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

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.

Genetic approaches reveal a healthy population and an unexpectedly recent origin for an isolated desert spring fish

Foskett Spring in Oregon's desert harbors a historically threatened population of Western Speckled Dace (Rhinichthys klamathensis). Though recently delisted, the dace's recruitment depends upon regular removal of encroaching vegetation. Previous studies assumed that Foskett Dace separated from others in the Warner Valley about 10,000 years ago, thereby framing an enigma about the population's surprising ability to persist for so long in a tiny habitat easily overrun by plants. To investigate that persistence and the effectiveness of interventions to augment population size, we assessed genetic diversity among daces inhabiting Foskett Spring, a refuge at Dace Spring, and three nearby streams. Analysis revealed a robust effective population size (Ne) of nearly 5000 within Foskett Spring, though Ne in the Dace Spring refuge is just 10% of that value. Heterozygosity is slightly lower than expected based on random mating at all five sites, indicating mild inbreeding, but not at a level of concern. These results confirm the genetic health of Foskett Dace. Unexpectedly, genetic differentiation reveals closer similarity between Foskett Dace and a newly discovered population from Nevada's Coleman Creek than between Foskett Dace and dace elsewhere in Oregon. Demographic modeling inferred Coleman Creek as the ancestral source of Foskett Dace fewer than 1000 years ago, much more recently than previously suspected and possibly coincident with the arrival of large herbivores whose grazing may have maintained open water suitable for reproduction. These results solve the enigma of persistence by greatly shortening the duration over which Foskett Dace have inhabited their isolated spring.

AdmixPipe v3: facilitating population structure delimitation from SNP data

Summary

Quantifying genetic clusters (=populations) from genotypic data is a fundamental, but non-trivial task for population geneticists that is compounded by: hierarchical population structure, diverse analytical methods, and complex software dependencies. AdmixPipe v3 ameliorates many of these issues in a single bioinformatic pipeline that facilitates all facets of population structure analysis by integrating outputs generated by several popular packages (i.e. CLUMPAK, EvalAdmix). The pipeline interfaces disparate software packages to parse Admixture outputs and conduct EvalAdmix analyses in the context of multimodal population structure results identified by CLUMPAK. We further streamline these tasks by packaging AdmixPipe v3 within a Docker container to create a standardized analytical environment that allows for complex analyses to be replicated by different researchers. This also grants operating system flexibility and mitigates complex software dependencies.

Availability and implementation

Source code, documentation, example files, and usage examples are freely available at https://github.com/stevemussmann/admixturePipeline. Installation is facilitated via Docker container available from https://hub.docker.com/r/mussmann/admixpipe. Usage under Windows operating systems requires the Windows Subsystem for Linux.

The Himalayan uplift and evolution of aquatic biodiversity across Asia: Snowtrout (Cyprininae: Schizothorax) as a test case

Global biodiversity hotspots are often remote, tectonically active areas undergoing climatic fluctuations, such as the Himalaya Mountains and neighboring Qinghai-Tibetan Plateau (QTP). They provide biogeographic templates upon which endemic biodiversity can be mapped to infer diversification scenarios. Yet, this process can be somewhat opaque for the Himalaya, given substantial data gaps separating eastern and western regions. To help clarify, we evaluated phylogeographic and phylogenetic hypotheses for a widespread fish (Snowtrout: Cyprininae; Schizothorax) by sequencing 1,140 base pair of mtDNA cytochrome-b (cytb) from Central Himalaya samples (Nepal: N = 53; Bhutan: N = 19), augmented with 68 GenBank sequences (N = 60 Schizothorax/N = 8 outgroups). Genealogical relationships (N = 132) were analyzed via maximum likelihood (ML), Bayesian (BA), and haplotype network clustering, with clade divergence estimated via TimeTree. Snowtrout seemingly originated in Central Asia, dispersed across the QTP, then into Bhutan via southward-flowing tributaries of the east-flowing Yarlung-Tsangpo River (YLTR). Headwaters of five large Asian rivers provided dispersal corridors from Central into eastern/southeastern Asia. South of the Himalaya, the YLTR transitions into the Brahmaputra River, facilitating successive westward colonization of Himalayan drainages first in Bhutan, then Nepal, followed by far-western drainages subsequently captured by the (now) westward-flowing Indus River. Two distinct Bhutanese phylogenetic groups were recovered: Bhutan-1 (with three subclades) seemingly represents southward dispersal from the QTP; Bhutan-2 apparently illustrates northward colonization from the Lower Brahmaputra. The close phylogenetic/phylogeographic relationships between the Indus River (Pakistan) and western tributaries of the Upper Ganges (India/Nepal) potentially implicate an historic, now disjunct connection. Greater species-divergences occurred across rather than within-basins, suggesting vicariance as a driver. The Himalaya is a component of the Earth's largest glacial reservoir (i.e., the "third-pole") separate from the Arctic/Antarctic. Its unique aquatic biodiversity must be defined and conserved through broad, trans-national collaborations. Our study provides an initial baseline for this process.

