Unraveling the genomic diversity and admixture history of captive tigers in the United States.

Genomic studies of endangered species have primarily focused on describing diversity patterns and resolving phylogenetic relationships, with the overarching goal of informing conservation efforts. However, few studies have investigated genomic diversity housed in captive populations. For tigers (Panthera tigris), captive individuals vastly outnumber those in the wild, but their diversity remains largely unexplored. Privately owned captive tiger populations have remained an enigma in the conservation community, with some believing that these individuals are severely inbred, while others believe they may be a source of now-extinct diversity. Here, we present a large-scale genetic study of the private (non-zoo) captive tiger population in the United States, also known as "Generic" tigers. We find that the Generic tiger population has an admixture fingerprint comprising all six extant wild tiger subspecies. Of the 138 Generic individuals sequenced for the purpose of this study, no individual had ancestry from only one subspecies. We show that the Generic tiger population has a comparable amount of genetic diversity relative to most wild subspecies, few private variants, and fewer deleterious mutations. We observe inbreeding coefficients similar to wild populations, although there are some individuals within both the Generic and wild populations that are substantially inbred. Additionally, we develop a reference panel for tigers that can be used with imputation to accurately distinguish individuals and assign ancestry with ultralow coverage (0.25×) data. By providing a cost-effective alternative to whole-genome sequencing (WGS), the reference panel provides a resource to assist in tiger conservation efforts for both ex- and in situ populations.

Predicting the direction of phenotypic difference.

Predicting phenotypes from genomic data is a key goal in genetics, but for most complex phenotypes, predictions are hampered by incomplete genotype-to-phenotype mapping. Here, we describe a more attainable approach than quantitative predictions, which is aimed at qualitatively predicting phenotypic differences. Despite incomplete genotype-to-phenotype mapping, we show that it is relatively easy to determine which of two individuals has a greater phenotypic value. This question is central in many scenarios, e.g., comparing disease risk between individuals, the yield of crop strains, or the anatomy of extinct vs extant species. To evaluate prediction accuracy, i.e., the probability that the individual with the greater predicted phenotype indeed has a greater phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We evaluated prediction accuracy using human data from tens of thousands of individuals from either the same family or the same population, as well as data from different species. We found that, in many cases, even when only a small fraction of the loci affecting a phenotype is known, the individual with the greater phenotypic value can be identified with over 90% accuracy. Our approach also circumvents some of the limitations in transferring genetic association results across populations. Overall, we introduce an approach that enables accurate predictions of key information on phenotypes - the direction of phenotypic difference - and suggest that more phenotypic information can be extracted from genomic data than previously appreciated.

DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data.

The ability to sequence ancient genomes has revolutionized the way we study evolutionary history by providing access to the most important aspect of evolution-time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and integrating multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool enables browsing sample metadata alongside additional layers of information, such as population structure, climatic data, and unpublished samples. Users can perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on the scientific questions that can be addressed through analysis of aDNA datasets.

Rescue by gene swamping as a gene drive deployment strategy.

Gene drives are genetic constructs that can spread deleterious alleles with potential application to population suppression of harmful species. As gene drives can potentially spill over to other populations or species, control measures and fail-safe strategies must be considered. Gene drives can generate a rapid change in the population's genetic composition, leading to substantial demographic decline, processes that are expected to occur at a similar timescale during gene drive spread. We developed a gene drive model that combines evolutionary and demographic dynamics in a two-population setting. The model demonstrates how feedback between these dynamics generates additional outcomes to those generated by the evolutionary dynamics alone. We identify an outcome of particular interest where short-term suppression of the target population is followed by gene swamping and loss of the gene drive. This outcome can prevent spillover and is robust to the evolution of resistance, suggesting it may be suitable as a fail-safe strategy for gene drive deployment.

Incorporating ecology into gene drive modelling.

Gene drive technology, in which fast-spreading engineered drive alleles are introduced into wild populations, represents a promising new tool in the fight against vector-borne diseases, agricultural pests and invasive species. Due to the risks involved, gene drives have so far only been tested in laboratory settings while their population-level behaviour is mainly studied using mathematical and computational models. The spread of a gene drive is a rapid evolutionary process that occurs over timescales similar to many ecological processes. This can potentially generate strong eco-evolutionary feedback that could profoundly affect the dynamics and outcome of a gene drive release. We, therefore, argue for the importance of incorporating ecological features into gene drive models. We describe the key ecological features that could affect gene drive behaviour, such as population structure, life-history, environmental variation and mode of selection. We review previous gene drive modelling efforts and identify areas where further research is needed. As gene drive technology approaches the level of field experimentation, it is crucial to evaluate gene drive dynamics, potential outcomes, and risks realistically by including ecological processes.

Trait-based approaches to predicting biological control success: challenges and prospects.

Identifying traits that are associated with success of introduced natural enemies in establishing and controlling pest insects has occupied researchers and biological control practitioners for decades. Unfortunately, consistent general relationships have been difficult to detect, preventing a priori ranking of candidate biological control agents based on their traits. We summarise previous efforts and propose a series of potential explanations for the lack of clear patterns. We argue that the quality of current datasets is insufficient to detect complex trait-efficacy relationships and suggest several measures by which current limitations may be overcome. We conclude that efforts to address this elusive issue have not yet been exhausted and that further explorations are likely to be worthwhile.

Extracting hierarchical features of cultural variation using network-based clustering.

High-dimensional datasets on cultural characters contribute to uncovering insights about factors that influence cultural evolution. Because cultural variation in part reflects descent processes with a hierarchical structure - including the descent of populations and vertical transmission of cultural traits - methods designed for hierarchically structured data have potential to find applications in the analysis of cultural variation. We adapt a network-based hierarchical clustering method for use in analysing cultural variation. Given a set of entities, the method constructs a similarity network, hierarchically depicting community structure among them. We illustrate the approach using four datasets: pronunciation variation in the US mid-Atlantic region, folklore variation in worldwide cultures, phonemic variation across worldwide languages and temporal variation in first names in the US. In these examples, the method provides insights into processes that affect cultural variation, uncovering geographic and other influences on observed patterns and cultural characters that make important contributions to them.

Limits to the evolution of dispersal kernels under rapid fragmentation.

Adaptive evolution of dispersal strategies is one mechanism by which species can respond to rapid environmental changes. However, under rapid anthropogenic fragmentation, the evolution of dispersal may be limited, and species may be unable to adequately adapt to fragmented landscapes. Here, we develop a spatially explicit model to investigate the evolution of dispersal kernels under various combinations of fragmentation dynamics and initial conditions. We also study the consequences of modelling an evolutionary process in which dispersal phenotypes continuously and gradually shift in phenotype space in a manner corresponding to a polygenic underlying genetic architecture. With rapid fragmentation rates, we observed the emergence of long-term transient states in which dispersal strategies are not well suited to fragmented landscapes. We also show that the extent and length of these transient states depend on the pre-fragmentation dispersal strategy of the species, as well as on the rate of the fragmentation process leading to the fragmented landscape. In an increasingly fragmented world, understanding the ability of populations to adapt, and the effects that rapid fragmentation has on the evolution of dispersal, is critical for an informed assessment of species viability in the Anthropocene.

Recent Evolutionary History of Tigers Highlights Contrasting Roles of Genetic Drift and Selection.

Species conservation can be improved by knowledge of evolutionary and genetic history. Tigers are among the most charismatic of endangered species and garner significant conservation attention. However, their evolutionary history and genomic variation remain poorly known, especially for Indian tigers. With 70% of the world's wild tigers living in India, such knowledge is critical. We re-sequenced 65 individual tiger genomes representing most extant subspecies with a specific focus on tigers from India. As suggested by earlier studies, we found strong genetic differentiation between the putative tiger subspecies. Despite high total genomic diversity in India, individual tigers host longer runs of homozygosity, potentially suggesting recent inbreeding or founding events, possibly due to small and fragmented protected areas. We suggest the impacts of ongoing connectivity loss on inbreeding and persistence of Indian tigers be closely monitored. Surprisingly, demographic models suggest recent divergence (within the last 20,000 years) between subspecies and strong population bottlenecks. Amur tiger genomes revealed the strongest signals of selection related to metabolic adaptation to cold, whereas Sumatran tigers show evidence of weak selection for genes involved in body size regulation. We recommend detailed investigation of local adaptation in Amur and Sumatran tigers prior to initiating genetic rescue.

Designing gene drives to limit spillover to non-target populations.

The prospect of utilizing CRISPR-based gene-drive technology for controlling populations has generated much excitement. However, the potential for spillovers of gene-drive alleles from the target population to non-target populations has raised concerns. Here, using mathematical models, we investigate the possibility of limiting spillovers to non-target populations by designing differential-targeting gene drives, in which the expected equilibrium gene-drive allele frequencies are high in the target population but low in the non-target population. We find that achieving differential targeting is possible with certain configurations of gene-drive parameters, but, in most cases, only under relatively low migration rates between populations. Under high migration, differential targeting is possible only in a narrow region of the parameter space. Because fixation of the gene drive in the non-target population could severely disrupt ecosystems, we outline possible ways to avoid this outcome. We apply our model to two potential applications of gene drives-field trials for malaria-vector gene drives and control of invasive species on islands. We discuss theoretical predictions of key requirements for differential targeting and their practical implications.

Network-based hierarchical population structure analysis for large genomic data sets.

Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic data sets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these data sets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community-detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We show the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrate how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic data sets.

Disease transmission and introgression can explain the long-lasting contact zone of modern humans and Neanderthals.

Neanderthals and modern humans both occupied the Levant for tens of thousands of years prior to the spread of modern humans into the rest of Eurasia and their replacement of the Neanderthals. That the inter-species boundary remained geographically localized for so long is a puzzle, particularly in light of the rapidity of its subsequent movement. Here, we propose that infectious-disease dynamics can explain the localization and persistence of the inter-species boundary. We further propose, and support with dynamical-systems models, that introgression-based transmission of alleles related to the immune system would have gradually diminished this barrier to pervasive inter-species interaction, leading to the eventual release of the inter-species boundary from its geographic localization. Asymmetries between the species in the characteristics of their associated 'pathogen packages' could have generated feedback that allowed modern humans to overcome disease burden earlier than Neanderthals, giving them an advantage in their subsequent spread into Eurasia.

Are both necessity and opportunity the mothers of innovations?

Baumard's perspective asserts that "opportunity is the mother of innovation," in contrast to the adage ascribing this role to necessity. Drawing on behavioral ecology and cognition, we propose that both extremes - affluence and scarcity - can drive innovation. We suggest that the types of innovations at these two extremes differ and that both rely on mechanisms operating on different time scales.

Ecological dynamics of the vaginal microbiome in relation to health and disease.

The bacterial composition of the vaginal microbiome is thought to be related to health and disease states of women. This microbiome is particularly dynamic, with compositional changes related to pregnancy, menstruation, and disease states such as bacterial vaginosis. In order to understand these dynamics and their impact on health and disease, ecological theories have been introduced to study the complex interactions between the many taxa in the vaginal bacterial ecosystem. The goal of this review is to introduce the ecological principles that are used in the study of the vaginal microbiome and its dynamics, and to review the application of ecology to vaginal microbial communities with respect to health and disease. Although applications of vaginal microbiome analysis and modulation have not yet been introduced into the routine clinical setting, a deeper understanding of its dynamics has the potential to facilitate development of future practices, for example in the context of postmenopausal vaginal symptoms, stratifying risk for obstetric complications, and controlling sexually transmitted infections.

Detecting hierarchical levels of connectivity in a population of Acacia tortilis at the northern edge of the species' global distribution: Combining classical population genetics and network analyses.

Genetic diversity and structure of populations at the edge of the species' spatial distribution are important for potential adaptation to environmental changes and consequently, for the long-term survival of the species. Here, we combined classical population genetic methods with newly developed network analyses to gain complementary insights into the genetic structure and diversity of Acacia tortilis, a keystone desert tree, at the northern edge of its global distribution, where the population is under threat from climatic, ecological, and anthropogenic changes. We sampled A. tortilis from 14 sites along the Dead Sea region and the Arava Valley in Israel and in Jordan. In addition, we obtained samples from Egypt and Sudan, the hypothesized origin of the species. Samples from all sites were genotyped using six polymorphic microsatellite loci.Our results indicate a significant genetic structure in A. tortilis along the Arava Valley. This was detected at different hierarchical levels-from the basic unit of the subpopulation, corresponding to groups of trees within ephemeral rivers (wadis), to groups of subpopulations (communities) that are genetically more connected relative to others. The latter structure mostly corresponds to the partition of the major drainage basins in the area. Network analyses, combined with classical methods, allowed for the identification of key A. tortilis subpopulations in this region, characterized by their relatively high level of genetic diversity and centrality in maintaining gene flow in the population. Characterizing such key subpopulations may enable conservation managers to focus their efforts on certain subpopulations that might be particularly important for the population's long-term persistence, thus contributing to species conservation within its peripheral range.

Revealing life-history traits by contrasting genetic estimations with predictions of effective population size.

Effective population size, a central concept in conservation biology, is now routinely estimated from genetic surveys and can also be theoretically predicted from demographic, life-history, and mating-system data. By evaluating the consistency of theoretical predictions with empirically estimated effective size, insights can be gained regarding life-history characteristics and the relative impact of different life-history traits on genetic drift. These insights can be used to design and inform management strategies aimed at increasing effective population size. We demonstrated this approach by addressing the conservation of a reintroduced population of Asiatic wild ass (Equus hemionus). We estimated the variance effective size (Nev ) from genetic data (N ev =24.3) and formulated predictions for the impacts on Nev of demography, polygyny, female variance in lifetime reproductive success (RS), and heritability of female RS. By contrasting the genetic estimation with theoretical predictions, we found that polygyny was the strongest factor affecting genetic drift because only when accounting for polygyny were predictions consistent with the genetically measured Nev . The comparison of effective-size estimation and predictions indicated that 10.6% of the males mated per generation when heritability of female RS was unaccounted for (polygyny responsible for 81% decrease in Nev ) and 19.5% mated when female RS was accounted for (polygyny responsible for 67% decrease in Nev ). Heritability of female RS also affected Nev ; hf2=0.91 (heritability responsible for 41% decrease in Nev ). The low effective size is of concern, and we suggest that management actions focus on factors identified as strongly affecting Nev, namely, increasing the availability of artificial water sources to increase number of dominant males contributing to the gene pool. This approach, evaluating life-history hypotheses in light of their impact on effective population size, and contrasting predictions with genetic measurements, is a general, applicable strategy that can be used to inform conservation practice.

Asymmetric disease dynamics in multihost interconnected networks.

Epidemic spread in single-host systems strongly depends on the population's transmission network. However, little is known regarding the spread of epidemics across networks representing populations of multiple hosts. We explored cross-species transmission in a multilayer network where layers represent populations of two distinct hosts, and disease can spread across intralayer (within-host) and interlayer (between-host) edges. We developed an analytic framework for the SIR epidemic model to examine the effect of (i) source of infection and (ii) between-host asymmetry in infection probabilities, on disease risk. We measured risk as outbreak probability and outbreak size in a focal host, represented by one network layer. Numeric simulations were used to validate the analytic formulations. We found that outbreak probability is determined by a complex interaction between source of infection and between-host infection probabilities, whereas outbreak size is mainly affected by the non-focal host to focal host infection probability. Hence, inter-specific asymmetry in infection probabilities shapes disease dynamics in multihost networks. These results highlight the importance of considering multiple measures of disease risk and advance our understanding of disease spread in multihost systems. The study provides a flexible way to model disease dynamics in multiple hosts while considering contact heterogeneity within and between species. We strongly encourage empirical studies that include information on both cross-species infection rates and network structure of multiple hosts. Such studies are necessary to corroborate our theoretical results and to improve our understanding of multihost epidemiology.

Application of network methods for understanding evolutionary dynamics in discrete habitats.

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.