Evaluation of a qualitative survey for early detection monitoring of New Zealand mudsnail

• Published in: Management of biological invasions 2021-06, Vol.12 (2), p.344-362
• Main Authors: Tank, Samantha, Herbst, Seth, Hayes, Daniel
• Format: Article
• Language: English
• Subjects: Abundance; Environmental monitoring; Introduced species; Invasive species; Rare species; Sampling
• ISSN: 1989-8649; 1989-8649
• DOI: 10.3391/mbi.2021.12.2.09

Early detection of an invasive species is the first critical step to managing their invasion. New Zealand mudsnails (Potamopyrgus antipodarum; hereafter NZMS) are a small gastropod native to New Zealand and a documented worldwide invader. Although many approaches for sampling NZMS have been used, no protocol has emerged as a standard for early detection monitoring in lotic environments. In order to document the occurrence of NZMS, we developed a qualitative sampling methodology and evaluated its effectiveness. The sampling methodology involved conducting two searcher visual surveys along stream margins in a 50-meter search range for a standard 20-minute search time. Qualitative estimates of abundance and the time when the first detection was made were documented by each searcher. We conducted a total of 227 surveys in 12 rivers in Michigan, USA in 2017. Survey data were analyzed using an occupancy model framework, resulting in a per survey detectability exceeding 96%. We ran the occupancy model in an atypical fashion to explore the impact that relative abundance had on detectability. As relative abundance increased, detectability of NZMS increased. We modeled shorter survey durations (i.e., 15, 10, and 5 minutes) to determine their impact on detectability and found that less than 2% of detections were lost when reducing the survey length from 20 to 5 minutes. We developed a novel decision support tool to help understand how the number of sites surveyed, the site level detection probability, and the occupancy level of a rare species interact to determine the overall probability of detection of a new invader. The decision support table can help guide sampling design choices by giving insight into what combination of choices provides the overall highest probability of detection across all sites combined.