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Information Transfer in a Complex and Noisy World Velilla Perdomo, E.

2020

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Download date: 04. Oct. 2021 Information Transfer in a Complex & Noisy World

A study through the lens of organisms using vibrational signals & cues

Estefania Velilla

1 Information Transfer in a Complex and Noisy World A study through the lens of organisms using vibrational signals and cues

2 This thesis should be cited as:

Velilla, E. (2020) Information Transfer in a Complex and Noisy World: A study through the lens of organisms using vibrational signals and cues. PhD thesis, Vrije Universiteit Amsterdam, The Netherlands.

ISBN: 978-94-6416-078-9

Cover and layout: Studio Migle, www.studiomigle.com Illustrations: Studio Migle (p. 20, 36, 60, 72, 88) Printed by: Ridderprint BV, www.ridderprint.nl

Copyright © 2020 by E. Velilla ([email protected]) 3 VRIJE UNIVERSITEIT

Information Transfer in a Complex and Noisy World A study through the lens of organisms using vibrational signals and cues

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. V. Subramaniam, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de Faculteit der Bètawetenschappen op donderdag 15 oktober 2020 om 9.45uur in de aula van de universiteit, De Boelelaan 1105

door Estefania Velilla Perdomo geboren te Bogotá, Colombia 4 promotor: prof.dr. J. Ellers copromotor: dr. W. Halfwerk

5 Contents

glossary 7 chapter 1 General Introduction 9 chapter 2 Gone with the wind: is signal timing in a 18 Neotropical katydid an adaptive response to variation in wind-induced vibratory noise? chapter 3 Variation in plant leaf traits affects 34 transmission and possibly detectability of herbivore vibrational cues chapter 4 The effect of vibrational noise on a 58 parasitoid-host interaction chapter 5 Vibrational noise from wind energy-turbines 70 negatively impacts earthworm abundance chapter 6 Effect of anthropogenic vibratory noise on 86 plant development and herbivory chapter 7 Summary and general discussion 101

References 113

Authors affiliation and addresses 141

List of publications 145

Summary (en) 147

Samenvatting (nl) 150

Acknowledgements 154

6 Glossary

Bioacoustics Cross-disciplinary science that combines biology and acoustics. Usually refers to the investigation of sound production, transmission and perception in organisms

Biotremology The study of production, transmission and perception of mechanical vibrations by organisms, and their effect on behavior

Cross-modal The process in which stimuli from one sensory modal- interference ity can affect the detection and processing of stimuli of another sensory modality

Cue A phenotypic trait that can be detected by a perceptual system, but has not been selected to induce a behavioral or physiological change in a receiver

Distraction A perceptual process during which noise reduces the detectability and discriminability of a signal or cue via an overload of an animal’s cognitive processing capacity

Information Knowledge obtained from biotic or abiotic stimuli

Masking A perceptual process during which noise reduces the detectability and discriminability of a signal or cue via a reduction in signal-to-noise ratio

7 glossary

Noise Environmental stimuli that interfere with the detection and processing of signals and cues

Signal A phenotypic trait that can be detected by a perceptual system and has specifically evolved to induce a behavioral or physiological change in a receiver

Signal-to-noise ratio Difference in intensity of a signal or cue relative to the background noise

Tremulatory signal Type of vibrational signal. It can be described as a trem- bling, shaking, sometimes jerking body motion that intro- duces substrate-borne vibrations into the plants on which the animals are perched

8 chapter 1

General Introduction

The environment is teeming with valuable information on which animals rely to make box 1.1 important decisions such as, where to find a The information transfer chain mate, or a prey, or when to avoid a predator (Dominoni et al. 2020). The reproductive The information transfer chain refers success and survival of animals, therefore to the process involving the produc- depends to a great extent on their ability tion of a stimulus that serves as infor- to extract relevant information from their mation (e.g. a pheromone mating environment (McNamara and Dall 2009; signal or vibrations in a plant induced Dominoni et al. 2020). Environments vary by herbivores chewing), its transmis- in their habitat complexity and in back- sion through a medium and, finally ground noise levels, of which both can affect its perception by a receiver. There are, the different stages of the information trans- thus, three stages in the information fer chain (Brumm and Slabbekoorn 2005, transfer chain: the production, trans- Box 1.1, Fig. 1.1). While there has been mission and perception of informa- extensive research on the effects of noise tion (Bradbury and Vehrencamp and environmental variation on information 1998). transfer for the acoustic domain, much less is known about these effects in the vibrational modality.

9 chapter 1

FACTORS AFFECTING THE INFORMATION TRANSFER CHAIN CONSEQUENCES

Noise

Reproductive Production Transmission Perception Survival success

Environmental Fitness variation

fig. 1.1 Factors affecting the production, transmission and perception of signals and cues and its conse- quences. Noise can affect both the production and the perception of information, and environmental variation can affect its transmission. Failure to perceive (extract) the relevant information can lead to lower reproductive success and survival, which can ultimately affect fitness. Depending on the context (e.g. mating versus predator-prey interactions), fitness can be negatively or positively impacted, as indicated by the red and green arrow in the fitness box, under consequences. The cases of increased fitness refer to predators/parasitoids whose cues are less easily perceived by their prey/hosts, increasing their chances of predation/parasitism. In those cases, the producer’s fitness increases and the receiver’s fitness decreases (Dominoni et al. 2020).

box 1.2 Sound versus vibrations

Sound (acoustic waves) propagates gate perpendicular to the direction longitudinally in the form of compres- or surface of the substrate through sion waves through a medium like which they are traveling (Hill 2014). air or water, or as transverse waves In contrast to airborne sounds, in in solids. In a longitudinal wave, the which all frequency components particle displacement is parallel to the travel with the same velocity, in bend- direction of wave propagation, and ing waves, high frequencies propa- in a transverse wave, the oscillations gate faster than low frequencies. As are perpendicular to the direction of a result, vibrational (bending) waves the wave. Substrate-borne vibrations, can change shape and duration when on the other hand, are mostly either they travel. Therefore, the strategies Rayleigh waves (through the earth) or used by animals relying on acoustic bending waves (in plants) (Hill 2014). versus vibrational information will Both of these types of waves propa- differ (Michelsen 2014).

10 chapter 1

The vibrational modality information across a wide range of contexts, including mating (e.g. Morris et al. 1994; Vibrational and acoustic communication Rodriguez 1999; Čokl et al. 2007; Bagwell were once considered part of the same et al. 2008; Mazzoni et al. 2009; Rodrí- scientific discipline, ‘bioacoustics’ (Hill et guez et al. 2015; Xiao et al. 2015), group al. 2019). However, vibrational and acous- communication (e.g. Faeth 1989; Cocroft tic waves have important mechanical and 1999, 2001; O’Connell-Rodwell et al. 2001; propagation differences (Box 1.2) that have Fletcher 2007), predator-prey dynamics (e.g. eventually led to the establishment of a new Warkentin 2005; Catania 2008; Halfwerk field dedicated solely to study of vibrational et al. 2014) and parasitoid-host interactions information, ‘biotremology’. In this section (e.g. Casas et al. 1998; Meyhofer and Casas I provide an introduction to the vibrational 1999; Broad and Quicke 2000; Djemai et modality. al. 2001). ‘Vibrational signal’ is often used as an The use of vibrational signals and cues umbrella term to refer to different kinds of is most prominent in animals living and/or vibrational signals. However, in arthropods, communicating on plants (Cocroft et al., up to four signaling categories can be distin- 2014). The use of plant-borne vibrations, guished based on shared signal parameters: however, comes with some challenges. For drumming, tremulation, stridulation and example, interactions occur mostly on a tymbal buckling (Hill 2008, 2009). In this local scale, from 30 cm – 4 m (Cocroft and thesis I use both the umbrella term ‘vibra- Rodriguez, 2005). Because animals relying tional signal’, as well as the more specific on plant-borne vibrations are typically much term, ‘tremulation’, which refers to a trem- smaller than the structures they vibrate, the bling, shaking, sometimes jerking, body amplitude of their signals and cues is low motion in katydids that induces substrate- at the source, limiting the long-distance borne vibrations into the plants where transmission of vibrations (Michelsen et al. animals are perched (Hill 2014). 1982). Additionally, plant-borne vibrational Vibrations can be perceived with signals and cues are transmitted as bending specific sensory systems or receptors (e.g. waves, for which the transmission velocity bristle, filiform, campaniform types) found increases with the square foot of frequency on different locations (e.g., thorax, head (Michelsen et al. 1982). As a consequence, wings, antennae) that are able to measure signals or cues with multiple frequencies can the oscillations at the boundary between become distorted over distance (reviewed media (reviewed in Cividini & Montesanto, in Cocroft and Rodriguez 2005). Another 2020). challenge for long-distance communica- Together with chemicals, vibrations are tion is that plant stems can act as frequency among the oldest modes of communication filters, attenuating frequencies differen- (Hill 2008; Cocroft et al. 2014; Hill et al. tially and thus, distorting signals and cues 2019). Hundreds of thousands of arthropod (Michelsen et al. 1982). species and many vertebrates use vibrational Besides the selective pressure from

11 chapter 1 plant structures, background noise (e.g. from lataria, where caterpillars were shown to wind and rain) can also play an important distinguish vibrations induced by rain from role in the evolution of vibrational commu- predator-induced vibrations (Castellanos nication systems. For example, the impact and Barbosa 2006). Moreover, embryos of of rainfall on leaves causes a character- the red-eyed tree frog Agalychnis callidryas, istic pattern of vibrational noise. In rainy which use vibrational cues induced by their environments like cloud forests and the snake predators to hatch prematurely and rain forests, signals that are detectable and escape predation, have been shown to hatch recognizable in the presence of rain might prematurely in response to rain-induced be selected for (e.g. pure tones) (Cocroft and vibrations (Warkentin 2005; Caldwell et al. Rodriguez 2005). 2010). Furthermore, wind has been shown Though the field of biotremology is to affect signal timing in the treehopper relatively young, it is growing and gain- Enchenopa binotata ‘Ptelea’ (McNett et al. ing recognition at a fast pace (Cocroft et 2010) and in the plant hopper Criomorphus al. 2014; Hill et al. 2019). As mentioned albomarginatus (Tishechkin 2007). above, the use of vibrational information Activities and infrastructure linked has been described across taxa for a variety to human population growth are progres- of contexts. Nevertheless, studies addressing sively changing the sensory environment the effects of noise and substrate variation of animals (Dominoni et al. 2020). Road- are still a minority in the field of biotrem- way traffic, construction work, wind energy ology. turbines and drilling activities also induce vibrational noise that can potentially affect Noise the many animals that rely on vibrational information. Although this is an increasingly Both natural and anthropogenic noise important problem, there are only a couple sources can affect animals in different of studies that have addressed this issue ways, including by disturbing the infor- in the field of biotremology. In one study, mation transfer processes and physiolog- anthropogenic vibrational noise induced ical processes (Brumm and Slabbekoorn by roadway traffic and wind turbines was 2005; Barber et al. 2010; Kight and Swad- shown to reduce the call rate of the terres- dle 2011; Francis and Barber 2013; Naguib trial toad Alytes obstetricans. Since females of 2013; Dominoni et al. 2020). For animals the same species prefer males with a higher using substrate-borne vibrations, wind and call rate, this effect potentially reduces rain represent the major sources of natural individual reproductive success (Caorsi et abiotic noise (Casas et al. 1998b; Cocroft al. 2019). In a different study, brood size in and Rodriguez 2005; Warkentin 2005; the burying beetle Nicrophorus marginatus Tishechkin 2007). The effect of rain-in- was found to be negatively related to seismic duced vibrational noise has for example noise (Phillips et al. 2020). been studied in connection to predation-risk Besides natural and anthropogenic evaluation in caterpillars Semiothisa aemu- abiotic noise sources, biotic noise can also

12 chapter 1 interfere with the production and perception performance of the European garden spider of signals and cues, such as in the south- Araneus diadematus, is negatively affected by ern green stinkbug Nezara viridula, where anthropogenic substrates. Noise amplitude N. viridula males exposed to heterospecific on artificial substrates was found to be lower signals showed a decrease in their response and spiders experiencing lower noise levels to the female signals (de Groot et al. 2010). were less sensitive to prey cues. Regardless of whether noise is of natu- ral, anthropogenic, abiotic or biotic origin, The knowledge gaps it has the potential to disrupt the informa- tion transfer chain (Box 1.1), which can have In general, little is known about the effect consequences for the reproductive success of noise and environmental variation and survival of animals, and therefore, ulti- on animals relying on vibrations. This is mately for their fitness (e.g. Halfwerk et al. particularly true for anthropogenic noise, 2011a). for which there are only a couple of studies in the field of biotremology. With the rapid Substrate variation changes in the sensory environment induced by humans, this is an extremely relevant Plants are the most widespread substrates topic to study. Moreover, we have a poor used in the transmission and perception of understanding of the effects of vibrational vibrational signals and cues (Cocroft and noise on multiple trophic levels. Most stud- Rodriguez 2005). However, plants can vary ies so far have focused on a one or two-spe- immensely in their traits and can therefore cies interaction effect. It is therefore of value affect the way that signals and cues are trans- to investigate how one vibratory noise source mitted and consequently also how they are can affect multiple trophic levels. perceived (Cocroft et al. 2006). Besides the While studies on the effect of substrate differences between plant species, the differ- variation have addressed the effect of plant ent parts within a plant (stem vs. leaves) can species or plant part on the transmission of also affect the transmission of vibrational vibrational signals, there are no systematic information. Plant stems, for example, can studies exploring the role of particular plant severely filter the frequency of signals (Bell traits. Investigating the role of plant traits 1980; Michelsen et al. 1982; Magal et al. on the transmission of vibrational informa- 2000), affect the frequency-dependence of tion could help us predict signal transmis- propagation velocity, and can sometimes sion within and between plants, and could contribute to temporal distortions from also help to understand host plant choice. reflections (Michelsen et al. 1982). Moreover, the effects of substrate variation Additionally, human-introduced have been mostly studied in the context of substrates (e.g. buildings) have also been mating signals, which are subject to contin- shown to affect the transmission of vibra- uous sexual selection, and can therefore tional information. For example, Wu and benefit from remaining repeatable (recog- Elias (2014) showed that the predatory nizable) across substrates. Exploring the

13 chapter 1 effects of substrate variation in the context (hours) and in the short-term (minutes of predator-prey dynamics or parasitoid-host and seconds). Both scales of variation can relationships could therefore shed new light be important for communication (Brown into the role of plant trait variation. and Handford 2003; McNett et al. 2010). Therefore, we predicted that katydids would This thesis time their signals to both the long-term and the short-term variation in wind patterns to In this thesis I address how both natural and cope with wind-induced vibrational noise. anthropogenic vibrational noise affect the In order to investigate this, we observed production and the perception of vibrational and recorded wind patterns throughout signals and cues. Furthermore, I address the night in the forests of Barro Colorado the effect of environmental variation on the Island (BCI), Panama. Then, we related transmission of vibrational cues. Finally, I katydid tremulatory activity to the wind explore the consequences of the effects of patterns by monitoring katydid signaling vibrational noise across different trophic activity throughout the night. Additionally, levels, from plants to meso- to macrofauna. we tested signal-timing to short-term fluc- To highlight the prevalence and the diversity tuations in wind levels by experimentally in the use of vibrational information, each exposing katydids to artificial wind gusts of chapter of this thesis focuses on a different different levels. By carrying out these obser- study system, and throughout this thesis I vations and experiments we could check cover a range of interactions (mating, para- whether katydids timed their vibrational sitoid-host and plant-insect interactions). signals to the periods at night when wind This thesis consists of seven chapters. levels we lowest. Furthermore, by exposing Following this general introductory chapter, katydids to short-term fluctuations in wind there are five original research chapters, and levels, we could observe whether katydids the thesis concludes with a summary and were signaling more during the wind-free general discussion chapter. periods. In chapter 3 we studied the role of Summary leaf-trait variation on the production and transmission of the vibrational chewing Chapter 2 explores the effect of wind on cues of a generalist caterpillar, Spodoptera the production of male tremulatory signals exigua. Different plant species and plant in the Neotropical katydid Copiphora brevi- parts are known to affect the transmission rostris. C. brevirostris males rely heavily of vibrational signals by introducing varia- on tremulatory signals to attract females. tion in amplitude attenuation and acting as Communication in C. brevirostris can a frequency filter. We had, however, little potentially be disturbed by wind because knowledge on the physical characteristics wind induces vibrational noise that over- of the plants (e.g. leaf traits) that played a laps in frequency range with their own role in amplitude attenuation and frequency signals. Wind can vary in the long-term filtering. Therefore, we systematically

14 chapter 1 measured plant leaf traits and related them which uses vibrations to detect and local- to amplitude and frequency characteristics ize its Drosophila host. Using shakers, we of a vibratory cue, at the production and exposed parasitoids and their host to noise transmission level. First, we measured leaf- of different levels. We then observed the traits (leaf thickness, punch force, specific amount of time female parasitoids spent in a leaf area and water content) of four species of foraging patch and the amount of time spent plants (Brassica oleracea var. capitata; Helian- searching for a host. To estimate any effects thus annuus; Beta vulgaris; Zea mays), which on the reproductive success of parasitoids, differ from each other in their leaf traits, we counted the number of successfully para- but which are all part of the natural diet of sitized hosts. S. exigua. Then, we recorded the chewing The effects of vibrational noise on vibrations of S. exigua on the four differ- different trophic levels are discussed in ent plant species and related the amplitude chapter 5 and chapter 6. In chapter and frequency characteristics of the chew- 5 we investigated the effect of vibrational ing vibrations to the variation in leaf traits. noise induced by wind-energy turbines Finally, based on the spectral characteristics on the abundance of earthworms and soil of the chewing recordings, we designed a mesofauna. Wind energy is the second synthetic playback stimulus to test the effect largest form of power generation capacity, of leaf-trait variation on the transmission of with most inland wind turbines placed on vibrational chewing cues. Using a calibrated agricultural fields. Wind energy turbines shaker, we reproduced the synthetic stimu- produce low frequency vibrations which can lus on the leaf and we recorded it with two potentially affect soil organisms. Among laser Doppler vibrometers simultaneously soil organisms, earthworms are particularly on different points of the leaf to characterize sensitive to vibrations as they use the vibra- transmission. tions of approaching moles to escape preda- Perception, at the end of the informa- tion (Catania 2008). The frequency range tion transfer chain is discussed in chapter of the vibrations induced by wind turbines 4. Here we explore the effect of vibrational overlap with the spectral characteristics of noise on the perception of a host vibra- foraging moles, suggesting the potential tional cue. Noise can affect the percep- of wind turbine-induced noise to mask the tion of signals and cues in different ways, cues of approaching moles. We therefore, including masking the signal or cue, and/or predicted that earthworm abundance would distracting the receiver (Brumm and Slab- be negatively related to wind turbine-in- bekoorn 2005; Naguib 2013). Regardless of duced vibrational noise. We expected meso- the mechanism, failure to extract relevant fauna (< 3mm), however, to not be able to signals or cues could lead to fitness effects perceive the low frequency vibrations due (e.g. Halfwerk et al. 2011a). In this chapter to their small size, and therefore to not be we test the effect of vibrational white noise affected by it. To investigate this, we used on the host-searching behavior and parasit- geophones to measure vibrational noise in ism success of the parasitoid Asobara tabida, the soil induced by wind turbines on seven

15 chapter 1 different agricultural fields where wind turbines were located. We measured vibra- tions at eight points along a transect start- ing at 2 m from the turbine and increasing exponentially up to 256 m. We then sampled earthworm and soil mesofauna abundance on four different points on the transect (8 m, 32 m, 64 m and 128 m). In chapter 6 we tested the effect of wind turbine-like vibrational noise on plant development and herbivory. Plants are known to react to either sounds or soil vibrations in different ways (e.g. germinating quicker or growing taller) (Takahashi and Jaffe 1984; Takahashi et al. 1991; Teixeira da Silva and Dobránszki 2014). Most studies, however, had tested plants with single tones. In this chapter we were interested in test- ing how plants reacted to wind turbine-like vibrational noise (pink noise). To do this, we exposed Pisum sativum seeds to high or low vibrational noise and we recorded the germi- nation time. We then allowed the plants to grow under high or low noise conditions and we recorded daily growth, time of flowering and time of fruiting. Finally, we tested whether plant expose to noise affected herbivory by a generalist caterpillar.

16 Copiphora brevirostris

17 chapter 2

Gone with the wind: is signal timing in a Neotropical katydid an adaptive response to variation in wind-induced vibratory noise? Estefania Velilla • Matías Muñoz • Nicol Quiroga • Laurel Symes • Hannah M. ter Hofstede Rachel A. Page • Ralph Simon • Jacintha Ellers • Wouter Halfwerk

Abstract

Wind, a major source of environmental 5:00 in the morning, which coincides with noise, forces invertebrates that communicate peak signaling period for male katydids. with plant-borne vibrations to adjust their Furthermore, we found that males produce signaling when communicating in windy significantly fewer tremulations when conditions. However, the strategies that exposed to wind rather than acoustic noise or animals use to reduce the impact of wind silence. Wind velocity significantly affected noise on communication are not well stud- the number of tremulations produced during ied. We investigated the effects of wind on the wind treatment, with fewer tremulations the production of tremulatory signals in the produced with higher wind velocities. Our Neotropical katydid Copiphora brevirostris. results show that katydids can time their First, we recorded katydid signaling activ- vibratory signaling both in the short-and ity and natural wind variation in the field. long-term to favorable sensory conditions, Additionally, we exposed katydid couples either through behavioral flexibility in during their most active signaling time response to short-term fluctuations in wind, period to artificial wind of different levels or as a result of an evolutionary process in and we recorded the number of tremulations response to predictable periods of low wind produced by the males. We found that wind conditions. levels are at their lowest between 2:00 and

Behav. Ecol. Sociobiol. (2020) 74:59 18 chapter 2 Introduction

Animals make decisions about when and recognition and shoaling in fish (Ward et where to search for mates, to look for food, al. 2008). The effect of noise on vibratory or to hide from predators based on signals communication is, however, greatly under- and cues available in their environment studied. (Bradbury and Vehrencamp 1998). The deci- Vibrational signals and cues are used sions made based on these signals and cues by hundreds of thousands of invertebrates, have crucial consequences for an animal’s and many vertebrates as well, and vibra- survival and reproductive success (Bradbury tion is the second-most widely documented and Vehrencamp 1998), emphasizing the communication modality after the chemi- importance of reliability of environmen- cal modality (Cocroft et al. 2014). Animals tal information (Dall et al. 2005). Noise communicating with plant-borne vibra- in the environment, however, can affect tions are particularly likely to be affected by the reliability with which information in noise caused by wind (reviewed in Cocroft the environment travels from sender to et al. 2014). As wind shakes the leaves and receiver, altering the behavior of senders and branches of plants, it induces vibrational hampering detection by receivers (Brumm noise, mostly at low frequencies (Barth et al. and Slabbekoorn 2005; Naguib 2013; Velilla 1988; Casas et al. 1998a; Cocroft and Rodri- and Halfwerk 2019). Consequently, animals guez 2005). Wind velocity can be predict- have evolved to cope with noise by adapting able for a given location in the course of a their behavior and/or their signal produc- day and night, whereas short-term fluctua- tion mechanisms (Brumm and Slabbekoorn tions in wind velocity can be highly unpre- 2005; Brumm 2006). dictable (Tishechkin 2007). Both scales of Research on the effects of noise on environmental variation can be important signaling has mostly focused on animals for communication, as has already been that communicate with airborne sound. shown in the airborne acoustic domain for However, noise can be referred to as any the swamp sparrow and the white-throated stimulus that reduces signal perception in sparrow (Brown and Handford 2003), a any sensory modality (Brumm and Slabbe- Neotropical treefrog (Schwartz and Wells koorn 2005; Brumm 2013; Velilla and Half- 1983), and the Eastern willet (Douglas werk 2019). For example, electric noise from and Conner 1999). Animals could adapt lighting storms affect predator-prey dynam- to communicating in windy conditions by ics among electric fish (Hopkins 1973), and increasing the amplitude of their signals or light noise from artificial lighting reduces by altering their signals to avoid spectral flashing activity in fireflies (Firebaugh and overlap. A different and less energy-costly Haynes 2016). Moreover, chemical noise strategy, however, would be to time signals from anthropogenic pollutants affects social to low-wind time periods and to wind-free 19 chapter 2 gaps, as has been shown for treehoppers and hours; Symes et al. 2020) and alternate with small homopterans (McNett et al. 2010; intervals of tremulations (Morris et al. 1994; Tishechkin 2013; Halfwerk et al. 2016b). Hamel et al. in preparation). Many Neotropical katydid species In this study, we test the hypothesis communicate with both acoustic and vibra- that C. brevirostris adaptively adjusts its tional signals. Most research on katydid signaling behavior to reduce the impact communication has focused on their acoustic of wind noise on signal transmission. We signals, although the vibrational signals and measured natural wind velocity variation signaling behavior of some katydid species in the field, observed signaling activity and have been recorded and described (Morris conducted a wind exposure experiment to et al. 1994; Romer et al. 2010; De Souza test the effect of wind over long- and short- et al. 2011; Sarria-S et al. 2016). Katydid term-time scales. In our signaling activity vibrational signals, also called tremulations, observations, we paired male and female are mate advertisement signals and are katydids to induce tremulatory behavior generated by rhythmic shaking of the abdo- and we studied the relationship between men, resulting in vibrations that propagate peak time in signaling activity and varia- through the plant substrate. These signals tion in wind levels. In our wind exposure contain most of the energy at low frequen- experiment, we exposed katydid couples cies < 100 Hz (Morris et al. 1994), and could to 3-minute wind gusts of different veloc- therefore be masked by wind noise. Copiph- ities, as well as two control conditions and ora brevirostris, a common species found counted the number of male tremulations mostly in the rainforest understory, produces during each condition. One control condi- both acoustic and vibrational signals and tion had no wind (here forth called “silent”) relies heavily on vibrational signaling and one control condition was exposure to (Morris et al. 1994). Copiphora brevirostris the sounds generated by the experimen- tremulates in the context of courtship and tal wind setup (referred to as the “sound” male-male interactions and does not need a treatment). We predicted that C. brevirostris female to be on the same substrate or even would: 1) signal at times of day when wind in the vicinity in order to signal (pers. obs). is typically low and 2) adjust their signaling A similar behavior has been recorded for activity in response to short-term fluctua- the congenic species Copiphora rhinoceros by tions in wind. Morris (1980), where males tremulate both reciprocally and in the absence of replies, and also do so in the presence and in the absence of females and/or conspecific males. The acoustic signals are short and mainly ultrasonic (30 ms duration and 32 kHz peak frequency; ter Hofstede et al., in press). The acoustic signals are only produced sporadi- cally throughout the night (44.4 calls per 24

20 chapter 2 Materials and Methods

Study area and animals Measuring natural wind variation

We conducted experiments at the Smith- We recorded wind data for five consecu- sonian Tropical Research Institute’s field tive nights from 22:00 at night until 08:00 stations in Gamboa, Panama from April in the morning, for three different trails to July 2018 and on Barro Colorado Island on BCI (Donato, Geostation, and Snyder (BCI) March to May 2019. Copiphora brevi- Molino) between March and May 2019 rostris were collected from the vegetation on the forest trails of BCI. These were along pipeline road in Gamboa and on the the same forest trails where katydids were forest trails of BCI. Animals were separated collected in the same year. A wind meter by sex, housed in net cages, and fed with (Kestrel 5500) was attached to a small tree at apple, dry cat food and water. Collection of approximately 1.5 meters height. Peak wind animals and experiments were approved by velocity was logged every minute, with a 2 the Ministry of Environment of Panama, second integration time. To explore general scientific permit No. SE/A-22-18. wind patterns throughout the night, we plotted the mean peak wind velocity per Characterizing C. brevirostris minute across all nights and trails (n = 15). tremulatory signal Additionally, we used the max peak wind velocity (highest wind velocity recorded per We recorded the tremulation of a single minute across all nights and trails) to guide male C. brevirostris using a Laser-Doppler our selection of wind gust levels. Using a Vibrometer (LDV) (Polytec PDV-100, LDV, we measured wind-induced vibrations sampling rate 44.1 kHz). The output of the from natural wind on an Oenocarpus sp. palm laser was recorded as a .wav file using an and wind-induced vibrations from artificial audio recorder (Tascam 60D MKII, 44.1 wind on the cage on which the animals were kHz, 16-bit resolution). We placed a male tested. In both cases we also recorded wind katydid together with a female in a custom- velocity with a wind meter. made nylon mesh cage (20 cm3) and we put a 1cm2 reflective tape on one side of the Measuring signaling activity cage. We recorded male tremulations when the male’s legs were positioned on the mesh Signaling activity was measured during containing the reflective tape and the female July 2018 in Gamboa, Panama. Using was not on the same panel of the cage. a camcorder with night vision (Sony DCR-SR45) we recorded the behavior of five katydid couples from 22:00 at night until 06:00 in the morning. We decided

21 chapter 2 to start at 22:00 based on pilot recordings mechanical engine of the fan. We directed where we found no activity in the earlier the wind toward (and as a control also away) hours of the evening. Katydids were placed from the cage using a PVC ventilation tube in a custom-made nylon mesh cage (20 cm3) with a diameter of 203mm. This was done with food and water. There were no plants manually by the experimenter who was pres- inside the cage. The animals were placed in ent in the room at the time of the experi- the cage 2-3 hours before starting obser- ments. Treatment levels were also adjusted vations to let them acclimatize. Obser- by hand by the experimenter. vations took place in a closed lab with an Katydid couples were placed in the ambient temperature of 29°C (SD ± 3°C), same experimental cage as in the signaling where they were protected from wind and activity experiment, provided with food and rain. There were no observers present in water. The pairs were chosen randomly from the room during the recordings. Katydids the communal cages. All individuals were were taken out of the experimental cage the used only once. Behavior was recorded with following morning and placed in a sepa- a camcorder (Sony FDR-AX33) coupled rate cage, separate from the animals that with a Led and IR light (Sony HVL-LEIR1, had not been tested yet. Individual animals 1500 lux). There were no plants inside the were used only once per experiment. We cage during the wind exposure experiment. released the katydids after all experiments Therefore, katydids signaled from the sides were finished to avoid re-catching the same of the cage. We chose to make our behav- animals. The videos were analyzed in VLC ioral recordings between 02:00 and 04:00 media player version 3.0.7.1. Male tremu- hours, because based on our signaling activ- lations were quantified from the videos by ity data, this was the peak period of signal- counting the total number of tremulations ing activity. produced every hour. In addition to different wind velocities (here after referred to as ‘wind’ treatment) we Wind exposure experiment also exposed katydid couples to two different control treatments. To control for any effect The wind exposure experiment was carried of the acoustic noise of the fan on katydid out between March and May 2019 in a signaling we exposed the katydid couples to closed lab on BCI with an ambient tempera- the sound of the fan only (here after referred ture of 29°C (SD ± 3°C), protected from to as ‘sound’ treatment). We did this by natural wind and rain. We exposed 16 katy- moving the ventilation tube to the side, so did couples to artificially created wind using that katydids would be exposed to the sound a computer fan (abm-papst S-Force Series of the fan, but not the artificial wind. Wind Axial, 200 x 50.88mm, 1220m3/h, 103W, velocity levels at the cage during the sound 48 V dc) mounted on a metal base. The fan treatment and the silent control were below was positioned in front of the cage (~1 meter 0.1 m/s, which was the detection thresh- away), but on a separate table to reduce the old of the wind meter. Acoustic noise levels transmission of vibrational noise from the at the cage during the wind and the sound

22 chapter 2 treatment varied from 60-70 dB SPL (A) silent levels, while acoustic noise remained measured with a Volcraft SPL meter (set to similar to the silent treatment (Fig. 2.1, fast and max) (for individual measurements Table 2.1). We also included a treatment see Table 2.1). Furthermore, using a LDV with no wind or sound (here after referred we measured the vibrations induced by wind to as ‘Silent’), which consisted of turning off on the cage. The sound treatment added very the fan. Each couple was exposed to Wind little, if any vibrational noise to the cage in and Sound treatments for four different comparison to measurements done when the wind velocities, plus the Silent control. Each fan was turned off. At the treatment level katydid couple was exposed to four 9-minute of 0.3 m/s, vibrational noise for the wind blocks made up of 3 minutes of wind expo- and the sound treatments was low, differing sure (of a certain velocity), 3 minutes of only by ± 1 dB from the silent treatment. sound treatment exposure, and 3 minutes of At 0.6 m/s vibrational noise remained rela- silent treatment for a total experimental time tively low, with wind vibrational noise levels of 36 minutes. The treatment and the treat- increased by 4 dB, and acoustic noise show- ment level (wind velocity) were balanced and ing little increase. The wind treatment at 0.9 randomized per trial. The wind velocities m/s had vibrational noise 15 dB higher than that katydids were exposed to were 0.3, the silent treatment, while acoustic noise 0.6, 0.9 and 1.5 m/s and were based on the remained similar to the silent treatment. natural wind variation we found in the field. 1.5 m/s wind produced vibrations that were Only 1.5 m/s was slightly higher than our almost 30 dB higher in amplitude than the maximum recorded wind velocity. table 2.1 Acoustic and vibrational noise levels produced by the fan and the wind from the fan during the different treatments and treatment levels. These measurements were intended to calculate the contribution of each treatment and treatment level to acoustic and vibrational noise levels. Vibrational noise levels for the sound treatment are similar for all treatment levels, indicating that the vibrations from the wind treatment are induced by wind and not by the vibrations from the mechanical motor of the fan or by sound airborne waves that come into contact with the experimental cage.

ACOUSTIC NOISE SPL(A) (dB) VIBRATIONAL NOISE RMS AMPLITUDE (dB)

Velocity (m/s) Silent Wind Sound Silent Wind Sound

0.3 60 6 .7 60.9 24 25 23

0.6 –– 61.8 61.2 –– 28 21

0.9 –– 65.1 62.8 –– 39 22

1.5 –– 70 66.2 –– 52 20

23 chapter 2

0 (a) 0 (b) Silent Sound -30 -30 Wind

-60 -60 -90 -90 0.3 m/s 0.6 m/s -120 0 (c) 0 (d)

-50 -50

Relative amplitudeRelative (dB) -100 -100 0.9 m/s -150 1.5 m/s -150 0 1 2 3 4 5 0 1 2 3 4 5 Frequency (kHz) fig. 2.1 Spectra of vibrational noise of the artificial wind treatment (fan on and directed towards the cage), and of the sound treatment (ventilation tube set aside) for all the wind velocity treatment levels. Wind velocity levels (m/s) are given in bottom right of each graph. Vibrational noise of the silent treatment is included in all the panels for comparison.

Signal and Statistical Analyses as a categorical variable making one-hour bins starting at 22:00 at night until 6:00 Signal analyses and statistical analyses were in the morning. We included male as our done with R version 3.3.1 (R Core Team, block variable. To test the effect of treat- 2016), run in the RStudio interface (RStu- ment and treatment level on the number of dio, Team 2015). male tremulations we fitted a generalized We calculated the power spectral linear mixed effect model from the R pack- density using a Hanning window of 2048 age “Lme4” (Bates et al. 2015) with Poisson samples to measure the spectral character- distribution, including male as a random istics the tremulatory signal of C. brevirostris effect. To test whether the fit of the statisti- and compared its distribution to the power cal model was improved by the inclusion of spectra for natural wind-induced vibrations treatment and treatment level we obtained and our experimental stimuli (artificial Wald Chi-square statistics from the “Anova” wind, the sound of the fan and silence). function in the statistical package “Car” We used a Friedman test to determine (Fox et al., 2011). We ran pairwise compar- the effect of time at night on the production isons with Bonferroni correction between of male tremulations. Our response variable the treatments at different treatment levels was the number of tremulations produced by with the R package “Emmeans” (Lenth & males per hour. Our grouping (predictor) Lenth, 2018). variable was time at night, which we treated 24 chapter 2 Results

Correspondence between signaling our recordings. These minor differences are and wind velocity probably due to differences in the substrate used during the recordings. While Morris We assessed spectral and temporal over- et al. (1994) recorded with accelerometers on lap in signaling and noise. C. brevirostris Heliconia plants, we recorded with a LDV on produced an average of 457 (SD ± 206) the mesh side of the experimental cage. It is bouts of tremulations between 22:00 at likely that the two substrates have different night and 6:00 in the morning. The trem- resonance properties. The majority of the ulation bouts lasted approximately 4-5 spectral energy of wind-induced vibrations seconds and consisted of two tremulations was also concentrated at low frequencies < per second. Most of the energy in tremu- 100 Hz (Fig. 2.3), indicating that wind has lations was concentrated at low frequen- the potential to mask the tremulatory signal cies between 80-100 Hz (Fig. 2.2, but see of C. brevirostris. Morris et al. 1994). Copiphora brevirostris tremulations recorded by Morris et al. (1994) show a slightly lower peak frequency than

(a)

1 sec 0 (b)

-20

-40 fig. 2.2 Waveform (a) and power spectrum (b) of the tremulatory -60 signal of a C. brevirostris male.

Relative amplitude (dB) Recording was made with a Laser-Doppler Vibrometer from the side of a nylon cage. 0 0.5 1.0 1.5 2.0 Frequency (kHz) 25 chapter 2

0 fig. 2.3 Amplitude spectra of (a) Male tremulation Artificial wind vibrations a tremulatory signal of male C. Natural wind vibrations brevirostris and (b) wind-induced –20 vibrational noise (0.9 m/s for both) recorded from an Oeno- carpus sp. palm and from the side –40 of the cage in which the animals were tested.

–60 Relative amplitude (dB) amplitude Relative

(b) –80 (a) 0 0.5 1 1.5 2 0 0.5 1 1.5 2 Frequency (kHz)

The average peak wind velocity across all Copiphora brevirostris tremulates trail sites was 0.2 m/s (SD ± 0.3 m/s), the more during wind-free gaps maximum was 0.9 m/s, and the minimum was 0 m/s, or below the detection level Males produced significantly fewer tremu- of the wind meter. Wind gusts (velocity lations when exposed to wind gusts of 1.5 above 0 m/s), lasted from a few seconds to m/s compared to the silent and the sound 10 minutes. The windiest part of the night treatment (Fig. 2.5, Table 2.2). Additionally, occurred from 22:00 at night to 2:00 in the males also produced significantly less trem- morning, with an average wind velocity ulations during the lower intensity wind of 0.4 m/s (SD ± 0.3 m/s), peaking at 0.9 treatment of 0.9 m/s in comparison to the m/s. The most wind-free gaps were found silent treatment, but not to the sound treat- from 2:00 to 6:00 in the morning, where ment (Fig. 2.5, Table 2.2). The inclusion of the average wind velocity was 0.05 m/s (SD treatment and treatment level significantly χ2 ± 0.1 m/s) and the peak was 0.7 m/s (Fig. improved the fit of the model ( (1) = 40.582, 2.4). Tremulatory activity was significantly P< 0.001). Males produced an average of 3.1 affected by time at night (Fig. 2.4, Fried- (SD ± 2.9) tremulations every three minutes man test, df=8, χ2 =34.725, P< 0.001), with during the lowest wind level, 2.1 (SD ± 0.9) a higher number of tremulations produced during 0.6 m/s, and 0.5 (SD ± 1.1) tremu- between 2:00 and 5:00 in the morning. Male lations when exposed to the highest wind katydids produced an average of 96 (SD ± level of 1.5 m/s. 48) tremulations per hour between 2:00 and 5:00 in the morning, whereas they produced an average of 18 (SD ± 18) tremulations per hour between 22:00 at night and 1:00 in the morning.

26 chapter 2

(a)

0.10

Mean peak 0.05 wind velocity (m/s) 0.00 22:00 00:00 02:00 04:00 06:00

(b) 0.75

0.50

Max peak 0.25 wind velocity (m/s) 0.00 22:00 00:00 02:00 04:00 06:00

200 (c)

150 Male M1 M2 100 M3 M4 M5 50 Number of male tremulations 0

22:00 00:00 02:00 04:00 06:00

Time fig. 2.4 (a) Mean peak wind velocity of all trails for the five nights of wind measurements. Peak wind velocity was recorded with a Kestrel 5500 wind meter, for every minute throughout the night, starting at 22:00 at night and ending at 07:00 in the morning. Blank spaces in the graph represent the times at night where wind velocity remained at 0 m/s for all nights, for all trails. (b) Maximum peak wind of all trails for the five nights of wind measurements. Most wind-free gaps occur between 2:00 and 6:00 in the morning. (c) Male katydid signaling activity. Colored lines represent the signaling activity of individual male C. brevirostris (N=5). Male katydids, coupled with females were video recorded overnight in a lab protected from wind and rain to quantify tremulations. Most tremulations are produced between 02:00 and 06:00 in the morning.

27 chapter 2

0.3 m/s 0.6 m/s a 10 a a 7.5 a a

a 5

2.5

0

a 0.9 m/s 1.5 m/s 10 ab b a 7.5 a Number of male tremulations

5

b 2.5

0 Silent Sound Wind Silent Sound Wind

Treatment fig. 2.5 Number of male tremulations produced by C. brevirostris during the different treatments per wind velocity block. The letters indicate significant pairwise differences (P< 0.05) calculated by means of a least square means test for multiple comparisons, with Bonferroni adjustment. The interquartile range was taken as the range from 0-25th percentile. From the mean, the whiskers show the highest and lowest value within 1.5 times the interquartile range.

28 chapter 2 table 2.2 Estimates, standard error, z ratio and P value from pairwise comparisons with P value Bonfer- roni adjustment from a linear mixed effects model testing the effect of treatment and treatment level on the number of tremulations produced by males. Per experimental trial, males were exposed to nine-min- ute blocks of three minutes silence, three minutes wind and three minutes fan sound per treatment level, randomizing the order of treatment and treatment level. The stars in the P value column indicate statistical significance. Results are given on the log (not the response) scale.

TREATMENT LEVEL CONTRAST ESTIMATE STD. ERROR Z RATIO P VALUE

0.3 m/s Control - sound 0.0307 0.175 0.176 0.176

Control - wind 0.1467 0.180 0.815 1.0000

Sound – wind 0.1160 0.181 0.640 1.0000

0.6 m/s Control - sound 0.1054 0.173 0.610 1.0000

Control - wind 0.3983 0.188 2.122 0.1015

Sound – wind 0.2929 0.192 1.527 0.3804

0.9 m/s Control - sound 0.1431 0.169 0.848 1.0000

Control - wind 0.4674 0.185 2.524 0.0348*

Sound – wind 0.3243 0.191 1.701 0.2666

1.5 m/s Control - sound -0.0481 0.178 -0.270 1.0000

Control - wind 0.9329 0.240 3.889 0.0003**

Sound – wind 0.9809 0.238 4.117 0.0001**

29 chapter 2 Discussion

In this study we investigated whether wind We did not find an effect of the sound affected the production of tremulatory only treatment in which we controlled for signals in C. brevirostris and whether males the sounds produced by our fan. These of this species 1) signal at times at night results suggest that C. brevirostris would also when wind is typically low and 2) adjust not adjust its vibrational signaling behav- their signaling activity in response to short- ior to the acoustic noise associated with term fluctuations in wind. We found that natural wind, such as the sound of moving wind levels were highest in the earlier part of branches and leaves. However, wind not the night between 22:00 and 2:00, whereas only induces sounds and vibrations, but the most wind-free gaps occurred between also fluctuations in airflow. Katydids, like 2:00 and 6:00 in the morning. In the lab, in other insects, have sensitive hairs that can the absence of wind, katydid signaling activ- detect airflow (Dupuy et al. 2011; Tuthill ity was highest between 2:00 and 5:00 in the and Wilson 2016), which can be important morning. Our results thus suggest that male for detecting attacking predators (Camhi et katydids timed their signaling to the periods al. 1978; Shimozawa et al. 2003). It is there- at night containing the most wind-free gaps fore, possible that the airflow associated under normal field conditions. Furthermore, with wind hinders the ability of katydids when male katydids were exposed to wind to detect approaching predators, increasing gusts, even during their peak time of signal- their perception of predation risk and lead- ing activity, they tremulated significantly ing to signaling inhibition. Based on our less than during the sound or silent treat- data we cannot conclude whether the effect ments. This effect was mainly driven by the on signaling is due to katydids detecting the highest wind treatment level 1.5 m/s and to vibrations induced by wind or detecting the a lesser extent by the 0.9 m/s treatment level. airflow itself. Future studies using shakers Although we never recorded wind veloc- to reproduce the vibrational noise induced by ity levels of 1.5 m/s in the field, C. brevi- wind are needed to further explore the effect rostris, as well as other katydid species can of vibrational noise on signaling. be found higher in the vegetation and even Animals using the acoustic or vibra- in the canopy where wind levels are known tional modality can adapt to communicating to be higher than lower in the vegetation in noisy environments in different ways. For (Paton 2017, pers. obs.). For example, the example, one strategy for senders to get their average BCI wind velocity level measured message across is to increase the amplitude in the canopy during the months of March of their signal, as has been recorded in birds and April 2017 was 2.3 m/s (SD ± 0.9 m/s), (Slabbekoorn 2013), mammals (Sinnott et with maximum levels of 7.7 m/s (SD ± 1.5 al. 1975), lizards (Brumm and Zollinger m/s) (Paton 2017). 2011), and frogs (Halfwerk et al. 2016a);

30 chapter 2 or to avoid spectral and temporal overlap, as a way to deal with wind. It is possible, e.g. robins (Fuller et al. 2007), great tits however, that C. brevirostris has a prefer- (Halfwerk and Slabbekoorn 2009), and ence for locations highly shielded from the bush crickets (Greenfield 1988). Our results wind, as has been shown in small Homop- indicate that C. brevirostris avoids tempo- tera, with animals concentrating mainly in ral overlap, adjusting its signal timing by places protected from the wind (Tishechkin signaling more during less noisy times at 2013). It would be interesting to look into night, and by signaling more during the the relationship between wind intensity and wind-free gaps. Similar patterns of signal number of katydids found in a particular timing in response to wind have also been substrate or in a particular location in the shown for Homoptera (Tishechkin 2007, forest. 2013) and Membracidae insects (McNett et Signal timing, which can be import- al. 2010), pointing towards a general trend ant in behavioral interactions like mating or for insects communicating with vibrational male-male interactions (Shaw and Galliart signals. Furthermore, because we only quan- 1987; Greenfield and Roizen 1993; Dyson tified the signaling activity and we did not et al. 1994; Greenfield 1994; Grafe 1996; record the signal during the experiments, we Brumm 2006; Höbel and Gerhardt 2007; cannot tell from our data whether C. brevi- Siegert et al. 2011; Symes et al. 2016), can rostris males are also adapting their signal be shaped by different selective pressures to avoid spectral overlap, or to increase the like characteristics of the transmission amplitude of their signals. medium, noise, sexual selection, predation Finally, senders can change the posi- and competition (Grafe 1996; Brumm 2006; tion from which they produce their signals Höbel 2010; McNett et al. 2010; Siegert to favor signal transmission (Nemeth et al. et al. 2011). Availability of females, preda- 2001), as shown in great tits (Halfwerk et tion pressure and weather conditions (wind al. 2012). We did not test plant-substrate and rain) have been suggested as factors preference for signaling. Therefore, we do shaping acoustic signal timing in nocturnal not know whether C. brevirostris chooses orthoptera, where wind and rain increase plants that favor signal transmission, for predation risk and decrease signal transmis- instance choosing plants that are stiffer sion (Walker 1983). Copiphora brevirostris and less responsive to wind. However, we is a common prey of foliage-gleaning bats collected katydids at night from different found in Gamboa and BCI (ter Hofstede et types of plants with different traits (e.g. large al. 2017), which are generally more active palm trees, heliconias, and small plants close before midnight (Belwood 1990). The pref- to the ground). Assuming these katydids erence of C. brevirostris for signaling later at would be signaling from the plants on which night, therefore, could also be explained by we found them, and in light of the large trait predator avoidance (Belwood and Morris variation in these plants, it is not likely that 1987). However, Lophostoma silvicolum and they choose a particular plant type that Micronycteris microtis, some of the main bat enhances signal transmission, especially predators of C. brevirostris, remain active

31 chapter 2 throughout the entire night (Lang et al. 2005; Kalka and Kalko 2006), suggesting that predator avoidance is not the only factor affecting signal timing. Bat activity may not lead to a reduction or complete cessation of all signaling activity, but perhaps to cessa- tion of acoustic calling activity only, at least in the forest understory. One explanation could be that C. brevirostris calls acoustically from higher in the vegetation, or even in the canopy before midnight to attract a female to a general focal area, and later descends into the understory to complete localiza- tion. Knowledge of the vertical distribu- tion of this species in the forest, and of its calling substrate preferences is needed to better understand the signaling strategies and mating interactions of C. brevirostris.

Acknowledgements

We are thankful to Eleanor Gourevitch, Ciara E. Kernan, Sharon Martinson, Amber S. Litterer and Luis Felipe Estrada for their help in the collection of C. brevirostris. Additionally, we’re thankful to Jen Hamel for her advice on the recording of tremulations. This research was made possible in part by the Dr. J.L. Dobberke Foundation. Estefania Velilla received additional funding in the form of a short- term fellowship from the Smithsonian Tropical Research Institute.

32 Spodoptera exigua

33 chapter 3

Variation in plant leaf traits affects transmission and possibly detectability of herbivore vibrational cues

Estefania Velilla • Jernej Polajnar • Meta Virant-Doberlet • Daniel Commandeur Ralph Simon • J. Hans C. Cornelissen • Jacintha Ellers • Wouter Halfwerk

Abstract

Many insects use plant-borne vibrations to that species-specific leaf traits can influence obtain important information about their transmission and potentially perception environment, such as where to find a mate of herbivore-induced chewing vibrations. or a prey, or when to avoid a predator. Plant Experimentally-induced vibrations attenu- species can differ in the way they vibrate, ated stronger on plants with thicker leaves. possibly affecting the reliability of infor- Amplitude and frequency characteris- mation, and ultimately the decisions that tics of chewing vibrations measured near are made by animals based on this informa- a chewing caterpillar were, however, not tion. We examined whether the produc- affected by leaf traits. Furthermore, we tion, transmission and possible perception found a significant effect of leaf area, water of plant-borne vibrational cues is affected by content and leaf thickness - important plant variation in leaf traits. We recorded vibra- traits against herbivory, on the vibrations tions of 69 Spodoptera exigua caterpillars induced by airborne noise. On larger leaves foraging on four plant species that differed higher amplitude vibrations were induced, widely in their leaf-traits (cabbage, beet- whereas on thicker leaves containing more root, sunflower and corn). We carried out a water airborne noise induced higher peak transmission and an airborne noise absorp- frequencies. Our findings indicate that tion experiment to assess whether leaf traits variation in leaf traits can be important for influence amplitude and frequency char- the transmission and possibly detection of acteristics, and background noise levels of vibrational cues. vibrational-chewing cues. Our results reveal

Ecology and Evolution (in review) 34 chapter 3 Introduction

Animals can extract and use a vast amount & Slabbekoorn, 2005;, Schaub et al., 2008; of information from their biotic and abiotic Wu & Elias, 2014;Ord et al., 2007). environment. This information can be A clear characterization of the sensory encoded by different sensory stimuli like environment is therefore crucial for under- sounds, odours or vibrations, and may guide standing its effects on the transmission and animals in many important life-history perception of environmental signals and decisions (Dall et al., 2005; Stevens, 2013). cues. However, characterizing the sensory Biotic environmental information such as environment can be challenging because it incidental cues (e.g. prey cues) or purpose- often entails a three-dimensional, complex ful signals (e.g. mating signals) are used to and large space through which information decide whom to mate with, where to search must travel. for food, or when to adopt predator avoid- Substrate-borne vibrations traveling ance behaviour (Dall et al., 2005; Stevens, through soil, rocks and plants are used as a 2013). Additionally, abiotic environmental sensory stimulus by hundreds of thousands information can warn animals of changes of invertebrates and many vertebrates as well in their habitat, such as the sound cues of (Cocroft et al., 2014; Cocroft & Rodriguez, upcoming rain or visual lightning cues of 2005). In particular on plants, vibrations an approaching storm (Geipel et al. 2019; travel relatively short distances in a one or O’Connell-Rodwell et al. 2001). However, two-dimensional plane and their active sensory conditions often vary across time range is determined primarily by the shape and space, which can have important conse- and size of particular plant species (Čokl et quences for the reliability and usefulness al., 2005; Mazzoni et al., 2014; Michelsen et of environmental information (Dall et al., al., 1982). Plants are highly complex struc- 2005; Stevens, 2013). tures and exhibit large variability in traits Reliable transmission and perception that can affect the transmission of vibra- of signals and cues can be affected by habi- tional signals and cues (Mankin et al., 2018). tat-dependent factors such as environmen- Within-species comparisons have shown tal complexity and noise levels (Brumm & that vibratory transmission properties vary Slabbekoorn, 2005; Morton, 1977; Rich- with plant age, turgidity and development ards & Wiley, 1980; Boncoraglio & Saino, and disappearance of organs and tissues 2007; Elias & Mason, 2014; Mortimer, (Bell, 1980). Also differences among plant 2017; Tobias et al., 2010). Environmental species may affect the production, transmis- or anthropogenic background noise has also sion and perception of vibratory signals and been documented to affect the perception of cues, leading to important consequences for signals and cues in a wide range of animals the animals that live on them (Cocroft et al. and for a wide range of contexts (Brumm 2006; Joyce et al. 2014). Signal variability

35 chapter 3 can affect the detection and proper assess- plant species would have an influence on cue ment of the signal or cue by the receiver, attenuation and absorption of environmen- possibly decreasing mate attraction or prey tal noise, and that the differences would be localization, as has been shown in spiders related to physical leaf-traits. (Gordon & Uetz, 2011; Rosenthal et al., 2019). For that reason, knowledge on the interspecific variation in transmission and sound-absorbing properties of plant tissues is important (Gagliano et al. 2012, 2017; Schöner et al. 2016). For example, leaf shape and leaf thickness may affect transmission of vibrations, or influence absorption of airborne sounds, and consequently interfere with detection of vibratory signals and cues. In this study we aimed to under- stand how leaf traits affect the production, transmission and detectability of chewing vibrations. We focused on the vibrations produced by the beet armyworm Spodoptera exigua caterpillar chewing on the leaves of different plant species. Chewing vibrations induced by foraging insects are known to travel throughout the herbaceous plant tissues and can be picked up by a wide range of organisms, including predators and para- sitoids (Meyhöfer and Casas 1999), and even the plant itself (Appel & Cocroft, 2014). We chose S. exigua, because of its diverse diet, which allowed us to compare vibrational chewing cues on four different host plant species with divergent leaf traits includ- ing eudicot species: sunflower Helianthus annuus, cabbage Brassica oleracea var. Capi- tata, beetroot Beta vulgaris, and the monocot corn Zea mays. We designed a set of experiments to quantify the extent of intra- and interspecies variability in the production and transmis- sion and detectability of chewing-induced vibrations along leaves. We predicted that

36 chapter 3 Methods

Animal rearing humidity. Quartz sand was used instead of potting soil to control for any variation in The beet armyworm S. exigua (Hübner; soil quality that could influence plant traits. Lepidoptera, Noctuidae) is a polyphagous A fixed amount of 50% Hoagland’s solu- insect pest with a worldwide distribution, tion was provided every second day. The and is considered a serious pest of vegetables, amount changed gradually with the growth field, and flower crops.S. exigua was reared of the plants (~10-100ml). Plants that were at 26°C ± 1°C and 80% relative humidity between six- and eight-weeks post germina- on a 12:12 h (L:D) photoperiod. Larvae tion were used in the experiments. were fed with a corn-based artificial diet, We measured four leaf traits in the lab: and adults were given 10% sucrose solution. Leaf area (cm2), leaf mass (g), leaf thickness Mating was facilitated by placing a male and (mm), and punch force (N), following the a female moth in a plastic round container handbook for standardized measurement with a mesh cloth sealing the top. The mesh of plant functional traits (Pérez-Harguin- cloth was used as a surface on which eggs deguy et al., 2013). Leaf area was measured were laid. Eggs were collected by remov- by scanning the leaves, and subsequently ing the cloth and cutting and placing the measuring the area with the program sections of the cloth containing eggs on diet- ImageJ. The dry weight was calculated filled petri dishes. As eggs hatched, larvae by first drying the harvested leaves for at developed on these diet-filled petri dishes. least 48 hours in a 70 °C oven, and then The life cycle of S. exigua under our rearing weighing them. Dry weight and leaf area conditions was completed between 25-30 were used to calculate the specific leaf area days and included 5 instars. Each instar SLA (cm 2/g) (one-sided area of a fresh leaf, transition took between three and five days. divided by its dry weight). Leaf thickness was measured with a manual calliper on nine Plant rearing and measuring of plant locations on the leaf, seven for corn (Fig. leaf traits 1). Punch force was measured as the maxi- mum (i.e. pulse) force needed for a 1 mm Four plant species were used for this exper- diameter blunt needle to puncture the leaf, iment: sunflower H. annuus, cabbage which was clamped tightly on either side B. oleracea var. Capitata, beetroot B. vulgaris, of the puncturing spot using a Mecmesin and corn Z. mays. Seeds were bought Ultratest Newton meter with Force Gauge from commercial companies: Seedo and AFG 1000-N (Mecmesin, Broadbridge 123Zaden. Plants were reared in growing UK). These measurements were done on the chambers at 20˚C - 25˚C with an 8:16 h same nine locations within eudicot leaves (L:D) photoperiod and with 70% relative and in, the same seven locations for corn.

