Sensors for Ecology and Makes a Strong Case for Deploying Integrated Sensor Platforms

Présentation de l’éditeur

Ecological sciences deal with the way organisms interact with one another and their environment. Using sensors to measure various physical and biological characteristics has been a common activity since long ago. However the advent of more accurate technologies and increasing computing capacities demand a better combination of information collected by sensors on multiple spatial, temporal and biological scales. This book provides an overview of current sensors for ecology and makes a strong case for deploying integrated sensor platforms. By covering technological challenges as well as the variety of practical ecological applications, this text is meant to be an invaluable resource for students, researchers and engineers in ecological sciences. This book benefited from the Centre National de la Recherche Scientifique (CNRS) funds, and includes 16 contributions by leading experts in french laboratories.

Key features • An overview of sensors in the field of animal behaviour and physiology, biodiversity and ecosystem. • Several case studies of integrated sensor platforms in terrestrial and aquatic environments for observational and experimental research. • Presentation of new applications and challenges in relation with remote sensing, acoustic sensors, animal-borne sensors, and chemical sensors. Sensors for ecology Towards integrated knowledge of ecosystems

Jean-François Le Galliard, Jean-Marc Guarini, Françoise Gaill

Sensors for ecology Towards integrated knowledge of ecosystems

Centre National de la recherche scientifique (CNRS) Institut Écologie et Environnement (INEE)

www.cnrs.fr Acr1932688304928-24242.pdf 1 20/03/12 16:12

Photographie de couverture / Cover Picture © CNRS Photothèque – AMICE Erwan UMR6539 – Laboratoire des sciences de l’environnement marin – LEMAR – PLOUZANE “A diver inspects a queen conch Strombus gigas during a scienti c expedition in Mexico. The queen conch is equipped with acoustic sensors, here nearby a receptor, in order to collect information on its behaviour and physiology in nature.”

sensors-001-344.indd 6 20/03/12 13:10 Contents

Foreword...... 11 I Ecophysiology and animal behaviour

Chapter 1 : Bio-logging: recording the ecophysiology and behaviour of animals moving freely in their environment Yan Ropert-Coudert, Akiko Kato, David Grémillet, Francis Crenner.... 17

Chapter 2 : Animal-borne sensors to study the demography and behaviour of small species Olivier Guillaume, Aurélie Coulon, Jean-François Le Galliard, and Jean Clobert...... 43

Chapter 3 : Passive hydro-acoustics for cetacean census and localisation Flore Samaran, Nadège Gandilhon, Rocio Prieto Gonzalez, Federica Pace, Amy Kennedy, and Olivier Adam...... 63

Chapter 4 : Bioacoustics approaches to locate and identify animals in terrestrial environments Chloé Huetz, Thierry Aubin...... 83 8 Contents II Biodiversity Chapter 1 : Global estimation of animal diversity using automatic acoustic sensors Jérôme Sueur, Amandine Gasc, Philippe Grandcolas, Sandrine Pavoine. 99

Chapter 2 : Assessing the spatial and temporal distributions of zooplankton and marine particles using the Underwater Vision Profiler Lars Stemmann, Marc Picheral, Lionel Guidi, Fabien Lombard, Franck Prejger, Hervé Claustre, Gabriel Gorsky...... 119

Chapter 3 : Assessment of three genetic methods for a faster and reliable monitoring of harmful algal blooms Jahir Orozco-Holguin, Kerstin Töbe, Linda K. Medlin...... 139

Chapter 4 : Automatic particle analysis as sensors for life history studies in experimental microcosms François Mallard, Vincent Le Bourlot, Thomas Tully...... 163 III Ecosystem properties Chapter 1 : In situ chemical sensors for benthic marine ecosystem studies Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel...... 185

Chapter 2 : Advances in marine benthic ecology using in situ chemical sensors Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel...... 209

Chapter 3 : Use of global satellite observations to collect information in marine ecology Séverine Alvain, Vincent Vantrepotte, Julia Uitz, Lucile Duforêt- Gaurier...... 227 Contents 9

Chapter 4 : Tracking canopy phenology and structure using ground-based remote sensed NDVI measurements Jean-Yves Pontailler, Kamel Soudani...... 243 IV Integrated studies

Chapter 1 : Integrated observation system for pelagic ecosystems and biogeochemical cycles in the oceans Lars Stemmann, Hervé Claustre, Fabrizio D’Ortenzio...... 261

Chapter 2 : Tropical rain forest environmental sensors at the Nouragues experimental station, French Guiana Jérôme Chave, Philippe Gaucher, Maël Dewynter...... 279

Chapter 3 : Use of sensors in marine mesocosm experiments to study the effect of environmental changes on planktonic food webs Behzad Mostajir, Jean Nouguier, Emilie Le Floc’h, Sébastien Mas, Romain Pete, David Parin, Francesca Vidussi...... 305

Synthesis and conclusion Jean-François Le Galliard, Jean-Marc Guarini and Françoise Gaill...... 331

NB: When cited in the text, a chapter of this book is identified according to the part it belongs to. For example, (III, 3) refers to the chapter 3 by Alvain et al. in the third (III) part of this book about Ecosystem properties.

Foreword

Altogether explorer, scientist, philosopher and one of the first world citizen, the German naturalist Alexander von Humboldt (1769-1859) is often considered as a founder of ecological sciences, though the word “ecology” was only coined several decades later by another German scientist, Ernst Haeckel (1834-1919). Equipped with the best sensors (thermometers, barometers, and so on) and familiar with advanced metrology techniques of its time, von Humboldt pioneered the field of plant biogeography, a discipline at the meeting point between botany, geography, climatology and geology. Von Humboldt major conceptual and methodological contributions consisted in collecting physical and geological data along with plant distribution maps to determine the physical and historical conditions favouring specific plant assemblages all over the world. With this approach, he ventured into previously unsuspected complex interactions between plants and their physical surroundings. Two centuries later, researchers are still striving to understand the ecological and evolutionary mechanisms that determine the distribution of plant and animal species.

Indeed, an accurate quantification of how organisms interact with each other and with their environment is at the heart of several grand challenges in modern ecological sciences from the description of bio-geochemical balances to the prediction of ecosystem dynamics. However, contrary to von Humboldt and his followers, we can now explore thoroughly the natural world, thanks to major technological improvements in our ability to measure physical, chemical and biological quantities. Sensors are now part of the standard toolbox of most ecological studies, and play an important role in both exploratory studies of nature, experimental approaches, and the development of predictive ecological models. With the advent of more advanced technologies and the strong opportunities offered by nowadays available computing capacities, we are in a better position to integrate ecological information from sensors across multiple spatial, temporal and biological scales. This book, sponsored by the Centre National de la Recherche Scientifique (CNRS) in France, presents an up-to-date overview of the use sensors for ecology by some leading CNRS laborato- 12 Foreword ries. The book covers some of the main technological challenges in our field from bio-loggers attached on animals to remote sensing imaging capacities installed on satellites, and provides many examples of practical applications chosen from ongoing CNRS programs. It is also tightly connected with the current frontiers in ecology and evolution throughout the world. We hope that the book will become an invaluable resource to students, researchers and engineers in ecological sciences.

Few books have reviewed methods and issues in the field of sensors for ecology. The reason is easy to understand: a great deal of techniques and sensor types do exist and are covered in specific reviews or journals. Keeping pace with the increasing number of sensors and technologies currently available is therefore a difficult task. Yet, this book provides in a synthetic way a balanced description of the new applications and challenges in ecological research that the use of remote, acoustic, animal- borne, chemical and genosensors represents. Here, we adopted a broad view on sensors – usually defined as a device that measures a physical quantity and converts it into a signal – to describe a quantity of tools including image and video analyses, biodiversity and life history sensors, and other less traditional methods. Furthermore, this book contains technical descriptions of some sensors even though it is not a handbook about sensor technologies. A technical treatment is crucial to understand, design and integrate sensors for the purpose of ecological research, but this issue is already addressed in many handbooks. Instead, it was decided to focus this presentation on relevant applications and practical problems faced by ecologists during their research programs. Lastly, the book differs from a traditional presentation based on a standard classification among sensor types and a discussion of the specific issues within each category of sensors (e.g. remote sensing versus chemical sensors). Ecological studies often need to integrate physical, chemical and biological data obtained with various types of sensors to get a comprehensive view of the state and dynamics of ecosystems. We therefore chose to make a strong case for the deployment of integrated, autonomous sensor platforms in the context of observational and experimental research infrastructures, and we present case studies where integrated data can inform predictive models. The integration of various types of sensors for ecological studies poses a series of complex problems, from the engineering and autonomy of the platform to the access and use of data for modelling. These difficulties go far beyond the design of a single sensor and are often specific of the scale at which sensors should be deployed. Foreword 13

The idea of this project started under the patronage of the CNRS Institut Écologie et Environnement in 2010 with the aim to produce a state-of- the-art review for sensors used in ecology in France, as well as to identify strengths and weaknesses in this field for the future. Under the supervision of Jean-Marc Guarini and Karine Heerah, a workshop was organised at the University Pierre and Marie Curie in Paris in January 2011. Twenty four contributions were made and the workshop’s program addressed remote sensing techniques, the use of sensors for in situ studies, and the use of sensors for experimental studies. From these contributions and the discussions that followed, a few were selected to be published in the four sections of this book. The first section deals with animal behaviour and physiology, a very active field of research that raises both strong technical constraints and ethical issues. In a second section, we discuss the use of sensors in intra-specific and inter-specific biodiversity studies, and provide key examples ranging from the use of acoustic sensors or genetic methods to image analysis. We then present a series of ecosystem studies relying on advanced remote sensing and chemical sensors; those studies focus on measuring feedbacks between organisms and their geo-chemical environment. Our last section groups three integrated ecological studies. Two discuss observation platforms of ecosystems and one describes an experimental marine ecology infrastructure. The latter demonstrates how sensors can be used to manipulate environmental conditions and study the effect of environmental changes on ecosystems. Each chapter was organised so that it reviews existing methods and sensors, and discusses current difficulties and requirements for future technological development.

We would like to thank the authors for their participation and their kind patience during the editorial process. Angeline Perrot assisted us during the two last months of this project and was extremely efficient at organ- ising reviews, editing all text and making the iconography tidy from an heterogeneous pool of figures and photographs. The editors would like to thank the CNRS and the Institut Écologie et Environnement (INEE) for their financial support, as well as the University Pierre and Marie Curie for hosting the workshop. Jean-François Le Galliard acknowledges the support of the TGIR Ecotrons program and from the UMS 3194 CEREEP- Ecotron IleDeFrance as well as the financial support from ANR Equipex PLANAQUA coordinated by École normale superieure and CNRS (contract ANR-10-EQPX-13).

Jean-François Le Galliard, Jean-Marc Guarini and Françoise Gaill January 16, 2012 Paris, France

I Ehi cop ys ology and animal behaviour

Chapter 1

Bio-logging: recording the ecophysiology and behaviour of animals moving freely in their environment

Yan Ropert-Coudert, Akiko Kato, David Grémillet, Francis Crenner

1. Setting the scene

1.1 Sensing with bio-loggers Bio-logging refers to the fastening of autonomous devices onto (mainly) free-ranging animals to collect physical and biological information (Naito, 2004; 2010; Cooke et al., 2004; Ropert-Coudert et al., 2005; see also Costa, 1988, although the term bio-logging was not used in these times). It should be noted here that bio-logging is sometimes referred to as biologging. However, as Naito (2010) pointed out, the latter term is misleading as it is used in molecular biology. As such bio-logging differs from telemetry in the sense that data are stored locally in the memory of the devices and not transferred via radio waves or other transmitting means. This move from biotelemetry to bio-logging was done in order to address practical difficulties related to data transmission. Thus, this comes, as no surprise that bio-logging was firstly used in ecological and physiological studies to investigate marine, far-ranging, diving species, as water represents a barrier to radio signals. Bio-logging studies were initially conducted on species with a body mass large enough to accommodate the large size of the very first loggers: seals and whales. As miniaturisation progressed, smaller species of seals and seabirds became target species for bio-logging approaches. Among seabirds, penguins (Sphenscidae) represent an intensively studied family because of their adaptation to aquatic life 18 Ecophysiology and animal behaviour and their consequently denser, larger and more robust body. Nowadays, bio-logging can be applied onto an impressive range of species, terrestrial or aquatic, whether these are mammals, birds or reptiles (see I, 2). Bio-logging developments are one step away from moving into the insect realm as radio-telemetry is already available to study terrestrial and flying insects (Vinatier et al., 2010; Wikelski et al., 2006). Immediate consequences of local storage are the necessity to retrieve the device to access the data and develop appropriate sensors to gather data about physical and biological information on relevant time scales. It therefore clearly appears that bio-logging primarily refers to a methodological approach and has generated research to improve existing technologies. Yet, bio-logging is more than a mere catalogue of tools and techniques. The possibility to obtain an uninterrupted flow of information pertaining to both the activity and physiology of animal and its immediate, physical surroundings revolutionised the way we consider several fields in biology. We could draw a parallel with the field of genetics and how it evolved from Gregor Mendel crossing variety of peas to the advanced technologies of molecular sequencing. Similarly, the ecologist with its notebook possesses now a suite of approaches to examine animals living freely in their environment. In this context, bio-logging applications ranges from physiological investigations to the comprehension of the functioning of ecosystems, by relating a change in physical parameters of the environment to a change in the behaviour of both a predator and its prey, at the same spatial and temporal scales (Ropert-Coudert et al., 2009a).

1.2. Bio-logging in the scientific community The word bio-logging was coined at the occasion of the first symposium about the topic held in 2003 in Tokyo, Japan. Over the past decade, three additional symposia took place: Saint Andrews (Scotland) in 2005, Pacific Grove (USA) in 2008, and Hobart (Tasmania) in 2011. The next bio-logging symposium will be organized in France and is tentatively scheduled for Strasbourg in September 2014. The number of manufacturers has steadily increased since the inception of Wildlife Computers (USA) in 1986, the first – to the best of our knowledge – bio-logging company ever. Nowadays, the core of the bio-logging production is concentrated in the North America and Japan (Ropert-Coudert et al., 2009b), but emerging companies in the UK (CTL), Iceland (Star-Oddi) or Italy (Technosmart) are gaining worldwide momentum (Table 1). A non-negligible proportion (a rough estimate of 20%) of bio-logging devices is still produced in research institutions, the so-called custom-made bio-loggers, and is thus accessible only through collaborations between researchers. In Europe, for example, research-driven developments are found in the Sea Mammal Part I – Chapter 1 19

Table 1: A non exhaustive list of the most-used bio-loggers together with their weights and the sensors they include, as well as the name of the manufacturers. UK

USA

Survey,

USA

USA Computers,

Antarctic Manufacturers

Lee,

ritish B Cefas technology Ltd, UK Star-Oddi, Iceland Little Leonardo, Japan TechnoSmart, Italy Mr. Oceangraphic Hole Woods Institution, Sea Mammal Research Unit, UK Swansea University, UK Wildlife transmission)

transmission)

(data

transmission)

(data

(data Argos

Argos

GPS, GMS

Sensors light, speed, GPS,

temperature, temperature, temperature,

Depth, GPS GPS Depth, Depth, Depth, temperature, light, speed, acceleration, magnetometer 370 2.71.7 Depth, temperature Temperature, light 130 Depth, temperature, speed, acceleration, magnetometer 22 22 225 300545 Depth, audio, pitch, roll, heading 370 Depth, temperature, conductivity 2.5 Light, wet or dry status 30 Depth, temperature, light, acceleration, magnetometer tag

Model (g) Weight I

Phone

SRDL tag Cefas G5 DST bird DST magneticORI400-D3GTW1000-3MPD3GT 19W400-ECG 9DSL400-VDT IIGiPSy Depth, temperature, magnetometer, tilt 82 57CatTraQ Depth, temperature, acceleration SPLASH10-F-400 Depth, temperature, image ECG DTAG CTD tag GPS Daily Diary 42 Mk 15 Mk9 20 Ecophysiology and animal behaviour

Research Unit of the University of St. Andrews, which organized the 2nd bio-logging symposium. In France, the only openly declared bio-logging development team is found at the Institut Pluridisciplinaire Hubert Curien in Strasbourg. The next big step for the bio-logging community will be to form a society so as to reach an official status and help structuring the community. Bio-logging is especially expected to play an important role in the forthcoming decade regarding conservation issues and will represent a crucial tool to assess large vertebrate species distribution and links between the physical environment and the biological response of animals to its variation (see Cooke, 2008).

2. Overview of bio-logging applications

2.1. Reconstructing the movement and feeding behaviour The ancestors of all bio-loggers are probably time-depth-recorders, commonly referred to as TDR in several instances. These devices record hydrostatic pressure according to time so as to reconstruct diving activity of sea animals. Oddly, the very first incarnation of a TDR, which was attached to a freely-diving Weddell seal Leptonychotes weddelli, consisted in coupling a kitchen timer with a pressure transducer (Kooyman, 1965; 1966). Subsequent devices also functioned on a mechanical basis, such as miniature pencils that were animated by pressure changes and drew the profiles of dives onto a miniature paper (e.g. Naito et al., 1990). The emergence of solid-state memories put an end to this era of clever handcrafting. Nowadays, TDR can weigh as less as 2.7g and are able to capture depth and temperature data every second for around 10 days. When associated with GPS, they provide localisation onto both the horizontal and vertical dimensions, on a large range of species. Originally, TDR delivered only a 2D view of the diving activity (depth according to time) but progresses in behaviour reconstruction came from the utilisation of accelerometers. Accelerometers record gravity-related and dynamic acceleration signals and can be used to provide specific information about the movements of the body, such as walking gait (e.g. Halsey et al., 2008) or head-jerking (e.g. Viviant et al., 2010). The potential of accelerometers to reconstruct time budget activity was demonstrated in several instances (e.g. Yoda et al., 1999; Ropert-Coudert et al., 2004a; Watanabe et al., 2005). The addition of gyroscopes and magne- tometers makes it possible to reconstruct the precise path of animals in the three dimensions. This approach, called “dead reckoning” (Wilson et al., 1991), is very prone to making substantial errors. For example, a Weddell seal diving for ca. 17mn would accumulate an error in its posi- Part I – Chapter 1 21 tion calculated via dead reckoning of nearly 100m over this period (see figure 5a in Mitani et al., 2003). While methods exist to take this error into account (Mitani et al., 2003), dead reckoning is yet to be implemented at time scales longer than a few days. Anyway, the precision of tracking techniques thanks to GPS development makes it unlikely that dead reckoning will become a major approach. Small body movements, such as limb movements (Wilson and Liebsch, 2003), can also be finely reconstructed using Hall sensors, i.e. sensors measuring the intensity of the magnetic field. In this case, a magnet placed on one mandibular plate facing a Hall sensor glued onto the other mandibular plate (figure 1) allows researchers to determine when a prey has been swallowed and, following a proper calibration, the size and type of prey (Wilson et al., 2002; Ropert-Coudert et al., 2004b).

Figure 1: Schematic representation of the jaw movement recorder on a gentoo penguin Pygoscelis papua (top) and a young wild boar Sus scrofa (bottom). A magnet and a Hall sensor, sensitive to the strength of the magnetic field are placed on the two mandibles, facing each other. When the mouth opens the Hall sensor senses a reduction in the intensity of the magnetic field and sends this information via a cable to the bio-logger attached on the body.

Finally, in the context of assessing animal movements on a world-wide scale, the two major developments of the recent decades feature the 22 Ecophysiology and animal behaviour advent of GLS (global location sensors) and of GPS (global positioning system). Global location sensors are miniaturised units that store light measurements at regular intervals, from which position can be estimated (using day length and noon time). Initially described by Wilson et al. (1992a), this method revolutionised migration studies because devices are particularly small (around 1g), cheap, and are able to record data up to several years. They can therefore be deployed year-round on a wide range of individuals and species (Fort et al., in press). Miniaturised GPS (the smallest ones currently weigh 5g or less) usually have shorter recording times yet far higher spatial resolution than GLS (a few meters versus a few tens of km). Their generalised use triggered a quantum leap in the spatial ecology of free-ranging animals (Ryan et al., 2004)

2.2. Reconstructing the internal temperature and heat flux Animal-borne bio-loggers also benefited physiological studies as these bio-loggers allowed researchers to investigate internal adjustments to the constraints of, for example, experiencing extremely low temperatures (Gilbert et al., 2008; Eichhorn et al., 2011). These feats cannot be realized in the confines of a laboratory. Reduced core temperature in the body of deep divers like the king penguins Aptenodytes patagonicus shed a new light on the physiological mechanisms involved in energy savings at great depths (e.g. Handrich et al., 1997). In parallel to the externally- attached bio-loggers that recorded mandibular activity (see above), measurements of temperature in the stomach (Wilson et al., 1992b; Grémillet and Plos, 1994) or the oesophagus of endotherms (Ancel et al., 1997; Ropert-Coudert et al., 2001) also permitted to explore when these animals fed onto their exothermic prey as their swallowing induced a drop in the temperature (see figure 2 and additional discussions around the principle and the limitations of this method in Hedd et al., 1995; Ropert-Coudert et al., 2006a). Heat flux measurement bio-loggers may also be used to study homeothermy in animals swimming in cold waters (e.g. Willis and Horning, 2005).

2.3. Reconstructing the heart effort: ECG vs. heart rate One challenge in ecophysiology is to determine energy expenditures of free-ranging animals. Field methods based on doubly-labelled water exist but these are long-term methods that integrate the energy expended over a period of few days (Speakman, 1997). Further to the point, these methods require multiple capture and handling, which are not always easy to implement in the field, especially for shy and sensitive species. Cormorants, for example, respond to handling with intense overheating. Last but not least, Part I – Chapter 1 23

Figure 2: Two temperature signals (°C) recorded by sensors placed in the upper part of the oesophagous (top) and in the stomach (bottom) of an Adélie penguin Pygoscelis adeliae fed with cold food items. Each ingestion is visualised as a sudden drop in the temperature, followed by a slow recovery. the doubly-labelled water method is expensive and implies a laboratory specifically equipped with isotope analysis facilities. In contrast, the measurement of heart rate can give an idea of the energy expended, as heart rates are linked with metabolic rates (Nolet et al., 1992; Green et al., 2001; Weimerskirch et al., 2002). Although the shape of the relationship is often unclear (Froget et al., 2002; Ward et al., 2002; McPhee et al., 2003), measuring heart rate still enables the estimation of the effort allocated to basal versus non-basal (e.g. locomotor) activities. Among the bio-logging approaches for measuring heart rate, two techniques have emerged: i) heart rate recorders (HRR) that detect the heart beat and store in their memory the interval between each heartbeat or the number of heart beats per certain time period; ii) electrocardiogram recorders (ECGR) that monitor and store the complete electric signals allowing to access the complete PQRS profile of a heartbeat. both systems measure the electrical activity of the heart transmitted via 2 or 3 electrodes placed in different parts of an animal’s body. HRR have an extended autonomy since they only count intervals (Grémillet et al., 2005) but are prone to error because the ability to distinguish heartbeats from electric noise due to muscular activity depends solely on an onboard algorithm. In contrast, ECGR requires a processing of the signal but this ensures that only heartbeats are counted (Ropert-Coudert et al., 2006b; 2009c). However, commercially available ECGR have limited autonomy. 24 Ecophysiology and animal behaviour

Figure 3: Recordings of heart rate on a captive mandrill Mandrillus sphinx. A. Photograph of the collar where devices are attached and the electrodes protruding from it. B. Collar mounted on the mandrill with the electrodes plugged on the skin and secured by bolts. C. A comparison of the heart rate recorded by two different devices: a heart rate counter (Polar Watch, blue) and an electrocardiogram (ECG recorder, red). The latter allows the user to visualize each heart beat as a PQRS complex and is thus much more reliable than heart rate directly given by the counter (calculated via an internal algorythm to which the user generally cannot access). The heart rate given by the counter shows large variation that are absent on the signals derived from the ECG. © Jacques-Olivier Fortrat.

The comparison of the heart rate signals of a sleeping mandrill Mandrillus sphinx directly derived from a commercially-available heart rate monitor (© Polar Electro, France) and the one calculated from an ECGR (Little Leonardo, Japan) illustrates well the risk of applying tools that are developed for a specific use (here, the Polar Watch is intended for measuring heart rate during human exercise) onto an animal model without prior Part I – Chapter 1 25 calibration work (figure 3). The need to reduce the risk of storing electromyograms generally leads researchers to implant the HRR in the body, while ECGR can either be implanted or externally attached. Implantation is not trivial as it involves anaesthesia and surgery, with all the associated risks, and is not always easy to perform in the field (see Green et al., 2004; Beaulieu et al., 2010).

2.4. Viewing the environment: image data logger Data contained in bio-loggers are used to reconstruct the activity and, in some cases, the environment in which the animals move. But the dream of all users is to be able to visualise directly what the animals are seeing. Images, if they do not give access to physiological information per se, are a smart and informative way of studying behaviour. Images are also attractive to a large audience as they do not always require specific knowledge to be interpreted. As communication towards the public becomes paramount to Science, this is a non negligible asset for bio-logging approaches that use digital-still picture recorders or even video recorders. The National Geographic Crittercam project was a pioneer in merging the scientific community with common people. However, their usefulness to answer scientific questions was often questioned. Digital-still cameras take images following a definite sampling interval which is not always adequate for short time events like prey capture. Yet, these techniques can provide unravelled insights into prey identification (figure 4, see also Davis et al., 1999; Watanabe et al., 2006), prey density (Watanabe et al., 2003), group behaviour (Takahashi et al., 2004a; Rutz et al., 2007) or the biomechanics of flight (Gillies et al., 2011). Video recording systems have limited autonomy and are still rather bulky to be used without the risk of impairing the performances and health of some animal models (see the bulkiness of a video recorder mounted on an emperor penguin in the figure 1 from Ponganis et al., 2000). Recent advances in miniaturisation allowed for these devices to be placed on the head of a flying seabird (Sakamoto et al., 2009). In an applied context, it has been recently proposed to use newly-developed, highly miniaturised digital-still picture recorders mounted on seabirds to monitor pirates fish- ing boat (Grémillet et al., 2010).

2.5. Reconstructing the environment: animals as bio-platforms The pioneers of bio-logging soon realised that this technology not only allowed the study of animals in their natural surroundings, but also to access their biotic and abiotic environment. Especially in the oceans, where sampling through the water column is impossible from satellites 26 Ecophysiology and animal behaviour and expensive from research vessels, this approach led to remarkable advances. As soon as time-depth-recorders were coupled with positioning devices and temperature sensors, the thermal structure of water masses could be assessed. This was first conducted in Antarctica by Wilson et al. (1994), which used penguins equipped with data loggers to map thermal gradients across the 100m of the Maxwell Bay. Not only did they assess this abiotic parameter, but they also cross-checked this information with an estimation of krill biomass in this water mass, which was based upon the predatory performance of the birds. This approach was revolutionary. Yet, temperature measurements were too coarse to be adequate for proper oceanography work. It is only a decade later that seabirds were equipped with loggers measuring ocean temperature to 0.005K and depth to 0.06m, values accurate enough to track the vertical movements of the thermocline off Scotland in the North Sea (Daunt et al., 2003). However, this approach was then only used to investigate areas that had been already studied, and had been sampled using conventional, ship-based surveys.

Figure 4: Image data loggers. A. A digitial-still-picture logger (Little Leonardo, Japan) mounted on a great cormorant Phalacrocorax carbo in Greenland (left, © David Grémillet) together with a view of the logger itself (B) and four examples of pictures taken by the logger (C). The examples show fish prey caught in the beak of the cormorant. Part I – Chapter 1 27

The next step consisted in using free-ranging marine animals fitted with bio-loggers to sample unknown areas. For instance, Charrassin et al. (2002) used temperature data collected by diving king penguins to identify a previously-unknown water mass off Kerguelen in the Southern Ocean. However, operational oceanography requires real-time assessments of biotic and abiotic parameters, for instance to parameterise models of ocean circulation and climatic processes (IV, 1). This was not possible using ancient archival tags fitted to marine predators, since those had to be recovered to download the data, sometimes weeks or months after the actual measurement. Such problem was solved by the use of a system integrating bio-physical sensors of the environment (e.g. water colour, temperature, salinity) and sensors of the animal’s movements (3D acceleration, depth and speed) with the Argos positioning and transmission system. Such tools are large, require substantial battery power, and can only be deployed on large marine mammals for the time being, in particular elephant seals (Mirounga leonina). However, they allowed a major step forward because elephant seals cruise the Southern Ocean in areas that are beyond the reach of satellite or vessel-based oceanography, especially in the marginal ice zone off Antarctica and at depths of more than 1000m (Charrassin et al., 2008). From these areas, devices fitted to these large, record-breaking divers can send new data which are now being routinely integrated into ocean physics models (Roquet et al., 2011).

2.6. Multi-information sensors: the special case of accelerometry A single parameter may not always be sufficient to address a scientific question, such as in the case of the dead reckoning technique that we mentioned earlier (section 2.1). However, the use of multiple sensors is not always possible since it generally leads to an increase in the bulkiness of the devices. Fortunately, accelerometry can be used to derive more information than only the posture or the activity of animals. For example, with sensitive accelerometers, it is possible to detect the faint signal of the heart rate in the movements of the cloacae of a bird and thus address physiological questions without the need for electrodes and/or implanted materials (Wilson et al., 2004). In addition, since a rough 70% estimate of the energy is expended through movements, overall dynamic body acceleration (ODBA) or partial dynamic body acceleration (PDBA), derived from 3-axes or 2-axes accelerometers, respectively, was proposed as an index of energy expenditures (Wilson et al., 2006). ODBA and PDBA are indeed significantly related to oxygen consumption in a variety of species, and both offer a good proxy of metabolic activity when combined with heart rate loggers (Halsey et al., 2008). Apart from accessing physiological parameters, these sensors can also be used to infer prey availability in the 28 Ecophysiology and animal behaviour environment. Changes in wing beat frequency and amplitude are increasingly used to infer prey encounter in birds (Ropert-Coudert et al., 2006b), while detection of head jerking movement are related to prey capture in marine mammals (Suzuki et al., 2009; Viviant et al., 2010).

3. The road to bio-logging is paved with good intentions but…

3.1. The standard bio-logging trade-off Increasing the life-time of a bio-logger while keeping the same level of performances leads to the following paradox. On the one hand, the amount of information stored is increased, and consequently the memory capacity has to increase too; on the other hand, the energy required to power the electronic circuit is increased, and so should be the battery size and weight in order to address this extra demand. Based on the power consumption of a unit, it is possible to adapt batteries of different capacities to the devices in order to adjust the working-time to the specific needs of a study. However, a longer working-time means larger and heavier batteries and bio-loggers, which may have an impact on the health of the species targeted or even become inappropriate (I, 2). This balance between small units with a lesser impact on the animal but reduced life time, and larger devices with enhanced functionalities but restrictions on their applicability, is a major problem seriously dealt with by the bio-logging community i) for ethical reasons, and ii) to ensure that the data collected are reliable and are as close to the norm as possible (Ropert-Coudert et al., 2007). Regarding the impact of bio-logger, one must be aware that animals are generally shaped to optimise their movement through a medium. Swimmers are hydrodynamically featured, while flying animals present a specific adaptations to reduce their body mass. Thus, any externally- attached item may impair these features and lead to an increase in energy expended or a change in behaviour. In parallel, we already mentioned the negative consequences of implanting bio-loggers. Guidelines are regularly produced to reduce the negative impact of bio-loggers (Casper, 2009). Bio- loggers, for example, should weigh less than 3% of the body mass of flying birds (Phillips et al., 2003) and less than 4-5% of the cross-section of the animal (Bannasch et al., 1994). Despite these guidelines, we believe that the scientific community should move forward to adopt a common code of conduct. Indeed, the bio-logging community is very mindful about the need to reduce the impact of devices, but newcomers may not always be aware of guidelines specifically designed for bio-logger deployments (see above). In some instances, referees are not aware of them and accept papers that present ethical concerns or which Part I – Chapter 1 29 results are questionable due to the negative influence of a bulky device on the performances of the animals. Which institution could be in charge of ensuring that the appropriate guidelines are followed? Some scientific journals have taken the lead in addressing this problem: for example, Animal Behaviour has very strict ethics regulations and ask the authors to address them before submission to peer review. The pressure to produce attractive results could, however, hinder these efforts as it sometimes pushes researchers to emphasise outputs against rigor (see Ropert-Coudert et al., 2007). Conversely, enforcements of strict rules would also be detrimental without consideration of the benefits that overstepping them could bring in terms of new scientific results.

3.2. Beyond sensors and devices: homogenising analyses and sharing data Originally, each research group using bio-logging approaches developed its own method for analysing the data generated by bio-loggers. This led to the emergence of several analytical programming codes that tackled the same question and therefore, to a divergence in the way bio-logging data were processed. For example, the bottom phase of a dive can be defined in several different manners, leading to values that are not comparable from one study to another. The trend of diversifying the analytical methods is also enhanced by the presence of free software like R that allows users to create and disseminate their own codes and thus their own definitions for various parameters. In addition, the possibility offered by most bio- loggers of selecting the frequency at which the sampling is done also leads to diversification and renders comparisons across data sets difficult. In physics, the “sampling theorem” states that the sampling frequency must be at least twice that of the signal’s highest component frequency (for a periodic signal) to avoid aliasing. Similarly, biologists suggested that the sampling interval should not represent more than 10% of the duration of the biological event that one wishes to measure (e.g. the lowest sampling frequency to measure a 600sec dive of a Weddell seal is 60sec, Boyd et al., 1993; Wilson et al., 1995). Not adopting a proper sampling protocol may lead to misinterpretation of the data and false biological conclusions (Ropert-Coudert and Wilson, 2004). Recently, the question has become a topic of reflexion on the occasion of various workshops. Can we (and should we) homogenise bio-logging data analysis? The difficulty to define the best practice in that case is twofold. First, devices always evolve and become more efficient or collect new types of data. Consequently new analytical methods are required to handle these novelties. Secondly, the analytical method depends upon the questions sought. In that sense, the currently best practice would not stay best for very long. Yet, we need to be able to compare datasets taken in 30 Ecophysiology and animal behaviour different locations, time and using different means, especially if we are to tackle large-scale questions. Methods like down-sampling, although necessarily frustrating, are keys to address such issues. We strongly advocate for working groups to explore paths for the homogenisation of analytical procedures within the framework of, for example, the Expert Group in Birds and Marine Mammals of the Scientific Committee for Antarctic Research (SCAR), or the newly-formed group of experts in accelerometry that was constituted on the last bio-logging symposium in Hobart. In addition to this issue, the use and share of data from bio-logging must be optimised. A whole book could be filled with the issue of data sharing, but only the surface will be scratched here. The million of data points that are now routinely recorded by data loggers and the multiplicity of the research teams using such an approach make it necessary to centralise, archive, and ultimately share the data. Some researchers had been collecting bio-logging information over several decades and onto a large range of individuals and species. Upon retirement, their data would be lost if no system stores them. This is only recently that specific data repository have emerged. The tendecy is now to multiply storage points, each scientific society recognizing the need for a database on their specific topic. For example, marine researchers studying the localisation and diving activity of polar top predators can store their data into the database managed by the SCAR (SCAR-marBIN and Antabif) that are themselves linked to marine databases at a larger scale (OBIS, SeaWiFS, etc.). This multiplica- tion and cross-sharing of datasets among databases, while duplicating the work, guarantee the permanence of a dataset as it will still be available even if one database is closed. An incentive to sharing the data is found in the recent effort to consider data sharing as a genuine publication, asso- ciating a DOI to a data set. As such, institutions evaluating a researcher’s output can value his/her effort towards the scientific community through this marker.

3.3. Bio-logging: an academic and commercial endeavour Efficient bio-logging equipment is generally achieved through a close collaboration between engineers and users. However, research institutions able to combine both expertises under the same roof are scarce. In some privileged situations, an academic collaboration can be developed between universities so as to link a department of biology and an engineering department for example. The highest technical sophistication can then be attained and complex and specific questions be answered. Once a prototype is created, engineers face more practical duties that may be less intellectually satisfying. Among those, the issue of proper conditioning and packaging of the device is critical. Most dysfunctions of bio-loggers Part I – Chapter 1 31 are due to practical packaging problems. Solving these problems requires a multidisciplinary and complex engineering approach. Once the equipment has finally been validated, biologists would request a large number of units and this is precisely when academic systems reach their limits. Indeed, academic bodies are (and probably should) not be involved into mass production as this would mean adopting an industrial approach to bio-loggers production. Industrial production implies that electronics hardware, software, connectic systems and batteries, circuit design and protection, casing and packaging, tests and validation, are all included at once in the reflexion process. Additionally at each stage of development, costs are balanced and they influence decisions at the next stage. Industries usually aim at producing the best device according to the cost it represents for them; and this is generally decided with consideration of the market, the number of potential customers and the most reasonable price per unit. Real and viable situations generally lay between these two positions. Subcontracting industrial fabrication could be an alternative for academic developers. Academic engineers and/or researchers could also create a start-up company based on what they developed to initially address their scientific needs. However, this involves an optimal knowledge of the scientific and technical need, as well as of the practical pro blems that may be encountered in the field while using the equipment. In a nutshell, everything reverts to the following question: is the demand originating from users asking for specific developments (greater performance, new sensors…) or from the engineers anticipating the application of new technologies? Both stimulations are probably necessary to draw an ambitious but realistic product specification.

4. Where do we go from here?

4.1. Going toward large-scale deployment For decades, the paucity of manufacturers, the expensive price of bio- loggers, their restricted memory or battery capacity, as well as the lack of adapted analytical tools precluded the deployment of numerous units at a time. Thanks to technological advances, such as those taking place in the mobile phone industry, some cheap, low consumption and consequently small bio-loggers have started to appear on the market. With these, large- scale deployments have become achievable. While occasionally dozen of devices had been deployed simultaneously to explore cooperative diving (Takahashi et al., 2004b), the first large-scale deployments, in both space and time, originated through programs like the Tagging of Pacific Pelagics (Topp, Block et al., 2003, see also http://www.topp.org/). Since 32 Ecophysiology and animal behaviour the inception of the Topp programs, thousands of tags have been attached to 22 top predator species in the Pacific, including whales, sharks, sea turtles, seabirds, pinnipeds and even squids. Mass production of devices is now a reality: it allows researchers to work at unprecedented spatial scales and on entire populations of studied animals. In this field, the United Kingdom has taken a huge step forward. For example, the long-life, minute geolocators developed by the British Antarctic Survey are deployed on a worldwide scale (e.g. Conklin et al. 2010). Recently, mass-production of GPS for mobile phone also created an alternative market where cheap GPS can be purchased by researchers who can re-conditioned them specifically to their needs. As an illustration of this, the IPHC bio-logging unit is modifying commercially-available GPS units (Cat Traq from Perthold Inc., http://www.mr-lee-catcam.de/ct_index_en.htm) to make them suitable for use on wild animals. However, there is a negative side to this large-scale enthusiasm: cheap devices do not always meet the usual scientific criteria. Lesser reliability or lower degree of technical information must be balanced with the benefits that can arise from the use of these mass-production bio-loggers. In other words, caution in the use of cheap devices must be taken to avoid impacting scientific excellence. Thorough calibration must be a premise to large-scale deployments.

4.2. Importance of multiple sensors As evoked briefly earlier in this chapter, the use of multiple sensors – when applicable – offers an added value by providing a much complete picture of the behaviour and physiology of the animals in their environment. The combination of simple sensors (e.g. pressure sensor and temperature sensor) became a standard in even the simplest data loggers, but genuinely multi-sensor loggers are still few. Among those, it is worth mentioning the “daily diary” unit developed by Prof. Rory Wilson at the University of Swansea. Despite their relatively small size ranging between 21 and 90g according to the size of animal, these bio-loggers can contain up to 14 different channels of both slow and fast sampling sensors working simultaneously (Wilson et al., 2008). Apart from the daily diary unit, multi-sensing devices, either developed by research teams or commercially available (Wildlife Computers, Little Leonardo, Greeneridge Science, etc.), are used in large body sized models, e.g. fin whales Balaenoptera physalus (Goldboegen et al., 2006). To extend the applicability of multi- sensing devices to smaller animals, special developments are needed (I, 2); for example, a drastic reduction in the consumption is a pre-requisite to a generalisation of multi-sensing to species smaller than a 1-2 kg animal. In addition, new chemical sensors to detect for example the level of oxygen in the water or the blood will pave the way for new generations of Part I – Chapter 1 33 multi-sensing bio-loggers with new requirements and constraints for the developers. Here, a distinction must be made depending on the acquisition rates of these new sensors. The deployment of sensors for quasi-static parameters for which the sampling interval is equal or longer than 1s (e.g. temperature, light, pressure…) would not cause any trouble as transducers use low power and the volume of data is small. However, the use of sensors for medium speed parameters sampled typically between 10 and 100Hz (e.g. accelerometers, gyroscopes, etc.) requires larger memory volume and greater energy to store the data. Even stronger difficulties are faced for sensors that acquire high speed parameters (more than 100Hz) like elec- trocardiograms, electromyograms, or electroencephalograms. Numerous technical problems occur, and a special electronic architecture is needed to manage the high volume of memory, high speed communication for data transfer, and so on. With the million of data points that the daily diary units can generate, the next challenge will be to develop a software able to handle, display and summarise the complex information delivered by the next generation of bio-loggers. Prof. Wilson thus invested an important amount of energy, resources and time in developing such a tool and did it in such a way that its utilisation can reach a larger public than the scientific community alone (Wilson et al., 2008, http://www.swan.ac.uk/biosci/research/smart/ smartsoftware/). The software allows users to interpret the data from the bio-loggers so as to truly reconstruct behaviour and visualise it. For example, data points from the magnetometer, gyroscope, accelerometer and altitude sensors are combined and the result on the screen is an albatross (a computer graphic one, of course) flying in three dimensions following the exact paths that the original albatross flew. Beyond the example of the daily diary, visualisation software to accommodate complex and large datasets and display them in a pleasant and efficient manner is becoming increasingly available. The statistical free software R is of course powerful and readily accessible but its lack of user-friendliness may sometimes limit its popularity for complex analyses and representations. Alternatives to R are numerous and we can only mention Igor Wavemetrics, which was recurrently presented at the last bio-logging symposium (http://www. wavemetrics.com/).

4.3. Combining the best of biotelemetry and bio-logging Biotelemetry – at least in theory – clearly has its advantages, especially as long as securing data is concerned. However, real-time data transmission is practically hampered by numerous factors leading to a temporary inter- ruption in communication, which in turn means a definite loss of measurements (Vincent et al., 2002; Costa et al., 2010). These blank periods 34 Ecophysiology and animal behaviour are generally due to technical limitations (e.g. signal attenuation, wave’s absorption by the environment, electromagnetic interferences, etc.) and to the behaviour of the animal to which the transmitter is attached (e.g. relative position of body and antenna, immersion in water or in a burrow, etc.). In comparison, bio-logging seems the perfect solution. Yet it suffers from an important drawback: the bio-logger has to be connected back to a computer at the end of the experiment to retrieve the data, which means that the animal has to be still alive, re-localised, re-captured and should still be carrying a bio-logger that is still functioning! In other word, deploying a bio-logger represents a binary game: if only one point goes wrong in the chain, no data are collected. Obviously, combining the capabilities of the two methods seems to be the solution. An ideal device would record permanently the data in an embedded memory, and would then transmit them regularly to a base station. Of course, this basic principle needs to be adjusted to each experimental situation. Data may be transferred following a fixed schedule, for instance when an animal returns to a fixed location in space and time. As a consequence this would only require a single base station installed within radio range of such a site where the animal is known to be found at regular interval, and with a bidirectional connection between the logger and the station. The base station would be filled gradually with data from the logger, and be downloaded by the user when needed. If the animal dis- appears only the data collected after the last transfer with the base station are lost. Alternatively, the base station can interrogate the environment at fixed schedules or be triggered manually to search for a telemetric logger within its reception range (see the approach developed by the University of Amsterdam, Shamoun-Baranes et al., 2011). The reverse strategy consists in asking the telemetric logger to regularly scan the radio-frequency environment, in order to search for a base station. In this case, scattering numerous base stations in a given experimental area would enhance the success rate of data transfer. These stations can also communicate between each other to optimise data organization and synchronisation. Additionally, each base station can communicate with a large number of telemetric loggers. The last step in this concept consists in bio-loggers able to communicate not only with base stations, but also among themselves, leading to a genuine network of communicating devices. Data would then be shared with all the loggers coming within communication range and then transferred to a base station when one logger is close to it. A non- negligible side aspect of such an approach is the possibility to investigate proximity between animals, including time, duration and possibly distance of encounters. While theoretically attractive, a fair amount of development has to be done to reach this grand challenge. Both advances in electronics and data communication protocols are required. Progresses Part I – Chapter 1 35 in theoretical studies over software that could be able to manage such complex sets of interactions are paramount to the future success of these bio-logging networks and cannot involve only one type of institutions.

5. Conclusion

Bio-logging has gone through several steps from mechanical to digital, and from bulkiness to miniaturisation. The field is now moving towards globalisation and large scale coverage. In the marine realm, bio-logging coupled to automatic identification and weighing systems such as those that exist in the Antarctic could serve as a basis for long term monitoring programs. Such observatories would thus act in parallel with weather or oceanographic stations to deliver data on Antarctic biodiversity. This concept can be extended to the terrestrial realm with a network of sensing nodes monitoring the state of terrestrial ecosystems over time. With the rapid modifications affecting all ecosystems on Earth, monitoring programs such as these are urgently needed. The diversification of the data collected, the increase in the temporal coverage and accessibility of bio- logged data, and the possibility for large number of units to be deployed in a given environment concur to promote bio-logging as the key approach for ecological sciences in the future.

Authors’ references

Yna Ropert-Coudert, Akiko Kato, Francis Crenner: Université de Strasbourg, Institut pluridisciplinaire Hubert Curien, UMR 7178, Strasbourg, France

David Grémillet: Centre d’écologie fonctionnelle et évolutive, UMR 5175, Montpellier, France University of Cape Town, FitzPatrick Institute, DST/NRF Excellence Centre, Rondebosch, South Africa

Corresponding author: Yan Ropert-Coudert, yan.ropert-coudert@iphc. cnrs.fr 36 Ecophysiology and animal behaviour

Aknowledgement

We thank Prof. Y. Naito for his dedication to promote bio-logging even after his retirement. We also thank Prof. Rory Wilson for sharing his pas- sion for the development and application of novel bio-loggers to a wide range of species.

References

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Speakman, J.R., 1997. Doubly labelled water: theory and practice, Chapman & Hall, London. Suzuki I., Naito Y., Folkow L. P., Miyazaki N., Blix A. S., 2009. Validation of a device for accurate timing of feeding events in marine animals. Polar Biology, 32, pp. 667-671. Takahashi A., Sato K., Naito y., Dunn M., Trathan P. N., Croxall J. P., 2004a. Penguin-mounted cameras glimpse underwater group behaviour. Proceedings of the Royal Society of London B, 271, pp. 281-282. Takahashi A., Sato K., Nishikawa J., Watanuki Y., Naito Y., 2004b. Synchronous diving behavior of Adelie penguins. Journal of Ethology, 22, pp. 5-11. Vinatier F., Chailleux A., Duyck P.-F., Salmon F., Lescourret F., Tixier P., 2010. Radiotelemetry unravels movements of a walking insect species in heterogeneous environments. Animal Behaviour, 80, pp. 221-229. Vincent C., McConnell B. J., Ridoux V., Fedak M. A., 2002. Assessment of Argos location accuracy from satellite tags deployed on captive gray seals. Marine Mammal Science, 18, pp. 156-166. Viviant M., Trites A. W., Rosen D. A. S., Monestiez P., Guinet C., 2010. Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers. Polar Biology, 33, pp. 713-719. Ward S., Bishop C. M., Woakes A. J., Butler P. J., 2002. Heart rate and the rate of oxygen consumption of flying and walking barnacle geese (Branta leucopsis) and bar-headed geese (Anser indicus). Journal of Experimental Biology, 205, pp. 3347-3356. Watanabe S., Izawa M., Kato A., Ropert-Coudert Y., Naito Y., 2005. A new technique for monitoring the detailed behaviour of terrestrial animals: A case study with the domestic cat. Applied Animal Behaviour Science, 94, pp. 117-131. Watanabe Y., Bornemann H., Liebsch N., Plötz J., Sato K., Naito Y., Miyazaki N., 2006. Seal-mounted cameras detect invertebrate fauna on the underside of an Antarctic ice shelf. Marine Ecology Progress Series, 309, pp. 297-300. Watanabe Y., Mitani Y., Sato K., Cameron M. F., Naito Y., 2003. Dive depths of Weddell seals in relation to vertical prey distribution as estimated by image data. Marine Ecology Progress Series, 252, pp. 283–288. Weimerskirch H., Shaffer S. A., Mabille G., Martin J., Boutard O., Rouanet J.-L., 2002. Heart rate and energy expenditure of incubating wandering albatrosses: basal levels, natural variation, and the effects of human disturbance. Journal of Experimental Biology, 205, pp. 475-483. Wikelski M., Moskowitz D., Adelman J. S., Cochran J., Wilcove D. S., May M. L., 2006. Simple rules guide dragonfly migration. Biology Letters, 2, pp. 325-329 Willis K., Horning M., 2005. A novel approach to measuring heat flux in swimming animals. Journal of experimental marine biology and ecology, 315, pp. 147-162. Part I – Chapter 1 41

Wilson. R P., Cooper J., Plötz J., 1992b. Can we determine when marine endotherms feed? A case study with seabirds. Journal of Experimental Biology, 167, pp. 267-275. Wilson R. P., Culik B. M., Bannasch R., Lage J., 1994. Monitoring Antarctic environmental variables using penguins. Marine Ecology Progress Series, 106, pp. 199-202. Wilson R. P., Ducamp J. J., Ress W. G., Culik b. M., Niekamp K., 1992a. Estimation of location: global coverage using light intensity, in: Priede, I.G. and Swift S. M. (Eds). Wildlife telemetry: remote monitoring and tracking of animals. Ellis Horwood, Chichester, pp. 131-134. Wilson R. P., Liebsch N., 2003. Up-beat motion in swinging limbs: new insights into assessing movement in free-living aquatic vertebrates. Marine Biology, 142, pp. 537-547. Wilson R. P., Pütz K., Grémillet D., Culik B. M., Kierspel M., Regel J., Bost C.-A., Lage J., Cooper J., 1995. Reliability of stomach temperature changes in determining feeding characteristics of seabirds. Journal of Experimental Biology, 198, pp. 1115-1135. Wilson R. P., Scolaro A., Quintana F., Siebert U., Straten M. T., Mills K., Zimmer I., Liebsch N., Steinfurth A., Spindler G., Müller G., 2004. To the bottom of the heart: cloacal movement as an index of cardiac frequency, respiration and digestive evacuation in penguins. Marine Biology, 144, pp. 813-827. Wilson R. P., Shepard E. L. C., Liebsch N., 2008. Prying into the intimate details of animal lives: use of a daily diary on animals. Endangered Species Research, 4, pp. 123-137. Wilson R. P., Steinfurth A., Ropert-Coudert Y., Kato A., Kurita M., 2002. Lip- reading in remote subjects: an attempt to quantify and separate ingestion, breathing and vocalisation in free-living animals using penguins as a model. Marine Biology, 140, pp. 17-27. Wilson R. P., White C. R., Quintana F., Halsey L. G., Liebsch N., Martin G. R., Butler P. J., 2006. Moving towards acceleration for estimates of activity- specific metabolic rate in free-living animals: the case of the cormorant. Journal of Animal Ecology, 75, pp. 1081-1090. Wilson R. P., Wilson M.-P. T., Link R., Mempel H., Adams N. J., 1991. Determinations of movements of african penguins Spheniscus demersus using a compass system: dead reckoning may be an alternative to telemetry. Journal of Experimental Biology, 157, pp. 557-564. Yoda K., Sato K., Niizuma Y., Kurita M., Bost C.-A., Le Maho Y., Naito Y., 1999. Precise monitoring of porpoising behaviour of Adelie penguins determined using acceleration data loggers. Journal of Experimental Biology, 202, pp. 3121-3126.

Chapter 2

Animal-borne sensors to study the demography and behaviour of small species

Olivier Guillaume, Aurélie Coulon, Jean-François Le Galliard, and Jean Clobert

1. Introduction

One of the main characteristics of ecological systems is their hierarchical organisation – communities are collections of species interacting with each other, species are groups of populations distributed spatially and connected through dispersal, and populations are made up of individuals. Despite the widespread opinion that individual variation is the raw material of ecological and evolutionary dynamics, ecological approaches at the level of communities or ecosystems have tended to ignore the large variation among individuals seen in their morphology, behaviour, or life histories (Bolnick et al., 2003). One of the reasons for this is that there are serious methodological constraints in our ability to identify and track individuals of most animal species within complex communities. Indeed, most communities are made up of relatively small species, which are extremely challenging to mark and equip with sensors. For example, a large part of the world’s mammals weigh less than 100g (see figure 1, Gardezi and da Silva, 1999) and the median body size of birds is around 30-40g (Blackburn and Gaston, 1994). Yet, small animal species contributed a lot to our understanding of ecological and evolutionary processes within natural populations, including population demography, dispersal ecology and evolutionary ecology (e.g. Clobert et al., 2001). They also play an important part in many terrestrial and aquatic ecosystems on Earth, where they include a large number of herbivores, small predators, as well as parasitoids, pollinators and plant mutualists. 44 Ecophysiology and animal behaviour

Figure 1: Relationship between species diversity (number of species) and species body mass (log-transformed, kg) in a large data set of the world’s mammal species after Gardezi and da Silva (1999). The distribution is significantly skewed to the right and indicates maximal species diversity for body mass around 25-63g. The dashed arrow represents the range of body mass (more than 100g) where animals can currently be fitted with some of the smallest bio-tracking and bio-logging devices (assuming the device must weigh less than 3-5% of the body mass, see table 1). This review focuses on animals weighing less than 100g, which represents most vertebrate species on earth and almost all invertebrate species. © The University of Chicago Press, 1999.

Several challenges for the development and proper implementation of animal-borne sensors on small terrestrial and aquatic animal species are identified. Appropriate detection and marking techniques are needed for the population ecology of many animal species, especially when rare and elusive ones are involved. In the field of movement ecology, gathering data on individual behaviour and movements of individuals in space and time, by using appropriate tracking technologies (also sometimes called bio-telemetry and referred to here as bio-tracking), is also fundamental. Finally, ecophysiological studies require gathering information on individual physiological state as well as on environmental conditions using micrometeorological and physiological sensors. This chapter aims at i) describing methods and sensors that have been used to collect such behavioural and demographic data on individuals of small animal species and ii) identifying the main current limitations and challenges of existing technologies and the most urgent areas of development in this domain. Part I – Chapter 2 45

Note that the methods reviewed here are also relevant for the juvenile forms of many large species. We define and review sensors in the broad sense so as to include individual marking techniques, bio-tracking techniques, and micrometeorological and physiological sensors sensu stricto. Animal-borne sensors may be defined as any device installed on an animal that measures a physical or chemical quantity and converts it into a signal which can be read by an observer or by an instrument, for example a micrometeorological quantity (e.g. temperature) or a physiological quantity (e.g. glucose level). Some tag systems that give information on the identity and devices that provide information on location of study animals, for example through the use of radio emitters or harmonic radar tags carried by the individuals, can be also considered as a type of biological sensors. Note that the coupled system of sensors and data loggers installed on the animal is called a bio-logger, and differs from simple bio-telemetry tools in the sense that data are stored locally in the memory of the devices and not transferred in real time via radio waves or other transmitting means. The development and use of bio-loggers with large animals is reviewed in chapter 1 of this book by Ropert-Coudert et al., and we will briefly review its applications with small animals here.

