DOI: 10.5061/dryad.zcrjdfnm9
title: Data from: Delineating important killer whale foraging areas using a spatiotemporal logistic model

description: Conservation management planning for highly mobile species requires an
 understanding of the distribution of areas that are biologically important
 to the species of concern. Collecting data on the locations of animal
 behaviors linked to biological characteristics, such as foraging, can be
 used to spatially describe biological important areas. However, spatial
 modeling of free-ranging animal behavior can be challenging, as behavioral
 observations of animals are often clustered, and sampling is commonly
 conducted at a higher frequency than changes in behavioral states,
 resulting in data that are usually highly autocorrelated in space and
 time. Here, we fit latent Gaussian process models to observational
 behavioral data to generate spatially-explicit predictions of foraging
 behavior within the critical habitat of an endangered population of
 fish-eating killer whales (Orcinus orca) in southern British Columbia,
 Canada, and northern Washington State, USA. We compare spatial models
 treating temporal autocorrelation in behavior in three ways: (1) ignoring
 temporal autocorrelation entirely; (2) traditional data-thinning to remove
 temporal autocorrelation, and; (3) using a temporal Gaussian process to
 account for temporal autocorrelation. Comparisons of autocorrelative
 structures for each model and visual comparison of broad spatial patterns
 demonstrate that our third approach yields more accurate results than when
 ignoring temporal autocorrelation entirely and higher precision results
 than when applying data-thinning methods. Within the identified areas of
 critical habitat, our models indicate two primary regions of intense
 killer whale foraging activity, and we delineate areas wherein the
 probability of foraging was particularly high as candidate locations for
 conservation management actions. This study underscores the value of
 refining our understanding of high-use areas for at-risk species by
 incorporating animal behavior data to inform area-based conservation
 measures.
description: # Delineating important killer whale foraging areas using a spatiotemporal
 logistic model
 [https://doi.org/10.5061/dryad.zcrjdfnm9](https://doi.org/10.5061/dryad.zcrjdfnm9) For any questions regarding data and analyses in this publication, please contact the corresponding author, Sheila Thornton[ sheila.thornton@dfo-mpo.gc.ca](mailto:eva.stredulinsky@dfo-mpo.gc.ca). The authors request that should this data (or any subset therein) be used in any analyses, presentations, publications, and/or other data products, it is encouraged that the corresponding author be notified, in order to ensure that the programs and personnel responsible for the collection, assembly, and interpretation of the data are consulted and adequately credited. We recommend citing Stredulinsky et al. (2023) with any use of images or data products in analyses, presentations, publications, and/or other data products. To contact the project lead of a particular study from which data was used in this analysis: (1) Noren_2006: Dawn Noren [dawn.noren@noaa.gov](mailto:dawn.noren@noaa.gov) (2) Holt_200709: Marla Holt [marla.holt@noaa.gov](mailto:marla.holt@noaa.gov) (2) Thornton_201821_FF & Thornton_201821_GBS: Sheila Thornton [sheila.thornton@dfo-mpo.gc.ca](mailto:sheila.thornton@dfo-mpo.gc.ca) ## Description of the data and file structure \=================================================================== STREDULINSKY ET AL. (2023) SRKW FORAGING DATA REPOSITORY \=================================================================== Data products hosted in this repository from Stredulinsky et al. (2023) "Delineating important killer whale foraging areas using a spatiotemporal Bernoulli approach". Behavioral Ecology, doi: [https://doi.org/10.1016/j.gecco.2023.e02726](https://doi.org/10.1016/j.gecco.2023.e02726 "Persistent link using digital object identifier") Data included in this repository: \================================= #### (1) Shapefiles delineating CFAs of high confidence (as seen in Figure 5) (n=6) Where probability of foraging exceeds 25% in more than 80% of model iterations 1 rasterfile provided for each region (Haro & Swiftsure), for 80% confidence bounds. Filenames: Haro_post_forage_exc_0.25_NAD83_BCAlbers.tiff, Swiftsure_post_forage_exc_0.25_NAD83_BCAlbers.tiff, #### (2) Shapefiles delineating FFAs of high confidence (as seen in Figure 6) (n=6) Where probability of foraging exceeds 50% in more than 80% of model iterations 1 rasterfile provided for each region (Haro & Swiftsure), for 80% confidence bounds. Filenames: Haro_post_forage_exc_0.5_NAD83_BCAlbers.tiff, Swiftsure_post_forage_exc_0.5_NAD83_BCAlbers.tiff, #### (3) Shapefiles delineating model extents for each region (Haro & Swiftsure) (n=2) Filenames: Haro_study_domain_simp.shp, Swiftsure_study_domain_simp.shp #### (4) Raw dataset (in .RDS format) required to replicate analyses from Stredulinsky et al. (2023). This dataset consists of 2624 behavioral observations of SRKW from four field studies, as described in Stredulinsky et al. (2023). Filename: SRKW_behav_modelling_archive_dataset.RDS Dataframe field descriptions: * STUDY: Character, Name of the field study in which the observation was made (n=4; Noren_2006, Holt_200709, Thornton_201821_FF, Thornton_201821_GBS) * YEAR: Integer, Year in which the observation was made * MONTH: Integer, Month in which the observation was made * SURVEY_ID: Integer, Unique identifier for each independent behavioral survey * TIME_UNIX_surv: Numeric, Minutes-into-survey when the observation was made (i.e. how many minutes after the first observation of the survey the given observation occurred) * LAT: Numeric, geographic latitude of the observation (in decimal degrees) * LON: Numeric, geographic longitude of the observation (in decimal degrees) * BEHAV: Integer, Behavior observed (0=Travel, 1=Forage) Projection information: \======================= All shapefiles are projected in NAD83 BC Albers (EPSG code: 3005)