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Monitoring the “health” of an ecological community is a critical component of conservation planning. We propose that aggregating intraspecific genetic variation across all species of an ecological community (Community Genetic Distribution; CGD) provides a new way to measure biodiversity that is unifying across taxa, economically scalable, and geographically transferable. Such community-scale data provides information about past dynamics that can unveil processes structuring contemporary biodiversity, and can identify communities that are resilient to perturbation. Using the CGD, high-throughput biodiversity genetic inventories (e.g. metabarcoding/eDNA) can be leveraged to identify the genetic signatures of pristine and disturbed systems. We show examples of the CGD from empirical systems, how it responds through space and time to human disturbance, and how it successfully recovers restoration and succession gradients from metabarcoding datasets with the goal of obtaining insight on community genetic health and developing indicator metrics which can identify communities that are resilient to perturbation. We outline ways in which the CGD complements and extends information in the suite of currently described essential biodiversity variables, and how it can contribute to the targets of the Kunming-Montreal Global Biodiversity Framework. 

Abstract The dynamic structure of ecological communities results from interactions among taxa that change with shifts in species composition in space and time. However, our ability to study the interplay of ecological and evolutionary processes on community assembly remains relatively unexplored due to the difficulty of measuring community structure over long temporal scales. Here, we made use of a geological chronosequence across the Hawaiian Islands, representing 50 years to 4.15 million years of ecosystem development, to sample 11 communities of arthropods and their associated plant taxa using semiquantitative DNA metabarcoding. We then examined how ecological communities changed with community age by calculating quantitative network statistics for bipartite networks of arthropod–plant associations. The average number of interactions per species (linkage density), ratio of plant to arthropod species (vulnerability) and uniformity of energy flow (interaction evenness) increased significantly in concert with community age. The index of specialization has a curvilinear relationship with community age. Our analyses suggest that younger communities are characterized by fewer but stronger interactions, while biotic associations become more even and diverse as communities mature. These shifts in structure became especially prominent on East Maui (~0.5 million years old) and older volcanos, after enough time had elapsed for adaptation and specialization to act on populations in situ. Such natural progression of specialization during community assembly is probably impeded by the rapid infiltration of non‐native species, with special risk to younger or more recently disturbed communities that are composed of fewer specialized relationships. 

Abstract Islands make up a large proportion of Earth's biodiversity, yet are also some of the most sensitive systems to environmental perturbation. Biogeographic theory predicts that geologic age, area, and isolation typically drive islands' diversity patterns, and thus potentially impact non‐native spread and community homogenization across island systems. One limitation in testing such predictions has been the difficulty of performing comprehensive inventories of island biotas and distinguishing native from introduced taxa. Here, we use DNA metabarcoding and statistical modelling as a high throughput method to survey community‐wide arthropod richness, the proportion of native and non‐native species, and the incursion of non‐natives into primary habitats on three archipelagos in the Pacific – the Ryukyus, the Marianas and Hawaii – which vary in age, isolation and area. Diversity patterns largely match expectations based on island biogeography theory, with the oldest and most geographically connected archipelago, the Ryukyus, showing the highest taxonomic richness and lowest proportion of introduced species. Moreover, we find evidence that forest habitats are more resilient to incursions of non‐natives in the Ryukyus than in the less taxonomically rich archipelagos. Surprisingly, we do not find evidence for biotic homogenization across these three archipelagos: the assemblage of non‐native species on each island is highly distinct. Our study demonstrates the potential of DNA metabarcoding to facilitate rapid estimation of biogeographic patterns, the spread of non‐native species, and the resilience of ecosystems. 

Abstract Current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research. 

Abstract BackgroundMacArthur and Wilson's theory of island biogeography has been a foundation for obtaining testable predictions from models of community assembly and for developing models that integrate across scales and disciplines. Historically, however, these developments have focused on integration across ecological and macroevolutionary scales and on predicting patterns of species richness, abundance distributions, trait data and/or phylogenies. The distribution of genetic variation across species within a community is an emerging pattern that contains signatures of past population histories, which might provide an historical lens for the study of contemporary communities. As intraspecific genetic diversity data become increasingly available at the scale of entire communities, there is an opportunity to integrate microevolutionary processes into our models, moving towards development of a genetic theory of island biogeography. Motivation/goalWe aim to promote the development of process‐based biodiversity models that predict community genetic diversity patterns together with other community‐scale patterns. To this end, we review models of ecological, microevolutionary and macroevolutionary processes that are best suited to the creation of unified models, and the patterns that these predict. We then discuss ongoing and potential future efforts to unify models operating at different organizational levels, with the goal of predicting multidimensional community‐scale data including a genetic component. Main conclusionsOur review of the literature shows that despite recent efforts, further methodological developments are needed, not only to incorporate the genetic component into existing island biogeography models, but also to unify processes across scales of biological organization. To catalyse these developments, we outline two potential ways forward, adopting either a top‐down or a bottom‐up approach. Finally, we highlight key ecological and evolutionary questions that might be addressed by unified models including a genetic component and establish hypotheses about how processes across scales might impact patterns of community genetic diversity. 

Abstract Large-scale studies on community ecology are highly desirable but often difficult to accomplish due to the considerable investment of time, labor and, money required to characterize richness, abundance, relatedness, and interactions. Nonetheless, such large-scale perspectives are necessary for understanding the composition, dynamics, and resilience of biological communities. Small invertebrates play a central role in ecosystems, occupying critical positions in the food web and performing a broad variety of ecological functions. However, it has been particularly difficult to adequately characterize communities of these animals because of their exceptionally high diversity and abundance. Spiders in particular fulfill key roles as both predator and prey in terrestrial food webs and are hence an important focus of ecological studies. In recent years, large-scale community analyses have benefitted tremendously from advances in DNA barcoding technology. High-throughput sequencing (HTS), particularly DNA metabarcoding, enables community-wide analyses of diversity and interactions at unprecedented scales and at a fraction of the cost that was previously possible. Here, we review the current state of the application of these technologies to the analysis of spider communities. We discuss amplicon-based DNA barcoding and metabarcoding for the analysis of community diversity and molecular gut content analysis for assessing predator-prey relationships. We also highlight applications of the third generation sequencing technology for long read and portable DNA barcoding. We then address the development of theoretical frameworks for community-level studies, and finally highlight critical gaps and future directions for DNA analysis of spider communities. 

Abstract In light of the current biodiversity crisis, molecular barcoding has developed into an irreplaceable tool. Barcoding has been considerably simplified by developments in high throughput sequencing technology, but still can be prohibitively expensive and laborious when community samples of thousands of specimens need to be processed. Here, we outline an Illumina amplicon sequencing approach to generate multilocus data from large collections of arthropods. We reduce cost and effort up to 50-fold, by combining multiplex PCRs and DNA extractions from pools of presorted and morphotyped specimens and using two levels of sample indexing. We test our protocol by generating a comprehensive, community wide dataset of barcode sequences for several thousand Hawaiian arthropods from 14 orders, which were collected across the archipelago using various trapping methods. We explore patterns of diversity across the Archipelago and compare the utility of different arthropod trapping methods for biodiversity explorations on Hawaii, highlighting undergrowth beating as highly efficient method. Moreover, we show the effects of barcode marker, taxonomy and relative biomass of the targeted specimens and sequencing coverage on taxon recovery. Our protocol enables rapid and inexpensive explorations of diversity patterns and the generation of multilocus barcode reference libraries across whole ecosystems.