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\begin{document}

\title{Spatiotemporal variability of abundance size-spectra in streams across
North America}



\author[1]{Justin Pomeranz}%
\author[2]{James R. Junker}%
\author[3]{Jeff Wesner}%
\affil[1]{University of Canterbury}%
\affil[2]{Affiliation not available}%
\affil[3]{University of South Dakota}%


\vspace{-1em}



  
  \date{May 08, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Accepted version of this manuscript can be found
here:~\url{https://doi.org/10.1111/gcb.15862}

The distribution of abundance and biomass within ecological communities
is related to trophic transfer efficiency from prey to predators. While
it is considered to be one of the few consistent patterns in ecology,
spatiotemporal variation of this relationship across continental-scale
environmental gradients is unknown. Using a database of stream
communities collected across North America (18-68\selectlanguage{ngerman}° N latitude, -4 to
25°C mean annual temperature) over 3 years, we constructed 162
mass-abundance relationships (i.e. size spectra). Size-spectra slopes
declined (became steeper) with increasing temperature. However, the
magnitude of change was relatively small, with median slopes changing
from -1.2 to -1.3 across a 29°C range in mean annual temperature. In
contrast, total community biomass increased 3-fold over the temperature
gradient. Our study suggests strong conservation of abundance
size-spectra in streams across broad natural environmental gradients.
This supports the emerging use of size-spectra deviations as indicators
of ecosystem health.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Spatiotemporal variability of abundance size-spectra in streams
across North America}

Justin P. F. Pomeranz\textsuperscript{1}*, Jeff S.
Wesner\textsuperscript{1}

\textsuperscript{1}Department of Biology, University of South Dakota.

* corresponding author, Justin Pomeranz

\textbf{Email:}jfpomeranz@gmail.com; jeff.wesner@usd.ed

ORCIDs: Justin Pomeranz: 0000-0002-3882-7666; Jeff Wesner:
0000-0001-6058-7972

\textbf{Running Title:} NEON Stream Size Spectra

\textbf{Keywords}

Biomass distribution, size spectra, community structure, allometric
scaling, body size, biogeography, macroecology, freshwater ecology,
macroinvertebrates

\textbf{Type of article:} Letters

\textbf{Number of words in:}

\textbf{Abstract:} 145

\textbf{Main text:} 3625

\textbf{Number of references:} 45

\textbf{Number of figures:} 3

\textbf{Number of tables:} 2

\textbf{Corresponding author:} Justin Pomeranz, jfpomeranz@gmail.com,
cell: 720.212.9517, \textbf{Mailing address:} University of South
Dakota, Department of Biology, 401 East Clark St, Vermillion, SD, USA
57069

\textbf{Author contributions:} JP designed the study, collected the
data, performed the modelling work, made the figures and wrote the first
draft of the manuscript, JW performed modelling work, made the figures,
and all authors contributed substantially to revisions.

\textbf{Data accessibility:} Upon article acceptance, all data and code
used in the present study will be archived in Data Dryad and Zenodo,
respectively.

\textbf{Abstract}

The distribution of abundance and biomass within ecological communities
is related to trophic transfer efficiency from prey to predators. While
it is considered to be one of the few consistent patterns in ecology,
spatiotemporal variation of this relationship across continental-scale
environmental gradients is unknown. Using a database of stream
communities collected across North America (18-68\selectlanguage{ngerman}° N latitude, -4 to
25°C mean annual temperature) over 3 years, we constructed 162
mass-abundance relationships (i.e. size spectra). Size-spectra slopes
declined (became steeper) with increasing temperature. However, the
magnitude of change was relatively small, with median slopes changing
from -1.2 to -1.3 across a 29°C range in mean annual temperature. In
contrast, total community biomass increased 3-fold over the temperature
gradient. Our study suggests strong conservation of abundance
size-spectra in streams across broad natural environmental gradients.
This supports the emerging use of size-spectra deviations as indicators
of ecosystem health.