2. Methodological issues

Studying small animals in their natural environment or in experimental infrastructures without interfering with their normal behaviour presents major challenges to ecologists. This is especially true when studies require equipping model species with animal-borne sensors to measure and record environmental or physiological parameters. The main methodological issue with small animals lies in their weight and volume, which poses an upper limit on the size of sensors and constrains any marking and attachment technique. Indeed, tags and sensors must obviously neither interfere with the natural behaviour, nor influence the survival of the animals that carry them. Guidelines generally recommend that sensors must weight less than 5% of the animal body mass for vertebrates, and less than 10% for invertebrates (Cochran, 1980; Cant et al., 2005). However, those figures must be adjusted according to the study species and populations. For example, in species for which running, flying or swimming is essential, the maximum tolerated weight of the device should even be lighter (e.g., Bedrosian and Craighead, 2007). The evaluation of the impacts of sensors, of their components, like the antenna, and of the fixation procedure has been performed in various contexts, such as meta-analyses of survival, growth or fecundity, which showed some significant detrimental effects 46 Ecophysiology and animal behaviour

(for example Bridger and Booth, 2003; Weatherhead and Blouin-Demers, 2004; Whidden et al., 2007). Hence, careful tests of animal-borne sensors and associated protocols should be conducted prior to deployment in field or experimental conditions. In addition, small animals or juvenile forms are often characterised by fast growth and high mortality, and some of these species may also be difficult to capture even when their populations are abundant. These demographic facts put some challenges on the ability to recapture animals, relocate their tags and design appropriate attachment techniques. For example, juvenile forms of many lizards must be equipped with sensors that must not interfere with their fast growth and strong susceptibility to predators, but at the same time should be inexpensive because of the high chance to loose sensors in the course of a standard demographic study (Le Galliard et al., 2011). In the following section, we review available animal-borne sensors used to study the demography and behaviour of animals, and discuss their applications and limits in the context of their implementation on small species.

3. Specifications of animal-borne sensors for small species

We list in table 1 some animal-borne sensors that can be commonly used to study demography and behaviour including bio-tracking, where the focus is on animal movements and data are often obtained remotely using telemetry, and bio-logging, where combined information on animal behaviour, physiology and habitat are typically recorded and data are stored locally on an autonomous device installed on free-ranging animals. Bio-tracking technologies compatible with demographic and behavioural studies of small animals include small tags used to mark and identify individuals from a short distance like magnetic wire tags and passive Radio Frequency Identification tags (RFID, Canner and Spence, 2011; Courtney et al., 2000; Bergman et al., 1992), also called passive integrated transponders (PIT). Other tracking devices such as harmonic radars, VHF transmitters or satellite based transmitters allow the localisation of small animals from a longer distance and therefore were used more often to study movement behaviour. Moreover, a range of more advanced bio-loggers can be used on small animals when remote transmission is not feasible and data should be stored locally. We review and discuss the use of these animal-borne sensors and techniques for small species. Other methods like video or camera traps have been used in some instances to obtain information on the abundance and ecology of small animals but they are less flexible and informative than bio-tracking and bio-logging technologies (see IV, 2, for a case study). Part I – Chapter 2 47

3. 1. Marking and tracking small animals with passive RFID tags It may be difficult to identify small animals with the help of techniques that avoid undue pain and stress, have no effect on fitness traits, and produce marks that are not easily lost. Non-invasive techniques, such as paint marks or bead-tags, do not ensure the identification of a large number of individuals and are often only temporary, except for rings. Thus, most demographic studies of small species involve more invasive techniques such as branding, toe-clipping or scale-clipping. To avoid the potential pain and stress caused by these techniques, passive integrated transponders (PIT) tags, also called passive RFID (radio frequency identification) tags, have been recommended because these ones provide permanent and reliable individual marks. This technology uses communication via radio waves to exchange data (identification number) between a reader and a passive electronic tag attached to the animal (Gibbons and Andrews, 2004). Examples of their use include tracking studies where “antennas” positioned in the natural habitat are connected to the reader. However, because the animal can only be detected at a short distance (see table 1), antennas must be located at places of maximal use by animals (e.g. dispersal corridors, nests or burrows, runways, etc.). We used this technology to study the spatial ecology of small mammals in northern Europe, even during the winter snow period (Hoset et al., 2008; Le Galliard et al., 2007). The custom-made system was developed by Harald Steen and Lars Korslund from the University of Oslo (Korslund and Steen, 2006). It consists of a tube-shaped single coil antenna (20 × 4cm) placed on the ground along runways to maximize recording rates, and attached to Trovan® LID665 oEM PIT tag decoders (LID665, EID Aalten bV, Aalten, Netherlands) that record PIT-tag number, date and hour each time a tagged vole passed through the antenna (see figure 2). There are however potential difficulties with PIT tags: they cannot be injected on the juvenile forms of most species, tags may get lost through the injection site, and injection as well as retention of these tags can cause small species pain and harm (e.g. Le Galliard et al., 2011).

3. 2. Harmonic radars for tracking very small animals Harmonic radar is becoming common to track small animals. Several studies recorded trajectories of hundreds of metres of very small animals, especially flying insects like beetles, honeybees or butterflies. Those studies focused on migration, dispersal, foraging, or flight behaviour and assessed environmental factors influencing movements (e.g. Chapman et al., 2011 and references therein). The harmonic radar system consists of an emitter that generates a micro-wave signal of a determined frequency. The tag – composed of a diode connected to a wire antenna – receives 48 Ecophysiology and animal behaviour

Category Sensor type Information Data acquisition Detection range

Identity and Scanning with a specific < 1 m with on- Passive RFID tag location radiofrequency source ground antenna Visual reading after Identity Requires recapture extraction Passive wire tag Identity and Scanning with a specific 3 cm with hand- location magnetic source held antenna Up to hundreds Scanning with a specific Harmonic radar Location of meters with on- Bio- radiofrequency source ground radars tracking Radio Identity and VHF receiver connected Up to 50 m with on- transmitter location to an antenna ground antenna

Satellite relay or VHF Global with Identity and GPS tracking receiver connected to an satellites, hundreds location antenna meters with VHF Identity and Satellite PTT Satellite relay (Argos) Global location Direct communication GLS logger Location Requires recapture port to data logger Direct communication GPS data logger Location Requires recaptures port to data logger Light, temperature; humidity, tilt; Bio- pressure and logging depth; magnetic field strength; Direct communication Data storage tag Requires recapture pitch and roll; port to data logger conductivity, salinity, dissolved oxygen, pH, imaging, EEG, ECG, EMG, etc Data storage tags, and GPS Global with and GLS loggers Satellite relay or VHF Combined Bio-logging + bio- satellite, hundreds combined with receiver connected to an tracking meters with VHF devices VHF and/or antenna relay Argos for remote transmission

Table 1. Specifications of some available sensors used in demographic and behavioural studies of small animals. Bio-tracking refers to the process of gathering remotely identity and/or location data on the study animal using passive or active devices, even if data may be stored locally prior to retrieval. Bio-logging differs from telemetry because data (location, animal physiology, or environment) are stored locally in the memory of the devices and must be downloaded via a communication port. Combined devices allow to store Part I – Chapter 2 49

Approximate lifespan Accuracy Smallest device of smallest device

< 1m 1 × 6 mm, 7.15 mg Unlimited

- 0.25 × 0.5 mm, small weight Unlimited

Few meters 12 mm long, 3 mg Unlimited

Few meters 10 × 5 mm, antenna 70 mm, 0.2 g Ten days

From hours in real-time to months Few meters 15g depending on the data acquisition and retrieval procedures

Hundreds of meters 5 g Several months

Hundred kilometres 0.5 g Up to several years

From hours to months depending Meters 22 × 14 mm, antenna 7 mm, 2 g on the numbers of locations

iButton (temperature): 17 × 6 mm, 1.49 g > 1 year

Geolocating archival tags (geolocation, internal and external temperature, - 6 months pressure, light, sea water switch): 8 × 20 × 6.7 mm, 1.9 g Archival Tag (temperature and pressure): 1 year 8 × 32 mm, 3.4 g EEG + GPS: 66 × 36 × 18 mm, 35g < 47 hours Argos + temperature sensor: 7.5 × 24 mm, Up to months depending on data antenna 200 mm, 5 g acquisition and retrieval procedures Meters with GPS, Argos + GPS + Temperature + activity: ten meters with Up to 3 years VHF, hundred 64 × 23 × 16.5 mm, antenna 178 mm, 22g meters with Argos Video AVED + VHF: 14 g Ten minutes

together both location, physiological and environmental data from various sensors and to transmit the stored data via a VHF radio or a satellite data relay network. RFID : RadioFrequency IDentification ; VHF : Very High Frequency ; GPS : global positioning system ; PTT : Platform Terminal Transmitter ; GLS : Global Location Sensing, AVEDs : animal-borne video and environmental data collection systems. 50 Ecophysiology and animal behaviour

Figure 2: Use of passive radiofrequency identification (RFID) tags for demographic studies of small mammals. A. Subcutaneous injection of a RFID tag on the back of a juvenile root vole (Microtus oeconomus). B, C. Custom made reader connected to a battery and an antenna, tube-shaped single coil antenna placed on the ground. © J.-F. Le Galliard. the signal and reemits at a doubled frequency that can be picked up by the receiver antenna. Two different transmitter-receiver systems are used to detect the tags (see figure 3). The first is a hand-held unit, originally designed to locate avalanche victims who wear tags on their clothes (e.g. Recco). This system is efficient to locate more or less 10cm tags from 50m above ground, less than 10 m on the ground and about 10cm below the ground surface (Mascanzoni and Wallin, 1986; o’Neal et al., 2005). The second, a ground-based scanning station, uses conventional radar plan position indicator technology (PPI) that gives the coordinates (range and azimuth) of the diodes (see Riley and Smith, 2002 for a detailed description). This system can be used to track smaller tags (more than 1cm) on Part I – Chapter 2 51 horizontal landscapes (Cant et al., 2005; ovaskainen et al., 2008) or for vertical looking up to 900m (Riley et al., 2007). The advantages of the harmonic radar system are multiple. Since tags are passive and do not require batteries, they have a potentially unlimited lifespan, their weight can be very low (down to a few milligrams), and they are rather cheap (less than 1€). However, this system also presents drawbacks. All diodes reemit at the same frequency. Therefore, for studies that need to individually identify the target, a complementary method is required (e.g. a visual mark to identify the individual once found). In addition, the radar signals can be absorbed or disturbed by landscape components that may work as barriers or reemit a background noise. The best performances have been obtained with PPI in agricultural landscapes (Cant et al., 2005; Ovaskainen et al., 2008) but this was achieved with the use of a heavy and complex system made out of a trailer, which is often uneasy to use in the field. Finally, the external antenna of the device can hinder the movements of individuals, especially for ground-dwelling species, and it also complicates the fixation process (Langkilde and Alford, 2002; o’Neal et al., 2005; Pellet et al., 2006).

Figure 3: Use of harmonic radar to track flying insects. Left to right, a tag attached on a honeybee (from Riley et al., 2007), the ground-based scanning station used to track flying insects (from ovaskainen et al., 2008). © J.R. Riley/outlooks on Pest Management, © O. Ovaskainen.

To summarise, harmonic radars represent one of the most promising opportunities to study movements on small spatial scales for small species, but only a limited number of research groups were able to use this 52 Ecophysiology and animal behaviour technology and few succeeded on other fauna than flying insects (Lovei et al., 1997; Langklide and Alford, 2002; O’Neal et al., 2005; Pellet et al., 2006). Technical advances are still necessary to improve the performances of these radars and to extend the field of investigations to new species and various ecological problems. Harmonic radars provide to date the best method to track very small animals (Chapman et al., 2011), but their use may require complex equipments that are not necessarily easy to implement under field conditions. Another major drawback of this system is that tracking simultaneously several individuals is difficult because signals from different individuals can not be differentiated.

3. 3. VHF radio tracking of small species Very high frequency (VHF, 30-300MHz) radio tracking systems consist of three parts: i) an emitter attached to an animal; ii) an antenna that picks up the signal sent by the emitter; iii) a receiver, to which the antenna is plugged, that decodes the signal to the operator. The antenna can be located on a car or hand held. The closer the orientation of the antenna is to the direction of the signal, the stronger the signal is received. The strength of the signal is used to calculate the location of the monitored animal using triangulation. VHF radio tracking (also called radio telemetry) has been used to monitor large animals since the early 1960s (e.g. large carnivores or large mammals, Lemunyan et al., 1959), because the size of the first emitters precluded its use on smaller species. Miniaturisation efforts have then allowed the application of the system on smaller species, e.g. bats and birds. The smallest emitters currently available weigh circa 0.2g, allowing thus the monitoring of species as small as arthropods, small reptiles and amphibians (e.g. Naef-Daenzer et al., 2005, Rock and Cree, 2008). For example, recent advances in radio telemetry allowed tracking movements of a Neotropical orchid bee, which is a small insect pollinator (Wikelski et al., 2010, see figure 4). This technology provided new and valuable knowledge to understand the ecology and evolution of pollination of orchids. However, the miniaturisation also comes at a cost since the duration of the miniaturised emitter is generally short (i.e. a few days to a few weeks for emitters < 1g), and the range of detectability of the emitter (i.e. the maximum distance at which the signal can be detected by the antenna- receptor system) is reduced to a few hundred meters for emitters < 1g compared to several kilometres for the biggest emitters assuming ground- to-ground conditions where the signal from an emitter on the ground picked up by a hand-held antenna. In the study by Wikelski et al. (2010) cited above, the lifespan of the emitters was around 10 days, which precluded, for example, to draw firm conclusions on the size of the home Part I – Chapter 2 53 ranges of the bees. Yet, the monitoring of the signal of fast moving animal can be improved by detecting the signal from the air with mobile antenna installed on e.g. an helicopter or with a fixed network of antenna installed on towers (Kays et al., 2011, Wikelski et al., 2010). For example, an automated radio telemetry system was built from receivers mounted on 40m towers topped with arrays of directional antennas and was later used to track the activity and location of radio-collared animals in a tropical rainforest (Kays et al., 2011; IV, 2) This automated platform can be installed on a permanent study plot for long-term monitoring of medium and large sized animals, but the system encounters difficulties when it comes to detect activity and movements of the smallest species (around 4-100g., Kays et al., 2011). Thus, there is definitely room for technological improvements of radio tracking systems for most juveniles of the small species still cannot be fitted with emitters, and because the lifespan of the smallest emitters precludes any long-term monitoring of individuals and reduces long range detection.

Figure 4: VHF tracking of small species. The transmitter (300mg) is glued to the bee thorax. © C. Ziegler from Wikelski et al. (2010).

3. 4. Satellite-based tracking for small species Satellite-based telemetry like Argos system utilises a platform transmitter terminal (PTT) attached to the animal that transmits an ultra high frequency (UHF, 300-3000MHz) signal by pulses according to pre-programmed time laps. This UHF signal can be detected by more than one of the satellites from the Argos network when these satellites pass over the tags. During this measurement window lasting a few minutes, the satellites can calculate the animal’s location based on the Doppler effect (i.e., the shift in pulse radio frequency due to the movement of the satellite 54 Ecophysiology and animal behaviour relative to the tag) and the satellite then relays this information to receiv- ing-interpreting sites located on the ground like the Argos system data processing centres. Satellites network from the Argos system allow theoretically locating a PTT anywhere on the earth with an accuracy of about 150 meters (Wikelski et al., 2007; Bridge et al., 2011). For determination of more accurate locations down to a few meters, a GPS (global positioning system) can be installed on the animal. The GPS tag sends an UHF signal to a specific satellites network different from the Argos satellite constellation. These satellites return then the signal to the tag which calculates its own position by trilateration. The number of location records per unit time is pre-programmed by users and data are then sent to a satellite relay and available using an internet interface for example (e.g. the Argos network). This GPS technology is more precise than direct telemetry by the Argos system (less than 10m accuracy) and now Argos devices often integrate a GPS tag that sends locations within the Argos transmission. However, the GPS system coupled to the Argos relay is also more energy-consuming, especially for real-time location. Thus, GPS devices require powerful batteries and have a relatively shorter lifespan than PTT devices of the same weight. Currently, the smallest available Argos transmitter weights approximately 5g, while the smallest device for Argos satellite-relay GPS tracking weights 22g with a maximum lifespan of several months depending on the frequency of data sampling and retrieval (Guilford et al., 2011, see table 1). Therefore, the use of satellite-based tracking technologies is still restricted to animals weighting more than 100g especially when it comes to research projects that require long-term tracking data (Wilkeski et al., 2007). To fulfill the lack of technology applicable to smaller animals, the Icarus initiative, abbreviation of the International cooperation for animal research using space headed by Martin Wikelski at the Max Planck Institute in Germany, aims at establishing a remote sensing platform for tracking over large spatial scales with transmitters as small as 1g (www.icarusinitiative.org). The project will require the deployment of low orbit altitude satellites to track the weak UHF signals of low weight transmitters located on the ground. A test of the method using an antenna attached to the International space station will start by 2014. An alternative to the very energy-consuming GPS tracking in real time is GPS data logging, where location data are stored locally on the tag attached to the animal. Several manufacturers develop GPS data loggers for wildlife tracking with a total weight starting at values as small as 2 grams and with an accuracy within 2.5m for on-ground measurements (table 1). The main limitation of these small GPS loggers is that the retrieval of the data requires recapturing the animals to download from the data logger. Thus, some of these devices also allow downloading the location data using Part I – Chapter 2 55 remote communication via satellites or via VHF, which brings the total weight of the equipment up to about respectively 15g and 30g. In addition, solar panels can be used to power the GPS directly in direct sunlight and to charge the battery of the logger. This technology helps reduce the weight of the battery while at the same time increasing the overall lifetime possibly up to several years and increasing the capacity to collect more location data. Nevertheless, the solar panels also cause some overload and the smallest available devices of this type weigh approximately 5g, which is still too heavy to fit on numerous small animal species. Irrespective of the method used (satellite Argos PTT or GPS tracking), another important limiting factor of positioning systems using satellites is that marine species cannot be tracked during the diving phase because the UHF signal used for satellite communication propagates badly in the sea depths.

3. 5. Bio-logging for small species More complex environmental and physiological parameters can be collected with the help of various types of data loggers (see table 1). Autonomous bio-loggers like data storage tags and iButton® devices can be set up on some small animals because of their relatively low weight (around 2g), but the data from these loggers cannot be retrieved from a distance. Animals must therefore be recaptured to upload the data, which may be time-consuming and difficult to practice under field conditions. For example, the iButton thermal loggers were used to gather data on body temperatures of small reptiles in the laboratory because these loggers are small and cheap (Lovegrove, 2009; Robert and Thompson, 2003). Similar thermal data can be obtained by using temperature-sensitive passive integrated transponders. In addition to this, data storage tags offer the possibility to measure a great variety of parameters including light intensity, tilt, pressure, depth, magnetic field strength, pitch and roll, conductivity, dissolved oxygen, pH, electroencephalograms, electrocardio- grams, or electromyograms (Walsh and Morgan, 2004; Van der Kooij et al., 2007; Jonsson et al., 2010) with minimum weights starting at approximately several grams. For example, a 3.4 g archival tag allows to measure both temperature and pressure during a minimum period of one year (table 1). However, more integrated and complex systems become inevita- bly heavier. For example, a neurologger used in combinaison with a GPS has been developed by Vyssotski et al. (2008) to analyse the neuronal activity of navigating homing pigeons and weights 35 grams. Data loggers combined with GPS, VHF or satellite transmitters are essential to our understanding of the behaviour of large animals (see I, 1) since these technologies offer the possibility to correlate location or movement with various bio-physical and physiological parameters (Ropert-Coudert 56 Ecophysiology and animal behaviour and Wilson, 2005). Yet, these loggers can be difficult to mount on small animals due to their large volume and their weight typically larger than 10-20g, which is the minimum weight for a current GPS data logger (see section 3.4 above). When the sole focus is on location, geolocators (or GLS logging) can provide an alternative lightweight technology for tracking individual movements on large spatial scales. The method consists to measure ambient light level during the day from which sunset and sunrise times are estimated from thresholds in light curves. The latitude and longitude are then derived from characteristics of daytime light cycles. Archival GLS loggers are now as small as 0.5g and can be fitted on small animal species, but the main limitations against the deployment of this technique are that i) animals must be recaptured to retrieve the data and ii) the accuracy of the measurements is rarely better than 100 km (Phillips et al., 2004). Nevertheless, this technology is adequate for tracking sea birds, migratory passerines or pelagic species (e.g. marine mammals, penguins) in order to determine long-distance movements, breeding season foraging ranges or broad-scale habitat preference from several months to multiple years. In addition to this range of bio-loggers, animal-borne video and environmental data collection systems (AVEDs) have been used for a long time to study large marine or terrestrial species (Moll et al., 2007), and have been recently upgraded to be suitable for a wider range of smaller species (Rutz and Bluff, 2008). For example, a device weighting about 14g and adapted to crows seems promising to collect animals’ eye view of resource use and social interactions along a known movement trajectory. Unfortunately, the system is still too heavy to fit on many small species and its current recording capacity does not exceed 1 hour. Advances are obviously necessary to improve the performances of this technology and to extend the field of investigations to smaller model species.

4. Designing the future generation of sensors for small animals

Our comparative analysis of available sensors highlights that current systems present major limits. The first one is that even basic sensors constitute a real burden for the small animals that have to carry them. Therefore, the main technical challenge is the miniaturisation of the components. An extra challenge is that this miniaturisation must be done without too much loss of capacity in the number of measured parameters, autonomy, or detectability. Another necessary improvement is to develop compatible fixation procedures. For large animals, various techniques (including glues, harnesses, subcutaneous or intraperitoneal implants, or ingestible tags) have Part I – Chapter 2 57 been experimented to attach the animal-borne device with a minimum hin- drance. However, ecologists who work on small species must often develop their own attachment techniques and materials since few standard are available or adapted to their models, and this process generally adds annoyances to a burden yet significant. The miniaturisation of sensors and associated equipments could limit this problem; however, technological advancements for harmless and weakly invasive fixation techniques are necessary. In addition, to address further questions in the field of animal ecology, we may wish to obtain novel kinds of information. This could be done by combining the “classical” data obtained with standard animal-borne sensors with new data obtained from other environmental sensors. In particular, a wide range of techniques are now available to assess the thermal environments (thermal imagery techniques, Lavers et al., 2005; Hristov et al., 2008), the weight of individuals (for automatic measurement of growth, body mass regulation and feeding activity, Rands et al., 2006), or the acoustic environment (for characterisation of behaviour and social interactions, see chapter by Huetz and Aubin). With such an integration in mind, the next generation of sensors for small animals will therefore allow investigating more accurately the internal and external factors influencing the responses of organisms to environmental variations.

Authors’ references

Olivier Guillaume, Jean Clobert: Station d’Écologie Expérimentale du CNRS à Moulis, CNRS USR 2936, Moulis, Saint-Girons, France

Aurélie Coulon: Muséum National d’Histoire Naturelle, Département Écologie et Gestion de la Biodiversité, Unité Conservation des Espèces, Restauration et Suivi des Populations (CERSP), MNHN-CNRS UMR 7204, Brunoy, France

Jean-François Le Galliard: Université Pierre et Marie Curie, Laboratoire Écologie et Évolution, CNRS UMR 7625 Paris, France École Normale Supérieure, Centre de recherche en écologie expérimen- tale et prédictive (Cereep) – Ecotron Ile de France, CNRS UMS 3194, St-Pierre-Lès-Nemours, France

Corresponding author: Olivier Guillaume, olivier.guillaume@EcoEx- moulis.cnrs.fr 58 Ecophysiology and animal behaviour

Acknowledgement

Jean-François Le Galliard acknowledges the support of the ANR grant Extinction (07-JCJC-0120) and a Marie Curie Fellowship.

References

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Chapter 3

Passive hydro-acoustics for cetacean census and localisation

Flore Samaran, Nadège Gandilhon, Rocio Prieto Gonzalez, Federica Pace, Amy Kennedy, and Olivier Adam

1. Introduction

Scientific observations of cetacean species (whales, dolphins, and relatives) are nowadays intensively collected by biologists for several purposes: identifying the species to monitor their population, locating individuals to collect data on the abundance and seasonal distribution of each species, obtaining detailed information about individuals (e.g. individual behaviour, food and health) and describing interactions among individuals (social group relationships, cross-species interaction). These observations are critical to measure the impacts of anthropogenic activities (e.g. underwater noise, ship strikes, fishery tools and bycatch) on populations and communities of cetaceans. Recently, Simmonds and Isaac (2007) and Simmonds and Eliott (2009) suggested to use whale observations to include relevant indicators in prediction models that describe the global warming of the planet. Odontocetes are top predators and mysticetes eat krill. Global warming will impact their alimentary resources since we know, for example, that krill mass is decreasing in the Austral Ocean. Moreover, some species use hot-spots for foraging and breeding. Observations of changes in the use of these hot-spots by cetaceans will give further insights into changes of their environment. 64 Ecophysiology and animal behaviour

Figure 1: Common observation methods for cetaceans. These methods include visual approaches by human observers, animal-borne sensors (I, 1), genetic methods and acoustic tools. The methods allow to obtain demographic, behavioural and genetic information. © Gandilhon/Breach, © Kennedy/NMML.

There are several methods available to observe and collect scientific data on cetaceans (figure 1). A classical and often-used method is based on visual observations, when cetaceans are identified by sight from a distance by a trained human observer. This technique is simple and trained observers can easily identify species, count the number of individuals or even identify individuals within a group using photo-identification techniques. Behavioural observations can also be conducted with this technique. However, this approach has also some drawbacks, such as the dependence on weather and availability of observation sites (shore, boat, etc), the impossibility to collect data at night, and the potential bias from the observers. Another and more advanced solution to observe and collect scientific data on cetaceans is to use automatic animal-borne sensors. This approach is newer and faces numerous technical challenges, including, for example, the need for sensors able to resist pressure differentials due to the water depth, and autonomous enough in terms of power supply and data storage (see chapter I, 1). We describe the principles and challenges of advanced detection methods based on passive acoustics monitoring (PAM). PAM consists in passively listening to the sounds emitted by animals. It is an attractive alternative to classical visual observations because cetaceans are vocally active and their sound can travel far underwater (Samaran et al., 2010b). In addition, species Part I – Chapter 3 65 of cetaceans can be detected remotely, according to the main features of their sounds (acoustic intensity, bandwidth), the acoustic propagation and the ambient noise level. Note that the extraction of individual acoustic signature is still a scientific challenge. The first step with acoustic methods consists in choosing the right electronic instrumentation to make recordings (hydrophone, amplifier, filters, data acquisition board, data transmission, storage unit). The following steps are dedicated to signal processing and pattern recognition to detect cetacean sounds in the recordings, to identify, date, and localise species observations, and estimate the number of individuals. These acoustic events can then be used by biologists to map the presence of even distribution (locations). The diversity of sounds produced by cetaceans is overviewed in the first part of this chapter. Then, a detailed account of the technological solutions to record and to analyse these sounds for the purpose of ecological studies of cetaceans is presented. Lastly, the application of these techniques is illustrated with case studies taken from our current projects.

2. Acoustic signatures of cetaceans’ species

Cetaceans are marine mammals that include 83 species from two major taxonomic units named Mysticetes (cetaceans without teeth) and Odontocetes (cetaceans with teeth). Cetaceans are present in all oceans, and their conservation statuses are different according to the species (http://www.iucn- redlist.org/). The sound repertoire of different cetacean species may vary from highly stereotyped repetitive sounds like the ones emitted by fin whales (Balaenoptera physalus) and the monotonous clicks of sperm whales (Physeter macrocephalus) to more complex communication calls, like those of bottlenose dolphins (Tursiop truncatus), killer whales (Orcinus orca) and humpback whales (Megaptera novaeangliae). A summary of the frequency range and source levels of sounds emitted by common cetacean species is given in table 1. These sounds are hypothesised to play a crucial role for communication among cetaceans, including group cohesion, searching for food, strategy for eating, mother-calf contact, individual recognition (acoustic signature), hunting, socialising, looking for a partner, delineating a territory, establishing a hierarchy, detecting predators and dangers, and orientation (Frankel, 1998). The intrinsic sound characteristics are non-linearly distorted from the acoustic propagation. Acoustic sound attenuation depends on distance between the cetacean source and the hydrophone and is also proportional to the frequency squared. Leroy’s equation gives the attenuation coefficient α (dB/km) depending on the frequency: 2 = 3 2 + fr f , ()f 6.10 f 0.16 2 2 fr + f 66 Ecophysiology and animal behaviour where f (kHz) is the frequency of the acoustic wave and fr is the relaxation frequency of the boric acid B(OH)3 present in the sea water. The fr value depends on the location and the empirical value is 1.6 kHz in the Mediterranean Sea for example. In addition, acoustic waves can be reflected at the sea surface and bottom, where the level of absorption depends on the nature of the seabed. Furthermore, the propagation speed of the sound in seawater is not constant: it depends importantly on salinity, pressure and temperature. All these parameters can be included in a mathematical model to describe acoustic propagation, based on the Helmholtz equation, where P is the pressure, and c 0 is the propagation speed of the sound in the seawater: 2 2 1 P = . P 2 2 0 c0 t For harmonic solution where P = pe i t and ω is the angular frequency (ω=2πF with F the frequency), the Helmholtz equation writes like: 2 2 p + k 2 p = 0, with the wavenumber k = = . c To solve this equation, different mathematic models and associated softwares could be applied including ray tracing (e.g. Bellhop software), normal modes models (e.g. Kraken software), the parabolic equation (e.g. RAM software) or the wavenumber integration (e.g. oases software). All of these can be downloaded on the Ocean acoustic library website (http:// oalib.hlsresearch.com).

Table 1: Features of sounds emitted by some common cetaceans’ species (adapted from Simmonds et al., 2004). To compare, a large tanker generates low frequency noise (<500Hz, 186db re 1µPa at 1m).

Broadband Frequency range Source level (kHz) (dB re 1µPa at 1m) Sperm whale clicks 163-223 [0.100 – 30] Spinner dolhin bursts 108-115 Bottlenose dolphins whistles 125-173 [0.8 – 24] Bottlenose dolphins clicks 212-228 [110 – 130] Risso’s dolphin 120 65 Fin whale moans 155-186 [0.03 – 0.75] Blue whale moans 155-188 Humpback whale song 144-174 [0.03 – 8] Humpback whale fluke 183-192 [0.03 – 8] and flipper slap Pilot whale 180 [0.5 – 20] Snapping shrimp 183-189 Part I – Chapter 3 67

The analysis of these sounds is difficult because of the presence of noise and non-linear distortion. Usually, different approaches are used to analyse vocalisations or short transient sounds (e.g. odontocete clicks, right whale gunshots). The first step consists in using the bandpass filter corresponding to the species studied, and possibly applying an enhancement of the signal if the amplifier gain is too low for the recordings. To detect the emitted sounds, one method is to set a threshold on the temporal energy signal. The use of the Teager-Kaiser operator represents an alternative that takes into account the fact that acoustic signals vary within the same species or individual, where signal variation is given by equation: ( s ) = s2 ()n sn()1 sn()+1 , where s(i) is the ith sample of the recorded signal (voltage corresponding to the acoustic pressure on the hydrophone, V/dB unit). The main advantages of this non-linear operator (in addition to its easy implementation) is that only three successive samples are needed to calculate Ψ. This operator, taking into account the signal variations, is used to track the instantaneous energy of the signal (for example for amplitude modulation and frequency modulation). Parametric models as autoregressive (AR) or/and mean average (MA) models are also a solution, especially for the detection of vocalisations or non-stationarity in the acoustic recordings. To analyse cetacean sounds, specialists in marine biology make extensive use of the Fourier transform or the spectrogram for the time evolution of frequency variations. More up-to-date methods exist such as the wavelet transform or the Hilbert Huang transform (Adam, 2008).

3. Acoustic observatories

Passive acoustics is based on the use of at least one hydrophone to record underwater sounds during a given time. These recordings typically include a large variety of sounds such as natural sounds (waves, rain, wind…), biological sounds (fish, shrimp, coral reef…) or sounds from human activities (sonar, airgun, marine traffic…). The choice of the recording equipment depends on several factors including the acoustic intensity and frequency bandwidth of sounds emitted by the cetacean species under investigation, the bathymetry of the area where it is deployed, and the ambient noise level, including those resulting from human activities. The choice of hydrophone also depends on the objectives of the study including the willingness to detect one or more specific cetacean species. If the target of the study is to detect cetaceans’ vocalisations over very long distances, the device will be chosen to maximise the amplitude of the signal up to 68 Ecophysiology and animal behaviour the saturating level that may result from marine noise and the minimum amplitude of vocalisations that are to be detected. The solution is to record continuously and keep the whole signal in memory to provide an a posteriori analysis. This recordings can be continuous time or during a specific period every day to optimise the memory size and the power consuming. The other solution is to process cetacean sounds immediately in situ, such that only time corresponding to cetaceans’ presence is stored in memory. This approach can save substantial energy and memory space.

3.1 Instantaneous acoustic observatories Here, the objective is to make recordings from a ship. Therefore, the acoustic system should be light and easily manoeuvrable. Typically, the equipments required are one or more hydrophones, their amplifier, the digital recorder or the data acquisition board and a computer. It is possible to buy this material for 6,000 to 7,000€ for a single hydrophone (sensitivity -170dB re 1V/uPa, [10-90kHz] omnidirectionnal). Digital recorders can now record at 192kHz on 24 bits and it is possible to use data acquisition cards with higher sampling rates and the possibility of different synchronised channels. Instantaneous acoustic observatories are usually used for opportunistic detections of cetaceans in a specific area. This approach may be supplemented by visual observations. The objective is to give information about the cetaceans’ distribution and localise potential hot-spots in a local region. We used this approach in Guadeloupe (French West Indies) to confirm the presence of elusive species like beaked whales, and in Madagascar to record different humpback whale singers.

3.2 Semi-permanent and permanent acoustic stations Semi-permanent or permanent acoustic stations are used for the purpose of monitoring a specific area (hot-spot of whale activity, a channel, a strait, or a harbour…). This system can be deployed with a buoy at the sea surface or can be anchored on the seabed. A good example of the first case is a new prototype that we deployed in Guadeloupe (French West Indies) around the end of 2010 (figure 2). This system was built by CeSigma (www.cesigma.com) and PLK Marine (www.plkmarine.com), and was implemented in a Fish Aggregating Device (FAD) in agreement with the fisheries committee of Guadeloupe. Technical features were a sampling frequency of 200kHz, samples coded on 24 bits, 4 channels, and 700Gb of storage, a Wifi transmission capacity, and continuous recordings (Gandilhon et al., 2010). This so-called “sonobuoy” is used to observe different species including sperm whales, humpback whales, bottlenose dolphins, spotted dolphins, rough-toothed dolphins. Part I – Chapter 3 69

Figure 2: Design of the acoustic system developed by CeSigma and PLK Marine for the scientific program Gualiba I coordinated by the team “Dynamique des écosystèmes Caraïbes” de l’Université des Antilles et de la Guyane and the Centre de neurosciences Paris Sud of University Paris Sud orsay. The sonobuoy consists of an autonomous recording and transmission device connected to a hydrophone recording ocean sounds continuously.

Another device is the autonomous underwater acoustic recorder for listening, manufactured and distributed by Multi-Electronique (www.multi- electronique.com). The entire system is based on a hydrophone, a data acquisition card, an external hard drive and power batteries. An acoustic latch releases the material from its anchor, back to the surface by a buoy. This type of material was used to monitor populations of great whales and some odontocetes for a year in the Mozambique Channel and on the south of Tromelin Island in the Indian Ocean. The buoy was deployed during the mission Eparses 2009 funded by the Marine protected areas, Terres australes et antarctiques françaises (Taaf), Centre d’études biological Chizé (CEbC-CNRS) and the Laboratoire domaines océaniques de l’université de Bretagne occidentale (LDO-UBO). The analysis was done a posteriori to automatically detect the cetacean sounds and extract the presence rate during the different months of the year. Other instruments of the same type have been employed since 2007 in collaboration with 70 Ecophysiology and animal behaviour the Pacific marine environment laboratory (NoAA), CNRS-CEbC, and the LDO-UBO to monitor large whales as part of the Subantarctic Indian ocean Program (see figure 3).

Figure 3: Design of the autonomous hydrophone of the Pacific marine environment laboratory (NoAA) and used for the scientific mission DEFLo-HyDRo. The sample frequency of the hydrophone is 250Hz and the data is coded 16 bits with a storage capacity of 80Go. The autonomy can be chosen from 18 to 24 months depending on if the recordings are continuous or during a short specific period of the day (10 min recordings every 6 hours for example). © NoAA/PML Vents program/Acoustics group (http://www.pmel.noaa.gov/vents/multimedia.html).

Regarding permanent observatories, marine ecologists using acoustic methods have exploited permanent infrastructures from other disciplines, like those of geophysics or physical oceanography. For example, in Europe, the ESONET project (European Seafloor Observatories NETwork, www. esonet-emso.org) brings together specialists in geophysics, chemistry, biochemistry, oceanography, marine biology and fisheries. Different underwater observatories are distributed all around the European coasts. In 2007, in collaboration with Prof. H. Glotin (LSIS, www.lsis.org), we proposed to add the marine mammal observation task to the neutrino detector installed on the ANTARES observatory (http://antares.in2p3. fr) deployed in the Mediterranean Sea (Hyères, France). The advantage of this system is that it provides permanent acoustic recordings throughout the whole year. The objective is to give an indication of the presence of sperm whales and fin whales even during winter, when weather conditions are not optimal for visual observations. Passive acoustics, in this case, is a unique solution to provide this kind of information of cetacean presence. There are other permanent observatories using acoustic detectors, such as the Mars ocean observatory testbed in Monterrey Bay (www.mbari.org/ mars) and the Neptune project in the Northeast Pacific ocean (www. neptune.washington.edu). The modules of these infrastructures are placed on the seabed, and power supply and data can be transmitted by a cable connected to facilities on shore. These projects have a section dedicated to marine biology and especially to cetacean observations. Part I – Chapter 3 71

4. Case studies

We worked on several cetacean species and the diversity of sounds that we dealt with explains the different methods we investigated.

4.1 Analysis of Sperm whale clicks Sperm whales emit two major types of clicks: regular clicks characterised by high intensities and inter-click intervals greater than 0.5ms, and creaks that are successions of clicks of variable amplitude and spacing of less than 0.5sec. The bio-mechanic of click generation is pretty well described in the literature by the model first introduced by Mohl et al. (2003) and subsequently modified by Laplanche et al. (2006). However, automatic detection of clicks can be challenging because the inter-click interval fluctuates over time, and the signal-to-noise ratio varies considerably according to the ambient noise and the position of the whale relative to the recording device (figure 4). Therefore, one needs to trade detection capacity off the risks of false alarms when setting and adjusting the detector.

Figure 4: Sperm whale clicks with underwater noise (upper panel) detected by using the Teager-Kaiser operator described previously (lower panel). Sperm whale clicks were recorded off the coast of Toulon (France) in August 2004. one sperm whale was presented the data shows regular clicks. 72 Ecophysiology and animal behaviour

Figure 5: Data retrieved from the acoustic sensing of Sperm whale clicks. A. Estimation of the Sperm whale length by measurement of the delay between the first pulse and the second pulse. The y-axis measures the relative amplitude of the pulse (normalized correlation between the original pulse and its reflection) and the z-axis corresponds to the number of successives clicks (here, 50 clicks). Sperm whale clicks are multipulsed signals. The first pulse comes from the “monkey lips” close to the blowhole and the other pulses come from reflections from the distal sac Part I – Chapter 3 73 and the frontal sac respectively located at the beginning and the end of the head. We can measure the time between 2 successive pulses: this delay is the time it takes for the acoustic wave to cross the head. From the known celerity of the acoustic wave in the head, this allows to estimate the head length and therefore the body length (Lopakta et al., 2006). B. Classification of Sperm whale clicks. Clicks were classified with the use of three parameters calculated after the Schur coefficients (for details, see Lopatka et al., 2006). Black circles: sperm whale clicks. Red circles: clicks from striped dolphins. C. This figure describes the different steps during the Sperm whale dives. Step 1, Sperm whale emits regular clicks at time intervals greater than 0.5sec to detect the presence of preys. Step 2, Sperm whale emits buzz at time intervals less than 0.5sec to get a better resolution of the “acoustic image” of this volume. Step 3, Sperm whale stops emitting clicks at the end of this part of the dives and will go back to step A for another prey research.

Sperm whale clicks have high frequency components (greater than 5kHz), so it is possible to use a high pass filter cutoff frequency exceeding 1kHz or more to overcome the ambient noise mostly due to the presence of maritime traffic. We tested several approaches to detect sperm whale clicks: Teager-Kaiser operator (see figure 4), autoregressive and moving average models, Schur algorithm, spectrogram and wavelet decomposition (Adam et al., 2005; Lopatka et al., 2005). The goals were firstly to minimise the false alarm rate, and secondly to obtain accurate estimates of the time of occurrence of clicks in order to localise the individuals by triangulation. We found that sperm whale clicks were characterised the best by reflection coefficients of the Schur model (Lopatka et al., 2006). By using the Schur model, it was then possible to characterise the morphology of individuals (the size of the sperm whale can be deduced from the inter-pulse), distinguish clicks of sperm whales from other transient sounds, and locate the individual with precision enough to rebuild its dive profiles (figure 5).

4.2 Detection and localisation of blue whales We have also used passive acoustic monitoring to study Antarctic blue whale populations in the Southern Ocean. Most of the year, Antarctic blue whale emits a low frequency call (from 28Hz to 20Hz, see figure 6A) that lasts for 15-20 sec with high intensity every minute. Algorithms for automatic whale call detection, extraction and discrimination were developed and used on a one-year continuous acoustic dataset (2003- 2004) recorded in the station located in sub Antarctic area near Crozet Islands under the framework of the International monitoring system of the comprehensive-test-ban treaty organisation (IMS-CTBTO). All data are available under contract with the Direction des applications militaires du commissariat à l’énergie atomique (CEA-DAM). The aim was to assess 74 Ecophysiology and animal behaviour

Figure 6: Acoustic study of Antarctic blue whale. A. Two successive Antarctic blue whale calls. Calls are stereotypical sounds, with 2 main frequencies at 28Hz and 20Hz. Antarctic blue whales emit these calls between 2 breathing phase. From this specific time-frequency pattern, these calls can be automatically detected. b. Number of Antarctic blue whale calls detected from the recordings of one hydrophone deployed in the North of Crozet Island from May 2003 to April 2004. the seasonal occurrence of blue whale in a specific area. The detection procedure was based on a matched filter model (Samaran et al., 2008). More than 170,000 blue whale calls were detected all year-round indicating their continuous presence in the region (figure 6B). Results revealed the seasonal occurrence and migration patterns of blue whales, providing information about ecology and habitats in this former commercial whal- ing area (Samaran et al., 2010c). A mathematical model RAM (Range-dependent Acoustic Model) was used to predict how sound levels changed with distance between vocalising whales and IMS receivers. This approach allowed estimating the size of the monitored area, which was estimated to be a radius of 200km. The distri- Part I – Chapter 3 75 bution of the estimated distances confirmed the presence of whales close to the Crozet Islands, showing the importance of this sub-Antarctic area for these endangered species especially during the austral summer feeding season (Samaran et al., 2010b). In addition, the triangular configuration of the calibrated hydrophones of the station allowed localising and tracking calling whales from observed differences in arrival times of the same signals at the three hydrophones. We could therefore estimate the movement and detection range between the recording system and the animals, which are critical data to understand the habitat of calling whales without human disturbance. The sound levels of received calls may also be used to estimate the level of sound emitted by the vocalising whales (Samaran et al., 2010a). The last objective of our analysis was to estimate the total number of vocalising whales. This task was not trivial and could be conducted along two methodological approaches. First, we could search for individual acoustic signature. Unfortunately, this is difficult for cetaceans in their natural environment, especially when they are far from the hydrophone, because of the non-linear distortion of sound due to the acoustic propagation (Musikas et al., 2009). Second, we could use the distance sampling method. This method is well-known for a wide range of applications with visual observations of wildlife, but it is particularly difficult to apply when little or no information is available about the call rate (Marques et al., 2009). Therefore, we suggested a third method based on a joint estimation of the number of calls emitted by each individual and the number of individuals in a specific area (Valsero et al., 2010). The two estimates are given by their probability functions, assumed to follow a Poisson distribution:

k s ()s PB()()s = k = e k! k μt ()μt PC()()t = k = e k! where B(s) is the number of whales in the area s (around the hydrophone) and C(t) the number of detected calls during time t. The estimated number of individuals in the study area at a time t can then be obtained by the method of moments using the maximum likelihood estimation (Valsero et al., 2010): 2 N μˆ = N N 2 ˆ = N 2 N s ()N 76 Ecophysiology and animal behaviour where N is the number of detected calls in the area s during t, N is the 2 mean and N is the variance. The method gives estimates of the number of individuals present in the Crozet Archipelago during the year between 0 and 4 individuals (95% confidence interval) based on the distribution of the time between successive calls and between 2 and 12 individuals based on the number of calls. This type of results is only possible with passive acoustics because visual observations cannot be conducted throughout the whole year in this area, highlighting the importance of having a permanent recording system installed there.

Table 2: Estimation of the number [min, max] of whale individuals around the Crozet Archipelagos by 2 distributions based on the distribution 1) of the time between successive calls and 2) of the number of calls in a specific area (Valsero et al., 2010).

Confidence interval (1) Confidence interval (2) Month 90% 95% 90% 95% May [1, 8] [1, 8] [3, 12] [2, 13] June [1, 8] [1, 9] [3, 12] [2, 13] July [1, 7] [0, 7] [2, 10] [2, 11] August [1, 7] [0, 7] [3, 11] [2, 12] September [0, 6] [0, 6] [3, 11] [2, 12] October [1, 6] [0, 7] [3, 10] [2, 11] November [1, 7] [0, 8] [3, 11] [2, 12] December [0, 6] [0, 7] [3, 11] [2, 12] January [0, 5] [0, 6] [3, 11] [2, 12] February [1, 7] [1, 8] [2, 10] [2, 11] March [0, 3] [0, 3] [2, 10] [2, 11] Avril [0, 6] [0, 7] [2, 10] [2, 11] Mean [0, 3] [0, 4] [3, 11] [2, 12]

4.3 Analysis of Humpback whale songs During the breeding season, Humpback whale males emit songs structured in a hierarchical manner where the basic building blocks are called sound units according to Payne and McVay (1971). Until now, whale experts classified these sounds manually by listening and labeling their recordings on spectrograms. Their objective was to extract the leitmotiv of the song for a specific population in a specific area for the breeding season (Noad et al., 2000; Cerchio et al., 2001). Of course, it is clear that Part I – Chapter 3 77 the choice of the well-adapted hydrophones for this application is crucial to improve the correct classification. To help the biologists, our objective was to develop an automatic analysis of Humpback whales songs (figure 7). This goal is quite difficult because, even when the hydrophone was just positioned in front of one singer, the recordings simultaneously included a lot of vocalisations from the other singers considered as background noise. So, to go further in the automatic classification task, we extracted different features of each sound unit, as duration, bandwith, harmonics, and frequency slopes. In addition, we proposed an original approach to increase the performance of our classifier: we defined the concept of subunits, which means that the sound units defined by Payne and McVay (1971) can be decomposed into one or more subunits. By this definition, we are willing to use the tonal information and the sound prosody (variation of the features in the frequency domain) to increase the correct classification. Our underlying idea is to show that the diverse number of sound unit types performed by the singers can be explained by a limited short number of subunits.

Figure 7: Segmentation of Humpback whale song recording into units (green area) and ambient underwater noise (red area).

The second step of our work was to extract information for each detected sound units. We first focused on the variation of the derivative of the five first main energetic frequencies. We then also defined different types of sound unit shape: sound unit with constant harmonic signals or chirps having increased or decreased, convex, concave or linear frequency variations. These features were extracted with different methods largely used in the Speech processing method (Kay, 1988) and based on the presence of at least one frequency in the sound units (Pace et al., 2009a). Classes were obtained by applying the unsupervised k-means algorithm, a statistical classification method, and the Davies-Bouldin criterion was used to evaluate the similarity within and between classes. This criterion made it possible to determine 78 Ecophysiology and animal behaviour the optimal number of classes for all vocalisations present in our dataset. Based on this method, 18 different groups of subunits were detected in a sample of 424 vocalisations (Picot et al., 2008; Pace et al., 2009b). We finally worked on hidden Markov models to take into account the possible variant duration of the subunits and to characterise the link between successive subunits (i.e. the syntax, see figure 8). The complete method for automatic classification of these sound units is a valuable tool for biologists willing to investigate the song evolution and the interactions between or within populations. In the future, we expect that this method will also make it possible to assign an acoustic signature to a specific Humpback whale individual.

Figure 8: Segmentation and classification of Humpback whale sound units. The sound units are not emitted in a random order by the singers. Thus we used the hidden markov model (HMM) to take advantage of the specific order of these sound units to detect and classify them. MFCC: Mel-frequency cepstrum coefficients, Δs: a measure the temporal rates of change of the MFCCs.

5. Conclusion and future work

Using passive acoustic monitoring to assess cetacean populations has several benefits in comparison with conventional survey methods such as Part I – Chapter 3 79 visual sightings. The animals can be studied continuously without any negative impact. This method is also less dependent on weather conditions than visual methods, and does not rely on animals surfacing in order to be detected. It can be applied globally, including remote areas where visual sightings are usually either too sparse, difficult to gather, or costly. Other advantages of passive acoustic monitoring are that it helps to identify areas of cetacean concentration, seasonal occurrence and distribution patterns; it can facilitate the long-term monitoring of cetacean abundance through variations in call rates over the years, and inform on where to establish marine protected areas. Passive acoustics is therefore an interesting complementary method for the cetacean observations.

Table 3: Advantages and drawbacks of two permanent acoustic systems

Advantages Drawbacks System with Electrical power by solar panels Weather conditions: buoy at the and/or wind turbines movement of waves, wind, sea surface Setting parameters (amplifier bad weather gain, sampling frequency) Risk of damage and theft Data transmission via HF, Wifi Real-time application System Discreet Electrical power deployed Less susceptible to surface Access to instrument on the sea activities (parameters settings) bottom Data transmission

Several passive acoustic approaches are possible, from instantaneous observations with a light deployable hydrophone to continuous observations with sonobuoys deployed either at the sea surface or on the seabed. The solutions offered by permanent observatories have advantages and drawbacks listed in table 3, and future work could be dedicated to build a system that retains the advantages of the two main techniques and elimi- nates the drawbacks. For this purpose, the power supply, data storage, and data transmission limits must be circumvented, especially for real- time applications. The best feasible solution is probably to set up fully cabled systems (data and power) at the sea bottom, even if this solution is quite expensive. Another important task is to develop automatic real-time analysis for the detection and, if possible, the localisation of cetaceans. These analyses would be best conducted in situ and the results could be directly sent to the biologists and the managers of marine protected areas and/or coastal areas. This could provide authorities with real-time monitoring tools to diminish the risk of collision between ships and cetaceans for example. 80 Ecophysiology and animal behaviour

Authors’ references

Flore Samaran: Université de la Rochelle, Observatoire Pelagis, Centre de Recherche des Mammifères Marins, ULR-CNRS UMS 3419, La Rochelle, France

Nadège Gandilhon: Université des Antilles et de la Guyane, Laboratoire de Biologie Marine, Equipe dynamique des écosystèmes Caraïbes, Pointe-à-Pitre, Guadeloupe, France

Rocio Prieto Gonzalez: Universidad de Valladolid, Departamento de Estadística e Investigación Operativa, Valladolid, Spain

Rocio Prieto Gonzalez, Amy Kennedy, Olivier Adam: Université Paris Sud orsay, Centre de Neurosciences Paris Sud, UPS- CNRS UMR 8195, Orsay, France

Federica Pace: University of Southampton, Institute of Sound and Vibration Research, Southampton England

Amy Kennedy: Alaska Fisheries Science Center, National Marine Mammal Laboratory, Seattle, USA

Olivier Adam: Université Pierre et Marie Curie, Institut Jean d’Alembert, Lutheries acoustique musicale, UPMC-CNRS UMR 7190, Paris, France

Corresponding author: Olivier Adam, [email protected]

References

Adam, O. Lopatka M., Laplanche C., Motsch J.-F., 2005. Sperm whale signal analysis: comparison using the autoregressive model and the wavelet transform. World Academy of Science, Engineering and Technology, 4, pp. 188-195. Adam O., 2008. Segmentation of killer whales vocalisations using the Hilbert Huang Transform. EURASIP Journal on Advances in Signal Processing, 2008, pp1-11. Part I – Chapter 3 81

Cerchio S., Jacobsen J. K., Norris T. F., 2001. Temporal and geographical variation in songs of humpback whales, Megaptera novaeangliae: synchronous change in Hawaiian and Mexican breeding assemblages. Animal Behaviour, 62, pp. 313-329. Frankel A. S., 1998. Sound production in: Perrin W. F., Wurisg B., Thevissen J. M. G. (Eds), Encyclopedia of Marine Mammals. Academic Press, San Diego, USA, pp. 1126-1137. Gandilhon N., Gervain P., Nolibe G., Louis M., Adam O., 2010. Creation of an autonomous system on moored Fish aggregating device (FAD) for a permanent acoustic monitoring of marine mammals and other perspectives for marine environment attention, Guadeloupe, F.W.I. 63th Annual Gulf and Caribbean Fisheries Institute (GCFI), Puerto Rico. Kay S. M., 1988. Modern Spectral Estimation: Theory and application. Prentice Hall, Upper Saddle River, USA. Laplanche C., Adam O., Lopatka M., Motsch, J.-F., 2006. Measuring the off-axis angle and the rotational movements of phonating sperm whales using a single hydrophone. Journal of the Acoustical Society of America, 119, pp. 4074-4082. Lopatka M., Adam o., Laplanche C., Zarzycki J., Motsch, J.-F., 2005. An attractive alternative for sperm whale click detection using the wavelet transform in comparison to the Fourier spectrogram. Aquatic Mammals, 31, pp. 463-467. Lopatka M., Adam O., Laplanche C., Zarzycki J., Motsch, J. F., 2006. Effective analysis of non-stationary short-time signals based on the adaptive Schur filter. Transactions on Systems, Signals and Devices, 1, pp. 295-319. Marques T. A., Thomas L., Ward J., Dimarzio N., Tyack P. L., 2009. Estimating cetacean population density using fixed passive acoustic sensors: an example with Blainville’s beaked whales. Journal of the Acoustical Society of America, 125, pp. 1982-1994. Mohl b., Wahlberg M., Madsen P., Heersfordt A., Lunds, A., 2003. The monopulsed nature of sperm whale clicks. Journal of the Acoustical Society of America, 114, pp. 1143-1154. Musikas, T., Samaran, F., Aupetit, M., Adam, O., 2009. Density estimation of Antarctic blue whales using automatic calls detection. First International workshop on density estimation of marine mammals using passive acoustics, Italy. Noad M. J., Cato D. H., Bryden M. M., Jenner M. N., Jenner K. C. S., 2000. Cultural revolution in whale songs. Nature, 408, pp. 537. Nosengo N., Riccobene G., Pavan, G., 2009. The neutrino and the whale. Nature, 462, pp. 560-561. Pace F., White P., Adam O., 2009a. Comparison of feature sets for humpback whale song classification. Proceedings of the fifth IOA International Conference on Bio-Acoustics, 31, pp. 136-144. 82 Ecophysiology and animal behaviour

Pace F., White P., Adam o., 2009b. Characterisation of sound subunits for humpback whale song analysis. 4th International Workshop on Detection and Localization of Marine Mammals using Passive Acoustics, Italy. Pace F., benard F., Glotin H., Adam o., White P., 2010. Subunit definition and analysis for humpback whale call classification. Applied Acoustics, 11, pp. 1107-1114. Payne R. S., McVay S., 1971. Songs of Humpback whales. Science, 173, pp. 585-597. Picot G., Adam O., Bergounioux M., Glotin H., Mayer F.-X., 2008. Automatic prosodic clustering of humpback whales song. New Trends for Environmental Monitoring using Passive Systems, pp. 1-6. Samaran F., Adam O., Motsch J.-F., Guinet C., 2008. Definition of the Antarctic and pygmy blue whale call templates. Application to fast automatic detection. Canadian Acoustics, 36, pp. 93-103. Samaran F., Adam O., Motsch J.-F., Cansi Y., Guinet C., 2010a. Source level estimation of two blue whale subspecies in southwestern Indian Ocean. Journal of the Acoustical Society of America, 127, pp. 3800-3808. Samaran F., Adam o., Guinet C., 2010b. Detection range modeling of blue whale calls in southwestern Indian ocean. Applied Acoustics, 71, pp. 1099-1106. Samaran F., Adam O., Guinet C., 2010c. Discovery of a mid-latitude sympatric area for two Southern Hemisphere blue whale subspecies. Endangered Species Research, 12, pp. 157-165. Simmonds M. P., Dolman S., Weilgart L. (Eds) 2004. Oceans Of Noise. The Whale and dolphin conservation society, Chippenham, UK. Simmonds M. P., Isaac S., 2007. The impact of climate change on marine mammals: early signs of significant problems. Oryx, 41, pp. 1-8. Simmonds M. P., Eliott W. J., 2009. Climate change and cetaceans: concerns and recent development. Journal of the Marine Biological Association of the United Kingdom, 89, pp. 203-210. Valsero, M. C., Prieto Gonzalez, R., Samaran, F., Adam, O., 2010. A spatio- temporal Poisson model to estimate the density of the Antartic blue whales in the Austral ocean. International Workshop on Applied Probability, Espagne. Chapter 4

Bioacoustics approaches to locate and identify animals in terrestrial environments

Chloé Huetz, Thierry Aubin

1. Needs for non-invasive methods to identify and locate animals

Population assessment and a proper understanding of behavioural strategies are central and urgent tasks in conservation biology. Nevertheless, up to now, field-based biological researches are held back by the difficulty, cost and intrusiveness of marking and tagging animals, and the relative ineffectiveness of manual data collection and analysis thereafter. Indeed, the monitoring of wild animals almost systematically presupposes their catching first. This invasive stage is not necessary when animals are acous- tically monitored (Gilbert et al., 1994; Hartwig, 2005). Almost all vocal species possess unique acoustic patterns that differ significantly from one to another individual, while following a common structure typical of the species. By using acoustic analysis methods, it is then possible to identify individuals or species emitting vocalisations (insects, frogs, birds and mammals). Sound sources have also the property to be localisable. Until recently, localisation of wild animals by acoustic methods was not widely used. This was mainly due to technical limitations, as the monitoring of simultaneous acoustic sources is problematic in the field. Indeed, among several requirements, simultaneous field recordings devices have to share features such as being wireless, waterproof, easily transportable, and with large memory capacities. Now that technologies exist to overcome these limitations, it has become possible to localise and track the movements of animals that generate sounds. Such systems have been first called acoustic 84 Ecophysiology and animal behaviour location systems (ALS) by McGregor et al. (1997), but are now currently named automatic acoustic survey systems (AASS). An accurate AASS must fulfil two conditions: it must allow to localise the sound source with precision and identify the emitter. Until now, AASS have been used mostly to detect marine mammals (e.g. Stafford et al., 1998; Mellinger and Clark, 2003; Clark and Clapham, 2004; see chapter I, 3). In contrast, AASS dedicated to the monitoring of terrestrial species are rather uncommon (even though Mennill et al. 2006 used it for a case study with birds). For cetaceans, sensors are hydrophones and AASS spatial precision for animal localisation is in the kilometre range. Animals can also be tagged with localisation systems such as Argos for more accurate localisation, but even these systems cannot provide an accuracy below the metre range. For terrestrial species, more accurate locations are often required, for example, to determine the relative positions of neighbouring birds. In addition, tagging small terrestrial animals is often impossible due to several technical reasons (small size, difficulty of catching animals, see chapter I, 2). Moreover, marine and terrestrial environments differ from an acoustic point of view, the latter being often less homogeneous, with more obstacles. Here we review and discuss the accuracy of AASS for monitoring the position of animals vocalising in different terrestrial environments. We first introduce the principles and purposes of acoustic location systems. Then, we propose a non-exhaustive review of the different methods that can be implemented in an AASS in order to automatically locate animals from the sounds they produce. We also present several existing methods used to extract from a vocalisation the individual and the species signatures. Finally, we illustrate the use of these technologies with some recent field applications concerning the location of birds in forest habitats.

