Incorporating microclimate into species distribution models
Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however,...
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Veröffentlicht in: | Ecography (Copenhagen) 2019-07, Vol.42 (7), p.1267-1279 |
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description | Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions. |
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Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. 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Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.</description><subject>Air temperature</subject><subject>Airborne lasers</subject><subject>Anthropogenic factors</subject><subject>Bioclimatology</subject><subject>Biodiversity</subject><subject>Biodiversity and Ecology</subject><subject>Biological evolution</subject><subject>Climate change</subject><subject>Climate effects</subject><subject>Digital Elevation Models</subject><subject>Ecological effects</subject><subject>Ecology, environment</subject><subject>Environmental Sciences</subject><subject>Habitats</subject><subject>Life Sciences</subject><subject>Microclimate</subject><subject>Mountains</subject><subject>remote sensing</subject><subject>Species</subject><subject>species distribution modelling</subject><subject>Weather stations</subject><issn>0906-7590</issn><issn>1600-0587</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLwzAUx4MoOKcXP0HBk0Lne0maNngaY7rBYBc9hzZNZkbXzKRT9u1tnXj0XR48fvz5vx8htwgT7OfRaL-ZAJM8PyMjFAApZEV-TkYgQaR5JuGSXMW4BUAqRTEiT8tW-7D3oexcu0l2TgevG7crO5O4tvNJ3BvtTExqF7vgqkPnfJvsfG2aeE0ubNlEc_O7x-Ttef46W6Sr9ctyNl2lmuUoUiM5lWhRVNxmoAspc1FUUjNqK8uZLShaijLjXICBXIOpK2tEjbbigHXGxuT-lPteNmof-nLhqHzp1GK6UsMNKMuooPQTe_buxO6D_ziY2KmtP4S2r6co5VJwweSQ-HCi-m9jDMb-xSKoQaQaRKofkT2MJ_jLNeb4D6nms_ULUiYE-wazW3Rr</recordid><startdate>201907</startdate><enddate>201907</enddate><creator>Lembrechts, Jonas J.</creator><creator>Nijs, Ivan</creator><creator>Lenoir, Jonathan</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons, Inc</general><general>Wiley</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7SS</scope><scope>C1K</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0003-0638-9582</orcidid><orcidid>https://orcid.org/0000-0002-1933-0750</orcidid><orcidid>https://orcid.org/0000-0003-3111-680X</orcidid></search><sort><creationdate>201907</creationdate><title>Incorporating microclimate into species distribution models</title><author>Lembrechts, Jonas J. ; Nijs, Ivan ; Lenoir, Jonathan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3716-e94291f16b4f50c899768b9c32fbf43f821f21954460e07c0edbfe6d1fb401d53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Air temperature</topic><topic>Airborne lasers</topic><topic>Anthropogenic factors</topic><topic>Bioclimatology</topic><topic>Biodiversity</topic><topic>Biodiversity and Ecology</topic><topic>Biological evolution</topic><topic>Climate change</topic><topic>Climate effects</topic><topic>Digital Elevation Models</topic><topic>Ecological effects</topic><topic>Ecology, environment</topic><topic>Environmental Sciences</topic><topic>Habitats</topic><topic>Life Sciences</topic><topic>Microclimate</topic><topic>Mountains</topic><topic>remote sensing</topic><topic>Species</topic><topic>species distribution modelling</topic><topic>Weather stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lembrechts, Jonas J.</creatorcontrib><creatorcontrib>Nijs, Ivan</creatorcontrib><creatorcontrib>Lenoir, Jonathan</creatorcontrib><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Ecography (Copenhagen)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lembrechts, Jonas J.</au><au>Nijs, Ivan</au><au>Lenoir, Jonathan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Incorporating microclimate into species distribution models</atitle><jtitle>Ecography (Copenhagen)</jtitle><date>2019-07</date><risdate>2019</risdate><volume>42</volume><issue>7</issue><spage>1267</spage><epage>1279</epage><pages>1267-1279</pages><issn>0906-7590</issn><eissn>1600-0587</eissn><abstract>Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. 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subjects | Air temperature Airborne lasers Anthropogenic factors Bioclimatology Biodiversity Biodiversity and Ecology Biological evolution Climate change Climate effects Digital Elevation Models Ecological effects Ecology, environment Environmental Sciences Habitats Life Sciences Microclimate Mountains remote sensing Species species distribution modelling Weather stations |
title | Incorporating microclimate into species distribution models |
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