37 chapter 3

The measuring points one, two, and three Recordings of caterpillar chewing covered the main vein, and the points four, vibrations five, six, seven, eight, nine the soft tissue (Fig. 1). The fresh weight and dry weight The purpose of these recordings was to were used to calculate the fresh weight to investigate the effect of variation in leaf dry weight ratio, which we interpreted as traits on the production of caterpillar the water content. chewing vibrational cues. We first made We measured leaf traits of two sets recordings of caterpillars chewing on differ- of plants. The first set (n= 101) consisted ent plant species and we then related the of plants paired up with the plants used in amplitude and frequency characteristics of the recordings of caterpillar chewing vibra- those recordings to variation in leaf traits. tions (chewing experiment). This paired A custom-built wooden box (90cm x 60 cm design allowed us to measure leaf traits in x 60 cm) with a Plexiglas top was used to plants undamaged by chewing while being reduce airborne noise during recording of representative of the leaf traits of chewed the chewing vibrations. The box was lined plants, but relied on the assumption that with noise absorbing foam and was placed paired plants used to measure traits repre- on a vibration reduction marble table with sent the leaf trait variation of the plants on passive suspenders to minimize substrate- which chewing measurements were done. borne noise of the building. The airborne Although this assumption may not have noise amplitude inside the box at the loca- been met for all pairs, it still seems reason- tion of the plant, was approximately 35 dB able compared to the alternative design (A) measured with an Extech SDL600 which would involve measuring leaf traits sound level meter set to (set to fast and max). and vibrations on the same leaf, given that Caterpillar chewing was recorded using chewing behaviour could be affected by a Laser-Doppler Vibrometer (LDV; Polytec prior measuring of the traits (e.g. damage PDV-100, set to 5 or 20 mm/s/V, sampling by punch force tests could have elicited rate 22 kHz). The output of the laser was secondary metabolites), and trait measure- acquired using a TASCAM DR-60D MKII ment could have been affected by foraging audio recorder (44,1 kHz, 16-bit resolution). (e.g. loss of mass or strength after herbiv- The recording level of the Tascam was set ory attack). The second set of plants (n=40) so that there would not be overload of the corresponds to the transmission experiment, signal. A reference signal using the same described in a section below. In this case, recording level was made against the vibra- there was no confounding effect of herbiv- tion-isolation table after each recording to ory, and we therefore used the same plants allow calculation of absolute amplitude. A to measure vibrations and leaf traits. reference signal can be made by generating a sinusoidal test signal of 2.80 V (RMS) with a frequency of approximately 1 kHz by setting the laser service mode to Output = Full (Polytec PDV-100 user manual, section

38 chapter 3

5). We recorded this test signal on the table plant species, we conducted a vibrational on which we placed our plants with caterpil- playback experiment. The purpose of this lars and we recorded it with the TASCAM experiment was to measure the transmis- with the exact same recording settings sion of a synthetic vibratory signal through- used per recording of caterpillar chewing out the leaves of the different plant species vibrations. A single plant was placed inside we used during the chewing vibrations the noise-isolation box, and a free-moving recordings. We then related these trans- caterpillar was placed on a leaf of the plant. mission measurements to the different leaf The caterpillar was always dropped on the traits. Using a Brüel & Kjær mini-shaker middle of the leaf, but it foraged freely and Type 4810, we played back a two-minute therefore the foraging position changed frequency sweep starting at 20 Hz and per trial. A piece of reflective tape placed ending at 2 kHz. The playback was corrected central on the measuring side of the leaf was for the frequency response of the shaker to used to enhance reflection of the laser beam. ensure constant velocity at all frequencies, Recordings started always around the same and adjusted to the RMS amplitude level time of day (~13:00). A recording started of chewing vibrations recorded in cater- when a caterpillar started eating, and the pillar trials. This adjustment was neces- recording lasted 30 seconds. The distance sary because the frequency response of the from feeding site to the measurement site mini shaker is highly non-linear in the low (reflective tape) was noted. The distances frequencies (< 100 Hz), biasing the playback ranged from 0.50 to 33.3 mm, with a mean to the frequencies > 100 Hz in a non-cor- ± sd distance of 12 ± 8.7 mm. Individual rected format. Therefore, by correcting our plants and caterpillars were used only once playback file to the frequency response of the for each recording. Recordings were done shaker, we made sure all frequencies were throughout three larval stages of the animals equally represented. We used two LDVs, (L2-L4/5). Before the recordings, caterpil- one to register vibrations next to the source lars were weighed and allowed to acclima- (Fig. 3.1) and the other on the nine different tize to the experimental chamber. We had points on the leaf, seven for corn (Fig. 3.1). 19 replicates for cabbage, 24 for beetroot, These points were the same points that were 18 for sunflower, and 16 for corn. However, used for measuring leaf thickness and punch eight recordings were too low in amplitude force (Fig. 3.1). Furthermore, we recorded and the chewing vibrations were not distin- the sweep on the adjacent leaf to test relative guishable from the background noise levels. energy loss during transmission. Therefore, our total sample size was reduced Vibrations were transferred via a rod to 69 caterpillar – plant combinations. mounted on the shaker and attached with Blue Tack adhesive to the underside of Transmission experiment the leaf on a point in the centre of the soft tissue, not touching the main vein, between To test which leaf-traits affected the trans- points five and eight (Fig. 3.1). The distance mission of caterpillar chewing cues among between each point and the stimulus was

39 chapter 3 noted. The distances ranged from 1 to 20 MPA40BT speaker positioned 60 cm from mm. We tested five plants per species. the plant, we played back 10 seconds of white noise at 70 dB (A) measured at the position Airborne-noise exposure experiment of the leaf with an Extech SDL600 sound level meter (set to fast and max) and recorded To test whether amplitude of vibrations with the LDV on three points (points 1:3, induced by airborne noise was affected by Fig. 3.1). Using an LDV we recorded the leaf traits, we did an acoustic noise playback vibrations induced by the airborne noise experiment with white noise (0.1 – 20kHz) playback. We tested five plants per species. (Rebar et al. 2012). Using a Behringer

(a) (b) (c) (d) 7

6 3 7 3 3 7 8 3 8 9 9 8 7 9

2 4 2 4 5 4 2 5 5 6 2 5 6 6 1 1 1 1 4

fig. 3.1 Schematic illustration of selection of points on a leaf for leaf trait measurements for the different plant species. (a) beetroot, (b) cabbage, (c) sunflower, (d) corn. Points 1, 2 and 3 covered the main vein, whereas other points were positioned on soft tissue. We did not consider whether a point on soft tissue covered a smaller vein or capillary. The yellow point represents the source of stimulation for the transmis- sion experiment. Due to the very different shape of corn leaves (d), the point amount and distribution was different.

Analysis of vibratory measurements Pro 1.5 software (Cornell Lab of Ornithol- ogy 2017), we selected ten chewing events Chewing recordings were first filtered with a per recording (see Fig. S3.1 in Supplemen- 100 Hz high pass filter in R version 3.3.1 (R tary materials for an example). We measured Core Team 2016), run in the RStudio inter- root mean square (RMS) amplitude from face (RStudio Team 2015) with the func- the waveform, and first and third quartiles. tion “fir” from the Seewave package version Peak frequency was taken from the spec- 2.1.4 (Sueur, 2008). Recordings were filtered trum (sampling frequency: 44100, window to remove high amplitude, low-frequency type: “Hanning”, window size: 1024, over- background building noise. Using Raven lap: 50). All measurements were done on the

40 chapter 3 filtered recordings. Reference recordings were filtered in the same way as chewing record- ings. RMS measurements from the reference recordings were used to calculate absolute RMS amplitude (mm/s) of chewing events. To calculate absolute RMS amplitude, we used the following formula:

mm RMS(measurement) RMSamp ( ) = *2.80 *LDV vel setting scaling factor s ( RMS(reference) )

The value of 2.80 represents the RMS Hence, our statistical analyses not only amplitude (in Volts) output of the LDV, explore differences in the average amplitude and the LDV velocity scaling setting was change (dB) and mean peak frequency (Hz) either 5 or 20 mm/s/V (Polytec PDV-100 measurements across all points on the same user manual, section 5). leaf, but also of the midvein points, as well as transmission via the stem to the nearest leaf. Analysis of vibratory sweep and acous- Noise recordings were also high-pass tic noise playbacks filtered and RMS amplitude (dB) and peak frequency (Hz) measurements obtained with The sweep recordings were high-pass Raven Pro. All recordings were normalized filtered in the same way as the chewing in R by dividing them by the maximum recordings. The main frequency range of amplitude of the loudest recording. chewing recordings was determined by plot- ting frequency spectrum of a representative Statistical analyses recording per plant species (Fig. 3.2). We determined RMS amplitude of reference Statistical analyses were done with R version recordings in Raven Pro, which were made 3.3.1 (R Core Team 2016), run in the RStu- for every recorded point, including the ones dio interface (RStudio Team 2015). on the adjacent leaves. Attenuation (dB) was calculated for every measurement point of Differences in leaf traits RMS(measurement) 20Log10 = RMS(reference) To evaluate differences in leaf traits across plant species we fitted linear mixed effects every individual plant using the following models from the package “Lme4” (Bates et formula: al., 2015). Because leaf traits were measured We also calculated peak frequency from on two batches of plants (the batch for the the spectrum (sampling frequency: 44100, chewing experiments and the batch for the window type: “Hanning”, window size: transmission experiment), we modelled 1024, overlap: 50). Because we did not “experiment” as a random effect with a measure transmission on the stem, we random intercept. For punch force and leaf decided to use the midvein points and the thickness we also included point on the leaf adjacent leaf as a proxy for transmission. as a random effect because we measured

41 chapter 3 these traits on nine different locations (seven on corn) on the leaf. We fitted a model per leaf trait with plant species as predictor. By means of a Tukey test from the statistical package “lsmeans” (Lenth, 2016) we conducted pairwise comparisons among species.

0 (a) (b)

–20

–40

–60 Filtered Beetroot Cabbage –80 Unfiltered 0 (c) (d) Airborne noise –20 Relative amplitude (dB) –40

–60

Corn Sunflower –80 0 1 2 3 0 1 2 3

Frequency (kHz) fig. 3.2 Normalized averaged amplitude spectra of the unfiltered and filtered chewing recordings, and of the airborne noise stimuli on (a) beetroot, (b) cabbage, (c) corn and (d) sunflower. Sampling rate: 44100 Hz, window type: Hanning, window size: 1024, overlap: 50, The dark grey dotted line represents the unfiltered recordings, and the yellow dotted line the airborne noise. The spectra show that most of the chewing cue remains unchanged after filtering.

Differences in RMS amplitude and lated pairwise comparisons among species mean peak frequency measurements by means of a Tukey HSD test. from chewing cues across plant species Effect of leaf traits on RMS amplitude and mean peak frequency measure- We fitted two multiple regression models ments from chewing vibrations with Gaussian distribution to evaluate differences in RMS amplitude (mm/s) and To determine which leaf traits contributed mean peak frequency (Hz) measurements to explaining variance in RMS amplitude across plant species. Caterpillar weight (g) and peak frequency measurements from and distance from chewing to measuring chewing vibrations, we followed an Infor- point (mm) were included as covariates. mation-Theoretic Approach (Burnham et Amplitude was log transformed. We calcu- al., 2011). We used the R package “MuMIn”

42 chapter 3

(Barton 2009) to evaluate 32 linear mixed Effect of leaf traits on amplitude effects candidate amplitude and frequency attenuation and mean peak frequency models. The linear mixed effects models measurements from the vibrational were produced with the package “Lme4” sweep playbacks (Bates et al., 2015). The leaf traits included in the RMS amplitude and peak frequency To test the effect of leaf traits on RMS candidate models were: SLA (cm2/g), leaf amplitude and mean peak frequency thickness (mm) and water content (g/g). measurements of the vibrational sweep We left out the traits, punch force, leaf area playback, we followed the same approach and leaf mass because of high collinearity as above. We evaluated 8 linear mixed between punch force and leaf thickness, effects candidate amplitude models and 8 and between leaf mass and SLA. and leaf frequency models. The candidate models area and SLA. Additionally, the models contained exactly the same leaf traits as in contained plant species as a random effect the analysis above. We repeated this process with a random intercept. Using Akaike’s to test the effect of leaf traits on amplitude Information Criterion for smaller sample and frequency measurements taken from the sizes (AICc) we assessed the explanatory adjacent leaf. value of the candidate models comparing them by their ΔAICc = (AICi – min AIC). Effect of leaf traits on amplitude and We considered all models with a ΔAICci mean peak frequency induced by ≤ 4. Akaike weights (w1) were computed airborne noise playbacks to determine the probability that a model was the best model. We then calculated the Once again, we followed an Information model average estimates with the subset of Theoretic Approach (Burnham et al., 2011). models that had a ΔAICci ≤ 4. Additionally, We evaluated 16 amplitude and frequency we calculated 95% confidence intervals. candidate models. In this analysis we For this analysis we used the mean leaf replaced SLA by leaf area because leaf area thickness across six of the nine measured is more likely to affect RMS amplitude points. These six points corresponded to the measurements. We calculated the averaged soft tissue of the leaf (points 4-9, Fig. 3.1), estimates for the models that were within and excluded the points along the midvein ΔAICci ≤ 4 of each other. (1-3, Fig. 3.1). We decided to exclude the All figures were created using the midvein points because the caterpillars we library “ggplot2” version 3.1.0 (Wickham, tested only foraged on soft plant tissue and 2016). never chewed from the midvein. Amplitude was log transformed in the amplitude model.

43 chapter 3 Results

Differences in leaf traits

All leaf traits differed significantly across and the lowest punch force, whereas cabbage species, except for water content (Lmm, and beetroot had the highest leaf thick- SLA (log transformed), F=65.54, P<0.01; ness and punch force. Although leaf traits punch force (square root transformed), displayed the same trends among plant F=360.71, P< 0.01; leaf thickness (log species in both experiments, they differed transformed), F=487.84, P< 0.01; leaf area largely between experiments, possibly due (log transformed), F=39.103, P<0.01;wa- to a mismatch in development at the time ter content, (log transformed), F=2.0498, of the experiments in spite of very similar P=0.1097, Fig. 3.3). In both experiments growing environments and treatment. sunflower and corn had the thinnest leaves

(b) (c) 4 (a) 100 6 3 75 4 2 50 2 1 25

0 Punch force (N) Leaf area (cm2) Leaf thickness (mm) 0 0 beetroot cabbage corn sunflower beetroot cabbage corn sunflower beetroot cabbage corn sunflower

80 (d) (e) 1000 60 750 40 500

20 250

Water content (g/g) 0

0 Specific leaf area (cm2/g) beetroot cabbage corn sunflower beetroot cabbage corn sunflower fig. 3.3 Boxplots showing variation in leaf traits across plant species. This graph combines the plants of both batches of experiments (chewing experiment, n=101 and transmission experiment, n=40). (a) leaf thickness is highest for cabbage and lowest for corn (b) punch force is also highest in cabbage and beetroot and lowest in corn and sunflower (c) leaf area was highest in corn (d) water content was also highest in corn (e) specific leaf area was highest in corn and sunflower, and cabbage had the lowest levels of specific leaf area. The effect of plant species was tested with a linear mixed effects model that included experimental batch as a random effect. Plant species had a significant effect on all traits. The interquartile range was taken as the range from 0 to 25th percentile. From the mean, the whiskers show the highest and the lowest value within 1.5 times the interquartile range.

44 chapter 3

Amplitude and mean peak frequency the lowest on beetroot (Fig 3.4). All plant measurements from chewing vibra- species differed significantly in amplitude tions differ across plant species measurements from each other, except cabbage and beetroot, and cabbage and corn Chewing vibrations recorded from different (Fig. 3.4). Mean peak frequency differed plant species differed in amplitude (Lm, F= significantly only in the cabbage – beetroot 40.795, P< 0.01). We recorded the high- comparison (p = 0.02, Fig. 3.4). est amplitude vibrations on sunflower and

c 1.00 fig 3.4 Boxplots showing the variation in RMS amplitude (mm/s) of chewing vibrations on the different plant species. 0.75 The letters indicate significant pairwise differences (P< 0.05) calculated by a Tukey HSD post 0.50 hoc test. The interquartile range was taken as the range from 0 to 25th percentile. From the mean, b a b the whiskers show the highest

RMS Amplitude (mm/s) RMS Amplitude 0.25 and the lowest value within 1.5 a times the interquartile range.

0.00 beetroot cabbage corn sunflower Plant species

Effect of leaf traits on RMS amplitude tions (Supplementary materials, Table S3.1). and mean peak frequency measure- However, neither of these had a signifi- ments from chewing vibrations cant effect on peak frequency measure- ments (Table 3.1). Caterpillar weight and None of the leaf traits we included in our distance from chewing to recording point candidate models significantly affected also appeared in the top models, but they amplitude measurements. Although leaf also did not have a significant effect on peak thickness appeared in the top models frequency measurements (Table 3.1). (Supplementary material, Table S3.1), it did not have a significant effect on amplitude (Table 3.1). Moreover, caterpillar weight had a highly significant positive effect on ampli- tude (Fig. 3.5, Table 3.1). Leaf thickness and water content appeared in all the models predicting vari- ation in peak frequency of chewing vibra- 45 chapter 3

fig. 3.5 Fitted values from the amplitude model (chewing experiment) explaining variation -1 in amplitude of caterpillar chewing cues plotted against caterpillar weight. The ampli- -2 tude model contained the leaf traits: leaf thickness, specific leaf area and water content. -3 Additionally, plant species and experimental batch were (chewing experiment) (chewing included as random effects with a random intercept. Amplitude

Fitted values from amplitude model amplitude from values Fitted -4 increases with the increase in caterpillar weight. The blue line 0.0 0.1 0.2 0.3 represents the linear relationship Caterpillar weight (gr) between the fit of the model and leaf thickness.

table 3.1 Conditional averaged estimates from candidate models testing the effect of leaf traits on the amplitude and peak frequency measurements from caterpillar chewing vibrations. All models within 4 AICc were considered. None of the leaf traits we measured significantly affect RMS amplitude or peak frequency measurements from chewing vibrations. 95% CI

PARAMETER ESTIMATE SE Z VALUE P VALUE LOWER UPPER

Log (amplitude) ~

Caterpillar weight (g) 6.87005 1.3485 5.001 < 0.001 4.178073 9.5635359

Leaf thickness (mm) -1.10835 0.6926 1.535 0.125 -2.466768 0.2996736

Peak frequency ~

Caterpillar weight (g) -185.389 137.411 1.349 0.177285 -454.708909 83.9307663

Distance (cm) -1.986 1.073 1.851 0.064227 -4.090220 0.1173903

Leaf thickness (mm) 61.322 61.143 1.003 0.315891 -58.515019 181.1594401

Water content (g/g) 3.238 3.934 0.823 0.410485 -4.472846 10.9488033

Effect of leaf traits on amplitude uation from the vibratory sweep playback attenuation and mean peak frequency (Supplementary material, Table S3.2). measurements from the vibrational However, only leaf thickness had a signifi- sweep playbacks cant effect (β= 4.7843, P< 0.01), with thicker leaves leading to higher attenuation (Fig. All the leaf traits we included in our candi- 3.6, Table 3.2). For the frequency model, date models appeared in the top models all leaf traits appeared in the top models explaining variation in amplitude atten- explaining peak frequency measurements 46 chapter 3

(Supplementary materials, Table S3.2), the null model, which contained only the though none of the traits had a significant random effects (point nested in species) and effect, and the 95% confidence intervals none of the leaf traits. The same was the largely overlapped zero (Table 3.2). case for the frequency model of the adjacent When we tested the effects of the same leaves (Supplementary materials, Table S3.3, leaf traits on amplitude attenuation of the S3.4). None of the traits explained signifi- vibratory sweep recorded on the adjacent cant variation in amplitude attenuation on leaves, the best model turned out to be the adjacent leaf.

30 fig. 3.6 Scatterplot showing the fitted values from the attenuation of the vibrational sweep playback (transmission experiment) plot- ted against leaf thickness, which 20 was the trait that significantly affected attenuation. The atten- uation model contained the leaf traits: leaf thickness, specific leaf area and water content. Addi- 10 tionally, point on the leaf nested in plant species was included as

(transmission experiment) (transmission a random effect with a random intercept. Attenuation is higher

Fitted values from attenuation model attenuation from values Fitted in thicker leaves. The blue line 0 1 2 3 4 represents the linear relationship Leaf thickness (mm) between the fit of the model and leaf thickness. table 3.2 Conditional averaged estimates from candidate models testing the effect of leaf traits on the attenuation and peak frequency measurements from vibrational sweep playbacks. All models within 4 AICc were considered. 95% CI

PARAMETER ESTIMATE SE Z VALUE P VALUE LOWER UPPER

Attenuation ~

Leaf thickness (mm) 4.7843 0.7994 5.985 <0.001 3.2175844 6.3510541

SLA (cm2/g) -0.1015 0.1728 0.587 0.5570 -0.4401452 0.2371717

Water content (g/g) 0.2314 0.7696 0.301 0.7638 -1.2770046 1.7398850

Peak frequency ~

Leaf thickness (mm) 0.4012 6.1126 0.066 0.948 -11.579329 12.3817850

SLA (cm2/g) 0.1992 0.3808 0.523 0.601 -0.547168 0.9455917

Water content (g/g) 1.2049 3.7212 0.324 0.746 -6.088454 8.4983418

47 chapter 3

Effect of leaf traits on amplitude and amplitude recordings on larger leaves (Fig. mean peak frequency induced by 3.7, Table 3.3). All leaf traits appeared in the airborne noise playbacks top models explaining variation in frequency induced by airborne noise (Supplementary Leaf area and water content were included materials, Table S3.5). Leaf thickness and in the top models explaining variation in water content had a significant effect on amplitude of vibrations induced by airborne frequency (Fig. 3.8, Table 3.3), with an noise playbacks (Supplementary materials, increase in both of these traits leading to Table S3.5). Leaf area had a significant higher peak frequency recordings. positive effect on amplitude, with higher

0 fig. 3.7 Fitted values from the amplitude model explain- ing variation in amplitude of -20 vibrations induced by airborne noise playbacks (airborne noise experiment) plotted against -40 leaf area, which was the trait that significantly affected the amplitude of vibrations. The -60 amplitude model contained the leaf traits: leaf thickness, leaf area and water content. Addi- by airborne noise (RMS dB) airborne noise by

Amplitude of vibrations induced vibrations of Amplitude -80 tionally, point on the leaf nested in plant species was included as a random effect with a random 10 20 30 intercept. Amplitude is higher Leaf area (cm2) on larger leaves. The blue line represents the linear relationship between the fit of the model and leaf thickness.

48 chapter 3 table 3.3 Conditional averaged estimates from candidate models testing the effect of leaf traits on the amplitude and peak frequency of vibrations induced by airborne noise playbacks. All models within 4 AICc were considered. 95% CI

PARAMETER ESTIMATE SE Z VALUE P VALUE LOWER UPPER

Log (amplitude) ~

Leaf area (cm2) 0.88388 0.29975 2.949 0.00319* 0.2963770 1.4713922

Water content (g/g) -0.43681 1.51265 0.289 0.77276 -3.4015498 2.5279280

Leaf thickness (mm) -0.03215 0.14415 0.223 0.82350 -0.3146789 0.2503756

Peak frequency ~

Water content (g/g) 30.678 13.218 2.321 0.0203* 4.771620 56.583662

Leaf thickness (mm) -3.402 1.515 2.245 0.0248* -6.371928 -0.431716

Leaf area (cm2) -3.632 4.187 0.867 0.3857 -11.839161 4.574371

(a) (b)

400

300

200

100 Fitted values from frequency model frequency from values Fitted experiment) noise (airborne 6 8 10 12 25 50 75 Water content (g/g) Leaf thickness (mm) fig. 3.8 Fitted values from the frequency model explaining variation in peak frequency of vibrations induced by airborne noise playbacks plotted against (a) water content and (b) leaf thickness. The frequency model contained the leaf traits: leaf thickness, leaf area and water content. Additionally, point on the leaf nested in plant species was included as a random effect with a random intercept. (a) vibrations recorded on leaves with higher water content had a higher peak frequency. (b) Higher leaf thickness also led to a higher peak frequency. The blue lines represent the linear relationship between the fit of the model and (a) water content and (b) leaf thickness.