\textbf{Introduction}

Many fundamental aspects of an organism's biology are controlled by body
size, including metabolic rate, life history characteristics, diet
breadth, and trophic position (Brown \emph{et al.} 2004;
Woodward\emph{et al.} 2005; White \emph{et al.} 2007). Communities,
particularly in aquatic environments, are often size structured and
characterized by a strong negative relationship between the abundance or
biomass of individuals and body size, known as the size spectrum (White
\emph{et al.} 2007; Blanchard \emph{et al.} 2009). The slope of this
relationship is related to trophic transfer efficiency (Trebilco
\emph{et al.} 2013; Sprules \& Barth 2015). Therefore, understanding the
distribution of biomass within communities connects individual- and
population-level traits to community structure, function, and ecosystem
dynamics (Dossena\emph{et al.} 2012; O'Gorman \emph{et al.} 2012;
Yvon-Durocher \& Allen 2012; Trebilco \emph{et al.} 2013).

Size spectra are one of the few well documented organizing principles in
ecology. A large body of literature has demonstrated the consistency of
size-spectra relationships in diverse ecosystems (Jennings \& Blanchard
2004; Trebilco \emph{et al.} 2013; Blanchard \emph{et al.} 2017;
Mazurkiewicz \emph{et al.} 2019, 2020). A strongly negative relationship
between the abundance or biomass of individuals for a given body size
range has been consistently documented in aquatic communities, to the
point that size-spectra parameters have been recommended as a potential
``universal'' indicator of ecological health (Petchey \& Belgrano 2010).

However, while estimated slope coefficients are universally consistent
in sign (i.e. always negative), their specific values can vary in
response to both natural and anthropogenic environmental drivers
(Yvon-Durocher \emph{et al.} 2011; Dossena \emph{et al.} 2012;
O'Gorman\emph{et al.} 2012; McGarvey \& Kirk 2018; Pomeranz \emph{et
al.} 2019). For example, because metabolic rates increase with both body
size and temperature, environmental warming may have asymmetrical
effects on community structure and biomass distributions (Brown \emph{et
al.} 2004; Brose \emph{et al.} 2012). Bergmann's and James' rule predict
that warmer regions will tend to have smaller species, or smaller-sized
populations within a species, respectively (Bergmann 1847; James 1970).
Further, the temperature-size rule states that warmer temperatures cause
smaller individual body sizes in ectotherm species (Atkinson 1994).
Finally, small body size is a predicted response to global warming
(Daufresne \emph{et al.} 2009; Gardner \emph{et al.} 2011). Therefore,
temperature is hypothesized to be one of the main drivers of variation
in size spectra (O'Gorman \emph{et al.} 2012, 2017). Other anthropogenic
impacts, including land use (Martínez \emph{et al.} 2016), acid mine
drainage (Pomeranz \emph{et al.} 2019), as well as natural variation,
including seasonal variation (McGarvey \& Kirk 2018), and resource
subsidies (Perkins \emph{et al.} 2018), have also caused slope estimates
to vary.

While these and other studies suggest that environmental conditions can
alter size spectra relationships, it is difficult to know how those
effects scale up to broader patterns in size-spectra at the continental
scale and across multiple years. A major limiting factor in large-scale
studies of size spectra is the logistical challenge of obtaining
consistently collected, processed, and analyzed data across a large
spatiotemporal scale. As a result, empirical studies of size spectra
share a fundamental limitation of small geographic scales and limited
sampling through time (typically a single sample). To overcome this
limitation, we estimated size spectra across stream sites in the
National Ecological Observatory Network (NEON). We had two primary
objectives in this study: 1) assess the broad geographical consistency
of size spectra across North American streams, 2) test the hypothesis
that size spectra vary as a function of temperature (measured as mean
annual temperature).

\textbf{Materials and Methods}

Quantitative samples of benthic macroinvertebrates from NEON
wadeable-stream sites from 2017-2019 were downloaded to determine
size-spectra relationships (National Ecological Observatory Network
2020). NEON is a National Science Foundation (USA) funded program which
collects standardized samples from 81 sites (24 streams) across a
variety of terrestrial and aquatic ecosystems in North America. Repeat
collections include automated instrument recordings and observational
field sampling throughout the year, and data are available as open
source data products (https://data.neonscience.org/home). The stream
sites range across a broad environmental gradient (Figure 1), spanning
from 18 to 68° N latitude (majority between 33 to 45° N), mean annual
temperatures of -4 to 25° C, and mean annual precipitation of 331 to
2530 mm (Table 1). Additionally, sites are in varying biomes and
terrestrial plant communities, and subject to different local geologies
and flow regimes.

