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\begin{document}

\title{Seasonal variation of brain size in a freshwater top predator}



\author[1]{Evan Versteeg}%
\author[1]{Timothy Fernandes}%
\author[1]{Matthew Guzzo}%
\author[2]{Frederic Laberge}%
\author[3]{Trevor Middel}%
\author[3]{Mark Ridgway}%
\author[1]{Bailey McMeans}%
\affil[1]{University of Toronto - Mississauga}%
\affil[2]{University of Guelph}%
\affil[3]{Ontario Ministry of Natural Resources and Forestry}%


\vspace{-1em}



  
  \date{June 03, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
1. Teleost fishes occupy a range of ecosystem and habitat types subject
to large seasonal fluctuations. Temperate fishes in particular, survive
large shifts in temperature, light availability, and access to certain
habitats across seasons. Yet, there is limited understanding of how
behavioral responses to a seasonally shifting environment might shape,
or be shaped by, the nervous system. 2. Here we quantified variation in
relative brain size and the size of five externally visible brain
regions in a freshwater top predator, lake trout (Salvelinus namaycush),
across six consecutive seasons in two different lakes. Acoustic
telemetry data from one of our study lakes was collected during the
study period from a different subset of individuals and used to infer
relationships between brain size and seasonal behaviors (habitat use and
movement rate). 3. Our results indicated that lake trout relative brain
size was larger in the fall and winter compared to the spring and summer
in both lakes. Larger brains coincided with increased use of nearshore
lake habitats and increased horizontal movement rates by lake trout in
the fall and winter based on acoustic telemetry. The telencephalon
followed the same pattern as whole brain size, while the other brain
regions (cerebellum, optic tectum, olfactory bulbs, hypothalamus) were
only smaller in the spring. 4. Seasonal shifts in total brain size might
reflect greater underlying changes in the size of the telencephalon.
These findings provide evidence that flexibility in brain size could
underpin shifts in behavior which could subserve functions associated
with differential habitat use during cold and warm seasons and allow
fish to succeed in seasonally variable temperate environments.%
\end{abstract}%



\sloppy


\textbf{Seasonal variation of brain size in a freshwater top predator}

\textbf{Abstract}

\begin{enumerate}
\tightlist
\item
  Teleost fishes occupy a range of ecosystem and habitat types subject
  to large seasonal fluctuations. Temperate fishes in particular,
  survive large shifts in temperature, light availability, and access to
  certain habitats across seasons. Yet, there is limited understanding
  of how behavioral responses to a seasonally shifting environment might
  shape, or be shaped by, the nervous system.
\item
  Here we quantified variation in relative brain size and the size of
  five externally visible brain regions in a freshwater top predator,
  lake trout (\emph{Salvelinus namaycush} ), across six consecutive
  seasons in two different lakes. Acoustic telemetry data from one of
  our study lakes was collected during the study period from a different
  subset of individuals and used to infer relationships between brain
  size and seasonal behaviors (habitat use and movement rate).
\item
  Our results indicated that lake trout relative brain size was larger
  in the fall and winter compared to the spring and summer in both
  lakes. Larger brains coincided with increased use of nearshore lake
  habitats and increased horizontal movement rates by lake trout in the
  fall and winter based on acoustic telemetry. The telencephalon
  followed the same pattern as whole brain size, while the other brain
  regions (cerebellum, optic tectum, olfactory bulbs, hypothalamus) were
  only smaller in the spring.
\item
  Seasonal shifts in total brain size might reflect greater underlying
  changes in the size of the telencephalon. These findings provide
  evidence that flexibility in brain size could underpin shifts in
  behavior which could subserve functions associated with differential
  habitat use during cold and warm seasons and allow fish to succeed in
  seasonally variable temperate environments.
\end{enumerate}



\textbf{Keywords}

Acoustic telemetry, behavior, brain regions, brain size, habitat
complexity, lake trout, phenotypic flexibility, seasonality.

\textbf{Introduction}

Teleost fishes exhibit exceptional flexibility in their behavioral and
physiological responses to changing environments, which has allowed them
to colonize an impressive number of habitats at different latitudes
(Dill 1983, Armstrong and Bond 2013). Previous work on freshwater fish
has highlighted the need to better document and resolve the role of
neural flexibility in underpinning life history strategies and ecology
among different species and populations (Gonda et al. 2013). Fish often
exhibit large variation in the proportional size of their brain regions
in association with ecological and sensory specialization (Kotrschal et
al. 1998, Gonzalez-Voyer and Kolm 2010). There is, however, a growing
body of experimental literature suggesting that novel environments can
influence fish brain size within the span of a few weeks or months (Park
et al. 2012, Herczeg et al. 2015, Turschwell and White 2016, Fong et al.
2019, Z\selectlanguage{ngerman}ávorka et al. 2020). If fish are capable of rapidly adjusting
their brain size to cope with new environments, then perhaps fish can
exhibit changes in brain size that allow them to succeed in the face of
seasonal and interannual changes in natural environments.

Seasonality in temperate lake ecosystems generates dramatic declines in
temperature and light levels during the late fall and winter due to
shifting daylight cycles and ice/snow cover. Species time the phenology
of many key life history events (e.g. \emph{Salvelinus
namaycush}reproduction; Martin 1957) and activity patterns with these
seasonal cycles. Some species, for example, suppress their activity in
response to winter conditions and wait to reproduce until the spring,
while others reproduce in the fall and remain active all winter (Shuter
et al. 2012). In either case, predictable seasonal habitat shifts are
commonplace among temperate fish species (e.g. to reach spawning sites
or overwintering areas or to access prey; Hanson et al. 2008, Shuter et
al. 2012). It is possible then, that coupled seasonal shifts in abiotic
conditions and fish behavior could increase cognitive demands during
particular seasons.

Brain and brain region size is correlated with performance in
cognitively demanding tasks (Kotrschal et al. 2013, Buechel et al. 2018)
and with variation in neuron numbers (Marhounová et al. 2019). As such,
brain size variation is generally conceived as relating to variation in
cognitive demands (i.e. sensory, motor, and integrative functions). To
date, only a single study has explored seasonal variation in the size of
one brain region (the telencephalon) in a wild fish, the round goby
(\emph{Neogobius melanostomus} ). This study found larger telencephalon
sizes during the reproductive season, which was associated with the
increased spatial processing demands of mating (McCallum et al. 2014).
It is still unknown however, whether whole brain size is seasonally
flexible in fishes, or if the size of individual brain regions can
change independently of one another across seasons.

Here, we investigated the seasonal variability in total brain mass and
individual region volumes (Figure. 1) of lake trout (\emph{Salvelinus
namaycush} ) from two lakes in Ontario, Canada. We first hypothesized
that seasonal changes in cognitive demands would drive changes in lake
trout brain size over a seasonal timescale. Second, we hypothesized that
any changes in overall brain size were either the result of: a) seasonal
demands for region-specific processing (i.e. a mosaic change in the size
of some brain regions and not others), as certain regions might be of
more or less utility depending on season-specific life history, or b)
ubiquitous changes in the size of each brain region (i.e. concerted
change in brain size; Finlay and Darlington 1995, Striedter 2005). Brain
size data were collected in both lakes over six consecutive seasons.
Trends in brain size variation were compared to habitat use and movement
rates data obtained by acoustic telemetry from one of our study lakes to
assess the relationship between seasonal patterns in brain size and
behavior.

\par\null

\textbf{Materials and methods}

\emph{Study Sites and sample collection}

Lake trout were collected seasonally from Lake of Two Rivers (hereafter
referred to as ``Two Rivers''), Ontario, Canada
(45\textsuperscript{o}34'42.6'' N, 78\textsuperscript{o}29'0.4'' W; 274
ha surface area, 38 m maximum depth) and Lake Opeongo (hereafter
referred to as ``Opeongo''), Ontario, Canada
(45\textsuperscript{o}41'46.8'' N, 78\textsuperscript{o}22'27.8'' W;
5800 ha surface area, 49.4 m maximum depth), each located within
Algonquin Provincial Park. Unlike Two Rivers, Opeongo supports pelagic
prey fish (e.g., lake cisco: \emph{Coregonus artedi} ). Mature lake
trout were sampled seasonally from fall 2017 to winter 2019 (Table 1)
using trap nets, gill nets, and angling equipment. The fish were
euthanized immediately upon capture via severing of the spinal cord, and
their lengths and weights collected (under approved University of
Toronto Animal Use Protocols). Fish heads were then removed and placed
in labelled containers with 10\% neutral buffered formalin (Fisher
Scientific Inc., New Jersey, USA). Fish that possessed undeveloped
gonads were considered immature and removed from analysis.

Following previous conventions (Guzzo et al. 2017), the period after
ice-off but before mean surface temperature (\textless{}6 m depth)
exceeded 15\selectlanguage{english}@C was denoted as spring, summer was defined as the period
during which surface temperatures reached or surpassed 15 @\selectlanguage{english}C, fall began
when lakes cooled to [?] 15@\selectlanguage{english}C and lasted until winter, defined as ice-on
to ice-off. Water temperatures were measured throughout the upper 6 m of
the water column in each lake using a string of data loggers (HOBO Temp
Pro H20-001, Onset, Cape Cod, MA) deployed over the deepest point of
each lake (Table 1).

