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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Northwest CASC > FY 2011 Projects > Disentangling the Effects of Climate and Landscape Change on Bird Population Trends in the Western U.S. and Canada ( Show direct descendants )

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_ScienceBase Catalog
__National and Regional Climate Adaptation Science Centers
___Northwest CASC
____FY 2011 Projects
_____Disentangling the Effects of Climate and Landscape Change on Bird Population Trends in the Western U.S. and Canada
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Habitat loss and climate change constitute two of the greatest threats to biodiversity worldwide, and theory predicts that these factors may act synergistically to affect population trajectories. Recent evidence indicates that structurally complex old-growth forest can be cooler than other forest types during spring and summer months, thereby offering potential to buffer populations from negative effects of warming. Old growth may also have higher food and nest-site availability for certain species, which could have disproportionate fitness benefits as species approach their thermal limits. We predicted that negative effects of climate change on 30-year population trends of old-growth-associated birds should be...
Abstract (from http://onlinelibrary.wiley.com/doi/10.1111/ddi.12456/full): Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial scales. However, in montane forest landscapes, high degrees of microclimate variability could influence occupancy dynamics and distributions of forest species. Using high-resolution bird survey and under-canopy air temperature data, we tested the hypothesis that the high vagility of...
Abstract (from Frontiers in Ecology and Evolution): Climate and land-use change are predicted to lead to widespread changes in population dynamics, but quantitative predictions on the relative effects of these stressors have not yet been examined empirically. We analyzed historical abundance data of 110 terrestrial bird species sampled from 1983 to 2010 along 406 Breeding Bird Survey (BBS) across the western USA. Using boosted-regression trees, we modeled bird abundance at the beginning of this interval as a function of (1) climate variables, (2) Landsat-derived landcover data, (3) the additive and interactive effects of climate and land-cover variables. We evaluated the capacity of each model set to predict observed...
Aim Assessing the influence of land cover in species distribution modelling is limited by the availability of fine-resolution land-cover data appropriate for most species responses. Remote-sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote-sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon. Location Central Coast Range, Cascade and Klamath Mountains Oregon, USA. Methods Information on bird observations was collected from 4598 fixed-radius point counts. Reflectance data...
Abstract (from http://onlinelibrary.wiley.com/doi/10.1111/gcb.12642/abstract): Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long-term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32-year period. Using boosted regression...