Phylogenetic relationships among Nearctic shrews of the genus Sorex (Insectivora, Soricidae) inferred from combined cytochrome b and inter-SINE fingerprint data using Bayesian analysis

Molecular Phylogenetics and Evolution 44 (2007) 192–203 www.elsevier.com/locate/ympev

Phylogenetic relationships among Nearctic shrews of the genus Sorex (Insectivora, Soricidae) inferred from combined cytochrome b and inter-SINE Wngerprint data using Bayesian analysis Aaron B.A. Shafer ¤, Donald T. Stewart Department of Biology, Acadia University, Wolfville, Nova Scotia, Canada B4P2R6 Received 14 July 2006; revised 22 November 2006; accepted 7 December 2006 Available online 20 December 2006

Abstract The Weld of molecular systematics has relied heavily on mitochondrial DNA (mtDNA) analysis since its inception. Despite the obvious utility of mtDNA, such data inevitably only presents a limited (i.e., single genome) perspective on species evolution. A combination of mitochondrial and nuclear markers is essential for reconstructing more robust phylogenetic trees. To evaluate the utility of one category of nuclear marker (short interspersed elements or SINEs) for resolving phylogenetic relationships, we constructed an inter-SINE Wngerprint for nine putative species of the genus Sorex. In addition, we analyzed 1011 nucleotides of the cytochrome b gene. Traditional neighbor-joining and maximum parsimony analyses were applied to the individual cytochrome b and inter-SINE Wngerprint data sets, along with Bayesian analysis to the combined data sets. We found inter-SINE Wngerprinting to be an eVective species level marker; however, we were unable to reconstruct deeper branching patterns within the Sorex genus using these data. The combined data analyzed under a Bayesian analysis showed higher levels of structuring within the Otisorex subgenus, most notably recognizing a monophyletic group consisting of sister-taxa S. palustris and S. monticolus, S. cinereus and S. haydeni, and S. hoyi. An additional noteworthy result was the detection of an historic mitochondrial introgression event between S. monticolus and S. palustris. When combining disparate data sets, we emphasize researcher diligence as certain types of data and processes may overly inXuence the analysis. However, there is considerable phylogenetic potential stemming from inter-SINE Wngerprinting. © 2006 Elsevier Inc. All rights reserved. Keywords: Short interspersed elements; Inter-SINE Wngerprint; Cytochrome b; Bayesian analysis; Phylogeny; Sorex; Otisorex

1. Introduction Despite being touted as a signiWcant new category of phylogenetic marker (Hillis, 1999; Miyamoto, 1999), Short interspersed elements (SINEs) have been utilized in relatively few systematic studies. SINEs are mobile elements that spread throughout the nuclear genome via retrotransposition. As repetitive entities, SINEs can be present in the genome in greater than 104 copies (Shedlock and Okada, 2000). With no known mechanism of removal, SINEs are inserted in a unidirectional manner (Batzer and Deininger,

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2002) and there is thought to be eVectively no risk of homoplasy (Nikaido et al., 1999). Furthermore, the absence of a SINE at a particular locus is thought to be the ancestral condition (Shedlock et al., 2004). SINEs data used in phylogenetic analysis have typically been generated by a lengthy process of probing, cloning, sequencing, and southern hybridization (e.g., Nikaido et al., 1999; Takahashi et al., 1998). However, some studies (e.g., Bannikova et al., 2005; Kostia et al., 2000) have found that simply using primers that are complementary to sections of the repetitive element will produce unique inter-SINE Wngerprints that contain valuable phylogenetic information. Since retroposable elements have been observed in a variety of mammals (e.g., Coltman and Wright, 1994), Wsh (e.g., Takasaki et al., 1996), birds (e.g., Chen et al., 1991),

A.B.A. Shafer, D.T. Stewart / Molecular Phylogenetics and Evolution 44 (2007) 192–203

reptiles (e.g., Endoh et al., 1990), and amphibians (e.g., Bucci et al., 1999), inter-SINE Wngerprinting could provide an eVective nuclear marker for inferring phylogenetic relationships within any of these groups. Furthermore, with evidence for mitochondrial recombination mounting (Lunt and Hyman, 1997; Tsaousis et al., 2005), and clear cases of introgression being documented (e.g., Ruedi et al., 1997; Glemet et al., 1998), the need for phylogenetically informative biparentally inherited markers is evident. As mitochondrial sequence data have been the systematists choice for nearly two decades, nuclear markers such as inter-SINE Wngerprints represent a new category of available markers with signiWcant phylogenetic potential (Hillis, 1999). To test the utility of inter-SINE Wngerprinting for resolving phylogenetic relationships among rapidly diverging clades, we applied this technique in combination with mitochondrial DNA (mtDNA) sequence data to a subset of North American Soricids. Among the extant members of the shrews (Soricidae), several genus and species level designations within the Soricinae (red-toothed shrews) have been subject to considerable controversy, likely as a consequence of the rapid radiation of this group. In the past 50 years, taxonomic and phylogenetic questions within the genus Sorex have been addressed using morphological, karyological, allozymic, mitochondrial sequence data, and most recently restriction fragment length polymorphisms and inter-SINE-PCR (e.g., Bannikova and Kraverov, 2005; Findley, 1955; Fumagalli et al., 1999; George, 1988; Ivanitskaya et al., 1994; Van Zyll de Jong, 1983). Many of the recent advances in Sorex phylogenetics have been the result of analysis of mtDNA data. In particular, the cytochrome b gene has been used extensively for Sorex evolutionary studies, as its rate of evolution was thought to be adequate for investigating the Sorex radiation (Fumagalli et al., 1999). Unfortunately, analysis of the cytochrome b data failed to resolve some of the deeper branching patterns with this genus, essentially producing a ‘star phylogeny.’ Furthermore, possible cases of mitochondrial introgression have been implicated between sister-taxa (Stewart et al., 1993), thus calling into question phylogenetic inferences based exclusively on mtDNA. Fortunately, inter-SINE Wngerprint analysis can be applied to groups that have diverged within the past 100 Myr, as mutational decay is not likely to have signiWcantly altered priming sites within that time frame (Edwards et al., 2005). Because SINEs are considered to be an appropriate marker for studying or reconstructing relationships among closely related species (Hillis, 1999), the genus Sorex represents an appropriate taxonomic group to test the utility of interSINE Wngerprinting. In this paper, we compare the phylogenetic utility of cytochrome b sequence data and inter-SINE Wngerprints applied to nine putative species of shrew representing the major species–complexes of new world Sorex. The major objectives of this study are: (i) to test the ability of interSINE Wngerprints to resolve deeper nodes in star-like phylogenies, (ii) to detect evidence of possible introgression,

