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\begin{document}

\title{Quantifying habitable water temperatures and thermal sensitivities among
species functional traits of stream invertebrates}



\author[1]{Hideyuki Doi}%
\author[2]{John Kominoski}%
\author[3]{Izumi Katano}%
\affil[1]{Kyoto University}%
\affil[2]{Florida International University}%
\affil[3]{Nara Women's University}%


\vspace{-1em}



  
  \date{June 02, 2024}


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\selectlanguage{english}
\begin{abstract}
Ectothermic species have body temperatures that reflect their
environment to varying degrees. Environmental temperature drives all
cellular and physiological functions, including metabolism, development,
growth, migration, and reproduction. Extreme temperatures are occurring
more frequently with climate change, and understanding the thermal
tolerance and adaptive traits of species is critical.We hypothesized
that 1) geographic location of stream ecosystems, such as elevation and
latitude, influence the habitable water temperature of lotic (stream)
invertebrates because the thermal habitat of species directly influences
their life cycle and consequently fitness and 2) species functional
traits (e.g., voltinism and feeding behavior) are influenced by
habitable temperature. Here, we tested these hypotheses across diverse
taxa and geographic regions using a dataset for stream invertebrates
traits across North America. We showed that maximum water temperature in
habitats and thermal breadth were significantly lower and narrower
across streams ranging in elevation, from 0 to 3000 m, suggesting that
invertebrate taxa across various elevations are less tolerant of warmer
water temperature. Also, we identified thermal sensitivity differences
among species traits, especially functional feeding group traits, as
these are related to habitat selection in stream ecosystems. Our
synthesis suggests that elevation and species traits can help predict
thermal breadth and thermal tolerance for different species under a
changing climate.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Anthropogenic climate change is having pronounced and accelerated
biological impacts on organisms and ecosystems (Pacifici et al., 2015;
Sinclair et al., 2016). Biologists are trying to understand and predict
these impacts. Inevitably, most of these impacts are mediated by the
behavioral and physiological responses of organisms to changing
biological variables (Doi et al., 2008; Radchuk et al., 2019). The
concept of species habitability with temperature applies community-level
understanding of species thermal tolerance using assembly rules that
estimate the presence or absence of species as well as their abundance
(Menezes et al., 2010). Yet, the need to move beyond descriptive to a
predictive understanding of species thermal tolerances is critical given
climate-driven increases in temperature (e.g., Pacifici et al., 2015;
Sinclair et al., 2016).

Most aquatic species, such as invertebrates, are ecothermic and
therefore have body temperatures that reflect their environment to
varying degrees (Angilletta, 2009; Sinclair et al., 2016). Extremely
high or low temperatures are lethal, and temperature determines all
cellular and physiological functions, including metabolism, development,
growth, migration, and reproduction and indirectly throughout changes in
prey community and environments (Grigaltchik et al., 2012; Ylla et al.,
2014; Nelson et al., 2020b; Schofield \& Kline, 2018; Shah et al.,
2021). Changes in water temperature have serious implications at
population, community, and ecological levels (e.g., Grigaltchik et al.,
2012; Sinclair et al., 2016), especially given the uncertainty in how
stream temperature regimes may change with different climate and
land-use changes (Kominoski \& Rosemond, 2012).

To date, attempts to measure vulnerability to climate change have
largely assessed species responses to temperature challenges, such as
lethal and critical thermal limits, i.e., habitable temperature (Deutsch
et al., 2008; Pinsky et al., 2019). Metabolic rates of ecotherms can
influence individual fitness, and consequently geographic distributions
and abundances (Angilletta, 2009; Shah et al., 2020; Terblanche \&
Chown, 2007; Vannote \& Sweeney, 1980), and related to their body
temperatures. For example, in aquatic ecosystems increased metabolic
demand with higher temperature can outpace the supply of oxygen from the
environment causing decreased performance and lowered tolerance to heat
stress (P\selectlanguage{ngerman}örtner et al., 2017). Therefore, understanding habitable
temperature across different environments would help us to predict
geographic variation in fitness, as well as species abundance and
distributions in response to global warming (Dillon et al., 2010).
Addressing the effects of climate change through the lens of exothermic
biology, especially species habitable temperature, will expand
understanding of how temperature affects the majority of species on
Earth.

Recent experimental temperature manipulations have quantified the
habitable temperature ranges of stream invertebrates (Nelson et al.,
2017a, 2017b, 2020a, 2020b; Shah et al., 2020). These studies suggested
that habitable temperature varied among the geographical location of the
habitats, such as elevation and latitude, and species traits, such as
feeding mode (Shah et al., 2017, 2020; Sunday et al., 2011). Shah et al.
(2017) showed the elevation effects of thermal breadth of many stream
macroinvertebrates in the temperate and trophic streams. Also, Nelson et
al. (2020a) reported the effect of thermal breadth diversity on energy
flux through a stream food web. A global analysis of thermal tolerance
among terrestrial and marine ecotherms found thermal tolerance breadths
increased with latitude (Sunday et al., 2017). Yet, how habitable
temperatures and thermal tolerances range across diverse stream
ecotherms from broad geographic regions and along elevation gradients is
needed to better predict the effects of changing temperatures on species
functional traits of inland waters (Shah et al., 2020).

In this study, we compared how geographic location and species traits
vary across North America. First, we hypothesized that geographic
location of stream ecosystems, such as elevation and latitude, influence
the habitable water temperature of lotic (stream) invertebrates, such
that habitats directly influence species' life cycles and consequently
their fitness and evolution (Kearney et al., 2020; Shah et al., 2020;
Sunday et al., 2011). Second, we hypothesized that species traits (e.g.,
voltinism and feeding behavior) are influenced by habitable temperature,
as these traits are directly related with species adaptations to
temperature (Sgrò et al., 2016). Also, thermal performance in the
different phylogeny (e.g., taxonomic category) is different, as these
traits are directly related with species adaptations to temperature.
Numerous tests of the two hypotheses have been performed (e.g., Kong et
al., 2019; Nelson et al., 2020b; Shah et al., 2021), but such tests have
only been conducted using temperature manipulations in the field and
laboratory for a limited number of species. Here, we tested our
hypotheses with variable taxa and geographic regions using a dataset of
stream invertebrate traits in North America (Vieira et al., 2006). The
dataset we used includes thermal preferences, i.e., maximum and minimum
water temperature surviving in, and the habitat conditions, such as
elevation and latitude of the collected points as well as various
species traits, including voltinism and feeding behavior for
over\emph{n} = 2,200 species. The dataset contained data from various
taxa, including bivalve, amphipods, and insects in streams across North
America, mostly in the United States. Use of a database summarized
various related data which allowed us to compare these factors to the
habitable water temperature of stream macroinvertebrate taxa across
broad geographic ranges in latitude and elevation.

\textbf{Materials and methods}

Database and obtained data

We used ``A database of lotic invertebrate traits for North America''
(Vieira et al., 2006) provided by the U.S. Geological Survey (USGS).
USGS's National Water-Quality Assessment Program in cooperation with
Colorado State University compiled the database. A total of \emph{N} =
14,127 records for over \emph{n} = 2,200 species, \emph{n} = 1,165
genera, and \emph{n} = 249 families have been compiled for the database
from \emph{n} = 967 reports, including journal papers and scientific
reports.

In this study, we focused on maximum and minimum water temperature
surviving in (°C, hereafter, max. and min. temperature). The data of
max. and min. temperature were from \emph{n} = 390 and \emph{n} = 440
records, respectively. Because species thermal tolerance data (maximum
lethal temperature, 5 records, thermal preference, 63 records with
various descriptions) records were very limited, we did not use these
data in our analyses in our analyses. We obtained the related geographic
factors, including maximum and minimum habitat elevation and the U.S.
state where the taxa recorded. Because of our testing for species traits
hypothesis,we also obtained the biological traits of the species,
including the mean and maximum body length (mm), functional feeding
guilds, voltinism (i.e., \textless{}1, 1, 2 and 3 year of the life
cycle), and species order of the taxa, which we obtained from the
dataset. We collected mean degree of latitude of the U.S. state where
the taxa recorded from the list of US state database
(https://www.latlong.net/category/states-236-14.html). We decided to use
a single database to summarize all variables in the same manner to
directly compare these factors to the thermal preference of the taxa.

Statistical analyses

We calculated the ``thermal breadth (°C)'' by maximum - minimum habitat
temperature (°C). We obtained \emph{n} = 306 data records of thermal
breadth (°C).

All statistics and graphics were performed using R ver. 4.0.2 (R Core
Team, 2020). We set the significant level, \selectlanguage{greek}α \selectlanguage{english}= 0.05. We performed
generalized linear models (GLMs) to predict the maximum and minimum
temperature and thermal breadth using ``glm'' function with the degree
of latitude N of the collected points, the elevation (above sea level,
m), and their mean body length as the body size index. We used the
Gaussian distribution as the error distribution for the GLM. We
performed the GLMs without the random factor due to too many data source
categories from 967 independent reports for the database. Before the GLM
analysis, we calculated the variance inflation factor (VIF) to check for
co-linearity of among factors. The maximum VIF was 10.2 for the
explanatory factors of GLM so we excluded the maximum elevation for all
GLMs because of significantly correlated with minimum elevation
(Pearson's coefficient by ``cor.test'' function, r = 0.692, P
\textless{} 0.0001). After the exclusion, the maximum VIF was 1.34
indicating that co-linearity among the factors would not significantly
influence the results of GLMs. We used simple GLM without random
factors, because the database does not have the suitable random factors
for the analysis.

For categorical data, including voltinism, functional feeding guilds,
and taxonomic Order, we performed a three-way analysis of variance
(ANOVA) and evaluated interactions using ``aov'' function. Also, we
simply used the ANOVA without the random factor due to 967 independent
reports for the database. When the ANOVA was significant, we performed
the post-hoc test using Tukey multiple comparisons with ``TukeyHSD''
function.

\textbf{Results}

Stream temperatures varied across habitats based on latitude and
elevation, as expected (Figs. 1, 2). Maximum stream temperatures were
lower at latitudes above than latitudes below 35\selectlanguage{ngerman}°N, but minimum stream
temperatures were similar across latitudes (Fig. 1, Table 1a,b). Thermal
breadths were lower at latitudes above compared to latitudes below 35°N
(Fig. 1, Table 1c). We did not observe broad categorical elevation
trends with any stream temperature metric (Fig. 2, Table 1 d.e, and f ).
Importantly, geographic information was not included for 74.8\% of
total\emph{n} = 229 records of stream maximum and minimum temperatures
and calculated thermal breadths (Figs. 1, 2). Maximum stream temperature
and thermal breadth were linearly related to habitat elevation (Fig. 3a
Table 2 s), whereas minimum stream temperature was not related to
elevation (Fig. 3a, Table 2). In contrast, stream latitude was not
linearly related with any stream habitat temperature variable (Fig. 3b,
Table 2).

We found that habitable maximum temperatures varied across voltinism and
among functional feeding guilds of species (Table 3, Figs. 4a-4c).
Specifically, multivoltine taxa had higher maximum stream temperatures
than univoltine and semivoltine taxa (Fig. 4a). Collector-gatherers
tended to have higher maximum stream temperatures (Fig. 4b), however,
effects of stream temperature among taxonomic Order were not clearly
evident (Fig. 4c, Table 3, 4). We detected significant interactions
among voltinism and feeding guilds (Table 3), however, we did not detect
differences in maximum temperatures and thermal breadth among
interactions with functional feeding guilds, voltinism, or taxonomic
Order (Figs. 4a-4c).

\textbf{Discussion}

Quantifying the habitable temperature range of ectotherms is critical to
predicting how global warming will impact different species (Huey \&
Kingsolver, 2019) and understanding the influence of temperature on the
minimum energy demands. Payne and Smith (2017) suggested that species
with narrower temperature ranges may simply reflect the acute effects of
temperature on biological rates of organisms living in warmer
environments. Water temperatures in shallow mountain streams can
dynamically change with changes in air temperature (Birrell et al.,
2020; Mohseni \& Stefan, 1999; Pilgrim et al., 1998), and water
temperatures in mountain streams are rapidly increasing worldwide with
air temperatures (e.g., Isaak \& Rieman, 2013). For example, in the
Pacific Northwest, stream water temperatures have increased by 0.22°C
per decade in the last decades (Isaak et al., 2012). We found the
invertebrate taxa across various elevations from 0 to 3000 m, with less
tolerances of warmer stream temperatures. We specifically tested the
thermal tolerance differences among functional feeding guilds and
voltinism. Our use of linear models to predict thermal breadth and
habitat temperature tolerances among diverse stream macroinvertebrates
can be used to better understand how elevation among temperate streams
constrains species traits (Shah et al., 2017, 2020). Although our model
results provide further inference towards predicting thermal responses
of stream communities and ecosystems under temperature increases,
holistic integration of dynamic taxon-specific variation to changing
temperatures is needed (Nelson et al., 2020b; Shah et al., 2021).

change effects on the fitness and habitats of stream invertebrates,
especially higher elevation stream invertebrate communities, are
predictable. We found that thermal tolerance was strongly correlated
with voltinism among stream taxa. For example, multi-voltinism such as
bivoltine and trivoltine taxa were found in streams with higher maximum
temperatures. Semi- and univoltine species have thermal tolerance for
colder water temperatures. Voltinism was explained by the combined
predictions of thermal adaptation (Kong et al., 2019), but typically,
semivoltine species generally inhabit in colder waters (Braune et al.,
2008; Danks, 2007; Huryn, 1990). Our synthesis, across a large
geographic range of temperate streams, suggests that there are tradeoffs
between higher thermal tolerance and energy allocation towards growth
that are generalizable across taxa with different life history
development strategies. In this study, we found the unexpected result
that longer-voltinism species may have thermal adaptation for warmer
temperatures. Therefore, our results provide further evidence that
thermal adaptation for warmer temperatures is a habitable trait of
longer-voltinism species.

Thermal ranges varied among stream functional feeding guilds. We found
higher maximum temperatures for collector-gatherer taxa than other
feeding guilds. Collector-gatherers can feed on various types of food
sources, including animal and detrital plant matter (Merritt et al.,
2017), and we have identified that they can tolerate a larger
temperature range than other feeding guilds. Collectively, this may
extend their habitats more than other species, such as shredders and
scrapers. Therefore, the apparently higher thermal tolerance of
collector-gatherers may be due to their ability to occupy a broader
range of habitats with more diverse food resources than other
macroinvertebrates. Among the functional feeding guilds, differences in
thermal sensitivity, e.g., tolerance and breath, have been observed
previously (Gilman et al., 2010; Grigaltchik et al., 2012; Kordas et
al., 2011; Pincebourde \& Casas, 2019; Shah, 2020; Vucic-Pestic et al.,
2011), but have not summarized. Our large-scale synthesis illustrates a
phenomenon of thermal tolerance differences among functional feeding
guilds, especially for collector-gatherers. These results could have
functional consequences for how increasing temperatures interact with
stream communities to alter organic matter processing (Ferreira \&
Canhoto, 2014; Ylla et al., 2014). Further studies needs to consider the
effects of thermal tolerance differences among functional feeding guilds
on the stream community and ecosystem function with climate change (Pyne
\& Poff, 2017).

Temperature variability in aquatic ecosystems is changing worldwide with
increased air and land surface temperatures and shifts in precipitation,
as well as human-driven flow alterations. Although minimum stream
temperatures are increasing in winter months in North American streams,
regulated streams and rivers can have lower stream water temperatures in
summer months compared to unregulated streams and rivers (Carlisle et
al., 2016). As temperature is a primary factor influencing metabolic
rates (Nelson et al., 2020b; Shah et al., 2021), organic matter and
biogeochemical processes (Ferreira \& Canhoto, 2014; Ylla et al., 2014)
and stream community composition (Carlisle et al., 2016; Nelson et al.,
2017a, 2017b, 2020a), understanding the structural and functional
constraints of increasing temperatures on stream ecosystems is critical
and likely to be highly variable among taxa (Nelson et al., 2020b; Shah
et al., 2021).

Although our synthesis focused on water temperature variation from a
large public database (Vieira et al., 2006), we should note that the
most extreme biological responses are often triggered by the synchronous
occurrence of multiple environmental stressors, e.g., water quality,
water flow, and UV radiation (Denny et al., 2011; Jackson et al., 2016).
Therefore, the consequences of multiple interacting environmental
changes on stream communities and their ecosystem functions is an
imperative to understanding climate-induced changes to streams. For
example, a study in a mountainous headwater stream in Portugal found
complex food web interactions from experimental warming, whereby the
presence of a dominant shredder increased fungal biomass and influenced
fungal composition on decomposing litter only in the reach with elevated
stream temperature (Domingos et al., 2015). Changes in stream
temperature from climate and land-use changes have complex effects on
stream community composition, biological interactions, and ecosystem
functioning. Shifts in food web relationships regardless of changes in
temperature or macroinvertebrate community composition can still impact
rates of organic matter processing in streams (Demi et al., 2019;
Domingos et al., 2015; Kominoski et al., 2011; Kominoski et al., 2013;
Rosemond et al., 2015). Understanding how variation in thermal tolerance
among stream taxa interacts with other global environmental changes is
critical for holistically assessing stream ecosystem integrity. Further,
studies that explicitly test for interactive effects of temperature and
other environmental changes are needed to elucidate the specific
physiological, genetic or environmental drivers behind functional
changes in stream ecosystems in a changing world.

