Biology subjects

Hedin, M.

Publications and source records attributed to Hedin, M..

3 recordsLinked to original sources

bioRxiv2021

The challenge of delimiting cryptic species, and a supervised machine learning solution

AO_SCPLOWBSTRACTC_SCPLOWThe 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 dataset 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, and uses modern computational tools ideally suited for species delimitation while also considering the biology and natural history of organisms to make more biologically informed species delimitation decisions. In principle, this approach is universally applicable for species delimitation of any taxon with genetic data, particularly for cryptic species.

evolutionary biology↗

bioRxiv2019

Phylogenetic systematics and evolution of the spider infraorder Mygalomorphae using genomic scale data

AO_SCPLOWBSTRACTC_SCPLOWThe Infraorder Mygalomorphae is one of the three main lineages of spiders comprising over 3,000 nominal species. This ancient group has a world-wide distribution that includes among its ranks large and charismatic taxa such as tarantulas, trapdoor spiders, and highly venomous funnel web spiders. Based on past molecular studies using Sanger-sequencing approaches, numerous mygalomorph families (e.g., Hexathelidae, Ctenizidae, Cyrtaucheniidae, Dipluridae and Nemesiidae) have been identified as non-monophyletic. However, these data were unable to sufficiently resolve the higher-level (intra- and interfamilial) relationships such that the necessary changes in classification could be made with confidence. Here we present the most comprehensive phylogenomic treatment of the spider infraorder Mygalomorphae conducted to date. We employ 472 loci obtained through Anchored Hybrid Enrichment to reconstruct relationships among all the mygalomorph spider families and estimate the timeframe of their diversification. We performed an extensive generic sampling of all currently recognized families, which has allowed us to assess their status, and as a result, propose a new classification scheme. Our generic-level sampling has also provided an evolutionary framework for revisiting questions regarding silk use in mygalomorph spiders. The first such analysis for the group within a strict phylogenetic framework shows that a sheet web is likely the plesiomorphic condition for mygalomorphs, as well as providing hints to the ancestral foraging behavior for all spiders. Our divergence time estimates, concomitant with detailed biogeographic analysis, suggest that both ancient continental-level vicariance and more recent dispersal events have played an important role in shaping modern day distributional patterns. Based on our results, we relimit the generic composition of the Ctenizidae, Cyrtaucheniidae, Dipluridae and Nemesiidae. We also elevate five subfamilies to family rank: Anamidae (NEW RANK), Euagridae (NEW RANK), Ischnothelidae (NEW RANK), Pycnothelidae (NEW RANK), and Bemmeridae (NEW RANK). The three families Hermachidae (NEW FAMILY), Microhexuridae (NEW FAMILY), and Stasimopidae (NEW FAMILY) are newly proposed. Such a major rearrangement in classification, recognizing eight newly established family-level rank taxa, is the largest the group has seen in over three decades since Ravens (1985) taxonomic treatment.

evolutionary biology↗

bioRxiv2018

An Empirical Demonstration of Unsupervised Machine Learning in Species Delimitation

One major challenge to delimiting species with genetic data is successfully differentiating species divergences from population structure, with some current methods biased towards overestimating species numbers. Many fields of science are now utilizing machine learning (ML) approaches, and in systematics and evolutionary biology, supervised ML algorithms have recently been incorporated to infer species boundaries. However, these methods require the creation of training data with associated labels. Unsupervised ML, on the other hand, uses the inherent structure in data and hence does not require any user-specified training labels, thus providing a more objective approach to species delimitation. In the context of integrative taxonomy, we demonstrate the utility of three unsupervised ML approaches, specifically random forests, variational autoencoders, and t-distributed stochastic neighbor embedding, for species delimitation utilizing a short-range endemic harvestman taxon (Laniatores, Metanonychus). First, we combine mitochondrial data with examination of male genitalic morphology to identify a priori species hypotheses. Then we use single nucleotide polymorphism data derived from sequence capture of ultraconserved elements (UCEs) to test the efficacy of unsupervised ML algorithms in successfully identifying a priori species, comparing results to commonly used genetic approaches. Finally, we use two validation methods to assess a priori species hypotheses using UCE data. We find that unsupervised ML approaches successfully cluster samples according to species level divergences and not to high levels of population structure, while standard model-based validation methods over-split species, in some instances suggesting that all sampled individuals are distinct species. Moreover, unsupervised ML approaches offer the benefits of better data visualization in two-dimensional space and the ability to accommodate various data types. We argue that ML methods may be better suited for species delimitation relative to currently used model-based validation methods, and that species delimitation in a truly integrative framework provides more robust final species hypotheses relative to separating delimitation into distinct \"discovery\" and \"validation\" phases. Unsupervised ML is a powerful analytical approach that can be incorporated into many aspects of systematic biology, including species delimitation. Based on results of our empirical dataset, we make several taxonomic changes including description of a new species.

evolutionary biology↗