Istituto Nazionale per la Fauna Selvatica, Ozzano dell'Emilia (BO), Italy;
School of Animal and Microbial Sciences, Whiteknights, Reading, England;
Dipartimento di Biologia Animale ed Ecologia, Università degli Studi di Perugia, Perugia, Italy;
Gruppo di Etologia ed Ecologia Comportamentale, Dipartimento di Biologia Evolutiva, Università di Siena, Siena, Italy
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Abstract |
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Introduction |
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The Sardinian wildcats belong to the F. silvestris libyca group (Ragni 1981
; Amori, Angelici, and Boitani 1999
) and originate from African wildcats which were introduced by Neolithic navigators into the island, as into Cyprus and Crete, about 6,0008,000 years ago (Davis 1987
) at an early stage of domestication, well before the domestication process was completed by the Egyptians about 4,000 years ago (Malek 1993
). Historical evidence of tamed or early-domesticated cats was found at Etruscan and Greek archaeological sites from the beginning of the fifth and fourth centuries b.c. in Italy (Keller 1908
; Ragni and Ragni 2001
). Thereafter, the Romans probably spread domesticated cats throughout continental Europe and Great Britain (Clutton-Brock 1999
).
Nowadays, domestic cats are distributed worldwide and virtually sympatric with European and African wildcats almost everywhere. Domestic cats and wildcats can interbreed and produce fertile offspring in captivity and in nature (Robinson 1977
; Ragni 1993
). The protracted coexistence of free-ranging domestic cats and wildcats lead one to suppose that interbreeding might be widespread and that "pure" wildcat populations would eventually no longer exist in parts of Europe (Suminski 1962
; French, Corbett, and Easterbee 1988
), the Middle East (Mendelssohn 1999
), and South Africa (Stuart and Stuart 1991
). Except for coat color variability, which is controlled by just a few genes (Robinson 1977
), domestication did not drastically modify the morphology of cats. Therefore, morphological and morphometrical studies did not find evidence of diagnostic traits suitable for identify hybrids and/or introgressed cat populations (Balharry and Daniels 1998
; Daniels et al. 1998
). Moreover, the fear of widespread hybridization made uncertain any identification of "pure" wildcats to be used as references for taxonomy and for studies of population diversity (Balharry and Daniels 1998
; Daniels et al. 1998
).
Studies using allozyme electrophoresis, DNA analyses of nuclear genes, and mitochondrial sequences (Randi and Ragni 1991
; Hubbard et al. 1992
; Randi et al. 2000
) indicated limited differentiation between wild-living and domestic cats, while the use of hypervariable nuclear markers (microsatellites) recently provided more stimulating results (Randi et al. 2000
; Beaumont et al. 2001
). Usually, microsatellites are variable enough to allow for the unequivocal identification of all the sampled individuals in a population. Thus, individuals can be used as units for clustering procedures, such as multivariate ordination of individual scores (Sneath and Sokal 1973
, pp. 245253), or genetic distance-based approaches (Bowcock et al. 1994
). These methods are simple and intuitive, but evaluating the consistency and statistical significance of clusters, which must be identified visually, may be problematic. Therefore, these methods are more suited to exploratory data analysis than to precise statistical inference (Pritchard, Stephens, and Donnelly 2000
). More efficient methods include a variety of maximum-likelihood assignment procedures (Paetkau et al. 1995
; Rannala and Mountain 1997
; Cornuet et al. 1999
) and Bayesian clustering models (Pritchard, Stephens, and Donnelly 2000
). In these procedures, individual genotypes can be assigned to populations irrespective of whether or not their potential source populations are known. The origin of individuals can be determined by calculating the probability of each individual multilocus genotype in each population, assuming that the individual comes from that population. Bayesian models aim to infer the structure of a data set by assuming that observations from each sample are random draws from unknown gene frequency distributions in which the marker loci are unlinked and at Hardy-Weinberg (HWE) and linkage (LE) equilibrium. Population structure within a data set is detected by the presence of Hardy-Weinberg and linkage disequilibrium and is modeled by assuming that the genotype of each individual is a mixture drawn at random from a number of different populations. The number of contributing populations can be estimated and, for a given number of populations, their gene frequencies and the admixture proportions for each individual are all jointly estimated. In this way, the sampled population is subdivided into a number of different subpopulations that effectively cluster the individuals. Then, individuals of a priori known or unknown origin may be assigned probabilistically to the subpopulations.
In this study, we analyzed mtDNA sequences and allele frequency variation at 12 feline microsatellite loci in Italian wild and domestic cats, with the following aims: (1) to estimate the extent of genetic differentiation between cats which were preclassified as wild and domestic using only morphological traits (prior phenotypic information), and (2) to infer the presence of genetically differentiated clusters assuming that all the samples may belong to a single indistinct "population," independently of any prior classification, by means of multivariate ordination, interindividual genetic distances, and Bayesian clustering. Once distinct populations were identified, we used Bayesian methods to assign (or exclude) outlier individuals to the populations and infer their ancestry independently of any prior information.
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Materials and Methods |
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Mitochondrial DNA Sequencing and Microsatellite Genotyping
We PCR-amplified about 1,100 bp of mtDNA using the primers CATDL1 (5'-AAC ATC CGT TCA TCA CCA TCG GGC-3') and CATDH1 (5'-GAA TAG CAC CCT GAC TGT CTG TGC G-3'), which match nucleotides 16068 and 191 of the domestic cat mtDNA (Lopez, Cevario, and O'Brien 1996
) and include 107 bp of the 3' terminal part of the cytochrome b gene, the entire tRNA-Pro and tRNA-Thr, and part of the 5' hypervariable domain of the mtDNA control region. These primers were designed to flank a portion of the mitochondrial genome excluding the feline nuclear mitochondrial transposition (numt; Lopez et al. 1994
). However, in a few cases, we amplified putative numt sequences which were divergent and phylogenetically basal to the true mtDNA sequences (detailed analyses of these findings will be reported in another paper). By comparing mtDNA and numt sequences, we designed the new primers FCAD16234H (5'-CCC TCC CTA AGA CTT CAA GGA AGA-3', which binds at position 16234 within the tRNA-Thr), FCAD16460LMT (5'-GGG GTG AGT TGG TGG TTA ATA GAG-3'), and FCAD16460LNU (5'-GGG TTG AGT TGG TGG TTA ATA GGA-3'), which bind at position 16460 of the mtDNA and numt sequences, respectively. These primers, alternatively paired with FCAD16234H, amplified a fragment of ca. 230 bp of either mitochondrial or nuclear origin. Moreover, we have amplified an mtDNA fragment ca. 750 bp long, including the complete lysine tRNA, the complete ATPase 8, and the first 100 bp of ATPase 6, using primers ATP68H87 (5'-GGC TCA AAC CAT AGC TTC ATA CC-3') and ATP68L94 (5'-GCA TAG GAA TTA GGG GGA CAG G-3'), which bind at positions 8502 and 9239, respectively.
PCRs were performed in 10-µl reactions (10 mM Tris-HCl, 2 mM MgCl2, 50 mM KCl, 0.1 µg BSA, 0.5 U of Taq DNA polymerase, 2.5 pmol of each primer) with 30 cycles of 94°C for 30 s, 55°C for 30 s, and 72°C for 1 min, followed by 10 min of final extension, in a Perkin Elmer 9600 thermocycler. PCR products were purified with shrimp alkaline phosphatase and S1 exonuclease (U.S. Biochemicals) and sequenced using ABI Dye Terminators. Sequences were analyzed in an ABI 373 automated sequencer, corrected using the software SEQUENCE NAVIGATOR MT 1.0, and aligned using ClustalX (Thompson et al. 1997
; ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalX/). The alignments were edited using SE-AL 1.0a1 (http://evolve.zoo.ox.ac.uk/Se-Al/Se-Al.html).
Twelve microsatellites (listed in table 2
) originally isolated in the domestic cat (Menotti-Raymond and O'Brien 1995
; Menotti-Raymond et al. 1999
) were PCR-amplified in 9-µl reaction volumes (1020 ng of genomic DNA, 1.5 pmol of each primer, 100 µM of each dNTP, 1.5 mM MgCl2, 16 mM (NH4)2SO4, 67 mM Tris-HCl [pH 8.8], 0.01% Tween-20, 1 µl DMSO, and 0.4 U of Taq DNA polymerase) using primers end-labeled with ABI dyes and 40 thermal cycles (94°C for 1 min, 5055°C for 30 s, 72°C for 30 s, and 72°C for 10 min). PCR products were analyzed in an ABI 373 automated sequencer. Allele sizes were estimated using the Southern Local method with ABI software GENESCAN 2.1, and individual genotypes were determined using GENOTYPER 2.1.
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Genetic Distances, Ordination Plots, and Clustering of Genotypes
The aligned mtDNA sequences were analyzed using PAUP* 4.0b2a (Swofford 1998
). The best-fit maximum-likelihood model of DNA substitution was the HKY model (Hasegawa, Kishino, and Yano 1985
) with among-sites substitution heterogeneity, which was selected by likelihood ratio tests among a suite of models of increasing complexity (Huelsenbeck and Crandall 1997
). The values of shape parameter
of the
distribution (Yang 1994
) and the transition/transversion (Ti/Tv) ratios were estimated by the data set using maximum likelihood with the HKY model and four discrete-rate categories. Phylogenetic trees were obtained by neighbor-joining (NJ; Saitou and Nei 1987
) with HKY+
DNA distances, and maximum parsimony (MP) with unordered and equally weighted characters. Robustness of the phylogenies was assessed by bootstrap percentages computed using 1,000 random resamplings with replacement. A minimum-spanning network among mtDNA haplotypes was constructed using ARLEQUIN.
Interindividual microsatellite genetic distances, including the 1 - proportion of shared alleles (DPS; Bowcock et al. 1994
) and deltamu (
µ2; Goldstein et al. 1995
) distances, were estimated with MICROSAT 1.5d (http://human.stanford.edu/microsat/microsat.html). Distance matrices were then used to construct NJ trees with the program NEIGHBOR in PHYLIP 3.5c (http://evolution.genetics.washington.edu/phylip.html). In addition, individual genotypes were ordinated in a multidimensional space by principal-component analysis (PCA) using the program PCAGEN (http://www.unil.ch/izea/softwares/pcagen.html).
Bayesian Clustering, Genetic Admixture Analysis, and Population Assignment
Pritchard, Stephens, and Donnelly (2000)
described a Bayesian clustering method (implemented in the program STRUCTURE; http://www.stats.ox.ac.uk/
pritch/home.html) which uses multilocus genotypes to infer population structure and simultaneously assign individuals to populations. This model assumes that there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned probabilistically to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. This method can be used to detect the presence of cryptic population structure and to perform assignment testing. Pritchard, Stephens, and Donnelly's (2000)
model assumes HWE and LE among the unlinked marker loci. Departures from HWE and LE lead the population to be split into subpopulations, to which individuals are assigned, and those with admixed ancestries are assigned to more than one source population. In this study, the posterior probabilities of K (i.e., the likelihood of K as a proportion of the sum of the likelihoods for different values of K) are estimated assuming uniform prior values on K between 1 and 5 (option MAXPOPS = 15). The presence of structure in the data set is revealed by the increasing likelihood of the data. The results presented in this study are based on 100,000 iterations, following a burn-in period of 10,000 iterations. A Bayesian assignment procedure is implemented in STRUCTURE, where individuals are assigned probabilistically to one or more predefined subpopulations using or not using prior population information.
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Results |
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Microsatellite variability was significantly partitioned among the three groups (FST = 0.13; RST = 0.30; P < 0.001; AMOVA), suggesting that phenotypic classification reflects significant genetic differences among cats. The estimated RST distances were more than two times FST, suggesting that cats differ in distributions of both allele frequency and allele size. Domestic cats and Sardinian wildcats showed the lowest pairwise FST and RST values (table 4 ), which, once again, supports current hypotheses on domestication.
Clustering of mtDNA Sequences and Ordination Plot of Individual Cats
Phylogenetic clustering of mtDNA sequences produced very similar NJ or MP trees. The unrooted NJ tree (fig. 3a
), obtained using the best-fit substitution model (HKY with -rate heterogeneity;
= 0.75 and Ti/Tv = 29, as estimated from the data), showed a number of lineages joining haplotypes which were found in more than one group, except lineage a, which included only European wildcat haplotypes. Lineage b is mainly a domestic cat lineage but includes haplotype Fca9, which was found also in three European wildcats (see the appendix at the journal website). Haplotypes from South African wildcats were distinct (lineage c), and the haplotypes from Sardinian wildcats split into two different lineages, including also domestic cats and European wildcats (lineages d and e). Phylogenetic signal from these sequences was weak, and most of the clades were not supported after 1,000 bootstrap replications (fig. 3b
), suggesting that mtDNAs of cats diversified rapidly. Weak phylogenetic resolution makes it difficult to use these mtDNA sequences to infer group distinction, divergence times, and eventual hybridization. However, mtDNA analyses indicated that (1) European wildcats sampled in Italy harbor at least two distinct haplotype groups (lineage a and the other haplotypes) diverging by about 2%, and (2) wildcats of African origin include at least three different lineages, thus suggesting that Sardinia was colonized perhaps more than once and certainly not with cats originating from South Africa.
