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2018
Should I stay or should I go? Complex environments drive the Should I stay or should I go? Complex environments drive the
developmental plasticity of flight capacity and flight-related developmental plasticity of flight capacity and flight-related
tradeoffs tradeoffs
Jordan R. Glass University of the Pacific
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SHOULD I STAY OR SHOULD I GO? COMPLEX ENVIRONMENTS DRIVE THE
DEVELOPMENTAL PLASTICITY OF FLIGHT CAPACITY AND FLIGHT-
RELATED TRADEOFFS
by
Jordan R. Glass
A Thesis Submitted to the
Graduate School
In Partial Fulfillment of the
Requirements for the Degree of
MASTER OF SCIENCE
College of the Pacific
Biological Sciences
University of the Pacific
Stockton, CA
2
2018
SHOULD I STAY OR SHOULD I GO? COMPLEX ENVIRONMENTS DRIVE THE
DEVELOPMENTAL PLASTICITY OF FLIGHT CAPACITY AND FLIGHT-
RELATED TRADEOFFS
by
Jordan R. Glass
APPROVED BY:
Thesis Advisor: Zachary R. Stahlschmidt, Ph.D.
Committee Member: Marcos Gridi-Papp, Ph.D.
Committee Member: Ryan Hill, Ph.D.
Department Chair: Craig Vierra, Ph.D.
Dean of Graduate School: Thomas H Naehr Ph.D.
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SHOULD I STAY OR SHOULD I GO? COMPLEX ENVIRONMENTS DRIVE THE
DEVELOPMENTAL PLASTICITY OF FLIGHT CAPACITY AND FLIGHT-
RELATED TRADEOFFS
Copyright 2018
by
Jordan R. Glass
4
DEDICATION
This thesis is dedicated to my wife, Christine M. Glass, for her unconditional love,
support, and patience.
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ACKNOWLEDGMENTS
I would like to thank Iris Chu, Dustin Johnson, Christina Koh, Nick Meckfessel, Carolyn
Pak, Sugjit Singh, and Jihee Yoon for experimental assistance, and Drs. Marcos Gridi-
Papp and Ryan Hill for their comments on this manuscript. I also appreciate funding from
the National Science Foundation (IOS-1565695 to ZRS) and University of the Pacific (to
ZRS).
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Should I stay or should I go? Complex environments drive the developmental plasticity
of flight capacity and flight-related trade-offs
Abstract
by Jordan R. Glass
University of the Pacific
2018
Animals must balance multiple, fitness-related traits in environments that are
complex and characterized by co-varying factors, such as co-variation in temperature and
food availability. Thus, experiments manipulating multiple environmental factors provide
valuable insight into the role of the environment in shaping not only important traits (e.g.,
dispersal capacity or reproduction), but also trait-trait interactions (e.g., trade-offs
between traits). We employed a multi-factorial design to manipulate variation in
temperature (constant 28°C vs. 28±5°C daily cycle) and food availability (unlimited vs.
intermittent access) throughout development in the sand field cricket, Gryllus firmus. We
found that fitness-related, life-history traits and trait trade-offs can be developmentally
plastic in response to variation in temperature and food availability. Variability in
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temperature and food availability influenced development, growth, body size,
reproductive investment, and/or flight capacity, and food availability also affected
survival to adulthood. Further, both constant temperature and unlimited food availability
promoted investment into key components of somatic and reproductive tissues while
reducing investment into flight capacity. We develop an experimental and statistical
framework to reveal shifts in correlative patterns of investment into different life-history
traits. This approach can be applied to a range of animal systems to investigate how
environmental complexity influences traits and trait trade-offs.
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TABLE OF CONTENTS
LIST OF TABLES .............................................................................................................. 9
LIST OF FIGURES .......................................................................................................... 10
Chapter 1: Introduction ......................................................................................... 11
Chapter 2: Methodology ....................................................................................... 15
Chapter 3: Results ................................................................................................. 21
Chapter 5: Discussion ........................................................................................... 27
REFERENCES ................................................................................................................. 32
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LIST OF TABLES
Table Page
1. Correlation Coefficients…..…………………………………………………… 18
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LIST OF FIGURES
Figure Page
1. PC1 Loadings…………………………………………………………………… 19
2. Survival Proportion……..……………………………………………………..... 22
3. Effects on Development Time, Growth Rate, Adult Body Mass, and Femur
Length……………………………..…………………………………………..... 23
4. Effects on Gonad Mass and Flight Muscle
Score…………………………………………………………………………..... 25
5. Female Investment Bias and Male Quality Index……………………………… 26
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Chapter 1: Introduction
Animals live in complex environments, which exhibit spatial and temporal
thermal heterogeneity. Environmental temperature is important because it influences a
range of biological processes, including behavior, energy use, locomotion, and
reproduction (Angilletta, 2009). Thus, animals are susceptible to the ongoing effects of
climate change (Parmesan et al., 1999; Root et al., 2003; Sinervo et al., 2010) whereby
environments are expected to continue to exhibit greater variation in temperature, in
addition to greater mean temperatures (IPCC, 2014). Further, this variation in
temperature may pose a greater risk to species and biodiversity than the gradual warming
characterized by shifts in mean temperature (Vasseur et al., 2014). However, variation in
temperature is not the only challenge many animals encounter. Food availability, like
temperature, is variable across space and time, and it can have large effects on animal
growth and development (Dunham, 1978; Jones, 1986; Shafiei et al., 2001). Additionally,
food availability and temperature can vary simultaneously (Mattson, 1980; Stamp, 1993)
and indicate the quality of a given environment (e.g., a high-quality environment may be
characterized by high food availability and a stable temperature). Thus, co-variation of
food availability and temperature can affect the perceived quality and distribution of
habitats (Roff, 1990, 1994a).
Animals have adapted to environmental heterogeneity (e.g., variation in
temperature and food availability) by employing a variety of locomotor strategies
(reviewed in Aidley, 1981). Flight, in particular, gives some terrestrial animals the ability
to travel greater distances than walking, and it allows flying animals to quickly leave
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poor quality environments in search of better habitats (Roff, 1994a). However, the act of
flight is energetically costly (Schmidt-Nielsen, 1972; Thomas & Suthers, 1972;
Bartholomew & Casey, 1978; Norberg, 2012). Further, building and maintaining flight
musculature, and synthesizing flight fuels (e.g., triglycerides) can greatly increase
metabolic demands (Zera & Mole, 1994; Zera, Mole, & Rokke, 1994; Zera & Denno,
1997). This energetic investment into flight can obligate trade-offs with other life-history
traits, such as investment into reproduction (reviewed in Zera & Harshman, 2001).
Researchers have gained a better understanding of how animals navigate flight-related
trade-offs by typically manipulating, at most, a single environmental variable (e.g.,
effects of food availability on a trade-off between flight and fecundity: Mole & Zera,
1993, 1994; Zera & Mole, 1994). Although single-variable manipulations contribute to
our understanding of how animals navigate trade-offs, it is still unclear how animals
navigate these trade-offs when experiencing shifts in complex environments
characterized by multiple co-varying environmental factors (e.g., variation in temperature
and food availability).
The quality of complex environments may mediate the prioritization of flight
capability relative to other important life-history traits (Mole & Zera, 1993, 1994; Zera &
Mole, 1994), which may be sex-specific. For example, females often allocate energy
resources more heavily toward reproduction (Williams, 1966; Shine, 1980; Reznick,
1985; Speakman, 2008) and this, in turn, may lead increase the likelihood that females
will encounter some types of reproduction-related trade-offs. In the wing-dimorphic sand
field cricket (Gryllus firmus Scudder), females of each discrete wing morph adopt a
different strategy to a flight-fecundity trade-off. During early adulthood, long-winged
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(LW) females specialize in dispersal by investing into functional flight musculature,
which comes at a cost to reproduction. Short-winged (SW) females sacrifice their ability
to fly in return for greater reproductive abilities (i.e., greater investment into ovary mass)
during early adulthood (reviewed in Zera, 2005). Males also exhibit trade-offs between
investment into flight capability and reproduction (mating-call duration: Crnokrak &
Roff, 1998; testes size: Saglam et al., 2008). In sum, the established flight-related trade-
offs in G. firmus provide a unique opportunity to examine the role of complex
environments in the plasticity of traits and trait-trait interactions.
Thus, we examined several dynamics of developmental plasticity in both sexes
and morphs of G. firmus due to multiple, co-varying environmental factors. Specifically,
we used a multi-factorial design to manipulate variation in temperature (constant 28°C
vs. 28±5°C daily cycle) and food availability (unlimited vs. intermittent access)
throughout development. The factorial design allowed us to test two hypotheses related to
the role of complex environments in the developmental plasticity of not only individual
traits, but also trait-trait interactions. First, we hypothesized that variability in
temperature and food availability influences the developmental plasticity of individual
life-history traits, such as growth, reproduction, and survival. We predicted an additive
effect of food and temperature where high food availability and stable temperatures
would enhance these fitness-related traits (e.g., abundant food and constant temperature
would lead to faster development and larger gonads). Second, we hypothesized that
variability in temperature and food influences the developmental plasticity of trait-trait
interactions, including trade-offs associated with investment into flight capability. Here,
we predicted that fluctuating temperatures and intermittent food availability would be
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non-ideal developmental conditions that would promote the prioritization of flight
capacity over other life-history traits, such as reproductive investment. By factorially
manipulating two ubiquitous environmental factors, this study will inform our
understanding of how dynamic environments influence important traits (i.e., survival,
developmental rate, and investment into reproductive and flight tissues) and trait trade-
offs in animals.
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Chapter 2: Methodology
Study Animals
The wing dimorphic sand field cricket (Gryllus firmus) is endemic to the
southeastern United States and ranges from Connecticut to Texas (Capinera et al., 2004).
Crickets (newly hatched: 1st instar; <5 d post-hatching; n=540) used in the study were
acquired from several selected, nearly true-breeding blocks that have been previously
described (Zera, 2005).
Experimental Design
To investigate the developmental plasticity of traits and trait-trait interactions, a 2
× 2 factorial design was employed on crickets individually reared in a 16 h photoperiod
and in translucent plastic containers (473 ml) with ad libitum access to water. Half of the
crickets were reared in an incubator (model I-36, Percival Scientific, Inc., Perry, IA,
U.S.) at a constant 28°C (“constant” temperature treatment), the temperature regime at
which this stock is typically reared (reviewed in Zera, 2005). The remaining crickets
were reared in an incubator (model I-36, Percival Scientific, Inc., Perry, IA, U.S.), which
created a sinusoidal diel temperature cycle (28±5°C; “fluctuating” temperature treatment)
that approximates the average diel temperature variation in Gainesville, FL, U.S. (where
the founders of the stock were collected) during the crickets’ active season (May –
September: National Weather Service). Crickets experienced one of two food treatment
levels: ad lib. access to food (commercial dry cat food) (“high” food treatment) or access
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to food for two 24-hour periods each week (“low” food treatment, which is ecologically
relevant due to crickets’ intermittent feeding habits: Gangwere, 1961).
At five days of adulthood (i.e., when the flight-fecundity trade-off peaks: Zera &
Larsen, 2001), crickets were weighed, euthanized, and stored at -20°C. Crickets were
later dissected, and their flight musculature (dorso-longitudinal muscles [DLM]) was
scored from 0 (DLM absent) to 1 (white, histolyzed, and non-functional DLM) to 2 (pink
and functional DLM) (King et al., 2011). Each cricket’s femur length (a proxy for body
size: Simmons, 1986) was measured, and its gonads were removed and dried to a
constant mass to determine gonad mass. Scoring wing musculature of G. firmus allowed
for an estimate of investment into flight, while determining gonad dry mass provided an
estimate of investment into reproduction (Roff & Fairbairn, 1991; Crnokrak & Roff,
1998). Growth rate (mm/d) was determined by dividing femur length by developmental
time.
Principle Component Analyses
A multivariate statistical method was used to reveal correlative patterns of
investment into different life-history traits, and to generate an index of resource
allocation strategy (sensu Stahlschmidt & Adamo, 2015; Bertram et al., 2017a,b). For
example, some females in our study allocated more toward flight capability relative to
reproduction and other life-history traits (see below). Specifically, two principle
component analyses (PCAs) were performed on dependent variables (developmental
time, growth rate, body mass, femur length, gonad mass, and DLM status). Male and
female data were analyzed independently because female-biased sexual dimorphisms in
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body size and gonad mass (described below) made data pooled across both sexes
irrevocably non-normally distributed. Two principle components (PCs) accounted for
significant percentages of the variation found in the data (see below), and they were used
as a dependent variable in subsequent analyses (e.g., linear mixed models, see ‘Statistical
Analyses’).
In the PCAs, the initial dependent variables were significantly correlated with one
another, with the exceptions of DLM not correlating with body size and mass in females
or with development time, growth rate, and testes mass in males (Table 1).
The Bartlett’s measure, which determines whether there is a significant pattern of
correlations in a given data set, for our data sets was highly significant (<0.001 for both
data sets). Yet, our data sets did not exhibit extreme multicollinearity (overly correlated
variables) because they each had an adequately large determinant of the correlation
matrix value (0.003 for both data sets). The Kaiser–Meyer–Olkin (KMO) measure of
sampling adequacy ranges from 0 (diffuse pattern of correlations) to 1 (compact pattern
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of correlations), and the KMO values for our data sets were 0.65 and 0.71 for females and
males, respectively, which are acceptable (Kaiser, 1974). Therefore, our male and female
data sets satisfied the assumptions of having significant and compact patterns of
correlations.
The first principal component extracted from our female data (PC1f) accounted
for 60% of the variation in the female data. PC1f loaded negatively onto growth rate,
body mass, and body size, and reproduction, and positively onto development time and
flight muscle (Figure 1).
Figure 1. PC1 loadings representing investment of female (white) and male
(grey) G. firmus into development, growth, body mass, body size, reproduction, and
flight muscle. Females appeared to exhibit a trade-off between investment into flight
capability and all other traits (i.e., PC1 for females [PC1f] represented an index of
dispersal bias). Males did not appear to exhibit this trade-off; rather, PC1 for males
(PC1m) indicated an index of overall quality.
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Thus, an individual with a high PC1f value had a high dispersal bias, which means it had
a greater investment into maintenance of flight musculature and capability, and a lower
investment into development, growth, body size, and reproduction (Figure 1). Similarly,
the first principle component extracted from our male data (PC1m) accounted for 59% of
the variation in the male data. However, PC1m loaded negatively onto development time,
and positively onto all other morphological traits (growth rate, body mass, and body size,
reproduction, and flight muscle; Figure. 1). Notably, PC1m loaded only weakly (0.05)
onto flight muscle (Figure 1). Thus, PC1m scores from our data signify a different metric
than PC1f, as we found no evidence of a negative correlation between flight capability
and gonadal investment in males (Roff & Fairbairn, 1993; but see Saglam et al., 2008)
(Figure 1). Rather, PC1m represented a quality index where a higher value reflected
greater investment into nearly all fitness-related traits (Figure 1).
Statistical Analyses
Data were tested for normality, transformed (e.g., natural log, log base ten, or
square root) when necessary, and analyzed using R (v.3.3.2 R Foundation for Statistical
Computing, Vienna, Austria). Two-tailed significance was determined at α < 0.05. To
examine the independent effects of variation in temperature and food availability,
analyses of variance (ANOVAs) were performed on development time, growth rate, and
body mass across all individuals (i.e., both sexes were analyzed together). Both body size
(femur length) and gonad mass data were analyzed independently for each sex because
female-biased dimorphisms that exist in G. firmus (e.g., in our study, sexes varied in
femur length [t test: t246 = 3.9, P < 0.001] and gonad mass [t test: t246 = 7.1, P < 0.001)
made data irrevocably non-normally distributed. For each ANOVA, temperature and food
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treatments, wing morphology (herein, “morph”: SW or LW), and sex were included as
main effects. Each ANOVA was also used to investigate the possible interactive effects
of temperature and food treatments, and only significant interactions are reported.
An ordinal logistic regression was performed on the categorical DLM scores
(scored from 0 to 2, see above) and temperature and food treatments, morph, and sex
were included as main effects. Similarly, a binary logistic generalized linear model was
used on data from each cricket to determine the main and interactive effects of variation
in temperature and food availability on survivorship (0: did not survive to adulthood; 1:
survived to adulthood). An ANOVA was performed on the PC1 scores for each sex
independently to determine if variation in temperature and food availability had a
significant effect on the trade-off between flight and fecundity in females (i.e., PC1f, an
index of dispersal bias: Figure 1) or on the overall quality of males (i.e., PC1m, an index
of quality: Figure 1).
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Chapter 3: Results
Survival increased with the availability of food (Z = 2.7, P = 0.006), but there was
no effect of temperature variability on survival (Z = 1.6, P = 0.12) (Figure 2).
Figure 2. Survival proportion of G. firmus reared under either “constant” (28°C)
or “fluctuating” (28±5°C) temperature treatments while given low (intermittent) or high
(ad lib.) access to food during development. Morph and sex were not considered, as sex
and wing morphology could not be determined in immature crickets.
For this analysis, morph and sex were not considered in the survival analysis as sex and
wing morphology could not be determined in immature crickets. Crickets reared in a
thermally fluctuating environment developed faster (mean: 72 d vs. 82 d spent in
development; F1,230 = 17, P < 0.001), and so did those given high food (mean: 74 d vs. 82
d; F1,230 =11, P < 0.001) and those that were LW (mean: 74 d vs. 82 d; F1,230 = 4.4, P =
0.037) (Figure 3A).
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Figure 3. Effects of variation in temperature and food availability during development on
(A) developmental time, (B) growth rate, (C) adult body mass, and (D) femur length.
Long-winged morphs are depicted by open columns, and short-winged morphs are
depicted by diagonal cross-hatched columns. Values are displayed as mean±s.e.m.
Additionally, there was an interactive effect of temperature, morph, and sex on
development time (F1,230 = 4.0, P = 0.047) (Figure 3A). Similarly, crickets reared in a
thermally fluctuating environment or high food availability grew faster (temperature:
mean: 0.19 mm/d vs. 0.16 mm/d; F1,230 = 18, P < 0.001, food: mean: 0.19 mm/d vs. 0.16
mm/d; F1,230 = 13, P < 0.001) (Figure 3B). Long-winged crickets also grew faster that SW
crickets (morph: mean: 0.19 mm/d vs. 0.17 mm/d; F1,230 = 6.5, P = 0.011), and females
grew faster than males (sex: mean: 0.19 mm/d vs. 0.17 mm/d; F1,230 = 4.0, P = 0.047)
(Figure 3B).
Individuals reared in a thermally fluctuating environment and those with high
food had greater body mass at adulthood (temperature: mean: 768.8 mg vs. 682; F1,230 =
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12, P < 0.001, food: mean: 762.7 mg vs. 663.5 mg; F1,230 = 13, P < 0.001) (Figure 3C).
Female crickets were significantly heavier than male crickets (sex: mean: 781.7 mg vs.
664.4 mg; F1,230 = 17, P < 0.001), and both males and females exhibited a difference in
body mass due to morph (i.e., LW individuals were heavier: mean: 765.6 mg vs. 662.1
mg; F1,230 = 14, P < 0.001) (Figure 3C). Female body mass was more sensitive to food
treatment than male body mass (food × sex: F1,230 = 9.0, P = 0.0029, Figure 3C). Females
given unlimited food access during development were larger (i.e., had longer femurs)
than those with limited access (mean: 13.29 mm vs. 12.71 mm; F1,117 = 8.2, P = 0.0049),
but there was no effect of temperature variability on body size (femur length: F1,117 = 2.9,
P = 0.089) (Figure 3D). Variation in temperature and food availability did not
significantly affect body size in male crickets (temperature: F1,113 = 2.5, P = 0.12, food:
F1,113 = 1.3, P = 0.26) (Figure 3D). However, LW males were significantly larger than
SW males (mean femur length: 12.79 mm vs. 11.99 mm; F 1,113 = 11, P = 0.0013) (Figure
3D).
Females in the high food treatment had dramatically heavier ovaries (mean: 31.5 mg vs.
10.2 mg; F1,117 = 41, P < 0.001: Figure 4A), but wing morphology and temperature
variability had no effect on ovary mass (morph: F1,117 = 3.3, P = 0.07, temperature: F1,117
= 0.21, P = 0.65).
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Figure 4. Effects of variation in temperature and food availability during development on
(A) gonad mass, and (B) flight muscle of G. firmus. Note: none of the SW males that
experienced high food and fluctuating temperature exhibited any flight muscle. Long-
winged morphs are depicted by open columns, and short-winged morphs are depicted by
diagonal cross-hatched columns. Values are displayed as mean±s.e.m.
Variation in temperature and food availability did not affect testes mass (F 1,110 = 0.048, P
= 0.83, food: F 1,110 = 13, P = 0.16) (Figure 4A). Crickets given high food greatly reduced
investment into flight muscle (T = 3.8, P < 0.001) (Figure 4B). This reduction in flight
muscle was most apparent in the LW morph, which specializes in dispersal capability
(effect of morph on DLM status: T = 5.9, P < 0.001) (Figure 4B).
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Females reared under fluctuating thermal environments exhibited greater
investment in development, growth, body size, and reproduction, while reducing
investment into flight capability (F1,117 = 6.0, P = 0.016) with similar effects on females
when reared in the high food treatment (F1,117 = 18, P < 0.001) (Figure 5A).
Figure 5. Effects of variation in temperature and food availability during development on
(A) female G. firmus bias to invest in maintenance of flight musculature at an expense to
other fitness-related traits, and (B) the quality of male G. firmus. Long-winged morphs
are depicted by open columns, and short-winged morphs are depicted by diagonal cross-
hatched columns. Values are displayed as mean±s.e.m.
Males reared in a fluctuating thermal environment had higher quality indices than those
reared in a constant temperature (F1,113 = 5.1, P = 0.026) (Figure 5B). Long-winged males
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were also higher quality than SW males (F1,113 = 12, P < 0.001) (Figure 5B). Food
availability did not affect the quality of males (F1,113 = 3.2, P = 0.076) (Figure 5B).
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Chapter 5: Discussion
In support of our hypotheses, we demonstrate that fitness-related, life-history
traits (e.g., flight capacity, growth, reproduction, and survival) and trait trade-offs can be
developmentally plastic in response to variation in temperature and food availability
(Figures 4 and 5). For example, high food availability and constant temperature promoted
investment into key components of somatic and reproductive tissues (Figures 3, and 4A)
while reducing investment into flight capacity (Figure 4B). However, contrary to our first
prediction, thermally fluctuating (not stable) environments promoted somatic growth
(Figures 3, and 5). Although we found no interactive effects of food and temperature
treatments on life-history traits, these environmental factors did have additive effects on
several traits (Figure 3A-C). Because temperature and food availability can naturally co-
vary (Mattson, 1980; Stamp, 1993), experiments manipulating multiple environmental
factors improve our understanding of how the environment shapes important traits
(Figures. 2 and 4; Todgham & Stillman, 2013; Kaunisto et al., 2016) and trait-trait
interactions (Figure 5A).
Fitness is strongly linked to survival, and food availability during development
influences survival across taxa (e.g., insects: Boggs & Freeman, 2005; amphibians: Scott
et al., 2007; reptiles: Garnett, 1986; fish: Wilkins, 1967, birds: Davis et al., 2005). In
support, individuals reared in the high food treatment in our study exhibited a 60%
increase in survival to adulthood relative to those in the low food treatment (Figure 2).
Food availability also promotes shorter development time, faster growth rate, and/or
larger adult size in both invertebrates and vertebrates (e.g., insects: Richardson, 1991;
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amphibians: Scott & Fore, 1995; reptiles: Dunham, 1978; Ballinger & Congdon, 1980).
Similarly, we observed that crickets reared on high-food diets were larger and heavier
than those given limited access to food (Figure 3C,D). However, high food availability
did not increase investment into all somatic traits—the construction and maintenance of
flight musculature was significantly reduced (not enhanced) in high food conditions
(Figure 4B). Presumably, individuals were more likely to invest in dispersal when
developing in low food (low quality) environments, which would allow them to leave to
find a higher quality environment. Thus, understanding the adaptive function of a given
trait is important to consider when determining its response to environmental variation.
Temperature, like food, can affect many biological processes, such as
development, growth, and reproduction (reviewed in Angilletta, 2009). Differences in
mean temperature can have strong effects on development time, growth rate, and body
mass in ectotherms (Ratte, 1985; Atkinson, 1994; Kingsolver et al., 2009; Williams et al.,
2012). However, independent of differences in mean temperature, we found that variation
in temperature experienced during development also tends to decrease the developmental
time and increase the growth rates, and body mass (Figure 3A-C), consistent with other
invertebrates (Ratte, 1985; Atkinson, 1994; Kingsolver et al., 2009; but see Kjærsgaard et
al., 2013) as well as vertebrates (Wijethunga et al., 2016; Pepin, 1991; Shine & Harlow,
1996; Booth, 1998). Thus, ectotherms typically benefit from experiencing variable
temperatures during their life-histories, as long as the rearing temperatures are within
physiological limits (Worner 1992; Shine & Harlow, 1996; Elphick & Shine, 1998;
Angilletta, 2009; Fischer et al., 2011; Colinet et al., 2015). The benefits of temperature
variability may be explained by the asymmetric effects of high and low temperatures on
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physiological processes (i.e., benefits of experiencing warmer temperatures outweigh the
costs of exposure to cooler temperatures; see Colinet et al., 2015).
Given enough time (i.e., generations), the optimal temperature for physiological
performance for animals is expected to match their environmental temperature (Leroi et
al., 1994; Gilchrist et al., 1997). We provide insight into this concept of thermal matching
because our experimental crickets were obtained from genetically isolated, true-breeding
lines that have been cultured at a constant temperature of 28°C for several decades (see
Mole & Zera, 1994; Zera, Potts, & Kobus, 1998; Zera, 2005; Zera et al., 2018). We found
that crickets did not perform better at the temperature regime under which they have been
selected (Figures 3A-C and 5). Thus, the optimal temperature for development in our
study system did not appear to be influenced by laboratory selection. Although it is
possible that the optimal temperature for G. firmus is <28°C, it is more likely that G.
firmus performs better at >28°C given the ‘warmer is better’ paradigm (Hamilton, 1973;
Bennett, 1987; Frazier et al., 2006). Thus, our study (and others’) indicates that some
traits can be fixed and seemingly insensitive to environmental selection, such as the
exposure to a homogeneous thermal environment over many generations (e.g., Alton et
al., 2017).
For decades, life-history evolution and trade-offs have been rich sources of
investigation (e.g., Pianka, 1981; Ricklefs, 1996; Roff, 2002). In this context, Gryllus
crickets have been frequently investigated due to the trade-off they exhibit between
investment into flight capability and female reproduction during early adulthood that is
mediated by a wing dimorphism (e.g., Roff, 1984; Roff, 1990; Mole & Zera, 1994; Zera
& Brink, 2000; Zera & Zhao, 2003). However, the association of wing morphology with
30
other fitness-related traits is still unclear (but see Rantala & Roff, 2005). Here, we found
that LW individuals developed and grew faster, and had greater adult body mass than
their SW counterparts (Figure 3A-C). However, in contrast to previous work (e.g., Mole
& Zera 1993, 1994), we observed no difference between the ovary masses of LW and
SW crickets during early adulthood (Figure 4A). Thus, complex environments may
obviate the well-established flight-fecundity trade-off—LW females in our study invested
more in flight capacity (Figure 4B) while also exhibiting comparable investment into
ovaries as SW females (Figure 4A). The flight-fecundity trade-off can also be eliminated
in stressful conditions because both morphs exhibit similar-sized ovaries when food is
less abundant or when they are immune challenged (ZRS & JRG, unpublished). We also
detected an association of wing morphology with male body size (i.e., LW males were
significantly larger than their SW counterparts), an effect we did not observe in females
(Figure 3D). This morph-specific variation in life-history traits may be attributed to genes
underlying wing morphology, which may be linked to other non-wing morphology genes,
exhibiting pleiotropic effects (Stirling, Roff, & Fairbairn, 1999; Stirling et al., 2001; Roff
& Fairbairn, 2012).
The present study found that fitness-related traits and trait trade-offs (i.e., between
flight capability and reproduction) can be developmentally plastic in response to variation
in temperature and food availability. Our results emphasize the importance of
manipulating multiple environmental factors when studying the environmental effects on
animals (e.g., Todgham & Stillman, 2013; Kaunisto et al., 2016). Further, we encourage
work examining the effects of mean (rather than variability in) temperature on the
developmental plasticity of trait interactions. For example, the flight-related trade-offs
31
may be obviated or enhanced at warmer temperatures. Future work can build upon our
approach by examining the developmental plasticity of trait-trait interactions across
developmental stages. For example, frog eggs and larvae (i.e., tadpoles) exhibit different
developmental plasticities in response to differences in mean temperature with regard to
fitness-related traits of morphology, physiology, and locomotion (Seebacher &
Grigaltchik 2014). We also develop an experimental and statistical framework (i.e.,
factorial manipulations of environmental factors combined with PCA to reveal shifts in
correlative patterns of investment into different life-history traits) that can be applied to a
range of animal systems to investigate how environmental complexity influences traits
and trait trade-offs.
32
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