A candle in the dark: Accounting for undetected species using Bayesian hierarchical models
Beasley, Emily M
Beasley, Emily M
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Abstract
Species occupancy, or the presence or absence of a species at a location, is important for informing conservation decisions. However, observations of ecological processes, such as occupancy, species richness, or species’ responses to environmental factors, are often biased due to observer error. Multi-species occupancy models (MSOMs) account for observer error by jointly modeling an ecological process— in this case, occupancy— and a survey process that is dependent on an ecological process. MSOMs can also model rare species by assuming all species come from a community-wide distribution: essentially, the rare species “borrow” information from those that are common. Using the same assumption, MSOMs can model species that were never detected during sampling using a method known as data augmentation. However, the lack of data for augmented species poses additional challenges: 1) augmentation provides no information about species identity, which must be assigned post analysis, and 2) the model tends to “pull” data-deficient species towards the center of the community distribution. Using a Bayesian approach may resolve these challenges by using informed priors for augmented species, but the efficacy of this method has yet to be tested. I will use simulated data to test whether the use of informed priors improves the accuracy of occupancy estimates. Specifically, I will compare the results of MSOMs using 1) uninformed priors, 2) informed priors, and 3) misinformed priors. Finally, I will apply data augmentation and informed priors to a real dataset of small mammal communities in Vermont to model undetected species that are known to occur in the study region.
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3:00pm-5:00pm
Graduate
Graduate
Date
2020-01-01