Comparing whole‐genome shotgun sequencing and DNA metabarcoding approaches for species identification and quantification of pollen species mixtures

Molecular identification of mixed‐species pollen samples has a range of applications in various fields of research. To date, such molecular identification has primarily been carried out via amplicon sequencing, but whole‐genome shotgun (WGS) sequencing of pollen DNA has potential advantages, including (1) more genetic information per sample and (2) the potential for better quantitative matching. In this study, we tested the performance of WGS sequencing methodology and publicly available reference sequences in identifying species and quantifying their relative abundance in pollen mock communities. Using mock communities previously analyzed with DNA metabarcoding, we sequenced approximately 200Mbp for each sample using Illumina HiSeq and MiSeq. Taxonomic identifications were based on the Kraken k‐mer identification method with reference libraries constructed from full‐genome and short read archive data from the NCBI database. We found WGS to be a reliable method for taxonomic identification of pollen with near 100% identification of species in mixtures but generating higher rates of false positives (reads not identified to the correct taxon at the required taxonomic level) relative to rbcL and ITS2 amplicon sequencing. For quantification of relative species abundance, WGS data provided a stronger correlation between pollen grain proportion and sequence read proportion, but diverged more from a 1:1 relationship, likely due to the higher rate of false positives. Currently, a limitation of WGS‐based pollen identification is the lack of representation of plant diversity in publicly available genome databases. As databases improve and costs drop, we expect that eventually genomics methods will become the methods of choice for species identification and quantification of mixed‐species pollen samples.     

A comparative study between outcomes of an in‐person versus online introductory field course

The COVID‐19 pandemic has disrupted many standard approaches to STEM education. Particularly impacted were field courses, which rely on specific natural spaces often accessed through shared vehicles. As in‐person field courses have been found to be particularly impactful for undergraduate student success in the sciences, we aimed to compare and understand what factors may have been lost or gained during the conversion of an introductory field course to an online format. Using a mixed methods approach comparing data from online and in‐person field‐course offerings, we found that while community building was lost in the online format, online participants reported increased self‐efficacy in research and observation skills and connection to their local space. The online field course additionally provided positive mental health breaks for students who described the time outside as a much‐needed respite. We maintain that through intentional design, online field courses can provide participants with similar outcomes to in‐person field courses.     

Podcasting as a tool to take conservation education online

Traditional forms of higher learning include teaching in the classroom on college campuses and in‐person adult‐focused public outreach events for non‐students. Online college degree programs and public outreach platforms have been steadily emerging, and the COVID‐19 pandemic has, at least temporarily, forced all related ecology and evolutionary biology programs to move to online delivery. Podcasting is a form of online mass communication that is rapidly gaining popularity and has the flexibility to be incorporated into the pedagogical toolbox for the online classroom and remote public outreach programming. Podcasting is also becoming more popular in the ecology and evolutionary biology field. Here, we describe the great potential of podcasting to transform the learning experience, present a case study of success from the United States, provide a table of podcast recommended by ecologist responding to a listserv, and provide a road map for adoption and utilization of podcasting for the future.     

Social environment and genetics underlie body site‐specific microbiomes of Yellowstone National Park gray wolves (Canis lupus)

The host‐associated microbiome is an important player in the ecology and evolution of species. Despite growing interest in the medical, veterinary, and conservation communities, there remain numerous questions about the primary factors underlying microbiota, particularly in wildlife. We bridged this knowledge gap by leveraging microbial, genetic, and observational data collected in a wild, pedigreed population of gray wolves (Canis lupus) inhabiting Yellowstone National Park. We characterized body site‐specific microbes across six haired and mucosal body sites (and two fecal samples) using 16S rRNA amplicon sequencing. At the phylum level, we found that the microbiome of gray wolves primarily consists of Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria, consistent with previous studies within Mammalia and Canidae. At the genus level, we documented body site‐specific microbiota with functions relevant to microenvironment and local physiological processes. We additionally employed observational and RAD sequencing data to examine genetic, demographic, and environmental correlates of skin and gut microbiota. We surveyed individuals across several levels of pedigree relationships, generations, and social groups, and found that social environment (i.e., pack) and genetic relatedness were two primary factors associated with microbial community composition to differing degrees between body sites. We additionally reported body condition and coat color as secondary factors underlying gut and skin microbiomes, respectively. We concluded that gray wolf microbiota resemble similar host species, differ between body sites, and are shaped by numerous endogenous and exogenous factors. These results provide baseline information for this long‐term study population and yield important insights into the evolutionary history, ecology, and conservation of wild wolves and their associated microbes.     

A pipeline for effectively developing highly polymorphic simple sequence repeats markers based on multi‐sample genomic data

Simple sequence repeats (SSRs) are widely used genetic markers in ecology, evolution, and conservation even in the genomics era, while a general limitation to their application is the difficulty of developing polymorphic SSR markers. Next‐generation sequencing (NGS) offers the opportunity for the rapid development of SSRs; however, previous studies developing SSRs using genomic data from only one individual need redundant experiments to test the polymorphisms of SSRs. In this study, we designed a pipeline for the rapid development of polymorphic SSR markers from multi‐sample genomic data. We used bioinformatic software to genotype multiple individuals using resequencing data, detected highly polymorphic SSRs prior to experimental validation, significantly improved the efficiency and reduced the experimental effort. The pipeline was successfully applied to a globally threatened species, the brown eared‐pheasant (Crossoptilon mantchuricum), which showed very low genomic diversity. The 20 newly developed SSR markers were highly polymorphic, the average number of alleles was much higher than the genomic average. We also evaluated the effect of the number of individuals and sequencing depth on the SSR mining results, and we found that 10 individuals and ~10X sequencing data were enough to obtain a sufficient number of polymorphic SSRs, even for species with low genetic diversity. Furthermore, the genome assembly of NGS data from the optimal number of individuals and sequencing depth can be used as an alternative reference genome if a high‐quality genome is not available. Our pipeline provided a paradigm for the application of NGS technology to mining and developing molecular markers for ecological and evolutionary studies.     

Demographic history and genetic diversity of wild African harlequin quail (Coturnix delegorguei delegorguei) populations of Kenya

Hunting wild African harlequin quails (Coturnix delegorguei delegorguei) using traditional methods in Western Kenya has been ongoing for generations, yet their genetic diversity and evolutionary history are largely unknown. In this study, the genetic variation and demographic history of wild African harlequin quails were assessed using a 347bp mitochondrial DNA (mtDNA) control region fragment and 119,339 single nucleotide polymorphisms (SNPs) from genotyping‐by‐sequencing (GBS) data. Genetic diversity analyses revealed that the genetic variation in wild African harlequin quails was predominantly among individuals than populations. Demographic analyses indicated a signal of rapid demographic expansion, and the estimated time since population expansion was found to be 150,000–350,000 years ago, corresponding to around the Pliocene–Pleistocene boundary. A gradual decline in their effective population size was also observed, which raised concerns about their conservation status. These results provide the first account of the genetic diversity of wild African harlequin quails of Siaya, thereby creating a helpful foundation in their biodiversity conservation.     

Species boundaries in the messy middle—A genome‐scale validation of species delimitation in a recently diverged lineage of coastal fog desert lichen fungi

Species delimitation among closely related species is challenging because traditional phenotype‐based approaches, for example, using morphology, ecological, or chemical characteristics, may not coincide with natural groupings. With the advent of high‐throughput sequencing, it has become increasingly cost‐effective to acquire genome‐scale data which can resolve previously ambiguous species boundaries. As the availability of genome‐scale data has increased, numerous species delimitation analyses, such as BPP and SNAPP+Bayes factor delimitation (BFD*), have been developed to delimit species boundaries. However, even empirical molecular species delimitation approaches can be biased by confounding evolutionary factors, for example, hybridization/introgression and incomplete lineage sorting, and computational limitations. Here, we investigate species boundaries and the potential for micro‐endemism in a lineage of lichen‐forming fungi, Niebla Rundel & Bowler, in the family Ramalinaceae by analyzing single‐locus and genome‐scale data consisting of (a) single‐locus species delimitation analysis using ASAP, (b) maximum likelihood‐based phylogenetic tree inference, (c) genome‐scale species delimitation models, e.g., BPP and SNAPP+BFD, and (d) species validation using the genealogical divergence index (gdi). We specifically use these methods to cross‐validate results between genome‐scale and single‐locus datasets, differently sampled subsets of genomic data and to control for population‐level genetic divergence. Our species delimitation models tend to support more speciose groupings that were inconsistent with traditional taxonomy, supporting a hypothesis of micro‐endemism, which may include morphologically cryptic species. However, the models did not converge on robust, consistent species delimitations. While the results of our analysis are somewhat ambiguous in terms of species boundaries, they provide a valuable perspective on how to use these empirical species delimitation methods in a nonmodel system. This study thus highlights the challenges inherent in delimiting species, particularly in groups such as Niebla, with complex, relatively recent phylogeographic histories.     

An open spatial capture–recapture model for estimating density,movement, and population dynamics from line‐transect surveys

The purpose of many wildlife population studies is to estimate density, movement, or demographic parameters. Linking these parameters to covariates, such as habitat features, provides additional ecological insight and can be used to make predictions for management purposes. Line‐transect surveys, combined with distance sampling methods, are often used to estimate density at discrete points in time, whereas capture–recapture methods are used to estimate movement and other demographic parameters. Recently, open population spatial capture–recapture models have been developed, which simultaneously estimate density and demographic parameters, but have been made available only for data collected from a fixed array of detectors and have not incorporated the effects of habitat covariates. We developed a spatial capture–recapture model that can be applied to line‐transect survey data by modeling detection probability in a manner analogous to distance sampling. We extend this model to a) estimate demographic parameters using an open population framework and b) model variation in density and space use as a function of habitat covariates. The model is illustrated using simulated data and aerial line‐transect survey data for North Atlantic right whales in the southeastern United States, which also demonstrates the ability to integrate data from multiple survey platforms and accommodate differences between strata or demographic groups. When individuals detected from line‐transect surveys can be uniquely identified, our model can be used to simultaneously make inference on factors that influence spatial and temporal variation in density, movement, and population dynamics.     

A genotyping‐in‐thousands by sequencing panel to inform invasive deer management using noninvasive fecal and hair samples

Studies in ecology, evolution, and conservation often rely on noninvasive samples, making it challenging to generate large amounts of high‐quality genetic data for many elusive and at‐risk species. We developed and optimized a Genotyping‐in‐Thousands by sequencing (GT‐seq) panel using noninvasive samples to inform the management of invasive Sitka black‐tailed deer (Odocoileus hemionus sitkensis) in Haida Gwaii (Canada). We validated our panel using paired high‐quality tissue and noninvasive fecal and hair samples to simultaneously distinguish individuals, identify sex, and reconstruct kinship among deer sampled across the archipelago, then provided a proof‐of‐concept application using field‐collected feces on SGang Gwaay, an island of high ecological and cultural value. Genotyping success across 244 loci was high (90.3%) and comparable to that of high‐quality tissue samples genotyped using restriction‐site associated DNA sequencing (92.4%), while genotyping discordance between paired high‐quality tissue and noninvasive samples was low (0.50%). The panel will be used to inform future invasive species operations in Haida Gwaii by providing individual and population information to inform management. More broadly, our GT‐seq workflow that includes quality control analyses for targeted SNP selection and a modified protocol may be of wider utility for other studies and systems where noninvasive genetic sampling is employed.     

The data‐index: An author‐level metric that values impactful data and incentivizes data sharing

Author‐level metrics are a widely used measure of scientific success. The h‐index and its variants measure publication output (number of publications) and research impact (number of citations). They are often used to influence decisions, such as allocating funding or jobs. Here, we argue that the emphasis on publication output and impact hinders scientific progress in the fields of ecology and evolution because it disincentivizes two fundamental practices: generating impactful (and therefore often long‐term) datasets and sharing data. We describe a new author‐level metric, the data‐index, which values both dataset output (number of datasets) and impact (number of data‐index citations), so promotes generating and sharing data as a result. We discuss how it could be implemented and provide user guidelines. The data‐index is designed to complement other metrics of scientific success, as scientific contributions are diverse and our value system should reflect that both for the benefit of scientific progress and to create a value system that is more equitable, diverse, and inclusive. Future work should focus on promoting other scientific contributions, such as communicating science, informing policy, mentoring other scientists, and providing open‐access code and tools.     

Molecular approaches reveal weak sibship aggregation and a high dispersal propensity in a non‐native fish parasite

Inferring parameters related to the aggregation pattern of parasites and to their dispersal propensity are important for predicting their ecological consequences and evolutionary potential. Nonetheless, it is notoriously difficult to infer these parameters from wildlife parasites given the difficulty in tracking these organisms. Molecular‐based inferences constitute a promising approach that has yet rarely been applied in the wild. Here, we combined several population genetic analyses including sibship reconstruction to document the genetic structure, patterns of sibship aggregation, and the dispersal dynamics of a non‐native parasite of fish, the freshwater copepod ectoparasite Tracheliastes polycolpus. We collected parasites according to a hierarchical sampling design, with the sampling of all parasites from all host individuals captured in eight sites spread along an upstream–downstream river gradient. Individual multilocus genotypes were obtained from 14 microsatellite markers, and used to assign parasites to full‐sib families and to investigate the genetic structure of T. polycolpus among both hosts and sampling sites. The distribution of full‐sibs obtained among the sampling sites was used to estimate individual dispersal distances within families. Our results showed that T. polycolpus sibs tend to be aggregated within sites but not within host individuals. We detected important upstream‐to‐downstream dispersal events of T. polycolpus between sites (modal distance: 25.4 km; 95% CI [22.9, 27.7]), becoming scarcer as the geographic distance from their family core location increases. Such a dispersal pattern likely contributes to the strong isolation‐by‐distance observed at the river scale. We also detected some downstream‐to‐upstream dispersal events (modal distance: 2.6 km; 95% CI [2.2–23.3]) that likely result from movements of infected hosts. Within each site, the dispersal of free‐living infective larvae among hosts likely contributes to increasing genetic diversity on hosts, possibly fostering the evolutionary potential of T. polycolpus.     

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture–recapture data

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles Myodes rufocanus from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.     

Implications of genetic heterogeneity for plant translocation during ecological restoration

Ecological restoration often requires translocating plant material from distant sites. Importing suitable plant material is important for successful establishment and persistence. Yet, published guidelines for seed transfer are available for very few species. Accurately predicting how transferred plants will perform requires multiyear and multi‐environment field trials and comprehensive follow‐up work, and is therefore infeasible given the number of species used in restoration programs. Alternative methods to predict the outcomes of seed transfer are valuable for species without published guidelines. In this study, we analyzed the genetic structure of an important shrub used in ecological restoration in the Southern Rocky Mountains called alder‐leaf mountain mahogany (Cercocarpus montanus). We sequenced DNA from 1,440 plants in 48 populations across a broad geographic range. We found that genetic heterogeneity among populations reflected the complex climate and topography across which the species is distributed. We identified temperature and precipitation variables that were useful predictors of genetic differentiation and can be used to generate seed transfer recommendations. These results will be valuable for defining management and restoration practices for mountain mahogany.     

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. AVAILABILITY: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.     

Genetic signature of human longevity in PKC and NF‐κB signaling

Gene variants associated with longevity are also associated with protection against cognitive decline, dementia and Alzheimer''s disease, suggesting that common physiologic pathways act at the interface of longevity and cognitive function. To test the hypothesis that variants in genes implicated in cognitive function may promote exceptional longevity, we performed a comprehensive 3‐stage study to identify functional longevity‐associated variants in ~700 candidate genes in up to 450 centenarians and 500 controls by target capture sequencing analysis. We found an enrichment of longevity‐associated genes in the nPKC and NF‐κB signaling pathways by gene‐based association analyses. Functional analysis of the top three gene variants (NFKBIA, CLU, PRKCH) suggests that non‐coding variants modulate the expression of cognate genes, thereby reducing signaling through the nPKC and NF‐κB. This matches genetic studies in multiple model organisms, suggesting that the evolutionary conservation of reduced PKC and NF‐κB signaling pathways in exceptional longevity may include humans.     

BASE: A novel workflow to integrate nonubiquitous genes in comparative genomics analyses for selection

Inferring the selective forces that orthologous genes underwent across different lineages can help us understand the evolutionary processes that have shaped their extant diversity and the phenotypes they underlie. The most widespread metric to estimate the selection regimes of coding genes—across sites and phylogenies—is the ratio of nonsynonymous to synonymous substitutions (dN/dS, also known as ω). Nowadays, modern sequencing technologies and the large amount of already available sequence data allow the retrieval of thousands of orthologous genes across large numbers of species. Nonetheless, the tools available to explore selection regimes are not designed to automatically process all genes, and their practical usage is often restricted to the single‐copy ones which are found across all species considered (i.e., ubiquitous genes). This approach limits the scale of the analysis to a fraction of single‐copy genes, which can be as low as an order of magnitude in respect to those which are not consistently found in all species considered (i.e., nonubiquitous genes). Here, we present a workflow named BASE that—leveraging the CodeML framework—eases the inference and interpretation of gene selection regimes in the context of comparative genomics. Although a number of bioinformatics tools have already been developed to facilitate this kind of analyses, BASE is the first to be specifically designed to allow the integration of nonubiquitous genes in a straightforward and reproducible manner. The workflow—along with all relevant documentation—is available at github.com/for‐giobbe/BASE.     

A new approach to interspecific synchrony in population ecology using tail association

Standard methods for studying the association between two ecologically important variables provide only a small slice of the information content of the association, but statistical approaches are available that provide comprehensive information. In particular, available approaches can reveal tail associations, that is, accentuated or reduced associations between the more extreme values of variables. We here study the nature and causes of tail associations between phenological or population‐density variables of co‐located species, and their ecological importance. We employ a simple method of measuring tail associations which we call the partial Spearman correlation. Using multidecadal, multi‐species spatiotemporal datasets on aphid first flights and marine phytoplankton population densities, we assess the potential for tail association to illuminate two major topics of study in community ecology: the stability or instability of aggregate community measures such as total community biomass and its relationship with the synchronous or compensatory dynamics of the community''s constituent species; and the potential for fluctuations and trends in species phenology to result in trophic mismatches. We find that positively associated fluctuations in the population densities of co‐located species commonly show asymmetric tail associations; that is, it is common for two species’ densities to be more correlated when large than when small, or vice versa. Ordinary measures of association such as correlation do not take this asymmetry into account. Likewise, positively associated fluctuations in the phenology of co‐located species also commonly show asymmetric tail associations. We provide evidence that tail associations between two or more species’ population‐density or phenology time series can be inherited from mutual tail associations of these quantities with an environmental driver. We argue that our understanding of community dynamics and stability, and of phenologies of interacting species, can be meaningfully improved in future work by taking into account tail associations.     

Molecular structure and evolution mechanism of two populations of double minutes in human colorectal cancer cells

Gene amplification chiefly manifests as homogeneously stained regions (HSRs) or double minutes (DMs) in cytogenetically and extrachromosomal DNA (ecDNA) in molecular genetics. Evidence suggests that gene amplification is becoming a hotspot for cancer research, which may be a new treatment strategy for cancer. DMs usually carry oncogenes or chemoresistant genes that are associated with cancer progression, occurrence and prognosis. Defining the molecular structure of DMs will facilitate understanding of the molecular mechanism of tumorigenesis. In this study, we re‐identified the origin and integral sequence of DMs in human colorectal adenocarcinoma cell line NCI‐H716 by genetic mapping and sequencing strategy, employing high‐resolution array‐based comparative genomic hybridization, high‐throughput sequencing, multiplex‐fluorescence in situ hybridization and chromosome walking techniques. We identified two distinct populations of DMs in NCI‐H716, confirming their heterogeneity in cancer cells, and managed to construct their molecular structure, which were not investigated before. Research evidence of amplicons distribution in two different populations of DMs suggested that a multi‐step evolutionary model could fit the module of DM genesis better in NCI‐H716 cell line. In conclusion, our data implicated that DMs play a very important role in cancer progression and further investigation is necessary to uncover the role of the DMs.     

Shotgun metagenomics of soil invertebrate communities reflects taxonomy,biomass, and reference genome properties

• Metagenomics – shotgun sequencing of all DNA fragments from a community DNA extract – is routinely used to describe the composition, structure, and function of microorganism communities. Advances in DNA sequencing and the availability of genome databases increasingly allow the use of shotgun metagenomics on eukaryotic communities. Metagenomics offers major advances in the recovery of biomass relationships in a sample, in comparison to taxonomic marker gene‐based approaches (metabarcoding). However, little is known about the factors which influence metagenomics data from eukaryotic communities, such as differences among organism groups, the properties of reference genomes, and genome assemblies.
• We evaluated how shotgun metagenomics records composition and biomass in artificial soil invertebrate communities at different sequencing efforts. We generated mock communities of controlled biomass ratios from 28 species from all major soil mesofauna groups: mites, springtails, nematodes, tardigrades, and potworms. We shotgun sequenced these communities and taxonomically assigned them with a database of over 270 soil invertebrate genomes.
• We recovered over 95% of the species, and observed relatively high false‐positive detection rates. We found strong differences in reads assigned to different taxa, with some groups (e.g., springtails) consistently attracting more hits than others (e.g., enchytraeids). Original biomass could be predicted from read counts after considering these taxon‐specific differences. Species with larger genomes, and with more complete assemblies, consistently attracted more reads than species with smaller genomes. The GC content of the genome assemblies had no effect on the biomass–read relationships. Results were similar among different sequencing efforts.
• The results show considerable differences in taxon recovery and taxon specificity of biomass recovery from metagenomic sequence data. The properties of reference genomes and genome assemblies also influence biomass recovery, and they should be considered in metagenomic studies of eukaryotes. We show that low‐ and high‐sequencing efforts yield similar results, suggesting high cost‐efficiency of metagenomics for eukaryotic communities. We provide a brief roadmap for investigating factors which influence metagenomics‐based eukaryotic community reconstructions. Understanding these factors is timely as accessibility of DNA sequencing and momentum for reference genomes projects show a future where the taxonomic assignment of DNA from any community sample becomes a reality.