\emph{Brain Mass and Region Volumes}

Following procedures from Edmunds et al. (2016a), the brains were
removed and trimmed of cranial nerves, and the spinal cord was cut at
the obex. Each brain was blotted thoroughly to remove excess formalin
and was then weighed (resolution of 0.0001 g) to obtain brain mass,
which was used to estimate brain size. Pictures were taken of the
dorsal, left, and ventral sides of the brain using an Olympus SZ61
dissection microscope and a Canon Powershot G9 digital camera and
PSREMOTE v1.7 software (Breeze and Breeze 2009). A calibration grid was
included in each picture. The height, length, and width of brain regions
visible on the resulting images (olfactory bulbs, telencephalon, optic
tectum, cerebellum and hypothalamus) were measured using the measuring
tool in ImageJ software (Rueden et al. 1997). Regional volumes
(mm\textsuperscript{3}) were estimated using the ellipsoid formula: V =
(L x W x H) \selectlanguage{greek}π/\selectlanguage{english}6, providing us with estimates of size for each brain
region. Volume measurements were conducted by the same person (EJV) and
measurement accuracy was assessed by repeating measurements of five
randomly selected brains ten times (Appendix S1: Table S1).

\emph{Acoustic telemetry}

Acoustic telemetry data were available from Lake of Two Rivers
throughout the study period, albeit for a different subset of lake trout
individuals (n=9). Fixed location reference tags within the array
provided a measure of array performance throughout the study period.
Estimated positions based on the Vemco (InnovaSea, WA, USA) positioning
algorithm were compared with known positions of the tags to quantify
positioning errors across seasons and years using methods proposed in
Smith (2013). Reference tags at 5 m depth had a lower average error than
tags placed at 18 m depth. Over 95\% of detections at 5 m had a
positional error of less than 6 m vs. 86\% at the 18 m depth. Overall
mean error was 2.41 m vs 5.24 m at 5 and 18 m depth, respectively.
Additional details about the telemetry array setup, performance, and
fish tagging can be found in (Appendix S2).

\emph{Statistical Analysis}

Seasonal differences in lake trout brain mass and each brain region
volume were explored using separate linear models (LM's).
Log\textsubscript{10} transformed brain mass or brain region volume was
the dependent variable, season, lake and sex were included as fixed
factors, and Log\textsubscript{10} transformed fork length as a
covariate (fork length was positively linearly related to brain mass and
the volume of the individual regions; Appendix S1: Table S2, Figure S1).
An alpha of 0.05 was used for all analyses. An interaction between lake
and season was included to evaluate if the effect of season on brain
size varied between lakes. Preliminary analyses indicated that the main
effect of sex and the interaction between sex and season did not
significantly influence the models, so sex was removed from the final
analysis. Similarly, if the main effect of sex was not a significant
parameter for a given model, it was removed. Following a significant
main effect of season, seasonal differences were identified using
Tukey's pairwise comparisons of estimated marginal means (EMMs). The
initial model for brain mass was run including all six seasons spanning
our two study years (Table 1). After finding no significant differences
between the two fall seasons or the two winter seasons (Figure 2a),
these seasons were combined (resulting in 4 seasons: spring, summer,
fall, winter) in all subsequent analyses of brain mass and individual
brain regions.

Acoustic telemetry data were modelled using generalized additive mixed
models (GAMMs; Wood 2011, 2017). After filtering the data (Appendix S3),
we performed four separate analyses on: 1) weekly mean depth, 2)
standard deviation (SD) of depth, 3) mean distance to shore, and 4) mean
horizontal movement rate (Appendix S1: Table S5). We fit two models for
each of these response variables. The first model, known as the
year-specific smoother model (YS), had separate seasonal smoothers for
each year (March 2017 -- March 2018 and March 2018 -- March 2019) by
including a factor-smoother interaction between week of the year and
year, with seasonality in behavior represented by week of the year and
treated as a cyclic cubic regression spline (knots = weeks 1-52). The
second model, known as the common smoother model, contained a single
seasonal smoother treated as a cyclic cubic regression spline common to
both years of data. In each model we included fish ID as a random
effect, and autoregressive and moving average (ARMA) correlation
structures were added to the models to account for autocorrelation. The
correlation structures used in each model, where p is the order of the
autoregressive (AR) part and q is the order of the moving average (MA),
were p = 2, q = 1 for mean depth, SD depth, and mean distance to shore
and p = 2, q = 2 for movement rate. Once the most appropriate
correlation structures were identified for each response variable, we
fit the two candidate models using maximum likelihood and ranked them
using AIC. The model with the lowest AIC value was chosen and a
difference in AIC\textless{}2 meant equal support for both models, in
which case the simpler CS model was chosen. We then refit the selected
model using restricted maximum likelihood to obtain parameter estimates
and significance (\selectlanguage{greek}α \selectlanguage{english}= 0.05).

\textbf{Results}

\emph{Hypothesis 1: Brain Size}

When including data for all six seasons sampled across our two study
years, Opeongo lake trout relative brain size decreased from fall and
winter highs to spring and summer lows before increasing again in the
fall and winter of the second study year (Figure 2a). In Two Rivers,
larger relative brain size in fall and winter was limited to the second
year of sampling (Figure 2a). Season and lake had significant effects on
lake trout relative brain size according to the LM, though there was no
significant season-lake interaction (Table 2). Fall and winter of the
first year did not significantly differ from fall and winter of the
second year (Figure 2a, Appendix S1: Table S3), and the data from each
of these seasons over two years were combined for subsequent analyses
(resulting in 4 seasons).

