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Semiarid rangelands throughout the western Great Plains support livestock production and many other ecosystem services. The degree to which adaptive multi-paddock (AMP) grazing management approaches can help achieve desired ecosystem services remains unclear. At the Central Plains Experimental Range in northeastern Colorado, a management-science partnership with a diverse stakeholder group is comparing collaborative adaptive rangeland management (CARM), designed to incorporate AMP principles, to traditional rangeland management (TRM), consisting of season-long grazing during the growing season. Each treatment was implemented on a set of 10, 130-ha pastures paired by soils, topography, and plant communities to evaluate how CARM affects vegetation (composition and production), livestock production (steer weight gain), and wildlife habitat (vegetation structure for grassland birds). For the first 5 years of the experiment, CARM cattle were managed as a single herd using AMP grazing with planned year-long rest in 20% of the pastures. Relative to TRM, CARM enhanced heterogeneity in vegetation structure across the landscape, benefiting two grassland bird species. However, this came at the cost of 12%-16% lower steer weight gains in CARM versus TRM and declining populations of a third bird species of conservation concern in both treatments. Here we discuss how increased understanding of ecological and social processes during the experiment's first 5 years led to changes in the CARM treatment and management objectives during the next 5 years. We also discuss how innovations in remote sensing, environmental sensors, ecosystem modeling, social learning, and economic analyses are being integrated into and supported by the CARM experiment., (© 2024 The Author(s). Journal of Environmental Quality published by Wiley Periodicals LLC on behalf of American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Snow dynamics influence seasonal behaviors of wildlife, such as denning patterns and habitat selection related to the availability of food resources. Under a changing climate, characteristics of the temporal and spatial patterns of snow are predicted to change, and as a result, there is a need to better understand how species interact with snow dynamics. This study examines grizzly bear (Ursus arctos) spring habitat selection and use across western Alberta, Canada. Made possible by newly available fine-scale snow cover data, this research tests a hypothesis that grizzly bears select for locations with less snow cover and areas where snow melts sooner during spring (den emergence to May 31st). Using Integrated Step Selection Analysis, a series of models were built to examine whether snow cover information such as fractional snow covered area and date of snow melt improved models constructed based on previous knowledge of grizzly bear selection during the spring. Comparing four different models fit to 62 individual bear-years, we found that the inclusion of fractional snow covered area improved model fit 60% of the time based on Akaike Information Criterion tallies. Probability of use was then used to evaluate grizzly bear habitat use in response to snow and environmental attributes, including fractional snow covered area, date since snow melt, elevation, and distance to road. Results indicate grizzly bears select for lower elevation, snow-free locations during spring, which has important implications for management of threatened grizzly bear populations in consideration of changing climatic conditions. This study is an example of how fine spatial and temporal scale remote sensing data can be used to improve our understanding of wildlife habitat selection and use in relation to key environmental attributes., Competing Interests: Please note that this research received funding from the following commercial sources: Alberta Newsprint Company, Canfor, Cenovus, Repsol, Seven Generations Energy, Shell Canada, TransCanada Pipelines, Teck Resources, West Fraser, Westmoreland Coal, and Weyerhaeuser. This does not alter our adherence to PLOS ONE policies on sharing data and materials and no competing interests exist.

Humans influence ecosystems on magnitudes that often exceed that of natural forces such as climate and geology; however, frameworks rarely include anthropogenic disturbance when delineating unique ecological regions. A critical step toward understanding, managing and monitoring human-altered ecosystems is to incorporate disturbance into ecological regionalizations. Furthermore, quantitative regionalization approaches are desirable to provide cost-effective, repeatable and statistically sound stratification for environmental monitoring. We applied a two-stage multivariate clustering technique to identify 'EcoAnthromes' across a large area - the province of Alberta, Canada - at 30 m spatial resolution, and using primarily remotely sensed inputs. The EcoAnthrome clusters represent regions with unique ecological characteristics based on a combination of natural ecological potential (e.g., climatic and edaphic factors) and disturbance, both natural and anthropogenic. Compared to existing expert-derived Natural Subregions in Alberta, the model-based EcoAnthromes showed greater class separation and explained more variance for an assortment of variables related to land cover, disturbance and species intactness. The EcoAnthromes successfully separated important ecological regions that are defined by complex assemblages of topography, climate and disturbance, such as gravel-bed river valleys, boreal forests, grasslands, post-fire recovery areas and highly disturbed agricultural, industrial and urban landscapes. In addition to presenting a flexible method for EcoAnthrome regionalization, we group and describe the EcoAnthromes created for Alberta and discuss how they can complement expert-derived regionalizations to aid in environmental management efforts, such as species recovery planning and monitoring for threatened species., (Copyright © 2019 Elsevier Ltd. All rights reserved.)