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\begin{document}

\title{Increased importance of cool-water fish at high latitudes emerges from
individual level responses to warming}



\author[1]{Aslak Smalås}%
\author[1]{Raul Primicerio}%
\author[2]{Kimmo Kahilainen}%
\author[3]{Petr Terentyev}%
\author[3]{Nikolay Kashulin}%
\author[3]{Elena Zubova}%
\author[1]{Per-Arne Amundsen}%
\affil[1]{UiT The Arctic University of Norway}%
\affil[2]{University of Helsinki}%
\affil[3]{Institute of the Industrial Ecology Problems of the North of the Kola Science Centre Russian Academy of Sciences}%


\vspace{-1em}



  
  \date{March 13, 2025}


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\selectlanguage{english}
\begin{abstract}
1. High latitude ecosystems are experiencing the most rapid warming on
earth, expected to trigger a diverse array of ecological responses.
Climate warming affects the ecophysiology of fish, and fish close to the
cold end of their thermal distribution are expected to increase somatic
growth from increased temperatures and a prolonged growth season, which
in turn affects maturation schedules, reproduction and survival,
boosting population growth. Accordingly, fish species living in
ecosystems close to their northern range edge should increase in
numerical importance and possibly displace cold-water adapted species.
2. We aim to document if and how population level effects of warming
mediated by individual level responses to increased temperatures, shift
community structure and composition in high latitude ecosystems. 3. We
studied 11 cool-water adapted freshwater fish populations in communities
dominated by cold-water adapted species to investigate changes in the
relative importance of cool-water fish during the last 30 years of rapid
warming in high latitude lakes. In addition, we studied the individual
level responses to warming to clarify the potential mechanisms
underlying the population effects. 4. Our long-term series` (1991-2020)
reveal a marked increase in numerical importance of the cool-water fish
species, perch, in ten out of eleven populations, and in most fish
communities the cool-water species is now dominant. Moreover, we show
that climate warming affects population level processes via direct and
indirect temperature effects on the individuals. Specifically, the
increase in abundance arises from increased survival of 0+ individuals,
faster juvenile growth and ensuing earlier maturation, all boosted by
climate warming. 5. The speed and magnitude of the response to warming
in these high latitude fish communities strongly suggest that cold-water
fish will be displaced by fish adapted to warmer water. Consequently,
management should focus on climate adaptation limiting future
introductions and invasions of cool-water fish and mitigating harvesting
pressure on cold-water fish.%
\end{abstract}%



\sloppy


\textbf{Introduction}

High latitude regions are experiencing the most rapid warming on Earth,
a trend projected to continue towards year 2100 (Parmesan 2006, IPCC
2013). Rapid warming strongly affects freshwater ecosystems (O'Reilly et
al. 2015), leading to changes in species abundance, phenology and
distribution (Hickling et al. 2006, Comte et al. 2013, Campana et al.
2020). Temperature driven changes in physiological rates of ectotherms
are expected to trigger a diverse array of ecological responses (Biro et
al. 2007, Huss et al. 2019, Rezende et al. 2019), with implications for
ecosystem structure and function (Benateau et al. 2019). Fish are
strongly influenced by ambient temperature, with species displaying
distinctive thermal niches (Hayden et al. 2014). Climate warming tends
to favor fish populations currently experiencing the cold end of their
species thermal range, typically in proximity of the northern limits of
a species distribution (Ficke et al. 2007, Campana et al. 2020). As
temperature increases, these populations are likely to outperform
competing species of colder temperature guilds (Hein et al. 2014, Hayden
et al. 2017). Evidence in support of, or against, these expectations is
presently lacking due to a paucity of long-term ecological studies of
freshwater fish communities in the rapidly warming Arctic (Amundsen et
al. 2019, Zubova et al. 2020).

