Work

Improved abundance trajectories with Bayesian population dynamics models: case study with a Hawaiian honeycreeper

Year: 2026

Type: article

Abstract: Many wildlife monitoring programmes collect annual data on population abundance. The resulting abundance estimates fluctuate over time partly because of true population change and partly because of observation error. These two components of variation can be separated by fitting the estimates to a population dynamics model within a Bayesian state-space modelling framework. By constraining the population trajectory to be biologically realistic, more precise estimates can be obtained. Independent biological knowledge can be incorporated through choice of model structure and by specifying informative prior distributions on demographic parameters. We illustrate the approach using a 31-year point transect study of the Hawai’i ’ākepa (Loxops coccineus). We fitted five models, each making different assumptions about how population change, recruitment and/or adult survival varied over time. Overall, the ’ākepa geometric mean growth rate was 1.02, indicating an increasing population over the 31-year time series, although there were periods of slow decline potentially associated with low recruitment and more rapid recovery associated with pulses of high recruitment. Abundance estimates derived from the population models were substantially more precise than the ‘raw’ point transect estimates: 95% credible interval (CrI) was on average 51.7% (s.d. = 14.1%) narrower.

Source: Royal Society Open Science

DOI: 10.1098/rsos.250528

Authors: Richard J. Camp, Len Thomas, Stephen T. Buckland, Steve J. Kendall

Institutions: United States Geological Survey, University of St Andrews, United States Fish and Wildlife Service

Language: English

Topic: Avian ecology and behavior

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $1,471