Quick viewing(Text Mode)

Species Interactions and Energy Transfer in Aquatic Food Webs

Species interactions and energy transfer in aquatic food webs

Jens Munk Nielsen ©Jens Munk Nielsen, Stockholm University 2015

Cover photos: © Jens Munk Nielsen, except bottomright: ©Helena Höglander

ISBN 978-91-7649-316-8

Printed in Sweden by Holmbergs, Malmö 2015

Distributor: Department of , Environment and Plant Sciences

ABSTRACT

Food webs are structured by intricate nodes of species interactions which govern the flow of organic matter in natural systems. Despite being long recognized as a key component in ecology, estimation of functioning is still challenging due to the difficulty in accurately measuring species interactions within a food web. Novel tracing methods that estimate species diet uptake and trophic position are therefore needed for assessing food web dynamics.

The focus of this thesis is the use of compound specific nitrogen and carbon stable isotopes and molecular techniques for assessing predator-prey interactions and in natural aquatic , with a particular focus on the species links between and .

The use of δ15N amino acid values to predict organism trophic position are evaluated through a meta-analysis of available literature which included measurements from 359 marine species (article I). Through a controlled feeding study isotope incorporation in aquatic organisms, across both plant- animal and animal-animal species linkages is further assessed (article II).

These studies showed that δ15N amino acid values are useful tools for categorizing animal trophic position. Organism feeding ecology influenced nitrogen trophic discrimination (difference in isotope ratio between and diet), with higher discrimination in compared to and (article I). Nitrogen isotope trophic discrimination also varied among feeding treatments in the laboratory study (article II). The combined findings from articles I & II suggest that researchers should consider using group specific nitrogen trophic discrimination values to improve accuracy in species trophic position predictions.

Another key finding in the controlled laboratory study (article II) was consistently low C isotope discrimination in essential amino acids across all species linkages, confirming that these compounds are reliable dietary tracers.

The δ13C ratios of essential amino acids were applied to study seasonal dynamics in zooplankton use in the Baltic Sea (article III). Data from this study indicated that zooplankton assimilate variable resources throughout the growing season. Molecular diet analysis (article IV) showed that marine copepod and cladoceran species ingested both autotrophic and heterotrophic resources.

Evidence from both articles III & IV also revealed that zooplankton feed on a relatively broad range of diet items but not opportunistically on all available food sources. Mesozooplankton feeding patterns suggested that energy and nutritional flows were channelled through an omnivorous zooplankton food web including microzooplankton prey items. Overall the results of this thesis highlight that stable isotope ratios in specific compounds and molecular techniques are useful tracing approaches that improve our understanding of food web functioning.

LIST OF ARTICLES

Article I Nielsen J.M, Popp B, Winder M (2015) Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia Vol. 178: 631-642

Article II Nielsen J.M, Reutervik K, Winder M, Hansson S (in prep) Amino acid stable isotope discrimination in diverse aquatic food chains.

Article III Nielsen J.M, Winder M (2015) Seasonal dynamics of zooplankton resource use revealed by carbon amino acid stable isotope values. Marine Ecology Progress Series Vol. 531: 143-154

Article IV Nielsen J.M, Fusilli M, Esparza-Salas R, Dalén L, Winder M (in prep) Marine zooplankton diet preferences across species, life stages and seasons using DNA barcoding.

Author contributions: Main contributions to study design, field sampling, data analyses and writing of articles I, II, III & IV. Shared co-authorship between J. M. Nielsen and M. Fusilli in article IV.

All accepted articles are reprinted with kind permissions from the publishers. The article Nielsen et al. (2015) Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms Vol 178, Issue 3, pp. 631-642, DOI 10.1007/s00442- 015-3305-7 was reprinted with permission of Springer. Nielsen & Winder (2015) Mar Ecol Prog Ser 531:143–154 is reproduced in this thesis under license from the copyright holder. The complete article from this source is not to be further copied separately from the thesis. This restriction ends 5 years after the initial publication date.

TABLE OF CONTENTS

ABSTRACT v

LIST OF ARTICLES vii

TABLE OF CONTENTS viii

INTRODUCTION 9

Food web dynamics and nutritional flows 9

Diet tracing 10

Diet tracing using molecular methods 12

Stable for diet tracing 13

Compound specific stable isotopes 13

Zooplankton feeding ecology 16

OBJECTIVES 18

AIM AND MAIN FINDINGS 19

CONCLUSIONS AND FUTURE PERSPECTIVES 24

Tracer techniques 24

Zooplankton feeding dynamics 25

Future perspectives 27

ACKNOWLEDGEMENTS 31

REFERENCES 32

INTRODUCTION

Food web dynamics and nutritional flows A primary field of research in ecology is the study of food webs (Elton 1927), which comprise a broad range of theoretical, mathematical and empirical studies (Layman et al. 2015). Food webs are constructed of multiple, potentially thousands of species interactions (Winemiller 1990), which shape trophic nodes and flow of organic matter within a food web. Certain species are often critical in shaping food web structure (Paine 1980), yet these can vary due to variable and strengths (Odum 1957, Dunne et al. 2002, Kratina et al. 2012). Despite being long recognized as fundamental to ecology, an integrated assessment of food web dynamics is still difficult due to the challenge in accurately assessing species diet usage and trophic position within a natural system. Such information is fundamental for categorizing food webs structure, complexity and energy pathways.

Trophic linkages in food webs can be assessed in terms of energy flows (Lindeman 1942), which also implies that the nutritional composition of organisms are important in shaping trophic linkages. Nutrients, comprising both atomic elements and organic molecules, provide the foundation for all living organisms. Single elements are indispensable for organisms as they provide the building blocks to the variety of organic compounds needed for basic functioning. Elemental stoichiometry (i.e. the relative balance of elements) thus provides a useful framework to assess how essential nutrients flow between organisms (Sterner & Elser 2002).

While elements are indispensable, most organic compounds can be synthesized or restructured by controlling enzymes. However, specific compounds that cannot be synthesized de-novo by heterotrophic organisms

9 must be acquired through assimilation of dietary components and are considered essential, such as highly unsaturated fatty acids (FA, Brett & Müller-Navarra 1997) and certain amino acids (AA, Wu 2009). The recognition that some molecules also act as essential compounds suggests that understanding of dietary nutrition and its influence on consumer growth goes beyond simple elemental stoichiometry (Müller-Navarra 2008). Often more than one nutrient simultaneously governs species growth (Raubenheimer & Simpson 2004, Wacker & Martin‐Creuzburg 2012). While stoichiometry is often included in dietary models, few of them take into account the role of nutritional quality of biochemical components (Perhar et al. 2012). Fully understanding the role of different biochemical compounds is crucial to better understand energy acquisition and nutritional transfer between animals.

Diet tracing Understanding nutritional flow across species trophic linkages relies on suitable tracing methods to assess the relative importance and contribution of dietary resources. In food web ecology a range of methods are used for gaining insight on species food acquisition. These include visual or molecular measures of stomach, faecal or gut content analyses (Pompanon et al. 2012, Clare 2014) and stable isotope analysis of elements such as nitrogen (N) and carbon (C) of bulk tissue (Post 2002, Boecklen et al. 2011) or biochemical compounds (Bec et al. 2011, McMahon et al. 2011).

