Spatial structure of roe deer populations: towards defining management units at a landscape scale

1. A crucial question in wildlife management concerns the definition of ecologically meaningful management units. Management action needs to be co-ordinated at the appropriate spatial scale. There are few practical tools for delimiting pertinent management units for large mammals such as ungulates. 2. Previous work has favoured a molecular approach to determine genetically distinct units, but this may be too costly for routine management. In addition, recent studies have revealed fine-scale heterogeneity in population dynamics of free-ranging ungulates in relation to environmental variability. 3. We combined spatial analysis of environmental heterogeneity (vegetation cover, topography and climate), animal morphology (jaw length) and genetic structure (micro -satellites) to define biologically meaningful population units for roe deer Capreolus capreolus in the Belluno province, north-eastern Italy. Jaw length is a sensitive measure of density-dependence and a good proxy for spatial and temporal variation in roe deer population growth. 4. Spatial analysis of environmental variables suggested that the study area should be divided into two, or possibly four, biogeographical regions in relation to variation in altitude and habitat type. There was significant spatial variability in jaw length across the province that clustered into two main regions (with shorter jaws in the north compared with the south), which matched the previously defined biogeographical regions. This spatial structuring was also supported by microsatellite analysis, which revealed two genetically distinct populations, one in the north and one in the south. 5. Synthesis and applications. A multiparameter approach, combining environmental information with data on indices of density-dependence such as jaw length, could be extremely useful for defining ecologically meaningful management units. Indeed, monitoring spatial and temporal variation in jaw length could provide deer managers with a simple way to index population structure and fluctuations in time and space.