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Unified models of biological diversity across organizational levels (genes, species, communities) provide key insight into fundamental ecological processes. Theory predicts that the strength of the correlation between species abundance and genetic diversity should be related to community age in closed communities (i.e. abundant species accumulate more genetic diversity over time than rare species). Following this rationale, we hypothesize that historical climatic events are expected to impact assembly processes, hence affecting both the species abundance distribution (SAD) and the species genetic distribution (SGD) in continental communities. Therefore, we predict that, if the congruence between SADs and SGDs depends on community age, then higher congruence would be observed in localities where climate has been more stable since the Last Glacial Maximum (LGM). We tested this prediction using relative abundance and nucleotide diversity (cox1-5′) data from 20 communities of leaf beetles along a latitudinal transect in the Iberian Peninsula. We observed that the congruence between SAD and SGD curves, measured as the correlation between the species' rank orders in both distributions, was significantly related to the change in mean annual temperature since the LGM, but not to current climatic conditions. Our results suggest that, despite the high connectivity of continental communities, historical climatic stability is still a relevant predictor of the congruence between species abundance and genetic diversity. Hence, the degree of congruence between SADs and SGDs could be used as a proxy of community stability, related not only to historical climatic variation but also to any other disrupting factors, including human pressure.

In Focus: Scalercio, S., Cini, A., Menchetti, M., Vodă, R., Bonelli, S., Bordoni, A., … Dapporto, L. (2020). How long is 3 km for a butterfly? Ecological constraints and functional traits explain high mitochondrial genetic diversity between Sicily and the Italian Peninsula. Journal of Animal Ecology. https://doi.org/10.1111/1365‐2656.13196. Biotic and abiotic factors can shape geographical patterns of genetic variation within species, but few studies have addressed how this might generate common patterns at the level of communities of species. Scalercio et al. (2020) have combined mtDNA sequence data and life‐history traits, to reveal a repeated pattern of genetic structure between Sicilian and southern Italian butterfly populations, which are separated by only 3 km of ocean. They reveal how intrinsic species traits and extrinsic environmental constraints explain this pattern, demonstrating an important role for wind. Moreover, the inclusion of almost 8,000 georeferenced sequences reveals that, in spite of also being present in southern Italy, almost half of Sicilian butterfly species are more closely related to populations from other parts of Europe, Asia or North Africa. We provide further discussion on the biogeographic barrier they identify, and the potential of community‐level DNA barcoding to identify processes that structure genetic variation across communities.

Improved taxonomic methods are needed to quantify declining populations of insect pollinators. This study devises a high-throughput DNA barcoding protocol for a regional fauna (United Kingdom) of bees (Apiformes), consisting of reference library construction, a proof-of-concept monitoring scheme, and the deep barcoding of individuals to assess potential artefacts and organismal associations. A reference database of cytochrome oxidase c subunit 1 (cox1) sequences including 92.4% of 278 bee species known from the UK showed high congruence with morphological taxon concepts, but molecular species delimitations resulted in numerous split and (fewer) lumped entities within the Linnaean species. Double tagging permitted deep Illumina sequencing of 762 separate individuals of bees from a UK-wide survey. Extracting the target barcode from the amplicon mix required a new protocol employing read abundance and phylogenetic position, which revealed 180 molecular entities of Apiformes identifiable to species. An additional 72 entities were ascribed to nuclear pseudogenes based on patterns of read abundance and phylogenetic relatedness to the reference set. Clustering of reads revealed a range of secondary operational taxonomic units (OTUs) in almost all samples, resulting from traces of insect species caught in the same traps, organisms associated with the insects including a known mite parasite of bees, and the common detection of human DNA, besides evidence for low-level cross-contamination in pan traps and laboratory procedures. Custom scripts were generated to conduct critical steps of the bioinformatics protocol. The resources built here will greatly aid DNA-based monitoring to inform management and conservation policies for the protection of pollinators.

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase the number of categ

New genetic diagnostic approaches have greatly aided efforts to document global biodiversity and improve biosecurity. This is especially true for organismal groups in which species diversity has been underestimated historically due to difficulties associated with sampling, the lack of clear morphological characteristics, and/or limited availability of taxonomic expertise. Among these methods, DNA sequence barcoding (also known as "DNA barcoding") and by extension, meta-barcoding for biological communities, has emerged as one of the most frequently utilized methods for DNA-based species identifications. Unfortunately, the use of DNA barcoding is limited by the availability of complete reference libraries (i.e., a collection of DNA sequences from morphologically identified species), and by the fact that the vast majority of species do not have sequences present in reference databases. Such conditions are critical especially in tropical locations that are simultaneously biodiversity rich and suffer from a lack of exploration and DNA characterization by trained taxonomic specialists. To facilitate efforts to document biodiversity in regions lacking complete reference libraries, we developed a novel statistical approach that categorizes unidentified species as being either likely native or likely nonnative based solely on measures of nucleotide diversity. We demonstrate the utility of this approach by categorizing a large sample of specimens of terrestrial insects and spiders (collected as part of the Moorea BioCode project) using a generalized linear mixed model (GLMM). Using a training data set of known endemic (n = 45) and known introduced species (n = 102), we then estimated the likely native/nonnative status for 4,663 specimens representing an estimated 1,288 species (412 identified species), including both those specimens that were either unidentified or whose endemic/introduced status was uncertain. Using this approach, we were able to increase