In the LM performed on the four seasons, season and lake had significant
effects on lake trout relative brain size (Table 2). Opeongo trout: a)
were larger than Two Rivers trout (mean fork length \selectlanguage{ngerman}± SD: 510 ± 54.6 vs.
410 ± 40.2 mm; mean body mass ± SD: 1574 ± 504 vs. 775 ± 228 g), b) had
a larger mean absolute brain size (0.832 ± 0.134 SD vs. 0.692 ± 0.134
g), but c) had a smaller relative brain size compared to Two Rivers
trout (i.e. after correcting for body size; Appendix S1: Table S2,
Figure S1). The effect of season on relative brain size did not differ
between lakes (season-lake interaction was not significant; Table 2).
Tukey's pairwise comparisons of seasonal EMMs demonstrated that relative
brain size was smaller during the spring and summer, and larger during
the fall and winter when the two study years were combined (Figure 2b,
Appendix S1: Table S3).

\emph{Hypothesis 2: Brain Region Sizes}

Next, we evaluated if changes in brain size were region-specific or if
changes involved all regions observable at the gross morphology level.
When using a LM with fork length as a covariate to correct for variation
in body size, the main effect of lake was significant: fish from Two
Rivers had relatively larger telencephala (lake: F\textsubscript{2,
134}= 250, P \textless{} 0.001), cerebella (lake: F\textsubscript{2,
134} = 260, P \textless{} 0.001), optic tecta (lake:
F\textsubscript{2,134} = 370.6, P \textgreater{} 0.001), and olfactory
bulbs (lake: F\textsubscript{2,134} = 205.4, P \textgreater{} 0.001)
than those from Opeongo. There was a significant interaction between
fork length and lake for hypothalamus size (F\textsubscript{3, 133} =
109, P = 0.003), where different allometric slopes produced Two Rivers
fish with larger hypothalami than Opeongo fish at smaller fork lengths
(Appendix S1: Figure S1).

The main effect of season and lake was also significant for all five
brain regions, yet none of the regions demonstrated a significant
interaction between season and lake. Thus, the seasonal patterns
observed in brain regions were not population-specific (Table 2).
Telencephalon size followed a trend similar to that of the whole brain
(smaller in spring and summer, larger in fall and winter; Figure 3a),
while cerebellum size was smallest during the spring, with no
significant differences between the fall, winter, and summer (Figure 3b,
Appendix S1: Table S3). Although season was a significant factor in the
LM, the optic tecum did not differ between any seasons based on the
Tukey tests (P \textgreater{} 0.06; Appendix S1: Table S4), however the
general trend in this region was for lower size in the spring (Figure
3c). Like the cerebellum, both the olfactory bulbs and the hypothalamus
were smallest during the spring, with no significant difference between
summer, fall, and winter (Figure 3d,e, Appendix S1: Table S4).

\emph{Acoustic Telemetry}

Model selection indicated that each of the movement and habitat use
metrics from Lake of Two Rivers trout were best modelled using a GAMM
with a year-specific weekly smoother rather than a common weekly
smoother (Appendix S1: Table S4). During the summer, lake trout occupied
greater depths (\textasciitilde{}12 m; Figure 4a) and were located well
over 300 meters away from shore (Figure 4b). Pronounced vertical
movement in the water column were characteristic of summer months (high
SD of depth, Figure 4c), while horizontal movements were lowest at this
time (Figure 4d). Conversely, lake trout occupied shallower depths
during the coolest weeks of the year (fall, winter, early spring: Figure
4a). Rapid depth changes occurred around weeks 20 and 40 (mid-May and
early October), with fish moving offshore to deeper water in the late
spring (immediately following the Spring sample collected in May) and
back nearshore into shallow water in early October (Figure 4a). Fish
remained in generally shallow water throughout the winter. These periods
of low depth occupancy in the spring, fall, and winter coincided with
shorter distances from shore (Figure 4b) and a low SD in depth (Figure
4c), indicating that the fish were not regularly changing position in
the water column during that time. Horizontal movement rates were lowest
in the summer and increased dramatically in the fall (Figure 4c).
Horizontal movement rates were higher in the second compared to the
first winter, however this did not seem to be due to differences in
system positioning error between winters.

\textbf{Discussion}

Our data suggest that the brain size of a temperate freshwater fish
changes within an annual timespan, mirroring seasonal changes in habitat
use and movement. Larger relative brain size in Two Rivers lake trout,
which do not have access to an offshore forage fish and as a result
forage more frequently in nearshore habitats (Vander Zanden and
Rasmussen 1996), is consistent with previous observations that fish
species and populations with a higher reliance on the nearshore habitat
have larger brains (Edmunds et al. 2016b, Axelrod et al. 2018). Seasonal
variation in relative brain size was apparent across both lakes, with
brains being smallest during the spring and summer and largest during
the fall and winter. It is unlikely that lake temperature alone was
responsible for these trends. Though larger brain sizes have been
associated with higher temperatures (and possibly metabolism; Gillooly
and McCoy 2014, Yu et al. 2014, Závorka et al. 2020), we observed that
lake trout brain size was smallest during the warmest season (summer).
Previous work has suggested that increases in brain size reflect
increased performance in cognitively demanding tasks (Kotrschal et al.
2013, Buechel et al. 2018). Our findings therefore support our first
hypothesis that fish alter their brain size seasonally, potentially in
accordance with seasonal variation in cognitive demands.

