Kristjan Herkül

This paper provides results of marine litter surveys carried out on 14 small uninhabited islands located in the coastal waters of Estonia, northeastern Baltic Sea. The islands were visited four times in total during 2019–2020. On each island, a litter survey was conducted on the beach and in terrestrial vegetation with a focus on macrolitter. Calculated over all surveys, the median value of macrolitter items per 100 m long beach section was 10.65, and the median density was 0.006 items m^–2. At the sub-basin level, the three islands located in the Gulf of Finland had the highest number of beach litter items per 100 m and the highest density (items m^–2), 38.05 and 0.017, respectively. The main litter material, representing 57.3% of all findings, was plastic; however, there were some variances across islands due to local conditions. The environmental variables most strongly correlated with differences in the composition of macrolitter were related to water movement and depth. Microlitter was found in low amounts (up to 60 items kg^–1) in the sediment of all studied islands. Litter items used as nest material were noted on all the islands with seabird colonies.

Figures S1–S4 and Tables S1 and S2 detailing the different aspects of marine litter dis tribution in the Estonian coastal sea region.

Herkül, K.; Vahtmäe, E.; Kutser, T.; Svirgsden, R., and Rohtla, M., 2024. Mapping shallow water coastal habitats using optical remote sensing and machine learning: Evaluating drone, airplane, and satellite-based imagery. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 519-523. Charlotte (North Carolina), ISSN 0749-0208. Mapping habitats in shallow coastal waters with conventional site inspections is costly and time-consuming due to the need for numerous sampling points, hindered by shallow water, rocks, and vegetation. Remote sensing (RS) imagery and mathematical modeling can reduce in situ efforts while improving mapping accuracy and resolution. This study tested various optical RS methods for mapping shallow water habitats in a coastal lagoon in the eastern Baltic Sea. On-site sampling, including bed substrate and macrophytes, was done through direct visual observations and underwater video recordings. Different RS imagery types were tested: drone and airplane-based orthophotos in RGB colors, airplane-based hyperspectral imagery, and Sentinel-2 multispectral satellite imagery. The Random Forest (RF) machine learning algorithm was used for supervised habitat distribution modeling. Field observation data and RS reflectance data were used to train RF models. Using the trained models, spatial predictions were made to cover the full extent of study areas with seamless maps of habitat variables. Cross-validation showed that all RS data types achieved moderate to nearly perfect prediction accuracy, effectively mapping the lagoon's habitat patchiness. It was concluded that the choice of RS method depends on the study area's size, characteristics, and desired spatial resolution.

The distribution data of 11 soft substrate charophyte and angiosperm species were analyzed. Our study aimed to elucidate the co-occurrence patterns among these sympatric macrophyte species and quantify their distribution areas. The central hypothesis of this study proposed that the observed co-occurrence patterns among the studied species deviate from what would be expected by random chance. Macrophyte occurrence data was derived from an extensive field sampling database. Environmental variables available as georeferenced raster layers including topographical, hydrodynamic, geological, physical, chemical, and biological variables were used as predictor variables in the random forest models to predict the spatial distribution of the species. Permutation tests revealed statistically significant deviations from random co-occurrence patterns. The analysis demonstrated that species tended to co-occur more frequently within their taxonomic groups (i.e., within charophytes and within angiosperms) than between these groups. The most extensive distribution overlap was observed betweenChara asperaWilld. andChara canescensLoisel., whileZostera marinaL. exhibited the least overlap with the other species. The mean number of co-occurring species was the highest inChara baltica(Hartman) Bruzelius whileZ. marinahad the largest share of single-species occurrences. Based on the distribution models,Stuckenia pectinata(L.) Börner had the largest distribution area.

Blue mussels of the genus Mytilus are important ecosystem engineers in intertidal and subtidal communities. The distribution of Mytilus mussels is influenced by a number of benthic and pelagic environmental variables (e.g., substratum type and availability, water movement, phytoplankton production, physical disturbance) as well as interactions between these variables. Because of its broad tolerance of environmental variation the Mediterranean species, Mytilus galloprovincialis, has the greatest ability of all blue mussels to colonise new geographic regions. Understanding how population genetic variation is related to, or caused by, environmental variation is important but has long been a challenge. The present study examined the genetic differentiation of native populations of M. galloprovincialis throughout its entire geographic range in the Mediterranean Sea, the Black Sea and the Sea of Azov using 53 single nucleotide polymorphisms (SNP loci). Mussels, in total 1004 individuals collected from 36 locations, were genotyped and combined with existing SNP data for mussels from 11 reference sites. Pairwise comparisons of F_ST values, correspondence analysis (CA) and STRUCTURE analysis all revealed four groups of populations: the Atlantic Ocean; the western Mediterranean; the Aegean Sea; and the Azov, Black and Marmara Seas. One population – from Algeria (Oran West) – was intermediate between the two main groups of the Mediterranean Sea and Atlantic Ocean. Seascape genetic analyses using GLM and DistLM analyses were employed to test site-specific genetic variation as a function of 13 environmental variables. The GLM identified five environmental variables that explained variation in site-specific F_ST values, whilst in the DistLM best-fit model only four were significant. These analyses suggest that a complex mix of environmental variables contribute to explaining genetic variation of M. galloprovincialis populations within the Mediterranean Sea, which most likely reflects the complex geological history of formation, isolation and reconnection among the regional sub-basins of the Sea.

