A review of techniques for spatial modeling in geographical, conservation and landscape genetics.

Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space.

A review of techniques for spatial modeling in geographical, conservation and landscape genetics

Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space.

Species richness and evolutionary niche dynamics: a spatial pattern-oriented simulation experiment.

Evolutionary processes underlying spatial patterns in species richness remain largely unexplored, and correlative studies lack the theoretical basis to explain these patterns in evolutionary terms. In this study, we develop a spatially explicit simulation model to evaluate, under a pattern-oriented modeling approach, whether evolutionary niche dynamics (the balance between niche conservatism and niche evolution processes) can provide a parsimonious explanation for patterns in species richness. We model the size, shape, and location of species' geographical ranges in a multivariate heterogeneous environmental landscape by simulating an evolutionary process in which environmental fluctuations create geographic range fragmentation, which, in turn, regulates speciation and extinction. We applied the model to the South American domain, adjusting parameters to maximize the correspondence between observed and predicted patterns in richness of about 3,000 bird species. Predicted spatial patterns, which closely resemble observed ones (r2=0.795), proved sensitive to niche dynamics processes. Our simulations allow evaluation of the roles of both evolutionary and ecological processes in explaining spatial patterns in species richness, revealing the enormous potential of the link between ecology and historical biogeography under integrated theoretical and methodological frameworks.

Macroevolutionary dynamics in environmental space and the latitudinal diversity gradient in New World birds.

Correlations between species richness and climate suggest non-random occupation of environmental space and niche evolution through time. However, the evolutionary mechanisms involved remain unresolved. Here, we partition the occupation of environmental space into intra- and inter-clade components to differentiate a model based on pure conservation of ancestral niches with higher diversification rates in the tropics, and an adaptive radiation model based on shifts in adaptive peaks at the family level allowing occupation of temperate regions. We examined these mechanisms using within- and among-family skewness components based on centroids of 3560 New World bird species across four environmental variables. We found that the accumulation of species in the tropics is a result of both processes. The components of adaptive radiation have family level skewness of species' distributions strongly structured in space, but not phylogenetically, according to the integrated analyses of spatial filters and phylogenetic eigenvectors. Moreover, stronger radiation components were found for energy variables, which are often used to argue for direct climatic effects on diversity. Thus, the correspondence between diversity and climate may be due to the conservation of ancestral tropical niches coupled with repeated broad shifts in adaptive peaks during birds' evolutionary history more than by higher diversification rates driven by more energy in the tropics.

Predicting continental-scale patterns of bird species richness with spatially explicit models.

The causes of global variation in species richness have been debated for nearly two centuries with no clear resolution in sight. Competing hypotheses have typically been evaluated with correlative models that do not explicitly incorporate the mechanisms responsible for biotic diversity gradients. Here, we employ a fundamentally different approach that uses spatially explicit Monte Carlo models of the placement of cohesive geographical ranges in an environmentally heterogeneous landscape. These models predict species richness of endemic South American birds (2248 species) measured at a continental scale. We demonstrate that the principal single-factor and composite (species-energy, water-energy and temperature-kinetics) models proposed thus far fail to predict (r(2) < or =.05) the richness of species with small to moderately large geographical ranges (first three range-size quartiles). These species constitute the bulk of the avifauna and are primary targets for conservation. Climate-driven models performed reasonably well only for species with the largest geographical ranges (fourth quartile) when range cohesion was enforced. Our analyses suggest that present models inadequately explain the extraordinary diversity of avian species in the montane tropics, the most species-rich region on Earth. Our findings imply that correlative climatic models substantially underestimate the importance of historical factors and small-scale niche-driven assembly processes in shaping contemporary species-richness patterns.