Testing for changes in population trends from low-cost ecological count data

Accurate and up-to-date knowledge of population trends is essential for effective biodiversity conservation, as is assessing the impact of conservation measures designed to alter these trends. Estimating population trends is challenging, however, either due to altogether insufficient data or due to so-called noisy data that do not readily allow for standard statistical analyses. In addition, many existing methods require monitoring data over long periods of time, which is in contrast to the quick interventions needed by conservation projects, especially when endangered species are involved.

To address these issues, we here present birp , a novel Bayesian tool that maximizes the power to test for population trends and changes in trends under arbitrary designs, including the canonical before-after (BA), control-intervention (CI) and before-after-control-intervention (BACI) designs often used to assess conservation impact. Our model builds on classic Poisson and negative binomial models for ecological count data and infers changes in population trends jointly from data obtained with multiple survey methods such as track counts, camera trap surveys, or distance sampling, and also from limited and noisy data not necessarily collected in standardized ecological surveys. By focusing on the change itself, our method side-steps common challenges of estimating population trends and does not need to know about absolute population densities or detection probabilities. birp is open-source and available as both a standalone command line tool as well as an R-package for fast and easy use.

We illustrate the power of our tool through extensive simulations and show that changes in trends are accurately estimated under various designs, even when data are noisy and sparse, and thereby enables biodiversity research also in regions that are remote and difficult to access.

Using birp , we further test for changes in population trends of Tasmanian devils in Australia and of apex predators and their main prey in the Central African Republic. Based on these results we give general guidelines on survey designs that maximize the power to detect trends.