Variation in whole brain size reflects underlying variation in
individual brain regions, which were also found to vary seasonally. The
telencephalon tracked the observed changes in brain size best (larger in
the fall and winter compared with spring and summer). Larger
telencephala have been associated with higher utilization of nearshore
habitats (Gonzalez-Voyer and Kolm 2010, Edmunds et al. 2016a). In their
investigations of the round goby \emph{Neogobius melanostomus,} McCallum
et al (2014) also found larger telencephalon sizes prior to the spawning
season. As lake trout move nearshore onto spawning shoals in the fall,
they navigate more structurally heterogeneous environments, while also
coordinating complex social behaviors during mating (Johnson et al.
2018). Nearshore habitat use was maintained throughout the winter (at
least in telemetered Two Rivers lake trout), which would also be
associated with navigating under reduced light conditions (due to
reduced photoperiod and snow and ice cover; Blanchfield et al. 2009).
Larger telencephala in the fall and winter could therefore reflect
increased cognitive demands associated with spawning in the fall and
nearshore habitat use during the fall and winter. Nearshore foraging is
also known to occur in spring (Guzzo et al. 2017) however, and
telemetered lake trout in our study had just begun to move away from
shore at the time of spring sampling. Smaller telencephala in the spring
and summer could, therefore, be associated with movement into offshore,
deeper water.

Unlike the telencephalon, seasonal changes in size of the other brain
regions were limited to a decrease in size in the spring. Nearshore
habitat use is higher in fall and winter compared to summer, and
reproduction occurs in the fall (Guzzo et al. 2017). Habitat use and
spawning alone are likely insufficient to explain the size of these
brain regions, which were of similar size in fall, summer, and winter.
The smaller size of the cerebellum, olfactory bulb, optic tectum, and
hypothalamus in spring vs. fall may potentially be driven by increased
lake trout foraging and growth during the spring (Fry 1939, Guzzo et al.
2017). Large increases in total body length during spring months may
reduce the relative brain size to body length ratio; such a dilution
would be expected if brain growth lags behind patterns in
global/systemic growth. Alternatively, reduced brain sizes during the
spring may be a product of energy trade-off mechanisms that mediate
energetically expensive tissues to optimize growth potential. Trade-offs
in brain and gut size, for example, have been observed in fish and other
vertebrates (Aiello and Wheeler 1995, Kotrschal et al. 2013, Monnet et
al. 2020, Rosenfeld et al. 2020). Investment in large, energetically
expensive digestive organs in support of growth in the spring could
entail temporary reduction in brain size to allow a more effective
allocation of resources to different parts of the organism (Armstrong
and Bond 2013). Whether such organ system trade-offs happen on a
seasonal scale is unknown. Future work is required to explore how
seasonally changing cognitive and energetic demands might govern the
size of the brain and its regions.

Previous work has identified seasonal brain size flexibility. The size
of the telencephalic hemispheres and whole brain of a benthic fish and a
shrew, respectively, have exhibited distinct seasonal patterning
(McCallum et al. 2014, Lázaro et al. 2018). Seasonal variation in the
size of a specific brain region, the hippocampus, has also been noted in
birds and mammals (Yaskin 2011). However, the inability to follow the
record brain metrics from the same individuals over time represents a
central limitation to our study (and previous studies on this topic),
given that, at this time, animals must be sacrificed to measure brain
sizes. We therefore cannot discount the possibility that individuals
captured during fall and winter tended to be those individuals within
the population that had larger brains. We have also included a limited
set of ecological traits (i.e., habitat use, movement rates, fall
reproduction by using mature individuals), making our inferences about
the role of brain size in these behaviors tentative until a larger suite
of traits can be explored. Additionally, we echo previous concerns about
the uncertain role of brain size in cognition, and stress caution when
interpreting its relationship with complex behaviors (Healy and Rowe
2007). However, the fact that we observed the same seasonal trend in
both lakes, and the observation that larger relative brain masses
coincided with increased nearshore habitat use and movement rates in
telemetered Lake of Two Rivers lake trout suggest that brain size of
lake trout is seasonally flexible in support of, or in response to,
changes in behavior. More work will be needed to elucidate which
specific behaviors are impacted by seasonally flexible changes in brain
size.

\par\null

\textbf{Conclusions}

Here, we find novel evidence that brain size changes seasonally in two
wild populations of a top-predator teleost fish. The whole brain and
telencephalon sizes were smaller in spring and summer, while the other
brains regions investigated were only smaller in the spring. These
distinct seasonal cycles could subserve different functions associated
with differential habitat use during cold and warm seasons, and
differential energetic allocation of resources between tissues in
support of foraging and growth in the spring. Further investigation will
be needed to elucidate the mechanisms that drive seasonal variation in
brain size and to identify the specific roles that this phenomenon plays
in the regulation of fish behavior and physiology.

\textbf{References}

Armstrong, J. B., and M. H. Bond. 2013. Phenotype flexibility in wild
fish: Dolly Varden regulate assimilative capacity to capitalize on
annual pulsed subsidies. Journal of Animal Ecology 82:966--975.

Axelrod, C. J., F. Laberge, and B. W. Robinson. 2018. Intraspecific
brain size variation between coexisting sunfish ecotypes. Proceedings of
the Royal Society B: Biological Sciences 285.

Breeze, C., and S. Breeze. 2009. PSREMOTE. Breeze Systems Limited,
Camberley.

Buechel, S. D., A. Boussard, A. Kotrschal, W. van Der Bijl, and N. Kolm.
2018. Brain size affects performance in a reversal-learning test.
Proceedings of the Royal Society B: Biological Sciences 285.

Dill, L. M. 1983. Adaptive Flexibility in the Foraging Behavior of
Fishes. Canadian Journal of Fisheries and Aquatic Sciences 40:398--408.

Edmunds, N. B., K. S. McCann, and F. Laberge. 2016a. Food Web Structure
Shapes the Morphology of Teleost Fish Brains. Brain, Behavior and
Evolution 87:128--138.

Edmunds, N. B., F. Laberge, and K. S. McCann. 2016b. A role for brain
size and cognition in food webs. Ecology letters 19:948--955.

Finlay, B. L., and R. B. Darlington. 1995. Linked regularities in the
development and evolution of mammalian brains. Science 268:1578--1584.

Fong, S., S. D. Buechel, A. Boussard, A. Kotrschal, and N. Kolm. 2019.
Plastic changes in brain morphology in relation to learning and
environmental enrichment in the guppy ( Poecilia reticulata ). The
Journal of Experimental Biology 222:jeb200402.

Fry, F. E. J. 1939. A comparative study of the lake trout fisheries in
algonquin park, ontario. Publications of the Ontario Fisheries Reserarch
Laboratory 46:1--69.

Gillooly, J. F., and M. W. McCoy. 2014. Brain size varies with
temperature in vertebrates. PeerJ 2014:1--8.

Gonda, A., G. Herczeg, and J. Merilä. 2013. Evolutionary ecology of
intraspecific brain size variation: a review. Ecology and Evolution
3:2751--2764.

Gonzalez-Voyer, A., and N. Kolm. 2010. Sex, Ecology and the Brain:
Evolutionary Correlates of Brain Structure Volumes in Tanganyikan
Cichlids. PLoS ONE 5:1--9.

Guzzo, M. M., P. J. Blanchfield, and M. D. Rennie. 2017. Behavioral
responses to annual temperature variation alter the dominant energy
pathway, growth, and condition of a cold-water predator. Proceedings of
the National Academy of Sciences of the United States of America
114:9912--9917.

Hanson, K. C., C. T. Hasler, S. J. Cooke, C. D. Suski, and D. P.
Philipp. 2008. Intersexual variation in the seasonal behaviour and depth
distribution of a freshwater temperate fish, the largemouth bass.
Canadian Journal of Zoology 86:801--811.

Herczeg, G., A. Gonda, G. Balázs, K. Noreikiene, and J. Merilä. 2015.
Experimental evidence for sex-specific plasticity in adult brain.
Frontiers in Zoology 12:1--7.

Huber, R., M. J. Van staaden, L. S. Kaufman, and K. F. Liem. 1997.
Microhabitat use, trophic patterns, and the evolution of brain structure
in african cichlids. Brain, Behavior and Evolution 50:167--182.

Johnson, N. S., D. Higgs, T. R. Binder, J. Ellen Marsden, T. Buchinger,
L. Brege, T. Bruning, S. Farha, and C. C. Krueger. 2018. Evidence of
sound production by spawning lake trout (Salvelinus namaycush) in lakes
huron and champlain. Canadian Journal of Fisheries and Aquatic Sciences
75:429--438.

Kotrschal, A., B. Rogell, A. Bundsen, B. Svensson, S. Zajitschek, I.
Brännström, S. Immler, A. A. Maklakov, and N. Kolm. 2013. Artificial
selection on relative brain size in the guppy reveals costs and benefits
of evolving a larger brain. Current Biology 23:168--171.

Kotrschal, K., M. J. Van staaden, and R. Huber. 1998. Fish brains\selectlanguage{english} :
evolution and environmental relationships. Reviews in Fish Biology and
Fisheries 8:373--408.

