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How to fail at species delimitation.

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TLDR
Researchers should apply a wide range of species delimitation analyses to their data and place their trust in delimitations that are congruent across methods, for in most contexts it is better to fail to delimit species than it is to falsely delimit entities that do not represent actual evolutionary lineages.
Abstract
Species delimitation is the act of identifying species-level biological diversity. In recent years, the field has witnessed a dramatic increase in the number of methods available for delimiting species. However, most recent investigations only utilize a handful (i.e. 2–3) of the available methods, often for unstated reasons. Because the parameter space that is potentially relevant to species delimitation far exceeds the parameterization of any existing method, a given method necessarily makes a number of simplifying assumptions, any one of which could be violated in a particular system. We suggest that researchers should apply a wide range of species delimitation analyses to their data and place their trust in delimitations that are congruent across methods. Incongruence across the results from different methods is evidence of either a difference in the power to detect cryptic lineages across one or more of the approaches used to delimit species and could indicate that assumptions of one or more of the methods have been violated. In either case, the inferences drawn from species delimitation studies should be conservative, for in most contexts it is better to fail to delimit species than it is to falsely delimit entities that do not represent actual evolutionary lineages.

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Citations
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Journal ArticleDOI

Multispecies coalescent delimits structure, not species

TL;DR: It is shown that the multispecies coalescent diagnoses genetic structure, not species, and that it does not statistically distinguish structure associated with population isolation vs. species boundaries.
Journal ArticleDOI

The BPP program for species tree estimation and species delimitation

TL;DR: An overview and a tutorial of the BPP program, which is a Bayesian MCMC program for analyzing multi-locus genomic sequence data under the multispecies coalescent model, is provided.
Journal ArticleDOI

ASAP: assemble species by automatic partitioning.

TL;DR: It is demonstrated that ASAP has the potential to become a major tool for taxonomists as it proposes rapidly in a full graphical exploratory interface relevant species hypothesis as a first step of the integrative taxonomy process.
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Species delimitation using genome-wide SNP data.

TL;DR: A recently introduced dynamic programming algorithm for estimating species trees that bypasses MCMC integration over gene trees with sophisticated methods for estimating marginal likelihoods, needed for Bayesian model selection, are combined to provide a rigorous and computationally tractable technique for genome-wide species delimitation.
References
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Journal ArticleDOI

The integrative future of taxonomy.

TL;DR: It is concluded that taxonomy needs to be pluralistic to improve species discovery and description, and to develop novel protocols to produce the much-needed inventory of life in a reasonable time.
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From Gene Trees to Species Trees

TL;DR: In this paper, a linear time algorithm is presented for computing all the losses in duplications associated with the least common ancestor mapping from a gene tree to a species tree, which can be approximated within factor 2 in polynomial time.
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Distinguishing migration from isolation: a Markov chain Monte Carlo approach.

TL;DR: The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations.
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Bayesian species delimitation using multilocus sequence data

TL;DR: A Bayesian modeling approach is used to generate the posterior probabilities of species assignments taking account of uncertainties due to unknown gene trees and the ancestral coalescent process and the method is illustrated by analyzing sequence data from rotifers, fence lizards, and human populations.
Journal ArticleDOI

Approximate Bayesian Computation (ABC) in practice

TL;DR: It is argued that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model if these principles are carefully applied.
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