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Context Bat conservation in the eastern United States faces threats from white nose syndrome, wind energy, and fragmentation of habitat. To mitigate population declines, the habitat requirements of species of concern must be established. Assessments that predict habitat quality based upon landscape features can aid species management over large areas. Roosts are critical habitat for many bat species including the endangered Indiana bat (Myotis sodalis) and the threatened northern long-eared bat (M. septentrionalis). Objectives While much is known about the microhabitat requirements of roosts, translating such knowledge into landscape-level management is difficult. Our goal was to determine the landscape-scale environmental variables necessary to predict roost occupancy for both species. Methods Using MaxLike, a presence-only occupancy modeling approach, with known roost sites, we identified factors associated with roosting habitat. Spatially independent roost locations were particularly limited for northern long-eared bats resulting in differences in study areas and sample sizes between the two species. Results Occupancy of Indiana bat roosts was greatest in areas with [80 % local forest cover within broader landscapes (1 km) with \40 % forest, \1 km of perennial streams but[1 km from intermittent streams and in areas with poor foraging habitat. Northern longeared roost occupancy was greatest in areas with[80 % regional but fragmented forest cover with greater forest edge approximately 4 km from the nearest major road. Conclusions Landscape features associated with roost occupancy differed greatly between species suggesting disparate roosting needs at the landscape scale, which may require independent management of roost habitat for each species.

Abstract. Forest management and ungulate herbivory are extant drivers of herbaceous-layer community composition and diversity. We conducted a white-tailed deer (Odocoileus virginianus) exclosure experiment across a managed landscape to determine how deer impacts interact with the type of forest management system in influencing herb-layer (all vascular plants < 0.5 m tall) species richness and composition. Our study took place 3 yr after harvest in a deciduous forest landscape being managed through even-aged (~4.1 ha openings) and uneven-aged (~1.4 ha openings) silvicultural systems. We expected the severity of deer impacts on herb layer species richness and composition to vary according to opening position, opening size, and the spatial scale of inference. At forest stand and landscape scales, species richness within silvicultural openings was greater outside compared to inside deer exclosures, and did not differ according to deer access in edges or the forest matrix. However, greater levels of species richness associated with deer access were driven by infrequently occurring forbs, and overall species composition did not differ. Notably, these species were not exotics or ferns. Deer reduced the density of large saplings and blackberry (Rubus spp.) shrubs in the smaller openings characteristic of uneven-aged management stands, but had no effect on sapling density in the larger openings characteristic of even-aged management stands. This result extends the forage maturation hypothesis to silvicultural systems, and is consistent with predictions that plant tolerance and avoidance of herbivory increase with resource availability. Deer may have facilitated the establishment of forbs in recently created silvicultural openings by temporarily slowing sapling regeneration, creating establishment sites through physical disturbance, and seed dispersal via epizoochory and endozoochory. This outcome is contingent upon declining deer visitation rates as woody vegetation matures as well as distance from source populations of exotic species. We conclude that ecological context, such as local ungulate abundance, disturbance, and landscape factors, influence how ungulates interact with forest management systems.

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Abstract. Sound forest management requires accurate forest maps at an appropriate scale. Forest cover data developed at a national scale may be too coarse for forest management at a local level. We demonstrated a two-stage unsupervised classification, integrating Continuous Forest Inventory (CFI) data and Landsat imageries, to classify forest types for Indiana State Forests (ISF) and 8-km surrounding areas. In the first stage, an automatic unsupervised classification assisted by CFI data was applied in ISF. In the second stage, the resultant forest cover information from the first stage was used to expand the classification area into the 8-km surrounding areas. Splitting the classification procedure into two stages made it possible to expand the classification area beyond the coverage of the CFI data. This data-aided unsupervised classification approach increased the repeatability of forest mapping. The resultant map contains five forest types: conifer, conifer-hardwood, maple, mixed hardwood, and oak-hickory forests. The overall accuracy was 81.9%, and the total disagreement was 0.176. The accuracies of conifer, conifer-hardwood, maple, mixed hardwood, and oak-hickory forests were 81.6, 63.4, 75.0, 33.3, and 90%, respectively. This forest mapping technique is suitable for automated mapping of forest areas where extensive plot data are available.