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\begin{document}

\title{Range edges of North American marine species are tracking temperature
over decades}



\author[1]{Alexa Fredston}%
\author[2]{Malin Pinsky}%
\author[3]{Rebecca Selden}%
\author[4]{Cody Szuwalski}%
\author[4]{James Thorson}%
\author[1]{Benjamin Halpern}%
\author[1]{Steve Gaines}%
\affil[1]{University of California Santa Barbara}%
\affil[2]{Rutgers University}%
\affil[3]{Wellesley College}%
\affil[4]{Alaska Fisheries Science Center}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\selectlanguage{english}
\begin{abstract}
Understanding range edges is key to addressing fundamental biogeographic
questions about abiotic and biotic drivers of species distributions.
Range edges are where colonization and extirpation happen, so their
dynamics are also important for natural resource management and
conservation. We quantified positions for 153 range edges of marine
fishes and invertebrates from three US continental shelf regions using
decades of survey data and a spatiotemporal model to account for changes
in survey design. We analyzed whether range edges maintained their edge
thermal niches---temperature extremes at the range edge---over time.
Most range edges (86\%) maintained either cold or warm temperature
extremes; 73\% maintained both. However, the substantial fraction of
range edges that altered their thermal niche underscore the multiplicity
of relevant drivers. This study suggests that temperate marine species
largely maintained their edge thermal niches during rapid change and
provides a blueprint for testing temperature tracking of species range
edges.%
\end{abstract}%



\sloppy


TITLE:

\emph{Range edges of North American marine species are tracking
temperature over decades}

SHORT RUNNING TITLE (45 character limit): Thermal niche tracking of
species range edges

AUTHORS:

Alexa Fredston\textsuperscript{1,2}*, fredston@rutgers.edu; Malin
Pinsky\textsuperscript{2}, malin.pinsky@rutgers.edu; Rebecca L.
Selden\textsuperscript{3}, bselden@wellesley.edu; Cody
Szuwalski\textsuperscript{4}, cody.szuwalski@noaa.gov; James T.
Thorson\textsuperscript{4}, james.thorson@noaa.gov; Steven D.
Gaines\textsuperscript{1}, gaines@bren.ucsb.edu; and Benjamin S.
Halpern\textsuperscript{1,5}, halpern@nceas.ucsb.edu

\textsuperscript{1} Bren School of Environmental Science \& Management,
University of California, Santa Barbara, Santa Barbara, CA, United
States

\textsuperscript{2} Department of Ecology, Evolution, and Natural
Resources, Rutgers, The State University of New Jersey, New Brunswick,
NJ, United States

\textsuperscript{3} Department of Biological Sciences, Wellesley
College, Science Center, Wellesley, MA, United States

\textsuperscript{4} Alaska Fisheries Science Center, National Marine
Fisheries Service, National Oceanic and Atmospheric Administration,
Seattle, WA, United States

\textsuperscript{5} National Center for Ecological Analysis and
Synthesis, University of California, Santa Barbara, Santa Barbara, CA,
United States

* \emph{Corresponding author:} 14 College Farm Road, New Brunswick, NJ
08901. Phone: (848) 932-9631. Fax: (732) 932-2587.

AUTHORSHIP STATEMENT: AF conducted the analysis, prepared the figures,
and drafted the manuscript. JTT was responsible for the development of
software used in the analysis. All authors contributed to the design of
the project, to interpretation and visualization of the results, and to
editing the manuscript.

DATA ACCESSIBILITY STATEMENT: If this manuscript is accepted, the data
supporting our analysis will be archived in a public repository and the
DOI will be included in the article.

ARTICLE TYPE: Letters

KEYWORDS (up to 10): biogeography, climate change, global warming,
thermal tolerance, range limit, range margin, thermal niche

NUMBER OF WORDS IN ABSTRACT AND MAIN TEXT: abstract 150, main text 4761

NUMBER OF REFERENCES: 69

NUMBER OF TABLES, FIGURES, AND TEXT BOXES: 0 tables, 3 figures, 0 text
boxes

ABSTRACT

Understanding range edges is key to addressing fundamental biogeographic
questions about abiotic and biotic drivers of species distributions.
Range edges are where colonization and extirpation happen, so their
dynamics are also important for natural resource management and
conservation. We quantified positions for 153 range edges of marine
fishes and invertebrates from three US continental shelf regions using
decades of survey data and a spatiotemporal model to account for changes
in survey design. We analyzed whether range edges maintained their edge
thermal niches---temperature extremes at the range edge---over time.
Most range edges (86\%) maintained either cold or warm temperature
extremes; 73\% maintained both. However, the substantial fraction of
range edges that altered their thermal niche underscore the multiplicity
of relevant drivers. This study suggests that temperate marine species
largely maintained their edge thermal niches during rapid change and
provides a blueprint for testing temperature tracking of species range
edges.

INTRODUCTION

Human-caused global climate change now affects, directly or indirectly,
all biomes and levels of biological organization (Scheffers et al.
2016). One of the most profound effects has been changes in the spatial
distributions of species that align with shifting climates---up
mountains, deeper in the oceans, and generally toward the poles(Parmesan
and Yohe 2003, Pecl et al. 2017). A strong correlation between regional
climate change and shifting species ranges has been documented in many
taxa (Chen et al. 2011, Pinsky et al. 2013). However, individualistic
responses and ``ecological surprises'' are also common (Poloczanska et
al. 2011, Zhu et al. 2012, La Sorte and Jetz 2012), underscoring the
need to consider the interplay of climatic constraints and non-climate
processes in determining the edges of species ranges (Sexton et al.
2009, Urban et al. 2016).

Range edges often arise where biotic or abiotic conditions prevent
persistence of a species. We use the definition of the fundamental niche
as the range of environments in which a species could theoretically
persist in the absence of biotic interactions, and the realized niche as
the range of environments in which the species is actually found (Godsoe
et al. 2017). Changing environments provide an opportunity to test
whether niches are conserved through time. If a species is shifting its
geographic range to track temperature, for example, it will occur at the
same temperature over time and the realized thermal niche will be
conserved. Conversely, if a species does not shift in concert with
temperature change, the geographic range may remain stable but the
realized thermal niche will change (La Sorte and Jetz 2012). In
addition, temperature extremes are more likely to be range-limiting than
means (Sunday et al. 2019), but either poleward or equatorward range
edges can theoretically be limited by either cold or warm temperature
extremes; e.g., a poleward range edge might occur where it becomes too
cold in winter for adults to survive, or where summers are not warm
enough for reproduction and/or juvenile survival (Hutchins 1947). We
define the \emph{edge thermal niche} as the thermal extremes found at a
species' range edge. Testing which temperature extreme is important for
which range edges has rarely been examined across full assemblages of
species (Ma et al. 2015, Urban et al. 2016). In this study, we test
whether range edges of marine species in three continental shelf regions
in the United States have conserved their edge thermal niches over
decades of environmental change.

Marine continental shelf species are ideal for studying these
biogeographical questions: they are shifting rapidly, experience
relatively few barriers to dispersal, and large-scale, long-term
datasets of their historical distributions and abundances exist (Pinsky
et al. 2020). Some studies on range edges and climate have predicted
that marine species should track temperature readily throughout their
range, because marine range edges occur on average at the limits of
species' thermal tolerances (Sunday et al. 2012, Stuart-Smith et al.
2017). Other theory---primarily developed in terrestrial systems but
supported by a recent test using marine data (Fredston-Hermann et al.
2020)---suggests that one or both range edges are driven by other
processes like species interactions and will not shift as much in
response to temperature change (Connell 1961, MacArthur 1972, Sax and
Gaines 2003, Poloczanska et al. 2011, Cahill et al. 2014, Louthan et al.
2015).

The extension of these tests to more regions and taxa is partially
limited by data availability, because quantifying range edge dynamics
requires large-scale and long-term biodiversity surveys (Parmesan et al.
2005). To date, very few studies have measured range edge dynamics at
high temporal resolution---a necessary prerequisite to understanding the
relationship between range edges and temperature change, particularly
because using too few time points can create misleading trends (La Sorte
and Jetz 2012, Brown et al. 2016, Fredston-Hermann et al. 2020).
Biodiversity survey programs also often use different sampling methods
and schedules, further complicating cross-regional comparative
biogeography. To address both of these constraints, we used large-scale
biodiversity survey data from the National Oceanic and Atmospheric
Administration (NOAA) beginning as early as 1968. We then fitted
spatiotemporal models to estimate biomass density and then range edge
positions through time, which allowed us to estimate range edge
uncertainty and to more clearly compare results across species and
regions.

