Spatial and Temporal Analysis of Forest and Grassland Changed at the Tallgrass Prairie Perserve

Geograph

A County-Based Northern Bobwhite Habitat Prioritization Model for Kentucky

Planning the management of northern bobwhite (Colinus virginianus) habitat at a statewide-scale is daunting. Native grassland restoration is difficult to manage in Kentucky because . 99% of the Commonwealth’s original grassland area has been lost to agriculture, succession, and development. We created a county prioritization model designed to target areas of grasslands and landowners likely to participate in conservation programs. Our goal was to identify 10% of the state as high priority for bobwhite habitat restoration. We created an east and west model divided by the Appalachian Mountains. The west model was designed to target production-oriented operators farming marginal lands, whereas the east model targeted reclaimed minelands. We used agricultural, landcover, and staff data to build county prioritization models in 2006 and 2011. The models targeted 16.6% and 17.6% of the state in 2006 and 2011, respectively. However, if areas of large, contiguous blocks of forests were excluded, the area total was much closer to 10%. Fifty percent of the high priority counties changed in the west model, and 33% of the counties in the east model changed over 5 years. Implementing a county prioritization model in conjunction with a finer-scale, biological targeted approach could focus conservation efforts with greater potential for success, but the models should be reconstructed at 5- to 10-year intervals to account for changes in conservation delivery potential. A modification of our technique may serve to validate or as an alternative to improve National Bobwhite Conservation Initiative 2.0

Automated Identification and Mapping of Woody Habitat Using Digital Ortho Imagery

Northern bobwhite (Colinus virginianus) restoration efforts operate at multiple spatial scales, from landscape (regional) levels to farm level (local). Choosing proper data sources, analysis techniques, and accounting for differences in scale (minimum mapping unit) between sources are critical first steps to successfully delivering habitat information useful for broad regional planning efforts and site specific research and management activities. To this end, we compared 3 methods of creating a habitat map and associated data: National Landcover Dataset (NLCD) 2006, hand digitized from 2010 National Agriculture Imagery Program (NAIP) imagery, and an Interactive Supervised Classification of 1-m NAIP imagery using ArcGIS 10.1. We analyzed a 3,660-ha portion of Peabody Wildlife Management Area in Muhlenberg and Ohio counties in west central Kentucky. We also compared percent cover of forest canopy closure using 2011 NLCD Percent Canopy Closure along with 10-m and 30-m aggregated datasets derived from image classification. Office inspection of aerial imagery and field verification yielded a 94% positive identification of woody vegetation. We found good agreement between NLCD 2006 and Image Classification for habitat classes. Hand digitizing did not compare well and this method is not recommended for creating digital habitat data. Percent Canopy closure yielded similar results between data sources. We found the smaller pixel size of the 10-m aggregate data to better identify small woody patches in open matrix. Use of 30-m national datasets to compare basic habitat across large areas is well warranted. Site specific research and management activities will benefit from image classification of 1-m imagery. We recommend additional research into the relationship between varying pixel size and habitat classification

A Focused Habitat Approach for Northern Bobwhite Restoration in Kentucky

The Kentucky Department of Fish and Wildlife Resources has measured northern bobwhite (Colinus virginianus) population trends since 1960. During that span, northern bobwhite steadily declined because clean agriculture, fescue-sod, plant succession, and development eroded habitat suitability. Multiple efforts have failed with regard to restoring northern bobwhite numbers. Over 3.5 million northern bobwhite were released by the Department over a three decade period. Habitat efforts on private lands were deployed for over 20 years with mixed results. Support for the habitat restoration efforts waned. In 2008, the Department unveiled a new strategy centered on restoring concentrated habitat in focal areas. From 2008 to 2013, the Department managed habitat and monitored breeding northern bobwhite on 5 focal areas that were distributed throughout the state. Focal areas ranged in size from 1,155 to 16,517 ha. A total of 109 breeding bird survey points were monitored annually with up to three repetitions. Habitat management activity was also tracked. We used distance sampling to model density-dependent and density-independent population growth. Across the study, there was a 0.992 probability that our populations were growing with a mean region-wide, density independent growth rate of 35.7% annually. We were able to grow populations in an array of landscapes that were dominated by agriculture and grasslands. Management actions maintaining ≥10% of the focus areas in early successional habitat consistently supported growing northern bobwhite populations. The unique nature of our focal areas made them poor laboratories for field study, so future multi-state collaboration may be essential to understand the factors driving northern bobwhite growth. A better understanding of northern bobwhite population growth as it relates to landscape, management, weather, and harvest metrics will improve management prescriptions for northern bobwhite habitat on larger landscapes in the future

Everyday discrimination and mood and substance use disorders: A latent profile analysis with African Americans and Caribbean Blacks

Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N = 4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience