Disease-driven mass mortality event leads to widespread extirpation and variable recovery potential of a marine predator across the eastern Pacific

Abstract

The prevalence of disease-driven mass mortality events is increasing, but our understanding of spatial variation in their magnitude, timing and triggers are often poorly resolved. Here, we use a novel range-wide dataset comprised 48 810 surveys to quantify how sea star wasting disease affected Pycnopodia helianthoides , the sunflower sea star, across its range from Baja California, Mexico to the Aleutian Islands, USA. We found that the outbreak occurred more rapidly, killed a greater percentage of the population and left fewer survivors in the southern half of the species's range. Pycnopodia now appears to be functionally extinct (greater than 99.2% declines) from Baja California, Mexico to Cape Flattery, Washington, USA and exhibited severe declines (greater than 87.8%) from the Salish Sea to the Gulf of Alaska. The importance of temperature in predicting Pycnopodia distribution rose more than fourfold after the outbreak, suggesting latitudinal variation in outbreak severity may stem from an interaction between disease severity and warmer waters. We found no evidence of population recovery in the years since the outbreak. Natural recovery in the southern half of the range is unlikely over the short term. Thus, assisted recovery will probably be required to restore the functional role of this predator on ecologically relevant time scales.

Full text

royalsocietypublishing.org/journal/rspb
Research
Cite this article: Hamilton SL et al. 2021
Disease-driven mass mortality event leads to
widespread extirpation and variable recovery
potential of a marine predator across the
eastern Pacific. Proc. R. Soc. B 288: 20211195.
https://doi.org/10.1098/rspb.2021.1195
Received: 26 May 2021
Accepted: 4 August 2021
Subject Category:
Global change and conservation
Subject Areas:
ecology, health and disease and epidemiology,
computational biology
Keywords:
sea star wasting disease, mass mortality event,
Pycnopodia helianthoides, temperature, species
distribution models, echinoderm
Author for correspondence:
S. L. Hamilton
e-mail: hamiltsa@oregonstate.edu
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.
c.5564419.
Disease-driven mass mortality event leads
to widespread extirpation and variable
recovery potential of a marine predator
across the eastern Pacific
S. L. Hamilton
1
, V. R. Saccomanno
2
, W. N. Heady
2
, A. L. Gehman
3,4
,
S. I. Lonhart
5
, R. Beas-Luna
6
, F. T. Francis
7
, L. Lee
8,9
, L. Rogers-Bennett
10,11
,
A. K. Salomon
12
and S. A. Gravem
1
1
Department of Integrative Biology, Oregon State University, Corvallis, OR 97331-4501, USA
2
The Nature Conservancy, San Francisco, CA, USA
3
University of British Columbia, Vancouver, BC V6T 1Z4, Canada
4
The Hakai Institute, Campbell River, British Columbia, Canada
5
NOAA’s Monterey Bay National Marine Sanctuary, Monterey, CA, USA
6
Universidad Autónoma de Baja California, Mexicali, Baja CA, Mexico
7
Fisheries and Oceans Canada, Ottawa, Ontario, Canada
8
Gwaii Haanas National Park Reserve, National Marine Conservation Area Reserve, and Haida Heritage Site,
Parks Canada, British Columbia, Canada
9
University of Victoria, Victoria, British Columbia, Canada
10
Bodega Marine Laboratory, University of California Davis, Davis, CA, USA
11
California Department of Fish and Wildlife, CA, USA
12
Simon Fraser University, BC V5A 1S6, Canada
SLH, 0000-0002-0156-7610; RB, 0000-0002-7266-3394
The prevalence of disease-driven mass mortality events is increasing, but our
understanding of spatial variation in their magnitude, timing and triggers
are often poorly resolved. Here, we use a novel range-wide dataset com-
prised 48 810 surveys to quantify how sea star wasting disease affected
Pycnopodia helianthoides, the sunflower sea star, across its range from Baja
California, Mexico to the Aleutian Islands, USA. We found that the outbreak
occurred more rapidly, killed a greater percentage of the population and left
fewer survivors in the southern half of the species’s range. Pycnopodia now
appears to be functionally extinct (greater than 99.2% declines) from Baja
California, Mexico to Cape Flattery, Washington, USA and exhibited
severe declines (greater than 87.8%) from the Salish Sea to the Gulf of
Alaska. The importance of temperature in predicting Pycnopodia distribution
rose more than fourfold after the outbreak, suggesting latitudinal variation
in outbreak severity may stem from an interaction between disease severity
and warmer waters. We found no evidence of population recovery in the
years since the outbreak. Natural recovery in the southern half of the
range is unlikely over the short term. Thus, assisted recovery will probably
be required to restore the functional role of this predator on ecologically
relevant time scales.
1. Introduction
While the prevalence of mass mortality events (MMEs) is increasing with
climate change [1,2], spatial variation in their timing, magnitude and triggers
often remain unknown rendering recovery potential difficult to predict and con-
servation interventions challenging to design. MMEs constitute ecological
disasters, and when they involve the loss of strongly interacting predators or
foundation species, effects can propagate throughout ecosystems. In coastal
© 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.

marine ecosystems, echinoderms, such as sea urchins and
sea stars, appear particularly susceptible to disease-driven
MMEs [3,4]. Furthermore, many echinoderm species are
strong ecological interactors as predators or major grazers
in their systems. Little is known, however, about the inter-
actions between echinoderm disease and changing ocean
conditions, making it difficult to determine when and
where these collapses may occur (but see [5,6]). Our limited
understanding of echinoderm disease-driven MMEs leaves
us unprepared to respond to events that can rapidly
alter population, community and ecosystem dynamics at
continental scales.
The sea star wasting disease (SSWD) epidemic, also known
as sea star wasting syndrome or asteroid idiopathic wasting
syndrome, began in 2013 and affected over 20 species of sea
stars along with the Pacific coastline from Mexico to the Aleu-
tian Islands [7,8]. Previous outbreaks of putative SSWD have
occurred, particularly in southern California, but have never
impacted stars on the scale observed since 2013 [4].
Pycnopodia helianthoides (hereafter Pycnopodia) appears to
be the species most impacted by SSWD, with declines reaching
99–100% in some areas [6,9–11]. Prior to the outbreak,
Pycnopodia was recognized as an important generalist meso-
predator across northeastern Pacific near-shore food webs
and can be an effective predator of small- and medium-sized
sea urchins on rocky reefs [12,13]. Via top-down pressure on
sea urchins, Pycnopodia can promote kelp abundance by affect-
ing sea urchin abundance, behaviour and grazing rates,
although the strength of this phenomenon varies substantially
across their range [10,12–14].