The impact of increasing temperatures on fish populations is mediated by
direct ecophysiological effects and indirect life history responses that
ultimately affect survival and reproduction. In ectotherms, temperature
limits biological rates, affecting for instance food intake and
metabolism and their balance determining the net energy gain of an
organism (Jobling 2002). Growth rate will therefore depend on ambient
temperature, with maximum growth being reached at an intermediate,
optimum temperature within the thermal niche of a species (Gvo\selectlanguage{polish}ž\selectlanguage{english}d\selectlanguage{ngerman}ík
2018). The growth rate of individuals living at temperatures below their
optimum might increase with climate warming, given sufficient food
availability (Huss et al. 2019, Smalås et al. 2020). Higher juvenile
growth rates lead to larger size at age and earlier maturation. Larger
size at age might increase survival, especially during early life
stages, because mortality in fish is largely size-dependent (Hurst 2007,
Perez \& Munch 2010). Thus, faster growth increases the probability of
reaching maturity, which is further enhanced by earlier maturation,
overall resulting in higher transition rates to the adult, reproductive
stage. Recruitment rates can be further enhanced by faster somatic
growth as young adult females become larger, thereby producing larger
clutches, thus climate warming is likely to boost population fecundity
(Heibo et al. 2005). In addition, some critical life stages, in
particular the egg and larvae, often have a narrower temperature range
for survival and successful development than other life stages (Dahlke
et al. 2020). Populations living close to the northern end of their
distribution, might in colder years suffer from temperatures that are
too low for successful development, especially in critical periods such
as survival over the first winter, and should therefore benefit from
climate warming (Hurst 2007, Dahlke et al. 2020).

The effects of climate warming on high latitude lakes go beyond
increasing water temperatures, and predicted changes in the aquatic
environments such as increased productivity, decreased dissolved oxygen
levels and altered seasonality, are likely to favor cool-water species
more than cold-water fish (Ficke et al. 2007, Rolls et al. 2017).
Increase in temperature and productivity will expectedly first favor
percids, and later cyprinids, over salmonids (Hayden et al. 2017). These
cool-water species have been shown to redistribute northwards and to
higher altitudes over the last few decades of rapid warming (Comte et
al. 2013, Hayden et al. 2014, Rolls et al. 2017). Therefore, it exists
an urge to document changes, but also to understand the mechanisms
behind climate driven changes in high latitude fish communities in order
to develop climate adaptation strategies that mitigate the possible
eradication or displacement of cold-water species in the Arctic. One
cool-water species moving northwards is the European perch (\emph{Perca
fluviatilis} ), hereafter perch, (Hayden et al. 2014), it has its
northern range edge in subarctic regions of Eurasia around 70°N, but
with a wide temperature range for growth, between 5-33°C, and an optimum
between 16-27 °C (Hokanson 1977, Karås 1990) (more detailed information
in Supplementary information).

Here, we address the effects of climate warming on perch populations at
the northern end of the species distribution, using long-term surveys of
high latitude freshwater fish communities (68-70°N). As a cool-water
adapted species, we expect perch to benefit from increasing temperatures
and a prolonged productive season, leading to increased abundance and
importance relative to cold-water adapted fish co-inhabiting the sampled
lakes. Several mechanisms underlie our expectation of an increase in the
relative importance of perch following the recent rapid warming.
Specifically, we focus on the life history and ecological implications
of temperature-induced increase in somatic growth rate, anticipating
that higher growth rates i) reduce the duration of critical life stages,
ii) increase size at age, and iii) anticipate maturation age; overall
improving survival and increasing recruitment rates and total population
fecundity (see Supplementary information, Fig. S2 for schematic
representation of mechanisms).

\textbf{Material and Methods}

\emph{Study area}

The study area is concentrated in northern Fennoscandia, with lakes
situated in Norway, Finland and Russia. All lakes are located north of
the Arctic Circle, towards the northern end of the distribution of
cool-water fish species (\textgreater{} 68\selectlanguage{english}@N) (See Supplementary
information, Fig. S3, for map of the study lakes and area). To assess
the relative importance of Eurasian perch, we compiled data from 11
lakes sampled on multiple occasions over the last 30 years. The eleven
study lakes were sampled between 2 and 26 times, with time series
ranging from 8 to 32 years (Appendix Table S1).

\emph{Fish sampling and individual data}

The total number of sampled fish across the 11 lakes was nearly 60 000,
of which 12~000 were perch, and the littoral catches from this data were
used to describe the development in proportion of perch in the 11 lakes
(Appendix Table S1, the fish community of the individual lakes are
described in the Supplementary information). Fish were collected using
multimesh gill-nets or gill net series in all three sampled habitats
(i.e. littoral, pelagic, profundal) of the lakes, but only fish from the
littoral zone were included in the analyses because the sampling in the
other habitats was more scattered in both time and space (See
supplementary information for details on the fish sampling procedure).
We used individual data from the two lakes most frequently and
intensively sampled (Lake Vaggatem and Lake Skrukkebukta), to assess
changes in life history parameters over time in the perch populations.
These two lakes were selected because of their long time series
(\textasciitilde{}1991-2020) with mostly annual sampling, including
daily data on water temperature, and detailed individual data collected
from year 2003 to 2020. A total of 2960 individual perch from Lake
Vaggatem (n=38-608 per year) and Lake Skrukkebukta (n=30-130) were
sampled. The individual fish sampling procedure and summary information
on individual- and population level data are described in the
supplementary information Appendix 1 (see Tables S2 \& S3).

\emph{Temperature}

For the two main study systems, Lake Skrukkebukta and Lake Vaggatem,
daily water temperature measurements were available from an automated
datalogger at the Skogfoss hydropower plant situated 25 km upstream from
Lake Skrukkebukta and 23 km downstream from Lake Vaggatem. We calculated
annual mean water temperature, mean summer temperature (Jun-Jul-Aug) and
mean autumn temperature (Sep-Oct-Nov) from the logger data. Mean annual
water temperature has increased significantly by 0.3\selectlanguage{ngerman}°C per decade
(p\textless{}0.01, F=9.361\textsubscript{1,25},
adj-R\textsuperscript{2}= 0.243, Appendix Table S4), mean autumn
temperature (September-November) has also increased significantly by 0.4
°C per decade (p\textless{}0.01, F=11.26\textsubscript{1,26},
adj-R\textsuperscript{2}= 0.275, Appendix Table S5), and mean summer
water temperature increased (June-August) by 0.4 °C per decade (Fig. 1)
(p=0.037, F=4.87\textsubscript{1,25}, adj-R\textsuperscript{2}= 0.13,
Appendix Table S6).

\emph{Statistical analysis}

The proportion of perch in the fish community was calculated as the
number of perch caught relative to the total number of fish caught in
the littoral zone of the different lakes. To estimate how the proportion
of perch in the catches changed over time we used a Linear Mixed Effect
model (LME) with location (lakes) as random effect, using the
nlme-package in R. The response variable was log(x+1) transformed.
Relative density of fish in terms of Catch-Per-Unit-Effort (CPUE) was
readily available for the two main study systems, Lake Skrukkebukta and
Lake Vaggatem. The CPUE was calculated as the number of fish caught per
100 m\textsuperscript{2} per night (or 12 hours). To investigate the
change in relative density over time, we estimated separate linear
models for the two lakes, with log(CPUE+1) as the response and year as
the predictor. To estimate the relationship between relative density and
water temperature, we again used CPUE on a natural logarithmic scale
with annual mean water temperature as predictor in a linear regression
model. We transformed the predictor to a weighted-moving-average over
the last two years with more weighting given to the latest year, to
better reflect any long-term effects of temperature on relative density
of perch. To investigate recruitment in the perch population, we used
the relative proportion of one-year-old individuals within the perch
population as a measure of the number of individuals surviving the first
winter, which is suggested to be dependent on water temperature (Dahlke
et al. 2020), but also food availability (or density of competitors) and
density of predators their first growth season. Relative proportion of
one-year-old individuals is calculated as the proportion of one-year-old
perch relative to the total CPUE of the perch populations. We used
linear regression with summer water temperature the preceding year on a
natural logarithmic scale to predict the relative proportion of
one-year-old individuals in the perch populations. In the statistical
analyses, we combined both lakes as the number of sampling points were
too low to treat them separately.

\emph{Back-calculated length at age and length increment}

The back-calculated length increment was estimated for a sub-sample of
perch from Lake Vaggatem and Lake Skrukkebukta. On the opercular bones
of individual perch, we measured the width of annual growth increments
as the distance between the opaque zones. We used these measurements in
addition to the total opercular length and the body length of the
individual perch to estimate length-at-age with the nonlinear
body-proportional hypothesis method. This method is commonly used for
perch (Thoresson 1996) and assumes that the deviation in body size from
the expected body size given by the operculum size does not change
through life (Thoresson 1996),

