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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.
Journal ArticleDOI

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 influence of a hot environment on parental cooperation of a ground-nesting shorebird, the Kentish plover Charadrius alexandrinus

TL;DR: The results suggest biparental care is essential during incubation in the Kentish plover in extremely hot environments and that once the eggs have hatched the risks associated with hot temperature are reduced: the chicks become mobile, and they gradually develop thermoregulation.
Journal ArticleDOI

The probability of topological concordance of gene trees and species trees.

TL;DR: The concordance of gene trees and species trees is reconsidered in detail, allowing for samples of arbitrary size to be taken from the species, and the term speciodendric is introduced to refer to genes whose trees are topologically concordant with species trees.
ReportDOI

MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering

TL;DR: A number of features of the software have been changed in this version, and the functionality has been expanded to include regularization for normal mixture models via a Bayesian prior.
Journal ArticleDOI

Inference of Population Structure Under a Dirichlet Process Model

TL;DR: The notion of the mean population partition is developed, which is the partition of individuals to populations that minimizes the squared partition distance to the partitions sampled by the MCMC algorithm.
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