No abstract

Ruffed grouse Bonasa umbellus populations in North America have declined as forests have matured and the extent of early successional forest habitat required by the species has diminished. When wildlife species decline because of habitat loss, determining where to focus habitat management efforts is difficult because both the wildlife population and the required habitat(s) are usually limited in distribution. We adopted a relatively new ecological modeling method, partitioned Mahalanobis D 2 , which allowed us to predict the distribution of potential ruffed grouse habitat across a landscape of management concern where high quality habitat was uncommon. We used presence data derived from radio-telemetry locations, and GIS habitat data from publicly available sources to create competing partitioned Mahalanobis D 2 models. The competing models identified important habitat variables and predicted ruffed grouse habitat distribution at 1-ha and 25-ha scales in southwestern Rhode Island, USA. The 1-and 25-ha models produced comparable overall classification accuracy (83.1% and 81.4%, respectively) but differed substantially in the area of predicted habitat (4,475.5 ha and 10,133.8 ha, respectively). We selected the more conservative 1-ha model as the 'best' model, and expanded it to a larger landscape extent. Once expanded, the model predicted 11,463 ha (15.5% of total land area) of potential ruffed grouse habitat for a 735-km 2 landscape in southwestern Rhode Island. This model identified those areas with varying proximities to the following features as likely to contain ruffed grouse habitat: early successional forests, river and stream corridors, mixed conifer forests, conifer forests, shrub wetlands and deciduous forests. Early successional forests were the most consistent component of habitat used by grouse, despite the fact that this habitat type was uncommon in our study area (<1% of total land area). Our model can be used to identify areas of existing ruffed grouse habitat for management focus.