Search USGS⌕ Search
Home/Archive/USGS

USGS · 70205578

Designing multi-scale hierarchical monitoring frameworks for wildlife to support management: A sage-grouse case study

Michael S. O’Donnell·David R. Edmunds·Cameron L. Aldridge·Julie A. Heinrichs·Peter S. Coates·Brian G. Prochazka·Steve E. Hanser
View original source ↗Export metadata ↓

Abstract

Population monitoring is integral to the conservation and management of wildlife; yet, analyses of population demographic data rarely consider processes occurring across spatial scales, potentially limiting the effectiveness of adaptive management. Therefore, we developed a method to identify hierarchical levels of organization (i.e., populations) to define multiple spatial scales, specifically intended to help guide appropriate conservation and management actions. This approach can support mobile species with high site fidelity where surveys occur on birthing/breeding grounds or migratory stopovers. Our approach used a graphbased clustering algorithm (Spatial K’luster Analysis by Tree Edge Removal) that explicitly included habitat selection information at multiple scales and further refined with constraint-based rules. We applied these concepts to greater sage-grouse leks (breeding grounds), a species of conservation concern, in two different ecological contexts (Nevada and Wyoming, USA). The constraint-based rules accounted for inter-lek movement distances based on literature and field studies in Nevada from 2012 to 2016, included methods to support a spatially balanced monitoring design, and identified barriers to movements among leks based on resistance surfaces. We evaluated the performance of our hierarchical clusters in Nevada using independent data from radio-marked sage-grouse, and we found the finest-scaled cluster level captured ~90% of sagegrouse movements and mid-level scales captured ~97–99% of movements. We expected comparable performance for Wyoming, where we lacked radio-marked sage-grouse for an evaluation, because genetic studies estimate similar dispersal distances to our ~15 km inter-lek movement distance in Nevada. For sage-grouse and other mobile species with high site fidelity, our approach to defining these frameworks could prove valuable for conservation and management applications, such as improving estimation of scale-dependent population trends and guiding the prescription of management actions at spatial scales that align with identified threats. Specific to sage-grouse, our analysis sets the stage for designing a monitoring framework that relies on comparison of short- and long-term population trends across our defined spatial scales and identifies and disentangles factors driving local (e.g., habitat quality) and regional (e.g., climate) population changes, thereby supporting scale-dependent management and research needs for adaptive management practices.

Explore related subjects

EcosphereArticle
90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 35.002085° to 45.005904° latitude; -120.005746° to -104.05216° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Michael S. O’Donnell, David R. Edmunds, Cameron L. Aldridge, Julie A. Heinrichs, Peter S. Coates, Brian G. Prochazka, Steve E. Hanser. 2019-09-25. Designing multi-scale hierarchical monitoring frameworks for wildlife to support management: A sage-grouse case study. https://doi.org/10.1002/ecs2.2872

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Evaluating harvest liberalization strategies on population dynamics of southern latitude temperate-breeding Canada geese

The recovery of the Canada goose ( Branta canadensis ) is one of North America's greatest conservation success stories. Today, continental abundance of temperate-breeding Canada geese (those breeding in southern Canada and lower 48 states in the United States) greatly exceeds historical levels. As a result of increased abundance, human–goose conflicts have also increased, ranging from private and agricultural property damage to human health concerns. Managers have primarily attempted to lower Canada goose populations using hunter harvest via liberalized hunting regulations (increased bag limits and total hunting days). To evaluate the effectiveness of harvest strategies for temperate-breeding Canada geese, managers need a better understanding of how liberalized hunting regulations affect population dynamics. We estimated survival and harvest probabilities, abundance, and recovery distribution of temperate-breeding Canada geese banded in Arkansas, USA, during 2005–2020. We found that adult harvest probabilities declined overall during the study and adult survival probabilities increased. Annual abundances of juvenile geese (≤1 year old) declined during the study, whereas there was no detectable trend in abundance of the adult population over time. Most Canada geese breeding in Arkansas were shot in Arkansas, indicating potential for population dynamics to be influenced largely by state-specific harvest regulations. However, our results suggest that harvest liberalization as a management tool may have limited capacity to further influence population dynamics of Canada geese. Declines in productivity and recruitment likely had a greater influence on the similar observed declines in juvenile and adult abundances, respectively, during our study. More research is needed to better understand the ecological mechanism affecting the population dynamics of Canada geese in both urban and rural environments.

Arkansas↗
USGS2026

Predicted habitat use for reintroduced grizzly bears in the transboundary North Cascades ecosystem

Grizzly bears ( Ursus arctos ) were once numerous in the North Cascades transboundary region of Washington State (United States) and British Columbia (Canada); however, few remain today. To support ongoing reintroduction evaluations, we used simulations based on movement models developed in the Northern Rocky Mountains to predict habitat use by a small founding group of grizzly bears in the North Cascades during the early stages of reintroduction. These simulations represent movements across a spatially explicit landscape based on individual-specific selection and movement parameters. We first evaluated predictive performance in three nearby populations of grizzly bears in the Squamish-Lillooet, McGillvary Mountains, and North Stein-Nahatlatch regions of the Coast Mountains, British Columbia. After applying an elevation-based calibration, predicted habitat use showed strong agreement with GPS location data from 73 collared bears. Across population–sex subgroups, Spearman rank correlations were ≥0.95, with 69.2%–83.3% of locations occurring within the top five habitat classes (covering 50% of mapped habitat) and 17.1%–36.2% in the top class (representing 10%). Overall, 24.5% and 77.9% of locations fell within the top class and top five classes, respectively. Seasonal validation showed strongest predictive performance from May to late summer or fall. Model predictions for the North Cascades indicate that habitat use during early reintroduction is likely to be concentrated in the central and northern mountainous portions of the ecosystem along the US–Canada border. These maps can guide recovery planning when no local bear data are yet available in the North Cascades transboundary region.

British Columbia, Washington↗
USGS2026

Holistic understanding of uncertainty for collaborative and proactive global change decision making

Global change is accelerating and pushing the planet's ecosystems beyond the range of historical observations, creating increasing uncertainty in future system conditions. Despite general agreement that proactive environmental action is warranted, environmental decision conversations often end by identifying additional data needed to reduce uncertainty before taking novel action. Given the inherent uncertainty in complex issues such as global change, quantitative data alone are likely insufficient to support proactive environmental action. Holistic understanding of uncertainty includes scientific quantification of uncertainty paired with emotional responses and transcendental grounding to help people work together toward proactive action in uncertain decision contexts. Holistic understanding arises from the four ways in which humans perceive the world, termed the Four Realms: Physical (e.g., how I observe), Mental (e.g., how I think), Emotional (e.g., how I feel), and Transcendental (e.g., how I connect to greater meaning or purpose). Environmental scientists and decision makers are generally trained in Physical and Mental Realm observation and analysis, but not in how to apply Emotional and Transcendental Realm understanding. Emotional and Transcendental processing occurs in scientists and decision makers whether it is acknowledged or not and contributes to different people interpreting the same information in different ways. Thus, when the role of Emotional and Transcendental Realms in an individual's interpretation process is not understood, it can derail conversations and perpetuate the status quo. Explicitly recognizing all Four Realms can bring people together across differences and inspire shared, novel decision making even in increasing uncertainty. To illustrate the benefits of holistic understanding, we share stories from our experiences in environmental decision contexts. Because accessing the Four Realms requires experiential and embodied techniques, while still relying on core scientific tenets of observation and analysis, we also present techniques for readers to learn to feel their own emotional understanding and connect to their own transcendental understanding. Holistic understanding can enhance data-driven decisions by recognizing that human responses to uncertainty inherently include interactions between emotions, thoughts, transcendental connections, and behavior. Ultimately, holistic understanding can help anchor data-driven decisions in intra- and interpersonal connections, inspiring action in the face of uncertainty.

Ecosphere↗