Using temporal sampling to improve attribution of source populations for invasive species

PLoS One. 2013 Jun 3;8(6):e65656. doi: 10.1371/journal.pone.0065656. Print 2013.

Abstract

Numerous studies have applied genetic tools to the identification of source populations and transport pathways for invasive species. However, there are many gaps in the knowledge obtained from such studies because comprehensive and meaningful spatial sampling to meet these goals is difficult to achieve. Sampling populations as they arrive at the border should fill the gaps in source population identification, but such an advance has not yet been achieved with genetic data. Here we use previously acquired genetic data to assign new incursions as they invade populations within New Zealand ports and marinas. We also investigated allelelic frequency change in these recently established populations over a two-year period, and assessed the effect of temporal genetic sampling on our ability to assign new incursions to their population of source. We observed shifts in the allele frequencies among populations, as well as the complete loss of some alleles and the addition of alleles novel to New Zealand, within these recently established populations. There was no significant level of genetic differentiation observed in our samples between years, and the use of these temporal data did alter the assignment probability of new incursions. Our study further suggests that new incursions can add genetic variation to the population in a single introduction event as the founders themselves are often more genetically diverse than theory initially predicted.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles*
  • Animals
  • Base Sequence
  • DNA, Mitochondrial / genetics*
  • Gene Frequency
  • Genetic Variation
  • Haplotypes*
  • Introduced Species*
  • Microsatellite Repeats
  • Molecular Sequence Annotation
  • Molecular Sequence Data
  • New Zealand
  • Phenotype
  • Specimen Handling / methods
  • Time Factors
  • Urochordata / genetics*

Substances

  • DNA, Mitochondrial

Associated data

  • GENBANK/GU328006
  • GENBANK/GU328007
  • GENBANK/GU328008
  • GENBANK/GU328009
  • GENBANK/GU328010
  • GENBANK/GU328011
  • GENBANK/GU328012
  • GENBANK/GU328013
  • GENBANK/GU328014
  • GENBANK/GU328015
  • GENBANK/GU328016
  • GENBANK/GU328017
  • GENBANK/GU328018
  • GENBANK/GU328019
  • GENBANK/GU328020
  • GENBANK/GU328021
  • GENBANK/GU328022
  • GENBANK/GU328023
  • GENBANK/GU328024
  • GENBANK/GU328025
  • GENBANK/GU328026
  • GENBANK/GU328027
  • GENBANK/GU328028
  • GENBANK/GU328029
  • GENBANK/GU328030
  • GENBANK/GU328031
  • GENBANK/GU328032
  • GENBANK/GU328033
  • GENBANK/GU328034
  • GENBANK/GU328035

Grants and funding

A University of Canterbury Postdoctoral Fellowship supported this study, with additional funding from the Ministry of Agriculture and Forestry, Biosecurity New Zealand (contract B0202), the National Institute of Water and Atmospheric Research, and a subcontract to NJG from the Biodiversity and Biosecurity OBI (C01X0502). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.