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Abstract Details
Activity Number:
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407
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #301078 |
Title:
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Estimating Open Population Site Occupancy Rates from Presence-Absence Data Lacking the Robust Design
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Author(s):
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David Dail*+ and Lisa Madsen
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Companies:
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Oregon State University and Oregon State University
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Address:
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Department of Statistics, Corvallis, OR, 97331,
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Keywords:
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Detection proability ;
Monitoring ;
N-mixture models ;
Open population ;
site occupancy
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Abstract:
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It is difficult to obtain accurate estimates of the site occupancy rates for a dynamic animal population when its presence during any survey period is detected with unknown probability. The existing likelihood models that account for unknown detection and allow open populations were proposed for the robust design setting, where several of the survey occasions occur within periods of known population closure. In this paper, we propose an alternative likelihood model that yields an estimator for the site occupancy rate during every survey period and does not require the robust design. We construct the marginal likelihood of the observed data by conditioning on, and summing out, the actual number of occupied sites during each survey period. A simulation study shows that on average the site occupancy rate estimates from the proposed model are less biased than the estimates from the original likelihood model. Both models are applied to a data set consisting of repeated presence- absence observations of American robins with yearly survey periods, with the proposed model yielding site occupancy rate estimates closer to the estimates obtained from a third model that allows point counts.
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