Laboratory work

A total of 407 raccoon samples (tissue n = 336, hair n = 56, buccal swabs n = 15) was collected from the core distribution in Germany (near the introduction sites of Edersee and Wolfshagen) as well as from the expanding range margins in Germany, Belgium and Luxembourg (Fig 1). Samples were collected from trapped, road-killed or legally hunted individuals. As invasive species, raccoons are legally considered to be a game species in Germany (German federal hunting law: Bundesjagdgesetz §2 Abs.1) and can therefore be harvested by licensed hunters outside the closed season without special permission. No animal was killed with the aim of providing samples for this study. All hunted individuals were legally shot and made available to the authors. All non-German samples originated from road-killed individuals. No special permit was required to take samples from road-killed individuals.

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Fig 1. Geographic origin of the 407 raccoon samples used in the present study.

One point can represent multiple samples. Hunting bag data: the number of individuals harvested between 2000/01 and 2002/03 in German administrative districts (light grey lines). Inset: mtDNA haplotype frequency distribution of a subset of 193 of the 407 raccoons [19]. Size of the pies corresponds to the number of samples in the administrative districts. Samples were collected in 14 of the 16 federal states in Germany: Brandenburg (BB), Berlin (BL), Baden-Württemberg (BW), Bavaria (BY), Hesse (HE), Mecklenburg-West Pomerania (MV), Lower Saxony (NI), North Rhine-Westphalia (NRW), Rhineland-Palatinate (RP), Schleswig-Holstein (SH), Saarland (SL), Saxony (SN), Saxony-Anhalt (ST) and Thuringia (TH). The inset also shows the location of Belgium (B) and Luxembourg (L).

https://doi.org/10.1371/journal.pone.0125441.g001

Buccal swabbing was performed with sterile diagnostic rayon dry swabs (Copan Diagnostics Inc., Murrieta, CA, USA). Swabs were stored in sterile tubes at -20°C. Tissue samples were stored frozen or in absolute ethanol. Hair samples were stored in filter paper with silica gel (ThoMar OHG, Lütau, Germany) at room temperature. Genomic DNA from tissue or swab samples was extracted using the DNeasy Blood and Tissue kit (QIAGEN Hilden, Germany) following the manufacturer’s protocol (replacing ATL buffer with 200 μl PBS buffer for the swab samples). We extracted the hair DNA with a modified Chelex100 protocol, using 250 μl of a 10% Chelex100 Resin solution (BioRad, München, Germany) with addition of 4 μl Proteinase K (18 mg/ml) per sample and an overnight lysis step [29].

All individuals were genotyped at 21 microsatellite loci: PLOT-01, PLOT-02, PLOT-03, PLOT-04, PLOT-05, PLOT-06, PLOT-07, PLOT-08, PLOT-10, PLOT-11, PLOT-13 [30], PLO-M15, PLO2-117, PLO-M3, PLO-M20, PLO2-14, PLO-M2, PLO-M17, PLO3–86 [31], PLM01 and PLM03 [32]. All loci were co-amplified in seven multiplex polymerase chain reactions (PCR). Multiplex 1 contained loci PLOT-01, PLOT-02, PLOT-03, PLOT-04, multiplex 2 loci PLOT-05, PLOT-06, PLOT-07, PLOT-08 and multiplex 3 loci PLOT-10, PLOT-11, PLOT-13. Multiplex 4 contained loci PLO-M15, PLO2-117, multiplex 5 loci PLO-M3, PLO-M20, PLO2-14, PLO-M2 and multiplex 6 loci PLO-M17, PLO3–86. Multiplex 7 contained loci PLM01, PLM03. Reactions were performed in a final volume of 10 μl containing 1–10 ng DNA, 5 μl Type-it Microsatellite PCR master mix (QIAGEN Hilden, Germany) and 0.3 μM of each primer. PCRs were performed in a Multigene Gradient Thermal Cycler (Labnet International Inc., Edison, NJ, USA) using the following program: one cycle of 15 min at 95°C, 32 cycles at 94°C for 30s, 90s at the multiplex-specific annealing temperature (60.5°C for Multiplex 1, 63°C for Multiplexes 2 and 3, 58.5°C for Multiplexes 4 and 7, 55.5°C for Multiplexes 5 and 6), followed by 40s at 72°C and a final extension for 30 min at 72°C. Hair and swab samples were amplified and scored twice to minimalize the risk of genotyping errors. The fluorescently labelled PCR products were analysed on a MEGABACE 1000 automated sequencer (GE Healthcare, Freiburg, Germany) and fragment lengths of the alleles were scored by eye using the Fragment Profiler 1.2 (Amersham Bioscience, Freiburg, Germany). Preliminary data analysis (results not shown) provided evidence for linkage disequilibrium between PLOT-04 and PLO-M17. We therefore excluded PLO-M17 from all analyses.

Genetic structure and descriptive statistics

We analysed the population genetic structure using two Bayesian genetic clustering algorithms. First, we analysed the data in STRUCTURE 2.3.4 [33]. We estimated the number of genetic subpopulations (K) by performing ten independent runs of K = 1–12 with 10⁶ Markov chain Monte Carlo (MCMC) iterations after a burn-in period of 10⁵ iterations, using the model with correlated allele frequencies and assuming admixture. ALPHA, the Dirichlet parameter for the degree of admixture, was allowed to vary between runs. After deciding on the most probable number of sub-populations based on the log-likelihood values (and their convergence) associated with each K, as well as on the ΔK method by Evanno et al. [34], we calculated each individual’s percentage of membership (q), averaging q over different runs of the same K. In order to facilitate geographical representation, the average q values for each administrative district (‘Landkreis’) were calculated and mapped using ArcGIS 10.1 (ESRI Inc., Redlands, CA, USA). Second, we also analysed our data using the ‘clustering of individuals’ algorithm implemented in BAPS v.6.0 [35], which infers the number of genetic clusters in a data set. We performed ten runs for each of K = 2–12.

For the subsequent analyses, populations were pre-defined by placing samples into the STRUCTURE cluster for which they showed the highest percentage of membership (q). We represented the results from K = 7, averaging q over eight runs with the highest log-likelihood values (see Results). We tested for the significance of heterozygote deficiency or excess [36] with the Markov chain method in Genepop 4.1.4 [37], with 10,000 dememorization steps, 500 batches and 10,000 subsequent iterations. Pairs of loci were tested for linkage disequilibrium using an exact test based on a Markov chain method as implemented in Genepop 4.1.4 The false discovery rate technique was used to eliminate false assignment of significance by chance [38].

We visualised the genetic differentiation among the samples with a Factorial Correspondence Analysis (FCA) in Genetix 4.05.2 [39] and performed genetic exclusion tests in the program GENECLASS 2.0 [40] to test the hypothesis that individuals assigned to a specific cluster but visualized as outliers in the FCA were in fact individuals that had recently been introduced to the population. Exclusion probabilities were calculated with the Monte Carlo method of Paetkau et al. [41] by simulating 10,000 multi-locus genotypes and by setting the threshold for exclusion of individuals to 0.001 [42]. The level of genetic differentiation between the genetic clusters was quantified with F_ST [35] in GenAlEx version 6.501 [43] and by an Analysis of molecular variance (AMOVA) using 9,999 permutations.

We tested the data set for isolation-by-distance (IBD) by analysing genetic relatedness between pairs of individuals as a function of geographical distance, using program SPAGeDi 1.2 [44]. The slope of this relationship offers a convenient measure of the degree of spatial genetic structuring. As suggested by Vekemans & Hardy [45], the Loiselle kinship coefficient (F_ij) [46] was chosen as a pairwise estimator of genetic relatedness, as it is a relatively unbiased estimator with low sampling variance. The slope was tested for a significant difference from zero by 10 000 permutations of locations of individuals. We performed an analysis on the whole data set, as well as on pairs of individuals assigned to the same STRUCTURE cluster only (using cluster-specific allele frequencies).