2. Localisation system: time delay of sound arrival estimation and triangulation

2.1. Principle of Automatic Acoustic Survey Systems Acoustic location systems use simultaneous recordings from an array of acoustic sensors (microphones or hydrophones) scattered over a particular area. From these recordings, two measurements can be extracted to compute the sound-source location: the level (or amplitude) differences between recordings, and the time delays between the sound arrival times at spatially separated microphones (see for review McGregor et al., 1997; Mennill et al., 2006). The latter, usually designated as the time-of-arrival Part I – Chapter 4 85 difference (TDoA) is estimated either i) by pair-wise cross-correlations of the sound waveforms recorded from the time-synchronised microphones or ii) by beamforming algorithms, which can be defined as the sum of all signals or their energy in the time domain (Valin et al., 2004) or in the frequency domain (Chen et al., 2006) properly time-delayed. The cross-correlation between two recordings shows one peak, corresponding to the TDoA of the same sound source at the two microphone positions.

Figure 1: Representation of our automatic acoustic survey system (AASS) localisation procedure. The first step consists in setting up the microphones and the wireless recording system (1). All pairwise distances between microphones are measured with a lasermeter (1.1), and all channels are recorded synchronously on a laptop computer (1.2). The offline analysis (2) follows three steps: from the 6-channels recordings, the animals’ vocalisations are extracted and band-pass filtered (2.1) to extract putative noise sources, then pairwise cross-correlations of the waveforms are performed for each vocalisation (2.2) in order to compute the time-of-arrival differences, which are then used to locate each vocalisation (2.3).

Sound source location can then be determined using the so-called triangulation principle. Indeed, the TDoAs of a sound between each microphone pair constrain the emitter’s location to a hyperboloid, and its 86 Ecophysiology and animal behaviour precise location can be resolved by intersecting hyperboloids from many pairs of receivers. Another possibility is to divide the space around the microphones into cells by a grid and compute all possible TDoAs for each cell. The location of a sound source is then computed by minimising the difference between the measured TDoAs with the theoretical ones. When using the beamforming algorithms, TDoA are estimated by searching the delays between recordings that maximise the output signal of the beamformer. Lastly, some methods use simultaneously the TDoA, the level (amplitude) difference, and the reflection of the sound on the surface and ground to locate with a greater precision the emitter (Cato, 1998).

Figure 2: Example of the output of our localisation system. In this case, a loudspeaker was placed outside the microphone configuration (red star) and was emitting seven vocalisations. Each vocalisation was then localised (blue circle). As shown on the zoom, the localisation error is small, on the centimetre range. our AASS consisted of an array of six wireless omnidirectional microphones recording simultaneously up to six-channel sound files on a laptop computer via an emitter-receiver station (figure 1). The microphones were set up throughout a given area in the field. Exploiting the speed of sound propagation through air, the system triangulates the position of sound sources on the basis of TDoAs at the microphones. These TDoAs Part I – Chapter 4 87 are estimated using a cross-correlation of the band-pass filtered waveforms. Instead of using the standard triangulation equations, we applied a simple exhaustive search in space like Hammer and Barrett (2001). Test experiments were conducted in the field in Brazilian rainforest and in European temperate forests in order to probe the accuracy of the system. A loudspeaker was placed at a given location around the microphones, and its distance to the microphones was measured with a lasermeter. Several sounds were played and recorded on the AASS. Figure 2 shows an example of the localisation system’s result for the playback of seven bird songs (computed source in blue circles). The “real position” of the emitter (the loudspeaker) is indicated with a red star and it can be seen that all songs are localised very close to the “real” position measured with the lasermeter.

2.2. Limits of Automatic Acoustic Survey Systems Acoustic localisation systems are prone to the same constraints and usually present the same limits as any kind of acoustic survey system. Indeed, sound localisation of animals is difficult when emitters are distributed widely in the open and when reverberations degrade temporal patterns in vocalisations (Spiesberger, 1999). Selective filtering due to obstacles in the environment can also modify the signal during propagation (Spiesberger, 1999; 2005). In noisy environments, multiple sounds coming from different species or individuals, and their background, can overlap with the signal of interest in the temporal and frequency domains, producing a jamming effect and leading to false localisations. An insufficient signal- to-noise ratio in turn impairs the localisation process (Quazi and Lerro, 1985) and methods to enhance the signal-to-noise ratio (e.g. a band-pass prefiltering) must be applied when possible. In general, any factor deteriorating the cross-correlations between recordings strongly affects the TDOA estimation and therefore can diminish the accuracy of the localisation process (Spiesberger, 1999; 2005). In addition, several constraints are specific to the use of an AASS for the purpose of localisation. The first necessary prerequisite of any localisation system is to know accurately the relative positions of the microphones (Quazi and Lerro, 1985). Indeed, in order to make use of the TDOAs and convert them into distances relative to the microphones, the whole microphone configuration has to be known, and subsequently, several pairwise distances between microphones have to be measured. The accuracy and the space coverage of the localisation process strongly depend on the inter-microphone distances. In fact, the optimisation of the spatial configuration of microphones faces trade-offs between a large spacing, an accurate measurement of their relative positions, and a good signal-to- 88 Ecophysiology and animal behaviour noise ratio of the recorded vocalisation on the different microphones. If most of the studies assess that, in theory, three microphones are necessary to localise in 2D, and four in 3D, it has been shown that one additional microphone is needed to obtain accurate estimates of sound location (4 in 2D and 5 in 3D, Spiesberger, 2001). Moreover, some redundancy can help the localisation process (Chen et al., 2003). Depending on the needed accuracy of the output localisation and on the studied environment, several methods can be used to measure the relative microphone positions. The position of the microphones can be estimated by the coordinates of a global-positioning system – GPS (Mennill et al., 2006). However, GPS positioning can hardly be used when animals need to be localised with great accuracy (less than 1m), or in obstructed environments such as dense forests. In these cases, a lasermeter can be used under the condition that no obstacle stops the laser beam and thus prevents the distance measures between pairs. This constrains the inter-microphone distances in thick vegetation or in irregular topography. Another constraint on the microphone configuration is the perfect time-synchronization that must be achieved between devices. A wireless or a wired synchronization system is therefore needed.

3. Identification systems: review of acoustic methods to extract species and individual signatures from animals’ vocalisations

3.1. Principles of identification systems The fact that animals can recognise one another by voice alone has been demonstrated repeatedly, especially in birds (for a review see Falls, 1982; Dhondt and Lambrechts, 1992) and mammals (for example Balcombe and McCracken, 1992, for bats; Caldwell et al., 1990, for dolphins; Sèbe et al., 2010, for lambs; Tooze and Harrington, 1990, for wolves). The information content of a signal is represented by structural sound features such as its spectrum or the temporal evolution of its amplitude and frequency modulations (see figure 3). A signal would be ideal for individual or species recognition if it is highly stereotyped within each individual or species, and if it significantly differs between individuals or species. Different methods are available for determining which parameters encode acoustic identity and allow recognition between species or between individuals. Part I – Chapter 4 89

Figure 3: Examples of display calls (above: spectrograms; below: oscillograms) emitted by three individuals of King penguins Aptenodytes patagonicus (from Aubin and Jouventin, 2002). 90 Ecophysiology and animal behaviour

Among the possible means to identify animal vocalisations, the most classical method is the visual inspection and labelling of oscillographs or spectrographs. This process is time consuming and dependent upon the judgement of one observer (Kogan and Margoliash, 1998). Beside this “manual” approach, some more or less automatic classification methods can be used. A first method is based on variance calculations realised on the temporal, amplitude and frequency parameters of a given signal. For each call parameter, the between-individual and within-individual coefficients of variation (CVbi and CVwi) can be calculated as follows: 1 CV = 100 (SD/Xmean )(1 + [ 4 n ]), where SD is the standard deviation, Xmean the mean of the sample and n the sample size (Sokal and Rohlf, 1995). To assess the potential for the coding of individual identity (potential of individual coding: PIC) for CVbi each parameter, the ratio E(CVwi), where E(CVwi) is the mean value of the CVwi of all individuals, is calculated (Scherrer, 1984; Robisson et al., 1993). For a given parameter, a PIC value greater than 1 means that this parameter may correspond to an individual parameter as its intra-individual variability is smaller than its inter-individual variability. In the same way, the potential of specific coding (PSC) can be evaluated with the same formula by using the between-species and within- species coefficients of variation. Beside this univariate approach using parameters with high PIC or PSC values, multivariate analyses (discriminant function analysis, DFA, principal component analysis, PCA, and artificial neural network analysis, ANN) can be performed (figure 4). All these methods provide classification procedures that assign each recorded call to its appropriate emitter (correct assignment) or reject the assignment (see Terry and McGregor, 2002). Other existing vocalisation classification techniques are based on traditional automatic speech recognition methods (Rabiner and Juang, 1993). one conventional method, the dynamic time warping (DTW; Anderson et al., 1996), is well suited for the detection of pure tones such as those in bird, bat and cetacean songs. This method compares the spectrograms (frequency versus time representation, see figure 3) of input sounds with those of a training data set of predefined templates (representative of sounds to detect and chosen by the investigator) by successive cross-correlations (Clark et al., 1987). These templates are the targeted database for the matching process. In contrast to the deterministic template matching of DTW, another method, the hidden markov model (HMM, Rabiner, 1989) uses a statistical representation. Briefly, an HMM is typically a collection of finite sets of states. Each state represents spectral properties in the form of Gaussian mixtures of spectral features, while temporal properties are represented by state transition probabilities. Each state has Part I – Chapter 4 91 a probability distribution over the possible output cases. Therefore, the sequence of cases generated by a HMM provides information about the sequence of states and are thus especially adapted for temporal pattern recognition of sounds such as sequences of successive notes in songs. This list of classifiers presented here is not exhaustive and it is possible to find in the literature a lot of other acoustic identification methods such as for example the spectral peak tracks method (SPTM) recently suggested by Chen and Maher (2006).

Figure 4: A principal component analysis taking into account 2 factors (F1 + F2 = 54% of the total variance) and based upon 18 acoustic parameters measured in the song of a tropical bird, the White-browed Warbler Basileuterus leucoblepharus. on this basis, the PCA separates 71% of the 21 individuals analysed. Each polygon corresponds to one individual (from Aubin et al., 2004).

3.2. Limits of identification systems All the methods described above are well suited to classify sounds, but they also have some limitations. The most important requirement for the reliability of the identification of vocal signature is that emitters produce individualised and stable vocalisations. Another necessity is to have a prior knowledge of the structure of the vocalisations emitted by the individuals 92 Ecophysiology and animal behaviour who will be then automatically identified. For example, as underlined by Terry et al. (2005), discriminant function analysis may assign all vocalisations to particular individuals if all individuals are known; thus this method cannot accommodate vocalisations from new individuals. All these methods, qualified as “supervised” methods, use in fact a training data set or templates that must be “learned” by the classifier. Instead, the HMM model is based on a statistical calculation; it can therefore accumulate more information and possibly generalise better than techniques based on fixed templates. The ANN classifiers often require a very high computational complexity. The DTW method does not use amplitude normalisation, so the results may be sensitive to amplitude differences between signals. The DTW and HMM methods do not perform well in noisy environments or for sounds with short duration and variable amplitude. In a word, all of the proposed methods accommodate well tonal or harmonic sounds, but are inappropriate when vocalisations containing aperiodic or noise-like components are involved.

4. Field applications

Studying animals in their natural habitat with a minimised influence on their behaviour is a key issue in ecology and ethology. Locating animals by the sounds they produce has the first advantage to be passive, meaning that the effect of the AASS on animal behaviour is insignificant. The second benefit is that terrestrial AASS can be used in habitats where visual location is difficult, such as dense vegetation (tropical forests, bushes, reed-beds). These systems may be also useful for studying secretive animals, difficult to observe because of large home ranges or nocturnal activity (see chapter IV, 2). They also ensure an accurate investigation of biological questions such as territorial defence, nest site, and mate fidelity. For example, they may be used to monitor multiple individuals simultaneously and, therefore, study behaviours such as territorial interactions between neighbours and duetting (Bower and Clark, 2005; Burt and Vehrencamp, 2005; Mennill and Vehrencamp, 2008). Thus, these systems seem particularly suitable for studying communication networks. For example, we are currently studying in the Amazonian forest the acoustic network of a typical bird of this habitat, the screaming piha Lipaugus vociferans. This species shows a remarkable form of lek display: females are attracted by singing assemblies of males and come for mating. Up to 25 males distributed on an area of about 600m of diameter are usually observed at a single lek. Each male has its own song posts and it counter-sings the other males. The use of an AASS will enable us to decipher this complex vocal organisation and particularly, to examine the singing interactions and movements of all the birds from a lek. Part I – Chapter 4 93

In a more general way, AASSs appear extremely useful to provide, with very little human interference, quantitative and qualitative indices of animal diversity in poorly accessible environments. Starting from this method, it is possible to develop concrete applications with an acoustic platform designed for the estimation of biodiversity of the fauna in different categories of environments. An automatic acoustic survey may help in the evaluation of the biological quality of a given habitat and appears as a useful tool for measuring the abundance of species or the impact of human activity on biodiversity (Sueur et al, 2008; see also II, 1). This method should sub- stitute to the traditional human visual or hear sampling realised on point counts (i.e. Uezu et al., 2005) to estimate the abundance of vocal species in remote or obstructed environments, such as rainforests. Thus, we believe that acoustic location and identification technology may provide a valuable and versatile tool for ecologists and ethologists.

5. Conclusion: future orientations, developments and needs for new sensors?

AASSs offer one of the best solutions for a non-invasive sensor that will enable biologists both to locate and identify individuals of a large number of “singing” species within a population in cheap, fast and automatic manner and in a wide range of environments. The emergence of this method is sped up by recent technological advances. Thus, commercially available autonomous digital recorders are now able to collect thousands of hours of audio data. The automatic identification of vocalisations is not always perfectly accurate, but the development of new algorithms methods in automatic human speech recognition has recently improved the process. Source localisation algorithms for monitoring vocal wildlife populations are now efficient, the limitations being mainly due either to imprecise microphone coordinates or the presence of a particular constellation of competing sound sources in the field. In the first case, the development of more accurate GPS would be an alternative to laser measurements to geolocate more precisely microphones. With such systems, it would also be possible to increase the distance between the microphone and thus monitor a wider area. In the second case, the noise generated by parasitic sounds overlapping the target sound, should be more or less removed by using specific artificial neural network analysis. Despite significant progress in source localisation theory and sensor network systems, progress toward developing prototype AASSs has been greatly slowed down by the absence of integrated platforms suitable for monitoring wild animals. An emphasis must be now placed on fully integrated systems (hardware and software) that are robust enough to be deployed in all kind of environments, and user-friendly enough to be used by biologists with little or no technical expertise. 94 Ecophysiology and animal behaviour

Authors’ reference:

Chloé Huetz, Thierry Aubin: Université Paris-Sud, Centre de Neurosciences Paris-Sud, UPS-CNRS UMR 8195, Orsay, France

Corresponding author: Thierry Aubin, [email protected]

Aknowledgement

We thanks F. Sèbe, H. Courvoisier and M.L. da Silva for technical support and help in the field. The work in the Amazonian forest (Brazil) was financially supported by a FP7-people-IEF grant.

References

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Chapter 1

Global estimation of animal diversity using automatic acoustic sensors

Jérôme Sueur, Amandine Gasc, Philippe Grandcolas, Sandrine Pavoine

1. Introduction

The estimation of biodiversity can be considered as one of the main challenges in modern biology. When dealing with ecology, evolutionary biology and conservation biology, there is an inescapable need to describe the composition and dynamics of biological diversity (Magurran, 2004). In ecology, the concept of biological diversity is mainly species-oriented, even if other evolutionary units or traits can also be used. In this context, biodiversity potentially refers to all species encountered in a given area at a specified time, including every potential species from underground bacteria to giant trees. Therefore, biodiversity assessment can turn out to be a time-consuming and complex task, as it relies on species inventory that may involve very different taxonomic groups. Exhaustive approaches such as the all taxa biodiversity inventory (ATBI) programs aim at inventorying the whole biodiversity mainly in tropical habitats (Gewin, 2002), but these programs are highly sensitive to the logistic and time-constraints of most inventory studies. An alternative to these approaches is to focus on one or a few taxa and consider them as biodiversity indicators (Pearson, 1994), but the choice of representative taxa is not trivial (Lawton et al., 1998). In addition, it is well known that patterns of species diversity for different taxa are sensitive to the observation scale. More precisely, there is a general congruence for species diversity between different taxa at a large area scale (more than 1km2) but not at a fine scale (less than 1km2, Weaver, 1995). This renders difficult the definition of an indicator taxon or even of several indicator taxa suppos- edly representative of the diversity in other forms of organisms (Ricketts 100 Biodiversity et al., 1999). Irrespective of the taxonomic breadth of any biodiversity assessment, the estimation of species biodiversity relies on inventories and species examination by one or several taxonomic experts that can be supported with genetic barcoding techniques (see chapter II, 3). Sampling in the field and identification in museum collections can require a considerable effort when the objective is to sample a large region for a long time period. To improve the rate of specimen collection, non-specialist taxonomic workers – or para-taxonomists – can separate morpho-species instead of identifying valid species. This solution is advocated by the rapid biodiversity assessment (RBA) programs that have been especially developed for the rapid exploration of biodiversity in tropical habitats (Oliver and Beattie, 1993; 1996; Oliver et al., 2000). Biodiversity assessment is often restricted to species richness, i.e. to the counting of the total number of species. However, a collection of species cannot be described solely by the number of items it includes. The abundance of each species has to be assessed to provide an estimation of species evenness. Evolutionary, ecological and life history characters of the species also describe facets of biodiversity (Brooks and McLennan, 1991; Vane-Wright et al., 1991; Grandcolas, 1998; Pavoine et al., 2009; Petchey et al., 2009). Species turnover along time and/or spatial scales is also required to take into account biodiversity dynamics. All of these requirements led to a plethora of biodiversity indices that have been developed for decades (Magurran, 2004; Buckland et al., 2005; Pavoine and Bonsall, 2010). In practice, a measure of biodiversity can be achieved with direct or indirect sampling. In the latter case, the use of a sensor should be ideally considered by employing a simple tool that returns an index of biodiversity. A human observer or a network of human observers might be considered as a “biodiversity sensor”, but may be biased by the experience of the observers and cannot be “deployed” in rough terrains for long periods of time. Another possibility is to work with local image, video capture instruments or with global satellite imagery. Satellite-based earth observations, or remote sensing, can produce environmental parameters from biophysical characteristics that can be indirectly used to assess species ranges and species richness patterns (Kerr et al., 2003, Turner et al., 2003; Wang et al., 2010; see also IV, 2). These methods are undoubtedly very attractive, but they rely on extremely expensive equipment and are difficult to adapt to small spatial scales. Like other methods, remote sensing often requires a time-consuming validation step. For instance, vegetation mapping with satellite images is based on a colorimetric calibration of pixels with a large set of direct vegetation samples. The use of a given sensor should be made according to a sampling strategy designed and evaluated carefully with respect to the type of data to Part II – Chapter 1 101 be collected. It is particularly important to identify the precision of the measurement system (data quality) and the level of accuracy that has to be reached along both time and space scales (data quantity). In most sampling strategies, there is a basic trade-off between precision and accuracy. In this context, we are currently developing an acoustic sensor that would produce a biodiversity index by analysing the sound produced by local animal communities. This approach could provide a portable, cheap, rea- sonably accurate and non-invasive animal diversity sensor that could be used at different space and time scales.

2. Sensing diversity through bioacoustics

Some animal species, including taxa often used in biodiversity studies, produce active sounds during their social interactions or in other contexts. For example, some fish and reptiles, most amphibians, birds, mammals, insects and other arthropods use sound for communication, navigation or predation acts. These acoustic signals generally produce a species-specific signature and several techniques in bioacoustics were developed to exploit these signals as an indication of species occurrence and as a tool for biodiversity studies (Obrist et al., 2010). The most elementary application is sensing by observers. This is usually achieved when following animal populations through aural listening and identification (e.g., Cano-Santana et al., 2008, for crickets). When based on a massive network of listeners, such a census method can generate large datasets of strong interest to ecologists (e.g. Devictor et al., 2008, for birds). Nonetheless, volunteer- based call surveys tend to be replaced by the automated digital recording system (ADRS), which is an electronic equipment that allows automatic data collection and generates a large amount of high-quality information about species biodiversity (e.g. Acevedo and Villanueva-Rivera, 2006, for amphibians). The problems alluded above for species identification with museum specimens is also true for species identification with sound. Acoustic identification is based on the experience of the observers, which can be biased due to sensory or training differences. As any other identification, it also relies on a taxonomic database providing information on the cor- respondence between every species and its acoustic signature. Automatic identification of the different songs embedded in the recording is rather complex and still suffers errors (e.g. Skowronski and Harris, 2006, for bats). These approaches are also difficult to deploy in complex acoustic environments like tropical forest soundscapes, where tens of signals mix up and many species still remain unknown (Riede, 1993). Reliable results can be obtained only when focusing on a single species with a rather 102 Biodiversity simple and loud call as demonstrated with the neotropical bird Lipaugus vociferans (see I, 4) and the Blue Whale Balaenoptera musculus in a marine context (see I, 3). Keeping in mind these constraints, we applied the concept of RBA to sounds produced by animals and even pushed the concept one step further. We recently suggested tackling the problem of diversity assessment at the community level by using bioacoustic methods (Sueur et al., 2008a). In the case of bioacoustics, the unit to work with is the acoustic community, which is defined as the sum of all sounds produced by animals at given location and time. The signals produced by different species can overlap, interfere and consequently reduce signal transmission between the emitter and the receiver of a focal species. Sound produced by other species is indeed considered as noise for the focal species and acts as a severe constraint on the evolution of conspecific signals (Brumm and Slabbekoorn, 2005). Consequently, species sharing the same acoustic space are supposed to show an over-dispersion of the frequency and time- amplitude parameters of their songs reducing the risk of interference. This has been reported in several acoustic communities (e.g., Lüddecke et al., 2000, for amphibians; Sueur, 2002, for cicadas; Luther, 2009, for birds). A measure of sound complexity could then work as a proxy of community richness and composition. The acoustic indices we are developing are mainly based on this concept of acoustic partitioning. We hereafter review the recording equipment and analysis we used to try and build an animal diversity acoustic sensor.

3. Listening and measuring acoustic diversity

A biodiversity sensor provides a measure of a single or a set of variables characterising biodiversity. Even if a sensor is composed of several probes and data analysers, it is often viewed as a all-in-one equipment that senses and analyses the environment concomitantly. Our method currently relies on two different equipments that are not used at the same time. However, we here consider that these sub-units constitute together a single sensor (figure 1). The first sub-unit is a digital sound-recorder that can be settled outdoor. The second sub-unit is a computer installed with software specifically developed to analyse sound diversity. Further statistical analyses on the acoustic indices, i.e. the biodiversity variables measured by the sensor, are not considered as part of the sensor but as part of data analysis processes. We hereafter detail the sampling protocols based on a single recorder or an array of recorders, the properties of the autonomous recorder currently in use and, eventually, the algorithm developed to compute the diversity indices from sound files. Part II – Chapter 1 103

Figure 1: Diagram showing the successive steps of the global estimation of animal diversity. Here, the biodiversity sensor is considered as the combination of different processes: recording, audio file conservation, signal analysis, and indices computation. These processes – in situ recording step with autonomous equipment and ex situ calculation of indices from stored acoustic data – are currently separated from each other. However, a portable all-in-one system might be developed in the future.

3.1. From a single manual recording spot to a network of autonomous recorders The sampling protocol is mainly constrained by the recording equipment available. Our method was first tested with a comparison between two closely spaced dry lowland coastal forests in Tanzania. The recording of the animal communities inhabiting these forests was achieved with a digital recorder (Edirol© R09) equipped with an omnidirectional microphone (Sennheiser© K6/ME62). Recordings were done by a single person at three times of the day and successively in the two forests. This procedure limited the sampling to a few days and to only two sampling sites. Such digital recorders also provide internal microphones that can be used to reduce costs. In this case, several items can be purchased to cover a wider area and a longer period of time. However, the recorders still have to be triggered and stopped manually, a condition that makes field work rather challenging. Unattended recorders were not available since the North American company Wildlife Acoustics© provided an autonomous digital field recorder (see details about this recording package in section 3.2). An autonomous system was absolutely necessary to design sampling protocols with synchronised units such as regular, cluster, multi-level, or stratified protocols. We first used three of these recorders to assess animal diversity within temperate woodlands by simultaneously recording a mature forest, a young forest and an edge forest (figure 2A, Depraetere et al., 2012). We then increased the number of recorders to estimate biodiversity endemism of three New-Caledonian sites. We planned a stratified sampling with four recorders set in each site. This ensured a repetition per site and allowed comparisons within and between study sites (figure 2B). Later, we tracked acoustic diversity of a typical tropical forest by deploying a network of 12 104 Biodiversity recorders regularly spaced on a 100 ×100m grid in French Guiana (see IV, 2). Each recorder was equipped with a microphone settled 1.5m high and a second microphone placed 20m high in the canopy (figure 2C). This 3D regular sampling covered 12ha of forest for more than 40 days. Eventually, we tried to transfer our method to freshwater habitats like forest ponds. This was achieved by adapting the autonomous recorder with a Reson© hydrophone and an Avisoft© pre-amplifier (figure 2D). This high-quality equipment is expensive (about 2700€ per unit) and sampling was therefore limited to three recording units. We therefore designed a rotating sampling by regularly moving the hydrophone position along transects.

Figure 2: The autonomous Wildlife Acoustics© recorder installed for outdoor studies. A. First version of the recorder (SM1) settled in a temperate woodland to estimate local bird acoustic community (Rambouillet, France). B. Second version of the recorder (SM2) with a single microphone in action (Mandjelia, New- Caledonia). C. The same recorder with two microphones, one 2 m high and the other one ready to be set 20 m up in the canopy (Nouragues experimental station, French Guiana). D. Recorder connected to an hydrophone to record underwater sound of a pond (Rambouillet, France).

3.2. The Song Meter: an autonomous acoustic sensor Wildlife Acoustics© developed two generations of autonomous digital recorders, namely the Song Meter SM1 and SM2 (figure 3). These stereo recorders, which weigh 1.6kg each and measure 20.3 × 20.3 × 6.4cm, Part II – Chapter 1 105 possess a stereo recording system with omnidirectional microphones that have a flat frequency response between 0.02 and 20kHz. These microphones can be directly connected to the main box, where data are stored, or can be settled up to 50m away from it. Given that terrestrial animals produce sound with an intensity of ca. 80dB at 1m re. 2×10-5 μPa (Sueur, unpublished data) and given that the microphones have a sensitivity of -36 ± 4dB, we can estimate that in a closed habitat, such as a forest, the microphone detects sounds up to around 100m from the source. A SM2 platform would then cover an area of approximatively 3,1ha. The recording sampling rate can be set from 4 to 48kHz with the standard SM2 and up to 384kHz with the ultrasonic SM2 option. The SM2 recorders are currently working with a lossless compression format (.wac) that can be written on four secure digital (SD) cards. The four SD slots provide 128Go storage space. Choosing an adequate sampling rate is not an easy task as it results from a trade-off between cost, data storage and the sound frequency used by animals. Increasing the sampling rate to high frequency requires a specific and expensive motherboard and, above all, generates very large sound files that are difficult to handle and to analyse. However, this is the only solution to record the acoustic activity of some insects and bats that emit ultrasound signals for communication or navigation. Up to now, we sampled the animal acoustic communities at a 44.1kHz sampling rate. A network of recorders generates thousands of files that need to be stored and analysed (see section 4.2). Using a higher sampling rate will certainly preclude the estimation of acoustic diversity by generating too high an amount of data. Electrical power is provided by four alkaline or LR20 batteries ensuring a maximum of 240 hours of recordings. Energy can also come from an external 12V battery potentially connected to a solar panel. Eventually, the SM2 platform provides also an internal temperature sensor and a connection for an external sensor. The additional data are written on the SD cards together with sound files. The main advantage of the Song Meter is that it can be easily programmed to record on simple time-of-day schedules or to implement complex monitoring protocols, even scheduling recordings relative to local sunrise, sunset and twilight. For instance, a schedule can be programmed to record regularly all day and night long, but also to record more intensively around sunrise and sunset, when dawn and dusk choruses of birds, insects and amphibians occur. 106 Biodiversity

Figure 3: The second version of the recorder (SM2) opened to show the main characteristics. A cable can be used to set the microphones away from the main box. Detailed characteristics can be obtained at http://www.wildlifeacoustics.com. © Wildlife Acoustics.

3.3. Computing the acoustic indices Biodiversity is traditionally decomposed into two levels, the average diversity within communities, or α diversity, and the diversity between communities, or β diversity. We therefore developed two acoustic indices aiming at estimating these two components of biodiversity (Sueur et al., 2008a). Both indices can be computed with the package seewave (Sueur et al., 2008b) of the free R environment (R Development Core Team, 2012). The first index, named H, is a Shannon-like index. The index H gives a measure of the entropy of the acoustic community by considering both temporal and frequency entropy. H is computed according to: H = Ht × Hf with 0 ≤ H ≤ 1, and Ht = - ∑ (A(t) × log(A(t)) / log (n)), and Hf = - ∑ (S( f ) × log(S( f )) / log (N)), where n = length of the signal in number of digitized points, A(t) = probability mass function of the amplitude envelope, S( f ) = probability mass function of the mean spectrum calculated using a short term Fourier transform (STFT) along the signal with a non-overlapping Hamming window of N = 512 points (figure 4). Part II – Chapter 1 107

Figure 4: The main two transforms used on raw recordings. A. Waveform or oscillogram of a sound recording. b. Amplitude envelope, A(t), obtained through the Hilbert transform. C. Mean spectrum, S( f ), obtained through a Fourier transform. Note the different x axes and that all y axes are in relative amplitude along a linear scale.

The H index increases logarithmically from 0 to 1 with species richness and evenness when considering species-specific calls (Sueur et al., 2008a). The index will be particularly high for a signal that has a flat amplitude envelope and a flat frequency spectrum. When only considering the spectral component of the index, a flat or multi-peak spectrum will give a higher Hf index than a single peak spectrum (figure 5 A, b). The H index was applied in Tanzania, and correctly revealed a higher acoustic diversity in the preserved part of the forest than in the disturbed part (Sueur et al., 2008a). However, Hf is not reliable when dealing with recordings made in the temperate woodland, where the acoustic activity is low and polluted 108 Biodiversity with environmental noise. In this particular case, we developed another index, named Acoustic Richness AR which was computed according to: AR = rank(Ht) × rank(M) × n-2, with 0 ≤ AR ≤ 1, where rank is the value position along the ordered samples, M is the median of the amplitude envelope and n the number of recordings (Depraetere et al., 2012). The second index, named D, is a simple acoustic dissimilarity measure. D is similarly composed of two sub-indices based on a difference between amplitude envelopes and frequency spectra respectively (figure 5 C). D is calculated like following: D = Dt × Df with 0 ≤ D ≤ 1, and

Dt = 0.5 × ∑ |A1 (t) – A2 (t)|, and

Df = 0.5 × ∑ |S1 ( f ) – S2 ( f )|, where A1(t), A2(t) are probability mass functions of the amplitude envelope for the two recordings under comparison, and S1( f ), S2( f ) are probability mass functions of the mean spectrum for the two recordings to be compared. The D index increases linearly with the number of unshared species between the two recordings, or communities (Sueur et al., 2008a). Both indices may suffer a bias as some species naturally produce signals with high temporal and/or spectral entropy. This is particularly the case of cicadas whose noise-like sound can be mistakenly interpreted as a high local diversity. Such bias can be buffered with a large sampling including a high number of time and space repetitions. The indices can also produce false values when background noise overlaps with the sound produced by the animal community (see section 4.1). Both indices are currently tested in different temperate and tropical habitats in this respect. Other acoustic indices have been developed elsewhere to monitor habitat state or community activity. Qi et al. (2008) divided the soundscape of an ecosystem following three frequency bands: the anthrophony, between 0.2 and 1.5kHz, the biophony, which starts at 2kHz with a peak at 8kHz, and the geophony, which can cover the entire spectrum with dominant low frequency. By computing a ratio between biological and anthropogenic signals, they coined an ecological estimator of ecosystem health. This original procedure does not give an estimation of local diversity but assesses the level of biological sound activity relative to anthropogenic activities. Pierreti et al. (2010) and Farina et al. (2011) designed an acoustic complexity index (ACI). This index computes time and frequency variability of a sound extrapolated from a spectrogram. The ACI appears to be correlated with the number of vocalisations produced by a bird community. However, this index assesses neither species diversity nor community turnover. The ACI index proved to be poorly sensitive to invariant noise, such as continuous noise from cars or aircrafts, but can be impacted Part II – Chapter 1 109 by unpredictable noise such as wind, running water or irregular human activity. All these acoustic indices, including H, D, and others in current development probably do not quantify the same facet of animal acoustic diversity.

Figure 5: Illustration of a spectral analysis on recordings made in two sites in New- Caledonia (France). A. A recording showing a broadband frequency spectrum with a high Hf index and a high number of peaks. b. A recording with a single dominant frequency peak generating a lower Hf index and less frequency peaks. C.

The difference between the two spectra used to compute the Df index. 110 Biodiversity

4. Sensitivity to noise level, sensor size and autonomy

4.1. Everything but noise Background noise is probably the primary issue in bioacoustics. Noise can significantly impairs acoustic observations and experiments by masking or distorting both time-amplitude and frequency parameters (Hartmann, 1998; Vaseghi, 2000). There are three main sources of noise to consider when recording outdoor: i) anthropogenic noise due to machinery, car, boat, plane, train traffic, or any other human activity, ii) biotic noise due to the activity of surrounding species, and iii) environmental noise due to rain, wind, river stream, waterfall, or sea wave (Brumm and Slabbekoorn, 2005; Laiolo, 2010). The estimation of animal diversity through acoustics is based on the recording of a whole community and as such does not face the classical problems encountered when trying to record a single species in the background noise generated by surrounding active species. However, anthropogenic and environmental noise can have negative effects on the results. In a few instances, anthropogenic noise can be removed by applying classical frequency filters (Stoddard, 1998). Recordings made close to an airport or a road with a regular traffic can be cleaned with a high-pass filter that will remove the low frequency band generated by plane or car engines. Such filters might exclude low pitch animal calling songs, but this can be accounted for when computing diversity indices. The main difficulty arises when recordings are polluted with unpredictable and/or broadband noise that can be interpreted erroneously as animal sounds. Removing such chaotic sound is a challenge to be solved in bioacoustics as well as in other acoustic disciplines (Rumsey and McCormick, 1992; Hartmann, 1998; Stoddard, 1998; Vaseghi, 2000). Usual frequency filters cannot be used as noise may overlap the frequency band used by the animal community. Other noise reduction algorithms use noise spectrum as a reference to be convoluted with the original signal. This solution might appear elegant but still suffers important limitations. First, the noise has to be constant in its frequency content, a condition rarely met in a natural acoustic environment. Second, it is necessary to identify accurately a time window where only noise occurs. This latter condition is very difficult to meet when faced with hundreds or thousands of recordings. Fortunately, some upstream solutions can be considered to reduce the anthropogenic and environmental noise (Obrist et al., 2010). When using an outdoor acoustic sensor, the most important parameter to consider is the direction and the protection of the microphone. The microphone can be oriented in a horizontal or vertical position as soon as its direc- tivity pattern is omnidirectional. A vertical upward position should be avoided when possible, as rain drops might directly strike the microphone Part II – Chapter 1 111 membrane. A vertical upside-down orientation might be the best solution in avoiding rain and lateral wind effects. More generally, adapting the orientation of the microphone to the local main sources of noises is usually advocated. For instance, the noise of running water or passing-by cars can be reduced by orienting the microphone perpendicularly to the source, and windscreens should be used to attenuate wind noise. Another upstream solution is to exclude data potentially corrupted with environmental noise. This can be achieved in three ways. The first option consists in cutting off the recording session when weather conditions are too bad. It is not yet available but could certainly be implemented quickly, given the availability of climate sensors in sound meter devices. The second option is to apply a signal-to-noise algorithm that indicates the occurrence of an important background noise. A threshold could be used as a reference to keep or to remove the files from the dataset. This solution is under development in our group. The third and last option, which is currently in use, is to gather climatic parameters from a local station and identify the time periods when the weather was too bad to allow a correct estimation of the acoustic diversity. This identification can be achieved automatically with a threshold applied on the climatic parameters or by running a redundancy analysis (RDA, Rao, 1964) to the acoustic indices with the climatic parameters as factors (Depraetere et al., 2012).

4.2. Optimal size of recorders As described above, the SM2 recorder weighs around 1.6kg and can be fitted with two microphones (figure 3). Hence, handling several of these units in a hard-to-reach environment requires a significant effort. A reduced size and weight would make field work easier and could also allow settling more units in the habitat. However, this has to be traded off against the size of the data that needs to be stored and analysed. A typical .wav file, which is the most popular uncompressed audio format, has a size of around 690kb/s (= 84ko/s) when sampled at a 44.01kHz rate. This means that one minute of recording is roughly equivalent to 5Mo for a single channel (mono) or around 10Mo for two channels (stereo). Sampling quickly generates x×102 hours of recording in x×103 files for a total x×102 osG data. A detailed above, the recorders have storage capacity of 128Go, which is enough for most applications sampled at 44.01kHz, but might appear limited for an over-month or over-year survey or for a long ultrasound monitoring. The next step of data transfer onto a hard disk for storage and conservation can take a significant time as writing speed is usually slow (around 6Mb/s = 0.7Mo/s). Eventually, the long- term storage of teraoctets of data can encounter some limits with a standard hard disk or server capacity. 112 Biodiversity

Regarding the calculation of indices, the larger the file, the slower the analysis process. Even if automated with R scripts, the analysis of thousands sound files is time-consuming. This is due to three main factors: i) the number of files to be analysed, ii) the size of each file, and iii) the time taken by R to work with large files. There is no easy way yet to coun- teract these three caveats. The number of files will increase as samples will be larger. The size of each file cannot be reduced. Compressed formats in particular, such as .mp3, cannot be used for obvious reasons of signal quality. The platform R is very convenient as it is free and open-source. It makes it a perfect tool for sharing our research and transferring our techniques to other laboratories. However, it may be relevant to look for other software solutions (see section 5.2).

4.3. Energy The SM2 recorder was developed to consume as less energy as possible, but current batteries ensure 240 hours of recording and therefore put a strong limit on the duration of sampling. A solution is to connect the recorder with a 12V battery fuelled with solar energy. However, if such autonomous energy system properly works in sunny areas, it is not adapted to cloudy or shaded areas like the understory of a tropical forest where a very low percentage of solar radiation reaches the ground.

5. What’s next?

5.1. Sampling Our method needs to be tested, validated and eventually applied in several acoustic conditions from different habitats. So far, we have tested it with both simulated and field acoustic communities (Sueur et al., 2008a; Depraetere et al., 2012). Tests on field communities concerned African tropical coast forest and temperate forest habitats. The latter test implied a modification of the indices to take into account the background noise and the low activity of the acoustic community. We are currently sampling several other places including mountain tropical forests in New Caledonia, neotropical evergreen forest in French Guiana, and evergreen monsoon forest in India. We are also transferring the technique to freshwater habitats by using hydrophones immerged in ponds. One of the aims of our method is to provide a long-term and large-scale sampling. We are currently sampling species diversity with a network of 10-16 sensors working about 40 days long over approximately 16 ha of tropical forest in French Guiana and India. This time period is too short Part II – Chapter 1 113 to track seasonal variations of species diversity. We would like to extend it to at least one year or even longer periods. Moreover, we plan to increase the number of sensors to monitor a larger area. Increasing the sampling time and network size will generate serious storage issues. A cut-off system that stops recordings when the meteorological conditions are not good enough could constitute a nice and cheap solution to overcome this difficulty. Another way could consist in sending directly the data from the recorder to a server through a satellite connection, as wireless connection to a base radio may be too slow for heavy sound files (see IV, 2 section 2.3). However such technological improvement mainly depends on the industry and may take some time to emerge.

5.2. Improving the indices As explained earlier, background noise is a central issue, and our indices, especially the index H, are particularly sensitive to noise. It is therefore necessary to develop new indices that are noise-resistant. Current research is ongoing in our laboratory to develop a new measurement of the richness based on the frequency peaks of the Fourier spectrum (figure 5). The spectrum can be smoothed or residual peaks due to noise can be filtered out so as to improve the measure in case of rain or wind noise. Amplitude or frequency threshold will be also applied on the envelope and the frequency spectrum respectively, to try to increase the signal-to- noise ratio. Whatever the index in use is, we also need to exactly identify which biodiversity information is collected by using the acoustic community as a proxy of animal diversity. Does the H index only embed a richness-evenness value or does it include phylogenetic and/or functional diversity information? Eventually, as outlined above, the signal analysis can be slow due to R process. Software directly written in C language will be developed on the next years to significantly speed up the analysis process.

5.3 Sharing the method with other scientists and citizens There is an important ethical requirement for making available the bioacoustic sensor and primary biodiversity data for later uses in terms of knowledge, engineering or conservation (e.g. Graham et al., 2004; Suarez and Tsutsui, 2004). The recording equipment we used so far can be purchased to the company Wildlife Acoustics©. The H and D index can be computed with the free R package seewave. The sensor and integrated bioacoustic system is therefore available to anyone. However, R does not have a user-friendly interface and we plan therefore to share the method soon through an interactive website. Any user will be able to upload recording 114 Biodiversity files for analysis. The acoustic indices will be returned to the user together with an optional graphical representation of the sound analysed (e.g., waveform, envelope, spectrogram, spectrum). On a long-term scale, the recorder and the signal analysis would not be separated but associated in a small and light all-in-one system. This system could be a ‘smartphone’ including a free application that computes the indices. Smartphones were proved to work as nice sensors for mapping the noise level of European cities (Maisonneuve et al., 2010). A similar citizen-science experience could be undertaken to assess animal acoustic diversity inside or around cities.

Authors’ references

Jérôme Sueur, Amandine Gasc, Philippe Grandcolas: Muséum national d’Histoire naturelle, Département Systématique et Évolution, UMR 7205 Paris, France

Sandrine Pavoine: Muséum national d’Histoire naturelle, Département Écologie et Gestion de la Biodiversité, UMR 7204 Paris, France

Corresponding author: Jérôme Sueur, [email protected]

Aknowledgement

This research has been supported by the INEE (CNRS) with a PEPS program and a PhD grant awarded to AG. Sampling in French Guiana was achieved thanks to a CNRS Nouragues 2010 grant. Sampling in New Caledonia was realised thanks to the ANR BIONEOCAL grant to PG. Main part of research was financed with the FRB BIOSOUND grant (Fondation pour la Recherche sur le Biodiversité). Marion Depraetere, Vincent Devictor, Stéphanie Duvail, Olivier Hamerlynck, Frédéric Jiguet, Isabelle Leviol, Pierre-Yves Martel, and Raphaël Pélissier participated at different steps to the development of this new sensing method or provided field data with which to compare our acoustic indices.

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Chapter 2

Assessing the spatial and temporal distributions of zooplankton and marine particles using the Underwater Vision Profiler

Lars Stemmann, Marc Picheral, Lionel Guidi, Fabien Lombard, Franck Prejger, Hervé Claustre, Gabriel Gorsky

1. Introduction

The last two decades, international multidisciplinary programs such as the Census Of Marine Life (COML), Joint GlObal Flux Studies (JGOFS), Global Ocean Ecosystem Dynamics (GLOBEC), Integrated Marine Biogeochemistry and Ecosystem Research (IMBER) conducted numerous cruises and sampled large areas of the oceans, often focusing on the first hundred meters of the water column. In parallel, advances in remote sensing technologies from satellites allowed synoptic descriptions of some physical and optical properties of the ocean surface used to assess epipelagic particle biomasses and primary production at a global scale (see III, 3). By contrast, pelagic ecosystems of mesopelagic water layers – also known as mid-water (100-1000m) – and deeper water layers remain widely unknown. Observing these pelagic ecosystems requires the use of large and often costly instruments launched from research vessels such as pumps, multinets, remotely operated vehicles (ROV), or submersibles. Furthermore, fragile zooplankton (ctenophores, medusae, siphonophores, appendicularians) or fragile aggregates are destroyed during collection with plankton nets, in situ water pumps, and/or sediment traps, which prevents the analysis of their spatial distribution. This challenge can partly be overcome by using non intrusive underwater optical and imaging technologies that appear to be promising tools for the study and quantifica- 120 Biodiversity tion of zooplankton community structures, diversity, as well as marine particles size spectra. The description of the meso- and bathypelagic fauna began to emerge with the use of ship-tethered cameras hooked on ROV (Lindsay et al., 2004; Lindsay and Hunt, 2005; Robison, 2004; Robison et al., 2005a; Steinberg et al., 1997). However, the deployment of these cameras is time- consuming and financially expensive, which prevents their wide use. Smaller instruments hooked on conventional gears – such as a rosette – or on autonomous platform – such as gliders and profilers (see IV, 1), may be more cost efficient and would provide valuable dataset on the spatial and temporal distributions of organisms and non living particles. Relatively few available instruments allow simultaneous in situ measurements of oceanic particles and zooplankton. Particles can be detected and measured by the laser in situ scattering and transmissometry (Agrawal and Pottsmith, 2000) based on scattering intensity. However, this instrument does not provide information on the shape of the particles and limits its use for zooplankton identification. The laser optical plankton counter records a shape approximation of particles crossing an array of light beams and can hardly set one particle apart another among various classes of particles and organisms (Herman et al., 2004). More recently, several instruments that employ image analysis to cha racterise and enumerate oceanic zooplankton have been developed and tested in the field (Benfield et al., 2007), including i) the video plankton recorder (Davis et al., 2005), ii) the shadowed imaged particle profiling and evaluation recorder (Sipper, Samson et al., 2001), iii) the in situ ich thyoplankton imaging system (Isiis, Cowen and Guigand, 2008), and iv) the zooplankton visualisation and imaging system (Zoovis, Benfield et al., 2007). Most of these instruments detect relatively large organisms (more than 100µm); however, there is an increasing interest in quantifying nano- and microplankton particles (Olson and Sosik, 2007; Sosik and Olson, 2007). Several systems using holographic imaging have been developed for this purpose (Alexander et al., 2000; Hobson et al., 1997; Katz et al., 1999; Pfitsch et al., 2007). Whether designed for small or large plankton, all these instruments collect images of a defined volume of water that can be processed to obtain unique information about the distribution, abundance, and behaviour of plankton on scales that cannot be investigated by conventional sampling systems such as nets and pumps. Most of the time, these instruments were used to document the in situ behaviour, taxonomic diversity, spatial distribution, and relative abundance of planktons. They were also used independently to study the dynamic of non-living particles in the water column. Ideally, both plankton and non-living particles should be studied simultaneously because of their interactions in the pelagic realm. These interac- Part II – Chapter 2 121 tions include for example zooplankton feeding on detritus produced at the surface leading to particle aggregation, fragmentation, and remineralisation in the water column. These interactions affect the transfer of large amounts of carbon from the surface to the deep sea – a process known as the “biological pump” – and contribute significantly to climate variability (Sarmiento and Le Quere, 1996; Volk and Hoffert, 1985). Therefore, in order to better understand the biological pump, it is crucial to evaluate simultaneously the distribution of the particulate matter and the zooplankton in the water column. The underwater vision profilers (UVPs) were designed and constructed in our laboratory at Villefranche-sur-Mer in order to achieve this goal (figure 1). Yet, particle and plankton-imaging systems present new challenges to the studies of aquatic biota. In this paper, we describe the fifth generation of the UVP (UVP5) design and calibrations. Moreover, we expose experimental results from different cruises showing the possibility of studying the biodiversity of zooplankton and the size spectra of particles.

Figure 1: Pictures of the underwater vision profiler UVP4 (A) and UVP5 as stand alone (b) and picture of UVP 5 in a 24 bottles Rosette CTD system (conductivity, temperature and depth, C). UVP4 is a large stand-alone package of nearly 1 m3 (300 kg) and incorporates a CTD, fluorometer and nephelometer sensors (Gorsky et al., 1992; Gorsky et al., 2000). The latest version called UVP5 (Picheral et al., 2010) is a smaller instrument (30kg) that can equip a standard rosette frame, interfaced with the CTD, and used down to 6000m deep instead of 1000m deep for UVP4. 122 Biodiversity

2. Description of the underwater vision profiler (UVP)

2.1. Main characteristics The underwater vision profilers (UVPs) were designed and constructed at the laboratory of Villefranche-sur-Mer to quantify simultaneously large particles (more 100 μm) and zooplankton in a known volume of water (Picheral et al., 2010). The UVP versions 2 to 4 had been operating since 1991 and they provided a database of more than 1300 inter-calibrated profiles of particle size distribution covering the global ocean. However these instruments required dedicated winch time on research ships, their maximum operating depth was 1000m, and the image acquisition at the ocean surface was limited because of daytime light saturation. In addition, their complexity required an onboard trained technician, which limited spreading their use over the oceanographic community. Nowadays, the UVP5 overcomes these limitations and can be set up for short or long-term deployments either as an autonomous system or as a complement to CTD (conductivity, temperature and depth) system. The UVP5 dimensions allow its incorporation into autonomous underwater vehicles (AUV), remotely operated vehicles (ROV), or drifting or geostationary mooring. In the near future, the ongoing miniaturisation of the sensors

Table 1: Underwater Vision Profiler 5 details

Camera housing pressure rated 6000 m Housing 2 independent glass cylinders for the lighting Camera 8 with internal memory storage Data storage Optional external drive 1.3 Megapixelup to 11fps processed images Camera and image 9 mm fixed focal lens analysis Pass band Filter centered on 625 nm Lighting Flash duration down to 100 μs Persistor CF2 piloting processor Piloting board Analog to digital conversion for external sensors Digital to analog output to CTD Power management Connection (camera Serial interface 100Mb network housing) Pressure digital sensor with 0.01% accuracy Embedded Sensors Pitch sensor Internal temperature sensor Rechargeable lithium-ion 6.3 A/29 V battery pack Power Continuous monitored during data acquisition Part II – Chapter 2 123 will lead to the development of autonomous camera systems that could be mounted on drifters and gliders working in network allowing real time “visual” monitoring of the biogeochemistry and the biology of the ocean (see IV, 1). The UVP5 instrumental package contains an intelligent camera and a lighting system encompassed into independent housings (figure 1). In addition, pressure and angle sensors are included to the system in order to monitor the UVP5 deployments and data acquisition. The hardware is also composed of an acquisition and piloting board, internet switch, hard drive, and dedicated electronic power boards whose details and characteristics are presented in table 1. Images can be recorded in fields of view ranging from 8 × 6 to 22 × 18cm at a distance of 40cm from the camera in red light environment in order to reduce zooplankton phototactic behaviour and to prevent contamination by the sunlight at the surface.

2.2 Calibration The manufacturing process of the UVP5 produces light-emitting diodes (LED) lighting systems and glass housings with unique optical characteristics. Therefore, each instrument requires individual calibration. In order to be able to estimate accurate concentrations and sizes of in situ marine particles, calibrations of the water volume and the size of particle within an image have to be done prior to the first deployment. A short description of the method is presented below but details can be found in Picheral et al. (2010). The calibration of the volume of the image has to be done independently for each of the two lights. A white sheet of paper, immerged in a tank with seawater, is placed at different distances from the LEDs. Pictures of the light field projected on the white paper are recorded and gathered in order to reconstruct the volume in 3D (Picheral et al., 2010, figure 2C). The size calibration protocol defines the equation and enables the conversion from a particle defined by a number of pixels to size (area) in metric unit. Due to light-scattering in the water, this relationship is not linear for small targets. It follows the rule Sm=A×SpB, where Sp is the surface of the particle in pixels and Sm is the surface in squared-millimetres. The calibration and determination of A and B involves diverse objects sorted into three major qualitative optical groups (dark, transparent, and heterogeneous) in order to represent the diversity of natural particles present in the environment. 124 Biodiversity

2.3. Zooplankton identification Since 2001, the UVP4 and UVP5 have provided images of macrozooplankton over the globe. All profiles have been analysed following the same protocol and using custom software routines to extract large objects (i.e. 500µm in maximum length). This size threshold was selected because most of the organisms cannot be identified below that size due to current insufficient resolution of the images. The sorting of the objects is computer-assisted as for the laboratory Zooscan system (Gorsky, 2010) and the computer prediction is visually validated by specialists to identify taxa. The size of the organisms is reported as well as its area or major and minor axes of the best fitted ellipse. This measure is best suited for dark and opaque organisms such as chaeognathes, radiolarians, fish, and large crustaceans, but cannot be used for gelatinous organisms.