Sites were sampled 1-4 times each year across the local growing season.
Most sites had at least two years with three samples, except for COMO
which only had one year with three samples (2018), and two samples
collected in both 2017 and 2019. Additionally, three sites only had data
available from a single collection in a single year; REDB in 2018, WALK
and WLOU both in 2019. Details of sample collection and processing
protocols are available at the NEON website. Briefly, macroinvertebrate
samples were collected from a known area using the sampling method most
suited to a site. Laboratory processing included subsampling for
taxonomic identification and size class measurement (nearest mm) and
estimating the total count per sample. Estimated total counts were
standardized to individuals per m\textsuperscript{2} by dividing by the
area sampled (Chesney 2019). Macroinvertebrate size classes (mm) were
converted to individual dry mass (M, in mg) using published
length-weight regression coefficients. Most (96\%) taxa had
taxon-specific length-weight regressions and these were used for model
development and assessment. Approximately 8\% of the observations were
flagged by NEON as being damaged, affecting their length measurements,
and were removed from the data. The final data set included length
measurements for \textgreater{}82,000 individuals and counts that
totaled \textgreater{}17 million individuals.

Size spectra slopes have been estimated using a variety of methods, with
binning methods being common in the published literature (White \emph{et
al.} 2008; Sprules \& Barth 2015; Edwards \emph{et al.} 2017). However,
recent comparative studies have shown binning methods to be inaccurate,
providing biased slope estimates, and recommend using maximum likelihood
methods (Edwards \emph{et al.} 2017). We fitted body size data (dry
weight estimated from length-weight regressions) of macroinvertebrates
from each collection to a bounded power law distribution with a
probability density function:

\begin{equation}
f\left(x\right)=\ \frac{\left(b+1\right)x^{b}}{x_{\max}^{b+1}-x_{\min}^{b+1}},\ \ b\ \neq 1\nonumber \\
 \end{equation}\begin{equation}
f\left(x\right)=\ \frac{1}{\log x_{\max}-logx_{\min}},\ \ b=1\nonumber \\
 \end{equation}

Where \emph{x} is body mass, \emph{b} is the scaling exponent (size
spectra slope), and the distribution is bounded by the minimum
(\emph{x\textsubscript{min}} ) and maximum (\emph{x\textsubscript{max}}
) body sizes observed in a collection. Maximum likelihood methods were
used to estimate the exponent, \emph{b} , using code modified from
Edwards et al. (2017). Using these methods, a more negative
\emph{b}exponent represents a steeper slope of the size spectra. Slopes
can be steeper due to either a relative increase in the abundance of
small body sizes, or a relative decrease in the abundance of large body
sizes, or a combination of the two. Regardless, steeper slopes represent
a relatively smaller proportion of large individuals compared to small
individuals within a community.

In addition to the size spectra slopes, we also examined how total
community biomass varied across the collections. First, we multiplied
the estimated biomass of individuals by their estimated density to
calculate mg of dry mass per m\textsuperscript{2} for each sample within
a collection (n = 8 samples per collection). The 8 samples per
collection were used as individual observations in the hierarchical
Bayesian analyses (see \emph{Statistical Analyses} ).

Macroinvertebrates were generally sampled three times within the growing
season from each site. However, due to the wide geographical
distribution of sites, there was significant variation in the sample
dates. For example, samples in Alaska (latitude 65-68\selectlanguage{ngerman}° N) were collected
between May and August, whereas samples in Alabama (latitude 33° N) were
collected between February and November. Because we are interested in
the effects of temperature on size spectra, we used the mean annual
temperature in degrees Celsius for each site.

