Data fusion of distance sampling and capture-recapture data

TL;DR

This paper presents a novel model-based data fusion approach for combining distance sampling and capture-recapture data to improve species distribution models by increasing spatial coverage and reducing parameter uncertainty.

Contribution

The authors developed a new statistical method that effectively fuses DS and CR data, addressing missing data issues and outperforming existing ad-hoc approaches in accuracy and efficiency.

Findings

The approach yields unbiased parameter estimates.

It increases efficiency over existing methods.

Demonstrated with Grasshopper Sparrow data.

Abstract

Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species-habitat relationships and abundance across space and time. Distance sampling (DS) and capture-recapture (CR) are two widely collected data types to learn about species-habitat relationships and abundance; still, they are seldomly used in SDMs due to the lack of spatial coverage. However, data fusion of the two data sources can increase spatial coverage, which can reduce parameter uncertainty and make predictions more accurate, and therefore, can be used for species distribution modeling. We developed a model-based approach for data fusion of DS and CR data. Our modeling approach accounts for two common missing data issues: 1) missing individuals that are missing not at random (MNAR) and 2) partially missing location information. Using a simulation experiment, we evaluated the performance of our modeling approach and compared it to existing approaches that use ad-hoc methods to account for missing data issues. Our results show that our approach provides unbiased parameter estimates with increased efficiency compared to the existing approaches. We demonstrated our approach using data collected for Grasshopper Sparrows (Ammodramus savannarum) in north-eastern Kansas, USA.