Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate
Exotic annual grass invasions in water‐limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived fr...
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Veröffentlicht in: | Ecological applications 2024-12, Vol.34 (8), p.e3028-n/a |
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description | Exotic annual grass invasions in water‐limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely‐sensed imagery. Accuracy metrics from our out‐of‐sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%–17% from 1989 to 2019 across all aspects at low‐ to mid‐elevation sites, while high‐ elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils. |
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The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely‐sensed imagery. Accuracy metrics from our out‐of‐sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%–17% from 1989 to 2019 across all aspects at low‐ to mid‐elevation sites, while high‐ elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.</description><identifier>ISSN: 1051-0761</identifier><identifier>EISSN: 1939-5582</identifier><identifier>DOI: 10.1002/eap.3028</identifier><identifier>PMID: 39284744</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Abundance ; Accuracy ; Animal models ; animals ; annual grass ; Annual variations ; Artemisia ; Bioclimatology ; biological invasion ; Bromus - physiology ; Bromus tectorum ; Climate Change ; Climate models ; Climate prediction ; cold ; Cold springs ; Current distribution ; data collection ; Geographical distribution ; Germination ; Grasses ; Impact prediction ; Indigenous plants ; indigenous species ; Introduced Species ; Invasive species ; Life history ; Microclimate ; Models, Biological ; Plant communities ; Plant Dispersal ; rate sum ; Remote sensing ; resistance and resilience ; risk ; SHAW model ; Soil ; Soil conditions ; Soil degradation ; Soil water ; Soils ; species ; spring ; Spring (season) ; Steppes ; Weather</subject><ispartof>Ecological applications, 2024-12, Vol.34 (8), p.e3028-n/a</ispartof><rights>2024 The Author(s). published by Wiley Periodicals LLC on behalf of The Ecological Society of America.</rights><rights>2024 The Author(s). Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.</rights><rights>Copyright Ecological Society of America Dec 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3638-114061aa185155e793289bc273afb431b7f46e3187e5d0a095a7f9860ce6ed423</cites><orcidid>0000-0002-1839-0671 ; 0000-0002-4216-4009</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Feap.3028$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Feap.3028$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39284744$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Terry, Tyson J.</creatorcontrib><creatorcontrib>Hardegree, Stuart P.</creatorcontrib><creatorcontrib>Adler, Peter B.</creatorcontrib><title>Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate</title><title>Ecological applications</title><addtitle>Ecol Appl</addtitle><description>Exotic annual grass invasions in water‐limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely‐sensed imagery. Accuracy metrics from our out‐of‐sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%–17% from 1989 to 2019 across all aspects at low‐ to mid‐elevation sites, while high‐ elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.</description><subject>Abundance</subject><subject>Accuracy</subject><subject>Animal models</subject><subject>animals</subject><subject>annual grass</subject><subject>Annual variations</subject><subject>Artemisia</subject><subject>Bioclimatology</subject><subject>biological invasion</subject><subject>Bromus - physiology</subject><subject>Bromus tectorum</subject><subject>Climate Change</subject><subject>Climate models</subject><subject>Climate prediction</subject><subject>cold</subject><subject>Cold springs</subject><subject>Current distribution</subject><subject>data collection</subject><subject>Geographical distribution</subject><subject>Germination</subject><subject>Grasses</subject><subject>Impact prediction</subject><subject>Indigenous plants</subject><subject>indigenous species</subject><subject>Introduced Species</subject><subject>Invasive species</subject><subject>Life history</subject><subject>Microclimate</subject><subject>Models, Biological</subject><subject>Plant communities</subject><subject>Plant Dispersal</subject><subject>rate sum</subject><subject>Remote sensing</subject><subject>resistance and resilience</subject><subject>risk</subject><subject>SHAW model</subject><subject>Soil</subject><subject>Soil conditions</subject><subject>Soil degradation</subject><subject>Soil