Gene flow and functional connectivity in the natterjack toad

Abstract

Functional connectivity is a key factor for the persistence of many specialist species in fragmented landscapes. However, connectivity estimates have rarely been validated by the observation of dispersal movements. In this study, we estimated functional connectivity of a real landscape by modelling dispersal for the endangered natterjack toad (Bufo calamita) using cost distance. Cost distance allows the evaluation of 'effective distances', which are distances corrected for the costs involved in moving between habitat patches in spatially explicit landscapes. We parameterized cost-distance models using the results of our previous experimental investigation of natterjack's movement behaviour. These model predictions (connectivity estimates from the GIS study) were then confronted to genetic-based dispersal rates between natterjack populations in the same landscape using Mantel tests. Dispersal rates between the populations were inferred from variation at six microsatellite loci. Based on these results, we conclude that matrix structure has a strong effect on dispersal rates. Moreover, we found that cost distances generated by habitat preferences explained dispersal rates better than did the Euclidian distances, or the connectivity estimate based on patch-specific resistances (patch viscosity). This study is a clear example of how landscape genetics can validate operational functional connectivity estimates.

Population structure and landscape genetics of two endangered frog species of genus Odorrana: different scenarios on two islands

“…Even neighboring populations can be isolated and genetically differentiated when unfavorable landscapes prevent migration between populations. Therefore, identifying how landscapes affect the gene flow and population structure is an intrinsic aspect in population and conservation biology (Manel et al, 2003;Coulon et al, 2004;Spear et al, 2005;Stevens et al, 2006).…”

“…As a result, studies in the field of landscape genetics are conducted by integrating landscape ecology and population genetics (Manel et al, 2003;Storfer et al, 2007). One of the successful schemes for revealing how landscapes affect population structure is examining the correlation between genetic distance, estimated from molecular markers, and cost distance, estimated from hypothetical cost surfaces, to establish the effect of multiple environmental and landscape factors using geographic information systems (GIS) (Coulon et al, 2004;Spear et al, 2005;Stevens et al, 2006;Spear and Storfer, 2008;Wang et al, 2009). In this scheme, 'costs' assignments are the most important and should be generated according to the mobility of target species .…”

“…However, direct observation of movements of individuals is often impossible in field studies . Therefore, 'costs' are predetermined by authors by incorporating ecological and behavioral evidence (Coulon et al, 2004;Spear et al, 2005;Stevens et al, 2006;Spear and Storfer, 2008) or by enumerating every combination of cost assignment . However, the ecology and behavior of some species, including endangered species, is not always clear.…”

A stochastic movement simulator improves estimates of landscape connectivity

“…However, the LCP approach implicitly assumes that individuals have perfect knowledge of the entire landscape (i.e., individual omniscience), allowing them to follow the optimal (i.e., least costly) route between any two locations. While LCPs may be better estimates of connectivity than simple structural connectivity estimates such as Euclidean distance, the additional variation explained is often low (e.g., Coulon et al 2004, Stevens et al 2006b). …”

Landscape features influence gene flow as measured by cost-distance and genetic analyses: a case study for giant pandas in the Daxiangling and Xiaoxiangling Mountains

“…Factors such as topographic (altitude, gradient, and slope) or anthropogenic factors (road construction, human residence) may also influence the movement or dispersal of individuals. Animal movement is modeled as a trade-off that mitigates many factors [19,26] and reflects the process of habitat selection.…”