Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection

Koshkina, V, Wang, Y, Gordon, A, Dorazio, R, White, M and Stone, L 2017, 'Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection', Methods in Ecology and Evolution, vol. 8, no. 4, pp. 420-430.


Document type: Journal Article
Collection: Journal Articles

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Title Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection
Author(s) Koshkina, V
Wang, Y
Gordon, A
Dorazio, R
White, M
Stone, L
Year 2017
Journal name Methods in Ecology and Evolution
Volume number 8
Issue number 4
Start page 420
End page 430
Total pages 11
Publisher Wiley-Blackwell
Abstract Two main sources of data for species distribution models (SDMs) are site-occupancy (SO) data from planned surveys, and presence-background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO-PB Model which uses repeated survey occupancy data and also incorporates detection probability. The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow-bellied glider (Petaurus australis) in south-eastern Australia, with the predictive performance of the Integrated Model again found to be superior. PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out-of-sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone.
Subject Conservation and Biodiversity
Landscape Ecology
Biostatistics
Keyword(s) imperfect detection
occupancy model
presence-background
sampling bias
site-occupany
spatial point process
species distribution models
DOI - identifier 10.1111/2041-210X.12738
Copyright notice © 2017 The Authors, Methods in Ecology and Evolution © British Ecological Society
ISSN 2041-210X
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