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USGS2011

Climate change, uncertainty, and natural resource management

Climate change and its associated uncertainties are of concern to natural resource managers. Although aspects of climate change may be novel (e.g., system change and nonstationarity), natural resource managers have long dealt with uncertainties and have developed corresponding approaches to decision-making. Adaptive resource management is an application of structured decision-making for recurrent decision problems with uncertainty, focusing on management objectives, and the reduction of uncertainty over time. We identified 4 types of uncertainty that characterize problems in natural resource management. We examined ways in which climate change is expected to exacerbate these uncertainties, as well as potential approaches to dealing with them. As a case study, we examined North American waterfowl harvest management and considered problems anticipated to result from climate change and potential solutions. Despite challenges expected to accompany the use of adaptive resource management to address problems associated with climate change, we conclude that adaptive resource management approaches will be the methods of choice for managers trying to deal with the uncertainties of climate change. ?? 2010 The Wildlife Society.

Journal of Wildlife Management↗

USGS1996

Spatial partitioning of environmental correlates of avian biodiversity in the conterminous United States

Classification and regression tree (CART) analysis was used to create hierarchically organized models of the distribution of bird species richness across the conterminous United States. Species richness data were taken from the Breeding Bird Survey and were related to climatic and land use data. We used a systematic spatial grid of approximately 12,500 hexagons, each approximately 640 square kilometres in area. Within each hexagon land use was characterized by the Loveland et al. land cover classification based on Advanced Very High Resolution Radiometer (AVHRR) data from NOAA polar orbiting meteorological satellites. These data were aggregated to yield fourteen land classes equivalent to an Anderson level II coverage; urban areas were added from the Digital Chart of the World. Each hexagon was characterized by climate data and landscape pattern metrics calculated from the land cover. A CART model then related the variation in species richness across the 1162 hexagons for which bird species richness data were available to the independent variables, yielding an R2-type goodness of fit metric of 47.5% deviance explained. The resulting model recognized eleven groups of hexagons, with species richness within each group determined by unique sequences of hierarchically constrained independent variables. Within the hierarchy, climate data accounted for more variability in the bird data, followed by land cover proportion, and then pattern metrics. The model was then used to predict species richness in all 12,500 hexagons of the conterminous United States yielding a map of the distribution of these eleven classes of bird species richness as determined by the environmental correlates. The potential for using this technique to interface biogeographic theory with the hierarchy theory of ecology is discussed. ?? 1996 Blackwell Science Ltd.

Biodiversity Letters↗