49 chapter 3 Discussion

Although vibrational signals and cues prob- tion has been given to the effect of substrate ably fall within the least known sensory variation on prey cues. This is particularly modality, they are extremely widespread interesting in light of the arms race between with hundreds of thousands of inverte- plants, their herbivores and the natural brate species and many vertebrates relying enemies of plant herbivores where vibrations on them (Cocroft et al., 2014). Vibrational have been demonstrated to mediate orien- signals and cues are used in a variety of tation towards prey (vibrotaxis) by insect interactions like mating (e.g. white-lipped parasitoids (Meyhöfer and Casas 1999) frog Leptodactylus albilabris; the prairie and predators (Pfannenstiel et al., 1995). mole cricket Gryllotalpa major; the meadow Additionally, a reverse interaction – avoid- katydid Conocephalus nigropleurum and the ance behaviour in response to semi-specific treehopper Ennya chrysura), predator-prey vibrational cues produced by foraging pred- (e.g. the Namib Desert golden mole Eremi- ators has also been shown (Castellanos & talpa granti namibensis; the sand scorpion Barbosa, 2006). In this study we investi- Paruroctonus mesaensis; the larvae of antlion gated the effect of leaf-trait variation on the Myrmeleon formicarius and the red-eyed tree production, transmission, and detectability frog Agalychnis callidryas), within group (e.g. of chewing cues of a generalist herbivore, the the leaf cutter ant Atta sexdens; treehopper beet armyworm S. exigua. Umbonia crassicornis and kangaroo rats of the We found the highest amplitude genus Dipodomys) (reviewed in Hill 2009), chewing vibrations on sunflower and the and plant-insect interactions (e.g. Pieris lowest on beetroot. These plants differed in rapae and Arabidopsis thaliana) (Appel & particular in their leaf thickness. However, Cocroft, 2014). when testing specifically for the effect of The role of substrate variation in leaf traits on chewing vibrations we did not vibrational signal transmission has received find a clear effect for most traits. Only leaf increasing attention in recent years (Casas thickness tended to show a negative relation and Djemai 2002; Casas et al. 2007; Cocroft with amplitude. Caterpillar weight had a et al. 2006; Cocroft and Rodríguez 2005; highly significant effect on amplitude, Čokl et al., 2004, 2005, 2007; Elias et al. with heavier caterpillars producing higher 2004; Joyce et al. 2008, 2014; Magal et al. amplitude cues. Moreover, distance from the 2000; Polajnar et al. 2012), and there is a chewing point to the recording point had a general consensus that substrate variation close to significant effect on peak frequency can affect the spectral and temporal char- measurements, with lower peak frequency acteristics of vibrational signals, potentially recorded at larger distances. Herbaceous affecting animal communication (McNett plant tissues act as a low-pass filter for vibra- & Cocroft, 2008). However, little atten- tions (Čokl et al. 2005, Cocroft et al. 2006),

50 chapter 3 so this result was also expected. propagating through the leaf are likely to Our analysis of the transmission exper- be higher in amplitude and therefore more iment revealed that leaf thickness signifi- likely to be detected. cantly predicted signal attenuation on the It is surprising that we did not find stimulated leaf, with thicker leaves leading clear effects of leaf traits on the amplitude to higher attenuation. These finding corrob- or frequency characteristics of caterpillar orate the findings on chewing vibrations per chewing vibrations, whereas we did find plant species, as well as for leaf thickness an effect on the transmission of a synthetic specifically. Recent similar research also vibrational stimulus. It could be that at the shows a negative correlation between leaf distances at which we recorded chewing thickness and vibrational noise recorded on vibrations, the effects of leaf traits are not yet the leaf (Li & Kang, 2018). We did not find, detectable, since the effect of the cue-pro- however, an effect of leaf traits on attenua- ducing animal had not yet attenuated much tion on the adjacent leaves. Unfortunately, and was still dominant over any reflec- we did not measure distance from one leaf tions. It is also possible that leaf traits affect to the next adjacent leaf or vein architec- production and transmission differently, and ture, which might be better predictors of that the two processes are not necessarily attenuation at that scale. Furthermore, mutually exclusive. Nonetheless, although none of the leaf traits we measured signifi- the effect of leaf thickness on amplitude of cantly predicted variation in peak frequency chewing cues was not significant, the confi- measurements. dence intervals barely overlapped zero, and Differences in amplitude within a leaf leaf thickness showed a similar trend as in are particularly important for Branconidae, the transmission experiment, with higher Eulophidae and Pteromalidae families of amplitude cues recorded in plants with the parasitoids, which parasitize hosts that are thinnest leaves. Alternatively, the lack of normally hidden in the substrate (e.g. endo- an obvious effect of leaf traits could indi- phytic Dipteran or Lepidoteran hosts). In cate that there is something happening at these cases vibrations are usually the main the production level. Perhaps caterpillars stimuli used (reviewed in Meyhofer and “prepare” their substrate before chewing Casas 1999). For example, Dejmai et al. it (e.g. via saliva secretions), standardizing (2004) showed that the parasitoid Sympiesis the toughness of the plant material they sericeicornis spent more time searching for consume, and consequently standardizing its leaf-miner host on a leaf, and produced the amplitude and frequency characteristics a higher number of oviposition insertions of their chewing cues. Several caterpillar when stimulated with simulated host vibra- species, including S. exigua are known to tions. Therefore, for parasitoids or predators regurgitate oral secretions on the leaves on searching for their host or prey within leaves which they feed (Alborn et al., 1997; Peiffer (e.g. predators or parasitoids of leaf-miners), & Felton, 2009; Vadassery et al., 2012). choosing plants with lower leaf thickness Although these oral secretions have been may be beneficial as chewing vibrations mostly studied for their effect on eliciting

51 chapter 3 plant defenses (Peiffer & Felton, 2009; is also consistent with the research of Li Vadassery et al., 2012), it is possible that and Kang (2018) who found a highly posi- they play an alternative role. A transmission tive correlation between leaf area and leaf experiment with actual foraging caterpillars vibrations induced by airborne noise. An where both foraging location and caterpillar interesting result was the effect of punch weight are controlled for would be an inter- force and water content on the frequency of esting follow up study. Such an experiment vibrations induced by airborne noise. Higher would tell us whether the observed lack of water content and higher punch force led effect of leaf traits is because of distance to vibrations with higher peak frequency, limitation, or whether it is caused at the possibly due to increased stiffness which production level. Another possible factor is is associated with higher natural frequen- leaf geometry which influences vibrational cies in finite structures that in turn influ- modes, but the effect is unpredictable and ence mechanical response to a sound field cannot yet be realistically investigated with- (Norton & Karczub 2003). out interfering with the performance of the In conclusion, we found that leaf traits living plant (de Langre 2019). do not affect the amplitude and frequency Background noise originating from characteristics of vibrational chewing cues, abiotic sources like wind and rain, or anthro- but they do affect their transmission and pogenic noise from traffic, can interfere with possibly their detectability. The discrep- detection of vibratory cues as shown for ancy between the effects of leaf traits on web-building spiders (Wu and Elias 2014). the production of chewing cues and their Acoustic noise can be picked up by elastic transmission and detectability is surprising. substrates like plants and continues travel- However, we speculate that “treatment” ing as vibrational noise within the substrate, of plant substrates by caterpillars prior to which can affect many vibrational interac- chewing could explain the lack of differ- tions, including predator-prey interactions ences in amplitude and frequency charac- (Virant-Doberlet et al. 2019). The impact teristics across plant species. of anthropogenic acoustic noise on commu- In this study we identified some possi- nities is thus even more pervasive than ble ecologically important linkages between commonly recognized. The frequency and plant and insect strategies mediated by intensity of pressure waves, as well as the chewing vibrations. Plants may for example difference of the acoustic impedances of air evolve thicker leaves to defend themselves and the substrate, affects the penetration of against their herbivores, such as caterpil- acoustic noise into the substrate (Cremer et lars or other types of invertebrate pests, al., 2005). We measured the amplitude and but by doing so, they may reduce effective frequency of vibrations induced by airborne use of vibratory cues by the predators and noise and found, not surprisingly, that leaf parasites that can eavesdrop on the chew- area significantly affected the amplitude of ing activity of these herbivores. Focusing vibrations induced by airborne noise, with on the role of species-specific leaf traits in larger leaves absorbing more noise. This plant-borne vibrations may also provide

52 chapter 3 insight into potential life-history trade-offs. Potential leaf trait candidates may include traits related to the architecture, diameter and internal structure of veins. Leaf surface properties related to epidermal structures and cuticles, rarely measured by plant ecologists, may also provide further traits of interest in this context. Trade-offs may also be operating across other life-history domains. For instance, a fast plant-growth strategy requires investment in leaves that might be more productive, e.g. through higher vein density to promote water trans- port (Brodribb et al. 2007); such a strat- egy may also lead to greater absorbance of vibration energy, which may also reduce the effectiveness of predators that use vibratory cues of herbivores. In-depth investigation into the role of plant morphology and phys- iology in influencing the sensory ecology of primary and secondary consumers will greatly increase our understanding of their complex eco-evolutionary relationships.

Acknowledgements

We are especially grateful to Richard van Logtestijn and Jurgen van Hal for their help with plant rearing and the measuring of leaf traits. Additionally, we would like to thank Biljana Pamucar for her assistance with animal and plant care, and Rick de Geus and Jip van ‘t Veer for their help with leaf trait measurements.

53 chapter 3 Supplementary Materials

table s3.1 Candidate models testing the effect of leaf traits on the amplitude a peak frequency measure- ment from chewing vibrations.

Log(amplitude) from chewing vibrations ~ AICc ΔAIC Weight

Caterpillar weight + leaf thickness + (1|species) 160.15 0 0.584

Caterpillar weight + (1|species) 161.03 0.88 0.375

Peak frequency from chewing vibrations ~

Caterpillar weight + distance + leaf thickness + water content + (1|species) 773.2 0 0.568

Caterpillar weight + leaf thickness + water content + (1|species) 775.9 2.69 0.147

Caterpillar weight + distance + leaf thickness + (1|species) 776.5 2.37 0.144

table s3.2 Candidate models testing the effect of leaf traits on the amplitude a peak frequency measure- ment from the vibrational sweep playbacks on the stimulated leaves.

Attenuation from sweep playbacks ~ AICc ΔAIC Weight

Leaf thickness + (1|species/point) 1093.5 0 0.544

Leaf thickness + SLA + (1|species/point) 1095.6 2.07 0.193

Leaf thickness + water content + (1|species/point) 1095.7.7 2.14 0.185

Leaf thickness + SLA + water content + (1|species/point) 1097.4 3.92 0.076

Peak frequency from vibratory sweep playbacks ~

(1|species/point) 1818.7 0 0.389

SLA + (1|species/point) 1820.5 1.82 0.156

Water content + (1|species/point) 1820.7 1.98 0.144

Leaf thickness + (1|species/point) 1820.8 2.12 0.134

SLA + water content + (1|species/point) 1822.6 3.94 0.054

Leaf thickness + SLA + (1|species/point) 1822.6 3.95 0.053

54 chapter 3 table s3.3 Candidate models testing the effect of leaf traits on the amplitude a peak frequency measure- ment from the vibrational sweep playbacks on the adjacent leaves.

Amplitude from vibratory sweep playbacks on adjacent leaf ~ AICc ΔAIC Weight

(1|species/point) 391.7 0 0.367

Leaf thickness + (1|species/point) 392.8 1.15 0.206

SLA + (1|species/point) 394.0 2.29 0.116

Water content + (1|species/point) 394.1 2.39 0.111

Leaf thickness + SLA + (1|species/point) 394.7 3.05 0.079

Leaf thickness + water content + (1|species/point) 395.3 3.6 0.060

Frequency from vibratory sweep playbacks on adjacent leaf ~

(1|species/point) 616.3 0 0.449

Leaf thickness + (1|species/point) 618.6 2.32 0.140

Water content + (1|species/point) 618.6 2.35 0.138

SLA + (1|species/point) 618.7 2.36 0.137

table s3.4 Conditional averaged estimates from candidate models testing the effect of leaf traits on the amplitude and peak frequency measurements from vibrational sweep playbacks on the adjacent leaf. All models within 4 AICc points of each other were considered. 95% CI

PARAMETER ESTIMATE SE Z VALUE P VALUE LOWER UPPER

Amplitude model

Leaf thickness -9.76930 8.42951 1.159 0.246 -26.2410072 6.7522303

SLA -0.07192 0.12999 0.553 0.580 -0.3267079 0.1828604

Water content -0.11257 0.75588 0.149 0.882 -1.5940643 1.3689200

Frequency model

Leaf thickness 12.6433 44.9853 0.281 0.779 -75.5263390 100.8128654

Water content 0.8146 3.6230 0.225 0.822 -6.2862267 7.9154940

SLA -0.1154 0.5806 0.199 0.842 -1.253435 1.022593

55 chapter 3 table s3.5 Candidate models testing the effect of leaf traits on the amplitude a peak frequency measure- ment from the vibrations induced by the airborne noise playback.

Vibrational noise amplitude induced by airborne noise ~ AICc ΔAIC Weight

Leaf area + (1|species/point) 499.1 0 0.389

Leaf area + punch force + (1|species/point) 500.8 1.66 0.169

Leaf area + water content + (1|species/point) 501.5 2.39 0.118

Leaf area + leaf thickness + (1|species/point) 501.6 2.42 0.116

Vibrational noise peak frequency induced by airborne noise ~

Leaf area + leaf thickness + (1|species/point) 715.4 0 0.210

Leaf thickness + water content + (1|species/point) 716.4 0.94 0.131

Leaf area + leaf thickness + water content + (1|species/point) 716.5 1.12 0.120

Leaf thickness + (1|species/point) 716.6 1.18 0.117

Punch force + water content + (1|species/point) 716.6 1.22 0.114

Leaf thickness + punch force + water content + (1|species/point) 717.4 2.01 0.077

Leaf area + leaf thickness + punch force + (1|species/point) 717.8 2.43 0.062

Leaf thickness + punch force + (1|species/point) 718.9 3.51 0.036

Leaf thickness + punch force + water + content + (1|species/point) 718.9 3.52 0.036

Leaf area + punch force + water content + (1|species/point) 719 3.61 0.035

10 Amplitude (dB) CE 3 CE= Chewing event 0 8

-10 6 CE 4 -20 Frequency (kHz) Frequency 4 CE 2 CE 7 CE 5 CE 9 2 -30 CE 1 CE 6 CE 8 CE 10

0 -40 Amplitude 0 1 2 3 4 5 6 7 Time (s) fig. s3.1. Example of chewing event selection. Events were selected by hand in Raven Pro 1.5. RMS ampli- tude was measured from the waveform and peak frequency from the power spectrum, for each selection. For our analysis we took the average RMS amplitude and peak frequency of the 10 selections. This is a recording of Spodoptera exigua foraging on a beetroot plant. CE indicates chewing event. 56 Asobara tabida

57 chapter 4

The effect of vibrational noise on a parasitoid-host interaction

Estefania Velilla • Ivor Keesmaat Wouter Halfwerk • Jacintha Ellers

Abstract

Noise can affect the ability of animals to tional white noise of different amplitude detect important information from the levels, and recorded host searching behav- environment, which can ultimately lead to ior and parasitism success. We found that a lower reproductive success and survival. parasitoids exposed to noise levels 12dB While a large percentage of invertebrates higher than ambient conditions reduced and many vertebrates use vibrational signals their host-searching time by an average of and cues, we know very little about how 42% and had a mean decrease of 56% in vibrational noise affects the perception of parasitism success. Our results suggest that information in these animals and how this vibrational noise can impact host-parasitoid in turn affects their fitness. In this study interactions, ultimately affecting parasit- we looked at the effects of vibrational noise oid fitness by reducing their reproductive on fitness components of a parasitoid wasp, success. Environments with high vibratory Asobara tabida, which relies on vibrations noise levels are likely to favor parasitoids to detect and localize its Drosophila host. relying primarily on other sensory modali- Here we exposed parasitoids and their ties such as, olfaction or vision to detect and hosts (Drosophila melanogaster) to vibra- localize their hosts.

58 chapter 4 Introduction

The environment provides animals with spring communication (e.g. Morris et al. information that is crucial for their repro- 1994; Pfannenstiel et al. 1995; Cocroft duction and survival, such as where to find 1999) and are in particular important in the a mate or a prey or when to avoid a pred- context or parasite-host interactions. ator (Stevens, 2013). Noise can affect the Several parasitoid wasps are known perception of this information in different to rely on vibrations to detect and localize ways, for example by masking the infor- their hosts (Lawrence 1981; Van Alphen mation, or by distracting or misleading the and Janssen 1981; Vet and van Alphen 1985; receiver (Brumm and Slabbekoorn 2005; Sokolowski and Turlings 1987; Sugimoto Barber et al. 2010; Naguib 2013; Domi- et al. 1988; Meyhofer et al. 1994, 1997; Van noni et al. 2020), thereby changing the Dijken and Van Alphen 1998). Vibrations response of receivers and affecting mating can be particularly crucial for parasitoid interactions (Francis et al. 2011; Halfwerk species that attack hosts that live hidden et al. 2011a; Costello and Symes 2014) and in the substrate (e.g. leaf mining, feeding predator-prey dynamics (Francis et al. 2009; in fruit or grains, underneath the bark of Simpson et al. 2015). Ultimately, the effects trees), since being concealed makes the of noise can lead to reduced reproductive and detection of visual and chemical stimuli breeding success, and a decline in survival more difficult (Meyhofer and Casas 1999). rates (Bayne et al. 2008; Blickley et al. 2012; Background noise, however, can hamper the Simpson et al. 2016) ability of parasitoids to detect and extract The effects of noise are well-studied for relevant information from their environ- the acoustic domain, in which most studies ment. Vibrational noise can originate from have focused on the effect of airborne traffic natural sources, such as wind and rain, but noise on acoustic communication (e.g. Bee also from anthropogenic sources such as and Swanson 2007; Lengagne 2008; Parris motorways, construction work and wind et al. 2009; Siemers and Schaub 2011; Half- energy turbines. Parasitoids foraging close werk et al. 2011b; Orci et al. 2016). However, to these sources can, therefore, be affected only a few studies have addressed this issue in their ability to detect the vibrations from in the vibrational modality (Polajnar and their hosts, which can ultimately lead to a Čokl 2008; McNett et al. 2010; Caorsi et al. lower reproductive success and lower fitness. 2019; Velilla et al. 2020), despite the preva- In this study we investigated the effect lent use and importance of this modality in of vibrational noise on the host search- the animal kingdom (Cocroft et al. 2014) ing behavior and parasitism success of the (Hill et al. 2019). Vibrational signals and endoparasitoid Asobara tabida. Asobara cues are for example used during mating, tabida females use the vibrations produced predator-prey dynamics, and parent-off- by their hosts (Drosophila of the obscura and

59 chapter 4 melanogaster group), to locate them within a foraging area (e.g. inside fermenting fruit) (Sokolowski and Turlings 1987). We exper- imentally exposed A. tabida wasps and their hosts, D. melanogaster larvae to vibrational white noise of different amplitude levels, and we observed the foraging behavior of the parasitoids and we quantified their parasit- ism success. We expected parasitoids forag- ing in high noise conditions to spend less time searching than parasitoids foraging in low or no noise conditions. Furthermore, we expected a lower parasitism success in the individuals exposed to high noise conditions in comparison to the individuals in low or no noise conditions.

60 chapter 4 Methods

Study species force SAP-702 2 x 20 W manufacturer No. RF-3511635 or Renkforce T21 2 x 50 W We obtained A. tabida wasps from the manufacturer No. RF-4602693). The trays Groningen Institute for Evolutionary Life were placed on tables, and the legs of the Sciences at the University of Groningen in tables were placed in buckets of sand to the Netherlands. The strain used for these minimize transmission of vibrational noise experiments was established as an isofe- from the building. male line in 2010 from a cross between two Our noise treatments consisted on lines, one collected in Sospel (France) and a low (0dB), a medium (6dB) and a high the other collected in Pisa (Italy). A. tabida (12dB) amplitude treatment. To calibrate has been maintained in D. melanogaster the treatments, we first measured the ambi- and reared in climate chambers at the Vrije ent vibrational noise levels on the center Universiteit Amsterdam at 20˚C, under 75% of the experimental tray using a Doppler relative humidity and 16:8 light: dark hours. laser vibrometer (LDV; Polytec PDV-100, set to 100 mm/s/V, sampling rate 22kHz) Noise exposure experiment connected to an oscilloscope (Rigol, DS1054, 4 Channel 50MHz, 1GSa/s) and The experiments were carried out between we matched the RMS amplitude of the June and October 2019 at the Vrije Univer- ambient noise levels for the 0dB treatment. siteit Amsterdam in a lab under ambient Additionally, to control for any confounding temperature conditions between 20-24˚C. effects of the electrical power of our setup, We exposed A. tabida and its host D. we included a “silent” treatment, which melanogaster to vibrational noise of increas- entailed playing back a silent file. We aimed ing amplitude levels, and we monitored host at testing 28 wasps per treatment level. searching behavior and parasitism success. However, one of our wasp batches failed Twenty-five second instar D. melanogaster and we were able to test on 24 wasps for the larvae were placed in a petri dish (30mm 0dB treatment. Using the formula below, diameter) containing a yeast-agar medium. we calibrated the levels for the 6dB and We placed four dishes with larvae on a 12dB treatments. TreatmentRMS(i) plastic dining tray (48.5 x 31cm) at equal 20*Log10 = ( ) AmbientnoiseRMS distances from each other. Four shakers (Monacor AR-30, Monacor International The trays containing the treatments GmbH & Co. Zum Falsch 36, 28307 0dB, 6dB and 12dB were placed on the Bremen, Germany) broadcasting vibrational same table, whereas the “ambient” treat- white noise were placed under the tray and ment was placed on a separate table. We were connected to an mp3 player (Lenco measured the amplitude levels on each of Xemio-200) and a stereo amplifier (Renk- the trays while all the treatments were play- 61 chapter 4 ing to make sure that one treatment did not To explore the effect of vibrational influence the other. noise on host searching behavior, we calcu- Observations on host searching lated the percentage of time that a wasp behavior were taken once a minute during spent in the bottom or top of the dish, and the first 15 minutes of every hour, for four also the percentage of time spent moving. hours, starting always between (10:00- To test whether our 0dB treatment was 11:00 a.m.). During the observations we representative of ambient conditions, we noted whether the parasitoids were posi- first fitted a linear regression with percent- tioned on the medium (henceforth referred age of time spent on the top of the dish or to as “bottom”) or on the underside of the percentage of time spent searching for a lid of the petri dish (henceforth referred to host as our response variable. We included as “top”). Observations of parasitoids in the control group (0dB and ambient) as our “bottom” position were assumed to be indic- main predictor, and date as a co-variate. ative for host foraging motivation and lack The effect of control groups was compared of noise-avoidance behavior, while parasit- with a pairwise test using the R function oids positioned on the “top” were assumed “lsmeans” from the “lsmeans” package to be uninterested in host foraging and/or (Lenth and Lenth, 2016). Additionally, displaying noise-avoidance behavior (since we fitted a multiple linear regression to test the lid was closed they were unable to actu- the effect of treatment level (0dB, 6dB and ally escape noise exposure). Additionally, 12dB) on the percentage of time spent on we recorded if wasps were moving on the the top of the dish and the percentage of medium with a pause-walk behavior typical time searching for a host. Furthermore, of host searching (Sokolowski and Turlings to model the effect of treatment level on 1987), and this was noted as “searching”. parasitism success, we followed the same After the four hours of observations, approach as above, first testing whether our the wasps were removed from the petri 0dB treatment was representative of ambi- dishes. The larvae remained in the petri ent conditions and subsequently fitting a dishes and were brought back to the rearing model to test the effect of treatment level climate chambers to continue their develop- on parasitism. Because of overdispersion in mental cycle, which in total lasted approx- our data, we used a generalized linear mixed imately 26-28 days. To assess the effect of effects model, with negative binomial distri- noise level on parasitism success, we counted bution using the R function “glmer.nb” from the number of A. tabida wasps that emerged the “lme4” package (Bates et al. 2015). We from the D. melanogaster pupae. included date as a random effect. We used qqplots from the R pack- Analyses age “ggplot2” (Whickham, 2016) and histograms to check normality of residuals. Statistical analyses were done with R version There was no indication of deviation from 3.3.1 (R Core Team, 2016), run in the RStu- the normality of residuals assumption. dio interface (RStudio Team, 2015).

62 chapter 4 Results

Although wasps spent increasingly more spent on the top of the dish or in percentage time on the top position of the dish as the of time searching for a host between the 0dB amplitude of our noise treatment increased and the ambient (pairwise comparison, β = (Table 4.1), noise amplitude did not 0.0146, SE= 0.0701, p = 0.83; percentage significantly predict the amount of time of time searching for a host, β = 0.277, SE= wasps spent on the top of the dish (linear 0.845, p = 0.74), indicating that our 0dB regression, F2,53 = 2.439, P= 0.09; Fig. 4.1 treatment was representative of ambient (a), for model estimates see Table 4.2). conditions. Furthermore, noise amplitude significantly predicted the percentage of time A. tabida spent searching for a host (linear regression,

F2,53= 3.969, P= 0.01), with wasps searching less as noise levels increased (β = -0.9, 1, t= -1.962, P = 0.05; Fig. 4.1 (b), for all model estimates see Table 4.3). There were no significant differences in percentage of time table 4.1. Mean percentage of time ± standard deviation (sd) spent by A. tabida on a position or moving during the different treatments.

TREATMENT TOP POSITION % (MEAN ± SD) SEARCHING % (MEAN ± SD)

Amb 17 ± 21 26 ± 18

0dB 20 ± 27 21 ± 21

6dB 24 ± 29 18 ± 18

12dB 33 ± 36 15 ± 16

63 chapter 4

(a) (b) 100 fig. 4.1. (a) percentage of time spent by A. tabida on the top of the petri dish. Although wasps exposed to the 12dB treatment spent more time on the top than 75 the other wasps, the effect of noise amplitude was not statisti- cally significant.

(b) Percentage of time spent 50 by wasps searching for a host. Searching consisted on a walk- pause movement. Wasps spent Percentagetime significantly less time searching 25 for a host with increasing noise levels.

0

amb 0dB 6dB 12dB amb 0dB 6dB 12dB Treatment

table 4.2 Estimates from a linear mixed effects model testing the effect of treatment level on the mean percentage of time spent by A. tabida on the top position. Testing date was included as a random effect with a random intercept. ESTIMATE STD. ERROR T VALUE P VALUE

(Intercept) -1.864e+04 9.513e+03 -1.959 0.05

Noise amplitude 8.874e-01 6.524e-01 1.360 0.17

Date 1.194e-05 6.089e-06 1.960 0.05

table 4.3 Estimates from a linear mixed effects model testing the effect of treatment level on the mean percentage of time spent by A. tabida searching for a host.

