Comparison of Sampling Techniques to Monitor Stream Amphibian Communities in Forested Regions of Alberta

Authors
Bev Gingras
Cynthia Paszkowski
Garry Scrimgeour
Sharon Kendall
Resource Date:
2000
Page Length
25

The objective of this study was to compare the effectiveness of four commonly used sampling techniques (pitfall traps, funnel traps, visual searches and call surveys) to detect stream amphibian communities along similar reaches of two boreal forest streams. Results from the four protocols were standardized in terms of catch–per-unit effort (CPUE). Standardization allows techniques to be evaluated in a larger context, correcting for differences in the number of traps deployed or the duration of sampling. CPUE can be the basis of comparing the information produced by different techniques, as well as, the time and money invested. In addition, CPUE results can be compared to determine if seasonal differences occur in amphibian presence or detectability. We also include results from an amphibian monitoring study completed at Elk Island National Park to provide some general comparisons of pitfall and funnel trap effectiveness for monitoring amphibians in upland areas of the aspen parkland ecoregion.

While an array of techniques can be used to monitor stream amphibian communities, the results from our study indicate that the visual survey technique is the most cost and information efficient to monitor stream amphibians in boreal forest sites.

Assuming that these results are applicable to other streams, we recommend that AFBMP monitor stream amphibians using visual surveys. Because catch rates of amphibians were highest in June and declined almost two-fold by August, we suggest that surveys be completed in June and July. To maximize the probability of detecting at least one individual, surveys should extend for at least 50 m. However, we suggest that surveys extend for 200 m during the initial years of the program, so that the relationship between effort and capture rates can be tested on independent and broader data sets.