Lazaro, J., M. Hertel, C. C. Sherwood, M. Muturi, and D. K. N. Dechmann.
2018. Profound seasonal changes in brain size and architecture in the
common shrew. Brain Structure and Function 223:2823--2840.

Marhounova, L., A. Kotrschal, K. Kverkova, N. Kolm, and P. N\selectlanguage{polish}ě\selectlanguage{english}mec. 2019.
Artificial selection on brain size leads to matching changes in overall
number of neurons. Evolution 73:2003--2012.

Martin, N. V. 1957. Transactions of the American Fisheries Society
Delineation of Sympatric Morphotypes of Lake Trout in Lake Superior.
Transactions of the American Fisheries Society 86:231--244.

McCallum, E. S., P. M. Capelle, and S. Balshine. 2014. Seasonal
plasticity in telencephalon mass of a benthic fish. Journal of Fish
Biology 85:1785--1792.

Park, P. J., I. Chase, and M. A. Bell. 2012. Phenotypic plasticity of
the threespine stickleback Gasterosteus aculeatus telencephalon in
response to experience in captivity. Current Zoology 58:189--210.

Pollen, A. A., A. P. Dobberfuhl, J. Scace, M. M. Igulu, S. C. P. Renn,
C. A. Shumway, and H. A. Hofmann. 2007. Environmental complexity and
social organization sculpt the brain in Lake Tanganyikan cichlid fish.
Brain, Behavior and Evolution 70:21--39.

Rueden, C., W. Rasband, C. Dietz, B. Northan, M. Hiner, L. Kamentsky, J.
Scindelin, A. Grislis, B. Dezonia, G. Harris, A. Fraser, and R. Lentz.
1997. Image J. SciJava.

Shuter, B. J., A. G. Finstad, I. P. Helland, I. Zweim\selectlanguage{ngerman}üller, and F.
Hölker. 2012. The role of winter phenology in shaping the ecology of
freshwater fish and their sensitivities to climate change. Aquatic
Sciences 74:637--657.

Smith, F. 2013. Understanding HPE in the VEMCO Positioning System (VPS).
Pages 1--31 Understanding HPE in the VEMCO Positioning System (VPS).

Striedter, G. F. 2005. Principles of Brain Evolution. Sinauer
Associates.

Turschwell, M. P., and C. R. White. 2016. The effects of laboratory
housing and spatial enrichment on brain size and metabolic rate in the
eastern mosquitofish, Gambusia holbrooki. Biology Open 5:205--210.

Wood, S. N. 2011. Fast stable restricted maximum likelihood and marginal
likelihood estimation of semiparametric generalized linear models.
Journal of the Royal Statistical Society. Series B: Statistical
Methodology 73:3--36.

Wood, S. N. 2017. Generalized additive models: an introduction with R.
CRC press.

Yaskin, V. A. 2011. Seasonal changes in hippocampus size and spatial
behavior in mammals and birds. Biology Bulletin Reviews 1:279--288.

Yu, Y., J. Karbowski, R. N. S. Sachdev, and J. Feng. 2014. Effect of
temperature and glia in brain size enlargement and origin of allometric
body-brain size scaling in vertebrates. BMC Evolutionary Biology
14:1--14.

Vander Zanden, M. J., and J. B. Rasmussen. 1996. A Trophic Position
Model of Pelagic Food Webs: Impact on Contaminant Bioaccumulation in
Lake Trout. Ecological Monographs 66:451--477.

Závorka, L., B. Koeck, T. A. Armstrong, M. So\selectlanguage{polish}ğ\selectlanguage{english}anci, A. Crespel, and S.
S. Killen. 2020. Reduced exploration capacity despite brain volume
increase in warm-acclimated common minnow. The Journal of Experimental
Biology 223:jeb223453.

