DOI: 10.5061/dryad.1rn8pk0ww
title: Data from: one shell of a problem: cumulative threat analysis of male sea turtles indicates high anthropogenic threat for migratory individuals and Gulf of Mexico residents

description: Human use of oceans has dramatically increased in the 21st century. Sea
 turtles are vulnerable to anthropogenic stressors in the marine
 environment because of lengthy migrations between foraging and breeding
 sites, often along coastal migration corridors. Little is known about how
 movement and threat interact specifically for male sea turtles. To better
 understand male sea turtle movement, and the threats they encounter, we
 satellite-tagged 40 adult male sea turtles of four different species. We
 calculated movement patterns using state-space modeling (SSM), and
 quantified threats in seven unique categories; shipping, fishing, light
 pollution, oil rigs, proximity to coast, marine protected area (MPA)
 status, and location within or outside of the U.S. Exclusive Economic Zone
 (EEZ). We found significantly higher threat severity in northern and
 southern latitudes for green turtles (Chelonia mydas) and Kemp’s ridleys
 (Lepidochelys kempii) in our study area. Those threats were pervasive,
 with only 35.9% of SSM points encountering no high threat exposure, of
 which 47% belong to just two individuals. Kemp’s ridleys were most exposed
 to high threats among tested species. Lastly, turtles within MPA
 boundaries face significantly lower threat exposure, indicating MPAs could
 be a useful conservation tool.
description: 2.1 Study Area/Species Collection We
 captured turtles as in Hart et al. [100] from 2009 – 2019. Forty adult
 male sea turtles of four different species were captured from four
 locations using a boat (jumping from a boat, snorkeling) or net capture
 via trawler. Sample sizes are as follows: Kemp’s ridley = 6, hawksbill =
 1, loggerhead = 8, green = 25. Capture location sample sizes are as
 follows: Dry Tortugas National Park = 24, Florida Keys National Marine
 Sanctuary = 6, Northern Gulf of Mexico = 9, Buck Island National Reef
 Monument = 1. We followed standard morphometric data collection, and
 attached platform transmitter terminals (PTT) to each turtle carapace
 using slow-curing epoxy (two-part Superbond epoxy; see Hart et al. [100]).
 Turtles were tracked using Wildlife Computers (Redland, Washington,
 U.S.A.) SPOT or SPLASH10 transmitters. Tracking data ranged from 8 June
 2009 to 7 August 2020 [100, 101]. 2.2 Collection and
 Calculation of Threats/State-Space Modelling We
 performed a switching state space model (SSM) on the raw location data in
 order to estimate each turtle’s true locations at regular time intervals
 due to significant positional uncertainty in the raw satellite data [101].
 Briefly, we used a Bayesian hierarchical movement model implemented in the
 R package ‘bsam’, using the ‘hDCRWS’ model specification and a time step
 of 1 day [103, 104, 105].  We set the Markov Chain Monte Carlo (MCMC)
 parameters following Roberts et al. [106], which used adaptive sampling
 for 7000 draws, taking 10,000 samples from the posterior distribution, and
 then thinning by five to reduce MCMC autocorrelation, resulting in 2000
 posterior samples from which to make inference. This process ultimately
 resulted in an improved dataset by eliminating location errors and
 provided one location point for each turtle per day. We
 collected a total of 8875 SSM points for threat analysis [101]. Through
 review of scientific literature and professional consultation, we
 collected data for seven primary threats to male sea turtles (Fishing,
 Shipping, Drilling Platforms, Light Pollution, MPA boundaries, located
 within or outside the U.S. Exclusive Economic Zone (EEZ), and coastal
 threat (within 10 km of a coastline) [8, 35, 36, 103, 107, 108, 109]. Raw
 location data have spatial accuracy ranges that vary between 500 m to 1.5
 km. Raw tracking data were therefore fit to a hierarchical,
 behavior-switching state-space model (SSM), which was then used to
 increase the accuracy of tracking data and to determine home ranges of
 each individual [103]. This allowed for estimation of the behavioral modes
 of individual turtles (unique behavioral patterns), regularization of the
 locations in time, and accounting for location error in the raw satellite
 data. In order to accurately depict the threats within the area of each
 SSM point (1.5 km), we created a 2-km radius buffer around each SSM turtle
 point using R [105], within which threats were assessed. The threat data
 were collected and prepared as described in the following
 sections. 2.3 Fishing Data Threats
 from fishing can come from a variety of sources (artisanal, longlining,
 commercial, nets and trawlers, etc.). Since 2016, all commercial fishing
 vessels within U.S. waters over 65 feet in length are required to have an
 AIS (Automatic Identification System) transponder tag attached, which
 tracks the ships every hour via satellite global positioning system (GPS)
 and ground-based receivers placed by the U.S. Coast Guard [111]. At
 present, only 2% of the world’s fishing vessels have AIS tags on board,
 but these ships account for more than 50% of total fishing effort [111].
 We used a fishing density raster layer of ground-based, AIS tracked ships
 as a representative subsample of fishing fleet intensity from
 marinecadastre.gov, a joint collaborative data repository for
 marine-related research by the National Oceanic Atmospheric Administration
 (NOAA), and the Bureau of Ocean Energy Management (BOEM) [112]. This layer
 includes tracks of fishing vessels that leave U.S. waters in the Gulf of
 Mexico and therefore provides a representative subsample for turtles that
 move beyond the U.S. EEZ boundary.  Fishing intensity
 was measured in grid cells 1 hectare in size, with each cell representing
 the total number of fishing craft that passed through that cell with an
 AIS transponder onboard within a given year. We added and averaged the
 total number of fishing vessels per cell from 2015–2017 to get the mean
 fishing intensity per cell. We then created a single raster layer for
 analysis using ArcGIS Pro ver. 2.5 [113]. Although only three years of
 fishing data were available, we assume fishing density was similar enough
 in previous years that the average cell values of the three years
 represent past fishing seasons. We averaged the fishing intensity score of
 all raster cells within each SSM turtle buffer and assigned that value as
 the fishing threat score for that point in ArcGIS Pro ver. 2.5
 [113]. 2.4 Shipping Data Shipping
 data were also obtained using AIS tagged ships, and were downloaded in
 their raw format courtesy of the U.S. Coast Guard with certain identifiers
 scrubbed for privacy. We were able to obtain data for the entire study
 period within from the Marine Cadastre data repository [112]. These data
 cover the entire study area and are a suitable, representative sub-sample
 of shipping data, clearly showing all shipping lanes within our study
 area.      AIS-tagged ships in the United States
 account for 50–60% of shipping activity [110]. We clipped all AIS data to
 each 2-km turtle buffer by date and then merged the data into a single
 vector shapefile to create a layer of shipping points that coincide with
 the presence of each 2-km SSM turtle buffer. Because of the large size of
 the AIS data files, we ran an RStudio instance on Google’s Cloud Computing
 Engine [105]. The total number of shipping points within each 2-km buffer
 was then added and assigned as the threat score for that SSM
 point. 2.5 Drilling Platform Data
 We downloaded drilling platforms point data, also referred to as
 oil rigs, oil platforms or drilling rigs to represent oil derived threats
 from the Marine Cadastre data repository [112]. We calculated the number
 of platforms within each turtle buffer by clipping the oil rig layer to
 the turtle layer and merging the data into a single Vector shapefile to
 create a layer of oil rig points within each SSM turtle buffer using
 ArcGIS Pro ver. 2.5 [113]. Drilling platforms were corrected by date to
 ensure they were in use during the date associated with the date of the
 2-km turtle buffer.      Marine Cadastre, although very
 useful in acquiring data for the United States, is missing drilling
 platform data for other parts of our study area, specifically Mexico and
 Cuba. In order to understand if turtles that left the U.S. EEZ encountered
 oil threats, we used a world, oil exploration shapefile called PETRODATA,
 that covers oil drilling hotspots around the world [114]. Upon comparison
 with our existing dataset, 99.7% of oil rig points fall within the
 PETRODATA polygon for the United States, Gulf of Mexico, oil exploration
 polygon. Therefore, we felt it was comparable because no public oil rig
 data are available for Mexico at the time of writing this manuscript. No
 international turtle points fell within the confines of oil polygons so
 further calculations were not necessary. 2.6 Light
 Pollution Data In 2011, the SUOMI VIIRS (Visible
 Infrared Imaging Radiometer Suite) satellite was launched to track
 multiple spatial data, such as snow and sea ice cover, active wildfires,
 sea and ice surface temperatures, and day/night light reflectance and
 radiance at high resolution [115]. We created our light pollution threat
 layer by combining all available light radiance raster data from NOAA’s
 Earth Observation Group public download domain and averaging the total
 radiance for each pixel [115]. In total, 54 raster files, ranging from
 January 13<sup>th</sup> – March 8<sup>th</sup>,
 2021, were combined by taking the average light radiance of each pixel,
 and then recording the average value of all pixels within each 2-km turtle
 buffer user R [105]. We assumed that the light radiance during the study
 period did not vary annually and that the light data we collected are
 representative of all years for which we have tracking data.
