Geology topics

Paul Beier

Publications and source records attributed to Paul Beier.

4 recordsLinked to original sources

USGS2017

Five-year external reviews of the eight Department of Interior Climate Science Centers: Southeast Climate Science Center

In 2008, the U.S. Congress authorized the establishment of the National Climate Change and Wildlife Science Center (NCCWSC) within the U.S. Department of Interior (DOI). Housed administratively within the U.S. Geological Survey (USGS), NCCWSC is part of the DOI’s ongoing mission to meet the challenges of climate change and its effects on wildlife and aquatic resources. From 2010 through 2012, NCCWSC established eight regional DOI Climate Science Centers (CSCs). Each of these regional CSCs operated with the mission to “synthesize and integrate climate change impact data and develop tools that the Department’s managers and partners can use when managing the Department’s land, water, fish and wildlife, and cultural heritage resources” (Salazar 2009). The model developed by NCCWSC for the regional CSCs employed a dual approach of a federal USGS-staffed component and a parallel host-university component established competitively through a 5-year cooperative agreement with NCCWSC. At the conclusion of this 5-year agreement, a review of each CSC was undertaken, with the Southeast Climate Science Center (SE CSC) review in February 2016. The SE CSC is hosted by North Carolina State University (NCSU) in Raleigh, North Carolina, and is physically housed within the NCSU Department of Applied Ecology along with the Center for Applied Aquatic Ecology, the North Carolina Cooperative Fish and Wildlife Research Unit (CFWRU), and the North Carolina Agromedicine Institute. The U.S. Department of Agriculture Southeast Regional Climate Hub is based at NCSU as is the National Oceanic and Atmospheric Administration (NOAA) Southeast Regional Climate Center, the North Carolina Institute for Climate Studies, the North Carolina Wildlife Resources Commission, the NOAA National Weather Service, the State Climate Office of North Carolina, and the U.S. Forest Service Eastern Forest Environmental Threat Assessment Center. This creates a strong core of organizations operating in close proximity focused on climate issues. The geographic area covered by the SE CSC represents all or part of 16 states and the Caribbean Islands and has overlapping boundaries with seven Landscape Conservation Cooperatives (LCCs): Appalachian LCC, Eastern Tallgrass Prairie and Big Rivers LCC, Gulf Coast Prairie LCC, Gulf Coastal Plains and Ozarks LCC, Peninsular Florida LCC, South Atlantic LCC, and Caribbean LCC. The SE CSC region also encompasses 134 U.S. Fish and Wildlife Service refuges and 89 National Park Service (NPS) units and is home to 11 federally recognized and 54 state recognized tribes.

Report↗

USGS2015

Adapting California’s ecosystems to a changing climate

Significant efforts are underway to translate improved understanding of how climate change is altering ecosystems into practical actions for sustaining ecosystem functions and benefits. We explore this transition in California, where adaptation and mitigation are advancing relatively rapidly, through four case studies that span large spatial domains and encompass diverse ecological systems, institutions, ownerships, and policies. The case studies demonstrate the context specificity of societal efforts to adapt ecosystems to climate change and involve applications of diverse scientific tools (e.g., scenario analyses, downscaled climate projections, ecological and connectivity models) tailored to specific planning and management situations (alternative energy siting, wetland management, rangeland management, open space planning). They illustrate how existing institutional and policy frameworks provide numerous opportunities to advance adaptation related to ecosystems and suggest that progress is likely to be greatest when scientific knowledge is integrated into collective planning and when supportive policies and financing enable action.

BioScience↗

USGS2014

Estimating landscape resistance to dispersal

Dispersal is an inherently spatial process that can be affected by habitat conditions in sites encountered by dispersers. Understanding landscape resistance to dispersal is important in connectivity studies and reserve design, but most existing methods use resistance functions with cost parameters that are subjectively chosen by the investigator. We develop an analytic approach allowing for direct estimation of resistance parameters that folds least cost path methods typically used in simulation approaches into a formal statistical model of dispersal distributions. The core of our model is a frequency distribution of dispersal distances expressed as least cost distance rather than Euclidean distance, and which includes terms for feature-specific costs to dispersal and sex (or other traits) of the disperser. The model requires only origin and settlement locations for multiple individuals, such as might be obtained from mark–recapture studies or parentage analyses, and maps of the relevant habitat features. To evaluate whether the model can estimate parameters correctly, we fit our model to data from simulated dispersers in three kinds of landscapes (in which resistance of environmental variables was categorical, continuous with a patchy configuration, or continuous in a trend pattern). We found maximum likelihood estimators of resistance and individual trait parameters to be approximately unbiased with moderate sample sizes. We applied the model to a small grizzly bear dataset to demonstrate how this approach could be used when the primary interest is in the prediction of costs and found that estimates were consistent with expectations based on bear ecology. Our method has important practical applications for testing hypotheses about dispersal ecology and can be used to inform connectivity planning efforts, via the resistance estimates and confidence intervals, which can be used to create a data-driven resistance surface.

Landscape Ecology↗

USGS2012

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.

PLoS ONE↗