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Resolving patterns of population genetic and phylogeographic structure to
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inform control and eradication initiatives for brown rats (Rattus norvegicus) on
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South Georgia.
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Stuart B Piertney1*, Andy Black2, Laura Watt1, Darren Christie2, Sally Poncet2, Martin
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A Collins2
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1Institute
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Building, Tillydrone Avenue, Aberdeen AB24 2TZ, UK
of Biological and Environmental Sciences, University of Aberdeen, Zoology
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2Government
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Stanley Falkland Islands
of South Georgia & South Sandwich Islands Government House
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*Corresponding author
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Stuart Piertney, School of Biological Sciences, University of Aberdeen, Aberdeen
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AB24 2TZ, UK
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Tel: 01224 272864
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Fax: 01224 272396
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Email [email protected]
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Running title: Rat eradication units on South Georgia
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Summary
1. The control and eradication of invasive species is a common management
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strategy to protect or restore native biodiversity. On South Georgia in the
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Southern Ocean, the brown rat Rattus norvegicus was brought onto the island
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with the onset of whaling and sealing activity in the 1800s, and has had a
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significant detrimental impact on key bird species of conservation concern.
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Efforts to eradicate rats from South Georgia using poisoned bait are ongoing.
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2. Despite the South Georgia rat eradication programme being the
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geographically largest and most ambitious eradication initiative to date, its
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success is facilitated by the potential that rat populations are effectively
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isolated by glacial barriers. This allows for localized eradication effort at
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manageable scales, leading to sequential eradication of individual populations
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with minimal risk of incursion from neighbouring areas.
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3. Here we use the levels of population genetic divergence estimated from 299
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single nucleotide polymorphism (SNP) loci and DNA sequence variation
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across 993 base pairs of the mitochondrial DNA cytochrome B locus to
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examine whether rat populations from nine glacially isolated areas on South
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Georgia are genetically distinct and so can be treated as independent
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eradication units.
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4. Bayesian clustering of individuals based on SNP similarity identified seven
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different genetic groups, which were confirmed using analyses based on
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pairwise genetic distance estimates and ordination of individuals using
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principal coordinate analysis. From a management perspective, these seven
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groups represent individual targets in baiting operations.
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5. Two mtDNA haplotypes were resolved across South Georgia, with a distinct
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geographic separation between the north-western and south-eastern
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populations. Approximate Bayesian Computation (ABC) was used to identify
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that this divergence was a consequence of two separate historical
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colonization events.
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6. Synthesis and applications. We illustrate that molecular markers are a
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valuable tool in species management and pest eradication given that the
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spatial distribution of genetic diversity can: i) identify demographically and
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genetically independent populations on which local eradication effort can be
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focussed; ii) distinguish between incomplete eradication and immigration in
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situations where individuals remain after eradication has been attempted and
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iii) identify the source of migrants when dispersal occurs over large spatial
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scales.
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Introduction
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The spread of invasive alien species represents the most insidious threat to the long-
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term persistence of native biodiversity (Genovesi 2009; Mack et al. 2000; Veitch et
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al. 2011; Vitousek et al. 1996). Mitigating the effects of alien species through active
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control or eradication has long been considered an absolute priority in management
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and conservation (Genovesi 2011). There is a large and growing list of successful
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eradication initiatives targeting different pest species (Genovesi & Carnevali 2011;
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Keitt et al. 2011) that have resulted in dramatic positive responses of resident
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populations of native species and recolonization by species that had been extirpated
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(Bellingham et al. 2010; Howald et al. 2010). However, because of the perception
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that large-scale eradication programmes are prohibitively expensive, logistically
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difficult to undertake and carry too high a risk of failure (Howald et al. 2007), most
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eradication initiatives have targeted small island populations where eradication
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efforts can be focussed (Cruz et al. 2009; Genovesi 2011; Howald et al. 2010).
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There remain very few attempts at population eradication over broader geographic
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areas (Fraser et al. 2013; Genovesi 2005; Howald et al. 2007; McClelland 2011).
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Over larger scales, effective species management and eradication programmes
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must make sufficient recourse to the patterns of population structure and dispersal to
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ensure that local eradication efforts are not swamped by larger-scale population
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processes. For successful eradication, the target populations must be clearly
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delimited with no potential for re-establishment through recolonization (Myers et al.
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2000). Population genetic analysis of the spatial distribution of intra-specific genetic
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variation is an extremely powerful approach for identifying population structure and
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defining management and conservation units (Fewster et al. 2011). Demographically
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independent population units can be inferred from high levels of population
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differentiation, with subsequent eradication effort focussed on populations with no
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potential for immigration. Conversely, populations that are exchanging individuals via
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dispersal will have similar allele frequencies and thus low genetic divergence, and
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must be treated with a combined target for eradication. Moreover, the diagnostic
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genetic signatures associated with distinct populations can be used to assign
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individuals to their source population and, as such, to identify the provenance of any
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migrants and the associated dispersal propensity for a given population.
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Here we demonstrate the utility of molecular genetic markers for defining populations
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as eradication units for the brown rat Rattus norvegicus on the sub-Antarctic island
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of South Georgia (54°S 37°W) in the Southern Ocean. Rats were unintentionally
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introduced to South Georgia in the early 1800s with the onset of land-based sealing
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and whaling activities on the island, and have subsequently colonized approximately
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two-thirds of South Georgia’s coastal habitat, primarily on the north-east side of the
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island. As is the case with invasive rats on many islands (Towns et al. 2006) they
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have had a significant impact on species of conservation importance, including the
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South Georgia pipit Anthus antarcticus, the South Georgia pintail Anas georgica,
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common diving-petrel Pelecanoides urinatrix, Antarctic prion Pachyptila desolata and
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blue petrel Halobaena caerulea (Pye & Bonner 1980), as well as the South Georgia
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diving petrel Pelecanoides georgicus, black-bellied storm-petrel Fregetta tropica,
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grey-backed storm-petrel Garrodia nereus and Wilson’s storm-petrel Oceanites
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oceanicus. There are ongoing initiatives to eradicate rats from South Georgia
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through the use of poisoned bait dropped from helicopters. Effort has been
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organized around three main phases of activity: the first was a geographically
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restricted initial trial centred around the Greene and Thatcher Peninsulas in March
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2011, the second covered the western half of the island in 2013, and the third
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covered the south-eastern portion of the island in early 2015. Combined, these three
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episodes covered the entire area over which rats had colonized and represent the
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geographically single largest invasive species eradication initiative yet attempted.
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The likelihood of overall success was considered enhanced because the rat-infested
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areas are potentially individually isolated coastal units, separated by glaciers,
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permanent snowfields and large bays. Targeted eradication in one area would
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therefore not be subsequently negated by recolonization from a neighbouring region,
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and as such the island could become rat free by sequential local eradication effort.
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This assumption that glaciers operate as barriers in preventing dispersal of rats
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between areas has previously been confirmed from significant levels of genetic
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divergence at microsatellite DNA polymorphisms between two adjacent, but glacially
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isolated rat populations on the Greene and Thatcher peninsulas (Robertson &
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Gemmell 2004), which were the targets in the initial trial phase of the eradication
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initiative.
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Here we extend this initial proof-of-concept study of Robertson & Gemmell (2004) to
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cover most of the rat-infested area of South Georgia, and expand from a relatively
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small number of microsatellite loci to a broad suite of single nucleotide
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polymorphisms (SNPs) from across the rat genome that provide greater resolving
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power for delimiting populations. Rat samples from nine different putatively glacially
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isolated regions were genotyped, from which the levels of genetic differentiation and
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demographic isolation can be assessed. Two geographically separate samples were
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analysed from one of these glacially isolated regions to assess any extent of genetic
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structure in the absence of glacial barriers. We test the hypothesis that rats from
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each of the glacially isolated regions form genetically and demographically distinct
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populations. If this is confirmed, then areas can be considered to be effectively
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isolated and therefore, may be suitable targets for eradication with little or no risk of
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re-invasion. Moreover, the geographic source of any individuals found following
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eradication efforts can be identified as being survivors of initial eradication efforts in
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that area, or immigrants from a different population.
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We resolve the geographical patterns of genetic structure using various approaches
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including pairwise comparison of genetic divergence between a priori defined
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populations, and the non-a priori assignment of individuals to one of a statistically
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inferred optimal number of genetic clusters. We also utilize approximate Bayesian
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computation (ABC; reviewed in Bertorelle et al. 2010) approaches to test competing
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demographic and biogeographic scenarios about the invasion of South Georgia and
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subsequent spread and admixture of individuals that best explain the spatial
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distribution of genetic diversity across the island.
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In parallel to analyses based on SNP loci, we also resolve the patterns of
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phylogeographic structure of the rat populations across South Georgia from
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mitochondrial DNA sequence variation. Given the initial colonization of the island by
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rats occurred approximately 250 years ago, any discontinuities in a phylogeographic
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topology must reflect input from different source populations rather than accumulated
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differences caused by mutation. As such, combined information from both nuclear
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and mitochondrial markers can provide insights into the invasion history of the island
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and the long-term patterns of population expansion and movement following initial
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invasion that can inform effective species management.
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Materials and methods
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Sampling and DNA extraction
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Tissue samples were obtained from a total of 349 individuals from across 10 putative
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populations (Table 1; Figure 1). The samples were taken before the onset of any
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eradication efforts, in the Austral summers of 2010–2011 (Greene, Maiviken and
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Grytviken) and 2011–12012 (all other populations). Nine of the populations are
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effectively geographically isolated by surrounding glaciers, permanent snowfields or
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large stretches of water. The Maiviken and Grytviken populations represent samples
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from two different locations on the same peninsula and are used to gauge genetic
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differentiation caused by geographical separation but in the absence of glacial
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barriers to gene flow. All samples were 3-mm tail-snips preserved in >90% ethanol
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and taken from animals snap-trapped in situ.
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DNA was extracted from tissue samples using Qiagen DNeasy Blood and Tissue
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Extraction Kit according to the manufacturer’s instructions.
