Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ...

• Main Authors: Braun, Camrin, Arostegui, Martin, Farchadi, Nima, Alexander, Michael, Afonso, Pedro, Allyn, Andrew, Bograd, Steven, Brodie, Stephanie, Crear, Daniel, Culhane, Emmett, Curtis, Tobey, Hazen, Elliott, Kerney, Alex, Lezama-Ochoa, Nerea, Mills, Katherine, Pugh, Dylan, Queiroz, Nuno, Scott, James, Skomal, Gregory, Sims, David, Thorrold, Simon, Welch, Heather, Young-Morse, Riley, Lewison, Rebecca
• Format: Software
• Language: unknown
• Published: Zenodo 2023
• Subjects: species distribution models; prediction; Ecological forecasting; spatial ecology; highly migratory species; Climate Change; Northwest Atlantic
• Online Access: https://dx.doi.org/10.5281/zenodo.7971531; https://zenodo.org/record/7971531

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark ( Prionace glauca ), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how ... : Funding provided by: NASA Headquarters Crossref Funder Registry ID: http://dx.doi.org/10.13039/100017437 Award Number: 80NSSC19K0187 ...