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The assignment of individual cats was inferred by STRUCTURE either without (USEPOPINFO = 0) or with (USEPOPINFO = 1) using prior population information. In the first case, domestic cats and European and Sardinian wildcats are probabilistically assigned to cluster I, II, or III. In the second case, we force sampling of all cat genotypes from one of the three different clusters, and STRUCTURE estimates the probability for each sample of having an ancestry in the other groups, either in the sampled generation or in the first or second past generations (q-values were computed with prior intergroup "migration" rate = 0.01; in this context, "immigrant" means "hybrid"). Probabilities of membership are the posterior values of qi (i = 1, 2, 3) for each individual, that is, the proportion of each individual genotype originating in one or in more than one cluster. Results (reported in table 6 ) showed that with USEPOPINFO = 0, cluster I grouped 94% (i.e., 46/49) of domestic cats with individual values of q1 0.92. Only three cats (preclassified as domestic cats) had individual q1 < 0.90, that is, Fca28, Fca32, and Fca35, which were nevertheless significantly associated with the domestic cat cluster I. Cluster II grouped 96% (i.e., 46/48) of European wildcats with q2
0.93. Two cats had q2 < 0.90, that is, Fsi228 (nevertheless significantly associated with cluster II) and Fsi284, which was associated in part with the domestic cat cluster I (q1 = 0.78) and in part with cluster II (q2 = 0.21). Cluster III joined 88% (15/17) of preclassified Sardinian wildcats with q3
0.97. One cat, Fli46, was significantly associated with cluster III, with q3 = 0.88, while Fli326 was unambiguously associated to the domestic cat cluster I, with q1 = 0.99.
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All of the Sardinian wildcats had q3 0.99 (cluster III) except Fli326, which was assigned to the domestic cat cluster I with q1 = 1.00 in the sampled generation. The mtDNA haplotype of Fli326 was unique, not shared with other cats or clearly associated with any wild or domestic lineages (figs. 3 and 4
). Although we cannot definitely exclude the possibility that this cat is a hybrid, mislabeling and wrong phenotypical classification are the most plausible explanations. In conclusion, we found 1 cat in the 114 studied (0.9%) that was genetically assigned to a different group if compared with the morphological preclassification.
We excluded the two outlier cats Fsi284 and Fli326 from the data set and assumed that all the other cats definitely belonged to three genetically distinct populations (clusters I, II, and III), which can be used as a reference for further testing of the assignment of the known hybrids to putative ancestral populations. Assignment was performed using STRUCTURE with prior information on the reference populations only, and not for the hybrid cats to be assigned. In this case, the q values of all reference cats were 1.00 (table 7 ), while all hybrids always had q values lower than 0.82. All of the known hybrids could be exactly identified as cats having admixed ancestry in more than one cluster.
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Discussion |
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In this study, we preclassified cats sampled in Italy using exclusively morphological markings, which, combined with lifestyle traits (behavior of domestic cats) and geographical origins (the wildcats sampled in Sardinia), allowed a clear-cut subdivision of three groupsEuropean wildcats, Sardinian wildcats and domestic catscorresponding to the three nominal subspecies of F. silvestris which are present in Italy: F. silvestris silvestris, F. silvestris libyca, and F. silvestris catus. Genetic diversification between preclassified cats was as follows: ST = 0.39 (as derived from mtDNA sequence divergence), FST = 0.13, and RST = 0.30 (as derived from microsatellite allele frequency or allele size variability), meaning that about 30% of the total genetic diversity was distributed among groups and that nongenetic classification identified groups that were genetically differentiated. All mtDNA haplotypes (with the exception of haplotype Fca9, which was shared between three domestic cats and three European wildcats; see the appendix at the journal website) were completely sorted among domestic cats and wildcats. The microsatellite RST distances were more than two times as large as FST, suggesting that divergence between wild and domestic cats cannot be explained by different distributions in allele frequencies alone; there must also be a shift in the mean allele length, implying divergence over a longer period, rather than recent drift (Slatkin 1995
). Moreover, the existence of many "private" alleles, some of which are at relatively high frequencies in domestic cats or wildcats, clearly suggests that wild and domestic cats are genetically differentiated and that there is little gene flow among them. Therefore, the hypothesis that frequent crossbreeding with free-ranging domestic cats might have strongly polluted the gene pool of wild populations (Suminski 1962
) was not confirmed by these data.
Genetic differentiation among cats was fully recognized by multivariate and NJ clustering procedures, which, without using prior population information, split the cats into separate clusters largely corresponding to the three nominal subspecies (figs. 5 and 6 ). However, these procedures led to the identification of a number of outliers, which were assigned differently from the nongenetic classification and apparently joined the "wrong" clusters. The known hybrid cats were poorly identified in both NJ and PCA procedures, which did not offer objective criteria for assigning individuals to populations.