We used GenAlEx to estimate the number of alleles (A), observed heterozygosity (H_O) and unbiased expected heterozygosity (_uH_E) for each STRUCTURE cluster and the number of private alleles (_pA) in a cluster. Allelic richness (A_R) was calculated using Fstat 2.9.3.2 [47]. Estimates were based on a minimum sample size of 13 diploid individuals. Relatedness coefficients were calculated in COANCESTRY 1.0.1.2. [48], which provides the Triadic Maximum Likelihood estimator TrioML based on Wang [49], estimating pairwise relatedness (r) by the use of a third individual as a reference, thus reducing the chance of genes identical in state being mistakenly inferred as identical by descent. We estimated effective population sizes (N_e) of each genetic cluster using the linkage disequilibrium method in program NeEstimator v.2.01 [50], estimated 95% confidence intervals using jackknifing and excluded rare alleles with frequencies less than 0.02.

Calculating minimum number of founders

There are several methods available to estimate the number of founders based on genetic data [51–54]. However, these methods require genetic information from the source population and assume no admixture between introduced populations. Our data set does not fulfil either condition. We therefore expanded an approach by Rasner et al. [55] and attempted to estimate the minimum number of founders required to introduce all empirically observed microsatellite alleles into each inferred STRUCTURE cluster. The genetic profiles of the founder individuals were generated by resampling the alleles in the empirical data set or by simulating genetic profiles based on allele frequencies. Custom-written scripts (S1 Script, S2 Script, S3 Script) for program R 3.1.0 [56] simulated different numbers of founder genotypes and ran 1,000 replicates for each founder size in order to estimate the smallest number of individuals whose genotypes contain all the alleles identified at the 20 microsatellite loci (assuming no physical linkage between loci and no past mutations). We considered the smallest minimum number of founders to be the simulated sample size that had a probability ≥ 0.05 of capturing all the observed alleles [55]. In order to account for the impact of sampling bias and the effect of genetic drift in small founder populations, we varied the R scripts in terms of the mechanisms for simulating founder genotypes: (1) we simulated genetic profiles by randomly sampling alleles—independently for each locus—without replacement from the genetic profiles present in the data set. (2) We simulated genetic profiles by resampling from the allele frequency distribution at each locus. (3) We simulated genetic profiles by resampling from allele frequency distributions assuming equal frequencies at each locus. While based on an oversimplified approach, this latter analysis was performed in an attempt to account for genetic drift. The maximum number of simulated individuals corresponded to the maximum amount of the individuals in a particular STRUCTURE cluster, except in the case of analysis 2, where threefold the maximum amount of individuals was simulated. In the case of the first two analytical approaches, we repeated the analysis after removing all genetic profiles that contained alleles that occurred at low frequency (<0.02) in the respective cluster [57] to avoid recently immigrated or admixed individuals inflating the estimate of the minimum number of original founders. Furthermore, we repeated the approaches after removing genetic profiles containing low-frequency alleles from a cluster, except if these alleles only occurred in the cluster under investigation.

Population genetic structure of raccoons in North America

The majority of the information on the large-scale genetic structure of North American raccoons is available for the Eastern United States [23]. When analyzing mtDNA diversity to evaluate the phylogenetic distinctiveness of four raccoon subspecies previously identified based on morphology, the authors identified 76 different control region haplotypes in a total of 311 samples [23]. The presence of three concordant lineages was inferred, which, however, did only partially correspond to the geographic ranges of the presumed sub-species. It was not possible to detect the geographic origin of German raccoons based on the analysis of mtDNA [22,23]. To the best of our knowledge, no comparable data has been published for microsatellite loci.

