ORCID Profile
0000-0001-9044-3873
Current Organisation
Virginia Tech
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Publisher: Public Library of Science (PLoS)
Date: 21-03-2018
Publisher: Elsevier BV
Date: 10-2018
Publisher: Public Library of Science (PLoS)
Date: 29-08-2019
Publisher: Elsevier BV
Date: 05-2020
Publisher: Cambridge University Press (CUP)
Date: 12-06-2019
DOI: 10.1017/S0376892919000055
Abstract: Species distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir ( Tapirus bairdii ) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the in idual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the in idual movements of species will likely lead to less accurate estimates of species intensity (number of in iduals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subs ling prior to implementation.
Publisher: Wiley
Date: 18-09-2017
DOI: 10.1111/DDI.12631
No related grants have been discovered for Marcella Kelly.