Dataset Information

Whole-genome data reveal the complex history of a diverse ecological community.

ABSTRACT: How widespread ecological communities assemble remains a key question in ecology. Trophic interactions between widespread species may reflect a shared population history or ecological fitting of local pools of species with very different population histories. Which scenario applies is central to the stability of trophic associations and the potential for coevolution between species. Here we show how alternative community assembly hypotheses can be discriminated using whole-genome data for component species and provide a likelihood framework that overcomes current limitations in formal comparison of multispecies histories. We illustrate our approach by inferring the assembly history of a Western Palearctic community of insect herbivores and parasitoid natural enemies, trophic groups that together comprise 50% of terrestrial species. We reject models of codispersal from a shared origin and of delayed enemy pursuit of their herbivore hosts, arguing against herbivore attainment of "enemy-free space." The community-wide distribution of species expansion times is also incompatible with a random, neutral model of assembly. Instead, we reveal a complex assembly history of single- and multispecies range expansions through the Pleistocene from different directions and over a range of timescales. Our results suggest substantial turnover in species associations and argue against tight coevolution in this system. The approach we illustrate is widely applicable to natural communities of nonmodel species and makes it possible to reveal the historical backdrop against which natural selection acts.

SUBMITTER: Bunnefeld L

PROVIDER: S-EPMC6048486 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Whole-genome data reveal the complex history of a diverse ecological community.

Bunnefeld Lynsey L Hearn Jack J Stone Graham N GN Lohse Konrad K

Proceedings of the National Academy of Sciences of the United States of America 20180626 28

How widespread ecological communities assemble remains a key question in ecology. Trophic interactions between widespread species may reflect a shared population history or ecological fitting of local pools of species with very different population histories. Which scenario applies is central to the stability of trophic associations and the potential for coevolution between species. Here we show how alternative community assembly hypotheses can be discriminated using whole-genome data for compon ...[more]

PMID: 29946026

Similar Datasets

Project description:Profound knowledge of demographic history is a prerequisite for the understanding and inference of processes involved in the evolution of population differentiation and speciation. Together with new coalescent-based methods, the recent availability of genome-wide data enables investigation of differentiation and divergence processes at unprecedented depth. We combined two powerful approaches, full Approximate Bayesian Computation analysis (ABC) and pairwise sequentially Markovian coalescent modeling (PSMC), to reconstruct the demographic history of the split between two avian speciation model species, the pied flycatcher and collared flycatcher. Using whole-genome re-sequencing data from 20 individuals, we investigated 15 demographic models including different levels and patterns of gene flow, and changes in effective population size over time. ABC provided high support for recent (mode 0.3 my, range <0.7 my) species divergence, declines in effective population size of both species since their initial divergence, and unidirectional recent gene flow from pied flycatcher into collared flycatcher. The estimated divergence time and population size changes, supported by PSMC results, suggest that the ancestral species persisted through one of the glacial periods of middle Pleistocene and then split into two large populations that first increased in size before going through severe bottlenecks and expanding into their current ranges. Secondary contact appears to have been established after the last glacial maximum. The severity of the bottlenecks at the last glacial maximum is indicated by the discrepancy between current effective population sizes (20,000-80,000) and census sizes (5-50 million birds) of the two species. The recent divergence time challenges the supposition that avian speciation is a relatively slow process with extended times for intrinsic postzygotic reproductive barriers to evolve. Our study emphasizes the importance of using genome-wide data to unravel tangled demographic histories. Moreover, it constitutes one of the first examples of the inference of divergence history from genome-wide data in non-model species.

Project description:BackgroundThe most prolific duck genetic resource in the world is located in Southeast/South Asia but little is known about the domestication and complex histories of these duck populations.ResultsBased on whole-genome resequencing data of 78 ducks (Anas platyrhynchos) and 31 published whole-genome duck sequences, we detected three geographic distinct genetic groups, including local Chinese, wild, and local Southeast/South Asian populations. We inferred the demographic history of these duck populations with different geographical distributions and found that the Chinese and Southeast/South Asian ducks shared similar demographic features. The Chinese domestic ducks experienced the strongest population bottleneck caused by domestication and the last glacial maximum (LGM) period, whereas the Chinese wild ducks experienced a relatively weak bottleneck caused by domestication only. Furthermore, the bottleneck was more severe in the local Southeast/South Asian populations than in the local Chinese populations, which resulted in a smaller effective population size for the former (7100-11,900). We show that extensive gene flow has occurred between the Southeast/South Asian and Chinese populations, and between the Southeast Asian and South Asian populations. Prolonged gene flow was detected between the Guangxi population from China and its neighboring Southeast/South Asian populations. In addition, based on multiple statistical approaches, we identified a genomic region that included three genes (PNPLA8, THAP5, and DNAJB9) on duck chromosome 1 with a high probability of gene flow between the Guangxi and Southeast/South Asian populations. Finally, we detected strong signatures of selection in genes that are involved in signaling pathways of the nervous system development (e.g., ADCYAP1R1 and PDC) and in genes that are associated with morphological traits such as cell growth (e.g., IGF1R).ConclusionsOur findings provide valuable information for a better understanding of the domestication and demographic history of the duck, and of the gene flow between local duck populations from Southeast/South Asia and China.

Project description:BackgroundA critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known.ResultsTo better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We first assessed the performance of these algorithms and tuned them on an influenza data set, for which the determinants of three complex phenotypes (infectivity, transmissibility, and pathogenicity) are known based on experimental evidence. This allowed us to show that chunking improves runtimes by an order of magnitude. Based on simulations, we showed that chunking also increases sensitivity of the predictions, reaching 100% with as few as 20 sequences in a small proteome as in the influenza case (5k sites), but may require at least 30 sequences to reach 90% on larger alignments (500k sites). While RRF has less specificity than random forest, it was never <50%, and RRF sensitivity was significantly higher at smaller chunk sizes. We then used these algorithms to predict the determinants of three types of drug resistance (to Ciprofloxacin, Ceftazidime, and Gentamicin) in a bacterium, Pseudomonas aeruginosa. While both algorithms performed well in the case of the influenza data, results were more nuanced in the bacterial case, with RRF making more sensible predictions, with smaller errors rates, than AB.ConclusionsAltogether, we demonstrated that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even when trained on small numbers of individuals. We further showed that our RRF algorithm may deserve more scrutiny, which should be facilitated by the decreasing costs of both sequencing and phenotyping of large cohorts of individuals.

Dataset Information

Whole-genome data reveal the complex history of a diverse ecological community.

Publications

Whole-genome data reveal the complex history of a diverse ecological community.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets