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, and 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 gigantic 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 assess the climate sensitivity of A. tridentata, we developed four predictive models, and 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, and as measured by change in cover,growth, or recruitment, or 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 approximately 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.
Source: usgs.gov