While the overarching aim among dietary measures is common in inferring resource use of an organism, alternative measures (e.g. measure of assimilation or ingestion) do not necessarily provide the same dietary information, differing both quantitatively and qualitatively (Traugott et al. 2013). Consequently, the type of dietary information sought determines

10 which measure or combination of methods are most useful, along with normal considerations of hypotheses, study design and the right spatial and temporal scale. Ingestion is a measure of what is captured by the species and thus provides information about what and potentially how many prey items an organism has grazed upon. Such information can be obtained from visual or molecular analysis of gut or stomach content. Assimilation can be defined as the actual uptake of specific nutrients from the ingested diet which are incorporated in a consumer’s tissue compartments. Assimilation, can for example, be measured using stable isotope analysis. Thus, assimilation can provide insight into which nutritional components are of importance for a particular consumer.

Another important consideration in dietary analysis is the transformation that the true diet components undergo before they are eventually measured either in the stomach or as isotope composition of a consumers tissue. DNA sequences of dietary items may be modified or partly broken by the time they are measured in a consumer gut (King et al. 2008, Thomas et al. 2014), and similarly the isotope ratio measured in a consumer’s tissue may not have the same isotope composition of the initial dietary component (DeNiro & Epstein 1981). Difficulty in accurately establishing such relationship between diet and consumer is a continuous focal point in isotope (McCutchan et al. 2003, McMahon et al. 2015a) and molecular (Thomas et al. 2014) ecology. Regardless of the analytical method used, accurate prediction of such transformation between original diet and measureable diet is a key aspect in making accurate ecological predictions from dietary analysis. For several dietary methods information on potential transformation between consumer and diet is still scarce and usually available from only a few species (Deagle et al. 2013, Hoen et al. 2014).

11 Diet tracing using molecular methods Molecular tools such as DNA barcoding analyses of environmental DNA are useful for gaining insight on species existence in the ambient water (Thomsen et al. 2012), or dietary ingestion from gut or faecal content (Pompanon et al. 2012). The use of DNA barcoding provides direct information on dietary intake, including separation of individual prey items to genera, taxa or species (Corse et al. 2010, Clare 2014). Diet analysis using DNA methods rely on adequate reference libraries to identify the various diet items, which can either be designed specifically by additional sampling of potential dietary resources (García-Robledo et al. 2013), or alternatively referenced from DNA databases, such as Genbank (Bohmann et al. 2011). Depending on the molecular marker used, DNA barcoding may give explicit information on both carnivory and herbivory (Clare 2014).

While detection of specific dietary items is a real strength of DNA barcoding, quantifying dietary proportions can be more challenging. Accuracy of diet quantification can be difficult both due to methodological uncertainties or if prey organisms have different number of DNA copies of genes per cell (Deagle et al. 2013), variation that is likely greater for species belonging to different genera or families (Clare 2014). Detecting is difficult since consumer DNA is also normally present in the diet sample (King et al. 2008). Despite these uncertainties the clear advantages of direct diet information and the small amounts of material needed for analysis make this a promising method for assessing species diet intake. Molecular techniques seem particularly suitable for estimating patterns of small animals such as zooplankton (Hu et al. 2014) which are often difficult to measure in their .

12 Stable isotope analysis for diet tracing Stable isotopes of C and N are valuable tools for depicting both species trophic information and diet assimilation (Peterson & Fry 1987, Post 2002). Stable isotopes occur in as atoms of the same element having different nuclear compositions, leading to small variations in mass. The ratio between elements of stable isotope is described as:

δ(‰) = [(Rsample / Rstandard) -1] × 1000 (1) where R = 13C / 12C or 15N / 14N of either bulk tissue or a specific compound.

The Rstandard is an international standard of atmospheric N2 for N or Pee Dee belemnite for C. Alteration in isotope composition between a consumer and its diet is known as trophic isotope discrimination (note: isotope fractionation or enrichment are used interchangeably). Inferring species trophic information or dietary assimilation relies on accurate knowledge of the isotope discrimination factor. Stable isotope analysis have proven to be very useful tools in ecology, however common for C and N of bulk tissue is that isotope discrimination can vary. Variation in bulk isotope discrimination is due to many co-occurring factors which can make interpretation difficult without good ecological knowledge of the system or organism in question, and we still need a better understanding of what shapes such variability (Vanderklift & Ponsard 2003, Boecklen et al. 2011). Most stable isotope work has focussed on bulk stable isotopes, which essentially is a measure of a mix of multiple compounds, such as lipids, protein and carbohydrates.

Compound specific stable isotopes A recent advance in stable isotopic techniques is the use of compound specific stable isotope analysis (CSIA). Though sharing similar properties to common bulk isotopes, C and N CSIA may provide additional resolution when assessing species diet use and trophic position. CSIA of compounds such as AAs provides an array of stable isotope measurements from a single

13 sample (~10-15 AAs). In principle such multiple tracers allow for separation of an increasing number of consumer dietary resources, something that is a common problem when using conventional bulk stable isotopes of only C and N (Brett 2014). Multiple tracers also allow for source differentiation using relative isotope ratios instead of absolute values (Larsen et al. 2009, McCarthy et al. 2013), which may eliminate some of the uncertainties associated with natural system variability when resources’ absolute isotope composition are temporally or spatially dynamic. It has also been proposed that multiple AA values can improve precision of trophic position estimation (Sherwood et al. 2011, Décima et al. 2013, Nielsen et al. 2015). However, to fully utilize multiple tracers more information on their isotope discrimination patterns is needed.

Application of AA CSIA of N isotopic composition is being increasingly used to infer trophic information of both aquatic (Chikaraishi et al. 2009, Bradley et al. 2015, Nielsen et al. 2015) and terrestrial species (Chikaraishi et al. 2014). A primary advantage of δ15N AA-CSIA is the dual information from trophic Fig. 1 AAs, which encode Change in consumer isotope ratios of trophic (red) and source AA (blue) with information about a species’ trophic position. The source AA isotope trophic position through ratio of all consumers on the food web 15 reflects the primary producers at the base predictable N trophic of the food web. Modified from discrimination, and source Chikaraishi et al. (2009).

14 AAs, which retain the N isotope composition of the primary producers that synthesized them (Fig 1, McClelland & Montoya 2002, Popp et al. 2007, Chikaraishi et al. 2009). Since N isotope discrimination can occur for several essential AAs due to biochemical reworking of the amino group (i.e. cleavage of C-N bonds), AAs are grouped into trophic and source AAs based on the degree of N isotope discrimination. The δ15N values of source AAs in consumers provide a measurement in a consumer’s tissue of the nitrogenous nutrients assimilated at the base of the food web (McClelland & Montoya 2002, Chikaraishi et al. 2009), something that can be difficult to accurately measure when using bulk isotope composition.

Contrary to N isotope ratios, δ13C AA values are commonly grouped into non-essential (non-E-AA) and essential (E-AA), respectively, based on consumer biosynthesis capabilities (Wu 2009). The δ13C values E-AAs which cannot be synthesized de-novo by have proven to be effective tracers in natural systems due to clear differentiation in relative δ13C ratios between different types of primary producers (Larsen et al. 2013), negligible trophic discrimination (Howland et al. 2003, McMahon et al. 2010), and consistency across changing environmental conditions (Larsen et al. 2015). Differences in δ13C E-AA values of aquatic invertebrate consumers have previously been useful in distinguishing between resource use of marine, terrestrial (Ellis et al. 2014, Vokhshoori et al. 2014) or primary producers (Fantle et al. 1999). Ecologists are continuing to explore new ways to apply AA-CSIA to answer useful biological questions, yet an important knowledge gap is what shapes variability in isotope trophic discrimination in individual AAs and among different types of consumers or diet sources.

