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 California
over the last decade and used the integrated camera trapping-based model
to extrapolate to the non-hunted areas of California to estimate a total
statewide average population size over the last five years of 59,851
individuals (90% credible interval: 49,412 – 70,611).  Data
included here include the regional AAH data by year, prior distributions
used to fit the model, regional camera trapping and local spatial
capture-recapture population estimates and associated standard errors, and
R jags code to run the model.