Established populations and founder events

Our results contradict the common assumption that the whole German raccoon population descended from two separate introduction events in central and eastern Germany [14–16, 22]. The optimal partition solutions suggested by STRUCTURE and BAPS were very similar, as both algorithms provided evidence for the presence of six geographically coherent genetic populations in the study area, with further genetic units formed by recently introduced individuals (see below). The two largest and most widespread of these clusters were centered in Hesse and Brandenburg, suggesting that they stemmed from the known introductions in 1934 at the Edersee (HE) and 1945 in Wolfshagen (BB). Furthermore, we were able to confirm the existence of a genetic cluster in eastern Saxony (SN), which had already been suggested as another independent founder event based upon mtDNA data (Fig 1, [22]). Raccoons were, in essence, absent from Saxony until the mid-1990s [58] and Biedrzycka et al. [59] did not observe the same mtDNA haplotype (PLO16) in neighbouring Poland and the Czech Republic, suggesting a recent and independent origin of the SN population.

The fourth major genetic cluster was located in the Harz Mountains and in north-central Germany (HA). It has, indeed, been reported that up to 60 raccoons escaped from fur farms in 1945 in the Harz Mountains [18], however, it was not possible to confirm or reject its existence based on mtDNA data [22]. In the present study, the presence of a private allele in the HA population, the low overlap with other clusters in the FCA and a level of allelic richness comparable to the other three main populations support the hypothesis of an independent introduction. The relatively low genetic differentiation (in terms of F_ST-values) between the HA and HE clusters probably resulted from recent admixture.

Both clustering methods also inferred the presence of two additional, smaller genetic clusters that were geographically coherent—the city of Kassel and Luxembourg/neighbouring regions—and therefore appeared to represent biologically meaningful units. The pattern of overlap in the FCA and the F_ST-based genetic differentiation estimates suggested that these KA and LU clusters were founded by individuals from the HE cluster. Since the first raccoons were sighted in the city of Kassel in the 1960s [60], this urban population has reached the highest raccoon densities (> 100 animals per km²) in Europe [61]. A reduced genetic exchange between rural and urban populations has been shown for the Chicago metropolitan area [26] and the Kassel individuals did not carry any private alleles. It is therefore possible that raccoons in Kassel were genetically differentiated as a result of a founding event and/or limited exchange (leading to genetic drift) between urban and rural areas. In contrast to KA, the LU cluster had private alleles. In addition to natural dispersers from central Germany, raccoons from a distinct genetic population (e.g. recently released captive individuals) probably contributed the genetic make-up of the LU individuals.

However, there are alternative explanations for the inference of the clusters observed in our data set. For example, the pattern of IBD observed when considering the whole data set has the potential to cause the inference of artificial partitions by the clustering methods [62]. However, the within-clusters IBD pattern is relatively weak, suggesting the existence of genuine genetic discontinuities [63]. Furthermore, the best explanation for the observation that the different clustering methods reached similar conclusions, that all but one cluster had private alleles and that clusters BB, HA, HE, SN were clearly distinct in the FCA analysis, is that these four main clusters were biologically meaningful and indicative of four different founding populations. The two smaller clusters (LU, KA) overlapped in the FCA with the HE individuals, with KA also having no private alleles. Furthermore, the high relatedness coefficients observed for clusters KA and LU could also be a source of artificial clusters, as the inclusion of family members in a genetic data set can cause STRUCTURE to detect clusters that represent closely related family lineages [64]. We therefore cannot confidently exclude the possibility that the two smaller clusters were indeed artifacts of the clustering methods. Further sampling is required to clarify the genetic status of the raccoons in the city of Kassel and in the very west of our study area.

Finally, the results of the FCA and the GENECLASS exclusion tests provided substantial support for the hypothesis that the seventh STRUCTURE cluster (RP) essentially consisted of recently introduced individuals. These individuals formed five distinct clusters in the BAPS analysis, but were assigned to a single cluster by STRUCTURE. Even though three individuals excluded (at least) at the P<0.01-level, had one of the five mitochondrial haplotypes typical for Germany [22], our microsatellite results provide strong evidence for the ongoing release/escape of raccoons into the wild.