ESTIMATE STD. ERROR T VALUE P VALUE

(Intercept) 1.679e+04 7.137e+03 2.352 0.02

Noise amplitude -9.602e-01 4.895e-01 -1.962 0.05

Date -1.073e-05 4.568e-06 -2.348 0.02

64 chapter 4

Parasitism success was generally low. On mixed effects model,β = -0.21, t= -2.801, P average 1.6 wasps emerged in the “ambi- < 0.01). There were no significant differences ent” and 0dB treatment, whereas on average between the 0dB and ambient treatment in 0.9 and 0.7 wasps emerged from the 6dB parasitism levels (pairwise comparison, β and 12dB treatments, respectively (Fig. = -0.277, SE= 0.845, p = 0.74), indicating 4.2). Noise amplitude significantly affected that our 0dB treatment was representative parasitism, with fewer wasps emerging with of ambient conditions. increasing noise levels (generalized linear

12.5

10.0

7.5

5.0 Emerged wasps

2.5 fig. 4.2 Number of emerged wasps after the noise exposure experiment. Noise levels had a significant effect on parasitism 0.0 success, with less wasps emerg- ing in higher noise conditions. amb 0dB 6dB 12dB Treatment

65 chapter 4 Discussion

Over 90% of insects are known to use vibra- when another stimulus (e.g. conspecific tional signals or cues (Cocroft et al. 2014), or heterospecific signals and natural or yet only a few studies have examined how anthropogenic noise), overlaps in temporal vibrational noise affects insects that depend and spectral characteristics with the signal on vibrational information (Polajnar and or cue, completely or partially masking the Čokl 2008; McNett et al. 2010; Caorsi et information (Brumm and Slabbekoorn al. 2019; Velilla et al. 2020). In this study 2005). Because we were not able to measure we tested the effect of vibrational noise on the vibrations of moving D. melanogaster, the host-searching behavior and parasitism it is difficult to tell from our data whether success of the parasitoid A. tabida. We show our noise playback masked the vibrational that vibrational white noise 12dB higher cues of the larvae. However, we suspect than ambient vibratory conditions signifi- that vibrational white noise played at an cantly affected the host searching behavior amplitude of 6dB and 12dB higher than of A. tabida, with parasitoids in the high the ambient noise levels would be sufficient noise treatment (12dB) spending approxi- to mask the vibrational cues of moving D. mately 42% less time searching for a host melanogaster larvae. than the parasitoids in the ambient treat- Irrespective of the underlying percep- ment. Moreover, noise levels 6dB higher tual mechanisms, our behavioral experi- than ambient conditions were enough to ments show that parasitoids spent less time cause a significant reduction in parasitism searching for their host and more time success, with the mean number of parasit- away from the medium. These observations oids emerging after this treatment decreas- suggest that females were actively trying to ing by approximately 56% in comparison to avoid the noise, although we cannot rule out those emerging in the ambient treatment. that noise was simply interfering with their The effect on parasitism was proportional to host searching behavior and that they were the treatment level, suggesting that higher therefore disoriented. Alternatively, noise noise levels could have an even stronger could lead to cross-sensory interference, impact. which is when noise interferes with infor- Many animals live in noisy habitats, mation processing in other sensory domains, which can make the detection and discrim- as has been shown in moths (Skals et al. ination of relevant information difficult 2005), dwarf mongooses (Morris-Drake et (Lohr et al. 2003). One of the major chal- al. 2016) and birds (Halfwerk and van Oers lenges animals face when trying to extract 2020). It is possible that the wasps in this relevant information from a noisy environ- experiment were distracted by vibrational ment is interference by masking (Brumm noise and therefore, did not perceive the and Slabbekoorn 2005). Masking occurs chemical cues of their hosts.

66 chapter 4

Parasitoids can use a wide range of it’s possible that other sources of noise like stimuli, including chemical, visual and vibrations induced by motorways or by wind vibratory cues for host location (Godfray energy turbines have a similar effect as the 1994; Quicke 1997). However, parasitoids effect we found with white noise. Wind attacking concealed hosts (e.g. leafminers) energy turbines are in particular interesting rely heavily on the vibrations produced by because there are increasingly more turbines the movement of hosts in order to locate placed in agricultural fields, where many them (Meyhofer and Casas 1999). Vibra- parasitoids are used as a biocontrol for agri- tions from moving hosts can provide direc- cultural pests. How successful a biocontrol tional information, in which case parasitoids program relies on the ability of parasitoids move towards the direction of the source to accurately locate, manipulate and accept emanating the vibrations, then shortly stop, their (Clarke et al. 2019). and then continue as another cue is produced Moreover, parasitoids that primarily – a behavior known as vibrotaxis (Fraenkel rely on other sensory modalities (e.g. olfac- and Gunn 1940). Alternatively, in a process tion and vision) to detect and locate their known as vibrokinesis, vibrations induced hosts may gain competitive advantage in by moving hosts can elicit a response in the noisy environments over parasitoids that rely parasitoid proportional to the intensity of on vibrations. Therefore, changes in envi- the vibrational stimuli, where a parasitoid ronmental conditions, like increases in noise searches in the vicinity of the source, but levels could alter the coexistence balance without detailed directional information among competing parasitoid species. (Fraenkel and Gunn 1940). Moreover, para- Furthermore, even though in this study sitoids can also use vibrational sounding to we have addressed the effects of vibrational locate their hosts, which is a form of echo- noise on the perception of a vibrational cue, location (Broad and Quicke 2000). During noise can also lead to cross-modal effects, vibrational sounding, parasitoids tap the affecting the perception of signals and cues substrate and uses the returning echos to across other modalities (Skals et al. 2005; locate a potential host (Broad and Quicke Morris-Drake et al. 2016). It is therefore 2000). Regardless of whether parasitoids use possible that parasitoids using olfactory or vibrotaxis, vibrokinesis or vibrational sound- visual cues to detect and localize their hosts ing, they must extract the relevant cues are also affected by noise. Further studies from the background in order to success- should examine the effect of noise on para- fully locate a host. The fitness of a parasitoid sitoids using different sensory modalities for depends on its ability to locate a host. There- host localization. fore, the effect of noise on host-searching behavior and parasitism success can lead to lower reproductive success and lower fitness, as we have shown in this study. In our study we have used vibrational white noise as playback stimulus. However,

67 chapter 4 Acknowledgements

We are grateful to Bregje Wertheim for providing the A. tabida cultures and to Janine Mariën and Rudo Verwij for their help with the rearing of D. melanogaster. Furthermore, we are grateful to Gerard Driessen for his advice on the experimental design on this study and to Yualli Haydee González for carrying out the pilot exper- iments.

68 Earthworms

69 chapter 5

Vibrational noise from wind energy-turbines negatively impacts earthworm abundance

Estefania Velilla • Eleanor Collinson • Laura Bellato Matty P. Berg • Wouter Halfwerk

Abstract

Human activities often have a profound turbines. Earthworm abundance showed impact on the sensory environment of a strong decrease with increasing vibra- organisms. Wind energy turbines are a tory noise. When comparing the nearest fast-growing potential source of anthro- sampling points in proximity of the wind pogenic vibrational noise that can affect energy turbines with the points furthest soil animals sensitive to vibrations and away, abundance dropped on average by thereby alter soil community functioning. 40% across all seven fields. The abundance Larger soil animals, such as earthworms, of small-sized soil animals (mesofauna, < are particularly likely to be impacted by the 3 mm in size) differed between crop fields, low-frequency turbine waves that can travel but was not related to local noise levels. Our through soils over large distances. Here results suggest that anthropogenic vibratory we examine the effect of wind turbine-in- noise levels can impact soil fauna, which has duced vibrational noise on the abundance important consequences for soil functioning. of soil animals. We measured vibrational Earthworms, for instance, are considered noise generated by seven different turbines to be crucial ecosystem engineers and an located in organically-farmed crop fields impact on their abundance, survival and in the Netherlands. Vibratory noise levels reproduction may have knock-on effects on dropped by an average of 23 ± 7 dB over important processes such as water filtration, a distance of 200 m away from the wind nutrient cycling and carbon sequestration.

Environmental Pollution (submitted) 70 chapter 5 Introduction

Animals rely on different sensory stimuli 2016). These platforms are needed to with- to acquire and process information from stand the weight of the turbine, and they may their environment. The ability to acquire additionally reduce some of the vibrational this information is essential for an animal’s noise created by the turbine. Nonetheless, reproductive success and survival (e.g. wind energy turbines still induce vibrations finding a mate and detecting a predator) in the soil, mostly in the low frequency range (Dominoni et al. 2020). However, activities (Stammler & Ceranna, 2016). However, we tightly linked to human population growth still have little knowledge about the vibra- are influencing the sensory environment by tional noise levels generated by wind energy emitting novel stimuli into natural areas turbines, how far this noise travels through such as artificial light at night, traffic sounds soils and how it affects soil animals. and subterranean drilling vibrations (Domi- The potential impact of noise is likely noni et al. 2020). These so-called sensory to depend on the overlap between an pollutants have been shown to cause nega- animal’s body size and the spectral distribu- tive effects on animal behavior and physiol- tion of the vibrations that travel through the ogy, which can translate to reduced survival soil. Vibrations generated by wind energy and reproduction, and ultimately to popula- turbines are typically low in frequency and tion declines (Barber et al. 2010; Kight and small-bodied animals (mesofauna, < 3 mm Swaddle 2011; Francis and Barber 2013). in size) will likely not be able to perceive, A growing potential source of anthro- or even experience any strain from these pogenic vibrational noise are wind energy low-frequency waves. Larger soil animals turbines, which are mainly found in rural or (macrofauna, > 1 cm in size), however, farming areas and sometimes cover a large might be able to perceive low-frequency land surface. Although wind energy turbines vibrations and can therefore be impacted by are less disturbing to the environment than turbine noise through a perceptual mech- fossil-fuel based energy sources, they have anism (Dominoni et al. 2020). Among been shown to affect animal behavior (Rabin macrofauna, earthworms are particularly et al. 2006; Thaker et al. 2018; Caorsi et al. sensitive to low-frequency vibrations as they 2019) and increase bird and bat mortality use vibrational cues to detect approaching (Johnson et al. 2003; Barrios and Rodríguez predators (Catania 2008; Farina 2014). 2004; Rydell et al. 2010; Bellebaum et al. Earthworms are crucial ecosystem engi- 2013a). The blades, the rotor and the shafts neers (Jones et al. 1997) that are well known from wind turbines are supported by a tower to influence soil structure, hydrology and that is usually anchored to a heavy concrete quality as well as plant production (e.g. and steel rebar platform reaching up to nine Clements et al., 1991; Van Groenigen et meters in depth (Stammler & Ceranna, al., 2014; Bertrand et al., 2015). Vibratory

71 chapter 5 noise can mask predatory cues and thereby impact earthworms (Dominoni et al. 2020). Alternatively, vibratory noise from turbines may mimic vibratory cues produced by approaching predators, such as moles, and earthworms may surface in response (Cata- nia 2008; Dominoni et al. 2020). In the current study we assessed the effect of wind turbine-induced vibrational noise on earthworm abundance, and on the abundance of soil mesofauna. We hypoth- esized that vibrational noise would have a body-size dependent effect on soil animals and expected a negative impact on earth- worms and no impact on the smaller meso- fauna. To test these predictions, we selected seven turbines stationed in agricultural fields. We first measured vibrational noise induced by wind turbines along a transect on exponentially increasing distances starting at 2 m up to 256 m from the turbines. We then measured earthworm and mesofauna abundance on four points on the transects (8 m, 32 m, 64 m and 128 m).

72 chapter 5 Materials and Methods

Study area

Sampling was done between the months other (Fig. 5.1, Table 5.1). Turbine numbers April and June 2019 in the fields belong- four to seven, located on site two, were posi- ing to the biological farms van Andel tioned next to each other (Fig. 5.1, Table Bio BV (Site 1; 52°18’19.4” N, 5°23’54.6 5.1), and approximately 2.5 km from the site. E) and Douwe Monsma Beheer (Site 2; A concrete platform, approximately 45 m x 52°18’32.9” N, 5°26’04.3” E) in the prov- 25 m surrounded the base of the turbines. A ince of Flevoland, the Netherlands. These small grass patch (~2-6 m wide) surrounded farms were located on reclaimed land and the concrete base of the turbines. All wind were completely surrounded by agricultural turbines in this study were three-blade fields. Turbine numbers one to three were turbines manufactured by Vattenfall. located on site one, in fields next to each table 5.1 Location, crop type, distance to the next turbine and coordinates of the wind turbines used in this study.

DISTANCE TO NEXT FARM TURBINE CROP COORDINATES TURBINE (M)

van Andel One No crop 401 52°18’25.2” N, 5°23’17.6” E

van Andel Two No crop 403 52°18’14.2” N, 5°23’28.2” E

van Andel Three No crop – 52°18’03.8” N, 5°23’42.4” E

Monsma Four Wheat 414 52°19’14.6” N, 5°25’56.6” E

Monsma Five Grass 410 52°18’32.9” N, 5°26’04.3” E

Monsma Six Spring onions 412 52°18’50.1” N, 5°26’13.0” E

Monsma Seven Carrots – 52°18’37.7” N, 5°26’21.5” E

73 chapter 5

a) b)

Wind Turbine 1 Wind Turbine 4

Wind Turbine 2 Wind Turbine 5

Wind Turbine 3 Wind Turbine 6

Wind Turbine 7

fig. 5.1 Satellite photos retrieved from Google of terrain where the wind turbines were located. (a) Turbines one to three, with no crops growing at the time of sampling, located on the van Andel Bio farm. (b) Turbines four to seven located on the Monsma farm, with wheat, grass, spring onions and carrot crops, respectively.

Measuring wind turbine-induced the soil. To prevent recording the vibrations vibrational noise generated by wind gusts directly hitting the geophone, we dug a 10 cm deep hole in the Vibrational noise was measured along two soil where we inserted the geophone in the 256 m long transects per turbine, for each soil at the bottom of the hole. We did not of the seven turbines. We measured vibra- cover the hole in the soil while the geophone tional noise at eight points along each tran- was recording. We recorded soil vibrations sect: 2 m, 4 m, 8 m, 16 m, 32 m, 64 m, 128 for one minute per point. Recording settings m and 256 m from the turbine. We used a were kept constant per transect, which vertical geophone (RTC 4.5 Hz, 395HM, allowed us to compare noise levels within The R.T. Clark Companies Inc. 10800 NW each transect (but not between transects, Garnett, Oklahoma City, OK 73114, USA) as recording levels on the Tascam recorder with a sensitivity of 23.4 V/m/s connected were not standardized). to an audio recorder (Tascam 60D MKII, We sampled vibrational noise on four 44,1 kHz, 16-bit resolution, Bahnstraße 12, different dates: 17 April, 25 April, 16 May 65205 Wiesbaden, Germany) to measure and 24 June. The wind force during those wind turbine-induced vibrational noise from days varied between three and four on the 74 chapter 5

Beaufort scale (12-28 km h-1), while air Determining soil mesofauna temperature ranged from approximately abundance 18°C to 30°C. Additionally, a hand-pushing penetrometer with a cone diameter of 3.3 We collected soil animal samples of the first mm was used to measure soil compaction. 5 cm of the top soil layer using a soil corer The soil compaction measurements were (10cm ø). We collected samples at 8 m, 32 made by pushing the penetrometer verti- m, 64 m and 128 m in the transect. At site cally into the soil at each point in a transect. one two transects were sampled per wind mile, while at site two only one transect Measuring earthworm abundance was sampled per wind mile. Therefore, we decided to take the average of the two tran- Earthworm abundance was measured in soil sects per wind mill for site 1 to avoid statis- blocks. We dug up 15625 cm3 of soil (25 cm tical issues regarding nesting of random x 25 cm x 25 cm), and deposited the soil in effects. a plastic tray (70 x 40cm). We then hand- Soil animals other than earthworms sorted the soil to search for earthworms, were extracted from the soil using Tullgren counting the total number of individuals. funnel extraction (following van Straalen Each sample was sorted-through with the & Rijninck, 1984), and collected in vials same precision, allocating 25 ± 5 min per with 75% ethanol. Taxonomic identifi- sample point. We monitored earthworm cation was done to the order or class level abundance for 46 sample points in total, under a Bresser ICD/LED binocular micro- along two transects per wind turbine at 8 scope 40x magnification. Furthermore, we m, 32 m, 64 m and 128 m for seven turbines. classified the mesofauna into three body- We started at 8 m because the soil closer size classes: small (< 3mm), medium (3 to the wind turbine (at 2 m and 4 m) was mm – 1cm), and large (> 1cm). However, more variable, often with some grass, which the medium and large body-size classes could influence earthworm abundance. The appeared only in two sample points and earthworm count at 32 m was done only for contained a very low count. Therefore, we transect one of turbines four, five, six and decided to exclude the medium and large seven. After earthworms were counted, we body-size class samples from our statistical returned them to the soil. We decided to analysis on mesofauna. group all earthworms together based on the assumption that they all play similar roles Data analyses in ecosystem services and interact with soil communities (Edwards, 2004). Therefore, We analyzed our recordings in Raven Pro. we did not make earthworm species or size Version 1.5. We calculated Root Mean distinctions. Square (RMS) amplitude (U) normalizing the measurements by the loudest recording from the transect (at 2 m distance from the wind mill). Relative RMS amplitude (dB)

75 chapter 5 was calculated with the following formula: single effect models testing the effect of our Recording(i) 20*Log10 = ( ) predictors on mesofauna abundance. Our Max recording candidate models included noise ampli- Furthermore, we inspected the spec- tude, distance to the wind turbine and tral characteristics of the vibrational noise soil compaction as predictors. In the set of recordings by plotting power spectra models explaining abundance of small soil (“Hanning” window, window size=2048). animals, we also included earthworm abun- Signal analyses and statistical analyses were dance as a predictor, since earthworms can done with R version 3.3.1 (R Core Team, have a major impact on other soil organisms 2016), run in the RStudio interface (RStu- (Eisenhauer 2010). We included transect dio Team, 2015). nested in wind turbine as a random effect To estimate how the relative noise with a random intercept. Using the Akaike’s amplitude dropped with distance, we used a Information Criterion for small sample linear mixed effects model from the package sizes (AICc) calculated with the function “lme4” (Bates et al. 2015). We included soil “dredge” from the R package “MuMIn” compaction and crop type (no crop, grass, (Barton 2009) we assessed the explanatory spring onions, wheat and carrots) as covari- value of the candidate models by comparing ates. We nested transects in wind turbines, their ΔAICc = (AICi – min AIC, model and included them as random effect vari- with the lowest AIC-value). We consid- ables with a random intercept and a random ered all models with a ΔAICci ≤ 4. Akaike slope. To test whether the fit of the statistical weights (w1) were computed to determine model was improved by the inclusion of our the probability that a model best described main factor “distance”, we obtained Wald the variation in abundance of earthworms Chi-square statistics from the “Anova” func- and small soil animals. We then calculated tion in the statistical package “Car” (Fox et the model average estimates with the subset al., 2012). of models that had a ΔAICci ≤ 4. Addition- ally, we calculated 95% confidence intervals Testing effect of vibrational noise on for this subset of models. abundance of macro- and mesofauna

We tested for an effect of vibrational noise on the abundance of earthworms and small- sized (<3mm) soil animals. Following the information theoretic approach (Burnham et al. 2011), we created two sets of candi- date models with abundance of earthworms and abundance of small soil animals as our response variables. We compared 20 single effect models testing the effect of our predic- tors on abundance of earthworms, and 64

76 chapter 5 Results

Wind turbine-induced vibrational noise

Vibrational noise was on average 23 dB ± found in the lower frequencies (Fig. 5.3). 7 dB SD louder at 2 m than at 256 m in We found a significant correlation the transect (see Table S5.1 for mean ± SD between distance to the wind turbines attenuation per distance point). The highest and vibrational noise levels, with vibra- attenuation at 256 m was 29 dB, measured tional noise levels decreasing with increas- on the field of turbine three at site 1, while ing distance (Lmm, distance, β= -0.08, the lowest attenuation at 256 m was 16 dB SE=0.006, P< 0.01, see Table S5.2 in supple- on the field of turbine seven at site 2 (Fig. mentary materials for estimates of all fixed 5.2). Most of the spectral energy in vibra- factors). tional noise induced by wind turbines was

0

–10

Turbine 1 Turbine 2 Relative amplitude (dB) –20 Turbine 3 Turbine 4 Turbine 5 Turbine 7

2 4 8 16 32 64 128 256 Distance fig. 5.2 Wind turbine-induced vibrational noise relative amplitude (dB) change with increasing distance (m) to the base of the wind turbine. We recorded noise levels on two transects per wind turbine. Standard deviation between the transects is shown with error bars per turbine per distance point.

77 chapter 5

Distance from turbine fig. 5.3 Mean spectra of vibra- 2 m tional noise in the soil induced 40 256 m by wind turbines, measured at 2 m and 256 m from the turbine. Sampling rate, 44100 20 Hz; window length, 2048. Most energy in wind turbine vibra- tional noise is biased towards the 0 low frequencies. Amplitude (dB) Amplitude

–20

0.0 0.5 1.0 1.5 2.0 Frequency(kHz)

Earthworm abundance is related to revealed that distance to turbine and vibra- noise amplitude and distance to wind tional noise levels were important predictors turbines of earthworm abundance (Table 5.2). Earth- worm abundance increased with distance to We counted an average of 183.6 ± 116.2 SD the turbines and therefore decreased with earthworms per meter squared, per wind increasing vibrational noise level (Fig. 5.4, turbine field. Turbine six at site two had Table 5.3). None of the crop types signifi- the lowest number of earthworms with a cantly affected earthworm abundance (Table mean of 86.8 ± 58.3 SD per meter squared, 5.3). Soil compaction and crop type were whereas turbine one at site one had the high- not considered important predictors, nor est mean count of 248 ± 168.9 SD earth- were they statistically significantly related worms per meter squared. to earthworm abundance on our sampling Our model selection procedure fields.

table 5.2 Top models from the information theoretic approach selection with a threshold of ΔAICc ≤ 4, explaining abundance of earthworms collected by the soil monitoring method. The best model included relative amplitude of vibrational noise and crop type as predictors.

Fixed and random effects AICc ΔAIC Weight

Relative amplitude + crop type + (1 + 1| wind turbine /transect) 301.4 0 0.503

Crop type + (1 + 1| wind turbine /transect) 302.5 1.02 0.302

Crop type + distance + (1 + 1| wind turbine /transect) 304.8 3.35 0.094

78 chapter 5

600 (a) 600 (b)

400 400

200 200 Earthworms m-1 Earthworms m-1

0 0

–20 –15 –10 –5 8 32 64 128 Relative amplitude (dB) Distance to wind turbine (m) fig. 5.4 (a) Scatterplot showing earthworm abundance in response to wind turbine-induced vibrational noise levels. Earthworm abundance statistically significantly decreased with increasing noise levels. The red line is the fit of a linear model testing the relationship between earthworm abundance and noise levels. (b) Boxplot showing earthworm abundance in response to distance to the base of the turbine. Earthworm abundance is statistically significantly higher further away from the from base of the turbine. table 5.3 Model-averaged means (Estimate), their conditional standard errors (SE), z value, p value and 95% confidence intervals (95% CI) for the fixed effects of relative amplitude, crop type (crop) and distance explaining abundance of earthworms. 95% CI

PARAMETER ESTIMATE SE Z VALUE P VALUE LOWER UPPER

Relative amplitude -0.35889 0.13962 1.897 0.0129* -0.64178638 -0.07599278

Crop:

Grass 2.19179 5.32917 0.398 0.6907 -8.60273484 12.98631093

No crop 2.03672 4.38416 0.450 0.6530 -6.84309861 10.91653972

Spring onions -5.92106 5.33132 1.075 0.2825 -16.71974234 4.87762537

Wheat -1.43302 5.36955 0.258 0.7962 -12.30550855 9.43946564

Distance 0.04672 0.01682 2.688 0.0072** 0.01264768 0.08079120

Abundance of small soil animals is not at site two had the highest mean number related to noise amplitude of soil animals (11904.7846 ± 4534.4711 SD animals per meter squared) and turbine The mean number of small-sized soil animals seven at the same site the lowest, (4534.4711 was 4883.781 ind. ± 4412.292 SD per meter ± 374.8314 SD animals per meter squared). squared per wind turbine field. Turbine five Only crop type was considered an import-

79 chapter 5 ant predictor of abundance of small-sized and in the fields with no crops (Lmm, grass, soil animals (Table 5.4), with most animals β= 88.3, SE=15.9, P< 0.01; no crop, β= 36.8, occurring in the field that contained grass SE=13.7, P= 0.01, respectively; Table 5.5). table 5.4 Top models from the information theoretic approach selection with a threshold of ΔAICc ≤ 4, explaining abundance of earthworms. The best model included only crop type as the main predictor.

