Data from: Delineating important killer whale foraging areas using a spatiotemporal logistic model
Data files
Apr 10, 2024 version files 950.25 KB
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Model_extents.zip
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Model_Rasters.zip
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Modelling_dataset.zip
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README.md
Apr 24, 2024 version files 949.53 KB
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Model_extents.zip
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Model_Rasters.zip
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Modelling_dataset.zip
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README.md
Abstract
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.
README: Delineating important killer whale foraging areas using a spatiotemporal logistic model
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.
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
(2) Holt_200709: Marla Holt marla.holt@noaa.gov
(2) Thornton_201821_FF & Thornton_201821_GBS: Sheila Thornton sheila.thornton@dfo-mpo.gc.ca
Description of the data and file structure
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STREDULINSKY ET AL. (2023) SRKW FORAGING DATA REPOSITORY
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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
Data included in this repository:
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(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:
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All shapefiles are projected in NAD83 BC Albers (EPSG code: 3005)