Hierarchical Units of Greater Sage-Grouse Populations Informing Wildlife Management Completed
Wildlife management boundaries frequently lack biological context, such as information on habitat resource availability and wildlife movements. To address this, we developed multiple levels of biologically relevant and hierarchically nested greater sage-grouse (Centrocercus urophasianus) population units that could facilitate management and conservation of populations and habitats.
Background
Wildlife populations are increasingly affected by local and regional landscape changes. These effects are direct (for example, habitat loss) and indirect (for example, noise, pollution, and traffic), potentially altering wildlife behavior or adaptability (for example, the loss of habitat connectivity, migration corridors, and breeding grounds). To improve wildlife management and conservation for sage-grouse, we developed methods that define hierarchically nested population units (Figure 1) based on habitat preferences and population connectivity. We worked closely with 11 state wildlife agencies to generate population/management units spanning political boundaries, allowing for the analysis of population monitoring data collected at sage-grouse breeding sites (leks) across space and time. The multi-scale approach supports assessing population changes at local and regional scales, thereby allowing managers to consider local and regional differences in connected populations.
Methods
We used information on potential connectivity (in other words, typical dispersal and movement distances) and functional connectivity (in other words, the willingness of individuals to move among preferred habitats) to describe the degree of subpopulation connectivity, defined as population structure (Figure 21). Population structure was informed from proximity of neighboring leks and habitat preferences, such as elevation and sagebrush cover (habitat), and avoidance of rugged terrain, large water bodies and inundated salt flats, and tree canopy cover. We then used these results and habitat conditions of terrain indices (5), percent cover of shrubland vegetation communities (10), and bioclimatic variables (5) summarized at leks (distances of 30 m to 6,400 m) to inform biologically meaningful groupings of leks across the study region.2 This lek aggregation requires consideration of population structure and conditions that maximize habitat similarities within population units and maximize habitat dissimilarities between population units.
Results
We developed 13 hierarchically nested sage-grouse cluster levels (Figure 3), each level representing a collection of subpopulations. The smallest population units (cluster level two) capture more than 92% of individual movements, where each bird is assigned to a single population. The evaluation of movements suggests that aggregating population demographic data at cluster level two will best account for capturing geographically closed population units (important for analyzing population demographic data) and can increase the precision of sage-grouse population models that inform wildlife management.
Research implications
Our analysis of population structure describes the relative importance of local populations and where to avoid landscape disturbances that may negatively affect population connectivity. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes, and habitat connectivity can provide information on dispersal and biodiversity. The hierarchical population units are supporting investigations of factors affecting population demographics while considering multiple scales and while reducing constraints of artificially defined jurisdictional boundaries. Therefore, these products will greatly assist state and federal agencies in making informed, targeted, and cost-effective decisions within an adaptive management framework, allowing managers to make real-time management decisions using population data.
Funders
U.S. Geological Survey (Ecosystem Mission Area, Status and Trends Program, Wyoming Landscape Conservation Initiative) and U.S. Bureau of Land Management.
Partners
California Department of Fish and Wildlife, Colorado Parks and Wildlife, Idaho Department of Fish and Game, Montana Fish, Wildlife & Parks, Nevada Department of Wildlife, North Dakota Game and Fish Department, Oregon Department of Fish and Wildlife, South Dakota Department of Game, Fish and Parks, Utah Division of Wildlife Resources, Wyoming Game and Fish Department, Washington Department of Fish and Wildlife, Bureau of Land Management, US Fish and Wildlife Service, US Forest Service, and Colorado State University.
References
1. O'Donnell, M. S., D. R. Edmunds, C. L. Aldridge, J. A. Heinrichs, A. P. Monroe, P. S. Coates, B. G. Prochazka, S. E. Hanser, L. A. Wiechman, T. J. Christiansen, A. A. Cook, S. P. Espinosa, L. J. Foster, K. A. Griffin, J. L. Kolar, K. S. Miller, A. M. Moser, T. E. Remington, T. J. Runia, L. A. Schreiber, M. A. Schroeder, S. J. Stiver, N. I. Whitford, and C. S. Wightman. 2021. Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study: Ecological Informatics, v. 63, p. 1-16, https://doi.org/10.1016/j.ecoinf.2021.101327.
2. O'Donnell, M. S., D. R. Edmunds, C. L. Aldridge, J. A. Heinrichs, A. P. Monroe, P. S. Coates, B. G. Prochazka, S. E. Hanser, and L. A. Wiechman. 2022. Defining biologically relevant and hierarchically nested population units to inform wildlife management. Ecology and Evolution 12:22, https://doi.org/10.1002/ece3.9565.