\emph{Statistical Analyses}

To estimate the effect of temperature on size spectra slopes
(\emph{b}exponent) and standing stock community biomass, we fit separate
hierarchical models with varying intercepts across sites and year
(McElreath 2020). We chose a Bayesian approach because it easily
incorporates prior information and hierarchical model structures (Hobbs
\& Hooten 2015; Dietze 2017). Because the exponent \emph{b} is
continuous and can be positive or negative, we used a Gaussian
likelihood to model size spectra slopes. The predictor variable was mean
annual temperature. To account for unexplained variation among sites and
years, we included random intercepts of \emph{site} and \emph{year} .
Priors for the intercept were set as \emph{Normal} (-1.5, 1). This puts
low prior probabilities on positive exponents and on exponents with
extreme negative values (e.g., \textless{} -4), reflecting a wide range
of possible values reported in the size spectra literature (White et al.
2007, Blanchard et al. 2009, Edwards et al. 2017). Prior distributions
for the effect of mean annual temperature were set as\emph{Normal}
(0,0.1), and \emph{Exponential} (2) for sigma and the standard deviation
of the random intercepts. To ensure that the prior distributions
contained reasonable prior prediction but did not overwhelm the
posterior inference, we used prior predictive simulation (\emph{see}
Supplemental Information, Fig. S1; Gabry et al. 2019).

For community biomass, we used a Gamma likelihood with a log link,
because biomass is a continuous and positive measure (Hobbs \& Hooten
2015). The model structure for biomass was the same as the model
structure for size spectra slopes. The prior for the intercept was set
as \emph{Normal} (7,1). This reflects a prior expectation that community
biomass values of \textasciitilde{}150 to \textasciitilde{}9000 mg dry
mass/m\textsuperscript{2} are reasonable, with a mean of
\textasciitilde{}1000. These point estimates are obtained by
exponentiating, due to the log-link, the prior mean (i.e. 7) plus and
minus two standard deviations (7-2 or 7+2). These values are compatible
with the range of values reported in the literature (Benke \emph{et
al.}1984; Grimm 1988; Warmbold \& Wesner 2018). The remaining priors
were\emph{Normal} (0,0.1) for the slope, \emph{Exponential} (2) for the
standard deviation of the random intercepts, and \emph{Gamma} (0.01,
0.01) for the shape parameter of the Gamma likelihood. As before, these
priors were specified using prior predictive simulation (Supplemental
Information, Fig. S1). Our prior and posterior distributions for
parameter coefficients are plotted in SI Figures S2 and S3. A
sensitivity analysis of our priors indicated our parameter coefficient
estimates were robust to halving and doubling the SD value (Fig. S6).
The full hierarchical model structure can be found in Supplemental
Information S1.

Models were specified using the \emph{brms} package (Bürkner 2018) in R
(R Development Core Team 2017), with posterior distributions derived
using Hamiltonian Monte Carlo in \emph{rstan} (Stan Development Team
2018). The models were run using 4 chains each with 6000 iterations, in
which the first half were discarded as warmup, resulting in 12000
posterior draws. Convergence was checked by ensuring that all r-hats
were \textless{}1.1, and by visually assessing trace plots (Gelman \&
Rubin 1992). To assess model fit, we used posterior predictive checks in
which we simulated ten data sets from the posterior distribution and
graphically compared them to the original data set (Gelman et al. 2013;
Gabry et al. 2019; Figure S4 and S5). Strong visual discrepancies
between the original data and simulated data indicate poor model fit
(Gabry et al. 2019). In our simulations, both models produced data that
were qualitatively similar to the original data, indicating good model
fit (SI, Figures S4 \& S5).

\emph{Data availability}

All of the data and R code to reproduce our results and figures will be
archived upon article acceptance. The data will be archived in an
appropriate repository (i.e., Dryad) and the code will be archived on
Zenodo.

\textbf{Results}

\emph{Size Spectra slopes}

The maximum likelihood estimates of the power law exponent (e.g., size
spectra slopes) for individual collections ranged from -0.73 to -1.80.
The median of the posterior distribution of size spectra slopes from the
hierarchical Bayesian model was -1.25 (95\% CrI (-1.39, -1.11); Table
2). Size-spectra slopes had a 98\% probability of being negatively
related to mean annual temperature (coefficient estimate = -0.004, 95\%
CrI (-0.007, 0.0004); Table 2), meaning that increasing temperature was
associated with relatively fewer larger body sizes compared to small
body sizes within a community (Figure 2A). However, the magnitude of
this decline was small, with median slopes changing from -1.2 to -1.3
across the 29°C temperature gradient. This range was smaller than the
among-sample range in measured slopes within sites (Figure 2A). Using
the posterior predictive distribution from our model to estimate
variation among sites, median slopes ranged from a low of -1.33 at site
GUIL (mean annual temperature = 25°C) to a high of -1.15 at site CARI
(mean annual temperature = -1°C; Figure 3A).