Large-scale climate changes influence the geographic distribution of biodiversity. Many taxa have been reported to extend or reduce their geographic range, move poleward or displace other species. However, for closely related species that can hybridize in the natural environment, displacement is not the only effect of changes of environmental variables. Another option is subtler, hidden expansion, which can be found using genetic methods only. The marine blue mussels Mytilus are known to change their geographic distribution despite being sessile animals. In addition to natural dissemination at larval phase—enhanced by intentional or accidental introductions and rafting—they can spread through hybridization and introgression with local congeners, which can create mixed populations sustaining in environmental conditions that are marginal for pure taxa. The Mytilus species have a wide distribution in coastal regions of the Northern and Southern Hemisphere. In this study, we investigated the inter-regional genetic differentiation of the Mytilus species complex at 53 locations in the North Atlantic and adjacent Arctic waters and linked this genetic variability to key local environmental drivers. Of seventy-nine candidate single nucleotide polymorphisms (SNPs), all samples were successfully genotyped with a subset of 54 SNPs. There was a clear interregional separation of Mytilus species. However, all three Mytilus species hybridized in the contact area and created hybrid zones with mixed populations. Boosted regression trees (BRT) models showed that inter-regional variability was important in many allele models but did not prevail over variability in local environmental factors. Local environmental variables described over 40% of variability in about 30% of the allele frequencies of Mytilus spp. For the 30% of alleles, variability in their frequencies was only weakly coupled with local environmental conditions. For most studied alleles the linkages between environmental drivers and the genetic variability of Mytilus spp. were random in respect to “coding” and “non-coding” regions. An analysis of the subset of data involving functional genes only showed that two SNPs at Hsp70 and ATPase genes correlated with environmental variables. Total predictive ability of the highest performing models (r² between 0.550 and 0.801) were for alleles that discriminated most effectively M. trossulus from M. edulis and M. galloprovincialis, whereas the best performing allele model (BM101A) did the best at discriminating M. galloprovincialis from M. edulis and M. trossulus. Among the local environmental variables, salinity, water temperature, ice cover and chlorophyll a concentration were by far the greatest predictors, but their predictive performance varied among different allele models. In most cases changes in the allele frequencies along these environmental gradients were abrupt and occurred at a very narrow range of environmental variables. In general, regions of change in allele frequencies for M. trossulus occurred at 8–11 psu, 0–10 °C, 60%–70% of ice cover and 0–2 mg m⁻³ of chlorophyll a, M. edulis at 8–11 and 30–35 psu, 10–14 °C and 60%–70% of ice cover and for M. galloprovincialis at 30–35 psu, 14–20 °C.

The current study focuses on trends in hydrophysical and -chemical parameters (e.g. temperature, salinity, dissolved oxygen, chlorophyll a (Chl a), pH and nutrients) in the Estonian coastal sea and offshore areas in relation to the biogeochemical processes and marine carbon dioxide system of the Baltic Sea. Analysis of 586 time series of these parameters, retrieved during national monitoring activities in 1993–2017, revealed a number of significant trends, which characterize the changes in the northeastern (NE) Baltic Sea. The number of significant trends in the surface layer was slightly higher in the coastal sea area than in the offshore area. No significant (e.g. climate change-related) temperature trends were revealed in the surface layers of the Estonian offshore area. Over a longer time frame (since the 1970s–1980s), the trends in hydrochemical parameters have shown improved ecological conditions in the Estonian coastal waters, however, further improvement is not so obvious. In fact, most nutrient trends were positive over the last two decades. A positive Chl a trend was detected in the offshore area of the Baltic Proper. Dissolved oxygen trends in the bottom layers were all negative. So far, not enough parameters have been monitored for the evaluation of marine acidification processes. Several important recommendations for further improvement of monitoring programmes are suggested.