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Summary Most populations of the European Pied Flycatcher Ficedula hypoleuca are decreasing. Different reasons for the decline are discussed, including biotic interactions and climate change. It is evident that many studies have been conducted in populations depending completely on nest boxes, but the influence of this artificial support on population dynamics is not well understood. We investigated the population dynamics of the Pied Flycatcher in the Kottenforst, an old-growth forest in western Germany, using recent data as well as historical records. We also determined the proportion of pairs breeding in nest boxes vs. natural nesting places. Specifically, we quantitatively analysed forest structure around tree holes occupied by the Pied Flycatcher. We found a continuous increase in population size since its establishment in the 1960s, which contrasts with overall long-term population trends in Europe as well as the regional trend. Whereas importance of nest boxes decreased over recent years, the majority of pairs are occupying tree holes for breeding, which are abundant in the richly structured, open old-growth forest. This forest structure seems to be optimal for the Pied Flycatcher since it allows flying insects to be hunted close to the nest. Finally, we discuss how forest structure and age as well as tree hole and insect availability may determine population trends of the Pied Flycatcher and highlight the importance of long-term studies. Abrahamczyk, S., Grimm, J., Fehn, M. & Stiels, D. (2023). Long-term decoupling of a local population trend of the European Pied Flycatcher Ficedula hypoleuca from nest box abundance indicates the importance of old-growth forest. Ardeola, 70: 185-200.

In (grapevine) breeding programs and research, periodic phenotyping and multi-year monitoring of different grapevine traits, like growth or yield, is needed especially in the field. This demand imply objective, precise and automated methods using sensors and adaptive software. This work presents a proof-of-concept analyzing RGB images of different growth stages of grapevines with the aim to detect and quantify promising plant organs which are related to yield.The input images are segmented by a Fully Convolutional Neural Network (FCN) into object and background pixels. The objects are plant organs like young shoots, pedicels, flower buds or grapes, which are principally suitable for yield estimation. In the ground truth of the training images, each object is separately annotated as a connected segment of object pixels, which enables end-to-end learning of the object features. Based on the CNN-based segmentation, the number of objects is determined by detecting and counting connected components of object pixels using region labeling.In an evaluation on six different data sets, the system achieves an IoU of up to 87.3% for the segmentation and an F1 score of up to 88.6% for the object detection.