Population, Phylogenetic, and Coalescent Analyses of Character Evolution in Gossamer- Winged Butterflies (Lepidoptera: Lycaenidae)
Item Type text; Electronic Dissertation
Authors Oliver, Jeffrey Catlin
Publisher The University of Arizona.
Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Download date 29/09/2021 17:22:36
Link to Item http://hdl.handle.net/10150/194227 POPULATION, PHYLOGENETIC, AND COALESCENT ANALYSES OF CHARACTER EVOLUTION IN GOSSAMER-WINGED BUTTERFLIES (LEPIDOPTERA: LYCAENIDAE) by Jeffrey Catlin Oliver ______ A Dissertation Submitted to the Faculty of the GRADUATE INTERDISCIPLINARY PROGRAM IN INSECT SCIENCE In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2007 2
THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Jeffrey Catlin Oliver entitled Population, phylogenetic, and coalescent analyses of character evolution in gossamer winged butterflies (Lepidoptera: Lycaenidae) and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy ______ Date: November 5, 2007 David R. Maddison ______ Date: November 5, 2007 Daniel R. Papaj ______ Date: November 5, 2007 Yves Carrière ______ Date: November 5, 2007 Michael W. Nachman ______ Date: November 5, 2007 Michael J. Sanderson Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. ______ Date: November 5, 2007 Dissertation Director: David R. Maddison 3
STATEMENT BY AUTHOR This thesis has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of sources is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be grated by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. SIGNED: Jeffrey Catlin Oliver 4
ACKNOWLEDGEMENTS I would like to thank all of my committee members for their feedback and support during my graduate work. David Maddison provided thorough instruction in evolutionary analyses and computational biology; because of his mentorship, I would like to think I have a richer understanding of phylogenetic biology and the philosophy of science. In addition to being instrumental in my decision to attend the University of Arizona, Dan Papaj offered a perspective on behavioral ecology and evolution that influenced my understanding of species interactions. Yves Carrière provided thoughtful critiques and encouragement during my education. Michael Nachman afforded me the background in population genetics necessary to pursue answers to my questions. Michael Sanderson, in addition to teaching me about macroevolution a decade ago, has improved my fundamental understanding of reconstructing and interpreting phylogenetic trees. My two undergraduate mentors, Brad Shaffer and Art Shapiro, were instrumental in my early development as a scientist. I thank them both for the invaluable opportunities they provided during my undergraduate education. The scientific community of the University of Arizona has helped in innumerable ways during my graduate education. I would like to thank past and present members of the Maddison and Papaj labs for constructive and thoughtful feedback on various studies I have presented. Members of the Nachman lab, especially Bret Payseur, were always helpful in matters of population genetics. I would especially like to thank Jeff Good and Kevin Oh for providing scientific and moral support during my time at the University of Arizona. Their engaging discussions and appreciation for H. lupulus were always refreshing. Finally, Katy Prudic has been instrumental as a field assistant, manuscript reviewer, cheerleader, and, most importantly, colleague during my graduate education. The administrative staff of the departments of Entomology and Ecology and Evolutionary Biology and the Center for Insect Science have always been helpful in matters concerning grants, employment, and coursework. Sharon Richards has been extraordinarily helpful, often going beyond the call of duty to aid students in need of help. This work would not have been possible without the gracious financial assistance from the National Science Foundation, the Environmental Protection Agency, Sigma Xi, the Center for Insect Sciences, the Graduate College of the University of Arizona, and the Xerces Society. 5
DEDICATION This work is dedicated to my parents, Sue and Dan Oliver. The road to this Ph.D. is paved with their endless support for my education. My appreciation for the natural world was undoubtedly nurtured by the numerous camping trips to Wolf Creek, Grover’s, and Owens Valley. Their encouragement and willingness to foster my curiosity form the basis of my desire to pursue a career of answering, and perhaps more importantly, asking questions. 6
TABLE OF CONTENTS
ABSTRACT ...... 9
INTRODUCTION ...... 11
An explanation of the problem and a review of the literature ...... 11
An explanation of the dissertation format ...... 12
PRESENT STUDY ...... 13
REFERENCES ...... 17
APPENDIX A GENETIC ISOLATION AND CRYPTIC VARIATION WITHIN
THE LYCAENA XANTHOIDES SPECIES GROUP (LEPIDOPTERA:
LYCAENIDAE) ...... 19
Abstract ...... 21
Introduction ...... 22
Materials and Methods ...... 25
Molecular Methods ...... 26
Phylogenetics ...... 26
Population Genetics ...... 27
Morphometrics ...... 29
Results ...... 31
Phylogenetics ...... 31
Population Genetics ...... 32
Morphometrics ...... 33
Discussion ...... 34 7
Taxonomic Implications ...... 38
Barriers to Gene Flow and Cryptic Variation ...... 39
Conclusions ...... 42
Acknowledgements ...... 43
References ...... 44
Tables, figures, and appendices ...... 50
APPENDIX B INFERRING SPECIES TREES FROM DEEP COALESCENCES
WHILE ACCOMMODATING GENE TREE UNCERTAINTY ...... 63
Abstract ...... 65
Introduction ...... 66
The Method ...... 68
The Data ...... 70
Simulated Data ...... 70
Empirical Data ...... 73
Results & Discussion ...... 76
Simulated data ...... 76
Empirical Data ...... 77
Conditions leading to positively misleading inferences under the deep coalescence
criterion ...... 79
Implications and Extensions ...... 81
Acknowledgments ...... 85
References ...... 87 8
Figures ...... 90
APPENDIX C EVOLUTION OF EXPLOITATION: ILLICIT SIGNALING IN A
LYCAENID-ANT INTERACTION ...... 101
Abstract ...... 103
Introduction ...... 104
Materials & Methods ...... 106
Study system ...... 106
Laboratory test for illicit signaling ...... 107
Evolution of association ...... 108
Results & Discussion ...... 112
Laboratory test for illicit signaling ...... 112
Evolution of association ...... 114
Illicit signaling and the evolution of exploitation ...... 116
Acknowledgments ...... 118
References ...... 119
Tables and figures ...... 123
APPENDIX D PERMISSION TO REUSE BLACKWELL CONTENT FOR THIS
DISSERTATION ...... 132
9
ABSTRACT
To understand the processes responsible for the diversity of life, one must consider evolutionary history. By incorporating a phylogenetic approach to studies of character evolution and species interactions, we may better understand the mechanisms governing this tangled bank. This work addresses fundamental questions regarding morphological and behavioral evolution, using gossamer winged butterflies (Lepidoptera:
Lycaenidae) as model systems.
By investigating the pattern of genetic variation in a group of closely related species of copper butterflies ( Lycaena ), I show morphological divergence occurred in the absence of gene flow between Lycaena xanthoides and L. editha . Additionally, genetic divergence between populations of L. xanthoides has occurred without considerable morphological divergence. These findings highlight the utility of genetic data for inferring species boundaries and identification of cryptic lineages.
Inferring evolutionary relationships among closely related species should benefit from multiple sources of information, e.g., unlinked genetic markers. Here I extend a method of reconstructing species relationships based on multiple reconstructed gene trees, using the number of incomplete lineage sorting events (‘deep coalescences’) as the objective function. This approach provides a more complete understanding of species’ histories by accommodating population level processes which may lead to discordance between gene genealogies and species phylogenies. The approach is evaluated with simulated and empirical data, and I discuss conditions which may result in misleading inferences. 10
Finally, incorporating data from multiple gene trees, I investigate the evolution of association in a lycaenid ant interaction. Lycaenid butterfly caterpillars are often involved in mutualisms with ant hosts: these larvae use volatile signals and provide carbohydrate rewards from the dorsal nectary organ (DNO) to associated ants to gain protection from natural enemies. However, larvae of some lycaenid species, such as L. xanthoides , do not possess the reward producing organ, yet are still found in association with ants. Evaluating the relationship in a phylogenetic framework, I show that L. xanthoides likely evolved from a non ant associated ancestor. This suggests that L. xanthoides has ‘cracked the code’ that other, honest signaling lycaenid larvae use to communicate to ants. Evolution of mutualisms between honest signaling larvae and ants will likely be affected by the impact of illicit signaling larvae.
11
INTRODUCTION “If the living world has not arisen from common ancestors by means of an evolutionary process, then the fundamental unity of living things is a hoax and their diversity is a joke.” Theodosius Dobzhansky, 1964
An explanation of the problem and a review of the literature
Species exist as biological entities with patterns of discontinuous phenotypic variation (Mayr, 1963; Coyne and Orr, 2004). However, the distinctness of taxa is called into question when morphological intermediates exist in areas of sympatry, reflecting either gene flow among variants of a species or hybridization without introgression between different species. Studying the partitioning of genetic variation provides a potential means to discern between the two possibilities.
Investigating the evolution of closely related taxa highlights the utility of multiple genetic markers in phylogenetic inference. Single gene histories may not accurately reflect the evolutionary history of species being investigated (Pamilo and Nei, 1988;
Maddison, 1997). Population level processes underlying the transmission of genes through time can often result in discordance among gene histories and species phylogenies (Tajima, 1983; Funk and Omland, 2003). By incorporating multiple loci and considering population genetic processes, we may gain a better understanding of evolutionary relationships of closely related taxa.
Equipped with informed estimates of evolutionary history, we can investigate the evolution of interactions between organisms. Signals passed between members of mutualistic relationship may be taken advantage of by illicit signalers or illicit receivers
(Otte, 1974;Haynes and Yeargan, 1999). Mutualisms between ants and lycaenid 12 butterflies may be vulnerable to invasion by illicit signalers, gaining protection from natural enemies by ants, without providing a nutritional reward. Myrmecophilous lycaenid species in a clade characterized primarily by myrmecoxenous taxa offer the opportunity to study the behavior and evolution of non rewarding participants in lycaenid ant interactions.
An explanation of the dissertation format
This work investigates morphological and behavioral evolution of lycaenid butterflies in a phylogenetic framework. Through genetic and behavioral analyses, I have shown how differences in wing characters evolve and how some lycaenid caterpillars may exploit ‘honest’ mutualistic interactions between other lycaenid caterpillars and their ant protectors. Three manuscripts are included as appendices.
Appendix A, “Genetic isolation and cryptic variation within the Lycaena xanthoides species group (Lepidoptera: Lycaenidae)” examines the patterns of genetic variation to evaluate morphological evolution in a group of closely related North
American butterfly taxa. Appendix B, “Inferring species trees from deep coalescences while accommodating gene tree uncertainty” extends methods of phylogenetic inference and provides a worked example evaluating the evolution of a wing character in blue butterflies. Appendix C, “Evolution of exploitation: illicit signaling in a lycaenid ant interaction” investigates the behavioral interactions between an ant and a non rewarding lycaenid larvae and evaluates the evolution of ant association in a group of largely myrmecoxenous taxa. 13
PRESENT STUDY
The methods, data, analyses, and conclusions of significant research are included in appendices in this document. Below is a summary of findings from each study, as well as summaries of results not included in appendices.
Appendix A used genetic and morphometric approaches to investigate the degree of isolation among the three members of the Lycaena xanthoides species group. Lycaena xanthoides , L. editha , and L. dione are predominantly allopatric and have been treated both as three separate species and as a single polytypic species. Using 618 bp of the mitochondrial gene COII, we found little phylogenetic resolution, but significant among taxa genetic variance partitioning. Divergence among these taxa has been relatively recent, as evidenced by relatively low pairwise sequence divergence. Also, the existence of two well supported clades within L. xanthoides sensu stricto , concordant with the
Transverse Ranges of southern California, indicates divergence within this taxon, and a possible cryptic species. Significant morphological differentiation between L. editha and
L. xanthoides supports the hypothesis that these taxa represent separate gene pools.
Populations occurring in a narrow zone where the two species’ ranges approach are characterized by intermediate morphology, suggesting incomplete morphological divergence or recent hybridization. These findings highlight the utility of genetic data in inferring species boundaries and the identification of cryptic lineages.
Morphological and electrophoretic data suggest a hybrid zone exists between
Lycaena xanthoides and L. editha in northern California and southern Oregon. The goal of this project was to determine if hybridization is occurring or has occurred recently 14 between these two closely related butterfly taxa, and, if so, to what degree alleles are introgressing beyond the hybrid zone. Sequencing of nuclear DNA markers revealed insufficient variation to address the question of hybridization ( elongation factor 1 alpha ,
28S , wingless , RNA polymerase II , arginine kinase ), so I attempted an Amplified
Fragment Length Polymorphism (AFLP) approach. After a lengthy optimization process,
I determined that I could not reliably score the markers. If nuclear sequence markers with sufficient variability are found, or if AFLP markers can be reliably scored, this research should be pursued.
Lycaena xanthoides is a relative host specialist, feeding on approximately 5 species of Polygonaceae, in the genus Rumex . Many populations feed on the native host,
Rumex salicifolius ; however, in the central valley of California, populations feed on R. crispus , an introduced Mediterranean plant. These populations do not have other host species available, and the goal of this project was to determine if local adaptation to the non native host has occurred. I performed standard female oviposition preference trials and larval performance assays on native and non native hosts, but I had insufficient samples from populations feeding on non native hosts. Although females from all populations appear to prefer to oviposit on the native host over the non native host, additional data are needed. Furthermore, I was unable to acquire enough native host plants to perform an adequate number of larval performance trials. The preliminary findings suggest that female preference for the native host is conserved, and larvae from all populations perform better on native hosts; additional trials are necessary to confirm these findings. 15
Morphological and mitochondrial investigations of the Lycaena xanthoides species group suggest a cryptic lineage within Lycaena xanthoides sensu stricto (see
Appendix A). I performed laboratory crosses to determine the extent, if any, of reproductive isolation between the northern and southern lineages of L. xanthoides . I collected larvae from 3 southern and 2 northern populations, and reared them to adulthood in the laboratory. I performed crosses by including an unmated female with two males (both males from the same source population) in an enclosure, provided with a nectar source and oviposition substrate (sprigs of Rumex salicifolius leaves). One direction (southern female X northern males) failed to produce eggs in any of 12 trials, while the converse crosses (northern female X southern males) resulted in eggs in 10 of
17 trials. Due to small sample sizes and relatively low success in controls (only 20%
40% of controls produced female offspring), results from these crosses remain preliminary. Backcross/F2 viability and fertility could not be measured due to low sample sizes and a lack of available host plants. Additional crosses are necessary to determine if there is reproductive isolation between the northern and southern lineage of
L. xanthoides .
Appendix B extends a method of reconstructing species relationships based on multiple reconstructed gene trees, using the number of incomplete lineage sorting events
(‘deep coalescences’) as the objective function. This method provides two important advances to phylogenetic inference based on multiple loci: (1) gene tree uncertainty is accommodated by drawing gene trees from a distribution of trees based on Bayesian
MCMC sampling and (2) species tree uncertainty is accommodated by providing support 16 values for relationships, based on multiple iterations of the tree searching procedure.
Most of these analyses are performed in the Mesquite software system; new software developed for Mesquite will be available in an upcoming release of Mesquite. To evaluate the method, I use simulated DNA sequence data reflecting the type of data commonly available to researchers, and compare the results to other methods of species tree inference. I also provide a worked example of implementation of the method on a pair of closely related genera of lycaenid butterflies, Everes and Cupido . I provide a description of the conditions resulting in misleading inferences, and discuss implications and extensions of this new approach.
Appendix C studies the behavior and evolution of ant associated lycaenid larvae which provide no rewards to their ant protectors. Here we test the hypothesis that L. xanthoides larvae function as illicit signalers, manipulating ant behavior when faced with a simulated predator attack, while providing no reward to ant associates. We evaluate the relationship in a phylogenetic framework and show that L. xanthoides likely evolved from a non ant associated ancestor. This suggests that L. xanthoides has ‘cracked the code’ that other, honest signaling lycaenid larvae use to communicate to ants. Evolution of mutualisms between honest signaling larvae and ants will likely be affected by the impact of illicit signaling larvae.
17
REFERENCES Coyne JA, Orr HA. 2004. Speciation. Sunderland, Massachusetts: Sinauer & Associates. Funk DJ, Omland KE. 2003. Species level paraphyly and polyphyly: frequency, causes and consequences, with insights from animal mitochondrial DNA. Annu Rev Ecol Evol Syst 34: 397 423. Haynes KF, Yeargan KV. 1999. Exploitation of intraspecific communication systems: illicit signalers and receivers. Ann Entomol Soc Am 92: 960 970. Maddison WP. 1997. Gene trees in species trees. Syst Biol 46: 523 536. Mayr E. 1963. Animal Species and Evolution. Cambridge, Massachusetts: Harvard University Press. Otte D. 1974. Effects and functions in the evolution of signaling systems. Ann Rev Ecol Syst 5: 385 417. Pamilo P, Nei M. 1988. Relationships between gene trees and species trees. Mol Biol Evol 5: 568 583. Tajima F. 1983. Evolutionary relationship of DNA sequences in finite populations. Genetics 105: 437 460. 18 19
APPENDIX A
GENETIC ISOLATION AND CRYPTIC VARIATION WITHIN THE LYCAENA XANTHOIDES SPECIES GROUP (LEPIDOPTERA: LYCAENIDAE) 20
Genetic isolation and cryptic variation within the Lycaena xanthoides species group (Lepidoptera: Lycaenidae) Jeffrey C. Oliver 1 and Arthur M. Shapiro 2 1Interdisciplinary Program in Insect Science and Department of Entomology, University of Arizona, Tucson, AZ 85721, USA. 2Section of Evolution and Ecology, University of California, Davis, CA 95616, USA. 21
Abstract
Species exist as biological entities with patterns of discontinuous phenotypic variation. However, the distinctness of taxa is called into question when morphological intermediates exist in areas of sympatry, reflecting either gene flow among variants of a species or hybridization between different species. Studying the partitioning of genetic variation provides a means to discern between the two possibilities. We used genetic and morphometric approaches to investigate the degree of isolation among the three members of the Lycaena xanthoides species group. Lycaena xanthoides , L. editha , and L. dione are predominantly allopatric and have been treated both as three separate species and as a single polytypic species. Using 618 bp of the mitochondrial gene COII, we found little phylogenetic resolution, but significant among taxa genetic variance partitioning.
Divergence among these taxa has been relatively recent, as evidenced by relatively low pairwise sequence divergence. Also, the existence of two well supported clades within L. xanthoides sensu stricto , concordant with the Transverse Ranges of southern California, indicates divergence within this taxon, and a possible cryptic species. Significant morphological differentiation between L. editha and L. xanthoides supports the hypothesis that these taxa represent separate gene pools. Populations occurring in a narrow zone where the two species’ ranges approach are characterized by intermediate morphology, suggesting incomplete morphological divergence or recent hybridization.
These findings highlight the utility of genetic data in inferring species boundaries and the identification of cryptic lineages. 22
Introduction
The pattern of discontinuity in population variation indicates that species are relevant biological entities, rather than typological anthropogenic constructs (Mayr
1963). Many species show variation in morphological characters, but occupy discrete clusters in morphological space, with little to no overlap among species (Coyne and Orr
2004). The maintenance of this gap in areas of sympatry is often used to infer reproductive isolation among the morphologically distinct taxa (Mayr 1963). Subtle differences in morphology among taxa or the presence of intermediates in areas of sympatry may confound the species problem, preventing the recognition of discrete clusters of variation. These “problem individuals” may be hybrids between different species or intermediates between forms of a single polytypic species.
Reconciling these two competing hypotheses is not simply a taxonomic exercise, but rather is a necessary step in understanding the processes of morphological divergence. A more explicit phrasing of the question of multiple versus single species is
“has morphological divergence accompanied reproductive isolation, or has morphological divergence occurred without isolation of gene pools?” To resolve this issue, assaying genetic variation is crucial, as reproductive isolation produces a pattern of discontinuous genetic variation, while conspecificity should result in a grade of genetic variation (Mallet 1995). If intermediates represent hybrids between taxa with substantial reproductive isolation, there will be little to no gene flow among the taxa, and discrete genetic clusters will be evident. However, if the intermediates represent transitional forms among variants within a polytypic species, there would be little evidence of genetic 23 dissimilarity among the morphological clusters, although genetic isolation by distance may be evident (Wright 1943). The characterization of genetic diversity provides a useful test of the two competing hypotheses explaining the existence of morphological intermediates: hybridization or intraspecific geographic variation.
We tested these two hypotheses in a group of morphologically similar butterflies of western North America. The Lycaena xanthoides complex is recognized as three closely related species by some (Pratt et al. 1991) and a single polytypic species by others
(Scott 1986). Lycaena xanthoides (Boisduval), L. editha (Mead), and L. dione (Scudder) are distributed primarily across the western United States, with predominantly allopatric ranges (Figure 1; Opler and Wright 1999). All three nominal species feed on plants in the buckwheat family (Polygonaceae), although there is little overlap in the specific host diets among the three species (Scott 1986, Ballmer and Pratt 1988). Lycaena xanthoides occurs from northern Baja California, Mexico to south central Oregon, although disjunct populations also occur in the Willamette Valley in Oregon (Pyle 2002). Lycaena editha inhabits the higher elevations of northeast California and the mountains along the northern edge of the Great Basin and into the northern Rocky Mountains. The third member of the complex, L. dione , is a species of the northern Great Plains, occurring at middle elevations east of the Rocky Mountains.
Although predominantly allopatric, in some regions the ranges of two members of the L. xanthoides group meet or overlap. Lycaena xanthoides and L. editha appear to meet along the California Oregon border, a region characterized by individuals morphologically intermediate between the two species (Scott 1986, Shapiro 1986). 24
Morphological analyses have been used to support both the polytypic species hypothesis
(Scott 1979) and the three species hypothesis (Pratt et al. 1991). Scott (1979) posited that
L. xanthoides and L. editha are conspecific, connected by clines of intermediate wing morphology. Based on larval and adult wing morphological characters, Pratt et al. (1991) concluded that the group represented three morphologically distinct species, although some contemporary hybridization may occur in contact zones between L. xanthoides and
L. editha .
In this study, we used mitochondrial DNA sequences and morphometric analyses to test the hypothesis that the L. xanthoides complex is a single polytypic species.
Although the utility of mitochondrial DNA has recently been questioned (Ballard and
Whitlock 2004), it remains a useful marker for phylogenetic and taxonomic inference, as long as the limitations are understood (Rubinoff and Holland 2005). We used a phylogenetic approach to assess support for species status of each of the three species and resolve the phylogenetic relationships among the members of the L. xanthoides species group. Additionally, we used a population genetic approach (AMOVA, Excoffier et al.
1992), to investigate variance partitioning within and among the three taxa of the L. xanthoides species group. This approach allowed us to test hypotheses about genetic isolation allowing for non monophyly of gene trees, as expected if divergences among taxa are recent (Neigel and Avise 1986).
Isolation by distance may occur within a single species, resulting in genetic differentiation between portions of a species’ range (Wright 1943). As the members of the L. xanthoides complex are predominantly allopatric, a pattern of genetic 25 differentiation may arise due to isolation by distance alone, in the absence of other reproductive isolating mechanisms (e.g. on average, L. editha populations are geographically closer to other L. editha populations than to either L. xanthoides or L. dione populations). We performed Mantel tests (Manly 2005) to test for isolation by distance, and partial Mantel tests to test for genetic differentiation while controlling for isolation by distance. Significant genetic differentiation above and beyond isolation by distance would not be expected under the polytypic species hypothesis.
Finally, we reinvestigated the morphological variation within and between L. xanthoides and L. editha . Wing patterns in butterflies not only provide characters for diagnosing species, but may also play an important role in mate recognition (Fordyce et al. 2002). To test for morphological differences between L. xanthoides and L. editha , we performed multivariate analyses on wing characters. These analyses allowed us to quantify the morphological variation, test for morphological divergence coincident with genetic divergence, and assess the utility of mitochondrial DNA in identifying evolutionarily distinct lineages.
Materials and Methods
We collected specimens of Lycaena xanthoides , L. editha , and L. dione from 62 different localities (Figure 1, Appendix I), and identified them based on traditional wing morphology characters (Opler and Wright 1999). Individuals from five localities (58 62,
Appendix I) were morphologically intermediate between L. xanthoides and L. editha , and were not assigned to a particular taxon. Our sampling of 474 specimens covered the 26 majority of L. xanthoides ’ range, and portions of L. editha ’s and L. dione ’s respective ranges (Figure 1). We also collected eight specimens representing five outgroup species
(L. rubidus , L. arota , L. cupreus , L. virgaureae , and L. phlaeas ) for phylogenetic analyses. Specimens were collected alive and preserved at 20°C or dried until the DNA extraction procedure.
Molecular Methods
Total genomic DNA was extracted from thoracic or leg tissue with DNeasy
Tissue Kit (Qiagen, Inc.; Valencia, CA) following the manufacturer’s instructions. We amplified a portion of the mitochondrial gene cytochrome oxidase subunit 2 (COII) with the primers PIERRE and EVA (Caterino and Sperling 1999), corresponding to positions
3147 3767 of the Drosophila yakuba COII sequence (Clary and Wolstenholme 1985).
The amplification protocol was an initial denaturation of 1:30 at 94°C, followed by 33 cycles of 94°C for 40 s, 45°C for 40 s, 72°C 45 s, with a final extension of 72°C for 7 min. This fragment was sequenced in both directions, using the amplification primers
PIERRE and EVA, on an Applied Biosystems 3730XL DNA Analyzer by the Genomic
Analysis and Technology Core (University of Arizona, Tucson, AZ). Sequences were aligned by eye with the aid of the program BioEdit (Hall 1999). A minimum spanning network of the ingroup haplotypes was created using Arlequin 3.0 (Excoffier et al. 2005).
Phylogenetics 27
To infer the phylogenetic relationships among the members of the L. xanthoides species group, we performed maximum parsimony and Bayesian analyses, using PAUP*
(Swofford 2001) and MrBayes 3.1.1 (Huelsenbeck and Ronquist 2001), respectively.
The sequence matrix was first pruned to remove redundant sequences, and this haplotype sequence matrix was used for all subsequent phylogenetic analyses. For maximum parsimony analyses, we performed a heuristic search (swap=TBR, addseq=random) of
1000 replicates. Parsimony clade support was assessed by performing a non parametric bootstrap analysis of 1000 replicates, where each bootstrap replicate consisted of 10 heuristic search replicates (swap=TBR, addseq=random).
For the Bayesian analyses, we performed two runs of four MCMC chains each for
10 million generations, sampling every 100 generations. Using likelihood ratio tests
(Sullivan and Swofford 1997), we selected an HKY+I model of sequence evolution, allowing each codon position different parameter estimates for the HKY+I model. We assessed convergence between the two runs by examining the standard deviation of split frequencies and a plot of log likelihood scores of the two runs. Convergence occurred when the standard deviation of the split frequencies was below 0.01 and the log likelihood scores of the trees did not increase in subsequent generations (Huelsenbeck and Ronquist 2001). Convergence occurred after 5 million generations, so only trees sampled after 5 million generations were used to generate a consensus tree and posterior probabilities of individual clades using the ‘sumt’ command in MrBayes.
Population Genetics 28
In addition to phylogenetic analyses, we also conducted AMOVA (Excoffier et al.
1992) on the L. xanthoides complex using the program Arlequin 3.0 (Excoffier et al.
2005). We performed two analyses: (1) Three groups, corresponding to each of the three species, L. editha , L. dione , and L. xanthoides , based on morphological identification and
(2) four groups, corresponding to L. editha , L. dione , and two groups of L. xanthoides based on our phylogenetic results (see Results). Using pairwise sequence differences, we partitioned variance into among group, within group among population, and within population among individual elements. For all AMOVA analyses, the five populations of intermediate morphology (58 62; Appendix I) were excluded because we could not assign them a priori to any group. This exclusion resulted in 421 specimens being included in the AMOVA analyses.
To determine if isolation by distance alone accounts for any observed genetic differentiation among the species, we performed Mantel tests (Manly 2005) implemented in IBD (Bohonak 2002) to test for associations between geographic distance and population pairwise F ST values calculated in Arlequin 3.0 (Excoffier et al. 2005). We log transformed F ST values and used the smallest observed non zero value for any F ST ≤ 0 before log transformation (Hellberg 1994). We calculated geographic distances between populations using the R software package (Casgrain and Legendre 2001), and log transformed them before IBD analyses. In addition to the geographic distance matrix, we included an indicator matrix, in which population comparisons were considered within group (0) or between groups (1), where the four groups corresponded to L. editha , L. dione, and two groups of L. xanthoides based on our phylogenetic results (see Results). 29
We performed partial Mantel tests with this indicator matrix, allowing us to test for genetic differentiation, controlling for isolation by distance. In the full and partial Mantel tests, we performed 10 4 randomizations and excluded populations with fewer than 10 individuals sampled. Because we could not assign individuals to a group for the partial
Mantel tests, we also excluded the five populations of intermediate morphology
(populations 58 62). This reduction resulted in only one L. dione population being analyzed, so we performed tests both including and excluding this population (31 and 30 populations, respectively). The exclusion of intermediates from these analyses will not affect our prediction: if isolation by distance alone accounts for any observed genetic differentiation among the species, the partial Mantel test controlling for isolation by distance will not support a relationship between the indicator matrix and genetic differentiation.
Morphometrics
We also investigated the morphological variation within and between L. xanthoides and L. editha . To test for morphological differences between L. xanthoides and L. editha , we performed multivariate analyses on wing morphological characters.
We measured a total of 11 wing characters (Figure 2), including some used in previous analyses of these taxa (Scott 1979, Pratt et al. 1991). For all measurements, the underside of the left forewings and hindwings were photographed with a Nikon Coolpix 990 digital camera with a millimeter scale level to the plane of the wings. All measurements were made in ImageJ 1.35p (Rasband 2006); only specimens for whom all 11 characters could 30 be measured were included in analyses. We were able to measure all 11 characters for the following number of females and males, respectively: L. editha 35, 114; L. xanthoides
65, 191; Intermediate 9, 34. All images used in this study have been deposited in
Morphbank (image records 136842 137363). For dimensional similarity, we square rooted area measurements before analyses. Males and females were analyzed separately, and all analyses were conducted in SAS 9.1 (SAS Institute 2004).
We first performed a principal components analysis (PCA), using a correlation matrix, on all L. editha , L. xanthoides , and intermediate specimens to describe the variation in morphology (Manly 2005). We then used the first six principal components to perform a multivariate analysis of variance (MANOVA) on specimens, excluding specimens from the five intermediate populations. We tested the hypothesis that L. editha and L. xanthoides possessed significantly different morphology, based on the principal components scores. We performed two additional MANOVAs, also using the first six principal components analyses: (1) assigning specimens from intermediate populations to L. editha , and (2) assigning specimens from intermediate populations to L. xanthoides . Finally, we performed discriminant function analyses (DFA) to determine if the intermediate populations were morphologically more similar to L. editha or L. xanthoides . We first used specimens from outside the zone of intermediacy to build a function to discriminate between L. editha and L. xanthoides (training phase). We then used this function to classify specimens from intermediate populations as L. editha or L. xanthoides (testing phase). 31
In addition to investigating the morphological variation between L. editha and L. xanthoides , we also tested for differences between members of the two clades of L. xanthoides recovered in our phylogenetic results. We first performed another principal components analysis, excluding data from intermediate populations, followed by a
MANOVA of the first six principal components. We then performed the training phase of a DFA on all data, excluding the intermediates, as an additional test for morphological differentiation among members of the two clades. That is, we used the DFA to determine if there were consistent morphological differences between specimens of L. xanthoides from clade ‘A’ and specimens from clade ‘B’.
Results
From the 474 ingroup individuals sampled, there were 28 distinct haplotypes and
30 polymorphic sites. All ingroup sequences were 618 bases long, while a single outgroup taxon, L. virgaureae , possessed a 3bp deletion. With the exception of one population (see “Phylogenetics”, below), no haplotypes were shared among the three species (Figure 3; Appendix I). Representative sequences for all haplotypes have been deposited in GenBank (accession numbers EF175476 EF175510).
Phylogenetics
The maximum parsimony and Bayesian analyses produced topologically congruent results (Fig 4). Both analyses supported a L. xanthoides species group + L. rubidus clade (83 Maximum Parsimony bootstrap clade support and 0.96 Bayesian 32 posterior probability). Monophyly of the L. xanthoides species group was not well supported by either analysis (65 MP bootstrap support and 0.31 posterior probability).
Relationships among the members of the L. xanthoides species group were also unresolved; neither analysis supported monophyly of L. xanthoides (6 MP support, 0.12 posterior probability), L. editha (16 MP support, 0.23 posterior probability), or L. dione
(23 MP support, 0.16 posterior probability). Interestingly, both analyses recovered two well supported clades within L. xanthoides (clades labeled ‘A’ and ‘B’ in Fig 4).
Populations of L. xanthoides never contained haplotypes from both clades: populations 1
20 possessed haplotypes from clade ‘A’, while populations 22 30 possessed haplotypes from clade ‘B’. The five L. xanthoides individuals sampled from the disjunct population in the Willamette Valley (population 21) possessed haplotypes found only in L. editha populations (Haplotypes P and U; Figure 3). The five populations morphologically intermediate between L. xanthoides and L. editha (populations 58 62) possessed only haplotypes Q and S (Figure 3), which were also found in numerous L. editha populations.
Population Genetics
Although the phylogenetic analyses did not show evidence of monophyly of any of the species of the L. xanthoides species group, a significant portion of the genetic variation was partitioned among species in AMOVA (Table 1). When populations were assigned to three groups, corresponding to the three species, among group variation accounted for approximately 57% of the total variation (Φ CT =0.57, P<0.001). The four group analysis assigned populations to species as in the three group analysis, but 33 populations of L. xanthoides were split into two groups, to correspond with the two clades recovered in the phylogenetic analyses (group 1: L. xanthoides clade ‘A’ +
Willamette Valley L. xanthoides , group 2: L. xanthoides clade ‘B’; Fig 3). In this
AMOVA, the among group component of variation increased to 89% (Φ CT =0.89,
P<0.001), with a corresponding decrease in the among population component of variation.
In our IBD analyses, the smallest positive pairwise F ST was 0.00268, so we replaced all values of F ST ≤ 0 by 0.00268 before log transformation. In our Mantel tests for isolation by distance, we found a significant correlation between geographic distance and genetic distance (including L. dione : r=0.3874, p<0.0001; excluding L. dione : r=0.3799, p<0.0001). In the partial Mantel tests, we found a significant relationship between type of comparison (within or between group) and genetic distance, when controlling for isolation by distance (including L. dione : r=0.5742, p<0.0001; excluding
L. dione : r=0.5734, p<0.0001).
Morphometrics
The results of the principal components analyses illustrate the morphological variation between L. editha and L. xanthoides (Table 2). The first two principal components scores reveal little overlap between L. editha and L. xanthoides (Figure 5).
Most individuals sampled from the intermediate populations do indeed possess morphology intermediate between L. editha and L. xanthoides . In the MANOVA analyses excluding intermediate populations, L. editha and L. xanthoides are significantly 34
different, based on the first six principal components (Females: Wilks’ Λ = 0.232, F 6,93 =
51.448, P < 0.0001; Males: Wilks’ Λ = 0.296, F 6,298 = 118.250, P < 0.0001). For the
MANOVA including intermediate populations, the differences between L. editha and L. xanthoides remained significant regardless of whether specimens from intermediate populations were assigned to L. editha (Females: Wilks’ Λ = 0.286, F 6,102 = 42.541, P <
0.0001; Males: Wilks’ Λ = 0.335, F 6,332 = 110.049, P < 0.0001) or assigned to L. xanthoides (Females: Wilks’ Λ = 0.309, F 6,102 = 37.979, P < 0.0001; Males: Wilks’ Λ =
0.422, F 6,332 = 75.725, P < 0.0001). In the training phase, the DFA correctly identified
96% and 97% of female and male specimens, respectively. In the testing phase, five of nine females from intermediate populations were classified as L. editha , while the other four were classified as L. xanthoides . Twenty of 34 intermediate males were classified as
L. editha , and 14 classified as L. xanthoides .
In analyses splitting L. xanthoides into two groups, the first six principal components demonstrated significant morphological differentiation among L. editha , L. xanthoides clade ‘A’, and L. xanthoides clade ‘B’ (Females: Wilks’ Λ = 0.130, F 12,184 =
27.171, P < 0.0001; Males: Wilks’ Λ = 0.208, F 12,594 = 59.122, P < 0.0001). In the discriminant function analysis, specimens were correctly assigned to one of three groups, based on haplotype identity, for 75% of the female specimens and for 86% of the male specimens (Figure 6).
Discussion 35
There are two clear implications from this work: (1) the three taxa previously defined on morphological characters represent isolated gene pools and (2) the isolation among these gene pools has been relatively recent. The three species were characterized by distinct genetic clusters (Figure 3), no populations contained haplotypes from more than one of these clusters, and there was almost no haplotype sharing among the three species. AMOVA analyses revealed significant among group partitioning of genetic variation when individuals were classified into groups based on morphological characters
(model 1 in Table 1). There was significant genetic differentiation among the taxa, even when controlling for isolation by distance. Finally, analyses of the morphological characters of L. xanthoides and L. editha show significant morphological differences between the two taxa. Morphological differentiation of L. xanthoides and L. editha is shown by significant MANOVA results, as well as a high success rate in DFA assignments (96% of female and 97% of male specimens correctly identified).
The isolation among these three taxa must have occurred recently. Mitochondrial sequence divergence was relatively low, ranging from 0.4% between L. xanthoides and L. editha to 1.5% between clades ‘A’ and ‘B’ of L. xanthoides . The observed levels of sequence divergence fall within the distribution observed between other lycaenid sister taxa, although the divergences among the L. xanthoides species group members occupy the lower end of the distribution. Briefly, sister species of the genus Agrodiaetus differ by 0 3% sequence divergence in cytochrome oxidase II (Kandul et al. 2004), and sister taxa M. teleius and M. nausithous differ by 1.2 2.8% sequence divergence (Als et al.
2004). There was also little phylogenetic resolution among the three species, as expected 36 for recently diverged taxa (Neigel and Avise 1986, Hudson and Coyne 2002, Funk and
Omland 2003). This adds to the growing body of evidence illustrating rapid speciation capabilities of Lepidoptera, especially Lycaenidae (Nice and Shapiro 1999, Aagaard et al.
2002, Eastwood and Hughes 2003, Lukhtanov et al. 2005). Two lines of evidence suggest that reproductive isolation may not be complete, or barriers have only recently been established between L. editha and L. xanthoides : the presence of common L. editha haplotypes in the Willamette Valley population of L. xanthoides , and the morphological intermediacy of populations occupying a geographic region between ‘typical’ L. editha and L. xanthoides populations.
The population of L. xanthoides in the Willamette Valley of Oregon is approximately 200 km north of the next nearest population of L. xanthoides . The intervening area is comprised of the Klamath and Siskiyou Mountain Ranges, inhabited by L. editha . The presence of L. editha haplotypes in Willamette Valley individuals suggests gene flow between L. editha and the Willamette Valley population may have occurred. The Willamette Valley L. xanthoides ’ population size has historically been low
(Severns and Villegas 2005), and hybridization with nearby L. editha populations may have allowed those haplotypes to introgress into the Willamette Valley and become fixed.
Introgressive hybridization is not uncommon in lepidopteran hybrid zones (e.g.
Dasmahapatra et al. 2002, Cianchi et al. 2003, Kronforst et al. 2006), and introgression of mitochondrial markers may sometimes obscure the genetic history of populations
(Gompert et al. 2006). Alternatively, the L. editha haplotypes in the Willamette Valley L. xanthoides may be due to incomplete lineage sorting. The current data are more 37 consistent with the hypothesis of introgressive hybridization, given the absence of any novel haplotypes in Willamette Valley L. xanthoides (‘neotypy’ of Omland et al. 2006).
Discerning between the two hypotheses, hybridization and introgression versus incomplete lineage sorting, would benefit from future analyses on multiple unlinked nuclear loci.
The populations with intermediate morphology, occupying regions of northern
California and southern Oregon, between populations of L. editha and L. xanthoides , also suggest that isolation among gene pools is recent or incomplete. Recent isolation among taxa may result in a pattern of incomplete morphological divergence, and the intermediate populations may be part of the L. editha gene pool, as all individuals sampled from intermediate populations possessed the common L. editha haplotypes Q and S. Alternatively, the intermediate populations may be evidence of recent hybridization between L. xanthoides and L. editha . This hypothesis is supported by unpublished allozyme surveys of individuals from an intermediate population (population
61 in this study) (H.J. Geiger and A.M. Shapiro, unpublished data). Briefly, for two loci diagnostic for the two species, alleles of both species were found in roughly equal frequencies, and genotype frequencies did not differ from Hardy Weinberg expectations.
If intermediate populations did have hybrid origins, the lack of L. xanthoides haplotypes in these populations could be attributed to higher immigration from L. editha populations, or an asymmetry in hybrid fitness (Muller 1942). The hybrid origin hypothesis is also supported by host use: the intermediate populations almost exclusively use non native hosts (Emmel and Pratt 1998), and this novel resource may have allowed expansion into 38 previously unoccupied regions (Bernays and Chapman 1994; Oliver 2006). The origin and current dynamics of these intermediate populations have the potential to provide valuable insight on the nature of reproductive isolation and morphological divergence.
Taxonomic Implications
Both genetic and morphological analyses presented here fail to support the polytypic species hypothesis: isolation among the three named taxa is evident. With the exception of the Willamette Valley population of L. xanthoides , there were no shared haplotypes among specimens morphologically classified a priori as L. xanthoides , L. editha , and L. dione , even in areas where geographic ranges abut. AMOVA and IBD also support the hypothesis that there is isolation among the gene pools of the three taxa. The morphological distinctness between L. editha and L. xanthoides revealed by multivariate analyses supports the position that they represent separate species. The maintenance of species integrity, despite hybridization, is known from other butterfly taxa as well
(Sperling and Harrison 1994, Sperling 2003).
The intermediate populations require further molecular study to ascertain their evolutionary history and taxonomic status. Emmel and Pratt (1998) recognized two subspecies from the region characterized by intermediate populations, L. xanthoides nigromaculata and L. editha pseudonexa , and maintained the species status of L. xanthoides and L. editha . In the morphological descriptions, L. xanthoides nigromaculata is distinguished from the nominate subspecies by, among other characters, larger ventral black spots while L. editha pseudonexa is distinguished from L. editha 39 editha by larger size and smaller ventral black spots. These descriptions of the two subspecies suggest convergence on a similar form and they do not explicitly differentiate
L. xanthoides nigromaculata and L. editha pseudonexa . The interpretation that L. xanthoides and L. editha both exist in this area and do not interbreed is not supported.
Only L. editha haplotypes were found in these intermediate populations, so either L. xanthoides nigromaculata is actually a variant of L. editha , or at least some recent hybridization has occurred between the two taxa. The latter hypothesis would likely be due to historical, not current, contact, as there are no “pure” parental populations near this zone of intermediacy. The biological relevance of L. editha pseudonexa is also questionable, as the authors describe disjunct populations with the pseudonexa phenotype occurring in the central Sierra Nevada Mountains, approximately 480 kilometers to the south, with intervening areas occupied by nominotypical L. editha populations.
Additional sources of data should be considered to verify the hypothesis that these intermediate populations represent hybridization between L. xanthoides and L. editha .
Barriers to Gene Flow and Cryptic Variation
Genetic and morphological variance partitioning support the hypothesis that at least three isolated gene pools exist within the L. xanthoides group. The three named taxa, L. xanthoides , L. editha , and L. dione , show genetic and morphological differentiation, and have predominantly allopatric ranges. Given the current geographic ranges, and the degree of genetic divergence in this group, we can provide preliminary inferences on the probable barriers to gene flow in this group. Considering a 40 mitochondrial molecular clock of 2.3% sequence divergence per million years (Brower
1994, but see Ballard and Whitlock 2004), the divergence among the members of the L. xanthoides species group occurred on the order of 0.2 0.7 million years ago. We hypothesize that genetic and morphological divergences occurred in glacial refugia
(Hewitt 2000), corresponding to current geographic ranges: L. xanthoides in lowland
California, L. editha in the northern Great Basin, and L. dione in the Great Plains.
Geological barriers would then include the Rocky Mountains (between L. editha and L. dione ) and the Sierra Nevada and Cascade Ranges (between L. editha and L. xanthoides ).
The most striking pattern to emerge from these analyses is the pattern of genetic and morphological variance partitioning within L. xanthoides . All individuals of clade
‘A’ (populations 1 20) were sampled north of the Transverse Ranges of California, while all individuals of clade ‘B’ (populations 22 30) were sampled in or south of the
Transverse Ranges (see Figures 1 and 4). These ranges form a major phylogeographic barrier in western North America for a variety of plant and animal taxa (Calsbeek et al.
2003). Haplotypes of clade ‘A’ and clade ‘B’ differ by an average 1.81% sequence divergence in COII. Two other lepidopteran taxa, Tegeticula maculata (Segraves and
Pellmyr 2001) and Hesperia comma (Forister et al. 2004), also show similar phylogenetic breaks across these mountain ranges. Tegeticula maculata shows an average 1.37% sequence divergence in cytochrome oxidase I between northern and southern populations, while the corresponding phylogeographical split in H. comma shows an average of 1.37% sequence divergence in COII. Although the Transverse Ranges cannot be a current barrier to gene flow within L. xanthoides ( L. xanthoides clade ‘B’ occurs at the higher 41 elevations of the Transverse Ranges), genetic differentiation of the two clades demonstrates that these mountain ranges were historically a barrier to gene flow.
This genetic differentiation within L. xanthoides begs the question: are there cryptic species in this group? Under a genealogical species concept (Baum and Shaw
1995) the well supported clades would suggest the existence of two genetically distinct species within L. xanthoides . Among group variance increased in a post hoc AMOVA, where L. xanthoides was split into two groups, corresponding to clades ‘A’ and ‘B’
(models 1 and 2 in Table 1). Additionally, post hoc MANOVA results demonstrated significant morphological differences between members of clade ‘A’ and members of clade ‘B’; this morphological divergence was supported by a considerable success rate in discriminant function analyses for both female and male specimens. The genetic and morphological divergence, in the presence of a known geological barrier, suggests that the differentiation is not spurious (Irwin 2002) and strongly argues for a distinction between the two lineages of L. xanthoides .
Cryptic lineages are being discovered with increasing frequency, and may be informative in future studies of biological diversification (Bickford et al. 2006). The L. xanthoides species group will be especially useful for comparisons of the processes leading to genetic divergence and morphological divergence. It is intriguing that the greatest genetic divergence (1.81%) was observed between northern and southern populations of L. xanthoides , demonstrating that molecular divergence and morphological divergence do not necessarily occur concurrently, although our post hoc analyses indicate some morphological differentiation within L. xanthoides . Additionally, 42 the divergence between the predominantly allopatric northern and southern L. xanthoides populations will allow tests of speciation via phylogenetic niche conservatism (Peterson et al. 1999, Wiens 2004). Future ecological niche modeling (Hugall et al. 2002) studies on L. xanthoides will influence our understanding of the processes responsible for biological diversification (Wiens and Donoghue 2004).
Conclusions
We conclude that the morphological divergence among the members of the L. xanthoides species group occurred primarily in the absence of gene flow, although the isolation among the taxa has been recent. Mitochondrial DNA remains a useful tool for detecting isolation among gene pools, given that mtDNA gene flow can occur even at low levels of immigration (Takahata and Slatkin 1984). The degree of isolation does require further study, as females are the heterogametic sex in Lepidoptera. Lepidoptera show strong adherence to Haldane’s Rule (Haldane 1922, Presgraves 2002) and maternally inherited markers may demonstrate a different pattern of gene flow than nuclear markers if there are sex based differences in hybrid fitness. The genetic and morphological differentiation within L. xanthoides suggests significant reproductive isolation, although we refrain from drawing any taxonomic conclusions at this time.
Finally, mtDNA markers remain useful for identifying units critical for conservation purposes (Shaffer et al. 2004) and previously unknown, evolutionarily distinct lineages
(Bickford et al. 2006).
43
Acknowledgements
Invaluable field assistance was provided by E.R. Bjerre, J.D. Oliver, S.C. Oliver, G.B.
Pauly, and especially K.L. Prudic. We also thank the numerous people who provided locality information and/or additional specimens: G.T. Austin, G.R. Ballmer, B. Bouton,
J.P. Brock, K.E. Davenport, J.A. Fordyce, M.A. Forister, B. Gendron, C. Harp, G.
Kareofelas, T.W. Kral, C.C. Nice, P.A. Opler, J.G. Pasko, G.F. Pratt, K.J. Ribardo, E.B.
Runquist, P.M. Severns, E.C. Snell Rood, A.D. Warren, and E. Weingartner. R.P.
Guralnick provided laboratory space during the early portion of this work. F.A.H.
Sperling, G.B. Pauly, D.R. Maddison, C.C. Nice, J.A. Fordyce, Z. Gompert, and three anonymous reviewers provided useful comments and discussion on previous drafts of this work. This work was funded by an EPA STAR Fellowship, a Xerces Society DeWind
Award, and a National Science Foundation DDIG to JCO 44
References Aagaard K, Hindar K, Pullin AS, et al. (2002) Phylogenetic relationships in brown argus butterflies (Lepidoptera: Lycaenidae: Aricia ) from north western Europe. Biological Journal of the Linnean Society , 75 , 27 37. Als TD, Vila R, Kandul NP, Nash DR, Yen SH, Hsu YF, Mignault AA, Boomsma JJ, Pierce NE (2004) The evolution of alternative parasitic life histories in large blue butterflies. Nature 432 , 386 390. Ballard JWO, Whitlock MC (2004) The incomplete natural history of mitochondria. Molecular Ecology, 13 , 729 744. Ballmer GR, Pratt GF (1988) A survey of the last instar larvae of the Lycaenidae (Lepidoptera) of California. Journal of Research on the Lepidoptera , 27 , 1 81. Baum DA, Shaw KL (1995) Genealogical perspectives on the species problem. In: Experimental and Molecular Approaches to Plant Biosystematics (eds Hoch PC, Stephenson AC), pp. 289–303. Missouri Botanical Garden, St. Louis, MO. Bernays EA, Chapman RF (1994) Host Plant Selection by Phytophagous Insects . Chapman and Hall, New York. Bickford D, Lohman DJ, Sodhi NS, Ng PKL, Meier R, Winker K, Ingram KK, Das I (2006) Cryptic species as a window on diversity and conservation. Trends in Ecology and Evolution , 22 , 148 155. Bohonak AJ (2002) IBD (Isolation by distance): A program for analyses of isolation by distance. Journal of Heredity , 93 , 153 154. Brower AVZ (1994) Rapid morphological radiation and convergence among races of the butterfly Heliconius erato inferred from patterns of mitochondrial DNA evolution Proceedings of the National Academy of Sciences , 91 , 6491 6495. Calsbeek R, Thompson JN, Richardson JE (2003) Patterns of molecular evolution and diversification in a biodiversity hotspot: the California Floristic Province. Molecular Ecology , 12 , 1021 1029 Casgrain P, Legendre P (2001) The R Package for Multivariate and Spatial Analysis, version 4.0 d6. User’s Manual. Département de sciences biologiques, Université de Montréal. Available at: http://www.bio.umontreal.ca/Casgrain/en/labo/R/v4/telecharger.html . 45
Caterino MS, Sperling FAH (1999) Papilio phylogeny based on mitochondrial cytochrome oxidase I and II genes. Molecular Phylogenetics and Evolution , 11 , 122 137. Cianchi R, Ungaro A, Marini M, Bullini L (2003) Differential patterns of hybridization and introgression between the swallowtails Papilo machaon and P. hospiton from Sardinia and Corsica islands (Lepidoptera, Papilionidae). Molecular Ecology , 12 , 1461 1471. Clary DO, Wolstenholme DR (1985) The mitochondrial DNA molecule of Drosophila yakuba nucleotide sequence, gene organization, and genetic code. Journal of Molecular Evolution , 22 , 252 271. Coyne JA, Orr HA (2004) Speciation . Sinauer Associates, Sunderland, MA. Dasmahapatra KK, Blum MJ, Aiello A, Hackwell S, Davies N, Bermingham EP, Mallet J (2002) Inferences from a rapidly moving hybrid zone. Evolution , 56 , 741 753. Eastwood R, Hughes JM (2003) Molecular phylogeny and evolutionary biology of Acrodipsas (Lepidoptera: Lycaenidae). Molecular Phylogenetics and Evolution , 27 , 93 102. Emmel JF, Pratt GF (1998) New subspecies of Lycaeninae from California and a type locality restriction for Chrysophanus cupreus W.H. Edwards (Lepidoptera: Lycaenidae). In: Systematics of Western North American Butterflies (ed Emmel TC), pp. 661 680. Mariposa Press, Gainesville, FL. Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics , 131 , 479 491. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online , 1, 47 50. Fordyce JA, Nice CC, Forister ML, Shapiro AM (2002) The significance of wing pattern diversity in the Lycaenidae: mate discrimination by two recently diverged species. Journal of Evolutionary Biology , 15 , 871 879. Forister ML, Fordyce JA, Shapiro AM (2004) Geological barriers and restricted gene flow in the holarctic skipper Hesperia comma (Hesperiidae). Molecular Ecology , 13 , 3489 3499. 46
Funk DJ, Omland KE (2003) Species level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annual Review of Ecology, Evolution, and Systematics , 34 , 397 423. Gompert Z, Nice CC, Fordyce JA, Forister ML, Shapiro AM (2006) Identifying units for conservation using molecular systematics: the cautionary tale of the Karner blue butterfly. Molecular Ecology , 15 , 1759 1768. Haldane JBS (1922) Sex ratio and unisexual sterility in hybrid animals. Journal of Genetics , 12 , 101 109. Hall TA (1999) BioEdit: a user friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series , 41 , 95 98. Hellberg ME (1994) Relationships between inferred levels of gene flow and geographic distance in a philopatric coral, Balanophyllia elegans . Evolution , 48 , 1829 1854. Hewitt G (2000) The genetic legacy of the Quaternary ice ages. Nature , 405 , 907 913. Hudson RR, Coyne JA (2002) Mathematical consequences of the genealogical species concept. Evolution , 56 , 1557 1565. Huelsenbeck JP, Ronquist F, (2001) MRBAYES: Bayesian inference of phylogeny. Bioinformatics , 17 , 754 755. Hugall A, Moritz C, Moussalli A, Stanisic J (2002) Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875). Proceedings of the National Academy of Sciences , 99 , 6112 6117. Irwin DE (2002) Phylogeographic breaks without geographic barriers to gene flow. Evolution , 56 , 2383 2394. Kandul NP, Lukhtanov VA, Dantchenko AV, Coleman JW, Sekercioglu CH, Haig D, Pierce NE (2004) Phylogeny of Agrodiaetus Hubner 1822 (Lepidoptera: Lycaenidae) inferred from mtDNA sequences of COI and COII and nuclear sequences of EF1 alpha: karyotype diversification and species radiation. Systematic Biolo gy, 53 , 278 298. Kronforst MR, Young LG, Blume LM, Gilbert LE (2006) Multilocus analyses of admixture and introgression among hybridizing Heliconius butterflies. Evolution , 60 , 1254 1268. 47
Lukhtanov VA, Kandul NP, Plotkin JB, Dantchenko AV, Haig D, Pierce NE (2005) Reinforcement of pre zygotic isolation and karyotype evolution in Agrodiaetus butterflies. Nature , 436 , 385 389. Mallet J (1995) A species definition for the Modern Synthesis. Trends in Ecology and Evolution , 10 , 294 299. Manly BFJ, (2005) Multivariate Statistical Methods , 3 rd edn. Chapman and Hall/CRC, Boca Raton, FL. Mayr E (1963) Animal Species and Evolution . Harvard University Press, Cambridge, MA. Muller HJ (1942) Isolating mechanisms, evolution and temperature. Biological Symposia , 6, 71 125. Neigel JE, Avise JC (1986) Phylogenetic relationships of mitochondrial DNA under various demographic models of speciation. In: Evolutionary Processes and Theory . (eds Karlin S, Nevo E), pp.515 534. Academic Press, Orlando, FL.
Nice CC, Shapiro AM (1999) Molecular and morphological divergence in the butterfly genus Lycaeides (Lepidoptera: Lycaenidae) in North America: evidence of recent speciation. Journal of Evolutionary Biology , 12 , 936 950. Oliver JC (2006) Population genetic effects of human mediated plant range expansions on native phytophagous insects. Oikos , 112 , 456 463. Omland KE, Baker JM, Peters JL (2006) Genetic signatures of intermediate divergence: population history of Old and New World Holarctic ravens ( Corvus corax ). Molecular Ecology , 15 , 795 808. Opler PA, Wright AB (1999) A Field Guide to Western Butterflies . Houghton Mifflin, New York. Peterson AT, Soberón J, Sánchez Cordero V (1999) Conservatism of Ecological niches in evolutionary time. Science , 285 , 1265 1267. Pratt GF, Wright DM, Ballmer GR (1991) Multivariate and phylogenetic analyses of larval and adult characters of the Editha complex of the genus Lycaena (Lepidoptera: Lycaenidae). Journal of Research on the Lepidoptera , 30 , 175 195. Presgraves DC (2002) Patterns of postzygotic isolation in Lepidoptera. Evolution , 56 , 1168 1183. 48
Pyle RM (2002) The Butterflies of Cascadia . Seattle Audubon Society, Seattle, WA. Rasband WS (2006) ImageJ 1.35p. U. S. National Institutes of Health, Bethesda, Maryland, USA. Available at: http://rsb.info.nih.gov/ij/ . Rubinoff D, Holland BS (2005) Between two extremes: Mitochondrial DNA is neither the panacea nor the nemesis of phylogenetic and taxonomic inference. Systematic Biology , 54 , 952 961. SAS Institute (2004) SAS version 9.1 SAS Institute, Cary, North Carolina, USA. Scott JA (1979) Geographic variation in Lycaena xanthoides . Journal of Research on the Lepidoptera , 18 , 50 59. Scott JA (1986) The Butterflies of North America: A Natural History and Field Guide . Stanford University Press, Stanford, CA. Segraves KA, Pellmyr O (2001) Phylogeography of the yucca moth Tegeticula maculata : the role of historical biogeography in reconciling high genetic structure with limited speciation. Molecular Ecology , 10 , 1247 1253. Severns PM, Villegas S (2005) Butterflies hanging on to existence in the Willamette Valley: a relict population of the great copper ( Lycaena xanthoides Boisduval). Northwest Science , 79 , 77 80. Shaffer HB, Fellers GM, Voss SR, Oliver JC, Pauly GB (2004) Species boundaries, phylogeography and conservation genetics of the red legged frog ( Rana aurora /draytonii ) complex. Molecular Ecology , 13 , 2667 2677. Shapiro AM (1986) Montane insular butterfly biogeography: fauna of Ball Mountain, Siskiyou County, California. Great Basin Naturalist , 46 , 336 347. Sperling, FAH (2003) Butterfly molecular systematics: from species definitions to higher level systematics. In: Butterflies: Ecology and Evolution Taking Flight . (eds Boggs CL, Watt WB, Ehrlich PR), pp.431 458. University of Chicago Press, Chicago, IL. Sperling, FAH, Harrison RG (1994) Mitochondrial DNA variation within and between species of the Papilio machaon group of swallowtail butterflies. Evolution , 48 , 408 422. Sullivan J, Swofford DL (1997) Are guinea pigs rodents? The importance of adequate models in molecular phylogenetics. Journal of Mammalian Evolution , 4, 77 86. Swofford DL (2001) PAUP*: Phylogenetic Analyses Under Parsimony (*and other methods) , version 4.0b10. Sinauer Associates, Sunderland, MA. 49
Takahata N, Slatkin M (1984) Mitochondrial gene flow. Proceedings of the National Academy of Sciences , 81 , 1764 1767. Wiens JJ (2004) Speciation and ecology revisited: Phylogenetic niche conservatism and the origin of species. Evolution , 58 , 193 197. Wiens JJ, Donoghue MJ (2004) Historical biogeography, ecology and species richness. Trends in Ecology and Evolution , 19 , 639 644. Wright S (1943) Isolation by distance. Genetics , 28 , 114 138. 50
Tables, figures, and appendices
Figure Legends
Figure 1. Geographic ranges and sampling locations of L. xanthoides (white), L. editha
(light grey), L. dione (dark grey). See Appendix I for locality information. Species’ ranges after Scott (1986) and Pyle (2002). Symbols refer to haplotypes found in population: A G ( ), H O ( ), P W ( ), and X CC ( ). See text and Figure 3 for haplotype descriptions.
Figure 2. Schematic of the underside left wings of Lycaena showing morphological characters used in this study. 1, Length of Cu 2 vein in hindwing; 2, Length of R 4+5 vein in forewing; 3, Maximum width of ventral hindwing M 3 submarginal white band, measured parallel to Cu 1 vein; 4, Maximum width of ventral hindwing black scaling between white band and orange lunule in cell Cu 1, measured parallel to Cu 1 vein; 5, Area of discal basal macule in ventral hindwing; 6, Area of M 1 discal macule in ventral hindwing; 7, Area of M 2 discal macule in ventral hindwing; 8, Area of Cu 1 discal macule in ventral hindwing; 9, Area of Sc+R 1 medial macule in ventral hindwing; 10, Area of
Cu 1 black aurora in ventral hindwing; 11, Area of R 4+5 subapical macule in ventral forewing.
Figure 3. Minimum spanning network of the ingroup COII haplotypes found in this study. Letters refer to individual haplotypes and numbers on branches represent number of nucleotide differences between haplotypes. White haplotypes were found in L. 51
xanthoides , grey haplotypes found in L. editha , and black haplotypes found in L. dione .
Haplotypes P and U were found in L. editha , as well as the Willamette Valley population of L. xanthoides .
Figure 4. Consensus of Bayesian and maximum parsimony analyses, with Bayesian consensus branch lengths shown. Numbers along branches show nodal support as
Bayesian posterior probability / maximum parsimony bootstrap support.
I Individuals from intermediate populations possessed either haplotype Q or haplotype S.
X Lycaena xanthoides individuals collected from the Willamette Valley possessed either haplotype P or haplotype U.
Figure 5. First two principal components scores plotted for specimens of L. editha ( ),
L. xanthoides ( ), Willamette Valley L. xanthoides ( ), and intermediate populations
(); (a) females (b) males.
Figure 6. Graph of two canonical variables used in discriminant function analyses splitting L. xanthoides into two groups and excluding specimens from intermediate populations. Lycaena editha ( ), L. xanthoides clade ‘A’ ( ), L. xanthoides clade ‘B’
( ), Willamette Valley L. xanthoides ( ); (a) females (b) males.
52
Tables Table 1. AMOVA Results (Excoffier et al. 1992) for population level analyses of the L. xanthoides species group.
Source of Variation Model Among Groups Among Populations, Among Individuals, Within Groups Within Populations (1) 3 Groups, based on d.f. = 2 d.f. = 54 d.f. = 364 species %var. = 57.21 %var. = 36.81 %var. = 5.98 ΦCT = 0.57** ΦSC = 0.86** ΦST = 0.94** (2) 4 Groups, splitting L. d.f. = 3 d.f. = 53 d.f. = 364 xanthoides %var. = 88.58 %var. = 5.66 %var. = 5.75 ΦCT = 0.89** ΦSC = 0.50** ΦST = 0.94**
** P < 0.001 53
Table 2. Loadings for the first six principal components and the percentage variation described by each; (a) females, (b) males. (a) Principal Component Character 1 2 3 4 5 6 1 0.2000 0.5530 0.0607 0.1117 0.2862 0.1397 2 0.1499 0.5626 0.1596 0.1070 0.3292 0.2800 3 0.0476 0.3890 0.3569 0.5659 0.5600 0.0686 4 0.2204 0.0665 0.5810 0.4905 0.1075 0.4014 5 0.3804 0.0248 0.0378 0.0840 0.2532 0.6089 6 0.4324 0.0372 0.0937 0.0224 0.0907 0.0606 7 0.4210 0.0078 0.1005 0.1171 0.1850 0.0792 8 0.4003 0.1058 0.1351 0.0168 0.0078 0.1973 9 0.3838 0.0489 0.2356 0.1746 0.2034 0.1230 10 0.1167 0.3079 0.4395 0.5608 0.5690 0.1464 11 0.2382 0.3338 0.4676 0.2209 0.1131 0.5292 % variation described 41.66% 19.38% 10.35% 8.89% 5.08% 4.28%
(b) Principal Component Character 1 2 3 4 5 6 1 0.1980 0.5810 0.0605 0.2429 0.1463 0.1125 2 0.1533 0.6072 0.0215 0.1399 0.2668 0.1482 3 0.0593 0.4149 0.0550 0.8074 0.3847 0.0085 4 0.2260 0.0295 0.5535 0.2048 0.5100 0.5377 5 0.3694 0.0375 0.0634 0.0323 0.0162 0.2710 6 0.4321 0.0143 0.0997 0.0688 0.0413 0.0364 7 0.4521 0.0256 0.0123 0.0007 0.0414 0.1083 8 0.3896 0.1402 0.1663 0.0927 0.1933 0.2690 9 0.3511 0.1042 0.3111 0.0965 0.0495 0.1563 10 0.1446 0.0901 0.6305 0.3496 0.612 0.0777 11 0.2432 0.2826 0.3878 0.2859 0.2851 0.6978 % variation described 39.75% 16.41% 11.78% 7.36% 6.27% 5.00% 54
Figure 1 55
Figure 2 56
Figure 3 57
Figure 4 58
Figure 5 59
Figure 6 60