\textbf{Tables}

\textbf{Table 1.} Sample sizes of lake trout for each season (n) and the
corresponding date range within which they were sampled from lakes
Opeongo and Two Rivers, Ontario. The mean daily surface water
temperature (\textless{} 6 m depth) is provided unless the loggers had
been removed due to ice cover.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Lake} & \textbf{Season} & \textbf{Sample Period} &
\textbf{Temp.} @\selectlanguage{english}\textbf{C} & \textbf{n}\tabularnewline
Opeongo & Fall 2017 & Oct. 17 - Nov. 2 & 12 & 9\tabularnewline
& Winter 2018 & March 13-16 & Ice cover & 12\tabularnewline
& Spring 2018 & May 11-17 & 4 & 16\tabularnewline
& Summer 2018 & Aug. 31 - Sept. 10 & 20 & 22\tabularnewline
& Fall 2018 & Oct. 30 -- Nov. 2 & 10 & 14\tabularnewline
& Winter 2019 & March 18-21 & Ice cover & 11\tabularnewline
Two Rivers & Fall 2017 & Oct. 30 & 11 & 6\tabularnewline
& Winter 2018 & March 19-21 & Ice cover & 6\tabularnewline
& Spring 2018 & May 8-9 & 4@ & 20\tabularnewline
& Summer 2018 & Aug. 20-21, Sept. 12-15 & 23, 19 & 17\tabularnewline
& Fall 2018 & Oct. 30 - Nov. 1 & 6 & 21\tabularnewline
& Winter 2019 & March 12-15 & Ice cover & 6\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Results of linear models exploring the main effects of
season, lake, and the season-lake interaction on total brain mass and
the volumes of individual regions (telencephalon, cerebellum, optic
tectum, olfactory bulb, and hypothalamus). Changes in brain mass were
first explored across all 6 continuous seasons (i.e., fall, winter,
spring, summer, fall, winter) before combining data into 4 seasons
(i.e., fall, winter, spring, summer) for subsequent comparisons of brain
mass and regional volumes.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Variable} & \textbf{Season} & \textbf{Lake} &
\textbf{Season*Lake}\tabularnewline
Brain mass (continuous) & P \textless{} 0.001 & P \textless{} 0.001 & P
= 0.180\tabularnewline
& F\textsubscript{5,128} = 13.7 & F\textsubscript{1,127} = 0.86 &
F\textsubscript{5,120}=1.55\tabularnewline
Brain mass (combined) & P = 0.003 & P \textless{} 0.001 & P =
0.32\tabularnewline
& F\textsubscript{3,130} = 5.00 & F\textsubscript{1,129} = 97.73 &
F\textsubscript{3,127} = 1.26\tabularnewline
Telencephalon volume & P \textless{} 0.001 & P \textless{} 0.001 & P =
0.311\tabularnewline
& F\textsubscript{3, 130} = 8.80 & F\textsubscript{1, 129} = 17.2 &
F\textsubscript{3, 124} = 1.2\tabularnewline
Cerebellum volume & P \textless{} 0.001 & P \textless{} 0.001 & P =
0.850\tabularnewline
& F\textsubscript{3, 130} = 10.5 & F\textsubscript{1, 129} = 99.9 &
F\textsubscript{3, 124}= 0.27\tabularnewline
Optic tectum volume & P = 0.017 & P \textgreater{} 0.001 & P =
0.785\tabularnewline
& F\textsubscript{3, 130} = 3.55 & F\textsubscript{1, 129} = 76.1 &
F\textsubscript{3, 124} = 0.36\tabularnewline
Olfactory bulb volume & P \textgreater{} 0.001 & P = 0.001 & P =
0.619\tabularnewline
& F\textsubscript{3, 130} = 6.09 & F\textsubscript{1, 129} = 10.8 &
F\textsubscript{3, 124}= 0.600\tabularnewline
Hypothalamus volume & P = 0.007 & P \textgreater{} 0.001 & P =
0.715\tabularnewline
& F\textsubscript{3, 130} = 4.17 & F\textsubscript{1, 129} = 85.8 &
F\textsubscript{3, 124} = 0.45\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Lake trout (top) and a lake trout brain with letters
indicating the regions measured in the present study (bottom). The
olfactory bulbs (A) process olfactory information. The telencephalon (B)
is primarily associated with spatial navigation and learning. The optic
tectum (C) processes visual and multimodal sensory information used for
orientation. The cerebellum (D) is associated with motor coordination
and learning. The hypothalamus (E) is involved in behavioral and
neuroendocrine control (Huber et al. 1997, Pollen et al. 2007).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Seasonal variation in relative brain size in lake
trout. The curves show estimated marginal means and 95\% confidence
intervals (shaded ribbons) of brain mass corrected for fork length in
trout from Lake of Two Rivers (red, solid line) and Lake Opeongo (blue,
dashed line). A) Model of relative brain size variation across six
consecutive seasons. B) Model combining two fall and winter sampling
seasons. Different letters denote significant seasonal differences (P
\textless{} 0.05) obtained by Tukey's test.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Seasonal variation in relative brain region size in lake
trout. The curves show estimated marginal means and 95\% confidence
intervals (shaded ribbons) of telencephalon (a), cerebellum (b), optic
tectum (c), olfactory bulbs (d), and hypothalamus (e) volumes corrected
for fork length in trout of Lake of Two Rivers (red, solid line) and
Lake Opeongo (blue, dashed line). Different letters denote significant
seasonal differences (P \textless{} 0.05) obtained by Tukey's test of
models combining two fall and winter sampling seasons.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4. Seasonal variation in lake trout behavior assessed by
telemetry. The weekly mean depth (A), distance to shore (B), standard
deviation (SD) of depth (i.e., vertical activity; C), and movement rates
(D) are shown. The trout were tagged with acoustic tags and tracked
using 3D telemetry during June 9, 2017 - June 7, 2019 in Lake of Two
Rivers, Ontario. The lines represent mean fits and ribbons are the 95\%
CI estimated using generalized additive mixed effects models with
year-specific cyclic cubic regression spline smoothers. Vertical lines
represent the sampling periods of trout used to measure brain size. The
thicker blue line for summer represents a longer sampling period during
that season (dates provided in Table 1).

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