Isotope discrimination can vary with the nutritional quality of assimilated dietary components (Robbins et al. 2010, Ventura & Catalan 2010,

15 McMahon et al. 2015b). Previous studies have shown a clear relationship between protein amounts and the isotope discrimination of both δ15N and δ13C values (Gaye-Siessegger et al. 2004, Bloomfield et al. 2011). Zooplankton bulk isotope values may also vary due to different nutritional quality of the prey (Matthews & Mazumder 2005, Ventura & Catalan 2010), while different dietary molecules can also vary in isotope composition (Degens 1969, Kürten et al. 2013). Interestingly, N AA isotope discrimination seem to vary both positively (Chikaraishi et al. 2015) and negatively (Bloomfield et al. 2011, McMahon et al. 2015b) with diet nutritional quality, highlighting the apparent complexity between animal physiology and isotope incorporation and there is still much scope for improved understanding of how isotopes and animal physiology are interlinked.

Zooplankton feeding ecology Zooplankton species are dominant intermediate species in aquatic ecosystems and important prey items for many species (Stibor et al. 2004) due to their high and high nutrient content (Beaugrand 2003). Yet, integrated assessment of how zooplankton species’ dynamics influence food web energy flow remains difficult due to continuous temporal and spatial environmental changes (Winder & Schindler 2004) and high species complexity (Sprules & Bowerman 1988). The limited biosynthesis capabilities of heterotrophs make the zooplankton-phytoplankton species link especially influential for nutritional fluxes since certain organic molecules must be taken up from the diet.

Optimal foraging theory predicts that species will target food items which provide the highest nutritional gain relative to the costs associated with foraging (Schoener 1971, Sih & Christensen 2001). Which dietary resource provides the best choice for a zooplankton consumer differs among animals

16 (Sprules & Bowerman 1988). Zooplankton feed on a range of dietary resources (Kleppel 1993), but target specific prey items which are nutritionally rich (Peters et al. 2006) or of a suitable size for capture (Berggreen et al. 1988, Katechakis et al. 2004). Zooplankton feeding likely fluctuate throughout the season dependent on the availability of specific dietary sources (Gentsch et al. 2008, Kürten et al. 2013) and the individual organisms’ nutritional requirements (Mayzaud 1976, Villar-Argaiz et al. 2002).

Many mesozooplankton (grazer size ~ 0.2-20 mm) can also feed on protists which provide important nutritional components (Boechat & Adrian 2006, Mitra et al. 2013). Furthermore, the role of microzooplankton (grazer size < 0.2mm) as trophic upgraders (i.e. packaging and thus enhancing the biochemical constituents of microalgae) and their ability to incorporate bacterial C in aquatic food webs is likely more important than often appreciated (Landry & Calbet 2004). The degree of omnivorous feeding in zooplankton likely plays an important role in energy transfer in aquatic systems and variation in feeding behaviour may alter food web structure substantially (Sprules & Bowerman 1988, Kratina et al. 2012). The non- static relationship between consumers and their diet assemblage (Grey et al. 2001, Nielsen & Winder 2015) makes accurate estimation of feeding behaviour of zooplankton in their ambient environment critical for understanding dynamics of energy flows in pelagic ecosystems (Dickman et al. 2008). However, zooplankton diet resources are often difficult to measure in natural environments and factors influencing zooplankton foraging and seasonal feeding patterns are still not well resolved.

17 OBJECTIVES

Accurate estimation of energy and nutrient transfer between species is an important part of categorizing food web dynamics. To measure such properties, reliable empirical methods which trace nutrient fluxes are essential. Dietary tracers, such as stable isotopes and molecular tools, have greatly improved our ability to assess such energy flows between species. Yet, we are still gaining new insights into how these tools can advance our knowledge on natural food webs. Therefore, through an integrated research approach, I studied the use of compound specific stable isotopes for assessing predator-prey interactions, and applied molecular and isotope tracer methods to categorize energy flow in natural aquatic ecosystems, with a particular focus on the species links between phytoplankton-zooplankton.

Part 1: Investigation into the variability in AA stable isotope ratios across and consumer type, assessed through a literature synthesis and meta-analysis (article I) and a controlled feeding experiment (article II).

Part 2: Understanding predator-prey interactions and energy transfer in aquatic ecosystems and across seasons, focusing on the link between phytoplankton and zooplankton, using different tracer methods: stable isotope analysis (article III) and molecular techniques (article IV).

18 AIM AND MAIN FINDINGS

Article I: Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms

In this article, we compiled the first meta-analysis of δ15N AA values from measurements of 359 marine species covering 4 trophic levels. We compared trophic position estimation using δ15N AA values to previous literature estimates from gut or stomach contents, and asked the following questions: 1. Are N trophic discrimination factors in different AAs constant across trophic position? 2. Does animal feeding ecology and form of N excretion affect the N trophic discrimination factor? 3. Which trophic and source AA δ15N ratios are most reliable for trophic position estimation, and can estimates which incorporate multiple trophic and source AA δ15N values provide more precise trophic position?

Our meta-analysis confirmed the applicability of using AA δ15N ratios as a broad-scale tool to predict trophic position of marine organisms at all levels of the food web. Within a given species, AA isotope ratios showed high co- variation, supporting the use of multiple AAs to predict consumer trophic position. Specifically, six trophic AAs all showed similar magnitude in trophic isotope discrimination among individual measurements suggesting ability to discriminate between trophic levels. Consistently low isotope discrimination in two source AAs showed that these compounds reflect the isotope ratios of primary producers at the base of the food web. Our findings suggest that the isotope composition of these compounds can be combined to retrieve species trophic position estimates.

19 Using a bootstrap approach we showed that in principle the N isotope composition from an increasing number of source and/or trophic AAs improved accuracy and precision in trophic position estimation. Our modelling approach also showed that both the absolute value of the trophic discrimination factor and uncertainty of this value is the most influential source of error for trophic position estimation, emphasizing the importance of applying accurate trophic discrimination factors to avoid under or underestimation of species trophic position.

Another key finding of our meta-analysis was that organism feeding ecology influences N trophic discrimination, with higher discrimination for herbivores compared to omnivores and carnivores. N isotope trophic discrimination was also lower for animals excreting urea compared to ammonium. These findings suggest that researchers should consider using group specific trophic discrimination values to improve accuracy in species trophic position predictions.

Article II: Amino acid stable isotope discrimination in diverse aquatic food chains

Here we studied multiple artificial freshwater food chains with various diet combinations, covering trophic levels 1-3 and measured bulk tissue and AAs isotope discrimination in C and N. We used model species of (Daphnia magna), fish (Poecilia sp.) and shrimp (Neocaridina heteropoda) to represent common aquatic consumers, reared on a basal resource of commercial fish pellet.

We estimated 1) N isotope incorporation rates of individual trophic AAs in a consumer (shrimp) during a diet shift, and 2) C and N discrimination for individual AA and bulk tissue, across trophic levels and resource type, and

20 assessed 3) if consumer isotope discrimination is influenced by nutritional quality of the diet.

We found consistently low C isotope discrimination in E-AAs across two trophic levels, confirming that these compounds are reliable dietary tracers. Relatively high N isotopic discrimination was present in most trophic AAs, while 15N trophic discrimination remained relatively low in most source AAs within feeding treatments. However, N isotope trophic discrimination in most trophic AAs also varied among feeding treatments, including consumers fed the same basal resource. For the majority of trophic AAs we accredited this variation in N isotope discrimination to variable diet imbalance, and we suggest that a consumer’s physiological adjustment in response to variable food qualities can influence the magnitude of N isotope trophic discrimination. Similar to our findings in article I, the laboratory data indicated that N trophic discrimination can vary and that group or taxa specific trophic discrimination factors are likely to provide improved trophic position estimation.

Article III: Seasonal dynamics of zooplankton resource use revealed by carbon amino acid stable isotope values

Here we used analysis of E-AA δ13C ratios in combination with bulk δ13C and δ15N values to study seasonal dynamics in resource use of a widespread copepod consumer (Acartia spp.) in the northern Baltic Proper.

Our results indicated that Acartia used a range of dietary phytoplankton groups, including , cryptophytes, diatoms, prasinophytes and cyanobacteria. However, the seasonal deviations between Acartia and δ13C E-AA values suggested that Acartia does not feed opportunistically on all available components but instead preferentially selects and assimilate

21 AAs from certain diet items. The degree of selective feeding seemed to vary across season. The distinct differences between Acartia and seston δ13C E- AA values further indicate that inferring trophic information from, for example, N isotope composition of consumers relative to seston can be misleading and such inference should be carefully evaluated or assessed in combination with additional data.

Low summer δ15N values in both Acartia and seston indicated that diazotroph cyanobacteria play an important role for Baltic Sea zooplankton either through direct or indirect utilization, in concurrence with previous observations (Rolff 2000). The fact that Acartia δ13C E-AA values seemed to encode seasonal variation putative dietary sources supported the application of δ13C E-AA values as a suitable tool to study consumer resource assimilation.

Article IV: Marine zooplankton diet preferences across species, life stages and seasons using DNA barcoding

Here we used DNA barcoding to detect zooplankton dietary ingestion in four common Baltic Sea zooplankton species: the copepods Acartia spp., Eurytemora affinis, Temora longicornis and the marine cladoceran Evadne nordmanni.

We assessed the available food resources in the and compared it to the diet composition of the different zooplankton species, life stages and seasons (summer, autumn). We hypothesized that resource intake and associated foraging patterns within a zooplankton were dynamic and depended on consumer species and the available diet composition. Specifically, we considered two alternative scenarios: 1) High prey biomass will lead to high dietary overlap between zooplankton species

22 as consumers can easily target preferred items, and, 2) Increased competition for resources will lead to higher levels of opportunistic feeding to fulfil nutrient requirements for growth, and thus also diet segregation among different zooplankton consumers.

Our molecular analysis provided direct identification of the majority of autotrophic and heterotrophic dietary resources commonly ingested by zooplankton species. All zooplankton consumers seemed to feed on a relatively broad range of diet items but they did not feed opportunistically on all available food sources. We found that zooplankton consumers, both copepods and cladocerans, generally overlapped considerably in diet intake. All consumers, including marine cladocerans, fed carnivorous on ciliate prey. Our study also indicated that larger nauplii and copepodites were capable of feeding on most of the same prey as adult copepods.

As competition for resources seemed to change across season, zooplankton consequently changed their foraging behaviour. Yet, the degree of selective feeding was not constant across season. Specifically, we found higher dietary overlap between zooplankton when dietary biomass was high indicating more specialized feeding, as the case during summer while in autumn increased competition resulted in lower dietary overlaps because zooplankton consumers likely fed more opportunistically.

23 CONCLUSIONS AND FUTURE PERSPECTIVES

Tracer techniques A key component when depicting the structure of food webs is the ability to place individual organisms within a food web. The broad scale applicability of employing δ15N AA values for predicting species trophic position was supported in both our meta-analysis (article I) and laboratory study (article II), in consensus with previous studies (Chikaraishi et al. 2009, Bradley et al. 2015). Our findings also highlighted the variability in species N isotope discrimination, similar to what has been shown in other studies (Dale et al. 2011, Décima et al. 2013, Vokhshoori & McCarthy 2014). The variability noted in our studies (articles I & II) indicates that researchers should carefully evaluate trophic discrimination factors and consider employing consumer specific values depending on taxa, dietary use (McMahon et al. 2015b), feeding behaviour (Nielsen et al. 2015) or excretion mechanism (Germain et al. 2013, McMahon et al. 2015a).

Another observation in our laboratory study was that isotope turnover times vary among different AAs (Article II, Bradley et al. 2014). Temporal tracing methods have been applied to retrieve insight on assimilation efficiencies of specific FAs (Mayor et al. 2011) and to assess how organic compounds such as FAs are accumulated and stored (Koussoroplis et al. 2013). Studies into isotope turnover times of individual AAs could similarly be very informative in studying the importance of specific AAs for species growth and nutritional usage.

An interesting pattern in our laboratory study (article II) was the consistent co-variation between bulk and AA δ13C ratios, suggesting that tracer methods can be combined to yield ecological information. Such combination

24 may enhance separation among an increasing number of dietary sources, something that is often problematic when using isotope ratios from only 2 or 3 compounds (Brett 2014, Phillips et al. 2014). It also implies that previous studies about variability in bulk stable isotope ratios can provide valuable knowledge about potential variation in AA-CSIA. These findings suggest that even if CSIA can provide increased resolution in disentangling species trophic information or diet use, bulk isotope and CSIA should be considered complementary methods, rather than prematurely dismissing the application of bulk stable isotope analysis (Bowes & Thorp 2015), which has shown to be a very valuable tool applicable for answering a range of ecological questions (Post 2002, Boecklen et al. 2011, Layman et al. 2012).

The advantage of having combined measurement of bulk and CSIA was informative when disentangling possible selective feeding from changes in trophic position in Baltic Sea zooplankton (article III). Our data suggested substantial selective feeding, as shown by the increasing deviations in C AA isotope composition between seston and zooplankton consumers during the growing season. Changes in the difference between δ15N isotope ratios of consuming zooplankton and seston have previously been used to infer changing trophic position across season (Boersma et al. 2015). However, our findings indicate that inferring trophic information from N isotope composition of consumers relative to seston should be treated with prudence, since differences in δ15N isotope ratios between seston and zooplankton consumers may be a consequence of differences in feeding selection rather than a change in trophic level (El-Sabaawi et al. 2010).

Zooplankton feeding dynamics In both our studies (articles III & IV) zooplankton were found to feed on a relatively broad range of resources. However, zooplankton did not feed

25 opportunistically on all available diet but targeted specific food sources including dinoflagellates, diatoms, cyanobacteria and ciliates (article IV). Different dietary resources vary in their nutritional composition (Brown et al. 1997) and thus complementary diet components are seemingly needed to adequately fulfil zooplankton energetic and physiological requirements (Kleppel 1993, Meunier et al. 2015). A common phenomenon in our study was ingestion of diet items which were usually above ~10 μm (approximated from size measures of the microscopy data) and usually consisting of good nutritional value in terms of lipids (Galloway & Winder 2015) and proteins (Vargas et al. 1998). Larger mobile prey such as many protists were also preferred, likely due to their high energetic gain (Klein Breteler et al. 1999, Veloza et al. 2006).

We found a clear depletion in the N isotope composition of both zooplankton and seston (article III). Such seasonal change in δ15N isotope ratios have previously been noted during summer blooms of cyanobacteria in the Baltic Sea (Rolff 2000, Karlson et al. 2015). A high abundance of cyanobacteria was also present in our genetic dietary data and indicated considerable zooplankton ingestion of these (article IV), in concurrence with previous findings (Motwani & Gorokhova 2013). Cyanobacteria are often considered a relatively poor food source in terms of nutritionally rich FAs (Galloway & Winder 2015). However, cyanobacteria including diazotroph species which often fuel N production in summer (Adam et al. 2015), can contain substantial amounts of N (Vargas et al. 1998) and may act as an important N source used to build proteinaceous tissue in aquatic organisms.

An advantage of the molecular data (article IV, 18S analysis) was the ability to assess the degree of zooplankton carnivory. The capability of all zooplankton, including marine cladocerans to feed on both and

26 prey such as ciliates suggests that most adult copepods should be categorized as omnivores (Paffenhöfer 1988, Sommer & Sommer 2004). Mesozooplanktons ability to feed on both autotrophic and heterotrophic prey influences the energy and nutritional flows among lower trophic levels species in aquatic food webs. Increased omnivory may enhance food web stability and persistence (Kratina et al. 2012) and microzooplankton can also introduce new energy and nutrients from bacterial components not accessible for mesozooplankton. However, since energy is lost at every trophic level increased omnivory may also introduce a longer and less efficient trophic chain. In article III no data was available on protist diet resources and the proportion of ciliate ingestion measured from molecular methods (article IV) does not provide quantitative prediction of trophic efficiencies of energy transfer. Nonetheless, our findings showed that nutritional flows in the planktonic food web channel primarily through an omnivory zooplankton , which include considerable amounts of ciliate prey.

Our genetic study (article IV) revealed a high overlap in resource use between different consumer genera. Such diet overlaps also suggest that indirect competition between zooplankton can be high, and during peak abundance in summer, marine cladocerans (Kankaala 1983, Möllmann et al. 2002) may also compete substantially with copepods for available heterotroph and autotroph resources in the Baltic Sea.

Future perspectives Molecular diet analysis showed to be a promising tool to depict individual diet items (article IV) and thus measure zooplankton ingestion in natural environments. Our molecular data seemed promising in identifying specific food items however, accurate quantification of specific diet resources was more challenging, something which seem a common problem in molecular diet analysis (Deagle et al. 2013). A sensible way to increase quantitative

27 estimates is improved insight into the amount of DNA a given algal species contains per individual cell or amount of C. This can be estimated by complementary laboratory experiments where a known quantity of mono- cultured cells or organism tissue is measured for amount of DNA sequences (Thomas et al. 2014). Such additional a priori information will be very useful in establishing relative contributions of diet intake in field samples. A similar approach also seems promising for employing in schemes where molecular assessment methods are used to quantify species abundances.

Fundamental requirements for an accurate diet tracer are predictable and preferably negligible trophic discrimination and consistent analytical reproducibility, which was the case for both δ13C values of bulk and E-AA values in our feeding study (article II). Precise diet tracers also require adequate separation of resource components. Such separation of diet sources has been established among largely different primary producer groups (Larsen et al. 2013). However, further improving the application of δ13C E- AA values for dietary tracing relies on elucidating two key issues which have not yet been studied. First, resolving to what degree dietary sources can be separated within a given primary producer domain, such as among either strictly aquatic primary producers or terrestrial plants. Second, measuring the turnover rates of individual E-AA stable isotope ratios, as this provides information of the temporal incorporation of specific AAs.

Future development of appropriate statistical frameworks will be highly valuable to improve application of CSIA and allow full exploitation of the information embedded in multiple tracer data (Galloway et al. 2014, Swanson et al. 2015). Diagnostic methods are also needed allowing evaluation of when CSIA data and associated models provide ecologically realistic results. Such diagnostic tools have proven to be valuable when

28 assessing 2 or 3 tracers (Smith et al. 2013, Brett 2014), and when dealing with complex multivariate data good diagnostic tools are even more crucial.

So far relatively few studies have taken advantage of combining ingestion (e.g. DNA barcoding) and assimilation (e.g. CSIA) measures (Traugott et al. 2013, Chiaradia et al. 2014). Assimilation of compounds relate to nutrient absorption and thus to growth and reproduction of a specific consumer. Stable isotope analysis is especially useful for categorizing such energy flow. Ingestion provides information about the effect of the upper trophic level on the lower trophic level. In principle different dietary methods should allow differentiation between the two and thus may be helpful in measuring energy flow, species interaction strengths and predation pressures. Yet, to what extent such mechanisms are actually possible to differentiate in complex natural systems remains to be tested.

Another way to potentially merge data from different types of dietary methods is by using DNA diet information as prior information in isotope mixing models (Chiaradia et al. 2014). Such a combination seems a promising way to further improve the output of statistical frameworks aiming at elucidating ecological processes. The advantages of both molecular and stable isotope methods for diet analysis are many and as we increase insight on the use of these dietary proxies, vast possibilities of potential ecological applications are likely to surface.

It is evident that food quality and not only food quantity plays an important role in food web processes and trophic interactions (Mitra & Flynn 2007, Perhar et al. 2012). Using appropriate diet tracers we showed that zooplankton continuously alters feeding behaviour in response to the changing dietary assemblage. Seasonal changes in diet assimilation influence the nutritional composition of zooplankton themselves and propagate to

29 higher trophic level predators (Möllmann et al. 2004), something that alters nutritional flows and community structure of aquatic food webs (Winder & Jassby 2011). If we are to gain further understanding on how energy transfers vary, it is necessary to look not only at elemental stoichiometry but also on specific biochemical requirements in primary producers and zooplankton.

Assessing how nutritional flows will be influenced by both natural and human mediated perturbations is an important future task. Recent climate warming has been shown to cause notable changes to phytoplankton distribution, species composition and abundance in both marine (Reid et al. 1998) and freshwater (Winder et al. 2012) systems. Also, future climate change is expected to increase bacterial activity and abundance of small- sized flagellates (Wohlers et al. 2009), potentially adding new energy pathways to aquatic food chains. Improved insight of how energy and nutrient is transferred through entire natural communities as well as identifying key trophic pathways that are especially crucial for shaping food web structures, should thus be a continued focal point in aquatic ecology.

30 ACKNOWLEDGEMENTS

First and foremost I would like to thank my supervisor Monika Winder for continued support and guidance. A special thanks to my assistant supervisor Sture Hansson as well as Alfred Burian, and past and present members of the Winder Lab group for enlightening ecological discussions.

I sincerely thank Katja Reutervik, Matteo Fusilli, Anna-Lea Golz, Helena Höglander, Stefan Svensson, Jennifer Griffiths, Linda Eggertsen, Maria Eggertsen, Olle Hjerne, Isabell Klawonn, Ragnar Elmgren, David Angeler, Peter Hambäck and Clare Bradshaw for valuable help in the field, laboratory, and/or for constructive comments and discussions during manuscript writing. Thank you to collaborators Brian Popp, Rodrigo Esparza-Salas and Love Dahlén.

I am especially grateful to the scientific staff at Askö, Eddie Eriksson, Eva Lindell, Carl-Magnus Wiltén, Susann Ericsson, Matthias Murphy and Barbara Deutsch and the Swedish National Environmental Monitoring Program group at Department of Ecology, Environment and Plant Sciences, Stockholm University for continuous help, support and data. Finally I would like to thank my family.

This thesis was funded by the German Science Foundation (DFG) under project number WI 2726/2-1.

