We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
How we count counts: Examining influences on detection during shoreline surveys of marine debris
AI summary Read the abstract
Researchers conducted field experiments to measure how accurately volunteers detect marine debris during shoreline surveys, finding that detection rates varied based on distance from the observer, debris color and size, and the type of shoreline surface. The study warns that inconsistent survey protocols make it difficult to compare debris data across studies and calls for more standardized monitoring approaches.
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.
More Papers Like This
Uncertainties in Visual Observations of Floating Riverine Plastic
AI summary Read the abstract
This study quantified the uncertainty in visual observation methods for monitoring floating plastic debris in rivers, finding that observer variability, counting distance, and lighting conditions introduce substantial error margins that are rarely acknowledged in published monitoring data.
Rocky shoreline protocols miss microplastics in marine debris surveys (Fogo Island, Newfoundland and Labrador)
AI summary Read the abstract
This study amended a standardized marine debris survey protocol for rocky shorelines, finding that standard sandy-beach methods underestimate litter on cobble beaches because plastics become buried under rocks. The findings highlight the need for habitat-specific monitoring protocols to accurately assess plastic contamination in diverse coastal environments.
Investigation of microplastic debris in marine surface waters using different sampling methods
AI summary Read the abstract
This thesis compared multiple sampling devices and methods for collecting and characterizing buoyant microplastic debris in marine surface waters, examining how sampling choices affect estimates of abundance and composition. Methodological standardization is needed to enable meaningful comparisons of microplastic levels across different marine studies.
Towards a North Pacific long-term monitoring program for ocean plastic pollution: A systematic review and recommendations for shorelines
AI summary Read the abstract
This systematic review examines shoreline monitoring methods for ocean plastic pollution in the North Pacific. Shorelines act as collection points for marine debris, but current studies often lack consistent methods, making it hard to compare results and understand the true scale of plastic pollution that eventually breaks into microplastics.
Increasing our understanding of coastal microplastics and mesoplastics: a comparison of sampling methodologies using volunteer researchers
AI summary Read the abstract
Researchers compared three different methods for sampling coastal microplastic and mesoplastic pollution using trained volunteers at three locations in southern England. They found that one citizen science method, the Big Microplastic Survey, consistently detected more plastic pollution and fewer zero counts than the other approaches. The study underscores the challenge of standardizing sampling methods and the importance of enabling meaningful comparisons across global monitoring efforts.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.