Editorial Type:
Article Category: Research Article
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Online Publication Date: 01 Mar 2016

Estimating Spring Salamander Detection Probability Using Multiple Methods

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Page Range: 126 – 129
DOI: 10.1670/15-041
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Abstract

Many studies suffer from imperfect detection probability, i.e., species are not detected when individuals may be present. In occupancy studies, detection probability is often treated as a nuisance variable. When used as a primary variable of interest, detection probability can be examined as a function of sampling covariates with the goal of maximizing the probability of encountering target species. Efforts to determine which methods maximize detection probability will benefit monitoring programs, particularly for species that are difficult to detect. We used three sampling methods, leaf litter bag (LLB) surveys, visual encounter surveys (VES), and flip and search (FS) methods to detect larval Spring Salamanders (Gyrinophilus porphyriticus). We estimated occasion-specific estimates of detection and used an analysis of variance to determine if detection probability varied among sampling methods. We found the FS method yielded higher detection estimates than did the LLB and VES. In addition, occupancy estimates derived from FS sampling changed drastically when compared among other single-method models, suggesting that LLB and VES gave biased estimates of occupancy related to a low probability of detecting Spring Salamanders at occupied sites. Furthermore, our results suggest the FS method provided higher detection probability estimates as compared to estimates derived from models that combined all sampling methods. In conclusion, efforts to monitor Spring Salamanders should rely on FS for sampling populations to maximize detection probability to reduce costs and increase effectiveness for large, widespread research projects.

Copyright: Copyright 2016 Society for the Study of Amphibians and Reptiles 2016
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Fig . 1

Study site locations in the Monongahela National Forest, West Virginia. Sites were located in Pocahontas, Greenbrier, and Randolph counties; shading represents the Monongahela National Forest boundary.


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Fig . 2

Sampling scheme: (A) We sampled three stream segments (rectangles) per stream. Two stream segments were located 100 m apart and the upstream (control) segments were separated by the treatment segments by a random distance of 120–150 m. (B) At each stream segment (rectangle), we placed three LLBs 4 m apart upstream of VES and FS locations. We conducted LLB and VES surveys in a 10-m stretch of the stream segment. Directly below VES, we chose five, 25-m2 areas to conduct FS sampling (we sampled each 25-m2 area once).


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Fig . 3

Results of the ANOVA comparing detection probabilities derived from constant models (ψ(.), p(.)) containing presence–absence data for each method (n = 5). FS = flip and search methods; LLB = leaf litter bag surveys, VES = visual encounter surveys.


Contributor Notes

Corresponding Author. E-mail: eedwards333@gmail.com
Accepted: 14 May 2015
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