| dc.description.abstract | Rangelands are heterogeneous working landscapes capable of supporting livestock production and biodiversity conservation, and heterogeneity-based rangeland management balances the potentially opposing production and conservation goals in these working landscapes. Within fire-dependent ecosystems, patch-burn grazing aims to create landscape patterns analogous to pre-European rangelands. Little work has tested the efficacy of patch-burn grazing in northern US Great Plains. We investigated patch contrast in above and belowground ecosystem properties and processes during the summer grazing seasons from 2017 – 2020 on three patch-burn pastures stocked with cow-calf pairs and three patch-burn pastures stocked with sheep. We focused on vegetation structure, plant community composition, forage nutritive value, grazer selection, livestock weight gain, soil nutrient pools, soil microbial community composition, and decomposition activity. We used mixed-effect models and ordinations to determine whether differences: along the time since fire intensity gradient, between ecological sites, and between grazer types existed. Despite no significant shifts in the plant community, structural heterogeneity increased over time as the number of time since fire patches increased and was higher than homogeneously managed grasslands. Grazing livestock preferred recently burned patches where the available forage had a higher nutritive value and lower available biomass than surrounding patches at a given point in time. With the exception of 2018, livestock weight gains were consistent. Soil nutrient pools and microbial abundances differed more by ecological site than by the time since fire intensity gradient, and ecological sites exhibited similar nutrient and microbial responses to the time since fire intensity gradient. That belowground response variables were mostly resistant to patch-burn grazing is supportive of further use of this management, especially given the desirable results with aboveground response variables. | en_US |
