Phylogeographical structure in the subterranean ... - Wiley Online Library

Molecular Ecology (2007) 16, 3453–3465

doi: 10.1111/j.1365-294X.2007.03398.x

Phylogeographical structure in the subterranean tuco-tuco Ctenomys talarum (Rodentia: Ctenomyidae): contrasting the demographic consequences of regional and habitat-specific histories Blackwell Publishing Ltd

M A T Í A S S . M O R A ,* E N R I Q U E P . L E S S A ,† A N A P . C U T R E R A ,* M A R C E L O J . K I T T L E I N * and ALDO I. VASSALLO* *Laboratorio de Ecofisiología, Departamento de Biología, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata. CC1 245, 7600 Mar del Plata, Argentina, †Laboratorio de Evolución, Facultad de Ciencias, Universidad de la República, Montevideo 11400, Uruguay

Abstract In this work we examined the phylogeography of the South American subterranean herbivorous rodent Ctenomys talarum (Talas tuco-tuco) using mitochondrial DNA (mtDNA) control region (D-loop) sequences, and we assessed the geographical genetic structure of this species in comparison with that of subterranean Ctenomys australis, which we have shown previously to be parapatric to C. talarum and to also live in a coastal sand dune habitat. A significant apportionment of the genetic variance among regional groups indicated that putative geographical barriers, such as rivers, substantially affected the pattern of genetic structure in C. talarum. Furthermore, genetic differentiation is consistent with a simple model of isolation by distance, possibly evidencing equilibrium between gene flow and local genetic drift. In contrast, C. australis showed limited hierarchical partitioning of genetic variation and departed from an isolation-by-distance pattern. Mismatch distributions and tests of neutrality suggest contrasting histories of these two species: C. talarum appears to be characterized by demographic stability and no significant departures from neutrality, whereas C. australis has undergone a recent demographic expansion and/or departures from strict neutrality in its mtDNA. Keywords: Ctenomys australis, Ctenomys talarum, phylogeography, subterranean rodents Received 26 January 2007; revision accepted 20 April 2007

Introduction In addition to ongoing ecological and demographic processes, population history influences the distribution and maintenance of genetic diversity. The recent colonization of new habitats or, alternatively, long periods of population stability have contrasting effects on the pattern of spatial apportionment of genetic variation (Matocq et al. 2000). Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how evolutionary history and landscape characteristics can structure populations (Hutchison & Templeton 1999). In this sense, mitochondrial DNA (mtDNA) markers have Correspondence: Matías Sebastián Mora, Fax: +54 223 4753150; E-mail: [email protected] © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

been shown to be effective to begin to reveal the present genetic structure and history of populations (Avise 1998). Many species occur in naturally fragmented environments, and when their dispersal abilities are restricted in relation to the spatial scale of habitat disruption, they generally occur in small genetic units in which genetic variation is low, whereas interpopulation divergence is high. This may be the case of the South American rodents of the genus Ctenomys (tuco-tucos), which is the most speciose among all subterranean rodent genera (Reig et al. 1990; Cook & Lessa 1998; Lessa & Cook 1998; Castillo et al. 2005). Populations of Ctenomys are composed of semi-isolated demes, occupying patches of habitat where soil hardness and particle size provides suitable conditions for burrowing activities (Busch et al. 2000). Species-specific habitat requirements, such as those related to body size differences

3454 M . S . M O R A E T A L . (see Lessa & Thaeler 1989) and other factors intrinsically associated with the species’ biology (i.e. pelage crypticity, Krupa & Gelusa 2000), strongly influence the type of soil subterranean rodents use to build their burrows, affecting the connectivity among local populations and resulting phylogeographical patterns. Ctenomys shares with other subterranean rodents restricted dispersal capabilities, small local effective population numbers, high territoriality, and in some cases socially-structured mating systems (Reig et al. 1990; Lacey 2000; Massarini & Freitas 2005). Here, we report results of a phylogeographical study of the Talas tuco-tuco C. talarum. Sampling was carried out so that a close comparison could be made with a recent study of the sand dune tuco-tuco (C. australis, Mora et al. 2006). Both species of Ctenomys are not closely related phylogenetically; unlike C. talarum, C. australis belongs to the mendocinus species group, which includes species like C. rionegrensis, C. porteousi, C. azarae and C. mendocinus (Massarini et al. 1991; D’Elía et al. 1999). C. talarum (mean body weight 130 g) and C. australis (mean body weight 380 g) occur in parapatry along the coast of the province of Buenos Aires, Argentina (Contreras & Reig 1965; Apfelbaum et al. 1991), between the cities of Necochea (38°37′S 58°50′W) and Bahía Blanca (39°30′S 61°40′W). Body size differences are correlated with their markedly different habitat preferences: C. talarum occupies both interdune and inland habitats with increasingly harder, humid and vegetated soils (Malizia et al. 1991; Vassallo 1993, 1998); in contrast, C. australis is restricted to a narrow coastal dune habitat characterized by friable soils and extremely low plant cover, occupying a very reduced effective area of approximately 100 km2, which corresponds to the species distribution range (Kittlein et al. 2004). In spite of these differences, both species have close associations with coastal habitats. Consequently, and especially in the case of C. australis, there is one predominant spatial dimension to their distributions: such one-dimensional distributions place restrictions to gene flow among populations of both species. Furthermore, the distribution of both species along the coast is interrupted by the same potential barriers, such as rivers and, in recent times, forestation (Contreras & Reig 1965; Kittlein et al. 2004). Interestingly, the geological histories of the two adjacent habitats used by these species differ markedly. The relatively unstable costal dunes resulted from recent fluctuations of the sea level in the mid-Holocene, 6000–1500 years ago (Isla et al. 2001). On the other hand, habitats occupied by C. talarum have a relatively more ancient origin: they are remnants from late Pleistocene aeolian deposits (Frenguelli 1950). A comparison of geographical genetic patterns of these two species thus offers a unique opportunity to assess the effect of their contrasting habitat requirements and possibly population history, on patterns of genetic structure, in the framework of a common regional history.

Hence, we assessed the geographical genetic structure of C. talarum and compared it with that of C. australis, addressing the following questions: (i) do these two subterranean rodent species have a similar phylogeographical structure determined by the occupation of a common geographical area, or rather, their phylogeographical structures differ as a result of different habitat requirements and/or differing biological attributes? (ii) to what extent do the predominantly one-dimensional distributions of these species, coupled with limited dispersal, impose an isolation-by-distance pattern of genetic variation? and (iii) how has the differential history of their habitats affected geographical genetic structure? Additional insights are provided by comparing these two species with data on Pearson’s tuco-tuco (C. pearsoni), a species that lives along the coast of Uruguay and, like C. talarum, is not restricted to Holocene sand dunes (Tomasco & Lessa in press), and with the Río Negro tuco-tuco (C. rionegrensis), an inland species that, nonetheless, is restricted to Holocene sand dunes in central western Uruguay (D’Elia et al. 1998; Wlasiuk et al. 2003; D’Anatro & Lessa 2006).

Materials and methods Sample collections We obtained tissue samples (toe snips, preserved in 95% ethanol for subsequent DNA extraction) from a total of 71 individuals of C. talarum, which were live-trapped with Oneida Victor N°0 snap-traps (Oneida Victor Inc. Ltd), with a rubber cover to avoid injuring the animals (see Zenuto & Busch 1998). Upon completion of these procedures the animals were immediately released into the burrow from which they were captured. Samples comprised between 6 and 22 individuals at each of six localities of C. talarum along the area sampled by Mora et al. (2006) for C. australis (Fig. 1 and Appendix). All parts of the study involving live animals followed guidelines of the American Society of Mammalogists (Animal Care & Use Committee 1998).

DNA extraction, PCR amplification and sequencing Total DNA extractions were performed with sodium dodecyl sulphate (SDS)-proteinase K-NaCl-alcohol precipitation (modified from Miller et al. 1988). A fragment of the control region of mtDNA was amplified by polymerase chain reaction (PCR) from all specimens of C. talarum using the primers TucoPro (5′-TTC TAA TTA AAC TAT TTC TTG-3, Tomasco & Lessa, in press) and TDKD (5′-CCT GAA GTA GGA ACC AGA TG-3′, Kocher et al. 1989). Amplification was carried out in a total volume of 12.5 µL containing the following components: 6.25 µL of DNA (4 µg/mL) used as a template, 0.5 units of Taq DNA polymerase, and the following reagents and final concentrations: 1X Taq © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

P H Y L O G E O G R A P H I C A L S T R U C T U R E I N C T E N O M Y S T A L A R U M 3455 deposited in GenBank under accession numbers EF531719EF531750.

Data analysis

Fig. 1 Geographical distribution of Ctenomys talarum and Ctenomys australis along the coast of southeastern Buenos Aires province, Argentina. Grey and black dashed areas show the distribution of C. talarum. The grey area represents the total distribution range of C. australis. The most important rivers and streams, the abbreviations, coordinates, and the numbers of the localities sampled are indicated; N: Necochea (38°37′S, 58°50′W), SC: Balneario San Cayetano (38°43′S, 59°26′W), ORE: Balneario Orense (38°48′S, 59°44′W), CL: Claromecó (38°51′S, 59°59′W), R: Balneario Reta (38°53′S, 60°19′W), QS: Río Quequén Salado (38°54′S, 60°28′W), BMS: Balneario Marysol (38°54′S, 60°31′W), MH: Monte Hermoso (38°59′S, 61°18′W) and PCO: Balneario Pehuen Có (39°0′S, 61°36′W). Population sampled for C. talarum (N, SC, R, QS, MH and PCO); and populations sampled for C. australis (N, SC, ORE, CL, R, BMS, MH and PCO). Modified from Mora et al. (2006).

polymerase buffer, dNTPs at 0.2 mm each, primers at 0.2 µm each and MgCl2 at 4 mm. PCR amplifications were performed in a Rapidcycler™ (Idaho Technology) with a program that consisted of an initial denaturation of 1 min at 94 °C, followed by 30 cycles of 1 min of denaturation at 94 °C, 1 min of annealing at 49 °C for C. talarum, and 1 min of extension at 72 °C, and a final extension of 5 min at 72 °C (following Tomasco & Lessa, in press). PCR products were purified by chromatography (Sephadex G50 fine, Pharmacia Biotech AB) performed following the guidelines of suppliers and sequenced at the core facility of the Facultad de Ciencias, Montevideo, using a Perkin Elmer ABI Prism 377 automated sequencer (Perkin Elmer) with the terminal primers used in PCR. Forward and reverse sequences were aligned and cross-checked as needed to resolve ambiguities. DNA extractions of C. talarum were deposited in the collection of the Laboratorio de Evolución, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay. The final sequences were 418 bp in length (compared to 403 bp of the same region in C. australis, as reported by Mora et al. 2006). The distinct sequences reported in this paper were © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

Electropherograms were scored and analysed using Sequence Navigator (Applied Biosystems) and aligned using clustal x (Thompson et al. 1997). Nucleotide variation was estimated as π (the average number of pairwise differences between haplotypes, which estimates θ = 2Nefµ; where θ is genetic diversity, Nef is the effective population size and µ is the mutation rate per locus per generation) and θw, an estimate of θ based on the number of segregating sites in the sample (Watterson 1975), using mega Version 1.02 (Kumar et al. 1993). arlequin Version 3.0 (Excoffier et al. 2005) and dnasp Version 4.0 (Rozas et al. 2003) were used for computing estimates of genetic variation within and among populations, global and pairwise estimates of gene flow (Hudson et al. 1992), and Tajima’s D (Tajima 1989) and Fu’s FS (Fu 1997) tests of neutrality within populations. The significance of these tests of neutrality was assessed using 1000 iterations (arlequin Version 3.0). Significant negative values of Tajima’s D and Fu’s FS are indicative of an excess of low frequency mutations, relative to expectations under the standard neutral model (strict neutrality of variants, constant population size and lack of subdivision and gene flow). Pairwise estimates of gene flow and linear geographical distances were used in each species to examine patterns of isolation by distance (Slatkin 1987, 1993; Slatkin & Hudson 1991). A Mantel (1967) test was used to assess significance of the correlation between gene flow estimates and geographical distances using 1000 permutations. The mean number of migrants per generation (Nm) was estimated as Nm = (1 – ΦST)/(2ΦST) and the geographical distance matrix was calculated using the ‘idrisi gis’ program (Eastman 1999). A minimum spanning tree of haplotypes was generated using arlequin (Excoffier & Smouse 1994; Excoffier et al. 2005). We examined the demographic history of populations by an analysis of ‘mismatch distributions’ to distinguish between populations that have been stable over time (according to an ‘equilibrium model’ with constant long-term Ne) from those that have experienced recent demographic and/or range expansion (and/or departures from strict neutrality; see Slatkin & Hudson 1991; Rogers & Harpending 1992; Harpending et al. 1998; Schneider & Excoffier 1999; Ramos-Onsins & Rozas 2002; Excoffier 2004). We employed parametric bootstrapping as implemented in arlequin to test the goodness of fit of the observed mismatch distribution to that expected under the sudden expansion model using the sum of squared deviations (SSD) statistic. In addition, we modified the program ‘ms’ (Hudson 2002) to get the distribution of the SSD statistic and assess the fit to

3456 M . S . M O R A E T A L . the observed mismatch distribution under the equilibrium model, following the same procedure implemented in arlequin (L. Excoffier, personal communication). In order to investigate the existence of hierarchical levels of population structure, an analysis of molecular variance (amova) was performed considering genetic distances between haplotypes and their frequencies using arlequin (Weir & Cockerham 1984; Excoffier et al. 1992; Excoffier et al. 2005). The effect of natural barriers on the partitioning of the genetic variance was tested at the regional level.

Results Variation among control region haplotypes C. talarum presented a total of 47 variable sites, of which 19 were singletons and 28 were parsimony informative sites, defining 32 haplotypes in the sample of 71 sequences analysed (Appendix). The high number of polymorphic sites in C. talarum resulted in high average haplotype and nucleotide diversity values (Table 1). Overall, the average number of nucleotide differences between haplotypes was of 9.0.

Genealogical relationships among haplotypes and geographical genetic variation of populations Most of the 32 haplotypes (68%) were apparently limited to single populations, and most individual populations showed relatively large numbers of haplotypes (overall haplotype

diversity was 0.93, with all populations being polymorphic; see Appendix). Furthermore, shared haplotypes were not found in populations separated by distances greater than 61 km (the distance between Balneario San Cayetano and Necochea; minimum spanning tree in Fig. 2a), signalling the restricted distribution of haplotypes. Two major groups of haplotypes, distinguished by 14 observed nucleotide differences and corresponding to the eastern and western portions of the study area, were found within C. talarum (Fig. 2a). Haplotype H14 found in Monte Hermoso (from the Western group) and haplotype H22, found in Quequén Salado (from the Eastern group; Figs 1 and 2) were the most divergent haplotypes, differing at 34 sites. Substantial variation is found within the Western group, with a large number of unique haplotypes (12 of 18 haplotypes; Fig. 2a). With the exception of H12 (found in only four individuals), the Western group did not show any haplotype at high frequency. In contrast, the populations from the Eastern group showed four haplotypes at high frequency (H1, H2, H21 and H31). A hierarchical analysis of variance (amova) showed substantial subdivision among C. talarum populations, mostly associated to differentiation among three regions separated by major rivers in the area (ΦCT = 0.77; Table 2). Variation among populations within groups and within populations were relatively low (5.83% and 17.5% of total variation, respectively) relative to the among group variance component (Table 2). The observed phylogeographical

Table 1 Number of sequences (N°S), number of haplotypes (N°Ha), number of polymorphic sites (N°PS), haplotype diversity (HaDi), nucleotide diversity (NuDi) and standard deviation (SD) in Ctenomys talarum and C. australis (Mora et al. 2006) from Argentina are shown. The total and overall values of these parameters are also given. Abbreviations of populations are shown in Fig. 1 Sample locations C. talarum N SC R QS HH PCO Total Overall C. australis N SC ORE CL R BMS HH PCO Total Overall

N°S

N°Ha

N°PS

HaDi

SD

NuDi

SD

15 9 22 7 8 10 71

6 5 6 3 7 8 32

7 9 7 4 16 7 47

0.7 0.72 0.77 0.53 0.99 0.96

0.114 0.159 0.065 0.209 0.063 0.059

0.004 0.003 0.005 0.003 0.014 0.005

0.002 0.003 0.003 0.002 0.007 0.003

0.93

0.015

0.022

0.002

0.81 0.81 0.9 0.15 0.71 0.71 0.81 0.86

0.119 0.13 0.161 0.126 0.181 0.105 0.13 0.111

0.0064 0.0049 0.0088 0.0004 0.0035 0.0043 0.0028 0.0055

0.003 0.003 0.005 0.001 0.003 0.003 0.002 0.004

0.83

0.038

0.0055

0.001

11 7 5 13 7 12 7 8 70

7 4 4 2 4 4 4 5 24

7 5 8 1 5 7 3 7 19

© 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

P H Y L O G E O G R A P H I C A L S T R U C T U R E I N C T E N O M Y S T A L A R U M 3457

Fig. 3 Relationship between pairwise geographical distances and estimates of gene flow (M) for Ctenomys talarum (solid circles and full line) and Ctenomys australis (open circles and broken line; from Mora et al. 2006) from Argentina, based on FST from mitochondrial control region sequences. The relationship between variables was statistically significant for C. talarum (see Results), but not for C. australis (r = –0.062, P > 0.05; see Mora et al. 2006).

A plot of pairwise geographical distances and estimates of gene flow (M, based on ΦST, see Hudson et al. 1992) is shown in Fig. 3. Genetic differentiation in C. talarum was consistent with a simple pattern of isolation by distance, possibly evidencing an equilibrium between gene flow and local genetic drift (r = 0.76, Mantel test, P < 0.05). A global estimate of migration rate per generation, calculated assuming an island model of population structure, was Nm = 0.12. While the average Nm value between populations distanced by less than 70 km was 2.4, the average Nm value among populations distanced at more than 200 km was 0.079. These results suggest that gene flow between populations of this species is limited to a small geographical scale.

Mismatch distribution and tests of neutrality

Fig. 2 Minimum spanning tree of 32 mitochondrial DNA (mtDNA) haplotypes of Ctenomys talarum (a) and 24 mtDNA haplotypes of Ctenomys australis (b, modified from Mora et al. 2006) from Argentina. Areas are proportional to haplotype frequencies, shading indicates localities, and cross hatches represent nucleotide differences between haplotypes. Haplotype numbers for C. talarum correspond to those shown in the Appendix. The dotted lines in (a) show both the major rivers and streams and the most important phylogeographical subdivisions associated with them (Western and Eastern regions). For further information on C. australis minimum spanning tree see Mora et al. (2006). Abbreviations of populations are shown in Fig. 1.

break between Eastern and Western geographical groups of C. talarum is associated to 72% of total variation in a model with two regions separated by Rio Quequén Salado (Fig. 1). © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

The global mismatch distribution of C. talarum was clearly multimodal (Fig. 4), suggesting constant population size and/or sustained subdivision for a long period of time, such that the SSD between observed and expected mismatch distributions did not show significant departures from an equilibrium model (SSD = 0.0269, P = 0.73). A sudden expansion model has a lower fit for this test statistic (SSD = 0.0263, P = 0.30). Mismatch distributions analysed separately for Eastern and Western groups showed similar patterns (Eastern populations [equilibrium model SSD = 0.026, P = 0.79; sudden expansion model SSD = 0.012, P = 0.37]; Western populations [equilibrium model SSD = 0.041 and P = 0.71; sudden expansion model SSD = 0.0026, P = 0.47]). However, Tajima’s D and Fu’s Fs indicate that the demographic histories of Western (D = –1.17, P = 0.07; Fs = –8.40, P < 0.001) and Eastern (D = –0.57, P = 0.32; Fs = –4.37, P = 0.06) populations differ, suggesting that these areas of the distribution of C. talarum have experienced different demographic events in their recent past (stability in the

3458 M . S . M O R A E T A L . Table 2 Hierarchical analysis of molecular variance (amova), using a square matrix of pairwise genetic distances between haplotypes. The fixation indices (Φ-statistics) are shown. Φ-statistics and significance of variance component (P) were tested by 1000 permutations according to Excoffier et al. (1992). The respective levels of subdivision of each population are shown in brackets. Abbreviations for locations are given in Fig. 1 Source of variation C. australis Three regions limited by the major rivers and streams

No clustering of subpopulations into regions

C. talarum Three regions limited by the major rivers and streams

No clustering of subpopulations into regions

Level of subdivision

ΦCT

[N-SC] [ORE-CLA-RET] [BMS-MH-PCO] [NEC] [SC] [ORE] [CLA] [RET] [BMS] [MH] [PCO]

0.03

[N-SC] [RET-QS] [MH-PCO] [N] [SC] [RET] [QS] [MH] [PCO]

0.77

P

0.22

< 0.001

ΦST

P

0.27

< 0.001

0.27

< 0.001

0.82

< 0.001

0.79

< 0.001

Discussion Hierarchical levels of genetic structure

Fig. 4 Observed and expected mismatch distributions for Ctenomys talarum (a) and Ctenomys australis (b) from Argentina. Open circles: observed distribution; solid circles: theoretical expected distribution under a population expansion model following Schneider & Excoffier (1999). F(i): frequency.

East and expansion in the West), further supporting the early subdivision of the geographical distribution of this species associated to the presence of a main barrier, the Río Quequén Salado (Fig. 1).

