Melding Wildlife Surveys to Improve Conservation Inference

TL;DR

This paper introduces a Bayesian integrated modeling approach that combines multiple wildlife survey datasets with different errors to improve conservation inference and spatial predictions for species of concern.

Contribution

The paper develops a novel Bayesian Markov melding framework for integrating diverse ecological surveys with varying errors, enhancing inference and prediction accuracy.

Findings

Integrated model outperforms independent analyses in predictive accuracy.

Model enables density prediction in unsampled regions.

Sensitivity analysis quantifies impact of reduced survey effort.

Abstract

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models that analyzed the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced survey effort.