\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[greek,ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{The evolutionary ecology of fatty-acid variation: implications for
consumer adaptation and diversification}



\author[1]{Cornelia Twining}%
\author[2]{Joey Bernhardt}%
\author[3]{Alison Derry }%
\author[4]{Cameron Hudson}%
\author[5]{Asano Ishikawa}%
\author[6]{Naoki Kabeya}%
\author[7]{Martin Kainz}%
\author[5]{Jun Kitano}%
\author[4]{Carmen Kowarik}%
\author[8]{Sarah Nemiah Ladd}%
\author[9]{Miguel Leal}%
\author[10]{Kristin Scharnweber}%
\author[1]{Jeremy Shipley}%
\author[4]{Blake Matthews}%
\affil[1]{Max Planck Institute of Animal Behavior}%
\affil[2]{McGill University}%
\affil[3]{Universite du Quebec a Montreal}%
\affil[4]{Eawag}%
\affil[5]{National Institute of Genetics}%
\affil[6]{Tokyo University of Marine Science and Technology}%
\affil[7]{WasserCluster Lunz}%
\affil[8]{Albert-Ludwigs-Universitat Freiburg}%
\affil[9]{University of Aveiro}%
\affil[10]{Uppsala Universitet}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The nutritional diversity of resources can affect the adaptive evolution
of consumer metabolism and consumer diversification. Omega-3 long-chain
polyunsaturated fatty acids (n-3 LC-PUFA) have a high potential to
affect consumer fitness, through their widespread effects on
reproduction, growth, and survival. However, few studies consider the
evolution of fatty acid metabolism within an ecological context. In this
review, we first document the extensive diversity in both primary
producer and consumer n-3 LC-PUFA distributions among major ecosystems,
between habitats, and among species within habitats. We highlight some
of the key nutritional contrasts that can shape behavioral and/or
metabolic adaptation in consumers, discussing how consumers can evolve
in response to the spatial, seasonal, and community-level variation of
resource quality. We propose a hierarchical trait-based approach for
studying the evolution of consumers' metabolic networks, and review the
evolutionary genetic mechanisms underpinning consumer adaptation to n-3
LC-PUFA distributions. In doing so, we consider how the metabolic traits
of consumers are hierarchically structured, from cell membrane function
to maternal investment, and have strongly environment-dependent
expression. Finally, we conclude with an outlook on how studying the
metabolic adaptation of consumers within the context of nutritional
landscapes can open up new opportunities for understanding evolutionary
diversification.%
\end{abstract}%



\sloppy




\textbf{Introduction}

Adaptive evolution of consumer metabolism in response to the
spatiotemporal variation of dietary resources can contribute to the
origin and maintenance of biological diversity. In natural populations,
consumers often face mismatches between the dietary supply of, and
physiological requirements for, both inorganic elements (e.g. carbon,
nitrogen, and phosphorus) and essential organic compounds (e.g. amino
acids, lipids, and vitamins). Ecosystem- and habitat-specific
differences in the nutritional quality of prey can generate divergent
selection, and thereby influence the evolutionary processes underlying
ecological speciation and adaptive radiation (Schluter 2000; Nosil
2012). Moreover, key metabolic adaptations, such as the ability to
synthesize essential compounds, can generate ecological opportunities
that enable consumer species to transition into novel adaptive zones
(Simpson 1945; Simpson 1953).

In natural populations of consumers, metabolic phenotypes in general,
and lipid phenotypes in particular, are important components of fitness
variation. Lipids are fundamentally important for energy storage, cell
membrane structure, and cellular functions (Sunshine and Iruela-Arispe
2017). Within lipids, the omega-3 (n--3) and omega-6 (n--6)
polyunsaturated fatty acids (PUFA) are important for somatic
development, especially nervous and gonadal tissues (Arts and Kohler
2009; Guo et al. 2016a; Tocher et al. 2019), cognition (McCann and Ames
2005; Cunanne et al. 2009; Hoffman et al. 2009), reproduction
(Martin-Creuzburg et al. 2009; Roqueta-Rivera et al. 2010; Chen et al.
2012; Sinendo et al. 2017), and survival (Matsunari et al. 2013; Fuiman
and Perez 2015; Kim et al. 2016; Mesa-Rodriguez et al. 2018; Twining et
al. 2018a).

Consumers likely face an allocation tradeoff involving their metabolic
capacity to synthesize PUFA and their capacity to acquire fatty-acids
(FA) from dietary sources. Indeed, the behavioral and metabolic
strategies to meet PUFA requirements vary widely across the tree of life
(Fig. 1). For example, detritivorous nematodes have a broad capacity to
synthesize PUFA from dietary carbohydrates as well as short-chain fatty
acid precursors (Watts and Browse 2002; Malcicka et al. 2018). However,
all vertebrates and many major groups of invertebrates lack the ability
to convert monounsaturated fatty acids (MUFA) such as oleic acid (OA) to
n--3 and n--6 PUFA (Kabeya et al. 2018). Therefore, a finch consuming
seeds that lack eicosapentaenoic acid (EPA, 20:5n-3) and docosahexaenoic
acid (DHA, 22:6n--3) must metabolically derive these compounds from
precursors (e.g., \selectlanguage{greek}α-\selectlanguage{english}linolenic acid, ALA, 18:3n--3) through enzymatic
conversion processes, including desaturation and elongation (Fig. 1C).
However some species of obligate carnivores, such as cats and tuna (Fig.
1E), are entirely unable to convert short-chain (C\textsubscript{18})
into the long-chain ([?]C\textsubscript{20;} LC) PUFA, and can only
acquire EPA and/or DHA directly from their diet (Rivers et al. 1975;
Betancor et al. 2020; Wang et al. 2020). Even those consumers that can
synthesize some EPA and/or DHA from the precursor compounds available in
low quality food (e.g., ALA), may face high metabolic costs that
manifest as reduced population growth rates. For example,
\emph{Daphnia}populations grow much slower (or not all) on a diet low in
n-3 LC-PUFA (e.g. cyanobacteria) compared to a diet high in n-3 LC-PUFA
(e.g.\emph{Nannochloropsis} or \emph{Cryptomonas} ; Fig. 1D;
Martin-Creuzburg et al. 2009; Martin-Creuzburg and von Elert 2009).

At the base of food chains there are two fundamental contrasts in n-3
PUFA availability that are particularly relevant for understanding how
spatiotemporal variation of resource quality can influence consumer
adaptation (Fig. 2). First, aquatic primary producers often contain both
EPA and DHA whereas terrestrial primary producers typically only contain
shorter chain n-3 fatty acids, such as ALA (Hixson et al. 2015; Twining
et al. 2016a; Colombo et al. 2017). As a result, terrestrial consumers
are fundamentally more limited by EPA and DHA availability than aquatic
consumers, and have evolved numerous adaptations to resolve this
nutritional constraint. Second, within aquatic systems, primary
producers in marine ecosystems have higher DHA content than in
freshwater ecosystems (Fig. 2). This DHA disparity has driven multiple
independent cases of consumer metabolic evolution associated with the
adaptation from marine to freshwater ecosystems (Ishikawa et al. 2019).
More generally, such fundamental nutritional contrasts among ecosystems,
as well as others occurring within ecosystems (e.g. among habitats, and
prey species), can contribute to evolutionary tradeoffs involving PUFA
acquisition and metabolism.

Previous work has documented how PUFA vary in relation to ecosystem
type, trophic level, taxonomy, and foraging behavior of species (e.g.,
Galloway and Winder 2015; Hixson et al. 2015; Colombo et al. 2017; Guo
et al. 2017), but not how variation is important for understanding the
prevailing diversity of consumer metabolism within and among species.
Here, we review the distribution of n-3 LC-PUFA in both primary
producers and consumers among major ecosystem (i.e., freshwater, marine,
terrestrial), between adjacent habitats within ecosystems (e.g.,
nearshore-offshore, stream-forests), and among co-occurring prey species
within habitats. We discuss how consumers can evolve in response to the
spatial, seasonal, and community-level variation of prey quality. In
doing so, we consider how the metabolic traits of consumers are
hierarchically structured, from cell membrane function to maternal
investment, and have strongly environment-dependent expression. Finally,
we discuss the evolutionary genetic mechanisms that underlie the
adaptation of consumers to PUFA limitation, and how such metabolic
evolution can be an important driver of consumer diversification in
ecosystems.

\emph{Heterogeneity of fatty acid distribution in nature: implications
for consumers}

Primary producers vary widely in their fatty acid composition across
ecosystems (Fig. 2A-C), but there are some stark contrasts within and
among ecosystems (Table S1). For example, vascular land plants, such as
angiosperms and gymnosperms, often contain little to no n-3 LC-PUFA,
whereas aquatic algae, such as diatoms and cryptophytes, are often laden
with both EPA and DHA (Fig. 2A-C). However, a number of non-vascular and
semi-aquatic plants, such as mosses, do contain EPA (e.g., Kalacheva et
al. 2009; Fig. 2B). Terrestrial primary producers also contain
significantly more fatty acids as ALA, the precursor to EPA and DHA,
compared to marine primary producers (Fig. 2A; Table S1). In addition to
these patterns with n-3 PUFA, terrestrial primary producers typically
contain a higher proportion of n-6, such as LIN, relative to n-3 PUFA,
like ALA, compared to aquatic primary producers (Hixson et al. 2015),
but the reasons for this are unclear. One possible explanation for these
patterns is the higher susceptibility of PUFA with more double bonds,
and LC-PUFA in particular, to peroxidation (Halliwell and Gutteridge
1985; Mueller 2004; M\selectlanguage{ngerman}øller et al. 2007), which is a greater risk in
terrestrial environments. Another important and well-documented pattern
is that marine primary producers have a significantly higher percentage
of fatty acids as EPA compared to either freshwater or terrestrial
primary producers (Fig. 2B; Table S1) as well as significantly more
fatty acids as DHA compared to terrestrial primary producers (Fig. 2C;
Table S1). The reasons for this pattern are also unclear, but might be
partly due to EPA and DHA conferring protection against high salinity
(Jiang and Chen 1999; Sui et al. 2010).

Within ecosystems, the distribution of PUFA of primary producers is
typically attributed to both species differences (Taipale et al. 2013)
and environmental conditions (Lang et al. 2011). For example, n-3
LC-PUFA are very abundant across several major groups of Eukaryotic
algae (Mühlroth et al. 2013), but are absent in Cyanobacteria (Twining
et al. 2016a). However, the composition and content of FAs can also be
highly variable among closely related species and individuals of the
same species (Lang et al. 2011; Galloway et al. 2012; Taipale et al.
2013; Charette and Derry 2016), possibly due to the strong influence of
environmental conditions (Lang et al. 2011). LC-PUFA molecules are
particularly unstable due to the susceptibility of their multiple double
bonds to oxidation and attack by reactive oxygen species (Shchepinov et
al. 2014). For instance, high temperatures increase reaction rates, such
that LC-PUFA degrade faster in warm environments (Hixson and Arts 2016).
In addition, phospholipids with double bonds, such as those found in
PUFA, may help cells maintain membrane fluidity at lower temperatures
(homeoviscous adaptation; Sinensky 1974; Feller et al. 2002). Thus, it
may be beneficial for organisms to have more LC-PUFA when it is colder
and more costly for them to protect LC-PUFA when it is warmer. In algae,
n-3 LC-PUFA content is often negatively correlated with both temperature
(Hixson and Arts 2016) and light levels (Amini Khoeyi et al. 2012; Hill
et al. 2011), and influenced by inorganic nutrient concentration (e.g.,
Guschina and Harwood 2009; Piepho et al. 2012). At constant temperature
and light levels, phosphorus limitation, for example, can decrease
overall lipid content but increase n-3 LC-PUFA production, possibly
reflecting the need to store lipids until growth conditions improve
(Guschina and Harwood 2009). However, when light, temperature, and
nutrients are simultaneously manipulated, fatty acid responses can be
highly variable across species and systems (e.g., Piepho et al. 2012;
Cashman et al. 2013; Guo et al. 2016b).

In consumers, the composition of PUFA reflects both the dietary sources
of lipids (e.g., ecosystem origin, prey availability) and the capacity
of consumers to metabolise different FA (Fig. 2, Hixson et al. 2015; Guo
et al. 2017). Insect species with an early aquatic life stage often
contain more n-3 LC-PUFA than those that are exclusively terrestrial
(Twining et al. 2018a), and are thus important sources of EPA for
insectivores, such as Eastern Phoebes (\emph{Sayornis phoebe} ) (Twining
et al. 2019). Many consumers acquire PUFA from multiple ecosystems in
order to meet their own nutritional requirements. For example, mammalian
carnivores can forage on aquatic resources to help increase their intake
of DHA relative to linolenic acid (18:2n-6; LIN), which is an abundant
n-6 PUFA in terrestrial primary producers (Koussoroplis et al. 2008).
Migratory consumers can accumulate n-3 LC-PUFA from PUFA-rich ecosystems
and use them for reproduction and offspring provisioning in more
PUFA-depauperate ecosystems (e.g. salmon migrating from the ocean to
freshwater streams; Heintz et al. 2004). Indeed, many species that
experience wide temporal variation in resource quality often exhibit
either plasticity (e.g., Katan et al. 2019) or genetic adaptation
(Ishikawa et al. 2019) associated with fatty acid metabolism.

Within ecosystems, consumers often experience contrasting distributions
of FA when foraging in multiple adjacent habitats. Within lakes, for
example, ecotypes of Eurasian perch (\emph{Perca fluviatilis} ) are
known to specialize on either littoral macroinvertebrates, which are
DHA-poor, or pelagic zooplankton, which have species (e.g. copepods)
that are DHA-rich (Fig. 3A). Intriguingly, in spite of the fact that DHA
is higher in pelagic prey, littoral perch typically have higher DHA than
pelagic perch. This might indicate that perch can thrive on a low-DHA
diet (Scharnweber et al., unpublished), via preferential DHA retention
(e.g., Hessen and Leu 2006; Heissenberger et al. 2010) and/or DHA
synthesis from precursors like ALA (e.g., Buzzi et al. 1996; Bell et al.
2001). In terrestrial systems, Tree Swallows vary widely in their access
to aquatic prey (McCarty and Winkler 1999; Stanton et al. 2016;
Michelson et al. 2018), which contain substantially more EPA than
terrestrial prey (Twining et al. 2018a; Twining et al. 2019; Fig. 3B).
Controlled diet studies show that Tree Swallow chicks, which are
inefficient at synthesizing EPA and DHA from ALA (Twining et al. 2018b)
grow faster, are in better condition, and have increased survival when
they consume either more aquatic insects or diets containing more EPA
and DHA (Twining et al. 2016b). Because nest sites vary considerably in
their distance to aquatic ecosystems, adults might trade-off food
quality with quantity when provisioning their young. Although
unexplored, this trade-off could select for increased efficiency of ALA
to EPA and DHA conversion in populations that breed in drier, upland
habitats that have a lower availability of high-quality freshwater prey.

Contrasting distribution of FA can, in some cases, drive adaptive
population divergence of consumers. For example, urban and rural
populations of Great Tits (\emph{Parus major} ) differ not only in their
diet (Andersson et al. 2015) and their fatty acid composition (Andersson
et al. 2015; Isaksson et al. 2017) but also in their expression of
the\emph{Elovl} and \emph{Fads} genes (Watson et al. 2017), which code
for the enzymes used to convert ALA and LIN to n-3 and n-6 LC-PUFA,
respectively. Specifically, rural tits have higher plasma EPA content
while urban tits have plasma higher arachidonic acid (ARA, 20:4n-6)
content (Andersson et al. 2015; Isaksson et al. 2017). The n-3 LC-PUFA
have anti-inflammatory properties while n-6 LC-PUFA, which are
synthesized from their shorter-chain n-6 precursor through the same
pathway as n-3 PUFA, have pro-inflammatory properties (Calder et al.
2002). Urban tits experience greater oxidative stress than do rural tits
(Isaksson et al. 2017; Watson et al. 2017) and also express
\emph{Elovl}and \emph{Fads} at lower rates compared to rural tits
(Watson et al. 2017). Thus, urban tits appear to suppress the production
of both n-3 and n-6 LC-PUFA in order to reduce inflammation and
oxidative damage in a more stressful environment (Watson et al. 2017).

\emph{The network and hierarchical structure of fatty acid traits}

The above examples illustrate the varied ways in which consumers adapt
to the heterogeneous distributions of n-3 LC-PUFA in nature (Fig. 2),
including via the evolution of metabolic capacity for biosynthesis of FA
and/or the foraging behaviors underlying the dietary acquisition of n-3
LC-PUFA. In light of this complexity, we suggest an integrative approach
that includes both investigating the individual enzymes and processes
involved in fatty acid synthesis within the metabolic network (Fig. 4;
Table 1), and situating these metabolic traits within a hierarchical
structure of functional traits leading to fitness variation (Fig. 5;
Table 2).

Although all organisms share core metabolic processes for fatty acid
synthesis (Fig. 4), consumer species vary widely in capacity to convert:
1) MUFA to PUFA (Module C, Fig. 4), 2) n--6 to n--3 PUFA (Module D, Fig.
4), and 3) C\textsubscript{18} n-6 and n-3 PUFA to LC-PUFA (Modules E
and F, Fig. 4). Each step within the n-3 LC-PUFA biosynthesis pathway is
governed by the presence and activity level of particular enzymes, as
well as by the presence and expression levels of specific genes.
Saturated fatty acids (SFA), such as stearic acid (18:0), are
synthesised \emph{de novo} through the fatty acid synthase (\emph{fasn}
) and SFA elongase system (Module A, Fig. 4). Stearoyl-CoA desaturase
(\emph{Scd} ) can then introduce a double bond at the \selectlanguage{greek}Δ\selectlanguage{english}9 position of the
fatty carbon chain, producing monounsaturated fatty acids, such as oleic
acid (OA, 18:1n-9) (Module B, Fig. 4). All eukaryotes appear to be able
to synthesize OA. In contrast, the biosynthesis from MUFA (OA; Module C,
Fig. 4) to PUFA, with multiple double bonds like linoleic acid (LIN,
18:2n-6), only exists in a limited number of consumers with the
methyl-end (\selectlanguage{greek}ω\selectlanguage{english}x) desaturase enzyme, \selectlanguage{greek}Δ\selectlanguage{english}12 desaturase (Blomquist et al.
1991). Most consumers neither possess the related methyl-end desaturase
enzyme ([?]15 desaturase) that is necessary to produce ALA from LIN, nor
the [?]17 and [?]19 desaturases to convert n-3 LC-PUFA from their n-6
LC-PUFA counterparts (Module D, Fig. 4). These enzymes introduce an
additional double bond between the terminal methyl group of a fatty acyl
chain and a pre-existing double bond, allowing the synthesis of PUFA
from MUFA, and, importantly, n-3 PUFA from n-6 PUFA. The methyl-end
desaturases were historically thought to exist only in plants, algae,
protists, fungi and a nematode (i.e. \emph{Caenorhabditis elegans} ),
but a recent study suggests that this gene family also occurs in
cnidarians, additional nematode species, lophotrochozoans (molluscs,
annelids, rotifers), and arthropods (copepods and at least two species
of insects) (Kabeya et al. 2018; Garrido et al. 2019; Kabeya et al.
2020). A much greater number of consumers are able to elongate and
desaturate n-6 and n-3 C\textsubscript{18}PUFA into corresponding n-6
and n-3 LC-PUFA (Modules E and F, Fig. 4). Network modules E and F
involve several front-end desaturases as well as fatty acid elongases
(elongation of very long-chain fatty acids protein, \emph{Elovl} ) and
exist, with varying efficiency, in consumers ranging from molluscs and
some arthropods (Monroig and Kabeya 2018) to chickens (Gregory and James
2014; Boschetti et al. 2016) and humans (Leonard et al. 2002; Nakamura
and Nara 2004), suggesting that these pathways have evolved multiple
times.

In light of the complexity of fatty acid metabolic networks, identifying
a set of modules and component traits can be a useful approach. As
illustrated in Fig. 4, we identify six core modules based on important
functional metabolic capacities (Fig. 4A-F, Table 1A), and further break
these down into constituent traits that define the reaction rates
between specific FA substrates and products (e.g., ALA to EPA conversion
capacity and efficiency, Table 1B). There is some value to such
simplifications because they reveal broad-scale patterns in metabolic
capacity across the tree of life. However, there is also substantial
pleiotropy, in that single genes can modify the activity of numerous
reaction rates across the overall metabolic network (Table 1B). For
example, in many teleosts, \emph{Fads2} gene products can influence
conversion rates of LIN to a series of n-6 LC-PUFA including ARA (Fig.
4, Module E), as well as ALA to a series of n-3 LC-PUFA including EPA
and DHA (Fig. 4, Module F). Nevertheless, treating both modules and
their component pathways as metabolic traits permits us to document
heritable variation within metabolic network modules (Box 1), and to
identify both the ecological and genetic mechanisms underlying their
adaptation. This is an important step for understanding the complex
evolution of metabolic networks (Olson-Manning et al. 2012; Watson et
al. 2014; Meli\selectlanguage{ngerman}án et al. 2018) and the role that metabolism plays in
evolutionary diversification more broadly.

The metabolic traits we summarize in Table 1 are also embedded within a
hierarchy of other potentially fitness-relevant consumer traits (Table
2). Natural selection acts upon the heritable intraspecific metabolic
traits in the context of other subordinate and emergent functional
traits in the hierarchy (Fig. 5; Henshaw et al. 2020; Laughlin et al.
2020). Where there is a heritable basis for metabolic traits, there is
the potential for adaptive evolution of consumer metabolism in response
to natural selection. Such evolution might involve fatty acid synthesis
and internal regulation, and/or of behavioral traits related to resource
acquisition (e.g., selective foraging) and/or life history traits (e.g.,
migration and phenology) (see references for Table 2; Fig. 5). The
evolution of metabolic traits might evolve independently, or as a
correlated response to other heritable traits, and culminate in changes
in physiological performance, immunocompetence, and cell membrane
fluidity (Table 1, Fig. 5). Such trait change has the potential to
influence numerous processes ranging from those affecting individual
molecules to those affecting an individual's lifetime reproductive
Darwinian fitness (Table 2, Fig. 5).

\emph{Evolutionary genetic mechanisms of metabolic adaptation}

When consumers experience selection for n-3 LC-PUFA synthesis, they can
increase enzymatic activity with three different types of genetic
processes: 1) gene copy number increases, 2) enzymatic activity changes
by amino acid substitutions, and 3) regulatory mutations that increase
transcription rates (Fig. 6). These three mechanisms differ in their
effect sizes and pleiotropy (i.e., the number of phenotypic traits
influenced by the gene). Copy number increases may have the strongest
effects on metabolic processes like fatty acid synthesis (Loehlin and
Carroll 2016; Loehlin et al. 2019), but are also likely to have
pleiotropic effects on other metabolic processes. This is because an
increase in copy number may affect expression in multiple tissues
throughout different ontogenetic stages (developmental pleiotropy)
and/or they may change the amounts of other organic compounds produced
as by-products when enzymes are multifunctional (biochemical pleiotropy)
(Fig. 6B). Pleiotropic changes may be neutral, favorable, or unfavorable
with respect to fitness. For instance, because n-3 and n-6 fatty acids
are elongated and desaturated via the same metabolic pathway (Fig. 4),
increased fatty acid desaturase and/or elongase activity may result in
increased production of both n-3 or n-6 LC-PUFA, depending on the
relative availability of n-3 and n-6 precursors. Amino acid
substitutions generally have even more pleiotropic effects than copy
number increases (Carroll 2005), but their effect sizes are reported to
be smaller in some cases (Loehlin et al. 2019). Regulatory mutations may
have relatively strong effects (Loehlin et al. 2019) and enable tissue-
or ontogenetic stage-specific expression, but may still be biochemically
pleiotropic. Importantly, these three types of mutations often occur
together. After gene duplication, these mutations can diverge in both
functional amino acid sequences and expression patterns (Ohno 1970;
Lynch 2007), becoming more specific (neo-functionalization) and thus
reducing pleiotropic effects while still having strong effect sizes
(Fig. 6C).

Examples of all three types of genetic mechanisms can be found within
the evolution of fatty acid metabolism. Copy number variation in fatty
acid desaturase (\emph{Fads} ) genes is widely observed in vertebrates
(Castro et al. 2012). For instance, Ishikawa et al. (2019) recently
found that freshwater threespine stickleback have increased \emph{Fads2}
copy number and thus greater capability to synthesize DHA, thereby
overcoming the nutritional constraints of freshwater ecosystems.
However, increased expression of\emph{Fads2} also results in increased
production of n-6 LC-PUFA, such as ARA, in sticklebacks, thus
demonstrating a biochemical pleiotropic effect (Ishikawa et al. 2019).
In humans, regulatory mutations are known to underlie adaptation to low
n-3 LC-PUFA diets (Fumagalli et al. 2015; Ye et al. 2017; Tucci et al.
2018). Derived alleles with higher \emph{Fads1} expression appear to
have enabled humans to survive better on cultivated, land plant-derived,
and n-3 LC-PUFA-deficient diets, allowing them to expand their
distribution (Ameur et al. 2012; Fumagalli et al. 2015; Tucci et al.
2018). In contrast, human populations that consume n-3 LC-PUFA-rich
diets with high amounts of fish and meat have the ancestral haplotypes
(Amorin et al. 2017). Amino acid changes that alter enzymatic functions
can also help consumers adapt to diets that vary in n-3 LC-PUFA content.
For example, although zebrafish (\emph{Danio rerio} ) have just one copy
of \emph{Fads2} , they have high \selectlanguage{english}[?]5 and \selectlanguage{english}[?]6 desaturase activities as a
result of amino acid changes (Hastings et al. 2001).
Neo-functionalization following duplication appears to be a common
genetic process (Ohno 1970; Zhang 2003). In fishes, for instance, the
acquisition of \selectlanguage{english}[?]4 activity occurred in one copy of \emph{Fads2} after
gene duplication (Li et al. 2010; Morais et al. 2012; Oboh et al. 2017).
Further genetic analysis of variation in fatty acid metabolism across a
greater diversity of taxa will help us to understand which mechanisms
are the most prevalent and how mechanisms differ in their effect sizes
and pleiotropy on fatty acid adaptive landscapes.

\emph{Metabolic adaptation and consumer diversification}

The network structure of fatty acid metabolism, including the modularity
and degree of pleiotropy, has important consequences for understanding
how organisms evolve and diversify over time. For instance, while the
primary photosynthetic pathways of plants are highly conserved, some of
its components have diversified widely, culminating in lineage-specific
pathway regulation and structure (Maeda 2019). As a consequence of
network structure, evolutionary changes at early steps within
photosynthesis can have more substantial effects on final products than
those that occur in later steps (Kacser and Burns 1981; Wright and
Rausher 2010; Olson-Manning et al. 2012). In consumers, the evolution of
metabolic networks has allowed them to utilize new resources or
synthesize essential organic compounds that were previously required
from diet (Borenstein et al. 2008; Wagner 2012). Within a lineage,
species can differ in the number and connectivity of modules in
metabolic networks as well as in synthesis activities across the
network. The evolution of carotenoid networks in birds, for example, has
led to considerable variation in the structure (i.e., gain and loss of
modules) and connectivity of functional modules (Morrison and Badyaev
2016), and, interestingly, has been implicated in the diversification of
avian color patterns (Badyaev et al. 2019a).

In the 1940s, Simpson posited that species could enter new `adaptive
zones' (Simpson 1945; Simpson 1953) via specific events, including
dispersal into new habitats, extirpation of predators, or through `key
innovations', namely those that relax or fundamentally change the
prevailing environmental sources of natural selection (Miller 1949;
Rabosky 2017). While microevolutionary dynamics might shape the existing
structure or control of metabolic networks (Figs. 4-5), large structural
changes in the network itself, such as the internalization of an
external dependency (e.g., the ability to synthesize a formerly
essential dietary fatty acid), might present a species with novel
ecological opportunity. In other words, the evolution of fatty acid
metabolism might afford species new opportunities to exploit novel
resources (e.g. terrestrial plants containing only ALA), and allow them
to persist and diversify in `adaptive zones'. For example, freshwater
threespine stickleback have reduced their external dependency on
DHA-rich resources, which are limited outside of marine habitats (Fig.
3A), by increasing their endogenous conversion rates from ALA to DHA, a
key innovation. Badyaev et al. (2019a) propose that such evolution in
the control of metabolic networks is fundamentally associated with
macroscale patterns of species diversity. Specifically, local metabolic
adaptation can culminate in shifts in network topologies, potentially
opening new opportunities for evolutionary diversity (Badyaev 2019b).
Currently, this is unexplored in the context of fatty-acid metabolism,
but there is considerable potential to do so in light of the
heterogeneity of FA within and among ecosystems (Fig. 2), genetic
variation and fitness relevance of FA acquisition and metabolism traits,
and examples of key innovations facilitating consumer diversification
(Ishikawa et al. 2019).

\emph{Conclusion}

There is a global metabolic network available for animals (Borenstein et
al. 2008), and we are still in the early stages of uncovering how this
network is structured across the animal tree of life (Fig. 1, Fig. 4,
Table 1) and how it is mechanistically linked to fitness variation of
consumers (Table 2, Fig. 5). Empirical studies have documented
substantial variation in the fatty acids of organisms, structured both
within and among ecosystems and among prey communities, which creates
ample opportunities for the evolution of consumer behavior and
metabolism. Indeed, there is growing evidence for evolutionary
diversification in the primary nodes and controls of the fatty acid
metabolic network (Fig. 4). Studying such metabolic diversity across
multiple scales (Fig. 4, 5), will allow us to understand more broadly
how consumers evolve traits related to both resource acquisition and
metabolism, and how they invade new environments and diversify.



\textbf{References}

1.

Ameur, A., Enroth, S., Johansson, A., Zaboli, G., Igl, W., Johansson,
Anna~C.V. \emph{et al.} (2012). Genetic Adaptation of Fatty-Acid
Metabolism: A Human-Specific Haplotype Increasing the Biosynthesis of
Long-Chain Omega-3 and Omega-6 Fatty Acids. \emph{The American Journal
of Human Genetics} , 90, 809-820.

2.

Amini Khoeyi, Z., Seyfabadi, J. \& Ramezanpour, Z. (2012). Effect of
light intensity and photoperiod on biomass and fatty acid composition of
the microalgae, Chlorella vulgaris. \emph{Aquac. Int.} , 20, 41-49.

3.

Amorim, C.E.G., Nunes, K., Meyer, D., Comas, D., Bortolini, M.C.,
Salzano, F.M. \emph{et al.} (2017). Genetic signature of natural
selection in first Americans. \emph{Proceedings of the National Academy
of Sciences} , 114, 2195-2199.

4.

Andersson, M.N., Wang, H.-L., Nord, A., Salmon, P. \& Isaksson, C.
(2015). Composition of physiologically important fatty acids in great
tits differs between urban and rural populations on a seasonal
basis.\emph{Frontiers in Ecology and Evolution} , 3.

5.

Arts, M.T. \& Kohler, C.C. (2009). Health and condition in fish: the
influence of lipids on membrane competency and immune response.
In:\emph{Lipids in Aquatic Ecosystems} (eds. Kainz, M, Brett, MT \&
Arts, MT). Springer New York New York, NY, pp. 237-256.

6.

Badyaev, A.V. (2019). Evolutionary transitions in controls reconcile
adaptation with continuity of evolution. \emph{Semin. Cell Dev. Biol.} ,
88, 36-45.

7.

Badyaev, A.V., Posner, A.B., Morrison, E.S. \& Higginson, D.M. (2019).
Cycles of external dependency drive evolution of avian carotenoid
networks. \emph{Nat. Commun.} , 10, 1596.

8.

Barrett, R.D. \& Schluter, D. (2008). Adaptation from standing genetic
variation. \emph{Trends in ecology \& evolution} , 23, 38-44.

9.

Barton, N.H. \& Keightley, P.D. (2002). Understanding quantitative
genetic variation. \emph{Nature Reviews Genetics} , 3, 11-21.

10.

Bell, J.G., McEvoy, J., Tocher, D.R., McGhee, F., Campbell, P.J. \&
Sargent, J.R. (2001). Replacement of fish oil with rapeseed oil in diets
of Atlantic salmon (Salmo salar) affects tissue lipid compositions and
hepatocyte fatty acid metabolism. \emph{The Journal of nutrition} , 131,
1535-1543.

11.

Betancor, M.B., Oboh, A., Ortega, A., Mourente, G., Navarro, J.C., de la
Gandara, F. \emph{et al.} (2020). Molecular and functional
characterisation of a putative elovl4 gene and its expression in
response to dietary fatty acid profile in Atlantic bluefin tuna (Thunnus
thynnus). \emph{Comp. Biochem. Physiol. B Biochem. Mol. Biol.} , 240,
110372.

12.

Bickel, R.D., Kopp, A. \& Nuzhdin, S.V. (2011). Composite effects of
polymorphisms near multiple regulatory elements create a major-effect
QTL. \emph{PLoS Genet} , 7, e1001275.

13.

Blomquist, G.J., Borgeson, C.E. \& Vundla, M. (1991). Polyunsaturated
fatty acids and eicosanoids in insects. \emph{Insect Biochem.} , 21,
99-106.

14.

Borenstein, E., Kupiec, M., Feldman, M.W. \& Ruppin, E. (2008).
Large-scale reconstruction and phylogenetic analysis of metabolic
environments. \emph{Proc. Natl. Acad. Sci. U. S. A.} , 105, 14482-14487.

15.

Boschetti, E., Bordoni, A., Meluzzi, A., Castellini, C., Dal Bosco, A.
\& Sirri, F. (2016). Fatty acid composition of chicken breast meat is
dependent on genotype-related variation of FADS1 and FADS2 gene
expression and desaturating activity. \emph{Animal} , 10, 700-708.

16.

Buzzi, M., Henderson, R.J. \& Sargent, J.R. (1996). The desaturation and
elongation of linolenic acid and eicosapentaenoic acid by hepatocytes
and liver microsomes from rainbow trout (Oncorhynchus mykiss) fed diets
containing fish oil or olive oil. \emph{Biochim Biophys Acta} , 1299,
235-244.

17.

Calder, P.C. (2002). Dietary modification of inflammation with
lipids.\emph{Proceedings of the Nutrition Society} , 61, 345-358.

18.

Carroll, S.B. (2005). Evolution at two levels: on genes and
form.\emph{PLoS Biol.} , 3, e245.

19.

Cashman, M.J., Wehr, J.D. \& Truhn, K. (2013). Elevated light and
nutrients alter the nutritional quality of stream
periphyton.\emph{Freshwater Biology} , 58, 1447-1457.

20.

Castro, L.F.C., Monroig, O., Leaver, M.J., Wilson, J., Cunha, I. \&
Tocher, D.R. (2012). Functional desaturase Fads1 (\selectlanguage{greek}Δ\selectlanguage{english}5) and Fads2 (\selectlanguage{greek}Δ\selectlanguage{english}6)
orthologues evolved before the origin of jawed vertebrates. \emph{PLoS
One} , 7, e31950.

21.

Cesar, A.S., Regitano, L.C., Mour\selectlanguage{ngerman}ão, G.B., Tullio, R.R., Lanna, D.P.,
Nassu, R.T. \emph{et al.} (2014). Genome-wide association study for
intramuscular fat deposition and composition in Nellore cattle.\emph{BMC
genetics} , 15, 39.

22.

Chaguaceda, F., Eklöv, P. \& Scharnweber, K. (2020). Regulation of fatty
acid composition related to ontogenetic changes and niche
differentiation of a common aquatic consumer. \emph{Oecologia} , 193,
325-336.

23.

Charette, C. \& Derry, A.M. (2016). Climate alters intraspecific
variation in copepod effect traits through pond food webs.\emph{Ecology}
, 97, 1239-1250.

24.

Chen, M., Liu, H. \& Chen, B. (2012). Effects of dietary essential fatty
acids on reproduction rates of a subtropical calanoid copepod, Acartia
erythraea. \emph{Mar. Ecol. Prog. Ser.} , 455, 95-110.

25.

Colombo, S.M., Wacker, A., Parrish, C.C., Kainz, M.J. \& Arts, M.T.
(2017). A fundamental dichotomy in long-chain polyunsaturated fatty acid
abundance between and within marine and terrestrial
ecosystems.\emph{Environ. Rev.} , 25, 163-174.

26.

Cunnane, S.C., Plourde, M., Pifferi, F., Bégin, M., Féart, C. \&
Barberger-Gateau, P. (2009). Fish, docosahexaenoic acid and Alzheimer's
disease. \emph{Prog. Lipid Res.} , 48, 239-256.

27.

Feller, S.E., Gawrisch, K. \& MacKerell, A.D. (2002). Polyunsaturated
fatty acids in lipid bilayers: intrinsic and environmental contributions
to their unique physical properties. \emph{Journal of the American
Chemical Society} , 124, 318-326.

28.

Fuiman, L.A. \& Perez, K.O. (2015). Metabolic programming mediated by an
essential fatty acid alters body composition and survival skills of a
marine fish. \emph{Proceedings of the Royal Society B: Biological
Sciences} , 282, 20151414.

29.

Fumagalli, M., Moltke, I., Grarup, N., Racimo, F., Bjerregaard, P.,
Jørgensen, M.E. \emph{et al.} (2015). Greenlandic Inuit show genetic
signatures of diet and climate adaptation. \emph{Science} , 349,
1343-1347.

30.

Galloway, A.W.E., Britton-Simmons, K.H., Duggins, D.O., Gabrielson, P.W.
\& Brett, M.T. (2012). Fatty acid signatures differentiate marine
macrophytes at ordinal and family ranks. \emph{J. Phycol.} , 48,
956-965.

31.

Galloway, A.W.E. \& Winder, M. (2015). Partitioning the Relative
Importance of Phylogeny and Environmental Conditions on Phytoplankton
Fatty Acids. \emph{PLoS ONE} , 10, e0130053.

32.

Garrido, D., Kabeya, N., Hontoria, F., Navarro, J.C., Reis, D.B.,
Martín, M.V. \emph{et al.} (2019). Methyl-end desaturases with \selectlanguage{english}[?]12 and
\selectlanguage{greek}ω\selectlanguage{english}3 regioselectivities enable the de novo PUFA biosynthesis in the
cephalopod Octopus vulgaris. \emph{Biochim. Biophys. Acta Mol. Cell
Biol. Lipids} , 1864, 1134-1144.

33.

Gregory, M.K. \& James, M.J. (2014). Functional characterization of the
duck and turkey fatty acyl elongase enzymes ELOVL5 and ELOVL2. \emph{J.
Nutr.} , 144, 1234-1239.

34.

Guo, F., Bunn, S.E., Brett, M.T. \& Kainz, M.J. (2017). Polyunsaturated
fatty acids in stream food webs -- high dissimilarity among producers
and consumers. \emph{Freshwater Biology} , 62, 1325-1334.

35.

Guo, F., Kainz, M.J., Sheldon, F. \& Bunn, S.E. (2016). The importance
of high-quality algal food sources in stream food webs -- current status
and future perspectives. \emph{Freshwater Biology} , 61, 815-831.

36.

Guo, F., Kainz, M.J., Sheldon, F. \& Bunn, S.E. (2016). Effects of light
and nutrients on periphyton and the fatty acid composition and somatic
growth of invertebrate grazers in subtropical streams. \emph{Oecologia}
, 181, 449-462.

37.

Guschina, I.A. \& Harwood, J.L. (2009). Algal lipids and effect of the
environment on their biochemistry. In: \emph{Lipids in aquatic
ecosystems} . Springer, pp. 1-24.

38.

Halliwell, B. \& Gutteridge, J.M.C. (1985). Free radicals in biology and
medicine.

39.

Hastings, N., Agaba, M., Tocher, D.R., Leaver, M.J., Dick, J.R.,
Sargent, J.R. \emph{et al.} (2001). A vertebrate fatty acid desaturase
with Delta 5 and Delta 6 activities. \emph{Proc. Natl. Acad. Sci. U. S.
A.} , 98, 14304-14309.

40.

Heintz, R.A., Nelson, B.D., Hudson, J., Larsen, M., Holland, L. \&
Wipfli, M. (2004). Marine subsidies in freshwater: Effects of salmon
carcasses on lipid class and fatty acid composition of juvenile coho
salmon. \emph{Transactions of the American Fisheries Society} , 133,
559-567.

41.

Heissenberger, M., Watzke, J. \& Kainz, M.J. (2010). Effect of nutrition
on fatty acid profiles of riverine, lacustrine, and aquaculture-raised
salmonids of pre-alpine habitats. \emph{Hydrobiologia} , 650, 243-254.

42.

Henshaw, J.M., Morrissey, M.B. \& Jones, A.G. 2020. Quantifying the
causal pathways contributing to natural selection. \emph{Evolution} .

43.

Hessen, D.O. \& Leu, E. (2006). Trophic transfer and trophic
modification of fatty acids in high Arctic lakes. \emph{Freshwater
Biology} , 51, 1987-1998.

44.

Hill, W.R., Rinchard, J. \& Czesny, S. (2011). Light, nutrients and the
fatty acid composition of stream periphyton. \emph{Freshw. Biol.} , 56,
1825-1836.

45.

Hixson, S.M. \& Arts, M.T. (2016). Climate warming is predicted to
reduce omega-3, long-chain, polyunsaturated fatty acid production in
phytoplankton. \emph{Glob. Chang. Biol.} , 22, 2744-2755.

46.

Hixson, S.M., Sharma, B., Kainz, M.J., Wacker, A. \& Arts, M.T. (2015).
Production, distribution, and abundance of long-chain omega-3
polyunsaturated fatty acids: a fundamental dichotomy between freshwater
and terrestrial ecosystems. \emph{Environmental Reviews} , 23, 414-424.

47.

Hoffman, D.R., Boettcher, J.A. \& Diersen-Schade, D.A. (2009). Toward
optimizing vision and cognition in term infants by dietary
docosahexaenoic and arachidonic acid supplementation: a review of
randomized controlled trials. \emph{Prostaglandins Leukot. Essent. Fatty
Acids} , 81, 151-158.

48.

Horn, S.S., Ruyter, B., Meuwissen, T.H., Moghadam, H., Hillestad, B. \&
Sonesson, A.K. (2020). GWAS identifies genetic variants associated with
omega-3 fatty acid composition of Atlantic salmon
fillets.\emph{Aquaculture} , 514, 734494.

49.

Hu, S., Wang, J., Han, T., Li, X., Jiang, Y. \& Wang, C. (2017). Effects
of dietary DHA/EPA ratios on growth performance, survival and fatty acid
composition of juvenile swimming crab (Portunus
trituberculatus).\emph{Aquac. Res.} , 48, 1291-1301.

50.

Isaksson, C., Andersson, M.N., Nord, A., von Post, M. \& Wang, H.-L.
(2017). Species-dependent effects of the urban environment on fatty acid
composition and oxidative stress in birds. \emph{Frontiers in Ecology
and Evolution} , 5.

51.

Ishikawa, A., Kabeya, N., Ikeya, K., Kakioka, R., Cech, J.N., Osada,
N.\emph{et al.} (2019). A key metabolic gene for recurrent freshwater
colonization and radiation in fishes. \emph{Science} , 364, 886-889.

52.

Jiang, Y. \& Chen, F. (1999). Effects of salinity on cell growth and
docosahexaenoic acid content of the heterotrophic marine microalga
Crypthecodinium cohnii. \emph{J. Ind. Microbiol. Biotechnol.} , 23,
508-513.

53.

Kabeya, N., Fonseca, M.M., Ferrier, D.E.K., Navarro, J.C., Bay, L.K.,
Francis, D.S. \emph{et al.} (2018). Genes for de novo biosynthesis of
omega-3 polyunsaturated fatty acids are widespread in animals. \emph{Sci
Adv} , 4, eaar6849.

54.

Kabeya, N., G\selectlanguage{ngerman}ür, \selectlanguage{polish}İ., \selectlanguage{english}Oboh, A., Evjemo, J.O., Malzahn, A.M., Hontoria,
F.\emph{et al.} (2020). Unique fatty acid desaturase capacities
uncovered in Hediste diversicolor illustrate the roles of aquatic
invertebrates in trophic upgrading. \emph{Philos. Trans. R. Soc. Lond. B
Biol. Sci.} , 375, 20190654.

55.

Kacser, H. \& Burns, J.A. (1981). The molecular basis of
dominance.\emph{Genetics} , 97, 639-666.

56.

Kalacheva, G.S., Sushchik, N.N., Gladyshev, M.I. \& Makhutova, O.N.
(2009). Seasonal dynamics of fatty acids in the lipids of water moss
Fontinalis antipyretica from the Yenisei River. \emph{Russ. J. Plant
Physiol.} , 56, 795-807.

57.

Katan, T., Caballero-Solares, A., Taylor, R.G., Rise, M.L. \& Parrish,
C.C. (2019). Effect of plant-based diets with varying ratios of omega 6
to omega 3 fatty acids on growth performance, tissue composition, fatty
acid biosynthesis and lipid-related gene expression in Atlantic salmon
(Salmo solar). \emph{Comp. Biochem. Physiol. D-Genomics Proteomics} ,
30, 290-304.

58.

Kelly, M., Tume, R., Fortes, M. \& Thompson, J. (2014). Whole-genome
association study of fatty acid composition in a diverse range of beef
cattle breeds. \emph{Journal of Animal Science} , 92, 1895-1901.

59.

Kim, J., Yin, T., Shinozaki, K., Lampe, J.W. \& Becker, L.B. (2016).
DHA-supplemented diet increases the survival of rats following
asphyxia-induced cardiac arrest and cardiopulmonary bypass
resuscitation. \emph{Sci. Rep.} , 6, 36545.

60.

Koussoroplis, A.-M., Lemarchand, C., Bec, A., Desvilettes, C., Amblard,
C., Fournier, C. \emph{et al.} (2008). From Aquatic to Terrestrial Food
Webs: Decrease of the Docosahexaenoic Acid/Linoleic Acid
Ratio.\emph{Lipids} , 43, 461-466.

61.

Lande, R. \& Arnold, S.J. (1983). The measurement of selection on
correlated characters. \emph{Evolution} , 1210-1226.

62.

Lang, I., Hodac, L., Friedl, T. \& Feussner, I. (2011). Fatty acid
profiles and their distribution patterns in microalgae: a comprehensive
analysis of more than 2000 strains from the SAG culture
collection.\emph{BMC Plant Biol.} , 11, 124.

63.

Laughlin, D.C., Gremer, J.R., Adler, P.B., Mitchell, R.M. \& Moore, M.M.
(2020). The Net Effect of Functional Traits on Fitness. \emph{Trends in
Ecology \& Evolution} .

64.

Lee, Y.W., Gould, B.A. \& Stinchcombe, J.R. (2014). Identifying the
genes underlying quantitative traits: a rationale for the QTN
programme.\emph{AoB Plants} , 6.

65.

Lemos, M.V., Chiaia, H.L.J., Berton, M.P., Feitosa, F.L., Aboujaoud, C.,
Camargo, G.M. \emph{et al.} (2016). Genome-wide association between
single nucleotide polymorphisms with beef fatty acid profile in Nellore
cattle using the single step procedure. \emph{BMC genomics} , 17, 213.

66.

Leonard, A.E., Kelder, B., Bobik, E.G., Chuang, L.-T., Lewis, C.J.,
Kopchick, J.J. \emph{et al.} (2002). Identification and expression of
mammalian long-chain PUFA elongation enzymes. \emph{Lipids} , 37,
733-740.

67.

Li, Y., Monroig, O., Zhang, L., Wang, S., Zheng, X., Dick, J.R. \emph{et
al.} (2010). Vertebrate fatty acyl desaturase with \selectlanguage{greek}Δ\selectlanguage{english}4
activity.\emph{Proc. Natl. Acad. Sci. U. S. A.} , 107, 16840-16845.

68.

Lin, G., Wang, L., Te Ngoh, S., Ji, L., Orb\selectlanguage{ngerman}án, L. \& Yue, G.H. (2018).
Mapping QTL for omega-3 content in hybrid saline tilapia. \emph{Marine
biotechnology} , 20, 10-19.

69.

Loehlin, D.W., Ames, J.R., Vaccaro, K. \& Carroll, S.B. (2019). A major
role for noncoding regulatory mutations in the evolution of enzyme
activity. \emph{Proc. Natl. Acad. Sci. U. S. A.} , 116, 12383-12389.

70.

Loehlin, D.W. \& Carroll, S.B. (2016). Expression of tandem gene
duplicates is often greater than twofold. \emph{Proc. Natl. Acad. Sci.
U. S. A.} , 113, 5988-5992.

71.

Lynch, M. (2007). \emph{The Origins of Genome Architecture} . Sinauer
Associates.

72.

Lynch, M. \& Walsh, B. (1998). \emph{Genetics and analysis of
quantitative traits} . Sinauer Sunderland, MA.

73.

Maeda, H.A. (2019). Evolutionary Diversification of Primary Metabolism
and Its Contribution to Plant Chemical Diversity. \emph{Front. Plant
Sci.} , 10, 881.

74.

Malcicka, M., Visser, B. \& Ellers, J. (2018). An Evolutionary
Perspective on Linoleic Acid Synthesis in Animals. \emph{Evol. Biol.} ,
45, 15-26.

75.

Martin-Creuzburg, D., Sperfeld, E. \& Wacker, A. (2009). Colimitation of
a freshwater herbivore by sterols and polyunsaturated fatty
acids.\emph{Proc. Biol. Sci.} , 276, 1805-1814.

76.

Martin-Creuzburg, D. \& von Elert, E. (2009). Good food versus bad food:
the role of sterols and polyunsaturated fatty acids in determining
growth and reproduction of Daphnia magna. \emph{Aquatic Ecology} , 43,
943-950.

77.

Matsunari, H., Hashimoto, H., Oda, K., Masuda, Y., Imaizumi, H., Teruya,
K. \emph{et al.} (2013). Effects of docosahexaenoic acid on growth,
survival and swim bladder inflation of larval amberjack (Seriola
dumerili, Risso). \emph{Aquac. Res.} , 44, 1696-1705.

78.

McCann, J.C. \& Ames, B.N. (2005). Is docosahexaenoic acid, an n-3
long-chain polyunsaturated fatty acid, required for development of
normal brain function? An overview of evidence from cognitive and
behavioral tests in humans and animals. \emph{Am. J. Clin. Nutr.} , 82,
281-295.

79.

McCarty, J.P. \& Winkler, D.W. (1999). Foraging Ecology and Diet
Selectivity of Tree Swallows Feeding Nestlings. \emph{Condor} , 101,
246-254.

80.

Melián, C.J., Matthews, B., de Andreazzi, C.S., Rodríguez, J.P., Harmon,
L.J. \& Fortuna, M.A. (2018). Deciphering the Interdependence between
Ecological and Evolutionary Networks. \emph{Trends Ecol. Evol.} , 33,
504-512.

81.

Mesa-Rodriguez, A., Maria Hernandez-Cruz, C., Beatriz Betancor, M.,
Fernandez-Palacios, H., Izquierdo, M.S. \& Roo, J. (2018). Effect of
increasing docosahexaenoic acid content in weaning diets on survival,
growth and skeletal anomalies of longfin yellowtail (Seriola rivoliana,
Valenciennes 1833). \emph{Aquac. Res.} , 49, 1200-1209.

82.

Michelson, C.I., Clark, R.G. \& Morrissey, C.A. (2018). Agricultural
land cover does not affect the diet of Tree Swallows in
wetland-dominated habitats. \emph{Condor} , 120, 751-764.

83.

Miller, A.H. (1949). Some ecologic and morphologic considerations in the
evolution of higher taxonomic categories. \emph{Ornithologie als
biologische Wissenschaft} , 84-88.

84.

Møller, I.M., Jensen, P.E. \& Hansson, A. (2007). Oxidative
Modifications to Cellular Components in Plants. \emph{Annu. Rev. Plant
Biol.} , 58, 459-481.

85.

Monroig, Ó. \& Kabeya, N. (2018). Desaturases and elongases involved in
polyunsaturated fatty acid biosynthesis in aquatic invertebrates: a
comprehensive review. \emph{Fish. Sci.} , 84, 911-928.

86.

Morais, S., Castanheira, F., Martinez-Rubio, L., Conceição, L.E.C. \&
Tocher, D.R. (2012). Long chain polyunsaturated fatty acid synthesis in
a marine vertebrate: ontogenetic and nutritional regulation of a fatty
acyl desaturase with \selectlanguage{greek}Δ\selectlanguage{english}4 activity. \emph{Biochim. Biophys. Acta} , 1821,
660-671.

87.

Morrison, E.S. \& Badyaev, A.V. (2016). Structuring evolution:
biochemical networks and metabolic diversification in birds. \emph{BMC
Evol. Biol.} , 16, 168.

88.

Mueller, M.J. (2004). Archetype signals in plants: the phytoprostanes.

\emph{Curr. Opin. Plant Biol.}

89.

M\selectlanguage{ngerman}ühlroth, A., Li, K., Røkke, G., Winge, P., Olsen, Y., Hohmann-Marriott,
M.F. \emph{et al.} (2013). Pathways of lipid metabolism in marine algae,
co-expression network, bottlenecks and candidate genes for enhanced
production of EPA and DHA in species of Chromista. \emph{Mar. Drugs} ,
11, 4662-4697.

90.

Nakamura, M.T. \& Nara, T.Y. (2004). Structure, function, and dietary
regulation of delta6, delta5, and delta9 desaturases. \emph{Annu. Rev.
Nutr.} , 24, 345-376.

91.

Nosil, P. (2012). \emph{Ecological Speciation} . Oxford University
Press.

92.

Oboh, A., Kabeya, N., Carmona-Antoñanzas, G., Castro, L.F.C., Dick,
J.R., Tocher, D.R. \emph{et al.} (2017). Two alternative pathways for
docosahexaenoic acid (DHA, 22:6n-3) biosynthesis are widespread among
teleost fish. \emph{Scientific Reports} , 7.

93.

Ohno, S. (1970). \emph{Evolution by Gene Duplication} . Springer,
Berlin, Heidelberg.

94.

Olson-Manning, C.F., Wagner, M.R. \& Mitchell-Olds, T. (2012). Adaptive
evolution: evaluating empirical support for theoretical
predictions.\emph{Nat. Rev. Genet.} , 13, 867-877.

95.

Orr, H.A. (2005). The genetic theory of adaptation: a brief
history.\emph{Nature Reviews Genetics} , 6, 119-127.

96.

Piepho, M., Arts, M.T. \& Wacker, A. (2012). Species-specific variation
in fatty acid concentrations of four phytoplankton species: does
phosphorus supply influence the effect of light intensity or
temperature? 1. \emph{J. Phycol.} , 48, 64-73.

97.

Rabosky, D.L. (2017). Phylogenetic tests for evolutionary innovation:
the problematic link between key innovations and exceptional
diversification. \emph{Philos. Trans. R. Soc. Lond. B Biol. Sci.} , 372.

98.

Remington, D.L. (2015). Alleles versus mutations: Understanding the
evolution of genetic architecture requires a molecular perspective on
allelic origins. \emph{Evolution} , 69, 3025-3038.

99.

Rivers, J.P., Sinclair, A.J. \& Craqford, M.A. (1975). Inability of the
cat to desaturate essential fatty acids. \emph{Nature} , 258, 171-173.

100.

Roqueta-Rivera, M., Stroud, C.K., Haschek, W.M., Akare, S.J., Segre, M.,
Brush, R.S. \emph{et al.} (2010). Docosahexaenoic acid supplementation
fully restores fertility and spermatogenesis in male delta-6
desaturase-null mice. \emph{J. Lipid Res.} , 51, 360-367.

101.

Schaeffer, L., Gohlke, H., Müller, M., Heid, I.M., Palmer, L.J.,
Kompauer, I. \emph{et al.} (2006). Common genetic variants of the FADS1
FADS2 gene cluster and their reconstructed haplotypes are associated
with the fatty acid composition in phospholipids. \emph{Human molecular
genetics} , 15, 1745-1756.

102.

Schluter, D. (1996). Adaptive radiation along genetic lines of least
resistance. \emph{Evolution} , 50, 1766-1774.

103.

Schluter, D. (2000). \emph{The ecology of adaptive radiation} . OUP
Oxford.

104.

Shchepinov, M.S., Roginsky, V.A., Brenna, J.T., Molinari, R.J., To, R.,
Tsui, H. \emph{et al.} (2014). Chapter 31 - Deuterium Protection of
Polyunsaturated Fatty Acids against Lipid Peroxidation: A Novel Approach
to Mitigating Mitochondrial Neurological Diseases. In: \emph{Omega-3
Fatty Acids in Brain and Neurological Health} (eds. Watson, RR \& De
Meester, F). Academic Press Boston, pp. 373-383.

105.

Simpson, G.G. (1945). Tempo and mode in evolution. \emph{Trans. N. Y.
Acad. Sci.} , 8, 45-60.

106.

Simpson, G.G. (1953). The Major Features of Evolution.

107.

Sinedino, L.D.P., Honda, P.M., Souza, L.R.L., Lock, A.L., Boland, M.P.,
Staples, C.R. \emph{et al.} (2017). Effects of supplementation with
docosahexaenoic acid on reproduction of dairy cows. \emph{Reproduction}
, 153, 707-723.

108.

Sinensky, M. (1974). Homeoviscous adaptation--a homeostatic process that
regulates the viscosity of membrane lipids in Escherichia
coli.\emph{Proceedings of the National Academy of Sciences of the United
States of America} , 71, 522-525.

109.

Stanton, R.L., Morrissey, C.A. \& Clark, R.G. (2016). Tree Swallow
(Tachycineta bicolor) foraging responses to agricultural land use and
abundance of insect prey. \emph{Can. J. Zool.} , 94, 637-642.

110.

Stern, D.L. \& Frankel, N. (2013). The structure and evolution of
cis-regulatory regions: the shavenbaby story. \emph{Philosophical
Transactions of the Royal Society B: Biological Sciences} , 368,
20130028.

111.

Sui, N., Li, M., Li, K., Song, J. \& Wang, B.S. (2010). Increase in
unsaturated fatty acids in membrane lipids of Suaeda salsa L. enhances
protection of photosystem II under high salinity.\emph{Photosynthetica}
, 48, 623-629.

112.

Sunshine, H. \& Iruela-Arispe, M.L. (2017). Membrane lipids and cell
signaling. \emph{Curr Opin Lipidol} , 28, 408-413.

113.

Taipale, S., Strandberg, U., Peltomaa, E., Galloway, A.W.E., Ojala, A.
\& Brett, M.T. (2013). Fatty acid composition as biomarkers of
freshwater microalgae: analysis of 37 strains of microalgae in 22 genera
and in seven classes. \emph{Aquat. Microb. Ecol.} , 71, 165-178.

114.

Tocher, D.R., Betancor, M.B., Sprague, M., Olsen, R.E. \& Napier, J.A.
(2019). Omega-3 Long-Chain Polyunsaturated Fatty Acids, EPA and DHA:
Bridging the Gap between Supply and Demand. \emph{Nutrients} , 11.

115.

Tucci, S., Vohr, S.H., McCoy, R.C., Vernot, B., Robinson, M.R.,
Barbieri, C. \emph{et al.} (2018). Evolutionary history and adaptation
of a human pygmy population of Flores Island, Indonesia. \emph{Science}
, 361, 511-516.

116.

Twining, C.W., Brenna, J.T., Hairston, N.G., Jr. \& Flecker, A.S.
(2016). Highly unsaturated fatty acids in nature: what we know and what
we need to learn. \emph{Oikos} , 125, 749-760.

117.

Twining, C.W., Brenna, J.T., Lawrence, P., Shipley, J.R., Tollefson,
T.N. \& Winkler, D.W. (2016). Omega-3 long-chain polyunsaturated fatty
acids support aerial insectivore performance more than food
quantity.\emph{Proc. Natl. Acad. Sci. U. S. A.} , 113, 10920-10925.

118.

Twining, C.W., Brenna, J.T., Lawrence, P., Winkler, D.W., Flecker, A.S.
\& Hairston, N.G., Jr. (2019). Aquatic and terrestrial resources are not
nutritionally reciprocal for consumers. \emph{Funct. Ecol.} , 33,
2042-2052.

119.

Twining, C.W., Lawrence, P., Winkler, D.W., Flecker, A.S. \& Brenna,
J.T. (2018). Conversion efficiency of \selectlanguage{greek}α-\selectlanguage{english}linolenic acid to omega-3 highly
unsaturated fatty acids in aerial insectivore chicks. \emph{J. Exp.
Biol.} , 221.

120.

Twining, C.W., Shipley, J.R. \& Winkler, D.W. (2018). Aquatic insects
rich in omega-3 fatty acids drive breeding success in a widespread
bird.\emph{Ecol. Lett.} , 21, 1812-1820.

121.

Wagner, A. (2012). Metabolic networks and their evolution. \emph{Adv.
Exp. Med. Biol.} , 751, 29-52.

122.

Wang, S., Wang, M., Zhang, H., Yan, X., Guo, H., You, C. \emph{et
al.}(2020). Long-chain polyunsaturated fatty acid metabolism in
carnivorous marine teleosts: Insight into the profile of endogenous
biosynthesis in golden pompano Trachinotus ovatus. \emph{Aquac. Res.} ,
51, 623-635.

123.

Watson, H., Videvall, E., Andersson, M.N. \& Isaksson, C. (2017).
Transcriptome analysis of a wild bird reveals physiological responses to
the urban environment. \emph{Sci Rep} , 7, 44180.

124.

Watson, R.A., Wagner, G.P., Pavlicev, M., Weinreich, D.M. \& Mills, R.
(2014). The evolution of phenotypic correlations and ''developmental
memory''. \emph{Evolution} , 68, 1124-1138.

125.

Watts, J.L. \& Browse, J. (2002). Genetic dissection of polyunsaturated
fatty acid synthesis in Caenorhabditis elegans. \emph{Proc. Natl. Acad.
Sci. U. S. A.} , 99, 5854-5859.

126.

Wright, K.M. \& Rausher, M.D. (2010). The evolution of control and
distribution of adaptive mutations in a metabolic
pathway.\emph{Genetics} , 184, 483-502.

127.

Xia, J.H., Lin, G., He, X., Yunping, B., Liu, P., Liu, F. \emph{et
al.}(2014). Mapping quantitative trait loci for omega-3 fatty acids in
Asian seabass. \emph{Marine biotechnology} , 16, 1-9.

128.

Ye, K., Gao, F., Wang, D., Bar-Yosef, O. \& Keinan, A. (2017). Dietary
adaptation of FADS genes in Europe varied across time and
geography.\emph{Nat Ecol Evol} , 1, 167.

129.

Zhang, J. (2003). Evolution by gene duplication: an update. \emph{Trends
Ecol. Evol.} , 18, 292-298.

130.

Zhang, W., Zhang, J., Cui, L., Ma, J., Chen, C., Ai, H. \emph{et
al.}(2016). Genetic architecture of fatty acid composition in the
longissimusdorsi muscle revealed by genome-wide association studies on
diverse pig populations. \emph{Genetics Selection Evolution} , 48, 5.

\emph{}

\textbf{Figures, Box, and Tables}

Fig. 1: Consumers differ in their capacity to synthesize key fatty acids
from precursors. Three of the major gaps in synthesis capacity (A)
include: 1) conversion of saturated fatty acids (SFA), which may be
derived from carbohydrates in diet, to the monounsaturated fatty acid
(MUFA) oleic acid (18:1n-9; OA) and the omega-6 (n-6) polyunsaturated
fatty acid (PUFA) linoleic acid (18:2n-6; LIN), 2) conversion of the n-6
PUFA LIN and arachidonic acid (20:4n-6; ARA) to the omega-3 PUFA
alpha-linolenic acid (18:3n-3; ALA) and eicosapentaenoic acid (20:5n-3;
EPA), respectively, and 3) conversion of the short-chain n-3 PUFA ALA
into the long-chain n-3 PUFA EPA and docosahexaenoic acid (22:6n-3;
DHA). Within primary producers (A), vascular terrestrial plants are only
capable of synthesizing fatty acids up to ALA whereas different species
of algae and non-vascular terrestrial plants (e.g., mosses and
liverworts) are also able to produce EPA and/or DHA. Consumers (B-E)
have evolved synthesis capabilities that differ based upon the
availability of key fatty acids in their diets. While some consumers (B)
are capable of synthesizing both short-chain and long-chain n-3 and n-6
PUFA from SFA and OA, others (C-D) require short-chain n-3 PUFA from
diet, and still others (E) must receive all key fatty acids directly
from diet. Some animals, such as soil nematodes (B), consume
PUFA-deficient resources like bacteria and organic matter and derive
only SFA and OA from their diet, which they use as precursors to
synthesize LA, ARA, ALA, EPA, and DHA. Others, such as finches and other
terrestrial birds (C), consume resources like seeds that contain only
SFA, OA, and LIN, as well as resources like terrestrial insects that
also contain ALA. They must therefore convert dietary LIN into ARA and
dietary ALA into EPA and DHA. Still others, like Daphnia and other
aquatic invertebrates (D), consume some resources that contain both
short-chain and long-chain PUFA, and are incapable of converting EPA to
DHA, but are also capable of synthesizing EPA from DHA, such as from the
Cryptophyte alga Cryptomonas, through the process of beta-oxidation
(BETA). Finally, some animals, like tuna (E) and other carnivorous
marine fishes, consume resources that contain the full set of key fatty
acids, including both EPA and DHA, and are unable to perform any of the
major synthesis steps in (A).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 2: Marine, freshwater, and terrestrial organisms differ in their
fatty acid composition, especially in terms of the omega-3 PUFA ALA,
EPA, and DHA. These ecosystem-based differences are most pronounced in
(A-C) primary producers, but also occur among (E-F) higher order
consumers. (A-C) Vascular terrestrial primary producers contain only the
shorter-chain omega-3 PUFA ALA, while aquatic primary producers, as well
as a few non-vascular terrestrial primary producers like Bryophytes,
also contain the longer-chain omega-3 PUFA EPA and/or DHA. (D-F)
Consumers contain more EPA and/or DHA than primary producers from the
same ecosystem, but also exhibit differences based on both ecosystem
(e.g., terrestrial versus marine mammals and terrestrial versus
freshwater insects) and trophic position (e.g., fish versus insects or
Cladocerans).

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/366595/articles/486301-the-evolutionary-ecology-of-fatty-acid-variation-implications-for-consumer-adaptation-and-diversification}

Fig. 3: A) The available prey of Eurasian perch vary widely in their DHA
content, both within and between habitats. Pelagic perch consume prey
with much higher DHA content (e.g. copepods and fish), whereas littoral
perch consume large amounts of DHA-poor macroinvertebrates. B) Tree
Swallows forage on insects originating from both aquatic and terrestrial
habitats, but aquatic prey tend to have higher EPA. Some EPA-poor taxa
like Hymenoptera and Thysanoptera are readily available in the
environment whereas other high-EPA taxa like Ephemeroptera, Trichoptera,
and Odonata are scarce (bars denote habitat availability). All of the
rare aquatic prey are preferentially selected by Tree Swallows, relative
to the more EPA-poor (points reflect variation in dietary proportions).
Perch prey and diet data are from Chaguaceda et al. (2020) and Tree
Swallow prey and diet data are from McCarty and Winkler (1999) and
Twining et al. (2018).

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/366595/articles/486301-the-evolutionary-ecology-of-fatty-acid-variation-implications-for-consumer-adaptation-and-diversification}

Fig. 4: A simplified fatty acid network including the following modules:
(A) \emph{de novo} synthesis of saturated fatty acids (SFA) via fatty
acid synthase and a SFA elongase (B) conversion of SFA to
monounsaturated fatty acids (MUFA) via [?]9 stearoyl-coA desaturase, (C)
conversion of the MUFA oleic acid (18:1n-9, OA) to the n-6 PUFA linoleic
acid (18:2n-6, LIN) via [?]12 methyl-end desaturase, (D) conversion of
omega-6 PUFAs like LIN and arachidonic acid (20:4n-6, ARA) to the
omega-3 PUFAs ALA (18:3n-3) and to EPA (20:5n-3) via the [?]15 and [?]17
omega desaturases respectively, (E) conversion of LIN to n-6 LC-PUFAs by
front-end desaturases and PUFA elongases (F) conversion of ALA to n-3
LC-PUFAs by front-end desaturases and PUFA elongases. Note that
Beta-oxidation process (multi-enzyme reaction) is also required to
synthesise some LC-PUFA including DHA.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 5: Hierarchical structure of inter-related functional traits
involved in fatty acids. Both shifts in subordinate traits (metabolic
traits; red box) and resource acquisition traits (behavioural,
morphological and life-history traits; blue boxes) enable an organism to
alter lipid traits and physiological traits (purple boxes), which
finally influence individual fitness.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 6: Relationship between genetics and ecology illustrated on fatty
acid adaptive landscapes. \textbf{a,} Different genetic mechanisms
underlying increases in metabolic enzymatic activities. The arrow
thickness indicates the gene expression levels. Gray circles and yellow
asterisks indicate enzymes with ancestral and new functions,
respectively. \textbf{b,} Effects of mutations on the metabolic
pathways. Because of a pleiotropic effect of the enzyme, both metabolic
pathways can be influenced. Font sizes reflect the amounts of
synthesized fatty acids X and Y. Px, precursor of fatty acid X; Py,
precursor of fatty acid Y. \textbf{c,} Adaptive (black) or non-adaptive
walks (gray) on two different types of adaptive landscapes. The X and Y
axes indicate the levels of fatty acids X and Y. Plus (+) indicates the
adaptive peak. In Environment A, simultaneous increases of fatty acids X
and Y are favored. In Environment B, however, only an increase in fatty
acid X but not fatty acid Y is favored.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Box 1: Why the genetics of adaptation matters for the evolutionary
ecology of fatty acids.

\emph{Quantitative genetic view of adaptation} - Quantitative genetics
models generally assume that traits are continuous, normally
distributed, and controlled by many genes with small effects. In a model
with a single trait, selection moves the trait distribution according to
the breeder's equation: Response to selection (\emph{R} ) is a product
of heritability (\emph{h} 2) and selection differential (\emph{S} )
(Lynch and Walsh 1998). Any heritable traits can evolve in response to
selection. In cases in which multiple traits are genetically correlated,
selection on one trait can bias the evolution of another genetically
correlated trait, and the evolutionary trajectories on adaptive
landscapes can be biased by a genetic variance-covariance matrix
(G-matrix; Lande and Arnold 1983; Schluter 1996; Schluter 2000).
Although quantitative genetics models are helpful to predict short-term
evolution of highly polygenic traits, genomic studies of fatty acid
composition have often identified loci with moderate to large effects
(Cesar et al. 2014; Xia et al. 2014; Lemos et al. 2016; Lin et al. 2018;
Horn et al. 2020), suggesting that an alternative view of the genetic
basis of adaptation may be more useful in some cases.

\emph{Genomic view of adaptation} - Recent advances in genomic
technologies have improved our ability to elucidate the genetic details
of adaptation. For instance, studies have helped document how aspects of
genetic architecture, such as the number, effect sizes, pleiotropy,
linkage, and genomic location of adaptive loci, can influence the speed
and reversibility of adaptive evolution (Barton and Keightley 2002).
Such studies also aim to identify causative genes. For instance, animal
breeding studies have sought to identify genes and quantitative trait
loci that control the fatty acid composition of meat (e.g., Kelly et al.
2014; Zhang et al. 2016). However, even when a locus with a major effect
is identified, this does not necessarily indicate the presence of a
single causative gene. Furthermore, even when a causative gene is
identified, it does not necessarily mean that a single mutation causes
the alteration of the gene function (Stern and Frankel 2013; Bickel et
al. 2011). Therefore, an additional goal of genetic adaptation studies
is to identify specific causative mutations (Lee et al., 2014;
Remington, 2015). For example, \emph{Fads2} duplication in freshwater
species derived from marine ancestors allows them to synthesize more n-3
LC-PUFA (Ishikawa et al. 2019). When adaptation occurs via standing
genetic variation, it may not be necessary to further dissect it into
the levels of individual mutations for predicting how adaptation
proceeds. This is because such adaptation occurs by replacement of
already-existing alleles with tightly linked adaptive mutations (Barrett
and Schluter 2008). However, to understand adaptation by \emph{de
novo}mutations, it is essential to determine the number and nature of
responsible causative mutations (Stern and Frankel 2013). For example,
the \emph{bab} locus explains over 60\% of phenotypic variance of
pigmentation in \emph{Drosophila} , but each single SNP explains only
1\% (Bickel et al., 2011). Similarly, different \emph{Fads1} and
\emph{Fads2} gene variants in humans explain between 1-28.5\% of the
variation in the PUFA content of blood phospholipids (Schaeffer et al.
2006). Once causative mutations that alter fatty acid synthesis are
identified, it is then possible to determine whether adaptive evolution
has occurred through a few large steps or multiple small steps (Orr
2005).

