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Context Technologies are now available to continuously monitor livestock foraging behaviours, but it remains unclear whether such measurements can meaningfully inform livestock grazing management decisions. Empirical studies in extensive rangelands are needed to quantify relationships between short-term foraging behaviours (e.g. minutes to days) and longer-term measures of animal performance. Aims The objective of this study was to examine whether four different ways of measuring daily foraging behaviour (grazing-bout duration, grazing time per day, velocity while grazing, and turn angle while grazing) were related to weight gain by free-ranging yearling steers grazing semiarid rangeland. Methods Yearling steers were fitted with neck collars supporting a solar-powered device that measured GPS locations at 5 min intervals and used an accelerometer to predict grazing activity at 4 s intervals. These devices were used to monitor steers in four different paddocks that varied in forage biomass, and across two grazing seasons encompassing a wide range of forage conditions. Steer weight gain (kg/steer.day) was measured in each paddock during each of three ∼60 day time intervals, and daily foraging behaviours were measured during 15–21 days in each interval. Results A model based on only two daily measurements of foraging behaviour, mean grazing bout duration (calculated at a 5 min resolution) and mean velocity while grazing explained 62% of the variation in animal weight gain. Conclusions Daily measurements of foraging behaviour vary substantially in response to varying foraging conditions in space and time, and can effectively serve as indicators of variation in cattle weight gain. Implications On-animal sensors that monitor foraging behaviour have the potential to transmit indicators to livestock managers in real time (e.g. daily) to help inform decisions such as when to move animals among paddocks, or when to sell or transition animals from rangeland to confined feeding operations.

Black-tailed prairie dogs (Cynomys ludovicianus) exhibit boom and bust cycles in landscapes where they are affected by outbreaks of plague caused by the introduced bacterium Yersinia pestis. We examined spatiotemporal dynamics of black-tailed prairie dog colonies in the Thunder Basin National Grassland, Wyoming over a period of 21 years. The colony complex experienced three plague epizootics during that time, and consequently three boom and bust cycles. The entire prairie dog colony complex collapsed over a 1-year period during the first and third epizootics, and over a 3-year period during the second epizootic. The boom and bust cycles were characterized by relatively rapid contractions in total area occupied by prairie dogs during a plague outbreak (e.g., >99% decline from 10,604 ha to 47 ha over 1 year [2017–2018]) followed by much slower recovery times (e.g., an increase from 410 ha to 10,604 ha over 11 years [2006–2017]). Prairie dogs occupied a total of 10,604 ha during at least one survey within the study period, but much of the area was not continuously occupied over time. We found that each of the three plague outbreaks occurred in years with highly connected prairie dog colonies and slightly above-average temperatures and summer precipitation, which were preceded by a dry year. Although plague outbreaks were associated with climatic conditions, we were unable to detect a role of climate in driving colony expansion. Our results illustrate the cyclic and extreme nature of fluctuations in black-tailed prairie dog colony size and distribution in a landscape where plague occurs and illuminate some of the drivers of these cycles. Further, our work shows how introduced diseases can dramatically influence populations of a keystone species, with important consequences for the broader ecological system.

On the Ground • Adaptive management should explicitly involve stakeholders, emphasize multiple iterations of identifying and prioritizing outcomes, and tightly link science-informed monitoring to decision-making benchmarks for effective feedback loops. • Short-term monitoring procedures should be simple, quick, and based on consistent methods that are focused on locations where meaningful change is expected or uncertainty is high. • Long-term monitoring procedures should emphasize consistent methodology across years that provides broader ecosystem context for multiple ecosystem services (e.g., watershed protection and grassland bird habitat). • Incorporating timely feedback from monitoring improves the capacity for rapid decision-making when benchmarks are attained and management should be modified.

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making. Thunder Basin Research Initiative; National Institute of Food and Agriculture [2020-67019-31153] Published version We are grateful to Paul Stapp, Eddie Childers, Buck Cully, Jeff Abeglen, Kristin Philbrook, Janice Naylor, Erin Considine, Andy Chappell, Jennifer Avising, Ruben Mares, Gwyn McKee, Peter McDonald, Travis Livieri, Lynne Deibel, Jessica Alexander, Daniel Kinka, Kristy Bly, Nicole Rosmarino, Tara Stephens, and numerous other biologists and field technicians for their data collection and sharing of prairie dog colony boundaries and other associated data across the Great Plains region. We thank David Eads, David Pellatz, and two anonymous reviewers for providing comments that helped to greatly improve the manuscript. This work is part of the Thunder Basin Research Initiative. Funding was provided by the National Institute of Food and Agriculture (Award no.: 2020-67019-31153). Public domain – authored by a U.S. government employee

Coverage of living (green) vegetation influences rangeland processes and biodiversity but remains a challenge to quantify at small spatial grain. We describe a technique for rapid airborne (unmanned aerial vehicle) measurements of continuous spatial coverage of living vegetation at a resolution (spatial grain) of 8 cm ground sampling distance. We then applied this technique at the pasture (paddock) scale (tens of hectares) in two contrasting grassland ecosystems (semiarid shortgrass steppe in northeastern Colorado, United States and mesic grassland in central Texas, United States) to determine effects of locally relevant grazing treatments on spatial variability and dependence (pattern) in fractional coverage of green vegetation (fractional vegetation cover; FVC). Site-specific regression models developed using reflectance in visible and near-infrared wavebands explained 90% and 89% of variance in FVC for semiarid and mesic grassland, respectively. Mean FVC was similar among shortgrass steppe pastures differing in grazing treatment (light or heavy grazing by cattle, moderate cattle grazing with prairie dogs present). In contrast, FVC was lower with rotational compared with continuous, year-long grazing in the mesic grassland. Heavy grazing in shortgrass steppe and rotational grazing in mesic grassland increased the spatial uniformity in FVC by reducing spatial variability and increasing spatial dependence in FVC, the latter by increasing the similarity in FVC values among spatially separated patches. Unmanned aerial vehicle−enabled remote sensing provides for FVC at sufficiently small spatial grain to characterize spatial variation in FVC at the pasture scale. Results can be used to evaluate the effectiveness of management actions intended to alter spatial variation in FVC to achieve conservation or diversity objectives. Enhanced capacity to monitor FVC at small spatial grain promotes adaptive management of grasslands.

Habitat loss and changing climate have direct impacts on native species but can also interact with disease pathogens to influence wildlife communities. In the North American Great Plains, black-tailed prairie dogs (Cynomys ludovicianus) are a keystone species that create important grassland habitat for numerous species and serve as prey for predators, but lethal control driven by agricultural conflict has severely reduced their abundance. Novel disease dynamics caused by epizootic plague (Yersinia pestis) within prairie dog colonies have further reduced prairie dog abundances, in turn destabilizing associated wildlife communities. We capitalized on a natural experiment, collecting data on prairie dog distributions, vegetation structure, avian abundance, and mesocarnivore and ungulate occupancy before (2015-2017) and after (2018-2019) a plague event in northeastern Wyoming, USA. Plague decimated black-tailed prairie dog populations in what was then the largest extant colony complex, reducing colony cover in the focal area from more than 10,000 ha to less than 50 ha. We documented dramatic declines in mesocarnivore occupancy and raptor abundance post-plague, with probability of occupancy or abundance approaching zero in species that rely on prairie dogs for a high proportion of their diet (e.g., ferruginous hawk [Buteo regalis], American badger [Taxidea taxus], and swift fox [Vulpes velox]). Following the plague outbreak, abnormally high precipitation in 2018 hastened vegetation recovery from prairie dog disturbance on colonies in which constant herbivory had formerly maintained shortgrass structure necessary for certain colony-associates. As a result, we observed large shifts in avian communities on former prairie dog colonies, including near-disappearance of mountain plovers (Charadrius montanus) and increases in mid-grass associated songbirds (e.g., lark bunting [Calamospiza melanocorys]). Our research highlights how precipitation can interact with disease-induced loss of a keystone species to induce drastic and rapid shifts in wildlife communities. Although grassland taxa have co-evolved with high spatiotemporal variation, fragmentation of the remaining North American rangelands paired with higher-than-historical variability in climate and disease dynamics are likely to destabilize these systems in the future.

Adaptive management of large herbivores requires an understanding of how spatial-temporal fluctuations in forage biomass and quality influence animal performance. Advances in remote sensing have yielded information about the spatial-temporal dynamics of forage biomass, which in turn have informed rangeland management decisions such as stocking rate and paddock selection for free-ranging cattle. However, less is known about the spatial-temporal patterns of diet quality and their influence on large herbivore performance. This is due to infrequent concurrent ground observations of forage conditions with performance (e.g., mass gain), and previously limited satellite data at fine spatial and temporal scales. We combined multi-temporal field observations of diet quality (weekly) and mass gain (monthly) with satellite-derived phenological metrics (pseudo-daily, using data fusion and interpolation) to model daily mass gains of free-ranging yearling cattle in shortgrass steppe. We used this model to predict grazing season (mid-May to October) mass gains, a key management indicator, across 40 different paddocks grazed over a 10-year period (n = 138). We found strong relationships between diet quality and the satellite-derived phenological metrics, especially metrics related to the timing and rate of green-up and senescence. Satellite-derived diet quality estimates were strong predictors of monthly mass gains (R