Download 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 Ecology, 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 food web
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 energy flow in natural aquatic ecosystems, with a particular
focus on the species links between phytoplankton and zooplankton.
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 plantanimal 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
consumer and diet), with higher discrimination in herbivores compared to
omnivores and carnivores (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 resource 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/s00442015-3305-7 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
Food web dynamics and nutritional flows
9
9
Diet tracing
10
Diet tracing using molecular methods
12
Stable isotope analysis 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 predation and competition 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
cannibalism 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
foraging patterns of small animals such as zooplankton (Hu et al. 2014)
which are often difficult to measure in their natural environment.
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 nature as atoms of the same element having
different nuclear particle 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
information
AAs,
the
from
which
dual
trophic
Fig. 1
encode
Change in consumer isotope ratios of
trophic (red) and source AA (blue) with
trophic position. The source AA isotope
ratio of all consumers on the food web
reflects the primary producers at the base
of the food web. Modified from
Chikaraishi et al. (2009).
information about a species’
trophic
position
predictable
15
N
through
trophic
discrimination, and source
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 heterotrophs 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 marsh
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 fish species (Stibor et al.
2004) due to their high abundance 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 nonstatic 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
trophic level 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 covariation, 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
(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 dinoflagellates, cryptophytes, diatoms, prasinophytes and
cyanobacteria. However, the seasonal deviations between Acartia and seston
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 EAA 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 water column 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 community were dynamic
and depended on consumer species and the available diet biomass
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 algae (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 autotroph and
26
heterotroph 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
food chain, 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 monocultured 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 environmental
monitoring 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 EAA 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 smallsized 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 plankton community.
The ISME journal, doi: 101038/ismej2015126
Beaugrand G (2003) Plankton effect on cod recruitment 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 Biology 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 Marine Biology 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. Limnology 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. Aquaculture 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 habitat 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 Ecosystem. 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 biodiversity
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. Estuaries 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-herbivore 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 spring: 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 fishes. 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 compoundspecific 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 CiliateExcluding 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 oceans. 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. Freshwater Biology 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. Functional Ecology 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 ocean. 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 sulfur. 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
penguin (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 Hydrobiology 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 productivity 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 149:14171429
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 foodweb 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 nonindependence 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 regime shift in the western North Atlantic indicated by compound-specific
δ15N of deep-sea gorgonian corals. 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 26:495500
Sprules W, Bowerman J (1988) Omnivory and food chain length in zooplankton
food webs. Ecology:418-426
Sterner RW, Elser JJ (2002) Ecological stoichiometry: 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 seawater
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 Estuary. Estuaries
and Coasts 34:675-690
Winder M, Schindler DE (2004) Climate change uncouples trophic interactions in an
aquatic ecosystem. 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 upwelling 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 37:117
40