Item talk:Q270791
From geokb
{
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"name": "Defining fine-scaled population structure among continuously distributed populations",
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"value": "70234150",
"url": "https://pubs.usgs.gov/publication/70234150"
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"value": "10.1111/2041-210X.13949",
"url": "https://doi.org/10.1111/2041-210X.13949"
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"journal": {
"@type": "Periodical",
"name": "Methods in Ecology and Evolution",
"volumeNumber": "13",
"issueNumber": "10"
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"inLanguage": "en",
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"name": "Methods in Ecology and Evolution"
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"datePublished": "2022",
"dateModified": "2022-10-17",
"abstract": "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. To facilitate the use of wildlife monitoring data for improved adaptive management, we developed a novel approach to define hierarchical tiers (multiple scales) of population structure.We defined population structure by combining graph theory with biological inference about dispersal capability (based on movement, gene flow, and habitat condition) and functional processes affecting movement (e.g. habitat selection across scales of landscape preferences). First, we developed least-cost paths between high fidelity sites (habitat patches) using a cost surface, informed from functional processes of habitat characteristics to account for resistance of inter-patch movements. Second, we combined the paths into a multi-path graph construct. Third, we used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into hierarchical tiers of connected subpopulations, denoting the degree that dispersal affected population structure.As a case study, we applied our approach across the greater sage-grouse (Centrocercus urophasianus) range, a species of conservation concern in western United States. We described the relative importance of local populations and where to potentially avoid landscape disturbances that may negatively affect population connectivity using centrality measures supported by graph theory, and we demonstrated close alignment of the resulting population structure with population densities.This method can be adapted for other species with site fidelity and used as a management tool to evaluate population trends and responses to landscape changes across different temporal and spatial scales.",
"description": "14 p.",
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"name": "British Ecological Society"
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"name": "Hanser, Steve E. shanser@usgs.gov",
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"name": "Fort Collins Science Center",
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"name": "Office of the AD Ecosystems",
"url": "https://www.usgs.gov/mission-areas/ecosystems"
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"name": "National Climate Change and Wildlife Science Center",
"url": null
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