How we count counts: Examining influences on detection during shoreline surveys of marine debris

Abstract

Shoreline surveys are a common approach for documenting loads of marine macrodebris (≥ 2.5 cm). When surveys are conducted repeatedly over time and space, patterns in source, abundance, geographic distribution, and composition can be detected. Yet to realize their full potential, monitoring programs that rely on surveys must grapple with high variability in debris abundance, and appropriately manage uncertainty when reporting estimates of debris quantity. A potentially important source of bias in estimating debris loads from shoreline monitoring datasets is variability in debris detection rates. With this in mind, we conducted field experiments using common strip-transect marine debris survey protocols, designed to test detection of macrodebris. We quantified how protocol, shoreline, and debris characteristics influence the detectability of marine macrodebris. Detection rates varied according to debris distance from observer (0–5 m), number of observers, debris characteristics (size, color), and shoreline substrate. Our results highlight considerations for monitoring program design. Comparisons across datasets should be approached cautiously given differences in survey protocols and sources of bias that may affect debris density estimates should be quantified and addressed. We hope these results will inform marine debris monitoring efforts that are optimized for intended data use and impact. • Debris size, color, and distance from observer influence macrodebris detection • Detection is also affected by substrate and number of observers performing surveys • Counts are minimums especially for small (2.5 - 5 cm) and dull colored items • Datasets from different survey methods may be biased in different ways • Adjustments to minimize or account for error will depend on study goals

Topics & Concepts