A demonstration of unsupervised machine learning in species delimitation

Machine learning approaches delimit cryptic taxa in a previously intractable species complex

Cryptic species are not diagnosable via morphological criteria, but can be detected through analysis of DNA sequences. A number of methods have been developed for identifying species based on genetic data; however, these methods are prone to over-splitting taxa with extreme population structure, such as dispersal-limited organisms. Machine learning methodologies have the potential to overcome this challenge. Here, we apply such approaches, using a large dataset generated through hybrid target enrichment of ultraconserved elements (UCEs). Our study taxon is the Aoraki denticulata species complex, a lineage of extremely low-dispersal arachnids endemic to the South Island of Aotearoa New Zealand. This group of mite harvesters has been the subject of previous species delimitation studies using smaller datasets generated through Sanger sequencing and analytical approaches that rely on multispecies coalescent models and barcoding gap discovery. Those analyses yielded a number of putative cryptic species that seems unrealistic and extreme, based on what we know about species' geographic ranges and genetic diversity in non-cryptic mite harvesters. We find that machine learning approaches, on the other hand, identify cryptic species with geographic ranges that are similar to those seen in other morphologically diagnosable mite harvesters in Aotearoa New Zealand's South Island. We performed both unsupervised and supervised machine learning analyses, the latter with training data drawn either from animals broadly (vagile and non-vagile) or from a custom training dataset from dispersal-limited harvesters. We conclude that applying machine learning approaches to the analysis of UCE-derived genetic data is an effective method for delimiting species in complexes of low-vagility cryptic species, and that the incorporation of training data from biologically relevant analogues can be critically informative.

A new long-read mitochondrial-genome protocol (PacBio HiFi) for haemosporidian parasites: a tool for population and biodiversity studies

BACKGROUND

Studies on haemosporidian diversity, including origin of human malaria parasites, malaria's zoonotic dynamic, and regional biodiversity patterns, have used target gene approaches. However, current methods have a trade-off between scalability and data quality. Here, a long-read Next-Generation Sequencing protocol using PacBio HiFi is presented. The data processing is supported by a pipeline that uses machine-learning for analysing the reads.

METHODS

A set of primers was designed to target approximately 6 kb, almost the entire length of the haemosporidian mitochondrial genome. Amplicons from different samples were multiplexed in an SMRTbell® library preparation. A pipeline (HmtG-PacBio Pipeline) to process the reads is also provided; it integrates multiple sequence alignments, a machine-learning algorithm that uses modified variational autoencoders, and a clustering method to identify the mitochondrial haplotypes/species in a sample. Although 192 specimens could be studied simultaneously, a pilot experiment with 15 specimens is presented, including in silico experiments where multiple data combinations were tested.

RESULTS

The primers amplified various haemosporidian parasite genomes and yielded high-quality mt genome sequences. This new protocol allowed the detection and characterization of mixed infections and co-infections in the samples. The machine-learning approach converged into reproducible haplotypes with a low error rate, averaging 0.2% per read (minimum of 0.03% and maximum of 0.46%). The minimum recommended coverage per haplotype is 30X based on the detected error rates. The pipeline facilitates inspecting the data, including a local blast against a file of provided mitochondrial sequences that the researcher can customize.

CONCLUSIONS

This is not a diagnostic approach but a high-throughput method to study haemosporidian sequence assemblages and perform genotyping by targeting the mitochondrial genome. Accordingly, the methodology allowed for examining specimens with multiple infections and co-infections of different haemosporidian parasites. The pipeline enables data quality assessment and comparison of the haplotypes obtained to those from previous studies. Although a single locus approach, whole mitochondrial data provide high-quality information to characterize species pools of haemosporidian parasites.

Pleistocene glaciation advances the cryptic speciation of Stellera chamaejasme L. in a major biodiversity hotspot

The mountains of Southwest China comprise a significant large mountain range and biodiversity hotspot imperiled by global climate change. The high species diversity in this mountain system has long been attributed to a complex set of factors, and recent large-scale macroevolutionary investigations have placed a broad timeline on plant diversification that stretches from 10 million years ago (Mya) to the present. Despite our increasing understanding of the temporal mode of speciation, finer-scale population-level investigations are lacking to better refine these temporal trends and illuminate the abiotic and biotic influences of cryptic speciation. This is largely due to the dearth of organismal sampling among closely related species and populations, spanning the incredible size and topological heterogeneity of this region. Our study dives into these evolutionary dynamics of speciation using genomic and eco-morphological data of Stellera chamaejasme L. We identified four previously unrecognized cryptic species having indistinct morphological traits and large metapopulation of evolving lineages, suggesting a more recent diversification (~2.67-0.90 Mya), largely influenced by Pleistocene glaciation and biotic factors. These factors likely influenced allopatric speciation and advocated cyclical warming-cooling episodes along elevational gradients during the Pleistocene. The study refines the evolutionary timeline to be much younger than previously implicated and raises the concern that projected future warming may influence the alpine species diversity, necessitating increased conservation efforts.

Microgeographic population structuring in a genus of California trapdoor spiders and discovery of an enigmatic new species (Euctenizidae: Promyrmekiaphila korematsui sp. nov.)