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SNP genotyping
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Individuals were genotyped at 299 single nucleotide polymorphisms (SNPs) by
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KBiosciences Ltd, using their KASPar technology. The SNPs examined were a
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subset of those described in Nijman et al. (2008) and were chosen to be as equally
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spread across the autosomes as possible (average interval distance between
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markers of 7.5Mb) and be polymorphic between the Wistar race and wild-caught
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samples from the Netherlands (see http://cascad.niob.knaw.nl/snpview). The list of
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markers used is provided in Appendix S1 in Supporting Information.
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The programme FSTAT 2.9.3.2 (Goudet 1995; 2002) was used to calculate: i) allele
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frequencies per population; ii) deviation from Hardy-Weinberg expectations by
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calculating whether FIS differs from zero by randomizing alleles within populations
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and enforcing a strict Bonferroni-corrected significance threshold to account for
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multiple tests; iii) pairwise genetic differentiation between populations using FST with
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significance tested by randomizing multi-locus genotypes between each population
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pair (1100 permutations) and incorporating a strict Bonferroni correction.
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Bayesian inference of genetic structure among samples was performed using
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Structure 2.3.3 (Pritchard et al. 2000). Individual membership coefficients for K
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genetic clusters were inferred using a Markov Chain Monte Carlo (MCMC) approach
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with 200 000 burn-in iterations and 100 000 MCMC iterations. Simulations were
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performed using the standard admixture ancestry model with correlated allele
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frequencies. The simulated number of clusters ranged from K = 2 to K = 10, and
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simulations for each K were run 20 times. Structure harvester v 0.6.7 (Earl 2011)
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was used to collate the results and infer the statistically best-supported K using both
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the maximum-likelihood method of Pritchard et al. (2000) and the ∆K approach of
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Evanno et al. (2005). Replicate runs for each K were aligned and averaged using
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Clumpp v1.1.2 (Jakobsson & Rosenberg 2007), and the resulting membership
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coefficient matrix for each K was visualized using Distruct 1.1 (Rosenberg 2004).
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The relationships among individuals and populations were visualized using a
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principle component analysis approach using the SNPRelate package (Zheng et al.
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2012) for R (R Core Team, 2013).
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To evaluate between different ecological scenarios that could explain the observed
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spatial distribution of the SNP genetic diversity across South Georgia, we utilized an
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approximate Bayesian computation (ABC) based approach using DIYABC v2.0
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(Cornuet et al. 2008; 2014). ABC compares between the observed data set and
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multiple simulated data sets generated under different hypothetical scenarios of
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population history and ancestry in a multidimensional space of summary statistics
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(Bertorelle et al. 2010). Those simulated data sets that are closest to the empirical
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data set are then used for posterior probability estimation of the scenarios and
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associated parameters to identify the most realistic scenario. The different
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hypotheses that were examined reflected alternative explanations for the patterns of
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genetic structure resolved using STRUCTURE and FST-based approaches, and are
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described in full in the results. Details of input parameters for the DIYABC analysis
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are provided in Appendix S2.
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Mitochondrial DNA sequencing
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A 993 base pair (bp) fragment of the rat mitochondrial DNA cytochrome B gene was
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PCR-amplified in 16 individuals from each of the ten putative populations using the
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primers H15915 5’-TCTCCATTTCTGGTTTACAAGAC-3’ and L14723 5’-
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ACCAATGACATGAAAAATCATCGTT-3’ (Pages et al. 2010). PCR reactions were
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performed according to Piertney et al. (2005) with products purified using the
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QIAquick PCR Purification Kit according to the manufacturer’s instructions. DNA
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sequencing was undertaken by Eurofins MWG using the same primers as used in
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PCR. Electropherograms were confirmed by eye and aligned in MEGA5. Basic Local
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Alignment Search Tool (BLAST) analysis was used to confirm the homology of
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sequences to the R. norvegicus mitochondrial cytochrome B.
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Results
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SNP genotyping
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All individuals returned unambiguous genotypes for each of the 299 SNP markers. A
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total of 238 of these markers were polymorphic across South Georgia. Levels of
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diversity were variable across the populations (Table 1) with observed
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heterozygosity ranging from 0.097 (Salisbury) to 0.244 (Grytviken) and proportion of
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polymorphic loci at the 99% level ranging from 0.321 (Right Whale) to 0.662 (Barff).
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No measure of genetic diversity was significantly positively correlated with sample
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size, inferring that differences among populations reflect actual natural variance and
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are not an artefact of the sampling. Three of the populations (Barff, Busen and
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Greene) showed a significant deviation from Hardy-Weinberg expectations as a
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consequence of a deficiency of heterozygote genotypes (Table 1).
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There was a considerable level of population genetic structure among populations
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(Table 2). Pairwise FST values ranged from 0.08 (Maiviken vs. Grytviken) to 0.59
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(Maiviken vs. Salisbury). All pairwise comparisons between populations were
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significantly greater than zero after sequential Bonferroni correction, except for: i)
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Prince Olav vs. Salisbury populations and ii) Maiviken vs. Grytviken.
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The pattern inferred from the FST analysis, which is based upon comparison among
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individuals assigned a priori to populations, was echoed by analysis without any pre-
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assignment of individuals to location. The optimal number of populations identified
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from across the overall sample was k=7, and this was consistent irrespective of
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whether the Evanno et al. (2005) or highest likelihood criteria was used. These
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seven groups equated to the sampled populations except that individuals from
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Prince Olav, Salisbury and Right Whale were combined as a single population, as
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were individuals from Grytviken and Maiviken (Figure 2).
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Ordination of individual genetic differences on a PCA plot again highlighted genetic
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clusters among the populations (Figure 3). Four clearly demarcated groups of
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individuals were resolved: i) Busen; ii) Blue Whale; iii) Prince Olav, Salisbury and
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Right Whale and iv) Greene, Barff, Gold, Maiviken and Grytviken. The first two of
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these groupings were consistent with population groupings resolved using
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STRUCTURE and pairwise FST comparison. However, PCA did not identify Maiviken
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plus Grytviken, Greene, Barff and Gold populations as separate entities. An
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equivalent PCA analysis that just includes these five populations still does not yield
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individual population grouping that mirror the k=7 groups (plot not shown).
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Mitochondrial DNA sequencing
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The 993 bp cytochrome B fragment was sequenced without any ambiguities for all of
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the 160 individuals examined. A total of two haplotype sequences were resolved
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across all individuals, defined by five variable nucleotide sites (positions 72, 301 and
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775 were C to T transition mutations; position 619 was an A to T transversion
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mutation; and position 937 was an A to G transition mutation). All 48 individuals
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across the Prince Olav, Salisbury and Right Whale populations were fixed for one
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haplotype, whereas all 112 individuals across all the other populations were fixed for
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the other haplotype. As such, there was no sequence polymorphism within any
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population.
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Colonization scenario assessment using ABC
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ABC was used to compare between scenarios of colonization of South Georgia with:
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i) a single colonization of the island by an unsampled ancestral population with no
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subsequent admixture but shared ancestral polymorphism among populations; ii) a
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single colonization of the island with admixture among populations; iii) two
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colonization events (consistent with the occurrence of the two mitochondrial DNA
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haplotypes across the island) with admixture within each group to explain the k=7
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distribution and iv) two colonization events with no admixture but shared ancestral
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polymorphism for the k=7 distribution. The models involving a single colonization
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performed poorly within the analysis, returning posterior probability distributions with
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mode <0.3. The highest posterior probabilities were obtained for scenario iv that
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included two colonization events and a lack of contemporary exchange across a
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population structure that mimicked the k=7 suite of clusters identified by the
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STRUCTURE analysis (P=0.68; 95% CI = 0.67–0.69). Model checking using six
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summary statistics not used in model selection as test statistics was performed using
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principle components analysis (PCA). The PCA ordination cloud derived from the
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posterior predictive distribution was tightly clustered and well centred on the target
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point corresponding to the real data set. This reaffirms that scenario iv is most
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appropriate to explain our observed pattern of genetic diversity.
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Discussion
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Patterns of population genetic structure
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The spatial distribution of genetic diversity across the 299 SNP markers examined
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within this study indicates high levels of genetic differentiation among populations of
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Rattus norvegicus on South Georgia. However, the underlying hypothesis that each
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of the glacially isolated regions on the island would represent a genetically and
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demographically independent unit was rejected, with putatively isolated populations
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that are separated by glacial barriers showing a lack a genetic differentiation that
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could only be explained by dispersal.
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All of the different analyses used to identify genetic differentiation (based upon FST,
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PCA and STRUCTURE-based assignment) found no genetic differences between
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the Grytviken and Maiviken samples. This is expected given that these two sampling
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locations are only 3-km apart on the same peninsula with no glacial barriers to gene
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flow separating the two locations. Rat populations in mainland populations have
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been shown to display genetic differentiation over scales of a few kilometres (e.g.
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Calmet et al. 2001), so a lack of genetic structure between Maiviken and Grytviken
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populations indicates dispersal of individuals in the absence of large-scale physical
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barriers.
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More difficult to reconcile is the lack of genetic differentiation between Prince Olav,
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Salisbury and Right Whale samples. These are areas separated by glacial barriers
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and as such are expected to show distinct genetic differentiation with no connectivity
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through dispersal. Both STRUCTURE-based analysis and PCA identified these three
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populations as a single genetic unit, and pairwise comparison of FST returned a non-
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significant value for Prince Olav vs. Salisbury, and only marginally significant values
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for comparison between Right Whale and Prince Olav or Salisbury. There are
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several mechanisms through which dispersal could be facilitated among these
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regions despite apparent barriers to dispersal. Firstly, there has been a documented
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recession and thinning of almost all of South Georgia’s glaciers as a consequence of
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global warming (Gordon & Timmis 1992; see also Cook et al. 2010). Within the last
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50 years, the Morris Glacier that separates Salisbury and Prince Olav has retreated
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to the extent that there is now a large area of open moraine and beach across which
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rats may pass. Similarly, the Brunonia glacier that is a putative barrier between Right
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Whale and Salisbury has also retreated considerably. However, it is only within the
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past five or so years that the foot of the glacier has hit bedrock that would form an
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obvious corridor over which rats could move. This would suggest that genetic
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similarity between Right Whale and Salisbury may have been mediated by longer-
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term movement of rats crossing the fjord during winter.
