Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous.

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation.

Validating graph-based connectivity models with independent presence-absence and genetic data sets.

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]).

Assessing the influence of the amount of reachable habitat on genetic structure using landscape and genetic graphs.

Genetic structure, i.e. intra-population genetic diversity and inter-population genetic differentiation, is influenced by the amount and spatial configuration of habitat.

Analysing landscape effects on dispersal networks and gene flow with genetic graphs.

Graph-theoretic approaches have relevant applications in landscape genetic analyses. When species form populations in discrete habitat patches, genetic graphs can be used (a) to identify direct dispersal paths followed by propagules or (b) to quantify landscape effects on multi-generational gene flow. However, the influence of their construction parameters remains to be explored.

Calmodulin distribution and the actomyosin cytoskeleton in Toxoplasma gondii.

The gliding motility of the protozoan parasite Toxoplasma gondii and its invasion of cells are powered by an actin-myosin motor. We have studied the spatial distribution and relationship between these two cytoskeleton proteins and calmodulin (CaM), the Ca(2+)-dependent protein involved in invasion by T. gondii.