Regional assemblages shaped by historical and contemporary factors: Evidence from a species-rich insect group

Monitoring insect biodiversity and comparison of sampling strategies using metabarcoding: A case study in the Yanshan Mountains, China

Insects are the richest and most diverse group of animals and yet there remains a lack, not only of systematic research into their distribution across some key regions of the planet, but of standardized sampling strategies for their study. The Yanshan Mountains, being the boundary range between the Inner Mongolian Plateau and the North China Plain, present an indispensable piece of the insect biodiversity puzzle: both requiring systematic study and offering opportunities for the development of standardized methodologies. This is the first use of DNA metabarcoding to survey the insect biodiversity of the Yanshan Mountains. The study focuses on differences of community composition among samples collected via different methods and from different habitat types. In total, 74 bulk samples were collected from five habitat types (scrubland, woodland, wetland, farmland and grassland) using three collection methods (sweep netting, Malaise traps and light traps). After DNA extraction, PCR amplification, sequencing and diversity analysis were performed, a total of 7427 Operational Taxonomic Units (OTUs) at ≥97% sequence similarity level were delimited, of which 7083 OTUs were identified as belonging to Insecta. Orthoptera, Diptera, Coleoptera and Hemiptera were found to be the dominant orders according to community composition analysis. Nonmetric multidimensional scaling (NMDS) analysis based on Bray-Curtis distances revealed highly divergent estimates of insect community composition among samples differentiated by the collection method (R = .524802, p = .001), but nonsignificant difference among samples differentiated according to habitat (R = .051102, p = .078). The study therefore appears to indicate that the concurrent use of varied collection methods is essential to the accurate monitoring of insect biodiversity.

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

Flight Mill Experiments and Computer Simulations Indicate Islands Recruit More Capable Flyers of Moths

Understanding the traits related to species colonization and invasion, is a key question for both pest management and evolution. One of the key components is flight, which has been measured for a number of insect species through radar and tethered flight mill systems, but a general understanding of insect flight at a community level is lacking. In this study, we used flight mill experiments to quantify flight abilities of moth species, and simulation experiments to study which moths in mainland China have the potential for cross-island dispersal. We found that moths from superfamily Geometroidea (family Geometridae) have the weakest flight ability among the seven Lepidoptera superfamilies, which is characterized by the shortest longest single flight (LSF), the shortest time corresponding to the longest single flight (TLSF) (timecorrespondingtothelongestsingleflight), the lowest total distance flown (TDF), and the lowest average speed during the flight (VTDF). Surprisingly, the family Pyralidae (superfamily Pyraloidea) has the highest flight endurance of all 186 species of 12 families in this study, which is unexpected, given its small size and morphological traits yet it shows the longest LSF and TLSF. The comparison between species common to mainland and islands shows that flight distance (LSF) may be more important for species spread than flight speed. The results of mainland-island simulations show that when P(LSF>CD) (the proportion of individuals whose LSF is greater than the closest distance (CD) between mainland and island to the total number of individuals in the population) is less than 0.004, it is difficult for moth species to disperse to across islands without relying on external factors such as airflow. Over extended periods, with the immigration of species with strong flight abilities, islands are more likely to recruit species with stronger flight abilities.

Identification of Species by Combining Molecular and Morphological Data Using Convolutional Neural Networks

Integrative taxonomy is central to modern taxonomy and systematic biology, including behaviour, niche preference, distribution, morphological analysis and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network (MMNet)) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently-available alternative methods when tested with 10 independent datasets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species) and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (>98%) in four datasets including closely related species from the same genus. The average accuracy of two modest sub-genomic (single nucleotide polymorphism) datasets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image versus gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multi-modal information for integrative taxonomy, such as image, audio, video, 3D scanning and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring and conservation of biodiversity.

DNA barcoding and community assembly-A simple solution to a complex problem

Identifying the current and past processes driving community assembly is critical in the effort to understand the Earth's biodiversity and its response to future environmental change. But while studies on community assembly often emphasize the role of contemporary ecological drivers, it has been particularly challenging to account for the effects of past processes in shaping present-day communities. In this issue of Molecular Ecology, Hao et al. (2020) provide a holistic analysis of factors driving the assembly of diverse communities of Lepidoptera in two mountain ranges in northeastern China. The authors use an impressively large data set and exceptionally comprehensive analyses to test how processes of range expansion and gene flow, speciation and extinction, dispersal limitation, environmental filtering and competition have led to present-day diversity patterns. A key novelty of this work is the exhaustive use of DNA barcodes, relatively simple yet powerful molecular markers, to tackle complex biological questions. The authors elegantly show the utility of DNA barcoding data for research beyond simple taxonomic assignment. Their approach is remarkable as it manages to integrate population genetics, phylogenetic history, species diversity and ecology into a well-rounded picture of community assembly. With this work, Hao et al. demonstrate the great promise of DNA barcoding for exhaustive community analysis of even highly diverse and complex systems, raising the bar for future research.