\(L_{a}=L_{A}({\frac{O_{a}}{O_{A}})}^{\beta_{1}}\),

where \(L_{a}\) is the back-calculated length-at-age a,
\(O_{a}\) the measured operculum radius at age a,
\(O_{A}\) the observed operculum size at time of capture, and
\(L_{A}\) the observed fish length at time of capture.
\(\beta_{1}\) is the linear regression slope coefficient estimated
from the log-log relationship between the body length and operculum
length at capture. A total of 1646 perch were used in the
back-calculation procedure. We back-calculated length increment
(mm·year\textsuperscript{-1}) for juvenile fish in the age group of 1-4
years with individual perch ranging from age 2 to 10 years, giving us
length increment data from year 1995 to year 2018. We did not asses
growth during the sampling year, because those estimates would be
dependent on sampling time within the year, which was not exactly the
same every year. A comparison between back-calculated length at final
winter before capture and length at capture revealed a good fit of the
back-calculation model (p\textless{}0.001, F= 2501 on 1 and 1644 df,
adj-R\textsuperscript{2}=0.94) (Appendix Fig. S6 \& Table S9).
Back-calculated length increment (mm·year\textsuperscript{-1}) for 1, 2,
3 and 4 year old perch were related to summer water temperature and
relative density in a Linear Mixed Effect model (LME) with sampling year
and age at capture as random effects, with the nlme-package in R. In
addition, we estimated cohort-mean (year class) length increment from
age 1 to 4, which was related to mean summer water temperature (°C) and
mean relative density (CPUE) for the same time period (three-year
moving-average) with linear regression.

\emph{Age at maturity}

We estimated age at maturity (A\textsubscript{50}, age at 50\%
probability of the individuals have reached maturation age) for each
cohort (year-class) with sufficient data in the time series for perch in
both Lake Vaggatem (n\textsubscript{cohorts}= 12) and Lake Skrukkebukta
(n\textsubscript{cohorts}= 4) (Appendix Fig. S13) using logistic
regression. We related the estimated cohort-specific
A\textsubscript{50}to the estimated total cohort-specific length
increment (age 1 to age 4 year old) using linear regression. We
estimated cohort-specific\(A_{50}\) to address how environmental
variables (water temperature and relative density) indirectly affected
age at maturity mediated through individual juvenile growth. In
addition, we estimated maturation age separately for males and females
to explore if it differed between the sexes and if the proportion of
males and females changed over time (Appendix 8, Fig. S11 \& S12).

Age at maturation is assumed to be plastic and depending on a
probabilistic maturation reaction norm (PMRN) describing the length- and
age-specific probabilities of maturation (Heino et al. 2002). To
illustrate how age at maturity changes with differing individual growth
rates and to highlight the population response to altered individual
growth rates, we calculated the PMRN from the long-term data on perch in
the Pasvik watercourse (See details on PMRN estimation routine in the
Supplementary Information, Appendix 8).

To investigate causal relationship between environmental variables and
age at maturation (A\textsubscript{50}) we used structural equation
modelling (SEM) with the ``piecewiseSEM'' package in R. We constructed
the SEM to assess direct and indirect effects of summer water
temperature and relative density on age at maturation
(A\textsubscript{50}) mediated through mean length increment
(mm·year\textsuperscript{-1}) from age 1 to age 4 year old perch. Summer
water temperature and relative density of perch were modelled as
exogenous random variables, influencing other variables, but not
themselves being influenced by other variables. The biotic variable
length increment (from age 1 to age 4, mm·year\textsuperscript{-1}) was
included as endogenous variable influenced by others and itself also
influencing other variables. Finally, age at maturity
(A\textsubscript{50}) was set as a response endogenous variable,
influenced by all other variables, but not influencing other variables.
All variables were standardized prior to the analysis. Figures and maps
were created by using the ggplot-package in R or BioRender.com, and
tables were made using the Sjplot-package in R.