Overall, we quantified conservatism of species' edge thermal niches over
time, drawing on repeated large-scale biodiversity surveys in three
North American marine regions to describe shifts in poleward and
equatorward range edges across 153 fish and invertebrate species. The
three study regions have divergent climatic histories that allowed us to
test for edge thermal niche conservatism in different temperature change
regimes: the Northeast US has warmed rapidly and almost continuously,
the Eastern Bering Sea has warmed episodically, and the US West Coast
has not warmed overall but periodically experiences dramatic temperature
fluctuations among years.

MATERIALS AND METHODS

\emph{Temperature and species distribution data sources}

We used National Oceanic and Atmospheric Administration (NOAA) long-term
surveys from three temperate marine continental shelf regions in the US:
the Northeast (annual spring survey 1968-2018), the West Coast
(triennial fall survey 1977-2004 and annual fall survey 2003-2018), and
the Eastern Bering Sea (annual summer survey 1989-2018; earlier years
omitted due to limited spatial extent (Lauth and Conner 2014)). These
surveys use trawl gear and a random stratified or fixed station sampling
design to target demersal and benthic fishes and invertebrates on the
continental shelf, up to several hundred meters deep. The West Coast and
Eastern Bering Sea raw datasets were downloaded using the R package
``FishData'', and the Northeast data were obtained from the 2019
OceanAdapt release (Stuart and Pinsky 2019), a data portal to access
NOAA trawl survey records (Lauth and Conner 2014, Politis et al. 2014,
Keller et al. 2017). The Northeast dataset, which was pre-processed by
OceanAdapt for quality control and taxonomic accuracy, contained records
for 74 species. We limited our West Coast analysis to the 54 species
that were recorded in both the triennial and the annual surveys. In the
Eastern Bering Sea dataset, we downloaded data on the 100 most
frequently observed taxa and proceeded with analysis for the 81 taxa
that were identified to species. We retrieved higher taxonomy for all
species using the R package ``taxize'' (Chamberlain and Sz\selectlanguage{ngerman}öcs 2013) and
grouped species as either fishes (belonging to classes Actinopterygii or
Elasmobranchii) or invertebrates (everything else). All data processing
and analyses were conducted in R version 3.6.0 (R Core Team 2018). All
code used is available at:
https://github.com/afredston/range-edge-niches.

Throughout our analysis, we compared distribution data for a given
species with temperature data from the preceding 12
months---specifically, the 12 months preceding the earliest possible
start month for each region's survey for analysis (March in the
Northeast, May in the West Coast, and July in the Eastern Bering Sea).
For example, range edges derived from the spring 1999 Northeast survey
were compared to temperature records from March 1998 to February 1999.

We used two historical sea surface temperature (SST) datasets. The NOAA
NCEI Optimum Interpolation Sea Surface Temperature dataset (NOAA NCEI
2018) is available daily from 1982 onward at 0.25ºx0.25º resolution; we
downloaded this dataset for all regions. We also downloaded the Hadley
Centre Global Sea Ice and Sea Surface Temperature dataset, available
monthly at 1º resolution from 1870 (Rayner 2003) to fill in earlier
years for the Northeast and West Coast regions. To ensure comparability
between the two data sources, we performed mean bias correction and
converted the daily SST records from the higher-resolution OISST dataset
into monthly means for each grid cell (see Appendix 1); all temperature
metrics described henceforth are based on monthly mean SSTs. We tested
for change in mean, minimum, and maximum monthly regional SSTs over time
by calculating the annual region-wide mean of grid cell-specific mean,
minimum (coldest month), and maximum (warmest month) SSTs, and
performing a linear regression of those region-wide means over time for
each region with a significance threshold of \selectlanguage{greek}α \selectlanguage{english}= 0.05.

Range edge positions were compared to warm and cold temperature
extremes, defined as the warmest and coldest months of the 12 months
preceding the survey (i.e., coldest month the previous winter and
warmest month the previous summer). To generate edge-specific estimates
of warm and cold extreme temperatures (see \emph{Range edge analysis} ),
we constructed generalized additive models (GAMs) of maximum and minimum
monthly temperatures in each year along the axis of measurement for each
region (see \emph{Spatiotemporal reconstruction of species ranges} )
using the ``mgcv'' package in R (Wood 2017). Each regional GAM predicted
warm or cold temperature extremes in each year, given a position along
the axis (one GAM was fit for all years in a given region with axis
position as a smoothed predictor estimated separately for each year, as
well as a separate year factor).

\emph{Spatiotemporal reconstruction of species ranges}

We estimated annual species range edges from the trawl survey data using
the spatial generalized linear mixed model implemented in the R package
VAST (Thorson and Barnett 2017, Thorson 2019). This model was designed
to estimate total abundance and spatial variation in density of species
using spatially referenced biomass observations. We fit VAST to data
that follow either stratified-random or fixed-station designs; in both
cases, VAST predicted densities over a fixed spatial domain. This
analysis enabled comparison across years even when survey methodologies
were revised and across regions with distinct survey protocols. In
addition, this approach controlled for differences in catchability,
enabling us to combine the two historical West Coast surveys (Thorson et
al. 2016a). The model structure is described in detail in the
supplementary materials (Appendix 2).

Of the 209 species-region combinations, VAST models converged for 205 of
them (72 of 74 in the Northeast, 53 of 54 on the West Coast, and 80 of
81 in the Eastern Bering Sea). This set included five species found in
both the West Coast and the Eastern Bering Sea (\emph{Atheresthes
stomias, Bathyraja interrupta, Clupea pallasii, Glyptocephalus
zachirus,} and \emph{Hippoglossoides elassodon} ), and one found in both
the Northeast and the West Coast (\emph{Alosa sapidissima} ), so the
total number of unique species was 199.

\emph{Range edge analysis}

We quantified species range edges as the 0.01 and 0.99 quantiles of
density along spatial axes running the length of each study region. We
chose these quantiles to capture the extremes of each species'
distribution; because edges were calculated from VAST's spatiotemporal
biomass estimates and not from the raw data, they were unlikely to be
influenced by the rare, high biomass observations that are common in
shoaling species such as fishes (Thorson et al. 2011). Species range
edges are conventionally measured in units of degrees latitude along a
north-south axis (e.g., Hickling \emph{et al.} 2005). However, in marine
regions with complex coastline topographies and/or coastlines that are
not oriented parallel to lines of longitude, coastal distance is a more
accurate metric of range edge position than latitude (Hare et al. 2010,
Bell et al. 2015, Fredston-Hermann et al. 2020). We therefore developed
a coastal distance metric for the West Coast and the Northeast (Figure
1A, 1B; methods in Appendix 2). We then associated points along the
coastline with the grid of VAST knots (see Appendix 2) by finding the
points with the minimum Euclidean distances. Matching points along the
coastline to the VAST knots enabled us to estimate density, and thus
range edge position, along the coastal distance axis. In the Eastern
Bering Sea, the coastal distance axis was less applicable because the
shelf is so wide that many species fall quite far from the coast and
also because the presence of islands makes the delineation of a smoothed
coastline more complex. In the Eastern Bering Sea, we therefore
estimated density along a line drawn from the Aleutian Islands (56\selectlanguage{ngerman}ºN,
161ºW) to the edge of the US Exclusive Economic Zone (62ºN, 176.5ºW)
through the Middle Domain, a hydrographic region with similar bathymetry
defined as lying between two oceanographic mixing zones that partition
the middle from inner and outer domains (Coachman 1986, Ortiz et al.
2016).

To ensure that the species analyzed had at least one range edge in the
study region, we eliminated range edges with mean positions over time
that fell close to the boundary of the study region, defined as less
than 10\% from the end of the axis of measurement in either direction
(Northeast axis length = 1368 km, West Coast axis length = 2037 km,
Eastern Bering Sea axis length = 1102 km). This removed 18 Northeast
species, 24 West Coast species, and 13 Eastern Bering Sea species. After
this filter, we proceeded with 153 range edges---55 in the Northeast, 25
on the West Coast, and 73 in the Eastern Bering Sea---across 145 fishes
and invertebrates from 17 taxonomic classes (eight had both equatorward
and poleward range edges; see Appendix 3). We note that for almost all
species, only one range edge fell within the study region (see Appendix
3). Thus, our analysis evaluated the evidence for our different
hypotheses by evaluating many isolated range edges, not by evaluating
both range edges of a single species.

We tested whether range edge positions had shifted significantly over
time with single-species Bayesian linear regressions of range edge
position on time (\emph{n} = 151 models; two models were removed that
did not estimate standard errors around range edge position due to
parameter reduction to aid convergence in VAST). Single-species models
were fitted using the ``rstanarm'' package (Goodrich et al. 2018) with
four chains, 40,000 iterations including 10,000 burn-in draws, and a
target average proposal acceptance probability of 0.99. We selected a
normally distributed vague prior with a mean of 0 and standard deviation
of 50 km/year; this standard deviation was chosen to exceed the upper
range of climate velocities in the oceans (Burrows et al. 2011). Range
positions were weighted by VAST-estimated standard errors
(\(\frac{1}{\text{SE}^{2}}\)) for each year so that estimated edge positions with
higher associated uncertainties were less influential (Thorson et al.
2016b). All of these models converged, as evaluated by Gelman-Rubin
convergence statistic below 1.1.

\emph{Edge thermal niche conservation}

We measured the edge thermal niche---the temperatures found at the range
edge---by predicting annual warm and cold temperature extremes at the
range edge position using region-specific GAMs (see \emph{Temperature
and distribution data sources} ). We then fitted Bayesian linear
regressions to test whether either the warm or cold extreme temperature
at a species' range edge had changed over time (\emph{n} = 306, 153
range edges for each of two temperature extremes). Single-species
Bayesian linear regressions were fitted using the ``rstanarm package''
(Goodrich et al. 2018) with four chains, 40,000 iterations including
10,000 burn-in draws, and a target average proposal acceptance
probability of 0.99. We selected a normally distributed vague prior with
a mean of 0 and standard deviation of 0.1 °C/year; this standard
deviation was chosen to exceed the largest rates of SST change across
all study regions (see \emph{Results} ). Models were weighted by
GAM-estimated standard errors (\(\frac{1}{\text{SE}^{2}}\)), so that estimated
temperatures with higher associated uncertainties were less influential.
Models converged for all range edges (Gelman-Rubin convergence statistic
below 1.1). We calculated the mean and 90\% Bayesian credible interval
from each single-species posterior distribution of the year coefficient
for either warm or cold temperature extremes.

If at least one of the two temperature metrics we measured at a range
edge---cold or warm extremes---was constant over time, the range edge
could be tracking that temperature and exhibiting edge thermal niche
conservatism (Hutchins 1947). We categorized range edges according to
whether the range edge maintained a constant warm extreme temperature at
the edge over time, a constant cold extreme temperature, both, or
neither, based on 90\% Bayesian credible intervals (Figure 2). In this
method, edge thermal niche conservatism could arise either from the
range edge shifting in space to track temperature, or the range edge
remaining stationary in a location where temperatures have not changed
over time. To disentangle these processes, we compared changes in the
edge thermal niche to changes in the range edge position (Figure 2).

RESULTS

From 1967 to 2018, minimum, mean, and maximum SST in the Northeast all
increased (Figure 1A), translating to more than one degree Celsius of
warming in every SST metric over the fifty years measured (respectively,
0.023 ± 0.007 °C/year, \emph{p} = 1.4 × 10\textsuperscript{-3}; 0.03 ±
0.004 °C/year, \emph{p} = 3.7 × 10\textsuperscript{-9}; 0.033 ± 0.006
°C/year, \emph{p} = 4.4 × 10\textsuperscript{-7}). On the West Coast
(Figure 1B), no significant trends occurred in any temperature metric
from 1976-2018, despite variation of ± 2 °C for individual years
(minimum SST 0.004 ± 0.008 °C/year, \emph{p} = 0.65; mean SST 0.002 ±
0.007 °C/year, \emph{p} = 0.77; maximum SST 0.003 ± 0.009
°C/year,\emph{p} = 0.77). In the Eastern Bering Sea (Figure 1C), warming
was evident in maximum SST change from 1988-2018, which increased 0.038
± 0.018 °C/year (\emph{p} = 0.049). Neither minimum nor mean SST
increased significantly in the Eastern Bering Sea, although both had a
positive relationship with year (minimum 0.008 ± 0.01 °C/year, \emph{p}
= 0.48; 0.022 ± 0.013 °C/year, \emph{p} = 0.10).

