Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non -military sites in the Kansas Flint Hills. The response variable was the longterm linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non -military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the nonmilitary site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage.
International audience ; Maintaining a land base that supports safe and realistic training operations is a significant challenge for military land managers which can be informed by frequent monitoring of land condition in relation to management practices. This study explores the relationship between fire and trends in tallgrass prairie vegetation at military and non -military sites in the Kansas Flint Hills. The response variable was the longterm linear trend (2001-2010) of surface greenness measured by MODIS NDVI using BFAST time series trend analysis. Explanatory variables included fire regime (frequency and seasonality) and spatial strata based on existing management unit boundaries. Several non-spatial generalized linear models (GLM) were computed to explain trends by fire regime and/or stratification. Spatialized versions of the GLMs were also constructed. For non-spatial models at the military site, fire regime explained little (4%) of the observed surface greenness trend compared to strata alone (7% - 26%). The non-spatial and spatial models for the non -military site performed better for each explanatory variable and combination tested with fire regime. Existing stratifications contained much of the spatial structure in model residuals. Fire had only a marginal effect on surface greenness trends at the military site despite the use of burning as a grassland management tool. Interestingly, fire explained more of the trend at the nonmilitary site and models including strata improved explanatory power. Analysis of spatial model predictors based on management unit stratification suggested ways to reduce the number of strata while achieving similar performance and may benefit managers of other public areas lacking sound data regarding land usage.
For millennia grasslands have provided a myriad of ecosystem services and have been coupled with human resource use. The loss of 46% of grasslands worldwide necessitates the need for conservation that is spatially, temporally, and socioeconomically strategic. In the Southern Great Plains of the United States, conversion of native grasslands to cropland, woody encroachment, and establishment of vertical anthropogenic features have made large intact grasslands rare for lesser prairie-chickens (Tympanuchus pallidicinctus). However, it remains unclear how the spatial distribution of grasslands and anthropogenic features constrain populations and influence conservation. We estimated the distribution of lesser prairie-chickens using data from individuals marked with GPS transmitters in Kansas and Colorado, USA, and empirically derived relationships with anthropogenic structure densities and grassland composition. Our model suggested decreased probability of use in 2-km radius (12.6 km(2)) landscapes that had greater than two vertical features, two oil wells, 8 km of county roads, and 0.15 km of major roads or transmission lines. Predicted probability of use was greatest in 5-km radius landscapes that were 77% grassland. Based on our model predictions, similar to 10% of the current expected lesser prairie-chicken distribution was available as habitat. We used our estimated species distribution to provide spatially explicit prescriptions for CRP enrollment and tree removal in locations most likely to benefit lesser prairie-chickens. Spatially incentivized CRP sign up has the potential to provide 4189 km2 of additional habitat and strategic application of tree removal has the potential to restore 1154 km(2). Tree removal and CRP enrollment are conservation tools that can align with landowner goals and are much more likely to be effective on privately owned working lands. ; Kansas Wildlife, Parks, and Tourism (Federal Assistance Grant) [KS W-73-R-3]; United States Department of Agriculture (USDA) Farm Services CRP Monitoring, Assessment, and Evaluation [12-IA-MRE CRP TA, KSCFWRU RWO 62, 7]; USDA Natural Resources Conservation Service, Lesser Prairie-Chicken Initiative ; We thank Kent Fricke, three anonymous reviewers, and the associate editor for providing reviews that improved the quality of the manuscript. We thank K. Schultz and A. Chappell for capturing and providing GPS data from lesser prairie-chickens captured on the Cimarron National Grasslands. B. Anderson, S. Baker, S. Bard, G. Brinkman, K. Broadfoot, R. Cooper, J. Danner, J. Decker, E. D. Entsminger, R. M. Galvin, N. Gilbert, A. Godar, G. Gould, B. Hardy, S.P. Hoffman, D. Holt, B. M. Irle, T. Karish, A. Klais, H. Kruckman, K. Kuechle, S. J. Lane, E. A. Leipold, J. Letlebo, E. Mangelinckx, L. McCall, A. Nichter, K. Phillips, J. K. Proescholdt, J. Rabon, T. Reed, A. Rhodes, B. E. Ross, D. Spencer, A. M. Steed, A. E. Swicegood, P. Waldron, B. A. Walter, I. Waters, W. J. White, E. Wiens, J. B. Yantachka, and A. Zarazua, provided much needed assistance with data collection. We greatly appreciate the logistic and technical support provided by J. C. Pitman, J. Kramer, M. Mitchener, D. K. Dahlgren, J. A. Prendergast, C. Berens, G. Kramos, and A. A. Flanders. Funding for the project was provided by Kansas Wildlife, Parks, and Tourism (Federal Assistance Grant KS W-73-R-3); United States Department of Agriculture (USDA) Farm Services CRP Monitoring, Assessment, and Evaluation (12-IA-MRE CRP TA#7, KSCFWRU RWO 62); and USDA Natural Resources Conservation Service, Lesser Prairie-Chicken Initiative. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. ; Public domain authored by a U.S. government employee
