A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir

Abstract

SummarySpecies 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 individual 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 individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals 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 subsampling prior to implementation.

Drivers for spatial modelling of a critically endangered seabird on a dynamic ocean area: Balearic shearwaters are non‐vegetarian

“…The prediction of the distributions of species is central to diverse applications in ecology, evolution, and conservation science (Elith et al, 2006). In particular, with species facing a high degree of global threat, distribution modelling may allow effective conservation strategies to be undertaken (Maiorano et al, 2019; Schank et al, 2019). The relatively long‐term dataset analysed (including 10 years from a period of 13 years) accounts for some interannual variability (Tummon et al, 2015), whereas previous studies of the distribution of this species covered more limited time periods (Arcos et al, 2009; Louzao et al, 2012).…”

Population status, connectivity, and conservation action for the endangered Baird's tapir

“…It is important to note that the resolution of the analysis can have significant effects on the results, for both the ISDM and the connectivity (i.e. least-cost) analysis (Etherington, 2016;Schank et al, 2019). We selected a resolution we deemed appropriate for the ISDM, but future research would benefit from further evaluating this choice for the leastcost path analysis.…”

“…The ISDM was fit using custom R code adapted from previous research (Dorazio, 2014;Schank et al, 2017Schank et al, , 2019.…”

Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice

“…For example, Fernandes et al (2018) showed that models with high fit (e.g., using Random Forest) were more negatively influenced by errors and biases (lower predictive success) compared with models with lower fit (e.g., GLMs), whereas models assuming a linear relationship (e.g., GLMs/GLMMs) are more affected by collinearity and multicollinearity. Schank et al (2019) showed that integrated distribution models can improve the handling of sampling bias whereas Tobler et al (2019) discuss the impact of measurement errors and correlation between objects in joint distribution models.…”