\emph{Range edge shifts over time}

Species' range edge shifts differed substantially, ranging from 24.5
km/year (equatorward edge of \emph{Homarus americanus} in the Northeast)
to -26.5 km/year (poleward edge of \emph{Sebastes semicinctus} in the
West Coast). Single-species edge positions over time are reported in
Appendix 3. When pooled over all species in a region, all regions had a
generally northward shift. Range edges shifted 3.9 km/year in the
Northeast from 1967-2018 (90\% CI 3.6 -- 4.2), 5.2 km/year on the West
Coast from 1976-2018 (90\% CI 4.2 -- 6.2), and 0.1 km/year in the
Eastern Bering Sea from 1988-2018 (90\% CI -0.2 -- 0.4; means and
credible intervals from single-species Bayesian linear regressions).
Separating these posterior distributions by range edge type (poleward or
equatorward) revealed opposing shifts in the two groups: equatorward
range edges overall shifted 5.3 km/year (90\% CI 5.0 -- 5.6), but
poleward range edges shifted -2.2 km/year (90\% CI -2.6 -- -1.8; means
and credible intervals from single-species Bayesian linear regressions).

The magnitude of range edge shifts was slightly greater in fishes than
in invertebrates. When both were pooled across all species and regions,
fishes shifted north 2.6 km/year (90\% CI 2.3 -- 2.9) and invertebrates
1.7 km/year (90\% CI 1.3 -- 2.1; means and credible intervals from
single-species Bayesian linear regressions). This difference in
magnitude of shift was driven by equatorward range edges of fishes
shifting faster---6.0 km/year \emph{versus} 4.0 km/year---than
invertebrates (90\% CIs 5.6 -- 6.4 and 3.6 -- 4.4, respectively), while
both groups of poleward edges shifted in the opposite direction at
similar rates (fishes -2.3 km/year, 90\% CI -2.8 -- -1.7, invertebrates
-2.0 km/year, 90\% CI -2.7 -- -1.3; means and credible intervals from
single-species Bayesian linear regressions).

\emph{Edge thermal niche tracking}

We estimated edge thermal niche tracking by testing whether the change
in minimum or maximum temperature at the range edge over time was
different from zero, based on 90\% Bayesian credible intervals from
Bayesian linear regressions of temperature on time. Of 153 species range
edges, we found that 131 (86\%) maintained at least one component of the
edge thermal niche (warm or cold extreme temperature) during the study
period. Further, for the majority of range edges---111, or 73\%---both
minimum and maximum temperatures were maintained over time. Of the 20
range edges consistent with only one temperature metric, 13 were
consistent with cold extremes and 7 were consistent with warm extremes.

In the West Coast and the Eastern Bering Sea, almost all range edges
tracked both temperature metrics (84\% and 97\%, respectively; Figure
3B, 3C). By contrast, only 34\% of range edges in the Northeast tracked
both temperature metrics, and the Northeast also contained 20 of the 22
range edges that did not track either warm or cold temperature extremes
(Figure 3A). Lack of tracking arose for different reasons in poleward
and equatorward edges: the poleward edges that did not maintain their
edge thermal niches often did not shift as the oceans warmed, or even
shifted south, while the equatorward edges that did not maintain their
edge thermal niches often shifted north faster than expected, into
cooler waters (Appendix 4).

DISCUSSION

We quantified temporal dynamics and thermal niche tracking over decades
for more than 150 marine range edges while using a novel spatiotemporal
modeling approach to standardize among the three study regions and their
differing survey methodologies. Across diverse geographies, historical
climates, and taxa, range edges of marine species were in general
conserving their thermal niches through space and time. Edge thermal
niche conservatism suggests that most species range edges are tracking
temperature change, consistent with evidence that many species
distributions are shifting through space (Pinsky et al. 2013, Hiddink et
al. 2015, Day et al. 2018, Lenoir et al. 2020) and supported by theory
from thermal ecophysiology (Sunday et al. 2012, Stuart-Smith et al.
2017, Pinsky et al. 2019). However, a non-negligible number of range
edges did not shift as predicted---especially in the Northeast, the
region with the greatest historical temperature increase---indicating
that temperature alone does not explain range edge dynamics for all
marine species. This study provides the first large-scale, multi-region
analysis of thermal niche tracking at range edges and describes novel
statistical approaches that are applicable to a wide range of taxa and
systems.

