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Riparian restoration increasingly employs dam removal, but with limited knowledge about post-removal ecological impacts. Comprehensive ecological study has occurred on few dam removals and rarely on associated terrestrial components. Research supporting the Elwha River restoration program, Washington, USA, helps fill this gap as the largest dam removal project to date. We developed forecasts of avian responses to post-removal forest restoration in Elwha reservoirs. We modeled abundance patterns for 8 species using avian inventory data and habitat variables at local, patch, and landscape scales. We evaluated models using Akaike's Information Criterion and determined the relative importance of each variable and scale using multi-model inference. Local habitat variables best explained abundance patterns for most species. We forecasted avian responses to forest restoration in the drained Lake Mills reservoir with and without conifers. Planting conifers hastened forecasted avian responses. Forecasts suggest birds associated with deciduous forests and edge habitats will colonize restored habitats rapidly, but birds associated with conifers may not respond markedly for more than a century. Birds can provide a measure of early restoration progress, but comprehensive restoration of Elwha avifauna will take longer than the dams have been in place. This conclusion echoes a familiar theme in Pacific Northwest forest restoration: anthropogenic impacts occur rapidly, but full restoration is comparatively slow. [ABSTRACT FROM AUTHOR]

The Northern Spotted Owl (Strix occidentalis caurina) population is declining throughout its range in the United States and adjacent Canada and is facing increasing pressure from the invading Barred Owl (Strix varia). In this study, we characterize Spotted Owl habitat associations and develop 2 new habitat selection models for the eastern Washington Cascade Range. Topographic and habitat data were compiled at 2 scales (0.25 and 1.0 mi) around 224 Spotted Owl activity centers, or sites, and at 160 random locations in the same geographic region, and used to develop models for predicting owl distributions. Univariate analysis found that owl sites occurred below 5000-ft elevation and were more likely to occur as area in the >71% crown-cover class increased. Owl sites were found to be more likely to occur closer to streams and to be rare in the Subalpine Fir (Abies lasiocarpa) vegetation type. The 9-25" tree size-class was a significant predictor of the distribution of owl sites. Habitat models were constructed that were moderately successful at predicting owl-site distribution. Models from the largest scale tested (1.0-mi radius) were the most predictive, at 80% accuracy. Top-ranked models included overstory canopy cover, tree size, elevation, precipitation, distance to stream, and tree species as predictors. The resulting models can be used to help identify likely sites for surveys and to inform conservation and landscape management activities associated with forest-health restoration. [ABSTRACT FROM AUTHOR]

The Mahalanobis distance statistic (D2) has emerged as an effective tool to identify suitable habitat from presence data alone, but there has been no mechanism to select among potential habitat covariates. We propose that the best combination of explanatory variables for a D2 model can be identified by ranking potential models based on the proportion of the entire study area that is classified as potentially suitable habitat given that a predetermined proportion of occupied locations are correctly classified. In effect, our approach seeks to minimize errors of commission, or maximize specificity, while holding the omission error rate constant. We used this approach to identify potentially suitable habitat for the Olympic marmot (Marmota olympus), a declining species endemic to Olympic National Park, Washington, USA. We compared models built with all combinations of 11 habitat variables. A 7-variable model identified 21,143 ha within the park as potentially suitable for marmots, correctly classifying 80% of occupied locations. Additional refinements to the 7-variable model (e.g., eliminating small patches) further reduced the predicted area to 18,579 ha with little reduction in predictive power. Although we sought a model that would allow field workers to find 80% of Olympic marmot locations, in fact, <3% of 376 occupied locations and <9% of abandoned locations were >100 m from habitat predicted by the final model, suggesting that >90% of occupied marmot habitat could be found by observant workers surveying predicted habitat. The model comparison procedure allowed us to identify the suite of covariates that maximized specificity of our model and, thus, limited the amount of less favorable habitat included in the final prediction area. We expect that by maximizing specificity of models built from presence-only data, our model comparison procedure will be useful to conservation practitioners planning reintroductions, searching for rare species, or identifying habitat for protection. [ABSTRACT FROM AUTHOR]