Information-based summary statistics for spatial genetic structure inference

XHQ was supported by China Scholarship Council.The measurement of biodiversity at all levels of organization is an essential first step to understand the ecological and evolutionary processes that drive spatial patterns of biodiversity. Ecologists have explored the use of a large range of different summary statistics and have come to the view that information-based summary statistics, and in particular so-called Hill numbers, are a useful tool to measure biodiversity. Population geneticists, on the other hand, have focused largely on summary statistics based on heterozygosity and measures of allelic richness. However, recent studies proposed the adoption of information-based summary statistics in population genetics studies. Here, we performed a comprehensive assessment of the power of this family of summary statistics to inform regarding spatial patterns of genetic diversity and we compared it with that of traditional population genetics approaches, namely measures based on allelic richness and heterozygosity. To give an unbiased evaluation, we used three machine learning methods to test the performance of different sets of summary statistics to discriminate between spatial scenarios. We defined three distinct sets, (i) one based on allelic richness measures which included the Jaccard index, (ii) a set based on heterozygosity that included FST and (iii) a set based on Hill numbers derived from Shannon entropy, which included the recently proposed Shannon differentiation, ΔD. The results showed that the last of these performed as well or, under some specific spatial scenarios, even better than the traditional population genetics measures. Interestingly, we found that a rarely or never used genetic differentiation measure based on allelic richness, Jaccard dissimilarity (J), showed the highest discriminatory power to discriminate among spatial scenarios, followed by Shannon differentiation ΔD. We concluded, therefore, that information-based measures as well as Jaccard dissimilarity represent excellent additions to the population genetics toolkit.Publisher PDFPeer reviewe

Dispersal and population structure at different spatial scales in the subterranean rodent Ctenomys australis

This study was funded by grants from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, PIP5838), Agencia de Promoción Científica y Tecnológica de la Argentina (PICTO1-423, BID-1728/OC-AR), and the programme ECOS-Sud France/Argentina (A05B01).Background: The population genetic structure of subterranean rodent species is strongly affected by demographic (e.g. rates of dispersal and social structure) and stochastic factors (e.g. random genetic drift among subpopulations and habitat fragmentation). In particular, gene flow estimates at different spatial scales are essential to understand genetic differentiation among populations of a species living in a highly fragmented landscape. Ctenomys australis (the sand dune tuco-tuco) is a territorial subterranean rodent that inhabits a relatively secure, permanently sealed burrow system, occurring in sand dune habitats on the coastal landscape in the south-east of Buenos Aires province, Argentina. Currently, this habitat is threatened by urban development and forestry and, therefore, the survival of this endemic species is at risk. Here, we assess population genetic structure and patterns of dispersal among individuals of this species at different spatial scales using 8 polymorphic microsatellite loci. Furthermore, we evaluate the relative importance of sex and habitat configuration in modulating the dispersal patterns at these geographical scales. Results: Our results show that dispersal in C. australis is not restricted at regional spatial scales (similar to 4 km). Assignment tests revealed significant population substructure within the study area, providing support for the presence of two subpopulations from three original sampling sites. Finally, male-biased dispersal was found in the Western side of our study area, but in the Eastern side no apparent philopatric pattern was found, suggesting that in a more continuous habitat males might move longer distances than females. Conclusions: Overall, the assignment-based approaches were able to detect population substructure at fine geographical scales. Additionally, the maintenance of a significant genetic structure at regional (similar to 4 km) and small (less than 1 km) spatial scales despite apparently moderate to high levels of gene flow between local sampling sites could not be explained simply by the linear distance among them. On the whole, our results support the hypothesis that males disperse more frequently than females; however they do not provide support for strict philopatry within females.Publisher PDFPeer reviewe

Deep learning in population genetics

KK is supported by a grant from the Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE), GSC 81, within the project GENOMIE QADOP. We acknowledge the support of Imperial College London - TUM Partnership award.Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, con volutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.Publisher PDFPeer reviewe

KLFDAPC : a supervised machine learning approach for spatial genetic structure analysis

CSC-University of St Andrews Joint Scholarship (to X.Q.); International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) from China Postdoc Council (to X.Q.); National Institute of General Medical Sciences (NIGMS) of the National Institute of Health (grant R35GM142783 to C.W.K.C.). Part of the computation for this work is supported by USC’s Center for Advanced Research Computing (https://carc.usc.edu).Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.Publisher PDFPeer reviewe

Supplementation of Dairy Cows under Alfalfa Grazing Conditions with Ground Corn

Two trials were carried out during the Autumn of 1991 and 1992 to investigate the effects of corn supplementation on lactational performance of dairy cows under alfalfa grazing. Forty two multiparous Holstein cows with 30-60 days in milk were used in a randomized continuous design with covariance analysis. The treatments were 0.0, 3.5 and 7.0 kg.day-1 of corn grain supplementation (T1, T2 and T3 respectively) in 1991 and 0.0, 3.0, 6.0 and 9.0 kg.day-1 (T1, T2, T3 and T4 respectively) in 1992. Six cows per treatment were used divided in 3 cows per grazing paddock. Dry matter (DM) intake (DMI) was estimated weekly for each group of cows. The pasture allowance was between 22-26 kg DM.cow-1.day-1. Pasture and grain DMI were 16.6 and 0.0, 13.5 and 3.2, 13.3 and 6.1 kg.cow-1.day-1 (T1, T2 and T3 respectively) in 1991; 16.8 and 0.0, 15.7 and 2.1, 14.0 and 4.1, 12.2 and 6.3 kg.cow-1.day-1 (T1, T2, T3 and T4 respectively) in 1992. The substitution rate was 0.66 kgDM pasture per kgDM corn. There was a lineal effect of supplementation on milk production the responses were 0.936 (1991) and 1.173 (1992) kg milk kgDM-1 corn. However, non significant effects (P \u3e0.05) on fat content (32.8 and 31.9 g. kg-1 milk) and protein contents (30.7 and 30.7 g. kg-1 milk) in milk during both years (1991 and 1992 respectively) were observed

High Moisture Sorghum Grain Silage: Effects of Tannin Content and Urea Treatment on the Performance of Dairy Cows

Grain sorghum silage tannin content effect was evaluated on milk production and chemical composition of Argentinean Holstein bred cows, and it are described dry matter (DM) and crude protein (CP) in situ digestion parameters and effective degradability. The base diet was constituted by alfalfa pasture, maize silage, and a protein – mineral supplement, differing in three treatments based on high moisture grain silage characteristic: LTS= Low tannin grain sorghum, HTS= High tannin grain sorghum, and HTSu= High tannin sorghum plus the addition of urea. Grain sorghum silage tannin content affects milk production, without significant alteration of chemical composition. Beside the effect of urea addition on grain sorghum tannin content, improvement in animal response was only moderate

Perturbation drives changing metapopulation dynamics in a top marine predator

Funding: O.E.G. was supported by the Marine Alliance for Science and Technology for Scotland, funded by the Scottish Funding Council (grant no. HR09011). E.L.C. was supported by a Newton Fellowship (Royal Society of London), Marie Curie Fellowship (EU Horizon2020) and a Rutherford Discovery Fellowship (Royal Society of New Zealand). A.J.H. and D.J.F.R. were supportedby NERC (grant no. SMRU 10/001).Metapopulation theory assumes a balance between local decays/extinctions and local growth/new colonisations. Here we investigate whether recent population declines across part of the UK harbour seal range represent normal metapopulation dynamics or are indicative of perturbations potentially threatening the metapopulation viability, using 20 years of population trends, location tracking data (n = 380), and UK-wide, multi-generational population genetic data (n = 269). First, we use microsatellite data to show that two genetic groups previously identified are distinct metapopulations: northern and southern. Then, we characterize the northern metapopulation dynamics in two different periods, before and after the start of regional declines (pre-/peri-perturbation). We identify source-sink dynamics across the northern metapopulation, with two putative source populations apparently supporting three likely sink populations, and a recent metapopulation-wide disruption of migration coincident with the perturbation. The northern metapopulation appears to be in decay, highlighting that changes in local populations can lead to radical alterations in the overall metapopulation's persistence and dynamics.PostprintPeer reviewe

