A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.

DZ, CK and GF acknowledge support from the German Science Foundation (DFG, grant no. ZU 361-1/1). BS acknowledges support by the German Science Foundation (grant no. SCHR1000/6-2) and by the Volkswagenstiftung and Niedersächsisches Ministerium für Wissenschaft und Kultur (METAPOLIS, ZN3121). BS and PJL acknowledge support by the joint BiodivERsA-project GreenFutureForest (grant no. 01LC1610A). WT was supported by the GAMBAS project funded by the French National Research Agency (ANR-18-CE02-0025). PB was partly funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, being managed by the U.S. Navy's Living Marine Resources program under Contract no. N39430-17-C-1982. CM acknowledges funding from National Science Foundation grant DBI 1565046 and DBI 1661510. JF acknowledges the support of the National Science Foundation (USA) grant no 1853697. GGA and JE acknowledge support of the Australian Research Council via grant DP180101852.

Peer reviewed