\textbf{Funding}

This project was partly supported by Institute for Sustainable Sciences
and Development, Hiroshima University.

\textbf{Author contributions}

H.D., J.S.K., and I.K. designed the research. H.D. analyzed the data and
wrote the first draft. All authors contributed to writing and editing
the final manuscript.

\textbf{Data availability}

All data were obtained from USGS data repository of ``A database of
lotic invertebrate traits for North America''
(https://pubs.er.usgs.gov/publication/ds187).

\textbf{Competing interests}

The authors declare no competing financial interests.

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Table 1 The linear model results for latitude and elevation category
effects on a, d) Max, b, e) Min temperature and c, f) thermal breadth.
Bold factors mean P \textless{} 0.05.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
a) Max. temperature & & & & & & & &\tabularnewline
\midrule
\endhead
& Estimate & Estimate & SE & SE & t & t & P & P\tabularnewline
\textbf{(Intercept)} & \textbf{26.2267} & \textbf{26.2267} &
\textbf{0.8504} & \textbf{0.8504} & \textbf{30.84} & \textbf{30.84} &
\textbf{2.00E\textsuperscript{-16}} &
\textbf{2.00E\textsuperscript{-16}}\tabularnewline
\textbf{Latitude \textgreater{}40} & \textbf{-7.8221} & \textbf{-7.8221}
& \textbf{0.9204} & \textbf{0.9204} & \textbf{-8.498} & \textbf{-8.498}
& \textbf{2.67E\textsuperscript{-15}} &
\textbf{2.67E\textsuperscript{-15}}\tabularnewline
\textbf{Latitude 35-40} & \textbf{-5.3919} & \textbf{-5.3919} &
\textbf{1.2909} & \textbf{1.2909} & \textbf{-4.177} & \textbf{-4.177} &
\textbf{4.23E\textsuperscript{-05}} &
\textbf{4.23E\textsuperscript{-05}}\tabularnewline
\textbf{R\textsuperscript{2}} & \textbf{0.2403} & \textbf{0.2403} & & &
& & &\tabularnewline
\textbf{P} & \textbf{1.38E\textsuperscript{-14}} &
\textbf{1.38E\textsuperscript{-14}} & & & & & &\tabularnewline
b) Min. temperature & & & & & & & &\tabularnewline
& Estimate & Estimate & SE & SE & t & t & P & P\tabularnewline
\textbf{(Intercept)} & \textbf{7.02} & \textbf{7.02} & \textbf{1.27098}
& \textbf{1.27098} & \textbf{5.523} & \textbf{5.523} &
\textbf{9.15E\textsuperscript{-08}} &
\textbf{9.15E\textsuperscript{-08}}\tabularnewline
Latitude \textgreater{}40 & 1.57543 & 1.57543 & 1.37562 & 1.37562 &
1.145 & 1.145 & 0.253 & 0.253\tabularnewline
Latitude 35-40 & -0.05478 & -0.05478 & 1.92936 & 1.92936 & -0.028 &
-0.028 & 0.977 & 0.977\tabularnewline
R\textsuperscript{2} & 0.0006497 & 0.0006497 & & & & & &\tabularnewline
P & 0.3435 & 0.3435 & & & & & &\tabularnewline
c) Thermal breadth & & & & & & & &\tabularnewline
& Estimate & Estimate & SE & SE & t & t & P & P\tabularnewline
\textbf{(Intercept)} & \textbf{19.207} & \textbf{19.207} &
\textbf{1.446} & \textbf{1.446} & \textbf{13.283} & \textbf{13.283} &
\textbf{2.00E\textsuperscript{-16}} &
\textbf{2.00E\textsuperscript{-16}}\tabularnewline
\textbf{Latitude \textgreater{}40} & \textbf{-9.398} & \textbf{-9.398} &
\textbf{1.565} & \textbf{1.565} & \textbf{-6.005} & \textbf{-6.005} &
\textbf{7.62E\textsuperscript{-09}} &
\textbf{7.62E\textsuperscript{-09}}\tabularnewline
\textbf{Latitude 35-40} & \textbf{-5.337} & \textbf{-5.337} &
\textbf{2.195} & \textbf{2.195} & \textbf{-2.431} & \textbf{-2.431} &
\textbf{0.0158} & \textbf{0.0158}\tabularnewline
\textbf{R\textsuperscript{2}} & \textbf{0.1579} & \textbf{0.1579} & & &
& & &\tabularnewline
\textbf{P} & \textbf{0.03748} & \textbf{0.03748} & & & & &
&\tabularnewline
d) Max. temperature & d) Max. temperature & & & & & & &\tabularnewline
& & Estimate & Estimate & SE & SE & t & t & P\tabularnewline
\textbf{(Intercept)} & \textbf{(Intercept)} & \textbf{21.05} &
\textbf{21.05} & \textbf{2.037} & \textbf{2.037} & \textbf{10.332} &
\textbf{10.332} & \textbf{7.10E\textsuperscript{-12}}\tabularnewline
elevation \textgreater{}2000m & elevation \textgreater{}2000m & -4.09 &
-4.09 & 2.881 & 2.881 & -1.42 & -1.42 & 0.165\tabularnewline
elevation 1000-2000m & elevation 1000-2000m & -1.944 & -1.944 & 2.597 &
2.597 & -0.748 & -0.748 & 0.46\tabularnewline
\textbf{R\textsuperscript{2}} & \textbf{R\textsuperscript{2}} &
0.0004934 & 0.0004934 & & & & &\tabularnewline
\textbf{P} & \textbf{P} & 0.3757 & 0.3757 & & & & &\tabularnewline
e) Min. temperature & e) Min. temperature & & & & & & &\tabularnewline
& & Estimate & Estimate & SE & SE & t & t & P\tabularnewline
\textbf{(Intercept)} & \textbf{(Intercept)} & \textbf{9.72} &
\textbf{9.72} & \textbf{2.0773} & \textbf{2.0773} & \textbf{4.679} &
\textbf{4.679} & \textbf{4.73E\textsuperscript{-05}}\tabularnewline
elevation \textgreater{}2000m & elevation \textgreater{}2000m & 0.39 &
0.39 & 2.9378 & 2.9378 & 0.133 & 0.133 & 0.895\tabularnewline
elevation 1000-2000m & elevation 1000-2000m & -0.4388 & -0.4388 & 2.6481
& 2.6481 & -0.166 & -0.166 & 0.869\tabularnewline
R\textsuperscript{2} & R\textsuperscript{2} & -0.05739 & -0.05739 & & &
& &\tabularnewline
P & P & 0.9511 & 0.9511 & & & & &\tabularnewline
f) Thermal breadth & f) Thermal breadth & & & & & & &\tabularnewline
& & Estimate & Estimate & SE & SE & t & t & P\tabularnewline
\textbf{(Intercept)} & \textbf{(Intercept)} & \textbf{11.33} &
\textbf{11.33} & \textbf{1.7} & \textbf{1.7} & \textbf{6.666} &
\textbf{6.666} & \textbf{1.38E\textsuperscript{-07}}\tabularnewline
elevation \textgreater{}2000m & elevation \textgreater{}2000m & -4.48 &
-4.48 & 2.404 & 2.404 & -1.864 & -1.864 & 0.0713\tabularnewline
elevation 1000-2000m & elevation 1000-2000m & -1.505 & -1.505 & 2.167 &
2.167 & -0.695 & -0.695 & 0.4922\tabularnewline
\textbf{R2} & \textbf{R2} & 0.04476 & 0.04476 & & & & &\tabularnewline
\textbf{P} & \textbf{P} & 0.1779 & 0.1779 & & & & &\tabularnewline
\bottomrule
\end{longtable}