Box 1 Figure: Relationship between the enzymatic activity and fitness in
two different environments (left) and a trajectory of adaptive walk
(gray arrow) biased by G-matrix on a two-dimensional adaptive landscape
(right). Px, precursor of fatty acid X; Py, precursor of fatty acid Y;
Plus (+), the adaptive peak.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Table 1.} Metabolic traits constituting the biosynthetic
pathways of fatty acids\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Pathway trait}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Trait type}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Mechanism}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Gene family}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Example taxa with functional pathway}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Example taxa without functional pathway}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
1) 18:0\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{De novo} fatty acid synthesis Elongation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Fatty acid synthase SFA elongases\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
All eukaryotes All eukaryotes\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
2) 18:0 18:1n-9 (SFA to MUFA)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
[?]9 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
First desaturases (Stearoyl-CoA desaturases)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
All eukaryotes\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
3) 18:1n-9 18:2n-6 (MUFA to PUFA)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
[?]12 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Omega desaturases (Methyl-end desaturases)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Plants, protists, fungi, certain invertebrates (Cnidarians, Nematodes,
Lophotrochozoans, some Copepods)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Deuterostomes\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
4) Omega-6 Omega-3\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
[?]15 desaturation (18:2n-6 18:3n-3)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Omega desaturases (Methyl-end desaturases)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Plants, protists, fungi, certain invertebrates (Cnidarians, Nematodes,
Lophotrochozoans, some Copepods)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Deuterostomes\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
[?]17 desaturation (C20 n-6 C20 n-3)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Omega desaturases (Methyl-end desaturases)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
[?]19 desaturation (C22 n-6 C22 n-3)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Omega desaturases (Methyl-end desaturases)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
5) ALA EPA and LIN ARA\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
[?]6 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Front-end desaturases\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Most vertebrates (\emph{Fads}2 gene), invertebrates\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Vascular plants (some can produce 18:4n-3) Most marine teleosts except
Elopomorpha (lack \emph{Fads}1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
[?]5 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Front-end desaturases\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Most vertebrates (\emph{Fads}1 gene); Fish with neofunctionalization of
\emph{Fads}2 (e.g., Zebrafish, Salmon); invertebrates (e.g., nematodes,
molluscs, sea urchins)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
[?]8 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Front-end desaturases\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Mammals, fish (\emph{Fads}2 gene), invertebrates (e.g., molluscs, sea
urchins)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Elongation (C18 C20)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PUFA elongases\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Most animals\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
6) EPA DHA and ARA 22:5n-6\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
[?]4 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Front-end desaturases\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Microalgae, protists, fungi, fish with neofunctionalization of
\emph{Fads2} (e.g., Rabbit fish)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Vascular plants (no PUFA elongase) Some acanthopterygian fish (lack [?]6
desaturation to C24)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
[?]6 desaturation\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Front-end desaturases\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Mammals, fish (\emph{Fads}2 gene)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0/1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Elongation (C20 C24)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PUFA elongases\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Microalgae, protists, fungi, most animals\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Types of intraspecific fatty acid traits that occur
along alternative pathways to influence individual fitness components
(Fig. 5). Full references are listed in our supplementary materials.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway to optimal organismal omega-3 fatty acid content}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Type of functional trait}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Examples}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Reference(s)}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{SUBORDINATE TRAITS}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{SUBORDINATE TRAITS}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{SUBORDINATE TRAITS}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 1: individual-level intracellular fatty acid conversion
and regulation}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 1: individual-level intracellular fatty acid conversion
and regulation}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 1: individual-level intracellular fatty acid conversion
and regulation}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 1: individual-level intracellular fatty acid conversion
and regulation}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Fatty acid metabolic traits} (Fig. 4) T1: fatty acid synthase
T2: SFA elongase T3: stearoyl-CoA desaturase T4: omega desaturase T5:
Front-end desaturase T6: PUFA elongase T7: Beta-oxidation genes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Castro et al. 2012 Castro et al. 2016 Kabeya et al. 2018 Boyen et al.
2020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 2: individual-level organismal resource acquisition and
life history}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 2: individual-level organismal resource acquisition and
life history}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 2: individual-level organismal resource acquisition and
life history}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway 2: individual-level organismal resource acquisition and
life history}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{BEHAVIOURAL ACQUISITION TRAITS}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{BEHAVIOURAL ACQUISITION TRAITS}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{BEHAVIOURAL ACQUISITION TRAITS}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Selective Foraging Behaviour\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Between prey items within an ecosystem: Calanoid copepods Rainbow trout
(\emph{Oncorhynchus mykiss}) Between seasons within an ecosystem
European whitefish (\emph{Coregonus lavaretus}) Tuatara (\emph{Sphenodon
punctatus}) Between ecotypes within an ecosystem: Killer whales
(\emph{Orcinus orca}) Threespine stickleback (\emph{Gasterosteus
aculeatus}) Between ecosystems: Eastern Phoebes (\emph{Sayornis
phoebe})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Eglite et al. 2019 Roy et al. 2020 Keva et al. 2019 Cartland-Shaw et al.
1998 Herman et al. 2005 Hudson et al. pers. comm.; Daneau-Lamoureux
pers. comm. Twining et al. 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Consumer Trophic Position\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Ontogeny in Eurasian perch (\emph{Perca fluviatilis}) Dietary plasticity
in larval anurans\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Chaguaceda et al. 2020 Whiles et al. 2010\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{LIFE HISTORY TRAITS}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{LIFE HISTORY TRAITS}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{LIFE HISTORY TRAITS}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Body size\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Freshwater calanoid copepods Round gobies and (\emph{Neogobius
melanostomus}) Monkey gobies (\emph{Neogobius fluviatilis})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Charette and Derry 2016 Ghomi et al. 2014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Maternal investment Egg Composition (maternal effects) Milk
Composition\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Daphnia egg composition Tropical nudibranch egg composition
(\emph{Aeolidilla stephanieae}) Mammalian milk composition\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Schlotz et al. 2013 Leal et al. 2012; Leal et al. 2013 Brenna et al.
2009; Hibbeln et al. 2019; Muhlhausler et al. 2011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Migration\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Migratory songbirds Migratory bats Southern hemisphere humpback whales
(\emph{Megaptera novaeanglidae})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Pierce and McWilliams 2005; Pierce and McWilliams 2014 McGuire et al.
2013 Waugh et al. 2012\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Phenology\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Colorado potato beetles (\emph{Leptinotarsa decemlineata})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Clements et al. 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Hibernation\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Black bears (\emph{Ursus americanus}) Yellow-bellied marmots
(\emph{Marmota flaviventris})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Iverson and Oftedal 1992 Hill and Florant 1999\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Dormancy \& diapause\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Harpacticoid copepods (\emph{Heteropsyllus nunni}) Lepidopterans\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Williams and Biesiot 2004 Vuka\selectlanguage{polish}š\selectlanguage{english}inovi\selectlanguage{polish}ć \selectlanguage{english}et al. 2015; Hemmati et al.
2017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Morphology (consumer feeding traits)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Three-spine stickleback\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Hudson et al pers. comm.; Daneau-Lamoureux et al. pers. comm.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Influenced by both Pathways 1 and 2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Influenced by both Pathways 1 and 2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Influenced by both Pathways 1 and 2}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Influenced by both Pathways 1 and 2}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Lipid traits}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Lipid traits}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Lipid traits}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Total lipids\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Fairy shrimp (\emph{Chirocephalus diaphanus}) Euphausiids
(\emph{Meganyctiphanes norvegica}) Calanoid copepods (\emph{Diaptomus
kenai})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Bocca et al. 1998 Mayzaud et al. 1999 Butler 1994\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Lipid classes (e.g., TAGs, polar lipids)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Euphausiids\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Saito et al. 2002 Mayzaud et al. 1999\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Fatty acid content: SFA (e.g., 16.0, 18.0) MUFA (e.g., OA) PUFAs (e.g.,
LA, ALA) LC-PUFA (e.g., DHA)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Daphnia EPA content Copepod DHA content Sex-specific differences:
Daphnia Tuatara Southern humpback whales\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Hessen and Leu 2006; Wacker and Martin-Creuzburg 2007; Sperfeld and
Wacker 2012 Charette and Derry 2016 Martin-Creuzburg et al. 2018
Cartland-Shaw et al. 1998 Waugh et al. 2012\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Body condition indexes -weight to length -fat mass\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Tree Swallows (\emph{Tachycineta bicolor}) Humans (\emph{Homo sapiens})
Round gobies and Monkey gobies\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Twining et al. 2016 Tan 2014; Elias and Innis 2001 Ghomi et al.
2014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Physiological traits}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Cell membrane fluidity\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Eastern newt (\emph{Notophthalmus viridescens}) Humans and Roundworms
(\emph{Caenorhabditis elegans})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Mineo et al. 2019 Ruiz et al. 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Mitochondrial function\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Ground squirrels Red-winged Blackbirds (\emph{Agelaius phoeniceus})
Humans\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Gerson et al. 2008 Price et al. 2018 Herbst et al. 2014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Inflammation\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Immune system performance in Wolf spiders Daphnia Largemouth bass
(\emph{Micropterus salmoides})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Fritz et al 2017 Schlotz et al 2012 Schlotz et al 2013 Schlotz et al
2016 Zhou et al 2020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Buoyancy\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Calanoid copepods (\emph{Calanoides acutus})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Pond and Tarling 2011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Metabolic Rate and Exercise Performance\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Thirteen-lined Ground Squirrels (\emph{Spermophilus tridecemlineatus})
White-throated Sparrows (\emph{Zonotrichia albicollis}) Yellow-rumped
Warblers (\emph{Setophaga coronata}) Atlantic salmon (\emph{Salmo
salar}) Mammals\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Gerson et al. 2008 Price and Guglielmo 2009 Dick and Guglielmo 2019
McKenzie et al 1998 Ruf et al 2006\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway Products: Individual-level fitness component
traits}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway Products: Individual-level fitness component
traits}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway Products: Individual-level fitness component
traits}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Pathway Products: Individual-level fitness component
traits}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Growth\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Daphnia Tree Swallows Eastern Phoebes Freshwater fish\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Brett and M\selectlanguage{ngerman}üller-Navarra 1997; Müller-Navarra et al. 2000; Ili\selectlanguage{polish}ć \selectlanguage{english}et al.
2019 Twining et al. 2016 Twining et al. 2019 Glencross 2009; Zhou et al.
2019 Lundova et al. 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Development\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Mammalian brain development Avian embryonic development\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
McNamara and Arsch 2019 Pappas et al. 2007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Reproduction\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Freshwater calanoid copepod fecundity Daphnia fecundity Eurasian perch
gonad size\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Charette and Derry 2016 Sperfeld and Wacker 2012; Ili\selectlanguage{polish}ć \selectlanguage{english}et al. 2019
Scharnweber and G\selectlanguage{ngerman}årdmark 2020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Survival\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Tree Swallow fledge success Sterlet survival (\emph{Acipenser
ruthenus})\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Twining et al. 2018 Lundova et al. 2018\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2-JB/Figure2-JB}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3ab/Figure-3ab}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-6/Fig-6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Box-figure/Box-figure}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