Small (< 1 cm) soil animal abundance ~ AICc ΔAIC Weight

Crop type + (1|wind turbine) 63.6 0 0.417

Crop type + earthworm abundance + (1| wind turbine) 65.9 2.33 0.130

Crop type + distance + (1| wind turbine) 66.8 3.22 0.083

amplitude + (1| wind turbine) 66.8 3.22 0.083

Crop type + soil compaction + (1| wind turbine) 67.3 3.76 0.064

Crop type + temperature + (1| wind turbine) 67.4 3.83 0.061

table 5.5 Model-averaged means (Estimate), their conditional standard errors (SE), z value, p value and 95% confidence intervals (95% CI) for the fixed effects of crop type (crop), distance to the wind turbine (distance), earthworm abundance (worms), relative amplitude of vibrational noise (amplitude), soil compac- tion and air temperature explaining abundance of mesofauna. 95% CI

PARAMETER ESTIMATE SE Z VALUE P VALUE LOWER UPPER

Crop

Grass 88.28877 15.96256 5.531 < 0.001 57.00272281 119.5748085

No crop 36.78331 13.73797 2.520 0.0117 8.17370129 65.3929242

Spring onions 7.34064 15.93400 0.461 0.6450 -23.88943067 38.5707036

Wheat 25.78486 15.90733 1.621 0.1050 -5.39294087 56.9626546

Distance 0.09334 0.09349 0.998 0.3181 -0.08990035 0.2765727

Worms 0.57817 0.63933 0.904 0.3658 -0.67488647 1.8312266

Amplitude -0.39614 0.84493 0.469 0.6392 -2.05216813 1.2598975

Soil compaction 0.11191 0.40853 0.274 0.7841 -0.68878938 0.9126064

Air temperature -0.45833 2.66578 0.172 0.8635 -5.68317427 4.7665076

80 chapter 5 Discussion

Here we assessed the relationship between the soil differed substantially between the subterranean vibrational noise levels and nearest points (2 m and 4 m) and remaining the abundance of soil animals. We hypoth- points of the transect. While we accounted esized that vibrational noise would impact for variation in soil compaction and crop the community in a size-dependent way. type, neither of these factors was related to Sampling seven different agricultural field earthworm densities, nor did it co-vary with we found that vibrational noise levels were distance to the wind turbines. significantly higher closer to the wind We did not find an effect of vibra- turbines and that earthworm abundance was tory noise on mesofauna abundance, which negatively related to vibrational noise levels. confirmed our prediction that any impact Moreover, we found no relationship between depends on an organism’s body size, as the noise levels and smaller-size soil animals. vibrations produced by wind turbines are Finally, we found no effect of earthworm typically biased towards the lower frequen- abundance on mesofauna abundance. cies. The larger the wavelength (the lower the frequency), the larger a sensory organ needs Earthworm abundance is negatively to be in order to pick up the fluctuations in related to vibrational noise pressure or particle motion. Alternatively, organisms detect deviation in pressure or We found that, on average, the number of motion by comparing the input between two earthworms decreased by 40% at the point or more organs simultaneously. Although furthest away from the turbines compared we do not know the exact organs used by to the closest point to the turbines where we earthworms to detect soil vibrations, their measured (64 m vs. 8 m). Our results confirm elongated body shape would make them well that earthworm abundance decreased equipped to detect low-frequency waves that substantially as amplitude of vibrational travel from their front to their back. noise increased. The maximum ampli- tude difference over the range at which we Sensory pollution consequences surveyed earthworms was on average 13dB. We therefore predict the impact of vibra- Human-induced sensory pollutants can tory noise to be even bigger when measured directly affect organisms through an over the whole transect, as vibrational noise impact on their perception, physiology and levels near the base of the turbine are up behavior (Brumm and Slabbekoorn 2005; to 30 dB higher than at our furthest sites Barber et al. 2010; Kight and Swaddle (> 200m from the turbine). However, we 2011; Naguib 2013; Velilla and Halfwerk did not survey earthworm densities close 2019). It is possible that wind turbine-in- to the turbine base as the composition of duced vibrational noise masks the vibra-

81 chapter 5 tional cues of approaching foraging moles, on other soil organisms. making earthworms in highly noisy areas Earthworms play a crucial role in more prone to predation (Dominoni et al. several soil processes including: soil forma- 2020). Moreover, even in situations where tion, soil structure, water infiltration, noise does not mask a signal or a cue, it can nutrient cycling, carbon sequestration, still affect the perception of that signal or climate regulation and primary production cue by distracting the receiver as (Dominoni (Bertrand et al., 2015; Blouin et al., 2013, et al. 2020) has been shown in Caribbean Edwards, 2004). Furthermore, earthworms hermit crabs Coenobita clypeatus and ground are known to play an important role in plant squirrels Spermophilus beecheyi (Rabin et al. production (Baker et al. 2006; Blouin et al. 2006; Chan et al. 2010). Finally, vibratory 2013) and in the distribution of other soil noise could be misleading to earthworms, organisms (Gutiérrez-López et al. 2010). who may not be able to distinguish between Therefore, the negative relationship we vibratory cues coming from an approaching find between wind turbine noise levels and predator such as a mole, and the subterra- earthworm abundance can potentially have nean waves from the turbines (Dominoni et cascading effects on other soil organisms al. 2020). As matter of fact, earthworms are and processes. Although renewable sources well known to be tricked by other organ- of energy are clearly the way forward, atten- isms, including humans who have devel- tion should be given to reducing vibrational oped so-called ‘worm grunting’ techniques noise levels in the soil induced by wind that mimic soil vibrations from approaching energy turbines. moles (Catania 2008). Either through masking, distraction or misleading effects, noise could decrease earthworm densities, and ultimately modify the distribution of species that depend or interact with earthworms. In our study we did not find evidence for an effect of varia- tion in earthworm abundance on the abun- dance of smaller soil animals. However, our measurements and observations were done in agricultural fields which are highly managed by humans (e.g. human-induced bioturbation homogenously throughout the field), possibly influencing the effects that earthworms would have under natural wild conditions, or in a different season. Further studies should examine the effect of vibra- tional noise on earthworm abundance in non-managed fields and its consequences

82 chapter 5 Acknowledgements

We are especially thankful to Piet van Andel and Jan-Harm Welleweerd from van Andel Bio and to Douwe Monsma from Douwe Monsma B.V. for providing us access to the fields where the measurements involved in this study were made. We are also grate- ful to Beth Mortimer for her advice on soil vibrations measuring techniques and for her advice on the hypotheses presented in this study. Furthermore, we thank Rudo Verwij and Jurgen van Hal for their help with the mesofauna analysis, Ivor Keesmaat for his help in the field data collection, Kees van Geestel for his advice on earthworm ecol- ogy, Oscar Franken for his input in the field sampling techniques and Jacintha Ellers for commeting on earlier versions of this manu- script.

83 chapter 5 Supplementary materials

table s5.1 Mean ± sd attenuation of wind turbine-induced vibrational noise (dB) at the different points in the transects.

DISTANCE (m) ATTENUATION (dB)

4 2 ± 3

8 5 ± 3

16 7 ± 3

32 10 ± 2

64 14 ± 3

128 18 ± 3

256 23 ± 7

table s5.2 Model estimates of model explaining variation in amplitude attenuation of wind turbine-in- duced vibrational noise in fields (n = 7).

FIXED EFFECTS ESTIMATE SE T VALUE P VALUE

Distance -0.087017 0.006 -15.349 <0.01

Soil compaction 0.003485 0.023 0.148 0.8830

Crop:

No crop 0.810133 1.542 0.525 0.6090

Grass 0.148951 2.004 0.074 0.9421

Spring onions -0.496189 2.493 -0.199 0.8455

Wheat 0.798357 1.935 0.413 0.6887

84 Pisum sativum

85 chapter 6

Effect of anthropogenic vibratory noise on plant development and herbivory

Estefania Velilla • Laura Bellato • Eleanor Collinson Wouter Halfwerk

Abstract

A growth in anthropogenic activities and as daily shoot-length growth. Moreover, infrastructure has led to increasing subter- we tested the direct and indirect effects of ranean vibratory noise levels. Inland wind vibrational noise on herbivory intensity by energy turbines, which are mostly located the generalist caterpillar Spodoptera exigua. in agricultural fields, are a fast-growing We found that plants exposed to high source of vibrational noise. Plants, which vibrational noise grew significantly faster are rooted in the soil are constantly exposed and taller than plants exposed to low vibra- to windmill-induced vibrations propagating tional noise. Additionally, plants treated through the ground. We have little under- with high noise germinated, flowered and standing on how anthropogenic seismic produced fruits quicker than those treated vibrations affect plant development and with low noise. However, the differences how that in turn can affect plant-insect in germination time, flowering time and interactions. In this study we investigated fruiting time between the treatments were the effect of windmill-like underground not statistically significant. Furthermore, we vibrational noise on plant development and did not find an effect of vibrational noise on on a plant-herbivore interaction. We exper- herbivory intensity. Vibrational noise could imentally exposed Pisum sativum plants have consequences for both natural plant from seed stage to seed production stage to communities and agricultural crops by alter- high and low vibrational noise levels and ing interspecific competition and by shifting monitored them daily. We recorded germi- growth-defence activation trade-offs. nation, flowering and fruiting time, as well 86 chapter 6 Introduction

The global increase in human infrastructure herbivores (Appel and Cocroft 2014). More- is well-known to affect ecosystems and the over, stimulating plants with sounds and organisms that live in them. An important vibrations of different frequency ranges has component of human activities associated been shown to affect germination timing with infrastructure is the production of (Uchida and Yamamoto 2002; Creath and substrate-borne vibrations. Road and rail- Schwartz 2004; Cai et al. 2014) and growth way traffic as well as construction work has (Takahashi et al. 1991). Besides the direct led to an increase in subterranean vibratory effects of vibrations on plants, vibratory noise levels, potentially influencing organ- noise can also affect organisms that inter- isms that live in the soil. Organisms ranging act with plants, hence indirectly affecting from animals (Cocroft and Rodriguez 2005; plants via species interactions. For example, Cocroft et al. 2014) to plants (Takahashi vibrations may deter beneficial soil fauna et al. 1991; Uchida and Yamamoto 2002; such as earthworms and therefore decrease Gagliano et al. 2012, 2017; Ghosh et al. soil fertility, or it can affect organisms in the 2016) to fungi (Leach, 1980) are known to trophic cascade of herbivores, predators and rely on vibratory signals and cues for their parasitoids. survival and reproduction (De Luca and Wind energy turbines are a fast-grow- Vallejo-Marín, 2013; Appel and Cocroft ing potential source of anthropogenic 2014; Veits et al. 2019). Human-induced vibrational noise. In Europe, wind energy vibratory noise levels may therefore impact remains the second largest form of power organisms across trophic levels. Despite the generation capacity, with a total net installed far-reaching ecological implications , only capacity of 168.7 GW (Wind in power 2017 a handful of studies have looked into the report). Most inland wind energy turbines effect of vibrational noise and have mostly are located in agricultural fields where plants focused on animals (Wu and Elias 2014; and the insects that interact with them are Gagliano et al. 2017; Roberts and Elliott potentially constantly exposed to vibrational 2017; Caorsi et al. 2019). noise. The few studies on the effects of vibra- In our current study we assessed tory noise on plants suggest that vibrational whether vibrational noise generated by cues are used across a range of different wind energy turbines influences plant-insect ecological contexts. Zea mays roots for exam- interactions. We first examined the effect ple grow towards vibrations associated with of vibrational noise on plant developmen- flowing water (Gagliano et al. 2017) and tal processes (e.g. germination, flowering Arabidopsis thaliana increase their anti-her- and fruiting time, and growth). In the plant bivory chemical defense when exposed to development experiment, we were interested the chewing vibrations induced by their to determine whether plants exposed to

87 chapter 6 vibrational noise germinated, flowered and fruited sooner or later than plants exposed to low vibrational noise, and whether there were differences in growth between the plants exposed to high or low vibrational noise. Next, we tested for direct and indi- rect effects of vibrational noise on herbivory. We experimentally exposed Pisum sativum seeds to high and low vibrational noise and tracked their development on a daily basis. After fruiting of the plants, we carried out an herbivory experiment, using the general- ist caterpillar Spodoptera exigua and full-fac- torial design. For the herbivory experiment we were interested in two questions: 1) does vibrational noise occurring at the moment of foraging affect herbivory? and 2) Does prior plant exposure to vibrational noise have carry-over effects on herbivory? For example, via changes in secondary metabo- lites? Caterpillars were placed on plants that either developed during low or high noise conditions, and were exposed to either high or low vibrational noise during the foraging period.

88 chapter 6 Materials and Methods

Study species chamber at 20°C with an 8:16 (L:D) photo- period and 70% relative humidity. The light- We decided to work with P. sativum plants ing in the experimental room contained because we were interested in linking our Photo Active Radiation (PAR) in the range vibrational noise field measurements to crop of 400-700 nm and some far-red lighting plants and because of evidence indicating the using LED lamps, in the range of 600-800 sensitivity of this species to vibrational noise nm. The light level in the experimental room (Gagliano et al. 2017). Organic P. sativum was 450 µmol/m2 /s. We exposed a total of seeds were obtained from the company 40 plants to vibrational noise from seed stage Intratuin (Nobelweg 10, 1097 AR Amster- to fruiting stage. From the 40 plants, 20 dam, The Netherlands). were exposed to high vibrational noise and For our herbivory experiments we the other 20 were exposed to low vibrational used the generalist caterpillar S. exigua. We noise (see below). Ten pots were placed on obtained S. exigua eggs from the company a plastic tray (48.5 x 31cm). Below the tray, Entocare (Haagsteeg 4, 6708 PM Wagen- four shakers (Monacor AR-30, Monacor ingen, The Netherlands). The animals were International GmbH & Co. Zum Falsch reared in climate rooms at 26°C ± 1°C and 36, 28307 Bremen, Germany) connected 80% relative humidity on a 12D/12N light to a constantly charging mp3 player (Lenco cycle. Caterpillars were fed ad-libitum with Xemio-200) and to a stereo amplifier (Renk- a corn-based artificial diet. For our herbiv- force SAP-702 2 x 20 W manufacturer No. ory experiments we used caterpillars in the RF-3511635 or Renkforce T21 2 x 50 W 5th instar. Caterpillars that took part in our manufacturer No. RF-4602693) broad- experiments were fed with P. sativum the casted vibrational pink noise. day before testing them to acclimatise them Our playback stimulus (pink noise) was to the diet. based on field recordings of subterranean vibrational noise induced by windmills (see Experimental procedures chapter 5 for methods on field record- ings of wind turbine-induced vibrations). We exposed P. sativum to high and low Since windmill-induced vibrational noise levels of vibratory noise throughout their was highly represented by energy in the low development (47 ± 3.5 days). Seeds were frequencies (< 100Hz), we decided to use planted individually on 26 June 2019 in 6 pink noise as a standardized playback stimu- cm diameter plastic plots containing organic lus, also biased towards the low frequencies, potting soil (Horticoop, Kalppolder 150, albeit in a wider range. Bleiswijk, The Netherlands). The entirety We measured the ambient vibratory of this experiment took place in a climate noise levels of our setup using a Laser-Dop-

89 chapter 6 pler vibrometer (LDV; Polytec PDV-100, low noise treatment. A difference of 12 dB set to 100 mm/s/V, sampling rate 22kHz) corresponds to the difference obtained from connected to an oscilloscope (Rigol, recording windmill induced soil vibrations DS1054, 4 Channel 50MHz, 1GSa/s). We at 8 m compared to 128 m. We chose 12 dB adjusted the amplitude by changing the since this was the maximum intensity-dif- settings of the amplifier. For the low-noise ference we could obtain with our speaker treatment we matched the RMS values setup without creating distortion. The obtained from the oscilloscope to the value amplitude of our vibrational noise stimuli obtained for the ambient conditions of the was calibrated on several positions on the room. For the high noise treatment, we set tray to ensure that all plants were exposed to the amplitude 12 dB higher compared to the equal vibrational noise amplitude (Fig. 6.1).

(a) Pink noise playback (b) Windmill induced vibrational noise 50

80

25

60

Amplitude (dB) Amplitude 0

40

High –25 At 2 m Low At 128 20 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5

Frequency(kHz) fig. 6.1 Power spectra of experimental playback stimulus and field recordings. (a) Power spectra from recordings of vibrational pink noise playback stimulus (“Hanning” window, window size = 2048) recorded with a Doppler laser vibrometer on the centre of an experimental tray containing 10 potted P. sativum plants. The high noise treatment (red line) was 12dB louder than the low noise treatment, and the low noise treatment (dark grey line) was approximately 2dB louder than ambient silent conditions calculated from root mean square readings from an oscilloscope. (b) Power spectra from normalized recordings (“Hanning” window, window size = 2048) of windmill-induced vibrational noise in the field at 2m (red line) and 128m (dark grey line) from the base of the wind turbine. Recordings were made with a vertical geophone.

90 chapter 6

We divided the treatments in two trays and late the day an event took place during the the trays were positioned opposite to each weekend. We noted germination date, date other (on opposite sides of the room), to of appearance of first flower and date of control for any lighting effects (Fig. 6.2). appearance of first fruit. Additionally, shoot Halfway through the experiment (19 days length was measured on a daily basis with after planting) we switched tray position so a metallic 30 cm-ruler. Plants were watered that plants in one side of the room would be by adding water to their tray allowing them exposed to the other side of the room, this to absorb as much water as needed. way avoiding confounding effects of location in the room. The seeds/plants were checked on a daily basis except for weekends. We extrap- olated based on developmental events recorded during the working week to calcu-

Tray A (a) Tray B

Low noise High noise B side Room (b) (c) amplifier

Roomside A Tray D Tray C

High noise Low noise

fig. 6.2 Graphical depiction of experimental set-up where we tested the effect of high and low vibratory noise on plant development. (a) Experimental table (light grey) with a plastic tray (blue) with four shakers on it. On top of the shakers another tray (dark grey) contained 10 P. sativum potted seeds/plants. We had two tables per treatment placed opposite and diagonally to each other. (b) the four shakers were attached to an amplifier, and the experimental stimulus was played from a constantly charging mp3 player attached to the amplifier. (c) We switched the position of the trays (light blue) 19 days after planting to control for confounding effects of room side.

91 chapter 6

For the herbivory experiment we exposed plants exposed to high vibrational noise caterpillars to high and low vibrational during the herbivory experiment, and the noise while they foraged on plants that had other half exposed to low vibrational noise. previously been exposed to either high or We used the change in caterpillar weight low vibrational noise during the plant devel- (delta weight) as a proxy to herbivory inten- opment experiment (Fig. 6.3). Half of the sity. Some caterpillars failed to forage at plants that were exposed to high vibrational all and were excluded from the results. The noise during the plant development experi- caterpillars that failed to forage fell into the ment were exposed to low vibrational noise soil and did not climb the plant again. The during the herbivory experiment, while the number of caterpillars that failed to forage other half were exposed to high vibrational was distributed across treatments. In total noise. The same applied for plants exposed we obtained data for 35 caterpillar-plant to low vibrational noise during the plant combinations. development experiment, with half of the

(a) (b) Treatment during herbivory experiment

High

Low

Treatment during plant development experiment

(c) (d) High

Low

fig. 6.3 Graphical illustration of herbivory experiment setup. We tested the effect of vibratory noise on herbivory of P. sativum by S. exigua. Noise could affect foraging in caterpillars directly, or indirectly via effects on plants previously exposed noise. Therefore, caterpillars were tested on the plants used during the plant development under noise conditions experiment. Caterpillars were placed on the leaves of plants and exposed to high or low noise conditions for 24 hours. (a) Half of the plants on this tray had been exposed to high noise conditions during the plant developing experiment, and the other half to low noise conditions. (b) Half of the plants on this tray had been exposed to high noise conditions during the plant developing experiment, and the other half to low noise conditions. (c) and (d) are replicates of (a) and (b), and therefore the same description applies. Plants were randomly (balanced) placed on the experimental trays. 92 chapter 6

We deprived the caterpillars (5th instar, normal distribution, and no transformation 22-27 days old) of food for 2 hours before or alternative choice of distribution family testing them. Caterpillars were weighed just produced a satisfactory model. Furthermore, before placing them on a plant. Individual to test the effect of high and low vibrational shakers were placed under individual plants. noise on the number of days it took for to Caterpillars were allowed to forage for 24 produce their first flower and their first fruit hours, after which they were removed and we used linear mixed effects models follow- immediately weighed. Foraging intensity ing Gaussian distribution. We included tray by caterpillars under different noise levels ID as a random effect with a random inter- could differ because caterpillars might be cept. Days to flowering and days to fruit- disturbed by noise, in which case we would ing, which were our response variables were expect foraging intensity to be lower under both squared-root transformed to achieve a high noise conditions, with caterpillars better model fit. The number of days it took showing a lower weight increase. Alterna- to flower and produce fruit was calculated tively, caterpillar chewing vibrations which from germination date and not from plant- can increase secondary metabolite emission ing date. To test the effect of high and low in plants (Appel and Cocroft 2014) could vibrational noise on plant growth we used be masked by our high noise treatment. The a linear mixed effects model with repeated masking of caterpillar-induced vibrations shoot length measurements as our response could result in lower emission of secondary variable. We nested plant identity in tray as metabolites, thus allowing caterpillars to a random effect, with a random intercept. forage for a longer amount of time. In this We tested whether vibrational noise case, we would expect a higher caterpillar affected herbivory using a linear regression, weight increase. For logistical reasons we did with caterpillar delta weight as our response not measure surface eaten from the leaves. variable. Our main predictors were treat- However, we expect surface eaten to be ment during the herbivory experiment (high highly correlated with increases in caterpil- or low vibrational noise at the time of forag- lar weight. Therefore, we used delta weight ing), treatment during the plant develop- as a proxy to herbivory intensity. ment experiment (treatment to which the plants had been exposed to in the previous Data analyses experiment) and final shoot length. We tested the interaction between treatment Statistical analyses were done with R version and previous treatment. We included plant 3.3.1 (R Core Team, 2016), run in the R final shoot length as a predictor to control studio interface (RStudio Team, 2015). for effects of plant size on the investment of We used a Kruskal Wallis test to test plant defences against herbivory. the effect of high and low vibrational noise All model residuals were inspected by on the number of days it took for seeds to means of Q-Q plots and histograms. There germinate. We used a non-parametric were no deviations from the normality or test here because our data did not follow variance homogeneity assumptions from

93 chapter 6 linear models. The effect of individual model predictors was established by full-null model comparisons by means of Wald Chi-square statistics from the “Anova” function in the statistical package “Car” (Fox et al., 2012).

94 chapter 6 Results

Vibrational noise affects plant growth and flowering time

Our repeated measures analysis using shoot high vibratory noise conditions (LMM, length as a proxy for growth revealed that high vibrational noise treatment, n = 39, β = vibrational noise had a significant effect on 2.9525, t = -2.191, p = 0.03; Fig. 6.4). growth, with plants growing taller under

50

Treatment 40 High Low 30

20 Shoot length

10

0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 Days since germination fig. 6.4 Boxplot showing increase in shoot-length (cm) over time (days since germination) for plants grow- ing under high (red) and low (grey) vibrational noise. Plants exposed to high (red) vibrational noise grew taller than plants exposed to low (grey) vibrational noise. The interquartile range was taken as the range from 0-25th percentile. From the mean, the whiskers show the highest and lowest value within 1.5 times the interquartile range.

95 chapter 6

Although seeds exposed to high noise vibratory noise produced their first flower germinated slightly earlier than seeds on average within 37.2 (SD ± 4.2) days after exposed to low noise, these differences germination, while the mean number of were not statistically significant (n= 39, days it took plants exposed to low vibratory χ2 Kruskal Wallis, (1) = 0.11, p= 0.73, Fig. noise to produce their first flower was 39.7 6.5 (a)). Noise treatment also tended to affect (SD ± 4.2) days after germination (Fig. 6.5 flowering time (Lmm,df = 1, F= 3.24, p = (b)). Fruiting time did not differ significantly 0.08), with plants exposed to high noise between the treatments (Lmm, df = 1, F = conditions flowering on average around 2.0476, p = 0.16, Fig. 6.5 (c)). 2 days earlier than plants exposed to low noise conditions. Plants treated with high

(a) (b) (c)

45 20 48

15 40 44

10 Days to first fruit Days to first flower Days to germination 35 40

5

High Low High Low High Low Treatment fig. 6.5 (a) Boxplot showing the number of days it took seeds exposed to high vs. low vibratory noise to germinate. Seeds exposed to high vibratory noise germinated approximately 1 day earlier than seeds exposed to low vibratory noise. (b) Number of days it took plants exposed to high vs. low vibratory noise to produce their first flower. Plants exposed to high vibratory noise produced their first flower approximately 2 days earlier than seeds exposed to low vibratory noise. The difference was near-significant. (c) Number of days it took plants exposed to high vs. low vibratory noise to produce their first fruit. Plants exposed to high vibratory noise produced their first flower approximately 1.5 days earlier than seeds exposed to low vibratory noise. The difference was not significant. The difference was not significant. The interquartile range was taken as the range from 0-25th percentile. From the mean, the whiskers show the highest and lowest value within 1.5 times the interquartile range.

96 chapter 6

Vibrational noise does not affect affected herbivory intensity. We found no herbivory carry over effects (Linear regression, df= 1, treatment during herbivory experiment, F We tested whether vibrational noise affected = 0.0263, P = 0.87; treatment during plant herbivory directly or indirectly. We found no development experiment, F = 0.7338, P = differences in herbivory intensity between 0.39; interaction between treatment during caterpillars foraging in high versus low herbivory experiment and treatment during vibrational noise. Furthermore, we exam- plant development experiment, F = 0.3648, ined whether the vibrational noise treat- P = 0.55; shoot length, F= 0.0183, P = 0.89, ment during the plant growth experiment Fig. 6.6).

Treatment during plant 0.06 development experiment High Low 0.03

0.00

–0.03 Caterpillar delta weight (mg)

High Low Vibrational noise treatment fig. 6.6 Experiment testing the effect of noise on herbivory intensity ofS. exigua caterpillars on P. sativum plants. This boxplot shows changes in caterpillar weight after foraging under high or low vibratory noise. The plants used in this experiment were the same plants used in the experiment that tested the effect of vibratory noise on plant development. To disentangles whether the effect on herbivory was a direct effect on the caterpillars or a carry-over effect from the plants from noise exposure during the previous experiment, half of the plants that were exposed to high noise during the plant development experiment were exposed to low noise during the herbivory experiment, and the other half to high noise. In the same manner, half of the plants that were exposed to low noise during the plant development experiment were exposed to high noise during the herbivory experiment, and the other half to low noise (Fig. 6.3). The difference was not signifi- cant. The interquartile range was taken as the range from 0-25th percentile. From the mean, the whiskers show the highest and lowest value within 1.5 times the interquartile range.

97 chapter 6 Discussion

The aim of this study was to examine stresses like bending, rubbing and shaking whether windmill induced vibrations can of plants (Goeschl et al. 1966; Takahashi influence plants and their herbivores. In a and Jaffe 1984), and more recently shown first experiment we tested whether vibra- to be induced by vibrations (Uchida and tional noise affected plant developmental Yamamoto 2002). Some studies have also processes like germination, flowering time, provided evidence that vibrations of certain fruiting time, and growth. Using the same frequencies positively influence not only plants, we tested in a follow-up experiment germination, but also root elongation, callus whether vibrational noise affected herbivory. growth, and cell cycling (Gagliano 2013; Our results show that plant develop- Chowdhury et al. 2014; Teixeira da Silva ment can be affected by vibrational noise and Dobránszki 2014). It could be that P. that is reminiscent of noise profiles found sativum is sensitive to very low frequency in the proximity of wind power turbines. vibrations (< 50Hz), as was shown for C. Plants exposed to high noise levels grew sativus and O. sativa (Takahashi et al. 1991). faster and taller compared to plants exposed Given that the frequency response of our to low noise levels. Furthermore, we found shakers was poor for the low frequencies (< that on average plants exposed to high noise 100Hz), it is possible that the effect of our germinated and flowered slightly faster than treatments was hindered by limitations in plants exposed to low noise. However, the our playback setup. Alternatively, it is possi- differences between treatments were not ble that our sample size was too small to statistically significant. detect an effect of our treatment on germi- Although we did not find statistical nation, flowering and fruiting time. We differences in germination, studies on other would not have experienced the same prob- plants have repeatedly shown that vibra- lem when we tested daily growth, because tions can influence this important devel- we used a repeated measures analysis, which opmental stage. Mechanical stimulation typically have higher statistical power (Guo with 50Hz vibrations has e.g. been shown et al. 2013). to promote germination in Cucumis sativus In our study we show that plants and Oryza sativa (Takahashi et al. 1991). exposed to the high vibrational noise treat- Uchida and Yamamoto (2002) also showed ment experienced significantly higher that vibrational stimulation with mono- growth than plants exposed to low vibra- tone frequencies in the range of 40-120 Hz tional noise. These different developmental affected germination of Arabidopsis thali- trajectories may have already been initiated ana. Ethylene synthesis, which is necessary at the start of germination. Our results seem for seed germination (Abeles et al. 1992), therefore consistent with previous studies, is known to be induced by mechanical which have shown that vibrations stimulate

98 chapter 6 the growth and development of several plant frequency vibrational noise than when stim- species (Takahashi et al. 1991; Collins and ulated with a lower amplitude of the same Foreman 2001; Mishra et al. 2016). noise stimulus. Furthermore, we found that Vibrations are not only important for P. sativum plants exposed to high vibrational plant developmental processes, but they can noise grew significantly taller than plants also be important for plant-insect interac- exposed to low vibrational noise. The ampli- tions. For instance, Arabidopsis thaliana tude difference at which we see this effect is increases its anti-herbivory defences when ecologically relevant for plants growing in stimulated with their herbivores’ chewing fields where windmills are located. vibrations (Appel and Cocroft 2014), and The effect of sound vibrations on plants Oenothera drummondii increases its nectar can be frequency and amplitude dependent sugar concentration when exposed to the (Chowdhury et al. 2014). Consequently, buzzing vibrations of its pollinator (Veits et not all plant species’ development could be al. 2019). We wanted to investigate whether affected by windmill-induced vibrations in vibrational noise affected plant-insect inter- the same way, possibly altering competition actions, for interactions in which vibrations rates between plant species. Although this provided valuable information. Plants that would not necessarily be a problem for culti- are constantly exposed to vibrational noise, vated fields with monocultures, it could have like plants in a windmill field, could have a consequences for natural plant communi- harder time detecting the chewing vibra- ties where interspecific competition is an tions of their herbivores making them more important determinant of the structure and vulnerable to higher herbivory intensity. In the dynamics of plant communities (Aerts this study, however, we found that vibra- 1999). Furthermore, investment in plant tional noise did not affect herbivory intensity growth might come at the expense of plant and we did not see any evidence of carry over defence activation, which is imperative for effects on herbivory from previously expos- plant survival (Huot et al. 2014), since plants ing plants to high or low vibrational noise. count with a limited pool of resources that Although we did not see a herbivory effect can be used for growth or for defence (Coley in our experiments, it is important to point et al. 1985; Simms and Rauscher 1987; out that we used caterpillar delta weight as Herms and Mattson 1992). Our results a proxy to herbivory intensity, which might highlight the susceptibility of plants to not be the most accurate measurement of vibratory noise. Future studies are needed to effects on herbivory, since fluctuations in understand the sensitivity of plants to vibra- weight might also be related to the individ- tions of various frequencies, and whether ual moving activity patterns of the cater- different developmental stages are affected pillars. differently by the different frequencies. In conclusion, we found that P. sativum germinates, flowers and produces fruit slightly quicker when stimulated with higher amplitude non-monotonic low

99 chapter 6 Acknowledgements

We thank Richard van Logtestijn for his help setting up the plant climate chambers and Hans Cornelissen for his advice on plant choice.