193

and (iii) to test the ability of inter-SINE Wngerprint analysis to identify recently diverged species. In addition, we combined the data sets to be analyzed under a Bayesian framework, and discuss the merits of combining disparate data sets in phylogenetic analyses. 2. Materials and methods 2.1. Specimens Specimens for inter-SINE Wngerprint analysis and cytochrome b sequencing were collected from a variety of locations across Canada and the United States (Table 1). Tissue samples were taken from 39 specimens representing a total of nine species (two outgroup, seven ingroup). All individuals are members of the genus Sorex with the ingroup representing the subgenus Otisorex. To root the tree, we included two species belonging to the subgenus Sorex, which has been previously shown to be an appropriate outgroup (Fumagalli et al., 1999; George, 1988; Stewart et al., 2002). All specimens are deposited either in the Royal Ontario Museum or will be deposited in the New Brunswick Museum and/or Acadia University Wildlife Museum. 2.2. Molecular techniques Total DNA was extracted from tissue samples (liver, heart, and/or kidney) using a DNeasy Tissue Kit (QIAGEN) following the manufacturer’s protocol. Total DNA was diluted to 25 g/l working stock, which was used for all subsequent reactions. The cytochrome b gene was ampliWed using primers L14841 (Kocher et al., 1989) and H15915 (Irwin et al., 1991) via the polymerase chain reaction (PCR). The PCR conditions consisted of an initial 2 min denaturing period at 94 °C, followed by 40 cycles of 45 s denaturing at 94 °C, 45 s annealing at 50 °C, and a 1 min extension period at 72 °C. The cycling period was followed by an additional 72 °C extension period of 2 min. PCR products were puriWed from a 1% agarose gel in 1£ TAE buVer using a gel extraction kit (QIAGEN). Double stranded sequencing was performed at Florida State University (Tallahassee) on an Applied Biosystems 3130 £ 1 gene analyzer with capillary electrophoresis. Cytochrome b sequences were aligned using BioEdit V.7.0.5.3 (Hall, 1999) and conWrmed by eye. For consistency with previous studies (e.g., Fumagalli et al., 1999), a total of 1011 nucleotides of each individual’s cytochrome b data was used for phylogenetic analysis. Some cytochrome b sequences were obtained from GenBank (see Table 1) and newly generated sequences were deposited in GenBank (Accession Nos. DQ788800–DQ788833). Inter-SINE-PCR was performed on a Soricidae speciWc SINE known as SOR (Borodulina and Kramerov, 2001). A Xuorescently labeled Cy5 amidite SOR speciWc primer, also aptly named SOR (Bannikova et al., 2005), was used for the PCR. As SINEs are situated 100–1000 bp apart (Bannikova et al., 2005), and are positioned in both up

194

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Table 1 List of specimens used in this study, their collection number, collection locality, Royal Ontario Museum (ROM) catalogue number, and GenBank accession numbers Specimens

Collection No.

Locality

ROM Catalogue No.

Genbank Accession No.

Arctic shrew (S. arcticus) Maritime shrew (S. maritmensis)

DTS 1022 NP5 KD319 KD404 DTS 907 DTS 515 DTS 1045 DTS 1147 DTS 405 DTS 424 DTS 898 DTS 903 DTS 124 DTS 131 DTS 350 DTS 399 DTS 457 DTS 599 DTS 818 DTS 941 DTS 071 DTS 312 DTS 335 DTS 946 DTS 947 DTS 978 DTS 1024 DTS 1382 DTS 1385 DTS 031 DTS 290 DTS 295 DTS 372 DTS 824 DTS 882 DTS 1081 DTS 1396 DTS 471 DTS 504

Saguenay, Quebec (QC) Sheridan County, Montana (Mont) Hampton, New Brunswick (NB) Kouchibouguac, New Brunswick (NB)¤ Windsor Forks, Nova Scotia (NS) Okotoks, Alberta (ALB) Cole Hbr, Nova Scotia (NS)¤ Cape Breton Island, Nova Scotia (NS) Maple Ridge, British Columbia (BC) Okotoks, Alberta (ALB)¤ Kananaskis, Alberta (ALB)¤¤ Kananaskis, Alberta (ALB) Second Falls, New Brunswick (NB) Second Falls, New Brunswick (NB)¤ Snowball, Ontario (ONT) Ghost River, Alberta (ALB) Seebe, Alberta (ALB)¤ Garlands, Nova Scotia (NS) Bayview, Prince Edward Island (PEI) Gogoma, Ontario (ONT)¤ Second Falls, New Brunswick (NB) Fredericton, New Brunswick (NB)¤ Snowball, Ontario (ONT)¤¤¤ Gogoma, Ontario (ONT) Gogoma, Ontario (ONT)¤ King City, Ontario (ONT)¤¤ Saguenay, Quebec (QC) Stewart Mtn., Nova Scotia (NS) Stewart Mtn., Nova Scotia (NS)¤ Bon Portage Island, Nova Scotia (NS) St. Georges, Newfoundland (NFLD) St. Georges, Newfoundland (NFLD)¤ Ghost River, Alberta (ALB)¤ Bayview, Prince Edward Island (PEI) Kananaskis, Alberta (ALB) Deer Island, New Brunswick (NB) Campobello Island, New Brunswick (NB)¤ Okotoks, Alberta Okotoks, Alberta¤

110255 N/A N/A N/A 110149 109832 110278 N/A 109733 109751 110140 110145 109467 109474 109686 109728 109779 109915 110061 110183 109417 109648 109671 110188 110189 110211 110257 N/A N/A 109378 109628 109633 109706 110067 110124 110313 N/A 109790 109821

DQ788809 DQ788810 DQ788807 DQ788808 DQ788806 DQ788800 DQ788802 DQ788801 DQ788832 DQ788804 DQ788833 DQ788803 AY310340a DQ788829 AY310342a AY310336a DQ788830 AY310337a AY310338a DQ788831 DQ788820 DQ788821 DQ788822 DQ788823 DQ788824 DQ788825 DQ788826 DQ788818 DQ788819 DQ788805 DQ788811 DQ788812 DQ788814 DQ788815 DQ788813 DQ788816 DQ788817 DQ788827 DQ788828

Water shrew (S. palustris) Montane shrew (S. monticolus)

Pygmy shrew (S. hoyi)

Smokey shrew (S. fumeus)

Long-tailed shrew (S. dispar) Common shrew (S. cinereus)

Hayden’s shrew (S. haydeni) a ¤

Previous GenBank submission. Phylogenetic tree reference, see Figs. 1–4.

and downstream direction, a single SINE speciWc oligonucleotide acts as both the forward and reverse primer to produce inter-SINE Wngerprint bands. The PCR conditions consisted of an initial 2 min 94° denaturing period, followed by 28 cycles of 30 s at 94°, 45 s at 65°, and 2 min at 72°. The cycling was followed by an additional extension period of 5 min at 72°. The PCR products were denatured and run in a long-read 7% polyacrylamide gel (Amersham Biosciences) on an ALFexpress II automated fragment analyzer (Amersham Biosciences). Each lane included two internal sizers of 150 and 300 bp. Three lanes were run containing only external sizers of 50 bp fragments ranging from 50 to 500 bp. Following the run, fragments were detected and band position scored as present or absent. Sizes were calculated by the ALFwin Fragment Analyser software 1.03 (Amersham Biosciences), calibrated according to the internal and external sizers, and

veriWed by eye. Inter-SINE Wngerprints were run on multiple gels (a total of six) to ensure repeatability. 2.3. Phylogenetic analysis and genetic distances The traditional phylogenetic methods of neighbor-joining (NJ), and maximum-parsimony (MP) were conducted separately on the cytochrome b and inter-SINE Wngerprint data sets. In addition, a Bayesian analysis was performed on the combined data set. For the cytochrome b NJ analysis, a genetic distance matrix was constructed using Kimura’s twoparameter model (which was chosen for comparison with previous studies), and trees were created using the software package MEGA 3 (Kumar et al., 2004). For the SOR fingerprint data, genetic distances were calculated using the Link et al. (1995) method and NJ analysis was performed using the software TREECON (Van de Peer and de Wachter, 1994), which

A.B.A. Shafer, D.T. Stewart / Molecular Phylogenetics and Evolution 44 (2007) 192–203

was selected for its ability to bootstrap binary data. For the MP analysis, the software package PAUP V.4.0b10 (SwoVord, 2002) was used for both the SINE Wngerprint and cytochrome b data sets. We implemented a full heuristic search with characters unweighted and unordered. The search included 100 stepwise, random addition replicates with treebisection-reconnection branch swapping. Support for both the NJ and MP phylogenetic analyses was evaluated using 1000 bootstrap replicates. Bayesian analysis of the combined data set was performed using the software MrBayes V.3.0 (Huelsenbeck and Ronquist, 2001). Because of variation in substitution rate per nucleotide position in the cytochrome b gene (Irwin et al., 1991), each codon position was partitioned and treated separately in a general time reversible (GTR) model under a distribution (see May-Collado and Agnarsson, 2006). Due to the binary nature of the SOR Wngerprint data (i.e., presence/ absence), a restriction site (binary) model was applied. The ascertainment bias associated with restriction site data sets can be applied to SINE Wngerprints, as absent SOR bands in all taxa cannot be observed; therefore, the lset coding option in MrBayes was set to “noabsencesites.” In addition, a -distribution was applied to the SOR Wngerprint data. As the data set is partitioned into four distinct blocks (1st, 2nd, and 3rd codon positions and the SINE data) in our Bayesian analysis, certain parameters (e.g., evolutionary rate) of the model were unlinked and allowed to evolve separately under each partition. The Markov chain Monte Carlo algorithm was run with four chains until the average standard deviation of split frequencies stabilized at <0.01. Accordingly, 500,000 generations were run with sampling every 100 generations and the Wrst 125,000 (25%) generations were discarded as a burn-in. We then used the remaining 3750 trees to construct a 50%-majority rule consensus tree. Average inter-speciWc genetic distances between recognizable clades were calculated using MEGA 3 (Kumar et al., 2004). However, because of the high degree of noise associated with the SOR data, the net between group mean was used to determine pairwise distances of the SOR data set. The net between group mean is calculated as the average distance between groups, minus the mean diVerence between within group distances, divided by two. The calculated distances, along with the reconstructed phylogenetic

195

relationships, were compared between data sets as well as with the Bayesian inference. 3. Results 3.1. Cytochrome b sequence divergence A 1011 bp portion of the cytochrome b gene was examined for 39 specimens from the genus Sorex. Among the 1011 sites compared, 314 (31%) were variable. Of these, 50 (16%) were at Wrst-codon positions, 8 (3%) were at secondcodon positions, and 254 (81%) were at third-codon positions. There were 295 phylogenetically informative sites, of which 44 (15%), 6 (2%), 245 (83%) were at Wrst, second, and third-codon positions, respectively. Similar to Fumagalli et al. (1999), there was a nucleotide composition bias, with guanine comprising only 13.9% of all nucleotides. The nucleotide compositions of the other nucleotides were: adenine, 28.8%, cytosine, 27.8%, and thymine, 29.5%. Transition substitutions were more frequent than transversion substitutions at an observed ratio of 5.3 among Otisorex and 4.6 overall. The inter-speciWc distances (Kimura two-parameter distances) of the sequenced Sorex samples ranged from 7% between S. cinereus and S. haydeni to 19% between S. arcticus and S. fumeus or S. hoyi (Table 2). The MP and NJ analysis of the cytochrome b sequence data produced fundamentally the same phylogenetic tree (Fig. 1), with minor variation in topology occurring within species. Four major Otisorex clades were supported (>70% bootstrap support) using both methods: (1) S. cinereus and S. haydeni; (2) S. hoyi; (3) S. monticolus and S. palustris; and (4) S. dispar and S. fumeus. In addition, the outgroup (subgenus Sorex) showed strong bootstrap support for a sister-taxon relationship. Unfortunately, neither phylogenetic method was able to resolve the relationship among these four deeper branchings of the subgenus Otisorex, essentially yielding an unresolved polytomy. 3.2. Inter-SINE Wngerprint data A total of 341 banding sites ranging in size from 150 to 500 bp was considered. Of these, 340 (99%) were variable

Table 2 Genetic distances (Kimura two-parameter model) of cytochrome b sequences between the outgroup S. arcticus, and S. maritimensis, and the ingroup members of S. hoyi, S. monticolus, S. palustris, S. cinereus, S. haydeni, S. dispar, and S. fumeus

S. arcticus S. maritimensis S. palustris S. monticolus S. hoyi S. fumeus S. dispar S. cinereus S. haydeni

S. arcticus

S. maritimensis

S. palustris

S. monticolus

S. hoyi

S. fumeus

S. dispar

S. cinereus

S. haydeni

— 0.09 0.18 0.18 0.19 0.19 0.19 0.17 0.18

— 0.17 0.17 0.18 0.17 0.16 0.16 0.15

— 0.06 0.13 0.13 0.12 0.10 0.11

— 0.14 0.14 0.12 0.11 0.11

— 0.14 0.13 0.11 0.13

— 0.10 0.12 0.12

— 0.11 0.11

— 0.07

—

196

A.B.A. Shafer, D.T. Stewart / Molecular Phylogenetics and Evolution 44 (2007) 192–203 S. cinereus (NFLD) 72/ 100/99

S. cinereus (NB) S. cinereus (NS) S. cinereus (PEI)

100/96

100/100

S. cinereus (ALB) S. cinereus (ALB)*

99/81

100/99

S. cinereus (NFLD)* S. cinereus (NB)*

100/100

S. haydeni (ALB) S. haydeni (ALB)*

100/99

S. hoyi (ALB) S. hoyi (ALB)*

100/100

S. hoyi (ONT)* 61/91

86/88

S. hoyi (NS) S. hoyi (PEI)

97/86

S. hoyi (NB)* 72/

100/100

S. hoyi (NB) S. hoyi (ONT) S. monticolus (BC)

100/98

100/99 97/80

S. palustris (NS) S. palustris (NS)* S. palustris (ALB)

63/71

S. monticolus (ALB)** 100/100 83/87

S. monticolus (ALB) S. monticolus (ALB)*

100/100

S. dispar (NS) S. dispar (NS)*

93/73

S. fumeus (ONT)** S. fumeus (NB)*

100/100

S. fumeus (ONT)*** 73/69

S. fumeus (NB) S. fumeus (ONT)* 52/

S. fumeus (ONT) S. fumeus (QC)

100/100

S. arcticus (QC) S. arcticus (Mont) S. maritimensis (NS)

100/100 99/69

S. maritimensis (NB) S. maritimensis (NB)*

Fig. 1. Fifty percent consensus tree of 39 Sorex specimens based on neighbor-joining and maximum parsimony methods of cytochrome b sequence data (totalling 1011 bp). Bootstrap values of both phylogenetic methods (NJ/MP) were obtained from 1000 replicates. Specimens are identiWed by species name followed by locality (¤ are used to distinguish individual specimens from the same locality in Table 1).

and parsimony informative. At least 40% of the variable characters are attributed to noise and to within species synapomorphies, as the proportion of parsimony informative characters decreases to approximately 60% when only one specimen from each species complex is included in the analysis. The net between group means of genetic distance (calculated using the Link et al. (1995) method) ranged from 11% between S. cinereus and S. haydeni to 24% between S. fumeus and S. dispar. Bannikova et al. (2005) found intraspeciWc variation ranging from 0 to 41% in Crocidura species. Because we used the net between groups distance, our

results for inter-speciWc variation, are well below that range (Table 3). The NJ and MP analysis of the inter-SINE Wngerprint data produced a tree (Fig. 2) similar to the cytochrome b tree; however, there were some notable diVerences. Six Otisorex clades were supported (>70% bootstrap support) using the NJ method: (1) S. cinereus and S. haydeni; (2) S. hoyi; (3) S. palustris; (4) S. monticolus; (5) S. dispar; and (6) S. fumeus. In addition, the inter-SINE Wngerprint analysis showed high resolution for deciphering individual level relationships (e.g., distinguishing “Eastern” and “Western”

A.B.A. Shafer, D.T. Stewart / Molecular Phylogenetics and Evolution 44 (2007) 192–203

197

Table 3 Net between group genetic distances (Link et al. (1995) method) of inter-SINE Wngerprint data between the outgroup S. arcticus, and S. maritimensis, and the ingroup members of S. hoyi, S. monticolus, S. palustris, S. cinereus, S. haydeni, S. dispar, and S. fumeus

S. arcticus S. maritimensis S. palustris S. monticolus S. hoyi S. fumeus S. dispar S. cinereus S. haydeni

S. arcticus

S. maritimensis

S. palustris

S. monticolus

S. hoyi

S. fumeus

S. dispar

S. cinereus

S. haydeni

— 0.13 0.14 0.15 0.15 0.19 0.18 0.14 0.14

— 0.13 0.17 0.13 0.16 0.22 0.14 0.19

— 0.12 0.08 0.10 0.17 0.11 0.15

— 0.14 0.19 0.18 0.14 0.18

— 0.13 0.18 0.11 0.14

— 0.24 0.13 0.18

— 0.19 0.20

— 0.11

—

S. hoyi clades; see Stewart et al., 2003). As in the cytochrome b phylogeny, there was strong support for the outgroup of S. arcticus and S. maritimensis forming a single clade. The inability of the MP analysis to support some of the groupings is likely due to the high level of noise associated with SINE Wngerprint data. It is worth noting that the inter-SINE Wngerprint data were replicable (same banding pattern) in subsequent trials. We therefore felt conWdent comparing Wngerprints of diVerent individuals from multiple gel runs. This was necessary as all samples analyzed could not be run simultaneously on a single gel. 3.3. Bayesian analysis of combined SINE and cytochrome b data The Bayesian analysis (Fig. 3) recovered three highly supported (>0.97 posterior probabilities) Otisorex groups: (1) S. cinereus and S. haydeni, S. monticolus and S. palustris, and S. hoyi; (2) S. fumeus; and (3) S. dispar. Within the Wrst group listed above, the Bayesian inference only weakly (pp D 0.54) supported S. monticolus, S. palustris, and S. hoyi forming a monophyletic group. The pairing of S. dispar and S. fumeus supported by the cytochrome b data was not observed, with S. dispar being placed as the most basal member of the Otisorex subgenus. Although the phylogenetic trees of individual data sets were essentially polytomies, the combined data sets using Bayesian inference suggested greater structuring within the subgenus Otisorex. For comparison, we have included the phylogenetic tree of the cytochrome b data using Bayesian analysis (Fig. 4).

technique was unable to resolve deeper branches. This is likely a result of a rapid Sorex radiation. Because of the paucity of shrew fossils, no clear evolutionary history of Nearctic Sorex is currently possible (Harris, 1998). However, the absence of pre-Pliocine fossils, tends to support Diersing’s (1980) view that the Sorex radiation in the new world occurred between the late Pliocene (»3 Mya) and the Pleistocene (»1.8 Mya). Our cytochrome b data also support Diersing’s hypothesis. Application of Fumagalli et al.’s (1999) Soricine molecular clock to our data suggests the major Otisorex radiation occurred approximately 5 Mya. The inability of inter-SINE Wngerprints to resolve deep root branches may therefore accurately reXect the new world Sorex evolution, with a ‘star phylogeny’ being more fact, than artifact. A similar ‘star phylogeny’ has been observed in the Chiroptera. Bannikova et al. (2002) constructed an inter-SINE Wngerprint using the mammalian interspersed repeat, and found only strong support for lower taxonomic levels. Again, this is likely a result of rapid radiation of Chiroptera upon exploitation of aerial niches, resulting in limited molecular synapomorphies (Ammerman and Hillis, 1992). However, higher support for deep root branching from inter-SINE Wngerprint analysis has been observed in hedgehogs (Bannikova et al., 2002) and artiodactyls (Kostia et al., 2000). As evidenced above and noted by Bannikova et al. (2002), the phylogenetic information produced by interSINE Wngerprinting varies greatly among organisms. Nonetheless, given the wide array of organisms where SINEs have been observed, the phylogenetic potential of inter-SINE Wngerprinting is signiWcant.

4. Discussion 4.1. The utility of inter-SINE Wngerprinting for phylogenetic analysis Inter-SINE Wngerprinting, using a primer speciWc for the SOR retroposon in Soricids, was an eVective marker for identifying species level groupings within new world shrews. The ability for inter-SINE Wngerprints to identify geographic clades (e.g., east and west S. hoyi), and to detect introgression (see Section 4.3), demonstrate its utility in phylogenetic studies. However, within Nearctic Sorex, this

4.2. Resolution of clades achieved by Bayesian analysis of combined data Sparking recent scientiWc debate is the so-called ‘Bayesian revolution’ occurring in systematic biology (see Beaumont and Rannala, 2004; Huelsenbeck et al., 2001). Bayesian phylogenetic inference allows for prior knowledge to be applied to an evolutionary model, and determines the probability of a hypothesis given the observed data. Nearly any character type, and even heterogeneous data sets, can be used to infer phylogenies under a Bayesian framework

198

A.B.A. Shafer, D.T. Stewart / Molecular Phylogenetics and Evolution 44 (2007) 192–203 100/98

S. haydeni (ALB) S. haydeni (ALB)*

66/ 86/50

98/

S. cinereus (NB) S. cinereus (NB)* S. cinereus (NFLD)

97/ 87/78

S. cinereus (ALB) S. cinereus (ALB)* S. cinereus (NS)

65/ 80/50

S. cinereus (NFLD)*

99/97

S. hoyi (ALB)

S. cinereus (PEI)

S. hoyi (ALB)*

86/

73/53

S. hoyi (ONT) S. hoyi (ONT)*

100/54 61/77

70/95

S. hoyi (NB) S. hoyi (NB)*

97/81 71/

S. hoyi (NS) S. hoyi (PEI) S. palustris (ALB)

71/ 100/74

S. palustris (NS) S. palustris (NS)* S. monticolus (BC)

99/84

S. monticolus (ALB)**

80/ 60/

S. monticolus (ALB)* S. monticolus (ALB)

100/100

S. dispar (NS) S. dispar (NS)*

/80

S. fumeus (ONT)* S. fumeus (ONT)

/51

S. fumeus (ONT)*** S. fumeus (ONT)**

100/99

S. fumeus (NB)

78/73

S. fumeus (NB)* S. fumeus (QC)

100/

S. arcticus (Mont) S. arcticus (QC) S. maritimensis (NS)

100/85 64/

S. maritimensis (NB) S. maritimensis (NB)*

Fig. 2. Fifty percent consensus tree of 39 Sorex specimens based on neighbor-joining and maximum parsimony methods of inter-SINE Wngerprint data. Bootstrap values of both phylogenetic methods (NJ/MP) were obtained from 1000 replicates. Specimens are identiWed by species name followed by locality (¤ are used to distinguish individual specimens from the same locality in Table 1).

with high computational eYciency (Nylander et al., 2004). In addition, unlike maximum likelihood methods, Bayesian analysis of combined and mixed data sets allows certain parameters to be decoupled in an evolutionary model (Jansa and Voss, 2005). The most recent extensive phylogenetic analysis of the Sorex shrews (Fumagalli et al., 1999) using cytochrome b sequence, placed S. arcticus and other shrews of the subgenus Sorex as the outgroup to the subgenus Otisorex. Unfortunately, that analysis was unable to resolve most of the deep root branching patterns within the genus. Our results, using Bayesian inference of combined cytochrome b sequence and inter-SINE Wngerprint data, show support

for structuring within the subgenus Otisorex. Similar to George’s (1988) allozyme data, S. fumeus and S. dispar are basal members of the Otisorex group; however, the Bayesian inference did not place these two species as sister-taxa as previously observed. Another result of this analysis is the monophyly of S. cinereus, S. haydeni, S. monticolus, S. palustris, and S. hoyi. The close phylogenetic relationship reported here among S. hoyi, S. palustris and S. monticolus is still tentative, as it is only weakly supported in our study. The highly supported paraphyletic S. monticolus group is consistent with the hypothesis of a historical introgression event. Therefore, the Bayesian analysis of the combined data set is an eVective tool for identifying introgression at

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199

S. cinereus (NFLD)* S. cinereus (NB) S. cinereus (NB)*

1.00 1.00

S. cinereus (NFLD)

1.00 0.54

0.68

S. cinereus (PEI) S. cinereus (NS)

1.00 1.00

S. cinereus (ALB) S. cinereus (ALB)

1.00

S. haydeni (ALB)* S. haydeni (ALB)*

1.00

0.97

S. hoyi (ALB) S. hoyi (ALB)*

1.00

S. hoyi (ONT)*

0.69

S. hoyi (NB)*

1.00

S. hoyi (NB)

0.79 0.92

S. hoyi (ONT)

0.54 0.79

S. hoyi (NS) S. hoyi (PEI) S. monticolus (BC)

0.80

1.00

S. monticolus (ALB)* S. monticolus (ALB)

1.00

S. monticolus (ALB)**

1.00

S. palustris (ALB)

1.00 1.00

1.00

S. palustris (NS)* S. palustris (NS) S. fumeus (NB)*

0.54 0.75

S. fumeus (NB) S. fumeus (QC)

1.00 0.62

S. fumeus (ONT)*** S. fumeus (ONT)**

0.71

S. fumeus (ONT)* S. fumeus (ONT)

1.00

S. dispar (NS) S. dispar (NS)*

1.00

S. arcticus (QC) S. arcticus (Mont) S. maritimensis (NS)

1.00 S. maritimensis (NB) S. maritimensis (NB)*

Fig. 3. The Bayesian tree for the combined cytochrome b and inter-SINE Wngerprint data sets. Numbers along internodes refer to posterior probabilities. Specimens are identiWed by species name followed by locality (¤ are used to distinguish individual specimens from the same locality in Table 1).

the species level. We also found high support for S.maritimensis as a distinct sister species to S. arcticus as observed by Stewart et al. (2002) based on cytochrome b data. When comparing the Bayesian analysis of the cytochrome b data to that produced of the combined data set, it is clear that the deep root branching is driven by the cytochrome b data. However, the phylogenetic positions of the terminal taxa and the posterior probabilities are inXuenced by both data sets in the combined analysis. Thus, we feel this study demonstrates the ability for combined data sets and Bayesian inference to help resolve previously undeWned relationships.

4.3. Monophyly of Sorex hoyi, S. monticolus, S. palustris, and the S. cinereus group A previous study by Fumagalli et al. (1999) found support for two independent monophyletic groups within Otisorex consisting of S. palustris and S. monticolus, and S. haydeni and S. cinereus, respectively. Our Wndings support this organization, but as a single monophyletic group, with the addition of S. hoyi as a sister species to S. monticolus and S. palustris. The aYliation of S. hoyi to S. moniticolus and S. palustris is not surprising as Stewart and Baker (1994b) found support for this relationship using mtDNA

200

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S. cinereus (NFLD)* S. cinereus (NB) S. cinereus (NB)*

1.00

S. cinereus (NFLD)

1.00

S. cinereus (PEI)

0.61

S. cinereus (NS)

1.00 1.00

S. cinereus (ALB) S. cinereus (ALB)

1.00

S. haydeni (ALB)* S. haydeni (ALB)*

1.00

0.99

S. hoyi (ALB) S. hoyi (ALB)* S. hoyi (ONT)*

1.00 S. hoyi (NB)*

0.82

S. hoyi (NB)

1.00 0.93

0.65

S. hoyi (ONT)

0.68 1.00

S. hoyi (NS) S. hoyi (PEI) S. monticolus (BC)

0.84

1.00

S. monticolus (ALB)* S. monticolus (ALB)

1.00

S. monticolus (ALB)**

1.00

S. palustris (ALB)

1.00 1.00

1.00

S. palustris (NS)* S. palustris (NS)

0.59

S. fumeus (NB)* S. fumeus (NB) S. fumeus (QC)

1.00

S. fumeus (ONT)*** S. fumeus (ONT)** S. fumeus (ONT)* S. fumeus (ONT)

1.00

S. dispar (NS) S. dispar (NS)*

1.00

S. arcticus (QC) S. arcticus (Mont) S. maritimensis (NS)

0.99 0.63

S. maritimensis (NB) S. maritimensis (NB)*

Fig. 4. The Bayesian tree (partitioned GTR model) for the cytochrome b sequences. Numbers along internodes refer to posterior probabilities. Specimens are identiWed by species name followed by locality (¤ are used to distinguish individual specimens from the same locality in Table 1).

control region sequences. Stewart and Baker (1994b), however, failed to resolve this group’s evolutionary relationship to other Otisorex species. We Wnd strong support for placing S. hoyi in the aforementioned monophyletic group, and suggest a sister-taxon relationship to the S. palustris and S. monticolus clade. A close genetic relationship between the S. monticolus complex and the water shrews (S. palustris and S. bendirii) has been previously observed (Demboski and Cook, 2001; Fumagalli et al., 1999; Stewart and Baker, 1994b); however, as for the pygmy shrews, several taxonomic designations have been proposed. At least two water shrews, S. palustris

and S. bendirii, are currently recognized in North America, with the latter’s range limited to the west coast (Hall, 1981). In contrast, S. palustris as it is currently recognized, is distributed across the western Cordillera, eastern United States, and boreal regions of Canada (Hall, 1981; Van Zyll de Jong, 1983). O’Neill et al. (2005) recently suggested that S. palustris may actually consist of two distinct species, a boreal species (S. palustris) and a Cordilleran species (S. navigator), as these two taxa exhibited considerable cytochrome b divergence (6.9%). In addition, our samples from Nova Scotia show 5% cytochrome b divergence from the O’Neill et al. (2005) boreal clade. With support for a

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distinct eastern S. palustris group from the cytochrome b sequence, inter-SINE Wngerprint data, and Bayesian analysis, a taxonomic reevaluation of subspecies of water shrews is currently underway (E. Mycroft pers. comm.). Similar to western S. palustris, S. monticolus is divided into distinct mtDNA clades inhabiting the coastal and continental regions of western North America. The cytochrome b sequence of the Alberta specimens of S. monticolus used in our analysis grouped with the northern continental samples of Demboski and Cook (2001), and the specimen from British Columbia grouped with their coastal clade (NJ analysis; data unpublished). The phylogenetic relationship among S. monticolus clades and water shrews remains controversial, as Demboski and Cook’s (2001) analysis demonstrated. Our Bayesian analysis, although limited to one sample, places the coastal S. monticolus group as basal to a clade consisting of S. palustris and continental S. monticolus. It is worth noting that analysis of the inter-SINE Wngerprint data alone demonstrated strong support for a monophyletic S. monticolus group, where analysis of the cytochrome b data indicated that S. monticolus is paraphyletic (Figs. 1 and 4). One possible explanation is secondary introgression between ancestral continental S. monticolus and boreal S. palustris, such that modern day S. palustris has retained the mtDNA of S. monticolus. Any subsequent analysis of the S. monticolus complex should include additional S. palustris specimens, as there does appear to be a mtDNA relationship between coastal S. monticolus and Cordilleran S. palustris/coastal S. bendirii (data unpublished). Given the conXicting phylogenies, we reiterate Demboski and Cook’s (2001) recommendation for a systematic reevaluation of the S. monticolus group, including analysis of both nuclear and mitochondrial genes. 4.4. Sorex fumeus and S. dispar complexes George’s (1988) allozyme analysis has been the only large scale phylogeny constructed that includes both S. dispar and S. fumeus. From the analysis, George (1988) placed S. dispar and S. fumeus as sister-taxa within the Otisorex subgenus. Subsequent molecular studies (Fumagalli et al., 1999; Stewart and Baker, 1994a) conWrmed the placement of S. fumeus within Otisorex. Our study unequivocally places S. dispar as a member of Otisorex using mtDNA sequence and inter-SINE Wngerprint data. However, our Bayesian analysis does not place these two species as sister-taxa, but as independent basal lineages to the aforementioned monophyletic group. This placement is not surprising, as both species have at various times, been placed in either subgenus (Findley, 1955; Junge and HoVman, 1981). A curious addition to this puzzle is the high SINE divergence (24%) between these two species, while the cytochrome b sequence NJ analysis pairs them together at only 10% divergence. We suggest several possible explanations for this incongruity: (1) mitochondrial introgression between lineages; (2) diVerential selection forces acting on each species, resulting in varying activity levels of the SOR

201

retroposon; (3) a lineage speciWc mutation in the retroposon that could increase its level of replication; and (4) chromosomal duplications therefore increasing SINE copies (Note: the karyotype of S. dispar is unknown). 4.5. Concluding remarks and future directions When interpreting phylogenies, one must distinguish between gene trees and species trees. This is especially pertinent when using nuclear and mitochondrial data sets and complex evolutionary models, as certain data sets and historical processes (e.g., introgression) may reduce phylogenetic accuracy. Malia et al. (2003) suggest that the best phylogenetic method should incorporate as much data as possible, while limiting the number of assumptions. However, we emphasize researcher diligence when analyzing multiple markers. In our Bayesian analysis, the resolution of deeper nodes and species level relationships appears to be inXuenced by both inter-SINE Wngerprints and cytochrome b data. We acknowledge that certain bifurcations resulting from the cytochrome b analysis (e.g., [coastal S. monticolus, [cordilleran S. monticolus and S. palustris]]) may have their taxonomic implications questioned; but, in this total evidence approach, understanding the individual marker’s evolutionary histories accompanied by the appropriate interpretation should be the course of action, and will ultimately strengthen phylogenetic inference. It is apparent that to resolve deeper nodes in the Sorex phylogeny, multiple sources of data must be utilized. O’leary et al. (2004) caution against the use of single genes for phylogenetic analysis, as they may be not be reXective of the true species tree (i.e., divergence of the ancestral populations gene pools). Moreover, Malia et al. (2003) stress the need for a shift towards species level analyses, as it is the least inclusive of all taxonomic classes. Phylogenetic analysis of species is preferred, as they are deWned on the criteria of diagnosability, rather than monophyly (see Prendini, 2001). Our analysis incorporates both ideas, as we address species level relationships, using a combined data set. O’leary et al. (2004) praise SINEs as a “good” phylogenetic character. This praise is a result of the aforementioned attributes, and also reXects the fact that SINEs represent an independant genome-wide marker (versus a single gene), thus being relatively devoid of certain evolutionary mechanisms associated with mtDNA (e.g., Numts, homoplasic deletions; see Broughton et al., 1998; Zhang and Hewitt, 2003). Furthermore, mitochondrial introgression between closely related species can signiWcantly inXuence phylogenetic inference (e.g., Prager et al., 1993). Fortunately, data sets such as morphological characters or allozymes need not be discarded, as they provide valuable evolutionary information, and can be incorporated into phylogenetic analyses. There is likely no one systematic panacea. Consequently, combining data sets along with the use of original, unique evolutionary models will greatly aid future systematic studies, and ultimately our understanding and interpretation of phylogenies.

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