Table 2 The linear model results for latitude and Min. elevation effects
on a) Max, b) Min temperature and c) thermal breadth. Bold factors mean
P \textless{} 0.05.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
a) Max. temperature & & & &\tabularnewline
\midrule
\endhead
& Estimate & SE & t & P\tabularnewline
\textbf{(Intercept)} & \textbf{18.516248} & \textbf{8.675901} &
\textbf{2.134} & \textbf{0.04206}\tabularnewline
Latitude & 0.038174 & 0.204745 & 0.186 & 0.85349\tabularnewline
\textbf{Min. elevation} & \textbf{-0.003588} & \textbf{0.001269} &
\textbf{-2.827} & \textbf{0.00875}\tabularnewline
\textbf{R\textsuperscript{2}} & \textbf{0.1713} & & &\tabularnewline
\textbf{P} & \textbf{0.03014} & & &\tabularnewline
b) Min. temperature & & & &\tabularnewline
& Estimate & SE & t & P\tabularnewline
(Intercept) & 9.96 & 9.08 & 1.097 & 0.282\tabularnewline
Latitude & -0.0756 & 0.214 & -0.353 & 0.727\tabularnewline
Min. elevation & -4.71E\textsuperscript{-05} & 0.00133 & -0.035 &
0.972\tabularnewline
R\textsuperscript{2} & -0.06896 & & &\tabularnewline
P & 0.9376 & & &\tabularnewline
c) Thermal breadth & & & &\tabularnewline
& Estimate & SE & t & P\tabularnewline
(Intercept) & 8.55361 & 8.924003 & 0.958 & 0.3463\tabularnewline
Latitude & 0.113774 & 0.210601 & 0.54 & 0.5935\tabularnewline
\textbf{Min. elevation} & \textbf{-0.003541} & \textbf{0.001306} &
\textbf{-2.712} & \textbf{0.0115}\tabularnewline
\textbf{R\textsuperscript{2}} & \textbf{0.1579} & & &\tabularnewline
\textbf{P} & \textbf{0.03748} & & &\tabularnewline
\bottomrule
\end{longtable}

Table 3 The ANOVA results for voltinism (Volt), functional feeding group
(FFG), and species order (Order) on a) Max, b) Min temperature and c)
thermal breadth. Bold factors mean P \textless{} 0.05.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
a) Max. temperature & a) Max. temperature & & & &\tabularnewline
\midrule
\endhead
& Df & Sum Sq & Mean Sq & F & P\tabularnewline
\textbf{Volt} & \textbf{3} & \textbf{474.9} & \textbf{158.3} &
\textbf{7.176} & \textbf{0.00046}\tabularnewline
\textbf{FFG} & \textbf{4} & \textbf{524.1} & \textbf{131.02} &
\textbf{5.939} & \textbf{0.000594}\tabularnewline
Order & 5 & 99 & 19.8 & 0.897 & 0.490664\tabularnewline
\textbf{Volt:FFG} & \textbf{5} & \textbf{590.7} & \textbf{118.13} &
\textbf{5.355} & \textbf{0.000563}\tabularnewline
Volt:Order & 3 & 52.3 & 17.44 & 0.791 & 0.505147\tabularnewline
FFG:Order & 4 & 67.2 & 16.8 & 0.762 & 0.555471\tabularnewline
Volt:FFG:Order & 1 & 9.2 & 9.21 & 0.418 & 0.521246\tabularnewline
b) Min. temperature & b) Min. temperature & & & &\tabularnewline
& Df & Sum Sq & Mean Sq & F & P\tabularnewline
Volt & 3 & 109.7 & 36.56 & 1.803 & 0.159\tabularnewline
FFG & 4 & 68 & 17.01 & 0.839 & 0.507\tabularnewline
Order & 5 & 81.1 & 16.22 & 0.8 & 0.555\tabularnewline
Volt:FFG & 5 & 18.3 & 3.65 & 0.18 & 0.969\tabularnewline
Volt:Order & 3 & 66 & 21.98 & 1.084 & 0.365\tabularnewline
FFG:Order & 4 & 46.1 & 11.53 & 0.569 & 0.687\tabularnewline
Volt:FFG:Order & 1 & 0.6 & 0.6 & 0.03 & 0.864\tabularnewline
c) Thermal breadth & c) Thermal breadth & & & &\tabularnewline
& Df & Sum Sq & Mean Sq & F & P\tabularnewline
Volt & 3 & 180.8 & 60.27 & 1.266 & 0.2968\tabularnewline
FFG & 4 & 395.8 & 98.95 & 2.079 & 0.0986\tabularnewline
Order & 5 & 229.5 & 45.9 & 0.964 & 0.4493\tabularnewline
Volt:FFG & 5 & 508.8 & 101.76 & 2.138 & 0.0772\tabularnewline
Volt:Order & 3 & 104.5 & 34.83 & 0.732 & 0.5382\tabularnewline
FFG:Order & 4 & 35.4 & 8.84 & 0.186 & 0.9447\tabularnewline
Volt:FFG:Order & 1 & 14.5 & 14.52 & 0.305 & 0.5834\tabularnewline
\bottomrule
\end{longtable}

