DOI: 10.5061/dryad.9kd51c5hg
title: Disease-driven mass mortality event leads to widespread extirpation and variable recovery potential of a marine predator across the eastern Pacific

description: 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 of 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’ range. Pycnopodia now appears to be functionally extinct
 (&gt; 99.2% declines) from Baja California, Mexico to Cape Flattery,
 Washington, USA and exhibited severe declines (&gt; 87.8%) from the
 Salish Sea to the Gulf of Alaska. The importance of temperature in
 predicting Pycnopodia distribution rose 450% after the outbreak,
 suggesting these latitudinal gradients 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 and
 assisted recovery will likely be required for recovery in the southern
 half of the range on ecologically-relevant time scales.
description: Thirty
 research groups from Canada, the United States, Mexico, including First
 Nations, shared 34 datasets containing field surveys of
 <i>Pycnopodia</i> (Table S1). The data included 48,810 surveys
 from 1967 to 2020 derived from trawls, remotely operated vehicles, SCUBA
 dives, and intertidal surveys. We compiled survey data into a standardized
 format that included at minimum the coordinates, date, depth, area
 surveyed, and occurrence of <i>Pycnopodia </i>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
 <i>Pycnopodia</i> 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 Gulf of Alaska, 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 California, southern California, and the
 Pacific coast of Baja California (Fig. S1; see Supplementary
 Material).
description: Documentation, data, and code accompanying Hamilton et al., 2021
 Pycnopodia Rangewide Assessment paper.
 <strong>Data</strong>
 MasterPycno_ToShare:  Dec_lat = latitude in decimal degrees.
 Numeric. Dec_lon = longitude in decimal degrees. Numeric. Depth = depth in
 meters. Numeric. Pres_abs = presence or absence of Pycnopodia on that
 survey. Binary. Presence = 1, absence = 0 Density_m2 = density in meters
 squared if available for that set of surveys. Numeric. NA = no density
 data available for that survey. Source = shorthand name of the group that
 shared the data with us and the type of data (e.g. trawl, dive). To get
 further info on who that dataset, group, and group contact, see Table S1.
 Character. Note: 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 in order to minimize the impacts of
 pseudoreplication on the dataset. Used in MaxentSWD_Final and
 Density-Inc_Models_Figs_Tables_ToShare.
 CrashEventsForRPlot: Crash Dates were determined 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 by 75% from pre-outbreak levels. Used in
 OutbreakTimelineFigs_ToShare.R EpidemicPhases: See
 manuscript methods for information on how the column ‘EpidemicPhases’ was
 created. “Start-End” specifies whether that date was the start or the end
 of that epidemic phase for that region. Used in
 OutbreakTimelineFigs_ToShare.R Incidence_2012-2019:
 Columns G-J were calculated by fitting a logistic regression model to the
 occurrence of Pycnopodia over time for each region. We fit a logistic
 regression model to the occurrence of Pycnopodia from 1/1/2012 to
 12/31/2019 to model the shape of the population decline for each region
 (Fig. 1a). From these models, we 1) estimated regional Pycnopodia
 occurrence rates on 1/1/2012 and 12/31/2019, 2) calculated the predicted
 occurrence value corresponding to a 75% decline in starting versus ending
 occurrence in each region, and 3) solved the inverse logistic equations
 for the date at which this occurrence value was predicted. All other
 columns are identifying information derived from MasterPycno_ToShare. Used
 in OutbreakTimelineFigs_ToShare.R
 MasterPycno_021821_SpatialJoin: Used to make Fig 5 for the
 remnant population analysis. Grid IDs were created in ArcGIS using a
 spatial join. Used in RemPop_Final.R Maxent_SWD
 OccurrenceData, BiasFiles, Maxent_EnvironmentalFiles folders all contain
 data that is used to run Maxent Analysis in Maxent terminal (available
 here: https://biodiversityinformatics.amnh.org/open_source/maxent/) Maxent_Output contains the output files generated by the Maxent terminal. Used in Maxent_SWD_Final to make Figures 3cd and 4. <strong>Code</strong> Density_Inc_Models_Figs_Tables_Toshare: Makes Fig 2, runs models on density and incidence changes across the phases of the pandemic ContinuedDeclines_Models_Figs_Tables_Toshare: Makes Fig S3, runs models on density and incidence changes for the years 2017-2020 (generally after the outbreak had subsided in many regions). OutbreakTimelineFigs_ToShare: Makes Fig1 Maxent_SWD_Final: Prepares Pre and Post-outbreak Pycnopodia incidence and bias files for Maxent program. Maxent analysis must then be run using these files and the Maxent program. For this analysis, we were not able to use Dismo or other R packages that run Maxent because these packages do not give you the ability the adjust the parameter Tau. Maxent may be downloaded at https://biodiversityinformatics.amnh.org/open_source/maxent/. Using the Maxent output files (included in the directory), this code then creates Figure 4 from the manuscript. Figure 3 were made using density data and Maxent output visualized in ArcGIS. RemPop_Final.R: Creates Figure 5, runs remnant population analysis. Uses MasterPycno_021821_SpatialJoin.