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Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Nevada), Interim

Dates

Publication Date
Time Period
2018

Citation

O'Donnell, M., Edmunds, D.R., Aldridge, C., Heinrichs, J.A., Coates, P.S., Prochazka, B.G., and Hanser, S., 2019, 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

nv_lvl7_coarsescale: Nevada hierarchical cluster level 7 (coarse-scale) for Greater sage-grouse 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 [...]

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Shapefile: nv_lvl7_coarsescale.zip
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Purpose

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.

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