Inter‐individual variability in dispersal behaviours impacts connectivity estimates

Abstract

The importance of landscape connectivity in determining biodiversity outcomes under environmental change has led to indices of connectivity becoming amongst the most widely used measures in conservation. Thus, it is vital that our understanding of connectivity and our use of indices describing it are reliable. Dispersal is the key ecological process involved in determining connectivity, and there is increasing evidence of substantial within‐population variability in dispersal behaviours. Here, we incorporate this inter‐individual variability into two approaches for estimating connectivity, least cost path analysis and stochastic movement simulation. Illustrative results demonstrate that including dispersal variability can yield substantially different estimates of connectivity. While connectivity is typically similar between nearby patches, the frequency of movements between patches further apart is often substantially increased when inter‐individual variability is included. Given the disproportionate role that unusual long‐distance dispersal events play in spatial dynamics, connectivity indices should seek to incorporate variability in dispersal behaviour.

Dispersal propensity, but not flight performance, explains variation in dispersal ability

“…Consequently, we believe the lure-based dispersal method worked adequately to assess the dispersal ability of these flies. This study contributes novel empirical evidence to our understanding of dispersal propensity [2,4,9,32] and highlights the importance of including intraspecific phenotypic variation. However, few studies consider plastic changes to phenotypic traits that may, in turn, influence key behaviours such as boldness, exploration and activity.…”

The range of juvenile movements of estuarine and coastal nursery dependent flatfishes: estimation from a meta-analytical approach

“…Movement behaviour affects the distribution of fish, but outcomes depend on interactions between movement patterns and local habitat structure and heterogeneity (Hanski, 1998;Fulford et al, 2011;Palmer et al, 2014). Movements and dispersal abilities are key ecological processes determining connectivity, and dispersal distances could be used to derive segregation between patches (Hanski, 1998;Palmer et al, 2014).…”

“…Movement behaviour affects the distribution of fish, but outcomes depend on interactions between movement patterns and local habitat structure and heterogeneity (Hanski, 1998;Fulford et al, 2011;Palmer et al, 2014). Movements and dispersal abilities are key ecological processes determining connectivity, and dispersal distances could be used to derive segregation between patches (Hanski, 1998;Palmer et al, 2014). However, distance of segregation provides information on the storage (over a given duration of the species' life history, depending upon the tracers that are used; Table 1) of separate pools, due to the lack of dispersal patterns across habitats (Secor and Rooker, 2005); i.e., an obvious dramatic overestimation of the home range.…”

MetaConnect, a new platform for population viability modelling to assist decision makers in conservation and urban planning

“…LCP length less than the maximum dispersal distance), the probability to reach a patch is inversely weighted by the LCP length (number of map cells crossed) or cumulative cost (total cost of all map cells crossed). We also adapted the Stochastic Movement Simulator (SMS) (Palmer, Coulon & Travis 2011; Aben et al 2014 ; Palmer, Coulon & Travis 2014; Coulon et al Submitted), which relaxes the assumption of omniscience inherent in the LCP approach. With the SMS rule, individuals make movement decisions based on the environment within a limited perceptual range and a tendency to directional persistence similar to that in a CRW.…”