3. Study of particle dynamics and zooplankton community structures at different spatial scales

3.1. Marine particles The UVPs were deployed more than 3000 times covering almost all oceans on Earth (figure 2). The first versions of the UVPs (2 and 3) were not able to efficiently distinguish the non-living particles from the zooplanktonic organisms. Therefore, earlier studies focused on the size spectra of all particles, assuming that most of them were nonliving particles. This hypothesis was then confirmed by the use of UVPs 4 and 5 showing that zooplanktons account for only 0.1 to 10% of the total number of particles in the water column (see next section). The most important biogeochemical information provided by the UVPs consists on the size spectra of large particles (more than 100µm). These particles, in the form of aggregates of individual particles of different origins, are the main vector of the vertical flux of carbon to the deep sea. In order to correctly estimate this flux, the concentration of particle per size bin (number per centimetre) must be converted to biovolume (cm3.cm-3) and to biomass (mg DryWeight.cm-3) assuming relationships between size and mass (Stemmann et al., 2008a). Then, the known relationship between size and settling speed can be used to estimate vertical flux (Guidi et al., 2008; Stemmann et al., 2004b). The coupling between small and meso-scale (scales from 5km to 100km) physical and biological processes in highly dynamic environments such as frontal zones, filaments, and equatorial systems was shown to influence the spatial patterns of carbon export. Vertical profiles of particle flux can Part II – Chapter 2 125 be analysed in a spatial context in order to provide estimates of carbon sequestration by the oceans at different scales. Previous deployments of the UVPs at high spatial resolution revealed that particle spatial patterns can be observed at scales as small as 10 to 100 km (Gorsky et al., 2002a; Gorsky et al., 2002b; Guidi et al., 2007; Stemmann et al., 2008c). Particle size spectra were also used in time series to constrain mathematical models of particle flux to the interior of the ocean (Stemmann et al., 2004a; 2004b). These analyses led to formulate the hypothesis that zooplankton organisms can detect large settling particles and can fragment them in numerous smaller parts that have slower settling speed. This process may generally affect carbon sequestration in the deep ocean.

Figure 2: Global map showing the location of sites that were studied using the different versions of the UVP (dark blue = UVP2, green = UVP3, light blue = UVP4, red = UVP5).

3.2. Comparison between zooplankton and non living particle size spectra The improvements of the optics and illumination of UVP4 and UVP5 enabled simultaneous estimations of the vertical distributions of both particles and zooplankton size spectra (figure 3). 126 Biodiversity

Figure 3: A. Vertical abundance (relative units) of two size classes of large particulate matter (LPM lines) and vertical day (upper right) and night (upper left) distributions of copepods during the California current ecosystem long-term ecological research (CCELTER) cruise off the Californian coast in autumn 2008. b. Typical UVP5 images of individuals from different macrozooplankton groups including copepoda (1), radiolarian (2), chaetognate (3), medusae (4), appendicularia (5), and euphausid (6). Part II – Chapter 2 127

Acoustic, optical, and imaging systems all face the same challenge when trying to distinguish between plankton and other particles in the water column. Plankton larger than 500µm includes crustacean (e.g. copepods and euphausiids), gelatinous taxa (e.g. medusae, tunicates), and eggs and fish larvae. other particles of the same size range include aggregates, abandoned houses of larvaceans, mucous webs of pteropods and all associated material, including living (protozoa and bacteria) and dead materials. Many of these “other particles” are fragile and are not retained and/ or preserved by filters or nets meshes (Gonzalez-Quiros and Checkley, 2006). Therefore, the contributions of organisms to the total number or the biomass of particles is not well known. Misrecognition between organisms and particles can have deep implication for the estimation of available biomass for higher trophic levels and for the estimation of vertical carbon fluxes. The laser optical plankton counter (LOPC) potentially dis- tinguishes automatically zooplankton from particles based on the opacity and size of the recorded objects (Checkley et al., 2008; Gonzalez-Quiros and Checkley, 2006; Jackson and Checkley, 2011). However, results provided by this instrument consist in a proxy for zooplankton since the recognition cannot be validated nor the taxa recognised. The UVP’s distinction is based on the automatic sorting of particles larger than 500µm followed by manual image analysis and visual verification of the plankton identifications by experts (Stemmann et al., 2008b; Stemmann et al., 2008d). During the Boum cruise on the Mediterranean Sea (summer 2008), the UVP was deployed on a longitudinal transect from the East to West basin for short-term stations and 3 sites were selected for their oligotrophic cha– racteristic (figure 4). The comparison between particles and zooplankton size spectra for the same size range (500µm-few mm) shows that the dominant zooplankton in abundance were radiolaria. More interesting, the results show almost for the first time that living organisms were only 1-15% of total particles detected by the UVP in the more than 500µm size range. These ratios are slightly lower than those reported earlier for the OPC (25%) and LOPC (20+/-14%) in the Californian Current system (Gonzalez-Quiros and Checkley, 2006; Jackson and Checkley, 2011). More data of such type should be acquired in different oceans to test whether the strong dominance of non-living particles is a common feature of pelagic ecosystem. 128 Biodiversity

Figure 4: Particles and zooplankton normalised number spectra obtained by the underwater vision profiler at 3 locations in the Eastern (left), Central (middle) and Western (right) Mediterranean Sea during the BOUM cruise in July 2008 (adapted from Stemmann and Boss, 2012). Particles were counted automatically from 60µm in equivalent spherical diameter (ESD) and thus include non living particles and zooplankton organisms. The different taxa were counted manually on the images only for size larger than 500µm from which they can be identified.

3.3. Appendicularians and the biological pump Appendicularians are zooplanktonic pelagic tunicates. They produce a mucous external filtration device called “the house” which allow them to filter small particles (0.2-50µm, see Lombard et al., 2011) from the seawater. Up to 26 houses can be produced within a day by a single individual (Sato et al., 2003), and once clogged, are discarded contributions to marine snow (Alldredge, 2005; Alldredge and Silver, 1988). Thus, the biogeochemical action of appendiculiarians includes mostly “repackaging” by filtering small particles and producing large ones. This effect on the biogeochemistry of particles and therefore on carbon fluxes was shown to be potentially important (Berline et al., 2011; Robison et al., 2005b). However, these organisms have been largely understudied until now mainly because of instrument limitations. Imaging systems such as the UVP overcome these limitations and provide simultaneous observations of their distribution and relation to particle stocks and fluxes. Appendicularians repackaging action were estimated from observations in the northeastern Atlantic ocean by the UVP4. Combined data of appendicularians and associated fluxes from UVP observations and from sediment traps suggested that the estimated pro- Part II – Chapter 2 129 duction of particulate matter by sub-surface appendicularians exceeded the observed total sinking flux at 200m (Lombard et al., 2010). This study supports the hypothesis that appendicularians play an important role in the production of particle fluxes (Alldredge, 2005). In addition, laboratory observation on discarded houses showed that empty appendicularian houses undergo a rapid deflation and compression process, decreasing their size and increasing their sinking speed (Lombard and Kiørboe, 2010). This process, combined with the previous estimation of discarded houses production, leads to the conclusion that up to 20-40% of the 300-500µm particles observed by the UVP in the upper 100m of the water column may be of appendicularian origin. In addition to producing discarded houses in the epipelagic layers, appendicularians are also supposed to be efficient at repackaging small particles by grazing into larger aggregates (more than 1mm) in the deep ocean (Alldredge, 2005). Using the UVP4 observations, the relationship between the changes in the vertical distributions of particles and zooplankton, including appendicularians, was investigated during the Mareco cruise in the North Atlantic (Stemmann et al., 2008b). The gelatinous fauna were consistently the most numerous between 400- 900m and in particular the appendicularians, that occurred mostly below 300m (figure 5). Particles vertical profiles showed that the equivalent spherical volume of particles (100µm

Figure 5: Vertical distribution of appendicularians (upper panel, bars are mean abundance and the stems are the standard deviation) and particles (lower panel, 100µm

3.4. Macrozooplankton spatial distribution in the mesopelagic layer The mesopelagic layer of oceans is located between the photic zone (the illuminated surface zone, where light penetrates the water down to a depth of 100m) and a depth of 1000m. It is bathed in half-light, which is why it is often referred to as the “twilight zone”. The mesopelagic zone represents one of the largest habitat on Earth, yet it is still widely unknown, especially when it comes to its biological composition. Since 2001, we have studied the in situ vertical (0-1000m) distribution of macrozooplankton during 12 cruises in 6 oceans (Mediterranean Sea, North Atlantic shelves, Mid-Atlantic ridge, tropical Pacific ocean, eastern Indian ocean, and sub-Antarctic ocean). Nine regions were identified based on the hydrological properties of the water column. They correspond to nine of the biogeochemical provinces defined by Longhurst (1995). Part II – Chapter 2 131

We tested if the zoogeography of macrozooplankton in the mesopelagic layer corresponds to these biogeochemical provinces (Stemmann et al., 2008d). The zooplankton community was sorted in 21 morphotypes and more than 5000 organisms were identified in the 100-1000 m depth layer. The numerically dominant groups were crustaceans (24%) followed by the medusae (18%), appendicularians (14%) chaetognathes (11%), fish (7%) and single-cell sarcodines of the group Star (6%, see figure 6). The other taxonomic groups were less than 5% of the total count each. However, pooling all single-cell sarcodines moved this group to second rank (23%) in term of frequency of occurrence. From a trophic perspective, the assemblages

Figure 6: Frequency of occurrence for the 20 taxonomic groups in the 9 regions. Note that the numerically dominant group of Crustacean has been removed from the list to increase the details in the other groups. Appendicularians (App.), Thaliacae (Thal.), Fish, Haliscera spp. medusa (Hal.), S. bittentaculata (Sol.), Aglantha spp. (Agl.), Aeginura grimaldii (Grim.), “other medusae” (Med.), chaetognath (Chaet.), lobate ctenophore (Lob.), cydippid ctenophore (Cyd.), siphonophore (Sipho.), single-cell sarcodine grouped by four (Radio CS.), colonial radiolarians (Radio C.), colonial radiolarians with double line (Radio CD.), Phaedorian (Phaeo.), single-cell sarcodine with spines (Spine.), double-cell sarcodine with spines (Spine 2.), spheres (Sphere.), and sarcodine with hairs (Star.). The regions are defined as: Northeast Atlantic shelves (NECS), Atlantic Arctic (ARCT), North Atlantic drift (NADR), Atlantic Subarctic (ARC), Subantarctic, ocean (SANT), North Atlantic Subtropical ocean, (NAST), South Pacific Subtropical Gyre (SPSG), Western Australia (AUSW), Mediterranean Sea (MEDI). The order of the region is set so the proportion of carnivorous organisms (in grey from Chaet. to Sipho.) decreases from left to right (modified from Stemmann et al., 2008). 132 Biodiversity of zooplankton could be lumped into three categories: gelatinous carnivores (cydippid stenophores, lobate ctenophores, medusae, siphonophores, chaetognathes), filter feeder detritivores (appendicularians and salps) and omnivores (sarcodines, crustaceans and fish). Interestingly, the proportion of carnivores decreased from 95% to 15%, from the high latitude regions (Northest Atlantic shelves, Atlantic Arctic, North Atlantic drift, Atlantic Subarctic, Subantarctic ocean) to the low latitude regions (Mediterranean sea, western Australia, South Pacific subtropical Gyre). The similarity in the community assemblages of zooplankton in the layer between 100 and 1000m was significantly higher within regions than between regions, for most cases. The regions with comparable compositions were located in the North Atlantic with adjacent water masses, suggesting that the assemblages were either mixed by advective transport or that environmental conditions were similar in mesopelagic layers. The data suggest that the spatial structuring of mesopelagic macrozooplankton occurs at large scales (e.g. basin scales) but not necessarily at smaller scales (e.g. oceanic front).

4. Conclusion

Results obtained using the UVP but also several other in situ imaging instruments have shown that bio-imagery techniques can provide useful data on plankton and particles spatial and temporal distribution in the upper kilometre of the ocean. In the next decade, rapid technological evolution toward miniaturisation in the optical sensors is expected, and will make possible the use of these sensors on autonomous platforms. Their extensive use may set a revolution in ocean plankton sciences equivalent to the revolution in medical practices for the last 15 years. Broader spatial and longer temporal coverage of plankton size spectra will soon be possible for global monitoring programs (see chapter IV, 1). Mathematical models for individual physiological and population change rates, biomasses flow between trophic levels, and functions of organisms or particle size, were also developed in the last decade. The new sets of data obtained by the wide use of imaging instruments are well adapted to calibrate and validate these models.

Authors’ references

Lars Stemmann, Marc Picheral, Franck Prejger, Hervé Claustre, Gabriel Gorsky: Université Pierre et Marie Curie, Laboratoire d’Océanographie de Villefranche, UPMC-CNRS UMR 7093, Villefranche-sur-Mer, France. Part II – Chapter 2 133

Lionel Guidi: University of Hawaii, Department of Oceanography, C-MORE, Center for Microbial Oceanography: Research and Education, Honolulu, USA

Fabien Lombard: Université de la Méditerranée, Laboratoire d’Océanographie Physique et Biogéochimique, UMR 6535, Campus de Luminy, Marseille, France

Corresponding author: Lars Stemmann, [email protected]

Aknowledgement

The authors would like to thank the many colleagues who helped us to develop our knowledge on plankton and particles dynamics as detected using their optical properties. Lars Stemmann was supported by funding from the 7th European Framework Program (JERICO) and by the PICS program of the CNRS. Fabien Lombard was supported by the French program ANR-10-PDOC-005-01 ‘Ecogely’. Lionel Guidi was supported by Center for Microbial Oceanography, Research and Education (C‑MORE; NSF grant EF-349 0424599), the HOT program (NSF grant OCE09‑26766) and the Gordon and Betty Moore Foundation (P.I. Pr David M. Karl).

References

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Chapter 3

Assessment of three genetic methods for a faster and reliable monitoring of harmful algal blooms

Jahir Orozco-Holguin, Kerstin Töbe, Linda K. Medlin

1. General introduction

Harmful marine algal taxa are globally distributed from tropical to polar latitudes, and occupy ecological niches ranging from brackish water, such as the Baltic Sea, to oceanic environments. It is generally acknowledged that the occurrence of harmful algal blooms is increasing and that pollution in coastal waters has contributed to this increase. Harmful algae can produce secondary compounds that are toxic to fish and shellfish (e.g. oysters, mussels) who feed on toxic algae. These compounds can cause amnesic, paralytic or diarrhetic traumas when fish and shellfish are eaten by humans (Hallegraeff, 1993). To mitigate health problems for human populations and negative effects to fisheries, aquaculture and tourism, accurate and cost-effective systems for identification and detection of toxic algae are urgently required. The monitoring of harmful algal blooms (HABs) is an European Union requirement and usually relies on visual confirmation of water discol- oration, fish kills, and laborious cell counts. These techniques are very time-consuming, require specialised or trained personnel, expensive equipment, and are ineffective when many samples have to be routinely analysed. Currently, up to five working days may be needed between specimen collection and the delivery of a diagnostic report of the species present, leaving little time for mitigation responses, which usually involve moving the caged pinfish or the mussel rafts to a new location. Other 140 Biodiversity mitigation efforts, such as precipitation of the bloom with clay particles, are not practised in Europe. Molecular methods are potentially faster and more accurate than traditional light microscopy methods, and have been used for identification of phytoplankton (Ayers et al., 2005; Diercks et al., 2008a; 2009; Gescher et al., 2008; Greenfield et al., 2006; O’Halloran et al., 2006). Recently, the analysis of small-subunit (SSU) ribosomal RNA (rRNA) genes has been established as an efficient way to characterise complex microbial samples (Amann et al., 1990). The method has proven to be of special value for the analysis of picophytoplankton samples, which are difficult to monitor by conventional methods because of their small size (Moon-van der Staay et al., 2001). This method also circumvents the selective step of laboratory cultivation (Giovannoni et al., 1990). Direct cloning and sequencing of the small subunit (SSU) ribosomal DNA (rDNA) from natural samples provide a broader view of the structure and composition of communities (López-Garcia et al., 2001). Because the rRNA database is increasing continually, it is possible to design probes from higher taxonomic groups down to the species level (Guillou et al., 1999; Groben et al., 2004). The species-specific probes can be applied for the analysis of phytoplankton communities with detection by flow cytometry, epifluorescence microscopy (Miller and Scholin, 1998; Lim et al., 1999), or other methods that take advantage of the hybridisation principle (Metfies and Medlin, 2004). These well-established approaches have the major disadvantage that they can only be used to identify one or a few organisms at a time, which makes it very time-consuming to get a broad view of a microbial sample. Thus, other methods have been developed in which multiple species can be identified at the same time (Metfies and Medlin, 2004). The identification of a region of the rRNA molecule that is species or group specific is essentially a genetic barcode. However most people working in the barcode field do not take into consideration how they could possible apply their barcode in a real life situation. For monitoring purposes, rRNA barcodes are put to use, either as a probe for hybridisation to a target or as a primer to amplify the target in a PCR reaction (see below). In designing a barcode, the position of the mismatch must be taken into account if using the barcode as a probe (mismatch in the middle) or as a primer (mismatch at the 3’ end) is chosen. The use of rRNA probes in fluorescence in situ hybridisation (FISH) reactions has often been used for identification of harmful microalgal species in field samples (e.g., Groben and Medlin, 2005), although it is not regularly included in monitoring programmes. FISH enables the direct visualisation of target cells by epi-fluorescence microscopy or by automated cytometric techniques. The whole cell stays intact and co-occurring phytoplankton species can be discriminated when counterstained with an overall DNA stain. Part II – Chapter 3 141

However, weak probe penetration, loss of cells during preparatory steps and autofluorescence of the target cells can mask the fluorescence signal. Thus, an unambiguous species differentiation may be difficult to achieve. Moreover, an extensive analysis of environmental samples with FISH is very time consuming, thus being inadequate to achieve the high sample throughput needed in routine monitoring programs (Touzet et al., 2009). The limited number of fluorochromes available also restricts the use of multiple probes at one time. Several cell free formats (DNA only) are currently available and have overcome the problems associated with FISH and the whole cell format. These include real-time quantitative polymerase chain reaction (qPCR), biosensors and microarrays. In the following sections, we will address issues related to the assessment of these laboratory methods for the monitoring of toxic algae and their possible use in automated devices and in situ monitoring programs of HAB. The monitoring of aquatic pathogens is essential to guarantee the safety and health of aquatic resources and the application of cell free methods to routine monitoring programs offers the best solution to assess good environmental status of all waters in a rapidly changing environment.

2. Quantitative polymerase chain reaction-based method

2.1. General principles The polymerase chain reaction (PCR) is one of the most powerful technologies in molecular biology. With PCR specific sequences, the number of DNA target molecules is amplified exponentially with each amplification cycle. The direct sequence amplification in PCR approaches enables the detection of low abundance targets and the detection of “hidden” DNA like target species consumed by a predator. However, an adverse aspect of PCR is the impossibility to visualise the target species to ensure unequivocally their presence in the sample and to assess any cross-reaction to any non-target organism (Kudela et al., 2010). With traditional qualitative “endpoint” PCR, no information about the quantity of starting material in the sample is available. Whereas in qPCR approaches, data are collected over the entire PCR cycle by using fluorescent markers that are incorporated into the PCR product during amplification. The quantity of the amplified product is proportional to the fluorescence generated during each cycle, which is monitored with an integrated detection system during the linear exponential phase of the PCR (Saunders, 2004). The accumulation of the PCR amplicon is measured as a change in fluorescence and is directly proportional to the amount of starting material (see figure 1). 142 Biodiversity

Figure 1: Amplification plot of 28S rDNA from Alexandrium tamarense in two different environmental samples from the Scottish East Coast (TaqMan approach). The excited fluorescence is plotted against the cycle number. The delta Rn is the magnitude of the signal generated by the given PCR conditions relative to a standard.

The qPCR can single out base pair differences: thus closely related species or populations can be distinguished. For environmental samples, an external standard for quantifying the amplified DNA is used. This could be a dilution of plasmids or DNA derived from laboratory cultures with a known concentration of the target template. To infer concentrations of the target species in an unknown sample, a standard curve must be made for each target species because of differences in DNA content per cell (Handy et al., 2008). When using a plasmid standard for quantifying cell numbers, it is essential to know the copy number of the ribosomal gene of the target species. In addition, one should take into account that the copy number of the rDNA genes may vary among different strains of an organism and species (Erdner et al., 2010). The most common used qPCR method is the SybR Green approach. In this assay, the fluorescent dye SYBR Green binds to the minor groove of double stranded DNA (dsDNA), which results in an increase of the fluorescent emission proportional to an increase in the dsDNA PCR amplicon formation after each cycle. Proper primer design is critical to avoid primer-dimers, which would be counted as amplified DNA because of the unspecific binding of SybR Green to all dsDNAs. Therefore, a melting curve analysis is performed that identifies primer-dimers by their lower Part II – Chapter 3 143 melting temperature compared to that of the target amplicon (Nolan, 2004). In other more sensitive and specific qPCR approaches, specific or non-specific primers together with a specific fluorigenic oligonucleotide probe are applied (e.g., TaqMan approach, molecular beacon and hybridisation probe assay). These assays apply fluorescence resonance energy transfer (Fret), which is the transfer of energy from an excited fluorophore, the donor, to another fluorophore, the acceptor, to generate enhanced fluorescence upon binding of the specific probe to its target (Cardullo et al., 1988). The use of specific primers and oligonucleotide probes, labelled with unique fluorescent dyes with different excitation wavelength, enables a rapid and quantitative enumeration of several organisms within one sample (multiplex PCR). The number of detectable target genes in one sample is limited by the number of available fluorescence reporter dyes for the separate probes. Consequently the detection is limited to six species in one sample. However, multiplex qPCR experiments have to be carefully optimised, and often require an elaborate adaptation, notably with increasing target species in one assay (Kudela et al., 2010). Potential drawbacks and limitations of qPCR could be, for instance, different DNA extraction yields depending on the extraction method used and the presence of humic substances that could influence the PCR reaction. These problems could be resolved or at least minimised by applying a high- quality DNA isolation method. Quantitative PCR can be easily performed immediately after in situ sampling onboard ship or on shore, but preserved samples can also be used. However, the preservation method can influence the results or even inhibit the reaction. The sensitivity of qPCR is considerably lower with formalin and glutaraldehyde preservation than with no preservation. Preservation using ethanol and freezing is preferred because it is still possible to detect and quantify target cells from fixed field samples after three years (Hosoi-Tanabe and Sako, 2005). Another commonly used fixative for phytoplankton samples is Lugol’s iodine, which has been reported to lower the sensitivity of some qPCR experiments (Bowers et al., 2000) but has also been successfully applied in others (Kavanagh et al., 2010). In HAB studies, multiplex qPCR experiments are applied less frequently, because of the extensive required optimisations to apply different primers and/or probes together in one environmental sample. Handy et al. (2006) successfully tested multiprobing using a single primer set with species specific probes in one assay versus multiplexing using specific primers and specific probes. They found that multiplexing was more efficient, albeit both methods were successful in detecting multiple raphidophyte species. More recently a new technology for qPCR has emerged. It is termed droplet qPCR and involves the Illumina® or 454 sequencing method. Tewhey 144 Biodiversity et al. (2009) were able to perform 1.5 million PCR with primers targeting 435 exons of 47 genes to screen genetic variation in large human populations. In this method, the genomic DNA template mixture contains all of PCR components except for the primers. The template is prepared by fragmenting genomic DNA using DNaseI to produce 2-4kb fragments. The template mixture is made into droplets and paired with primer pair droplets and both droplets enter the microfluidic chip at a rate of about 3,000 droplets per second. As the primer pair droplets are smaller than the template droplets, they move faster through the channels until they contact the preceding template droplet. Field-induced coalescence of these droplet pairs results in the two droplets merging to produce a single PCR droplet, which is collected and processed as an emulsion PCR reaction (Tewhey et al., 2009).

2.2. Case studies of harmful algae Quantitative PCR has been used as a sensitive and accurate alternative to microscopic cell counts for estimating changes in cell densities of harmful algal species in natural phytoplankton samples. In particular, qPCR enables the differentiation between morphologically similar species, such as the dinoflagellate Cryptoperidiniopsis brodyi (Steidinger et al., 2006), which co-occurs with Pfiesteria species and is indistinguishable by light microscopy, but is easily identified by using qPCR (Park et al., 2007). Another benefit over microscopic counts is the sensitive enumeration and identification of fragile species, which might not be easily preserved, such as raphidophytes (Handy et al., 2008) or species that can only be reliably identified with electron microscopy, such as Prymnesium cells. Multiplexing has also been successfully applied to detect the harmful species Prymnesium parvum (Manning and La Claire, 2010). Moreover, qPCR can be much faster and more reliable than traditional counting methods and cryptic species can be more easily identified (Manning and La Claire, 2010). Therefore, qPCR has become a standard method in detecting harmful algae (Fitzpatrick et al., 2010). The target genes for the primers and probes in HAB qPCR applications are the internal transcribed spacer I-5.8S rRNA gene, or the 18S/28S rRNA gene, depending on the DNA sequence divergence between closely related species. A variety of primers targeting these ribosomal genes are hitherto available for the detection and identification of various HAB species in qPCR applications (table 1). Part II – Chapter 3 145

Table 1: Summary of qPCR studies for the detection of HAB species and the toxins they produce.

QPCR On Midtal Taxon Toxins Study Approach Phylochip Galluzzi et al. Genus Alexandrium SYBR Green Yes 2004 Toxic North American clade of Dyhrman et the A. catenella/ Saxitoxin SYBR Green Yes al. 2006 and fundyense /tamarense 2010 species complex Unknown Zhang and Pfiesteria shumwayae SYBR Green No fish killer Lin 2005 Zamor et al. Prymnesium parvum prymensins SYBR Green Yes 2011, Galluzzi et al. 2008 Genus Pseudo- Domoic Fitzpatrick et SYBR Green Yes nitzschia acid al. 2010 Cysts of toxic North American clade of Erdner et al. the A. catenella/ Saxitoxins SYBR Green Yes 2010 fundyense /tamarense species complex Unknown Taqman Bowers et al. Pfiesteria species No fish killer probes 2000 Toxic North American and Temperate Asian Hosoi-Tanabe Taqman clade of the A. Saxitoxin Yes and Sako probes catenella/fundyense 2005 /tamarense species complex A. minutum, A. ostenfeldii, A. tamutum, Mediterranean, North American and Saxitoxins Taqman Töbe et al. Western European and Yes probes unpublished ribotypes of the A. sprilloids catenella/ fundyense /tamarense species complex from European waters Cysts of the Temperate Asian ribotype of A. Taqman Kamikawa et Saxitoxins Yes catenella/ fundyense/ probes al. 2007 tamarense species complex Table 1 – to be continued 146 Biodiversity

Table 1 – continued QPCR On Midtal Taxon Toxins Study Approach Phylochip Bowers et al. 2006, Coyne et al. Harmful Unknown Taqman Yes 2005, Handy raphidophytes Fish killer probes et al. 2006, Kamikawa et al. 2006 Okadaic Hybridization Kavanagh et Dinophysis species Yes acid probes al. 2010 Hybridization Touzet et al. A. minutum Saxitoxins Yes probes 2009 Lingulodinium Hybridization Moorthi et al. Not toxic No polyedrum probes 2006

2.3. Future prospects Multiplex qPCR assays should be improved for routine testing in HAB studies, with several probes recognising different HAB species in one single environmental sample, in order to accelerate the identification of several species and lower substantially analysis costs and time per sample. Further development of primer and probes for HAB species and the application of the variety of available probes will alleviate the deployment of the method, circumventing long primer and probe testing procedures. Costs of real-time PCR instruments are decreasing, so that in future qPCR instruments will be standard tools in HAB studies. New high throughput technologies, such as the open array technology using the qPCR method, are available. Here, the benefits from microarrays and the data quality of PCR are combined. The open array is a new nanoliter fluidics platform for low volume solution phase reactions, which enables the analysis of thousands of samples in parallel. The application of such a high throughput method will also considerably alleviate the analysis of large amounts of environmental samples in HAB studies in future. In addition, qPCR has now been adapted for use in a buoy (Preston et al., 2011) and it is only a matter of time until qPCR primers for toxic algae are added because the environmental sample processor buoy is already capable of detecting toxic algae using a sandwich hybridisation method with chemiluminescence detection (see section 3.3 below). Part II – Chapter 3 147

3. Electrochemical biosensor-based methods

3.1. General principles DNA (RNA)-based biosensors have been used in numerous fields ranging from medical diagnosis to forensic and environmental research (Heidenreich et al., 2010; Liu et al., 2010). An electrochemical biosensor is a self-contained integrated device capable of providing specific (semi)- quantitative analytical information using a biological recognition element (biochemical receptor), which is retained in direct spatial contact with an electrochemical transduction element (Thévenot et al., 1999). This transducer transforms the recognition event into a measurable signal by means of a potentiostat (see complete set-up in figure 2). Whereas a chemiluminescent biosensor requires a spectrophometer or a camera to record a change of colour, electrochemical genosensors use molecular probes to detect target nucleic acids in a sample by recording changes in an electrochemical signal. DNA (RNA) strands are used as the recognition element that can discriminate any target within a mixed assemblage by specific hybridisation of the capture and signal probes to the complementary target strand. The method provides a high selectivity, sensitivity and accuracy, typically from the millimolar to the femtomolar range with more or less 5% accuracy.

Figure 2: Principles of a lab-benched electrochemical genosensor. A. Experimental set-up including the electrochemical sensor interface, the chip connector and the chip. B. Design of the three electrode cell printed on a ceramic substrate (© Dropsens, http://www.dropsens.com/). 148 Biodiversity

Biosensors are also powerful tools for species detection. Although some chemiluminescent biosensors have been developed (Scholin et al., 1996), those based on the direct electrochemical detection of nucleic acid target molecules have successfully been applied by linking DNA or RNA hybridisation events onto an oligonucleotide-modified electrode surface (Drummond et al., 2003). The simplicity, low power requirements, speed and accuracy of electrochemical biosensors have made them attractive candidates to overcome traditional limitations in HAB studies (Diercks et al., 2008b; 2011; Metfies et al., 2005). Moreover, the ability of electrochemical or chemiluminescent sensors to identify directly nucleic acids in complex samples is a valuable advantage over other approaches, such as qPCR that requires target purification and amplification (Liao et al., 2007). The study of toxic algal blooms with genosensors is greatly facilitated by the use of DNA (rRNA) probes. The detection strategy is usually based on a sandwich hybridisation assay (SHA) in which a target DNA or RNA is bound by both a capture and a signal probe (Rautio et al., 2003; Zammatteo et al., 1995). Only one of the two probes needs to be specific for the target species. A capture probe is immobilised on a semiconduct- ing transducer platform, e.g. carbon or gold. If the target sequence binds to the capture probe in the first hybridisation event, its detection takes place via a second hybridisation event with a signal probe linked to a recorder molecule, such as fluorochromes or digoxigenin. An antibody linked to the recorder molecule is coupled to a horseradish-peroxidase (HRP) enzyme for electrochemical signal amplification. HRP converts electrochemically inactive substrates to an electroactive product that can be detected amperometrically, where the measured current is proportional to the analyte concentration in a sample (Metfies et al., 2005). Figure 3 shows the typical amperometric signal expected for a DNA- based biosensor using gold as transducer platform (signal c) as compared to the negative control and background (signals b and a, respectively). For monitoring of water samples, a calibration curve has to be determined for each probe set to assess the current density (nA.mm-2) for 1 ng RNA. For each target species, the RNA concentration per cell has to be investigated. Subsequently the cell concentration of the target species in a water sample can be calculated from the electrochemical signals. By using a different substrate the anti-digoxigenin antibody conjugated to HRP reacts to produce a green coloured product, the intensity of the reaction can be measured in a spectrophoto- meter or captured by a camera, thus forming a chemiluminescent biosensor. Part II – Chapter 3 149

Figure 3: Analytical signal recorded by a DNA-based biosensor at a fix potential of -0.15 V. The signal includes (a) background noise, (b) negative control current, and (c) positive control current with a DNA-based biosensor using gold as transducer surface. The signal of the positive control relative to the negative control is proportional to DNA concentration in the sample.

Detection assays of oligonucleotide probes involving the amplification of hybridisation signals through enzyme tracer molecules have the advantage of being ultrasensitive (Ronkainen-Matsuno et al., 2002). The assay format maximizes discrimination of the target sequences, and RNA purification is not required. Reactions are rapid, easy to execute and amenable to automation. Quantification of the target species can be performed by using smaller, portable and inexpensive instrumentation and several probe sets have already been developed for this purpose. Such probes can simultaneously be measured by using the multichip connector shown in Figure 2. However, few reliable genosensors have been applied so far because one of their main drawbacks is their lack of robustness. Conditions, such as pH, temperature and ionic strength, and short-term stability, must be considered.

3.2 Case studies of harmful algae The sandwich hybridisation assay with chemiluminescent detection was first introduced by Scholin et al. (1996) for the detection of Pseudo- nitzschia species in California waters. A benchtop device is available and other prototypes operated from a buoy are currently being tested. Probes were initially designed with sequence data from local populations, which proved to be non-specific when applied to other areas. This difficulty underlies the need to use a sequence database based on global isolates 150 Biodiversity when probes are designed to make them universal. Currently, DNA probes for Pseudo-nitzschia spp., Alexandrium spp., Heterosigma akashiwo, Chattonella spp., Fibrocapsa japonica, a variety of Karenia spp., Karlodinium veneficum and Gymnodinium aureolum are available using the chemiluminescent detection system in a semiautomatic robotic system (Scholin et al., 2003). Other species for whom sandwich hybridisation probes have been developed (e.g. Coccholodinium polykrikoides, Mikulski et al., 2008), have yet to be applied in a semiautomatic system. In New Zealand, this method has gained national accreditation and is used to monitor shellfish harvests (Ayers et al., 2005). A chemiliuminescent SHA detection in a microliter plate format was also adapted as a rapid means to test probe specificity and some probe sets for 10 toxic algae were validated (Diercks et al., 2008c). Fibre optic genosensors have been applied to harmful algal cell enumeration of Alexandrium fundyense, Pseudo-nitzschia australis and Alexandrium ostenfeldii (Anderson et al., 2006). Biosensors for the detection and identification of the toxic dinoflagellate Alexandrium ostenfeldii and A. minutum were developed by Metfies et al. (2005). Development and adaptation of a multiprobe biosensor for the simultaneous detection of 16 target species of toxic algae has been conducted but is not commercially available (Diercks et al., 2008a; Diercks et al., 2011). More recently, elucidation of the different steps of the biosensor fabrication process from the electrochemical point of view, proof of concept with different algal species, and evaluation of the influence of the transducer platform geometry and material has been published (Orozco et al., 2011a). Probe orientation and effect of the digoxigenin-enzymatic label in a sandwich hybridization format to develop toxic algae biosensors have also been evaluated (Orozco et al., 2011b). However, a system enabling the identification of a broad spectrum of toxic algal species and the in situ quantification of very low cell concentrations of cells is still unavailable and very much needed.

3.3 Future prospects In the past decade, the application of biosensor technology has gained significant impact in microbial ecology. In the European Union (EU) FP6-project Alagadec, a portable semi-automated electrochemical biosensor-system was developed in order to facilitate the detection of toxic algae in the field. This device enables the electrochemical detection of microalgae from water samples in less than two hours, without the need of expensive equipment. In the future, autonomous biosensors will be combined with in situ measurement systems for monitoring of the marine environment. The Scholin chemiluminescent SHA has been adapted for real time measurements in a buoy, the environmental sample processor Part II – Chapter 3 151

(ESP, Greenfield et al., 2006). Toxin analysis by antibody/antigen detection methods (Elisa) and most recently qPCR (Preston et al., 2011) have also been added to this platform, which will serve the need for high resolution monitoring of marine phytoplankton in order to evaluate consequences of environmental change in the oceans. Whereas the chemiluminescent robotic system is already commercialised, the hand held electrochemical device is still a prototype and is not available for purchase. Nevertheless, probes exist only for a limited number of phytoplankton and must be validated for each region where they are applied, calibration curves must be generated for each probe set, and high sample volume (ca. 5L) are required if the cell densities are relatively low (under the limit of detection of the methods). Validation of probe signals against total rRNA and over the growth cycle of the algae under different environmental conditions has to be carried out to infer cell numbers before the method can be applied to wild samples. In addition, manual RNA isolation should be done by a trained molecular scientist, because a large amount of good quality target rRNA is required for these assays. The manual isolation of RNA is currently the limiting factor of all systems. It has been found out that different users can isolate different qualities of rRNA from the same sample. An automated RNA isolation, developed during the Algadec-project and the lysis methods available in Scholin’s environmental sample processor should overcome these difficulties.

4. Microarrays-based method

4.1. General principles At the core of the DNA microarray technology is a DNA microchip that contains an array of oligonucleotides, PCR-products, or cDNAs spotted onto a small surface, e.g. a glass slide. Recently developed DNA- microarray-technology allows the simultaneous analysis of up to 250,000 probes at a time (Lockhart et al., 1996). DNA-microarray technology has enormous potential to be used as a method to analyse samples from complex environments, because it provides a rapid tool without a cultivation step. Target nucleic acids are labelled with a fluorescent dye prior to their hybridisation to the probes on the DNA chip. The fluorescence pattern on the DNA-chip after the hybridisation of the target DNA is then analysed with a fluorescent laser-scanner (DeRisi et al., 1997, see figure 4). When these probes detect species, the microarray has been termed a phylochip and these are essentially barcodes for species and their automated application. 152 Biodiversity

Figure 4: Spotting scheme for the first generation Midtal microarray. The microarray consists in two supergrids made out of four grids installed on a microscopic slide. Each position in the grid represents a spot of ca. 50µm in diameter where a given probe is immobilised. Each probe (colour coded) is spotted four times. This generation of the microarray has 960 spots, covering 112 probes for toxic algal species and higher taxon levels, and various positive and negative control probes.

The basis of the immuno-microarray is an immunoassay that has been miniaturised. In immunoassays, a competitive format is applied where the anti- gens are miniaturised in diminutive spots on a small surface. Fluorescently labelled antibodies compete with the analytes in the sample to conjugate with the miniaturised antigen. Unbound antibodies from the sample are then free to bind to the microarray, and the lower the signal, the higher the concentration of analytes (toxins) present in the field sample. Fluorescence emission is measured with techniques such as confocal or CCD microarray scanners. For optimal use in a monitoring program, it is absolutely necessary to validate the signal intensity against known cell counts under various environmental conditions, because absolute cell numbers are the basis for HAb studies. The principal advantage of the method is that thousands of probes can be miniaturised on a single chip. The primary disadvantage is Part II – Chapter 3 153 the high cost, which is a ca. 25€ per sample (Gescher et al., 2010) and the need to make a calibration curve for each probe on the chip. The first DNA-microchip to study microbial diversity was to analyse samples from nitrifying bacteria, which are difficult to study by cultivation (Guschin et al., 1997). A hierarchical set of oligonucleotide probes targeting the 16S ribosomal RNA was created to analyse the bacterial samples on the DNA-chip. Hierarchical ribosomal RNA probes are now available for many species of algae (Groben and Medlin, 2005), and some of them are available in a microarray format (Metfies and Medlin, 2004, Gescher et al., 2008a; 2008b, Midtal: www.midtal.com). These techniques have been tested in the field and have shown congruence with results obtained by flow cytometry and FISH hybridisation (Metfies et al., 2010; Gescher et al., 2008b).

4.2. Case studies of harmful algae There are no published case studies directly applying microarrays to toxic algae. However, microarrays have been the subject of several EU projects. In FP5 Picodiv and Micropad projects, microarrays were developed for algae and protozoa, and results from chip hybridisation were favourably compared to other measurements of diversity, i.e., direct cell counts and clone libraries (Medlin et al., 2006). In these two projects, the microarrays were in early stages of development and proof of principle was the major outcome because it was discovered that probes made for fluorescence in situ hybridization (FISH) could not be directly transferred to a microarray chip format (Metfies and Medlin, 2008). With a few exceptions, nearly every probe had to be modified for a successful use in the microarray chip format. Problems with transferring FISH probes to a microarray chip format led workers in the EU project Midichip to modify their probes and microarrays for cyanobacteria. The updated method involved additional steps as compared to the one step hybridisation found on most microarrays. The FP6 project FISH AND CHIPS made use of prototype findings to develop a microarray chip for phytoplankton at the class level. Field data were analysed over three years with rRNA as the preferred target molecule (Gescher et al., 2008b, Metfies et al., 2010). A microarray for toxic species in the dinoflagellate genus Alexandrium was also developed but not field tested (Gescher et al., 2008a). In the EU project Aquachip, pathogenic bacteria were the target of interest. This project developed a chip for five bacteria but they were not widely tested with environmental samples. In addition, the detection system developed for this chip was based on a microtiter plate system with detection under a fluorescent microscope. This is not a standard protocol that can be used in a commercial microarray chip reader, and therefore was never commercialised. In EU FP7 project Midtal (www.midtal.com), a species microarray with 163 hierarchical probes for species of 154 Biodiversity toxic algae is in early stages of development (figure 4). Field testing has just begun with high correlation between microarray signal intensity and field counts. The toxin microarray in Midtal, which is based on surface plasmon resonance, detects changes in mass when the antibody binds to the toxin. This microarray can simultaneously detect 4 different toxins (saxitoxins, neosaxitoxin, okadaic acid, and domoic acid) in a competitive assay format.

4.3. Future prospects A common problem in all of the phylochip assays is the wide variation in signal strength of the various probes. In Midtal, the signal of the probes on the microarrays was enhanced by increasing probe length to 25 nucleo- tides instead of 18 and also by adding a longer spacer region to lift the probes above the surface of the microarray so that there is more space for the hybridisation to occur. A fragmentation protocol to break the RNA into small pieces has also been optimised to prevent strong secondary structure formation for signal enhancement (figure 5). Calibration curves will be produced for each probe on the microarray. This is the most time- consuming step needed to make the microarray quantitative because cul- ture experiments have to be established to measure the amount of RNA per cell under different abiotic conditions and to equate RNA content to cell numbers accurately. However, once these calibrations are done, the microarray becomes a very valuable and fast tool to measure community responses over broad ranges in space and time.

Figure 5: Hybridisation of fragmented RNA in increasing fragmentation temperature. The probes tested in this study are: bathy01 (a), Pra507 (b), Chlo02 (c), Crypt01 (d), CryptoA (e), Crypt01-25A (f), Crypt03-26 (g), ATWE03 (h), DinoE-12 (i), LPoLyJ (j), Prym01-A (k), Pela02 (l), PNFRAGA (m), Psnmulti A-17 (n), Psn seriA+11 (o), and NS04 (p). Probes with lower signals are enhanced by fragmentation by increasing temperatures from 40°C to 70°C. Data shown are the signal to noise ratio of the hybridisation signal according to the probes tested at different temperatures. Part II – Chapter 3 155

Authors’ references

Linda K. Medlin: Université Pierre et Marie Curie, Laboratoire d’Océanographie Micro bienne, UPMC-CNRS UMR 7621, Banyuls-sur-mer, France

Jahir Orozco-Holguin: University of California, San Diego (UCSD), Department of Nano Engineering, La Jolla, USA

Kerstin Toebe: Alfred Wegeneer Institute for Polar and Marine Research, Bremerhaven, Germany

Corresponding author: Linda K. Medlin, [email protected]

Aknowledgement

J. O. was supported by a Postdoctoral Fellowship from The Institut National des Sciences de l’Univers (INSU), France. This work was partially supported by EU FP7 MIDTAL.

References

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Chapter 4

Automatic particle analysis as sensors for life history studies in experimental microcosms

François Mallard, Vincent Le Bourlot, Thomas Tully

1. Introduction

Biodiversity is expected to be heavily damaged by the alarming effects of climate change in the next decades and centuries (Leadley et al., 2010). Assessing how populations will respond to environmental change is crucial if one wants to predict the consequences of global change on biodiversity. The density, phenotypic structure and genetic composition of a population are shaped by extrinsic variations of the environment, intrinsic regulatory mechanisms such as density dependence mechanisms, and complex interactions between both extrinsic and intrinsic factors. These processes determine whether deleterious environmental changes can lead either to a local population extinction (Drake and Griffen, 2010; Griffen and Drake, 2008; Sinervo et al., 2010) or its rescue through plastic or genetic adaptation (Bell and Gonzalez, 2009; 2011; Chevin and Lande, 2010). Thus, it is especially important to monitor population changes and get accurate measurements of the factors that regulate the size and structure of a population so as to understand how they will react to environmental changes. Studying how life history traits (individual fitness or demographic components) respond to environmental changes is widely done in evolutionary ecology. In this context, the most common traits under study in population dynamics are age and size at maturity, reproductive output (fecundity, egg size, intervals between reproductive events), longevity and mortality rates (Braendle et al., 2011). These traits are determined 164 Biodiversity by a combination of genetic and environmental effects. When the value of a trait changes according to an environmental factor, it is considered as phenotypically plastic (Scheiner, 1993). The shape of its relationship with the environmental factor (also called a reaction norm) is essential to assess the population responses to environmental change (Flatt, 2005). By linking life-history variation with the genetic makeup of an organism, the interplay between population dynamics and evolutionary dynamics can also be addressed (Saccheri and Hanski, 2006). As a matter of fact, most populations are composed of a mixture of different categories of individuals and, even in an extreme case of a clonal population where all individuals share the same genotype, there is variation in age, size or body condition for instance. Life-history variation has to be taken into account and measured to better understand how a population behaves (De Roos, 2008; Tuljapurkar et al., 2009). Thus, one of the dreams of a population ecologist would be to follow in parallel the dynamics and structure of a population, and the life-histories of every individual the population is made up of. This would enable the understanding of how population growth, population dynamics, individual phenotype and life-history traits influence one another (Pelletier et al., 2007; Coulson et al., 2006; Ozgul et al., 2009; Pettorelli et al., 2011). Unfortunately, following simultaneously the dynamic of a whole population and the growth and reproductive trajectory of its individuals remains a Holy Grail quest especially for animal populations in the wild because of the mobility and elusive- ness of the tracked individuals. In this chapter, we discuss the use of a new generation of sensors, based on automated image analysis from microcosm experiments, to address limitations of ecological methods in previous studies.

2. State of the art and objectives

Several studies in the wild have quantified to which extent the population growth and dynamics are controlled by the underlying life-history strategies of individuals within the population (Coulson et al., 2006; Pettorelli et al., 2011). These studies are based on longitudinal follow-up both at the population and individual levels, the individuals being marked and recaptured. A longitudinal follow-up of individuals is crucial if one wants to address fundamental questions about the patterns of life-history traits throughout life. However, a longitudinal follow-up of wild animals is in general very time-consuming and can only be applied to a part of the studied population. Part II – Chapter 4 165

Given the difficulty of gathering relevant demographic data on wild animals both at the population and individual levels, researchers have looked for complementary and more convenient experimental model approaches, in particular microcosms experiments (Benton et al., 2007). Because of their relatively short generation time and ease of rearing, several small arthropods have been used as model organisms in microcosm experiments in order to study in parallel population dynamics and life- history traits including Daphnia (Drake and Griffen, 2009; Hebert, 1978), Drosophila (Mueller et al., 2005), mites (Benton and Beckerman, 2005), and collembola (Ellers et al., 2011; Tully and Ferrière, 2008). Data collection, however, is often made visually in most microcosm experiments. This may be accurate enough when populations are sufficiently small and close to extinction (Drake and Lodge, 2004; Pike et al., 2004) but it becomes very time-consuming or impossible to do for larger populations. For instance, experimental mite populations are studied by daily counts of individuals using a binocular microscope and hand-held counter. When the density is too high to be measured visually, it is estimated by extrapolating measurements made on a sub-sample of the population (Bowler and Benton, 2011; Plaistow and Benton, 2009). This procedure not only takes time but it is also prone to errors, including differences among observers, and it only gives coarse measurement of the life-history. Alternatively, measurements can be made on digital images of individuals or populations. Digital images are ideal sources of information for phenotypic and demographic studies in microcosm experiments, because images can be collected very rapidly, they are cheap and can be stored and re-observed if necessary, and the procedure of taking images is generally harmless. For example, Stemman et al. (II, 2) describes how image analysis can be used to identify and separate particles and plankton species by size in pelagic, marine environments. In images from microcosm experiments, the extraction of life-history data can be made by hand on a computer using appropriate image analysis software such as ImageJ (Abramoff et al., 2004) to estimate for instance egg and body sizes (Tully and Ferrière, 2008). However, this non-automated procedure is time-consuming and quickly becomes impractical when one wants to closely follow hundreds of individuals each laying hundreds of eggs, or dozens or more populations composed of hundreds of individuals each. Automatic counting and measuring is then needed. Some commercial softwares provide such services but they are often unaffordable and closed source. Previous studies designed and proposed image analysis methods to automatically track or count small organisms such as small arthropods in the laboratory (Krogh et al., 1998, Auclerc et al., 2010; Lukas et al., 2009). 166 Biodiversity

The method of Krogh et al. (1998) is adapted for white collembolan species and requires immobilising the individuals by CO2 anaesthesia and transferring them on an even, black surface. The method therefore requires in practice long and delicate manipulations. Other image ana lysis methods usually require a very contrasted and even background, which is rarely the case in microcosms, or a polarised filter to prevent light reflection on the background. Hooper et al. (2006) developed an image analysis setup for measuring Daphnia population size, but this method does not allow recursive automatic counting because non- daphnid objects (noise and impurities on the glass) had to be manually deleted before automatic enumeration of the Daphnia on their pictures. Using these different methods as a routine for counting and measuring individuals in an experimental population must therefore be banished. Some authors have used morphological image processing tools to reduce the noise in the background and help to identify the individuals (here collembolan) on the images (Marçal and Caridade, 2006). However, this method is prone to errors: it does not permit the elimination of particles that look like collembolans and dead collembolans will be counted and measured. We present hereafter a method that we developed to automate the measurements of i) some fundamental life-history traits (size, growth, fecundity) of a collembolan that is used as a model organism and ii) the density and fine scale size structure of collembolan populations reared in microcosms. Our method can be applied in general to count and measure some animals (or some particles) that are moving on a motionless background. It requires simple material (a digital camera, a stand and a good lighting device) and the freely available, open-source image processing software ImageJ (Abràmoff et al., 2004). We first give some details about our model organism before presenting the principle of our method and the device settings that we use. We then explain more precisely how the images are processed to extract relevant information from the background. Lastly, examples of analyses with large data sets collected a on large number of individuals are presented, such as the follow-up of growth trajectory of isolated individuals, the growth of a cohort, or the fluctuations of both population size and structure. Part II – Chapter 4 167

3. Description of the methods

3.1. The biological model In our study, we used the springtail Folsomia candida (Collembola, Isotomidae) as a model organism. This species is convenient to breed in the laboratory (Fountain and Hopkin, 2005) and is used as a model organism in ecotoxicology, ecology and evolution (Fountain and Hopkin, 2001; Tully and Lambert, 2011). The collembolans are bred in small boxes of about 5cm in diameter whose bottoms have been filled with a 2 to 3cm thick layer of plaster of Paris. Plaster of Paris is perfect for growing collembolans since it keeps a high level of moisture in the boxes. It also has the advantage of providing a flat two-dimensional environment, which is ideal to observe and count all the creepy-crawlies wandering in the box. To enhance the contrast between the collembolans that are white and their background, the plaster was darkened with some Indian ink. However, a completely dark and homogeneous background is not necessary for efficient image processing (see below). More details about our rearing conditions of the collembolan can be found in Tully and Ferrière (2008) and Tully and Lambert (2011).

3.2. Camera settings and lighting unit We used a digital camera (Nikon D300) equipped with a 60mm macro lens and fixed on a camera stand. The camera is connected to a computer and is driven through the software Camera Control Pro from Nikon that enables the adjustment and control of the camera settings (figure 1). We took some 8 bit grey pictures of 4,288 × 2,848 pixels, saved as slightly compressed .JPEG files. We used several LED bulbs such as powerful Pikaline bulbs (16W, 650 lumens) that generate relatively constant, homogeneous and strong lighting. The lighting unit has to provide a light as homogeneous as possible but our image analysis can compensate for some heterogeneity in the lighting as discussed below. However, the stability of lighting conditions between pictures in a stack is more important. The powerful lighting unit enables to shoot with short aperture time (1/100) and small aperture (F36), which ensures sharp pictures with large depth of field. Artificial lighting using fluorescent light bulbs is not recommended because the light intensity fluctuates at 50Hz frequency, which generates substantial lighting heterogeneity between pictures when using an aperture time shorter than 1/50s. We also avoid using incandescent light bulbs since they produce a lot of heat that can harm or disturb our organisms. 168 Biodiversity

Figure 1: The camera stand with the camera and the LED lighting unit on, ready to take pictures of the rearing box in the centre.

3.3 Principle of analysis Our method consists in taking a set of several (usually three to five) images of our rearing boxes under the same conditions. Between each image of a box, we blow lightly in it to ensure that each living individual has moved between the first and the last shot. These images are then compared using the ImageJ software (http://rsbweb.nih.gov/ij/) to generate a new image composed of all elements that remained motionless within the set (figure 2). This generated image, called the still background, is then subtracted to each picture. This produces a stack of images that only contains the mobile elements, here the collembolans that have moved between pictures. These elements are then counted and measured after scaling the images. Part II – Chapter 4 169

Figure 2: The principle of image analysis using the ImageJ multitracker procedure. A. one of the images of the original set. Left, the rearing box contains a population of collembolans and can be scaled relative to a graph paper (on the top) or a black square automatically recognised by the plugin. Right, a detailed view of one area of the rearing box. B. The motionless image obtained by comparing four different pictures from the same set to keep the elements that did not move. This motionless image is then used to detect the outline of the box – the area of interest to detect particles (C). D. A subtraction between the original image and the motionless picture followed by a thresholding procedure yields a new image where the particles can be counted and measured. 170 Biodiversity

The structure and size of the population is stored in a text file. The principle of this analysis is inspired by the particle analysis procedure developed in the ImageJ multitracker plugin (Kuhn, 2001). An almost perfectly even and contrasted background is not needed to calculate the still background, which allows the measurements to be taken directly in the rearing boxes and minimise disturbances (see figures 2, 3 and 4). Only the moving particles are measured so that dead animals are discarded from the counting.

3.4. Image analysis Once the still background is removed from the set of pictures, the ImageJ software can be used to count and measure the particles. A thresholding procedure is then needed to transform our 8-bits image that contains 256 levels of grey into a black and white 2-bits image. One has to choose a threshold value that is the grey level above which the pixels will become white and under-which they will become dark. It is then possible to count and measure the white particle on the black background (figure 3). To facilitate this thresholding procedure when a single picture is analysed, users have to i) control the overall luminosity on each picture and to ii) maximise the contrast between the particles of interest and its background. The first constraint ensures selecting particles with the same precision within a picture (homogeneous lighting). The second one allows getting a straight discrimination between particles and the background such that precise and repeatable measurements can be obtained with different thresholds in a broader range of values. Our measurement method guarantees great precision while allowing a reliable automatic thresholding: removing the motionless background corrects relative lack of enlightenment homogeneity and increases the contrast between moving particles and the background that becomes homogeneously black (figure 3). The user no longer needs to choose by hand an appropriate threshold and the software can be programmed to automatically run the analyses. However, our method is sensitive to local variations of lighting between pictures within the stack. In some cases, the moving particles can create shadows that darken their surrounding substrate, which may reduce the efficiency of the removal of the motionless pixels. This may generate some noise in the background. Providing omnidirectional lighting reduces the formation of shadows and easily prevents these annoying effects. Part II – Chapter 4 171

Figure 3: Comparison of two particle analysis methods. A. The original picture to be analysed: the collembolans lay on an inhomogeneous substrate. B, C and D. The same picture from which a thresholding procedure has been applied. In B, the motionless background has been removed before the process and the particles are reliably detected. Without removal of the motionless background, no threshold level gives satisfying results: in C, the threshold level is too low and some unwanted background elements are kept (red arrow); whereas in D, the level is both too high to select all the individuals (red arrow) and too low to remove all the non-living elements.

3.5. Time requirement and versatility This automatic particle measuring and counting method enabled us to analyse a large number of samples in a relatively short amount of time. For instance, it takes about a little less than two hours to shoot a hundred populations (5 pictures/populations) and to sort these pictures on the computer. It then takes about one hour for the plugin to analyse the whole 500 pictures and count and measure all the individuals in these populations (20 to 30sec per set of 5 pictures on a 2.5GHz computer). Although the procedure was developed and adapted to our collembolan system, it is versatile enough to be tuned and adapted to many different systems as long as there is a still background on which some particles randomly move. To scale our measurements into millimetres rather than 172 Biodiversity pixels we designed an automatic scaling script based on the recognition of a black square of a known area (see figure 2). Including the camera (Nikon D300s, about 1,200€), the lens (Nikon 60mm f/2.8G ED AF-S Micro NIKKOR, about 550€), the stand (starting at about 90€ for the Kaiser 5361) and the software Camera Control Pro (about 130€, running on both Mac and Windows computer), our measurement system costs about 2,000€.

4. Case studies

We use our automated particle analysis as a routine procedure in the laboratory. We developed several specific java-coded plugins that we can directly run within ImageJ. The software allows us to choose between a large number of measurements performed on each particle including position on the picture, area, and centre of mass, but also bounding rectangles or fitted ellipse parameters and many more. We present below different types of analyses based on this method to suggest ideas for broader applications. We recommend using the program R to manipulate and analyse such data (R Development Core Team, 2011).

4.1. Movement analysis Our automated particle analysis was used to track an individual. We collected a set of pictures (1 every 6sec during about 3/4h) with a fixed webcam controlled by a webcam capture application (Dorgem, Fesevur). The different steps of the analyses are described on figure 4. Some difficulties may arise from temporal variation in the still background, which is likely to occur from variation in light intensity or from some changes in the background due for instance to the drying of the plaster in long term follow-ups. One way to circumvent these difficulties is to calculate the motionless background picture and analyse images on shorter periods. But this is very time-consuming and is prone to error when an individual does not move during this period. The image analysis gives the different particle positions during the follow-up. The few tiny particles that may also be detected can be easily discriminated from our collembolan by setting for instance a size threshold in ImageJ before the analysis. In figure 4, one can see that the animals first explore relatively exhaustively the rearing box before finding refuge in the upper left side of the box. The method was used to track a single animal, but tracking several animals at the same time is also possible using the MultiTracker plugin from ImageJ. Part II – Chapter 4 173

Figure 4: Image analysis method customised for tracking individuals. A. The first picture of the set shows the starting position of the collembola. B. This picture shows the still background. C. This picture is obtained from first picture substracted from the background. D. This picture is the sum of the 300 pictures in the stack from which the background has been removed. The particle analysis is robust to the substrate or lighting heterogeneities. E. The successive positions of the individual are measured by particle analyses. 174 Biodiversity

4.2. Life-history analysis One of the major advantages of our automated image analysis method is that it provides a straightforward estimate of the number and the size of the particles without altering their shape by smoothing or other image treatments. We used it as a routine procedure to measure and counts cohorts of collembolan, but also to get fecundity measurements by isolating the eggs and then counting the active juveniles once the eggs have hatched. The method helps characterise life-history traits (Tully and Ferrière, 2008): it allows measuring growth trajectories and reproductive events for instance. On figure 5, we illustrate some data collected from a cohort of more than 3,500 individuals that has been kept and followed under controlled conditions of temperature, food and density. The adults were transferred regularly to fresh rearing boxes and the eggs kept separately until hatching to be counted. Body length (figure 5B) and fecundity (figure 5C) were measured using our automatic particle analysis. Although the counting measurements is pretty accurate (figure 2 and 3), the body length measurements can be biased when some individuals are curved or are measured in weird positions. To avoid that, we scored each particle using ratios of its width, length, perimeter and area (figure 5A). We used this score to select the collembolan on which a reliable measurement can be made. This allows us to reach a precision of about 0.1 to 0.15mm on the mean cohort body length (figure 5B). These two types of measurements (number and size of particles) can also be coupled together to follow populations made of a mixture of cohorts. The data can be conveniently arranged to draw histograms of the relative abundance by size class (figure 5D), which allows calculating the size distribution of the population.

4.3. Population dynamics Another application of our method is the survey of a population dynamics by following the population’s size and structure. This was done on a weekly basis for about a year on our collembolan populations. A standard population survey focuses on counting the number of individuals at each time step to produce a time series of the population size or density. Although the information is incomplete to embrace all the richness of a population dynamic, it is an essential first step. This kind of information is easily obtained from the results of the automatic particle measuring process by calculating the average number of particles per date. Figure 6A shows such a time series. Large fluctuations of the population size can be observed. It rises from 366 individuals to more than 2000 with no specific temporal pattern. Rapid increase in the number of individuals can be explained by the simultaneous hatching of numerous clutches. But it is not possible with this method to tell if the observed decreases are caused by death of adults or of juveniles, even though these two mortality processes have very different meanings regarding the future dynamic of the population. Part II – Chapter 4 175

3.5 3.0 Particle Score A B 0 7 1 8 3.0 2 9 2.5 3 10 4 11 2.5 5 12 6 2.0 2.0 1.5

Length (mm) 1.5 Length (mm) 1.0 1.0 0.5 0.5

0.0 0.0

0.0 0.2 0.4 0.6 0.8 1.0 050 100 150 200 250 300

Width (mm) Age (days) 50 Pictures 500 Mean C D 200 20 100 50 10 20 5 Number of individuals 10 5 cundity (eggs hatched / individual week) Fe 2 2 1 1

050 100 150 200 250 300 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Length (mm) Age (days) Figure 5: Examples of life-history trait measurements on a cohort of individuals kept in a constant environment. A. The measurements of about 28,000 50-days old individuals are scored on their form (width versus length area). This score enables unreliable results to be removed in order to reach high precision measurements of body length in the cohort. b. Growth trajectory of a cohort. A subset of 100 individuals is measured regularly and particles with an unreliable score have been removed. C. Fecundity measurements (log scale). The adults of the cohort are regularly transferred to fresh rearing boxes. The eggs laid in the old boxes are stored in controlled conditions and the juveniles are counted after hatching. Each blue point is a measure of fecundity based on 10 individuals during one week and red points are mean values. D. Population structure measured on a collembolan population (number of individuals on log scale). The population structure was measured independently on six pictures of the population (grey lines), which illustrates the good repeatability of our measurement method. The black line represents the estimated average structure. 176 Biodiversity

Figure 6: Population dynamics of collembolan population assessed by automated image analysis. A. Time series of the number of individuals in a collembolan population during about 300 days. The number of individuals is on a log scale. B. Time series of the measured total biosurface (on a log scale) of the same population.

Another way to represent the population dynamics is to calculate the total biosurface rather than the total number of individuals. The biosurface is the surface occupied by a living individual on a given area, here the rearing box, and is proportional to the biomass of the population. Figure 6B displays the time series of the biosurface measured by summing the surface of all particles at a given date. The observed biosurface oscillations are smoother than the density ones (figure 6). Biosurface tend to rise throughout the year which suggests that although individuals are hatching and dying regularly, some juveniles are growing and reach adulthood, thus increasing the whole population’s biosurface. To encompass the richness of details provided by our method and to provide insights into the dynamics of the size structure of the population, we plotted the population’s size structure during the year (figure 7A). The population size-structure is clearly bimodal with a group of small and another group of large individuals (figure 5D). At the beginning of the follow-up, the two groups are close to one another: the small newly hatched individuals are about 0.2 to 0.3mm long and the larger individuals (adults) are 0.5 to 1.25mm long. Thereafter, the mean Part II – Chapter 4 177 length of juveniles remains stable while birth events are clearly visible as orange and red spots. These birth events correspond to the spikes in the number of individuals shown in figure 6A and the abrupt drops following the spikes can now be attributed to events of high newly- hatched juvenile mortality. The group of larger individuals manage to grow from about 0.75mm at the beginning of the follow-up to 1.15mm after 200 days whereas their number remains almost stable. This suggests that the increasing trend in biosurface (figure 6B) is due to adult growth rather than recruitment of young individuals into adulthood. However this does not mean that young individuals do not recruit at all but either that the time scale of the measure is too large to capture those recruitment events or that too few individuals are reaching adulthood simultaneously. When the population is grown at a lower temperature, the population dynamic is different (figure 7B): adults reach a much higher size (ca. 1.75mm) and some cohorts of juveniles succeed in recruiting into the adult population. These waves of recruitment can be clearly seen around 100 days, after 300 days, and before 400 days. This shows that our measurement method can be used to analyse the detailed dynamics of population structures and to extract life-history data from these dynamics (average size at birth and adult size, maximum body length, average growth rate, etc.).

5. Conclusion

Our automated particle analysis method was initially designed to count and measure small moving organisms in experimental microcosms. This method relies on the idea of analysing pictures of the same sample and removing the motionless background to count and measure the moving particles. This method is simple, cheap and efficient and can readily be made automatic using the freely available software ImageJ. Although we have applied the method to experimental collembolan populations for studying movements, life-history traits and population dynamics, it is clear that this kind of sensor can be adapted to many other experimental systems and research issues. It is possible to apply more sophis- ticated image treatments such as smoothing filters once the background has been removed in order to improve the efficiency of particle recognition especially when some particles are so close that they are in contact or partially overlaping. Using colour pictures could also brings out new possibilities to increase the contrasts and discriminate particles of interest from some background noise (Harman, 2011). In addition, we 178 Biodiversity

Figure 7: Size-structured population dynamics. A. Dynamics of the population size structure at a given temperature. The colour score scales with the number of individuals (on a log scale) of a size class at a given time. The number of individuals can be less than one if an individual is not counted on all of the slices during the picture analysis. b. Dynamics of the population size structure at another temperature. Here, we can observe larger adults (more than 1.5mm) and several waves of birth events followed by recruitment. Part II – Chapter 4 179 anticipate that several improvements could be implemented such as the discrimination of several species or types of individuals mixed together based on shape and colour recognition. In this way, the behaviour of phenotypically contrasted individuals (with some colour mark for instance) could be followed inside a population and the invasion dynamics of two competing species or phenotypically different strains could be tracked down.

Authors’ references

François Mallard, Vincent Le Bourlot, Thomas Tully: École normale supérieure, Laboratoire Écologie et Évolution, UPMC- CNRS-ENS UMR 7625, Paris, France

Thomas Tully: Université Paris 4 – Sorbonne, IUFM de Paris, Paris, France

Corresponding author: Thomas Tully, [email protected]

Acknowledgement

This research was supported by a french grant from the Programme inter- disciplinaire du vivant Longévité et vieillissement funded by the Centre National de la Recherche Scientifique (CNRS).

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Sinervo, B. Mendez-de-la-Cruz F., Miles D. B., Heulin B., Bastiaans E., Cruz M. V. S., Lara-Resendiz R., Martinez-Mendez N., Calderon-Espinoza M. L., Meza-Lazaro R. N., Gadsden H., Avila L. J., Morando M., De la Riva I. J., Sepulveda P. V., Rocha C. F. D., Ibarguengoytia N., Puntriano C. A., Massot M., Lepetz V., Oksanen T. A., Chapple D. G., Bauer A. M., Branch W. R., Clobert J., Sites J. W., 2010. Erosion of lizard diversity by climate change and altered thermal niches. Science, 328, pp. 894-899. Tuljapurkar S., Steiner U. K., Orzack S. H., 2009. Dynamic heterogeneity in life histories. Ecology Letters, 12, pp. 93-106. Tully T., Ferrière R., 2008. Reproductive flexibility: Genetic variation, genetic costs and long-term evolution in a collembola. PloS One, 3, e3207. Tully T., Lambert A., 2011. The evolution of postreproductive life span as an insurance against indeterminacy. Evolution, 65, pp. 3013-3020. III Ecosystem properties

Chapter 1

In situ chemical sensors for benthic marine ecosystem studies

Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel

1. Introduction

Benthic marine environments concentrate living and non-living entities that rule major biogeochemical processes such as the remineralisation of carbon, inorganic carbon fixation by phototrophs or chemoautotrophs, the recycling of nitrogen, phosphorus, sulphur or metals, and the precipitation and burial of minerals. These processes generate steep physical and chemical gradients within sedimentary or porous rocky substrates, as well as in the boundary layer above them. The quantification of the rates of these transformations and the resulting fluxes across the benthic- pelagic interface has prompted the development of in situ chemical sensors over the last three decades. Yet the initial momentum on chemical sensor application for benthic environment came from functional ecology studies in naturally “extreme” environments, such as microbial mats in hypersaline lakes (Jørgensen and Revsbech, 1983) or deep-sea hydrothermal vents (Johnson et al., 1986; Luther III et al., 2001a). Here, the main requirement was to assess the distribution of micro- or macro-organisms in relation to in situ chemical gradients, while minimising disturbance of these gradients. Some of these tools were later converted to the monitoring of a variety of marine environments, including environments impacted by human activities (Moore et al., 2009). This cross-fertilisation led to the development of different techniques available for benthic ecologists. Some of these techniques have been commercialised (e.g.: Unisense, AIS, SAtlantic) and became accessible to non-specialised users. However, despite their advantages over sampling approaches, in situ sensors still remain under-used. In this chapter, we 186 Ecosystem properties intend to provide an overview of available sensing techniques and present their respective advantages for various applications in benthic ecology. Existing gaps and promising new technologies are also briefly presented. Chemical sensors dedicated to the monitoring of water masses in coastal observatories (e.g. Johnson et al., 2007) are out of the scope of this review. Instead of discussing analytical performances of sensors per se, which are detailed in several review papers and books (Taillefert et al., 2000; Buffle and Horvai, 2000; Moore et al., 2009), we intend to clarify the capabilities and limitations of these tools for the assessment of chemical constraints on biodiversity. Therefore, the focus is on the detection or quantification of chemical compounds and their interaction with biological processes in the benthic ecosystem, including compounds such as oxygen, carbon dioxide (CO2), hydrogen sulphide (H2S), or other biologically relevant parameters like pH. Quantifying fluxes of nutrients and carbon budgets in marine environments is often indirectly derived from the measurements of these compounds. This specific application of sensors was considered more broadly in a previous review (Viollier et al., 2003).

2. Why is in situ chemical sensing needed?

The dynamic mixing zone between sulphide-rich fluids and seawater at deep-sea hydrothermal vents provides a striking illustration of the need for in situ chemical sensing in marine environment. Using a prototype underwater colorimetric chemical analyser, Johnson et al. (1988) first described the spatial and temporal heterogeneity of this particular benthic habitat where oxygen and sulphide can coexist, whereas they are considered as mutually exclusive in most marine environments. Water sampling in this habitat is unable to preserve the chemical disequilibrium. The relaxation of chemical systems to more stable thermodynamic states during sample recovery results in strong biases in the characterisation of habitats chemistry (Le Bris et al., 2006). Such sampling biases are not restricted to deep-sea hydrothermal vents, but can occur wherever local hydrodynamics promotes diffusive or advective transport of reduced compounds into oxygenated water, creating chemically unstable conditions. These metastable chemical conditions, which are supporting chemosynthetic metabolic pathways for microbial communities, can only be accurately characterised by using in situ sensors (e.g. Laurent et al., 2009; Vopel et al., 2005). On the other hand, it is generally assumed that pore water chemical gradients that are governed by slow molecular diffusion rates are stable in sediment cores. Most of the time, these profiles are therefore measured on cores, shortly after sampling. Cores are sometimes maintained under Part III – Chapter 1 187 controlled temperature, close to in situ conditions, in order to minimise changes. Despite this, differences between in situ and laboratory pore water chemical profiles from sediment cores were highlighted, which illustrated the sensitivity of these profiles to sampling conditions and encouraged direct in situ sensing approaches (Preisler et al., 2007). The risk of disturbance is obviously more critical when the sample undergoes large pressure and temperature changes. Sediment cores collected in deep-sea environments, in which dissolved gases (e.g. CO2, methane) can be enriched, are subject to degassing during recovery, affecting chemical profiles and leading to significant biases (De Beer et al., 2006). In addition, one of the main advantages of in situ sensing relative to direct sampling is to provide a better view of temporal and spatial variability from larger data sets. This is particularly crucial in extreme and remote environments where sampling is difficult and time-constrained. The capacity to monitor environmental fluctuations also provides a much more relevant view of the chemical constraints exerted on communities and on the processes ruling these fluctuations (III, 2). For all these reasons, studies of deep-sea benthic ecosystems have made important contributions to metho dological advances in sensors and analysers (figure 1). As reviewed in Moore et al. (2009), many of the technologies and analytical principles available to date for coastal monitoring and shallow water studies (e.g. colorimetric analysers or micro-profilers) originated from early prototypes developed for extreme and remote environments. Nowadays, the need is expanding to polar and sub-seafloor environments, bringing new constraints on the use of in situ sensors and supporting new developments.

Figure 1: Examples of deep-sea chemical sensing devices. A. Microprofiler for high- resolution sediment oxygen, pH, T and sulfide profiles at a methane seepage area (© Ifremer/ MEDECO-cruise, F. Wenzhöfer). B. Electrochemical probe integrating both pH and H2S and temperature sensors in the mixing plume of ‘CrabSpa’, a diffuse of hydrothermal vent on the top of a lava pillar 2500m deep, colonized by bacterial mats and Bythogreid crabs (© WHOI /S. Sievert). These extreme benthic environments have contributed importantly to technological advances in the field of benthic chemical sensors. 188 Ecosystem properties

3. A variety of techniques with different capabilities

3.1. Spectrophotometric flow analyzers Continuous-flow analysis (CFA) and flow-injection-analysis (FIA) devices are routinely used in laboratory to analyse large series of aquatic samples, and have been adapted for use on various oceanographic platforms, including landers and submersibles operating on the seafloor. The detection principle is usually based on spectrophotometric and fluorimetric methods after mixing with reagents or dyes (table 1). For hydrothermal habitat studies, the first in situ measurements of sulphide were obtained at 2500m deep from a submersible device based on the Cline colorimetric method (Johnson et al., 1986). In these chemically diverse environments, multichannel instruments made it possible to perform sulphide analysis in combination with silicate (Johnson et al., 1994; Le Bris et al., 2000), nitrate (Le Bris et al., 2000), iron (Chapin et al., 2002; Sarradin et al., 2005) and manganese (Sarrazin et al., 1999). The main advantage of these methods is the possibility of in situ calibration using standards (Le Bris et al., 2000). Indeed, chemical methods require repeated calibration, as close as possible to measurement conditions, in order to correct drifts and account for interference effects. In an attempt to avoid the use of reagents and standards, an UV spectrophotometry method based on the deconvolution of complex spectra reflecting the mixture of several absorbing species (e.g. nitrate, sulphide, iodine) was suggested (Johnson and Coletti, 2002). However, although the UV nitrate sensor Isus is now commercially available and has achieved a certain level of success in the field of coastal monitoring, the use of this device for benthic ecosystem studies has not yet been reported. A number of studies confirmed the performances of flow devices for the mapping of steep mixing fluid-seawater interfaces above the seafloor (Johnson et al., 1988; 1994; Sarrazin et al., 1999; Le Bris et al., 2000; 2006). New generation devices were built using miniaturised flow actuators solenoid valves and optimised for application in environmental monitoring (e.g. Johnson et al., 2007; Vuillemin et al., 2009). Flow-measurement methods using a colorimetric or fluorescent dye that acts as a pH indicator have been successfully implemented from ships and buoys to investigate the CO2-carbonate system at the air-sea interface (Feely et al., 1998). The transfer of such techniques to pH, DIC, alkalinity and pCO2 measurement in benthic systems has not yet been achieved. For applications at the interface between the seafloor and the water column, other techniques are being preferred. Indeed, in flow analysis, the risk of clogging is large when transferring the sample across the reaction pathway to the detection cell. This restricts its use to the aqueous phase only, excluding environments with high particle load or colloids, and the application of flow analysis in close vicinity to animals or bacterial mats is therefore delicate. For this reason and despite the wide range of methods Part III – Chapter 1 189 available, the use of flow analysers in benthic environments remained limited. If the chemical parameter to be measured can be converted into a physical (electrical or optical) signal, electrodes or optodes provide a more adequate solution, circumventing the problem of sample drawing by deporting the measurement point to the environment itself (see below).

Table 1: Summary of the sensing techniques used in benthic environments, their respective advantages and drawbacks.

Group of Operating Available chemical Benthic use Key Advantages sensors Principle species in marine / Limitation environments - - Flow ana Spectropho S(-II), NO2 +NO3 , Mixing zone In situ calibration, lyzers tometry and silicates, Fe(II), above the high precision, Johnson et al, micro-flow Mn(II), seafloor at wide range 1986, Le Bris techniques hydrothermal of analytes / et al 2000, after mixing vents sensitivity to Vuillemin et the sample with DIC, alkalinity, pH clogging, not al. 2009 reagents/dyes Only in the selective to water column chemical speciation 2+ Single analyte Potentiometry pH, Ca , CO2, H2S, From shallow to High spatial 2- electro and S , N2O, O2, H2 deep sediments; resolution, low chemical amperometry including energy use / sensors with selective hydrothermal long-term drifts, Revsbech et al membrane vents and microsensor 1983, De Beer electrodes methane seeps. fragility 2002, Cai and Interfaces Reimers 2003, between Le Bris et al. organisms and 2001 the environment 2- Multi-analyte Voltammetry, O2, H2S, Sx , Fe(II), Vent habitats, Access to chemical 2- electro working Mn(II), S2O3 sediments, speciation / long chemical electrode term instability, sensors surface Cu(II), Pb(II), Zn(II), sensitivity Luther et al generally Cd(II) Metal-rich to electrode 1999, Buffle modified (e.g. coastal waters poisoning and Tercier gold amalgam 2000 electrode)

Optodes Measurement O2, pH Methane No analyte Glud et al of fluorescence seeps, shallow consumption, 2003, Konig et intensity and and deep-sea 2D mapping / al 2005 lifetime of a dye sediments decrease sensitivity sensitive to the over time, high parameter to be detection limit measured and low precision

New spectro Raman CH4, H2S, H2, pCO2 Deep-sea Detection of new metry and mass organics hydrothermal analytes / cost, methods. spectrometry vents and limited to aquatic Wankel et al methane seeps media 2010

The list of tools and associated references are not exhaustive. The cited advantages and limitations are within the context of this review article. 190 Ecosystem properties

3.2 Single-parameter potentiometric sensors The simplest electrochemical technique, potentiometry, uses the potential difference between a reference electrode and an ion sensitive electrode (ISE). The logarithmic relationship between the potential and the concentration (or activity) of the detected ion by the ISE is referred as the Nernst law. The pH glass electrode is by far the most widely used potentiometric sensor in benthic environments. Potentiometric pH measurements were obtained from the shallowest to the deepest environments, both inside the sediment (Wenzhöfer et al., 2000; De beer et al., 2006; Reimers, 2007) and above it, in the acidic plume of hydrothermal vents (Le Bris et al., 2001), within the tube of an extremophilic polychaete worm living on the wall of hydrothermal chimneys (Le Bris et al. 2003, see figure 2), or on the surface of degrading organic falls (Laurent et al., 2009). The low sensitivity of miniaturized pH electrodes to pressure is an advantage to work at various depths (up to at least 3000m deep), but temperature effects are larger and should be accounted for (Le bris et al., 2001). A wide range of analytes can be similarly measured by potentiometry based on ion-specific membranes although only a few were used in situ (De Beer, 2002). The Ca2+ sensor, which was used to assess saturation thresholds for calcification is of particular interest (Cai and Reimers, 2003). pCO2 was also quantified by a glass sensor covered with a gas permeable membrane (the so-called Severinghaus sensor), in combination with pH (Cai and Reimers, 2000; Al-Horani et al., 2003).

Figure 2: Measurement of pH inside the tube of an Alvinella pompejana hydrothermal worm tube. The diameter of the pH glass electrode is 2mm. It is combined with a temperature sensor and a fluid flow sampler inlet (red arrow). Part III – Chapter 1 191

The sulphide potentiometric sensor is the most widely used and best studied of these sensors. A potentiometric sulphide sensor based on a silver wire coated with silver sulphide was first suggested by Berner (1963). It was later miniaturised by Revsbech et al. (1983) who were the first to study sediment profiles with a potentiometric sulphide microelectrode of 200µm tip diameter, enhancing spatial resolution and reducing disturbance on in situ gradients. From this, the use of electrochemical techniques developed rapidly, because of their capacity to achieve the spatial resolution required for biogeochemical studies within microbial mats and sediments (Taillefert et al., 2000; De Beer, 2002 for review). Aside from the capacity to be miniaturised, potentiometric sulphide electrodes have several advantages for benthic ecology studies, such as relatively low sensitivity to temperature and pressure and large detection range (e.g. from less than 1µM to more than 1mM for sulphide, Vismann et al., 1996). At higher concentrations, they suffer from low accuracy due to their logarithmic response, but these electrodes still respond reproducibly to sulphide at least up to 20mM. Conversely, deviations from the Nernst law and long response times can affect the in situ performance of the potentiometric sulphide electrodes at low sulphide levels (Müller and Stierli, 1999; Ye et al., 2008). To solve this problem, these authors proposed epoxy-based silver sulphide sensors with a much larger nernstian measurement range, but the in situ performances of these sensors are not known. A main difficulty arising from the use of potentiometric sensors comes from potential drifts, which result from the fact that sensors can only be calibrated in laboratory conditions before deployment. Drift may occur when the reactive surfaces of the reference and sensing electrodes undergo fouling by bacterial biofilms or by adsorption of mineral and organic colloids. The conversion of the AgCl surface of the reference electrode to silver sulphide upon exposure to H2S, or the ageing of the crystal- line layer of the sensing surface are also identified as possible causes of drift (Janz and Ives, 1968). Furthermore, since Ag/Ag2S electrodes are sensitive to S2-, pH measurements need to be performed along with in situ deployments, raising similar constraints with pH electrode responses. Ecological studies, however, do not always require highly accurate chemical data, and the temporal and spatial variability of analytes, in a semi- quantitative approach, may be more informative than few quantitative values to elucidate the dynamics of natural systems (Le Bris et al., 2008). However, when quantitative assessment of fluxes at the sediment water interface is required, potentiometric sensors are often replaced by other techniques. 192 Ecosystem properties

3.3 Single-parameter electrochemical sensors by amperometry Amperometry is one of the most used techniques for microsensors nowadays (table 1). Amperometric microsensors measure the current generated at the surface of a working electrode to which a fixed difference of potential is applied according to a reference electrode. In the so-called Clark sensor for oxygen, the applied potential enables the reduction of oxygen at the working electrode made of gold or platinum. A gas-permeable membrane prevents other potential oxidants from reacting on the electrode, while maintaining a fixed pH and ionic strength at the electrode surface. Under these conditions, the measured current is proportional to the rate of the reaction, controlled by the flux of oxygen passing the membrane, and ultimately the concentration of oxygen in the medium. Revsbech (1989) presented an oxygen microsensor built on the principle of the Clark electrode. The addition of a third electrode, the guard cathode, enabled the improvement of the accuracy, the response time and the stability of measurements, by preserving the miniaturised reference electrode from reduction or oxidation currents. On the same principle, an amperometric microsensor was developed to measure H2S (Jeroschewski et al., 1996). This sensor was used in a wide range of sulphidic environments such as coastal sediments (Kühl et al., 1998), hypersaline lakes (Wieland and Kühl, 2000), shallow water hydrothermal vents systems (Wenzhöfer et al., 2000) and deep-sea methane seeps (De Beer et al., 2006). The H2S microsensor is sensitive to temperature and salinity changes, but unlike the potentiometric sensor, its response is linearly related to the concentration of H2S (Jeroschewski et al., 1996). For this reason, amperometric sensors are particularly useful under acidic conditions where the H2S form of free sulphide is dominant. It can also be utilised under moderate alkaline conditions (pH< 8.5), but when pH increases, the proportion of H2S over HS- decreases to such an extent that it becomes more advantageous to use the potentiometric sulphide ISE method (Taillefert et al., 2000). Amperometric microsensors were made commercially available to a wide range of users by the group that designed them at the Aarhus University, Denmark (Unisense, http://www.unisense.dk). Amperometic sensors are also available for hydrogen, hydrogen sulphide and N2O resulting of nitrate reduction by microbes. For sediments, a new bio-electrochemical sensor has been designed, which combines an amperometric N2O sensor and a microbial community that converts nitrate into this compound (Larsen et al., 1996). Despite the success of these microsensors, an important limitation should be kept in mind: the fragility of the glass tip usually less than 100µm makes the acquisition of profiles over 10 cm or more extremely difficult in sediments inhabited by macrofauna forming carbonate shells or chi- Part III – Chapter 1 193 tin tubes (De Beer et al., 2006). The pressure sensitivity of amperometric sensors is also much higher than those potentiometric sensors, since the measurement is related to the partial pressure of a volatile compound (Glud et al., 2003). Strong temperature variations also influence the per- meability of the membrane and have to be corrected (Wenzhöfer et al., 2000). Drift and limited lifetime is another limitation of amperometric sensors. Particularly, electrochemical reactions occurring at the electrodes results in changes in the chemical composition of the internal electrolyte. The degree of this change depends on the current intensity and on the electrolyte volume. Such changes are particularly significant in the case of microsensors, due to their very small size, and can cause signal drift and limit the operational lifetime. For example, reduction of sensitivity has been reported after a prolonged use of the H2S sensor (Jeroschewski et al., 1996), and the lifetime of the commercial sensor is considered to be limited to several months

3.4 Multiparameter voltammetric techniques Compared to potentiometry and amperometry, voltammetric techniques are attractive for benthic studies because they can detect several analytes with the same electrode and have fast response times (less than 10sec), therefore avoiding problems due to the use of multiple electrodes in spatially or temporally heterogeneous environments (Wang et al., 1998; Luther III et al., 1999; 2001b). In voltammetry, a variable potential is applied between a working electrode and a reference electrode. In cyclic voltammetry, the most in situ used method, this potential is swept from a minimum to a maximum value, and reverse. At an appropriate potential, an analyte is oxidized or reduced at the working electrode resulting in a current peak recorded on a so-called voltammogram (see figure 3 for an example). Reviews of the voltammetric techniques include those by Tercier and Buffle (1993) and Luther III et al. (2008). Like in amperometry, the current is measured between the working electrode and an auxiliary (or guard) electrode. By using an underwater voltammetric device, Luther III et al. (2001a) investigated sulphide speciation (i.e. different electroactive forms of sulphur(-II)) down to 2500m deep and temperature up to 80°C, and emphasised significant differences in the chemical environment of hydrothermal fauna. The working electrode used by these authors was made of a mercury film formed on a gold disc (diameter 100 µm). One of the main advantages of this gold amalgam electrode lies in its high over- potential for the reduction of water, expanding the range of analytes that can be measured (Kühl and Steuckart, 2000). Concentrations of thiosul- fate, polysulphides and iron(II, III) and manganese(II), oxygen and iodine were also obtained with the use of voltammetric methods (Luther III et 194 Ecosystem properties al., 2001b; 2008). For sulphide measurements, solid electrodes such as Pt, Au, Ag and various types of carbon substrates were also used without Hg film (review in Buffle and Tercier-Weber, 2005) and have the advantage of simplicity and ruggedness although their analytical performances are more limited.

Figure 3: A typical voltammogram obtained using a solid state gold-amalgam microelectrode (100µm tip diameter) maintained in a hole inside a degrading woody substrate in seawater. The figure illustrates the oxidation (positive) and reduction (negative) currents recorded while the potential is scanned from -0.1 V to -1.8 V and back at a rate of 1V/s (cyclic voltammetry). The height of the negative current intensity peak is proportional to the concentration of sulphide (about 440µM here) after several weeks of immersion of the wood in a seawater aquarium.

Several metals like manganese, copper, silver, mercury, arsenic, and other toxic metals can be measured in laboratory by using voltammetric techniques. An automated flow-through device has been designed for monitoring of Mn, Cu, Pb and Cd in natural waters (buffle and Tercier-Waeber, 2000). However, since the methods usually require pH buffering, their in situ use for benthic ecosystem studies is not straightforward, despite their interest for the study of contaminated sediments. The recent development of nanomaterials is one of the promising ways to develop new electrochemical and optical sensors, especially for organic molecules that require higher oxidation potential. For example, the simultaneous detection of inorganic sulphide and organic sulphide species in a single voltammetric scan may be possible with carbon nanotubes. This electrode enables H2S and thiols signals to be discriminated, while they overlap on other electrode materials like gold-amalgam for example (Lawrence et al., 2004a; 2004b). Part III – Chapter 1 195

3.5 Optodes Optodes are based on the measurement of fluorescence intensity or lifetime of a dye that is modulated by a chemical substance to be measured. This fluorescent molecule is embedded in a polymeric membrane integrated in an optical measurement system able to induce (using a pulsed UV light source) and to quantify (using a CCD camera or a detector with high temporal resolution) the fluorescent signal (Klimant et al., 1995; Glud et al., 1996; 2003). The first oxygen optodes were developed with the ruthenium(II)-tris-(4,7-diphenyl-1,10-phenantrolin) complex, which fluorescence is quenched by oxygen favouring the relaxation of the excited form, but similar dyes are now available providing a higher resolution (König et al., 2005). other fluorescent pH indicators are used for pH measurements (Stahl et al., 2006). One of the main limitations of optodes is the progressive degradation of the dye while exposed to the excitation light (Glud et al. 2003). To improve accuracy, the fluorescent lifetime, independent of the dye concentration in the matrix, is generally used instead of the intensity signal. Fibre optics allowed microoptodes to be designed with advantages for high-resolution profiles similar to those achieved with microelectrode (Glud et al., 1999). The main advantage of optodes, over amperometric electrodes, is that they do not consume the analyte. As a result the size of the optode is not limited and planar optodes of several square centimetres were developed for an implementation in aquaria (Glud et al., 1996; Stahl et al., 2006) and recently for in situ measurements down to great depth (Glud et al., 2005). However, the time response of these planar optodes is in the range of minutes, much longer than the fast-responding microoptodes.

3.6 New in situ techniques Advanced analytical techniques are developing rapidly for in situ marine applications (Moore et al., 2009 for review). The adaptation for underwater use of a new set of spectrometric methods, such as Raman spectroscopy (Brewer et al., 2004), surface plasmon resonance (SPR, Boulart et al., 2008) or membrane inlet mass spectrometry (Mims, Schluter and Gentz, 2008; Wankel et al., 2010) further expanded the potential of in situ detection of less reactive chemical compounds such as methane, hydrocarbons, or organic pollutants. Their adaptation to various immersion depths will provide a better view of the fine-scale gradients of these compounds at the scale of patchy macrophyte and macrofauna aggregations on the seafloor. The technique has been used recently from remote operated vehicles (ROVs) for the measurement of a variety of analytes on the seafloor, 196 Ecosystem properties

ranging from simple volatiles like H2S, CO2 and methane down to 2500m (Wankel et al., 2010). These new techniques also hold the promise of disentangling dissolved organic carbon complexity directly in situ with spatial resolution similar to inorganic compounds profiles. While amino acids, sugars, volatile organic acids are commonly measured in laboratory, there are still no tools to access these biochemical tracers directly in situ. Sensors should also target organic pollutants or signaling molecules that are likely to play a significant role in marine benthic ecosystems at low concentration levels, but very low detection thresholds are a major challenge and will likely require highly selective biosensing techniques to be developed. Carbon nanotube-based biosensors developed for other purposes could be adapted to describe benthic marine ecosystems (see the review by Merkoci, 2006). Current sensors are however also lacking a number of ecologically important inorganic components, the major one being sulphate, which is a main electron acceptor for organic matter degradation in anoxic environment.

4. Performances of in situ chemical sensing techniques for benthic studies

4.1. Measurement ranges The tools mentionned previously were generally developed for a given application, i.e. tightly linked to the ecological issue that was addressed. Their application to different contexts may suffer from significant limitations. For instance, while amperometric sensors are selective to H2S, less than 90% of the free sulphide is present under this acidic form in typical seawater and most pore waters of the benthic environment, where pH is above 7.5. De Beer et al. (2006) reported that the detection limit of this microsensor is about 10µM sulphide at pH 8. In such conditions, the sensitivity of this sensor is therefore insufficient to detect significant exposure to this toxic compound, considering that a few micromolar is already deleterious for marine aerobes (Vismann et al., 1991). Potentiometry is better adapted to characterise the potential toxicity of low sulphide content in the environment. Conversely, voltammetry offers a much lower detection limit (0.2µM according to Luther III et al., 2008) but also accounts for the less toxic ionic fraction, HS-. Assessing the toxicity of the environment required pH to be measured - in parallel, in order to assess the ratio of H2S and HS in the total concentration of free sulphide. Part III – Chapter 1 197

A similar issue was raised about oxygen. Amperometric microsensors were first developed to describe oxygen sediment profiles from which the consumption of oxygen in sediment can be quantified (Revsbech et al., 1983). These microsensors use the Clark amperometric electrode principle and have a detection limit estimated to be 1µM. While this is suitable to quantify oxygen consumption rates, this detection limit is insufficient to establish true anoxic conditions in which anaerobic ammonium oxidation or another strict anaerobic microbial processes could occur. This limit led to the development and commercialisation of a new tool, the Stox sensor with enhanced sensitivity. The detection limit of this sensor is much lower (0.01µM, www.unisense.com). In comparison, the other methods used to detect oxygen in sediment or overlying water have a much higher detection threshold: 10µM for voltammetry and 7µM for optodes (Moore et al., 2009). Conversely, the application range of a chemical sensor may be limited by its saturation threshold. For in situ colorimetric sulphide analysers, the maximum concentration lies around 200µM (Sarrazin et al., 1999) and was extended to about 1mM using a non linear calibration method (Le Bris et al., 2003). Similarly, amperometric sulphide sensors are expected to have a maximum concentration range of 200µM for H2S (Borum et al., 2005), expanding its limit to a few millimolar of free sulfide in alkaline conditions. This is important to be acknowledged given that the concentration of sulphide often exceeds 1mM in organic rich sediments. Potentiometric sensors with logarithmic response are sensitive over a much larger measurement range, although their precision is limited at high concentration.

4.2 Temperature dependence and other interferences For the purpose of habitat comparison or rate quantification, chemical concentrations may require to be quantified with maximum accuracy. In this case, beside the classical issues of sensitivity and reproducibility, measurement conditions are to be accounted in the choice of the sensor. When temperature is prone to change, which is often the case in natural environments, the temperature influence on the sensor response is an important criterium. Colorimetric detection methods are sensitive to temperature (and pressure) changes due to their influence on flow rates and reaction kinetics (Le Bris et al., 2000). In comparison, electrochemical techniques with solid state electrode are less affected by temperature change, unless a selective membrane is used. Diffusion through a membrane is highly dependent on temperature, and correction is needed while using amperometric microsensors in thermally variable environments (Wenzhöfer et al., 2000). Glass electrodes also display a signifi- 198 Ecosystem properties cant temperature dependence that needs to be accounted (Le Bris et al., 2001). The sensitivity of the technique used to potential interferences of seawater major ions should additionally be considered in the calibration protocol, especially in environments with variable salinities (estuaries, brines, salt-marshes).

4.3 Chemical speciation The capacity of sensing techniques to measure various chemical species of the same compound is also of major importance while considering the toxicity or bioavailability of a chemical compound. Rather than quantifying the total concentration of a dissolved element encompassing both biologically active and inactive forms, as done in reagent-based colorimetry, electrochemistry and selective membrane-based spectrometry target single species or group of reactive species. For example, among all sulphide species, the H2S form has the strongest deleterious effect on non-adapted organisms, due to its capacity to pass gas exchange membrane. Bulk sulphide analysis on water or pore water samples with the Cline or the iodometric methods does not enable the discrimination of this toxic sulphide form from iron complexes, which are abundant in most sulfidic marine environments and much less toxic. While in situ colorimetric analysers based on laboratory methods typically measure the total concentration of soluble sulphide (Le Bris et al., 2003), electrochemical techniques give access to various forms of free sulphide, and amperometric microsensors are selective to H2S (Jeroschewski et al., 1996). Potentiometric sulphide sensors are sensitive to S2- and com- - bined with pH measurement allows the calculation of both HS and H2S. Voltammetry does not discriminate these two forms but provides a much better view of the different labile sulphide species in the environment, including dissolved or colloidal iron sulphides (FeSaq or FeS°) and poly- 2- sulphides (Sn ). The total concentration of sulphide (i.e. all labile sulphur compounds in the redox state S-II) is also directly accessible in cyclic voltammetry.

5. Expanding the temporal stability of chemical sensors

Today, temporal variability of marine environments over days to months is still mostly addressed using physical sensors (temperature, salinity, see IV, 1). Chemical sensors were applied to the harshest benthic environments, but the typical measurement durations were often less than a few hours and successful examples of long-term deployments are quite rare in the literature. The principle of unattended in situ measurement over Part III – Chapter 1 199 several months in the surrounding of hydrothermal vent macrofauna has been demonstrated for ferrous iron Fe(II) by flow colorimetry using low energy osmotic pumps and solenoid valves (Chapin et al., 2002). Since that time, similar devices have been designed based on solenoid valves and peristaltic pumps (e.g. Vuillemin et al., 2009 for Fe(II)) for unattended long-term deployment. Before these systems can be used routinely, several issues are still to be solved including power, mechanical and electronic ruggedness, in addition to standard solution and reagent instability. Tools with higher ruggedness and simpler operation procedures were proposed for this purpose. For example, UV spectrometry overcomes the addition of reagents and has been considered as an alternative, though it has not been validated for benthic environment studies. Electrochemistry is another option, which offers the advantage of mechanical ruggedness (no moving parts like pumps and valves) and low energy requirements. However, in this case, calibration issues are important since calibration is not done in situ but in laboratory and therefore raise the problem of long- term stability of measurement during deployment. Currently, the availability of integrated sensor systems for long term continuous monitoring from shallow to great depth is scarce, but a few examples of autonomous deployments over several days to weeks are known. A first deep-sea autonomous voltammetric analyzer, the Isea system, designed by AIS (www.aishome.com), was successfully deployed to record the concentration of H2S po for u t 3 days on an animal assemblage of the East Pacific Rise (Luther III et al., 2008; Lutz et al., 2008). These series revealed that

H2S can change over two orders of magnitude (from non-detectable levels to 30μM) within minutes to hours several times during a few days. Using potentiometric electrodes, Ye et al. (2008) found a similar pattern in the same deep-sea environment with total dissolved H2S fluctuating periodically over about 12 hours, reflecting tidal influence. The detection system was integrated in a flow device allowing calibration of the electrode in situ. Autonomous miniaturised potentiometers (designed by NKE, France) and equipped with lab-made pH and sulphide electrodes were adapted for the purpose of long-term deployment (Le Bris et al., 2001; 2008; see figure 4). Although they were mostly used for short term measurements during submersible dives of a few hours (Le Bris et al., 2001; 2006), their unattended use over 3 days in a shallow water mangrove swamp revealed the potentiality of these simple and easy-to-handle tools for autonomous monitoring in various environments (Laurent et al., 2009). Solid-state voltammetric and potentiometric electrodes are particularly suitable for harsh conditions within the environment of benthic fauna. Using microelectrodes, these techniques are also well suited for low cost, energy 200 Ecosystem properties

Figure 4: Deployment of chemical sensors for benthic studies. A. Potentiometric sensors deployed for continuous sulfide and pH measurements over two weeks on a hydrothermal mussel bed at 2500 meter depth (9°50’N, East Pacific Rise) © Le bris/MESCAL/Ifremer. b. Programmation of autonomous sensors for unattended in situ monitoring of a wood fall experiment over 4 months 500m deep (Lacaze Duthiers canyon, Mediterranean sea) ©Le bris/LECob/UPMC. C. Autonomous underwater potentiometers (NKE) equipped with laboratory-made pH and sulfide electrodes similar to those used in A and b. The length of the ruler is 30 cm. and size-limited monitoring devices (Pizeta et al., 2003). Since efforts to integrate both sensors and their electronics in shallow water housings (Unisense and MPI divers system) have expanded, the capacity for in situ deployments increased (Vopel et al., 2005). In addition to commercialised one-dimensional sediment profilers (Unisense), recently developed systems offer access to three-dimensional mapping and a Part III – Chapter 1 201 wider range of application, such as coral reef microhabitats (Weber et al., 2007). Expanding the autonomy of these chemical sensors over several days to months is the most crucial need today, but the most difficult task is to ensure the stability of electrode response. Uncontrollable matrix interferences, lack of specificity or selectivity, problems of reversibility, fouling and drift highlighted by analytical chemists impeded the transfer of electrochemical techniques for in situ long-term monitoring (Tercier and Buffle, 1993; Muller and Stierli, 1999). In this prospect, promising laboratory works showed that the potentiometric Ag/Ag2S electrode could have a stable response to a certain level over two months (Lacombe et al., 2007), at least when operated with a laboratory instrument in artificial seawater and ambient temperature. However the stability of electrochemical analyses in situ when sensors are exposed to colloids, biofilms, and varying pH and temperature has to be better comprehended. In situ testing of completely autonomous and submersible devices over several months on degrading wood in a mangrove waters, confirmed their relative stability in a microbiologically active environment (Le Bris et al., submitted, Yücel et al., submitted). Deployments over two to three weeks in deep sea hydrothermal environments confirmed calibration stability at ambient pressure but in situ stability still needs to be address under simulated pressure and temperature conditions (Contreira et al., 2011).

6. Conclusion

Compared to the profusion of sensor principles and techniques available for ex situ analyses of benthic marine environments, only a few are operational in situ today. Among the sensors adapted to underwater use even fewer sensors have achieved sufficient reliability and ruggedness for common use in benthic ecology. Microelectrodes developed since the 1980s have significantly improved our capacity to characterise physicochemical gradients at the sediment-water interface in relation with the structure and activities of microbial communities. However, the inherent fragility of microelectrodes resulted in a biased sampling of benthic habitats toward soft sediments, excluding the habitat of macro-organisms with hard shells or tubes or rocky substrates. A significant step forward is still awaited for making in situ sensors operational for a wider range of ecosystem studies. There is a great need to encourage the integration of new sensing techniques developed by analytical chemists as part of experimental designs in ecology. For this, however, a paradigm change is required: high frequency but low precision data obtained in situ in natural environments can be of much higher relevance to the ecological processes studied than 202 Ecosystem properties few high quality analyses performed on samples in controlled laboratory conditions. The challenge is to gather interdisciplinary expertise, at the interface of geochemistry, ecology and analytical chemistry in order to appreciate the advantages and drawbacks of the various techniques and their possible combination. Indeed no standard set of in situ sensors in benthic ecology exists, but rather a bunch of available techniques that need to be combined to answer specific scientific issues and develop comprehensive mechanistic models.

Authors’ references

Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel: Université Pierre et Marie Curie, Laboratoire d’écogéochimie des environnements benthiques, CNRS-UPMC FRE3350, Banyuls-sur-Mer, France.

Corresponding author: Nadine Le Bris, [email protected]

Acknowledgement

This review was supported by UPMC, CNRS, the TOTAL fundation and the European commission, within the SENSEnet ITN network (International sensor development network GA 237868). The TOTAL fundation supports the post-doctoral grant of Mustafa Yücel, as part of the chair “Extreme marine environments, biodiversity, and global change”.

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Advances in marine benthic ecology using in situ chemical sensors

Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel

1. Introduction

Seafloor ecosystems play a significant role in energy transfer, nutrient recycling and carbon storage in marine environments, while sustaining diverse biological communities in a variety of benthic habitats. The abiotic properties of benthic environments reflect available resources and physical or chemical constraints on biodiversity, and are themselves modulated by biological activity. However, insufficient knowledge of the interplay between organisms and communities and the dynamic components of their environment prevents their role in ecosystem functioning to be fully understood. Opening this black box is particularly crucial to understand how these complex and dynamic interactions governs ecosystem responses to disturbances. In situ chemical sensing techniques are of primary importance in this context. They have provided the ability to describe how hydrodynamics, microbial activity, engineer species and chemical kinetics combine and shape in situ gradients in marine benthic systems. Complementing the inventory of available techniques and their respective advantages and limitations (see chapter III, 1), this chapter illustrates major insights in marine benthic ecology gained from the use of in situ sensors, and discusses the potentialities offered by the development of autonomous sensing devices for in situ experimental approaches. 210 Ecosystem properties

2. Biological activity and habitat chemical heterogeneity

2.1 Chemical gradients at interfaces Opposite vertical gradients in electron acceptors (dissolved oxygen, nitrate, sulphate as well as particulate iron and manganese oxides) and electron donors (ammonium, sulphide), characterise marine redox interfaces where organic matter is degraded by microbes using redox reactions as an energy source (Schulz and Zabel, 2006). These gradients exist both in benthic and in pelagic environments. In the latter, deep suboxic layer (tens to hundreds of meter thick) may form like the one probed using in situ sensors in the black Sea (Glazer et al., 2006). This transition occurs along much more shorter distances in benthic systems. A particular example is the redox gradients occurring over decimetre to meter-scale in the plumes of hydrothermal vents, where fluids are mixed with seawater above the seafloor. Within these narrow interfaces, specialised chemolithotrophic communities settle and grow, exploiting the energy provided by the combination of inorganic electron donors and acceptors in their environment. Steep changes in sulphide, pH, as well as nitrate, oxygen, and iron, have been documented at the scale of chemosynthetic assemblages, with the use of sensors operated by deep-sea submersibles (see figure 1, Johnson et al., 1988; Le Bris et al., 2000; 2003). The co-variation of chemical parameters with temperature, taken as a dilution tracer, revealed the combined influence of physical process (mixing) and biological activity (consumption or biologically induced heat exchange) on these gradients (Johnson et al., 1988; 1994; Le Bris et al., 2005; 2006a).

Figure 1: In situ sensing of sulphide and pH gradients at the scale of an aggregation of hydrothermal tubeworms Riftia pachyptila at 2015 meter depth in the Gulf of California. © S. Sievert, Woods Hole Oceanographic Institute. Part III – Chapter 2 211

Due to the slow diffusion of solutes in sediment pore waters, gradients in sediment pore waters often occur over even shorter distances (e.g. decimetre to sub-millimeter scales). Besides being indicative of the rate of carbon and nutrients remineralisation processes in the sediment, these gradients reflect the environmental constraints exerted on microbial communities and on benthic fauna. Particularly, the oxygen penetration depth can extend from only a few millimetres to several centimetres below the sediment-water interface. This penetration layer defines the habitat of microbial aerobes. As reviewed in Stockdale et al. (2009), positioning of the lower limit of the oxic layer can only be resolved using in situ micro- sensing techniques. While oceanographic chemical probes (mostly for pH and oxygen) have been available since the mid-twentieth century, their use by microbial ecologists for sediment studies only traces back to the early eighties (Revsbech et al., 1983). Miniaturisation of electrode tips down to c.a. 10µm for O2 offered significant advantage over sampling, as it allowed resolving chemical changes over distances as short as 25µm, which is required to highlight the processes sustaining microbial mats at the sediment water-interface. Strong gradients were thus revealed and related to the vertical zonation of bacteria and diatoms over a few millimetre-thick microbial mat from core samples of sediments (Jørgensen and Revsbech, 1983). The technique is still widely used, most of the time combined with the measurement of other major tracers of biogeochemical processes in marine sediments, such as sulphide. Sulphide is produced by the reduction of sulphate diffusing from seawater and has strong ecological implications. Besides being toxic to aerobes, sulphide is used as an electron donor by chemolithotrophic microbes to gain energy. Amperometric microsensors provided high-resolution profiles of the most toxic sulphide form, H2S, in addition to oxygen, and sometimes pH. By using these sensors, micropro- files were obtained directly in situ through microbial mats and the under- neath sediments at shallow hydrothermal vents (Wenzhöfer et al., 2000) or greater depth (Gundersen and Jørgensen, 1992; De Beer et al., 2006). These data illustrated steep sulphide concentration increases from zero to as high as millimolar levels. Beyond assessing sulphate and oxygen consumption rates, and related carbon remineralization rates, these profiles offered useful information on the drivers of microbial diversity and distribution in marine sediments. Chemical profiles of oxygen and sulphide in different sulphidic microbial mats of a deep-sea mud volcano in the eastern Mediterranean Sea, revealed strong habitat preferences for different sulphide-oxidising microbe types (Grünke et al., 2011). Beside their catabolic requirements, it was also proposed that sulphide gradients are used by motile microbes as chemical cue to position themselves within the sediment. A steep 212 Ecosystem properties sulphide concentration increase would thus prevent the sulphide oxidising Beggiatoa strains, which stores nitrate from shallow sediment layers, to get lost in deeper sulphide-rich regions of the sediment (Preisler et al., 2007). The use of an amperometric nitrate microsensor in combination with oxygen, pH and sulphide microsensors greatly improved the understanding of the ecology of these widely distributed chemolithotrophic microbes in marine sediments. For example, a comparison of in situ nitrate profiles in mats of Beggiatoa, a sulphide-oxidizing bacterium, with the total concentration of nitrate after bacterial cell lease confirmed the storage of nitrate in intracellular vacuoles. In turn, this storage capacity allows sulphide-oxidising bacteria to survive for days in the sediments, outside of the upper part of the vertical redox zonation where nitrate is supplied (Preisler et al., 2007). Sedimentary profiles of various iron species is also of major ecological relevance. The coupling of iron and sulphide chemistries in sediment is known to control sulphide toxicity and bioavailability. In Beggiatoa mats, chemical oxidation of H2S with iron oxides unexpectedly appeared to be the main sulphide consuming process (Preisler et al., 2007). Furthermore, the formation of less labile precipitated forms of iron sulphide exerts a major control on biogeochemical cycling and habitat conditions (Stockdale et al., 2009). In situ voltammetry on gold-amalgam micro-electrodes proved to be a very powerful technique to access the dissolved or colloid forms of reduced and oxidised iron and sulphide involved in these processes. G. W. Luther III from the University of Delaware and his group made this technique fully operational for in situ use up to 3000m deep within redox interfaces above the seafloor (Brendel and Luther III, 1995; Luther III et al., 2008). A first deep sea in situ sediment profiling voltammetric system has recently been applied in the hydrothermal environment of the Loihi Seamount, describing the intricate iron cycling in this iron-rich environment (Glazer and Rouxel, 2009).

2.2. Habitat heterogeneity on and below the seafloor By offering a much better spatial coverage and resolution than achievable from sampling, in situ measurements allowed to document horizontal changes in chemical gradients. Changes occurring at scales ranging from centimetres to metres or tens of metres are often related to the distribution of fauna and macrophyte assemblages. For example, higher sulphide levels in seagrass bed sediments compared to non-vegetated surrounding sediments were documented by Hebert and Morse (2003). The higher sulphide level was attributed to the enrichment of organic material in sediment. Furthermore, Borum et al. (2005) highlighted one to two order of magnitude increases in sulphide from 2 to 6cm deep (up to more than Part III – Chapter 2 213

500µM) between densely vegetated areas and Thalassia testidum (a seagrass) die-off patches. At hydrothermal vents, the mapping of oxygen, sulphide, iron and manganese and other chemical factors at the surface of faunal assemblages, using both colorimetric analysers and electrochemical sensors, revealed steep transitions between the habitats of different dominant chemosynthetic species (Sarrazin et al., 1999; Le Bris et al., 2000; Le Bris et al., 2006; Podowski et al., 2010). Organic fall degradation on the seafloor, such as whale skeletons, similarly induces marked lateral chemical zonation with distinct sulphide maxima in the sediment that were described using in situ amperometric microprofilers (Treude et al., 2009). At kilometre scale (i.e. the diameter of the Haakon Mosby mud volcano), Nieman et al. (2006) and De Beer et al. (2006) found strong differences in the sulphide and oxygen gradients characterising areas occupied by tubeworms, microbial mats, as well as the centre of the volcano without visible colonisation. Interestingly, these authors also pointed deeper sulphate penetration and sulphide production in the sediments colonised by tubeworms illustrating their large influence on sediment profiles. The effect of bioturbation and bioirrigation on the fluxes at the sediment- water interface is often accounted for by defining a specific diffusion coefficient, which is used to extrapolate fluxes over larger areas. In reality, faunal activity results in very complex and diverse gradient shapes, which cannot be reduced to a simple diffusion gradient according to the Fick’s first law (Glud et al., 2005; Bertic and Ziebis, 2009). Even when classical diffusion profiles are obtained, significant changes in microsensor profiles are reported within centimeter distances, reflecting the heterogeneous organisation of meiofaunal and microbial communities within sediments (Glud et al., 2005; Stahl et al., 2006). Similar to macrofauna, macrophytes create marked zonation around their rhizosphere, both due to the enrichment in labile organic compounds and the oxygen release in the sediment by roots (Hebert and Morse, 2003, Borum et al., 2005). By using a gold-amalgam electrode, Hebert and Morse (2003) not only documented large differences between adjacent vegetated and unvegetated sediments, but they also found up to 80 µM difference in the maximum sulphide content between two cores collected 1.5cm apart in the rhizosphere region of a vegetated sediment. Despite these studies, the relevance of spatial heterogeneity on nutrients and carbon fluxes has been largely overlooked in marine benthic ecology. The lateral heterogeneity that was already recognised from multiple series of sediment cores can now be captured at centimetres scale with microsensors and down to even millimetres with planar optodes. Assessing the influence of habitat heterogeneity on microbial activities will help to better integrate this unevenness into ecological models. As recently 214 Ecosystem properties illustrated by Bertic and Ziebis (2009), microsensors offer the opportu- nity to link benthic biogeochemical processes with biological diversity, thus addressing the control of burrowing organisms on the diversity and activity of microbial communities. These tools are undoubtedly opening new ways to understand the mechanisms underlying the control of bioengineers on microbial processes, and ultimately on the functioning and function of benthic ecosystems. Planar optodes provide access to sediment heterogeneity in two dimensions, and are very efficient tools to address the relationships between chemical gradients in sediments and the behaviour of benthic organisms (figure 2). Oxygen planar optodes, in particular, revealed steep changes due to the filling of macrofauna burrows with oxygenated water contrasting with the surrounding sediment pore waters, and the diffusion through their wall from and to the surrounding sediment (König et al., 2005; Glud et al., 2005). Similarly, planar optodes revealed unexpected pH heterogeneity resulting from carbon remineralisation within the sediment. Steep pH changes were observed across burrow walls, and within microniches in the sediment (Stahl et al., 2006). For example, the photosynthetic activity of a small benthic diatom at the sediment surface was characterised by a remarkable pH increase from 8.0 in the water to up to 8.6. Today, however, most of these techniques are applied ex situ in mesocosms and have only been rarely operationally used in situ (Glud et al., 2005).

Figure 2: A two dimensional image of the oxygen variability at the sediment water interface showing the irrigation of burrows with oxygenated water by marine invertebrates. © F. Wenzhöfer and R. Glud. Part III – Chapter 2 215

Lateral heterogeneity is a major problem when two or more chemical sensors are needed to assess a chemical factor from equilibrium calculation.

This is particularly the case for assessing total CO2 (or dissolved inorganic carbon, DIC) from the in situ measurement of pH and pCO2, or the total sulphide concentration from pH and H2S microsensors. A mismatch between the profiles of different chemical parameters obtained within several centimetres distance can lead to strong biases in the definition of the chemical factor of interest (De Beer et al., 2006). The same problem arises when addressing the constraints exerted on calcifying organisms at the scale of their microhabitat, which requires to combine the measure- 2+ ment of pH, pCO2 and Ca (Cai and Reimers, 2003). In the heterogeneous media characterising most benthic habitats, the design of integrated in situ probes combining different sensors within a short distance is therefore a critical need. A compromise has to be found, since disturbance of the environmental gradient increases with the diameter of the probe, particularly in sediments.

3. Temporal dynamics of benthic ecological processes

3.1. Reactive chemical species and chemical speciation Aquatic chemical systems are dynamic molecular assemblages, whose composition and properties are governed by kinetic and thermodynamic laws (Stumm and Morgan, 1996). The influence of living organisms on chemical assemblages often results in non-equilibrium states allowing + reactive chemical species to coexist (e.g. O2, H2S, NH4 ). These reactive chemicals can be used as energy sources, but they also constitute potential stress factors and constrain the ability of organisms to settle and grow. Quantifying the concentration of reactive chemical species within characteristic microniches is therefore of main relevance to ecological studies. Conventional colorimetric analytical methods allow the quantification of total concentrations of elements, sometimes selecting a given redox states (e.g. FeII, S-II), but they are unable to discriminate between reactive forms even when operated in situ. By using in situ amperometric and potentiometric microelectrodes, it is possible to selectively detect a single reactive form of a chemical element (see III, 1). In situ voltammetry can further discriminate different chemical species and has been particularly useful in distinguishing various sulphide-rich environments in terms of toxicity and energy availability for chemoautotrophs by comparing the respective contents of free sulphide and of sulphides complexed with iron (Luther III et al., 2001a; 2001b). Furthermore, the technique allows to measure other 2- 2- forms of reduced sulfur, thiosulphates (S2O3 ) or polysulphides (Sn ) and 216 Ecosystem properties can therefore assess the importance of these intermediate compounds in the biological sulphide oxidation (Waite et al., 2008; Gartman et al., 2011). Colloidal forms of metals are other reactive species playing a significant role in benthic ecology and biogeochemistry. Cyclic voltammetry revealed that iron sulphide colloids are electrochemically active and can be particularly abundant in metal rich reducing environments. These labile macromolecules determine not only sulphide speciation – thus limiting the toxicity of sulphide – but also iron speciation with significant effects on its mobility (Rickard and Luther III, 2007). Taillefert et al. (2000a) showed that soluble Fe(III) colloids detected in sediment pore waters can have important implications on the mineralisation rate of organic matter. This highly reactive Fe(III) can diffuse in and out of sediments, supply- ing an electron acceptor at locations where particulate iron oxides are no more available. In addition, reduction of soluble Fe(III) by sulphide was identified as a major removal process for toxic sulphide in the sediments (Preisler et al., 2007).

2.2 Short-term dynamics: from laboratory to natural conditions Physical and biological factors not only constrain the spatial distribution of in situ chemical gradients, they also generate substantial temporal variability that has long been difficult to investigate in benthic ecosystems. Day-night transitions are of particular ecological relevance. With the help of chemical sensing techniques, the challenge of capturing light-dark variability was first tackled from ex situ experiments on sediment cores. Jørgensen et al. (1983) thus described how the interplay of diatoms with photosynthetic and non-photosynthetic sulphur bacterial communities shaped chemical gradients over day-night cycles within a benthic microbial mat collected in a hypersaline lake. Monitoring the oxygen gradients at the sediment interface in response to light exposure revealed profound deviation from oxygen saturation, due to the activities of benthic phototrophic primary produc- ers (Revsbech et al., 1986). In a narrow photosynthetic layer – less than a few millimetres thick – oxygen reached up to three times the atmospheric saturation level in light conditions, in complete opposition with the steep oxygen consumption profile in the dark. Daily changes in oxygen within algae microhabitat with temporary anoxia and hyperoxia were similarly documented by Pöhn et al. (2001) in artificial light exposure conditions. Sensors have moreover fostered the investigation of the reciprocal relationships between key bioengineers and chemical factors variation in their environment. As an example, seagrass beds are known not only for their carbon storage efficiency and the nutritional and sheltering role of their canopy above the sediment, but also for the diurnal control they exert on biogeochemical processes inside the sediment. Significant light-dark Part III – Chapter 2 217 changes in microsensor vertical profiles were documented from a series of sediment cores collected at different times of the day on a seagrass bed, highlighting the influence of the rhizosphere activity on oxygen profiles and subsequent biogeochemical processes (Hebert and Morse, 2003; Borum et al., 2005). The most important ecological consequence of this process is the reduction of toxic sulphide in the upper layer of the sediment. Indeed, the oxygen released from the roots during the day promotes the oxidation of sulphide, and protect the plants from toxic sulphide invasion as it was demonstrated by using microsensors in microcosms (Borum et al., 2005). Using similar microsensors, these authors monitored in situ daily changes in oxygen and sulphide in healthy patches of seagrass Thalassia testidum and adjacent areas including die-off patches. These direct measurements confirmed that, without detoxification by oxygen, the sulphide produced in non-vegetated sediment can reach higher levels and diffuse outward, further increasing deleterious effects on adjacent seagrass beds. The fast response of amperometric microsensors is particularly well suited to address temporal variability at even shorter scales. For example, the monitoring of oxygen use of an microelectrode over one hour allowed to resolve fluctuation in oxygen content from 0 to 80% of the overlying water saturation within a benthic polychaete burrow in a mesocosm (Kristensen, 2000). This study highlighted the influence of burrow ventilation by the worm not only on the burrow habitat itself but also on the surrounding sediment, through diffusion of oxygen across the burrow wall. Kristensen (2000) also pointed out the need for more experimental testing of the impact of such burrowing activity, which implicitly called for an extension of the use of microsensors directly in situ as done by Volkenborn et al. (2007). From 2D video imaging on sediment cores obtained with planar optodes, these authors have recently quantified the influence of Arenicola marina, a common burrowing annelid, on oxygen penetration in sediment through an ex situ approach. Oxygenated seawater pumping by the worm through its burrow promotes lateral diffusion of oxygen through burrow walls to the surrounding sediment. The use of in situ microsensors to compare chemical gradients between a 400m2 exclusion zone and a densely colonized adjacent area confirmed the influence of this process over large scales, but it also revealed that burrow irrigation is not the sole influence of these organisms on oxygen transport rates in the sediment. The combination of local heterogeneities formed by burrow openings and excreted material at the sediment surface with hydrodynamic forcing by currents and waves promoting diffuse fluxes at the sediment-water interface also appeared as a major control on oxygen penetration in sediment. This example illustrates well the importance of in situ studies, in combination to ex situ approaches using sensors. Another example is provided by Vopel et al. (2001; 2005), who studied the two transport mechanisms enabling sulphide and oxygen to be sup- 218 Ecosystem properties plied to the chemosynthetic bacterial symbionts of Zoothamnium niveum, a colonial ciliate typically found in mangrove peat sulphidic habitats. Using microsensors in aquarium, these authors described how the contraction and extension of the ciliate stalk enhanced advective transport of alternatively sulphide and oxygen over durations a few seconds (Vopel et al., 2001). In situ measurements on mangrove rootlets colonized by these ciliates, further documented the capacity of this symbiosis to exploit natural hydrodynamics. In high flow conditions, boundary-layer modulations over degrading peat results in fluctuating sulphide and oxygen concentrations over ciliate groups, whereas in quiescent periods advective transport generated by the ciliate is needed for the supply of these compounds to the symbionts. Most continuous in situ monitoring studies remained limited to very shallow environments, like salt marshes or mangrove swamp, where the use of in situ sensors is facilitated because electronics can be maintained out of the water and only the electrode is immersed and can be positioned by an operator (Taillefert, 2000b; Vopel et al., 2005) Today, however, underwater sensing devices allowing autonomous deployments are becoming available for a variety of environmental conditions, including remote deep-sea conditions (III, 1),. As far as tracers of ecological processes are concerned, the need for highly accurate and precise measurements may be balanced by the benefits gained from the characterisation of temporal changes. The study of wood degradation and colonisation in tropical shallow waters provided an illustration of the advantages of continuous autonomous monitoring over durations exceeding a few hours. Large sulphide and pH variation on the surface of a natural wood fall in a mangrove swamp were determined over almost three days in correlation with tide (figure 3 and Laurent et al., 2009). The succession of high sulphide and low pH periods with low sulphide and higher pH periods was attributed to the influence of tidal currents on local hydrodynamics. This study emphasized the need for continuous measurement series over several days capturing tidal fluctuation in order to fully describe habitat redox conditions. Similar wide temporal fluctuations partly attributed to the forcing of tidal current on vent fluid mixing have been recently highlighted from the use of autonomous voltammetric and potentiometric sensing device deployed over days to weeks in deep-sea hydrothermal vent habitats (Luther III et al., 2008; Contreira et al., 2011). The availability of fully autonomous systems that can be operated unattended over longer periods will enlarge the capacity to tackle the transient dynamics of ecological processes directly in situ in the future. With the objectives of documenting the dynamics of establishment of sulphidic conditions suitable for chemoautotrophy on organic falls, pioneer works have just explored the capacity to perform continuous chemical monitoring on experimentally immersed organic substrates over duration exceeding two months (figure 4 and Le Bris et al. submitted; Yücel et al., submitted). Part III – Chapter 2 219

Figure 3: Fluctuation of sulphide (A) and pH (b) at the surface of a woody substrate naturally immersed in a tropical mangrove swamp as measured with sulphide (green signal) and pH sensors (red signal). The tide is represented by the blue curve (adapted from Laurent et al., 2009). 220 Ecosystem properties

Figure 4: Autonomous sulphide and pH sensors used for an experimental study of the degradation and colonisation of woody debris over three months in a mangrove swamp. The electrode tips are attached to the surface (green arrow) and inserted (red arrow) into a piece of Coco nuciphera (© o. Gros UAG).

4. Conclusion

In situ chemical sensing supported a significant step forward in the understanding of the interaction of microbial consortia and macrofaunal assemblages in marine benthic habitats, as well as their role in the spatial and temporal dynamics of chemical gradients. Processes governing nutrient recycling, organic matter transformation and energy transfer, environmental toxicity, and the resulting chemical constraints on biological diversity are important ecological issues that have been addressed by using chemical sensors. These tools hold great promises for the investigation of marine biodiversity and ecosystem functioning relationships, whose underlying mechanisms remain largely unknown. Modularity, robustness, small size, and cost effectiveness of sensors largely improved, which maximised their capacity to be implemented as part of in situ experimental designs. Analogous to the miniaturised modules that equip Mars landers (Kounaves et al., 2010), new generations of integrated and autonomous sensing devices are to be developed by combining different techniques to access the full range of relevant parameters for specific questions. Exchange of knowledge and technical expertise between soil and terrestrial aquatic sciences and marine benthic ecology will undoubtedly foster these development efforts. Part III – Chapter 2 221

Authors’ references

Nadine Le Bris, Leonardo Contreira-Pereira, Mustafa Yücel: Université Pierre et Marie Curie, Laboratoire d’écogéochimie des environnements benthiques, CNRS-UPMC FRE3350, Observatoire océanologique, Banyuls-sur-Mer, France.

Corresponding author: Nadine Le Bris, [email protected]

Acknowledgement

This review was supported by UPMC, CNRS, the TOTAL foundation and the European Commission, within the SENSEnet ITN network (International sensor development network GA 237868). The TOTAL foundation supports the post-doctoral grant of Mustafa Yücel, as part of the Chair “Extreme marine environments, biodiversity, and global change”.

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studies of the photosynthesis and O2, H2S and pH profiles of a microbial mat. Limnology and Oceanography, 28, pp. 1062-1074. Revsbech N. P., Madsen b., Jørgensen b. b., 1986. oxygen production and consumption in sediments determined at high spatial resolution by computer simulation of oxygen microelectrode data. Limnology and Oceanography, 31, pp. 293-304. Rickard D., Luther III G.W., 2007. Chemistry of iron sulfides. Chemical Reviews, 107, pp. 514-562. Sarrazin J., Juniper S. K., Massoth G., Legendre P., 1999. Physical and chemical factors influencing species distribution on hydrothermal sulfide edifices of the Juan de Fuca Ridge, northeast Pacific. Marine Ecology Progress Series, 190, pp. 89-112. Schulz H. D., Zabel M. (Eds), 2006. Marine Geochemistry, 2nd edition. Springer Berlin Heidelberg, New York, USA. Stahl H., Glud A., Schröder C. R., Klimant I., Tengberg A. Glud R. N., 2006. Time-resolved pH imaging in marine sediments with a luminescent planar optode. Limnology and Oceanography: Methods, 4, pp. 336-345. Stockdale A., Davison W., Zhang H., 2009. Micro-scale biogeochemical heterogeneity in sediments: A review of available technology and observed evidence. Earth-Science Reviews, 92, pp. 81-97. Stumm W., Morgan J. J., 1996. Aquatic Chemistry, 3rd edition. Wiley, New York, USA. Taillefert M., Bono A. B., Luther III G. W., 2000a. Reactivity of freshly formed Fe(III) in synthetic solutions and (pore)waters: voltammetric evidence of an aging process. Environmental Science and Technology, 34, pp. 2169-2177. Taillefert M., Luther III G. W., Nuzzio D. b., 2000b. The application of electrochemical tools for in situ measurements in aquatic systems: a review. Electroanalysis, 12, pp. 401-412. Treude T., Smith C. R., Wenzhoefer F., Carney E., Bernardino A. F., Hannides A. K., Kruger M., Boetius A., 2009. Biogeochemistry of a deep-sea whale fall: sulfate reduction, sulfide efflux and methanogenesis. Marine Ecology Progress Series, 382, pp. 1-21. Part III – Chapter 2 225

Volkenborn N., Polerecky L., Hedtkamp S. I. C., Van Beusekom J. E. E., De Beer D., 2007. Bioturbation and bioirrigation extend the open exchange regions in permeable sediments. Limnology and Oceanography, 52, pp. 1898-1909. Vopel K., Pöhn M., Sorgo A., Ott J., 2001. Ciliate-generated advective seawater transport supplies chemoautotrophic ectosymbionts. Marine Ecology Progress Series, 210, pp. 93-99.

Vopel K., Thistle D., Ott J., Bright M., Roy H., 2005. Wave-induced H2S flux sustains a chemoautotrophic symbiosis. Limnology and Oceanography, 50, pp. 128-133 Waite T. J., Moore T. S., Childress J. J., Hsu-Kim H., Mullaugh K. M., Nuzzio D. B., Paschal A. N., Tsang J., Fisher C. R., Luther III G. W., 2008. Variation in sulfur speciation with shellfish presence at a Lau Basin diffuse flow vent site. Journal of Shellfish Research, 27, pp. 163-168. Wenzhöfer F., Holby O., Glud R. N., Nielsen H. K., Gundersen J. K., 2000. In situ microsensor studies of a shallow water hydrothermal vent at Milos, Greece. Marine Chemistry, 69, pp. 43-54. Yücel M., Galand P. E., FagervoldS. K., Le bris N. Submitted. Transition from suboxic to sulfidic conditions during early wood-fall degradation in seawater.

Chapter 3

Use of global satellite observations to collect information in marine ecology

Séverine Alvain, Vincent Vantrepotte, Julia Uitz, Lucile Duforêt-Gaurier

1. Introduction: ocean colour features, a state of the art

The term “ocean colour” encompasses the retrieval and description of parameters linked with oceanic phytoplankton from optical measurements. The remote sensing of ocean colours has been used for more than 30 years and now provides key information on the dynamics of the oceanic phytoplankton (Morel and Prieur, 1977; Mobley et al., 1993; Antoine et al., 1996; Bricaud et al., 1998; Loisel et al., 2006). Phytoplankton com- prises microscopic plant-like organisms living in the illuminated surface layers of the ocean. The existence of phytoplankton is of a fundamental interest as they form the base of the aquatic food webs, providing an essential ecological function for all aquatic life. Like terrestrial plants, phytoplankton uses pigment antennae to capture the energy of photons. Among these phytoplankton pigments, total chlorophyll-a (i.e. the sum of chlorophyll-a, divinyl-chlorophyll-a, and chlorophyllide a) is a commonly used proxy of total phytoplankton biomass. Chlorophyll-a selectively modifies the flux of photons that penetrates the ocean surface layer. It absorbs the red and blue wavelengths and scatters the green ones. For this reason, the colour of the ocean changes from blue to green depending on the concentration and type of phytoplankton populations. Thus, by studying the colour of light scattered from the oceans, in other words ocean colour, optical sensors onboard satellites enable to quantify the chlorophyll concentration and observe its interactions with other constituents (Mobley et al., 1993; Antoine et al., 1996; Bricaud et al., 1998). 228 Ecosystem properties

Visible and near-infrared passive radiometers onboard spacecrafts provide useful data on spatial and temporal scales, unattainable by shipboard sampling. This was well demonstrated by the first satellite dedicated to the observation of ocean colour, the coastal zone colour scanner (CZCS) launched in 1978. Since then, a number of advanced ocean-colour satellites have been launched, including SeaWiFS (sea viewing wide field of view sensor, from August 1997 to December 2010), Modis (moderate resolution imaging spectroradiometer) and Meris (medium-spectral resolution imaging spectrometer), which are still in activity. However, the ocean colour observation from space faces some important limitations. Indeed, the information obtained from satellite observation is restricted to the near-surface layer of the ocean (Gordon and McCluney, 1975). The thickness of this layer typically varies from a few metres to about 60m, depending on the presence of optically-significant constituents in the water and the wavelength considered (Smith and Baker, 1978). Products derived from satellite data are therefore integrated content over the first penetration depth. Another limitation is that a large part ocean colour measurements in the visible spectrum is caused by the atmosphere and aerosols that diffuse and absorb light. The atmosphere is responsible for about 90% of the blue light detected by a satellite sensor. However, the portion of the signal that carries information from the ocean and the atmosphere can be de-convoluted. This is currently done by using atmospheric correction algorithms that are still being improved. In the past few years, the analysis of ocean colour satellite data has moved beyond the estimation of chlorophyll-a concentration to include new parameters. This includes the ability to determine the dominant phytoplankton groups in the surface waters (Aiken et al., 2009; Alvain et al., 2005: Uitz et al., 2006; Raitsos et al., 2008; Kostadinov et al., 2009; Brewin et al., 2010), to obtain information on particle size distribution (Loisel et al., 2006), or to retrieve information about other biogeochemical components such as particulate organic carbon (POC) and coloured detrital matter (Stramski et al., 1999; Loisel et al., 2002; Siegel et al., 2002). This chapter presents an overview of these newly available parameters from remote sensing of ocean colour. We conclude by a synthesis of most important challenges and ongoing developments. Part III – Chapter 3 229

2. Overview of newly available parameters from remote sensing

2.1. Particulate organic carbon Inherent optical properties (IOPs) describe the absorption and scattering properties of ocean water and its constituents. A recent method to analyse remote sensing data consists in deriving the surface content of particulate organic carbon (POCsurf) from the inherent optical properties, as presented in Loisel et al. (2002). The natural variations of optically- significant substances in seawater can be deduced from the measurements of the total backscattering coefficient of seawater, bb, which is not sensitive to the presence of dissolved material. The bb coefficient can be parti- tioned into two components, bb=bbp+bbw where bbw si the backscattering coefficient of seawater (Morel and Prieur,

1977) and bbp, is the backscattering coefficient of particles. The bbp variability is determined primarily by changes in the abundance of the particle assemblage and also, secondarily, by the composition of the assemblage. In a remote-sensing context, the backscattering coefficient of seawater is not measured directly, but is derived by the inversion of the natural light field reflected back from the ocean and detected by satellite ocean colour sensors (Loisel and Stramski, 2000; Loisel and Poteau, 2006). A simple linear relationship calibrated for a study area is then used between POCsurf and bbp (Claustre et al., 1999; Loisel et al., 2001). Previous studies at regional (Stramski et al., 1999; Loisel et al., 2001) and global scales (Loisel et al., 2002) have demonstrated the feasibility of estimating POC from bbp, and figure 1 displays global maps of the near-surface concentration (POCsurf) for the SeaWiFS period 1997-2008 in June and January.

The global distribution of POCsurf follows the major gyre system and other large scale circulation features of the ocean. Low surface POC concentrations are encountered in subtropical gyres, where large scale -3 downwelling is expected. For example, POCsurf is less than 50mg.m in the South Pacific gyre. Elevated near-surface POC concentration in the range 100-200mg.m-3 are encountered at high and temperate latitudes (e.g. Antarctic circumpolar current, subarctic gyres, or temperate North Atlantic). Compared to subtropical gyres, these areas are characterized by a high chlorophyll concentration supported by inputs of nutrients injected from below the euphotic layer by advection or vertical mixing, or from terrestrial sources. 230 Ecosystem properties

Figure 1: Global maps of the particulate organic plankton near-surface concentration calculated from SeaWiFS observations, during the period 1997-2008 in June and January using the method of Loisel et al. (2002).

2.2. Phytoplankton functional types Phytoplankton plays an important role in many global biogeochemical cycles. However, the photosynthetic efficiency and biogeochemical impacts of phytoplankton depend strongly on the functional types of phytoplankton species. Thus, monitoring the spatial and temporal distribution of dominant phytoplankton functional groups is of critical importance. For a given chlorophyll-a concentration (Chl-a), phytoplankton groups scatter and absorb light differently according to their pigments composition, shape and size. However, the first order signal retrieved from ocean colour sensors in open oceans, the normalized water leaving radiance (nLw), varies with Chl-a (Gordon et al., 1983; Morel et al., 1988) and cannot be easily used to extract information about phytoplankton groups Part III – Chapter 3 231 present in the oceanic surface layer. To circumvent this difficulty, different approaches have been developed in the past few years. When changes in nLw are significant enough between phytoplankton groups, they can be detected from their specific radiances measurements (Sathyendranath et al., 2004; Ciotti et al., 2006). When reflectance changes are not significant enough to separate one group from another, empirical or semi-empirical methods have to be developed. This last case is particularly relevant when the objective is to detect phytoplankton groups defined from a biogeochemical or size point of view at global scale. The Physat algorithm (Alvain et al., 2005; Alvain et al., 2008) has been developed based on an empirical relationship between coincident in situ phytoplankton observations and remote sensing measurements anomalies. The Physat method has been applied to the SeaWiFS satellite archive, and more recently to MODIS. Monthly Physat data have been used to retrieve the monthly climatology maps for January and June, shown in figure 2.

Figure 2: Dominant phytoplankton groups climatology maps over 1997-2008 period (SeaWiFS), from Physat, for June and January. Physat method allows to separate dominant phytoplankton groups from remote sensing measurements. 232 Ecosystem properties

Physat has a domain of applicability ranging from concentrations of Chl-a higher than 0.04mg m-3, so as to discard ultra-oligotrophic waters where it is unlikely that a dominant group can be found using ocean-colour data, to Chl-a lower than 3mg.m-3 so that waters possibly contaminated by coastal material are excluded. The Physat approach is based on the identification of specific signatures in spectra classically measured by ocean colour sensors. It has been established by comparing two kinds of simultaneous and coincident measurements: normalised SeaWiFS water leaving measurements (nLw) and in situ measurements of phytoplankton biomarker pigments performed in the framework of the Gep&Co program (Dandonneau et al., 2004). Five dominant phytoplankton groups are currently identified: diatoms, nanoeukaryotes, Synechococcus, Prochlorococcus and Phaeocystis-like. Note that the Physat method allows the detection of these groups only when they are dominant. The key step in the success of methods such as Physat is to associate in situ measurements with remote sensing measurements after having removed the first order variations due to the Chl-a concentration and classically used in previous ocean colour products. This step is done by dividing the actual nLw by a mean nLw model (nLw ref) for each wavelength (λ), established from a large remote sensing dataset of nLw(λ) and Chl-a: nLw*(λ) = nLw(λ) / nLw ref(λ, Chl a)

By dividing nLw by this reference, we obtain a new product, noted nLw*, which is used in Physat. Indeed, it was shown that main dominant phytoplankton groups sampled during the GeP&Co program were associated with a specific nLw* spectrum. It is thus possible to define a set of criteria to characterise each group as a function of its nLw* spectrum. These criteria can thus be applied to the global daily SeaWiFS archive in order to obtain global monthly maps synthesis of the most frequently detected dominant group, as shown in figure 2. Note that when no group prevails over the period of one month, the pixels are associated with an “uniden- tified” group. The geographical distribution and seasonal succession of major dominant phytoplankton groups were studied in Alvain et al. (2008) and are in good agreement with previous studies and in situ observations (Zubkov et al., 2000; DuRand et al., 2001; Marty and Chivérini, 2002; Dandonneau et al., 2004; Longhurst, 2007; Alvain et al., 2008). However, as for all empirical ocean colour methodology, validation based on in situ measurements has to be pursued every time a suitable dataset is available. In situ observations are indispensable in any stage of satellite development. Therefore, constructing and maintaining fully consistent coupled biogeochemical and optical records is a high priority. Part III – Chapter 3 233

2.3. Phytoplankton size classes and associated primary production Another approach to discriminating phytoplankton groups from space consists in using the surface Chl-a concentration (Chl-asurf) retrieved from ocean colour measurements as an index of phytoplankton community composition. Chlorophyll-based approaches typically rely on the general knowledge that, in open oceans, large phytoplankton cells (mostly diatoms) develop in eutrophic regions (e.g. upwelling systems) where new nutrients are available, whereas small phytoplankton are preferentially associated with the presence of regenerated forms of nutrients and dominate phytoplankton assemblage in oligotrophic environments. On the basis of such trends, Uitz et al. (2006) proposed a method for deriving the contributions of three pigment-based size classes (micro-, nano-, and picophytoplankton) to depth-resolved chlorophyll-a biomass using Chl-asurf as input parameter. This method was developed through the statistical analysis of an extensive phytoplankton pigment database (2419 sampling stations) obtained from high performance liquid chromatography (HPLC) analysis of samples from a variety of oceanic regions. Using an improved version of the diagnostic pigment criteria of Vidussi et al. (2001), Uitz et al. (2006) computed phytoplankton class-specific vertical profiles of Chl-a for each station included in the pigment database, from which the desired statistical relationships were established. Essentially, seven pigments were selected as biomarkers of specific taxa, which were then assigned to one of the three size classes according to the average size of the organisms. Some limitations of this method were recognised in the past (Vidussi et al., 2001; Uitz et al., 2006). For example, certain diagnostic pigments are shared by various phytoplankton taxa and some taxa may have a wide range of cell size. Yet, this method enables characterising the taxonomic composition of the entire phytoplankton assemblage while providing relevant information on its size structure (Bricaud et al., 2004). For example, microphytoplankton essentially include diatoms, nanophytoplankton include primarily prymnesiophytes, and picophytoplankton are often prokaryotes (cyanobacteria) and small eukaryotic species. The approach of Uitz et al. (2006) provides quantitative information on the composition of phytoplankton community within the entire upper water column rather than just the surface layer accessible to ocean colour satellites. In addition, this approach can be extended to the estimation of primary production associated with the pigment-based size classes, using a bio-optical model (Morel, 1991) coupled to class-specific photo-physiological properties (Uitz et al., 2008). In a nutshell, Uitz et al. (2008) investigated relationships between phytoplankton photo-physiology and community composition by analysing a large database of HPLC pigment determinations and measurements of phytoplankton absorption spectra and photosynthesis vs. irradiance curve parameters collected in various open ocean waters. An empirical model that describes 234 Ecosystem properties

Figure 3: Seasonal climatology (1998-2007) of total and phytoplankton class- specific primary production for the December-February period (boreal winter/ austral summer; left-hand side panels) and for the June-August period (boreal summer/austral winter; right-hand side panels). (a, e) Total primary production in absolute units of gC.m-2.d-1. (b-d) and (f-h) percent contribution of class-specific production to total primary production. Total primary production, attributed to the entire algal biomass, was obtained by summing the contributions of each class. (adapted from Uitz et al., 2010). Part III – Chapter 3 235 the dependence of algal photo-physiology on the community composition and depth within the water column, essentially reflecting photoacclimation, was proposed. The application of the model to the set of in situ data enabled the identification of vertical profiles of photo-physiological properties for each phytoplankton size class. Figure 3 illustrates the seasonal climatology of phytoplankton class-specific and total primary production obtained by applying the class-specific approach to a 10-year time series of Chl-asurf data from SeaWiFS (Uitz et al., 2010). Temperate and subpolar latitudes in each hemisphere exhibit high total primary production values in summer, especially in the North Atlantic Ocean. In contrast, oligotrophic subtropical gyres are associated with low values and show weak seasonality. Microphytoplankton appear as a major contributor to primary production in temperate and subpolar latitudes in spring-summer, especially in the North Atlantic (more than 50%) and in the Southern Ocean (30-50%). Their contribution reaches a maximum of about 70% in near-coastal upwelling systems year-round, but is reduced drastically in subtropical gyres. Nanophytoplankton appear ubiquitous and account for a significant fraction of total primary production (30-60%). The relative contribution of picophytoplankton to primary production represents up to 40-45% in subtropical gyres and decreases to 15% in the northernmost latitudes in summer. The proposed approach enabled to produce ocean colour-derived climatology of primary production at a phytoplankton class-specific level in the world’s open oceans (Uitz et al., 2010). Such information represents a significant contribution to our ability to understand and quantify marine carbon cycle. It also provides a benchmark for monitoring the responses of oceanic ecosystems to climate change in terms of modifications of phytoplankton biodiversity and associated carbon fluxes.

3. Challenges posed by current developments

3.1. Dissolved organic matter (DOM) Besides the latter parameters, new developments in ocean colour remote sensing are needed especially for studying the dynamics of the dissolved organic matter. The dissolved organic carbon (DOC) is operationally defined as the fraction of organic carbon smaller than 0.2µm. It accounts for almost all the organic carbon of the ocean (Chen and Borges, 2009) being equivalent in magnitude to the atmospheric CO2 stock. DOC can be degraded by microbial activity and sunlight action and converted into

CO2. Hedges (2002) reported that an increase of 1% in the DOC degradation rates would lead to a source of CO2 equivalent or greater than that 236 Ecosystem properties represented by the fossil fuel combustion. Therefore it appears crucial, for understanding the global carbon cycle, to investigate the dynamics of this biogeochemical compartment, which is still poorly constrained. This is particularly true for the coastal ocean, where DOC fluxes are potentially large and highly variable in time and space, due to numerous driving factors taking effect on these very heterogeneous ecosystems, such as biological activity, land-sea interactions, and strong hydrodynamic forcing. In that context, efforts are needed to develop research activities aiming to estimate DOC concentrations and fluxes from space. Recent studies have emphasised the potential of satellite imagery for retrieving DOC concentrations with a satisfying accuracy (Mannino et al., 2008; Del Castillo et al., 2008; Fichot and Benner, 2011). The current main limitation for estimating DOC concentrations from radiative measurements stands in the crucial need of a relevant correlation between DOC concentration, which is uncoloured, and CDOM (coloured dissolved organic matter), which represents the coloured part of the marine dissolved material and is therefore measurable from space. Significant DOC-CDOM relationships were documented for various coastal ecosystems, especially those influenced by rivers discharges (e.g. Ferrari, 2000; Mannino et al., 2008; Del Castillo et al., 2008). However, the diversity in the origin of the dissolved material as well as the potential decoupling in the sensitivity of DOC and CDOM to various environmental factors (e.g. biological activity, photo-degradation processes…) induces regional and seasonal variations in the CDOM-DOC relationships. Environmental effects can significantly alter or preclude the establishment of a significant link between CDOM and DOC, for example, in the oceanic waters and coastal ecosystems not influenced by terrestrial inputs. Therefore, our ability to derive dissolved organic carbon contents from satellite measurements is still limited. The understanding of environmental effects through dedicated in situ or laboratory studies represents the major challenge for developing DOC inversion algorithms in the next years. These algorithms will provide, in a near future, relevant insights for global ocean carbon cycle study.

3.2. Scaling down and up from regional scales to global scale Coastal oceans have fast changing and contrasted optical properties, which prevents the development of a “simple”, general algorithm to derive in-water bio-optical and biogeochemical parameters for the whole ocean from satellite information. Therefore, open ocean or coastal algorithms are usually developed to focus on an area-specific range of optical variability. However, these algorithms have some limitations related to their high dependency upon the data set used for their development, as well Part III – Chapter 3 237 as to the difficulty to capture the numerous high frequency processes affecting regional bio-optical relationships. Moreover, the scaling-up of such regional approaches to derive biogeochemical parameters at large scale (i.e. global) would require to consider a patchwork of algorithms developed on a mosaic of regions. This seems to be difficult to set up in practice. Another approach consists in taking explicitly into account the optical diversity of the marine environment within the algorithms development procedure. This was shown to be crucial for explaining the dispersion found around the bio-optical relationships (Loisel et al., 2010). In practice, this original approach aims to classify the different regions according to their optical properties as described by the marine reflectance spectra. Further, region-specific algorithms (empirical or semi-analytical) are developed and applied to the defined optical regions. The main advantage of this classification-based approach is that it is independent of the location and time period, being thus more universal than classical approaches and potentially applicable to large-scale studies. The potential of this classification-based approach for improving the performance of the inversion procedure has been recently emphasised for the retrieval of the Chl-a (Mélin et al., 2011) and SPM concentrations (Vantrepotte et al., submitted).

3.3. Theoretical studies If the last years have seen the development of different approaches to distinguish phytoplankton groups from space, the current techniques are usually based on empirical methods (see above). Despite the fact that remotely sensed measurements are generally well matched with in situ measurements, the underlying theoretical foundation is still to be addressed. In a recent study published by Alvain et al. (2011), a radiative transfer model called Hydrolight (Mobley et al., 1993) was used to reconstruct the signals used in Physat. A sensitivity analysis of the method to the following three model parameters was conducted: the specific phytoplankton absorption, the dissolved organic matter absorption, and the particle backscattering coefficients. This last parameter explained the largest part of the variability in the radiative anomalies. Our results also showed that specific environments associated with each group must be considered imperatively. This study represents a first step toward a better understanding and future improvement of phytoplankton groups detection methods based on specific signal identification. In a near future, further advances are expected from the improvement of the optical sensors themselves, especially their spectral and spatial resolution. This may also pave the way for the development of new algorithm based on both phytoplankton groups and their environmental conditions (such as the content in dissolved organic matter). 238 Ecosystem properties

3.4. Geostationary sensors The recent development of geostationary ocean colour sensors will increase the precision of the remote sensing measurements and will provide relevant insights for the study of marine biogeochemical cycles. Geostationary satellites continuously view the same region of the Earth’s surface. The size of the observed region depends on the spacecraft specification. It thus allows obtaining high quality and frequent observations of a defined area. Such an instrument is therefore useful in order to follow the response of marine ecosystems to short-term variations in the environmental conditions. In particular, it is of interest for monitoring the effects of rivers plumes, tidal front, and mixing on the biotic and abiotic material present in coastal areas or assessing the effects of exceptional events (storms, red tides, dissemination of sediments or pollutants). Data derived from geostationary satellites will also provide relevant information for biogeochemical modelling purposes as well as for research activities related to the biogeochemical cycles at daily scales. The South Korean instrument on board the COMS-1 satellite (Goci, geostationary ocean colour imager), launched in 2010, is the first ocean-colour sensor in a geostationary orbit. The target area of Goci covers a large region (2500 × 2500km) around the Korean peninsula. It resolution is of 500 m while it acquires data at a 1-hour frequency. The other ocean colour geostationary missions that are currently planned (Ocapi-CNES, GeoCape- NASA) will increase the spatial coverage and the number of information delivered by such sensors.

3.5 Cross-using remote sensing data Considering the recent variety of new ocean colour products, cross using studies will open a large range of new applications. For example, information on dominant phytoplankton groups could be analysed concomitantly with maps of POC and particle size distribution, hence providing new insights into biogeochemical or ecological processes. An illustration is shown in figure 1 and 2 for a Northern area (45°N-52°N, 30°W-15°W) and a Southern area (47°S-40°S, 65°E-80°E). The two areas are almost identical in terms of Chl-a concentration but distinct in terms of POC concentration. This difference also exists in terms of phytoplankton groups. The region in the Southern Ocean is dominated by diatoms whereas the region in the northern Atlantic is dominated by nanoeukaryotes. The comparison of the spatial distribution of Chl-a, POC and dominant phytoplankton groups prompts the following question. Is it possible to identify from space, and at a global scale, some differences in the “POC vs Chl-a” relationship based on the dominant phytoplankton group as detected by the Physat tool? Further investigations are required to fully answer this question but our simple example illustrates how Part III – Chapter 3 239 much cross-using of remote sensing data will be necessary and useful in a near future.

Authors’ references

Séverine Alvain, Vincent Vantrepotte, Lucile Duforet-Gaurier: Université du Littoral de la Côte d’Opale-Lille Nord, Laboratoire d’Océa nologie et Géosciences, Lille1-ULCO-CNRS UMR 8187, Lille, France

Julia Uitz: Université Pierre et Marie Curie, Laboratoire d’Océanographie de Villefranche, UPMC-CNRS UMR 7093, Villefranche-sur-Mer, France

Corresponding author: Séverine Alvain, [email protected]

Acknowledgement

The authors would like to thank the NASA SeaWiFS project and the NASA/GSFC/DAAC for the production and distribution of the SeaWiFS data.

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Tracking canopy phenology and structure using ground-based remote sensed NDVI measurements

Jean-Yves Pontailler, Kamel Soudani

1. Introduction and general context

The NDVI (normalised-difference vegetation index) is the most popular remotely sensed spectral vegetation index. It was defined in the early seventies (Kriegler et al., 1969; Rouse et al., 1973; Tucker, 1977; 1979) for remote sensing purposes, and has been mostly used to track green vegetation from satellites, airplanes and ground-based spectral measurements. The first measurements of vegetation indices from space were conducted in 1972 thanks to Landsat 1 and its embedded MSS (multispectral scanner) and this technique carries on nowadays with satellites such as Spot or Modis Terra. The NDVI is an estimate of the amount of visible light absorbed by canopies and can correlate with gross primary photosynthesis (Running et al., 2004). The measurement of NDVI exploits the fact that green vegetation absorbs largely the incident red radiation but reflects a large part of the infrared radiation. By contrast, a bare soil does not exhibit such differences. Thus, NDVI is defined as a normalised difference between red (RED) and near-infrared (NIR) reflectance given by the equation:

NDVI = (NIR – RED) / (NIR + RED)

The NDVI value ranges from -1.0 to 1.0: negative values indicates water and snow, values close to zero indicates bare soil, and high positive values indicates sparse vegetation (0.2 to 0.5) or dense green vegetation (up to 0.85). NDVI can also be related to the leaf area index (LAI), green 244 Ecosystem properties biomass, photosynthetic activity or to ground cover, which are relevant parameters in canopy and forest ecosystem modelling (Bréda et al., 2002). Several factors affect the accuracy of satellite-based NDVI measurements, for example, the presence of clouds or atmospheric dust in the solar and sensor viewing directions and topography. These impose geometric and atmospheric corrections (Soudani et al., 2006). Surprisingly, very few studies make use of ground-based NDVI measurements to record vegetation dynamics in situ in spite of the limited constraints imposed by this technique. This chapter describes a new low-cost laboratory-made NDVI sensor devoted to track in situ temporal variations in canopy structure and plant phenology.

2. Designing a new NDVI sensor

2.1. General concept Most authors agree on the fact that an accurate NDVI estimate requires measurements in a narrow red band centred around the chlorophyll absorption band (between 650 and 680nm) and in a narrow or broad near infrared band placed in the 750-1100mm range (Thenkabail et al., 2000; Elvidge and Chen, 1995). In these conditions, a relevant choice for the light detectors of an on-ground NDVI sensor would be to couple silicon photodiodes that operate in the red and near-infrared ranges with interference filters that transmit the required bandpass only. This operating mode is sound but such detectors typically show a low sensitivity due to the necessity of collimating incident radiation (i.e. removing oblique beams) to allow the interference filters to work properly. The narrow red band is more affected by very low output levels, especially over dense canopies that absorb a large part of incoming red light. In these conditions, we chose not to use interference filters but instead made use of long-pass filters providing a sharp cut-off. Also, we took benefit of the peculiar shape of the spectral response curve of gallium arsenide phosphide photodiodes (GaAsP, Hamamatsu Photonics, Japan) when designing the red detector.

2.2. Sensor design and calibration The red detector uses a large GaAsP photodiode (TO-8 package) coupled with a Schott RG645 glass-tinted long-pass filter (Schott Glaswerke, Mainz, Germany). Thanks to this combination, only the portion of the GaAsP response curve corresponding to the longest wavelengths is used (figure 1A). As a consequence, the detector has a narrow response curve Part III – Chapter 4 245 centred on 655nm. The near-infrared detector is equipped with a silicon photodiode (TO-5 package) and a Schott RG780 glass-tinted long-pass filter. It has therefore a broad response curve centred on 825nm.

Figure 1: Characteristics of RED and NIR detector of the custom-made NDVI sensor. A. Relative response of the RED detector. The relative response results from the shape of a gallium arsenide phosphide photodiode (GaAsP) and from the cut-off of a long-pass filter. B. Relative response of RED and NIR (near infrared) detectors.

The relative spectral response of the two channels (figure 1B) was monitored using a halogen stabilised light source and a monochromator (H10, Jobin Yvon, France), combined with simultaneous energy measurement with a pyranometer (CE 180, Cimel, France). The body of the sensor is 85mm long and 38mm in diameter. It is made of polytetrafluoroethylene (Teflon) surrounded by a stainless steel housing (figure 2A). The two detectors face a 5mm thick acrylic diffuser (Altuglass 740, Altulor, France). Current is converted to voltage with shunt resistors. To provide a high sensitivity, the value of the resistors was selected as high as possible without affecting signal linearity. As a result, no amplification is necessary contrary to similar sensors using silicon diodes and interference filters (Methy et al., 1987). The order of magnitude of the output level is about 25mV so the sensor can be connected directly to most commercial data loggers. Other characteristics of the sensor can be found in Pontailler et al. (2003). Prior to its deployment in situ, the sensor is calibrated against a spectroradiometer (Li-1800, Li-Cor, Nebraska, USA), considering bandwidths equal to 640-660 nm and 780-920 nm for RED and NIR, respectively. 246 Ecosystem properties

Figure 2: The custom-made NDVI sensor developed at the Laboratoire Écologie- Systématique-Évolution (CNRS, Université Paris Sud, Agro Paris Tech , UMR 8079, Orsay, France). A. Cross-section of the NDVI sensor. b. NDVI sensor performing routine measurements at barbeau Carboeurope site.© J.-y. Pontailler.

2.3. Operating mode The sensor has a view angle of 100° with a greater sensitivity in the centre of the field of view and is “looking” downwards. Users can restrict this angle by using a black hood, which decreases both channels outputs equally so that NDVI values remain unaffected. The fact that a single sensor provides no reflectance data but measures radiance should be kept in mind. Computing NDVI from radiance assumes that the RED/NIR ratio of incident light is constant, which is not totally true due to the presence of clouds or variations in solar angle. Of course, it is possible to add a second sensor looking upwards to obtain true reflectance data. However, this second sensor has to be cosine-corrected and must be frequently cleaned because it is exposed to bad weather. That is why we promote the use of a single sensor looking downwards. It is a robust maintenance-free solution, well adapted to routine measurements. Data filtering based on time, sun elevation, or incident radiation may be helpful to increase accuracy. Part III – Chapter 4 247

3. In situ deployment to monitor vegetation phenology

Up to now, 47 sensors were manufactured and installed on various sites including flux towers within Carboeurope network, crops, meadows, Mediterranean vegetation and shrubs or rain forest. All sensors were identical and calibrated in the same way in our laboratory before shipping. During measurements, sensors were linked to Campbell CR10x or CR1000 data loggers (Campbell Scientific, Utah, USA) using 0-50 or 0-250mV input ranges. Zeroes (night values) were checked and offsets were adjusted if necessary. Measurements were performed at one minute interval and half-hourly average values were recorded. Data obtained when incident radiation was low were rejected (below 250Wm-2) and filtered data were then averaged daily.

3.1. Measurements over forests In forests stands, NDVI sensors were installed on top of towers measuring carbon and water vapour fluxes by eddy covariance. Sensors had a view angle restricted to approximately 60° and were obliquely set up at 20° from vertical (figure 2B). They were located from 4 to 20m over the top of the canopy. The NDVI sensor nicely tracks phenological events over deciduous forests. In an oak stand located in Barbeau Carboeurope site (Quercus sessiliflora, Seine-et-Marne, France), the course of NDVI over time highlights several phases (figure 3A). Before budburst, the sensor mostly detects branches, trunks, leaf litter and bare soil. NDVI values are stable around 0.4 and display some slight variations due to modifications of litter moisture and direct versus diffuse radiation regimes. After budburst, NDVI increases rapidly during leaf expansion and maturation that lasts about 30 days until it reaches its maximum value in the late spring at day-of-the-year 130. Then, NDVI is almost constant during summer from early May to the end of September. This period constitutes the main season of growth in temperate deciduous forests. The NDVI plateau during the growing season is characterised by a very slow and regular decrease of NDVI values from 0.9 to 0.8, which may be caused by imperceptible colour changes in the canopy (slight yellowing) and probably sun-view geometry variations throughout summer. Our research team performed eddy-covariance measurements in Carboeurope deciduous sites, and observed at the same period a similar regular decrease of carbon uptake probably due to early foliage senescence (Granier et al., 2000). This phenomenon is not always taken into account by process-based models of canopy functioning. During this stage of NDVI maximum, the radiance measured by the red channel is very low and NDVI signal approaches saturation. Later in the season, leaf yellowing and fall both cause a strong decrease of NDVI values. Colour and density changes partly overlap, and the deconvolution of signal components 248 Ecosystem properties is not easy. We can notice that the NDVI curve matches pretty well with the evolution of the fraction of photosynthetically active radiation (PAR) transmitted by the canopy during the same period, indicating that NDVI is a good indicator of the amount of absorbed visible light (figure 3A).

Figure 3: Seasonal and inter-annual variations in NDVI measurements over a deciduous forest study plot. A. Temporal profiles of NDVI and transmitted PAR (photosynthetically active radiation) in Carboeurope oak forest site in Barbeau (France) in 2006. B, C. NDVI temporal profiles at Barbeau site over six years (2005- 2010) and annual carbon net exchange of the stand from 2006 to 2010. Year 2007 was characterised by a stronger carbon storage flux, which was detected by NDVI measurements (red arrow) Part III – Chapter 4 249

Figure 4: Seasonal variation in NDVI measurements over two evergreen forest study plots. A. NDVI temporal profile at le bray Carboeurope pine site in 2007. b. NDVI temporal profile in Paracou site (French Guyana, rain forest) in 2007 during two dry seasons (yellow bands).

The sensor also highlights inter-annual variations of phenological events. Figure 3B shows the evolution of NDVI over a 6-year period in Barbeau Carboeurope flux site. Few inter-annual changes are noticeable between day-of-the-year 130 and day-of-the-year 275 but spring and autumn greatly differ from one year to another. These changes are of importance. For instance, an early budburst was observed during the warm spring of 2007, 250 Ecosystem properties inducing an exceptional annual carbon uptake as shown by eddy-covariance measurements on the same site (Delpierre et al., 2009). Carbon assimilation is considerable at this period of the year because canopy photosynthetic capacity is intact. By contrast, a late foliage senescence has a limited impact on carbon budget because aged leaves have a lower photosynthetic capacity and because there is less available light at this period of the year. Of course, the length of the leafy period is not the only parameter that influences the carbon uptake of deciduous forests. We can notice on figure 3B that year 2009 had a reduced net carbon budget in spite of a relatively long leafy period. This may be due both to the negative impact of several cold weeks that occurred immediately after leaf expansion and an increase of ecosystem respiration in autumn because of a particularly mild weather. A NDVI sensor can also track the phenology of evergreen tree species, but with small variations of NDVI due to subtle phenological changes in these ecosystems. figure 4A shows NDVI values measured in a pine forest in Le Bray Carboeurope site (Pinus pinaster, Gironde, France). The sensor can monitor the rise and growth of new shoots during the spring from day-of-the-year 90 to day-of-the-year 160. In the tropical rain forest in Paracou Carboeurope site (French Guiana), NDVI varies little over the year, as one would expect in evergreen moist forests. Nevertheless, we observe two periods with a decline in NDVI of variable magnitude. These two periods of NDVI decline are concomitant with the two dry seasons, the short dry season called “the little summer of March” and the major dry season from July to November (figure 4B).

3.2. Measurements over savannahs and crops In a savannah (Congo), a NDVI sensor was useful to describe the evolution of the herbaceous cover (figure 5A). A moderate wet season was observed from day-of-the-year 60 to day-of-the-year 160, followed by a dryer period. A fire occurred on day-of-the-year 185, lowering NDVI values to zero. A fast regrowth took place from day-of-the-year 300 onwards after a fairly long dry period. Similarly, crops (figure 5B) exhibit large temporal changes from ploughing to harvest. The colour and ground cover of crops therefore both vary intensely, and NDVI sensors may be useful to characterise these changes. figure 5B shows the temporal pattern of NDVI in a winter wheat field in Belgium. NDVI reaches a maximum value in May (day-of-the-year 130) and drops to zero within two months due to an intense yellowing. The harvest does not modify the signal. Later sparse vegetation appears up to the end of the vegetation period (day-of- the-year 260). Part III – Chapter 4 251

Figure 5: Seasonal variation in NDVI measurements over a savannah and a winter wheat study plot. A. NDVI temporal profile in Tchizalamou site (Congo, savannah) in 2008 during wet and dry seasons. b. NDVI temporal profile in Lonzée site (Belgium, winter wheat) in 2007, before and after havest.

4. Use of the NDVI sensor to estimate the leaf area index and plant biomass

4.1. Leaf area index The leaf area index (LAI) is defined as the total one-sided area of leaf tissue per unit ground surface area. It is a key parameter to describe the state of a forest ecosystem and a relevant input to productivity models. Several 252 Ecosystem properties methods have been devised for estimating LAI. Destructive methods that require direct sampling and allometric methods – which establishes quantitative relations between several easy to measure dimensions in the canopy (tree size and/or density, leaf characteristics…) and leaf area index (Ceulemans and al., 1993) – are cumbersome, time consuming and site dependent. Conversely, optical methods are fast and appropriate for monitoring spatial and temporal dynamics of canopy structure (Bréda et al., 2002).

Our NDVI sensor was used to keep track of LAI in an elevated CO2 experiment conducted in Florida (Pontailler et al., 2003). The vegetation studied was a scrub-oak natural ecosystem made up of three main tree species (Quercus myrtifolia, Q. geminata and Q. chapmannii). Four 4m2 contrasted calibration plots were marked out in February and four similar plots were marked out in June. The vegetation was low in stature and we used a handheld version of the sensor equipped with a dual LCD display to measure NDVI (36 measurements per plot). Immediately after these measurements, the plots were manually defoliated, the area of a sub-sample of the leaves – ca. 0.1m2 – was measured using a leaf area meter (LI-3100, Li-Cor, Nebraska, USA), and all leaves, including those sub-sampled, were dried to constant weight at 80°C and weighed. In these conditions, we could assess “true LAI” values from direct, destructive samples. The best fit between “true LAI” and the NDVI measurements was obtained using a negative exponential function (r2=0.987, figure 6A). We observed a moderate saturation effect in the range of values determined here, making it possible to obtain reliable estimations of LAI up to 3 or even more. The explanation for this moderate saturation is that even if the canopy reflectance in the red band reaches an asymptote at LAI values of 2-3, its reflectance in the near-infrared continues to increase with an increase in leaf area (Peterson and Running, 1989; Soudani et al., 2006). Nevertheless, the accuracy of this technique decreases when LAI increases, and NDVI is also affected by background materials when LAI is low. Contrary to other field techniques used to assess LAI, the approach based on NDVI measurement has the advantage to consider green plant parts only, ignoring branches and woody stems. Also, ground- based NDVI is well adapted to low canopies where other techniques may be difficult to implement.

4.2. Estimating biomass and ground cover The NDVI measurements are also useful to estimate biomass non-destruc- tively. A good linear relationship was obtained between NDVI and har- vested green biomass by Buttler and Landolt (unpublished data) in a peat bog stand in the East of France (figure 6B). As observed when performing Part III – Chapter 4 253

Figure 6: Use of NDVI for plant biomass studies. A. Relationship between NDVI and “true” LAI (leaf area index, total leaf tissue area per unit ground surface area) in a scrub oak ecosystem in Florida. B Relationship between NDVI and biomass in a peat bog stand.

LAI measurements, saturation also occurs when determining biomass in dense plots. The ability of NDVI to discriminate between green vegetation and bare soil makes it also useful to determine the ground cover of stands. This has to be considered carefully because density inside green spots often varies together with ground cover, and both are taken into account when performing measurements. This technique appears more adapted to assess trends rather than absolute values, even when a cautious calibration process is applied. 254 Ecosystem properties

5. Comparison with satellite-based remote sensing data

Despite the technological maturity of remote sensing technologies onboard of numerous satellites and the significant progress achieved for the last decade in this field, the potential use of remote sensing to monitor phenology and vegetation dynamics remains severely limited by atmospheric conditions in general and especially by the cloud cover (Soudani et al., 2008). Thus, ground-based NDVI measurements provide the data needed for the calibration and validation of satellite observations and products. Figure 7 shows the NDVI temporal profiles measured for three consecutive years over a deciduous mature forest in Barbeau Carboeurope site using our in situ NDVI sensor. These data are compared with those obtained from bands 1 (red) and 2 (near infrared) of Modis onboard Terra satellite platform for the same area. Both sensors show similar patterns and may be used to extract the main phenological markers that characterise the seasonality of vegetation in deciduous forests. Discrepancies between in situ NDVI sensor and Modis Terra based NDVI may be explained by numerous factors. First, an important reason for the observed discrepancies is the differences in the spectral responses of the two sensors. Shapes and bandwidths of spectral responses of Modis bands 1 and 2 are different from those of our in situ NDVI sensor. Second, other contributing factors include atmospheric effects, and view and illumination conditions. Finally, differences in spatial resolution may also be important here because the area observed by the in situ NDVI sensor (around 100m²) is rather small compared to Modis pixel size (a few hectares).

Figure 7: NDVI temporal profiles measured using a NDVI sensor located on top of a flux tower (squares, red lines) and derived from Modis satellite data (circles, blue line) over three years in Barbeau Carboeurope oak site. Part III – Chapter 4 255

6. Conclusion

The sensor described in this chapter is rugged and maintenance-free. We designed it ten years ago and we have used it since then in a variety of conditions where it always showed a satisfying reliability. For example, several units performed continuous measurements on top of flux towers for six years without failure or noticeable drift. The cost of this instrument is affordable, about 200€ without labour costs, and the instrument can be purchased from ESE laboratory (www.ese.u-psud.fr/). Our on- ground NDVI sensor appears well adapted to track vegetation phenology routinely, to assess leaf area index or ground cover in relatively sparse canopies and estimate standing biomass provided calibration is done. This sensor partly fills the lack of low-cost sensors available to scientists that are not ready to invest in expensive spectrometers to perform simple spectral measurements.

Authors’ references

Jean- Yves Pontailler, Kamel Soudani: Université Paris-Sud 11, Écophysiologie végétale, bilan carboné et fonc tionnement des écosystèmes, Laboratoire Écologie, systématique, évolu tion, CNRS-UPS-AgroParisTech UMR 8079, Orsay, France

Corresponding author: Kamel Soudani, [email protected]

Aknowledgement

The authors thank GIP ECOFOR and F-ORE-T « Observatoires de Recherche en Environnement (ORE) sur le Fonctionnement des Écosystèmes Forestiers » ECOFOR, INSU, Ministère de l’Enseignement Supérieur et de la Recherche for funding the NDVI project. We express our thanks to Eric Dufrêne who has strongly supported this project. We would like to express our profound gratitude to Laurent Vanbostal and Daniel Berveiller for their help in manufacturing NDVI sensors and performing measurements. We also thank all our collaborators who were involved in the data collection process in all study sites. 256 Ecosystem properties

References

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Soudani K., François C., le Maire G., Le Dantec V., Dufrêne E., 2006. Comparative analysis of Ikonos, Spot and ETM+ data for Leaf Area Index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, 102, pp. 161-175. Thenkabail P. S., Smith R. b., De Pauw E. 2000. Hyperspectral vegetation indices and their relationship with agricultural crop characteristics. Remote Sensing of Environment, 71, pp. 158-182. Tucker, C.J. 1977. Use of near infrared/red radiance ratios for estimating vegetation biomass and physiological status, NASA/GSFC Report X-923- 77-183, NASA Goddard Space Flight Center, USA. Tucker C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, pp. 127-150.

IV I nteGRATED STUDIES

Chapter 1

Integrated observation system for pelagic ecosystems and biogeochemical cycles in the oceans

Lars Stemmann, Hervé Claustre, Fabrizio D’Ortenzio

1. Exploring the under-sampled ocean at a time of global changes

Our climate is changing at unprecedented rates, and there is an urgent need to improve the observation at global scales of pelagic ecosystems biodiversity and functioning. The oceans constitute the largest habitats on Earth for a highly diverse and numerous flora and fauna, and play a major role in the carbon cycle and climate. Ocean carbon sources and sinks are controlled by both physical (Sabine et al., 2004) and biological (Volk and Hoffert, 1985) processes that take place at various temporal and spatial scales. Based on global biogeochemical modelling and on the use of paleoproxies from sedimentary archives, the sedimentation of biogenic particulate matter from the euphotic zones of the ocean, a process named biological carbon pump, was shown to contribute significantly to climate variability (Sarmiento and Le Quere, 1996; Volk and Hoffert, 1985). However, the uncertainties in our understanding of the biological pump’s functioning in today’s oceans remain important. For example, recent reviews about the export of biogenic particles to the deep ocean showed that there is no consensus on the mechanisms controlling its spatial and temporal variability (Boyd and Trull, 2007; Burd et al., 2010). In particular, the roles of zooplankton and bacteria are not well understood. Large temporal and spatial scales of marine surface production can be studied using satellite data (see III, 3). Alternatively, underwater autonomous floats technology has improved, thus allowing the exploration of the deeper layers of pelagic environments (Claustre et al., 2010b; Johnson 262 Integrated studies et al., 2009). Run since almost a decade, the international Argo project, which currently has an array of about 3,000 floats deployed in the world ocean, has proven to be an invaluable tool in modern physical oceanography (figure 1). The Argo project provides, on a routine basis and with unprecedented detail, the heat and salt content of the upper kilometre ocean, as well as water mass circulation. In addition, the BIO-Argo research group intends to add biogeochemical sensors to the current floats (Claustre et al., 2010a; 2010b; IOCCG, 2011). To summarise individual researchers as well as agencies have recognised the fact that autonomous platforms array could provide 3D information not attainable by satellite platforms, where the vertical dimension is missing. Such a platform can also determine near surface properties when cloud cover impedes observations from space. Therefore, it seems useful and timely to coordinate the work of different groups to obtain coherent data sets to determine global patterns of nutrients, plankton and marine particles distribution in the oceans. Until now, studies of the biological pump based on Argo floats have used chemical or physical sensors, and have therefore overlooked the effects of living organisms. This is because available sensors were not adapted to record individual organisms but rather did bulk measurements of their biomasses. Bio-optical and imaging sensors dedicated to the identification and quantification of organisms living in pelagic environments are now being developed (see II, 2). The current limitation of Argo floats shall soon be overcome thanks to the miniaturisation of these sensors, which make them compatible for implementation on the Argo floats. The abundance and size distribution of plankton and particles are among the relevant variables of pelagic environments that are not measured with standard floats (but see III, 3 for remote sensing techniques). The analysis of the size distribution of planktonic taxa is important because metabolic processes and many ecological traits, including population abundance, growth rate and productivity, spatial niche, trophic, competitive and facilitative relationships between species, are influenced by the body size of the organism (Brown et al., 2004; Gillooly, 2000; 2001; 2002). In addition, most marine organisms are opportunistic feeders and their prey size is limited by the diameter of their mouth. Therefore, predator–prey relationships are, in many marine systems, importantly determined by size (Hansen et al., 1997; Jennings and Warr, 2003). Furthermore, the particle diameter can describe multiple particle properties such as mass and settling speed or flux (Alldredge and Gotschalk, 1988), rate of colonisation by microbes and zooplankton (Kiørboe, 2000; Kiørboe et al., 2002, 2004) and coagulation rate (Jackson, 1990). The rate of biogeochemical activity such as aggregate remineralisation by bacterial activity or zooplankton consumption can also be proportional to the size of Part IV – Chapter 1 263

Figure 1: The Argo float is a global array of autonomous floats measuring pressure, temperature and salinity. A. Map showing the location of 3161 Argo floats in the world ocean (in May 2010, http://www.argo.ucsd.edu/). B. Schematic operating sequence of an Argo float. See section 2 for a full explanation of the sampling strategy and see figure 2 for an image of the deployment of a Provor float. © Argo, © jcommops. particles and plankton (Kiørboe and Thygesen, 2001; Ploug and Grossart, 2000). Hence, because size of organisms or particles captures so many aspects of ecosystem functioning, the size distribution of plankton and 264 Integrated studies particles in a volume of water can be used to synthesise a succession of co-varying traits into a single dimension (Woodward et al., 2005). Based on autonomous vehicles equipped with bio-optical sensors, pilot projects were launched or are planed for studying the size distribution of pelagic communities at different temporal and spatial scales (Boss et al., 2008; Kortzinger et al., 2004; Niewiadomska et al., 2008). If networks and arrays are implemented from these pilot projects to coordinate the efforts at the international level, a revolution in biological and biogeochemical oceanography will happen. The community will have access to an unprecedented observational array of vertically resolved “biogeochemical” and ecological variables (see next section for details). Developing such an in situ automated observation system will constitute an essential step towards a better understanding of biogeochemical cycles and ecosystem dynamics, especially at spatial and temporal scales that have been unexplored until now. Two main outcomes can be expected from a well-designed integrated observation system (Claustre et al., 2010a). The scientific outcomes include a better exploration and an improved understanding of both present state and change and variability in ocean biology and biogeochemistry over a large range of spatial and temporal scales (see figure 1). Associated with this, the reduction of uncertainties in the estimation of biogeochemical fluxes is an obvious target and the assessment of zooplankton resources for higher trophic levels (fish) is also an achievable important goal. Beside these scientific objectives, the operational and long-term outcomes are the development of sound predictions of ocean biogeochemistry and ecosystem dynamics as well as the delivery of real-time and open-access data to scientists, users and decision makers. It is also expected that reduced uncertainties will result in better policy. The present chapter reviews recent sensor technical developments and scientific results from pilot projects that investigated pelagic ecosystems using autonomous vehicles. It is a synthesis of two recent articles (Claustre et al., 2010b; Stemmann and Boss, 2012). The following sections were set to i) describe autonomous vehicles, ii) describe miniaturised present and future sensors that can describe habitats (physical and geochemical environment) and plankton communities (phyto and zooplankton), iii) suggest framework for data control and quality and iv) propose their integration in modelling and observing systems. Part IV – Chapter 1 265

2. The various platforms in support of a pelagic autonomous observation system

Autonomous floats spend most of their life drifting at depth where they are stabilised by being neutrally buoyant at the “parking depth” pressure where they have a density equal to the ambient pressure and a compress- ibility that is less than that of sea water (figure 2A). In the Argo mode, the floats pump fluid into an external bladder at typically 10-day intervals, and rise to the surface for about 6 hours while measuring temperature and salinity. Satellites determine the position of the floats when they surface, and receive the data transmitted by the floats. The bladder then deflates and the float returns to its original density and sinks to drift until the cycle is repeated. The floats can also be configured remotely to another prescribed resting depth. In the Argo array, floats are designed to make about 150 such cycles. With their long lasting capacities (3 years in the Argo array), floats are particularly useful to follow the temporal dynamics of the pelagic ecosystems in large-scale physical structures such as long lasting gyres. In contrast to floats, gliders can be steered and maintained in particular areas providing the spatial structure for all variables measured by the sensors on-board at relatively slow speed (30km.day-1 horizontally, see figure 2B). They are suitable platforms for any sustained observational system aimed at monitoring bio-physical coupling at the coastal interface between shelf and open ocean because they can operate at sub- to meso-scale (1km to 100km). The improvements in glider technology were accompanied by the emergence of glider ports or centres. These logistical centres, very often in the proximity of a laboratory, are key to the success of these systems. The development of a “global bio glider network” in the near future will have to rely on a cluster of these local, national or international (e.g. Everyone’s gliding observatories) centres. The endurance (around 4 months) and range (2000km) of gliders constrain the procedure by requiring repetitive deployments, but gliders are already capable to cover large parts of the global ocean. On a longer term and with the continuing improvement of technology (e.g. increasing endurance and range), transoceanic and repeated transects from glider port to glider port will likely become possible. Animal-borne systems (see figure 2C) can nicely complement gliders and floats (Teo et al., 2009). Recently, animal- borne instruments have been designed and implemented to provide in situ hydrographic data from parts of the oceans where little or no other data are currently available such as for example the Southern ocean (Bailleul et al., 2010; Roquet et al., 2011). The animal-platform community is only beginning and no continuous deployments is underway (see I, 1 for more details). 266 Integrated studies

Figure 2: Three platforms used to study the pelagic environment. A. PRoVoR float about to be immerged in the ocean for a three years journey (© Ifremer). B. Glider (© DR). C. Elephant seal equipped with sensors glued at on the top of his head (©C. Guinet/CNRS). Part IV – Chapter 1 267

3. Relevant pelagic ecosystem variables at global scales

3.1. Monitoring the pelagic habitats with core physical and geochemical variables The broad-scale global array of temperature and salinity profiling floats, known as Argo, has already grown to be a major component of the ocean observing system. The final array of 3,000 floats now provides 100,000 temperature/salinity (T/S) profiles and velocity measurements per year distributed over the global oceans at an average 3-degree spacing (figure 1). Floats cycle at a 2,000m depth every 10 days, with 3-4 year life- times for individual instruments. All Argo data are publically available in near real-time via the global data assembly centers (GDACs) after an automated quality control (QC), and in scientifically quality controlled form, delayed mode data, via the GDACs within six months of collection. Hence, basic physical data about the salinity and temperature of the pelagic habitat are readily available. In addition to these physical sensors, geochemical sensors are now being developed and deployed on Argo floats. Oxygen sensors are currently being installed on floats for multi- year periods with little or no drift in sensor response (Kortzinger et al., 2004; Riser and Johnson, 2008). As for June 2009, more than 200 floats have been deployed with oxygen sensors, with about 150 currently active (Johnson et al., 2009). Nitrate sensors based on direct optical detection are now also deployed on floats, and they operate for more than 500 days (Johnson and Coletti, 2002). Detection limits are on the order of 0.5 to 1μM. Although not sufficient to measure euphotic zone nitrate concentrations in many regions, these sensors can resolve annual cycles in meso- trophic, bloom-forming regions. Measurements with gas tension devices (McNeil et al., 2006) can be combined with oxygen concentrations to determine the partial pressure of molecular nitrogen (N2) in seawater.

Finally, prototype pCO2 sensors were tested on floats but several technical problems (long time constants of sensors) have to be solved before their operational use. Yet, major improvements in the pCO2 sensors can be expected in the near future. Bio-optical sensor technologies have also been refined so that they can now be deployed on autonomous platforms (Bishop and Wood, 2009; Boss et al., 2008). Particle load is the main driver of water turbidity or transparency in the open ocean. Turbidity can be quantified by the measurement of the backscattering coefficient using a backscattering metre, while transparency is measured by the particle attenuation coefficient using a transmissometer. In open ocean waters, particulate organic carbon (POC) is the main source of particles, and both optical measurements can be converted to a concentration of POC with reasonable 268 Integrated studies accuracy (Bishop and Wood, 2009). The long-term deployment of bio- optical sensors is possible on Argo floats because these platforms spend much of their time deep down in cold and dark waters. Consequently, biofouling is less of an issue than when sensors are permanently fixed in the upper ocean, for example, on moorings, or on benthic surfaces (see III, 1).

3.2. Monitoring the plankton communities Theo Bi -Argo community has already implemented multispectral optical sensors to estimate chlorophyll-a as a proxy for phytoplankton biomass. It can be measured by fluorescence, and miniature fluorescence sensors are now available to equip a variety of platforms (e.g. gliders, floats, animals). When converting chlorophyll-a data into biomass data, one must however take into account the issues of variable pigment to carbon ratios and variable fluorescence to chlorophyll concentration ratios, which are caused by non photochemical quenching, changes in species composition, and changes in temperature. Chlorophyll fluorescence and light scattering (proxy for POC) in the upper 1000m were measured in the North Atlantic for three years (Boss et al., 2008). In the future, coccolithophorids carbonate shells might be detected from the background of nano-sized phytoplankton cells by their specific optical birefringence properties (Guay and Bishop, 2002). Much less work has been carried out to characterise the zooplankton, which constitutes a major trophic level of pelagic ecosystems. Checkley et al. (2008) combined a sounding oceanographic lagrangian observer float with a laser optical plankton counter (LOPC) and a fluorometer to make an autonomous biological profiler, the SOLOPC. The instrument senses plankton and other particles over a size range of 100µm to 1cm in profiles to 300m in depth and sends data ashore via satellite. Objects sensed by the LOPC include aggregates and zooplankton, the larger of which can be distinguished from one another by their transparency. The instrument was deployed during several weeks in the Californian current system (Checkley et al., 2008). In the future, these imaging systems will make monitoring particles and zooplankton at the same time possible (see II, 2). Acoustic sensors from gliders (Davis et al., 2008) or from floats were also used to detect plankton and non living particles (Jaffe et al., 2007). But the interpretation of acoustic backscatter at a single frequency is complicated by several factors. Marked spatial changes in intensity of acoustic backscatter do not necessarily imply changes in zooplankton or fish biomasses, as differences in body size, species composition, elastic properties of the animals, or orientation, can also markedly influence Part IV – Chapter 1 269 acoustic signals (Roberts and Jaffe, 2007; 2008). These instruments have not yet been deployed over long periods of time but we expect that a strong development and wide use of these instruments will be seen the next decade. The use of flow cytometry for plankton organisms smaller than about 20μm (pico and nano-size range) provides an alternate way to automatically obtain taxonomic information in this size range (Olson and Sosik, 2007). Molecular sensors are also now being developed for coastal observatories to remotely detect marine microbes and small invertebrate (Scholin et al., 2009). In situ flow cytometers and molecular sensors represent a promising avenue in this respect, although their size and energy consumption prevent them, for the moment, from being part of an operational open ocean observation system, for which long term autonomy and cost efficient sensors are important.

3.3. From observations to predictions using modelling framework Building a global observation system to describe and predict the functioning of the pelagic ecosystem requires a stepwise approach with regional- scale, pilot projects. Pilot studies that combine in situ sensors deployed on long-endurance platforms with satellite sensors, ship cruises, and data assimilation of biogeochemical-ecological models must be carried on to obtain a proof of the concept. In particular, data assimilation into different types of models is essential to interpret spatial and temporal variability, and to convert the sum of local measurements into quantitative rate estimates over large regions of the ocean. For more than two decades, pelagic ecosystem modeling has focused on the role and functioning of ecosystem components described as “boxes”. In these box models, the marine ecosystem is divided into several dynamic compartments. The first models of marine ecosystems dynamics contained only one variable, the phytoplankton (Fleming, 1939; Riley and Bumpus, 1946), but models including three variables – nutrient, phytoplankton, zooplankton – were quickly developed (Riley et al., 1949). Thereafter, the development of computers enabled the number of variables to increase up to seven by adding bacteria, particulate and dissolved detritus and ammonia as a second source of nutriment (Fasham et al., 1990). This latter model became a standard for the subsequent development of biogeochemical models, including the development of biogeochemical models with up to 11 compartments (Aumont et al., 2003; Le Quere et al., 2005) or models involving a greater number of phytoplankton types from which merging communities can be extracted (Follows et al., 2007). Despite these improvements, little attention has been paid 270 Integrated studies

Figure 3: Conceptual scheme showing how data from the new generations of sensors can be integrated into improved models of the pelagic ecosystems. Improved models include coupled dynamics of nutrients (blue ellipse), size-structured populations of phytoplankton (P, green symbols) and zooplankton (Z, dark orange symbols) and a size-structured pool of detritus (D, orange symbols) in the upper ocean layer and the midwater layer. Black arrows represent the flow of mass from one compartment to the other. The flow of mass of detritus due to physical processes (mixing and particle settling) from the upper to the midwater layer depends on the size of detritus. Grey arrows represent mass transfer between the size classes within the same group. In the future, detritus and some zooplankton groups detected using imaging systems may be replaced by a size-based description. However, not all compartments may be described with size when size is less important for bio-geochemical processes or less variable. This is particularly true for the phytoplankton because these species don’t prey on each other or they have very specific functions (N2 fixators, calcifiers) and several zooplankton taxa (tunicates, jellyfish). Crustaceans may be good candidate for a size based description because they share many metabolic pathways and life cycle dynamics. The proposed model should be simple enough to be included in 3D biogeochemical models. Only vertical processes are represented here and land- oceans transports or bentho-pelagic coupling are not represented. Part IV – Chapter 1 271 to zooplankton because of the complexity of this group and the small number of global data sets (Carlotti and Poggiale, 2010). The zooplankton is often represented as a closure variable with fixed rates in compartment models while their dynamic trophic interactions with the phytoplankton may be important to understand the ecosystem dynamics. Robust models relating climate change to fish production require also an adequate description of the zooplanktonic intermediar- ies between phytoplankton and fish in end-to-end models. Therefore, acquisition of in situ data is needed for testing mechanistic end-to-end models and optimising the balance between fidelity and simplicity in the zooplankton component. At the same time, the deeper ocean (below the mixed layer) was treated as a black box because of the lack of data. In particular, the description of particle sinking to the deep oceans still mostly relies on exponential or power law functions (Armstrong et al., 2002; Betzer et al., 1984; Martin et al., 1987; Suess, 1980). However, marine particle fluxes display strong regional and temporal variability in response to production regimes and their seasonality. The relationship between surface ocean ecosystem structure and variability is not captured by these simplified approaches. A recent model (Gehlen et al., 2006; Kriest and Evans, 2000) provides an interesting alternative suitable for global scale applications (figure 3). It relies on the explicit parameterisation of particle interactions (aggregation/disaggregation) where particle number and size are state variables. The sinking speed is computed as a function of particle size distribution. This approach relies however on simplifying assumptions that have not been fully validated by comparing with data on particle dynamics. For instance, the description of the particle size spectrum by a constant exponent contradicts observations where variability with depth of the latter was reported (Guidi et al., 2009). This variability most likely reflects the impact of zooplankton feeding and microbial degradation on particle size spectra (Stemmann et al., 2004a; 2004b). In this case, measuring organisms and particles size distribution would also lead to general improvement of the description and dynamics of zooplankton in models. 272 Integrated studies

Figure 4: Schematic representation of the outcomes of a future array of oceanic floats equipped with biological sensors to measure various pelagic ecosystems components as well as standard physical and chemical sensors. In the future, the nutrient(N)-phytoplankton(P)-zooplankton(Z)-detritus(D) conceptual scheme of most biogeochemical models will be quantified by the observation of new ecosystem components.

4. The key to success: agreed procedures, data management and distribution

In principle, the different observational approaches from ships, satellites, or autonomous vectors can be regarded as stand-alone initiatives with their own rationales, objectives, analysis tools and synthesis products. In fact, this was the path followed previously, even though many scientists are often involved in more than one approach. Overcoming the separa- Part IV – Chapter 1 273 tion between the different observational approaches is a major objective for the scientific community for the next decade. The technology for observing key oceanic biogeochemistry and ecosystem variables has progressively matured to the point where it is now amenable to a global dissemination (figure 4). Additionally, data sources will be much more diverse than today, going essentially from ship-based data acquisition to an increased contribution of data acquired through remotely operated platforms. Within a few years, our community will thus acquire tremendous amounts of biological data in addition to the standard physical and chemical data. An integrated observation system will be operationally useful and scientifically relevant if and only if this huge data acquisition effort is supported by an efficient data management system, able to meet both basic scientific and operational goals. Indeed, the success in implementing these new cost effective technologies in our observation strategy will heavily rely on our capacity to make all data easily available. Nevertheless, such data management system is still to be designed and implemented. The important criteria that preclude this implementation are, notably, the availability of real-time quality-controlled (QC) data for operational applications and the production of delayed-model QC data required for climate-related studies. In some ways, these prerequisites are orthogonal to the historic habits or constraint in relation with biological data management. First of all, with the exception of satellite data, biologists were not used to the management of very large datasets because most biological data acquisition was done during discrete measurements performed from ship-based platforms. Secondly, there are generally some hurdles to make biological data publically available. While efforts in this direction are underway, much remains to be done and the community has to consider this aspect of data management as a priority. Finally, and in relation with the preceding point, the biological oceanographer community is even less used to the constraints involved in the production and distribution of data in near-to-real-time. A technological evolution is thus required in the way we manage data to gua rantee public access and to deliver real-time data and products, when required. This likely represents the most challenging issue for our community, at least as challenging as the required technological developments themselves.

Authors’ references

Lars Stemmann, Hervé Claustre, Fabrizio D’Ortenzio: Université Pierre et Marie Curie, Laboratoire d’Océanographie de Ville franche, UPMC-CNRS UMR 7093, Villefranche-sur-Mer, France.

Corresponding author: Lars Stemmann, [email protected] 274 Integrated studies

Aknowledgement

This work is a synthesis of two recent articles (Claustre et al., 2010b; Stemmann and boss, 2012). Authors were supported by funding from 7th European Framework Program (Research Infrastructure JERICO and GROOM projects). Lars Stemmann is supported by the CNRS/UPMC Chaire on Biodiversity and Functioning of Pelagic Ecosystems.

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Tropical rain forest environmental sensors at the Nouragues experimental station, French Guiana

Jérôme Chave, Philippe Gaucher, Maël Dewynter

1. Introduction

Although they only cover about 7-10% of landmasses, tropical forests hold over two thirds of terrestrial biological diversity, and over 40% of its carbon stocks. It is therefore unsurprising that scientists and the public alike show so much interest in this biome. Historically, however, active research in tropical ecology was restricted to a few well-equipped research sites where scientists – mostly permanently based in North America and in Europe – could settle down from a few months to a few years to conduct their research program. In tropical rainforests, large scale logging programs have been initiated only since the 1960s, as much of Europe and North America had already been deforested centuries ago. Thus, when it comes to forested ecosystems, the fundamental difference between the tropical and the temperate zone is that the tropics still hold unlogged forests, whereas the overall area of unlogged forests in Europe and North America today cover less than a few hundreds of hectares. Ecological processes that take place in mixed old-growth, tropical rainforests are different from that of plantations, and they call for different research methods and measurement techniques. Indeed, in plantations one or a few tree species dominate the ecosystem dynamics, trees are even-aged, and decomposition and tree mortality are much lower than in natural forests. Hence, spatial and temporal heterogeneity are both lower in plantations than in unmanaged tropical forests (Ghazoul and Sheil, 2010). 280 Integrated studies

Another peculiarity of the tropics is the prevalence of harmful diseases, a major impediment to welfare development over the 20th century, with diseases such as mosquito-borne viruses (yellow fever, dengue fever, chikungunya) or various malaria strains. Thus epidemiological research has been a major impetus for the development of research programs in the tropics. In French Guiana, Institut Pasteur has been established as early as 1940, when research infrastructure in this French department was altogether lacking. Major tropical ecology programs were initiated based on the infrastructure provided by these epidemiological research programs, such as Panama’s antenna of the Smithsonian Institute first established on Barro Colorado Island, in an area that was disrupted in 1913 by the flooding of Gatun lake, central Panama. An emerging concern for biological conservation during the 1970s motivated other programs, such as the Manu Biosphere Reserve, along a tributary of the rio Madre de Dios, South-east Peru, which was originally established to study the biology of the then endangered black caiman. None of these initiatives were primarily driven by issues in environmental science. Capacity building in tropical environmental science was prompted by a succession of international research programs, which started with the International biological program (Golley, 1993). Our expertise in this question lays in the management of the Nouragues ecological research station, a scientific research station managed by the CNRS since 1986 and located in the tropical rain forest of French Guiana, within the Nouragues natural reserve. At the Nouragues station, research programs have been deployed to monitor relevant biological and physical measures to understand the ecological dynamics of this important and endangered ecosystem.

The structural heterogeneity of tropical forest ecosystems explain the difficulties of implementing quantitative research programs in the tropics. Because of rapid advance in environmental sensors and sampling technologies, it appears important to review progress in environmental monitoring and sensing applied to tropical forests. Environmental sensors have a long history and they have been intensively researched for a wide range of applications. Although the focus of this contribution is on tropical forest ecology applications of sensors, it seems relevant to emphasise the major classes of sensors as used in research programs recently. Sensors fall into three main categories (see table 1): physical environmental sensors, chemical sensors, and biological sensors. Physical sensors measure micrometeorological variables, including light intensity, wind speed, and air moisture. Chemical sensors were mostly developed for monitoring carbon dioxide concentration. However, more recent applications have expanded its range to nitrous oxides and to Part IV – Chapter 2 281 volatile organic compounds. Biological sensors measure a wide array of biological phenomena (see below).

Table 1. Examples of major sensor modalities with comments on cost, reliability, and power requirements. Adapted from Rundel et al., 2009.

Sensor Example Comments category Inexpensive to intermediate cost, Physical Temperature reliable, low power requirements Relative humidity Intermediate, reliable, low power Leaf wetness Inexpensive, reliable, low power Inexpensive to moderate, issues Soil moisture with calibration and measurement units, low power Wind speed and direction Intermediate, very reliable, (sonic anemometer) moderate power Expensive, reliable, moderate Chemical Atmospheric carbon dioxide power, requires careful calibration Soil carbon dioxide Intermediate, reliable, low power Expensive, reliable, moderate Soil carbon dioxide efflux power, requires careful calibration Expensive, under development for Nitrate reliable terrestrial deployments Not available for terrestrial Phosphorus deployments Moderately expensive, reliable, Biological Digital imagers moderate power Expensive, variable power Minirhizotron cameras requirements Commercial probes: moderately Sap flow sensors expensive, calibration issues Acoustic sensors Moderate, reliable, moderate power

There are several constraints to the implementation of environmental sensors in the tropics. First, the conditions are not always favourable to a sensor because of very high moisture levels, temperature exceeding 28°C most of the day, and the occurrence of frequent thunderstorms – climatic conditions that may have an adverse effect on electronic components. Second, electricity and internet connection are not always ensured. At the 282 Integrated studies

Nouragues station, a dual solution of a hydroelectric power plant and solar panels was chosen, yet we have experienced several episodes of electric power failure over the past few years. Finally, the maintenance of most instruments is expensive and difficult because on-site support is lacking for many of the sensors. In the forthcoming sections, possible options for environmental sensor deployment in the tropical rain forest of the Nouragues station are reviewed.

2. Environmental sensors

2.1. Meteorology Many projects in ecology make direct use of environmental variables. These encompass climate, major elements in the environment (especially carbon and nitrogen), the soil, water and air chemistry, and light radiation. Environmental sensors need to be deployed when these values are expected to vary at a sufficiently high frequency so as to impact ecological processes. While the measurement of climatic variables sounds like a trivial task, specific challenges occur in tropical forest environments. Indeed, norms specify that a rainfall gauge should not be placed closer than 10 times the height of the closest obstacle. In tropical forests, trees are obvious obstacles, and they commonly grow 40m high or even higher. Hence, a clear cut of almost 1km² area should be made to ensure that the specifications are met. For this reason, it is virtually impossible to set up rainfall gauges at ground level in closed forest environments. One solution would be to set up the meteorological station atop a tower taller than the canopy, a difficult challenge in itself. Light measurement (either photosynthetically active radiation or total intensity), and the other main meteorological measures (air moisture, air temperature) suffers from the same limitation. A direct consequence of this limitation is that climate variables now routinely used in ecological modelling are poorly interpolated in many tropical areas. For instance, the large Worldclim database includes only some 10 reference points for the whole 83000km² of French Guiana (http:// www.worldclim.org/; Hijmans et al., 2005). The university of East Anglia CRU Global climate dataset includes mean climate variables for the period between 1960 and 1990, and is derived from a global dataset of climatic normals, numbering 19,295 stations for precipitation and 12,092 for temperature, and interpolated onto a 0.5° latitude by 0.5° longitude grid (New et al. 2002). This dataset included less than 30 points of temperature measurement over Amazonia, and a significantly sparser sampling of rainfall over the same area. The absence of fine-grained meteorological data Part IV – Chapter 2 283 limits our capability to draw conclusions about climate change effects in tropical rainforests. For example, Fonty et al. (2009) reported changes in floristic composition at the top of the Nouragues inselberg, central French Guiana, and a staggering 2°C increase of the annual temperature mean in the village of Régina, around 40 km North East of Nouragues, from the the Meteo France data set of 1955-2005. These authors concluded that climate change was a potential trigger for the floristic changes observed at Nouragues. However, temperatures recorded by the meteorological station in Regina may have been biased upward by land-use change and population increase around the municipality of Régina. Temperature measurements at the Nouragues station failed to detect any significant trend over the period 1999-2009 (figure 1).

Figure 1: Daily temperature from 1999 to 2009 at the inselberg site, Nouragues experimental Station. In blue: minimal temperature; in red: maximal temperature; in black: mean temperature (estimated as the arithmetic mean of minimal and maximal daily temperatures). Measurements for the period 1999-2002 were taken by an automated meteorological station. Measurements reported for the period 2003-2009 were recorded by permanent staff of the station (gaps correspond to periods of inactivity at the station). 284 Integrated studies

2.2 Eddy flux covariance Over the past two decades, an extension of micrometeorological stations has met with a great success in relation with questions in ecosystem science. This method, called eddy-flux correlation, makes use of micrometeorological sensors (including anemometers) coupled with CO2 concentration sensors (for a detailed theoretical account, see Baldocchi 2003). The idea is to measure CO2 concentration below and above any vegetated surface and eventually to measure the carbon balance of a range of vegetation types, thus bridging the gap between ecophysiological theories – applied at the leaf level – and whole ecosystem processes. The gradient in CO2 concentration is directly related to the net flux of CO2 between the turbulent air layer and the inside of the vegetation. More precisely, the vertical

CO2 flux is proportional to the covariance between fluctuations in the vertical component of air velocity, w, and the CO2 mixing ratio c (c = ρc/

ρa where ρc is CO2 density and ρa is air density). For a tropical rain forest, it is therefore necessary to set up CO2 sensors below and above the canopy, and this can only be achieved by the construction of a tower climbing well above the canopy. Early attempts to implement this strategy were carried out in Brazil, near Manaus, by a British group led by Prof John Grace (Grace et al., 1995). Some technical problems made the long-term operation of such an instrument quite difficult (see Carswell et al., 2002). One of them is that the above-canopy air layer is not turbulent at night, violating one of the main assumptions of the eddy flux covariance method at night, when the forest is expected to release CO2 through respiration. The day-night change in the above-canopy turbulent air profile is far more pronounced in the tropics than in the temperate zone, making this problem even more acute than in temperate forests. This bias would result in an underestimation of the CO2 out flux at night time, resulting in an overestimation of the CO2 storage capacity of tropical forests. Statistical methods have been devised to deal with such anomalies, but it is to be expected that they are more serious when the terrain is hilly than when it is flat, a serious concern in many cases. Indeed, the horizontal flux of CO2 (CO2 leakage effect) may confound the vertical CO2 balance if the above-canopy air layer is not turbulent at night. Despite these problems, the technology has met with a great success over the past decade and eddy-flux covariance towers are now part of a worldwide network of sites called Fluxnet to measure global patterns of exchanges of CO2 between terrestrial ecosystems and the atmosphere (Baldocchi et al., 2001). Because of the logistical demands of such an instrumentation and associated manpower, an eddy-flux tower has not been established at Nouragues, but one is operating near the coast, at the Paracou research station, oper- Part IV – Chapter 2 285 ated by the Institut National de la Recherche Scientifique (INRA, Bonal et al., 2008). Less than 10 eddy-flux covariance towers are now operating in Amazonia (almost all of them in Brazil), and their number is far less per unit area than in temperate areas, due to establishment cost and challenging logistics. Currently, only three sites have sufficient information to fully resolve the carbon balance of an Amazonian rain forest.

2.3 Distributed sensor networks The strategy of measuring meteorological variables by means of a single station is akin to assuming that fine scale spatial variation in microm- eteorology may be ignored. However, the tropical forest environment is highly heterogeneous and for some applications it is critically important to describe and monitor this fine-grained variability. Montgomery and Chazdon (2002), for instance, measured with great details the amount of light that is available for the seedlings of shade tolerant tree species in the understory of a tropical forest of Costa Rica. They showed that light availability was essential to seedling survival and growth, but that only detailed monitoring of long-term light availability could determine the status of the plant. The light environment of a forest understory is however extremely difficult to measure. One often uses the technique of hemispherical photographs of the canopy taken from the ground. These photographs are then analysed so as to threshold obstacles and measure the overall amount of sky (usually a few percent, Norden et al., 2007). This requires considerable manpower, as these photographs should be taken at dawn or dusk, so as to avoid the problem of saturating the image with direct sunlight. Photosynthesis theory tells us that other variables – among which temperature, soil hygrometry, and soil chemistry – should be also important in determining the status of a seedling. One solution to this problem is to devise networks of environmental sensors. When sensing a rough environment, it is important to have many independent points of measurement. The challenges associated to this goal are that the nodes need to have a good autonomy (low energy demand and/or solar power availability). They also should preferably be versatile and allow wireless communication. A cheap technical implementation of this idea consists in placing small sensors at many points on the ground level and conducting periodic data collection campaigns. All- in-one probes are easy to set up and retrieval may be achieved through a simple USB port. One example is the EL-USB-2 (Lascar Electronics, Salisbury, UK), a sensor module that sells at about 50€ apiece. Another promising instrument is the iButton, a rugged and small sensor that easily withstands the moist and warm conditions of the tropical rain forest 286 Integrated studies environment (produced and sold by Maxim Inc., Sunnyvale, CA, USA, figure 2). The iButton may measure basic environmental variables, but a much larger range of such sensors is available in the market. Indeed, this market is driven by the need for continuous monitoring of the storage conditions for food products. Such a technology has a long history, although its application to a wide array of problems (crop health status, medical tracking of patients in hospitals, detection of hazards in remote areas) is recent. A more advanced version of a distributed sensor network would be to have the probes exchange information between themselves and with a central monitoring unit. It would then be possible to extend the network to some arbitrarily large spatial scales and have the measured data propagate from one sensor to the next up until it reaches the ‘master’ unit. Although the technique described seems appealing, because it can be made redundant and may be designed to spread over large sampling areas, no convincing examples are available as of now in the literature. One company, Crossbow (now purchased by Memsic Andover MA, USA), has developed easily deployable sensor networks, the most recent version being the eko Pro series system (figure 2). Thus far, it has mostly been used for precision agriculture and crop monitoring (especially wine in California). One great advantage of the eko Pro series nodes is their versatility: the node can be fitted with four sensors that measure soil moisture and temperature, soil water content, ambient temperature and humidity, leaf wetness and solar radiation. A test of this instrument is underway in the tropical rain forest environment of the Nouragues experimental research Station. An extension of the above concept of environmental sensor networks may be using a relatively low number of sensors but retaining the capacity to sample large areas by moving sensors inside the study site. One implementation of this idea is cable-based robotic systems in long term and rapidly deployable configurations, called networked info-mechanical systems (Rundel et al., 2009). This solution has the advantage of covering a large area with just one sensor, but it requires a lot of engineering and wiring, which may make its implementation clumsy in the understory of a tropical rain forest. A related idea has been implemented in sensor- bearing robots. However, given the complex environment of tropical rain forests, it is unlikely that robots will be a useful solution in a foreseeable future. Part IV – Chapter 2 287

Figure 2: A. The iButton rugged environmental sensor with data transfer device. b. The eko Pro series node (yellow box) installed at the blue oak Ranch Reserve, University of California. Nodes read data from the local weather station and wirelessly transmit the data to the base radio. The data sent from all nodes is received at the base radio and is forwarded to the gateway. The gateway then stores data from the sensor network and makes it accessible via a web GUI interface. © B. Decencière, © M. Hamilton. 288 Integrated studies

3. Biological monitoring

3.1 Monitoring plant physiology Plants have also been a prime target for monitoring physiological status. Photosynthetic exchange capacity is now measured through very precise instrumentation, which gives access to the short-term response of the leaf to changes in the light and CO2 microenvironment (LiCOR 6400 photosynthesis analyser, Lincoln, USA). It is possible to monitor continuously the leaf assimilation rate of a plant using such instrument. Of course, such leaf-level measurements are critical to interpret correctly the outputs of eddy-flux covariance sensors (see section 2.2 above). At a coarser grain of resolution, it is also useful to get a simple normalised measurement for photosynthetic capacity that may be repeated for a large number of plants or across many plants, so as to assess the acclimation to environmental conditions. Portable instruments such as the Spad-meter measure the activity of the photosystem, thus the concentration in chlorophyll (Coste et al., 2010), in just a few seconds. A related portable instrument, the Dualex 4, commercialised by the French company Force-A (http:// www.force-a.eu/) jointly measures chlorophyll content and polyphenol concentration (especially flavonoids). It is also possible to measure how fast sap flows from the roots to the leaves in a woody plant by using the difference of temperature between two ther- mocouples. This clever idea was first implemented by André Granier from Inra Nancy (Granier, 1985), and is now commonly referred to as a Granier sapflow probe. This method uses two sensors, each containing a thermocouple inserted perpendicularly into the sapwood. The downstream sensor is heated and the measured difference in temperature between the sensors narrows as sap flux density increases. Granier (1985) established empiri- cally the relationship between temperature difference and sap flux density by testing the system in detached stem segments through which water was allowed to flow at known rates. The Granier probes are now commonplace in tropical ecosystem science (see O’Brien et al., 2004), and are routinely coupled with the study of sites equipped with eddy-flux towers. They are especially useful in tracking the water status of large trees. Indeed, plants under water stress close their stomata and thus lead to a reduction in sap flow, resulting in an increase of the plant’s water use efficiency. A third emerging application in monitoring the biological activity of plants from the ground is related to the emission of chemical volatile organic compounds (VOCs). Plants emit VOCs when they are stressed either by peculiar environmental conditions or by predation. Their flowers also often emit VOCs to signal their presence to cohorts of pollinators, which may highly be specialised. The diversity of VOCs is wide and represents a fascinating way Part IV – Chapter 2 289 by which plants may exchange information with their ecological partners (Raguso, 2008). However, a few molecules contribute to a disproportionate amount to the chemistry of the atmosphere and for this reason are being studied far more intensively. It is the case of isoprene, a terpene molecule with five carbon atoms whose half-life in the atmosphere is over 1 hour. It has been estimated that isoprene alone contributes to almost half of VOC emissions by vegetation (in mass) at a global scale (Guenther et al., 1995). Classical methods to monitor VOCs have not made use of sensors, but rely on the idea of trapping (in fact adsorbing) VOCs in a physical matrix, and desorbing the VOCs at high temperature to funnel them into a gas spectrometer coupled with a molecular analyser. This somewhat cumbersome procedure does not easily undergo automation. However, some recent progress was made. First, fast isoprene sensors have been developed based on the principle of chemiluminescence (Guenther and Hills, 1998) and are now used for monitoring in tropical environments. The second category of VOC sensors detects and quantifies the presence of all monoterpenes (carbon based volatiles with 10 carbon atoms). A frontier in this research on VOC sensors is to retrieve the full information about VOC emitted by the vegetation in real time and in the field. Recent advances include the automation of VOC trapping techniques including one method called relaxed eddy accumulation (REA). Instruments such as those are now being tested in tropical forest environments and one has been mounted atop the eddy-flux tower in Paracou, French Guiana. Up to now, these methods still require a downstream desorption of the trapped molecules into a gas chro- matographer, but technological advances may soon make it possible to analyse the VOCs in real time and without resorting to adsorption.

3.2 Monitoring animal movement and physiological status Understanding the home range and displacement of elusive tropical rain forest animals is one of the most exciting questions in tropical ecology. For this reason, a tremendous amount of research has been devoted to develop technological and statistical techniques to estimate the density and biology of these animals. At the Nouragues experimental research Station, these animals include large mammals such as tapirs, peccaries, deer, monkeys and large birds such as black curassows or gray-winged trumpeters. One classic method is to survey transects at a slow speed (less than 1km/h) and observe animals. The distance to the animal, together with the angle, may be used to estimate population density. Of course, other techniques may be devised. One of them consists in randomly establishing camera traps in the survey area. Each time an animal crosses the line of a laser (or other detection techniques), a photograph of the animal is taken. In some cases, it is possible to identify the photographed individual. A long-term 290 Integrated studies monitoring program for large vertebrates based on this technology has been developed at the Nouragues station by Cecile Richard-Hansen and her colleagues from the Office national de la chasse et de la faune sauvage (see figure 3). This research group was able to successfully identify tapirs and jaguars multiple times in the study area.

Figure 3: A. C. Richard-Hansen setting up a camera trap. b. Photograph of a tapir moving in font of a camera trap. C. Camer traps to record animal presence and movements in the Nouragues experimental station. The camera traps were installed along walking paths (red lines). © C. Richard-Hansen. Part IV – Chapter 2 291

In a similar way, video surveillance equipment was adapted to record the behaviour of animals living in the canopy. The equipment used at the Nouragues station consists of a small waterproof camera connected to a portable video recorder, settled to motion detection, and fitted in a waterproof case. The electric power is supplied by lihium-ion batteries because of their lightness with regard to lead-ion batteries at comparable capacity. This equipment has been successfully used to study the nesting behaviour of the Red-throated Caracara, Ibycter americanus, one of the rare raptors living socially in small groups of less than ten individuals. The scientific knowledge concerning this relatively common species was so poor that even its nest was unrecorded. It was found out that the dominant female of the group lays and incubates a unique egg. In addition, all individuals of the group feed the chick mainly with wasp nests, large millipedes and fruits (McCann et al. 2010). The next step will be using this video equipment in order to find out how this bird can avoid the stings when it is attacking wasp nests. The equipment was also tested to study the breeding behaviour of nocturnal and arboreal frogs Trachycephalus resinifictrix which are breeding in the water holes of canopy trees (Gaucher, unpublished results). A bucket simulating a water hole used by the male singers in trees was settled 5m high and equipped with this video materiel. A continuous recording for a week shows that the bucket can attract up to four males together which are fighting to exclude each other. Also, two different species of small snakes (one diurnal, one nocturnal) were detected, and these fed upon tadpoles. Finally, one individual of Dendrobates tinctorius deposited its tadpole in the water bucket where it was also feeding on the eggs and tadpoles of T. resinifictrix. In non-tropical environments, the preferred method for monitoring individuals of large vertebrate species consists in capturing them and equipping them with a collar so that the animals can be located by radio-tracking or satellite tracking (see I, 2). This idea is faced with several problems in the tropical forest environment, among which those caused by missing data from tracking devices. First, it is quite hard to capture a wild animal, being rare and skilled at detecting humans. Second, not all sensors can be used to geo-locate the animal because of the dense forest understory. One solution has been implemented by the group of Martin Wikelski, then at Princeton University. They installed a number of large towers on Barro Colorado Island, Panama, to follow the movement of a range of animals. The idea is to set up transmitters on an animal, and to monitor its movement by triangulation between the antenna-receptor systems. The transmitters used by this team are less than 1cm long (http://www.princeton. edu/~wikelski/research/transmitters.htm) and the signal is propagated through FreeWave radios that ensure proper propagation from the sensors 292 Integrated studies to the lab (figure 4). Other examples of animal geolocation in tropical rain forests include birds (such as toucan, again in Panama), and bats. Because of their wide ecological spectrum, their fascinating biology, and their high diversity in the tropics, bats have especially attracted a lot of research. Transmitter-equipped bats were followed to detect their nesting sites, their foraging strategy and their metabolic phases (for an overview of the bats of French Guiana, see Charles-Dominique et al., 2001). Similar techniques could be applied to track the displacement of even tiny objects such as plants’ seeds (see Wright et al., 2008).

Figure 4: Example of the movement of one radio-tracked agouti (in green), and of one ocelot (in yellow) in the ca. 15 km2 barro Colorado Island, Panama. The trajectory of both animals distinctively shows that the agouti was killed and eaten by the ocelot (red area). Vertical black bar on the background represent the location of the triangulation antennae. © R. Kays.

Several applications of environmental sensors in animal ecology involve the continuous monitoring of the physiological status of a study animal, such as heart beat, transpiration, or movement speed (see I, 1). For example, it is now possible to continuously monitor the blood chemistry in diving emperor penguins (blood gases, O2 content, hemoglobin concen- Part IV – Chapter 2 293 tration, lactate concentration, Ponganis et al., 2007). To our knowledge, these involved applications for monitoring animal biological status still have to be implemented in tropical terrestrial animals, as these species need to move in complex environments and cannot be equipped with heavy instrumentation.

3.3 Monitoring biodiversity A major frontier in biodiversity research is that long-term species monitoring was restricted. Long-term data usually are limited to a small number of emblematic species, often birds or terrestrial mammals, and these data are almost totally lacking in the tropics. Long term monitoring in the tropics has focused on frogs (especially in Central America, a hotspot for dendrobatid frogs), birds (inspired by initiatives that began in temperate areas such as the breeding bird survey), and trees. Yet, these monitoring programs require tremendous amounts of manpower and are extremely expensive. For instance, the census of a large permanent tree sampling plot with some 10,000 trees mapped, tagged, and measured requires at least 2 weeks of work for an experienced team of approximately 10 techni- cians. In French Guina, the most recent census of the 22ha of permanent plots at Nouragues was conducted in November 2008, and more regular ones are now being carried out in the Paracou station and at other places through the Guyafor project, a joint effort of Cirad-ONF and CNRS. Recently, a two-year long research project was conducted at the Nouragues station to assess the feasibility of a sampling method of abundance and diversity of common birds based on the French protocol of the “Suivi temporel des oiseaux oommuns” (Stoc program). The Stoc program has been developed in mainland France during the last decade (Julliard et al., 2004), but had never been implemented in a tropical environment. Mist nets were set up in the understory and monitored since October 2008 and until April 2010. A total of 453 birds were caught, 367 were ringed (all species except hummingbirds), and 86 marked animals were recaptured, for a total of 65 bird species. The first results of the Stoc, with a recapture rate about 20%, are very encouraging. However, this valuable survey had to be discontinued because of limited manpower at the station. Would it be possible to devise autonomous instruments to monitor species diversity at one site in a tropical rain forest environment? The challenge is to screen at a fast pace signals that could be unambiguously assigned to a species or a taxon. Several options have recently opened up. One of them is based on the impressive advances offered by high-throughput DNA sequencing. Sequencing technologies are currently evolving at a fast pace and should offer unique opportunities to make a significant progress for biodiversity surveys in the near future. One idea developed recently in 294 Integrated studies the ANR (Agence nationale de la recherche) project Metabar led by Pierre Taberlet from the University of Grenoble and implemented partly in the tropical environment of Les Nouragues is to combine concepts from metagenomics (analysis of cellular microbial DNA from the soil) and from the recently emerged field of DNA barcoding (use of small DNA fragments that serve to discriminate among species). Virtually any soil contains enough extracellular DNA from decomposed tissues (even degraded and in small quantities) to be extracted, amplified, and sequenced using next generation sequencers. An analysis of sequence repositories shows that, for most taxonomic groups, it is possible to find small DNA regions (ideally around 30bp) that will efficiently discriminate across the taxa (Jørgensen et al., 2011). Using this approach, it should be possible to survey the diversity of taxa that make a substantial portion of the biomass in the soil environment, such as termites, earthworms, plants or fungi. Based on this strategy, a routine protocol could be established to evaluate the long-term trends, but also inter-annual variations in biodiversity, for a broad range of taxa. Such a DNA-based biodiversity sensor would require the development of semi-automated procedures to extract and preserve environmental DNA before the laboratory analysis phase. It would also need a substantial and long-term financial investment to secure the funds for regular high-throughput sequencing runs. Another application relies on the sound produced by active animals such as amphibians, fish, birds, mammals, insects and other arthropods, all of which use sound for communication, navigation or predation. Bioacoustics is the discipline concerned with the development of sensors specialised in detecting animal sounds (see I, 4) and analysing them for their ecological relevance, or as an indication of species occurrence and as a tool for biodiversity studies. Applications of these techniques are routinely made in tropical forest environments for birds (some ornithologists are able to identify virtually all the species based on their song alone), but also for anurans. Automated methods for recording were developed and implemented in tropical forest environments (see II, 1; Acevedo and Villanueva-Rivera, 2006; see also Pijanowski et al., 2011 for a review). One company is now selling a fully packed solution for frog monitoring, called the froglogger (http://www.frogloggers.com/). We should also emphasise here that the method may be extended to the study of ultrasounds emitted by bats, an important component of the mammal community in neotropical rain forests. However, more than twenty years after a hallmark publication of Fenton and Bell (1981), the acoustic identification of neotropical bats is still at the stage of the research. The main problem is to build a reference library of ultrasound that would characterise the bat species, while taking in account intraspecific variability, which constitutes a considerable challenge. Studies by Michel Barataud and the Groupe chiroptères de Part IV – Chapter 2 295

Guyane have contributed to the development of a large data base of sound sequences in natural conditions of flight. The data collection method uses detectors of ultrasounds heterodyne–time expansion Pettersson D1000X, D980 and D 240X (Pettersson Elektronik ABTM), and digital recorders on card (EdirolTM) or minidisc (SharpTM).

4. Remote sensing of the environment

Remote sensing has played a key role in efforts to understand ecological, hydrological and land-use processes in the tropics. The main advantage with remote sensing is that observations are made at a spatial scale and temporal resolution that captures the regional-level effects, and yet, can offer an outstanding level of detail. The disadvantage is that the measurements are sometimes difficult to validate at a scale that matches the patterns expressed in the satellite observations. Also, it is often difficult to establish the causal mechanisms contributing to the remotely sensed patterns.

4.1 Measuring rainfall from space One approach to bypass the lack on ground-based meteorological measurements makes use of remote sensing instrumentation embarked in the tropical rainfall monitoring mission (TRMM, Kummerow et al., 2000), with a wall-to-wall coverage, but a relatively coarse spatial resolution. Gaining a better knowledge on the climatic environment of the tropical forest biome still represents an important challenge because many of the climatic processes observed in the tropical belt have no equivalent in temperate zones, and are thus understudied, in spite of their critical relevance to human welfare. This notably includes quantitative data on the El Nino southern oscillation phenomenon, which causes serious droughts and associated fires quite regularly in South East Asia and America. Consequences for the flora and the fauna of tropical rain forests are also important, as the lack of rain may result in a limited fruit development in many keystone species, and thus lead the animal that feed upon it to star- vation, as was first documented by Robin Foster (see Wright et al., 1999 for a more quantitative and updated description). The TRMM produces a best-estimate precipitation rate within grids of 0.25° by 0.25° in a global band extending from 50 degrees South to 50 degrees North latitude. The satellite has onboard a 2.3cm wavelength radar, called precipitation radar, that provides a global image of rainfall accumulation every three hours in pixels of about 1° in latitude and longitude. These values may be cumu- lated at a daily or monthly basis (see figure 5 for a long-term average). 296 Integrated studies

Figure 5: Average rainfall in millimeters per day over the period 1998-2011 across the tropics. High rainfall zones are evident in the Choco area in Colombia, in Papua New Guinea, and western borneo. A mean rainfall of 8mm/day (or about 3,000mm/yr) is observed over most of Amazonia. © Tropical Rainfall Monitoring Mission’s website (http://trmm.gsfc.nasa.gov/).

4.2 Measuring forest status from space Remote sensing of the biosphere has a long history which may be traced back to the very birth of the history of photography itself. Indeed the photographer Gaspard-Félix Tournachon (also known as Nadar) photographed the Petit bicêtre (nowadays Petit Clamart) from a captive balloon as early as 1858. However, the technology was largely developed for mili- tary purposes during the great wars, and resulted in a range of applications in the visible range, that culminated with Landsat and Spot instruments in the 70s. Both instruments passively record a few bands in the visible range (plus one in the infrared) and this signal may be related to the activity of the vegetation because of the absorbance spectrum of photosynthesis. One measurement of the vegetation activity is the normalised difference vegetation index (NDVI), which was successfully used to map the world-wide extent of terrestrial biomes over the 1980s and well into the 1990s (see III, 4). Mapping ecosystems is of crucial importance, especially to understand potential routes of dispersal, suitability of habitats, and other ecological features. One outstanding contribution to geogra- phers and ecologists alike is due to the group led by Valéry Gond from Montpellier, who recently provided a detailed map of the Guiana Shield based on optical remotely sensed instruments (figure 6, adapted from Gond et al., 2011). Not only does this map clearly shows the great heterogeneity of the Guiana Shield, which expands from Southern Colombia to French Guiana, and includes a significant part of the Amazon and Orinoco watersheds, but it also evidences the presence of dry areas along the so-called Roraima corridor, which connects the Venezuelan llanos to the Serra dos Carajás in brazil, through the Rupununi of Guyana and the Sipaliwini savanna, South of Surinam. Part IV – Chapter 2 297

Low dense forest / included savanna Tree / savanna on poor drainage soils (RSLC 18) (RSLC 25 and RSLC 26)

High forest with regular canopy mostly on terra firme (RSLC 19)

High forest with disrupted canopy Agriculture settlement / city (RSLC 20) (RSLC 32)

Mixed high and open forest (RSLC 21)

Open forest / euterpe palm forest Permanent / temporary waterbody (RSLC 22) (from RSLC 2 to RSLC 13 and RSLC 27, RSLC 29, RSLC 31 and RSLC 33) Figure 6: Map of landscape types of the Guiana Shield based on a classification of 1-km2 remotely sensed pixels recorded by the VEGETATIoN sensor onboard the Spot-4 satellite covering a period of 12 months. In the legend, RSLC stands for remotely sensed landscape classes. The maroon and green color scale represents the five dominant forest landscape types. © V. Gond (see Gond et al., 2011 for a full review).