Incipient speciation in allopatric Etheostoma rupestre (Percidae: Etheostomatinae) lineages, with the description of three new subspecies

In recent years, new species descriptions for the North American darters have proliferated. Most species concepts accepted by contemporary ichthyologists require that a valid species be both monophyletic and diagnoseable, yet many lineages exhibit modal or range differences in morphological characteristics without individuals being diagnosable. Such scenarios present difficulties with regards to proper taxonomic recognition of divergent lineages and often prohibit appropriate conservation action. Following the example of recent authors, we provide meristic, geometric morphometric, and pigmentation data to support the recognition of three subspecies of Etheostoma rupestre, a species endemic to the Mobile Basin. These morphological data cohere with previous genetic work for E. rupestre. The nominate subspecies Etheostoma rupetsre rupestre (Tsais Rock Darter) is endemic to the Tombigbee River and Black Warrior River watersheds in Alabama and Mississippi and is characterized by having lower numbers of lateral blotches, lower range and mean of lateral line scales, lower modal number of scales above the lateral line, and lower degrees of nape squamation than other subspecies. Etheostoma rupestre piersoni (Shamrock Darter), ssp. nov., is endemic to the Cahaba and Alabama River Watersheds in Alabama and is characterized by intermediate counts of lateral blotches and higher scale counts and nape squamation than E. r. rupestre. Etheostoma rupestre uphapeense (Jade Darter), ssp. nov., is restricted to several small, disjunct populations in the Coosa and Tallapoosa watersheds in Alabama, Georgia, and Tennessee. Etheostoma r. uphapeense is characterized by having a higher mean number of lateral blotches than both other subspecies and higher scale counts than E. r. rupestre. While E. r. rupestre and E. r. piersoni are widespread and abundant within their respective ranges, E. r. uphapeense has a disjunct range and is often uncommon where it occurs. Etheostoma r. uphapeense should be monitored where it occurs to discern population trends.

Fish out of water: Genomic insights into persistence of rainbowfish populations in the desert

How populations of aquatic fauna persist in extreme desert environments is an enigma. Individuals often breed and disperse during favorable conditions. Theory predicts that adaptive capacity should be low in small populations, such as in desert fishes. We integrated satellite-derived surface water data and population genomic diversity from 20,294 single-nucleotide polymorphisms across 344 individuals to understand metapopulation persistence of the desert rainbowfish (Melanotaenia splendida tatei) in central Australia. Desert rainbowfish showed very small effective population sizes, especially at peripheral populations, and low connectivity between river catchments. Yet, there was no evidence of population-level inbreeding and a signal of possible adaptive divergence associated with aridity was detected. Candidate genes for local adaptation included functions related to environmental cues and stressful conditions. Eco-evolutionary modeling showed that positive selection in refugial subpopulations combined with connectivity during flood periods can enable retention of adaptive diversity. Our study suggests that adaptive variation can be maintained in small populations and integrate with neutral metapopulation processes to allow persistence in the desert.

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.