TY - CONF T1 - A statistical approach for predicting grassland degradation in disturbance-driven landscapes T2 - 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp) Y1 - 2015 A1 - Jacquin, Anne A1 - Goulard, Michel A1 - Hutchinson, J.M.S. A1 - Hutchinson, S.L. AB -

The relationship between fire and long-term trends in tallgrass prairie vegetation was assessed at Fort Riley and Konza Prairie Biological Station (KPBS) in Kansas. Linear trends of surface greenness were previously estimated using BFAST and MODIS MOD13Q1 NDVI composite images from 2001 to 2010. To explain trends, fire frequency and seasonality (fire regime) was determined and each site was divided into spatial strata using administrative or management units. Generalized linear models (GLM) were used to explain trends by fire regime and/or stratification. Spatialized versions of GLMs were also computed address unexplained spatial components. Non-spatial models for FRK showed fire regime explained only 4% of trends compared to strata (7-26%). At KPBS, fire regime and spatial stratification explained 14% and 39%, respectively. At both sites, improvements in performance were minimal using both fire and strata as explanatory variables. Model spatialization resulted in a 5% improvement at FRK, but with weak spatial structure in the residuals, and was not necessary at KPBS as the existing stratification most of the spatial structure in model residuals. All models at KPBS performed better for each explanatory variable and combination tested. Fire has only a marginal effect on vegetation trends at FRK despite its widespread use as a grassland management tool to improve vegetation health, and explains much more of the trends at KPBS. Analysis of predictors from spatial models with existing stratification yielded an approach with fewer strata but similar performance and may provide insight about additional explanatory variables omitted from this analysis.

JF - 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp) PB - IEEE CY - Annecy, France UR - https://ieeexplore.ieee.org/document/7245759 ER - TY - JOUR T1 - The timing of climate variability and grassland productivity JF - Proceedings of the National Academy of Sciences of the United States of America Y1 - 2012 A1 - Craine, J.M. A1 - Jesse B. Nippert A1 - Elmore, A.J. A1 - Skibbe, A.M. A1 - Hutchinson, S.L. A1 - N. Brunsell AB -

Changes in precipitation amount and variability have the potential to alter the structure and function of grasslands, but we know little about how changes in the timing of precipitation might affect grasslands. Here, we analyze long-term records from a tallgrass prairie to show that shifts in the timing of precipitation during the growing season have little effect on primary productivity or grass reproduction, but can greatly affect grazer performance. While greater late-season precipitation increases the weight gain of adult and young bison, greater mid-season precipitation decreases their weight gain. In addition, calving rates are lower after years with greater mid-season precipitation and higher after years with greater late-season precipitation. As well-timed drought can actually increase grazer weight gain and reproduction, it will be necessary to generate predictions of within-season distribution of precipitation to successfully forecast future grazer performance.

VL - 109 UR - https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0706.2012.20400.x ER - TY - Generic T1 - The water budget, climate variability, and climate impacts assessment in Northeast Kansas Y1 - 2009 A1 - Wilson, I.E. A1 - J. J. Harrington A1 - McLauchlan, K.K. A1 - Martinson, E.J. A1 - Hutchinson, S.L. VL - 32 ER - TY - JOUR T1 - Development of water usage coefficients for the fully-watered tallgrass prairie JF - Transactions of the American Society of Agricultural Engineers Y1 - 2008 A1 - Hutchinson, S.L. A1 - Koelliker, J.K. A1 - Alan K. Knapp VL - 51 ER -