Estimating wildlife populations and their dynamics using multiple data sources and a hierarchical integrated model: the case of California's black bears

Accurate monitoring of wildlife populations is critical to their effective conservation and management. For populations that are actively harvested, age-at-harvest (AAH) data provide a valuable source of information about their age and sex structure. Bayesian state space models have been developed to harness this AAH data along with prior knowledge of species’ ecology to estimate population sizes, trends, and underlying demographic rates.
We extended these state-space models further to integrate abundance models using camera trapping data to both inform initial population size and extrapolate to areas without AAH data. Additionally, we formulated a hierarchical integrated model that models the data and populations separately by region while still sharing information across regions to account for socio-ecological differences and informing adaptive local management.
We applied our state-space model to estimate the population size and dynamics of black bears within the hunted areas of Cali..., , **Estimating wildlife populations and their dynamics using multiple data sources and a hierarchical integrated model: the case of California's black bears**

Dataset DOI: [10.5061/dryad.9p8cz8wvp](10.5061/dryad.9p8cz8wvp)

**Description of the data and file structure**

Data included in this repository include processed black bear harvest data, camera trap (Royle-Nichols) model results, and local spatial capture-recapture model results. The data are formatted for use in a hierarchical integrated model to estimate black bear population size and dynamics across California from 2013-2024. 

**Files and variables**

**File: bear_IPM_data.RDS**

**Description:** This RDS file contains the data needed to run the JAGS model in R. 

The RDS file contains:

Y: a constant defining the number of years included in the data and model,

A: a constant defining the number of age classes in the data and model,

hr.idx: a matrix with sex = 2 rows and age classes = 10 columns indexing the log hazard ratio...,