31

REFERENCES

Adam B, Klawonn I, Svedén JB, Bergkvist J, Nahar N, Walve J, Littmann S, Whitehouse MJ, Lavik G, Kuypers MM (2015) N2-fixation, ammonium release and N-transfer to the microbial and classical food web within a community. The ISME journal, doi: 101038/ismej2015126

Beaugrand G (2003) Plankton effect on cod in the North Sea. Nature 426:661-664

Bec A, Perga M-E, Koussoroplis A, Bardoux G, Desvilettes C, Bourdier G, Mariotti A (2011) Assessing the reliability of fatty acid-specific stable isotope analysis for trophic studies. Methods in Ecology and Evolution 2:651-659

Berggreen U, Hansen B, Kiørboe T (1988) Food size spectra, ingestion and growth of the copepodAcartia tonsa during development: Implications for determination of copepod production. Marine 99:341-352

Bloomfield AL, Elsdon TS, Walther BD, Gier EJ, Gillanders BM (2011) Temperature and diet affect carbon and nitrogen isotopes of fish muscle: can amino acid nitrogen isotopes explain effects? Journal of Experimental and Ecology 399:48-59

Boechat IG, Adrian R (2006) Evidence for biochemical limitation of population growth and reproduction of the rotifer Keratella quadrata fed with freshwater protists. Journal of Plankton Research 28:1027-1038

Boecklen WJ, Yarnes CT, Cook BA, James AC (2011) On the Use of Stable Isotopes in Trophic Ecology. Annual Review of Ecology, Evolution, and Systematics 42:411-440

Boersma M, Mathew KA, Niehoff B, Schoo KL, Franco‐Santos RM, Meunier CL (2015) Temperature driven changes in the diet preference of omnivorous copepods: no more meat when it's hot? Ecol Lett

Bohmann K, Monadjem A, Noer CL, Rasmussen M, Zeale MR, Clare E, Jones G, Willerslev E, Gilbert MTP (2011) Molecular diet analysis of two African free-tailed bats (Molossidae) using high throughput sequencing. PloS one 6:e21441

Bowes R, Thorp J (2015) Consequences of employing amino acid vs. bulk-tissue, stable isotope analysis: a laboratory trophic position experiment. Ecosphere 6:art14

Bradley CJ, Wallsgrove NJ, Choy CA, Drazen JC, Hetherington ED, Hoen DK, Popp BN (2015) Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. and Oceanography: Methods 139 (2015): 476-493

32 Brett MT (2014) Resource polygon geometry predicts bayesian stable isotope mixing model bias. Marine Ecology Progress Series 514:1-12

Brown M, Jeffrey S, Volkman J, Dunstan G (1997) Nutritional properties of microalgae for mariculture. 151:315-331

Chiaradia A, Forero MG, McInnes JC, Ramírez F (2014) Searching for the true diet of marine predators: incorporating Bayesian priors into stable isotope mixing models. PloS one 9:e92665

Chikaraishi Y, Ogawa NO, Kashiyama Y, Takano Y, Suga H, Tomitani A, Miyashita H, Kitazato H, Ohkouchi N (2009) Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol Oceanogr Meth 7:740-750

Chikaraishi Y, Steffan SA, Ogawa NO, Ishikawa NF, Sasaki Y, Tsuchiya M, Ohkouchi N (2014) High‐resolution food webs based on nitrogen isotopic composition of amino acids. Ecology and evolution 412 (2014): 2423-2449

Chikaraishi Y, Steffan SA, Takano Y, Ohkouchi N (2015) Diet quality influences isotopic discrimination among amino acids in an aquatic vertebrate. Ecology and Evolution 5:2048-2059

Clare EL (2014) Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. Evolutionary applications 7:1144-1157

Corse E, Costedoat C, Chappaz R, Pech N, MARTIN JF, Gilles A (2010) A PCR‐ based method for diet analysis in freshwater organisms using 18S rDNA barcoding on faeces. Molecular ecology resources 10:96-108

Dale JJ, Wallsgrove NJ, Popp BN, Holland KN (2011) Nursery use and foraging ecology of the brown stingray Dasyatis lata determined from stomach contents, bulk and amino acid stable isotopes. Marine Ecology Progress Series 433:221-236

Deagle BE, Thomas AC, Shaffer AK, Trites AW, Jarman SN (2013) Quantifying sequence proportions in a DNA‐based diet study using Ion Torrent amplicon sequencing: which counts count? Molecular ecology resources 13:620-633

Décima M, Landry MR, Popp BN (2013) Environmental perturbation effects on baseline 15N values and zooplankton trophic flexibility in the southern California Current . Limnol Oceanogr 58:624-634

Degens E (1969) Biogeochemistry of stable carbon isotopes. Organic geochemistry. Springer

DeNiro MJ, Epstein S (1981) Influence of diet on the distribution of nitrogen isotopes in animals. Geochimica et Cosmochimica Acta 45:341-351

33 Dickman EM, Newell JM, Gonzalez MJ, Vanni MJ (2008) Light, nutrients, and food-chain length constrain planktonic energy transfer efficiency across multiple trophic levels. Proc Natl Acad Sci U S A 105:18408-18412

Dunne JA, Williams RJ, Martinez ND (2002) Network structure and loss in food webs: robustness increases with connectance. Ecol Lett 5:558-567

El-Sabaawi RW, Sastri AR, Dower JF, Mazumder A (2010) Deciphering the seasonal cycle of copepod trophic dynamics in the Strait of Georgia, Canada, using stable isotopes and fatty acids. and Coasts 33:738-752

Ellis GS, Herbert G, Hollander D (2014) Reconstructing Carbon Sources in a Dynamic Estuarine Ecosystem using Oyster Amino Acid δ13C Values from Shell and Tissue. Journal of Shellfish Research 33:217-225

Elton C (1927) Animal ecology. 207 pp. Sidgwick & Jackson, LTD London

Fantle MS, Dittel AI, Schwalm SM, Epifanio CE, Fogel ML (1999) A food web analysis of the juvenile blue crab, Callinectes sapidus, using stable isotopes in whole animals and individual amino acids. Oecologia 120:416-426

Galloway A, Eisenlord M, Dethier M, Holtgrieve G, Brett M (2014) Quantitative estimates of isopod resource utilization using a bayesian fatty acid mixing model. Marine Ecology Progress Series 507:219-232

Galloway AW, Winder M (2015) Partitioning the relative importance of phylogeny and environmental conditions on phytoplankton fatty acids. PloS one 10:e0130053

García-Robledo C, Erickson DL, Staines CL, Erwin TL, Kress WJ (2013) Tropical plant- networks: reconstructing species interactions using DNA barcodes. PloS one 8:e52967

Gaye-Siessegger J, Focken U, Abel H, Becker K (2004) Individual protein balance strongly influences 15N and 13C values in Nile tilapia, Oreochromis niloticus. Die Naturwissenschaften 91:90-93

Gentsch E, Kreibich T, Hagen W, Niehoff B (2008) Dietary shifts in the copepod Temora longicornis during : evidence from stable isotope signatures, fatty acid biomarkers and feeding experiments. Journal of Plankton Research 31:45-60

Germain LR, Koch PL, Harvey J, McCarthy MD (2013) Nitrogen isotope fractionation in amino acids from harbor seals: implications for compound-specific trophic position calculations. Marine Ecology Progress Series 482:265-277

Grey J, Jones RI, Sleep D (2001) Seasonal changes in the importance of the source of organic matter to the diet of zooplankton in Loch Ness, as indicated by stable isotope analysis. Limnology and Oceanography 46:505-513

34 Hoen DK, Kim SL, Hussey NE, Wallsgrove NJ, Drazen JC, Popp BN (2014) Amino acid δ15N trophic enrichment factors of four large carnivorous . Journal of Experimental Marine Biology and Ecology 453:76-83

Howland MR, Corr LT, Young SMM, Jones V, Jim S, Van Der Merwe NJ, Mitchell AD, Evershed RP (2003) Expression of the dietary isotope signal in the compound- specific 13C values of pig bone lipids and amino acids. International Journal of Osteoarchaeology 13:54-65

Hu S, Guo Z, Li T, Carpenter EJ, Liu S, Lin S (2014) Detecting in situ copepod diet diversity using molecular technique: development of a Copepod/Symbiotic Ciliate- Excluding Eukaryote-Inclusive PCR Protocol. e103528

Kankaala P (1983) Resting eggs, seasonal dynamics, and production of Bosmina longispina maritima (PE Müller)(Cladocera) in the northern Baltic proper. Journal of Plankton Research 5:53-69

Karlson AM, Duberg J, Motwani NH, Hogfors H, Klawonn I, Ploug H, Svedén JB, Garbaras A, Sundelin B, Hajdu S (2015) Nitrogen fixation by cyanobacteria stimulates production in Baltic food webs. Ambio 44:413-426

Katechakis A, Stibor H, Sommer U, Hansen T (2004) Feeding selectivities and food niche separation of Acartia clausi, Penilia avirostris (Crustacea) and Doliolum denticulatum (Thaliacea) in Blanes Bay (Catalan Sea, NW Mediterranean). Journal of Plankton Research 26:589-603

King R, Read D, Traugott M, Symondson W (2008) INVITED REVIEW: Molecular analysis of predation: a review of best practice for DNA‐based approaches. Molecular ecology 17:947-963

Klein Breteler W, Schogt N, Baas M, Schouten S, Kraay G (1999) Trophic upgrading of food quality by protozoans enhancing copepod growth: role of essential lipids. Marine Biology 135:191-198

Kleppel G (1993) On the diets of calanoid copepods. Marine Ecology Progress Series 99:183-183

Koussoroplis AM, Kainz MJ, Striebel M (2013) Fatty acid retention under temporally heterogeneous dietary intake in a cladoceran. Oikos 122:1017-1026

Kratina P, LeCraw RM, Ingram T, Anholt BR (2012) Stability and persistence of food webs with omnivory: Is there a general pattern? Ecosphere 36 (2012): art50 3

Kürten B, Painting SJ, Struck U, Polunin NV, Middelburg JJ (2013) Tracking seasonal changes in North Sea zooplankton trophic dynamics using stable isotopes. Biogeochemistry 113:167-187

Landry MR, Calbet A (2004) Microzooplankton production in the . Journal of Marine Science 61:501e507

35 Larsen T, Bach LT, Salvatteci R, Wang YV, Andersen N, Ventura M, McCarthy MD (2015) Assessing the potential of amino acid 13 C patterns as a carbon source tracer in marine sediments: effects of algal growth conditions and sedimentary diagenesis. Biogeosciences 12:4979-4992

Larsen T, Taylor DL, Leigh MB, O'Brien DM (2009) Stable isotope fingerprinting: a novel method for identifying plant, fungal, or bacterial origins of amino acids. Ecology 90:3526-3535

Larsen T, Ventura M, Andersen N, O’Brien DM, Piatkowski U, McCarthy MD (2013) Tracing Carbon Sources through Aquatic and Terrestrial Food Webs Using Amino Acid Stable Isotope Fingerprinting. PloS one 8:e73441

Layman CA, Araujo MS, Boucek R, Hammerschlag-Peyer CM, Harrison E, Jud ZR, Matich P, Rosenblatt AE, Vaudo JJ, Yeager LA, Post DM, Bearhop S (2012) Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biological reviews of the Cambridge Philosophical Society 87:545-562

Layman CA, Giery ST, Buhler S, Rossi R, Penland T, Henson MN, Bogdanoff AK, Cove MV, Irizarry AD, Schalk CM (2015) A primer on the history of food web ecology: Fundamental contributions of fourteen researchers. Food Webs 4:14-24

Lindeman RL (1942) The trophic-dynamic aspect of ecology. Ecology 23:399-417

Matthews B, Mazumder A (2005) Temporal variation in body composition (C : N) helps explain seasonal patterns of zooplankton 13C. 50:502-515

Mayor DJ, Cook K, Thornton B, Walsham P, Witte UF, Zuur AF, Anderson TR (2011) Absorption efficiencies and basal turnover of C, N and fatty acids in a marine Calanoid copepod. 25:509-518

Mayzaud P (1976) Respiration and nitrogen excretion of zooplankton. IV. The influence of starvation on the metabolism and the biochemical composition of some species. Marine Biology 37:47-58

McCarthy MD, Lehman J, Kudela R (2013) Compound-specific amino acid δ15N patterns in marine algae: Tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the . Geochimica et Cosmochimica Acta 103:104-120

McClelland JW, Montoya JP (2002) Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83:2173-2180

McCutchan JH, Lewis WM, Kendall C, McGrath CC (2003) Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and . Oikos 102:378-390

McMahon KW, Fogel ML, Elsdon TS, Thorrold SR (2010) Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. Journal of Animal Ecology 79:1132-1141

36 McMahon KW, Fogel ML, Johnson BJ, Houghton LA, Thorrold SR (2011) A new method to reconstruct fish diet and movement patterns from 13C values in otolith amino acids. Can J Fish Aquat Sci 68:1330-1340

McMahon KW, Polito MJ, Abel S, McCarthy MD, Thorrold SR (2015a) Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo (Pygoscelis papua). Ecology and Evolution 5:1278-1290

McMahon KW, Thorrold SR, Elsdon TS, McCarthy MD (2015b) Trophic discrimination of nitrogen stable isotopes in amino acids varies with diet quality in a marine fish. Limnology and Oceanography 60:1076-1087

Meunier CL, Boersma M, Wiltshire KH, Malzahn AM (2015) Zooplankton eat what they need: copepod selective feeding and potential consequences for marine systems. Oikos

Mitra A, Flynn KJ (2007) Importance of interactions between food quality, quantity, and gut transit time on consumer feeding, growth, and trophic dynamics. The American Naturalist 169:632-646

Mitra A, Flynn KJ, Burkholder J, Berge T, Calbet A, Raven JA, Granéli E, Glibert PM, Hansen PJ, Stoecker DK (2013) The role of mixotrophic protists in the biological carbon pump. Biogeosciences Discussions 10

Motwani NH, Gorokhova E (2013) Mesozooplankton grazing on picocyanobacteria in the Baltic Sea as inferred from molecular diet analysis. PloS one 8:e79230

Müller-Navarra DC (2008) Food Web Paradigms: The Biochemical View on Trophic Interactions. International Review of 93:489-505

Möllmann C, Kornilovs G, Fetter M, Köster F (2004) Feeding ecology of central Baltic Sea herring and sprat. Journal of fish biology 65:1563-1581

Möllmann C, Köster F, Kornilovs G, Sidrevics L (2002) Long-term trends in abundance of cladocerans in the Central Baltic Sea. Marine Biology 141:343-352

Nielsen JM, Popp BN, Winder M (2015) Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia:1-12

Nielsen JM, Winder M (2015) Seasonal dynamics of zooplankton resource use revealed by carbon amino acid stable isotope values. MEPS 531:143-154