The aetiological agent(s) driving SSWD remain unidenti-
fied. Current hypotheses focus on (i) a viral-sized aetiological
agent (e.g. sea star-associated densovirus) and (ii) low oxygen
at the surface of the skin maintained through subsequent bac-
terial proliferation [7,15]. Additionally, the relationship
between temperature and SSWD is unresolved. In laboratory
studies, the lesion growth rate increased with increasing temp-
erature, but evidence for warm temperatures triggering SSWD
is mixed [16–18]. Some studies showed a positive relationship
between the timing of the outbreak and temperature [6,18,19],
while others found no relationship [8,20] or a negative relation-
ship [21]. Differences in disease detection could explain these
variable field observations. SSWD is a fast-paced disease accel-
erating at the scale of weeks to months, so peak prevalence
of infection is difficult to detect from seasonal or annual
monitoring programmes [7]. Thus, the relationship between
environmental triggers of an outbreak can easily be confounded
with pandemic disease dynamics [22].
While previous papers have documented that SSWD
caused dramatic losses in Pycnopodia in some places
[7,9,10], here we compiled 48 810 surveys on Pycnopodia pres-
ence and density from 34 data contributors ranging from Baja
California, Mexico, to the Aleutian Islands, USA, to create the
most comprehensive dataset to date to quantify impacts
to the species across its entire range. Using this unique data-
set, we evaluate the population-level impacts of SSWD on
Pycnopodia by asking the following. (i) How did the timing
of the SSWD epidemic vary across Pycnopodia’s range?
(ii) How did SSWD change the abundance and spatial distri-
bution of Pycnopodia? (iii) How did environmental variables
that predict Pycnopodia distribution differ pre- and post-out-
break? (iv) Is there evidence of population recovery in the
years since populations first collapsed?
2. Methods
(a) Data collection and compilation
Thirty research groups from Canada, the United States, Mexico,
including First Nations, shared 34 datasets containing field sur-
veys of Pycnopodia (electronic supplementary material, table
S1). The data included 48 810 surveys from 1967 to 2020 derived
from trawls, remotely operated vehicles, scuba dives and interti-
dal surveys. We compiled survey data into a standardized format
that included at minimum the coordinates, date, depth, area sur-
veyed and occurrence of Pycnopodia for each survey. When
datasets contained more than one survey at a site in the same
day (e.g. multiple transects), we divided the total Pycnopodia
count in all surveys by the total survey area and averaged the
latitude, longitude and depth as necessary. Using breaks in
data coverage, political boundaries and biogeographic breaks,
we assigned each survey to one of twelve regions: Aleutian
Islands, west Gulf of Alaska (GOA), east GOA, southeast
Alaska, British Columbia (excluding the Salish Sea), Salish Sea
(including the Puget Sound), Washington outer coast (excluding
the Puget Sound), Oregon, northern California, central Califor-
nia, southern California and the Pacific coast of Baja California
(electronic supplementary material, figure S1).
(b) Timeline of epidemic and population declines
We developed two timelines to define (i) epidemic phases describ-
ing how the epidemic progressed and (ii) population phases
describing how Pycnopodia populations changed over time
(electronic supplementary material, table S2).
(i) Epidemic phases
For each region, epidemic timelines were divided into four phases
punctuated by three dates as follows: (i) pre-epidemic phase;
(ii) date SSWD first observed; (iii) emerging epidemic phase;
(iv) outbreak date; (v) epidemic phase; (vi) crash date and
(vii) post-epidemic phase (electronic supplementary material,
figure S2). To describe SSWD emergence, we used datasets from
MARINe (electronic supplementary material, table S1) and
queried the date of the first symptomatic sea star observed at
594 sites distributed from Baja California, Mexico, to the western
GOA, USA (see http://data-products/sea-star-wasting/). We
used observations for both Pisaster ochraceus and Pycnopodia
because P. ochraceus has more observations than Pycnopodia
enabling more accurate estimates of outbreak timing among
regions (n= 450 and n= 247 sites, respectively). P. ochraceus
showed a slightly earlier date of first observation than Pycnopodia,
but the timelines were otherwise very similar (See electronic
supplementary material, figure S3).
We defined ‘date SSWD first observed’as the earliest record
of a symptomatic Pycnopodia or P. ochraceus in each region (elec-
tronic supplementary material, figure S2). This date defined the
break between ‘pre-epidemic’and ‘emerging epidemic’phases.
We defined ‘outbreak date’by fitting a normal curve to the dis-
tributions of dates when SSWD was first observed at each site
and calculated the 10th percentile; this served as the break
between ‘emerging epidemic’and ‘epidemic’phases. The 10th
percentile was chosen because we reasoned that when 10% of
sites show signs of SSWD, the disease has probably transitioned
to an outbreak, rather than persisting as isolated cases of infec-
tion. Further, our detection of disease at 10% of sites probably
means the actual number of sites infected is much higher. The
time elapsed between the ‘date SSWD first observed’and the
‘outbreak date’was considered the ‘emerging epidemic’phase.
As the epidemic progressed and Pycnopodia populations
declined, we used trends in Pycnopodia occurrence (site-level
presence or absence) to estimate ‘crash date’, defined as the
date when the occurrence rate of Pycnopodia in a region decreased
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
2

by 75% from pre-outbreak levels. A 75% decline in occurrence
was chosen because it is a substantial decline and because this
threshold gave date estimates in all regions that were the most
similar to the crash timelines reported elsewhere [8–10,12,21,23].
‘Crash date’defined the break between the ‘epidemic’and the
‘post-epidemic’phases.
We defined ‘emergence duration’as the time elapsed
between ‘date SSWD first observed’and the ‘outbreak date’,
which indicated how quickly the disease progressed in each
region. The difference in time between the outbreak date and
crash date in a region defined the ‘epidemic duration’.For
further details, see electronic supplementary material, figure S2.
(ii) Population phases
To define the effect of SSWD on Pycnopodia populations, we
delineated three population phases: historical, decline and current
(electronic supplementary material, figure S2). The ‘outbreak date’
in each region (defined above) determined the break between the
‘historical’and ‘decline’phases. The ‘current’period includes
data from 2017 to 2020. Region-specific dates associated with the
‘post-epidemic’phase were not used to define ‘current’population
phase because (i) not all regions are necessarily in the ‘post-
epidemic’phase (see electronic supplementary materials) and
(ii) many regions had recent crash dates (e.g. 2018 for Alaskan
regions) with limited data in the ‘post-epidemic’phase. Population
phases were used in density and occurrence analyses, species
distribution models and remnant population analysis.
(c) Influence of sea star wasting disease on global
sunflower sea star populations
To determine how Pycnopodia has been affected by SSWD, we
examined how density and occurrence varied with population
phase and region. We compared historical and current popu-
lations (defined above) in each region when possible. We
modelled deep (greater than 25 m depth) and shallow (less than
or equal to 25 m depth) populations separately because Pycnopo-
dia were much more common at depths less than or equal to
25 m, and data from deep depths were unavailable for most
regions. We performed all models in R v. 4.0.0 and RStudio v.
1.2.5042 [24]. For density models, we built zero-inflated general-
ized linear models [25] of Pycnopodia counts, using log
10
(area
searched) as the offset variable, Poisson likelihoods and log link
functions, fit by Type II sums of squares. For occurrence models,
we constructed a generalized linear model [26] of Pycnopodia
occurrence rate, using area searched as a covariate, binomial like-
lihoods and logit link functions, fit by Type II sums of squares. In
some regions, low sample sizes led to low confidence in our esti-
mates of occurrence and density, therefore we used grey shading
in our tables to delineate values with low confidence. For further
details on this modelling process and regional data limitations, see
electronic supplementary materials.
(d) Abiotic correlates of the population decline
We used MaxEnt species distribution models to (i) quantify abiotic
conditions associated with Pycnopodia before and after SSWD and
(ii) predict the distribution of remaining populations [27]. We cre-
ated two MaxEnt models, one estimating the distribution of
Pycnopodia prior to the SSWD outbreak (2009–2012) using 6206
observations and the other estimating the distribution of current
populations (2017–2020) using 1702 observations. We used prior
studies to select important abiotic variables [28,29] and eliminated
highly correlated variables [30]. Abiotic variables in each model
were the 90th percentile of sea surface temperature and mean
chlorophyll from 2009 to 2012 and 2017 to 2020 for pre-outbreak
and current models, respectively (NASA MODIS Aqua: https://
oceancolor.gsfc.nasa.gov/data/aqua/), mean salinity from a
long-term climatology (NOAA: https://www.nodc.noaa.gov/
OC5/regional_climate/), depth (NOAA ETOPO1: https://www.
ngdc.noaa.gov/mgg/global/), and substrate type (UC Boulder
dbSEABED: https://instaar.colorado.edu/~jenkinsc/dbseabed)
(see electronic supplementary materials for further details).
Datasets were clipped to the study area, defined as 0–456 m
depth (our deepest observation of Pycnopodia) from 112.637° W,
24.874° N (our southernmost observation) and 170.196° W,
52.508° N (our northernmost/westernmost observation) [31].
Google Earth Engine was used to create temperature and chlor-
ophyll metrics from MODIS data, and all other analyses were
completed in R Studio [24,32]. We used our compiled Pycnopodia
dataset to create 5000 background points for each model that
mirrored the spatial sampling bias of the data itself [30]. Using
the package ‘ENMeval’, we chose to use linear and quadratic fea-
tures and a regularization parameter= 1 based on combined
information from the training and evaluation Area Under the
Curve metrics and Akaike’s information criterion (see electronic
supplementary materials for further details) [33]. We adjusted
the default average species probability parameter by calculating
the average occurrence rate from the pre-outbreak (0.61%) and
current periods (0.14%) from the compiled dataset [30].
(e) Current status and recovery potential
(i) Population density
To visualize changes in Pycnopodia density in shallow depths
(less than 25 m) from historical (1987–outbreak date) to current
populations (2017–2020), we used ArcGIS Pro 2.7 to generate a
grid of 16 km
2
hexagonal cells across Pycnopodia’srange. For
each time period, we used a spatial join to nest the available den-
sity surveys within each cell (historical, n= 3984; current, n=
1344) and calculated mean density within each cell for both
time periods. Jenks natural break classification was selected to
symbolize density due to the high variance within the dataset.
(ii) Remnant populations
To determine where persistent remnant Pycnopodia populations
have been found since 2017, we used ArcGIS Pro 2.7 to generate
a grid of 16 km
2
hexagonal cells along with Pycnopodia’srange.
We used a spatial join to nest the 6284 available surveys from
shallow depths for 2017–2020 within each cell. We retained
only those cells with surveys performed in at least three of the
4 years from 2017 to 2020. From these better-surveyed cells, we
calculated the percentage of surveys with Pycnopodia occurrence,
which indicated the persistence of the remnant population. Each
cell was then classified as ‘absent’= 0%, ‘rare’= less than 25%,
‘common’= less than 90% and ‘very common’≥90%. Note that
this method does not evaluate remnant Pycnopodia population
dynamics. Remnant populations designated as common or
even very common using this method can include populations
that are (i) unaffected by SSWD and stable, (ii) affected by
SSWD yet stable or (iii) affected by SSWD and declining.
3. Results
(a) Latitudinal gradients in epidemic timing
Epidemic timelines showed that the date of first SSWD
observed occurred in 2013 for nearly all regions (figure 1b;
electronic supplementary material, table S2). Emergence dur-
ation (orange bar in figure 1b) was notably variable among
regions. In British Columbia, the Washington outer coast,
all California regions and Baja California, SSWD became an
‘outbreak’(approx. 10% sites infected) within a few weeks
to two months. The emergence duration was nearly a year
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
3

in Oregon and over seven months in the Salish Sea, despite
the Salish Sea having the earliest record of a SSWD-afflicted
animal (30 March 2013). Southeast Alaska’s emergence dur-
ation was similar to Oregon (10.1 months) but the
emergence duration in the eastern GOA was nearly 19
months.
Epidemic duration (light purple bar in figure 1a) and the
crash date (solid points in figure 1b) showed a marked latitu-
dinal gradient, indicating that populations crashed more
quickly in the southern part of the range (figure 1; electronic
supplementary material, table S2). The logistic regression
model showed significant declines in occurrence over time,
which varied by region (electronic supplementary material,
table S3). Populations crashed in Baja California within 2.1
months of the outbreak date and in southern California
within 6.3 months. Declines took less than two years in cen-
tral California, less than three years in northern California,
Oregon and the east GOA, and around 4 years on the
Washington outer coast, the Salish Sea, British Columbia
and southeast Alaska. The west GOA and Aleutian Islands
had an estimated 17-month epidemic duration, but limited
sampling in these regions made these estimates uncertain.
For this analysis and others, lower data availability for
much of Alaska and parts of British Columbia created greater
uncertainty in regional estimates for these areas. We suspect,
however, that the observed latitudinal gradient here is not
driven only by generally lower sampling effort northward
because northern regions with high sampling effort, such as
southeast Alaska, also exhibited late outbreak dates and
long emergence durations.
(b) Latitudinal gradients in population declines
After the SSWD outbreak, Pycnopodia density declined range
wide by 94.3% and the magnitude of this decline was similar
in shallow and deep depths (92.5% and 96.5%, respectively,
figure 2; electronic supplementary material, table S4). In shal-
low depths (where the vast majority of animals are found),
the magnitude and significance of the decline differed by
region (figure 2; electronic supplementary material, table S4
and table S5: population phase: p= 0.423
7,3523
; region × popu-
lation phase: p< 0.0001
7,3523
). Estimated density declines were
greater than 87.9% in 11 of 12 regions and were greater than
99.2% in all regions of the outer coast of the contiguous USA
and Mexico, with no Pycnopodia observed in Oregon,
southern California, and Baja California since at least 2017
(figure 3; electronic supplementary material, table S4). In
the Salish Sea, the British Columbia, southeast Alaska and
the east GOA, declines were also severe (92.4%, 87.9%,
96.0% and 93.8%, respectively).
