Using spatial capture-recapture methods to estimate long-term spatiotemporal variation of a wide-ranging marine species
Data files
Jun 20, 2025 version files 842.72 KB
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Model_input_objects.RData
840.24 KB
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README.md
1.94 KB
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SCR_model_fitting.R
533 B
Abstract
Determining population status to inform mitigation of anthropogenic threats requires statistical approaches that investigate spatial and temporal variation. In the face of climate change, it is increasingly important to differentiate between changes in population size and redistributions of populations. This is especially true for wide-ranging species such as the blue whale. An abundance of eastern North Pacific blue whales has previously been estimated using (non-spatial) closed capture-recapture and distance sampling methods, but the estimates show opposite and diverging trends over the last 30 years. Evidence that the distribution has been expanding could explain the apparent disparity, due to the confounding effects of spatial variation in sampling and the changing distribution. To investigate this, we apply, for the first time, spatial capture-recapture (SCR) methods to blue whale photo-identification data from small boat surveys to estimate abundance. The study area was defined as the length of the continental USA coastline, extending approximately 100 km offshore. Average annual effort from 1991 to 2023 was 97 days, resulting in 7,358 sightings of 1,488 unique individuals. We find significant support for non-linear spatiotemporal variation. In all years, there were higher densities at lower latitudes, but there were notable decadal cyclical fluctuations in the number of animals using the study area. This large variation in the numbers of animals using these waters motivates further study into the relationship with environmental changes. Our results are an important step in spatially-explicit modelling of observational blue whale data, which highlight the value of including spatial and temporal data and are relevant to any marine mammal species monitored using photo-identification.
Dataset DOI: 10.5061/dryad.wh70rxx13
Description of the data and file structure
These data relate to spatial-capture recapture modelling of eastern North Pacific blue whale abundance as described in the associated paper (Whittome et al. 2025). Photo-identification data were collected by the Cascadia Research Collective. These data were then processed for input to the R package secr (Efford 2024) to conduct spatially-explicit capture-recapture modelling. The data input files and model run code are provided. The data input files only contain summarised location information of whale encounters. A grid (with a cell size of approximately 44km) was applied to an area which spanned the extent of the encounters, and the centroid of each grid cell was considered the ‘trap’ with encounters allocated to the closest trap (see associated publication for further detail).
Files and variables
Model_input_objects.RData
This file contains prepared input objects required for spatially-explicit capture-recapture modelling in the package secr. It includes the multi-session capthist and mask objects.
Code/software
R software (R Core Team 2024) with package secr (Efford 2024) is required to load the data and run the code. Modelling was carried out using R version 4.4.1. and secr version 4.6.6.
SCR_model_fitting.R
This R script provides the code to fit the spatially-explicit capture-recapture model. It loads data provided from the file 'Model_input_objects.RData'.
Efford, M. 2024. secr: Spatially explicit capture-recapture models. R package version 4.6.6. doi:10.32614/CRAN.package.secr.
R Core Team. 2024. R: A language and environment for statistical computing. [accessed 2024 Aug 2]. https://www.R-project.org/.
