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
Hauptverfasser: Terry, Tyson J., Hardegree, Stuart P., Adler, Peter B.
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container_title Ecological applications
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creator Terry, Tyson J.
Hardegree, Stuart P.
Adler, Peter B.
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 (&gt;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 &amp; 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). 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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 (&gt;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 &amp; 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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 (&gt;2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.</abstract><cop>Hoboken, USA</cop><pub>John Wiley &amp; 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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source MEDLINE; Wiley Online Library Journals Frontfile Complete
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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