Occurrence declined range wide by 52.3% ( figure 2; elec-
tronic supplementary material, table S4), and this decline was
significant in shallow and deep depths (64.13% and 55.3%,
respectively; electronic supplementary material, table S5:
p
1,3714
< 0.0001 and p
1,2148
< 0.0001, respectively). In shallow
depths, regional patterns were similar to those for density
declines (figures 2 and 3a,b; electronic supplementary material,
table S4 and table S5: region× population phase: p<
0.0001
7,3714
) with more severe declines in Oregon and south-
ward (greater than 92.2% decline). In the Salish Sea, British
Columbia, southeast Alaska and the east GOA, declines were
substantial though less severe than southern regions (52.9%,
68.9%, 20.8% and 58.9%, respectively). Too few data were avail-
able to make confident estimates in the west GOA and the
Aleutian Islands. Overall, Pycnopodia appears functionally
extirpated along the southern 2700 km stretch of coastline
from Baja California, Mexico, to Cape Flattery, Washington,
USA, and experienced substantial declines in northern regions.
(c) Temperature became more important in predicting
Pycnopodia distributions
Prior to the outbreak of SSWD, MaxEnt models predicted a
relatively even distribution of Pycnopodia from Baja California
to the Aleutian Islands, and the predicted probability of
Pycnopodia occurrence rarely dropped below 60% in coastal
areas (figure 3c). Depth was by far the strongest predictor
western AlaskaŸ
pre-epidemic epidemic
no data
post-epidemic
emerging epidemic
population
crashed
2020
(a)
(b)
2019
2018
2017
2016
2015
2014
2013
2012
2020
2019
2018
2017
2016
date
2015
2014
2013
2012
present at
10% of sites
SSWD first
observed
east Gulf of Alaska
southeast Alaska
British Columbia*
Salish Sea
Washington outer coast*
Oregon
northern California
central California
southern California
Baja CaliforniaŸ
1.00
region
0.75
0.50
0.25
predicted incidence of Pycnopodia
0
western Alaska
east Gulf of Alaska
southeast Alaska
British Columbia*
Salish Sea
Washington outer
coast*
Oregon
northern California
central California
southern California
Baja California
Figure 1. (a) Timeline of epidemic phases between January 2012 and
December 2019 by region. Pre-epidemic phase (yellow) includes dates
before the ‘date SSWD first observed’, when the first recorded symptomatic
sea star was reported in each region (unknown in western Alaska). The emer-
ging epidemic phase (orange) spans from the ‘date SSWD first observed’to
the ‘outbreak date’when 10% of the sites within a region had reported
SSWD observations. Epidemic phase (violet) spans the ‘outbreak date’to
the ‘crash date’(defined above) and indicates how quickly the disease
caused population declines. The post-epidemic phase (purple) includes
dates after the crash date, though SSWD may still be present and driving
further declines in the future. Caret: some dates inferred based on the
dates in neighbouring regions. Asterisk: British Columbia and Washington
outer coast exclude the Salish Sea. (b) Logistic model predictions for the
occurrence of Pycnopodia helianthoides over the course of the epidemic by
region. These models were used to estimate the ‘crash date’(filled circles)
of the populations in each region, defined as a 75% decline in occurrence
from January 2012 to December 2019. (Online version in colour.)
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
4

of Pycnopodia occurrence with permutation importance of
nearly 75% of the total predictive capacity (figure 4a; elec-
tronic supplementary material, table S6) [28]. The predicted
probability of Pycnopodia dropped exponentially as depth
increased, approaching zero around 300 m ( figure 4b).
Compared to the pre-outbreak model, the probability of
Pycnopodia occurrence plummeted range wide in the current
model. MaxEnt models predicted nearly 0% probability in
Baja California and southern California, and less than 10%
probability across the outer coast of the US as far north as
48.4° latitude, around Cape Flattery, Washington (figure 3d).
Moving northwards along inner coastal waters from Puget
Sound to the Aleutian Islands, the current model predicted
somewhat higher probabilities of occurrence around 15–25%.
Along central British Columbia, southeast Alaska and the
Aleutian Islands, the current model identified pockets of
higher probabilities around 30–60% (figure 3d).
The importance of various abiotic variables in predicting
Pycnopodia occurrence also differed between the pre-outbreak
and current models. The importance of temperature increased
more than fourfold to nearly 40% permutation importance
and was the most important predictor along with depth
(figure 4a). Prior to the outbreak, the relationship between
the probability of Pycnopodia and temperature formed a unim-
odal curve that peaked around 16°C (figure 4b). After the
outbreak, this curve shifted dramatically towards colder temp-
eratures, peaking around 5°C and decaying down to nearly 0%
probability by 23°C (figure 4b). Conversely, depth maintained
a similar relationship with predicted probability, although the
peak at shallow depths fell to approximately 18% probability
as opposed to approximately 75% pre-outbreak. Among the
remaining variables, mean chlorophyll increased in impor-
tance to 10.7% permutation importance, substrate rose to
6.3% and mean salinity fell to become the least important
variable (electronic supplementary material, table S6).
(d) No population recovery since 2017
We found no clear evidence that Pycnopodia have begun to
recover on a large scale. Though some sites have seen the
0
0.05
0.10
0.15
density of Pycnopodia (m−2)
phase
historical
decline
current
0
0.25
0.50
0.75
1.00
Aleutians
west Gulf of Alaska
east Gulf of Alaska
southeast Alaska
British Columbia*
Salish Sea
Washington outer coast
Oregon
northern California
central California
southern California
Baja California
re
g
ion
occurrence of Pycnopodia (survey−1)
region
Aleutians
west Gulf of Alaska
east Gulf of Alaska
southeast Alaska
British Columbia*
Salish Sea
Washington outer coas
t
Oregon
northern California
central California
southern California
Baja California
(a)
(b)
Figure 2. Mean (±s.e.) Pycnopodia helianthoides (a) density (m
2
) and (b) occurrence in shallow depths (less than 25 m) among the 12 regions and population
decline phases (historical, decline and current, see electronic supplementary material, table S2) over the SSWD outbreak. Asterisk: Washington outer coast and British
Columbia exclude the Salish Sea. (Online version in colour.)
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
5

recruitment of small animals (A.L.G. & S.A.G. 2017–2020,
personal observation), we observed no increases in Pycnopodia
density in any region since 2017 (electronic supplementary
material, figure S4). In fact, the southern regionsfrom Baja Cali-
fornia to the outer coast of Washington have ‘flat-lined’at near-
zero densities. Further, those regions with remaining animals
either show no recovery (east GOA) or a continued decline in
density from 2017 to 2020 (southeast Alaska, British Columbia,
Salish Sea; p< 0.001 for each region). However, fits by region
were quite low (R< 0.09 in all regions) because the remaining
densities in these regions were variable.
