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150325s2007 xx |||||o 00| ||eng c |
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|a (DE-627)JST087440326
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|a (JST)20204086
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Tredick, Catherine A.
|e verfasserin
|4 aut
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|a Sub-Sampling Genetic Data to Estimate Black Bear Population Size: A Case Study
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|c 2007
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|a Text
|b txt
|2 rdacontent
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|a Computermedien
|b c
|2 rdamedia
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|a Online-Ressource
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|2 rdacarrier
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|a Costs for genetic analysis of hair samples collected for individual identification of bears average approximately US$50 [2004] per sample. This can easily exceed budgetary allowances for large-scale studies or studies of high-density bear populations. We used 2 genetic datasets from 2 areas in the southeastern United States to explore how reducing costs of analysis by sub-sampling affected precision and accuracy of resulting population estimates. We used several sub-sampling scenarios to create subsets of the full datasets and compared summary statistics, population estimates, and precision of estimates generated from these subsets to estimates generated from the complete datasets. Our results suggested that bias and precision of estimates improved as the proportion of total samples used increased, and heterogeneity models (e.g., $M_{h[\text{CHAO}]}$ ) were more robust to reduced sample sizes than other models (e.g., behavior models). We recommend that only high-quality samples (>5 hair follicles) be used when budgets are constrained, and efforts should be made to maximize capture and recapture rates in the field.
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|a Copyright 2007 International Association for Bear Research and Management
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|a American black bear
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|a budget constraints
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|a noninvasive genetic sampling
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|a population estimates
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|a sub-sampling
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|a Ursus americanus
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|a Social sciences
|x Population studies
|x Population estimates
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|a Information science
|x Data products
|x Datasets
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|a Biological sciences
|x Biology
|x Zoology
|x Animals
|x Mammals
|x Bears
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Statistical theories
|x Estimation theory
|x Estimation bias
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|a Biological sciences
|x Biology
|x Genetics
|x Genotypes
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|a Biological sciences
|x Biology
|x Zoology
|x Animals
|x Mammals
|x Bears
|x Black bears
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|a Social sciences
|x Population studies
|x Population characteristics
|x Population size
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|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Statistical results
|x Statistical properties
|x Estimate reliability
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|
4 |
|a Mathematics
|x Applied mathematics
|x Statistics
|x Applied statistics
|x Inferential statistics
|x Statistical estimation
|x Estimation methods
|x Estimators
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|a Biological sciences
|x Biology
|x Genetics
|x Population genetics
|x Ecological genetics
|x Population Estimation and Trends
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|a research-article
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1 |
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|a Vaughan, Michael R.
|e verfasserin
|4 aut
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1 |
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|a Stauffer, Dean F.
|e verfasserin
|4 aut
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1 |
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|a Simek, Stephanie L.
|e verfasserin
|4 aut
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1 |
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|a Eason, Thomas
|e verfasserin
|4 aut
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0 |
8 |
|i Enthalten in
|t Ursus
|d International Association for Bear Research and Management, 1998
|g 18(2007), 2, Seite 179-188
|w (DE-627)503327328
|w (DE-600)2210269-3
|x 19385439
|7 nnns
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1 |
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|g volume:18
|g year:2007
|g number:2
|g pages:179-188
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|u https://www.jstor.org/stable/20204086
|3 Volltext
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|d 18
|j 2007
|e 2
|h 179-188
|