All Data

This data set includes information on collections of surface water and fecal samples from wild gulls (Larus spp.) at two locations on the Kenai Peninsula, Alaska, USA. Samples were screened for Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) and tested for resistance to multiple antibiotics.

Elevation projections from the WARMER-Mangroves model for J N. "Ding" Darling National Wildlife Refuge across a range of sea-level rise scenarios (53, 115, and 183 cm by 2100). The model was calibrated using dated soil cores sampled from the basin hydrologic zone. These data support the following publication: Buffington, K.J., Thorne, K.M., Krauss, K.W., Conrad, J.K., Drexler, J.Z., and Zhu, Z.,

Accurate elevation data in coastal ecosystems are crucial for understanding vulnerability to sea-level rise. Lidar has become increasingly available; however, in tidal wetlands such as mangroves and salt marsh, vertical bias from dense vegetation reduces accuracy of the delivered 'base earth' products. To increase accuracy of elevation models across south Florida, we applied the LEAN technique to

Monitoring change in genetic diversity in wildlife populations across multiple scales could facilitate prioritization of conservation efforts. We used microsatellite genotypes from 7,080 previously collected genetic samples from across the greater sage-grouse (Centrocercus urophasianus) range to develop a modelling framework for estimating genetic diversity within a recently developed hierarchical

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of

The Trinity River Basin is a major source of water for a large part of Texas that includes the rapidly growing Dallas-Fort Worth and Houston metropolitan areas. An application of the Precipitation-Runoff Modeling System (PRMS) was used to conduct hydrologic simulations for the Trinity River Basin surface water model. Climate variables including daily precipitation, daily maximum temperature, and d

Coastal management decisions are complex and include challenging tradeoffs. Decision science offers a useful framework to address such complex problems. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecologic

This dataset contains information regarding the acreages of land currently (as of 2004) enrolled in the Conservation Reserve Program (CRP) distributed by county and the year the CRP contract was initiated (1987-2004, excluding 1994 and 1995). Additionally, it contains total acreages of land enrolled in the CRP distributed by county and the contract year (1987-2003). USDA Farm Service Agency's (FSA

Proportion of agricultural land cover within 18-km radius developed using a circular focal moving window analysis.

Agricultural land cover in the study area for the conservation assessment of Greater Sage-grouse conducted by the Western Association of Fish and Wildlife Agencies. This dataset was developed from Sagestitch, an Eastern Washington Shrubsteppe Mapping Project, several state-level Gap Analysis Program (GAP) land cover products (AZ, CA, NM, OR, and WA), National Land Cover Data (NLCD) (ND,SD,NE), and

Boundary of the conservation assessment of Greater Sage-grouse and sagebrush habitat conducted by the Western Association of Fish and Wildlife Agencies. The boundary is derived from the pre-settlement distribution of the Sage-grouse.

Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes