Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Nevada and Wyoming, Interim
Dates
Publication Date
2020-01-02
Time Period
2018
Citation
O'Donnell, M., Edmunds, D.R., Aldridge, C., Heinrichs, J.A., Coates, P.S., Prochazka, B.G., and Hanser, S., 2020, Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Nevada and Wyoming, Interim: U.S. Geological Survey data release, https://doi.org/10.5066/P9J0B7JR.
Summary
We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization [...]
Summary
We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different population growth rates among smaller clusters. Equally so, the spatial structure and ecological organization driving scale-dependent systems in a fragmented landscape affects dispersal behavior, suggesting inclusion in population monitoring frameworks. Studies that compare conditions among spatially explicit hierarchical clusters may elucidate the cause of differing growth rates, indicating the appropriate location and spatial scale of a management action.
The data presented here reflect the results from developing a hierarchical monitoring framework and then applying these methods to Greater Sage-grouse in Nevada and Wyoming, US. When using these data for evaluating population changes or when identifying a spatially balanced sampling protocol, all cluster levels are designed to work together and therefore we recommend evaluating multiple cluster levels prior to selecting a single cluster level, if a single scale is desired, when analyzing population growth rates or other analyses, as these data are intended for multi-scale efforts. In other words, let your data decide which scale(s) are appropriate for the given species. These cluster levels are specific to Greater Sage-grouse but they may be appropriate for other sagebrush obligate species, but the user will need to make this determination.
The products from this study aim to support multiple research and management needs. However, these data represent an interim data product because there may be errors associated with clusters along the edges of the state boundaries (due to the lack of lek data in neighboring states). We are planning to release new data that we will develop for the Greater sage-grouse range. We recommend using the new data products once available instead of these data products.
These data will remain online as they are associated with the following citation, which provides a detailed explanation of the methods used to develop these data:
O’Donnell, Michael S., David R. Edmunds, Cameron L. Aldridge, Julie A. Heinrichs, Peter S. Coates, Brian G. Prochazka, and Steve E. Hanser. 2019. Designing multi-scale hierarchical monitoring frameworks for wildlife with high site fidelity to support conservation: a sage-grouse case study. Ecosphere
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Related External Resources
Type: Related Primary Publication
O’Donnell, M. S., Edmunds, D. R., Aldridge, C. L., Heinrichs, J. A., Coates, P. S., Prochazka, B. G., & Hanser, S. E. (2019). Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study. Ecosphere, 10(9). doi:10.1002/ecs2.2872
Considering range-wide declines in sage-grouse populations and regional variability of population sizes, targeted management actions are needed at spatial scales that align with factors causing population change. There is a need to better understand mechanisms driving population changes, allowing for targeted management actions to help conserve populations. Yet, to our knowledge, repeatable multi-scaled and biologically-informed methods to support population monitoring of sage-grouse has yet to be developed. We developed a biologically-informed approach to clustering habitat and population units to improve opportunities for multi-scale monitoring and evaluation of broadly distributed populations using a spatially balanced framework. In doing so, we aimed to (1) use a statistical and repeatable approach, (2) include biologically relevant landscape and habitat characteristics, and we desired a framework that (3) is spatially hierarchical, (4) discretizes the landscape while capturing connectivity (habitat and movements), and (5) supports management questions at different spatial scales.
Several intended uses of these sampling units include the following:
1. The sampling units can inform a spatially balanced monitoring framework (for example, Generalized Random Tessellation Stratified sampling framework).
2. The sampling units can inform groupings of population counts, which in turn can be used to evaluate population growth rates.
3. The sampling units can inform the examination of relationships between the groupings of quantified landscape changes and population changes.
4. The sampling units can inform the partitioning of the landscape for developing seasonal habitat model development.