100 chapter 7

Summary and general discussion

In this thesis I investigated the effects of using various study systems instead of one noise and substrate variation on the produc- to address a comprehensive problem and tion, transmission and perception of vibra- the outstanding questions that result as a tional signals and cues, and how that in consequence. Finally, I make suggestions for turn affects animals relying on vibrational future directions and I end with a conclud- information. Here, I summarize here the ing remark. main findings of each chapter, and discuss these results in a broader ecological context. Furthermore, I identify the limitations of

101 chapter 7 Summary

In this dissertation I show that noise and lowest, and signaled significantly less during substrate variation have important effects on wind gusts above 0.9 m/s, even during its all stages of information transfer in a variety peak time of signaling activity. Wind of study systems, and that individuals may represents one of the major sources of envi- adjust their behavior accordingly. ronmental noise for animals communicat- ing with substrate born vibrations (McVean Production and Field 1996; Casas et al. 1998b; Cocroft and Rodriguez 2005; Tishechkin 2007; Animals use different sensory stimuli (e.g. McNett et al. 2010). Therefore, we propose chemicals, sounds, light, vibrations) to that signal timing is an adaptation to avoid advertise important information such as signal masking by vibrational noise induced warning to conspecifics of the presence of by wind (see discussion in chapter 2 for a predators or availability to mate (Brad- alternative hypothesis). Wind is considered bury and Vehrencamp 1998; Stevens 2013). to be an important source of selection in This information is therefore crucial for an grassland and forest canopies (Cocroft and animal’s survival and reproductive success Rodriguez 2005). However, in our study (Dominoni et al. 2020). However, back- we demonstrate that wind is likely also an ground noise can affect an animal’s abil- important source of selection for animals ity to detect relevant information (Brumm communicating in the forest understory. and Slabbekoorn 2005; Francis and Barber Besides timing their signals, animals 2013). While we know a lot about the communicating in noisy environments effects of noise on the production of acous- can rely on several mechanisms to increase tic signals, we have little knowledge on how their chances of being perceived by the noise affects vibratory signaling. Therefore, target receiver. The producer of a signal the purpose of chapter 2 was to inves- can increase the amplitude of a signal (e.g. tigate how a potential source of vibratory Halfwerk et al. 2016a), repeat the signal noise affected animals communicating with more often (e.g. Brumm et al. 2004), adjust vibrations, and how these animals coped the frequency to avoid spectral overlap (e.g. with it. We show that wind noise affected Polajnar and Čokl 2008), or change loca- the production of vibratory signals in the tion of signal production to enhance signal Neotropical katydid Copiphora brevirostris, transmission (e.g. Tishechkin 2007). The and that male katydids time their signal- strategy that an animal will use to optimize ing to the long- and short-term fluctuations signal detection will probably depend on in wind velocity patterns. We found that the characteristics of the background noise, C. brevirostris signaled significantly more among other things. When a source of during the times at night when wind is noise is static or affects a particular location

102 chapter 7

(e.g. wind turbines), it is more likely that interactions. This is especially relevant for animals change the location from where predators and parasitoids of concealed prey/ they produce a signal. However, if a source hosts (e.g. leafminers), which rely heavily on of noise is continuous (e.g. other insects vibrational cues propagated through leaves choruses or roadway traffic), animals will to detect and localize their prey/hosts. probably recur to strategies like increasing This transmission study was carried out the amplitude of a signal or adjusting the in two different years (2016 and 2017), thus frequency to avoid spectral overlap. growing two batches of plants and measur- ing their leaf traits on those two years. An Transmission unexpected result from this experiment was that we found large variation in leaf traits Substrate variation is known to affect within species between years. Although we vibrational signal transmission in several used the same company to purchase seeds, ways, including severe frequency filtering and we planted the seeds and grew the plants and changes to the frequency dependence under the same temperature, light, humid- propagation velocity (Bell 1980; Michelsen ity and nutrient regimens in both years, we et al. 1982; Magal et al. 2000). However, still encountered large variation in leaf traits. most studies on transmission of vibrational Between-year variation in leaf traits is prob- signals and cues have compared transmis- ably due to slight differences in plant devel- sion on different plant species or parts of opmental stages at the time we measured the plants, or artificial versus natural substrates leaf traits for the experiments. The fact that (Michelsen et al. 1982; Magal et al. 2000; leaf traits vary throughout the development Miklas et al. 2001; Cocroft et al. 2006a). In of plants is important because this means chapter 3, we decided to use a (leaf) trait- that species-based predictions of signal based approach to systematically study the transmission are likely to be less accurate effects of substrate-induced changes in the if the variation of leaf traits associated with transmission of vibrational cues. Our results plant developmental stage is not taken into suggest that variation in leaf traits can affect account. Using a trait-approach to predict the transmission of vibrational information. transmission of vibrations might be more More specifically, we found that leaf thick- reliable than a species-approach prediction. ness explained variation in amplitude atten- uation during the transmission of vibrational Perception chewing cues induced by the generalist caterpillar Spodoptera exigua. Vibrational Extraction of relevant information from chewing cues produced on thicker leaves the environment is a crucial step preced- were lower in amplitude than chewing cues ing all information-driven behaviors (e.g. produced on thinner leaves. An effect on moving in the direction of a potential mate amplitude attenuation of caterpillar chew- or reacting to the trail of a prey), and just as ing cues can have consequences for pred- background noise can affect the production ator-prey, parasite-host and plant-insect of signals, it can also affect their perception.

103 chapter 7

Noise can affect signal or cue perception by higher than ambient) spent significantly masking the signal, and by distracting or less time searching for a host, which trans- misleading the receivers (Brumm and Slab- lated into a significant decrease in parasitism bekoorn 2005; Velilla and Halfwerk 2019; success. In our study we were not able to Dominoni et al. 2020). Masking occurs draw conclusions on the mechanism respon- when there is (full or partial) temporal and sible for the changes in behavior and parasit- spectral overlap between the signal and noise ism success in A. tabida. However, regardless (e.g. Legendre et al. 2012) thus making it of the mechanism, noise can lead to serious difficult or impossible for animals to assess consequences like increased mortality (e.g. the encoded information (Raboin & Elias, Simpson et al. 2016) and decreased repro- 2019). Distraction of the receiver results in a ductive success (e.g. Halfwerk et al. 2011a,), shift of attention away from relevant infor- which ultimately leads to a lower fitness. mation (Naguib 2013; Velilla and Halfw- Our findings have important conse- erk 2019). The distracting effects of noise quences not only for the parasitoids, which have been shown for example, in Caribbean experience a decline in fitness due to a hermit crabs, where crabs exposed to boat reduced reproductive success, but also for motor noise allowed simulated predators the hosts, and potentially plants affected by to approach closer than crabs not exposed the hosts, including plant species of societal to noise (Chan et al. 2010), indicating an importance. Plants benefit from parasitoids immediate fitness cost to living in noisy controlling their herbivore enemies, since conditions. Moreover, noise can also mislead without this bio-control, plants are more the receiver. For example, highway noise can vulnerable to herbivory. Moreover, the use share many features with natural stimuli of parasitoids as bio-control to regulate agri- like rain, wind and ocean surf, mislead- cultural pests is a common practice in agri- ing animals that are in search of optimal culture. Therefore, agricultural fields close breeding or feeding conditions (Geipel et to sources of anthropogenic-noise (e.g. close al., 2019b). Most knowledge on the effects of to wind energy turbines or highways) could noise on the perception of vibrational signals be less efficient in controlling pests when and cues has been based on behavioral stud- using parasitoids that rely on vibrational ies, and therefore, little is known about the information. The use of vibrations in para- neural and brain aspects that lead to behav- sitoids has been reported for the following ioral changes. species: Asobara tabida, Biosteres longicauda- In chapter 4, we assessed how tus, Choetospila elegans, Chrysocharis laricinel- anthropogenic-like noise (pink noise), lae, Coeloides brunneri, Coeloides dendroctoni, affected the perception of a host cue in Dapsilarthra rufiventris, Diachasma alloeum a parasitoid-host interaction. We show Diglyphus isaea, Leoptopilina longipes, Opius that anthropogenic-like noise affects the melleus, Phaenocarpa persimilis, Sympiesis seri- host-searching behavior and parasitism ceicornis (reviewed in Meyhofer et al. 1997). success of the endoparasitoid Asobara tabida. Given that the use of vibrational cues is Parasitoids exposed to high noise (12dB found in at least four families, the trait must

104 chapter 7 have evolved more often, indicating its broad Earthworms are particularly sensitive spread and potentially its importance. to vibrations in the soil, since they use vibra- tions to detect approaching predators, such Consequences for different trophic as moles (Catania 2008). It therefore makes levels sense that earthworms avoid areas with high vibrational noise. Furthermore, earthworms So far in the previous chapters we inves- play a crucial role in soil processes, influ- tigated how noise and the environment encing soil structure, hydrology and plant affected information transfer and species production (Clements et al. 1991; Blouin interactions in a one or two species dyad and et al. 2013; Van Groenigen et al. 2014; at the individual level. However, organisms Bertrand et al. 2015). Therefore, a reduction interact with different trophic levels, and an in earthworm abundance could have serious effect on one species could have cascading consequences for soil health. effects for several other species, leading to While testing the effect of wind community and/or population effects. With turbine-like vibrational noise on plant devel- chapter 5 and chapter 6, we aimed at opment in chapter 6, we found that Pisum investigating the cascading effects of vibra- sativum seeds exposed to high vibrational tional noise in the soil induced by wind noise (12dB higher than ambient conditions) turbines on abundance of soil mesofauna germinated significantly sooner than seeds and earthworms, and on plant development. exposed to low vibrational noise. Further In chapter 5, we found that wind exposure to high noise during plant devel- turbine noise affected earthworm abun- opment led to plants growing significantly dance, but not mesofauna abundance, with taller and quicker than plants exposed to earthworm count decreasing significantly low noise. (average of 40%) with proximity to wind Different plant species are known to turbines. We propose that the potential react differently to vibrations, depending on impact of substrate-borne noise is likely to the amplitude and the frequency of the stim- depend on the most prominent wavelengths ulus (Chowdhury et al. 2014). Therefore, not of the vibrations that travel through the soil all plant species would react in the same way and that overlap with an animal’s body size. as we show in this chapter. Consequently, Since vibrations generated by wind turbines by inducing different effects on different are very low in frequency, small-bodied species, competition rates between plant animals (mesofauna, < 3 mm in size) are not species could be altered. This is not a serious likely to be affected by these low-frequency problem for agricultural fields with mono- waves. Larger soil animals (macrofauna, > 1 cultures, since all plants would react in the cm in size), on the other hand, might be able same way. However, it could affect natu- to perceive low-frequency vibrations and ral plant communities where interspecific can therefore be impacted by turbine noise competition is an important determinant through a perceptual mechanism (Domi- of the structure and the dynamics of plant noni et al 2020). communities (Aerts 1999). Studying this

105 chapter 7 question in natural non-managed habitats would make an interesting follow up study. In Europe, wind energy is currently the second largest form of power generation capacity, with a total net installed capacity of 169.3 GW (Pineda and Tardieu 2018). In the Netherlands, more specifically, there are around 2,300 wind turbines placed on either wind parks or agricultural fields (nvde.nl). In this study we measured wind turbine-in- duced vibrations up to 256 meters away from the turbine base and we could not find the distance at which the noise amplitude attenuation flattened. At 2 meters from the base of the turbine vibrational noise was on average 23 dB louder than at >200 meters. Animals living close to the turbines there- fore experience extremely different sensory conditions than further away. Several stud- ies have addressed the detrimental effects of wind turbines on wild life (Johnson et al. 2003; Barrios and Rodríguez 2004; Rabin et al. 2006; Rydell et al. 2010; Bellebaum et al. 2013b; Thaker et al. 2018; Caorsi et al. 2019). Yet, we have very little knowledge on the impact of wind turbine noise on soil fauna or plants, despite the fact that turbines are anchored in the soil. While renewable energy is clearly the way forward, we need to consider the impact of vibrational noise on soil organisms when re-thinking wind turbine structures.

106 chapter 7 Outstanding questions

Vibrational information is used by a wide quantified. range of organisms, from plants to elephants, Another question that remains is and in a variety of contexts including, court- whether there is any relationship between ship/mating, predator-prey, parasitoid-host, seasonal wind patterns and seasonal vari- parent-offspring, and other interactions. ation in reproduction when comparing Given the pervasiveness of vibrational species that communicate with vibrations information and the diversity of contexts versus species that rely mostly on acoustic in which it is used, I decided to cover a signals? Descriptive studies relating seasonal range of species and ecological contexts in variation in reproduction in acoustically this thesis. The advantage of taking this versus vibrationally communicating species broad scale approach, is that it highlights would provide interesting knowledge on the significance of the environmental vari- the effects of wind on life history strate- ation problem for different organisms and gies. Furthermore, we tested katydids in an for different contexts, allowing for a more artificial substrate (nylon cage). However, wholistic understanding of the impacts C. brevirostris can be found on a range of of noise on the vibrational modality. The plants which possibly move in response downside however, is that we lack deeper to wind differently than a nylon cage (e.g. understanding for each of the study systems. noise induced by wind on a palm leaf prob- Next, I discuss the questions that remain ably has a higher amplitude than on a nylon from each of the studies presented in this cage). Therefore, testing animals on a range thesis and I propose experiments to address of natural substrates might provide a more them. realistic assessment of the effects of wind, In chapter 2, we show that Copiphora possibly showing that lower wind veloci- brevirostris signaling is affected by wind. But ties (e.g. < 0.9 m/s) can already affect katy- we do not have any information regarding did signaling. Additionally, systematically the consequences of this disturbance. Does studying substrate choice in wind and no wind affect signal detection and vibrotaxis wind conditions, as well as studying signal in females, potentially leading to a lower transmission on these substrates could tell reproductive success? Conducting vibro- us something about other strategies katydids taxis experiments in wind and no-wind use to reduce noise effects. conditions could tell us something about Furthermore, in chapter 3 we show the female’s ability to detect and react to that leaf thickness affects the transmission male signaling. Alternatively, reproductive of chewing vibrational cues from foraging success could be tested by exposing katydids caterpillars, and that this could have conse- to varying wind velocities in a large arena, quences for predator-prey, parasite-host or where mating events are monitored and plant-insect interactions. However, we did

107 chapter 7 not test the receiver side (predators or para- vibrational noise induced by wind turbines. sitoids), and therefore we lack knowledge on One question that remains, however, is the biological consequences of our findings. whether some species of earthworms are Is the amplitude attenuation variation we more susceptible to vibrational noise than find significant enough to decrease percep- others. In our study we did not differentiate tion in receivers? Playback experiments between earthworm species. However, some with shakers using vibrational chewing species might be more sensitive to vibrations cues to test receiver behavior could be used than others (e.g. due to predation pressure by to address this question. Furthermore, leaf moles). Therefore, disentangling earthworm traits can vary according to the developmen- species composition along a transect (see tal stage of the plant and to changes in the methods of chapter 5) could shed some environment (e.g. humidity, light and nutri- light on the vulnerability of the group as a ent availability). Therefore, it would be inter- whole to vibrational noise, and on the possi- esting to measure how the production and ble consequences, as an effect on different transmission of vibrational cues are affected earthworm species could lead to different within plant species along a gradient of trait consequences for soil health, plant devel- variation. Moreover, experiments testing opment and distribution of soil mesofauna. receiver behavior along a gradient of leaf Another remaining question is whether trait variation would address the biological vibrational noise induced by wind turbines significance of the former experiment. indirectly affects other soil species that are We demonstrate in chapter 4 that not necessarily sensitive to vibrations, via vibrational noise affects host-searching effects on earthworms. This question could behavior and parasitism success in Asobara be addressed by experimentally exposing tabida. However, we do not have informa- soil animal communities to vibrational noise tion on the mechanisms by which parasit- with and without earthworms. oids are affected. Does noise mask the hosts’ Finally, in chapter 6 we concluded vibrational cues? Or does it have a distract- that vibrational noise affected plant develop- ing effect? To get a better indication of the ment, with seeds germinating significantly masking potential of our noise stimuli, the quicker under high vibratory noise, than vibrational cues produced by Drosophila under low noise. Additionally, we found melanogaster larvae should be recorded and that plants also grew significantly taller compared to the noise stimuli to examine under high noise versus low noise condi- overlap in spectral characteristics. Addi- tions. Given the effects on earthworm tionally, testing parasitoids for the effects abundance found in chapter 5, a ques- of noise on host-searching behavior and tion that remains here is how the effects parasitism success with on-band/off-band on earthworm abundance interact with the experiments would tell us something about effects on plant development, since earth- the distracting effects of noise. worms are known to play an important role In chapter 5, we concluded that in plant processes. For example, earthworms earthworm abundance is negatively related to can induce changes in the physiochemical

108 chapter 7 properties of the soil, they can act as biocon- trol of pests and parasites, stimulate symbi- osis and produce plant growth regulators by stimulating microbial activity (Bertrand et al. 2015). Experiments testing plant devel- opment under different noise conditions with and without worms would address this question. Additionally, controlled field experiments testing plant development (of mixed plant species) under noise conditions could tell us something about the effects of noise on plant species competition.

109 chapter 7 Future directions

In a world that is increasingly being shaped using that modality (e.g. by masking their by humans, it is timely and relevant to study signals), but can also have cross-modal how human-induced environmental changes effects (affecting the perception of signals affect animal behavior and physiology, espe- and cues in other modalities) (Morris-Drake cially when these effects can have conse- et al. 2016; Halfwerk and van Oers 2020), quences for an individual’s fitness. Many indicating that noise potentially has much scientists have realized this issue and have wider implications. Many animals rely on thus focused their efforts on studying how more than one sensory modality to extract different species are affected by different information from their environment (e.g. anthropogenic stressors including, acous- some katydids can use both acoustic and tic noise, artificial light and chemical vibrational signaling for attracting mates, pollutants, and how animals adapt to it. and certain parasitoids use chemical cues Vibrational anthropogenic noise has unfor- in combination with vibrational cues to tunately received scarce attention. This is detect and localize their hosts). Therefore, understandable, as biotremology is still an addressing the effect of vibrational noise emerging field (Hill et al. 2019). Neverthe- on different sensory modalities for animals less, we now know that the use of vibrational sensitive to vibrations would provide a better information is extremely widespread in the indication of the wider implications of noise. animal kingdom, with hundreds of thou- For example, an experiment where parasit- sands of invertebrates and many vertebrates oids are exposed to vibrational noise and are using vibrational signals and cues (Cocroft et tested for localization of a general foraging al. 2014). Given the worrying recent decline area (patch with kairomone cues, lacking in arthropod numbers (Vogel 2017), and the actual hosts) would address cross modal ubiquity of vibrational communication in effects of vibrational noise on the percep- arthropod species (Cocroft and Rodriguez tion of a chemical cue. This would be highly 2005; Cocroft et al. 2014; Hill et al. 2019), relevant for parasitoids (including those that special attention should be given to factors do not use vibrational cues) foraging in agri- that exacerbate this decline. In this section cultural fields with wind turbines, where the I propose several research avenues for the substrate is constantly exposed to vibrational effects of vibrational noise on animal behav- noise. ior and fitness. Consequences at the community and Cross-modal effects population level

There is increasing evidence that noise in a Studying the community composition and particular modality not only affects animals population effects of noise can be challeng-

110 chapter 7 ing because of the massive effort it requires An evolutionary perspective to sample and experimentally control areas representative of communication ranges As urbanized areas increase, the sensory (e.g. a large forest area for birds). For arthro- environment of animals can swiftly and pods, however, this would be much more drastically change. Whether animals survive feasible. Noise can negatively affect popu- and thrive in the new sensory conditions lations by decreasing breeding density, or probably depends on how well and quickly it can positively affect them by leading to they can adapt to them. Quick adapta- a noise-associated decline in a major pred- tions can occur in the form of epigenetic ator (Slabbekoorn and Halfwerk 2009). changes, as has been shown in Collembola Furthermore, noise can lead to community (Orchesella), where females exposed to heat effects by directly affecting species, result- shock stress during the non-reproductive ing in communities dominated by species phase produced offspring with a higher heat resilient to noise, and by affecting species shock tolerance than their mothers (Zizzari that are not necessarily affected by noise, and Ellers 2014). Arthropods with a quick but that are dependent on interactions with generation turnover make ideal systems to species affected by noise. Population effects study transgenerational effects and adap- can be examined by measuring breeding tations. For example, exposing animals to density and predation rates, and commu- vibrational noise at different developmen- nity effects can be investigated by looking tal phases or throughout all phases, then at the community composition of commu- testing their response (e.g. stress levels, nities exposed to noise (e.g. species diversity anti-predator responses, mating behavior), and species richness). Experiments using and subsequently testing the response of mesocosms exposed to different levels their offspring under no noise conditions of vibrational noise (e.g. with soil animal would tell us something about the thresh- communities) could provide valuable infor- old at which adaptations are triggered and mation on biodiversity and population which species are likely to thrive in a noisy changes in response to noise. First, testing world. single species populations would tell us which species are sensitive and most vulner- able at the population level. Second, testing natural communities would contribute to our understanding of the effects of noise on biodiversity and would provide information regarding direct versus indirect effects of noise.

111 chapter 7 Concluding remark

Probably because of our strong reliance as humans on sound, vision and smell, we understand acoustic, visual and chemical communication better than vibrational communication. However, advances in technology have made it possible for us to discover the fascinating world of vibrations. As our knowledge on vibrational signals and cues expands, we are better able to create accurate synthetic signals, play them back faithfully, and recreate realistic vibratory environments. Arthropods communicat- ing with substrate-borne vibrations provide the perfect arena to study information transfer, effects of noise, and adaptations to communicating in noisy environments in a controlled environment.

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140 Authors affiliation and addresses

Matty P. Berg Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Community and Conservation Ecology Group Groningen Institute of Evolutionary Life Sciences University of Groningen Nijenborgh 7 9747 AG Groningen The Netherlands

Laura Bellato Department of Ecological Science Vrije Universiteit Amsterdam, De Boelelaan 1085 1081HV Amsterdam The Netherlands

Eleanor Collinson Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Daniel Commandeur Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

141 authors affiliation and addresses

Hans C. Cornelissen Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Jacintha Ellers Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Wouter Halfwerk Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Hannah ter Hofstede Department of Biological Sciences Dartmouth College 78 College Street Hanover, NH 03755 USA

Smithsonian Tropical Research Institute Apartado 0843-03092 Panamá República de Panamá

Ivor Keesmaat Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

142 authors affiliation and addresses

Matias Muñoz Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Nicol Quiroga Departamento de Ciencias Ecológicas Facultad de Ciencias Universidad de Chile Santiago Chile

Rachel A. Page Smithsonian Tropical Research Institute Apartado 0843-03092 Panamá República de Panamá

Jernej Polajnar National Institute of Biology Večna pot 111 1000 Ljubljana Slovenia

Ralph Simon Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

143 authors affiliation and addresses

Laurel Symes Center for Conservation Bioacoustics Cornell Lab of Ornithology 159 Sapsucker Woods Road Ithaca, NY 14850 USA

Smithsonian Tropical Research Institute Apartado 0843-03092 Panamá República de Panamá

Estefania Velilla Department of Ecological Science Vrije Universiteit Amsterdam De Boelelaan 1085 1081HV Amsterdam The Netherlands

Smithsonian Tropical Research Institute Apartado 0843-03092 Panamá República de Panamá

Meta Virant-Doberlet National Institute of Biology Večna pot 111 1000 Ljubljana Slovenia

144 Publications

Refereed journals

Published

Velilla, E., Symes, L., ter Hofstede, H.M., Muñoz, M., Quiroga, N.A., Halfwerk, W. (2020). Gone with the wind: Signal timing in a Neotropical katydid as an adaptive response to variation in wind-induced vibratory noise. Behav. Ecol. Socio- biol. 74:59. https://doi.org/10.1007/s00265-020-02842-z

Jouta, J., De Goeij, P., Lok, T., Velilla, E., Camphuysen, C. J., Leopold, M., ... & Piersma, T. (2018). Unexpected dietary preferences of Eurasian Spoonbills in the Dutch Wadden Sea: spoonbills mainly feed on small fish not shrimp. Journal of ornithology, 159(3), 839-849.

Goedknegt, M. A., Havermans, J., Waser, A. M., Luttikhuizen, P. C., Velilla, E., Camphuysen, K. C., ... & Thieltges, D. W. (2017). Cross-species comparison of parasite richness, prevalence, and intensity in a native compared to two invasive brachyuran crabs. Aquatic Invasions, 12(2).

Submitted

Velilla, E., Polajnar, J., Virant-Doberlet, M., Commandeur, D., Simon, R., Cornelissen, H.C., Ellers, J., Halfwerk, W. Vari- ation in plant leaf traits affects signal-to-noise ratio of herbi- vore vibrational cues. In review at Ecology and Evolution.

Velilla, E., Collinson, E., Berg, M.P., Halfwerk, W. Vibrational noise from wind-energy turbines negatively affects earth- worm abundance. Submitted at Environmental pollution.

145 publications

In preparation

Velilla, E., Keesmaat, I., Halfwerk, W., Ellers, J. The effect of vibrational noise on a parasitoid-host interaction.

Other

Velilla, E., Halfwerk, W. (2019). Adjustments to Facilitate Communication in Noisy Environments. In: Choe, J.C. (Ed.), Encyclopedia of Animal Behavior, (2nd ed.). vol. 1, pp. 598-605. Elsevier, Academic Press.

