DOI: 10.5061/dryad.g79cnp5wv
title: Data from: One-stage spatial mark-resight analysis reveals an increasing grizzly bear population with declining density near roads

description: Wildlife ecologists throughout the world strive to monitor trends in
 population abundance to help manage wildlife populations and conserve
 species at risk. Spatial capture-recapture studies are the gold standard
 for monitoring density, yet they can be difficult to apply because
 researchers must be able to distinguish all detected individuals. Spatial
 mark-resight (SMR) models only require a subset of the population to be
 marked and identifiable. Recent advances in SMR models with radio-collared
 animals required a two-staged analysis. We developed a one-stage
 generalized SMR (gSMR) model that used detection histories of marked and
 unmarked animals in a single analysis. We used simulations to assess the
 performance of one- and two-stage gSMR models. We then applied the
 one-stage gSMR with telemetry and remote camera data to estimate grizzly
 bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky
 Mountains. We estimated abundance trends for the population and
 reproductive females (females with cubs of the year). Simulations suggest
 one- and two-stage models performed equally well. One-stage models are
 more dependable as they use exact likelihoods whereas two-stage models
 have shorter computation times for large datasets. Both methods had
 &gt; 95% credible interval coverage and minimal bias. Increasing the
 number of marked animals increased the accuracy and precision of abundance
 estimates and &gt; 10 marked animals were required to obtain
 coefficients of variation &lt; 20% in most scenarios. The grizzly bear
 population increased slightly (growth rate λmean = 1.02) to a 2023 density
 of 10.4 grizzly bears/1000 km2. Reproductive female abundance had high
 interannual variability and increased to 1.0 bears/1000 km2. Population
 density was highest within protected areas, within high quality habitat
 and far from paved roads. The density of activity centers declined near
 paved roads over time. Mechanisms of decline may have included direct
 mortality and shifting activity centers to avoid human activity. Our study
 demonstrates the influence of human activity on localized density and
 importance of protected areas for carnivore conservation. Finally, our
 study highlights the widespread utility of remote camera and
 telemetry-based spatial mark-resight models for monitoring spatiotemporal
 trends in abundance.
description: The gSMR models combine remote camera detections of marked
 animals, unmarked animals, and telemetry data to estimate the baseline
 detection rate, home range scale parameter, and spatially explicit
 estimates of density. Our study area encompassed 15,483
 km<sup>2</sup> and included Banff, Kootenay, and Yoho Nation
 Parks and the Ya Ha Tinda ecosystem within the Rocky Mountains of Canada.
 The remote camera data contains detection histories from 25 marked,
 radio-collared grizzly bears and detections of unmarked grizzly bears
 recorded at 625 remote cameras from 2012 to 2021. Telemetry data contains
 daily global positioning system (GPS) locations from fifteen female and
 ten male grizzly bears. We provide source code to estimate spatial and
 temporal trends in grizzly bear density as well as the density of female
 grizzly bears with cubs of the year. We describe each data set and
 associated attributes in tbl_DataDescription_2023-11-13.csv. 
description: # Data from: Grizzly bear spatial mark-resight analysis reveals a stable
 population with declining densities near roads
 [https://doi.org/10.5061/dryad.g79cnp5wv](https://doi.org/10.5061/dryad.g79cnp5wv) ## Description of the data and file structure *tbl_DataDescription_2023-11-13.csv* provides a description of all data sets and column attributes for grizzly bear gSMR analysis. Here is a summary of each data set. | **DataSet** | **Description** | | :------------------------------------ | :------------------------------------------------------------------------------------------------------------- | | tbl\_specs.csv | table of constants used for models | | pix\_xy.csv | table of pixel coordinates with spatial attributes. 4 km spacing. | | site\_cam.csv | Site attributes of remote camera locations | | occ\_long\_cam.csv | Occurrence data for grizzly bears in long format.  14 day occasions | | detect\_year\_cam.csv | Detection history data summarised by year | | detect\_marked.csv | Detection data for marked (radio-collared) grizzly bears | | gps\_day.csv | table of GPS locations with one location per day for each animal. | | d2\_cam.csv | matrix for squared euclidean distances between each pixel (row) and each camera site (column) | | pixel\_lookup.csv | matrix lookup table that links x.id and y.id (pixel coordinates) to a unique pixel number in pix\_xy. | | tbl\_juvenile.csv | table of parturition records and survival records (when known) for radio-collared female grizzly bears | | statespace\_2023.sqlite | Shapefile of state-spaced used for the joint gSMR analysis.  UTM Zone 11 NAD 83 | | MountainParks\_2007\_YaHaTinda.sqlite | Shapefile of study area including national parks and the Ya Ha Tinda ecosystem.  UTM Zone 11 NAD 83 | | sites\_camera\_2023.sqlite | sqlite file of remote camera locations and raw detection rates.  UTM Zone 11 NAD 83 | | tbl\_area\_parks.csv | Area in km2 of Banff National Park, Kootenay National Park, Yoho National Park, and the Ya Ha Tinda study area | ## Sharing/Access information Data was collected by Parks Canada and the University of Montana. ## Code/Software We provide R code for the simulation study and grizzly bear gSMR analysis with the following scripts: **Simulation Study** *1a. gSMR simStudy functions and nimble code.R*: R script with functions to generate spatial mark-resight data and nimble code for fitting two-stage and joint gSMR models. *1b. gSMR Run SimStudy One and Two Stage gSMR.R*: R script to specify scenario parameters and to run simulation study. This script calls upon *1a. gSMR simStudy functions and nimble code.R* **Grizzly bear analysis** *2a. nimble_efficient_functions.R*: R script with nimble functions that help reduce computation time. *2b. nimble model gSMR grizzly.R*: R script with the nimble code for the grizzly bear joint gSMR model. *2c. fit one stage gSMR model grizzly bears.R*: R script for importing data and fitting the grizzly bear joint gSMR model. The script calls upon *2a. nimble_efficient_functions.R and* 2b. nimble model gSMR grizzly.R. **Female grizzly bear with cub of year analysis** *3b. nimble model gSMR grizzly_COY.R*: R script with the nimble code for the female grizzly bear with cub of the year joint gSMR model. *3c. fit one stage gSMR model grizzly_COY.R*: R script for importing data and fitting the grizzly bear joint gSMR model. The script calls upon *2a. nimble_efficient_functions.R* and *3b. nimble model gSMR grizzly_COY.R*. We used R version4.4.0 and the following packages for the analysis: stringr_1.5.1 nimble_1.1.0 sf_1.0-16 ggplot2_3.5.1 lubridate_1.9.3 tibble_3.2.1 tidyr_1.3.1 dplyr_1.1.4