DOI: 10.5061/dryad.wh70rxx13
title: Using spatial capture-recapture methods to estimate long-term spatiotemporal variation of a wide-ranging marine species

description: 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.
description: # Using spatial capture-recapture methods to estimate long-term
 spatiotemporal variation of a wide-ranging marine species Dataset DOI:
 [10.5061/dryad.wh70rxx13](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/](https://www.R-project.org/).