When we investigated localized (16 km
2
) persistence of rem-
nant populations from 2017 to 2020, we found no cells with
common or very common observations of Pycnopodia from
Oregon to the southern range limit, and only two cells had
common populations on the Washington outer coast (figure 5).
In the Salish Sea and north, the numberof cells with common or
very common observations increased, peaking at 60% of the
cells in southeast Alaska. While the Aleutian Islands and west
GOA had no regularly surveyed cells, we expect that common
observations could be found there based on the increased prob-
ability of Pycnopodia in these regions predicted by the SDM
models (figure 3) and cells with common observations in
nearby regions of east GOA and southeast Alaska ( figure 5).
4. Discussion
We document the functional extirpation of Pycnopodia across
2700 km of coastline from Baja California, Mexico to Cape
Flattery, WA, USA and severe declines across the rest of their
(a) historical
1976–SSWD
average density (m2)
probability of Pycnopodia
0
£6.7
£1.6
£0.4
£0.2
£0.08
0
NA
0 1000 km
Gulf of
Alaska
Gulf of
Alaska
(b) current
2017–2020
(c) pre-SSWD
2009–2012
(d) current
2017–2020
1000 km
0.85
0.6
0.45
0.3
0.15
0
Figure 3. Density (m
2
)ofPycnopodia helianthoides in shallow water (less than 25 m) from (a) historical (1976 to the outbreak date of SSWS) and (b) current
(2017–2020) surveys. Grey cells represent areas where no surveys were conducted during the relevant timeframe, but were conducted within the dataset timeframe.
MaxEnt species distribution model logistic predictions for Pycnopodia helianthoides (c) immediately pre-SSWD outbreak (2009–2012) and (d) currently (2017–2020).
(Online version in colour.)
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
6

range. Regions with warmer temperatures had faster, more
severe population declines and fewer survivors. Currently,
Pycnopodia populations show few signs of recovery, and popu-
lations in the northern half of the range may still be declining.
The power of this analysis derived from the continental-scale
collaboration that combined data from more than 30 contribu-
tors working across countries and sectors. If disease- and
climate-driven MMEs continue to increase in frequency, this
kind of multinational collaboration and data sharing will be
critical to responding to these events, particularly for wide-
ranging species like Pycnopodia. Our analysis sounds an
urgent alarm for managers, policy-makers, conservationists
and ocean-lovers across the Pacific Coast of North America.
Without intervention, Pycnopodia are unlikely to recover to
pre-wasting levels from Baja California to the outer coast of
Washington in the near future. The persistence of the remnant
populations throughout the rest of the range is also in ques-
tion. Further, the widespread and potentially long-lasting
loss of Pycnopodia may have ecosystem-level consequences,
particularly for kelp forests, where this loss may erode their
resilience via increased urchin grazing [10,12,13,34].
(a) Latitudinal gradient in the speed and severity of sea
star wasting disease
A strong latitudinal gradient structured the rate of regional
Pycnopodia population crashes, suggesting that regional factors
could be driving variation in disease response. Populations
crashed within a few months in Baja California and southern
California, 2 years in the rest of California and in 3–5 years in
Oregon and northward. Populations may still be experiencing
declines throughout Alaska (figure 1b), which is supported by
ongoing evidence of diseased Pycnopodia in many regions
(P. Raimondi & K. Gavenus 2021, personal communication).
The increased rate of disease spread in the southern latitudes
suggests that environmental conditions either increased host
susceptibility and/or disease transmission, or that genetic
variability in the host or disease leads to a higher transmission
rate (e.g. [35]). It will be difficult to disentangle these possibili-
ties until a causative agent of SSWD has been identified.
The severity of SSWD-driven population declines also
showed a marked latitudinal pattern. Pycnopodia populations
appear to be approaching functional extirpation from
Baja California, Mexico, to Cape Flattery, WA, USA. In our
dataset, no Pycnopodia were observed in Baja California
since 2015, none in California since 2018, and only a handful
in Oregon and the Washington outer coast since 2018
(for more detail see [11]). In the Salish Sea and northward,
Pycnopodia populations experienced severe declines but the
chance of encountering an individual during a survey is
greater than or equal to 32% in most of these northern
regions. These remaining northward populations are patchily
distributed, but occasionally harbour high densities of larger
Pycnopodia. As with the rate of disease spread, the drivers of
this variability could lie with the host, the disease or environ-
mental interactions between the two. However, the variation
in mortality, particularly within the northern regions, creates
an excellent opportunity for future research.
The 4.5-fold increase in the importance of temperature in
predicting Pycnopodia distribution post-outbreak suggests temp-
erature could be a driving force behind the observed latitudinal
patterns in the speed and severity of the disease. After SSWD,
the relationship between Pycnopodia occurrence and temperature
became strongly negative from 5 to 20°C, suggesting a
60
40
20
permutation importance (%)probability of Pycnopodia
0
0.75
0.50
0.25
0
90th percentile
temperature (C)
10 15
90th perc. temp. (C)
20 25 −500 −400−300
depth (m) mean chloro. (mg m−3)mean salinity (PSU)
−200−100 0 0 10 20 30 25.0 27.5 30.0 32.5 123
substrate type
45
depth (m) mean chlorophyll
(mg m−3)
mean salinity
(PSU)
substrate
category
phase
pre-outbreak
current
Figure 4. (a) Permutation importance of variables in MaxEnt model predictions of Pycnopodia helianthoides occurrence pre-outbreak (2009–2012) and current
(2017–2020). (b) MaxEnt logistic output response curves showing the probability of Pycnopodia occurrence across the represented range of each variable pre-out-
break (2009–2012) and currently (2017–2020). (Online version in colour.)
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
7

disease-mediated shift in temperature associations. This is con-
sistent with experimental studies that have shown warmer
temperatures cause SSWD to progress more quickly and increase
sea star mortality [16–18]. These studies documented increased
individual-level impacts of SSWD over a range of 9–19°C,
which mirrors the decreasing incidence of Pycnopodia over this
range of temperatures currently.
Across systems, elevated temperatures generally increase
virulence, growth rates and overwintering success of many
pathogens, and heat stress in host organisms shifts energy allo-
cation towards metabolic demands, leaving fewer resources for
immunological functions [36,37]. Thus, the putative link
between temperature and SSWD speed and severity is unsur-
prising. While we infer that temperature drove the latitudinal
patterns documented here, this association is correlational
and does not rule out confounding variables of temperature
such as latitude, coastline complexity or nutrients (electronic
supplementary material, figure S5). For instance, an alternative
hypothesis for the geographic patterns seen here is that if a lati-
tudinal gradient exists in genetic resistance to SSWD, with
greater resistance in the northern half of the range than the
south, then this could have created the same pattern in
SSWD impacts that we infer temperature did. Additionally,
this continental-scale analysis glosses over important
regional-scale variability, and regional to local-scale investi-
gations of the relationship between abiotic variables and
population-level resistance to SSWD are warranted. While
our analysis is strongly suggestive, it is not conclusive.
Additionally, whether climate change or warm tempera-
tures triggered the outbreak remains unknown. Harvell et al.
[6] showed that warm temperature anomalies explained
more than a third of the variance in Pycnopodia outbreak
timing in the Salish Sea [6]. Furthermore, Aalto et al. [19] mod-
elled the initial outbreak spread dynamics and suggested that
warm temperatures can trigger disease and increase mortality
[19]. Conversely, several studies found that warmer ocean
temperatures were not associated with SSWD outbreak
timing in Pisaster ochraceus in Oregon and California [8,21].
Though we lack a mechanistic understanding of whether
temperature or climate change triggered the SSWD outbreak,
this study adds to existing evidence that the speed and severity
of SSWD are greater in warmer waters.
A recent hypothesis advanced from laboratory exper-
iments suggests that elevated dissolved organic matter or
low-dissolved oxygen triggers SSWD [15]. Because continen-
tal scale, near shore estimates of these variables do not exist at
high enough spatial resolution to be incorporated into our
models, we were unable to test this hypothesis. However,
to our knowledge, no large-scale hypoxic event occurred
prior to the SSWD epidemic. Further, large-scale hypoxic
events have occurred periodically in places like Oregon
[38] in recent decades with no subsequent outbreaks of
SSWD. The proposed link between elevated dissolved
organic matter, low-dissolved oxygen and SSWD remains a
hypothesis that requires further evaluation in the field.
(b) Supporting recovery
We found little evidence of region-wide recovery in Pycnopodia
since 2017, and many southern regions show evidence of
functional extirpation. Although we are aware of recent juven-
ile recruitment events in the GOA, southeast Alaska and
British Columbia (K. Gavenus & P. Raimondi 2021, personal
communication; A.L.G. 2017–2021, personal observation), in
British Columbia juveniles appear to be failing to grow into
adults, presumably because of recurring outbreaks of SSWD
(A.L.G. 2017–2021, personal observation). Spatial variability
in the impacts of SSWD creates variable recovery pathways
for Pycnopodia. For example, protecting surviving adults in
more northern regions will likely be critical for natural recov-
ery. While Pycnopodia are not targeted in fisheries, adults
may be killed as bycatch in trap and trawl fisheries,
(T. Frierson 2021, personal communication) and bycatch mor-
tality should be considered in recovery planning.
Southward, natural recovery will probably be impeded
by low larval availability and Allee effects. We believe the
time has come for active recovery of this IUCN-listed Criti-
cally Endangered species in the southern half of its range
east Gulf of Alaska n = 2
n = 5
n = 29
n = 124
n = 6
n = 2
n = 4
n = 12
n = 43
n = 11
absent rare common very common
southeast Alaska
British Columbia*
Salish Sea
Washington outer coast*
Oregon
northern California
central California
southern California
Baja California
0 0.25 0.50
frequency
0.75 1.00
Figure 5. The frequency with which Pycnopodia helianthoides remnant populations were observed from 2017 to 2020 in each region. Surveys were aggregated into
16 km
2
grid cells and grid cells were only included if they contained shallow (less than 25 m) surveys from at least three different years from 2017 to 2020. n= the
number of grid cells that fit this criterion (n= 0 for Aleutians and west GOA; not shown). Each grid cell was classified by the per cent of total surveys that observed
Pycnopodia: absent = 0%, rare = less than 25%, common = less than 90% and very common ≥90%. Asterisk: British Columbia and Washington outer coast exclude
the Salish Sea. (Online version in colour.)
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
8

[11]. Active recovery strategies include captive breeding plus
reintroduction of young animals and translocations of adult
animals from extant to locally extinct areas. The recent invest-
ment shows that captive breeding is feasible, but the capacity
and effort required to scale breeding programmes to support
recovery over large areas requires further investigation
(J. Hodin 2021, personal communication). Recent work by
Schiebelhut et al. [39] suggests a genetic underpinning for
SSWD resistance, so it may be advisable to selectively breed
resistant adults or to reintroduce a high number of younger,
smaller and genetically diverse animals [39]. Comparatively,
translocations are lower cost compared to captive rearing.
However, translocation is problematic due to a lack of
robust donor populations, the logistics of crossing inter-
national borders, losses of re-introduced animals to SSWD
in transplanted locations, and risks of SSWD and other
unintended introductions into target areas.
Closing key research gaps will increase the capacity for
recovering Pycnopodia populations. Research into the aetiology
of SSWD, how disease susceptibility varies among individuals,
life stages and populations, and how environmental factors
influence susceptibility and resistance are crucial. We also
lack a basic understanding of important life-history infor-
mation for Pycnopodia, including reproductive phenology,
growth rates and genetic structure. Finally, while multiple
studies have found that Pycnopodia can reduce grazing by sea
urchins in subtidal kelp forests, we lack information on the
variability in the magnitude and spatial scale of this interaction
across Pycnopodia’s range [10,12,13]. Understanding the eco-
logical, economic and social impacts of Pycnopodia recovery
as a tool for restoring degraded kelp forest ecosystems is
urgently needed given recent collapses in kelp forests within
its range [34].
In times of rapidly changing ocean conditions, the plight of
Pycnopodia highlights the importance of enhancing long-term
monitoring (LTM) programmes to allow us to better monitor,
maintain and strengthen the resilience of marine ecosystems.
We cannot overstate the importance of well-coordinated LTM
to this effort and future MME work. The ‘what’and ‘how’of
LTM is also key. For example, if size frequency and vital rates
data were available for Pycnopodia, size-based population
models could have been constructed to help assess population
growth rates and project time to quasi-extinction. We see a
need to add information on organism size frequency, health,
genetic diversity and ecological interactions to the ongoing
LTM of population incidence and density. Additionally, citizen
science, a crucial component of this study, increases the spatial
scale and frequency of LTM and increases the likelihood of
detecting incipient MMEs. For wide-ranging marine species,
cross-boundary coordination of consistent minimum monitor-
ing standards and data sharing pathways are critical. Overall,
remarkable circumstances call for remarkable investment in
and development of broad-scale LTM programmes.
5. Conclusion
This study documents the disease-driven extirpation of a
marine predator over 2700 km of coastline. Eight years after
the SSWD outbreak began, the causative agent(s) of the dis-
ease remain unknown. This mismatch between the severity
of the epidemic and the state of knowledge highlights the
paucity of tools and support available to understand and
respond to disease-driven MMEs, particularly in species
that are neither commercially important nor charismatic. Cur-
rently, very few management, conservation or policy efforts
have been developed to respond to MMEs in marine wildlife.
Science, funding, management, conservation and policy often
move slowly, yet if the frequency of MMEs continues to
increase, institutions will need to respond much more quickly
than they have to the SSWD epidemic. Increasing the
capacity to monitor a wide variety of species, detect early
warning signs of MMEs and rapidly research and respond
to them will be increasingly important in the coming years.
Data accessibility. The compiled dataset and code to replicate the ana-
lyses conducted and figures created for this paper are available
from the Dryad Digital Repository: https://doi.org/10.5061/dryad.
9kd51c5hg [40].
Authors’contributions. S.L.H.: conceptualization, data curation, formal
analysis, funding acquisition, investigation, methodology, project
administration, visualization, writing-original draft, writing-review
and editing; V.R.S.: formal analysis, investigation, methodology, visu-
alization, writing-original draft, writing-review and editing; W.N.H.:
conceptualization, funding acquisition, writing-original draft, writ-
ing-review and editing; A.L.G.: formal analysis, investigation,
methodology, writing-original draft, writing-review and editing;
S.I.L.: formal analysis, methodology, visualization, writing-original
draft, writing-review and editing; R.B.-L.: methodology, writing-orig-
inal draft, writing-review and editing; F.T.F.: methodology, writing-
original draft, writing-review and editing; L.L.: methodology, writ-
ing-original draft, writing-review and editing; L.R.-B.: methodology,
writing-original draft, writing-review and editing; A.K.S.: method-
ology, writing-original draft, writing-review and editing; S.A.G.:
conceptualization, data curation, formal analysis, funding acqui-
sition, investigation, methodology, project administration,
supervision, visualization, writing-original draft, writing-review
and editing.
All authors gave final approval for publication and agreed to be
held accountable for the work performed therein.
Competing interests. We declare no competing interests.
Funding. This work was supported by the Nature Conservancy and a
National Science Foundation Graduate Research Fellowship.
Acknowledgements. We thank Lindsey Aylesworth, Tristan Blaine, Jenn
Burt, Mark Carr, Henry Carson, Jenn Caselle, Ryan Cloutier, Isabelle
Côté, Tom Dean, Eduardo Diaz, David Duggins, George Esslinger,
Jan Freiwald, Alejandro Frid, Taylor Frierson, Rani Gaddam, Katie
Gavenus, Donna Gibbs, the Haida Nation, the Heiltsuk Nation,
Chris Jenkins, Cori Kane, Aimie Keller, the Kitasoo/Xai’xais
Nation, Brenda Konar, Kristy Kroeker, Andy Lauermann, Julio
Lorda, Dan Malone, Scott Marion, Dan McNeill, Fiorenza Micheli,
Melissa Miner, Gaby Montaño, the Nuxalk Nation, Dan Okamoto,
Christy Pattengill-Semmens, Mike Prall, Pete Raimondi, Nancy
Roberson, Dirk Rosen, Jessica Schultz, Ole Shelton, Jorge Torre, Guil-
lermo Torres-Moye, Jane Watson, Ben Weitzman, Greg Williams and
the Wuikinuxv Nation and their associated institutions (electronic
supplementary material table S1) for their willingness to share data
for this effort. We thank Norah Eddy, Joe Gaydos, Drew Harvell,
Jason Hodin, Erin Meyer, Kirsten Alvstad and Josh Havelind for
their help and guidance. Please see the electronic supplementary
material for a full list of acknowledgements. The scientific results
and conclusions, as well as any views or opinions expressed herein,
are those of the authors and do not necessarily reflect the views of
NOAA or the Department of Commerce.
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
9

References
1. Fey SB, Siepielski AM, Nusslé S, Cervantes-Yoshida
K, Hwan JL, Huber ER, Fey MJ, Catenazzi A, Carlson
SM.2015 Recent shifts in the occurrence, cause, and
magnitude of animal mass mortality events. Proc.
Natl Acad. Sci. USA 112, 1083–1088. (doi:10.1073/
pnas.1414894112)
2. Tracy AM, Pielmeier ML, Yoshioka RM, Heron SF,
Harvell CD. 2019 Increases and decreases in marine
disease reports in an era of global change.
Proc. R. Soc. B 286, 20191718. (doi:10.1098/rspb.
2019.1718)
3. Feehan CJ, Scheibling RE. 2014 Effects of sea
urchin disease on coastal marine ecosystems.
Mar. Biol. 161, 1467–1485. (doi:10.1007/s00227-
014-2452-4)
4. Stokstad E. 2014 Death of the stars. Science 344,
464–467. (doi:10.1126/science.344.6183.464)
5. Lester SE, Tobin ED, Behrens MD. 2007 Disease
dynamics and the potential role of thermal stress in
the sea urchin, Strongylocentrotus purpuratus.
Can. J. Fish. Aquat. Sci. 64, 314–323. (doi:10.1139/
f07-010)
6. Harvell CD et al. 2019 Disease epidemic and a
marine heat wave are associated with the
continental-scale collapse of a pivotal predator
(Pycnopodia helianthoides).Sci. Adv. 5, eaau7042.
(doi:10.1126/sciadv.aau7042)
7. Hewson I et al. 2014 Densovirus associated with
sea-star wasting disease and mass mortality. Proc.
Natl Acad. Sci. 111, 17 278–17 283. (doi:10.1073/
pnas.1416625111)
8. Miner CM et al. 2018 Large-scale impacts of sea star
wasting disease (SSWD) on intertidal sea stars and
implications for recovery. PLoS ONE 13, e0192870.
(doi:10.1371/journal.pone.0192870)
9. Montecino-Latorre D, Eisenlord ME, Turner M,
Yoshioka R, Harvell CD, Pattengill-Semmens CV,
Nichols JD, Gaydos JK.2016 Devastating
transboundary impacts of sea star wasting disease
on subtidal asteroids. PLoS ONE 11, e0163190.
(doi:10.1371/journal.pone.0163190)
10. Schultz JA, Cloutier RN, Côté IM. 2016 Evidence for
a trophic cascade on rocky reefs following sea star
mass mortality in British Columbia. PeerJ 4, e1980.
(doi:10.7717/peerj.1980)
11. Gravem SG et al. 2020 Pycnopodia helianthoides.
International Union for the Conservation of Nature.
12. Burt JM, Tinker MT, Okamoto DK, Demes KW, Holmes
K, Salomon AK. 2018 Sudden collapse of a
mesopredator reveals its complementary role in
mediating rocky reef regime shifts. Proc. R. Soc. B 285,
20180553. (doi:10.1098/rspb.2018.0553)
13. Eisaguirre JH, Eisaguirre JM, Davis K, Carlson PM,
Gaines SD, Caselle JE. 2020 Trophic redundancy and
predator size class structure drive differences in kelp
forest ecosystem dynamics. Ecology 101, e02993.
