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Supporting Information – Marrotte, Gonzalez and Millien
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Habitat sampling protocol
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We measured habitat characteristics amongst the forest fragments where the mice were sampled.
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Forest sites were surveyed using 8 quadrats of 4 m2 evenly distributed over the sampling site
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between July and August 2011. A total of 32 variables were used to describe the habitat found in
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each forest patch (Table 2).
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Within each of these quadrats, all the trees with a diameter at breast height (DBH) greater than 9
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cm were counted, measured and identified to the species level. For each site, the total number of
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trees, the total number of tree species, the total basal area and the mean & maximum DBH were
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then calculated. The vertical complexity of the understory between 0 and 2 meters was assessed
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by binning this vertical range by increments of 0.5 meters. The presence or absence of vegetation
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between [0-0.5], [0.5-1.0], [1.0-1.5] and [1.5-2.0] meters was recorded and for each quadrat every
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presence was summed to reduce this data to a single descriptor. As a result, this variable ranged
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from 0 to 4, where a value of 0 refers to the absence of plants and a value 4 refers to the presence
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of vegetation at each level. To summarize this descriptor at the patch level, the sum of the 8
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quadrats was calculated for each forest patch.
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To measure the horizontal density of the understory vegetation, a measuring rod was installed at
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the center of each quadrat. The rod consisted of alternating colored increments of 10 cm in
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length, with a total length of 100 cm. Within each quadrat the rod was observed from distances of
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4, 3, and 2 meters while recording the lowest height visible on the rod at 3 observation heights of
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1.0, 0.8, and 0.5 meters. This produced 9 visual obstruction measurements per quadrat, which
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were summarized at the patch level by calculating the average of the 8 quadrats for all 9
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measurements.
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Within each quadrat, the ground cover was estimated visually by estimating the independent
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density of the following elements: bare ground, rocks, grass plants, ferns, stumps, roots, logs,
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branches, litter and shrubs. To summarize these ground cover measurements to the patch level,
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the average of the 8 quadrats was taken for each type of ground cover.
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For a canopy measurement a square piece of transparent plastic 15x15 cm containing 25 3x3 cm
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squares was used to measure the canopy openness. While looking up vertically, the number of
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squares filled with foliage was recorded and the number was then transformed to a percentage.
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To reduce these measurements to the patch level the average of the 8 quadrats was calculated.
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Litter depth was measured within each of the 8 quadrats and an average was used to summarize
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these measurements to the patch level.
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The elevation and slope angle were recorded at each quadrat and average of all 8 quadrats was
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taken to reduce these variables to the patch level.
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The area and perimeter were than measured from the digitized forest patches and we also
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calculated the perimeter to area ratio.
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Supplementary Figures legend
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Figure S1. Within the univariate sensitivity analysis we tested 10 landscape features (Table 1).
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This is an example of testing the urban land use class. In this resistance surface, the urban land
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use is assigned a value of 104 or 10,000 resistance units relative to the flat surface and then
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Circuitscape is used to compute pairwise ecological distances (effective resistance) between each
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location.
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Figure S2. Cumulative current map created using Circuitscape. Once a resistance surface is
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derived, Circuitscape is used to compute the effective resistance between each pair of sites. This
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figure illustrates the cumulative current of each pairwise computation of the urban univariate
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resistance surface from Figure S1. This current flow can be interpreted as simulated gene flow
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between populations (McRae 2006).
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Figure S3. Within-site index estimates of the overall top model. Since the direction of the habitat
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quality estimate is unknown there are 2 possible outcomes. The type 1 model is when site D has
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the most suitable habitat. Type 2 model is when site A has the most suitable habitat.
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Figure S4. Sensitivity analysis of the habitat estimates of the overall top model. For each site, the
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type 1 habitat estimate was varied between 0 and 1 while holding the other habitat quality
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estimates and the landscape resistance estimates static.
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Figure S5. Boxplots illustrating the distribution of the estimated resistance b parameter of the
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linear barriers (Richelieu River and Highway 116) land use classes (Agriculture, Orchard, Urban
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and Other) for the 975 models that converged from the numerical optimization routine.
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Figure S6. Sensitivity analysis of the resistance b parameter of the Richelieu River and Highway
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116 linear barriers and the agriculture, forest, orchard, urban and other land use classes.
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