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Populations of many species of grassland‐associated butterflies, moths, and bumblebees in the Great Plains of North America are experiencing steep declines due to habitat loss and degradation—primarily conversion of grasslands to row‐crop agriculture and invasion of woody plants and exotic cool‐season grasses—and insecticide use. These declines are exacerbated by the generally limited dispersal ability of insects, which make local populations susceptible to extirpation following habitat fragmentation. Interest in pollinator conservation has grown substantially in recent years, but information to guide pollinator conservation across the region is limited. We used pollinator biology along with principles of landscape ecology and metapopulation dynamics to develop a simple decision matrix based on patch size and inter‐patch distances to guide landscape‐scale grassland conservation efforts in the U.S. northern Great Plains. When applied to spatial land cover data depicting potential pollinator habitat, the matrix uses landscape characteristics to guide placement of conservation treatments to help ensure persistence of target pollinator populations. Patch size and connectivity thresholds can be set to match characteristics of target species, but in all cases, local management will be necessary to ensure that fine‐grained features such as nectar sources and host plants are present. Applying the matrix to habitat layers for a nine‐state analysis region showed substantial geographic variation in conservation needs, opportunities, and potential treatments. We also demonstrate that non‐native planted cover such as alfalfa and certain grasslands enrolled in the Conservation Reserve Program can substantially enhance landscape structure by increasing grassland patch size and core area while decreasing distance between patches. Pollinators dependent on native prairie will also benefit from planted cover that provides nectar sources and serves as a buffer from pesticides associated with croplands. Consistent with the principles of strategic habitat conservation, targeted monitoring and research will be necessary to validate and adapt the model to meet local conditions. [ABSTRACT FROM AUTHOR]

Climate projections for the Midwestern United States predict southerly climates to shift northward. These shifts in climate could alter distributions of species across North America through changes in climate (i.e., temperature and precipitation), or through climate-induced changes on land cover. Our objective was to determine the relative impacts of land cover and climate on the abundance of five bird species in the Central United States that have habitat requirements ranging from grassland and shrubland to forest. We substituted space for time to examine potential impacts of a changing climate by assessing climate and land cover relationships over a broad latitudinal gradient. We found positive and negative relationships of climate and land cover factors with avian abundances. Habitat variables drove patterns of abundance in migratory and resident species, although climate was also influential in predicting abundance for some species occupying more open habitat (i.e., prairie warbler, blue-winged warbler, and northern bobwhite). Abundance of northern bobwhite increased with winter temperature and was the species exhibiting the most significant effect of climate. Models for birds primarily occupying early successional habitats performed better with a combination of habitat and climate variables whereas models of species found in contiguous forest performed best with land cover alone. These varied species-specific responses present unique challenges to land managers trying to balance species conservation over a variety of land covers. Management activities focused on increasing forest cover may play a role in mitigating effects of future climate by providing habitat refugia to species vulnerable to projected changes. Conservation efforts would be best served focusing on areas with high species abundances and an array of habitats. Future work managing forests for resilience and resistance to climate change could benefit species already susceptible to climate impacts. [ABSTRACT FROM AUTHOR]

Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush ( Hylocichla mustelina) and blue-winged warbler ( Vermivora cyanoptera) from 2 time periods (1995-1998 and 2006-2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006-2007. Competing (Deviance Information Criteria <5) blue-winged warbler models accounted for 67% of the variability and suggested positive associations with forest edge and proportion of forest at the 8,000-ha scale, and negative associations with forest patch area (800 ha) and wetness (800 ha and 80,000 ha). The regional model performed best for blue-winged warbler predicted abundances from point counts conducted in Iowa during 1995-1996 ( rs = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined ( rs = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data ( rs = 0.03, P = 0.92 for Iowa and rs = 0.13, P = 0.51 for all areas, 2006-2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by land managers, but we caution that the utility of such models may decrease over time for species preferring relatively ephemeral habitats if model inputs are not updated accordingly. © 2012 The Wildlife Society. [ABSTRACT FROM AUTHOR]