Table 4 Tukey post-hoc comparison for a) voltinism and b) functional
feeding group on Max temperature. Bold factors mean P \textless{} 0.05.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
a) Voltinism & &\tabularnewline
\midrule
\endhead
comparison & differences & P\tabularnewline
1-\textless{}1 & -2.16851 & 0.71915\tabularnewline
2-\textless{}1 & 3.236363 & 0.4831368\tabularnewline
3-\textless{}1 & -2.26363 & 0.9621085\tabularnewline
\textbf{2-1} & \textbf{5.404878} & \textbf{0.0076061}\tabularnewline
3-1 & -0.0951 & 0.9999963\tabularnewline
2-3 & -5.500 & 0.6131607\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
b) FFG & &\tabularnewline
\midrule
\endhead
comparison & differences & P\tabularnewline
Collector gatherer -Collector filterer & -3.03943 &
0.0397763\tabularnewline
\textbf{Predator-Collector filterer} & \textbf{-5.6287} &
\textbf{0.0000004}\tabularnewline
\textbf{Scraper/grazer-Collector filterer} & \textbf{-3.93106} &
\textbf{0.0001579}\tabularnewline
\textbf{Shredder-Collector filterer} & \textbf{-6.53636} &
\textbf{0.0000001}\tabularnewline
Predator-Collector gatherer & -2.58932 & 0.1044006\tabularnewline
Scraper/grazer-Collector gatherer & -0.89163 & 0.8866317\tabularnewline
\textbf{Shredder-Collector gatherer} & \textbf{-3.49693} &
\textbf{0.0224365}\tabularnewline
Scraper/grazer-Predator & 1.69769 & 0.2977081\tabularnewline
Shredder-Predator & -0.90760 & 0.9186897\tabularnewline
Shredder-Scraper/grazer & -2.60530 & 0.0685934\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Stream temperature ranges (Max., Min. temperature and thermal
breadth) across a categorical grouping of habitat latitude from the
Vieira et al. (2006) database.

Boxes and bars in the box plots indicate medians \selectlanguage{ngerman}± inter-quartiles and ±
1.5 × inter-quartiles, respectively. Points represent individual data
values. The smooth lines indicate the distribution of the data using
violin plots. The violin plot outlines illustrate kernel probability
density, i.e., the width of the enclosed area represents the proportion
of the data located.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Stream temperature ranges (Max., Min. temperature and thermal
breadth) across categorical grouping of habitat elevation from the
Vieira et al. (2006) database.

Boxes and bars in the box plots indicate medians \selectlanguage{ngerman}± inter-quartiles and ±
1.5 × inter-quartiles, respectively. Points represent individual data
values. The smooth lines indicate the distribution of the data using
violin plots. The violin plot outlines illustrate kernel probability
density, i.e., the width of the enclosed area represents the proportion
of the data located.

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

\end{center}
\end{figure}

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

\end{center}
\end{figure}

Figure 3. Stream temperature ranges (Max., Min. temperature and thermal
breadth) and a) minimum elevation recorded and b) with the degree of
latitude N. The blue line with shaded area indicates the results of
regression with 95\% confidential interval of the significant slope in
the GLMs (P \textless{} 0.05 for the coefficient, Table S1). The habitat
temperature ranges (Max., Min. temperature and optimal range) with
maximum elevation recorded showed in Fig. S1.

a)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\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}

b)c)

Figure 4. Macroinvertebrates habitat temperature ranges (Max, Min
temperature, and thermal breadth) and a) voltinism, b) their functional
feeding guilds (FFGs), and c) species order. The boxes and bars in the
box plots indicate median \selectlanguage{ngerman}± inter-quartiles and ±1.5 × inter-quartiles,
respectively. The points represent individual data values with the
colors indicating the body size category (blue= Small (body length
\textless{} 9 mm), green = Mid (9-16 mm), red= Large (\textgreater{}16
mm), and grey = no data).

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

