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The Importance of Scale: Assessing and Predicting Brook Trout Status in its Southern Native Range

Occupancy models are of increasing interest to managers and natural resource decision makers. Assessment of status and trends, as well as the specific drivers influencing occupancy, both may change as a function of scale, and analyses conducted at multiple scales can help identify important mechanisms leading to changes in distributions. We analyzed extensive fine-scale occupancy data across the southern historic range of the brook trout, Salvelinus fontinalis to determine which landscape metrics and thresholds were useful in predicting brook trout presence across three relevant spatial scales and how brook trout occupancy varied by scale. Percentage occupancy declined markedly with increased spatial resolution, as 52% of watersheds (HUC10) but only 32% of subwatersheds (HUC12) and 14% of catchments (HUC14) were occupied. Across all three scales, habitats which were exclusively occupied by native brook trout (without non-native trout) were rare (<10%). CART models using GIS-derived landscape predictor variables were developed for three classification cases: Case 1:(brook trout; no brook trout), Case 2 (brook trout; non-native trout only; no trout), and Case 3 (brook trout only; brook and non-native trout; non-native trout only and no trout). Model results were sensitive to both scale and the number of classification categories with respect to classification accuracy, variable selection and variable threshold values. Classification accuracy tended to be lowest at the finest (catchment) scale potentially reflecting stochastic population processes and barriers to movement. Classification rates for the overall models were: Case 1: Watershed (80.19%); Subwatershed (85.06%); Catchment (71.13%); Case 2: Watershed (69.31%); Subwatershed (68.72%); Catchment (57.38%); Case 3: Watershed (58.91%); Subwatershed (59.83%); Catchment (47.59%). Our multiscale approach revealed soil permeability (positive) and atmospheric pollution (negative) to be important predictors. The predicted occupancy and observed status of brook trout appear to be influenced by the scale the data are collected and reported.

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