Impact of ocean barriers, topography, and glaciation onthe phylogeography of the catfish Trichomycterusareolatus (Teleostei: Trichomycteridae) in Chilebij_1224 876..892

PETER J. UNMACK1*, ANDRE P. BENNIN1, EVELYN M. HABIT2,4,PEDRO F. VICTORIANO3,4 and JERALD B. JOHNSON1,5

1Evolutionary Ecology Laboratories, Department of Biology, Brigham Young University, Provo,UT 84602, USA2Unidad de Sistemas Acuáticos, Centro de Ciencias Ambientales EULA-Chile, Universidad deConcepción, Casilla 160-C, Concepción, Chile3Centro de Investigaciones en Ecosistema Patagónicos CIEP Coyhaique, Chile4Departamento de Zoología, Facultad de Ciencias Naturales y Oceanográficas, Universidad deConcepción, Casilla 160-C, Concepción, Chile5Monte L. Bean Life Science Museum, Brigham Young University, Provo, UT 84602, USA

Received 4 September 2008; accepted for publication 9 January 2009

We examined the role of several earth history events on the phylogeographic distribution of the catfish Tricho-mycterus areolatus in Chile using the cytochrome b gene. We explored three biogeographic hypotheses: that sealevel changes have resulted in the isolation of populations by drainages; that glaciation has impacted geneticdiversity; and that ichthyological subprovince boundaries correspond to phylogeographic breaks in our focal species.We found seven well-supported clades within T. areolatus with high levels of genetic divergence. The strongestsignal in our data was for an important role of sea level changes structuring populations. Five of the seven cladesmapped cleanly to the geographic landscape and breaks corresponded closely to areas of narrowest continentalshelf. In addition, few haplotypes were shared between rivers within clades, suggesting that only limited localmovement of individuals has occurred. There was no relationship between the levels of genetic diversity and theproportion of individual drainages covered by glaciers during the last glacial maximum. Two phylogeographicbreaks within T. areolatus did match the two previously identified faunal boundaries, but we found three additionalbreaks, which suggests that faunal breaks have only limited utility in explaining phylogeographic patterns. Theseresults imply that the narrow continental shelf coupled with sea level changes had a strong influence on theobligate freshwater fishes in Chile. © 2009 The Linnean Society of London, Biological Journal of the LinneanSociety, 2009, 97, 876–892.

ADDITIONAL KEYWORDS: Andes Mountains – biogeography – community assemblages – continental shelf– cytochrome b – Hatcheria macraei – introgression – sea level change – South America.

INTRODUCTION

A strong geographic bias exists in the global distri-bution of phylogeographic studies, with the majorityof research having focused on taxa from the northernhemisphere (i.e. North America and Europe)(Avise, 2000). This disparity has prompted a call

for increased studies in the southern hemisphere(Beheregaray, 2008). Southern South America pro-vides a series of unusual geographic features thatmake it particularly interesting for studying the phy-logeography of the southern hemisphere. Most promi-nent is the Andes mountain range that runs north tosouth along the western edge of South America. Thesemountains formed in the Tertiary and create a barrierto biotic exchange between lowland habitats on either*Corresponding author. E-mail: peter.pub@unmack.net

Biological Journal of the Linnean Society, 2009, 97, 876–892. With 4 figures

© 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 97, 876–892876

side of the range. In addition, the distance betweenthe high mountains to the east and the ocean to thewest is relatively short, with most of central Chilebeing only 100–220 km wide. This results in anextremely strong elevational gradient from the highAndes down to sea level, with rivers running prima-rily from the mountains west to the ocean. Suchextreme elevation gradients could have a major influ-ence on small-scale climatic patterns in this area,which, in turn, could affect the distribution of sometaxa. It is also important to note that the AndesMountains decrease in elevation from 6960 m in thenorth near Santiago to below 1000 m in the south inContinental Chiloé. Consequently, for species affectedby elevation, we might expect biotic exchange across

the Andes to be more common at southern latitudesthan at northern latitudes.

Southern South America has also been subjectto large-scale climatic fluctuations associated withmajor glaciations during the Pleistocene. Mostnotable are repeated cycles where major ice sheetsformed and covered vast parts of the southernmostregions of western South America. Current geologicalhypotheses suggest that glaciers covered most ofsouthern Chile and extended as a single continuousunit north along the higher elevations of the Andes toaround 38° south (Fig. 1B) (Hulton et al., 2002). Manysmaller isolated glaciers occurred on higher mountainpeaks north of this main ice sheet (Grosjean et al.,1998; Harrison, 2004). Hence, glacial events have had

Figure 1. Geographic features of the study area presented in two panels. Dots on both maps represent collectionlocalities and the dotted line represents the political boundary between Chile and Argentina. A, map of central Chileshowing rivers and the extent of exposed land when sea level is lowered 150 m during glacial periods, as indicated by thegrey-shaded area, comprising information that we used to evaluate hypothesis 1. The Río Mataquito and Río Toltén areunderlined to denote the boundaries between the Central, South-central, and Southern fish subprovinces of Dyer (2000),comprising boundaries that we used to evaluate hypothesis 3. B, map of central Chile with site numbers included for ourcollection localities; details of these sites are found in Table 2. The grey-shaded area represents the estimated maximumextent of the major ice sheet during the last glaciation, comprising information used to evaluate hypothesis 2. The insertmap shows the extent of the study area relative to the broader geographic region.

PHYLOGEOGRAPHY OF T. AREOLATUS 877

© 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 97, 876–892

an important influence on the distribution of speciesin southern Chile (Villagrán, 1990; Villagrán & Hino-josa, 2005). Indeed, only taxa that were able to persistin refuges or that could readily migrate along a northto south gradient would have persisted. Those unableto move, such as freshwater aquatic taxa, would havesuffered local extirpation. Hence, we expect manyspecies that currently occupy the southernmostregions of Chile to be recent colonists. Combined, theunique geography and climate of southern SouthAmerica provide a clear framework for exploring thecurrent distribution of species throughout this region.

One group of organisms likely to be stronglyimpacted by geological and climatic events in south-ern South America are those restricted to freshwaterhabitats. Movement for these species is typicallylimited by connections among riverine systems, withthe ocean providing a barrier at each river terminus.As a result of the the topographic nature of this area,most rivers are relatively short and flow from east towest (Fig. 1). Thus, the only escape from unfavorableconditions (e.g. due to climate change or glacialadvance) is to move downstream, which would be oflimited utility when glaciers are advancing fromsouth to north. Opportunities for south to north move-ment could occur across drainage divides or if riversconnect along the continental shelf during low sealevels associated with glacial periods (Clark & Mix,2002). However, Chile has an extremely narrow con-tinental shelf: a sea level fall of 150 m only widensthe coastline of Chile by 10–60 km (Fig. 1A). Conse-quently, few rivers were likely to coalesce duringglacial periods, limiting the opportunity for coloniza-tion across river systems. Thus, we expect freshwatertaxa in southern South America to be stronglyimpacted by glaciation with limited opportunities forrecolonization after glacial events.

The factors responsible for endemism and thusbiogeographic province boundaries are also likely toimpact widespread species that cross those bound-aries (Avise, 2000). Under such circumstances, wemight expect to find congruence between biogeo-graphic province boundaries and genetic breakswithin widespread species (Burton, 1998). Chile iscurrently divided into three major ichthyologicalprovinces based on the distribution of endemic fresh-water fishes: Titicata in the north; Chilean in thecentral region; and Patagonian in the south (Dyer,2000). Most of the obligate freshwater fish species inChile (18) inhabit the Chilean Province and haverelatively restricted ranges that span one or only afew major rivers (Dyer, 2000). In view of these pat-terns of endemism, Dyer (2000) divided Chilean Prov-ince into three subprovinces: Central, South-centraland Southern subprovinces, as delimited in Fig. 1A.The boundaries between these subprovinces are delin-

eated by the Río Mataquito and Río Toltén, respec-tively. It is unclear whether these biogeographicsubprovinces are associated with phylogeographicbreaks in broadly-distributed species.

The trichomycterid catfish Trichomycterus areola-tus (Valenciennes) is ideal for understanding Chileanbiogeography. It is one of the most widespread obli-gate freshwater fish in Chile, occurring withinChilean Province in all drainages from the Río Limarísouth to Chiloé Island, a distance of approximately1400 km (Fig. 1). Trichomycterus areolatus appears tohave broad environmental tolerances and is typicallywidespread and abundant within most rivers that itoccupies (Habit, Victoriano & Campos, 2005; Habit,Belk & Parra, 2007). This species can be found fromsmall headwater streams to low elevation rivers nearthe coast that have riffles with a variety of substrates,especially gravel and rocks (Arratia, 1983; Garcia-Lancaster, 2006). This makes it an ideal group forexploring the impact of glaciation and climate changeon aquatic taxa across central Chile. It is also one ofthe few species that crosses the two ichthyologicalsubprovince boundaries within the Chilean Province(Fig. 1A; Dyer, 2000).

In the present study, we used T. areolatus to exploreseveral biogeographic hypotheses. We generated thesehypotheses using three kinds of data: continentalshelf width along the coast of Chile; historic climaticpatterns and known glacial cycles during Pleistocene;and natural fish community breaks based on existingfaunal subprovince boundaries. Using these data, wefocused on three general questions. First, we askedwhat role have sea level changes had in maintainingisolation of major river basins? Given the narrowcontinental shelf, we expect sea level changes to onlyhave local impacts on T. areolatus population connec-tivity, with the narrowest portions of the continentalshelf corresponding to the largest phylogeographicbreaks (Fig. 1A). Second, is there a difference ingenetic diversity between drainages with significantice cover during glaciation versus those that werelargely unglaciated (Fig. 1B, Table 1)? We mightexpect to observe a decrease in genetic variation inglaciated basins due to the major decrease in basinsize as well as the locally colder climatic conditionsand impacts from deglaciation. Finally, do phylogeo-graphic breaks in T. areolatus correspond to subprov-ince biogeographic boundaries identified for fishes(Fig. 1A; Dyer, 2000)?

MATERIAL AND METHODSSTUDY TAXA AND SAMPLING

Sample sizes at each collection site varied from one toeleven, but were typically five or eight individuals, for

878 P. J. UNMACK ET AL.

© 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 97, 876–892

a total of 302 T. areolatus from 43 locations spanningthe entire distribution of this species in Chile (Fig. 1,Table 2). Our sampling strategy was designed to coverthe breadth of their range for phylogeographic analy-sis. Our sampling scheme has the added advantagein that we are able to comprehensively documentwithin-species diversity and, by including twoclosely-related species as outgroup taxa (see below),we were able to explore current species boundaries.This approach is particularly appropriate for under-studied groups where molecular genetic studies oftenreveal the presence of cryptic taxa (Johnson, Dowling& Belk, 2004; Wong, Keogh & McGlashan, 2004;Hammer et al., 2007). We initially included a fewindividuals from two outgroup species: Hatcheriamacraei (Girard) from Argentina and southern Chileand Bullockia maldonadoi (Eigenmann) from centralChile (Fig. 1, Table 2). Our preliminary analyses indi-cated that T. areolatus did not form a monophyleticgroup, but was paraphyletic with respect to H.macraei. Consequently, we expanded our geographiccoverage for H. macraei to include 12 individuals fromsix collection sites that span the range of this out-group species. We also included broad geographicsampling of B. maldonadoi, with 26 individuals takenfrom seven collection sites (Table 2).

GEOSPATIAL ANALYSIS

We employed a geographic information system ap-proach to quantify several environmental factorsneeded to evaluate our three general hypotheses. Forexample, datasets used to generate maps (e.g. Figs 1

and 3) were obtained from the Digital Chart of theWorld (ESRI, 1993) and manipulated in ArcInfoand ArcMap, version 9.1 (Environmental SystemsResearch Institute). Bathymetric data (Figs 1A, 3)were obtained via the ETOPO2v2 Global Gridded2-min Database (NGDC, 2006). The glaciation layer(Fig. 1B) was originally redrawn by J. Milne (sensuClapperton, 1993; Turner et al., 2005; Ruzzante et al.,2006). Drainage basin boundaries were obtained fromthe HydroSHEDS dataset developed by the WorldWildlife Fund (Lehner, Verdin & Jarvis, 2006). Drain-age basins were combined with the glaciation layer todetermine the percentage cover of glaciers by riverbasin.

DNA ISOLATION, AMPLIFICATION, AND SEQUENCING

We extracted genomic DNA from muscle tissue fromeach specimen using the DNeasy Tissue Kit (QiagenInc.). We amplified the cytochrome b (cyt b) geneusing two primers that flanked this gene: Glu315′-GTGACTTGAAAAACCACCGTT-3′ and Cat.Thr295′-ACCTTCGATCTCCTGATTACAAGAC-3′. Final con-centrations for polymerase chain reaction (PCR)components per 25 mL reaction were: 25 ng of tem-plate DNA, 0.25 mM of each primer, 0.625 units of TaqDNA polymerase, 0.1 mM of each dNTP, 2.5 mL of10 ¥ reaction buffer and 2.5 mM MgCl2. Amplificationparameters were: 94 °C for 3 min followed by 35cycles of 94 °C for 30 s, 48 °C for 30 s, and 72 °C for90 s, and, finally, 72 °C for 7 min. PCR products wereexamined on a 1.5% agarose gel using SYBR safe DNAgel stain (Invitrogen). The PCR products were purifiedusing a Montage PCR 96 plate (Millipore). Mostsequencing reactions and clean-up were performedusing a Parallab 350 robot (Parallabs). Somesequences were also obtained via cycle sequencingwith Big Dye 3.0 dye terminator ready reaction kitsusing 1 : 16th reaction size (Applied Biosystems).Sequencing reactions were run at an annealing tem-perature of 52 °C in accordance with the ABI manu-facturer’s protocol. Sequenced products were purifiedusing sephadex columns. Sequences were obtainedusing an Applied Biosystems 3730 XL automatedsequencer at the Brigham Young University DNASequencing Center.

PHYLOGENETIC ANALYSIS OF DNA SEQUENCE DATA

We edited DNA sequences using Chromas Lite,version 2.0 (Technelysium), imported them intoBIOEDIT, version 7.0.5.2 (Hall, 1999) and alignedthem by eye. Sequences were checked for unexpectedframe shift errors or stop codons in MEGA, version4.0 (Tamura et al., 2007). The editing resulted in a1076-bp fragment for each individual included in the

Table 1. Extent of ice cover by river basin at the lastglacial maximum (presented from north to south; for ref-erence, see Fig. 1)

River basin % Glaciated

Imperial 30Toltén 66Valdivia 66Bueno 67Maullín 90Ralun 100Puelo 93Yelcho 92Pico-Palena 85Cisnes 90Aysén 90Baker 86

Basins north of the Río Imperial had negligible amounts ofglaciation and are not shown. Values are based on thecurrent coastline.

PHYLOGEOGRAPHY OF T. AREOLATUS 879

© 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 97, 876–892

Tab

le2.

Col

lect

ion

loca

lity

data

Pop

ula

tion

code

Spe

cies

Loc

alit

yR

iver

basi

nS

ubp

rovi

nce

Lat

itu

deL

ongi

tude

ND

egre

esM

inu

tes

Sec

onds

Deg

ress

Min

ute

sS

econ

ds

1T

AR

íoG

uam

pull

aL

imar

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entr

al-3

040

6.5

-71

322.

68

2T

AR

íoC

hoa

paC

hoa

paC

entr

al-3

137

50.9

-71

916

.08

3T

AR

íoP

etor

caat

Zap

alla

rP

etor

caC

entr

al-3

215

46.5

-70

5736

.08

4T

AE

ster

oL

imac

he

atL

imac

he

Aco

nca

gua

Cen

tral

-32

5928

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117

6.7

85

TA

Río

Aco

nca

gua

atS

anF

elip

eA

con

cagu

aC

entr

al-3

244

25.4

-70

4521

.48

6T

AE

ster

oE

lB

ello

toM

aipo

Cen

tral

-33

1149

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110

57.9

87

TA

Est

ero

Arr

ayan

,S

antu

ario

Río

Arr

ayan

Lo

Bar

nec

hea

Mai

poC

entr

al-3

322

25.1

-70

3140

.611

8T

AR

íoen

Agu

ila

Su

rat

An

gost

ura

Mai

poC

entr

al-3

354

40.9

-70

4348

.38

9T

AR

íoen

An

gost

ura

del

Pai

ne

Mai

poC

entr

al-3

357

16.2

-70

428.

35

10T

AR

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ach

apoa

lN

orte

Rap

elC

entr

al-3

411

37.0

-70

4513

.95

11T

AE

ster

oTo

poca

lma

Topo

calm

aC

entr

al-3

47

29.6

-71

5631

.72

12T

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Lo

Val

divi

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dary

-34

598.

8-7

118

24.5

513

TA

Río

Mau

leM

aule

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th-c

entr

al-3

533

4.9

-71

4137

.65

14T

AE

ster

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eloc

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outh

-cen

tral

-35

3749

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233

43.6

815

TA

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639

10.2

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2712

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th-c

entr

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633

2.8

-72

531

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ster

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ongu

énat

Pu

ente

Car

mel

itas

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dali

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outh

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-36

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30

27.0

37

20T

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ster

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le,

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asde

laG

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lién

Sou

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649

52.9

-72

5512

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21T

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oat

Pu

ente

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outh

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-36

4946

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34

12.3

5

22T

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bío

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th-c

entr

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721

6.5

-71

5354

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outh

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tral

-37

218.

6-7

153

6.2

326

TA

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Du

quec

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bío

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th-c

entr

al-3

732

9.0

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4213

.56

27T

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eco

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outh

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3217

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135

39.8

228

TA

Río

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Leb

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outh

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tral

-37

104.

8-7

310

26.3

530

TA

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Pu

nta

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Pu

nta

Lav

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Sou

th-c

entr

al-3

79

29.3

-73

3443

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31T

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lco

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th-c

entr

al-3

735

40.8

-73

2329

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32T

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cupi

lC

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cupi

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outh

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tral

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4751

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313

17.5

533

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Impe

rial

Impe

rial

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th-c

entr

al-3

843

34.3

-73

56.

98

880 P. J. UNMACK ET AL.

© 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 97, 876–892

34T

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peri

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outh

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247

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ry-3

859

8.5

-72

379.

98

36T

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ltén

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90

49.2

-72

3033

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outh

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-39

2714

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246

59.7

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TA

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roV

aldi

via

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ther

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951

12.2

-72

4527

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ern

-39

488.

4-7

243

27.0

240

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946

32.7

-72

2734

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41T

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n-4

026

21.8

-72

5447

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outh

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-40

4545

.7-7

258

14.9

743

TA

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Bla

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outh

ern

-41

1230

.6-7

337

6.7

844

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10

10.9

-73

367.

98

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2-7

247

47.5

546

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123

8.0

-72

5522

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47T

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-42

2228

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348

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2955

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159

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-31

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loca

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study. Phylogenetic analyses were performed usingboth parsimony and likelihood approaches usingPAUP* 4.0b10 (Swofford, 2003). We conducted ourmaximum parsimony (MP) analysis using a heuristicsearch with 1000 random additions and tree bisectionand reconnection (TBR) branch swapping. For ourmaximum likelihood (ML) analysis, we identified thebest-fitting model of molecular evolution using theAkaike Information Criterion (AIC) in MODELTEST,version 3.7 (Posada & Crandall, 1998). MODELTESTidentified GTR+I+G as the best model with thefollowing parameter estimates: Lset Base = (0.27820.2887 0.1424) Nst = 6 Rmat = (8.3552 82.6934 2.36395.3730 26.7399) Rates = gamma Shape = 0.8469 Pin-var = 0.5956. Due to the large number of haplotypesin the full dataset (147 haplotypes), we were con-strained by computation time in our ability to runbootstrap analyses for both the MP and ML analyses.To overcome this limitation, we created a trimmeddataset of 55 haplotypes that were used to estimatebootstrap values. To construct the trimmed data set,we chose the two most divergent haplotypes plusseveral intermediate haplotypes from each clade. Thisgave a total 45 haplotypes for T. areolatus, six hap-lotypes for H. macraei, and four haplotypes for B.maldonadoi. Most haplotypes within a clade differedonly by a few base pairs, thus minimizing the chancesof getting significantly different results based onwhich haplotypes were included. Nodal support onthe reduced dataset was estimated with PAUP* bybootstrap with 1000 replicates for MP using a heu-ristic search with ten random additions of taxa andTBR branch swapping. For ML, we used GARLI 0.951(Zwickl, 2006) to estimate 1000 bootstrap replicatesusing the default values recommended for the soft-ware. The ML tree topology was estimated withPAUP* via a heuristic search with five randomadditions of taxa and TBR branch swapping usingthe GTR+I+G model of evolution with the para-meters: Lset Base = (0.2784 0.2859 0.1448) Nst = 6Rmat = (6.7769 66.9881 1.5695 3.2757 20.9228)Rates = gamma Shape = 0.9155 Pinvar = 0.6229. Alltree lengths reported for MP include both informativeand uninformative characters. Trees were editedusing DENDROSCOPE, version 1.4 (Huson et al.,2007) to arrange haplotypes in a north to south ori-entation.

Finally, in our discussion, we explore levels ofsequence divergence between our focal species andother freshwater taxa that occupy some of the samedrainages investigated in the present study. To makethese comparisons with freshwater crabs of the genusAegla, we secured published mitochondrial (mt)DNAdata directly from M. Perez-Losada (Pérez-Losadaet al., 2002). We used crab data from the genes COIand COII because these typically have closer sub-

stitution rates to cyt b than do other mitochondrialribosomal genes that were available. We used model-corrected sequence divergence in the crabs, whichrequired identifying a best-fitting model of molecularevolution using AIC in MODELTEST as outlinedabove. We found this model to be TrN+I+G withthe parameters: Lset Base = (0.3180 0.1390 0.1283)Nst = 6 Rmat = (1.0000 14.4898 1.0000 1.0000 8.9973)Rates = gamma Shape = 0.8667 Pinvar = 0.6814. Pair-wise genetic divergence among haplotypes acrossdrainages were then calculated in PAUP* using theseparameter estimates.

PHYLOGEOGRAPHIC ANALYSIS

To explore factors that have shaped the distribution ofT. areolatus through space and time, we employed aset of phylogeographic analytical approaches. We firstemployed an analysis of molecular variance (AMOVA)to determine how genetic variation was partitionedacross the geographic landscape (Excoffier, Smouse &Quattro, 1992). If genetic structuring is driven byisolation among drainages due to the saltwater oceanboundary, then we expect to observe high levels ofstructuring among drainages. Similarly, if fish com-munity provinces are indicators of historical events,then we expect to see breaks in our T. areolatus dataset coincident with fish community boundaries. Weused the AMOVA framework to create two competingmodels corresponding to these two hypotheses. OurAMOVA models are distinguished by the way that the43 T. areolatus collecting sites are grouped. Caseswhere high amounts of variation can be explainedbetween groups suggest an important historic barrierto gene flow exists, coincident with the structure ofthe model. Our two models were constructed asfollows (for geographic reference, see Fig. 1). (1) Toevaluate the role of sea level changes in maintainingisolation of river basins, we divided populations into24 groups that coincide with the 24 river drainagesfrom which T. areolatus were collected (Table 2).These groups were delineated by collection sites: 1, 2,3, 4–5, 6–9, 10, 11, 12, 13, 14, 15–16, 19–20, 21–23/26–27, 28, 30, 31, 32, 33–34, 35–36, 37–40, 41–42,43–44, and 45–46. (2) To evaluate the associationbetween fish community boundaries and T. areolatusphylogeography, we divided populations into threegroups based on the three geographic subprovincesdefined by Dyer (2000; Fig. 1A, Table 2). These groupswere: Central subprovince (sites 1–11); South-centralsubprovince (sites 13–16, 19–23, 26–28, and 30–34);and Southern subprovince (sites 37–46). We excludedpopulations from Río Mataquito (site 12) and RíoToltén (sites 35–36) because Dyer (2000) listedthese drainages as being the boundary between sub-provinces, but did not assign them to a specific sub-

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province. All of these analyses were executedin ARLEQUIN, version 3.1 (Excoffier, Laval &Schneider, 2006) with significance assessed using 104

random permutations of the dataset.We evaluated the effects of glaciation on T. areola-

tus populations by examining the levels of geneticdiversity among drainages across the range of thisspecies. Most drainages in southern Chile that weresubstantially covered by ice (> 85%) during the lastglacial maximum currently do not contain T. areola-tus (Dyer, 2000), with the exception of Río Maullín ofwhich 90% was covered. However, all other drainagesat the southern extent of the range that were par-tially covered by ice (30–90% cover) do contain T.areolatus. We were interested in comparing popula-tions from these sites with those from more northerndrainages where glaciers were absent or minor incoverage. If glaciation was important in shaping thegenetic diversity in this species, we might expect tofind a negative association between the percentage ofdrainage that was covered with ice and the level ofgenetic diversity now present in that drainage. To testthis hypothesis required calculating the genetic diver-sity for fishes in each drainage as well as calculatingthe percentage of each drainage that was historicallycovered by ice (as described above). Three drainages(i.e. Maipo, Imperial, and Toltén) had individualsfrom multiple clades: we separated these drainagesby clade for calculating genetic diversity because thepresence of multiple clades would artificially raisegenetic diversity measures relative to surroundingdrainages with only single clades. We calculatedmtDNA genetic diversity using the measures: numberof haplotypes, haplotype diversity (HD) and nucleotidediversity (p) with the software DnaSP, 4.50.3 (Rozaset al., 2003). We then used linear regression to test fora significant relationship between the percentage ofglacial coverage by drainage and each of our threemeasures of genetic diversity; these analyses wereexecuted in SYSTAT, version 10 (SPSS, 2000).

RESULTS

The distribution of genetic variation in T. areolatusallowed us to evaluate each of our three biogeographichypotheses. We first describe patterns of sequencediversity for the cyt b gene within T. areolatus; wethen present the mtDNA gene tree and show how itmaps to the geographic landscape where sampleswere collected; and, finally, we describe phylogeo-graphic structuring and patterns of genetic diversityamong populations.

SEQUENCE DIVERSITY

We found 125 haplotypes of T. areolatus, ten haplo-types of H. macraei, and 12 haplotypes of B. maldona-

doi (GenBank accession FJ772091-FJ772237; seeSupporting information, Appendix S1). Of the 1076 bpsequenced, 849 characters were invariant, 50 charac-ters were variable but parsimony uninformative, and175 characters were parsimony informative. Our MPsearch with all characters weighted equally recovered15 599 most parsimonious trees with a length of 436(CI = 0.598, RI = 0.930). ML analysis recovered onetree with a likelihood score of -4310.22826 (Fig. 2).The trimmed dataset contained 55 haplotypes. Of the1076 bp sequenced, 875 characters were invariant, 55characters were variable but parsimony uninforma-tive, and 144 characters were parsimony informative.The MP search, with all characters weighted equally,recovered 1637 most parsimonious trees with a lengthof 355 (CI = 0.634, RI = 0.893). ML analysis recoveredthree trees with a likelihood score of -3629.74808(Fig. 3). Overall, the trees were well resolved, withgood to strong support (Hillis & Bull, 1993) at mostdeeper nodes demonstrated by bootstrap values > 70for both MP and ML analyses (Fig. 3).

PHYLOGENETIC RELATIONSHIPS

We found seven well-supported clades within T.areolatus (labelled as clades A through G in Figs 2, 3).Five of the seven clades map cleanly to the geographiclandscape with no shared haplotypes between geo-graphic regions. One exception was clade B, whichcontained haplotypes that were geographically dis-tributed in areas primarily represented by clades Cand D. Specifically, two individuals with haplotype 21occurred at site 7 in the Río Maipo drainage, andeight individuals with haplotype 24 occurred at site14 in the Estero Reloca drainage (Figs 1, 3). Haplo-type 21 was rare in the Río Maipo drainage (two of 32fish sampled). By contrast, all eight fish sampled fromEstero Reloca drainage were from clade B. The secondexception was a small region of geographic rangeoverlap between haplotypes found in clades E and F.Clade E haplotypes were geographically distributedfrom the southern boundary of clade D at the RíoBiobio drainage south to Río Toltén; Clade F haplo-types were distributed from Río Imperial south to RíoBueno (Fig. 1A). Thus, there is a region of overlapbetween clades E and F in the Río Imperial and RíoToltén drainages (sites 33–36 in Fig. 1; draingesdescribed in Fig. 3). Of the 18 individuals sampled inthese drainages, 13 were from clade E and five werefrom clade F (Fig. 2). However, all four localities fromthese drainages had haplotypes from both clades (seeSupporting information, Appendix S1).

Unexpectedly, we also found that the outgroup H.macraei nested within the southernmost T. areolatusclade G (Figs 2, 3). Moreover, it did not form a singlemonophyletic group within this clade, but, instead,

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was represented by two small lineages nested withina set of T. areolatus haplotypes. The first H. macraeilineage (haplotypes H7 to H10) was composed of indi-viduals collected from the Río Baker drainage at thesouthern range of T. areolatus in Chile and from theRío Colorado drainage far to the north in Argentina(localities 51 and 52 in Fig. 1B). The second H.macraei lineage (haplotypes H1 to H6) was composed

of individuals from the Manso and Aysén drainages inChile (sites 49 and 51; Fig. 1B) and from the Negroand Chubut drainages across the Andes mountains inArgentina (sites 53 and 54; Fig. 1B).

GENETIC DIVERSITY

We evaluated patterns of genetic diversity by compar-ing genetic variation within each of our major clades

17,18,19,20

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41,42,43,44,45,46

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A

Bullockia

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0.01 substitutions/site

Figure 2. Maximum likelihood (ML) phylogram of Trichomycterus areolatus haplotypes generated from 1076 bp of thecytochrome b gene. The phylogeny includes 125 haplotypes of T. areolatus, 12 haplotypes of the outgroup Bullockiamaldonadoi (prefixed with B), and ten haplotypes of the outgroup Hatcheria macraei (prefixed with H). Branch lengthswere estimated via ML under the GTR+I+G model of evolution. The tree was rooted with B. maldonadoi. Numbers to theright of the haplotypes indicate the collection localities present in each clade; haplotypes underlined and linked witharrows are shared between clades. Letters to the right of the tree comprise labels for the seven major clades. Details foreach haplotype, including locations where haplotypes are found and the number of individuals with that haplotype, areprovided in the Supporting information (see Supporting information, Appendix S1).

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and within each of the 22 river systems. Levels ofgenetic diversity were generally high within clades:maximum model-corrected sequence divergencewithin the seven major clades was in the range 0.8–2.6% (Table 3). The maximum percentage of sequencedivergence among clades was in the range 2.3–9.1%.Genetic diversity was also high within most riversystems, as measured both by haplotype diversity andby nucleotide diversity (Table 4). For example, half ofthe rivers (12 of 24 sampled) had haplotype diversity

measures greater than 0.9 and only seven riversystems had hapotype diversity measures less than0.5. Nucleotide diversity measures showed comparablepatterns (Table 4). The two exceptions were the RíoLimarí and Río Choapa which showed no geneticvariation among individuals for the 16 fish sampledacross these two basins (see Supporting information,Appendix S1).

We found no evidence that genetic diversity in T.areolatus was affected by Pleistocene glaciation. For

Figure 3. Maximum likelihood (ML) phylogram of Trichomycterus areolatus haplotypes generated from 1076 bp of thecytochrome b gene from a subset of haplotypes provided in Fig. 2. This tree includes 45 haplotypes of Trichomycterusareolatus, four haplotypes of Bullockia maldonadoi (prefixed with B), and six haplotypes of Hatcheria macraei (pre-fixed with H). Haplotype designations match those shown in Fig. 2 and the Supporting information (see Supportinginformation, Appendix S1). Branch lengths were estimated using ML under the GTR+I+G model of evolution. Bootstrapvalues shown are derived from maximum parsimony and ML analyses (listed in that order) based on 1000 psue-doreplicates each. The dashed bootstrap value indicates support < 50%. The tree was rooted with B. maldonadoi.Letters to the right of the haplotypes represent major clade designations. The geographic distribution of each T.areolatus clade is indicated by the brackets; the geographic distribution of H. macraei is not shown but this speciesoccurs to south and west of the distribution of T. areolatus. Dashed arrows indicate the geographic location of thosehaplotypes within clades that do not map to the geographic regions indicated by the brackets. Overlapping bracketsfor the Río Imperial and Río Toltén indicate the four populations sampled in these drainages that contain haplotypesfound in both clades E and F. The grey-shaded area along the Chilean coast indicates the extent of exposed land whensea level is 150 m lower.

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Table 3. Genetic distance matrix comparing the seven major clades and two outgroup species shown in Fig. 1

A B C D E F G Hatcheria Bullockia

A 0.015B 0.035–0.057 0.026C 0.057–0.072 0.057–0.071 0.008D 0.057–0.076 0.058–0.075 0.020–0.033 0.016E 0.047–0.069 0.049–0.066 0.016–0.026 0.010–0.023 0.008F 0.056–0.071 0.061–0.079 0.030–0.041 0.025–0.040 0.026–0.031 0.008G 0.065–0.091 0.066–0.086 0.030–0.045 0.032–0.052 0.031–0.045 0.033–0.048 0.015Hat. 0.071–0.094 0.064–0.093 0.034–0.046 0.034–0.053 0.036–0.046 0.036–0.050 0.001–0.026 0.022Bul. 0.040–0.072 0.041–0.068 0.070–0.099 0.071–0.091 0.065–0.085 0.070–0.094 0.080–0.102 0.077–0.108 0.054

Trichomycterus areolatus clades are indicated by letters A to G; outgroups are listed by their genera names Hatcheria andBullockia. Values on the diagonal show the maximum divergence observed within each clade. Values below the diagonal arethe minimum and maximum genetic distances among haplotypes between clades are reported for each comparison. Geneticdistances were corrected under the GTR+I+G model of evolution (see text).

Table 4. Genetic diversity measures by river drainage basin for Trichomycterus areolatus for the cytochrome b gene

River NPolymorphicsites

# ofhaplotypes HD p

Limarí 8 0 1 0.000 0.00000Choapa 8 0 1 0.000 0.00000Petorca 8 8 7 0.964 0.00263Aconcagua 16 22 12 0.967 0.00441Maipo 32 60 10 0.790 0.00729Maipo clade B 2 3 2 1.000 0.00279Maipo clade C 30 10 10 0.761 0.00182Rapel 5 11 5 1.000 0.00410Topocalma coast 2 1 2 1.000 0.00093Mataquito 5 2 3 0.700 0.00093Maule 5 4 4 0.900 0.00168Reloca coast 8 3 3 0.464 0.00070Itata 10 11 4 0.778 0.00428Andalién coast 45 25 15 0.906 0.00444Biobío 24 20 13 0.942 0.00480Lebu 5 2 2 0.400 0.00074Lavapie coast 1 0 1 0.000 0.00000Lebu 2 coast 8 7 5 0.786 0.00206Cayucupil coast 5 3 4 0.900 0.00130Imperial 16 42 12 0.942 0.01477Imperial clade E 11 22 8 0.891 0.00650Imperial clade F 5 3 4 0.900 0.00130Toltén 13 34 7 0.795 0.01409Toltén clade E 6 11 5 0.933 0.00385Toltén clade F 7 1 2 0.286 0.00027Valdivia 18 15 12 0.941 0.00319Bueno 15 16 7 0.657 0.00253Llico coast 16 3 4 0.517 0.00054Maullín 13 13 6 0.782 0.00253Butalcura coast 16 3 3 0.242 0.00035

Values presented include the number of polymorphic sites, the number of haplotypes, haplotype diversity (HD), andnucleotide diversity (p) Rivers are listed from north to south. Smaller short coastal rivers are identified by coast after theirname. Three rivers (Maipo, Imperial, and Toltén) contained individuals from multiple genetic clades. These wereseparated into clades, as well as being calculated as a whole.

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each of the genetic diversity measures that we calcu-lated, there was no relationship between geneticdiversity and the percentage of a drainage coveredwith ice (Fig. 4). The slopes of each of these lines werestatistically indistinguishable from zero: number ofhaplotypes (slope = 0.016, P = 0.56); haplotype diver-sity (slope = 0.001; P = 0.545); and nucleotide diver-sity (slope = 0.000; P = 0.401).

PHYLOGEOGRAPHY

Trichomycterus areolatus populations are highlystructured. Most of the genetic variation within thisspecies is partitioned among river drainages. That is,there is very little evidence for gene flow betweendifferent river basins (FCT = 0.85, P < 0.001). OurAMOVA under the ‘sea level change’ hypothesisrevealed that 84.9% of the total genetic variationcould be explained among river drainages, whereasonly 2.1% could be explained among collecting siteswithin drainages, and 13.0% within collecting sites(Table 5). We found that fish biogeographic regionswere comparatively poorer predictors of the partition-ing of genetic variation in T. areolatus (FCT = 0.45,P < 0.001). A moderate 45.3% of total genetic varia-tion could be explained among the three fish biogeo-graphic regions, with 44.7% among sites within fishcommunity regions, and 10.0% within sites them-selves.

The distribution of haplotypes across the geo-graphic landscape also reveals a limited amount ofgene flow among drainage systems. Of the 125 T.areolatus haplotypes identified in the present study,only 14 are shared among two or more river systems(see Supporting information, Appendix S1). Thosethat do share haplotypes tend to drain into the oceanin areas where there is a wide continental shelf thatcould potentially allow the rivers to connect duringlow sea level events. For example, the Río Maiposhares one haplotype each with three adjacent riversystems: the Río Rapel shares haplotype 28, the RíoEstero Topocalma shares haplotype 29, and the RíoMataquito shares haplotype 33. Another example ofthis can be found in the Itata, Andalién, and Biobíobasins, which share nine haplotypes in various com-binations. Interestingly, the Río Andalién and RíoBiobío have river mouths that almost connect atcurrent sea level, and clearly connect at slightly lowersea levels (Mardones, 2002). Finally, the Imperial,Toltén, and Valdivia drainages all share haplotype 95and, again, we find a wide continental shelf in thisarea (Figs 1, 3; see also Supporting information,Appendix S1). Another interesting phylogeographicfeature is that at the northernmost range of the T.areolatus where the continental shelf is most narrow,we find that two river drainages (i.e. the Río Limaríand Río Choapa) contain only a single haplotype(haplotype 1) for all 16 individuals sequenced.

DISCUSSIONSUPPORT FOR COMPETING HYPOTHESES

The focus of the present study was to describe thephylogeography of T. areolatus, aiming to betterunderstand the historical biogeography of Chile.

Genetic Diversity and Glaciation History

Percent of Drainage Historically Glaciated

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s

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0.007C)

Figure 4. Plots showing relationship between the per-centage of drainage basins covered by glaciers during thePleistocene and (A) number of haplotypes; (B) hapolotypediversity; and (C) p (nucleotide diversity). The line in eachplot marks the least square regression line. None of thelines has a slope significantly different from zero, indicat-ing that percentage of glaciation does not have a signifi-cant effect on the response variables.

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Freshwater aquatic species are particularly compel-ling as biogeographic models in this area given theirpotential susceptibility to geological events, to climatechange and glaciation, and to dispersal barriers suchas salt water in the ocean and changes in land for-mations. In view of the geological and climatic historyof Chile, we generated three non-exclusive competinghypotheses that could explain the current distributionof genetic variation in T. areolatus. We considersupport for each of these hypotheses in turn.

Sea level changesThe topography of central Chile and the ocean barrierappear to be the important factors that limit popu-lation connectivity between river basins. This isreflected by the low numbers of haplotypes sharedbetween rivers, a pattern indicative of limited move-ment of T. areolatus between basins. In manysystems, a decline in sea level allows rivers to coa-lesce on the exposed continental shelf, especially inareas where the continental shelf is wide. However, inChile, the continental shelf is very narrow and fewrivers are likely to connect during low sea levels(Fig. 1A). Interestingly, all but one of the phylogeneticbreaks identified between T. areolatus clades corre-spond to areas with narrow continental shelf widths(Fig. 3). However, some areas with narrow continen-tal shelves also separate several populations withinclades. The one case where we found a distinct breakbetween clades but without a narrow continentalshelf occurred between clades E and F (Fig. 3). Thepatterns between these clades are complex, however,because the results of the present study suggest long-term isolation despite this being an area where a widecontinental shelf occurs. However, some recent sec-ondary contact has also occurred because the bound-ary between clades E and F overlaps across the RíoImperial and Río Toltén (Figs 2, 3).

We did find some evidence for shared haplotypesacross drainages (e.g. the Limarí and Choapa basinsin the north, Fig. 1; see Supporting information,Appendix S1). If the ocean is a barrier to T. areolatus

movement, cases of shared haplotypes acrossdrainages present a testable hypothesis that faunalexchange between these basins has occurred acrossdrainage divides rather than via coastal connections.An alternative explanation for shared haplotypesacross drainages is that of incomplete lineage sorting,rather than recent faunal exchange, where drainagesmay have historically been interconnected and laterbecame isolated, resulting in shared haplotypesamong systems but without any contemporary geneflow.

GlaciationWe found no association between the percentage ofglaciation in drainages and contemporary levels ofgenetic diversity (Fig. 4). These results suggest thatpopulations of T. areolatus in partially glaciatedbasins were not negatively affected by glaciers in away that resulted in the loss of genetic diversity.Populations forced downstream by headwater glaciersapparently were large enough to maintain typicalvariation. It is clear, however, that glaciations havehad a major effect on the southern distributional limitof T. areolatus; this species is not found anywhere onthe mainland of Chile south of Río Maullín. All riversfrom Río Maullín south experienced extensive glacia-tion with more than 85% of their catchments beingcovered by ice during the last glacial maximum(Table 1). Interestingly, there is a difference in thelocation of the ice within the catchments north andsouth of Río Maullín. Drainages from Río Maullínnorth were only glaciated in their headwater reachesand, thus, presumably fish recolonized from down-stream reaches and unglaciated tributaries. South ofRío Maullín ice covered all but the uppermost reachesof the drainages (Fig. 1B). This presents severalpotential explanations as to why T. areolatus does notoccur in drainages south of Río Maullín. It is possiblethat they simply never occurred there. Alternatively,they did occur at an earlier time but were extirpatedand thus unable to recolonize because movementbetween coastal rivers appears to be difficult (see

Table 5. Results of the analysis of molecular variance tests evaluating two competing hypotheses to explain thepartitioning of genetic variation in Trichomycterus areolatus

Hypothesis (numberof groups)

Amonggroups

Among populationswithin groups

Withinpopulations FCT

River Basins (24) 84.9 2.1 13.0 0.85*Subprovinces (3) 45.3 44.7 10.0 0.45*

*P < 0.001.The total genetic variation in the data set is partitioned into three categories: among groups, among populations withingroups, and within populations. Groups were defined a priori to evaluate support for each hypothesis. Those hypotheseswith more variation explained ‘among groups’ show greater relative support. Total genetic variation sums to 100%.

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above). Although the last glaciation did not com-pletely cover these catchments, an earlier glaciationmay have. The largest glacial event in southern SouthAmerica occurred 1–1.15 Mya (Coronato, Martinez &Rabassa, 2004) and may have more extensivelycovered these basins, extirpating any T. areolatuspopulations. It is notable that no obligate freshwaterfishes found in central Chile, except Percichthystrucha (Cuvier & Valenciennes), occur further southon mainland Chile than Río Maullín (Dyer, 2000),which suggests that this glacial boundary isextremely important in the biogeography of Chileanfishes.

Faunal breaksThere is some evidence that phylogenetic differenceswithin T. areolatus correspond to the fish communitysubprovince breaks identified by Dyer (2000).However, it is important to recognize that we foundfive phylogenetic breaks between T. areolatus lineageswithin the Chilean Province (between clades A, C, D,E, F, and G; Fig. 3), whereas Dyer (2000) only hypoth-esized two faunal breaks within this province basedon patterns of endemism in the fish communities. Thenorthern break is identified to be in the vicinity of theRío Mataquito, and the southern break at the RíoToltén (Fig. 1A). We did find evidence in T. areolatusfor a separation of clades just south of the RíoMataquito. Samples from the Río Maipo south to RíoMataquito were all recovered as a single closely-related clade (C) with the major separation beingbetween Río Mataquito and Río Maule (Fig. 3). Wealso found a separation between clades in the vicinityof Río Toltén; however, divergence patterns here weremore complex. Clade E contained haplotypes fromjust south of Río Biobío to the Río Toltén. Clade Fcontained haplotypes from Río Imperial south to RíoValdivia. Thus, haplotypes from the Río Imperial andRío Toltén were shared between each clade (Fig. 3; seeSupporting information, Appendix S1). This impliesthat a historical barrier may have existed in this areabut the break was later crossed by T. areolatus,resulting in shared haplotypes between the Río Impe-rial and Río Toltén. Thus, it appears that the geo-graphic divides among Dyer’s fish communities arepartially reflected in the phylogeography of T. areola-tus, but that the overall pattern of geographic struc-turing in this species is more complex than thatpredicted by fish community boundaries alone.

COMPARATIVE PHYLOGEOGRAPHY

The results obtained for T. areolatus are consistent inthose found in the other fish species examined inChile. Ruzzante et al. (2006) examined the genera

Percilia and Percichthys from seven and threeChilean rivers, respectively, and found no sharing ofhaplotypes among rivers (although their sample sizeswere often too small to detect rare haplotypes). Popu-lations of Percilia from each river were geneticallydistinct, being separated by between six and sixteenDNA mutational steps. Percichthys from Río Andaliénand Río Biobío were only one mutational step apart;however, populations in the next river north, RíoItata, differed from these two drainages by at least 30steps. Such levels of divergence suggest long isolationbetween most percichthyid populations within theserivers, which is a pattern similar to those found in T.areolatus. The only other aquatic group examinedgenetically across Chile is the freshwater crabs of thegenus Aegla (Pérez-Losada et al., 2002). Most of thesespecies are restricted to single river basins, with somebasins having up to three species present. Only twospecies occur in more than one river basin: Aeglaabtao Schmitt and Aegla laevis (Latreille). Geneticdivergences between geographically isolated Aeglaspecies are typically large (1.7–14.1%; mean = 7.1%).Genetic divergence is also substantial among riverbasins within crab species: A. abtao from Río Buenoand Río Valdivia differed by 2.6% (compared to 3.7–4.8% for T. areolatus from the same basins) and A.laevis from Río Maipo and Maule differed by 2.6–2.8%(compared to 2.0–2.8% for T. areolatus from the samebasins). Interestingly, levels of genetic divergencebetween Aegla species and T. areolatus clades are of asimilar magnitude. However, most Aegla species areendemic to individual rivers, whereas the majorclades within T. areolatus occur across multiple riverbasins (Fig. 3), with considerably lower genetic diver-gences within clades (Table 3). These results suggestthat most freshwater crab species have typically beenisolated between individual river basins for consider-ably longer time frames than have T. areolatus if theirrates of molecular evolution are similar.

TAXONOMIC IMPLICATIONS

The results obtained in the present study have anumber of interesting implications relative to thetaxonomy of T. areolatus, as well as the status of themonotypic genus Hatcheria. There are at least twopossible explanations for the placement of H. macraeiwithin T. areolatus. It could be caused by introgres-sive hybridization of the mitochondrial genome of T.areolatus into H. macraei at different times in theirhistory. Some introgression could be relatively recentbased on the placement of several H. macraei haplo-types (H1–H6) within clade G, as well as the closeplacement of H. macraei individuals (H7–H10) to theremainder of clade G (Figs 2, 3). An alternativehypothesis for the close relationship of H. macraei to

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T. areolatus is that the current taxonomy is incorrect,with H. macraei actually representing an individuallineage within T. areolatus that has morphologicallydiverged and specialized from other ancestralT. areolatus lineages. That H. macraei has beendescribed as a distinct genus indicates that there areclear morphological differences to define these fish asbeing distinct from other species (Arratia et al., 1978).The next step in the molecular work aiming to dis-tinguish between these competing hypotheses willcomprise an examination of DNA from multiplenuclear genes and an expansion of the sample sizeand geographic sampling within H. macraei, as wellas the use of additional outgroup taxa. If data fromnuclear genes are consistent with our mtDNA results,this would indicate that H. macraei evolved froman independent lineage of T. areolatus. If, however,the results of the nuclear DNA are consistent withcurrent taxonomy then it provides evidence forintrogressive events of T. areolatus mtDNA intoH. macraei populations.

The taxonomy of T. areolatus itself might warrantadditional consideration. Deep phylogenetic structur-ing within T. areolatus coupled with the observedpatterns of geographic isolation among clades sug-gests that some of these clades are sufficiently dis-tinct to be considered as independent taxonomicentities. Model-corrected sequence divergence amongmajor clades varied in the range 1.0–9.1% (Table 3),with the upper level at a value commonly observedbetween sister species or even genera in other fishes(Johns & Avise, 1998). Given the observed patterns ofphylogenetic divergence, the next step is to determinewhether these would also meet the criteria of uniquespecies under morphological and ecological speciesconcepts (Sites & Marshall, 2004). Some preliminarydata already exist indicating differences in morphol-ogy between populations from clades A and C (Pardo,2002).

CONCLUSIONS

The paucity of phylogeographic work in the SouthernHemisphere (Beheregaray, 2008) presents an impor-tant opportunity: it promises to reveal the role ofearth history events in shaping Southern Hemispherebiodiversity. In the present study, we focused on therole of glaciation and ocean barriers in driving thehistory of the freshwater fish T. areolatus in Chile. Bycontrast to studies conducted on many freshwateraquatic taxa in North America and Europe, we findthat colonization of new habitats after glaciation isuncommon and gene flow among drainage systems islow. This can be best explained by the relatively shortrivers that run east to west from high elevations inthe Andes directly into the Pacific Ocean, and by a

narrow continental shelf; when combined, these land-scape features create little opportunity for geneticexchange via river capture over land or for rivercoalescence during low sea levels. In T. areolatus, thishas resulted in high levels of genetic structuringamong drainages. It also creates ideal conditions forlocal adaptation and genetic differentiation amonggeographically isolated populations. Hence, theengine creating new biodiversity in Chile for aquatictaxa may largely be controlled by earth historyevents. How such earth history events affect othertaxa in the Southern Hemisphere remains an excitingprospect for future discovery.

ACKNOWLEDGEMENTS

We thank Néstor Ortiz and Waldo San Martín fortheir help with collecting tissue samples in the field.Benjamin Groves provided assistance in the labora-tory. We are also grateful for feedback from KeithCrandall on earlier versions of the analyses andmanuscript. This work was funded by a grant fromthe US National Science Foundation (NSF) PIREprogram (OISE 0530267), and projects DIUC-Patagonia 205.310.042-ISP, DIUC 203.113.064-1.0and DIUC 204.310.041-1.0 from Universidad de Con-cepción to EMH and PFV. Laboratory work was aidedby instrumentation provided by NSF (DBI-0520978).Additional funding to J.B.J. from the BYU KennedyCenter supported A.P.B. in the conduction of fieldwork.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:

Appendix S1. Table of haplotype counts in Trichomycterus areolatus, Hatcheria macraei (prefixed with H), andBullockia maldonadoi (prefixed with B) based on the cytochrome b gene. The collection site is listed across thetop, the sample size from each population is given in the last column. Each haplotype is listed along the leftside with the total number of individuals with each haplotype listed in the last column.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materialssupplied by the authors. Any queries (other than missing material) should be directed to the correspondingauthor for the article.

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