Dispersal and habitat connectivity in complex heterogeneous landscapes: an analysis with a GIS‐based random walk model

Abstract

Dispersaland habitat connectivity in complex heterogeneous landscapes: an analysis with a GIS-based random walk model. -Ecography 19: 97-106.A grid-based random walk model has been developed to simulate animal dispersal, taking landscape heterogeneity and linear barriers such as roads and rivers into account. The model can be used to estimate connectivity, and has been parameterized for the badger in the central Netherlands. The importance of key parameters was evaluated by meilns of sensitivity analysis. Results agree with field observations. and give interesting insight into the isolation of populations and potential populations. The model can be applied to obtain knowledge about dispersal processes in complex landscapes. P. Schippers und J . P. Knuupen. Winund Sturing Centre for Integrured Land, Soil undWutrr Rrseurcli (SC-DLO), P.O. Box 12s. N L -6 7 0 A C Wugeningen. The Nerlierlunds. -1. Verhoom cvtd R. C. vun Apekloorn. Inst. for Forestry mid Nuture Reseurch (IBN-DLO).

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

“…Where multiple species or life stages are involved, the data requirements are large and future work should address the need to synthesize connectivity models parametrized for multiple organisms of interest. Strong habitat preferences do not guarantee use of that habitat for dispersal (Schippers et al 1996;Vignieri 2005), and an organism's decisions along the way may be more important in determining the overall path than the cumulative resistance over the entire trajectory (Brooker et al 1999). Here, we explicitly consider the uncertainty associated with typical, least cost path techniques by developing simulation approaches to examine the implications of alternative connectivity models in two infectious disease applications.…”

“…Most functional connectivity studies summarize the EGD between a pair of nodes by taking the single minimum resistance path, assuming the cost of this path to be the most informative measure of node connectivity. Clearly, the distribution of EGDs (along all possible paths) between a pair of nodes provides a more complete representation of the diversity of connectivity modes (Boone & Hunter 1996;Schippers et al 1996), including those that pass through contiguous corridors, fragmented habitat patches and indirect paths (Theobald 2006). EGD distributions are estimated for S. japonicum and O. hupensis using a series of environmental resistance models parametrized with ecological, experimental and behavioural data.…”

Is green infrastructure an effective climate adaptation strategy for conserving biodiversity? A case study with the great crested newt

“…the likelihood that an individual newt will disperse, which for adults was assumed to be density dependent (Table 2); (b) probability to immigrate into another patch, determined by the distance from the original patch and the permeability of the intermediate landscape, as estimated using a grid-based movement model, described next. Patch connectivity, defined as the probability of an individual newt that leaves one patch to arrive at each other patch, was estimated using a grid-based movement model (Schippers et al 1996). The model simulated a correlated random walk on a grid (each cell with eight neighbours), with the probability of moving to a neighbour cell depending on this cell's preference value multiplied by a normalized weight, which depends on the direction of the previous move.…”

Where do we go from here? Dispersal simulations shed light on the role of landscape structure in determining animal redistribution after reintroduction

“…For both scenarios and for each movement rule, we simulated 999 exploratory paths, made up of 2000 steps starting from the release-site, and we calculated: a) the arrival rate in the neighbouring valleys (Schippers et al 1996); b) the number of times each grid cell was included in the exploratory paths. We graphically compared the arrival rates in the three valleys with the frequency of actual deer locations collected in [2004][2005][2006].…”