Possum dispersal kernels based on straight-line distance and accumulated-cost.

<p>Examples of the dispersal kernel calibration from connectivity values of observed possum dispersal events for (A) the highest-ranked cost-surface landscape representation (DTR in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>), and (B) the uniform landscape representation (D in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>). A stacked histogram of the possum dispersal events categorised by sex is shown along with the fitted lognormal dispersal kernel's probability density function (PDF), and both the cumulative distribution function (CDF) and the inverted cumulative distribution function (1-CDF).</p

The locations of six studies that provided empirical possum dispersal data.

<p>Dispersal of possums was measured either directly using radio-telemetry or indirectly using landscape genetics, and each study was associated with a variety of different landscape environments across the North Island of New Zealand categorised on the basis of differences in climate, landform, and soils <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone.0088293-Leathwick1" target="_blank">[53]</a>.</p

Examples of least-cost modelling versus straight-line distance based dispersal models.

<p>For (A) an arbitrary section of landscape and possum dispersal starting location, dispersal is modelled using (B) the highest-ranked cost-surface landscape representation (DTR in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>) for which connectivity is measured by accumulated-cost, and (C) the uniform landscape representation (D in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>) for which connectivity is measured by distance. The connectivity values converted to the associated probability density and cumulative distribution values given the associated dispersal kernel (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-g006" target="_blank">Figure 6</a>).</p

Dispersal events as straight-lines and least-cost paths.

<p>Examples of nine of the 65 radio-collared brushtail possum dispersal events used to calibrate the dispersal kernels. To allow for the effect of incorporating landscape features to be shown, both the straight-line distances associated with the uniform landscape representation (D, in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>) and the least-cost paths associated with the highest ranked cost-surface landscape representation (DTR, in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>) are shown. While the straight-line distances do not consider the landscape at all, the least-cost paths avoid both rivers and areas without tree and scrub cover.</p

Comparing genetic distance with straight-line distance and accumulated-cost.

<p>Examples of two of the 48 landscape representations used to measure the direct transfer costs between 77 neighbouring possum sampling locations for which genetic distances had been measured. Assuming (A) a uniform landscape representation (D, in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>), connectivity was measured as straight-line distance that (B) explained little of the variation in genetic distances. By using (C) a cost-surface landscape representation that incorporated transfer costs associated with rivers and the absence of tree and scrub cover (DTR, in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone-0088293-t002" target="_blank">Table 2</a>), connectivity was measured as the accumulated-cost of least-cost paths that (D) explained approximately a third of the variation in genetic distance.</p

Intra-feature cost-surface weightings.

<p>The form of the relationships used to rescale the values of each landscape feature to between zero and one, and to control the intra-feature weighting of the values from each landscape feature.</p

Straight-line distance based dispersal kernels.

<p>These dispersal kernels are used to model dispersal for male and female brushtail possums (<i>Trichosurus vulpecula</i>) in New Zealand <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088293#pone.0088293-Ramsey1" target="_blank">[52]</a>.</p

The six GIS raster datasets representing the landscape features thought to affect possum dispersal, the ecological justification for their choice, and the associated weight ranges that represent the perceived relative importance of each landscape feature.

<p>The six GIS raster datasets representing the landscape features thought to affect possum dispersal, the ecological justification for their choice, and the associated weight ranges that represent the perceived relative importance of each landscape feature.</p

A summary of the landscape genetics based ranking of the 48 landscape representations, with the parameterisation of the associated accumulated-cost dispersal kernel.

a<p>Each landscape representation measured connectivity as a function of distance (D) and the traversal costs resulting from the inter-feature weights of elevation (E), plan curvature (P), tree and scrub cover (T), river order (R), highway traffic volume (H), and bridge length (B).</p>b<p>Landscape representations were ranked by their Akaike weight (<i>w</i>_i). Only the top ranked cost-surface landscape representations (Σ<i>w</i>_i≤0.95) plus the uniform landscape representation are listed.</p><p><i>r</i>²  =  coefficient of determination (in all cases <i>p</i>≤0.03), μ_log  =  the mean of the logarithm of the lognormal dispersal kernel, σ_log  =  the standard deviation of the logarithm of the lognormal dispersal kernel, maxDC  =  maximum observed dispersal connectivity value.</p

NZ Stoat Simulation R code

This file contains the annotated R code for the mitochondrial genetic drift simulation for the New Zealand stoat population since 1883 - showing the consequences of the founding bottleneck size