The recognition and delineation of cryptic species remains a perplexing problem in systematics, evolution, and species delimitation. Once recognized as such, cryptic species complexes provide fertile ground for studying genetic divergence within the context of phenotypic and ecological divergence (or lack thereof). Herein we document the discovery of a new cryptic species of trapdoor spider, Promyrmekiaphila korematsui sp. nov. Using subgenomic data obtained via target enrichment, we document the phylogeography of the California endemic genus Promyrmekiaphila and its constituent species, which also includes P. clathrata and P. winnemem. Based on these data we show a pattern of strong geographic structuring among populations but cannot entirely discount recent gene flow among populations that are parapatric, particularly for deeply diverged lineages within P. clathrata. The genetic data, in addition to revealing a new undescribed species, also allude to a pattern of potential phenotypic differentiation where species likely come into close contact. Alternatively, phenotypic cohesion among genetically divergent P. clathrata lineages suggests that some level of gene flow is ongoing or occurred in the recent past. Despite considerable field collection efforts over many years, additional sampling in potential zones of contact for both species and lineages is needed to completely resolve the dynamics of divergence in Promyrmekiaphila at the population-species interface.

Discerning structure versus speciation in phylogeographic analysis of Seepage Salamanders (Desmognathus aeneus) using demography, environment, geography, and phenotype

Numerous mechanisms can drive speciation, including isolation by adaptation, distance, and environment. These forces can promote genetic and phenotypic differentiation of local populations, the formation of phylogeographic lineages, and ultimately, completed speciation. However, conceptually similar mechanisms may also result in stabilizing rather than diversifying selection, leading to lineage integration and the long-term persistence of population structure within genetically cohesive species. Processes that drive the formation and maintenance of geographic genetic diversity while facilitating high rates of migration and limiting phenotypic differentiation may thereby result in population genetic structure that is not accompanied by reproductive isolation. We suggest that this framework can be applied more broadly to address the classic dilemma of "structure" versus "species" when evaluating phylogeographic diversity, unifying population genetics, species delimitation, and the underlying study of speciation. We demonstrate one such instance in the Seepage Salamander (Desmognathus aeneus) from the southeastern United States. Recent studies estimated up to 6.3% mitochondrial divergence and four phylogenomic lineages with broad admixture across geographic hybrid zones, which could potentially represent distinct species supported by our species-delimitation analyses. However, while limited dispersal promotes substantial isolation by distance, microhabitat specificity appears to yield stabilizing selection on a single, uniform, ecologically mediated phenotype. As a result, climatic cycles promote recurrent contact between lineages and repeated instances of high migration through time. Subsequent hybridization is apparently not counteracted by adaptive differentiation limiting introgression, leaving a single unified species with deeply divergent phylogeographic lineages that nonetheless do not appear to represent incipient species.

The Artefactual Branch Effect and Phylogenetic Conflict: Species Delimitation with Gene Flow in Mangrove Pit Vipers (Trimeresurus purpureomaculatus-erythrurus Complex)

Mangrove pit vipers of the Trimeresurus purpureomaculatus-erythrurus complex are the only species of viper known to naturally inhabit mangroves. Despite serving integral ecological functions in mangrove ecosystems, the evolutionary history, distribution, and species boundaries of mangrove pit vipers remain poorly understood, partly due to overlapping distributions, confusing phenotypic variations, and the lack of focused studies. Here, we present the first genomic study on mangrove pit vipers and introduce a robust hypothesis-driven species delimitation framework that considers gene flow and phylogenetic uncertainty in conjunction with a novel application of a new class of speciation-based delimitation model implemented through the program Delineate. Our results showed that gene flow produced phylogenetic conflict in our focal species and substantiates the artefactual branch effect where highly admixed populations appear as divergent nonmonophyletic lineages arranged in a stepwise manner at the basal position of clades. Despite the confounding effects of gene flow, we were able to obtain unequivocal support for the recognition of a new species based on the intersection and congruence of multiple lines of evidence. This study demonstrates that an integrative hypothesis-driven approach predicated on the consideration of multiple plausible evolutionary histories, population structure/differentiation, gene flow, and the implementation of a speciation-based delimitation model can effectively delimit species in the presence of gene flow and phylogenetic conflict.

Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation

Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely solely or primarily on molecular data, with other layers often incorporated only in a post hoc qualitative or comparative manner. A major limitation is the difficulty of devising quantitative parametric models linking different datasets in a unified ecological and evolutionary framework. Machine Learning (ML) methods offer flexibility in this arena by easily learning high-dimensional associations between observations (e.g., individual specimens) across a wide array of input features (e.g., genetics, geography, environment, and phenotype) to delimit statistically meaningful clusters. Here, I implement an unsupervised method using Self-Organizing (or "Kohonen") Maps (SOMs) for such purposes. Recent extensions called "SuperSOMs" can integrate multiple layers, each of which exerts independent influence on a two-dimensional output grid via empirically estimated weights. The grid cells are then delimited into K distinct units that can be interpreted as species or other entities. I show empirical examples in salamanders (Desmognathus) and snakes (Storeria) with layers representing alleles, space, climate, and traits. Simulations reveal that the SuperSOM approach can detect K = 1, tends not to over-split, reflects contributions from all layers, and limits large layers (e.g., genetic matrices) from overwhelming other datasets, desirable properties addressing major concerns from previous studies. Finally, I suggest that these and similar methods could integrate conservation-relevant layers such as population trends and human encroachment to delimit management units from an explicitly quantitative framework grounded in the ecology and evolution of species limits and boundaries.

From beasts to bytes: Revolutionizing zoological research with artificial intelligence

Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.

An Opiliones-specific ultraconserved element probe set with a near-complete family-level phylogeny

Sequence capture of ultraconserved elements (UCEs) has transformed molecular systematics across many taxa, with arachnids being no exception. The probe set available for Arachnida has been repeatedly used across multiple arachnid lineages and taxonomic levels, however more specific probe sets for spiders have demonstrated that more UCEs can be recovered with higher probe specificity. In this study, we develop an Opiliones-specific UCE probe set targeting 1915 UCEs using a combination of probes designed from genomes and transcriptomes, as well as the most useful probes from the Arachnida probe set. We demonstrate the effectiveness of this probe set across Opiliones with the most complete family-level phylogeny made to date, including representatives from 61 of 63 currently described families. We also test UCE recovery from historical specimens with degraded DNA, examine population-level data sets, and assess "backwards compatibility" with samples hybridized with the Arachnida probe set. The resulting phylogenies - which include specimens hybridized using both the Opiliones and Arachnida probe sets, historical specimens, and transcriptomes - are largely congruent with previous multi-locus and phylogenomic analyses. The probe set is also "backwards compatible", increasing the number of loci obtained in samples previously hybridized with the Arachnida probe set, and shows high utility down to shallow population-level divergences. This probe set has the potential to further transform Opiliones molecular systematics, resolving many long-standing taxonomic issues plaguing this lineage.

Phylogeography and cohesion species delimitation of California endemic trapdoor spiders within the Aptostichus icenoglei sibling species complex (Araneae: Mygalomorphae: Euctenizidae)

Species delimitation is an imperative first step toward understanding Earth's biodiversity, yet what constitutes a species and the relative importance of the various processes by which new species arise continue to be debatable. Species delimitation in spiders has traditionally used morphological characters; however, certain mygalomorph spiders exhibit morphological homogeneity despite long periods of population-level isolation, absence of gene flow, and consequent high degrees of molecular divergence. Studies have shown strong geographic structuring and significant genetic divergence among several species complexes within the trapdoor spider genus Aptostichus, most of which are restricted to the California Floristic Province (CAFP) biodiversity hotspot. Specifically, the Aptostichus icenoglei complex, which comprises the three sibling species, A. barackobamai, A. isabella, and A. icenoglei, exhibits evidence of cryptic mitochondrial DNA diversity throughout their ranges in Northern, Central, and Southern California. Our study aimed to explicitly test species hypotheses within this assemblage by implementing a cohesion species-based approach. We used genomic-scale data (ultraconserved elements, UCEs) to first evaluate genetic exchangeability and then assessed ecological interchangeability of genetic lineages. Biogeographical analysis was used to assess the likelihood of dispersal versus vicariance events that may have influenced speciation pattern and process across the CAFP's complex geologic and topographic landscape. Considering the lack of congruence across data types and analyses, we take a more conservative approach by retaining species boundaries within A. icenoglei.

Species delimitation using genomic data to resolve taxonomic uncertainties in a speciation continuum of pelagic seabirds

Speciation is a continuous and complex process shaped by the interaction of numerous evolutionary forces. Despite the continuous nature of the speciation process, the implementation of conservation policies relies on the delimitation of species and evolutionary significant units (ESUs). Puffinus shearwaters are globally distributed and threatened pelagic seabirds. Due to remarkable morphological status the group has been under intense taxonomic debate for the past three decades. Here, we use double digest Restriction-Site Associated DNA sequencing (ddRAD-Seq) to genotype species and subspecies of North Atlantic and Mediterranean Puffinus shearwaters across their entire geographical range. We assess the phylogenetic relationships and population structure among and within the group, evaluate species boundaries, and characterise the genomic landscape of divergence. We find that current taxonomies are not supported by genomic data and propose a more accurate taxonomy by integrating genomic information with other sources of evidence. Our results show that several taxon pairs are at different stages of a speciation continuum. Our study emphasises the potential of genomic data to resolve taxonomic uncertainties, which can help to focus management actions on relevant taxa, even if they do not necessarily coincide with the taxonomic rank of species.

High-resolution species assignment of Anopheles mosquitoes using k-mer distances on targeted sequences

The ANOSPP amplicon panel is a genus-wide targeted sequencing panel to facilitate large-scale monitoring of Anopheles species diversity. Combining information from the 62 nuclear amplicons present in the ANOSPP panel allows for a more senstive and specific species assignment than single gene (e.g. COI) barcoding, which is desirable in the light of permeable species boundaries. Here, we present NNoVAE, a method using Nearest Neighbours (NN) and Variational Autoencoders (VAE), which we apply to k-mers resulting from the ANOSPP amplicon sequences in order to hierarchically assign species identity. The NN step assigns a sample to a species-group by comparing the k-mers arising from each haplotype's amplicon sequence to a reference database. The VAE step is required to distinguish between closely related species, and also has sufficient resolution to reveal population structure within species. In tests on independent samples with over 80% amplicon coverage, NNoVAE correctly classifies to species level 98% of samples within the An. gambiae complex and 89% of samples outside the complex. We apply NNoVAE to over two thousand new samples from Burkina Faso and Gabon, identifying unexpected species in Gabon. NNoVAE presents an approach that may be of value to other targeted sequencing panels, and is a method that will be used to survey Anopheles species diversity and Plasmodium transmission patterns through space and time on a large scale, with plans to analyse half a million mosquitoes in the next five years.

