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Comparative studies suggest remarkable similarities among food webs across habitats, including systematic changes in their structure with diversity and complexity (scale-dependence). However, historic aboveground terrestrial food webs (ATFWs) have coarsely grouped plants and insects such that these webs are generally small, and herbivory is disproportionately under-represented compared to vertebrate predator–prey interactions. Furthermore, terrestrial herbivory is thought to be structured by unique processes compared to size-structured feeding in other systems. Here, we present the richest ATFW to date, including approximately 580 000 feeding links among approximately 3800 taxonomic species, sourced from approximately 27 000 expert-vetted interaction records annotated as feeding upon one of six different resource types: leaves, flowers, seeds, wood, prey and carrion. By comparison to historical ATFWs and null ecological hypotheses, we show that our temperate forest web displays a potentially unique structure characterized by two properties: (i) a large fraction of carnivory interactions dominated by a small number of hyper-generalist, opportunistic bird and bat predators; and (ii) a smaller fraction of herbivory interactions dominated by a hyper-rich community of insects with variably sized but highly specific diets. We attribute our findings to the large-scale, even resolution of vertebrate, insect and plant guilds in our food web. This article is part of the theme issue ‘Connected interactions: enriching food web research by spatial and social interactions’. 

Physical samples and their associated (meta)data underpin scientific discoveries across disciplines, and can enable new science when appropriately archived. However, there are significant gaps in community practices and infrastructure that currently prevent accurate provenance tracking, reproducibility, and attribution. For the vast majority of samples, descriptive metadata is often sparse, inaccessible, or absent. Samples and associated (meta)data may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkages or provenance tracking as new information is generated over time. The Physical Samples Curation Cluster has therefore developed ‘A Scientific Author Guide for Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, community feedback, and assessing real-world examples to identify community and infrastructure needs. We identified areas of work needed to enable authors to efficiently reference samples and related data, link related samples and data, and track their use. Our goal is to help improve the discoverability, interoperability, use of physical samples and associated (meta)data into the future. 

Abstract The impact of preserved museum specimens is transforming and increasing by three-dimensional (3D) imaging that creates high-fidelity online digital specimens. Through examples from the openVertebrate (oVert) Thematic Collections Network, we describe how we created a digitization community dedicated to the shared vision of making 3D data of specimens available and the impact of these data on a broad audience of scientists, students, teachers, artists, and more. High-fidelity digital 3D models allow people from multiple communities to simultaneously access and use scientific specimens. Based on our multiyear, multi-institution project, we identify significant technological and social hurdles that remain for fully realizing the potential impact of digital 3D specimens. 

Abstract While museum voucher specimens continue to be the standard for species identifications, biodiversity data are increasingly represented by photographic records from camera traps and amateur naturalists. Some species are easily recognized in these pictures, others are impossible to distinguish. Here we quantify the extent to which 335 terrestrial nonvolant North American mammals can be identified in typical photographs, with and without considering species range maps. We evaluated all pairwise comparisons of species and judged, based on professional opinion, whether they are visually distinguishable in typical pictures from camera traps or the iNaturalist crowdsourced platform on a 4-point scale: (1) always, (2) usually, (3) rarely, or (4) never. Most (96.5%) of the 55,944 pairwise comparisons were ranked as always or usually distinguishable in a photograph, leaving exactly 2,000 pairs of species that can rarely or never be distinguished from typical pictures, primarily within clades such as shrews and small-bodied rodents. Accounting for a species geographic range eliminates many problematic comparisons, such that the average number of difficult or impossible-to-distinguish species pairs from any location was 7.3 when considering all species, or 0.37 when considering only those typically surveyed with camera traps. The greatest diversity of difficult-to-distinguish species was in Arizona and New Mexico, with 57 difficult pairs of species, suggesting the problem scales with overall species diversity. Our results show which species are most readily differentiated by photographic data and which taxa should be identified only to higher taxonomic levels (e.g., genus). Our results are relevant to ecologists, as well as those using artificial intelligence to identify species in photographs, but also serve as a reminder that continued study of mammals through museum vouchers is critical since it is the only way to accurately identify many smaller species, provides a wealth of data unattainable from photographs, and constrains photographic records via accurate range maps. Ongoing specimen voucher collection, in addition to photographs, will become even more important as species ranges change, and photographic evidence alone will not be sufficient to document these dynamics for many species.