Sage Grouse Management

Contributed Oral

 
A Range-Wide, Long-Term Greater Sage-Grouse Monitoring Database to Inform Management Across Eleven Western States, USA
Julie Heinrichs, Leslie Schreiber, Adrian Monroe, Michael Schroeder, Peter Coates, San Stiver, Brian Prochazka, Nyssa Whitford, Catherine Wightman, Steve Hanser, Lief Wiechman, Thomas Christiansen, Avery Cook, Shawn Espinosa, Lee Foster, Kathleen Griffin,
Long-term monitoring of natural resources is imperative for increasing our understanding of ecosystem processes, services, and how to manage those ecosystems to maintain or improve function. Challenges with using these data may occur because methods of monitoring changed over time, multiple organizations collect and manage data differently, and monetary resources fluctuate, affecting many aspects of data (e.g., more or less monitoring on an annual basis depending on level of funding). We demonstrate the methods and challenges for acquiring, uniting, and standardizing a range-wide greater sage-grouse (Centrocercus urophasianus) lek count database (lek is a breeding site where males compete for ability to mate with visiting females). The database was comprised of long-term lek count monitoring data, which we compiled using data provided by all 11 western USA states where greater sage-grouse occur and dated back to the 1950’s in some of these states. We used automated and repeatable methods to standardize data via custom open-source software (grsg_lekdb) to improve the scientific integrity of future sage-grouse population assessments within and among states. Data standardization included reconciling uses of different terminology and expunging unusable data, resulting in the removal of 26% of data records due to database insertion errors and modifications to >1 million values to correct formatting and typing errors. We used the data to inform population monitoring and modeling needs, including a range-wide population trends analysis based on hierarchical, nested lek clusters (multi-scaled monitoring framework) and a Targeted Annual Warning System (TAWS) to inform managers of leks and populations in decline. Our approaches corrected spatial and aspatial data errors, maximized inclusion of usable data, and supported applications of ecoinformatics that identified data to inform detection probabilities, population trends, and monitoring guidelines. We show the importance of data management and how ecoinformatics can improve the usefulness of data for future research needs.
 
Greater Sage-Grouse Population Structures and a Hierarchical Monitoring Framework to Inform Management
Michael O’Donnell, David Edmunds, Cameron Aldridge, Julie Heinrichs, Adrian Monroe, Peter Coates, Brian Prochazka, Steve Hanser, Lief Wiechman
Population monitoring is important to wildlife and land management agencies, but analyses of population data rarely account for processes occurring across both spatial and temporal scales. We present a multi-scaled framework to inform long-term monitoring and population trend assessments of Greater sage-grouse (Centrocercus urophasianus) across the western United States. Using a newly developed standardized database of sage-grouse leks (breeding sites), we defined population structure (connectivity) with least-cost paths between neighboring leks while relying on a resistance surface to identify the smallest cost-weighted distance path between leks. We then uniquely incorporated various factors that encompassed dispersal capabilities, seasonal habitat conditions, dispersal distances informed from genetic flow, and functional processes (scale effects) of habitat selection to decompose the least-cost paths into sub-populations. Lastly, we assessed multi-scaled habitat selection needs with constraint-based rules of connectivity (reflected as sub-populations) using a landscape partitioning approach known as the Spatial “K”luster Analysis by Tree Edge Removal clustering algorithm (SKATER). This unique combination of methods provided a biologically-informed methodology of grouping breeding populations at multiple nested scales (clusters). We evaluated the resulting hierarchical framework (13 cluster levels) based on the assumption of closed populations using >1.7 million telemetry locations (2006 – 2021) and 2,821 unique birds. We found that fine-scaled clusters captured 92% of birds’ time spent within their home cluster. The hierarchical framework is intended to support numerous needs, including a hierarchical and spatially balanced framework for population monitoring, demarcation of multi-scaled units for assessing population trends, and a newly developed Targeted Annual Warning System (TAWS) that identifies leks and population clusters undergoing noticeable population declines relative to regional trends.
 
A Novel Approach to Estimate Range-Wide Population Trends for Greater Sage-Grouse at Multiple Spatial Scales
Peter Coates, Brian Prochazka, Michael O’Donnell, Cameron Aldridge, David Edmunds, Adrian Monroe, Mark Ricca, Greg Wann, Steve Hanser, Lief Wiechman, Michael Chenaille
Incorporating spatial and temporal scales into greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse) population monitoring strategies is challenging and rarely implemented. Sage-grouse populations are characterized by temporal oscillations, making trend estimation sensitive to start and stop years. Accounting for environmental and demographic stochasticity is critical to reliably estimating population trends and identifying deterministic factors on the landscape more amenable to management action. We used a standardized database of lek counts within a hierarchical Bayesian state-space model and a biologically-informed, multi-scale network of breeding populations, known as ‘clusters,’ to estimate trends across different spatiotemporal scales. While accounting for oscillations in population abundance, our models estimated 37.0, 65.2, and 80.7% range-wide declines across short (17 years), medium (33 years), and long (53 years) temporal scales, respectively. Models also predicted 12.3, 19.2, and 29.6% of populations (defined as clusters of neighboring leks) consisted of over 50% probability of extirpation at 19, 38, and 56-year projections from 2019, respectively, based on averaged annual rate of change in apparent abundance across two, four, and six oscillations (average period of oscillation is 9.6 years). At the lek level, models predicted 45.7, 60.1, and 78.0% of leks with over 50% extirpation probabilities over the same time periods, respectively, mostly located on the periphery of the species’ range. Recent rates of decline were greater in western portions of the range, particularly the Great Basin, where wildfire and invasive grasses are prominent. Conversely, some areas in the eastern range exhibited evidence of population growth in recent decades. This modeling framework can serve as the foundation for a ‘Targeted Annual Warning System’ decision support tool to direct management efforts toward populations with the greatest need and may be modified to evaluate the effectiveness of conservation efforts.
 
Assessing Range-Wide Population Performance of Greater Sage-Grouse Using a Targeted Annual Warning System
Peter Coates, Michael O’Donnell, Cameron Aldridge, David Edmunds, Adrian Monroe, Mark Ricca, Greg Wann, Steve Hanser, Lief Wiechman, Michael Chenaille, Brian Prochazka
When local perturbations are absent, greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) populations exhibit long-term oscillations in abundance, which are driven by large-scale climatic patterns. Concurrent to long-term oscillations is short-term “noise”, which is governed by environmental and demographic stochasticity. Multi-scale spatiotemporal fluctuations in abundance make population performance assessments difficult, especially when additional information regarding metapopulation structure is absent. By incorporating metapopulation structure into a Bayesian hierarchical population monitoring framework developed for sage-grouse across their range, we were able to identify moments of aberrant decline at lek sites (i.e., traditional breeding grounds) and local populations defined as clusters of leks. Within this framework we defined aberrant decline at these scales as a negative trend that is also declining at a rate below the estimated trend at a much broader spatial scale. Multi-year assessments of aberrant decline accounted for environmental and demographic stochasticity, as well as observation error, and identified populations exhibiting strong evidence of climatically corrected negative trends. Post hoc analyses that simulated management intervention at the local scale used metapopulation stability as a target for identifying optimal management intervention thresholds. Using this framework, we identified population declines that are likely attributable to disturbances on the landscape rather than environmental stochasticity or intrinsic factors across broader regions, which can help immediately inform when and where increased monitoring or direct management intervention may be needed to reverse negative trends.

Virtual
Location: Virtual Date: November 3, 2021 Time: 1:00 pm - 2:00 pm