\emph{Community biomass}

Community biomass showed higher variation among sites (Figure 2B) with
collection-specific medians ranging from 22.3 to 6,729 mg dry mass per
m\textsuperscript{2}. The global median of the posterior samples from
the hierarchical Bayesian model was 1,333 mg dry mass per
m\textsuperscript{2} (95\% CrI (247, 10 929)). Community biomass had a
95\% probability of being positively related to mean annual temperature
(coefficient estimate = 0.04 (CrI (-0.008, 0.09); Table 2). After
exponentiating, this indicates that each unit increase in mean annual
temperature causes an average of a 4\% increase in biomass. From the
posterior predictive distribution, median biomass values ranged from a
low of 285 mg dry mass per m\textsuperscript{2} at site CARI (mean
annual temperature = -1°C) to a high of 11,309 mg dry mass per
m\textsuperscript{2} at site LEWI (mean annual temperature = 12°C,
Figure 3B).

\textbf{Discussion}

We analyzed the largest collection of size spectra relationships in
stream communities that we are aware of, and we found that slope
estimates varied in response to a broad temperature gradient.
Size-spectra slopes represent the efficiency of energy transfer from
small, abundant, individuals to fewer large predators (Trebilco \emph{et
al.} 2013) with clear implications for ecosystem functioning
(O'Gorman\emph{et al.} 2012; Cross \emph{et al.} 2015). Shallow slopes
indicate efficient transfer of energy by supporting a relatively higher
proportion of larger individuals, while steeper slopes indicate
inefficient energy transfer with relatively fewer large individuals. The
steeper slopes reported here with increasing mean annual temperatures
implies that warmer sites have fewer large individuals relative to the
number of small individuals within a site compared to colder sites.

These results help to resolve previous uncertainty in how size spectra
slopes scale with temperature. Variation in size spectra slopes is
driven by variation in body size distributions and body size is in turn
altered by temperature, either through reductions in taxon-specific body
size, species turnover, or through changes in community structure
(Bergmann 1847; Atkinson 1994; Daufresne \emph{et al.} 2009;
Winder\emph{et al.} 2009; O'Gorman \emph{et al.} 2012). Thus, it is
widely expected size-spectra slopes should vary across temperature
gradients, though the direction of change is uncertain (Daufresne
\emph{et al.}2009; Dossena \emph{et al.} 2012; O'Gorman \emph{et al.}
2012). Theory predicts that warm environments should favor smaller
individuals, and this is supported by empirical (James 1970; Atkinson
1994; Daufresne\emph{et al.} 2009) and experimental observations
(Yvon-Durocher\emph{et al.} 2011; Dossena \emph{et al.} 2012). However,
O'Gorman\emph{et al.} (2017) found that warmed Icelandic streams had
shallower slopes, perhaps due to increased nutrient availability and
changes in trophic transfer efficiency, leading to increased top-down
effects of consumers on diatoms. In contrast, Dossena \emph{et al.}
(2012) found that slopes declined with temperature, but the effect
varied over seasons. Mazurkiewicz \emph{et al.} (2020) found no
relationship between marine benthic size spectra and temperatures in
arctic systems. These contrasting outcomes, derived from different
experimental approaches, generate uncertainty in how size spectra slopes
should scale with temperature across large spatial gradients. The
results presented here support the hypothesis that slopes become steeper
in response to increasing temperature.

However, while size spectra slopes scaled with temperature, the overall
change was relatively small, with median slopes declining by only
\textasciitilde{}0.1 units across the temperature gradient. Direct
comparisons of this effect size with previous studies of size spectra
responses to temperature (e.g., Yvon-Durocher et al. 2012, O'Gorman et
al. 2017) are hampered by the different approaches to estimating size
spectra exponents (Edwards et al. 2017). However, in a 30-year dataset
from the International Bottom Trawl Survey (ICES 2015), Edwards et al.
(2020) found that size spectra slopes calculated using maximum
likelihood varied \textasciitilde{}0.4 units among years. In addition,
among sample variation at NEON sites in this study was
\textasciitilde{}0.3 to 0.8 units, with measured slopes at one site
varying from -1 to -1.8 among sample dates. Placing our results into
this context, the influence of temperature appears small relative to
variation due to other factors, including natural variation over time.

In contrast to the negative relationship of temperature with
size-spectra slopes, community biomass was positively related to mean
annual temperature. This is also in agreement with predicted effects of
increasing environmental temperature supporting more small-bodied
individuals. For example, an increase in community biomass could be
driven by small-bodied individuals alone, if their increase was larger
than the relative decrease in larger-bodied individuals. It is important
to account for community biomass, as increased biomass at lower trophic
levels may be able to support biomass at higher trophic levels
(O'Gorman\emph{et al.} 2012), even if trophic transfer efficiency is
affected by temperatures (Trebilco \emph{et al.} 2013). However, the
magnitude of the random effects of \emph{site} were large, and the
effect of temperature was relatively small. Including additional
predictor variables thought to affect community biomass, such as
productivity or nutrient availability at the base of the food web (Morin
\emph{et al.}2001; Daan \emph{et al.} 2005), may help explain additional
variation.

Given the relatively small influence of temperature and the overlap in
site-specific averages of size spectra (Figure 3A), our results provide
an opportunity to use our range of size-spectra as a baseline indicator
in studies of disturbance. Size-spectra relationships have been proposed
as a universal indicator of ecological health, with deviations from
``natural'' size spectra representing disturbed systems (Jennings \&
Blanchard 2004; Petchey \& Belgrano 2010; Trebilco \emph{et al.} 2013).
Defining ``natural'' is difficult without accounting for variation among
broad spatial and temporal scales. By accounting for the effect of
temperature on size spectra slopes in relatively undisturbed systems
across 50 degrees of latitude over three years, our results reveal
bounds that could help to gauge the severity of size spectra change in
response to disturbance. For example, one approach would be to compare
size spectra from disturbed sites to the posterior predictive
distribution of size-spectra at a similar site in our study, with
deviations outside of the expected range of natural variation indicating
the level of disturbance. This may represent a powerful tool for
assessing ecological condition. Indeed, as NEON data continues to be
collected, it will be possible to compare our predictions to
size-spectra collected after intense disturbances, such as extremely
high or low flow events, temperature anomalies due to climate change,
wildfires, flow debris, etc. This represents an exciting opportunity to
test responses to disturbances at higher levels of organization, which
has typically been difficult or impossible due to the large logistical
efforts needed to collect community-wide data across broad spatial
scales. Furthermore, data on post-disturbance size spectra within the
NEON sites will provide valuable information on community recovery, and
the magnitude, direction, and expected duration of altered size spectra.

\textbf{Acknowledgments}

We thank CJ Perovich for thoughtful discussion, and IA Sutton for help
with maximum likelihood methods. This work was funded by an NSF EAGER
award (DEB \#1837233) to JSW.

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\textbf{Tables and Figures}

\textbf{Table 1.} Site-level information for stream sites in the
National Ecological Observatory Network.

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/367796/articles/487073-spatiotemporal-variability-of-abundance-size-spectra-in-streams-across-north-america}

\textbf{Table 2.} Population-level coefficient estimates and 95\%
credible intervals for the effects of degree days on size spectra slopes
(\emph{b} exponent) and community biomass.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Model} & \textbf{Coefficient} & \textbf{Estimate} &
\textbf{Q2.5} & \textbf{Q97.5} & \textbf{R-hat}\tabularnewline
\midrule
\endhead
\emph{b} exponent & y-intercept & -1.2 & -1.3 & -1.1 & 1\tabularnewline
& degree days & -0.003 & -0.007 & 0.0004 & 1\tabularnewline
Biomass & y-intercept & 6.7 & 6.2 & 7.5 & 1\tabularnewline
& degree days & 0.04 & -0.008 & 0.08 & 1\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 1.} Map of site locations across North America.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 2} . Effect of mean annual temperature on the slope and
intercept of normalized size-spectra relationships. Horizontal lines and
shading indicate the median and 95\% CrI of the change in slopes or
intercepts across degree day values. Dots indicate individual
collections.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 3} . Posterior distributions of slope (a) and biomass (b)
across sites where color corresponds to mean annual temperature. Black
vertical lines within density distributions show the median value for a
given collection. Sites are ordered by decreasing mean annual
temperature, e.g., GUIL is the warmest site at 25\selectlanguage{ngerman}°C, while OKSR is the
coldest at -4°C.

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