Species ranges are shifting poleward around the globe, both on land and
in the sea (Chen et al. 2011, Poloczanska et al. 2013). Local patterns
in climate change have helped to explain distributional shifts of many
species, especially those that did not shift directly toward the poles
(Pinsky et al. 2013, Lenoir and Svenning 2015). These findings suggest
that species' range shifts can be at least partially explained by
spatial shifts in their climatic niches (Loarie et al. 2009, Burrows et
al. 2011). While many global change studies have not measured range edge
displacement, those that have often report major poleward shifts in
range edges, particularly at the poleward range edge (Parmesan et al.
1999, Hickling et al. 2005). However, most studies on range edges have
used a small number of time points (e.g., Hickling et al. 2005)---and
often just two---limiting inference about climatic niche tracking.
Marine species are predicted to track their climatic niches more closely
than terrestrial species: they have exhibited greater range shifts to
date, and are more physiologically vulnerable to warming (Pinsky et al.
2019, 2020, Lenoir et al. 2020). Testing for climatic niche tracking is
fundamentally related to measuring range edge dynamics,
because---especially in the oceans---range edges are expected to
coincide with climatic niche limits, specifically thermal limits (Sunday
et al. 2012).

By measuring thermal niche conservatism at the range edge, we tested for
a relationship between range edge position and the isotherms
representing winter and summer temperatures over time (Sunday et al.
2015, Fredston\selectlanguage{english}-Hermann et al. 2020). A finding that a range edge
remained in the same winter or summer temperatures over time can have
several interpretations. It does not necessarily imply that the isotherm
moved: an edge may track its thermal niche either by shifting in the
same direction and at the same rate as an isotherm, or by remaining in
place when the isotherm is stationary. The two regions in our study with
high levels of edge thermal niche tracking, the West Coast and the
Eastern Bering Sea, both had relatively little temperature change when
averaged over the study period; thus, range edges in those regions that
did not move much were typically classified as tracking the edge thermal
niche.

Recent work on marine heatwaves has underscored the need to move beyond
means to measure climatic extremes and variability in studies of global
change biology, including in the oceans (Smale et al. 2019). We
quantified edge position in relation to temperature extremes precisely
for this reason, especially given the marked recent increases in warm
extreme (summer) temperatures in the Northeast and the Eastern Bering
Sea. Yet our results revealed that range edges remained in the same cold
extreme (winter) temperatures approximately as often as they did for
summer temperatures, suggesting that winter temperatures may be an
underappreciated covariate of range dynamics at both poleward and
equatorward range edges (Dana 1853, Morley et al. 2017). Foundational
biogeographic theory provides a hypothesis for this: poleward range
edges could be influenced either by summer temperatures limiting
reproduction and growth or by winter temperatures limiting survival; and
equatorward range edges could be influenced either by summer
temperatures limiting survival or by winter temperatures limiting growth
and reproduction (Hutchins 1947). Further work could test whether this
is a biologically plausible explanation for these temperate marine
species. Longer time series, extensive analysis of different dimensions
of temperature change, or additional oceanographic data products such as
high-resolution hindcast sea bottom temperature data could be used in
the future to tease apart more precisely which temperature metrics best
explain range edge dynamics and why.

Our finding that the region with the greatest historical temperature
increase exhibited the lowest frequency of edge thermal niche tracking
underscores the critical importance of considering non-temperature and
indirect processes that may influence species distributions. In the
Northeast, we documented equatorward range edges that shifted much
further north than expected based on temperature---into cooler
waters---and poleward range edges that did not shift or shifted south
(Appendices 3 and 4). This could arise due to density-dependent habitat
selection if these species were declining in abundance, causing each
range edge to collapse toward the range center (Blanchard et al. 2005).
At the equatorward edge, competition or predation from the south could
be driving edge retraction (Kordas et al. 2011); for example, the rapid
contraction of the equatorward range edge of American lobster could be
due to increased predation from species like black sea bass shifting up
the coast (McMahan and Grabowski 2019) or increased mortality from a
temperature-related disease (Groner et al. 2018). At the poleward edge,
species interactions (HilleRisLambers et al. 2013), priority effects
(Fukami 2015), dispersal limitation (Poloczanska et al. 2011), or a lack
of non-thermal habitat (McHenry et al. 2019) could all inhibit northward
shifts. Sessile invertebrates are particularly vulnerable to dispersal
limitation if prevailing currents do not align with local climate
velocities, as in the Northeast (Molinos et al. 2017, Fuchs et al.
2020). Changes in non-temperature abiotic drivers such as dissolved
oxygen are likely to also influence range edge dynamics (Deutsch et al.
2015, Howard et al. 2020).