Another important application of remote sensing is monitoring the status of vegetation as a response to changing climates. This is a difficult task however, as the controversy initiated by the publication by Scott Saleska and colleagues (Saleska et al., 2007) emphasises. These authors used NASA’s Terra satellite to argue that the canopy of the Amazon rain forest greened up during the 2005 drought, which affected much of the Amazon watershed. They inferred that the rain forest could be resilient to dryness, at least for short periods, and undermined independent evidence that 298 Integrated studies

Amazonian trees may have suffered directly from this drought (Phillips et al., 2009). However, Saleska et al’s discovery has been criticised because their methodology and data were inappropriate, and led to flawed conclusions (Samanta et al., 2010). Indeed, interpretation of satellite imagery in the visible spectrum may be contaminated by atmospheric aerosol, water vapour and sub-pixel cloud hazes. Because most remote sensors are able to detect the roughness of a surface or its optical spectrum, what happens below the canopy of a forest is sel- dom visible. This causes serious problems for applications involving GPS units (though these have significantly improved over the past years), and also for measuring forest structure. A technique to scan below the canopy is the light detection and ranging application (Lidar), which measures the distance from a source to a point based on the return time of a laser pulse, at a high frequency. Aircraft-borne instruments have been used to measure the topography of terrain below the canopy, because a fraction of the pulses reach the ground level. In 2007, canopy height has been measured over 600ha at the Nouragues station through a simple difference of canopy altitude and ground altitude as measured by a helicopter-borne Lidar instrument. Using these data, one can monitor the large-scale forest structure, locate treefall gaps and even the dynamics of treefall gap formation, and better understand the landscape-scale variation of ecosystem features. One of the grails of tropical forest science is to be able to measure biomass stocks in tropical forests, because tropical forests represent an important carbon stock and these stocks are in the process of being incorporated in financial markets through schemes such as reducing emissions from deforestation and forest degradation. Some proposals were made to relate canopy height to biomass stocks, and they show some promise at the landscape scale (Asner et al., 2010). However, other techniques were proposed as alternatives, including a radar sensor operating at the P band that may equip a future satellite currently in discussion at the European space agency, and called BIOMASS (http://www.esa.int/esaLP/ SEMFCJ9RR1F_index_0.html). Some preliminary tests of this instrument were performed in 2009 in French Guiana, through collaboration between Onera, ESA and the local French research teams. Flights over the Paracou station and over the Nouragues station yielded fascinating results, some of which are still being processed.

4.3 Measuring biodiversity from space One of the dreams of remote sensing science is to detect more subtle patterns than geographical or physical ones. This may be more than science- fiction today but the use of hyperspectral sensors may help to achieve this goal. Hyperspectral sensors retrieve the reflection of a surface, not Part IV – Chapter 2 299 with a few wavelengths like in classical remote sensing imagery, but with hundreds of wavelengths. In section 3.2, we mentioned that the absorption pattern of light in the UV spectrum and in red could be jointly used to assess the concentration of chlorophyll and of flavonoids, one important class of secondary compounds in plant tissue. In fact, the continuous absorption spectrum of vegetation may yield bands that are typical to other compounds, and therefore be able to detect part of the chemical composition of the canopy from a remote sensor. A research program on this issue has been implemented by Asner’s group at the Carnegie institution for science (Asner and Martin, 2011). By focusing a beam to single canopy, their technology is now able to retrieve part of the chemical composition, but also biophysical parameters, and possibly provide a fast index of functional diversity in tropical forest environments (Asner et al., 2011).

5. Conclusion

Many of the techniques briefly presented here are by no means unique to tropical forest environments, but they share some features. These environmental sensors are comparatively simpler and more rugged than in many temperate applications. One critical factor is obviously the moisture of the tropical environment that has a deleterious effect on the electronics of many components of the sensor. Another constraint is the topic of research. While much of modern ecology in the temperate zone deals with ecosystem science (with a strong focus on the measurement of fluxes), and experimental approaches (hypothesis testing on model species), the breadth of research topics in the tropics includes also an important but exceedingly difficult part: the documentation of basic patterns of diversity. Thus programs aimed at developing environmental sensors in the tropics should acknowledge this fact and strive to provide tools adapted to documenting the environment in which species live (i.e. their niche). Many of these sensors are aimed at measuring physiological or chemical processes for specific species. While the development of such sensors would seem like a natural outgrowth of fundamental research programs, it appears that the link between basic science and sensor development remains loose in ecology and evolutionary biology (with a few noticeable exceptions). Finally, we have emphasised several possibilities towards the development of biodiversity sensors, i.e. semi-automated instruments that would provide partial information on the status of biodiversity, but that could be maintained for long time scales, so that early-warning signals to radical changes on biodiversity could be detected early on. 300 Integrated studies

Authors’ references

Jérôme Chave: Université Paul Sabatier, Laboratoire Évolution et diversité biologique, UPS-CNRS-ENFA UMR 5174, Toulouse, France

Philippe Gaucher: CNRS-Guyane, USR 3456, Cayenne, France

Maël Dewynter: Office National des Forêts, Réserve de Montabo, Cayenne, France

Corresponding author: Jérôme Chave, [email protected]

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Chapter 3

Use of sensors in marine mesocosm experiments to study the effect of environmental changes on planktonic food webs

Behzad Mostajir, Jean Nouguier, Emilie Le Floc’h, Sébastien Mas, Romain Pete, David Parin, Francesca Vidussi

1. Introduction

Marine ecosystems, and their biological components in particular, play a crucial role in the regulation of Earth climate and in the biogeochemical cycles of major elements such as carbon, nitrogen, phosphorus, or sulphur (Pielke, 2008). Moreover, marine ecosystems are reservoirs of biodiversity that remain partially undiscovered (Webb et al., 2010). In addition, these ecosystems, in particular coastal zones, provide a large variety of ecological services to our society. In the context of a changing environment at global and local scales, studying the ecological and evolutionary responses of marine organisms, from cell to population level and from communities to food web level, becomes more than ever essential. The observation of marine ecosystem from permanent fixed observatory systems provides essential information about ecosystem variability at local or regional scales. These observatory systems can be linked together and form a web to gather data that can be interpreted at large spatial and temporal scales and coupled with remote-sensing data from satellites (see III, 3 and II, 2). However, it is necessary to complement these large-scale field studies with observations and experiments of isolated assemblages of marine micro-organisms. The isolation of a portion of water mass including associated plankton assemblages can be realised 306 Integrated studies into an enclosed experimental unit (e.g. a mesocosm) to study ecological processes from cells to communities and food webs. The confined organisms and associated water mass can also be subjected to different experimental treatments in order to study responses to known forcing factors. This mesocosm experimental approach, complementary to the observational approach, provides additional fundamental information about the ecology of organisms and their interactions with biotic and abiotic factors. Information from field observation and from experimentation can be merged and used for predictive modelling. The main objective of this chapter is to illustrate and discuss the use of sensors to study physico-chemical and biological variables as well as sensors used to apply treatments in marine mesocosm experiments. These sensors can also be used as feedback controls of the applied treatments in marine mesocosm experiments. Some of these sensors are also used in marine observatories and have been presented elsewhere in the context of other methodological approaches (see II, 2 and III, 1). However, we focus here on constraints from using these sensors in mesocosm experimentation to study confined planktonic assemblages and obtain detailed information on ecosystem functioning. It is necessary to stress out that some of the sensors and automated systems presented in this chapter were developed for specific objectives within the framework of our scientific projects.

2. Experimental marine ecology using mesocosm approach

A major concern in environmental marine science is to determine and predict the response of pelagic ecosystems to factors such as increasing pCO2 and consequent decrease of water pH as well as increasing temperature and stratification, influencing both light and nutrient availabilities. At the same time, in applied aquatic science, the means to preserve or restore coastal and marine environments to ensure their sustainable ecological services attract an increasing attention. In addition, it is becoming more and more important to understand how some marine resources can be newly produced and sustainably exploited, including microalgae used for biofuel production, for example. The water column of marine ecosystems is a natural habitat for many organisms that have adopted the planktonic life mode for the main part of their life cycle (see II, 2). Although the planktonic organisms can move in the water column, these organisms cannot actively swim like fish and their movement is rather dictated by water mass advection. Part IV – Chapter 3 307

Plankton microbial organisms ensure the most fundamental functions in marine ecosystems such as primary production and remineralisation, and play a key role in the biogeochemical cycles of major elements on Earth (Falkowski et al., 2008; Falkowski, 1994; Falkowski and Woodhead, 1992). Study of microbial planktonic processes such as nutrient assimilation, production, growth, and respiration, as well as investigation of their interaction with other plankton assemblages are most of the time carried out in the microcosms.

Figure 1: Relationship between the size of a marine organism (adapted from Sieburth et al., 1978) and the water volume required for studying a community assemblage. The most important organisms in the marine ecosystem functioning are Femto-plankton (0.02-0.2µm, viruses and small heterotrophic Archea and bacteria), Pico-plankton (0.2-2µm, larger heterotrophic bacteria, cyanobacteria, small eukaryotic phototrophs, and heterotrophic flagellates), Nano-plankton (2-20µm, most of phytoplankton assemblages, large heterotrophic flagellates and small ciliates), Micro-plankton (20-200µm, large phytoplankton, large ciliates including thintinides, and different stages of metazooplankton including copepods), Meso-plankton (0.2-20mm, large phytoplankton, protozooplankton and metazooplankton), Macro-plankton (2-20cm) and Mega-plankton (20-200cm).

As an operational way to study planktonic assemblages, Sieburth et al. (1978) distinguished these assemblages according to their sizes (see 308 Integrated studies figure 1). To investigate physiology, life cycle and interaction between femto-, pico-, nano- and micro-plankton the experiments can be conducted in the laboratory using some litres of water containing all these assemblages (i.e. microcosm experiments). However, experimental systems of community responses to perturbations should include most of the components of the food web to investigate both direct effects of the studied stressors on organisms and indirect effects through trophic responses, for example trophic cascades (e.g., Vidussi et al., 2011). For this, an experimental water volume of at least thousand litres (i.e. mesocosm) that contain all or most of the biological components of the plankton food web (virus, bacteria, phyto and zoo-plankton, fish larvae) is necessary (figure 1). Mesocosms can also host benthic organisms interacting with the plankton (i.e. filters feeders) or small fish (i.e. plankton feeders). The utilisation of mesocosms has proved essential to provide new results on the topics of global change effect on marine systems, such as the study of the effects of increasing ultraviolet B radiation (e.g. Mostajir et al., 1999), the increase of CO2 and acidification (e.g. Riebesell et al., 2007), the increase of water temperature (e.g. Lewandowska and Sommer, 2010), and simultaneous increases of water temperature and ultraviolet B radiation (Vidussi et al., 2011). New fundamental concepts have emerged from mesocosm experiments (i.e. concerning food web functioning, Thingstad et al., 2007), and mesocosm experiments are also an essential tool in eco-toxicology (e.g. Sargian et al., 2005; Pestana, 2009). Until now, studies of marine ecosystems in the water column of the mesocosms have been generally performed from discrete samplings at different time interval from hours to days or weeks. Yet, ecological processes at the microbial level can occur at short time scales in the order of hours, and the absence of adequate high-frequency sampling can hide some fundamental processes taking place between two discrete sampling episodes. Thus, automated sensors in mesocosm experiments have been used recently to monitor physicochemical and biological variables and hence provide fundamental information at high frequency with little perturbation of mesocosms and a reduced human effort (Vidussi et al., 2011). Another fundamental advantage of using automated sensors is that they allow regulation and realistic simulation of some experimental treatments including climatic variables (Nouguier et al., 2007). We present in this chapter different sensors that can be used in mesocosm experiments. Most examples and related data presented here are from experiments carried out in the Mediterranean centre for marine ecosystem experimental research operated jointly by the CNRS and the University of Montpellier 2 (Medimeer, http://www.medimeer. univ-montp2.fr/, see figure 2). Part IV – Chapter 3 309

Figure 2: In situ moored mesocosms (up to 12) around the Medimeer floating platform (ca. 30m) installed in Thau lagoon (Mediterranean coastal lagoon, South of France).

3. Sensors to monitor ecological variables in marine mesocosms

Several variables in the water column can be studied during the mesocosm experiments. These environmental variables include the water column physical properties (e.g. temperature, salinity, and light radiation), the biogeochemical properties (e.g. dissolved nutrients, oxygen concentration, and particle load), and proxies of biological components (e.g. fluorescence of phytoplankton). These variables can be continuously monitored at a high resolution by a set of automated in situ sensors.

3.1. Monitoring of the water column physico-chemical properties and variability 3.1.1. Water temperature The monitoring of temperature along a vertical profile within a mesocosm gives information on the degree of stratification or homogenisation of the water column. Moreover, because biological processes are temperature- dependent, monitoring of water temperature can contribute to understand 310 Integrated studies responses of organisms to experimental treatments. Among the usual temperature sensors, such as thermocouple and platinum resistor (Pt100 and Pt1000), thermistors are well adapted to the measurement of natural water temperature. Their resistance varies inversely with temperature, but, more importantly, their baseline resistance value and coefficient of variation are generally large. This permits the use of long cables and allows an easy half bridge measurement on data loggers for the long term recording of numerous temperature channels. An improvement by a software correction is used to minimise linearisation errors and obtain measurements from 0 to 40°C with an accuracy from 0.01 to 0.02°C. Figure 3A shows an example of water temperature monitored at three depths during a mesocosm experiment. This shows firstly the daily variations of water temperature in the mesocosm, and also demonstrates the efficiency of water mass mixing done with a pump in the mesocosm. Diel variations of water column temperature tend to follow the variation of atmospheric temperature.

3.1.2. Conductivity The conductivity of water is its ability to transmit the electric current. The electrically charged ions move in presence of an electrical field or an alternative magnetic field (see III, 1). To measure the water conductivity in aquatic environments, two techniques are available. The first one is the electrode cell method that is mostly used for measuring low conduc- tivities in pristine environments. Here, the conductivity is obtained from measurement of the electric current generated by an alternative electric field between the electrodes of the cell. The second one relies on the electromagnetic induction method, which is used for high conductivity measurements in various types of marine environments. Sensors using this method are rather immune to biofouling, and the measured electric current flowing through the water is generated by the alternative electric field from a ring transformer. As the relative proportions of the main ionic constituents are nearly constant, the conductivity can be directly converted to salinity for a given temperature using available standards (Weyl, 1964). During the mesocosm experiments, conductivity measurements were conducted with AADI Aanderaa CS3119 AIW (Aanderaa Data Instrument AS, Norway) to monitor the temporal variations of salinity (figure 3B). The trend after one day of measurement shows a salinity increase in surface waters, indicating the occurrence of evaporation.

3.1.3. Nutrients concentrations The nutrients commonly measured during experiments are nitrate, nitrite, ammonia, phosphate, silicate, dissolved organic nitrogen and phosphorus species. Nutrients are one of the essential elements for the growth of Part IV – Chapter 3 311

Figure 3: In situ sensors installed on the Medimeer platform. A. Monitoring of daily variations of water temperature measured every 2 minutes at three depths during a mesocosm experiment. B. Monitoring of salinity in the surface water of a mesocosm measured every 2 minutes.

phytoplankton and bacteria. The continuous nutrient concentration monitoring in real time by in situ nutrient analyser probes allows determining sources, sinks, and dynamics of different nutrients in natural environments (Vuillemin et al., 2009) and can be adapted for the mesocosm experiment. This permits for example long-term study of water quality regarding pollutants, effluents and nutrient loads. Indeed, a continuous measurement of nutrient concentrations helps to identify the effect of anthropogenic nutrient sources on planktonic communities including the triggering of blooms by the nutrient load of continental waters (Lefebvre, 2006). Most in situ nutrients analyser-probes, such as SubChem Analyzer and Wiz probes, measure automatically various dissolved chemical species, using wet chemical techniques of in-flow analysis based on standard laboratory analytical methods (spectrophotometry and fluorimetry) developed as long as a century ago (Varney, 2000; Thouron et al., 2003). These analytical probes are equipped with one or multiple reagent-deliv- ering modules and standards, and one or mutiple electro-optical detectors – their number depends on the measurement of one or several nutrients (nitrate, nitrite, phosphate, ammonia, see Blain et al., 2004; Lefebvre, 2006). The frequency of measurement depends on the amount of reagent and standard loaded in the probe, and the number of nutrients measured by the probes. To avoid the drifts and the degradation of optics, reagents, calibration standards, and also biofouling in the sensor sampling line, a frequent maintenance is necessary, which includes reagents and standards change, complete clean up and in situ recalibration of the probe. 312 Integrated studies

3.1.4. Turbulence In natural aquatic environments, turbulence is created by wind shear at the surface, horizontal shear at the pycnocline (i.e., density gradient) and sediment surface, breaking surface and internal waves, and buoyancy effects such as convection. Turbulent mixing at a small scale can influence predator-prey interactions (Rothschild and Osborn, 1988; Browman, 1996; Dower et al., 1997), particle capture (Shimeta and Jumars, 1991), aggregation and disaggregation (MacIntyre et al., 1995), small-scale patchiness (Moore et al., 1992; Squires and yamazaki, 1995), and species-specific growth inhibition (Gibson and Thomas, 1995). However, turbulence is also important for mixing large water masses with distinct physical p roperties or nutrient flux in the same water column (Jørgensen and Revsbech, 1985; Dade, 1993; Denman and Gargett, 1995; Karp-Boss et al., 1996). Therefore, in the aquatic ecosystems, turbulence is one of the significant environmental factors influencing the plankton ecology. In mesocosm experiments, turbulence regimes can be simulated to adjust fluxes of nutrients, dissolved gases and particle aggregations and disinte- grations. There are various techniques ensuring turbulent mixing in the mesocosms such as rotating paddles and oscillating grids in tanks, flexible- walled in situ enclosures (Menzel and Case, 1977; Grice and Reeve, 1982) and other mixing schemes including bubbling (Eppley et al., 1978; Sonntag and Parsons, 1979) and pumping (Kotak and Robinson, 1991). These techniques can provide reasonable representation of some aspects of natural turbulent mixing but usually not all of them (Sanford, 1997). Indeed, it is not expected that any single mixing design for a mesocosm experiment will reproduce all aspects of natural turbulence. Hence significant differences between natural turbulence and that generated in the water column of a mesocosm are frequent (Brumley and Jirka, 1987; Fernando, 1991). Turbulent mixing can be monitored during experiments by using several types of sensors including laser doppler velocimeters (Hill et al., 1992), small acoustic (Kraus et al., 1994; Trivett and Snow, 1995) and electromagnetic velocity sensors (Howarth et al., 1993), flow meters and particle image velocimetry. In particular, the particle image velocimetry system, developed for fluid dynamics studies (Gray and Bruce, 1995) and based on the assumption that the particles follow the flow, allows estimating accurately the velocity field and the small-scale distribution of flow and turbulence by tracking small particle movement through series of frame. Fluorometers are also widely used to study processes of diffusion and mixing based on dye dispersion technique, since fluorescent dyes such as rhodamine and fluoresceine are detectable at very low concentrations. This dye dispersion technique consists in injecting a small amount of a fluorescent neutrally buoyant dye either into the surface or mid-depth, and measuring fluorescence at several locations in the mesocosms over Part IV – Chapter 3 313 time. The mixing time and velocity are defined by tracer concentration reaching a specified degree of uniformity inside the mesocosm.

3.2. Monitoring the plankton food web components 3.2.1. Chlorophyll and phycoerythrin concentrations Measurements of fluorescence have been used for many years in marine science to measure concentrations of chlorophyll (Lorenzen, 1966; Mignot et al., 2011). Phytoplankton species have specific pigments and the detection of some of these pigments gives information about their concentrations, as well as phytoplankton biomasses and diversity. The fluorescence level can be monitored by sensors and modern compact ones have a wide range of optical configurations and a wide range of wavebands to measure chlorophyll a (phycoerythrin and phycocyanin) and other pigments. This has paved the way for new developments such as use of multiples excitation wavelengths for discriminating between phytoplankton taxa (Paresys et al., 2005). In the mesocosm experiment, the continuous measurement of chlorophyll a and phycoerythrin concentrations by fluorescence sensors is used to monitor the presence and the temporal dynamic of the algal bloom (chlorophyll a measurement) or cyanobacteria (phycoerythrin measurement). It is also used to determine the diel variations of pigment concentration (particularly chlorophyll a), which is affected by change of phytoplanktonic biomass and photoacclimation processes. Figure 4 shows an example of diel variations for chlorophyll a concentration monitored for the first time during a mesocosm experiment.

Figure 4: Diel variations of chlorophyll a concentration monitored every 2 minutes by a Wetlabs sensor in a mesocosm experiment. 314 Integrated studies

3.2.2. Particulate organic carbon of small particles In open ocean waters, particulate organic carbon (POC) is the main source of particles (see III, 3 and IV, 1). Particle load is the main driver of water turbidity in the open ocean, which can be quantified by the backscattering coefficient measurement with backscattering meter. The bulk backscattering coefficient (bbp) measurements are affected by particle size distribution and composition of seawater constituents (colloids, bacteria, phytoplankton, biogenic detritus, minerogenic particles, and bubbles). Recent studies have shown that the POC concentration of small particles can be estimated with reasonable accuracy from particle backscattering coefficient at 470 or 510nm, provided mineral particles are absent (Stramski et al., 1999; Loisel et al., 2001; Balch et al., 2005). Some optical sensors measure the backscattering coefficient at different wavelengths and, through some calculations, give the spectral slope γ of the particulate backscattering coefficient. Based on theoretical and in situ studies (e.g. Reynolds et al., 2001; Stramska et al., 2003), the γ slope has been proposed as a proxy of the suspended marine particle size distribution for particles smaller than 10µm (Loisel et al., 2006). Backscattering coefficient measurements at high frequency can be performed during mesocosm experiments using commercial sensors such as the Wetlabs Eco-Triplet sensors, which operates at 470, 532, 650 and/or 880nm. These sensors were used to determine the dynamic of organic particles smaller than 10µm during two mesocosm experiments in the Medimeer platform (data not shown).

3.2.3. Plankton identification, size and abundance Planktons i the main component of the pelagic food web and is an indicator of the ecological status of water masses (Manizza et al., 2005; Wyatt and Ribera d’Alcala, 2006). Plankton is also an indicator of water quality and is influenced by natural and anthropogenic changes in the environment (Belin and Berthome, 1991). Apart from the possibility to use in situ imagery system for observations and identifications of marine zooplankton and particles (see II, 2), in situ flow cytometry is a powerful tool to investigate microorganisms such as phytoplankton in their natural environment (Dubelaar et al., 1999; Olson et al., 2003; Sosik et al., 2003; Dubelaar et al., 2004; Thyssen et al., 2008). One of the first in situ flow cytometer, the FlowCytobot was designed to cover the full-size range of phytoplankton (from 1µm up to several 100µm) during several month deployments (Olson et al., 2003; Sosik et al., 2003). The first generation of FlowCytobot was optimised for the analysis of pico- and small nanoplankton (around 1 to 10μm) and used fluorescence and light scattering signals from a laser beam (Olson et al., 2003; Sosik et al., 2003). Part IV – Chapter 3 315

To complement this device, a submersible imaging FlowCytobot (Olson and Sosik, 2007) has been developed to identify the taxonomy of natural plankton assemblages (nano- and microplanktonic organisms and detritus) in the size range from around 10 to 100μm. This new generation combines a video and image in-flow system to cover the size range from 10 to 100µm (Olson and Sosik, 2007; Sosik and Olson, 2007) and a flow cytometric technology to both capture images of organisms for identification and measure chlorophyll fluorescence associated with each image. Like the laboratory-based FlowCam (Sieracki et al., 1998), the automatic classification procedures of the various plankton types uses an approach based on a combination of image feature types including size, shape, symmetry, and texture characteristics, as well as orientation invariant moments, dif- fraction pattern sampling, and co-occurrence matrix statistics (Sosik and Olson, 2007). Moreover, the measurements of chlorophyll fluorescence allow to discriminate phototrophic (i.e. phytoplankton) from heterotrophic cells. The fully automated CytoBuoy is another submersible flow cytometer that delivers data in real-time when operated online (Dubelaar et al., 1999; Cunningham et al., 2003; Dubelaar and Jonker, 2000). CytoBuoy allows estimating phytoplankton biomass and discriminating between different phytoplankton groups with an approach that combines the morphological, pulse-shape (large horizontal to vertical aspect ratio) and high-frequency analysis of particle crossing a laser beam. With additional efforts in sensor miniaturisation and in reduction of power consumptions, the continuous and long-term use of a submersible flow cytometer will be soon possible during mesocosm experiments. We have not used a submersible flow cytometer yet in the Medimeer’s mesocosms. This instrument is still too large and it is difficult to immerse it in the mesocosms where different treatments are applied without increasing disturbance or contamination. Also, the cost of the system is still prohibitive when one wants to replicate sensors across mesocosms. An alternative solution could be to use an automated pumping system to provide the water sample from different mesocosms to a flow cytometer installed in the laboratory nearby the mesocosm platform. This would allow to perform continuous plankton monitoring with minimal cost and disturbance.

3.3. Assessment of the food web functioning In aquatic ecology, mesocosm experimentations are performed in order to assess the functioning of the food web and the web energy transfer. To characterise the resilience of the ecosystem studied, the direct and indirect effects of biological and environmental interactions (trophic cascades, 316 Integrated studies

feedbacks) on the ecosystem functioning (e.g. CO2 exchanges, nitrogen or phosphorus cycles, aerosols production) should be assessed. Currently, only few sensors are developed and used to automatically monitor such parameters and these sensors focus on net O2 exchanges or bivalve filtration activity.

3.3.1. Dissolved oxygen concentration and community oxygen metabolism Oxygen is involved in most of biological and chemical processes in aquatic systems. The net O2 production and respiration rates can be calculated by measuring the changes in the dissolved O2 concentrations in the course of the experiment during day-time and night-time respectively. Therefore, the dissolved O2 can be used as a proxy of the net community oxygen production. Moreover, as photosynthesis and respiration stoichiometrically relate on oxygen and carbon fluxes, the net O2 production and respiration rates can be converted into net carbon production and respiration rates by using equations with photosynthetic and respiratory quotients of conversion (Dickson and Orchardo, 2001). The photosynthetic quotients are various, because they have been calculated on the basis of the biochemical composition of phytoplankton (Laws, 1991), direct measurements of natural communities (Williams and Robertson, 1991; DeGrandpre et al., 1997) and modelling studies (Williams, 1993). Two families of sensors are used to measure dissolved oxygen (see III, 1). The sensors based on galvanic or polarographic oxygen electrodes are generally used for short-term measurements because frequent changes of the membrane and calibration are necessary. Thus, most of the oxygen sensors used in marine mesocosm experiments are based on oxygen dynamic luminescence quenching of a platinum porphyries complex. These optode sensors are used for long term recording of dissolved O2 in marine and brackish waters because of their strength, their immunity to biofouling and their easiness of cleaning. Since salinity and water temperature influence the dissolved O2 concentration in marine system, O2 sensor is generally associated with a temperature sensor and a conductivity or salinity sensor via a data logger in order to realise the necessary correction in real time. In flow-through mesocosms, flow-respirometry technique is applied by measuring difference in concentration in the outflowing water compared to the inflowing water (Griffith et al., 1987). In open-top mesocosms, sea-air exchange has to be taken into account and the assumption that the respiration rate is constant in the light and in the dark has to be made (Leclerc et al., 1999). The common gross photosynthesis and respiration for a community can then be calculated based on ampero- Part IV – Chapter 3 317 polarographic electrode measurements over 24 hours and by using a multiple regression method. Figure 5 shows an example of dissolved

O2 concentrations monitoring at high frequency during a mesocosm experiment performed in Medimeer. The dissolved O2 concentration increase during light period indicates that phytoplanktonic O2 production exceeded plankton community respiration, whereas the O2 concentration decrease during dark period suggests an important community respiration activity.

Figure 5: Monitoring of oxygen saturation every 2 minutes in the water column of a mesocosm. Oxygen saturation was measured with an optode sensor.

3.3.2. Bivalve activity in relation with plankton communities In coastal marine environments, filter feeders such as bivalves can control the planktonic communities by filtering their surrounding water including small living and non-living particles. To study the relationship between plankton dynamics and bivalve filtration, it is necessary to be sure that the bivalves are alive and physiologically active. The bivalve physiological activity can be assessed continuously by measuring the frequency of valve gape. For mussels, valve gape is measured by automated remote-sensing technologies such as fiber-optic imaging (Franck et al., 2007) or Hall effect sensor system (Robson et al., 2007). Robson et al. (2009) also used a Hall effect sensor system to measure the pumping flux from the exhalant siphon. A Hall effect sensor is a transducer that varies its output voltage in response to changes in a magnetic field. These changes are induced by changes in the distance between the sensor and a magnet positioned few millimetres away. 318 Integrated studies

Figure 6: Monitoring of oyster valve gape every 2 seconds at the beginning (day 1, dash black line) and at the end (day 9, plain grey line) of a mesocosm experiment. The oyster Crassostrea gigas remains open most of the time at the beginning while its periods of activity are shortened at the end of the experiment (with fewer opening period).

The Hall effect sensor was used in a gape activity measurement system during a mesocosm study carried out on Medimeer in order to measure oysters (Crassostrea gigas) filtration pressure (Mostajir et al., unpublished data). During this experiment, each oyster resting on its bottom shell was glued on a PVC plate where a hall sensor was inserted. The other part of the hall sensor system was a magnet attached on a PVC stick glued on the oyster top shell. In this way, changes in magnetic field were produced by variations in the distance between the sensor and this magnet. Twenty oysters were introduced to two mesocosms and sensors were connected to a data logger (CR23X, Campbell Scientific). The signal (output voltage) was sampled every 2 seconds. The physiological activity of the twenty oysters, related to the degree of valve aperture at two levels (open and close), was monitored continuously over the nine days of the experiment (figure 6). The results highlighted that oyster behaviour of particle filtration, as indicated by duration of valve aperture, correlated with plankton abundances. At the beginning of the experiment (day 1), plankton was abundant and valves were opened all day long. When food became scarce at the end of the experiment (day 9) valves remained closed some hours per days (Mostajir et al., unpublished data). Part IV – Chapter 3 319

4. Automated sensors to regulate marine mesocosm experiments

Sensors in aquatic mesocosm studies can also used for the automated feedback control of the experimental conditions. Automatic regulation is crucial for the long-term and continuous control of marine mesocosms, and to ensure a good repeatability between treatments. Automated sensors are widely used in indoor mesocosm facilities. For example, automated sensors control the chemical doses of pollutants and stressors in the Experimental stream facility (U.S. Environmental protection agency, USA), the water level in the Multiscale experimental ecosystem research center facility (University of Maryland, USA), and the water motion inside a mesocosm (Falter et al., 2006). In outdoor mesocosms, especially those immersed in the sea, regulation of the experimental conditions is more problematic than in indoor mesocosms because of their higher temporal variability and because of stronger constraints on accessing an energy source. Still, the mixing turn-over time generated by a pump of which flow rate is controlled by voltage input can be automated using the feedback of a flow meter set in the outflow circuitry (Medimeer team, unpublished data). In addition, recent developments have focused on sensor-based regulation systems of different global changes scenarios. Up to now, the feedback control device focused on increase of carbon dioxide (CO2), temperature and ultraviolet B radiation (UVBR: 280-320nm). Regulation of these variables is important to elucidate the response of marine organisms as well as those of the whole food web to these stressors, as they are expected to change in the near feature from global anthropogenic changes. Models predict an increase of average atmospheric pCO2 which induces marine acidification (IPCC, 2007). Water temperature is also expected to increase as a consequence of global warming, and incident UVBR at the sea surface are expected to be modified as a concomitant effect of ozone depletion and global warming (Weatherhead and Andersen, 2006; IPCC, 2007).

Kim et al. (2008) designed an in situ mesocom facility for CO2 enhancement studies by adapting the set up used in previous experiments (Engel et al., 2005; Delille et al., 2005; Kim et al., 2006). The continuous feedback control only concerned the gas concentration in the headspace above the mesocosm measured with online infrared analysers (LI-COR 820). Other experimental setups focused only either on temperature (Liboriussen et al., 2005) or on UVBR (Roy et al., 2006). Here, we present a set-up of sensors used in the combined regulation of UVBR and water temperature increase in the outdoor immersed mesocosms of Medimeer. The experiment and technical set up are thoroughly described in our previously published articles (Nouguier et al., 2007; Vidussi et al., 2011). The developed setup aimed to maintain a proportional enhancement between the control mesocosms 320 Integrated studies and the treatment mesocosms, where an increase of 3.1°C was applied for the temperature and an increase of 20% was applied for the UVBR. The main objective was to ensure that the enhanced treatments perfectly tracked natural temporal variations in temperatures and incident UVBR in real-time. A data logger (CR23X, Campbell Scientific) monitored the reference measurements, the feedback control measurements, calculated the deviation between the measured values and the target values, and sent a proportional command to heating elements and UV lamps (figure 7). For the two temperature-increased mesocosms, the reference temperature was calculated from the mean of the temperature measured every 30 seconds at three depths in the control mesocosms using thermistor probes (Campbell Scientific 107). Changes in the reference temperature commanded the functioning of a submersible heating element inside treatment mesoscosms (Galvatec, France). The feedback control temperature was calculated for warmed mesocosms from the mean temperature measured at 3 depths by using thermistors probes. As shown on figure 8A, the variations

Figure 7: Scheme of the experimental design for increasing UVBR and water temperature in the mesocosm relative to water temperature and incident UVBR measured in the control mesocosms (without heating and UVBR increase). For further information see Nouguier et al. (2007). Part IV – Chapter 3 321 of the temperature in the warmed mesocosm matched the variations in the control mesocosms and a constant difference of 3.1°C was maintained. For the UVBR enhanced treatments, the reference incident irradiance was measured every 30 seconds by an ultraviolet B sensor SKU 430 (Skye Instruments) situated outside and corrected with a set coefficient taking into account shading and transmission reduction at the centre of the mesocosms. Variations in incident UVBR commanded the regulation of ultraviolet b fluorescent lamps (Philips TL20RS/01) controlled by electronic ballasts (bAG electronics AD18.22310) and allowed to maintain a constant +20% increase of UVBR in UVBR enhanced treatments. The stability of the +20% constraint is achieved thanks to a ultraviolet B sensor SKU 430 (Skye Instruments) positioned indoor in a structure imi- tating the upper part of the treatment mesocosms. This latter structure and controlling sensor receive the same regulation command from the immersed mesocosms. It was placed in the workshop of Medimeer to accurately measure the radiation delivered by the ultraviolet B lamps as the sensor needs to be set on a stable plane surface. In addition, the correct functioning of the ultraviolet B lamps is checked in real-time for each lamp by a custom-built photodiode system (sglux TW30DZ). As shown on figure 8B, the variations of the UVBR in the illuminated mesocosm matched the variations in the control mesocosm and a constant enhancement of 20% was maintained during the day.

Figure 8: Use of sensors to manipulate environmental conditions in a mesocosm experiment. A. In the mesocosm experiment carried out in April 2005, after the heating started (1), the temperature gradually increased over 2 hours until an enhancement of 3°C is reached. The temperature regulation (2) was then fully in operation maintaining a difference on 3.1°C on average and tracking perfectly the variations of the temperature in the control mesocosm. Water temperature of mesocosms was monitored every 30 seconds. B. Data from a mesocosm experiment carried out in April 2005 demonstrate that the UV regulation running from 9:45 to 17:45 controlled the 20% enhancement delivered by UVB lamps: the artificial UVBR intensity (black line) followed the short-term variations in the natural incident UVBR. UVBR was monitored every 30 seconds. 322 Integrated studies

5. Towards a new generation of sensors

In marine mesocosm experiments, several environmental parameters can be monitored with the use of automated sensors as described in section 3. Some of these sensors are voluminous (sometimes 0.5m high) and difficult to deploy in relatively small mesocosms (around 1m of diametre and 1m3 water volume). There is therefore a need to reduce the size of the current sensors for future applications in mesocosm experiments. In addition, several measurement biases may arise from biofouling. This is the case in particular for fluorescence sensor, which will require frequent cleaning especially in eutrophic waters where biofilm development can be very fast. In this case, a sensor with a wiper should be used such as the Wetlabs fluorescence sensors. Additional efforts in the equipment of copper wired filter avoiding the creation of biofouling may also be recommended. Finally, a reduction of the power and reagents consumption of some chemical sensors is necessary to enable their routine use in oceanic monitoring during long-term deployments. As previously mentioned, data coming from the use of several sensors inside mesocosms can be combined to investigate the global characte ristics or responses of the food web components under a simulated perturbation. Unfortunately, there are still obvious lacks in the availability of sensors providing key information about the diversity and dynamics of aquatic microorganisms. Some miniaturised geno-sensors dedicated to the identification of microorganisms can distinguish species or groups of microorganisms, and would provide very useful information about the food web dynamics in the mesocom experiments (see II, 3 for toxic algae). Miniaturised camera sensors with associated software allowing image analysis would also be useful to study the interaction between micro organisms or between microorganisms and metazooplankton such as predation (see II, 2). More generally, the development of the next generation of biological sensors and their use in mesocosm experiments would help to bring a huge step forward regarding our knowledge in marine ecology. This is likely to be the case regarding urgent scientific questions about the consequences of anthropogenic changes on the local and global scales.

Authors’ references

B ehzad Mostajir, Emilie Le Floc’h, Sébastien Mas, David Parin: Université de Montpellier 2, Centre d’écologie marine expérimentale Medimeer (Mediterranean centre for marine ecosystem experimental research), CNRS UMS 3301, Montpellier, France. Part IV – Chapter 3 323

Behzad Mostajir, Jean Nouguier, Romain Pete, Francesca Vidussi: Université de Montpellier 2, Écologie des systèmes marins côtiers (Ecosym), CNRS, IRD, IFREMER, Université Montpellier 1 UMR 5119, Montpellier, France

Corresponding author: Behzad Mostajir, [email protected]

Aknowledgement

MEDIMEER was funded by ECOSYM laboratory, CNRS INEE, Institut Fédératif de Recherche 129 Armand Sabatier, CNRS-GDR 2476, and Région Languedoc-Roussillon. We acknowledge support of the European project MESOAQUA, Grant agreement n° 22822, to provide some of the sensors and some of the related mesocosm experiments from which some data are presented here. The UV-B and temperature increase mesocosm experiment (UVTEMP project) and the oyster mesocosm experiment (Oyster and Fish project) were founded by the French National Program in Coastal Science (PNEC-Chantier Lagunes Mediterréennes). Special thanks to Eric Fouilland for the constructive comments in the first version of the manuscript.

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Synthesis and conclusion

Jean-François Le Galliard, Jean-Marc Guarini and Françoise Gaill

1. Finale

Advancesn i ecological sciences depend on a tight interaction between observation of nature, experimentation, and modelling. Relevant to all three approaches is the availability of high-quality data about physical, chemical and biological variables, and therefore the development of appropriate instruments and sensors. The content of this book demonstrates that the use of sensors has allowed great conceptual progresses including understanding animal physiology and behaviour, assessing biodiversity and quantifying states and processes of entire ecosystems. Sensors are now critical to explore ecological systems that remain difficult to access or sample. For example, sensors were necessary to the discovery and description of new life supports under “extreme” environmental conditions such as deep-sea ecosystems, the analysis of physiological and behavioural adaptations of animals in polar conditions, or the observation from space of global surface compartments of marine ecosystems. One of the most recent outcomes concerns the quantification of the biological diversity in its broadest ecological sense, encompassing both the number and abundance of species and intra-specific variation in the form and life-history of organisms (see section II). The ability to identify automatically, to locate and to count organisms is clearly a major improvement in the “toolbox” of ecology. However, there is still a long way to go to design shared procedures and fully autonomous platforms as those required for marine pelagic environments. Sensors play a critical role in ecosystem studies focusing on measuring stocks (e.g., plant, soil, or carbon biomass), fluxes of molecules in solid, liquid or gaseous media, and functional processes (e.g., photosynthesis). In these perspectives, sensors have greatly improved both in terms of quality and diversity over the past last years. They enable faster, more accurate and less invasive quantification of whole-ecosystem components in the field (section III). Once again, the trends and needs are in the development of autonomous platforms based 332 Synthesis and conclusion on networks of sensors. This is clearly an important step that needs to be fulfilled to be able to describe and model entire ecosystems at local, regional or global scales, or under experimental conditions (section IV).

2. Next generation sensors: synthesis

2.1. Bio-logging and bio-tracking Technological advances in the fields of material design, optics and imagery, chemistry, communications, and computers have sped up the development of next generation sensors providing higher quality as well as new types of data (Benson et al., 2010; Porter et al., 2009). Yan Ropert-Coudert et al. (I, 1) have demonstrated this very clearly regarding bio-logging, which focuses on measuring physiological and behavioural variables of animals in their natural environment. For this, they used sensors and loggers that were directly attached on the animal (i.e. bio-loggers). The quantity and quality of variables that modern bio-loggers can record has steadily increased, because technology improved at the same time power sources, data storage capacities, sensors characteristics and communication devices, allowing trends towards smaller dimensions and lighter weight. New technologies recently developed in other disciplines (image and video acquisition devices, miniaturised GPS, biomedical sensors) are also added on modern bio-loggers. Nowadays, the community of bio-logging is equipped with a broad range of sensors that started a conceptual revolution in eco-physiological studies. The next steps in this field of ecology will be to develop smaller bio-loggers able to track and study smaller animals, as stressed by Guillaume et al. (II, 2). This is a relevant point for a large community of researchers who addresses already many key issues (e.g., surveys of avian migration or decline of pollinators), for which model species are still too small to be equipped with the bio-loggers currently available (Bridge et al., 2011). Another important step forward in eco-physiology is the development of non invasive remote sensing techniques that can detect movement, behaviour, or physiological state of animals without interfering with their normal behaviour. Samaran et al. (I, 3) as well as Huetz and Aubin (I, 4) have demonstrated how passive acoustic sensors can be used to identify species, track animals and study their behaviour in both aquatic and terrestrial environments. Similar progresses in animal behaviour and physiology have been made recently using other techniques like remote image sensing, video analyses and thermography (McCafferty et al., 2011). Synthesis and conclusion 333

2.2. Biodiversity and ecosystem functioning Working out accurate biodiversity scenarios and understanding the coupling between biodiversity and ecosystem functioning are two of the great challenges of modern ecology (Leadley et al., 2010; Loreau, 2010). A large range of advanced physical, chemical and biological sensors are now available to measure eco-geochemical processes (Benson et al., 2010; Porter et al., 2009). Today, technological advances emphasise autonomy, ruggedness, and resistance to drift of standard “lab-benched” analytical sensors before their deployment in the field. The chapters by Le Bris et al. (III, 1 and III, 2) and that of Mostajir et al. (IV, 3) advocate for more autonomous and less invasive sensors because it is critical to study the short scale spatial heterogeneity and temporal variability of chemical gradients. However, these authors also pointed out that there are still critical limits against the long-term deployment of chemical sensors in the field, and the quantity of environmental factors that can be recorded for the purpose of long-term monitoring programs often remains small. The idea to measure and monitor proxies of biodiversity automatically has led to the development of a new generation of “biodiversity sensors” during the last decade (this book). Ideally, these sensors should be able to track automatically species or morph abundance and diversity in space and time, and could also be used to quantify biodiversity below the species taxonomic level accurately. As exemplified in the four chapters specifically dedicated to biodiversity and in other chapters alluding to the same matter throughout the book, this idea will require the use of multi-source data from remote sensing methods, image analyses, or genosensors. Sueur et al. (II, 1) reviewed advanced acoustic methods to estimate the species diversity of singing amphibians, birds and insects. Imagery and spectrometry sensors carried into aircrafts and satellites are also used routinely to study biodiversity (see III, 3 and IV, 2). The more advanced instruments are characterised by greater spatial and spectral resolutions (Ustin et al., 2004; Wang et al., 2010). For example, imaging spectroscopy of ground surfaces at high resolutions (less than 10m and with bandwidths of 10-20 nms) can be used to identify tree species, to map vegetation and land covers, or to track invasive plant species. With standard multispectral sensors such as those implemented on Landsat or Modis satellites, a new range of analytical imagery solutions help to deconvulate the spectral signals in order to detect additional biological items. Alvain et al. (III, 3) have demonstrated how such analytical progresses have led to build accurate maps of major phytoplanktonic groups at the surface of the global ocean. Moreover, the use of remote sensing imagery is not limited to the taxonomic identification and biodiversity mapping only, but expands rapidly toward the development of indices of carbon or dry matter stocks, estimates of biochemical variables, 334 Synthesis and conclusion and calculation of functional processes (e.g., Kokaly et al., 2009). This trend is discussed using data from satellite imagery, light detection and ranging application (Lidar), or radar satellites in two chapters, the first one by Alvain et al. (III, 3) on oceanic ecosystems and the second one by Chave et al. (IV, 2) proposing a review on tropical rainforests studies. Pontallier and Soudani (III, 4) have shown that remote sensing technologies from space also stimulate the development of in situ sensors that can be deployed above plant canopy, on flux towers for example. These in situ sensors have a great potential to characterise the dynamics of vegetation in real time with limited costs and maintenance. Another application of “digital eyes” technology is the pattern recognition in natural samples. The cameras developed by Gorsky, Stemman and their research group (II, 2) are, for instance, capable of sorting particles from pelagic oceanic samples by type and by size, including detecting non- living particles and identifying a substantial fraction of marine zooplankton. This technological improvement has led to a tremendous change in the capacity to quantify marine particles compared to what was done with sediment traps or discrete sampling in the water column. Pattern recognition techniques are also used in plant physiology, agronomy and eco-morphology to measure the form of plants and animals, and are sometimes grouped together with eco-physiology, functional genomics and metabo- lomics into the so-called “phenomics” techniques of biotechnology. Plant phenomics is under rapid development in agronomy but relies on greenhouse protocols that are inadequate for ecological studies and use imagery techniques that still require to be tested in the field (Eberius and Lima- Guerra, 2009). Yet, the transfer of lab-benched phenomics into field conditions holds much promise for those interested in the understanding of phenotypic diversity and biological adaptations (Houle, 2010). Mallard et al. (II, 4) describes how a simple pattern recognition method can be used in microcosms to locate, count and measure the size of individual arthropods. Similar approaches need to be tested and implemented in the field and should be extended to study other taxonomic groups. Concerning the characterisation of biodiversity, an alternative to remote sensing and image analysis mentioned above is the use of genetic barcodes from water or soil samples, or living tissues. A first approach, called environmental metabarcoding, requires the sampling, extraction and amplification of multiple barcodes and the subsequent sequencing with high throughput technologies (Jørgensen et al., 2012). However, metabarcoding techniques are difficult to adapt into autonomous sensors in the field. Some laboratory-based alternatives allow near-real-time detection of toxic marine algae and other microorganisms. They have the potential to be deployed in the field in a near future, and were described here by Orozco et al. (II, 3). These alternatives rely on the existence of taxon- Synthesis and conclusion 335 specific genetic barcodes, but seek for detecting these barcodes (using amplification or hybridisation techniques) rather than sequencing them exhaustively. The field portable versions of these lab-benched techniques are called genosensors and some of them are under development for few years in marine ecology (Paul et al., 2007; Scholin, 2010). This includes the autonomous microbial genosensor (AMG) and the environmental sample processor (ESP) used for real-time studies of bacterioplankton and invertebrates (figure 1). Jahir orozco et al. (II, 3) have demonstrated that it is still extremely difficult to standardise genetic protocols when the interest is to calculate real cell counts. In addition, the method is suitable for fluids, but it is not easy to implement in hard substrates, such as soils and benthic sediments. Therefore, much progress remains to be done to import genetic methods into autonomous sensors installed in the field.

Figure 1: This field-portable genosensor, called the environmental sample processor (ESP), can be deployed during several months to monitor the diversity and toxicity of marine bacterioplankton. The ESP is a collaborative project coordinated by Christopher Scholin and funded by the Monterey bay Aquarium Research Institute, National Science Foundation, and NASA. It is based on the electrochemical biosensor based methods described by Jahir orozco et al. in this book, and allows real-time monitoring of a diversity of marine species. © Kim Fulton-bennet/2006 MAbRI. 336 Synthesis and conclusion

2.3. Towards integrated knowledge of ecosystems Despite strong differences among sensor types and technologies, several authors of this book drew similar perspectives for the future. The first shared opinion is that recent progresses in ecology have been achieved through similar technological improvements including increased miniaturisation, increased autonomy, and increased communication capacities. The need for sensors that present these three traits is critical in ecology when observations must be conducted for long periods of time in remote areas (Porter et al., 2009) and must be as non-invasive as possible. There is however an obvious trade-off between miniaturisation and increased communication capacities on one side and autonomy on the other side. Various alternatives exist to reduce consumption (including pre-programmation of sampling and communication rates) and to increase power capacities (including renewable energy sources as solar panels and wind or hydro-turbines). Miniaturisation is also important because the smaller the sensors the less they interfere with the dynamics or the behaviour of the studied systems. Most ecology laboratories in France are far from reaching the ability to address all of these technological challenges when designing and constructing sensors. Therefore, a stronger priority must be given to collaborative research and development projects involving environmental scientists, engineers and specialists in informatics or telecommunication. Most studies also emphasise the need for adequate and representative spatial coverage to study spatial ecological processes. Even if it is not a major issue for remote sensing from space, the question of inadequate spatial coverage raises serious concerns to interpretation and analysis of data collected by in situ sensors. The challenge rests on the fact that most ecological phenomena exhibit spatial structures at multiple scales from a few millimetres to hundreds of kilometres, and that most ecological systems demonstrate characteristic patterns only at certain spatial scales (Levin, 1992; Wiens, 1989). Several technological solutions are now available to cope with this problem, depending on the spatial scale of investigation. At broad spatial scales (i.e. hundreds of kilometres range), ecological patterns are best addressed by remote sensing and network of autonomous stations such as buoys on oceans and towers on continents. These types of measurements have a long tradition in oceanography, forestry and hydrology. At regional and local scales, standard networks of sensors typically had small spatial and temporal resolution until the recent development of wireless technologies (Porter et al., 2005). The wireless network technology, of which Chave et al. discuss one potential application (IV, 2), allows high-frequency observations, intensive and inexpensive sampling over large areas up to tens of kilometres, non-intrusive sampling of study sites, and real-time reactions (figure 2). Generally speaking, long-term approaches conducted at large spatial scales are beyond the scope of a single laboratory program and Synthesis and conclusion 337 require coordination and shared procedures among an entire international community of scientists. This is well demonstrated by the Argos oceanographic network described by Stemmann et al. (IV, 1).

Figure 2. A wireless network of sensors developed by the Fraunhofer Institute for Microelectronic Circuits and Systems (IMS, Germany) installed on the grounds of the Northwest German Forestry Testing Facility in Göttingen. The wireless network enables real-time measurements of environmental data during a deployment period of up to 12 months. © Fraunhofer IMS.

In addition to specific requirements of temporal and spatial coverage, most ecologists emphasise the strong need to integrate multiple types of information, which usually implies to collect at the same time and in the same place both physical, chemical and biological data (e.g. Sagarin and Pauchard, 2010). The integration of multiple sensors is an extremely difficult task, given the costs, amount of data types and need for evolv- ability of multi-sensor platforms. Modular equipments provide the best solution to accommodate multiple sensor types and anticipate technological changes. Strong improvements in data streaming, data processing and storing capacities must be achieved compared to standard procedures to customise multi-sensors network. Priority must be given to real-time acquisition and processing, open source tools, and freely available data sets, which requires a strong cooperation with computer scientists (Benson et al., 2010). This book demonstrates the feasibility and usefulness of some sensor networks that collect physical and chemical data in oceanography 338 Synthesis and conclusion or forestry (Le Bris et al., III, 1; Stemman et al., IV, 1; Chave et al., IV, 2). There is now a clear need to expand these networks in order to collect at the same time biological data on the presence and abundance of species of interest (see section IV).

4. Next generation sensors: perspectives for infrastructures

Well-organised networks of observational and experimental infrastructures are critical for the development and use of next generation sensors. These infrastructures can help to design and test new types of sensor and are ideal places to promote the long-term deployment of sensors and to test the relevance of integrated information from multiple sensors types. For example, observational and experimental programs conducted in the Long-term Ecological Research (LTER) network, National Ecological Observatory Network (Neon) and ocean observatories initiative in the USA have all been devoted to the development of new sensors technology (Benson et al., 2010; Porter et al., 2009). Several authors of this book are involved in similar infrastructures at a national or international level. For the last decade, the CNRS in France has promoted a range of observational and experimental infrastructures, which should form the basis for the development of next generation sensors for ecology. Regarding observational infrastructures, various specialised environmental science observatory networks managed or supported by the CNRS exist, including a network dedicated to greenhouse gas monitoring (Icos, http://www.icos-infrastructure.eu/), a critical zone exploration network dedicated to geochemical and hydrological processes on continents (http://www.czen.org/), and several national and international oceanographic networks such as the Naos project coordinated by Ifremer (http://www.naos-equipex.fr/). In addition, the CNRS is strongly involved in the management and support of a LTER-like network that includes 10 study sites scattered in France and overseas (http://www.cnrs. fr/inee/outils/za.htm). This network includes regional study sites where the coupled dynamics of ecosystems and societies are investigated on the long-term with a multidisciplinary approach. All these study sites rely strongly on sensor networks for monitoring the environmental and ecosystem processes. Regarding experimental infrastructures, the project Anaee (analysis and experimentation on ecosystem), supported by the CNRS in partnership with Inra, is registered on the roadmap of the European Esfri program with a preparation phase planned from 2011 to 2014 (http://www.anaee. com/). It will bring together the major experimental facilities in continental Synthesis and conclusion 339

Figure 3: Infrastructures in experimental ecology managed by the Institute Ecology and Environment of the CNRS in France belong to the ANAEE European research infrastructure. In ANAEE, research infrastructures encompass in vivo approaches (small scale, high manipulative power of Ecotrons), semi-natural situations (intermediate scales, medium manipulative power), and natural situations (larger spatio-temporal scales, low manipulative power). A-b. Ecotrons are newly created infrastructures that enable highly controlled manipulation of terrestrial and aquatic organisms, communities and ecosystems. In France, this infrastructure consists in the Ecotron de Montpellier (A) specialised in the analysis of terrestrial ecosystems, and the Ecotron Ile-de-France (B) specialised in the analysis of aquatic ecosystems. C-D. The semi-natural platform includes facilities that enable manipulation of terrestrial and aquatic organisms, communities and ecosystems under partially controlled situations. In France, it includes of the national aquatic ecology platform (C) located nearby Paris, and a large infrastructure of experimental meta-ecosystems located nearby Toulouse (D). E-F. In natura experimental sites are long-term experimental facilities allowing simultaneous measurements of key ecosystem variables and parameters through a multi-disciplinary approach. They include experimental grasslands and forests supported by the CNRS and several other institutional partners such as the Nouragues tropical rainforest (E) and the alpine grasslands of the Lautaret (F). © CNRS. 340 Synthesis and conclusion ecosystem sciences in a distributed and coordinated network (Lemaire, 2008). Selected experimental platforms from Anaee will allow simultaneous manipulation and monitoring of ecosystem processes by collaborative approaches. Meeting the challenges of Anaee requires a sustained research effort with a range of complementary approaches from highly controlled facilities to natural systems (see Figure 3). We anticipate that the financial support provided for these sites will stimulate collaborations and programs to develop sensors that ecologists will use to explore natural ecosystems, test ecological theories, and develop predictive models.

Authors’ references

Jean-François Le Galliard: Université Pierre et Marie Curie, Laboratoire Écologie et Évolution, CNRS-UPMC-ENS UMR 7625, Paris, France École normale supérieure, CEREEP – Écotron Ile-de-France, CNRS-ENS, UMS 3194, Saint-Pierre- lès-Nemours, France

Jean-Marc Guarini: Université Pierre et Marie Curie, Laboratoire d’Écogéochimie des Environnements Benthiques, CNRS-UPMC, FRE 3350, Banyuls-sur-Mer, France

Françoise Gaill: CNRS, Institut Écologie et Environnement, Paris, France

Corresponding author: Jean-François Le Galliard, galliard@biologie. ens.fr

Acknowledgement

Jean-François Le Galliard acknowledges the support of the TGIR Ecotrons program, and of the ANR program “Investissement d’avenir: Equipement d’excellence” (ANR PLANAQUA 10-EQPX-13-01) coordinated by UMS 3194 CEREEP-Ecotron IleDeFrance. Synthesis and conclusion 341

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