Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions

Vesicular stomatitis (VS) is the most common vesicular livestock disease in North America. Transmitted by direct contact and by several biting insect species, this disease results in quarantines and animal movement restrictions in horses, cattle and swine. As changes in climate drive shifts in geogr...

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Autores principales:	Dylan Burruss, Luis L. Rodriguez, Barbara Drolet, Kerrie Geil, Angela M. Pelzel-McCluskey, Lee W. Cohnstaedt, Justin D. Derner, Debra P. C. Peters
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	big data machine learning vesicular stomatitis land use macrosystems climate change Science Q
Acceso en línea:	https://doaj.org/article/327e4cdc49f04afcaabbb85ad015263d
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spelling	oai:doaj.org-article:327e4cdc49f04afcaabbb85ad015263d2021-11-25T17:15:20ZPredicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions10.3390/cli91101592225-1154https://doaj.org/article/327e4cdc49f04afcaabbb85ad015263d2021-10-01T00:00:00Zhttps://www.mdpi.com/2225-1154/9/11/159https://doaj.org/toc/2225-1154Vesicular stomatitis (VS) is the most common vesicular livestock disease in North America. Transmitted by direct contact and by several biting insect species, this disease results in quarantines and animal movement restrictions in horses, cattle and swine. As changes in climate drive shifts in geographic distributions of vectors and the viruses they transmit, there is considerable need to improve understanding of relationships among environmental drivers and patterns of disease occurrence. Multidisciplinary approaches integrating pathology, ecology, climatology, and biogeophysics are increasingly relied upon to disentangle complex relationships governing disease. We used a big data model integration approach combined with machine learning to estimate the potential geographic range of VS across the continental United States (CONUS) under long-term mean climate conditions over the past 30 years. The current extent of VS is confined to the western portion of the US and is related to summer and winter precipitation, winter maximum temperature, elevation, fall vegetation biomass, horse density, and proximity to water. Comparison with a climate-only model illustrates the importance of current processes-based parameters and identifies regions where uncertainty is likely to be greatest if mechanistic processes change. We then forecast shifts in the range of VS using climate change projections selected from CMIP5 climate models that most realistically simulate seasonal temperature and precipitation. Climate change scenarios that altered climatic conditions resulted in greater changes to potential range of VS, generally had non-uniform impacts in core areas of the current potential range of VS and expanded the range north and east. We expect that the heterogeneous impacts of climate change across the CONUS will be exacerbated with additional changes in land use and land cover affecting biodiversity and hydrological cycles that are connected to the ecology of insect vectors involved in VS transmission.Dylan BurrussLuis L. RodriguezBarbara DroletKerrie GeilAngela M. Pelzel-McCluskeyLee W. CohnstaedtJustin D. DernerDebra P. C. PetersMDPI AGarticlebig datamachine learningvesicular stomatitisland usemacrosystemsclimate changeScienceQENClimate, Vol 9, Iss 159, p 159 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	big data machine learning vesicular stomatitis land use macrosystems climate change Science Q
spellingShingle	big data machine learning vesicular stomatitis land use macrosystems climate change Science Q Dylan Burruss Luis L. Rodriguez Barbara Drolet Kerrie Geil Angela M. Pelzel-McCluskey Lee W. Cohnstaedt Justin D. Derner Debra P. C. Peters Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions
description	Vesicular stomatitis (VS) is the most common vesicular livestock disease in North America. Transmitted by direct contact and by several biting insect species, this disease results in quarantines and animal movement restrictions in horses, cattle and swine. As changes in climate drive shifts in geographic distributions of vectors and the viruses they transmit, there is considerable need to improve understanding of relationships among environmental drivers and patterns of disease occurrence. Multidisciplinary approaches integrating pathology, ecology, climatology, and biogeophysics are increasingly relied upon to disentangle complex relationships governing disease. We used a big data model integration approach combined with machine learning to estimate the potential geographic range of VS across the continental United States (CONUS) under long-term mean climate conditions over the past 30 years. The current extent of VS is confined to the western portion of the US and is related to summer and winter precipitation, winter maximum temperature, elevation, fall vegetation biomass, horse density, and proximity to water. Comparison with a climate-only model illustrates the importance of current processes-based parameters and identifies regions where uncertainty is likely to be greatest if mechanistic processes change. We then forecast shifts in the range of VS using climate change projections selected from CMIP5 climate models that most realistically simulate seasonal temperature and precipitation. Climate change scenarios that altered climatic conditions resulted in greater changes to potential range of VS, generally had non-uniform impacts in core areas of the current potential range of VS and expanded the range north and east. We expect that the heterogeneous impacts of climate change across the CONUS will be exacerbated with additional changes in land use and land cover affecting biodiversity and hydrological cycles that are connected to the ecology of insect vectors involved in VS transmission.
format	article
author	Dylan Burruss Luis L. Rodriguez Barbara Drolet Kerrie Geil Angela M. Pelzel-McCluskey Lee W. Cohnstaedt Justin D. Derner Debra P. C. Peters
author_facet	Dylan Burruss Luis L. Rodriguez Barbara Drolet Kerrie Geil Angela M. Pelzel-McCluskey Lee W. Cohnstaedt Justin D. Derner Debra P. C. Peters
author_sort	Dylan Burruss
title	Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions
title_short	Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions
title_full	Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions
title_fullStr	Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions
title_full_unstemmed	Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions
title_sort	predicting the geographic range of an invasive livestock disease across the contiguous usa under current and future climate conditions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/327e4cdc49f04afcaabbb85ad015263d
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Predicting the Geographic Range of an Invasive Livestock Disease across the Contiguous USA under Current and Future Climate Conditions

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