Abstract

Improving detection rates for elusive species with clumped distributions is often accomplished through adaptive sampling designs. This approach can be extended to include species with temporally variable detection probabilities. By concentrating survey effort in years when the focal species are most abundant or visible, overall detection rates can be improved. This requires either long-term monitoring at a few locations where the species are known to occur or models capable of predicting population trends using climatic and demographic data. For marbled salamanders (Ambystoma opacum) in Massachusetts, we demonstrate that annual variation in detection probability of larvae is regionally correlated. In our data, the difference in survey success between years was far more important than the difference among the three survey methods we employed: diurnal surveys, nocturnal surveys, and dipnet surveys. Based on these data, we simulate future surveys to locate unknown populations under a temporally adaptive sampling framework. In the simulations, when pond dynamics are correlated over the focal region, the temporally adaptive design improved mean survey success by as much as 26% over a non-adaptive sampling design. Employing a temporally adaptive strategy costs very little, is simple, and has the potential to substantially improve the efficient use of scarce conservation funds.

Highlights

  • Improving sampling designs for difficult-to-detect rare species has been the focus of much recent work [1,2,3,4,5,6]

  • Our results demonstrate the potential utility of a temporally adaptive sampling framework

  • For species with inherently low detectability and substantial population fluctuations correlated across a region, concentrating sampling effort in the most productive years maximizes detection rates

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Summary

Introduction

Improving sampling designs for difficult-to-detect rare species has been the focus of much recent work [1,2,3,4,5,6]. Imperfect detection is a critical problem when field data are used for a variety of tasks, including analyzing habitat associations, untangling metapopulation dynamics, and managing resources [4, 7,8,9,10]. To improve both modeling accuracy and the efficient use of limited conservation resources, researchers seek field methods that maximize detection probability. Adaptive sampling has long been advocated as an appropriate strategy for surveying species with clumped distributions [11,12,13,14,15]. The number and distribution of PLOS ONE | DOI:10.1371/journal.pone.0120714 March 23, 2015

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