Browsing by Author "Renwick, Katherine"

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Disentangling climate and disturbance effects on regional vegetation greening trends
(2018-11) Emmett, Kristen; Poulter, Benjamin; Renwick, Katherine
Productivity of northern latitude forests is an important driver of the terrestrial carbon cycle and is already responding to climate change. Studies of the satellite-derived Normalized Difference Vegetation Index (NDVI) for northern latitudes indicate recent changes in plant productivity. These detected greening and browning trends are often attributed to a lengthening of the growing season from warming temperatures. Yet, disturbance-recovery dynamics are strong drivers of productivity and can mask direct effects of climate change. Here, we analyze 1-km resolution NDVI data from 1989 to 2014 for the northern latitude forests of the Greater Yellowstone Ecosystem for changes in plant productivity to address the following questions: (1) To what degree has greening taken place in the GYE over the past three decades? and (2) What is the relative importance of disturbance and climate in explaining NDVI trends? We found that the spatial extents of statistically significant productivity trends were limited to local greening and browning areas. Disturbance history, predominately fire disturbance, was a major driver of these detected NDVI trends. After accounting for fire-, insect-, and human-caused disturbances, increasing productivity trends remained. Productivity of northern latitude forests is generally considered temperature-limited; yet, we found that precipitation was a key driver of greening in the GYE.
Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub
(2017-09) Renwick, Katherine; Curtis, Caroline; Kleinhesselink, Andrew; Schlaepfer, Daniel; Bradley, Bethany; Aldridge, Cameron; Poulter, Benjamin; Adler, Peter
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.