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The initial colonization of Prince Olav, Salisbury and Right Whale could have been
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mediated by fur sealing activity. When fur sealing was at its peak in the early 1800s,
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the 'mother ships' used Prince Olav as their home base, from where small sailing
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vessels were dispatched, usually to the west end where the fur seals were most
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abundant. These shallops, carrying men, stores, food and skins may also have
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carried rats, thereby providing a dispersal route. Also, sailing ships were frequently
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wrecked along the coastline of South Georgia, and particularly at the north-west end
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of the island en route to and from the island’s main port in Prince Olav Harbour. This
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in itself could facilitate movement of rats over large geographic distances crossing
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glacial barriers to dispersal. However, whilst this may facilitate the initial colonization
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of rats over large areas, it is unlikely to explain the current lack of genetic divergence
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between these areas, as the time since this initial colonization is such that genetic
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drift would be expected to generate significant genetic structure between effectively
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isolated populations.
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The other populations of rat on South Georgia (Gold, Barff, Greene, Busen and Blue
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Whale) each showed significantly high levels of pairwise genetic divergence from
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FST, and formed separate genetic units in a STRUCTURE analysis to indicate that
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the samples represent genetically independent populations. That said, the PCA
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analysis did overlay the Greene, Barff and Gold samples with the Grytviken and
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Maiviken samples as a single cloud in ordination, indicating more genetic similarity.
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This highlights a potential limitation of using patterns of population genetic structure
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to infer dispersal following a relatively recent and small founder event: extant and
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truly isolated populations can share sufficient ancestral polymorphism over local or
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regional scales so that they do not appear as distinct. Approaches such as ABC offer
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the capacity to tease apart the relative effects of contemporary dispersal from
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ancestral polymorphism by examining whether the genetic data best fits a model with
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or without ongoing admixture of populations. In this case models that best fit the
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genetic data for those populations within the PCA-identified cloud were those that
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had ancestral polymorphism but not admixture, indicating that in accordance with
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STRUCTURE and FST based analyses, they do represent isolated populations with
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minimal dispersal.
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Management implications for the eradication of rats on South Georgia
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Such an understanding of the patterns of population genetic structure across South
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Georgia greatly informs efforts to eradicate rats from the island. Any attempt to
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extirpate rats from across South Georgia in a single concerted effort is logistically
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impracticable (Robertson & Gemmell 2004). These genetic data however identify
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smaller eradication units that are effectively isolated, and as such more localized
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eradication efforts can be utilized with low risk of recolonization from neighbouring
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areas during the interval between successive baiting operations. That said, this
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assumes that genetic isolation is caused by barriers to dispersal rather than intrinsic
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density-dependent priority effects (Fraser et al. 2015) whereby immigrant rats are
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excluded by resident populations. Local extirpation would relax such effects, allowing
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for large-scale recolonization. Given the density of rats on South Georgia, this
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appears an unlikely scenario. Notwithstanding, the data have definitively identified
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seven separate eradication units – i) Right Whale, Salisbury and Prince Olav; ii) Blue
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Whale; iii) Busen; iv) Maiviken and Grytviken; v) Greene; vi) Barff and vii) Gold.
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Clearly, identification of the three most northerly populations (Prince Olav, Salisbury
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and Right Whale) as a single genetic cluster highlights a major challenge for
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eradication given the large area these populations encompass. Moreover, if their
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historical isolation has been removed by the retreat of glaciers and the opening up of
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channels for dispersal, then any overall strategy to focus on more localized
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eradication units is clearly time limited given ongoing global warming continues to
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reduce glacial extent across the island (Cook et al. 2010). Given that the three
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phases of eradication that have been undertaken on South Georgia to date have
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covered all of the populations identified by the genetic data as belonging to the same
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eradication unit, there is no reason to believe that eradication should not be
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successful. In the event that rats are subsequently found following eradication
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activity, the high levels of genetic divergence between the populations provides
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diagnostic genetic profiles for each eradication unit that allows the provenance of
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any individuals that may be found in future to be ascertained. As such it can be
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established whether eradication efforts were insufficient to remove all rats from a
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given area, or whether there is dispersal across barriers leading to recolonization
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from elsewhere.
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Historical patterns of colonization
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Analysis of the mitochondrial DNA sequence data provides another level of
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information on genetic structure above that yielded from the SNP loci. Only two
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mitochondrial haplotypes were resolved across all the 160 individuals examined.
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These were separated by five mutations, indicating that one haplotype is not derived
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from the other in situ, but they are a consequence of different colonization events
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into South Georgia. The two haplotypes are very distinctively structured across the
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island with all the individuals from Prince Olav, Salisbury and Right Whale being one
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haplotype, and all other individuals from Blue Whale in the north to the Gold
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population in the south-east being the other haplotype. These patterns are consistent
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with one colonization being associated with sealing activity centred on Prince Olav,
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and the other with whaling activity centred on Grytviken. Indeed, levels of SNP
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diversity (both observed heterozygosity and mean number of alleles per locus) are
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greatest for the two different mitochondrial groupings in Prince Olav and Grytviken.