Recent population admixture

We found strong evidence for recent admixture of the separate founder populations. It is perhaps surprising that some 70 years after the last major introduction event (not considering Saxony) the founder populations are still genetically distinct. While the inner-German border fortifications might have contributed to a reduced admixture between east and west, the core distribution areas of the eastern HA and BB clusters were relatively isolated until an increase in population density was detected since 1990 [65]. Given that the BB cluster still had the lowest overall rate of admixture, the river Elbe might have acted as a natural barrier, limiting raccoon dispersal [27]. Population admixture may have increased genetic diversity in each founding cluster and contributed to the significant increase in raccoon abundance over the last 20 years. Such an increase of genetic variation during biological invasions by combining genetic variation from multiple source populations has been described as a key factor in a number of successful invasions [2, 66].

Genetic diversity

Even though not directly comparable to the present study due to differences in the suite of microsatellite used for genotyping, the average number of alleles observed in the two largest clusters (HE: A = 7.0; BB: A = 6.8) as well as the average heterozygosity values reported here (0.56 ≤ H_O ≤ 0.61 and 0.60 ≤ uH_E ≤ 0.62) were lower than comparable estimates from the raccoon’s native range [26–28]. The higher loss of diversity in the case of mitochondrial DNA [22] was probably caused by the different effective population sizes of both markers. The microsatellite diversity results are in line with our simulation analysis, which suggested that the number of founders was substantially larger than commonly assumed (see below). Despite the lower genetic diversity in the introduced compared to the native range, our study confirms that German raccoons have a relatively high genetic diversity compared to other invasive mammals. It is clear that the species is not an example of a genetic depauperate, but successful invader.

Estimating the number of founders

While the number of individuals released can have a significant impact on the probability of invasion success [8] the presumed number of founders differed between the main clusters. According to historical records, HE might have been founded by as few as four individuals, BB by 25 and the HA population by 60 raccoons [13, 17, 18]. Our analyses partly deviate from these reported values. Some of our samples could even be excluded from the existing populations and must therefore be considered recently introduced individuals [42]. It is likely that, over the years, individuals kept as pets and escapees have entered the different populations [11].

The founder population size estimates based on resampling from the allele frequency distribution (analysis 2) were too high and very likely uninformative. In the subsequent discussion, we therefore focus the method based on re-sampling of the empirical data set (analysis 1). The highest estimates of founder individuals were obtained when all individuals (and their alleles) assigned to a cluster were considered in the analysis. Because of the recent admixture between the different founder populations, these estimates might well be adequate representations of the minimum number of individuals that have contributed to the overall genetic composition of the current clusters.

In order to attempt to infer the number of original founders, we removed all the individuals from a cluster whose genetic profiles contained alleles occurring at low frequency (< 0.02) [57]. The corresponding estimates suggested that the large BB cluster was founded by at least 36 individuals, while clusters HA and HE could have been founded by as few as 28 individuals. These results suggest that the two main populations HE and BB had a larger and HA a smaller number of founders than suggested by the historical record. The estimates for the number of founders would even be higher if one allows for the possibility that some of the low-frequency private alleles observed in BB and HE stemmed from the original founders but declined as a result of genetic drift. This latter scenario appears to apply to the younger SN cluster as all the low-frequency alleles were private. The analysis that included all the SN alleles therefore probably provided a more accurate estimate of founder population size (23 individuals) than the reduced data set.

The advantage of our new method is that it is very simple and does not require information on the allele frequencies in the source population(s). However, the estimates are affected by sample size and resampling from the allele frequency distribution clearly leads to overestimating founder numbers. Also the method cannot adequately account for genetic drift and does not allow for mutations creating new alleles. At the very least, the method provides some useful information regarding relative estimates of founder numbers (if sample sizes are broadly comparable). When we assumed all alleles in a cluster to have equal frequencies (to account for genetic drift), the BB estimate was in line with the historic information, while the HE cluster was estimated to have been founded by 21 individuals. In other words, it is likely that population HE at least had a larger number of founders than suggested by the historic record, even when considering the most conservative analysis.

Our study illustrates that it is precarious to fully trust the historical record of founder events in invasion biology. We proved the existence of at least four independent founding events with probably a substantially higher number of founders than commonly assumed.