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Integrating distance sampling and presence‐only data to estimate species abundance
Author(s) -
Farr Matthew T.,
Green David S.,
Holekamp Kay E.,
Zipkin Elise F.
Publication year - 2021
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1002/ecy.3204
Subject(s) - abundance (ecology) , covariate , abundance estimation , jackal , sampling (signal processing) , range (aeronautics) , distance sampling , inference , ecology , environmental science , statistics , computer science , mathematics , biology , materials science , filter (signal processing) , computer vision , artificial intelligence , composite material
Integrated models combine multiple data types within a unified analysis to estimate species abundance and covariate effects. By sharing biological parameters, integrated models improve the accuracy and precision of estimates compared to separate analyses of individual data sets. We developed an integrated point process model to combine presence‐only and distance sampling data for estimation of spatially explicit abundance patterns. Simulations across a range of parameter values demonstrate that our model can recover estimates of biological covariates, but parameter accuracy and precision varied with the quantity of each data type. We applied our model to a case study of black‐backed jackals in the Masai Mara National Reserve, Kenya, to examine effects of spatially varying covariates on jackal abundance patterns. The model revealed that jackals were positively affected by anthropogenic disturbance on the landscape, with highest abundance estimated along the Reserve border near human activity. We found minimal effects of landscape cover, lion density, and distance to water source, suggesting that human use of the Reserve may be the biggest driver of jackal abundance patterns. Our integrated model expands the scope of ecological inference by taking advantage of widely available presence‐only data, while simultaneously leveraging richer, but typically limited, distance sampling data.