\textbf{Results}

\emph{The proportion of perch is increasing in high latitude lakes}

The proportion of perch in the littoral zone of the sampled lakes
substantially increased over the study period (Fig. 2a). All lakes with
more than two sampling points showed an overall increase in proportion
of perch with time. The overall mean trend reveals that the proportion
of perch increased exponentially over time (p\textless{}0.01,
mar-R\textsuperscript{2}=0.356, Table S10), from under 10\% in the early
1990s to above 70\% in most lakes during the last decade, however with
large variation between lakes (Fig. 2a). The relative density (CPUE)
data in Lake Skrukkebukta and Lake Vaggatem shows a similar trend as the
overall proportion data (Fig. 2b), with a substantial increase in
relative density of perch in the littoral zone of both lakes
(Skrukkebukta: t=8.014 on 22 d.f., p\textless{}0.01,
adj-R\textsuperscript{2}= 0.73. Vaggatem: t=4.042 on 24 d.f.,
p\textless{}0.01, adj-R\textsuperscript{2}= 0.38, Appendix Table S11 \&
S12). This was related to an increase in water temperature where
relative density of perch increased with annual mean water temperature
(Weighted-Moving-Average over the last two years) in both Lake
Skrukkebukta and Lake Vaggatem (Fig. 2c). In Lake Skrukkebukta the
relative density has increased with 1.83
ln-CPUE·°C\textsuperscript{-1}of temperature increase (t=3.788 on 20
d.f., p\textless{}0.01, adj-R\textsuperscript{2}=0.389, Appendix Table
S13), while in Lake Vaggatem, the increase was weaker, but significant
with an increase of 0.89 ln-CPUE·°C\textsuperscript{-1} of temperature
increase (t=3.788 on 20 d.f., p=0.048, adj-R\textsuperscript{2}=0.141,
Appendix Table S14).

\emph{Survival of juveniles increases with summer water temperature}

We used the relative proportion of 1-year olds in the perch population
in Lake Vaggatem and Lake Skrukkebukta as a proxy for number of
individuals surviving the first critical winter. The relative proportion
of one year old perch increased with mean summer water temperature
(Jun-Aug) the preceding year (Fig. 3). The relative proportion of one
year old perch was significantly related to temperature
(p\textless{}0.001, adj-R\textsuperscript{2}=0.35), increasing by 3.2 \%
per degree centigrade increment in preceding year summer water
temperature (°C) (t=3.550 on 21 d.f., p\textless{}0.001, Appendix Table
S15).

\emph{Faster juvenile growth with higher summer water temperature}

The overall trend for the juvenile perch was that annual length
increment increased with summer water temperature and decreased with
relative density of perch in both Lake Vaggatem and Lake Skrukkebukta
(Fig. 4, Appendix Fig. S7-10). The combined length increment
(mm·year\textsuperscript{-1}) from age 1 to age 4 year old perch
increased substantially with increasing 3-year-mean summer water
temperature and decreased similarly with an increase in 3-year-mean
relative density (Fig. 5a \& 5b). The combined length increment (age 1
to 4 year) for Lake Vaggatem and Lake Skrukkebukta perch was
significantly related to temperature and density in a linear regression
model (p=0.004, adj-R\textsuperscript{2}=0.28), increasing by 8.5 mm per
degree centigrade of temperature increment (t=2.481 on 31 d.f., p=0.019)
and decreased by 6.8 mm per 10 CPUE increment (t=-3.806 on 31 d.f.,
p=0.001) (Appendix Table S16). In addition, there was a difference in
intercept between the lakes, where the length increment was larger in
Lake Vaggatem compared to Lake Skrukkebukta (Appendix Table S16).
However, for the individual age groups, the effect of water temperature
and relative density on length increment varied. For the youngest age
group (1 year old) there was no significant change in length increment
(mm·year\textsuperscript{-1}) with either increasing summer water
temperature or relative density of perch (Fig. 4, Appendix: Fig. S7,
Table S17 \& S18). For all the other juvenile age groups (2 to 4 year
old), length increment (mm·year\textsuperscript{-1}) increased
significantly with increasing summer water temperature in both lakes,
whereas only in Lake Vaggatem length increment decreased significantly
with relative density of perch (Fig. 4, Appendix: Fig. S6-S10, Table
S19-S24) (See Supplementary information for more detailed description of
results).

\emph{Earlier maturation age with faster growth}

Age at maturation (A\textsubscript{50}) differed between males and
females in the perch populations in Lake Vaggatem and Lake Skrukkebukta;
males matured on average at an age of 4.1 years whereas females matured
on average at an age of 7.5 years (Appendix Fig. S11). The difference in
age at maturity between the sexes did not change over time (t=-0.767 on
12 d.f., p=0.45) (Appendix Fig. S12). The observed increase in combined
length increment (mm·1-4years\textsuperscript{-1}) substantially
affected the cohort specific age at maturation (A\textsubscript{50})
negatively (Fig. 5c), with -0.8 years reduction per cm increase in
length increment (t=-3.783 on 14 d.f., p=0.002,
adj-R\textsuperscript{2}=0.47) (Appendix Table S25 \& Fig. S13). The
effect of summer water temperature and relative density on age at
maturity was mediated through growth (length increment) for perch in
Lake Vaggatem and Lake Skrukkebukta, as illustrated by the structural
equation model (SEM) results (Fig 6a). We found a positive effect of
cohort-specific (age 1 to age 4 year old) mean summer water temperature
and a negative effect of relative density of perch in the same time
period on length increment of perch from age 1 to age 4 year, which
further affected age at maturity (A\textsubscript{50}) negatively (Fig.
6a). Figure 6b illustrates these relationships theoretically, where
individuals with higher growth rate, due to temperature increase or
reduced density, will reach maturation age earlier than populations
experiencing lower growth rates according to the estimated PMRN from the
perch populations in Lake Vaggatem and Lake Skrukkebukta.

\textbf{Discussion}

We find that the numerical importance of perch in fish communities at
the northern edge of its distribution increased substantially during the
last three decades of rapid warming. The positive trend was registered
in ten out of the eleven lakes investigated. For our main two study
lakes, Lake Vaggatem and Lake Skrukkebukta, the trend was driven by an
increment in perch density associated with the increase in water
temperature. These two perch populations showed higher recruitment with
warming, with the relative proportion of 1 year old fish in catches
increasing significantly with an increment in mean summer water
temperature. The demographic responses to warming were concomitant with
individual level effects on somatic growth, which increased with
temperature across most young age classes, resulting in earlier
maturation. The latter is an indirect effect of warming mediated by
increased temperature-dependent growth rate of juveniles, an
eco-physiological response, and phenotypic plasticity in maturation
schedules, a life history adaptation. The resulting earlier maturation
and larger size at age of juveniles help explain the increased
recruitment rates promoting perch population density at higher
temperatures. Somatic growth displayed negative density-dependence,
which may mask individual and demographic responses to warming in field
studies.

During the recent period of warming, the increased numerical importance
of perch was accompanied by positive temperature effects on juvenile
growth in our two reference lakes. The yearly mean growth significantly
increased with temperature across all investigated juvenile age classes,
with the exception of the 1 year old age group. The cohort mean
cumulative length increment from age 1 to 4 years increased by 8.5 mm
(\textasciitilde{}12\%) per degree centigrade increment in summer water
temperature. Individual perch growth displayed substantial negative
density-dependence, decreasing by 6.8 mm per 10 CPUE units increase in
relative density, in line with earlier findings for this and other
species (Byström \& García-Berthou 1999, Olin et al. 2017). The observed
positive effect of temperature on perch somatic growth was expected
considering that in our lakes mean summer water temperature varied
between 10-14 °C, which, although within the species temperature
tolerance range (Karås \& Thoresson 1992), is well below the optimum
temperature for perch growth, estimated to be within 16-27°C (Hokanson
1977). Positive effects of higher summer water temperatures on perch
growth rates have been described in regions where temperature
variability is within the thermal tolerance range of the species
(Jeppesen et al. 2012, Huss et al. 2019), where the increased size at
age was maintained also in adult age classes owing to faster growth in
young stages (Huss et al. 2019, Gårdmark et al. 2020). The projected
future increase in ambient temperatures will increase metabolic demands
(Huey \& Kingsolver 2019), but at high latitudes, lake productivity
mediated by catchment greening is expected to increase with warming
(O'Beirne et al. 2017), and should ensure sufficient food availability
to support growth (Kao et al. 2015). The documented and projected
positive effects of warming on growth of perch living at its northern
range edge affect its life history, demography, and ecological
interactions.

The faster growth induced by warming resulted in earlier maturation of
perch. The indirect effect of warming, estimated and summarized via a
structural equation model, is substantial, with maturation age
(A\textsubscript{50}) decreasing by 0.8 years per cm increase in
juvenile length increment (from age 1 to 4 years). The adaptive plastic
response in maturation age is dependent on the eco-physiological process
of somatic growth, which in turn is affected by the ambient temperature
and food availability (Ward et al. 2017). A reduction in maturation age
as a consequence of increased growth has been documented repeatedly in
fish (Reznick 1993, Haugen 2000). Furthermore, an increase in juvenile
growth rate has been associated with a subsequent increase in
reproductive output in fish (Ward et al. 2017), and an increase in
reproductive output is seen as a direct effect of an increase in water
temperature also for perch (Heibo et al. 2005). The climate driven
increase in temperature-dependent growth thus results in larger size at
age and earlier maturation, increasing the perch populations
reproductive output and recruitment, thus promoting population growth,
as seen in other stocks (Ward et al. 2017).