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There is also a significant negative correlation between distance from these focal
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populations and levels of SNP diversity within populations. The Blue Whale and Gold
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populations have relatively low levels of allelic diversity as the most northerly and
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southerly populations from Grytviken, and there is a gradual reduction in number of
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alleles from Prince Olav to Salisbury to Right Whale Bay. These geographically
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peripheral populations did not show a deficiency of heterozygote genotypes across
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the SNPs as may have been expected following sequential founder events and
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inbreeding in small populations. Three other populations, however (Barff, Busen and
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Greene) did show significant deviations from Hardy-Weinberg expectations, which
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provides further evidence that these populations are effectively isolated given the
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effects of drift and inbreeding are not negated by gene flow. Whilst relative levels of
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SNP diversity in the two mitochondrial groupings could be used to infer the size of
447
the initial founding populations, this could be affected by an ascertainment bias
448
associated with SNPs being chosen based upon their diversity among European
449
populations, and the true provenance of the rats from whaling and sealing activity
450
being unclear.
451
452
Overall, this study has demonstrated the utility of molecular markers for defining a
453
genetic landscape of demographically and genetically independent populations that
454
can be used as targets for eradication without the potential for recolonization
455
between areas. Moreover, it has identified population-specific genetic signatures that
456
can be used in future to identify the provenance of rats found in any area after an
457
eradication effort, and thus identify whether individuals survived initial eradication
458
efforts or are colonizers from neighbouring locations. This reinforces how useful
459
molecular techniques can provide an important tool in applied ecological issues and
460
can guide conservation and management (Fewster et al. 2011).
461
462
463
Acknowledgements
464
The project is indebted to Anton Wolfaardt, Darren Peters, Tom Hart, Leigh-Anne
465
Wolfaardt, Mark Tasker and Kalinka Rexer-Huber for their efforts in helping collect
466
samples in sometimes challenging conditions. Funding for the project was supplied
467
by the UK Government’s Overseas Territories Environmental Programme (OTEP).
468
Thanks to the Captains and crew of the FPV Pharos SG for deploying and retrieving
469
teams from various field camps around South Georgia and for providing support while
470
ashore. The team would also like to thank the Government Officers and British
471
Antarctic Survey personnel based at King Edward Point (KEP) for their hospitality and
472
support while based at KEP or within Cumberland Bay.
473
474
Data accessibility
475
476
SNP genotypes: DRYAD entry DOI: doi:10.5061/dryad.9133p
477
References
478
479
Bellingham, P.J., Towns, D.R., Cameron, E.K., Davis, J.J., Wardle, D.A.,
480
Wilmshurst, J.M. & Mulder, C.P.H. (2010). New Zealand island restoration:
481
seabirds, predators and the importance of history. New Zealand Journal of
482
Ecology, 34, 115-136.
483
484
Bertorelle, G., Benazzo, A. & Mona, S. (2010) ABC as a flexible framework to
485
estimate demography over space and time: some cons, many pros. Molecular
486
Ecology, 19, 2609-2625.
487
Calmet, C., Pascal, M. & Samadi S. (2001) Is it worth eradicating the invasive pest
488
Rattus norvegicus from Molene archipelago? Genetic structure as a decision
489
making tool. Biodiversity and Conservation, 10, 911-928.
490
Cook, A.J., Poncet, S., Cooper, A.P.R., Herbert, D.J. & Christie, D. (2010). Glacier
491
retreat on South Georgia and implications for the spread of rats. Antarctic
492
Science, 19, 1-9.
493
494
Cornuet, J.M., Santos, F., Beaumont, M.A., Robert, C.P., Marin, J.M., Balding, D.J.,
495
Guillemaud, T. & Estoup, A. (2008) Inferring population history with DIYABC: a
496
user-friendly approach to Approximate Bayesian Computations. Bioinformatics,
497
24, 2713-2719.
498
499
500
Cornuet J.M., Veyssier J., Pudlo P., Dehne-Garcia A., Gautier M., Leblois R., Marin,
J.M. & Estoup, A. (2014) DIYABC v2.0: a software to make Approximate Bayesian
501
Computation inferences about population history using Single Nucleotide
502
Polymorphism, DNA sequence and microsatellite data. Bioinformatics, 30, 1187-
503
1189.
504
Cruz, F., Carrion, V., Campbell, K.J., Lavoie, C., & Donlan, C.J., (2009) Bio-
505
economics of large-scale eradication of feral goats from Santiago Island,
506
Galapagos. J. Wildlife Management, 73, 191-200.
507
508
Earl D (2011) Structure Harvester v0.6.1.
http://taylor0.biology.ucla.edu/structureHarvester/.
509
Estoup, A., Lombaert, E. & Marin J.-M. (2012) Estimation of demo-genetic model
510
probabilities with Approximate Bayesian Computation using linear discriminant
511
analysis on summary statistics. Molecular Ecology Resources, 12, 846-855.
512
513
Evanno, G., Regnaut, S. & Goudet, J. (2005). Detecting the number of clusters of
514
individuals using the software STRUCTURE: a simulation study. Molecular
515
Ecology, 14, 2611–2620.
516
Fewster, R.M., Miller, S.D. & Ritchie, J. (2011) DNA profiling – a management tool
517
for rat eradication. In: Veitch, C. R., Clout, M. N. & Towns, D. R. (eds.) (2011)
518
Island Invasives: Eradication and Management. Proceedings of the International
519
Conference on Island Invasives. Gland, Switzerland: IUCN and Auckland, New
520
Zealand: CBB. pp 426-431.
521
522
Fraser, C.I., Banks, S.C. & Waters, J.M. (2014) Priority effects can lead to an
underestimation of dispersal and invasion potential. Biological Invasions, 17, 1-8
523
Fraser, E.J., MacDonald, D.W., Oliver, M.K., Piertney, S.B. & Lambin, X. (2013)
524
Using population genetic structure of an invasive mammal to target control efforts
525
– an example using the American mink in Scotland. Biological Conservation, 167,
526
35-42
527
528
Genovesi, P. (2005) Eradications of invasive alien species in Europe: A review.
Biological Invasions, 7, 127-133.
529
Genovesi, P. (2009) Invasive alien species in a changing world. Biodiversity, 10, 3-4.
530
Genovesi, P. (2011) Are we turning the tide? Eradications in times of crisis: how the
531
global community is responding to biological invasions. In: Veitch, C. R., Clout,
532
M. N. & Towns, D. R. (eds.) (2011) Island Invasives: Eradication and
533
Management. Proceedings of the International Conference on Island Invasives.
534
Gland, Switzerland: IUCN and Auckland, New Zealand: CBB. pp 5-10.
535
536
Genovesi, P. & Carnevali, L. (2011). Invasive alien species on European islands:
537
eradications and priorities for future work. In: Veitch, C. R., Clout, M. N. &
538
Towns, D. R. (eds.) (2011) Island Invasives: Eradication and Management.
539
Proceedings of the International Conference on Island Invasives. Gland,
540
Switzerland: IUCN and Auckland, New Zealand: CBB. pp 56-62.
541
542
543
544
545
Gordon, J.E. & Timmis, R.J. (1992). Glacier fluctuations on South Georgia during the
1970s and early 1980s. Antarctic Science, 4, 215-226.
Goudet, J. (1995) FSTAT (version 1.2): a computer program to calculate f-statistics.
J Hered., 86,186–485.
546
547
Goudet, J. (2002) FSTAT—a program to estimate and test gene diversities and
fixation indices version 2.9.3.2. http://www2.unil.ch/popgen/softwares/fstat.htm.
548
549
550
551
Howald, G., Donlan, C.J. & Galvan, J.P. (2007) Invasive rodent eradication on
islands. Conservation Biology, 21, 1258–1268.
Howald, G., Donlan, C.J., Faulkner, K.R., Ortega, S., Gellerman, H., Croll, D.A. &
552
Tershy, B.R. (2010) Eradication of black rats Rattus rattus from Anacapa Island.
553
Oryx, 44, 30-40.
554
Jakobsson, M. & Rosenberg, N.A. (2007) CLUMPP: a cluster matching and
555
permutation program for dealing with label switching and multimodality in analysis
556
of population structure. Bioinformatics, 23, 1801–1806.
557
Keitt, B, Campbell, K. Saunders, A., Clout, M., Wang, Y., Heinz, R., Newton, K. &
558
Tershy, B. (2011). The Global Islands Invasive Vertebrate Eradication Database:
559
A tool to improve and facilitate restoration of island ecosystems. In: Veitch, C. R.,
560
Clout, M. N. & Towns, D. R. (eds.) (2011) Island Invasives: Eradication and
561
Management. Proceedings of the International Conference on Island Invasives.
562
Gland, Switzerland: IUCN and Auckland, New Zealand: CBB. pp 74-77.
563
564
Mack, R. N., Simberloff, D., Lonsdale, W. M., Evans, H., Clout, M. & Bazzaz, F. A.
565
(2000). Biotic invasions: causes, epidemiology, global consequences and control.
566
Ecological Applications, 10, 689-710.
567
568
569
McClelland, P.J. (2011). Campbell Island – pushing the boundaries of rat
eradication. In: Veitch, C. R., Clout, M. N. & Towns, D. R. (eds.) (2011) Island
570
Invasives: Eradication and Management. Proceedings of the International
571
Conference on Island Invasives. Gland, Switzerland: IUCN and Auckland, New
572
Zealand: CBB. pp 204-207.
573
574
Myers, J.H., Simberloff, D., Kuris, A.M. & Carey, J.R. (2000) Eradication revisited:
dealing with exotic species. Trends in Ecology and Evolution, 15, 316-320.
575
Nijman, I.J., Kuipers, S., Verheul, M., Guryev, V. & Cuppen E (2008) A genome-wide
576
SNP panel for mapping and association studies in the rat. BMC genomics, 9, 95-
577
104.
578
Piertney, S.B., Stewart, W.A., Lambin, X., Telfer, S., Aars, J. & Dallas, J.F. (2005)
579
Phylogeographic structure and post-glacial evolutionary history of water voles
580
(Arvicola terrestris) in the United Kingdom. Mol Ecol, 14, 1435-1444.
581
582
583
584
585
586
587
588
589
Pritchard, J.K., Stephens, M. & Donnelly, P. (2000) Inference of population structure
from multi-locus genotype data. Genetics, 155, 945-959.
Pye, T. & Bonner, W.N. (1980) Feral brown rats (Rattus norvegicus) on South
Georgia (Southern Atlantic Ocean). Journal of Zoology, 192, 237-255.
R Core Team (2013) R: A language and Enviornment for Statistical Computing. R
foundation for statistical Computing. Vienna Austria.
Robertson, B.C. & Gemmell, N.J., (2004) Defining eradication units to control
invasive pests. Journal Applied Ecology, 41, 1042-1048.
Towns, D. R., Atkinson, I. A. E. and Daugherty, C. H. (2006). Have the harmful
590
effects of introduced rats on islands been exaggerated? Biological Invasions, 8,
591
863-891.
592
Veitch, C. R., Clout, M. N. & Towns, D. R. (eds.) (2011) Island Invasives: Eradication
593
and Management. Proceedings of the International Conference on Island
594
Invasives. Gland, Switzerland: IUCN and Auckland, New Zealand: CBB. xii +
595
542pp.
596
Vitousek, P.M., DAntonio, C.M., Loope, L.L. & Westbrooks, R. (1996) Biological
597
invasions as global environmental change. American Scientist, 84, 468-478.
598
Zheng, X., Levine, D., Shen, J., Gogarten, Laurie, S.M., Weir B.S (2012) A High-
599
performance Computing Toolset for Relatedness and Principal Component
600
Analysis of SNP Data. Bioinformatics doi:10.1093/bioinformatics/bts606
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Table 1. Levels of SNP variation in 10 populations of Rattus norvegicus from South
603
Georgia. n = number of samples genotyped; Hexp = expected heterozygosity; Hobs =
604
observed heterozygosity; FIS = Wright’s statistic for deviation from Hardy-Weinberg
605
expectations for panmixia; P(0.95 or 0.99) = proportion of the 299 SNP markers that
606
were polymorphic at the 95% or 99% level, respectively; Na = mean number of
607
alleles per locus
608
609
Population
n
Hexp
Hobs
FIS
P(0.95)
P(0.99)
Na
Right Whale
34
0.109
0.111
-0.006
0.291
0.321
1.321
Salisbury
34
0.098
0.097
0.034
0.274
0.328
1.328
Prince Olav
29
0.107
0.106
0.025
0.308
0.341
1.341
Blue Whale
54
0.124
0.128
-0.021
0.328
0.358
1.368
Busen
36
0.211
0.195
0.091
0.565
0.615
1.615
Maiviken
12
0.219
0.236
-0.038
0.575
0.615
1.615
Grytviken
36
0.236
0.244
-0.020
0.609
0.642
1.642
Greene
32
0.213
0.192
0.110
0.562
0.622
1.622
Barff
37
0.232
0.220
0.066
0.615
0.662
1.662
Gold
45
0.213
0.209
0.028
0.545
0.599
1.599
610
Table 2: Pairwise point estimates of genetic differentiation (unbiased estimator of
611
FST) for ten populations of R. norvegicus across South Georgia. Non-significant
612
values (after Bonferroni correction) are underlined
615
Blue Whale
Grytviken
Gold
Greene
Maiviken
Prince Olav
Right whale
0.17
0.47
0.12
0.12
0.11
0.14
0.52
0.52
0.53
0.43
0.18
0.21
0.21
0.20
0.53
0.54
0.54
0.46
0.47
0.49
0.51
0.48
0.46
0.49
0.16
0.14
0.08
0.50
0.51
0.52
0.13
0.18
0.53
0.53
0.55
0.17
0.56
0.56
0.58
0.56
0.57
0.59
0.26
0.15
0.28
Salisbury
Busen
614
Busen
Blue Whale
Grytviken
Gold
Greene
Maiviken
Pronce Olav
Right whale
Salisbury
Barff
613
616
Figure 1: Map of South Georgia showing locations of the sampled populations.
617
Numbers in parentheses indicate the eradication phase for which each population
618
was targeted. The shaded area identifies glaciers or regions with permanent snow
619
cover.
620
621
622
Figure 2: Individual membership coefficients inferred from Bayesian inference of
623
genetic structure within STRUCTURE across all ten populations of R. norvegicus on
624
South Georgia for the most likely number of genetic groups (k=7), which are defined
625
by seven colours. Black lines demarcate a priori populations and each individual is
626
represented by a single vertical line. Coefficients are averaged across runs due to
627
multiple solutions among replicates, using the standard admixture model including
628
sampling locations as prior information.
629
630
631
632
633
634
Figure 3: Principal component analysis (PCA) plot showing genetic relationships
635
amongst 349 R. norvegicus individuals from across ten populations on South
636
Georgia. Populations are colour coded, with values equating to those specified in
637
Figure 1. The two principal components account for 40.4% of the variation.
638
639
640
Supporting information
641
Additional supporting material may be found in the online version of this article:
642
Appendix S1. List of SNP loci genotyped, and locations within the rat genome.
643
Appendix S2 – input parameters for the DIYABC analysis.
644