 2.7 MPA, EEZ, and Proximity to Coast Data We
 downloaded both the MPA Layer and EEZ Layer as vector layers from the
 Marine Cadastre data repository [112]. We created the coastal threat
 vector layer in ArcGIS Pro version 2.5 by making a 10-km buffer around all
 available land within the study region [113]. The SSM points that were
 &gt; 10 km from coastline, or within the U.S. EEZ or an MPA boundary
 were assigned a value of “0” to indicate threat absence, whereas points
 that were &lt; 10 km from the coast or outside of the U.S. EEZ or an
 MPA boundary were assigned a value of “1” to indicate threat
 presence.      Because Marine Cadastre focuses on
 primarily U.S. waters, we needed data on international MPAs, specifically
 for Cuba and Mexico. Those data were downloaded from the IUCN’s World
 Database on Protected Areas website [116]. We followed the same format of
 assigning values of “0” for turtles that were within the confines of those
 MPAs, and values of “1” for turtles outside the confines of those
 MPAs. 2.8 Statistical Analyses To
 directly add and compare the effects of individual threats, we
 standardized all threat categories to a mean of zero and standard
 deviation of one, which allowed us to take the sum of all threats directly
 and create a cumulative threat score for each SSM point. Through
 preliminary data exploration, we discovered the data were substantially
 non normal, and greatly spatially autocorrelated. As a result, we removed
 SSM points that were within 4 km of one another. Data removal reduced the
 number of SSM points from 8875 to 474. Despite removal, spatial
 autocorrelation still existed, but the degree to which it existed was
 reduced. Moran’s I of cumulative impact scores changed from 0.458 (p
 &lt; 0.001) at distance class I to Moran’s I of 0.313 (p &lt;
 0.001). Complete removal of spatial autocorrelation from our dataset would
 have thinned the data to too few points to be able to run an analysis on;
 therefore, we decided to strike a balance between reducing the data, yet
 also minimizing the degree to which spatial autocorrelation existed in our
 dataset. To test the prediction that individual or
 combined threats varied with species along a latitudinal gradient, we
 tested our thinned data using PERMANOVAs, a permutation-based test for
 significance as the data were still very non normal. PERMANOVAs were run
 using the R package ‘vegan’ [117]. We tested this using the interactive
 effects of species and latitude responding to threat. We included the
 threat x species interaction because we expected that species may vary in
 their response to spatially varying threats. Because of the potential for
 areas with variables of high threat to be clustered, data may not show a
 direct, linear relationship with latitude. To better understand any
 latitudinal relationships present in our data, we ran breakpoint
 regressions between latitude and threat (individual threats vs. latitude
 and cumulative threat scores vs. latitude) to see if the breakpoint model
 was a better fit to the data. Due to the large spatial gap present and low
 sample size, we removed turtle 14, the lone hawksbill of this study from
 this portion of the analysis. All data were given an alpha of 0.95 for
 detecting statistical significance. All data were analyzed using R
 [105].     In order to test the prediction that threats
 vary by species, we first calculated median values (due to the presence of
 outliers) of threat scores for each of the four numerical threats
 (Shipping = 0, Light = 1.3, Fishing = 1.2, Oil = 0) and used presence
 scores for the remaining three (Coast = 1, MPA = 1, EEZ = 1). Values above
 the median value for numerical threats or that had a score of 1 for
 categorical threats were categorized as ”high threat,” whereas values
 below the median or a score of zero, respectively, were considered ”low
 threat.” We then calculated the percentage of days during the study period
 an individual turtle encountered high threats by dividing the number of
 days a high threat was encountered by the sum of their SSM points. We
 calculated average values for each species from these percentages to
 understand how often sea turtle species were exposed to each threat during
 the study period. Preliminary data analysis discovered our data were very
 non normal. Therefore, we ran PERMANOVAs on each threat category percent
 by species. Turtle 14, the single hawksbill captured for our study, was
 removed from this part of the analysis. To test the
 prediction that turtles within MPA boundaries experienced lower threat, we
 recorded the mean time each individual turtle spent outside of an MPA
 using their SSM points. If individuals spent more than the mean value
 (23.6%) outside of an MPA, they were counted as a “non-MPA” turtle.
 Individuals that spent more time than the mean value within an MPA were
 counted as “MPA” turtles. Three threat variables of continuous data
 (Light, Shipping, Fishing) that were categorized as high threat were
 compared between non-MPA and MPA turtles for statistical significance with
 a Welch’s T-Test in R [105]. T-Test analyses were modified for unequal
 variance and if the equal variance assumption was violated.
 To test the prediction that concentration of turtle SSM points is
 lowest in areas of high threat, we created a 10x10 km grid cell fishnet
 over the study region and then using the ‘Spatial Join’ tool to merge all
 points within each grid of the fishnet in ArcGIS Pro version 2.5 [111].
 The number of turtle points within each grid cell was treated as a form of
 density for that specific area. Threat values of each grid cell were
 averaged if more than one turtle point was present. We then ran linear
 regression analyses to test for relationships between density and each
 individual threat, and several combinations of threat layers: All Combined
 Threats, Oil Threat (Oil, Shipping, and Coastal layers), Boat Threat
 (Fishing, Shipping, and Coastal, EEZ layers), and Fishing Threat (Fishing,
 Coastal, EEZ, and MPA layers). We additionally ran breakpoint regressions
 on our data to determine if density responded to threat nonlinearly or in
 linear segments using the R package ‘segmented’ [118].
 To better understand threat interactions through time, we added
 the total number of high threat categories encountered on a given day for
 each SSM point and then plotted the threat on a color gradient (from 0–6)
 by month of the year and individual turtle, which clearly displays the
 daily number of high threats each turtle was exposed to during the
 tracking period. Turtles were sorted by capture location, MPA status, and
 species. Plotting data in such a manner can provide a visual
 interpretation of threats by individual, as well as show the timing of
 threats by month. All data were analyzed using R version 4.1.0
 [105].
description: Please see the attached metadata release for navigating our
 dataset. Attached is all the transformed data, locations where raw data
 was accessed, and code for figures and statistical analysis. Coordinates
 have been removed to protect sensitive species locations.Therefore some of
 these data transformations in the code cannot be run. Requests for
 coordinate data can be made to Dr. Kristen Hart: kristen_hart@usgs.gov
description: # Data from: one shell of a problem: cumulative threat analysis of male
 sea turtles indicates high anthropogenic threat for migratory individuals
 and Gulf of Mexico residents
 [https://doi.org/10.5061/dryad.1rn8pk0ww](https://doi.org/10.5061/dryad.1rn8pk0ww) ## Description of the data and file structure These data are for Ashford et al. 2022 This data release contains the necessary R code to reproduce the data and results from this experiment. Data can be found at the USGS public data release ttps://doi.org/10.5066/P958OAKJ. Please be aware that because these data contain the locations of sensitive species, coordinates have been removed which mean some of the code cannot be run. Please see the following for the contents of each folder: 1\. Code for Predictions and Figures: a. Density and Threat: The R code that was used to test the assumption that sea turtle density could predict threat. b. Figure 2 R Code: Contains the R code necessary to recreate figure 2 in the paper. c. Interactive Effects of Species and Latitude: This folder contains the code necessary to recreate the statistics and figures (Figures 3-5) for the prediction that Species and Latitude interact with threat. d. Threat x Species and Threat x MPA x Migration Figure: This folder contains the code necessary to recreate the statistics and figures (Figures 1, 2, 6, 7, and all Supplemental Figures) for the prediction of MPA status and threat as well as Species and threat. Any questions about the data release or code can be sent to Micah Ashford: [mashford87@gmail.com](mailto:mashford87@gmail.com)