The University of Southampton
University of Southampton Institutional Repository

Algorithmic approaches to aid species' delimitation in multidimensional morphospace

Algorithmic approaches to aid species' delimitation in multidimensional morphospace
Algorithmic approaches to aid species' delimitation in multidimensional morphospace
Background
The species is a fundamental unit of biological pattern and process, but its delimitation has proven a ready source of argument and disagreement. Here, we discuss four key steps that utilize statistical thresholds to describe the morphological variability within a sample and hence assess whether there is evidence for one or multiple species. Once the initial set of biologically relevant traits on comparable individuals has been identified, there is no need for the investigator to hypothesise how specimens might be divided among groups, nor the traits on which groups might be separated.

Results
Principal components are obtained using robust covariance estimates and retained only if they exceed threshold amounts of explanatory power, before model-based clustering is performed on the dimension-reduced space. We apply these steps in an attempt to resolve ongoing debates among taxonomists working on the extinct Eocene planktonic foraminifera Turborotalia, providing statistical evidence for two species shortly before the lineage's extinction near the Eocene/Oligocene boundary.

Conclusion
By estimating variance robustly (samples containing incipient species are unlikely to be scaled optimally by means and standard deviations) and identifying thresholds relevant to a particular system rather than universal standards, the steps of the framework aim to optimize the chances of delineation without imposing pre-conceived patterns onto estimates of species limits.

1471-2148
1-11
Ezard, Thomas H.G.
a143a893-07d0-4673-a2dd-cea2cd7e1374
Pearson, Paul N.
76269a23-3411-45a1-bc81-b3a668ef1d13
Purvis, Andy
ea5716f3-8fdf-4275-8c67-578005614348
Ezard, Thomas H.G.
a143a893-07d0-4673-a2dd-cea2cd7e1374
Pearson, Paul N.
76269a23-3411-45a1-bc81-b3a668ef1d13
Purvis, Andy
ea5716f3-8fdf-4275-8c67-578005614348

Ezard, Thomas H.G., Pearson, Paul N. and Purvis, Andy (2010) Algorithmic approaches to aid species' delimitation in multidimensional morphospace. BMC Evolutionary Biology, 10 (175), 1-11. (doi:10.1186/1471-2148-10-175).

Record type: Article

Abstract

Background
The species is a fundamental unit of biological pattern and process, but its delimitation has proven a ready source of argument and disagreement. Here, we discuss four key steps that utilize statistical thresholds to describe the morphological variability within a sample and hence assess whether there is evidence for one or multiple species. Once the initial set of biologically relevant traits on comparable individuals has been identified, there is no need for the investigator to hypothesise how specimens might be divided among groups, nor the traits on which groups might be separated.

Results
Principal components are obtained using robust covariance estimates and retained only if they exceed threshold amounts of explanatory power, before model-based clustering is performed on the dimension-reduced space. We apply these steps in an attempt to resolve ongoing debates among taxonomists working on the extinct Eocene planktonic foraminifera Turborotalia, providing statistical evidence for two species shortly before the lineage's extinction near the Eocene/Oligocene boundary.

Conclusion
By estimating variance robustly (samples containing incipient species are unlikely to be scaled optimally by means and standard deviations) and identifying thresholds relevant to a particular system rather than universal standards, the steps of the framework aim to optimize the chances of delineation without imposing pre-conceived patterns onto estimates of species limits.

This record has no associated files available for download.

More information

Published date: June 2010
Organisations: Environmental

Identifiers

Local EPrints ID: 344725
URI: http://eprints.soton.ac.uk/id/eprint/344725
ISSN: 1471-2148
PURE UUID: f9c48c14-e9e8-4cbf-9b6b-82e5a4f00559
ORCID for Thomas H.G. Ezard: ORCID iD orcid.org/0000-0001-8305-6605

Catalogue record

Date deposited: 31 Oct 2012 11:54
Last modified: 22 Jun 2024 01:46

Export record

Altmetrics

Contributors

Author: Thomas H.G. Ezard ORCID iD
Author: Paul N. Pearson
Author: Andy Purvis

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×