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

Quantitative genetics of body size evolution on islands: an individual-based simulation approach

According to the island rule, small-bodied vertebrates will tend to evolve larger body size on islands, whereas the opposite happens to large-bodied species. This controversial pattern has been studied at the macroecological and biogeographical scales, but new developments in quantitative evolutionary genetics now allow studying the island rule from a mechanistic perspective. Here, we develop a simulation approach based on an individual-based model to model body size change on islands as a progressive adaptation to a moving optimum, determined by density-dependent population dynamics. We applied the model to evaluate body size differentiation in the pigmy extinct hominin Homo floresiensis, showing that dwarfing may have occurred in only about 360 generations (95% CI ranging from 150 to 675 generations). This result agrees with reports suggesting rapid dwarfing of large mammals on islands, as well as with the recent discovery that small-sized hominins lived in Flores as early as 700 kyr ago. Our simulations illustrate the power of analysing ecological and evolutionary patterns from an explicit quantitative genetics perspective

Performance and Consistency of Indicator Groups in Two Biodiversity Hotspots

In a world limited by data availability and limited funds for conservation, scientists and practitioners must use indicator groups to define spatial conservation priorities. Several studies have evaluated the effectiveness of indicator groups, but still little is known about the consistency in performance of these groups in different regions, which would allow their a priori selection.We systematically examined the effectiveness and the consistency of nine indicator groups in representing mammal species in two top-ranked Biodiversity Hotspots (BH): the Brazilian Cerrado and the Atlantic Forest. To test for group effectiveness we first found the best sets of sites able to maximize the representation of each indicator group in the BH and then calculated the average representation of different target species by the indicator groups in the BH. We considered consistent indicator groups whose representation of target species was not statistically different between BH. We called effective those groups that outperformed the target-species representation achieved by random sets of species. Effective indicator groups required the selection of less than 2% of the BH area for representing target species. Restricted-range species were the most effective indicators for the representation of all mammal diversity as well as target species. It was also the only group with high consistency.We show that several indicator groups could be applied as shortcuts for representing mammal species in the Cerrado and the Atlantic Forest to develop conservation plans, however, only restricted-range species consistently held as the most effective indicator group for such a task. This group is of particular importance in conservation planning as it captures high diversity of endemic and endangered species

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting