Replicate analyses of OSPAR beach litter data.

Replicate surveys of beach litter have seldom been performed in the past. In this study, replicate surveys of beach litter were conducted on the beach north of Hörnum (Sylt, Germany), from 2015 to 2019, applying a slightly modified OSPAR protocol of beach litter monitoring. Descriptive statistics and power analyses were calculated on data resulting from these replicate surveys, to find out whether the scatter of replicate beach litter data decreases and the statistical power increases with increasing numbers of replicate surveys. From 2015 to 2019, mean total abundances, given as numbers of litter items, ranged from 19 to 185 litter items on a 50 m section of beach. With increasing numbers of replicate surveys, the scatter given by the coefficient of variation (CV) significantly increased up to 113%. Statistical power considerably increased with increasing numbers of replicate beach sections, e.g. from 82% (two beach sections) to nearly 100% (five beach sections) for a given reduction of beach litter of 50%. Based on these results from a morphologically straight coastline, the use of replicate surveys would be sensible for the future monitoring of beach litter. However, there is high need for studies, which consider coastlines with varying morphology.

Baseline and power analyses for the assessment of beach litter reductions in the European OSPAR region.

Marine litter pollution is a global environmental problem. Beach litter is a part of this problem, and is widely monitored in Europe. The European Marine Strategy Framework Directive (MSFD) requires a reduction of beach litter. A reduction of 30% has been proposed in the European Plastics Strategy. The aims of this study are to develop (a) a method to calculate sufficiently stable and precise baseline values for beach litter, and (b) to derive a method of power analysis to estimate the number of beach litter surveys, necessary to detect a given reduction, using these baseline values. Beach litter data from the OSPAR (Oslo Paris Convention) region were used, and tailor-made statistical methods were implemented in open source software, litteR. Descriptive statistics and Theil-Sen and Mann-Kendall trend analyses were calculated for the most abundant beach litter types, for 14 survey sites. The length of a baseline period necessary to obtain a specified precision of the mean baseline value, expressed as Coefficient of Variation (CV), was calculated. Power analyses were performed using Monte Carlo simulations combined with Wilcoxon tests to determine significant deviations of the simulated datasets from the baseline mean values. For most survey sites, the mean length of monitoring periods necessary to achieve a CV < 10% amounts to four to five years with four surveys a year. The mean number of surveys necessary to detect a statistically significant reduction of 30% with 80% power ranges from 14 to 20. Power analyses show that a reduction of 10% is difficult to detect, because more than 24 surveys are needed. In contrary, a reduction of 40-50% can be detected easily with a small (<12) number of surveys. The new methods could also be applied to other areas where similar beach litter surveys are performed.

OSPAR standard method and software for statistical analysis of beach litter data.

The aim of this study is to develop standard statistical methods and software for the analysis of beach litter data. The optimal ensemble of statistical methods comprises the Mann-Kendall trend test, the Theil-Sen slope estimation, the Wilcoxon step trend test and basic descriptive statistics. The application of Litter Analyst, a tailor-made software for analysing the results of beach litter surveys, to OSPAR beach litter data from seven beaches bordering on the south-eastern North Sea, revealed 23 significant trends in the abundances of beach litter types for the period 2009-2014. Litter Analyst revealed a large variation in the abundance of litter types between beaches. To reduce the effects of spatial variation, trend analysis of beach litter data can most effectively be performed at the beach or national level. Spatial aggregation of beach litter data within a region is possible, but resulted in a considerable reduction in the number of significant trends.

A multi-criteria evaluation system for marine litter pollution based on statistical analyses of OSPAR beach litter monitoring time series.

During the last decades, marine pollution with anthropogenic litter has become a worldwide major environmental concern. Standardized monitoring of litter since 2001 on 78 beaches selected within the framework of the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) has been used to identify temporal trends of marine litter. Based on statistical analyses of this dataset a two-part multi-criteria evaluation system for beach litter pollution of the North-East Atlantic and the North Sea is proposed. Canonical correlation analyses, linear regression analyses, and non-parametric analyses of variance were used to identify different temporal trends. A classification of beaches was derived from cluster analyses and served to define different states of beach quality according to abundances of 17 input variables. The evaluation system is easily applicable and relies on the above-mentioned classification and on significant temporal trends implied by significant rank correlations.

Estimation of the impact of prevailing weather conditions on the occurrence of oil-contaminated dead birds on the German North Sea coast.

Chronic oil pollution by illegal oil dumping in the North Sea is difficult to quantify. Beached, oil-contaminated sea birds, however, may be used as an indirect indicator. Reconstructing the drift of oil slicks and sea bird corpses in the southern North Sea for the period 1992-2003 by means of a two-dimensional numerical transport model driven by re-analysed weather data, we show with an example of two common sea bird species that the variability observed within the number of corpses registered during beached bird surveys for the German coast primarily reflects the inter-annual variability of prevailing weather conditions. This should be taken into account when interpreting the data. We propose normalisation of beached bird survey data based on numerical drift simulations to improve the recognition of trends in the level of chronic oil pollution.