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

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...

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Bibliographic Details
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: Dataset
Language:English
Published: Dryad 2023
Subjects:
FOS Earth and related environmental sciences
species distribution models
prediction
Ecological forecasting
spatial ecology
highly migratory species
Climate change
Morse
Northwest Atlantic
morse
Online Access:https://dx.doi.org/10.5061/dryad.h44j0zpr2
https://datadryad.org/stash/dataset/doi:10.5061/dryad.h44j0zpr2
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Summary: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 each ... : Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned > ...

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