Survival of individuals during different life stages is a process which
could be directly affected by ambient temperature either through
temperature-specific developmental rates, temperature-dependent
mortality rates or time spent in different life stages mediated by
individual somatic growth (Sponaugle et al. 2006, Mirth et al. 2021). We
show that the proportion of 1 year olds in the populations increased
with summer water temperature during their 0+ summer. Increasing summer
water temperatures has been shown to increase recruitment in cool-water
fish (Kokkonen et al. 2019). The effect of increased summer temperature
is usually related to larger body size and condition in autumn that
subsequently lower the winter mortality (Hurst 2007, Estlander et al.
2017). Developmental rates increase with water temperature in
ectotherms. For instance, mortality and the duration of the perch
egg-stage are decreasing with temperature, with normal development of
eggs occurring in the temperature range of 7-18 °C (Küttel et al. 2002).
Therefore, an increase in summer water temperature might increase the
number of surviving hatchlings as more eggs might develop normally and
the shorter duration of the egg-stage might decrease the predation risk
at this life stage. Embryos and hatchlings are defined as critical life
stages with a narrow thermal range (Dahlke et al. 2020), and at the
northern edge of perch distribution an increase in spring and summer
water temperature might have been pivotal for an increase in recruitment
and subsequent increase in density and relative importance of the perch
populations.

Considering the rapid warming experienced in the study area during the
last three decades, an increased numerical importance of a cool-water
species could be expected (Ficke et al. 2007, Rolls et al. 2017), but a
total shift in fish community dominance was surprising. The population
process outlined above helps explain the sudden response to climate
warming, and suggests that similar responses should be expected in other
populations of cool-water species at their northern range edge, unless
kept in check by negative ecological interactions. However, many of the
lakes near the northern range edge of perch are salmonid dominated
systems, with little resistance capacity against percid fish at higher
temperatures (Hayden et al. 2014). Shifts in dominance from cold-water
fish to more cool- or warm-water fish have been documented in other
freshwater systems (Jeppesen et al. 2012, Hansen et al. 2017). Climate
change impact on high latitude freshwater ecosystems is predicted to
further increase as warming favours resident and invasive cool-water
species, potentially displacing native cold-water salmonids from these
ecosystems (Hayden et al. 2017, Hansen et al. 2017, Campana et al.
2020). Perch is a generalist fish that has high capacity as a resource
competitor in littoral habitats, and subsequently also as a predator,
for native cold-water species such as whitefish (Hayden et al. 2014).
Such ecological interactions with resident cold water species, mediating
higher order effects of climate change, may change in character and
outcome under warming (Urban et al. 2011). Climate induced changes in
size-structured interactions may have cascading effects within the food
web, and the outcome is dependent on thermal niche, population size
structure and the existing ecological interactions within the ecosystem
(Gårdmark et al. 2020). Cold-water sympatric species will be more
vulnerable as cool-water fish increase in competitive and predatory
capacity with warming, possibly causing major alterations within fish
communities in high latitude lakes. In a wider perspective, cool-water
perch dominance may eventually shift towards warm-water cyprinid (roach,
bleak) fish dominance along increasing temperature and productivity in
lakes where cyprinids are present or able to immigrate (Hayden et al.
2017).

\textbf{Conclusion}

Our study documents that warming triggers a rapid numerical increase of
cool-water fish at their northern range edge. The causal links between
individual and population effects of warming considered in this study
help to account for the speed and magnitude of the population responses.
The magnitude of these responses is such that dominance is shifting from
salmonids to percids, warning of an ongoing reorganization of high
latitude fish communities. Evidently, water-temperature increase from
climate change has already favoured cool-water fish at high latitudes,
and future projected climate warming will accentuate this development,
potentially at the further expense of cold-water salmonids. Climate
adaptation strategies must therefore focus on limiting the ecological
impact of warmer water fish in high latitude ecosystems. Consequently,
management should focus on climate adaptation limiting future
introductions and invasions of cool-water fish and mitigating harvesting
pressure on cold-water fish.

