Counting using deep learning regression gives value to ecological surveys

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 [Formula: see text] of 0.92. Initial performance in the seal images is lower (RMSE of 23.46 seals and [Formula: see text] 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 [Formula: see text] 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 ([Formula: see text] of 0.92 and 0.77 for the rings and seals, respectively), directly usable in ecological research

Is summer growth reduction related to feeding guild?:A test for a benthic juvenile flatfish sole (<i>Solea solea</i>) in a temperate coastal area, the western Wadden Sea

Flatfish species are an important target for fisheries. During their juvenile stage they concentrate in coastal nursery areas. Food conditions in these areas are an important factor determining habitat quality and ultimate survival. Recently, growth reduction in summer has been observed in plaice, Pleuronectes platessa, feeding on both epibenthic and benthic prey. In the current study, we test the hypothesis that summer growth reduction is a consequence of a reduced availability of benthic prey by analysing summer growth in a fully benthic feeding flatfish, juvenile sole (Solea solea). Summer growth was studied for contrasting years with respect to preceding winter water temperature conditions to exclude possible irreversible non-genetic adaptations of growth to water temperature. Individual growth, estimated from otolith daily rings, was compared with predictions of maximum growth at the prevailing temperature. In line with expectations, 0-group sole showed strong summer growth reduction, supporting the notion that summer growth reduction is related to feeding modes. Summer growth reduction underlines the importance of a good definition of how and over what time period growth as indicator of habitat quality is estimated and compared