Odum HT (1957) Trophic structure and of Silver Springs, Florida. Ecological Monographs:55-112

Paffenhöfer G-A (1988) Feeding rates and behavior of zooplankton. Bulletin of Marine Science 43:430-445

37 Paine RT (1980) Food webs: linkage, interaction strength and community infrastructure. The Journal of animal ecology:667-685

Perhar G, Arhonditsis GB, Brett MT (2012) Modelling the role of highly unsaturated fatty acids in planktonic food web processes: a mechanistic approach. Environmental Reviews 20:155-172

Peters J, Renz J, Beusekom J, Boersma M, Hagen W (2006) Trophodynamics and seasonal cycle of the copepod Pseudocalanus acuspes in the Central Baltic Sea (Bornholm Basin): evidence from lipid composition. Marine Biology 149:1417- 1429

Peterson BJ, Fry B (1987) Stable isotopes in ecosystem studies. Annual review of ecology and systematics:293-320

Phillips DL, Inger R, Bearhop S, Jackson AL, Moore JW, Parnell AC, Semmens BX, Ward EJ (2014) Best practices for use of stable isotope mixing models in food- web studies. Canadian journal of zoology 92:823-835

Pompanon F, Deagle BE, Symondson WO, Brown DS, Jarman SN, Taberlet P (2012) Who is eating what: diet assessment using next generation sequencing. Molecular ecology 21:1931-1950

Popp BN, Graham BS, Olson RJ, Hannides CCS, Lott MJ, López‐Ibarra GA, Galván‐Magaña F, Fry B (2007) Insight into the Trophic Ecology of Yellowfin Tuna, Thunnus albacares, from Compound-Specific Nitrogen Isotope Analysis of Proteinaceous Amino Acids. 1:173-190

Post DM (2002) Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and Assumptions. Ecology 83:703-718

Raubenheimer D, Simpson SJ (2004) Organismal stoichiometry: quantifying non- independence among food components. Ecology 85:1203-1216

Reid PC, Edwards M, Hunt HG, Warner AJ (1998) Phytoplankton change in the North Atlantic. Nature 391:546-546

Robbins CT, Felicetti LA, Florin ST (2010) The impact of protein quality on stable nitrogen isotope ratio discrimination and assimilated diet estimation. Oecologia 162:571-579

Rolff C (2000) Seasonal variation in 13C and 15N of size-fractionated plankton at a coastal station in the northern Baltic proper. Marine Ecology Progress Series 203:47-65

Schoener TW (1971) Theory of feeding strategies. Annual review of ecology and systematics:369-404

38 Sherwood OA, Lehmann MF, Schubert CJ, Scott DB, McCarthy MD (2011) Nutrient in the western North Atlantic indicated by compound-specific δ15N of deep-sea gorgonian . Proceedings of the National Academy of Sciences 108:1011-1015

Sih A, Christensen B (2001) Optimal diet theory: when does it work, and when and why does it fail? Animal behaviour 61:379-390

Smith JA, Mazumder D, Suthers IM, Taylor MD (2013) To fit or not to fit: evaluating stable isotope mixing models using simulated mixing polygons. Methods in Ecology and Evolution 4:612-618

Sommer F, Sommer U (2004) δ15N signatures of marine mesozooplankton and seston size fractions in Kiel Fjord, Baltic Sea. Journal of Plankton Research 26:495- 500

Sprules W, Bowerman J (1988) Omnivory and food chain length in zooplankton food webs. Ecology:418-426

Sterner RW, Elser JJ (2002) : the biology of elements from molecules to the biosphere. Princeton University Press

Stibor H, Vadstein O, Diehl S, Gelzleichter A, Hansen T, Hantzsche F, Katechakis A, Lippert B, Løseth K, Peters C, Roederer W, Sandow M, Sundt-Hansen L, Olsen Y (2004) Copepods act as a switch between alternative trophic cascades in marine pelagic food webs. Ecol Lett 7:321-328

Swanson HK, Lysy M, Power M, Stasko AD, Johnson JD, Reist JD (2015) A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96:318-324

Thomas AC, Jarman SN, Haman KH, Trites AW, Deagle BE (2014) Improving accuracy of DNA diet estimates using food tissue control materials and an evaluation of proxies for digestion bias. Molecular ecology 23:3706-3718

Thomsen PF, Kielgast J, Iversen LL, Møller PR, Rasmussen M, Willerslev E (2012) Detection of a diverse marine fish fauna using environmental DNA from samples. PloS one 7:e41732

Traugott M, Kamenova S, Ruess L, Seeber J, Plantegenest M (2013) Empirically characterising trophic networks: what emerging DNA-based methods, stable isotope and fatty acid analyses can offer. Adv Ecol Res 49:177-224

Wacker A, Martin‐Creuzburg D (2012) Biochemical nutrient requirements of the rotifer Brachionus calyciflorus: co‐limitation by sterols and amino acids. Functional Ecology 26:1135-1143

Vanderklift MA, Ponsard S (2003) Sources of variation in consumer-diet d15N enrichment: a meta-analysis. Oecologia 136:169-182

39 Vargas M, Rodriguez H, Moreno J, Olivares H, Del Campo J, Rivas J, Guerrero M (1998) BIOCHEMICAL COMPOSITION AND FATTY ACID CONTENT OF FILAMENTOUS NITROGEN‐FIXING CYANOBACTERIA. Journal of phycology 34:812-817

Veloza AJ, Chu FLE, Tang KW (2006) Trophic modification of essential fatty acids by heterotrophic protists and its effects on the fatty acid composition of the copepod Acartia tonsa. Marine Biology 148:779-788

Ventura M, Catalan J (2010) Variability in amino acid composition of alpine crustacean zooplankton and its relationship with nitrogen-15 fractionation. Journal of Plankton Research 32:1583-1597

Villar-Argaiz M, Medina-Sánchez JM, Carrillo P (2002) Linking life history strategies and ontogeny in crustacean zooplankton: implications for homeostasis. Ecology 83:1899-1914

Winder M, Berger S, Lewandowska A, Aberle N, Lengfellner K, Sommer U, Diehl S (2012) Spring phenological responses of marine and freshwater plankton to changing temperature and light conditions. Marine Biology 159:2491-2501

Winder M, Jassby AD (2011) Shifts in zooplankton community structure: implications for food web processes in the upper San Francisco . Estuaries and Coasts 34:675-690

Winder M, Schindler DE (2004) Climate change uncouples trophic interactions in an . Ecology 85:2100-2106

Winemiller KO (1990) Spatial and temporal variation in tropical fish trophic networks. Ecological Monographs 60:331-367

Wohlers J, Engel A, Zollner E, Breithaupt P, Jurgens K, Hoppe HG, Sommer U, Riebesell U (2009) Changes in biogenic carbon flow in response to sea surface warming. Proc Natl Acad Sci U S A 106:7067-7072

Vokhshoori NL, Larsen T, McCarthy MD (2014) Reconstructing δ13C isoscapes of phytoplankton production in a coastal system with amino acid isotope values of littoral mussels. MEPS 504:59-72

Vokhshoori NL, McCarthy MD (2014) Compound-Specific δ15N Amino Acid Measurements in Littoral Mussels in the California Upwelling Ecosystem: A New Approach to Generating Baseline δ15N Isoscapes for Coastal Ecosystems. PloS one 9:e98087

Wu G (2009) Amino acids: metabolism, functions, and nutrition. Amino acids 37:1- 17

40

Top View