Esdm: A tool for creating and exploring ensembles of predictions from species distribution and abundance models. Issue 11 (11th September 2019)

• Record Type: Journal Article
• Authors: Woodman, Samuel M.; Forney, Karin A.; Becker, Elizabeth A.; DeAngelis, Monica L.; Hazen, Elliott L.; Palacios, Daniel M.; Redfern, Jessica V.
• Editors: Goslee, Sarah
• Is Part Of: Methods in ecology and evolution. Volume 10:Issue 11(2019)
• Page Start: 1923
• Page End: 1933
• Publication Date: 2019-09-11
• Subjects: abundance -- blue whale Balaenoptera musculus -- ensemble -- model evaluation -- model predictions -- probability of occurrence -- species distribution models -- species management; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13283
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Species distribution models (SDMs) are a valuable statistical approach for both understanding species distributions and identifying potential impacts of environmental changes or management decisions to species, but multiple SDMs for the same species in a region can create confusion in decision‐making processes. One solution is to create ensembles (i.e. combinations) of predictions from existing SDMs. However, creating ensembles can be challenging if the predictions were made at different spatial resolutions, using different data sources, or with different prediction value types (e.g. abundance and probability of occurrence). We present esdm, an r package that allows users to create an ensemble of SDM predictions overlaid onto a single base geometry. These predictions can be evaluated (e.g. through among‐model uncertainty or AUC, TSS and RMSE metrics), mapped, and exported. esdm includes a built‐in GUI created using the r package shiny, which makes the package accessible to non‐r users. We provide an overview of esdm functionality and use esdm to create an ensemble of predictions from three blue whale Balaenoptera musculus SDMs for the California Current Ecosystem.