Sammendrag
The three-year research project “Quantum Leap” was recently completed in Norway. The project utilised citizen science data from beach litter cleanups 2015-2019 in combination with two separate professional data collection initiatives: (1) randomised quantitative sampling to determine spatial patterns in beach litter densities, and (2) beach litter deep dives to investigate litter in-depth for source identification and dating. In this presentation the authors present a case study with the results of the evaluation of the national citizen science data collection initiative with respects to data quality, current potential uses, suggested changes, and possible future roles in monitoring. Norway’s national beach cleanup citizen science data collection initiative was run by the NGO Keep Norway Beautiful for many years and transferred to the governmental Norwegian Centre against Marine Litter in 2020. The program has many strengths with respects to well established, relatively simple protocols available through a user-friendly online portal and app. However, the program also has limitations in terms of poorly communicated research goals and pre-planned data usages, and a lack of recorded metadata and effort data. The latter in particular limits the possible uses of the present dataset. Trend analyses, both in space and time, are statistically challenging despite a very large dataset. Citizen science data were, however, used successfully to identify common “worst offender” litter items in different geographic regions. An inherent challenge with data collected in relation to cleanup effort, irrespective of whether the data are collected by citizen scientists or professionals, is the targeted or opportunistic nature of site selection where cleanup crews purposefully select polluted or conveniently accessible beaches. Consequently, site selection is not probabilistic and cannot be considered representative of the coastline as a whole, and if the data are used quantitatively to estimate average litter densities these will generally be overestimates as clean and low-density locations are typically excluded from the dataset. This is evident when comparing the citizen science data to quantitative sampling in the same regions. The citizen science data tied to cleanup actions are therefore not readily suitable for monitoring changes in overall litter densities. However, with some adjustments to the protocol, the program could be highly useful in monitoring other aspects of beach litter pollution levels, particularly pertaining to specific litter sources and categories.
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