water</subject><subject>Soils</subject><subject>species</subject><subject>spring</subject><subject>Spring (season)</subject><subject>Steppes</subject><subject>Weather</subject><issn>1051-0761</issn><issn>1939-5582</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNqNkV1rFTEQhoMo9kPBXyABb7zo1kw-dpMrKaVWoaVe6HXIZmdPU_Ykx2S30n9vjj0tVRCcmxnIM-9M5iXkDbBjYIx_QLc5FozrZ2QfjDCNUpo_rzVT0LCuhT1yUMoNq8E5f0n2hOFadlLuk3iZBpxCXFF_jW5eZVcKHUKZc-iXOaR4RF2_xMFFj7WMA81YNikWpHOifgprN2PtdXGF1BXq6LhEv22kaaQlhYmug89pR74iL0Y3FXy9y4fk-6ezb6efm4ur8y-nJxeNF63QDYBkLTgHWoFS2BnBtek974Qbeymg70bZogDdoRqYY0a5bjS6ZR5bHCQXh-Tjve5m6dc4eIxzdpPd5LpFvrPJBfvnSwzXdpVuLUALrDWiKrzfKeT0Y8Ey23UoHqfJRUxLsQKU5EoDk_-BspZJxaWp6Lu_0Ju05FhPUSmpleiEeDK7Hq6UjOPj4sDs1nBbDbdbwyv69ulHH8EHhyvQ3AM_w4R3_xSyZydffwv-Ag5otDI</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Terry, Tyson J.</creator><creator>Hardegree, Stuart P.</creator><creator>Adler, Peter B.</creator><general>John Wiley & Sons, Inc</general><general>Ecological Society of America</general><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>SOI</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1839-0671</orcidid><orcidid>https://orcid.org/0000-0002-4216-4009</orcidid></search><sort><creationdate>202412</creationdate><title>Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate</title><author>Terry, Tyson J. ; Hardegree, Stuart P. ; Adler, Peter B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3638-114061aa185155e793289bc273afb431b7f46e3187e5d0a095a7f9860ce6ed423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Abundance</topic><topic>Accuracy</topic><topic>Animal models</topic><topic>animals</topic><topic>annual grass</topic><topic>Annual variations</topic><topic>Artemisia</topic><topic>Bioclimatology</topic><topic>biological invasion</topic><topic>Bromus - physiology</topic><topic>Bromus tectorum</topic><topic>Climate Change</topic><topic>Climate models</topic><topic>Climate prediction</topic><topic>cold</topic><topic>Cold springs</topic><topic>Current distribution</topic><topic>data collection</topic><topic>Geographical distribution</topic><topic>Germination</topic><topic>Grasses</topic><topic>Impact prediction</topic><topic>Indigenous plants</topic><topic>indigenous species</topic><topic>Introduced Species</topic><topic>Invasive species</topic><topic>Life history</topic><topic>Microclimate</topic><topic>Models, Biological</topic><topic>Plant communities</topic><topic>Plant Dispersal</topic><topic>rate sum</topic><topic>Remote sensing</topic><topic>resistance and resilience</topic><topic>risk</topic><topic>SHAW model</topic><topic>Soil</topic><topic>Soil conditions</topic><topic>Soil degradation</topic><topic>Soil water</topic><topic>Soils</topic><topic>species</topic><topic>spring</topic><topic>Spring (season)</topic><topic>Steppes</topic><topic>Weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Terry, Tyson J.</creatorcontrib><creatorcontrib>Hardegree, Stuart P.</creatorcontrib><creatorcontrib>Adler, Peter B.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Ecological applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Terry, Tyson J.</au><au>Hardegree, Stuart P.</au><au>Adler, Peter B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate</atitle><jtitle>Ecological applications</jtitle><addtitle>Ecol Appl</addtitle><date>2024-12</date><risdate>2024</risdate><volume>34</volume><issue>8</issue><spage>e3028</spage><epage>n/a</epage><pages>e3028-n/a</pages><issn>1051-0761</issn><eissn>1939-5582</eissn><abstract>Exotic annual grass invasions in water‐limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely‐sensed imagery. Accuracy metrics from our out‐of‐sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%–17% from 1989 to 2019 across all aspects at low‐ to mid‐elevation sites, while high‐ elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>39284744</pmid><doi>10.1002/eap.3028</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-1839-0671</orcidid><orcidid>https://orcid.org/0000-0002-4216-4009</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Abundance Accuracy Animal models animals annual grass Annual variations Artemisia Bioclimatology biological invasion Bromus - physiology Bromus tectorum Climate Change Climate models Climate prediction cold Cold springs Current distribution data collection Geographical distribution Germination Grasses Impact prediction Indigenous plants indigenous species Introduced Species Invasive species Life history Microclimate Models, Biological Plant communities Plant Dispersal rate sum Remote sensing resistance and resilience risk SHAW model Soil Soil conditions Soil degradation Soil water Soils species spring Spring (season) Steppes Weather |
title | Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate |
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