Common spatial pattern

Abstract: Recognition and analysis of spatial autocorrelation has defined a new par- adigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available software, to examine breeding habitat relationships for three common Neotropical migrant songbirds in the southern Appalachian Mountains of North Carolina and Tennessee, USA. In preliminary models that ignored space, the abundance of all three species was cor- related with both local- and landscape-scale habitat variables. These models were then modified to account for broadscale spatial trend (via trend surface analysis) and fine-scale autocorrelation (via an autoregressive spatial covariance matrix). Residuals from ordinary least squares regression models were autocorrelated, indicating that the assumption of independent errors was violated. In contrast, residuals from autoregressive models showed little spatial pattern, suggesting that these models were appropriate. The magnitude of habitat effects tended to decrease, and the relative importance of different habitat variables shifted when we incorporated broadscale and then fine-scale space into the analysis. The degree to which habitat effects changed when space was added to the models was roughly correlated with the amount of spatial structure in the habitat variables. Spatial pattern in the residuals from ordinary least squares models may result from failure to include or adequately measure autocorrelated habitat variables. In addition, con- tagious processes, such as conspecific attraction, may generate spatial patterns in species abundance that cannot be explained by habitat models. For our study species, spatial patterns in the ordinary least squares residuals suggest that a scale of 500-1000 m would be ap- propriate for investigating possible contagious processes.

Abstract: Grazing can alter the spatial heterogeneity of vegetation, influencing ecosystem processes and biodiversity. Our objective was to identify why grazing causes increases in the spatial heterogeneity of vegetation in some cases, but decreases in others. The immediate effect of grazing on heterogeneity depends on the interaction between the spatial pattern of grazing and the pre-existing spatial pattern of vegetation. Depending on the scale of observation and on the factors that determine animal distribution, grazing patterns may be stronger or weaker than vegetation patterns, or may mirror the spatial structure of vegetation. For each possible interaction between these patterns, we make a prediction about resulting changes in the spatial heterogeneity of vegetation. Case studies from the literature support our predictions, although ecosystems characterized by strong plant-soil interactions present important exceptions. While the processes by which grazing causes increases in heterogeneity are clear, how grazing leads to decreases in heterogeneity is less so. To explore how grazing can consistently dampen the fine-scale spatial patterns of competing plant species, we built a cell-based simulation model that features two competing plant species, different grazing patterns, and different sources of vegetation pattern. Only the simulations that included neighborhood interactions as a source of vegetation pattern produced results consistent with the predictions we derived from the literature review.

Abstract: Many ecological phenomena are sensitive to spatial heterogeneity and fluxes within spatial mosaics. Landscape ecology, which concerns spatial dynamics (including fluxes of organisms, materials, and energy) and the ways in which fluxes are controlled within heterogeneous matrices, has provided new ways to explore aspects of spatial heterogeneity and to discover how spatial pattern controls ecological processes.

Abstract: Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes.