Development and Delivery of Species Distribution Models to Inform Decision-Making

Abstract

Information on where species occur is an important component of conservation and management decisions, but knowledge of distributions is often coarse or incomplete. Species distribution models provide a tool for mapping habitat and can produce credible, defensible, and repeatable information with which to inform decisions. However, these models are sensitive to data inputs and methodological choices, making it important to assess the reliability and utility of model predictions. We provide a rubric that model developers can use to communicate a model's attributes and its appropriate uses. We emphasize the importance of tailoring model development and delivery to the species of interest and the intended use and the advantages of iterative modeling and validation. We highlight how species distribution models have been used to design surveys for new populations, inform spatial prioritization decisions for management actions, and support regulatory decision-making and compliance, tying these examples back to our model assessment rubric.

Ensemble modeling of the potential distribution of the whale shark in the Atlantic Ocean

“…This study is part of an emerging effort to understand the impact of climatic change on charismatic and vulnerable marine species. We endeavored to follow the most recent best‐practice recommendations for correlative modeling (Araújo et al, ; Sofaer et al, ), particularly in marine environments (Robinson, Nelson, et al, ). Namely, (a) we used actual observation data including both presence and surveyed absence localities (i.e., places where observers were active and did not detect the target species)—which is an uncommon asset in species distribution and niche modeling studies, particularly those targeting marine species; (b) We used the same spatial resolution for species occurrence and environmental data, filtering out any records with insufficient precision; (c) We employed a systematic procedure for selecting relevant predictor variables, avoiding correlated or noninformative variables and backing up their ecological meaningfulness with the literature; (d) We computed and displayed the predictions of a range of different modeling algorithms, and addressed model‐based uncertainty by assessing prediction variance; (e) We cross‐evaluated model predictions over a range of random test samples using both threshold‐dependent and threshold‐independent metrics; we selected models based on their predictive performance on the test samples; and we built the final models using the complete (training plus test) dataset.…”

Calibration of probability predictions from machine‐learning and statistical models

“…The example here is that of an Australian bird species' distribution (as presence-absence data at a scale of 50 × 50 km 2 ), using climate and land-cover predictors. The example itself is immaterial and merely illustration [R code (R Core Team, 2019) and data can be found in Supporting Information Appendix S1], although in the context of species distribution analysis presence-absence data and predicted occurrence probability are particularly common (some recent examples are Derville, Torres, Iovan, & Garrigue, 2018;Marca et al, 2019;Martínez et al, 2018;Robinson, Ruiz-Gutierrez, & Fink, 2018;Sabatini et al, 2018;Sofaer et al, 2019).…”

“…Correct probability predictions are particularly important, as Platt (2000) points out, when they form part of an actual probability-based decision, or when they are averaged with other methods, so that a common measure is required. In the specific field of species distribution models of presence-absence data, Pearce and Ferrier (2000) featured such calibration prominently, yet hardly any study or even standard has picked it up (Araújo et al, 2019;Peterson et al, 2011;Sofaer et al, 2019); for notable exceptions see Franklin (2010); Johnston et al (2015Johnston et al ( , 2019; Guisan, Thuiller, and Zimmermann (2017) and Fink et al (2020).…”

A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat

“…We first summarized the two modeling approaches, including data inputs, predictor variables, and suitability criteria to highlight similarities and differences in model structure (Table 1). We classified individual model inputs, procedures, and outputs according to the rubric established by Sofaer et al (2019) in Table S1. We also summarized the relative importance of predictor variables used in each modeling approach based on sensitivity analyses.…”

“…Some studies have advocated for a more pragmatic modeling approach that reduces the researchimplementation gap (Guisan et al, 2013;Schmolke et al, 2010;Sofaer et al, 2019). An ideal modeling approach should address previous impediments to the use of models in decision-making by: (a) explicitly identifying the management problem and objectives; (b) defining and evaluating the consequences of alternative actions; (c) assessing model sensitivity, accuracy, and uncertainty; and (d) clearly communicating results in a manner that directly addresses the natural resource problem (Addison et al, 2013;Guisan et al, 2013;Sofaer et al, 2019). Here, we provide a case study that utilizes an ensemble modeling approach, incorporates these considerations, and can directly inform conservation decision-making in a heavily populated mid-Atlantic (USA) coastal zone.…”