Time-to-Detection Occupancy Modeling: An Efficient Method for Analyzing the Occurrence of Amphibians and Reptiles
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.Abstract
Location of the study area and sampled ponds at Point Reyes National Seashore, California, USA, 2017.
Sierran Treefrog (Hyliola sierra) detection rate (λ) based on (A) pond area, (B) pond complexity, and (C) percent emergent vegetation. (D) California Red-Legged Frog (Rana draytonii) λ based on mean pond depth, and Rough-Skinned Newt (Taricha granulosa) λ based on (E) pond area and (F) survey date at Point Reyes National Seashore, California, USA, 2017. All plots hold other variables constant at their mean values. Bold lines represent posterior medians; the gray-shaded areas represent posterior 95% credible intervals. Tick marks along the x-axis indicate observed values.
Example detection probability (p) curves based on survey duration representing the effects of (A) percent emergent vegetation for Sierran Treefrogs (Hyliola sierra) and (B) survey date for Rough-Skinned Newts (Taricha granulosa) at Point Reyes National Seashore, California, USA, 2017. In (A) the black solid line represents a pond with 0% emergent vegetation cover, and the gray dashed line represents a pond with 75% emergent vegetation cover; in (B) the black solid line represents a survey conducted on 1 May, and the gray dashed line represents a survey conducted on 1 July. In both cases, other variables were held constant at their mean values. Bold lines represent posterior medians; light lines represent posterior 95% credible intervals.
Sierran Treefrog (Hyliola sierra; A,B) probability of occurrence (ψ) based on (A) fish detection and (B) pond area, and Rough-Skinned Newt (Taricha granulosa) ψ based on (C) fish detection and (D) pond area at Point Reyes National Seashore, California, USA, 2017. Circles and bold lines represent posterior medians; error bars and the gray shaded area represent posterior 95% credible intervals. Tick marks along the x-axis in (B) and (D) indicate the area of surveyed ponds.
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