intSDM: A reproducible framework for integrated species distribution models

Abstract

AbstractIntegration of data is needed to address many of the problems currently threatening biodiversity. There has been an exponential increase in quantity and type of biodiversity data in recent years, including presence-absence, counts, and presence-only citizen science data. Species Distribution Models (SDMs) are frequently used in ecology to predict current and future ranges of species, and are a common tool used when making conservation prioritization decisions. Current SDM practice typically underutilizes the large amount of publically available biodiversity data and does not follow a set of standard best practices. Integrating data types with open-source tools and reproducible workflows saves time, increases collaboration opportunities, and increases the power of data inference in SDMs. Here, we address the discipline-wide call for open science and standards in SDMs by (1) proposing methods and (2) generating a reproducible workflow to integrate different available data types to increase the power of SDMs. We provide an R package, intSDM, which reduces the learning curve for the use of integrated SDMs and makes them an accessible tool for use by non-programmers. We provide code and guidance on how to accommodate users’ diverse needs and ecological questions with different data types available on the Global Biodiversity Information Facility (GBIF), the largest biodiversity data aggregator in the world. Finally, we provide a case study of the application of our reproducible workflow by creating SDMs for vascular plants in Norway, integrating presence-only and presence-absence species occurrence data, climate, and habitat data.