Combining Species Delimitation, Species Trees, and Tests for Gene Flow Clarifies Complex Speciation in Scrub-Jays

Complex speciation, involving rapid divergence and multiple bouts of post-divergence gene flow, can obfuscate phylogenetic relationships and species limits. In North America, cases of complex speciation are common, due at least in part to the cyclical Pleistocene glacial history of the continent. Scrub-jays in the genus Aphelocoma provide a useful case study in complex speciation because their range throughout North America is structured by phylogeographic barriers with multiple cases of secondary contact between divergent lineages. Here, we show that a comprehensive approach to genomic reconstruction of evolutionary history, i.e., synthesizing results from species delimitation, species tree reconstruction, demographic model testing, and tests for gene flow, is capable of clarifying evolutionary history despite complex speciation. We find concordant evidence across all statistical approaches for the distinctiveness of an endemic southern Mexico lineage (A. w. sumichrasti), culminating in support for the species status of this lineage under any commonly applied species concept. We also find novel genomic evidence for the species status of a Texas endemic lineage A. w. texana, for which equivocal species delimitation results were clarified by demographic modeling and spatially explicit models of gene flow. Finally, we find that complex signatures of both ancient and modern gene flow between the non-sister California Scrub-Jay (A. californica) and Woodhouse's Scrub-Jay (A. woodhouseii), result in discordant gene trees throughout the species' genomes despite clear support for their overall isolation and species status. In sum, we find that a multi-faceted approach to genomic analysis can increase our understanding of complex speciation histories, even in well-studied groups. Given the emerging recognition that complex speciation is relatively commonplace, the comprehensive framework that we demonstrate for interrogation of species limits and evolutionary history using genomic data can provide a necessary roadmap for disentangling the impacts of gene flow and incomplete lineage sorting to better understand the systematics of other groups with similarly complex evolutionary histories.

Evolutionary Genetics of Cacti: Research Biases, Advances and Prospects

Here, we present a review of the studies of evolutionary genetics (phylogenetics, population genetics, and phylogeography) using genetic data as well as genome scale assemblies in Cactaceae (Caryophyllales, Angiosperms), a major lineage of succulent plants with astonishing diversity on the American continent. To this end, we performed a literature survey (1992–2021) to obtain detailed information regarding key aspects of studies investigating cactus evolution. Specifically, we summarize the advances in the following aspects: molecular markers, species delimitation, phylogenetics, hybridization, biogeography, and genome assemblies. In brief, we observed substantial growth in the studies conducted with molecular markers in the past two decades. However, we found biases in taxonomic/geographic sampling and the use of traditional markers and statistical approaches. We discuss some methodological and social challenges for engaging the cactus community in genomic research. We also stressed the importance of integrative approaches, coalescent methods, and international collaboration to advance the understanding of cactus evolution.

Phylogenomics of paleoendemic lampshade spiders (Araneae, Hypochilidae, Hypochilus), with the description of a new species from montane California

Hypochilus is a relictual lineage of Nearctic spiders distributed disjunctly across the United States in three montane regions (California, southern Rocky Mountains, southern Appalachia). Phylogenetic resolution of species relationships in Hypochilus has been challenging, and conserved morphology coupled with extreme genetic divergence has led to uncertain species limits in some complexes. Here, Hypochilus interspecies relationships have been reconstructed and cryptic speciation more critically evaluated using a combination of ultraconserved elements, mitochondrial CO1 by-catch, and morphology. Phylogenomic data strongly support the monophyly of regional clades and support a ((California, Appalachia), southern Rocky Mountains) topology. In Appalachia, five species are resolved as four lineages (H.thorelli Marx, 1888 and H.coylei Platnick, 1987 are clearly sister taxa), but the interrelationships of these four lineages remain unresolved. The Appalachian species H.pococki Platnick, 1987 is recovered as monophyletic but is highly genetically structured at the nuclear level. While algorithmic analyses of nuclear data indicate many species (e.g., all H.pococki populations as species), male morphology instead reveals striking stasis. Within the California clade, nuclear and mitochondrial lineages of H.petrunkevitchi Gertsch, 1958 correspond directly to drainage basins of the southern Sierra Nevada, with H.bernardino Catley, 1994 nested within H.petrunkevitchi and sister to the southernmost basin populations. Combining nuclear, mitochondrial, geographical, and morphological evidence a new species from the Tule River and Cedar Creek drainages is described, Hypochilusxomotesp. nov. We also emphasize the conservation issues that face several microendemic, habitat-specialized species in this remarkable genus.

Using natural history to guide supervised machine learning for cryptic species delimitation with genetic data

The diversity of biological and ecological characteristics of organisms, and the underlying genetic patterns and processes of speciation, makes the development of universally applicable genetic species delimitation methods challenging. Many approaches, like those incorporating the multispecies coalescent, sometimes delimit populations and overestimate species numbers. This issue is exacerbated in taxa with inherently high population structure due to low dispersal ability, and in cryptic species resulting from nonecological speciation. These taxa present a conundrum when delimiting species: analyses rely heavily, if not entirely, on genetic data which over split species, while other lines of evidence lump. We showcase this conundrum in the harvester Theromaster brunneus, a low dispersal taxon with a wide geographic distribution and high potential for cryptic species. Integrating morphology, mitochondrial, and sub-genomic (double-digest RADSeq and ultraconserved elements) data, we find high discordance across analyses and data types in the number of inferred species, with further evidence that multispecies coalescent approaches over split. We demonstrate the power of a supervised machine learning approach in effectively delimiting cryptic species by creating a "custom" training data set derived from a well-studied lineage with similar biological characteristics as Theromaster. This novel approach uses known taxa with particular biological characteristics to inform unknown taxa with similar characteristics, using modern computational tools ideally suited for species delimitation. The approach also considers the natural history of organisms to make more biologically informed species delimitation decisions, and in principle is broadly applicable for taxa across the tree of life.

Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data

For epidemic prevention and control, the identification of SARS-CoV-2 subpopulations sharing similar micro-epidemiological patterns and evolutionary histories is necessary for a more targeted investigation into the links among COVID-19 outbreaks caused by SARS-CoV-2 with similar genetic backgrounds. Genomic sequencing analysis has demonstrated the ability to uncover viral genetic diversity. However, an objective analysis is necessary for the identification of SARS-CoV-2 subpopulations. Herein, we detected all the mutations in 186 682 SARS-CoV-2 isolates. We found that the GC content of the SARS-CoV-2 genome had evolved to be lower, which may be conducive to viral spread, and the frameshift mutation was rare in the global population. Next, we encoded the genomic mutations in binary form and used an unsupervised learning classifier, namely PhenoGraph, to classify this information. Consequently, PhenoGraph successfully identified 303 SARS-CoV-2 subpopulations, and we found that the PhenoGraph classification was consistent with, but more detailed and precise than the known GISAID clades (S, L, V, G, GH, GR, GV and O). By the change trend analysis, we found that the growth rate of SARS-CoV-2 diversity has slowed down significantly. We also analyzed the temporal, spatial and phylogenetic relationships among the subpopulations and revealed the evolutionary trajectory of SARS-CoV-2 to a certain extent. Hence, our results provide a better understanding of the patterns and trends in the genomic evolution and epidemiology of SARS-CoV-2.

Combining genomic, phenotypic and Sanger sequencing data to elucidate the phylogeny of the two-clawed spiders (Dionycha)

The importance of morphology in the phylogenomic era has recently gained attention, but relatively few studies have combined both types of information when inferring phylogenetic relationships. Sanger sequencing legacy data can also be important for understanding evolutionary relationships. The possibility of combining genomic, morphological and Sanger data in one analysis seems compelling, permitting a more complete sampling and yielding a comprehensive view of the evolution of a group. Here we used these three data types to elucidate the systematics and evolution of the Dionycha, a highly diverse group of spiders relatively underrepresented in phylogenetic studies. The datasets were analyzed separately and combined under different inference methods, including a novel approach for analyzing morphological matrices with commonly used evolutionary models. We tested alternative hypotheses of relationships and performed simulations to investigate the accuracy of our findings. We provide a comprehensive and thorough phylogenetic hypothesis for Dionycha that can serve as a robust framework to test hypotheses about the evolution of key characters. We also show that morphological data might have a phylogenetic impact, even when massively outweighed by molecular data. Our approach to analyze morphological data may serve as an alternative to the proposed practice of arbitrarily partitioning, weighting, and choosing between parsimony and stochastic models. As a result of our findings, we propose Trachycosmidae new rank for a group of Australian genera formerly included in Trochanteriidae and Gallieniellidae, and consider Ammoxenidae as a junior synonym of Gnaphosidae. We restore the family rank for Prodidomidae, but transfer the subfamily Molycriinae to Gnaphosidae. Drassinella is transferred to Liocranidae, Donuea to Corinnidae, and Mahafalytenus to Viridasiidae.

Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system

Delimiting species boundaries is a major goal in evolutionary biology. An increasing volume of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and demographic fluctuations. New methods based on model selection, such as approximate Bayesian computation, approximate likelihoods, and machine learning are promising tools arising in this field. Here, we introduce a framework for species delimitation using the multispecies coalescent model coupled with a deep learning algorithm based on convolutional neural networks (CNNs). We compared this strategy with a similar ABC approach. We applied both methods to test species boundary hypotheses based on current and previous taxonomic delimitations as well as genetic data (sequences from 41 loci) in Pilosocereus aurisetus, a cactus species complex with a sky-island distribution and taxonomic uncertainty. To validate our method, we also applied the same strategy on data from widely accepted species from the genus Drosophila. The results show that our CNN approach has a high capacity to distinguish among the simulated species delimitation scenarios, with higher accuracy than ABC. For the cactus data set, a splitter hypothesis without gene flow showed the highest probability in both CNN and ABC approaches, a result agreeing with previous taxonomic classifications and in line with the sky-island distribution and low dispersal of P. aurisetus. Our results highlight the cryptic diversity within the P. aurisetus complex and show that CNNs are a promising approach for distinguishing complex evolutionary histories, even outperforming the accuracy of other model-based approaches such as ABC.

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.

Excluding loci with substitution saturation improves inferences from phylogenomic data

The historical signal in nucleotide sequences becomes eroded over time by substitutions occurring repeatedly at the same sites. This phenomenon, known as substitution saturation, is recognized as one of the primary obstacles to deep-time phylogenetic inference using genome-scale data sets. We present a new test of substitution saturation and demonstrate its performance in simulated and empirical data. For some of the 36 empirical phylogenomic data sets that we examined, we detect substitution saturation in around 50% of loci. We found that saturation tends to be flagged as problematic in loci with highly discordant phylogenetic signals across sites. Within each data set, the loci with smaller numbers of informative sites are more likely to be flagged as containing problematic levels of saturation. The entropy saturation test proposed here is sensitive to high evolutionary rates relative to the evolutionary timeframe, while also being sensitive to several factors known to mislead phylogenetic inference, including short internal branches relative to external branches, short nucleotide sequences, and tree imbalance. Our study demonstrates that excluding loci with substitution saturation can be an effective means of mitigating the negative impact of multiple substitutions on phylogenetic inferences. [Phylogenetic model performance; phylogenomics; substitution model; substitution saturation; test statistics.]

Species in lichen-forming fungi: balancing between conceptual and practical considerations, and between phenotype and phylogenomics

Lichens are symbiotic associations resulting from interactions among fungi (primary and secondary mycobionts), algae and/or cyanobacteria (primary and secondary photobionts), and specific elements of the bacterial microbiome associated with the lichen thallus. The question of what is a species, both concerning the lichen as a whole and its main fungal component, the primary mycobiont, has faced many challenges throughout history and has reached new dimensions with the advent of molecular phylogenetics and phylogenomics. In this paper, we briefly revise the definition of lichens and the scientific and vernacular naming conventions, concluding that the scientific, Latinized name usually associated with lichens invariably refers to the primary mycobiont, whereas the vernacular name encompasses the entire lichen. Although the same lichen mycobiont may produce different phenotypes when associating with different photobionts or growing in axenic culture, this discrete variation does not warrant the application of different scientific names, but must follow the principle "one fungus = one name". Instead, broadly agreed informal designations should be used for such discrete morphologies, such as chloromorph and cyanomorph for lichens formed by the same mycobiont but with either green algae or cyanobacteria. The taxonomic recognition of species in lichen-forming fungi is not different from other fungi and conceptual and nomenclatural approaches follow the same principles. We identify a number of current challenges and provide recommendations to address these. Species delimitation in lichen-forming fungi should not be tailored to particular species concepts but instead be derived from empirical evidence, applying one or several of the following principles in what we call the LPR approach: lineage (L) coherence vs. divergence (phylogenetic component), phenotype (P) coherence vs. divergence (morphological component), and/or reproductive (R) compatibility vs. isolation (biological component). Species hypotheses can be established based on either L or P, then using either P or L (plus R) to corroborate them. The reliability of species hypotheses depends not only on the nature and number of characters but also on the context: the closer the relationship and/or similarity between species, the higher the number of characters and/or specimens that should be analyzed to provide reliable delimitations. Alpha taxonomy should follow scientific evidence and an evolutionary framework but should also offer alternative practical solutions, as long as these are scientifically defendable. Taxa that are delimited phylogenetically but not readily identifiable in the field, or are genuinely cryptic, should not be rejected due to the inaccessibility of proper tools. Instead, they can be provisionally treated as undifferentiated complexes for purposes that do not require precise determinations. The application of infraspecific (gamma) taxonomy should be restricted to cases where there is a biological rationale, i.e., lineages of a species complex that show limited phylogenetic divergence but no evidence of reproductive isolation. Gamma taxonomy should not be used to denote discrete phenotypical variation or ecotypes not warranting the distinction at species level. We revise the species pair concept in lichen-forming fungi, which recognizes sexually and asexually reproducing morphs with the same underlying phenotype as different species. We conclude that in most cases this concept does not hold, but the actual situation is complex and not necessarily correlated with reproductive strategy. In cases where no molecular data are available or where single or multi-marker approaches do not provide resolution, we recommend maintaining species pairs until molecular or phylogenomic data are available. This recommendation is based on the example of the species pair Usnea aurantiacoatra vs. U. antarctica, which can only be resolved with phylogenomic approaches, such as microsatellites or RADseq. Overall, we consider that species delimitation in lichen-forming fungi has advanced dramatically over the past three decades, resulting in a solid framework, but that empirical evidence is still missing for many taxa. Therefore, while phylogenomic approaches focusing on particular examples will be increasingly employed to resolve difficult species complexes, broad screening using single barcoding markers will aid in placing as many taxa as possible into a molecular matrix. We provide a practical protocol how to assess and formally treat taxonomic novelties. While this paper focuses on lichen fungi, many of the aspects discussed herein apply generally to fungal taxonomy. The new combination Arthonia minor (Lücking) Lücking comb. et stat. nov. (Bas.: Arthonia cyanea f. minor Lücking) is proposed.

Gene Flow Increases Phylogenetic Structure and Inflates Cryptic Species Estimations: A Case Study on Widespread Philippine Puddle Frogs (Occidozyga laevis)

In cryptic amphibian complexes, there is a growing trend to equate high levels of genetic structure with hidden cryptic species diversity. Typically, phylogenetic structure and distance-based approaches are used to demonstrate the distinctness of clades and justify the recognition of new cryptic species. However, this approach does not account for gene flow, spatial, and environmental processes that can obfuscate phylogenetic inference and bias species delimitation. As a case study, we sequenced genome-wide exons and introns to evince the processes that underlie the diversification of Philippine Puddle Frogs-a group that is widespread, phenotypically conserved, and exhibits high levels of geographically-based genetic structure. We showed that widely adopted tree- and distance-based approaches inferred up to 20 species, compared to genomic analyses that inferred an optimal number of five distinct genetic groups. Using a suite of clustering, admixture, and phylogenetic network analyses, we demonstrate extensive admixture among the five groups and elucidate two specific ways in which gene flow can cause overestimations of species diversity: (1) admixed populations can be inferred as distinct lineages characterized by long branches in phylograms; and (2) admixed lineages can appear to be genetically divergent, even from their parental populations when simple measures of genetic distance are used. We demonstrate that the relationship between mitochondrial and genome-wide nuclear p-distances is decoupled in admixed clades, leading to erroneous estimates of genetic distances and, consequently, species diversity. Additionally, genetic distance was also biased by spatial and environmental processes. Overall, we showed that high levels of genetic diversity in Philippine Puddle Frogs predominantly comprise metapopulation lineages that arose through complex patterns of admixture, isolation-by-distance, and isolation-by-environment as opposed to species divergence. Our findings suggest that speciation may not be the major process underlying the high levels of hidden diversity observed in many taxonomic groups and that widely-adopted tree- and distance-based methods overestimate species diversity in the presence of gene flow.