The DNA of coral reef biodiversity: predicting and protecting genetic diversity of reef assemblages

Conservation of ecological communities requires deepening our understanding of genetic diversity patterns and drivers at community-wide scales. Here, we use seascape genetic analysis of a diversity metric, allelic richness (AR), for 47 reef species sampled across 13 Hawaiian Islands to empirically demonstrate that large reefs high in coral cover harbour the greatest genetic diversity on average. We found that a species’s life history (e.g. depth range and herbivory) mediates response of genetic diversity to seascape drivers in logical ways. Furthermore, a metric of combined multi-species AR showed strong coupling to species richness and habitat area, quality and stability that few species showed individually. We hypothesize that macro-ecological forces and species interactions, by mediating species turnover and occupancy (and thus a site’s mean effective population size), influence the aggregate genetic diversity of a site, potentially allowing it to behave as an apparent emergent trait that is shaped by the dominant seascape drivers. The results highlight inherent feedbacks between ecology and genetics, raise concern that genetic resilience of entire reef communities is compromised by factors that reduce coral cover or available habitat, including thermal stress, and provide a foundation for new strategies for monitoring and preserving biodiversity of entire reef ecosystems

Diversity from genes to ecosystems : a unifying framework to study variation across biological metrics and scales

This work was assisted through participation in “Next Generation Genetic Monitoring” Investigative Workshop at the National Institute for Mathematical and Biological Synthesis, sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Hawaiian fish community data were provided by the NOAA Pacific Islands Fisheries Science Center's Coral Reef Ecosystem Division (CRED) with funding from NOAA Coral Reef Conservation Program. O.E.G. was supported by the Marine Alliance for Science and Technology for Scotland (MASTS). A. C. and C. H. C. were supported by the Ministry of Science and Technology, Taiwan. P.P.-N. was supported by a Canada Research Chair in Spatial Modelling and Biodiversity. K.A.S. was supported by National Science Foundation (BioOCE Award Number 1260169) and the National Center for Ecological Analysis and Synthesis. All data used in this manuscript are available in DRYAD (https://doi.org/dx.doi.org/10.5061/dryad.qm288) and BCO-DMO (http://www.bco-dmo.org/project/552879).Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organisation (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap we present a unifying framework for the measurement of biodiversity across hierarchical levels of organisation. Our weighted, information-based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon’s entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.) we applied the framework to a real dataset on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics, and eco-evolutionary dynamics.Publisher PDFPeer reviewe

Patterns of phenotypic plasticity and local adaptation in the wide elevation range of the alpine plant Arabis alpina

OEG was supported by the Marine Alliance for Science and Technology for Scotland (MASTS).1.  Local adaptation and phenotypic plasticity are two important characteristics of alpine plants to overcome the threats caused by global changes. Among alpine species, Arabis alpina is characterised by an unusually wide altitudinal amplitude, ranging from 800 to 3,100 m of elevation in the French Alps. Two non‐exclusive hypotheses can explain the presence of A. alpina across this broad ecological gradient: adaptive phenotypic plasticity or local adaptation, making this species especially useful to better understand these phenomena in alpine plant species. 2.  We carried out common garden experiments at two different elevations with maternal progenies from six sites that differed in altitude. We showed that (1) key phenotypic traits (morphotype, total fruit length, growth, height) display significant signs of local adaptation, (2) most traits studied are characterised by a high phenotypic plasticity between the two experimental gardens and (3) the two populations from the highest elevations lacked morphological plasticity compared to the other populations. 3.  By combining two genome scan approaches (detection of selection and association methods), we isolated a candidate gene (Sucrose‐Phosphate Synthase 1). This gene was associated with height and local average temperature in our studied populations, consistent with previous studies on this gene in Arabidopsis thaliana. 4.  Synthesis. Given the nature of the traits involved in the detected pattern of local adaptation and the relative lack of plasticity of the two most extreme populations, our findings are consistent with a scenario of a locally adaptive stress response syndrome in high elevation populations. Due to a reduced phenotypic plasticity, an overall low intra‐population genetic diversity of the adaptive traits and weak gene flow, populations of high altitude might have difficulties to cope with, e.g. a rise of temperature.PostprintPeer reviewe