Abstract
Avian point counts vary over space and time due to actual differences
in abundance, differences in detection probabilities among counts, and differences
associated with measurement and misclassification errors. However, despite the
substantial time, effort, and money expended counting birds in ecological research
and monitoring, the validity of common survey methods remains largely untested,
and there is still considerable disagreement over the importance of estimating detection
probabilities associated with individual counts. Most practitioners assume that
current methods for estimating detection probability are accurate, and that observer
training obviates the need to account for measurement and misclassification errors
in point count data. Our approach combines empirical data from field studies with
field experiments using a system for simulating avian census conditions when most
birds are identified by sound. Our objectives are to: identify the factors that influence
detection probability on auditory point counts, quantify the bias and precision
of current sampling methods, and find new applications of sampling theory
and methodologies that produce practical improvements in the quality of bird
census data.
We have found that factors affecting detection probabilities on auditory counts,
such as ambient noise, can cause substantial biases in count data. Distance sampling
data are subject to substantial measurement error due to the difficulty of estimating
the distance to a sound source when visual cues are lacking. Misclassification errors
are also inherent in time of detection methods due to the difficulty of accurately
identifying and localizing sounds during a count. Factors affecting detection probability, measurement errors, and misclassification errors are important but often ignored components of the uncertainty associated with point-count-based abundance
estimates.
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