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

• Main Authors: Hamilton, Sara, Saccomanno, Vienna, Heady, Walter, Gehman, Alyssa-Lois, Lonhart, Steve, Beas-Luna, Rodrigo, Francis, Fiona, Lee, Lynn, Rogers-Bennett, Laura, Salomon, Anne, Gravem, Sarah
• Format: Software
• Language: unknown
• Published: Zenodo 2021
• Subjects: Baja; Gulf of Alaska; Canada; Pacific; British Columbia; First Nations; Alaska; Aleutian Islands
• Online Access: https://dx.doi.org/10.5281/zenodo.5116545; https://zenodo.org/record/5116545

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 (> 99.2% declines) from Baja California, Mexico to Cape Flattery, Washington, USA and exhibited severe declines (> 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. : Documentation, data, and code accompanying Hamilton et al., 2021 Pycnopodia Rangewide Assessment paper. Data 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. Code 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. Funding provided by: Nature Conservancy Crossref Funder Registry ID: http://dx.doi.org/10.13039/100014596 Award Number: Funding provided by: National Science Foundation Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001 Award Number: Graduate Research Fellowship : Thirty research groups from Canada, the United States, Mexico, including First Nations, shared 34 datasets containing field surveys of Pycnopodia (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 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 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).