146 Summary

The environment is teaming with valuable organisms and contexts, this thesis covered information that helps animals make deci- different invertebrate species, and interac- sions about where to find a mate, where to tions including, courtship and plant-insect find food and water, and whether to escape interactions, and parasite-host dynamics. imminent danger, among other things. The To address the questions of this thesis, reproductive success and survival of animals, I used a combination of environmental data therefore, partly depends on how success- collection, field work in the Neotropics and fully they extract relevant information from in the Netherlands and behavioral experi- their environment (e.g. detecting a court- ments. Below, I outline the main findings ship signal or a predator’s cue). However, of this thesis. environmental complexity, including back- ground noise, can make it difficult to detect Noise affects the production and and interpret crucial information, affecting perception of vibrational signals animals’ reproduction and survival, and ulti- and cues mately their fitness. While we have ample knowledge on how noise (e.g. traffic noise Natural noise or rain noise) affects animals using acoustic information, we know relatively little about We investigated the effect of wind, a natural how vibrational noise affects animals relying noise source, on the production of vibra- on information from vibrations. tional signals in a Neotropical katydid Vibrations are used by a wide variety of (chapter 2). Some katydids communicate organisms including hundreds of thousands using vibrations, they produce vibrational of invertebrate species, many vertebrates and signals to attract mates. Their communica- even plants. Vibrational signals and cues can tion can potentially be disturbed by wind, be used across different contexts includ- because wind induces vibrational noise that ing, courtship and mating, predator-prey overlaps in frequency range with their own dynamics, parasite-host and plant-insect signals. We measured wind levels in the interactions, male-male competition, and Neotropical forests of Barro Colorado Island parent-offspring relationships. (Panama), and we then observed katydids The purpose of this thesis was to inves- throughout the night (under controlled tigate how noise and environmental vari- non-windy conditions) to establish their ation affect the production, transmission signaling patterns throughout the night. and perception of vibrational signals and Additionally, we experimentally exposed cues, and how that in turn affects species katydids to short wind gusts (using a fan) interactions. To highlight the widespread to understand how short-term fluctuations use of vibrational signals and cues across in wind levels affected their signaling. Our

147 summary results show that katydids can time their plant-insect interactions. For the first part vibratory signaling both in the short- and of this study (chapter 5), we visited several long-term to favorable sensory conditions, windmills located in agricultural fields in either through behavioral flexibility in the Netherlands and we measured the vibra- response to short-term fluctuations in wind tional noise levels at increasing distances or as a result of an evolutionary process in from the windmills. We then surveyed response to predictable periods of low-wind earthworm abundance and abundance of conditions. small-sized soil animals. We found that vibrational noise levels close to the windmills Anthropogenic noise (at 2 m) were on average 23 dB higher than further away from the windmills (at 256 m), Human population growth and the activi- and that earthworm abundance decreased ties that result thereof are quickly changing substantially with proximity to windmills the sensory environment of many organ- (40% reduction on average). Abundance of isms. In chapters 4, 5, and 6 of this thesis, small-sized soil animals, on the other hand, I studied how anthropogenic vibrational was not affected by vibrational noise levels noise from windmills and highways affected or distance to the windmills. The potential different invertebrates in different contexts. impact of noise is likely to depend on the In chapter 4, we studied how highway-like overlap between an animal’s body size and noise influenced a parasitoid-host relation- the spectral distribution of the vibrations ship. We studied a parasitic wasp species that travel through the soil. Since most of that relies on vibrational cues for findings the spectral energy in windmill vibrations its host. We exposed these wasps and their is in the low frequencies, small soil animals hosts to vibrational noise of different levels will likely not be able to perceive, or even and we observed how much time wasps experience strain from these low-frequency spent searching for a host and how successful waves. However, an effect on earthworm they were at parasitizing a host. We found abundance might have important conse- that noise levels significantly influenced host quences for soil structure and soil processes, searching behavior and parasitism success, for which earthworms play a crucial role. with wasps exposed to high noise spending For the second part of this study (chap- less time searching and being less success- ter 6), we investigated how windmill-like ful at parasitizing. Environments with high vibrational noise affected plant develop- vibratory noise levels are likely to favor para- ment. We experimentally exposed plants sitoids relying primarily on other sensory from seed stage to seed production stage to modalities, such as olfaction or vision, to high and low vibrational noise levels and detect and localize their hosts. monitored them daily. We recorded germi- In chapters 5 and 6, we studied nation, flowering and fruiting time, as well how vibrational noise induced by wind- as daily shoot-length growth. Additionally, mills affected soil macro- and mesofauna, we tested the direct and indirect effects of and how it affected plant development and vibrational noise on herbivory intensity by a

148 summary generalist caterpillar. We found that plants Future directions exposed to high vibrational noise grew significantly faster and taller than plants There is increasing evidence that noise can exposed to low vibrational noise, and that have cross-modal effects, indicating that plants treated with high noise germinated, noise in one sensory modality (e.g. vibratory flowered and produced fruits quicker than noise) potentially has much wider implica- those treated with low noise. We did not tions, affecting the production or perception find, however, an effect on herbivory inten- of signals and cues in other modalities (e.g. sity. An effect of noise on plant development vision or hearing). Many animals rely on could have consequences for both natural more than one sensory modality to extract plant communities and agricultural crops information from their environment. There- by altering interspecific competition and by fore, addressing the effect of vibrational shifting growth-defense activation trade- noise on different sensory modalities for offs. animals sensitive to vibrations would provide a better indication of the wider implications Environmental variation affects the of vibrational noise. Furthermore, noise transmission of vibrational cues can lead to community effects by directly affecting species, resulting in communi- Plants are the most widespread substrates ties dominated by species resilient to noise, to receive or transmit vibrational informa- and by indirectly affecting species that are tion. However, plant species and plant parts not necessarily affected by noise, but that can vary significantly, possibly affecting are dependent on interactions with species the transmission of vibrational signals and affected by noise. Population effects can be cues. To assess how environmental variation examined by measuring breeding density affected the transmission of a vibratory cue and predation rates. Community effects can (chapter 3), we systematically measured leaf be investigated by looking at the commu- traits in four plant species, and we related nity composition of communities exposed the transmission of vibratory chewing cues to noise. Experiments using mesocosms from a generalist herbivore caterpillar to exposed to different levels of noise would variation in leaf traits. We found that leaf provide valuable information on biodiver- traits varied significantly between plant sity and population changes in response to species, and that some leaf traits influenced noise. Finally, evolutionary experiments the transmission and possibly the detect- are needed to understand whether and how ability of herbivore vibrational cues. Our animals adapt to quickly changing sensory findings are particularly interesting in light environments. of the arms race between plants, their herbi- vores and the natural enemies of plant herbi- vores where vibrations have been shown to mediate orientation towards prey by insect parasitoids.

149 Samenvatting

De omgeving zit vol met waardevolle nalen, en hoe dat interacties tussen verschil- informatie, die dieren kunnen gebruiken lende soorten beïnvloedt. Trillingen vormen om belangrijke beslissingen te maken. Zo een belangrijke bron van informatie voor vele moeten dieren bijvoorbeeld beslissen waar soorten in verschillende contexten. Daarom ze een paringspartner, voedsel en water behandelt deze thesis verschillende ongew- zoeken, en hoe ze daarbij gevaar kunnen ervelde soorten en verschillende interacties, ontlopen. Hoe succesvol een dier is, hangt waaronder baltsgedrag, interacties tussen daarom gedeeltelijk af van hoe goed een dier planten en insecten en de dynamiek tussen informatie uit de omgeving kan halen (zoals parasieten en hun gastheren. het opmerken van een paarsignaal of een Om de vragen van deze thesis te beant- teken van een roofdier). De omgeving kan woorden, heb ik een combinatie gebruikt complex zijn en ruis bevatten, waardoor het van data over het milieu, veldwerk in de detecteren en interpreteren van relevante tropen en Nederland, en gedragsexperi- informatie moeilijk kan zijn. Dit heeft menten. Hieronder zijn de belangrijkste effect op de overleving en voortplanting, bevindingen van deze thesis opgesomd. een daarmee de biologische ‘fitness’ van een dier. Onderzoekers hebben al veel kennis Vibrationeel lawaai beïnvloeden de vergaard over het effect van akoestisch productie en perceptie van trillingen lawaai (verkeerslawaai of regen) op dieren die geluid uit hun omgeving gebruiken als Natuurlijke vibrationeel lawaai informatiebron, maar we weten weinig af van hoe vibrationeel lawaai dieren beïnv- We hebben het effect van wind, een natu- loedt die afhankelijk zijn van informatie uit urlijke bron van vibrationeel lawaai, onder- trillingen over hun omgeving. zocht op de productie van trillingen in een Vele organismes zijn afhankelijk van tropische sabelsprinkhaansoort (hoofd- trillingen als informatiebron, waaronder stuk 2). Sommige sabelsprinkhanen honderdduizenden ongewervelde dier- communiceren met trillingen, ze gebruiken soorten, vele gewervelde diersoorten en deze trillingen om potentiële paringspart- zelfs planten. Trillingen worden gebruikt in ners aan te trekken. Hun communicatie verschillende contexten, zoals bij het paren, zou door wind verstoord kunnen worden, bij het vinden van voedsel en bij het zorgen omdat de wind trillingen veroorzaakt die voor nakomelingen. overlappen met dezelfde frequenties die Het doel van deze thesis was om het de sabelsprinkhanen gebruiken om te effect te onderzoeken van vibrationeel lawaai communiceren. Om dit te onderzoeken en variatie in de omgeving op de productie, hebben we windniveaus gemeten in de transmissie en de perceptie van trillingssig- tropische regenwouden op Barro Colorado

150 samenvatting

Island in Panama. Ook hebben we sabel- In ons experiment hebben we parasitoïde sprinkhanen ’s nachts onder gecontroleerde wespen en hun gastheren aan trillingen windstille omstandigheden geobserveerd van verschillende sterktes blootgesteld, en om te onderzoeken hoe ze communiceren geobserveerd hoeveel tijd de wespen nodig gedurende de nacht. Daarnaast hebben we hadden om hun gastheren te vinden. We een experiment gedaan, waarin we sabel- vonden dat de vibrationeel lawaai een signif- sprinkhanen hebben blootgesteld aan kort- icant effect had op het zoekgedrag van de durende windvlagen (met een ventilator) wespen en hun parasiteersucces: wespen om te begrijpen hoe kortdurende schom- die waren blootgesteld aan hoge trillingen melingen in windsterktes hun communi- zochten minder lang naar gastheren en catie kan beïnvloeden. De resultaten laten waren ook minder succesvol. We conclud- zien dat sabelsprinkhanen de timing van eren dat omgevingen met veel verstoringen hun trillingen kunnen aanpassen, zowel op van trillingen geschikter zijn voor parasi- korte als lange termijn, afhankelijk van de toïden die niet afhankelijk zijn van trill- windomstandigheden. Dit zou kunnen zijn ingen om hun gastheren te vinden, maar doordat de sabelsprinkhanen flexibel zijn in van andere modaliteiten zoals geur of zicht. het produceren van trillingen afhankelijk In hoofdstuk 5 en 6 hebben we van de wind in de omgeving, of het zou een onderzocht hoe trillingen geproduceerd resultaat kunnen zijn van een evolutionair door windturbines invloed kunnen hebben proces waarin de sabelsprinkhanen tijdens op bodemdieren, plantengroei en interac- voorspelbaar niet-winderige momenten hun ties tussen planten en insecten. In het eerste trillingen produceren. gedeelte van deze studie (hoofdstuk 5), hebben we verschillende windturbines in Antropogene vibrationeel lawaai landbouwvelden in Nederland bezocht. Hier hebben we de trillingssterkte gemeten De menselijke bevolking is sterk aan het op verschillende afstanden van de windtur- toenemen, en dat brengt vele snelle veran- bines. Daarnaast hebben we de hoeveelheid deringen op aarde met zich mee. In hoofd- regenwormen en andere kleine bodemdie- stuk 4, 5 en 6 van deze thesis, bespreek ren geteld, dieren die wellicht beïnvloed ik hoe antropogene vibrationeel lawaai van worden door de trillingen afkomstig van windturbines en snelwegen invloed kunnen de windturbines. We vonden dat de trill- hebben op verschillende ongewervelde ingssterktes dichtbij de windmolen (op dieren in diverse contexten. In hoofdstuk 2 m) gemiddeld 23 dB hoger waren dan 4, hebben we bestudeerd hoe vibrationeel verder weg (op 256 m). De hoeveelheid lawaai, gebaseerd op snelwegtrillingen, de regenwormen nam ook toe verder van de relatie tussen een parasitoïde en zijn gasth- windturbines af (gemiddeld 40% toename). eer beïnvloedt. Parasitoïden hebben een Het voorkomen van andere kleine bodem- gastheer nodig voor hun ontwikkeling, en dieren was niet gerelateerd aan de afstand sommige parasitoïden vinden hun gastheren van de windturbine. De mogelijke impact op basis van de trillingen die ze maken. van vibrationeel lawaai hangt waarschijn-

151 samenvatting lijk af van de grootte van een dier en van Variatie in de omgeving beïnvloedt de de spectrale kenmerken van de trillingen transmissie van trillingen in de grond. De meeste energie in de trill- ingen in de bodem zit in de lage frequenties, Planten zijn de meest voorkomende die kleine bodemdieren waarschijnlijk niet substraten om informatie in trillingen te kunnen waarnemen. Echter, een verander- ontvangen of door te geven. Echter kan het ing in het voorkomen van regenwormen zou doorgeven van trillingen sterk variëren met belangrijke consequenties kunnen hebben de plantensoort en zelfs met verschillende voor de bodemstructuur en bodemproces- delen van een plant. Om te onderzoeken sen, waar regenwormen een centrale rol in hoe variatie in de omgeving de transmissie spelen. van trillingen kan beïnvloeden (hoofdstuk Voor het tweede deel van deze studie 3), hebben we verschillende kenmerken van (hoofdstuk 6) hebben we gekeken naar bladeren van vier plantensoorten gemeten het effect van vibrationeel lawaai (vergeli- en dit hebben we gerelateerd aan transmissie jkbaar met trillingen van windturbines) op van trillingen veroorzaakt door het kauwen de ontwikkeling van planten. In een exper- van een herbivoor. We vonden dat verschil- iment hebben we planten blootgesteld aan lende plantensoorten in hun bladkenmerken lage en hoge trillingssterktes, en hebben verschilden, en dat sommige bladken- we dagelijks gevolgd hoe de planten zich merken de transmissie van de trillingen van zaadje tot een volgroeide plant ontwik- beïnvloedden. Dit zou kunnen bepalen hoe kelden. We hebben de tijd tot ontkieming, makkelijk trillingen geproduceerd door bloei en vruchten, en dagelijkse groei van herbivoren door hun natuurlijke vijanden de planten gemeten. Daarnaast hebben we worden opgemerkt. Onze bevindingen zijn gekeken naar het effect van vibrationeel met name interessant in het kader van de lawaai op het eetgedrag van een herbivoor, wapenwedloop tussen planten, herbivoren een rups. We vonden dat de planten bloot- en de natuurlijke vijanden van de herbivoren, gesteld aan sterke trillingen significant aangezien het is aangetoond dat trillingen sneller groeiden dan de planten blootgesteld een rol spelen in het lokaliseren van de aan zwakke trillingen. Ook ontkiemden herbivoren door hun natuurlijke vijanden. en bloeiden de planten bij sterke trillingen eerder, en produceerden ze sneller vruchten. Toekomstige onderzoek Echter zagen we geen effect van trilling- sintensiteit op het eetgedrag van de rupsen. Er is meer en meer bewijs dat vibrationeel Een effect van trillingen op de ontwikkel- lawaai effect kan hebben op meer dan ing van planten zou consequenties kunnen één modaliteit, dat wil zeggen dat ruis in hebben voor zowel natuurlijke plantenge- een modaliteit (bijv. trillingen) effect kan meenschappen als voor landbouwgewassen, hebben op een andere modaliteit (bijv. zicht doordat de trillingen effect kunnen hebben of gehoor). Als vibrationeel lawaai ook op de competitie tussen soorten en tussen de andere modaliteiten kan beïnvloeden, zijn er balans tussen groeien en verdedigen. potentieel veel grotere implicaties van deze

152 samenvatting verstoringen. Vele diersoorten zijn afhanke- lijk van meer dan één modaliteit om infor- matie uit de omgeving te halen. Daarom zou het bestuderen van het effect van vibra- tioneel lawaai op meerdere modaliteiten een betere indicatie kunnen geven van de bredere implicaties van vibrationeel lawaai. Daarnaast kan vibrationeel lawaai leiden tot verstoringen in een gemeenschap bestaande uit verschillende soorten. Dit kan gebeuren doordat bijvoorbeeld vibrationeel lawaai één soort met een centrale rol in het web beïnvloeden, waardoor ook soorten die zelf bestendig zijn tegen vibrationeel lawaai maar afhankelijk zijn van interacties met de eerste soort toch indirect worden beïn- vloed. De effecten op een soort op popula- tieniveau kunnen worden onderzocht door te kijken naar de hoeveelheid nakomelingen die dieren krijgen, en naar hoeveel dieren er door roofdieren worden gepakt. Het effect op gemeenschappen kan worden onderzocht door te kijken naar de dieren in de gemeen- schappen die worden blootgesteld aan vibra- tioneel lawaai. Experimenten waarin dieren worden blootgesteld aan vibrationeel lawaai van verschillende sterktes zouden waar- devolle informatie kunnen opleveren over biodiversiteit en populatieschommelingen in reactie op vibrationeel lawaai. Ten slotte, evolutionaire experimenten zijn nodig om te onderzoeken of en hoe snel dieren zich kunnen aanpassen aan snel veranderende sensorische omgevingen.

153 Acknowledgements

I first want to thank all the animals that new meaning. made this PhD thesis possible. Without Eef (Evert), you have so often been my you: katydids, caterpillars, parasitoids and public when I needed to practice a presen- flies, none of this would have been possible. tation, that you could probably give my I want to thank my wonderful supervi- presentations yourself. Thank you for always sors, Jacintha Ellers and Wouter Halfwerk. being willing to listen to my talks and to my Jacintha, it has been inspiring and encour- internal discussions, even when you didn’t aging to develop my scientific career under understand them or when you had your own your supervision and support. Thank you things going on. I really enjoy that we both for all the discussions, the advice, for your go through the same process of doing a PhD trust in me, and for the fun times as well, and with only a few words or no words at all of course. Thank you for listening when I we can understand each other. It has been wanted to take a different direction in my a difficult year, but I admire your ability PhD and for helping me achieve it. Wouter, to still find humor in everyday things and I already got a sense of the kind of person to carry on, accepting there are good and you are when you invited me for a second bad moments. Thank you for all your love, interview in the office that later became my patience, humor, and support. I love you. office. You ordered pizza and pulled in a Si estos agradecimientos estuvieran plastic bag with cold beers that was hang- escritos en orden cronológico empezaría por ing outside the window. We discussed ideas acá. A mis papás les debo todo. Gracias por for the project. You made an interview seem darme la infancia que me inspiró a seguir like a fun discussion about science. At one esta carrera y a volverme la persona que soy. point you said very casually, “so… do you La infancia en la que crecí nadando con want the job?”. I had to appear just as cool, delfines y tiburones y donde la naturaleza of course. So, even though my heart was que tanto me ha inspirado me rodeaba a pounding with excitement, I controlled my diario. Ustedes dos han sido mis mayores breathing and I casually replied, “sure… why admiradores. Gracias por creer ciegamente not?”. You and I both had no idea of what we en mí. Se que no siempre entienden los were getting into when I started this PhD. temas con los que trabajo, pero están siem- But I am happy to have been your first PhD pre interesados y dispuestos a escucharme. student, and I am thankful for the experi- Mamá, cuando me quise ir de Colombia por ence of finding our way through the complex segunda vez en el 2009, tú me apoyaste y me world of biotremology together. Thanks for animaste, aun sabiendo que te haría falta. all the time you dedicated to me. I also have Tú has estado siempre ahí para mí y has to say that you gave the question “how do sido parte de todas las etapas importantes you feel about technical equipment?” a whole en mi vida. Gracias por toda tu dedicación,

154 acknowledgements tu amor sin límites, ¡y por creer tan ciega- zo trots geweest… ik zie je voor me, klaar mente en mí! om te vieren met je raspa en je aanstekelijke Pa, tú has llenado mi vida de aventu- caribische vrolijkheid. ras y de encuentros mágicos con animales. Nana y Shimana, con ustedes di el paso Desde pequeña me enseñaste que el mar a la vida de adulto cuando apenas era una oscuro y profundo solo da miedo cuando no adolescente. Gracias por todo el apoyo en nos atrevemos a mirar que hay abajo. Gracias esos 5 años que viví con ustedes y por darme por enseñarme a nadar sola en el maravilloso muchas herramientas que me han sido vali- mar profundo. Me encanta que eres el tipo osas en esta vida, incluyendo herramientas de persona que ve un tiburón y en vez de que usé en este doctorado. Gracias por tanto salirse del agua gritando, me tomas de la amor. Ari, contigo empecé hace 12 años la mano para ir a verlo de cerca. Me enseñaste vida universitaria, y desde entonces no he a amar a la naturaleza con respeto. Gracias dejado de estudiar. Extraño estudiar contigo por ese regalo tan grande. y nuestras discusiones sin fin sobre todo y Vero, mi pedacito de hogar caribeño sobre nada. en Europa. Me ha hecho muy feliz tenerte I want to thank Judith, Matias, Andrew tan cerca y poder compartir tanto contigo. and Ralph for all the great sensory ecology Gracias por siempre estar orgullosa de mí y discussions, the beers, the coffee, the walks, por contar siempre con mi opinión. Juanpa, the field work and of course the friendship. tú y yo crecimos de la misma forma, así que Judith, I’m really grateful that you agreed más que nadie entiendes mi fuente de inspi- to accompany me as my paranymph in this ración. Gracias por todo tu cariño e interés occasion. I am excited to finish my PhD, but en mi trabajo en estos últimos 4 años. sad to leave this great group! Francesco, Mi tía Claudia, te estoy infinitamente thanks for being such a caring roommate. agradecida por uno de los mejores regalos I’ve really enjoyed our talks, museum visits, que he recibido, mi educación y mi introduc- dinners, and drinks at the Stelling. Also, I ción a Holanda. Gracias por ese empujón tan am thankful to my colleagues at the Animal grande que me llevó tan lejos. Gracias por Ecology department with whom I have tu generosidad y por creer en mí. Extraño greatly enjoyed discussions, outings, drinks, nuestros fines de semana en el apartamento and more: Wendy, Dre, Claudia, Anouk, de Sweelinckplein y nuestras conversaciones Matty, Toby, Joris, Kees, Jurgen, Simon, sin fin. De igual manera estoy inmensa- Malin, Dick, Jeroen, Ruben, Oscar, Yumi, mente agradecida con mis tíos Patricia y Nico, Janine, Riet and Rudo. Ricardo por ser parte de esa contribución a Moreover, I want to thank a very mi educación. special group of people, without whom I Alma, Steven en Heleen, we hebben would have probably never gone into ecol- in de afgelopen jaren zoveel samen meege- ogy. To start with, the Texel Chicks: Ineke, maakt. Ik ben ontzettend blij dat jullie mijn Eva, Emma and Susanne. Thank you for familie zijn geworden! Dankjewel voor your friendship and for setting the bar so jullie liefde, steun en interes. Alma, jij was high. Also, during my time at NIOZ I

155 acknowledgements met some wonderful people from whom I listening to me about my projects and for also learned a lot about ecology: Thomas, the nice discussions and for being my Dutch Roeland, Allert, Roos, Jan, Piet, Ginny, language practice partners. Sjoerd and Lise. Every chapter of my thesis entailed a A big part of my PhD has entailed different study system and method. This co-supervising several students: Biljana, would not have been possible without all Daniel, Yualli, Eleanor, Laura, Ivor and the people I collaborated with in the differ- Dhaishendra. Your projects have been ent projects. crucial for my PhD. Thank you for your hard work. I also want to thank Jens and Nicol for Katydid project your help developing two different projects. Jens, I was so impressed with your work, and Laurel, I’m so glad I approached you at that I am so happy for you that you have found conference in Germany. So many good your PhD. I am sure it will be a success! things have come out of that short interac- Nicol, no pude haber encontrado a una mejor tion. Thank you and Hannah for your gener- persona para apoyarme en el proyecto con los osity, for allowing me to join your group katydids en Panamá. ¡Gracias por toda tu when you only barely knew me, for making ayuda! Fue un placer trabajar contigo. all of your equipment available to me, and Anne, dankje voor je vriendschap en voor for your valuable feedback. I enjoyed the je steun tijdens de goede en de moeijlijke short time we spent together in Panama. perioden in de laatste jaren, en ook voor Mati, we spent so many hours in silence at de gezellige etentjes en voor je hulp met night in the jungle looking for katydids, and Nederlands leren. Jij bent een van de weinige cutting plants during the day for our trans- vrienden van mij die geen bioloog/ecoloog is, mission experiment, and doing experiments. maar toch ben je vaak geintereseerd in mijn Thank you for all your help and for all the werk en je stelt altijd interessante vragen. interesting discussions. We had a great time, Marleen, we hebben in de afgelopen 6 of 7 both in the field and sneaking in to the pool jaar veel leuke dingen samen gedaan! Ik heb at the resort. Thanks also to Eleanor and ook in de laatste maanden veel genoten van Luis Felipe for helping me collect katydids. onze leuke wandelingen (je weet zoveel over planten!) en van de interessante conversaties. Plant traits and caterpillar chewing Ik ben heel dankbaar dat je mijn paranymf vibrations project bent. Migle, I am so happy you agreed to Hans, thank you for all your help with brain- make the cover of my thesis and to format storming about this project, for guiding me this book! I have had a great experience with plant traits and for putting resources working together and it has also been a at my disposal. Thank you also for your good excuse to talk to each other and see constant positivism, even when our paper each other more often. Also, thanks to was rejected for the fourth time! Jernej and Anastasia, Adrienn and Maddie for always Meta, you were involved with the second

156 acknowledgements part of this project. Thank you for receiving Finally, I would like to thank the exam- me in your lab in Slovenia, for teaching me ination committee; Steve Simpson, Hans about transmission of vibrational signals, Slabbekoorn, Hans Cornelissen, Martine for coming over to Amsterdam to carry out Maan and Erik Poelman for investing time experiments with me and for all the feedback in my thesis, for being present at my public and help you’ve given me in our (still not defense, either physically or virtually and for ending) publishing process. Daniel, thank your valuable feedback. you for carrying out some of the experiments and for helping out with plant trait measure- ments, even when you had already finished your masters project. Jurgen and Richard, I also want to thank you both for all your help during this project with plant rearing and plant trait measurements.

Asobara tabida project

Special thanks to Gerard Driessen for all the advice on A. tabida parasitism. Also, thanks to Janine and Rudo for allowing me to use the Drosophila culture they maintained, and to Bregje Wertheim for providing us with A. tabida on several occasions. I am grateful to Yualli and Ivor for carrying out the experi- ments from this project.

Wind turbine project

A big thanks to Eleanor, Laura and Ivor who carried out the fieldwork in this project. I also want to thank Matty for his collabora- tion, providing expert advice on how to work with soil animals. Additionally, I want to thank Kees for all his help with earthworm ecology and Oscar for his help with brain- storming over sampling possibilities.

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