Predator-prey interactions provide key information on the role of each species in the community and an overall assessment of the stability of food webs. DNA metabarcoding has the potential to provide highly informative data which substantially enhance trophic interactions analysis, by providing higher taxonomic detail compared to earlier methods. Here we show, using the Cabo Verde seabird community, that the integrated analysis of trophic networks based on DNA metabarcoding of faecal samples can increase considerably our understanding of the trophic interactions in whole communities. Results revealed that these seabird species prey mostly on fish, with most seabirds relying heavily on very few prey species, which are also targeted by fisheries. This community shows high specialization and modularity levels, i.e., is dominated by seabird species with specialized diets. Such network structure has implications for its management and conservation because specialist predators are especially vulnerable to prey depletion. The Cape Verde shearwater (Calonectris edwardsii), identified as the main network connector species, was confirmed to be a suitable sentinel species of changes in this marine food web. Our results clearly show that network analysis can be used effectively to maximize the potential of DNA metabarcoding in studying trophic interactions of complex communities.

AbstractTens of millions of images from biological collections have become available online in the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. Whilst image analysis has become mainstream in consumer applications, it is still only used on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if the images of collection objects could be made accessible as a single corpus. In this paper, we make the case for building infrastructure that could support image analysis of collection objects. We show that such an infrastructure is entirely feasible and well worth the investment.IntroductionOwing to their central role in cataloguing the world’s biodiversity, global biological collections likely hold samples of most known macro-biodiversity. As such, they are an irreplaceable asset for research of all kinds, including ecology, conservation, natural history and epidemiology  \cite{Bradley_2014,Cook_2014,Davis_2019,Antonelli_2020}. They are also seen as an important and underused resource to address numerous questions in the context of biodiversity under global change \cite{Soltis_2017,Meineke_2018,Hussein_2022}. Thus ensuring access to, and integrating data from these collections is globally important for the future. Conservation and sustainable use of biodiversity are fundamental to the 2030 Agenda of the \cite{secretariat_of_the_convention_on_biological_diversity_biodiversity_2016} and achieving its sustainable development goals is only realistic with the collections that underpin accurate naming and knowledge of biodiversity.To keep pace with the demand for access to collections, digital imaging of biological collections has progressed at pace (Fig. 1). As of September 2022, the Global Biodiversity Information Facility (GBIF) has more than 49 million preserved or fossil specimens with an image. For just the nearly 400 million specimens of plants held in collections globally \cite{thiers_worlds_2020}, there are almost 38 million (9%) occurrences with images on GBIF. This number is expected to grow substantially. For example, the digitisation of the Kew herbarium, which holds over 7 million specimens will add to already major digitization programs in Australia, China, Europe and the United States among others \cite{willis_science_2018,Nelson_2018,Borsch_2020,chinese_virtual_herbarium_299000_2021}.\ref{240550}\ref{205447}

Although sexual dietary differentiation is well known in birds, it is usually linked with significant morphological dimorphism between males and females, with lower differentiation reported in sexually monomorphic or only slightly dimorphic species. However, this may be an artefact of poor taxonomic resolution achieved in most conventional dietary studies, which may be unable to detect subtle intraspecific differentiation in prey consumption. Here we show the power of multi-marker metabarcoding to address these issues, focusing on a slightly dimorphic generalist passerine, the black wheatear Oenanthe leucura. Using markers from four genomic regions (18S, 16S, COI and trnL), we analysed faecal droppings collected from 93 adult black wheatears during the breeding season. We found that sexes were rather similar in bill and body features, though males had a slightly thicker bill and longer wings and tail than females. Diet was dominated in both sexes by a very wide range of arthropod species and a few fleshy fruits, but the overall diet diversity was higher for males than females, and there was a much higher frequency of occurrence of ants in female (58%) than male (29%) diets. We hypothesise that the observed sexual differentiation was likely related to females foraging closer to their offspring on abundant prey, while males consumed a wider variety of prey while foraging more widely. Overall, our results suggest that dietary sexual differentiation in birds may be more widespread than recognised at present, and that multi-marker DNA metabarcoding is a particularly powerful tool to unveiling such differences.