title: Temporally adaptive sampling: a case study in rare species survey design with marbled salamanders (Ambystoma opacum).
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.
authors: Biology Department, Bryn Mawr College, Bryn Mawr, Pennsylvania, United States of America.
authors: Noah D
authors: ND
authors: Charney
authors: Natural Heritage & Endangered Species Program, Division of Fisheries and Wildlife, Westborough, Massachusetts, United States of America.
authors: Jacob E
authors: JE
authors: Kubel
authors: Northfield, Massachusetts, United States of America.
authors: Charles S
authors: CS
authors: Eiseman
mesh_terms: D000557:Ambystoma; D000818:Animals; D008404:Massachusetts; D012494:Sampling Studies; D062211:Spatio-Temporal Analysis