By contrast, the Bayesian procedure jointly assigns a probability to the number of populations and to the membership of each individual in each population, allowing extraction of precise quantitative information from the data set. The multilocus genotypes from individual cats fit the genetic model (i.e., the assumptions that genetic markers are independent in HWE and LE) better if samples are split into at least three distinct populations. When samples were assigned to three inferred clusters without using any prior population information, the domestic cats and the European and Sardinian wildcats were assigned to clusters I, II, and III, respectively, with the average proportion of individual memberships q > 0.90 and without significant ancestry in the other clusters. Only two cats (Fsi284 and Fli326) among the many putative outliers which were apparently "misplaced" in the multivariate or distance-based clustering procedures had significant ancestry in other clusters (table 6 ). When ancestry of the outlier cats was further investigated using prior population information, results suggested that putative European wildcat Fsi284 may be a hybrid with statistically significant ancestry in the first past generation of the domestic cat population, while the Sardinian wildcat Fli326 had about 100% probability of belonging to the sampled generation of the domestic cat population. Therefore, Fli326 may represent a case of a misidentified or mislabeled sample.
The European wildcats used in this study were sampled from across the entire species' distribution range in Italy. Only 1 (Fsi284) of 48 genotyped European wildcats had admixed ancestry and was probably a hybrid with the domestic cat. Moreover, three additional putative European wildcats (Fsi70, Fsi73, and Fsi285) showed mtDNA haplotype Fca9, which was shared with three domestic cats. In both minimum-spanning network and NJ trees, haplotype Fca9 appears to be related to other domestic cat haplotypes and not to wildcat haplotypes (figs. 3 and 4
). Therefore, although mtDNA haplotypes did not convey strong phylogenetic information, it is probable that Fca9 is a domestic cat haplotype. Nevertheless, the Bayesian assignment procedure classified Fsi70, Fsi73, and Fsi285 as European wildcats. The putative hybrid cat Fsi284 might derive from recent crossbreeding, while cats Fsi70, Fsi73, and Fsi285 could have a more ancient ancestry with domestic cats. Cats Fsi70, Fsi285, and Fsi284 were collected in Tuscany Maremma, on the Thyrrenian (western) coast of central Italy, and cat Fsi73 came from a central Apennines area geographically very close to Maremma. These localities map on the northernmost edge of the zoogeographical range of F. silvestris silvestris in Italy (fig. 1
), which is thought to have been stable from the end of the last glaciation (Ragni et al. 1994
), and were historically densely settled by humans and by potentially free-ranging domestic cats.
These findings suggest that despite a long period of sympatry and syntopy, hybridization is negligible and is limited to particular areas at the geographical and ecological edges of the wildcat distribution in central Italy. However, more samples, and probably more microsatellite loci, should be analyzed to obtain quantitative estimates of the rate of crossbreeding in the Italian wildcat population. The microsatellites used in this study are widely spaced on the same chromosome or on separate chromosomes (Menotti-Raymond et al. 1999
). Backcrossing of first-generation hybrids into the wildcat population will dilute the proportion of domestic parental genotypes through the generations, and linkage disequilibrium will be negligible after a few generations of backcrossing. Therefore, except for the introgressed nonrecombining mtDNA, evidence of episodic hybridization in the past might have been lost, and the identification of past hybridization might require an exponentially increasing number of molecular markers (Goodman et al. 1999
). Thus, the existence of distinct groups of wildcats (European and African wildcats) does not necessarily mean that we have identified "pure" populations with no introgression, but rather that we have identified cats that show little evidence of recent domestic cat ancestry.
Results of Bayesian admixture analyses validate the morphological protocols used to identify wild and hybrid phenotypes of F. silvestris and have implications for the conservation of wildcat populations in the Mediterranean region (Stahl 1993
; Nowell and Jackson 1996
). Despite national and international protection in most European countries, the wildcat is threatened and declining throughout most of its range due to habitat destruction, direct persecution, accidental killing, transmission of viral diseases, and possible hybridization with feral cats. To enforce legal protection, it is therefore important to improve a set of morphological, behavioral, and molecular traits diagnostic for the wildcat and to map the regional distribution of "pure" wildcat populations, which must be protected with high priority.
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Acknowledgements |
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Footnotes |
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1 Keywords: Felis silvestris
wild and domestic cat
hybridization
Bayesian clustering
assignment test
admixture analysis
2 Address for correspondence and reprints: Ettore Randi, Istituto Nazionale per la Fauna Selvatica, Via Cà Fornacetta 9, 40064 Ozzano dell'Emilia (BO), Italy. met0217{at}iperbole.bo.it
.
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