Summary results of a principal component analysis based on variables describing the characteristics of habitat patch-networks of water voles in southwestern Portugal (N = 69).

<p>Total variance explained 88.2%. Rotation Method: Varimax with Kaiser Normalization. Values in bold indicate |factor loadings| >0.50.</p

Performance of the optimal management solutions for the five management strategies.

<p>Level diagrams showing the objective outcomes of optimal solutions in each of the six objectives (A–F, X axis). Colors correspond to the number of different landowner management groups of each solution. The Y axis (the same across all plots) represents the Euclidean distance to the ideal solution, i.e., a theoretical solution that achieves the best possible values in all objectives simultaneously. Distances were computed giving equal weights to cost (A), fire risk (B–D) and biodiversity (E–F) objectives (see text for details). The arrow in each objective axis point to the direction that is to be achieved during optimization (minimize or maximize).</p

Summary of optimal management solutions in each restriction scenario.

<p>Each bar represents the approximate general composition of all the solutions fulfilling each restriction scenario, and of those ten that minimize the distance to the ideal solution in the no-restriction scenario. Each segment (within a bar) represents one group of landowners, whose proportion is given by segment length. Shade intensity is proportional to the average management interval of the respective group (indicated below). Groups that are flexible in terms of proportion of owners are represented with oblique boundaries extending between the approximate minimum and maximum allowable proportions.</p

Objective dynamics in a landscape managed according to compromise solutions.

<p>Plots showing the values taken by the six objectives (A–F) along the simulation period (X axis) of the three compromise solutions (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086001#pone.0086001.s008" target="_blank">Figure S8</a>). Each line corresponds to a solution and depicts the mean values (±standard deviations) across 100 simulations of different random landscapes subjected to the management regime of the solution. Objective values used during the optimization algorithm correspond to the average (A) or the minimum (B–F) taken along the whole simulation period (A) or discarding the first 20 years (B–F, vertical dashed line).</p

Summary results of information-theoretic model selection and multimodel inference performed separately for each season to compare seasonal relationships between mosaic occupancy of water voles across spatial resolutions, and the mosaic gradients describing habitat-networks (H1, H2, H3) and matrix types (M1, M2, M3), and the autocovariate terms (ATC) for spatially correlated responses (see text).

<p>The table provides Akaike weights of the best fitting models (wi) for each response variable, the number of models including each predictor, the selection probabilities, and model averaged regression coefficients with 95% confidence intervals. Predictors included in the best models are underlined. Coefficient estimates whose 95%CI excluded 0 are presented in bold.</p

Location of the study region and sampling sites (land mosaics) used to investigate water vole occupancy according to patch-network and matrix characteristics.

<p>Examples of four land mosaics with different patch-network and matrix characteristics are also presented. Triangles, circles and squares represent sampling sites surveyed respectively in 2006 (n = 20), 2007 (n = 37), and 2008 (n = 18). Colours indicate the sampling season of surveys: dry season (black, n = 38) and wet season (grey, n = 37) (see text for details).</p

Summary statistics of habitat-network and matrix variables recorded per land mosaic, and overall and seasonal occupancy patterns of water voles in south-western Portugal.

a<p>Sample size (N) is not constant, because some variables could only be computed for a subset of the land mosaics studied, and because different mosaics were sampled in the wet and the dry seasons.</p

Summary results of a principal component analysis based on matrix variables characterising the land mosaics surveyed for water voles in southwestern Portugal (N = 75).

<p>Total variance explained 77.5%. Rotation Method: Varimax with Kaiser Normalization. Values in bold indicate |factor loadings| >0.50.</p

Summary results of information-theoretic model selection and multimodel inference on the relationships between mosaic occupancy of water voles across spatial resolutions and the mosaic gradients describing habitat-networks (H1, H2, H3) and matrix types (M1, M2, M3), and the autocovariate terms (ATC) for spatially correlated responses (see text).

<p>The table provides Akaike weights of the best fitting models (wi) for each response variable, the number of models including each predictor, the selection probabilities, and model averaged regression coefficient with 95% confidence intervals. Predictors included in the best models are underlined. Coefficient estimates whose 95%CI excluded 0 are in bold.</p

Akaike weights (wi) of univariate models fitted to test alternative water vole response curves (linear or quadratic) to the main mosaic gradients describing the habitat-network and the matrix.

<p>Comparisons included the null model (i.e. fitted only to the random component). The directions of associations between land mosaic occupancy measures and predictors are presented for response curves used in multivariate analysis: (+) positive, (−) negative, (∩) unimodal (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069976#pone.0069976.s002" target="_blank">Fig. S2</a>).</p