Evaluating the Effectiveness of Range Riding at Reducing Conflicts Between Livestock and Native Carnivores Across the American West

Background: The potential effectiveness of range riding depends, in part, on the goals of the rancher using a rider. Some producers use riders primarily to reduce depredation through human presence, while others focus on monitoring herd health and predator activity. Rancher partners participating in the Conflict Reduction Consortium hosted by Western Landowners Alliance were particularly interested in the potential of riding to reduce indirect losses.

As part of our existing Conservation on Workinglands Conservation Innovation Grant for the past year, (CoW-CIG) we ran focus groups that met monthly or more frequently with ranchers from across the West to understand what value riding provided to them, how best to measure that value, and what methods would be useful and feasible to measure that value. Our existing funding allows us to examine the influence of varied rider strategies on 1) annual depredation rates, 2) historical indirect losses, and 3) chemical indicators of stress in cattle herds. However, our producer partners have requested we add to this research list. Specifically, they requested 1) interviews with ranchers to contextualize important ranch-level decision-making related to using a rider and 2) cattle behavioral data (specifically landscape use and foraging behaviors) so that the influence on varied rider strategies on behavioral indicators of predator-induced stress on cattle can be evaluated. To add these two methods, we are using the funding that the Graduate Student Western SARE provided. These additions will improve rancher profitability through reducing conflict, enhancing stewardship through an improved understanding of how a rider can reduce cattle stress that leads to indirect losses, and improving overall quality of life for ranchers and their communities by helping to empower operational decision-making.

Objectives (1-2) and Hypotheses (ai - ii):

1. Identify whether varied range riding activity alters behavioral indicators of stress in cattle;
2. As rider intensity, time spent within proximity of the herd, and time riding at dusk, dawn, or at night increase,
3. seasonal herd vigilance will decrease, and
4. average time spent in high-quality foraging areas will increase.
5. Conduct unstructured interviews with livestock producers to capture the unique operational, environmental, and economic context driving decision making on husbandry techniques like range riding, and to capture the unique challenges of livestock production as related to predator conflict.

Objective 1: To examine the influence of varied rider activity on cattle behavior, we have been, and will continue to collect several data streams during the spring 2023 and 2024 grazing seasons across three operations - one in Washington, and two in Montana. These include data on rider activity, habitat features and forage quality/quantity, predator spatial use, and cattle behavioral activity. All of these operations have active wolf and grizzly bear populations.

Range Rider Data

This, and last grazing season, participating riders completed rider data sheets. We encouraged daily data collection but to accommodate time constraints of riders, we also allowed for weekly data collection (Nickerson_Daily_Rider, Nickerson_Weekly_Rider). Most riders also recorded their riding tracks in a GPS unit. Riders were trained each spring on data collection for both GPS units and data sheets that were provided at the start of the season. Rider GPS tracks will be used to compare rider landscape use and proximity to cattle location data (recorded by the rider) and carnivore location data. Rider data sheets provide data on 1) rider intensity (frequency and duration of rides, and how often a rider uses management in the field while riding such as moving cattle, fixing fence, etc.), 2) time within proximity versus away from the herd (and activity when away), 3) time of day or night riding, and 4) observations while monitoring the herd and of predator activity. Combined, these data comprehensively define rider intensity, use of the landscape, timing, and monitoring.

Environmental Data

Environmental data will be collected this fall and next spring through our producer partners and existing open-sourced data such as ArcGIS Pro, MODIS, Western Regional Climate Centers, and through data-sharing agreements with wildlife agencies. Examples of needed data include seasonal drought conditions, forage quality and quantity, water sources, whether herds are receiving supplemental feed, and alternative native prey densities. These variables of interest and covariates will be used to isolate the influence of varied rider behavior on herd behavioral stress from other potential stressors like heat, cold, illness, and distance to water.

Predator Data

We collected data on predator locations using three methods: 1) rider observations recorded via rider data sheets (see attachments), 2) data provided by wildlife agencies through data sharing agreements, and 3) game camera grids deployed on grazing allotments/pastures (already purchased). On each operation, we deployed 30 cameras in three grids of 10 cameras. Grid locations were selected based on areas of high use by cattle and were moved over the course of the season to match the timing of when cattle were moved to new grazing areas. Together, all three methods will provide a more robust understanding of predator presence than each could provide alone.

Cattle Behavioral Data

To explore the influence of riding on behavioral indicators of stress in livestock, and the potential influence of behavioral stress on weaning weights, reproduction, and illness, we have been measuring two metrics: 1) cattle vigilance, and 2) cattle landscape use behavior. Vigilance is the amount of time cattle spend moving or on the lookout for predators rather than eating, ruminating, or resting, all of which contribute to weight gain, reproductive success, and reduced illness. We collected cattle vigilance data from camera trap photos of cattle captured via our existing camera trap grids (10 cameras per grid), and via rider observations recorded on rider data sheets. At the end of the season, all cattle photos will be categorized as either vigilant (head up above shoulders and not chewing, running) or not vigilant (head down at or below shoulders feeding, head up above shoulders chewing, walking, or lying down/resting/ ruminating). Because cattle are often in groups, we count the total number of individuals in each photograph and the proportion of cows, our cattle of interest, that are vigilant/not vigilant. Photos from last season (2022) are being coded now. Rider data sheets from last season are also being thematically analyzed for cattle behavior and given a similar scoring of vigilant or not vigilant. Thus, at the end of the season, each of the three herds will have 31 date-specific vigilance scores – a scoring for each game camera deployed, and a scoring from each recorded rider data sheet. Scorings will then be averaged to a daily vigilance score.

To collect livestock landscape use data, we deployed VHF collars and ear tags on three operations. VHF collars have allowed the riders to more easily locate and record cattle locations on rider data sheets, which will not only allow for improved cattle location data, but also a comparison between riders with, and without VHF assistance.

We prioritized collaring lead cows at the three operations, with a minimum of 20% of each herd receiving a collar or ear tag. Cattle were collared at the start of the season with help from ranchers, riders, and their employees. Cattle location data will be used to evaluate the quality and quantity of forage available in areas where herds are spending most of their time. Furthermore, this information will be compared to the varied behavior of riders and predators to understand if riders reduce predator-induced stress in cattle, therefore improving foraging behavior that leads to higher weaning weights.

To map high-priority grazing areas at each of the three operations, we will use ArcGIS Pro and MODIS to map out areas of high forage quality (NDVI), forage quantity (NPP), and acceptable distance to water over all utilized allotments and pastures. We will then bring these maps to our producer and rider partners for confirmation and adjustment if needed. Where possible, priority areas will reflect seasonal changes in green-up based on the dates that livestock were present. This will likely happen between grazing seasons (2023 and 2024).

By modeling both herd vigilance and herd landscape use/foraging behavior as a function of varied rider activity and varied predator activity, these data will allow us to answer the following research questions:

1. Does varied rider activity influence the proximity of predators to cattle?
2. Does varied rider activity influence vigilance in cattle?
3. Does varied rider activity influence the quality of foraging areas used by cattle?

Cattle Chemical Data:

On seven ranches our first season (2022) and 11 ranches this season (2023), we collected hair samples from cow tails for cortisol and thyroid function analyses. We sampled at least 20% of the herd in both the spring and fall efforts. Fall samples from 2022 and spring samples from 2023 have yet to be analyzed by the Smithsonian, but spring 2022 cortisol samples from four of our ranches showed a wide range in cortisol levels across herds (pregrazing 2022_cortisol_SAREReport_IDremoved). For this reason, we changed our sampling protocol to at least 20% of the herd from 10% to capture more inter- and intra-herd diversity.

By incorporating cattle behavioral analysis into our range riding methods, the potential influence of a rider on indirect losses can be more accurately determined, since it is likely that behavioral stress responses to predation risk have a larger influence on weaning weights, reproduction, and illness than chemical responses to stress alone. The analysis of both behavioral and chemical responses are needed to accurately model herd stress, and the methods outlined above will allow us to measure whether riders can improve cattle foraging time, resting time, and the quality of forage used by a herd.

Objective 2: As mentioned above, conflict reduction tools are often created and evaluated without the direct involvement of ranchers. This can result in tools being researched at inappropriate temporal or spatial scales, or testing within a limited scope that does not account for the diverse, complex, and sometimes limiting relationships between an operation’s social, ecological, and economic dynamics. In turn, this can result in tools or solutions that are not feasible to deploy or maintain, are cost prohibitive, or simply ineffective in certain contexts. The coproduction of our research questions and methods have ensured that our temporal and spatial scales are sufficiently diverse, but capturing qualitative data will be critical to ensuring our findings are representative of diverse landowner needs and circumstances.

To capture this complexity, we have been conducting unstructured interviews (Interview Questions CIG) with all 30+ of our producer and rider partners operating in Washington, Montana, Wyoming, Oregon, California, New Mexico, and Arizona - including all producer partners from the existing CoW-CIG, and the three additional producer partners involved in the research outlined above for Objective 1. All operations have active wolf populations, and operations in Washington, Wyoming, and Montana also have active grizzly bear populations. Interview questions asked producers to reflect on riding as a tool, to describe their riding-related husbandry practices and operational protocols, identify production limitations that may influence the effectiveness of a range rider, and describe anticipated challenges to accurately analyzing riding’s potential. Ranchers were encouraged to lead conversations in whatever direction they would like, and interviews were recorded and transcribed for analysis. Transcriptions will be coded and analyzed using inductive thematic analysis. Inductive thematic analysis is a qualitative data analysis technique where themes are derived from the data themselves as opposed to being predetermined, then categorized after data collection. We will categorize and code distinct responses until saturation of categories is met, and response frequencies achieved. Interview findings will be crucial to capturing the diverse and complex relationships driving rancher decision-making on the operation and provide the needed descriptions for the development of future technical and financial support programs like those provided by NRCS. So far, about 75% of our partners have been interviewed, and interviews will continue throughout the year.