Occupancy models provide a reliable method of estimating species distributions while accounting for imperfect detectability. The cost of accounting for false absences is that detection and nondetection surveys typically require repeated visits to a site or multiple-observer techniques. More efficient methods of collecting data to estimate detection probabilities would allow additional sites to be surveyed for the same amount of effort, which would support more precise estimation of covariate effects to improve inference about underlying ecological processes. Time-to-detection surveys allow the estimation of detection probability based on a single site visit by one observer, and therefore might be an efficient technique for herpetological occupancy studies. We evaluated the use of time-to-detection surveys to estimate the occupancy of pond-breeding amphibians at Point Reyes National Seashore, California, USA, including variables that affected detection rates and the probability of occurrence. We found that detection times were short enough, and occupancy was high enough, to estimate reliably the probability of occurrence of three pond-breeding amphibians at Point Reyes National Seashore, and that survey and site conditions had species-specific effects on detection rates. In particular, pond characteristics affected detection times of all commonly detected species. Probability of occurrence of Sierran Treefrogs (Hyliola sierra) and Rough-Skinned Newts (Taricha granulosa) was negatively related to the detection of fish and pond area. Time-to-detection surveys can provide an efficient method for estimating detection probabilities and accounting for false absences in occupancy studies of reptiles and amphibians.
|Publication Subtype||Journal Article|
|Title||Time-to-detection occupancy modeling: An efficient method for analyzing the occurrence of amphibians and reptiles|
|Series title||Journal of Herpetology|
|Publisher||The Society for the Study of Amphibians and Reptiles|
|Contributing office(s)||Western Ecological Research Center|
|Other Geospatial||Point Reyes National Seashore|
|Google Analytics Metrics||Metrics page|