Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change
Grassland loss has been extensive worldwide, endangering the associated biodiversity and human well-being that are both dependent on these ecosystems. Ecologists have developed approaches to restore grassland communities and many have been successful, particularly where soils are rich, precipitation...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-05-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fpls.2017.00730/full |
| _version_ | 1828778576658300928 |
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| author | Kristin Kane Diane M. Debinski Chris Anderson John D. Scasta David M. Engle James R. Miller |
| author_facet | Kristin Kane Diane M. Debinski Chris Anderson John D. Scasta David M. Engle James R. Miller |
| author_sort | Kristin Kane |
| collection | DOAJ |
| description | Grassland loss has been extensive worldwide, endangering the associated biodiversity and human well-being that are both dependent on these ecosystems. Ecologists have developed approaches to restore grassland communities and many have been successful, particularly where soils are rich, precipitation is abundant, and seeds of native plant species can be obtained. However, climate change adds a new filter needed in planning grassland restoration efforts. Potential responses of species to future climate conditions must also be considered in planning for long-term resilience. We demonstrate this methodology using a site-specific model and a maximum entropy approach to predict changes in habitat suitability for 33 grassland plant species in the tallgrass prairie region of the U.S. using the Intergovernmental Panel on Climate Change scenarios A1B and A2. The A1B scenario predicts an increase in temperature from 1.4 to 6.4°C, whereas the A2 scenario predicts temperature increases from 2 to 5.4°C and much greater CO2 emissions than the A1B scenario. Both scenarios predict these changes to occur by the year 2100. Model projections for 2040 under the A1B scenario predict that all but three modeled species will lose ~90% of their suitable habitat. Then by 2080, all species except for one will lose ~90% of their suitable habitat. Models run using the A2 scenario predict declines in habitat for just four species by 2040, but models predict that by 2080, habitat suitability will decline for all species. The A2 scenario appears based on our results to be the less severe climate change scenario for our species. Our results demonstrate that many common species, including grasses, forbs, and shrubs, are sensitive to climate change. Thus, grassland restoration alternatives should be evaluated based upon the long-term viability in the context of climate change projections and risk of plant species loss. |
| first_indexed | 2024-12-11T16:45:05Z |
| format | Article |
| id | doaj.art-6f55103494424b45a7bae6d32d3d8e36 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-11T16:45:05Z |
| publishDate | 2017-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-6f55103494424b45a7bae6d32d3d8e362022-12-22T00:58:14ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2017-05-01810.3389/fpls.2017.00730238003Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate ChangeKristin Kane0Diane M. Debinski1Chris Anderson2John D. Scasta3David M. Engle4James R. Miller5Department of Natural Resources and Environmental Science, University of Nevada RenoReno, NV, USADepartment of Ecology, Evolution and Organismal Biology, Iowa State UniversityAmes, IA, USADepartment of Agronomy, Iowa State UniversityAmes, IA, USADepartment of Ecosystem Science and Management, University of WyomingLaramie, WY, USADepartment of Natural Resource Ecology and Management, Oklahoma State UniversityStillwater, OK, USADepartment of Natural Resources and Environmental Sciences, University of IllinoisUrbana, IL, USAGrassland loss has been extensive worldwide, endangering the associated biodiversity and human well-being that are both dependent on these ecosystems. Ecologists have developed approaches to restore grassland communities and many have been successful, particularly where soils are rich, precipitation is abundant, and seeds of native plant species can be obtained. However, climate change adds a new filter needed in planning grassland restoration efforts. Potential responses of species to future climate conditions must also be considered in planning for long-term resilience. We demonstrate this methodology using a site-specific model and a maximum entropy approach to predict changes in habitat suitability for 33 grassland plant species in the tallgrass prairie region of the U.S. using the Intergovernmental Panel on Climate Change scenarios A1B and A2. The A1B scenario predicts an increase in temperature from 1.4 to 6.4°C, whereas the A2 scenario predicts temperature increases from 2 to 5.4°C and much greater CO2 emissions than the A1B scenario. Both scenarios predict these changes to occur by the year 2100. Model projections for 2040 under the A1B scenario predict that all but three modeled species will lose ~90% of their suitable habitat. Then by 2080, all species except for one will lose ~90% of their suitable habitat. Models run using the A2 scenario predict declines in habitat for just four species by 2040, but models predict that by 2080, habitat suitability will decline for all species. The A2 scenario appears based on our results to be the less severe climate change scenario for our species. Our results demonstrate that many common species, including grasses, forbs, and shrubs, are sensitive to climate change. Thus, grassland restoration alternatives should be evaluated based upon the long-term viability in the context of climate change projections and risk of plant species loss.http://journal.frontiersin.org/article/10.3389/fpls.2017.00730/fullrestorationgrasslandsMaxentspecies distribution modelsclimate change |
| spellingShingle | Kristin Kane Diane M. Debinski Chris Anderson John D. Scasta David M. Engle James R. Miller Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change Frontiers in Plant Science restoration grasslands Maxent species distribution models climate change |
| title | Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change |
| title_full | Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change |
| title_fullStr | Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change |
| title_full_unstemmed | Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change |
| title_short | Using Regional Climate Projections to Guide Grassland Community Restoration in the Face of Climate Change |
| title_sort | using regional climate projections to guide grassland community restoration in the face of climate change |
| topic | restoration grasslands Maxent species distribution models climate change |
| url | http://journal.frontiersin.org/article/10.3389/fpls.2017.00730/full |
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