An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods

Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtained from double digestion restriction site-associated DNA sequencing (ddRADseq) for species in the family Antarctophilinidae. We also reevaluated the fossil record associated with this taxon to provide further insights into the origin of the group. Novel approaches to identify distinctive genetic lineages, including unsupervised machine learning variational autoencoder plots, were used to establish species hypothesis frameworks. In this sense, three undescribed species and a complex of cryptic species were identified, suggesting allopatric speciation connected to geographic or bathymetric isolation. We further observed that the shallow waters around the Scotia Arc and on the continental shelf in the Weddell Sea present high endemism and diversity. In contrast, likely due to the glacial pressure during the Cenozoic, a deep-sea group with fewer species emerged expanding over great areas in the South-Atlantic Antarctic Ridge. Our study agrees on how diachronic paleoclimatic and current environmental factors shaped Antarctic communities both at the shallow and deep-sea levels, promoting Antarctica as the center of origin for numerous taxa such as gastropod mollusks.

The choices we make and the impacts they have: Machine learning and species delimitation in North American box turtles (Terrapene spp.)

Model-based approaches that attempt to delimit species are hampered by computational limitations as well as the unfortunate tendency by users to disregard algorithmic assumptions. Alternatives are clearly needed, and machine-learning (M-L) is attractive in this regard as it functions without the need to explicitly define a species concept. Unfortunately, its performance will vary according to which (of several) bioinformatic parameters are invoked. Herein, we gauge the effectiveness of M-L-based species-delimitation algorithms by parsing 64 variably-filtered versions of a ddRAD-derived SNP data set collected from North American box turtles (Terrapene spp.). Our filtering strategies included: (i) minor allele frequencies (MAF) of 5%, 3%, 1%, and 0% (= none), and (ii) maximum missing data per-individual/per-population at 25%, 50%, 75%, and 100% (= no filtering). We found that species-delimitation via unsupervised M-L impacted the signal-to-noise ratio in our data, as well as the discordance among resolved clades. The latter may also reflect biogeographic history, gene flow, incomplete lineage sorting, or combinations thereof (as corroborated from previously observed patterns of differential introgression). Our results substantiate M-L as a viable species-delimitation method, but also demonstrate how commonly observed patterns of phylogenetic discordance can seriously impact M-L-classification.

Visualizing population structure with variational autoencoders

Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs)-generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data-for visualizing population genetic variation. VAEs incorporate nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.

A revised phylogeny of the New Caledonian endemic genus Troglosiro (Opiliones : Cyphophthalmi : Troglosironidae) with the description of four new species

The Cyphophthalmi genus Troglosiro (the only genus of the family Troglosironidae) is endemic to New Caledonia, representing one of the oldest lineages of this emerged part of Zealandia. Its species are short-range endemics, many known from single localities. Here we examined the phylogenetic relationships of Troglosironidae using standard Sanger-sequenced markers (nuclear 18S rRNA, 28S rRNA, and mitochondrial 16S rRNA and cytochrome c oxidase subunit I) and a combination of phylogenetic methods, including parsimony under Direct Optimization and maximum likelihood with static homology. We also applied a diversity of species delimitation methods, including distance-based, topology-based and unsupervised machine learning to evaluate previous species designations. Finally, we used a combination of genetic and morphological information to describe four new species – T. dogny sp. nov., T. pin sp. nov., T. pseudojuberthiei sp. nov. and T. sharmai sp. nov. – and discuss them in the broader context of the phylogeny and biogeographic history of the family. A key to the species of Troglosiro is also provided. urn:lsid:zoobank.org:pub:93541314-8309-468C-BB77-B34C3A81137E

Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance.

Ultraconserved yet informative for species delimitation: Ultraconserved elements resolve long-standing systematic enigma in Central European bees

Accurate and testable species hypotheses are essential for measuring, surveying and managing biodiversity. Taxonomists often rely on mitochondrial DNA barcoding to complement morphological species delimitations. Although COI-barcoding has largely proven successful in assisting identifications for most animal taxa, there are nevertheless numerous cases where mitochondrial barcodes do not reflect species hypotheses. For instance, what is regarded as a single species can be associated with two distinct DNA barcodes, which can point either to cryptic diversity or to within-species mitochondrial divergences without reproductive isolation. In contrast, two or more species can share barcodes, for instance due to mitochondrial introgression. These intrinsic limitations of DNA barcoding are commonly addressed with nuclear genomic markers, which are expensive, may have low repeatability and often require high-quality DNA. To overcome these limitations, we examined the use of ultraconserved elements (UCEs) as a quick and robust genomic approach to address such problematic cases of species delimitation in bees. This genomic method was assessed using six different species complexes suspected to harbour cryptic diversity, mitochondrial introgression or mitochondrial paraphyly. The sequencing of UCEs recovered between 686 and 1,860 homologous nuclear loci and provided explicit species delimitation in all investigated species complexes. These results provide strong evidence for the suitability of UCEs as a fast method for species delimitation even in recently diverged lineages. Furthermore, we provide the first evidence for both mitochondrial introgression among distinct bee species, and mitochondrial paraphyly within a single bee species.