Wildlife management boundaries frequently lack biological context, such as information on habitat resource availability and wildlife movements. To address this, we developed multiple levels of biologically relevant and hierarchically nested greater sage-grouse (Centrocercus urophasianus) population units that could facilitate management and conservation of populations and habitats.
Background
Wildlife populations are increasingly affected by local and regional landscape changes. These effects are direct (for example, habitat loss) and indirect (for example, noise, pollution, and traffic), potentially altering wildlife behavior or adaptability (for example, the loss of habitat connectivity, migration corridors, and breeding grounds). To improve wildlife management and conservation for sage-grouse, we developed methods that define hierarchically nested population units (Figure 1) based on habitat preferences and population connectivity. We worked closely with 11 state wildlife agencies to generate population/management units spanning political boundaries, allowing for the analysis of population monitoring data collected at sage-grouse breeding sites (leks) across space and time. The multi-scale approach supports assessing population changes at local and regional scales, thereby allowing managers to consider local and regional differences in connected populations.
Methods
We used information on potential connectivity (in other words, typical dispersal and movement distances) and functional connectivity (in other words, the willingness of individuals to move among preferred habitats) to describe the degree of subpopulation connectivity, defined as population structure (Figure 21). Population structure was informed from proximity of neighboring leks and habitat preferences, such as elevation and sagebrush cover (habitat), and avoidance of rugged terrain, large water bodies and inundated salt flats, and tree canopy cover. We then used these results and habitat conditions of terrain indices (5), percent cover of shrubland vegetation communities (10), and bioclimatic variables (5) summarized at leks (distances of 30 m to 6,400 m) to inform biologically meaningful groupings of leks across the study region.2 This lek aggregation requires consideration of population structure and conditions that maximize habitat similarities within population units and maximize habitat dissimilarities between population units.
Results
We developed 13 hierarchically nested sage-grouse cluster levels (Figure 3), each level representing a collection of subpopulations. The smallest population units (cluster level two) capture more than 92% of individual movements, where each bird is assigned to a single population. The evaluation of movements suggests that aggregating population demographic data at cluster level two will best account for capturing geographically closed population units (important for analyzing population demographic data) and can increase the precision of sage-grouse population models that inform wildlife management.
Research implications
Our analysis of population structure describes the relative importance of local populations and where to avoid landscape disturbances that may negatively affect population connectivity. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes, and habitat connectivity can provide information on dispersal and biodiversity. The hierarchical population units are supporting investigations of factors affecting population demographics while considering multiple scales and while reducing constraints of artificially defined jurisdictional boundaries. Therefore, these products will greatly assist state and federal agencies in making informed, targeted, and cost-effective decisions within an adaptive management framework, allowing managers to make real-time management decisions using population data.
Funders
U.S. Geological Survey (Ecosystem Mission Area, Status and Trends Program, Wyoming Landscape Conservation Initiative) and U.S. Bureau of Land Management.
Partners
California Department of Fish and Wildlife, Colorado Parks and Wildlife, Idaho Department of Fish and Game, Montana Fish, Wildlife & Parks, Nevada Department of Wildlife, North Dakota Game and Fish Department, Oregon Department of Fish and Wildlife, South Dakota Department of Game, Fish and Parks, Utah Division of Wildlife Resources, Wyoming Game and Fish Department, Washington Department of Fish and Wildlife, Bureau of Land Management, US Fish and Wildlife Service, US Forest Service, and Colorado State University.
References
1. O'Donnell, M. S., D. R. Edmunds, C. L. Aldridge, J. A. Heinrichs, A. P. Monroe, P. S. Coates, B. G. Prochazka, S. E. Hanser, L. A. Wiechman, T. J. Christiansen, A. A. Cook, S. P. Espinosa, L. J. Foster, K. A. Griffin, J. L. Kolar, K. S. Miller, A. M. Moser, T. E. Remington, T. J. Runia, L. A. Schreiber, M. A. Schroeder, S. J. Stiver, N. I. Whitford, and C. S. Wightman. 2021. Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study: Ecological Informatics, v. 63, p. 1-16, https://doi.org/10.1016/j.ecoinf.2021.101327.
2. O'Donnell, M. S., D. R. Edmunds, C. L. Aldridge, J. A. Heinrichs, A. P. Monroe, P. S. Coates, B. G. Prochazka, S. E. Hanser, and L. A. Wiechman. 2022. Defining biologically relevant and hierarchically nested population units to inform wildlife management. Ecology and Evolution 12:22, https://doi.org/10.1002/ece3.9565.