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• W4296151649 abstract "1 Abstract 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) have typically been used in ecology to estimate current and future ranges of species, and are a common tool used when making conservation prioritisation decisions. However integration of these data in a model-based framework is needed to address many of the current large-scale threats to biodiversity. Current SDM practice typically underutilizes the large amount of publicly available biodi-versity data and does not follow a set of standard best practices. Integrating different data types with open-source tools and reproducible workflows saves time, increases collaboration opportunities, and increases the power of data inference in SDMs. We aim to address this issue by (1) proposing methods and (2) generating a reproducible workflow to integrate different available data types to increase the power of SDMs. We provide the R package intSDM , as well as 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 proposed reproducible workflow by creating SDMs for vascular plants in Norway, integrating presence-only and presence-absence species occurrence data and climate data." @default.
• W4296151649 created "2022-09-17" @default.
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• W4296151649 date "2022-09-17" @default.
• W4296151649 modified "2023-10-15" @default.
• W4296151649 title "_<i>int</i>SDM: A reproducible framework for integrated species distribution models" @default.
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• W4296151649 doi "https://doi.org/10.1101/2022.09.15.507996" @default.
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