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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ftdatacite:10.5281/zenodo.7971532 2023-06-11T04:15:25+02:00 Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... 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 2023 https://dx.doi.org/10.5281/zenodo.7971532 https://zenodo.org/record/7971532 unknown Zenodo https://zenodo.org/communities/dryad https://dx.doi.org/10.5061/dryad.h44j0zpr2 https://dx.doi.org/10.5281/zenodo.7971531 https://zenodo.org/communities/dryad Open Access Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 info:eu-repo/semantics/openAccess species distribution models prediction Ecological forecasting spatial ecology highly migratory species Climate Change SoftwareSourceCode Software article 2023 ftdatacite https://doi.org/10.5281/zenodo.797153210.5061/dryad.h44j0zpr210.5281/zenodo.7971531 2023-06-01T12:19:14Z 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 ... Software Northwest Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
species distribution models prediction Ecological forecasting spatial ecology highly migratory species Climate Change |
spellingShingle |
species distribution models prediction Ecological forecasting spatial ecology highly migratory species Climate Change 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 Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
topic_facet |
species distribution models prediction Ecological forecasting spatial ecology highly migratory species Climate Change |
description |
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 ... |
format |
Software |
author |
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 |
author_facet |
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 |
author_sort |
Braun, Camrin |
title |
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
title_short |
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
title_full |
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
title_fullStr |
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
title_full_unstemmed |
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
title_sort |
data and code for: building use-inspired species distribution models: using multiple data types to examine and improve model performance ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.7971532 https://zenodo.org/record/7971532 |
genre |
Northwest Atlantic |
genre_facet |
Northwest Atlantic |
op_relation |
https://zenodo.org/communities/dryad https://dx.doi.org/10.5061/dryad.h44j0zpr2 https://dx.doi.org/10.5281/zenodo.7971531 https://zenodo.org/communities/dryad |
op_rights |
Open Access Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.797153210.5061/dryad.h44j0zpr210.5281/zenodo.7971531 |
_version_ |
1768372210536284160 |