Estimating the density of wildlife populations is still a difficult task, especially when you work with spatially open populations and you must relax the assumption of closure, which is the basis of most methods currently used. Further difficulties arise when obtaining density estimates at small spatial scales. Using eight years (1996-2003) to monitor data from a roe deer Capreolus capreolus population that lives in a sub-Mediterranean environment in central Italy, we were able to estimate local density (at a spatial scale of one home range) by using a large sample of radio-marked animals. Local density estimates could be obtained only in zones in which radio-marked deer were available in sufficient numbers. To estimate local density in the whole study area, we developed a calibration model, which allowed us to infer density where radio-marked deer were absent or scarce. To do this, we computed the mark-resight density estimates (using radio-marked animals) and related these estimates to linear and non-linear functions of animal count and surface area of fields, to obtain a set of density estimators. Then, we selected a linear combination of such estimators, whose quality was assessed by cross-validation. Our results show that the method we propose can be effective in investigating small-scale spatial structure of density in a roe deer population. We see several potential applications of this method for both research and management purposes.
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Vol. 16 • No. 3