C. talarum is characterized by strong geographical subdivision of variation in mtDNA. This translates into a complex network of haplotypes (most of which have restricted distributions), a bimodal mismatch distribution and a high portion of genetic variation attributable to differences among regions. In addition, the overall pattern is similar to that expected under the isolation-by-distance or the stepping-stone models (Slatkin 1993). In contrast, the sand dune tuco-tuco (C. australis) shows a combination of a few widespread haplotypes and numerous unique ones, coupled with lower nucleotide diversity, a star-like haplotype network, a unimodal mismatch distribution and no significant correlation between geographical distances and estimated levels of gene flow (Mora et al. 2006; see also Fig. 2b). Major rivers in the area appear to be associated with strong levels of genetic differentiation in C. talarum. However, those barriers do not correspond to significant changes of the genetic makeup of populations in the case of C. australis. These two species share an underground lifestyle and broadly similar associations with coastal environments. Furthermore, they were sampled across the same geographical regions and, whenever both were present, in the same localities. So, how have they come to show such contrasting levels and patterns of genetic variation? We address this question and offer a general explanation based on the biological differences between the species and their © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

P H Y L O G E O G R A P H I C A L S T R U C T U R E I N C T E N O M Y S T A L A R U M 3459 habitats and their possible historical and demographic consequences. Our scenario assumes neutrality of the mitochondrial genome, but it is followed by a brief discussion of the consequences of possible departures from neutrality. First, it is clear that at equilibrium we should expect to observe an isolation-by-distance pattern in both species, because the dispersal ability of these two subterranean species is limited relative to the extent of the study area. As pointed out by Solomon (2003), ecological factors and life-history can be viewed as a complex set of variables that interact to influence phylopatry in rodents; these interactions should determine the cost of and constraints on dispersal. Data on rates of dispersal are available for only a few species of subterranean rodents and, in general, have been assumed to be very limited (see Patton & Smith 1990; Steinberg & Patton 2000, in pocket gophers; and Reig et al. 1990; Zenuto & Busch 1998; Busch et al. 2000; Lacey 2000; El Jundi & Freitas 2004; Cutrera et al. 2005; Cutrera et al. 2006, in tuco-tucos). As we said above, C. talarum and C. australis, like other species of Ctenomys, are characterized by restricted mobility and its dispersal capability highly depends on dune fragmentation and particular habitat conditions (Cutrera et al. 2006; Mora et al. 2006). It should be noted, that differences in body size and predation risk between C. talarum and C. australis have an important influence on their dispersal rates (Vassallo et al. 1994), determining differences in gene flow between populations. In this sense, working with microsatellites, Cutrera et al. (2005) showed high levels of genetic structure at microgeographical scale (local populations) and very low potential of dispersal in C. talarum. In contrast, working with radio-telemetry and capture-mark-recapture studies, M. S. Mora et al. (unpublished data) found that C. australis seems to present fewer restrictions to mobility than the former species. The association of both C. talarum and C. australis to the coast constrains their geographical genetic structures to one major dimension. One-dimensional distributions, coupled with limited dispersal ability, facilitate the establishment and detection of isolation by distance (Slatkin 1987, 1993; Slatkin & Barton 1989). However, the precise association of these two species with coastal dunes is different in nature. C. australis is strictly limited to the first line of coastal dunes, whereas C. talarum lives in the next line of dunes, as well as in other habitats. Although poorly studied, there are additional known populations of C. talarum in inland areas. However, the species does not appear to be as abundant in such areas as it is along the coast. This patchy inland distribution might be related to changes in soil composition during the late Pleistocene and early Holocene (Frenguelli 1950; Quintana 2004). During the Pleistocene/Holocene transition, the Argentinean Pampa presented some inner palaeodune areas of fixed sandy soils composed of a lower percentage © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

of clay soils than we observe nowadays (e.g. Eastern of Sierras del Sistema de Tandilia; Fig. 1). These Pleistocene aeolian sedimentary deposits would have allowed the establishment of C. talarum in the Sistema de Tandilia (Quintana 2004). Palaeontological data support a progressive decrease in the abundance of inland C. talarum’s populations since the late Pleistocene (10 375 years before present). East of Sierras del Sistema de Tandilia, and in other areas in the Province of Buenos Aires, C. talarum disappeared some 170 ± 60 years ago (see Quintana 2004). Nowadays, inland populations of C. talarum have been found only in the localities of Sierras del Sistema de Ventania (southern-central Buenos Aires Province), El Saladillo and El Cazón (northern-central Buenos Aires Province [Quintana 2004]; Fig. 1). It should also be noted that there are areas along the coast in which C. talarum is absent (e.g. Balneario Orense, Claromecó and Balneario Marysol; M. S. Mora, personal observation; Fig. 1). The development of agroecosystems during the last two centuries could have also negatively impacted C. talarum populations. In sum, the current populations of C. talarum appear to be relicts of a more extended historical distribution (see also Quintana 2004). The possible elimination of inland populations of C. talarum, however, should not erase the signature of longterm stability and geographical structure of the species along the eastern range of the coast. In contrast, C. australis may have colonized the current formation of coastal sand dunes in relatively recent times, i.e. within the last 6000 years (Mora et al. 2006), more probably following the evolution of the sand dune system since the middle Holocene (from 6000 to 1500 years ago, dated by radiocarbon; Isla et al. 2001). Even though the flexibility in soil occupation is higher in C. talarum than C. australis (related to an intrinsic tolerance to changes in soils hardness; Vassallo 1998), greater percentage of clayey soils and the historical arrangement of the major drainage basins of Buenos Aires province could have been important factors in the occupation of these areas in the former species. On the whole, C. talarum may have been less affected by changes in the coastal dune habitat. Probably, both Río Quequén Salado and Río Sauce Grande (Fig. 1) should have historically conformed to a steep boundary zone of limited genetic exchange between eastern and western regions in C. talarum (Fig. 1). This is consistent with the large number of nucleotide differences between both phylogeographical units. In contrast, Mora et al. (2006) found that C. australis does not show a marked geographical structuring of its haplotype diversity, consistent with a scenario of lack of equilibrium between gene flow and genetic drift as evidenced by other analyses (e.g. Mantel test, Fig. 3). C. australis more likely reflects the imprinting of a recent range expansion, rather than current levels of gene flow (Turgeon & Bernatchez 2001; Garnier et al. 2004; Michaux et al. 2005).

3460 M . S . M O R A E T A L .

Historical population processes The observed pattern of mismatch distributions suggests a contrasting historical demographic history of these two species, revealing a signal of long-term population stability in C. talarum and a possible historical population expansion in C. australis (Fig. 4a and b). Patterns of mismatch distributions from populations of constant size are characteristically ragged and erratic (e.g. Slatkin & Hudson 1991; Matocq et al. 2000). The multimodal mismatch distribution found in C. talarum is consistent with long-term demographic stability (Slatkin & Hudson 1991; Rogers & Harpending 1992). This multimodal pattern is also associated with a strong geographical subdivision (Fig. 4a). In addition, regional Tajima’s D-tests performed on Eastern and Western distribution ranges of C. talarum, as well as Fu’s FS test performed on the Eastern range, did not show departures from strict neutrality, which also supports a population equilibrium scenario. However, and differently to the East, Fu’s FS test performed on the Western species distribution range suggests a sudden expansion event. Overall, the results are consistent with demographic and geographical stability. A population expansion should result in a unimodal distribution of pairwise differences (Rogers & Harpending 1992; Harpending & Rogers 2000; Whorley et al. 2004; Michaux et al. 2005). The mismatch distribution observed in C. australis is consistent with a historical population expansion (Fig. 4b; see also Mora et al. 2006). Tajima’s D and Fu’s Fs are consistent with this scenario (Mora et al. 2006). However, departures from strict neutrality of the mitochondrial genome cannot be ruled out in either species, and different selective regimes can mimic the various demographic scenarios outlined above. For example, selective sweeps and selection against slightly deleterious mutations can result in a pattern of haplotype diversity similar to that produced by a population expansion (Whittam et al. 1986; Bertorelle & Slatkin 1995; Harpending et al. 1998; Bazin et al. 2006; Mora et al. 2006; see Wlasiuk et al. 2003 for a possible example in tuco-tucos).

Equilibrium and nonequilibrium populations In general, one-dimensional spatial systems have a reduced variance in geographical genetic attributes, as compared to two-dimensional species’ populations, increasing the chance to detect isolation-by-distance patterns (Slatkin & Barton 1989; Slatkin 1993). It should therefore be easier to detect a pattern of isolation by distance in one-dimensional than in two-dimensional species’ distributions (Slatkin 1987, 1993; Slatkin & Barton 1989). On the other hand, the absence of an isolation-by-distance pattern suggests that a species is far from equilibrium between gene flow and genetic drift and may have only recently colonized parts of

its current distribution (Slatkin 1993; Hutchison & Templeton 1999; Wlasiuk et al. 2003). Because the distribution of C. australis is markedly more linear than that of C. talarumr, we also expected to find an isolation-by-distance pattern in C. australis. Mora et al. (2006; as outlined above) suggest that a history of recent demographic expansion in this species can account for its limited geographical structure. In contrast, our analyses of C. talarum were consistent with the expectations of equilibrium between local genetic drift and gene flow. As pointed out by Garnier et al. (2004), a significant pattern of isolation by distance does not necessarily mean spatially homogeneous gene flow (Lacey 2000; Steinberg & Patton 2000). Rather, the data suggest that gene flow occurs more frequently between neighbouring populations (gene flow limited by distance between patches and by barriers like rivers). This pattern is consistent with the view that dispersal among subterranean rodents can be low because they are highly dependent on their local habitat (Zenuto & Busch 1998; Cutrera et al. 2005, 2006). Cutrera et al. (2005) used microsatellites to show high levels of genetic structure at a microgeographical scale (local populations) in C. talarum, inferring very low potential movements of individuals. A similar result was observed by Tomasco & Lessa (in press) in C. pearsoni. Local populations of this species are also associated to a nearly linear, predominantly coastal distribution and showed a pattern consistent with a regime of differentiation by genetic drift under limited gene flow. Although C. pearsoni occupies sandy soils in eastern Uruguay, like C. talarum, it also lives in hard soils on western Uruguay (Table 3). It is possible that greater flexibility in soil occupation has facilitated local adjustments of these two species to sea level changes while maintaining an overall pattern of isolation by distance. Global gene flow estimates (Nm) based on sequence data (ΦST; Hudson et al. 1992) yielded lower values for C. talarum (NmC. talarum = 0.12) than for C. australis (NmC. australis = 1.47; see Mora et al. 2006). It should be noted, however, that these estimates of gene flow from ΦST statistics are based on an island model of population structure, assuming equilibrium between gene flow and genetic drift (Wright 1978). Historical demographic expansions, such as that proposed for C. australis, represent clear departures from such assumptions and would result in overestimates of current gene flow. Interestingly, and in contrast with C. talarum and C. pearsoni, C. australis, which is limited to a nearly linear geographical distribution, did not show a significant correlation between pairwise geographical distances and estimates of gene flow, allowing us to rule out a pattern of isolation by distance (Fig. 3; see also Mora et al. 2006). This species yielded higher genetic estimates of migration rates per generation (Nm), showing a lower genetic structure and apparently a greater individual interchange among demes. However, © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd

Species

Distribution

Habitat

Population structure (and markers used for inference)

C. talarum

From Magdalena to Bahía Blanca, along the eastern and southeastern Atlantic coast; and in two inland populations (‘Sierra de la Ventana’ and ‘El Saladillo’), of Buenos Aires Province, Argentina.

Well vegetated, grassy and inter-dune habitats along the coast. More or less sandy and friable to hard, clayey soils.

C. australis

Southeastern of Buenos Aires province, Argentina.

C. pearsoni

Departments of Soriano, San José, Colonia, Canelones, Maldonado and Rocha, Uruguay. Predominately along the coast. Restricted geographical range in Uruguay (50 × 60 Km.), Department of Río Negro. Additional populations in Entre Ríos and Corrientes, Argentina.

C. rionegrensis

IBD

Demographic inference (assuming neutrality)

Strongly structured geographically at low geographical scale (microsatellites and mtDNA).

Yes

Population stability

Limited to sandy soils. Related to the low productivity and unstable conditions of the dune habitat. Associate to a habitat instability.

Slighly structured geographically (mtDNA and allozymes).

No

Recent population expansion associated with recent changes in sea-level fluctuations during Quaternary dune formation. Mutation-drift disequilibrium.

Well-vegetated soils with variable clay-loam fractions, and coastal sand dunes.

Strongly structured geographically (mtDNA).

Yes

Population stability

Limited to sandy and friable soils.

Strongly structured geographically (microsatellites and mtDNA). Moderate structure (allozymes).

No

Recent population expansion associate to the Quaternary dune formation. Mutation-drift disequilibrium.

Source This work Cutrera et al. (2005) Busch et al. (2000) Vassallo (1998) Malizia et al. (1991) Reig et al. (1990) Pearson et al. (1968) Mora et al. (2006) Kittlein et al. (2004) Vassallo (1998) Vassallo et al. (1994) Apfelbaum et al. (1991) Contreras & Reig (1965) Tomasco & Lessa (in press) Lessa & Langguth (1983)

Wlasiuk et al. (2003) D’Elía et al. (1998)



Table 3 Distribution, habitat characteristics, inferences of population structure with different molecular markers, consistency with a simple model of isolation by distance (IBD), and demographic inference assuming neutrality for Ctenomys talarum and C. australis from Argentina, and for C. pearsoni and C. rionegrensis from Uruguay. The sources are also given

3462 M . S . M O R A E T A L . there is growing evidence that many species have not yet reached migration-drift equilibrium, and the observed patterns of genetic structure reflect the imprinting of historical population processes rather than current levels of gene flow (Milá et al. 2000; Turgeon & Bernatchez 2001; Garnier et al. 2004; Whorley et al. 2004). Finally, a similar pattern of genetic structure is found in C. rionegrensis which, like C. australis, is also associated to sand dune habitats. C. rionegrensis showed a pattern consistent with a historical population expansion, possibly associated to the history of the sand dune habitat it occupies in central western Uruguay (Wlasiuk et al. 2003; D’Anatro & Lessa 2006). Apparently, in both sand dune dwellers, the moderate to high estimates of gene flow obtained from mtDNA data are inflated by the impact of recent range expansions. A forthcoming multilocus analysis with independent loci would surely be needed to distinguish between historical and current processes in these species. The successful development of primers for the study of microsatellite loci in tuco-tucos (Lacey et al. 1999) and their application across species (Lacey 2001; Wlasiuk et al. 2003) will help identify the processes generating phylogeographical patterns in Ctenomys.

Conclusions To sum up, contrasting patterns of genetic structure between C. talarum and C. australis reflect different evolutionary histories resulting from their different associations with coastal habitats and contrasting dispersal abilities. C. australis is restricted to the first line of dunes and shows limited geographical subdivision, evidence of historical demographic expansion (likely linked to both the recent evolution of its habitat and its greater dispersal ability), and departs from an isolation-by-distance pattern of genetic differentiation. In contrast, C. talarum is strongly structured geographically in a pattern broadly consistent with isolationby-distance expectations, in agreement with its limited dispersal ability and a habitat that was likely less affected by late Pleistocene-Holocene changes in coastal environments. In addition, differences between eastern and western phylogeographical units suggest a more complex demographic history in C. talarum, suggesting a more recent colonization of the Western part of its distribution. Although comparative data on the genetic structure of tuco-tucos are still limited, these contrasting patterns are paralleled by studies of C. rionegrensis (Wlasiuk et al. 2003), a sand-dune specialist, and C. pearsoni (Tomasco & Lessa, in press), a coastal species with broader habitat tolerance.

Acknowledgements The authors would like to thank I. Tomasco, C. Abud, A. D’Anatro and A. Parada for their invaluable help during laboratory training

in molecular techniques, and to L. Excoffier for statistical advice on mismatch distributions. We are also grateful to all the members of the Laboratorio de Ecofisiología, Universidad Nacional de Mar del Plata, and the Laboratorio de Evolución, Facultad de Ciencias, Universidad de la República del Uruguay, for their permanent support and advice. Financial support was provided by Fondo Nacional para la Investigación Científica y Tecnológica (FONCYT, PICT 01-12507) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, PIP-5838). M. S. Mora is a fellow, and M. J. Kittlein and A. I. Vassallo are researchers of CONICET.

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Matías Sebastián Mora is studying the phylogeography and population genetics of the genus Ctenomys, focusing on the dynamics of regional migration and conditions of regional persistence of these species. Enrique P. Lessa studies many aspects of the phylogenetics, population genetics and phylogeography of mammals, especially rodents. Ana Paula Cutrera is interested in population genetics and the interaction among demography, behaviour and genetic variation in natural populations of subterranean rodents. Marcelo Javier Kittlein studies metapopulation ecology and genetics, and many aspects of applied statistics. Aldo Iván Vassallo studies morphological and evolutionary aspects of subterranean rodents.


Polymorphic sites (excluding ambiguous sites) at the control region of mitochondrial DNA for Ctenomys talarum (N = 71; sequence size: from 1 to 418). The distribution of each haplotype (N°H: number of haplotype) by locality and the total number of individuals per haplotype (T) are shown. Sample sizes are given in parentheses. Abbreviations of populations are shown in Fig. 1. Ctenomys australis data are reported in Mora et al. (2006). GenBank accession numbers are shown in brackets Haplotypes by locality

N°H

Polymorphic sites

H1 [EF531719] H2 [EF531720] H3 [EF531721] H4 [EF531722] H5 [EF531723] H6 [EF531724] H7 [EF531725] H8 [EF531726] H9 [EF531727] H10 [EF531728] H11 [EF531729] H12 [EF531730] H13 [EF531731] H14 [EF531732] H15 [EF531733] H16 [EF531734] H17 [EF531735] H18 [EF531736] H19 [EF531737] H20 [EF531738] H21 [EF531739] H22 [EF531740] H23 [EF531741] H24 [EF531742] H25 [EF531743] H26 [EF531744] H27 [EF531745] H28 [EF531746] H29 [EF531747] H30 [EF531748] H31 [EF531749] H32 [EF531750]

T . . . . . . . . . . . . G G . . . . . . . . . . . . . . . . .

T . . . . . . . . . . . . . . . . . . . . . . . . . . C . . . .

T . . . . . . . . . . . . . . . . . . . C C C C . . . . . . . .

C . . A . . . . . . . . . . . . . . . . . . . . . . . . . . . .

C . . . . . . . . . . . . G . . . . . . . . . . . . . . . . . .

C . . . . . . . . . . . . . . . A . . . . . . . . . . . . . . .

T . . . . . . . . . . . . C . . . . . . . C . . . . . . . . . .

A . . . . . C . . . . . . G . . . . . . . . . . . . . . . . . .

A . . . . . . . . C . . . . . . . . . . . . . . . . . . . . . .

A . . . . . . . . . . . . . . . . . . . . . . . . . . C . . . .

A . . . . . . . . . . . . C . . . . . . . . . . . . . . . . . .

C . . . . . . . . . . . . G . . . . G . . . . . . . G . . . . .

A . . . . . . . . . . . . . . . . . . . . . . . T . . . . . . .

A . . . . . . . . . . . . . . . . C . . . . . . . . . . . . . .

A . C . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

T C . C C C C C C C C C C C C C C C C C C C C C . . . . . . . .

T . . . C . . . . . . . . C . . . . . . . . . . . . C . . . . .

A . . . . . . . . . . . . C . . . . . . . . . . . . . . . . . .

T . . . . . . . . . . . . . . . . . . . . . . . . C . . . . . .

C . . T T T T T T T T T T T T T T . . . . . . . . . . . . . . .

G . . A A A A A A A A A A A A A A . . . . . . . . . . . . . . .

G . . . . . . . . . . . . . . . . . . . . . A . . . . . . . . .

G . . T T T T T T T T T T T T T T . . . . . . . . . . . . . . .

G . . . . . . . C . . . . . . . . . . C . C . . . . . . C . . .

A . . G G G G G G G G G G G G G G . . . . . . . . . . . . . . .


C . . T T T T T T T T T T T T T T . . . T T . . . . . . . . . .


C . . T T T T T T T T T T T T T T . . . . . . . . . . . . . . .

A . . T T T T G T T G G G G G G . . . . . . . . . . . . . . . .

C . . . . . . . . . . . . . T T . . . . . . . . . . . . . . . .

T . . C C C C C C C C C C C C C C . . . . . . . . . . . . . . .

T . . . . . . . . . . C C C C C C . . . . . . . . . . . . . . .

C T . . . . . . . . . . . . . . . T T T T T T T . . . . . . . .

G . . . . . . . . . C . . . . . . . . . . . . . . . . . . . . .

C . . A A A A A A A A A A A A A A . . . T T . . . . . . . . . .

A . . . . . . . . . . . . . . . T . . . . . . . . . . . . . . .

G . . . . . . . . . . . . . . . . . . . . . . . . . . . . T . .


T . . A A A A A A A A A A A A A A . . . . . . . . . . . . . . .

T C . . . . . . . . . . . . . . . C C C C C C C . . . . . . . .

T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C

C . . . . . A . . . . . A . . . A . . . . . . . . . . . . . . .

C . . . . . . . . . . . . . . . A . . . . . . . . . . . . . . .

T . . . . . . . . . . . . . . . . . . . . . . . . . . . C . . .

C . . . . . . . . . . . . . . . . . . . . . . . . . . . . T T T

A . . . . . . . . . . . . . . . . . . . . . . . . . . . T T T

N (15)

SC (9)

3

5

R (22)

QS (7)

MH (8)

PCO (10)

10 1 1 1 2 1 1 1

2 1 1 1 1 1 1 1 6

5 1

3 1 1 1 1 1 1 1 8 1

1 1 2 1

T (71) 8 10 1 1 1 2 1 1 1 1 1 4 1 1 1 1 1 1 1 1 11 1 3 1 1 1 1 1 1 1 8 1



Appendix