Coefficients of Spearman correlations of the mean resource share of individuals with (a) the proportion of individuals in high-quality cells and (b) the proportion of individuals experiencing competition for four hypothetical species under different landscape scenarios.

<p>Bars marked with an asterisk (*) indicate the coefficients are statistically significant at <i>P</i><0.05.</p

Sample patterns of spatial distribution of individuals under different landscape scenarios.

<p>Hypothetical species are parameterized by environmental tolerance <i>C</i>_envir and mean moving distance <i>D</i>_mean. Black dots represent individuals residing in cells of <i>Q</i>≥0.5 and free of competition, while red dots are individuals residing in cells of <i>Q</i><0.5 or experiencing competition. Greener colour indicates higher habitat quality.</p

Variables, parameters, and initial conditions used in the model.

<p>Variables, parameters, and initial conditions used in the model.</p

Summary of the percentages of the variation in response variables explained by factors scale of spatial autocorrelation in habitat quality (<i>S</i>), species environmental tolerance (<i>C</i>_envir), and mean moving distance (<i>D</i>_mean). Detailed ANOVAs for each response variable are presented in Table S1.

<p>Summary of the percentages of the variation in response variables explained by factors scale of spatial autocorrelation in habitat quality (<i>S</i>), species environmental tolerance (<i>C</i>_envir), and mean moving distance (<i>D</i>_mean). Detailed ANOVAs for each response variable are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107742#pone.0107742.s003" target="_blank">Table S1</a>.</p

Flow diagrams of the submodels in the simulation model that determine mortality (a), reproduction (b), and (c) movement for a single individual within a time step.

<p>Flow diagrams of the submodels in the simulation model that determine mortality (a), reproduction (b), and (c) movement for a single individual within a time step.</p

Population responses of four hypothetical species to the scale of spatial autocorrelation in habitat quality <i>S</i>.

<p>(a) population size; (b) mean resource share of individuals; (c) mean mortality rate of individuals; (d) proportion of individuals in high-quality cells (<i>Q</i>≥0.5); (e) proportion of individuals experiencing competition. The figure shows mean±1 SD for 50 replicates for each variable.</p

Niche overlap values and results of similarity tests for each pair of crested newt candidate species (the three <i>Triturus karelinii</i>'s) and recognized species as determined with PCA-env (below diagonal) and ENFA (above diagonal).

<p>Similarity tests compare one (candidate) species with the background of the other and the reverse. If one or both comparisons are significant, results for the comparison of (candidate) species listed from top to bottom with the background of the one listed from left to right are noted before the slash; after the slash the reverse combination is noted. *  =  significantly larger (p<0.05) and ns  =  non-significant.</p

Spotting East African Mammals in Open Savannah from Space

<p>A GeoEye-1 satellite image was acquired on 11th August 2009 to capture the migration of the large-sized herd animals (i.e., wildebeest and zebra) in the Maasai Mara National Reserve, Kenya. The use of the satellite image was authorized by the DigitalGlobe Foundation and made the data fully disclosed and publicity available.</p

Niches of the different crested newt (candidate) species based on PCA-env.

<p>Each (candidate) species' niche is displayed on the same referential: a multi-dimensional scale represented by the first two axes of a principal component analyses summarizing the entire study area. Grey shading reflects the density of the occurrences of each (candidate) species in each cell. The solid and dashes contour lines illustrate 100% and 50% of the available environment in the study area. The correlation circle (bottom left) shows the contribution of the climatic variables on the two axes of the PCA and the percentage of inertia explained by the two axes.</p

Segmentation parameters for object-based analysis.

<p>Segmentation parameters for object-based analysis.</p