Summer growth reduction in juvenile flatfish has been observed in various temperate coastal areas, suggesting a general mechanism. One possible mechanism that might explain this phenomenon is related to trophic limitation. After the spring phytoplankton bloom macrozoobenthic infauna becomes less active above the sediment, thereby affecting the time spent by predatory flatfish on searching for prey and hence, causing a reduction in food intake and in growth. Here, our aim is to gather evidence to substantiate this so-called "summer growth reduction" hypothesis by analyzing summer growth for 0-group flounder Platichthys flesus at the Balgzand intertidal area in the western Dutch Wadden Sea, under the prediction that flounder, as a more epibenthic predator, would suffer less or not at all from summer growth reduction in contrast to 0-group plaice Pleuronectes platessa, a more benthic feeder. Summer growth was studied for three contrasting years with respect to preceding winter water temperature conditions (cold, average and warm year) to exclude possible irreversible non-genetic adaptation of growth to water temperature conditions. Growth performance was analyzed by combining information on individual growth based on otolith daily ring analysis with predictions of maximum growth (= under optimal food conditions) based on a Dynamic Energy Budget model. In line with expectations and in contrast to 0-group plaice, no trend in growth performance over time was found suggesting that 0-group flounder showed no growth reduction after summer, providing further basis for a future testing of the trophic limitation hypothesis.

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, which opens opportunities to automatise counting tasks. In this study, we use machine learning to automate counting objects of interest without the need to label individual objects. By leveraging already existing image-level annotations, this approach can also give value to historical data that were collected and annotated over longer time series (typical for many ecological studies), without the aim of deep learning applications. We demonstrate deep learning regression on two fundamentally different counting tasks: (i) daily growth rings from microscopic images of fish otolith (i.e., hearing stone) and (ii) hauled out seals from highly variable aerial imagery. In the otolith images, our deep learning-based regressor yields an RMSE of 3.40 day-rings and an $$R^2$$ R 2 of 0.92. Initial performance in the seal images is lower (RMSE of 23.46 seals and $$R^2$$ R 2 of 0.72), which can be attributed to a lack of images with a high number of seals in the initial training set, compared to the test set. We then show how to improve performance substantially (RMSE of 19.03 seals and $$R^2$$ R 2 of 0.77) by carefully selecting and relabelling just 100 additional training images based on initial model prediction discrepancy. The regression-based approach used here returns accurate counts ($$R^2$$ R 2 of 0.92 and 0.77 for the rings and seals, respectively), directly usable in ecological research.

The food web structure of a coastal fish community (western Dutch Wadden Sea) was studied based on stomach content data from samples collected between 2010 and 2018. In total, 54 fish species were caught and 72 different prey items were identified. Fish species consumed from only a few up to >30 different prey species, suggesting the presence of both opportunistic and more specialized feeders. We found no significant differences between years or switches in food source with fish size. The trophic positions of the Wadden Sea fish community ranged from 2.0 to 4.7, with most trophic positions above 3.0. In the past, (near)-resident species were the most abundant guild in spring, and juvenile marine migrants in autumn. At present, all guilds are present in similar but low abundances. The (near)-resident community consisted of about 20 species that fed primarily on amphipod crustaceans, brown shrimps and juvenile herring. There was only a slight overlap in diet with the group of juvenile marine migrants (5 species of juvenile flatfishes and clupeids), whose preferred prey were copepods, polychaetes and brown shrimps. About 15 species of marine seasonal visitors showed an overlap in diet with both the (near)-resident and the juvenile marine migrants, especially for brown shrimps and to a lesser extent herring and gobies. Our results illustrate (1) the pivotal position of a few key prey species (amphipod crustaceans, brown shrimps, juvenile herring and gobies) for the coastal Wadden Sea fishes and (2) that the substantial prey overlap in the diet of some predators cannot exclude intra- and inter-specific competition among these predators.