This study is the first to use a spatiotemporal modeling approach to
estimate range edge dynamics and estimate a standard error around range
edge positions, which we see as important methodological advances. Using
the VAST model, we calculated rates of range edge shift that were
similar in magnitude to those calculated from raw survey data in the
Northeast (Fredston\selectlanguage{english}-Hermann et al. 2020). Our results are not directly
comparable to previous work, however, because---unlike analyses of raw
distribution data---VAST attributes some variation in recorded
observations and abundances to observation error. Continued extensions
of VAST and similar models to distribution data will facilitate more
rigorous evaluation of historical range edge dynamics, even for datasets
with known inconsistencies and biases in sampling.

While we found that range edge positions almost always maintained their
edge thermal niche, year-over-year temperatures at the range edge were
often variable (Appendix 4), and near-term (i.e., annual, not
multi-decadal) projections of a species' edge thermal niche are unlikely
to predict exactly where it will shift. Further, a non-negligible
fraction of range edges did not shift at all, shifted opposite the
predicted direction, or ``overshot'' temperature change and shifted into
cooler waters. Our methods provide a blueprint for assessing whether
range edges have tracked their thermal niches and for identifying
species of concern that do not appear to be shifting as expected based
on temperature alone. Classifying species by edge thermal niche tracking
can inform management and conservation because different interventions
are likely required for a species that shifts in response to warming
(e.g., transboundary management) than for a species that remains
stationary in the face of warming (e.g., assisted migration). To move
beyond categorizing all results of no thermal niche tracking as
``individualistic responses,'' future research can test edge thermal
niche conservatism against---or jointly with---other biogeographic
hypotheses that integrate the influence of species interactions,
population dynamics, eco-evolutionary processes, and other important
abiotic and biotic drivers. Future progress on range edge dynamics will
be accelerated by mechanistically testing predictions about which
temperature and non-temperature processes should be limiting for which
range edges against biogeographical data. Testing multiple
contemporaneous processes (including density-dependent range expansion
and contraction) and their net effects will provide insight into, and
ultimately enable prediction of, range edge dynamics in a changing
climate.

ACKNOWLEDGMENTS

We thank current and past NOAA staff for collecting, curating, and
publishing the data used in this study. E. M. Howard and R. Mendelssohn
provided expert advice regarding oceanographic and spatial data
analysis. We also thank A. J. Allyn and D. Ovando for code review and
feedback.

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FIGURES

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/369147/articles/488065-range-edges-of-north-american-marine-species-are-tracking-temperature-over-decades}

\textbf{Figure 1.} Maps of study regions---Northeast (A), West Coast
(B), and Eastern Bering Sea (C)---with the continental shelf shaded in
blue (300 m cutoff for Northeast and Eastern Bering Sea, 600 m for West
Coast). Inset plots show regional minimum (blue), mean (orange), and
maximum (red) monthly SST (annual means). Black dashed lines show axes
of measurement for range edges in each region, for which the origin
point is marked with an X.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/369147/articles/488065-range-edges-of-north-american-marine-species-are-tracking-temperature-over-decades}

\textbf{Figure 2.} Example schematic showing range edge position with
standard errors over time (first column), edge thermal niche over time
(second column), and the posterior distribution of estimated edge
thermal niche change (third column) for the equatorward edge
of\emph{Gadus morhua} in the Northeast (A), the poleward edge
of\emph{Paralithodes camtschaticus} in the Eastern Bering Sea (B), and
the equatorward edge of \emph{Sebastes pinniger} on the West Coast (C).
Time-series and distributions in blue represent cold extreme
temperatures and red represents warm extreme temperatures. Horizontal
bars in the third column represent 90\% Bayesian credible intervals. A
Bayesian credible interval that included zero (the vertical dotted line)
was interpreted as no change over time. Canary rockfish icon by Harold N
Eyster via phylopic.org under a Creative Commons License
http://creativecommons.org/licenses/by/3.0/.

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/369147/articles/488065-range-edges-of-north-american-marine-species-are-tracking-temperature-over-decades}

\textbf{Figure 3.} Distribution of range edge shifts over time (x-axis)
grouped by whether the edge thermal niche was conserved over time
(y-axis) in the Northeast (A), the West Coast (B), and the Eastern
Bering Sea (C). Each point corresponds to a single range edge. Range
edges could track cold (blue), warm (red), both (purple), or neither
(grey) temperature extremes. The thickness of each horizontal band
represents a relative weighting of point densities along the x-axis.

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