\textbf{Author contribution}

A.S., R.P., and P.-A.A. conceived the ideas and planned the paper. A.S.,
P.-A.A., K.K., P.M.T., N.A.K., and E.M.Z. collected the data and did the
preliminary data processing. A.S. and R.P. analysed the data with
substantial input from all co-authors. A.S. led the writing. All authors
contributed significantly to the drafts and approved the paper.

\textbf{Acknowledgements}

Thanks are due to the numerous people helping with collecting, sampling
and sharing the data included here. No conflict of interest to declare.
Data will be available in a Dryad-repository upon acceptance of the MS.

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

\end{center}
\end{figure}

\textbf{Figure 1.} Water temperature in the Pasvik watercourse from year
1992 to 2020. Annual mean temperature (blue circles). Mean autumn
temperature (Sep-Nov) (green triangles). Mean summer temperature
(Jun-Aug) (orange squares). Temperature data is missing from January
1998-July 1999.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 2.} Development of perch populations in the littoral zone
of study lakes from year 1990 to year 2020. a) Proprtion of perch in the
study lakes, the black line shows the overall trend given by the
Linear-mixed-effect model (Appendix Table S11). b) Relative density
(ln-CPUE, no. of fish per 100m\textsuperscript{2} per 12h) of perch in
the littoral zone of Lake Vaggatem (green) and Lake Skrukkebukta (blue)
from year 1990 to year 2020. c) Relative density (ln-CPUE) of perch
dependent on annual mean water-temperature (Weighted-Moving-Average,
WMA, over the last two years) in Lake Skrukkebukta (blue) and Lake
Vaggatem (green) in the Pasvik watercourse.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 3.} Relative proportion of one-year-old individuals in
Lake Skrukkebukta (blue) and Lake Vaggatem (green) perch populations on
a natural logarithmic scale related to the preceding summer water
temperature (\selectlanguage{ngerman}°C). Relative proportion of one-year-old perch is
calculated as the proportion of one-year-old individuals relative to the
total CPUE (100m\textsuperscript{2}/12h)\textsuperscript{-1} in the
littoral zone of the two lakes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 4.} Back-calculated length increment
(mm\selectlanguage{ngerman}·year\textsuperscript{-1}) for age 1 year (top left), 2 years (top
right), 3 years (bottom left) and 4 years (bottom right) perch from Lake
Vaggatem (green) and Lake Skrukkebukta (blue) in the Pasvik watercourse
dependent on summer water temperature (°C). Points represent the mean,
whiskers represent the bootstrapped 95 \% confidence interval of the
mean, trend line with shading represent linear regression with standard
error.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 5.} Relationship between the average length increment
from age one to four years (mm\selectlanguage{ngerman}·1-4 years\textsuperscript{-1}) for the
different cohorts of perch and a) summer water temperature (three-year
moving-average, SMA), b) relative density (three-year moving-average,
CPUE) with solid lines representing the multiple linear regression model
results (predictors centred and scaled). c) The relationship between age
at maturation (A50, given by logistic regression) and average length
increment from age one to four years for the different perch cohorts
from 1998-2013, with the solid line representing the linear regression
model and shaded area depicting the standard error of the model. NB.
Lakes were pooled in the linear regressions because no significant
difference in the slope between lakes were detected.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\textbf{Figure 6.} a) Structural equation model showing the relationship
between predictor variables affecting length increment (combined from
age 1 year to age 4 year olds) and the effect of increasing length
increment on age at maturation in the perch populations in Lake Vaggatem
and Lake Skrukkebukta. Arrows represent causal pathways, highlighted
black lines represent significant relationships and grey lines represent
non-significant relationship within the model. Numbers in boxes denote
the effect size of standardized coefficients and R\textsuperscript{2} is
shown for each endogenous variable. b) Relationship between somatic
growth and maturation in perch in the Pasvik watercourse illustrated by
the maturation reaction norm, length at age for perch in the Pasvik
watercourse (grey dots) with blue dotted line representing a slow growth
rate, i.e. ``cold and high density situation'' (10th percentile) and
orange dotted line representing a fast growth rate, i.e. ``warm and low
density situation'' (90th percentile). The population estimated
probabilistic maturation reaction norm (PMRN) midpoint (solid line), the
25\textsuperscript{th} and 75\textsuperscript{th} percentile (dashed
lines).

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