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Full-Text Articles in Life Sciences
Changing Forest Structure Across The Landscape Of The Sierra Nevada, Ca, Usa, Since The 1930s, Christopher R. Dolanc, Hugh D. Safford, James H. Thorne, Solomon Z. Dobrowski
Changing Forest Structure Across The Landscape Of The Sierra Nevada, Ca, Usa, Since The 1930s, Christopher R. Dolanc, Hugh D. Safford, James H. Thorne, Solomon Z. Dobrowski
Forest Management Faculty Publications
Understanding the dynamics of forest structure aids inference regarding future forests and their distributions around the world. Over the last few decades, several papers have addressed changing forest structure in the Sierra Nevada, CA, USA, but these studies were limited in scope. We carried out a broad comparison of forest density and composition in the 1930s versus the 2000s for the west slope of the central and northern Sierra Nevada, using the two most extensive data sets available. Forests in this region have endured a long, complex history of human disturbance, and are now experiencing climatic shifts. We subdivided the …
Can Fire Atlas Data Improve Species Distribution Model Projections?, Shawn M. Crimmins, Solomon Z. Dobrowski, Alison R. Mynsberge, Hugh D. Safford
Can Fire Atlas Data Improve Species Distribution Model Projections?, Shawn M. Crimmins, Solomon Z. Dobrowski, Alison R. Mynsberge, Hugh D. Safford
Forest Management Faculty Publications
Correlative species distribution models (SDMs) are widely used in studies of climate change impacts, yet are often criticized for failing to incorporate disturbance processes that can influence species distributions. Here we use two temporally independent data sets of vascular plant distributions, climate data, and fire atlas data to examine the influence of disturbance history on SDM projection accuracy through time in the mountain ranges of California, USA. We used hierarchical partitioning to examine the influence of fire occurrence on the distribution of 144 vascular plant species and built a suite of SDMs to examine how the inclusion of fire-related predictors …