Given bees' central effect on vegetation communities, it is important to understand how and why bee distributions vary across ecological gradients. We examined how plant community composition, plant diversity, nesting suitability, canopy cover, land use, and fire history affected bee distribution across an open-forest gradient in northwest Indiana, USA, a gradient similar to the historic Midwest United States landscape mosaic. When considered with the other predictors, plant community composition was not a significant predictor of bee community composition. Bee abundance was negatively related to canopy cover and positively to recent fire frequency, bee richness was positively related to plant richness and abundance of potential nesting resources, and bee community composition was significantly related to plant richness, soil characteristics potentially related to nesting suitability, and canopy cover. Thus, bee abundance was predicted by a different set of environmental characteristics than was bee species richness, and bee community composition was predicted, in large part, by a combination of the significant predictors of bee abundance and richness. Differences in bee community composition along the woody vegetation gradient were correlated with relative abundance of oligolectic, or diet specialist, bees. Because oligoleges were rarer than diet generalists and were associated with open habitats, their populations may be especially affected by degradation of open habitats. More habitat-specialist bees were documented for open and forest/scrub habitats than for savanna/woodland habitats, consistent with bees responding to habitats of intermediate woody vegetation density, such as savannas, as ecotones rather than as distinct habitat types. Similarity of bee community composition, similarity of bee abundance, and similarity of bee richness between sites were not significantly related to proximity of sites to each other. Nestedness analysis indicated that species composition in species-poor sites was not merely a subset of species composition at richer sites. The lack of significant proximity or nestedness effects suggests that factors at a small spatial scale strongly influence bees' use of sites. The findings indicate that patterns of plant diversity, nesting resource availability, recent fire, and habitat shading, present at the scale of a few hundred meters, are key determinants of bee community patterns in the mosaic open-savanna-forest landscape.

Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate Max-Ent models, one considering the species as a single population and two of disjunct populations. Principal component analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species vs. population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.

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