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High diet overlap is assumed to cause competition between the three dominant pelagic planktivorous mesopredators in the Baltic Sea, sprat, herring, and stickleback11,24,25. Despite this assumption, stickleback populations have increased dramatically over the past decades, which raises the question of whether and how resources are partitioned26. While previous studies of fish diet overlap have mainly relied on microscopic identification of gut content, we implemented a DNA metabarcoding approach targeting two different gene regions, the 18S rRNA gene (18S) and the mitochondrial cytochrome c oxidase I gene (COI) to reveal the taxonomic diversity of prey, and a qPCR step to quantify rotifers that are at times abundant in the Baltic Sea. Our study highlights consistency between methods, with DNA metabarcoding resolving the plankton-fish link at the highest taxonomic resolution. Our results suggest a unique niche of stickleback that may enable high population growth in the open water, despite high competition between mesopredators, although this finding needs to be confirmed at larger scale. More than half of the DNA found in herring and sprat stomach contents was assigned to Pseudocalanus, supporting previous observations of high diet overlap between the two clupeids11,12. On the other hand, the diet of stickleback differed substantially from the two clupeids, with rotifers appearing as main prey DNA in spring. The high rotifer biomass in the environment and lack of competition from other predators indicate that this novel niche utilization may support the drastic increase of pelagic stickleback in the Baltic Sea.We find that copepods dominated the gut content of the two clupeids sprat and herring. Pseudocalanus and Temora occupied most of the sequence reads of the clupeid metabarcoding, two species that are often reported as preferred prey in previous studies11,12. Despite high contributions of these two copepods, Pseudocalanus was more than four times as abundant as Temora in clupeid gut contents. A strong preference for this copepod with marine origin can further confirm the increased competition between the clupeids, as Pseudocalanus has decreased due to decreased salinity12 and shares a similar vertical distribution as clupeid during daytime27. Our study using metabarcoding further reveals a large relative quantity (11%) of the ctenophore Mertensia in the gut samples of both clupeids. Similar, Clarke et al.28 reported an important contribution of gelatinous zooplankton to upper trophic levels in the Southern Ocean. Despite high abundances of ctenophores in the Baltic Sea and their assumed importance in marine food webs19, they are not reported as food for planktivorous fish. A possible explanation is the difficulty observing them microscopically, as their digestion rate is faster than crustaceans29, and no hard parts remain in the digestive system. Further, COI detected the presence of cladocerans, which was confirmed by the microscopic survey, but underrepresented with 18S that strongly amplify copepods20. Interestingly, more than twice annelid COI reads, including the benthic macroinvertebrates Bylgides and Marenzellaria, were associated to stickleback (15%) and herring (8%) than to sprat (4%), highlighting their ability to migrate vertically. These interactions suggest that together stickleback and herring contribute to benthic-pelagic coupling when oxygen is not restricting vertical migration in the southern Baltic Sea30.Sprat and herring share a similar feeding niche, which may explain previously observed declines in body mass and stomach fullness, and supports the theory of competition between the two species31. In contrast, stickleback revealed little diet overlap with the other mesopredators. The low relative abundances of Pseudocalanus (1–8%) in metabarcoding analyses indicates that the density-dependent competition may not limit the population growth of stickleback. The copepods that were shared in the diet of stickleback, sprat, and herring were Temora, Acartia, and Centropages have increased over the last decades, as opposed to Pseudocalanus32. Our results show that stickleback are able to feed on a broader spectrum of prey and highlight that stickleback utilizes the rotifer Synchaeta baltica as prey, which is an important component of the plankton community composition in the Baltic Sea18,20. Due to the difference of prey size, we can expect an overrepresentation of copepod to rotifer sequences compared with microscopic count data. High predation rate on S. baltica is supported by both the qPCR assay as well as microscopic counts, although only the eggshells were visible but not the soft-bodied rotifer. Despite the considerably lower carbon content per S. baltica (ca. 6 µg C ind−1) compared to copepods (ca. 20 µg C ind−1)33, the high number of rotifers likely act as a major food source for stickleback. These results propose that stickleback, due to their opportunistic feeding behaviour34 and smaller size35, have a distinct feeding niche from sprat and herring in the open water, as they feed on a smaller size class of zooplankton compared to the clupeids. Thus, we cannot assume the same process of competition between clupeids and stickleback.Rotifers can at times be very abundant in the Baltic Sea, reaching densities up to 25,000 ind m−3, but their natural predators are poorly studied. An increasing trend in biomass of the two main rotifer genera (Synchaeta and Keratella) was observed since the 1990s36. In a recent study, we showed that rotifers might occupy a unique feeding niche, as direct grazers of dinoflagellate spring bloom, as well as in the recycling of organic matter in summer20. The low level of predation on rotifers by clupeid adults ( More

Ecosystem services are the contributions of nature to human well-being — for example, the provision of raw materials, carbon sequestration and recreation. Although relatively new, the study of these essential services has developed rapidly and is now included in many global policies and assessments. However, mapping and modelling these services is restricted by the availability of data that can account for the multidimensional traits of ecosystem services and model coupled social–ecological systems. Traditional datasets, including surveys, interviews, and focus groups, are often not viable on the scale necessary for many ecosystem service assessments. More

We surveyed three islands of Lake Chany: Uzkoredkii (54° 58′ 15′′ N, 77°27′04′′ E), Reden’kii (54° 56′ 05′′ N, 77° 22′ 27′′ 52 E), Korablik (54° 59′ 31′′ N, 77° 40′ 38′′ E). The studied intertidal habitats are rarely reached by humans.Gull nests were counted in colonies by regular surveys over eight years (1993, 1994, 1996–1998, 2001–2003) on the islands of Lake Chany. Colonies were visited daily or sometimes every other day. To minimize the disturbance caused by the investigation, the time spent working, within view of the gulls was restricted to a maximum of forty minutes per study plots. We noted nest content at every visit for the presence of eggs or chicks. In total, there were 1 164 nests under observation. Nests contained 1 (n = 140), 2 (n = 518), 3 (n = 504) or 4 (n = 2) eggs. Modal clutch size of the great black-headed gull is two or three eggs, varying seasonally. The length and width of the eggs were measured using Vernier calipers (division accuracy 0,1 mm) and numbered with a waterproof marker. Egg volumes were estimated using Hoyt’s equation: Volume = 0.51 * Length * Width * Width/100013. We determined the volume of 2117 great black-headed gull eggs.As the laying of eggs has already started by the first visit to the colony, the date of the beginning of egg laying was calculated by subtracting the average length of the incubation period of great black-headed gulls (27 days) from the hatching date of first chick in the nest (n = 559 nests). If the hatching date was not known, the clutch initiation date was determined by subtracting the number of days of incubation from the date that the nest was first discovered (n = 469 nests). The stage of incubation was estimated from the change in position of an incubated egg placed in water14,15. The technique’s accuracy varied throughout incubation and mean prediction error fall between 0–4 days. On average, egg flotation estimated an embryo’s developmental age to within 1.9 ± 1.6 days (mean ± 1 SD)16. Only 47 nests were found during egg laying. Great black-headed gulls usually laid eggs at intervals of two days. Incubation started as soon as the first egg was laid, so eggs hatched asynchronously, one or two days apart.Whenever possible, we determined the within-clutch laying sequence of eggs (1st, 2nd, 3rd, and 4th). A complete laying sequence was established by observation in 47 cases. In about 48% of clutches the position in laying sequence was established on the basis of the sequence of hatching. In other cases, if we could distinguish within-clutch distinct flotation levels of eggs, we numbered eggs according to the stage of incubation. Sometimes this technique for distinguishing egg laying order were used in other seabirds17,18.We recorded the pipping date (i.e. appearance of star-like bursts) and the actual hatching date of the individual eggs. Wet chicks were registered as hatchlings of that day; dry chicks were registered as 1 day old. Chicks older than two days left the nest and moved to a location nearby. Newly hatched gull chicks were captured by hand at nests, ringed, and measured. We determined wing, tarsus, and head length using a ruler with zero-stop and vernier calipers and body weight measured using Pesola spring balances for 747 chicks of great black-headed gulls, and 457 of them hatched from eggs that were measured. More

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