Print PageThe diversity of tools available for landscape genetic analysis, which examines the influence of landscape heterogeneity on microevolutionary processes, is expanding rapidly. Individual-based, and spatially-explicit population models (IB-SEPMs) provide one means for developing and evaluating statistical methods for inferring how landscape patterns affect biological processes. The use of genetic data to make indirect inferences regarding dispersal patterns often requires an assumption that population units are in migration-drift equilibrium, an assumption often violated by populations of conservation concern.
The present study uses and IB-SEPM to evaluate the ability of both demographic and genetic data to infer dispersal processes in a population of Red-cockaded Woodpeckers (RCWs) undergoing recovery. RCWs are a cooperative breeding species in which family groups defend territories in predominantly longleaf pine savannahs. The study population is spread across three public landowners in coastal North Carolina. Demographic data at different time periods were available at all three sites. However, detailed data permitting the derivation of a connectivity matrix and a pedigree, permitting calculation of genetic indices, was available only at one site.
Five patterns were derived from the monitoring data: abundance over time, territory occupancy, connectivity, average minimum genetic distance among breeding groups (Dij), and unique alleles within breeding groups (Ai). The IB-SEPM was used to generate these same patterns across 200,000 parameterizations describing alternative dispersal hypotheses. The plausibility of each parameterization was evaluated by estimating the likelihood of each simulated pattern given the observed pattern, -log(L[S|O]). By minimizing the -log(L[S|O]) for each pattern individually and in combination with other patterns, we contrast the information content among patterns.
Results/Conclusions
When demographic and genetic patterns were used to estimate dispersal parameters separately, we often found the patterns led to different conclusions (i.e., patterns are not redundant). However, when coarse demographic patterns were combined with genetic patterns, we found that the median expected value for the dispersal parameters was very close to those observed when coarse demographic patterns were combined with connectivity data in the absence of genetic information. In other words, substituting Dij and Ai for connectivity is effective in non-equilibrium populations. However, the dispersion of dispersal parameter values was greater compared to when connectivity was included. The utility of landscape genetic approaches can only be appreciated if further studies contrast the utility of both demographic and genetic data to infer landscape processes. Our results with RCWs, stress the added value of combining demographic and genetic data when evaluating complex landscape processes.
