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Non-spatial regression models are rarely adequate for exploring ecological phenomena, especially in settings where the processes operate at large spatial scales and when model covariates do not explain all variation present in the outcome variable. Given the complexity of ecological processes, it is often unrealistic to assume a set of stationary regression coefficients can capture space-varying and scale-dependent relationships between covariates and an ecological response. Spatially-varying coefficients (SVC) models fit within a Bayesian inferential framework provide a statistically robust method to explore potential space-varying and scale-dependent impacts of covariates. Our study objective was to assess the utility of SVC models for capturing non-stationary relationships between temporal community dynamics in avian assemblages and variation in environmental factors. We also wanted to compare the inference drawn from SVC models to that obtained from space-varying intercept models and also models that do not acknowledge any spatial structure beyond what is introduced by the covariates. Our analysis examines the temporal turnover, expressed as a proportion, of avian communities across New York State, USA. Given the expected outcome is non-Gaussian, we detail a generalized linear model specification of the proposed model structures. Our results show the SVC model outperformed the spatially-varying intercept and non-spatial models in terms of model fit and model predictive inference. Further, by fitting these models within a Bayesian inferential framework, we were able to make inferences about the spatial impact of covariates and other process parameters, as well as obtain full posterior predictive inference about the rate of turnover at new, unobserved locations. We conclude that SVC models provide a flexible framework for exploring and accounting for non-stationary mechanisms driving ecological patterns. [ABSTRACT FROM AUTHOR]

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980–2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products. [ABSTRACT FROM AUTHOR]

Managed ecosystems experience vulnerabilities when ecological resilience declines and key flows of ecosystem services become depleted or lost. Drivers of vulnerability often include local management actions in conjunction with other external, larger-scale factors. To translate these concepts to management applications, we developed a conceptual model of feedbacks linking the provision of ecosystem services, their use by society, and anthropogenic change. From this model we derived a method to integrate existing geodata at relevant scales and in locally meaningful ways to provide decision-support for adaptive management efforts. To demonstrate our approach, we conducted a case study assessment of southeast Alaska, where managers are concerned with sustaining fish and wildlife resources in areas where intensive logging disturbance has occurred. Individual datasets were measured as indicators of one of three criteria: ecological capacity to support fish/wildlife populations ( provision); human acquisition of fish/wildlife resources ( use); and intensity of logging and related land-use change ( disturbance). Relationships among these processes were analyzed using two methods—a watershed approach and a high-resolution raster—to identify where provision, use and disturbance were spatially coupled across the landscape. Our results identified very small focal areas of social-ecological coupling that, based on post-logging dynamics and other converging drivers of change, may indicate vulnerability resulting from depletion of ecosystem services. We envision our approach can be used to narrow down where adaptive management might be most beneficial, allowing practitioners with limited funds to prioritize efforts needed to address uncertainty and mitigate vulnerability in managed ecosystems. [ABSTRACT FROM AUTHOR]