(doi:10.1002/ecy.2993)
14. Duggins DO. 1983 Starfish predation and the
creation of mosaic patterns in a kelp-dominated
community. Ecology 64, 1610–1619. (doi:10.2307/
1937514)
15. Aquino CA et al. 2021 Evidence that microorganisms
at the animal-water interface drive sea star wasting
disease. Front. Microbiol. 11, 610009. (doi:10.3389/
fmicb.2020.610009)
16. Bates A, Hilton B, Harley C. 2009 Effects of
temperature, season and locality on wasting disease
in the keystone predatory sea star Pisaster
ochraceus.Dis. Aquat. Organ. 86, 245–251. (doi:10.
3354/dao02125)
17. Kohl WT, McClure TI, Miner BG. 2016 Decreased
temperature facilitates short-term sea star wasting
disease survival in the keystone intertidal sea star
Pisaster ochraceus.PLoS ONE 11, e0153670. (doi:10.
1371/journal.pone.0153670)
18. Eisenlord ME et al. 2016 Ochre star mortality during
the 2014 wasting disease epizootic: role of population
size structure and temperature. Phil. Trans. R. Soc. B
371, 20150212. (doi:10.1098/rstb.2015.0212)
19. Aalto EA et al. 2020 Models with environmental
drivers offer a plausible mechanism for the rapid
spread of infectious disease outbreaks in marine
organisms. Sci. Rep. 10, 5975. (doi:10.1038/s41598-
020-62118-4)
20. Hewson I, Bistolas KSI, Quijano Cardé EM, Button
JB, Foster PJ, Flanzenbaum JM, Kocian J, Lewis CK.
2018 Investigating the complex association
between viral ecology, environment, and
northeast Pacific sea star wasting. Front. Mar. Sci. 5,
77. [cited 2020 Nov 4]. (doi:10.3389/fmars.2018.
00077)
21. Menge BA, Cerny-Chipman EB, Johnson A, Sullivan J,
Gravem S, Chan F. 2016 Sea star wasting disease in
the keystone predator Pisaster ochraceus in Oregon:
insights into differential population impacts, recovery,
predation rate, and temperature effects from long-
term research. PLoS ONE 11, e0153994.
22. Baker RE, Yang W, Vecchi GA, Metcalf CJE, Grenfell
BT. 2020 Susceptible supply limits the role of
climate in the early SARS-CoV-2 pandemic. Science
369, 315–319. (doi:10.1126/science.abc2535)
23. Konar B et al. 2019 Wasting disease and static
environmental variables drive sea star assemblages in
the Northern Gulf of Alaska. J. Exp. Mar. Biol. Ecol.
520, 151209. (doi:10.1016/j.jembe.2019.151209)
24. R Studio Team. 2020 RStudio: integrated development
for R. Boston, MA; See http://www.rstudio.com/.
25. Jackman S. 2020 Pscl: classes and methods for R
Australia. R package version 1.5.5. Sydney, New South
Wales: Political Science Computational Laboratory.
United States Studies Centre, University of Sydney.
26. Zeileis A, Kleiber C, Jackman S. 2008 Regression
models for count data in R. J. Stat. Softw. 27,1–25.
(doi:10.18637/jss.v027.i08)
27. Phillips SJ, Anderson RP, Schapire RE. 2006
Maximum entropy modeling of species geographic
distributions. Ecol. Model. 190, 231–259. (doi:10.
1016/j.ecolmodel.2005.03.026)
28. Hemery LG, Marion SR, Romsos CG, Kurapov AL,
Henkel SK. 2016 Ecological niche and species
distribution modelling of sea stars along the Pacific
Northwest continental shelf. Divers. Distrib. 22,
1314–1327. (doi:10.1111/ddi.12490)
29. Bonaviri C, Graham M, Gianguzza P, Shears NT. 2017
Warmer temperatures reduce the influence of an
important keystone predator. J. Anim. Ecol. 86,
490–500. (doi:10.1111/1365-2656.12634)
30. Merow C, Smith MJ, Silander JA. 2013 A practical
guide to MaxEnt for modeling species’distributions:
what it does, and why inputs and settings matter.
Ecography 36, 1058–1069. (doi:10.1111/j.1600-
0587.2013.07872.x)
31. Hijmans RJ, van Etten J. 2012 raster: geographic
analysis and modeling with raster data.
32. Gorelick N, Hancher M, Dixon M, Ilyushschenko S,
Thau D, Moore R. 2017 Google earth engine:
planetary-scale geospatial analysis for everyone.
Remote Sens. Environ. 202,18–27. (doi:10.1016/j.
rse.2017.06.031)
33. Muscarella R, Galante PJ, Soley-Guardia M, Boria
RA, Kass JM, Uriarte M, Anderson RP. 2014
ENMeval: an R package for conducting spatially
independent evaluations and estimating optimal
model complexity for Maxent ecological niche
models. Methods Ecol. Evol. 5, 1198–205. (doi:10.
1111/2041-210X.12261)
34. Rogers-Bennett L, Catton CA. 2019 Marine heat
wave and multiple stressors tip bull kelp forest to
sea urchin barrens. Sci. Rep. 9, 15050. (doi:10.1038/
s41598-019-51114-y)
35. Carnegie RB, Ford SE, Crockett RK, Kingsley-Smith
PR, Bienlien LM, Safi LSL, Whitefleet-Smith LA,
Burreson EM.2021 A rapid phenotype change in
the pathogen Perkinsus marinus was associated
with a historically significant marine disease
emergence in the eastern oyster. Sci. Rep. 11,
12872. (doi:10.1038/s41598-021-92379-6)
36. Harvell CD. 2002 Climate warming and disease risks
for terrestrial and marine biota. Science 296,
2158–2162. (doi:10.1126/science.1063699)
37. Shields JD. 2019 Climate change enhances disease
processes in crustaceans: case studies in lobsters,
crabs, and shrimps. J. Crustac. Biol. 39, 673–683.
38. Chan F, Barth JA, Lubchenco J, Kirincich A, Weeks H,
Peterson WT, Menge BA.2008 Emergence of anoxia
in the California current large marine ecosystem.
Science 319, 920. (doi:10.1126/science.1149016)
39. Schiebelhut LM, Puritz JB, Dawson MN. 2018
Decimation by sea star wasting disease and rapid
genetic change in a keystone species, Pisaster
ochraceus.Proc. Natl Acad. Sci. USA 115,
7069–7074. (doi:10.1073/pnas.1800285115)
40. Hamilton SL et al. 2021 Data from: Disease-driven
mass mortality event leads to widespread
extirpation and variable recovery potential of a
marine predator across the eastern Pacific. Dryad
Digital Repository. (doi:10.5061/dryad.9kd51c5hg)
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 288: 20211195
10