New approaches to species delimitation and population structure of anthozoans: Two case studies of octocorals using ultraconserved elements and exons

As coral populations decline worldwide in the face of ongoing environmental change, documenting their distribution, diversity and conservation status is now more imperative than ever. Accurate delimitation and identification of species is a critical first step. This task, however, is not trivial as morphological variation and slowly evolving molecular markers confound species identification. New approaches to species delimitation in corals are needed to overcome these challenges. Here, we test whether target enrichment of ultraconserved elements (UCEs) and exons can be used for delimiting species boundaries and population structure within species of corals by focusing on two octocoral genera, Alcyonium and Sinularia, as exemplary case studies. We designed an updated bait set (29,181 baits) to target-capture 3,023 UCE and exon loci, recovering a mean of 1,910 ± 168 SD per sample with a mean length of 1,055 ± 208 bp. Similar numbers of loci were recovered from Sinularia (1,946 ± 227 SD) and Alcyonium (1,863 ± 177 SD). Species-level phylogenies were highly supported for both genera. Clustering methods based on filtered single nucleotide polymorphisms delimited species and populations that are congruent with previous allozyme, DNA barcoding, reproductive and ecological data for Alcyonium, and offered further evidence of hybridization among species. For Sinularia, results were congruent with those obtained from a previous study using restriction site associated DNA sequencing. Both case studies demonstrate the utility of target-enrichment of UCEs and exons to address a wide range of evolutionary and taxonomic questions across deep to shallow timescales in corals.

Defining relictual biodiversity: Conservation units in speckled dace (Leuciscidae: Rhinichthys osculus) of the Greater Death Valley ecosystem

The tips in the tree of life serve as foci for conservation and management, yet clear delimitations are masked by inherent variance at the species-population interface. Analyses using thousands of nuclear loci can potentially sort inconsistencies, yet standard categories applied to this parsing are themselves potentially conflicting and/or subjective [e.g., DPS (distinct population segments); DUs (Diagnosable Units-Canada); MUs (management units); SSP (subspecies); ESUs (Evolutionarily Significant Units); and UIEUs (uniquely identified evolutionary units)]. One potential solution for consistent categorization is to create a comparative framework by accumulating statistical results from independent studies and evaluating congruence among data sets. Our study illustrates this approach in speckled dace (Leuciscidae: Rhinichthys osculus) endemic to two basins (Owens and Amargosa) in the Death Valley ecosystem. These fish persist in the Mojave Desert as isolated Plio-Pleistocene relicts and are of conservation concern, but lack formal taxonomic descriptions/designations. Double digest RAD (ddRAD) methods identified 14,355 SNP loci across 10 populations (N = 140). Species delimitation analyses [multispecies coalescent (MSC) and unsupervised machine learning (UML)] delineated four putative ESUs. F _ST outlier loci (N = 106) were juxtaposed to uncover the potential for localized adaptations. We detected one hybrid population that resulted from upstream reconnection of habitat following contemporary pluvial periods, whereas remaining populations represent relics of ancient tectonism within geographically isolated springs and groundwater-fed streams. Our study offers three salient conclusions: a blueprint for a multifaceted delimitation of conservation units; a proposed mechanism by which criteria for intraspecific biodiversity can be potentially standardized; and a strong argument for the proactive management of critically endangered Death Valley ecosystem fishes.

Molecular species delimitation in the primitively segmented spider genus Heptathela endemic to Japanese islands

Determining species boundaries forms an important foundation for biological research. However, the results of molecular species delimitation can vary with the data sets and methods that are used. Here we use a two-step approach to delimit species in the genus Heptathela, a group of primitively segmented trapdoor spiders that are endemic to Japanese islands. Morphological evidence suggests the existence of 19 species in the genus. We tested this initial species hypothesis by using six molecular species-delimitation methods to analyse 180 mitochondrial COI sequences of Heptathela sampled from across the known range of the genus. We then conducted a set of more focused analyses by sampling additional genetic markers from the subset of taxa that were inconsistently delimited by the single-locus analyses of mitochondrial DNA. Multilocus species delimitation was performed using two Bayesian approaches based on the multispecies coalescent. Our approach identified 20 putative species among the 180 sampled individuals of Heptathela. We suggest that our two-step approach provides an efficient strategy for delimiting species while minimizing costs and computational time.

Integrative species delimitation reveals cryptic diversity in the southern Appalachian Antrodiaetus unicolor (Araneae: Antrodiaetidae) species complex

Although species delimitation can be highly contentious, the development of reliable methods to accurately ascertain species boundaries is an imperative step in cataloguing and describing Earth's quickly disappearing biodiversity. Spider species delimitation remains largely based on morphological characters; however, many mygalomorph spider populations are morphologically indistinguishable from each other yet have considerable molecular divergence. The focus of our study, the Antrodiaetus unicolor species complex containing two sympatric species, exhibits this pattern of relative morphological stasis with considerable genetic divergence across its distribution. A past study using two molecular markers, COI and 28S, revealed that A. unicolor is paraphyletic with respect to A. microunicolor. To better investigate species boundaries in the complex, we implement the cohesion species concept and use multiple lines of evidence for testing genetic exchangeability and ecological interchangeability. Our integrative approach includes extensively sampling homologous loci across the genome using a RADseq approach (3RAD), assessing population structure across their geographic range using multiple genetic clustering analyses that include structure, principal components analysis and a recently developed unsupervised machine learning approach (Variational Autoencoder). We evaluate ecological similarity by using large-scale ecological data for niche-based distribution modelling. Based on our analyses, we conclude that this complex has at least one additional species as well as confirm species delimitations based on previous less comprehensive approaches. Our study demonstrates the efficacy of genomic-scale data for recognizing cryptic species, suggesting that species delimitation with one data type, whether one mitochondrial gene or morphology, may underestimate true species diversity in morphologically homogenous taxa with low vagility.