Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub |
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| Authors: | Katherine M Renwick Caroline Curtis Andrew R Kleinhesselink Daniel Schlaepfer Bethany A Bradley Cameron L Aldridge Benjamin Poulter Peter B Adler |
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| Affiliation: | 1. Department of Ecology, Montana State University, Bozeman, MT, USA;2. Graduate Program in Organismic and Evolutionary Biology, University of Massachusetts, Amherst, MA, USA;3. Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA;4. Section 5. of Conservation Biology, University of Basel, Basel, Switzerland;6. Department of Botany, University of Wyoming, Laramie, WY, USA;7. School of Forestry & Environmental Studies, Yale University, New Haven, CT, USA;8. Department of Environmental Conservation, University of Massachusetts, Amherst, MA, USA;9. Department of Ecosystem Science and Sustainability, Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO, USA;10. US Geological Survey, Fort Collins Science Center, Fort Collins, CO, USA;11. Biosphere, NASA GSFC, Greenbelt, MD, USA;12. Biospheric Sciences Laboratory (Code 618), NASA Goddard Space Flight Center, Greenbelt, MD, USA |
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| Abstract: | A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments. |
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| Keywords: |
Artemisia
climate change correlative models model comparison process‐based models sagebrush vegetation change |
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