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Activity Number:
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577
- Statistical Models in Ecology
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #306765
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Title:
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Accounting for Location Uncertainty in Model-Based Distance Sampling Methods
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Author(s):
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Trevor Hefley* and Alice Boyle and Narmadha Mohankumar
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Companies:
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Kansas State University and Kansas State University and Kansas State University
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Keywords:
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ecological statistics;
inhomogeneous point process;
integrated population model;
spatio-temporal statistics
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Abstract:
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Distance sampling of plants and animals is commonly used to obtain estimates of abundance while correcting for errors in detection. Model-based approaches allow broader use of distance sampling data but require that the exact location of each individual is recorded. From conception, distance sampling data collection was envisaged as recording the distances from a point or line transect and the detected individuals. Consequently, the exact location of individuals is rarely recorded. We show how model-based methods for distance sampling data can be extended enabling inference for individual-level species-habitat relationships when the exact location is not recorded. We illustrate our approach using a simulation experiment and by modeling the habitat use of Dickcissels (Spiza americana) using data collected for a long-term ecological experiment.
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Authors who are presenting talks have a * after their name.
