Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models

Abstract While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically...

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Autores principales:	Pietro Milanesi, Francesca Della Rocca, Robert A. Robinson
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2020
Materias:	climate change dynamic predictors land‐use change nonstationarity R spatiotemporal model Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/9170ff4e47d2439a8164bd3ba0b73229
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spelling	oai:doaj.org-article:9170ff4e47d2439a8164bd3ba0b732292021-11-04T13:06:10ZIntegrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models2045-775810.1002/ece3.5938https://doaj.org/article/9170ff4e47d2439a8164bd3ba0b732292020-01-01T00:00:00Zhttps://doi.org/10.1002/ece3.5938https://doaj.org/toc/2045-7758Abstract While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically consider the environmental predictors as static; specifically, in most studies using species distribution models, predictor values are averaged over a 50‐ or 30‐year time period. This could lead to a strong bias due to monthly/annual variation between the climatic conditions in which species' locations were recorded and those used to develop species distribution models or even a complete mismatch if locations have been recorded more recently. Moreover, the impact of land‐use change has only recently begun to be fully explored in species distribution models, but again without considering year‐specific values. Excluding dynamic climate and land‐use predictors could provide misleading estimation of species distribution. In recent years, however, open‐access spatially explicit databases that provide high‐resolution monthly and annual variation in climate (for the period 1901–2016) and land‐use (for the period 1992–2015) conditions at a global scale have become available. Combining species locations collected in a given month of a given year with the relative climatic and land‐use predictors derived from these datasets would thus lead to the development of true dynamic species distribution models (D‐SDMs), improving predictive accuracy and avoiding mismatch between species locations and predictor variables. Thus, we strongly encourage modelers to develop D‐SDMs using month‐ and year‐specific climatic data as well as year‐specific land‐use data that match the period in which species data were collected.Pietro MilanesiFrancesca Della RoccaRobert A. RobinsonWileyarticleclimate changedynamic predictorsland‐use changenonstationarityRspatiotemporal modelEcologyQH540-549.5ENEcology and Evolution, Vol 10, Iss 2, Pp 1087-1092 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	climate change dynamic predictors land‐use change nonstationarity R spatiotemporal model Ecology QH540-549.5
spellingShingle	climate change dynamic predictors land‐use change nonstationarity R spatiotemporal model Ecology QH540-549.5 Pietro Milanesi Francesca Della Rocca Robert A. Robinson Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models
description	Abstract While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically consider the environmental predictors as static; specifically, in most studies using species distribution models, predictor values are averaged over a 50‐ or 30‐year time period. This could lead to a strong bias due to monthly/annual variation between the climatic conditions in which species' locations were recorded and those used to develop species distribution models or even a complete mismatch if locations have been recorded more recently. Moreover, the impact of land‐use change has only recently begun to be fully explored in species distribution models, but again without considering year‐specific values. Excluding dynamic climate and land‐use predictors could provide misleading estimation of species distribution. In recent years, however, open‐access spatially explicit databases that provide high‐resolution monthly and annual variation in climate (for the period 1901–2016) and land‐use (for the period 1992–2015) conditions at a global scale have become available. Combining species locations collected in a given month of a given year with the relative climatic and land‐use predictors derived from these datasets would thus lead to the development of true dynamic species distribution models (D‐SDMs), improving predictive accuracy and avoiding mismatch between species locations and predictor variables. Thus, we strongly encourage modelers to develop D‐SDMs using month‐ and year‐specific climatic data as well as year‐specific land‐use data that match the period in which species data were collected.
format	article
author	Pietro Milanesi Francesca Della Rocca Robert A. Robinson
author_facet	Pietro Milanesi Francesca Della Rocca Robert A. Robinson
author_sort	Pietro Milanesi
title	Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models
title_short	Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models
title_full	Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models
title_fullStr	Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models
title_full_unstemmed	Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models
title_sort	integrating dynamic environmental predictors and species occurrences: toward true dynamic species distribution models
publisher	Wiley
publishDate	2020
url	https://doaj.org/article/9170ff4e47d2439a8164bd3ba0b73229
work_keys_str_mv	AT pietromilanesi integratingdynamicenvironmentalpredictorsandspeciesoccurrencestowardtruedynamicspeciesdistributionmodels AT francescadellarocca integratingdynamicenvironmentalpredictorsandspeciesoccurrencestowardtruedynamicspeciesdistributionmodels AT robertarobinson integratingdynamicenvironmentalpredictorsandspeciesoccurrencestowardtruedynamicspeciesdistributionmodels
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Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models

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