Appendix I. Sampling localities, sizes, and haplotype distribution. N 1 = number of specimens included in molecular analyses, N 2 = number of specimens included in morphometric analyses; L. dione specimens were not included in morphometric analyses. Species # Locality N1 N2 Haplotypes (Count) L. xanthoides 1 Lagoon Valley Regional Park, Solano Co. CA. 10 7 A(8), B(2) 38.3361 °N, 122.0156 °W. 2 North Sacramento, Sacramento Co. CA. 38.5996 10 13 A(10) °N, 121.4718 °W. 3 Colfax, Placer Co. CA. 39.0954 °N, 120.9488 °W. 10 8 A(4), B(6) 4 Lily Pond, Colusa Co. CA. 39.3134 °N, 122.7104 11 16 A(10), °W. C(1) 5 Mendocino Pass, Glenn Co. CA. 39.7943 °N, 10 17 A(10) 122.935 °W. 6 Pine Grove, Lake Co. CA. 38.8282 °N, 122.7311 3 3 A(3) °W. 7 Chiles Valley, Napa Co. CA. 38.5568 °N, 122.365 10 6 A(6), B(4) °W. 8 Carmel Valley, Monterey Co. CA. 36.4001 °N, 10 8 A(6), D(4) 121.5773 °W. 9 Pinnacles Campground, San Benito Co. CA. 10 3 A(10) 36.4896 °N, 121.1511 °W. 10 Tehachapi Mountains, Kern Co. CA. 35.0718 °N, 10 20 A(3), B(5), 118.4836 °W. E(2) 11 Caliente, Kern Co. CA. 35.291 °N, 118.627 °W. 2 2 B(1), E(1) 12 Cuyama River, Santa Barbara Co. CA. 34.8931 °N, 10 13 A(5), B(4), 119.534 °W. G(1) 13 Greenhorn Mountains, Kern Co. CA. 35.6684 °N, 10 18 B(8), E(2) 118.5297 °W. 14 Mitchell Canyon, Contra Costa Co. CA. 37.9204 5 4 A(4), F(1) °N, 121.9418 °W. 15 Sherwin Grade, Tulare Co. CA. 35.9776 °N, 3 2 B(2), E(1) 118.4672 °W. 16 Freeman Creek, Tulare Co. CA. 36.135 °N, 3 3 B(3) 118.4739 °W. 17 Silver Canyon, Inyo Co. CA. 37.4031 °N, 11 12 A(11) 118.2353 °W. 18 Rock Creek, Mono Co. CA. 37.5282 °N, 118.6382 10 15 A(10) °W. 19 Snow Mountain, Colusa Co. CA. 39.3457 °N, 1 3 B(1) 122.7569 °W. 20 Kennedy Meadows, Tulare Co. CA. 36.0459 °N, 1 0 B(1) 118.1332 °W. 21 Eugene, Lane Co. OR. 44.0497 °N, 123.1791 °W. 5 3 P(4), U(1) 22 Lake of the Woods, Kern Co. CA. 34.8123 °N, 9 7 I(9) 119.0108 °W. 23 Buckman Springs Road, San Diego Co. CA. 7 5 H(1), I(6) 32.7169 °N, 116.5012 °W. 24 Lake Hemet, Riverside Co. CA. 33.6702 °N, 10 16 H(1), I(8), 116.6993 °W. J(1) 25 Blue Jay Camp, Orange Co. CA. 33.6531 °N, 6 6 H(2), I(4) 117.4517 °W. 61
26 Pine Creek, San Diego Co. CA. 32.8548 °N, 10 7 I(10) 116.5228 °W. 27 Lake Henshaw, San Diego Co. CA. 33.2337 °N, 10 15 H(5), I(4), 116.7574 °W. J(1) 28 Lake Silverwood, San Bernardino Co. CA. 10 15 I(7), O(3) 34.2717 °N, 117.2883 °W. 29 Lebec, Kern Co. CA. 34.8243 °N, 118.8796 °W. 10 9 K(9), L(1) 30 Mojave River Wash, San Bernardino Co. CA. 2 0 I(1), M(1) 34.3078 °N, 117.332 °W. L. dione 31 Red Deer River, Alberta, Can.. 51.8333 °N, 113 10 X(2), Y(2), °W. Z(4), BB(1), CC(1) 32 Manchester, Cascade Co. MT. 47.6 °N, 111.4389 2 X(2) °W. 33 Beartooth Creek, Lewis & Clark Co. MT. 46.8655 1 AA(1) °N, 112.006 °W. 34 Power, Teton Co. MT. 47.7333 °N, 111.5089 °W. 1 Z(1) 35 Oklahoma City, Oklahoma Co. OK. 35.6433 °N, 1 Y(1) 97.2125 °W. L. editha 36 Warner Mountains, Modoc Co. CA. 41.1666 °N, 10 10 Q(9), R(1) 120.2963 °W. 37 Ochoco Mountains, Crook Co. OR. 44.4502 °N, 6 0 Q(4), W(2) 120.7492 °W. 38 North Fork Payette River, Valley Co. ID. 44.3028 10 13 P(5), Q(1), °N, 116.0813 °W. T(2), U(1), V(1) 39 Long Gulch Road, Elmore Co. ID. 43.559 °N, 10 9 P(4), Q(4), 115.6075 °W. T(2) 40 Quincy, Plumas Co. CA. 39.9397 °N, 120.9525 14 16 Q(14) °W. 41 Donner Summit, Nevada Co. CA. 39.3266 °N, 10 14 P(6), Q(1), 120.393 °W. S(3) 42 Clio, Plumas Co. CA. 39.7432 °N, 120.5802 °W. 9 6 Q(8), S(1) 43 Olympic Village, Placer Co. CA. 39.198 °N, 5 5 P(3), Q(1), 120.2329 °W. S(1) 44 Spring Garden, Plumas Co. CA. 39.8949 °N, 9 11 Q(9) 120.7861 °W. 45 Little Truckee River Headwaters, Nevada Co. CA. 2 1 Q(1), S(1) 39.4939 °N, 120.4202 °W. 46 Sumpter, Baker Co. OR. 44.7485 °N, 118.2034 10 9 Q(9), S(1) °W. 47 Little Camas Reservoir, Elmore Co. ID. 43.3285 2 2 P(1), Q(1) °N, 115.392 °W. 48 Maggie Summit, Elko Co. NV. 41.6844 °N, 10 9 Q(5), S(5) 116.0415 °W. 49 Camas Creek, Lake Co. OR. 42.2139 °N, 120.2269 10 0 Q(9), S(1) °W. 50 West Cascade Mountains, Lane Co. OR. 43.5501 10 0 Q(10) °N, 122.3842 °W. 51 East Cascade Mountains, Klamath Co. OR. 10 0 Q(10) 42.8976 °N, 121.8298 °W. 62
52 Wallowa Mountains, Wallowa Co. OR. 45.6218 11 0 Q(11) °N, 117.7233 °W. 53 Tioga Pass, Tuolumne Co. CA. 37.911 °N, 9 9 P(8), U(1) 119.2579 °W. 54 Crooked Creek, Inyo Co. CA. 37.4987 °N, 118.173 3 4 P(3) °W. 55 Franktown, Washoe Co. NV. 39.2415 °N, 10 16 P(1), S(9) 119.8466 °W. 56 Lang Crossing, Nevada Co. CA. 39.3192 °N, 4 10 P(3), S(1) 120.6586 °W. 57 Piute Pass, Mono Co. CA. 38.241 °N, 119.5107 3 4 P(2), S(1) °W. Intermediate 58 Trinity Alps, Trinity Co. CA. 41.1215 °N, 6 5 Q(1), S(5) 122.8164 °W. 59 Meiss Lake, Siskiyou Co. CA. 41.9026 °N, 10 10 Q(8), S(2) 122.0724 °W. 60 South Cascade Mountains, Jackson Co. OR. 8 0 S(8) 42.287 °N, 122.373 °W. 61 Gazelle, Siskiyou Co. CA. 41.5207 °N, 122.5203 19 28 S(19) °W. 62 Siskiyou Mountains, Jackson Co. OR. 42.0915 °N, 10 0 S(10) 122.4908 °W.
63
APPENDIX B
INFERRING SPECIES TREES FROM DEEP COALESCENCES WHILE ACCOMMODATING GENE TREE UNCERTAINTY 64
Title: Inferring species trees from deep coalescences while accommodating gene tree uncertainty. Jeffrey C. Oliver Interdisciplinary Program in Insect Science and Department of Entomology, University of Arizona, Tucson, AZ 85721, USA.
65
Abstract
Inferring evolutionary relationships among closely related species should benefit from multiple sources of information, e.g. unlinked genetic markers. Existing methods of inferring species trees from gene trees are available; however, most suffer from one or more liabilities, such as unrealistic constraints or failure to accommodate incomplete lineage sorting. Here I extend a method of reconstructing species relationships based on multiple reconstructed gene trees, using the number of incomplete lineage sorting events
(‘deep coalescences’) as the objective function. This method provides two important advances to phylogenetic inference based on multiple loci: (1) gene tree uncertainty is accommodated by drawing gene trees from a distribution of trees based on Bayesian
MCMC sampling and (2) species tree uncertainty is accommodated by providing support values for relationships, based on multiple iterations of the tree searching procedure.
Most of these analyses are performed in the Mesquite software system; new software developed for Mesquite will be available in an upcoming release of Mesquite. To evaluate the method, I use simulated DNA sequence data reflecting the type of data commonly available to researchers, and compare the results to other methods of species tree inference. I also provide a worked example of implementation of the method on a pair of closely related genera of lycaenid butterflies, Everes and Cupido . I provide a description of the conditions resulting in misleading inferences, and discuss implications and extensions of this new approach.
66
Introduction
As gene sequence data for more loci become available, it is becoming increasingly popular to base estimates of phylogeny on multiple gene genealogies.
Because a single gene tree does not necessarily reflect the evolutionary relationships of the species being investigated (Pamilo and Nei, 1988; Maddison, 1997), it is essential to base phylogenetic inferences on multiple, unlinked loci. Incorporating data from multiple loci has been implemented in a variety of methods. One widely used approach concatenates the sequences from the different loci, to form a single, ‘super’ sequence for phylogenetic analyses; however, recent simulations suggest that this concatenation approach may lead to erroneous inferences (Kubatko and Degnan, 2007). Additionally, the concatenation approach assumes all loci share the same history: this constraint of identical branch lengths and tree topology among gene genealogies may be unreasonable, especially among recently diverged taxa (Pamilo and Nei, 1988).
There are increasingly more methods to incorporate independent loci in phylogenetic inference, allowing each locus a unique evolutionary history. One approach is to analyze each locus separately, then generate a consensus tree as a best estimate; however, the utility of approach is limited, given the low probability of reciprocal monophyly in gene genealogies for recently diverged taxa (Hudson and Coyne, 2002;
Rosenberg, 2003). The lack of reciprocal monophyly is a very real phenomenon: Funk and Omland (2003) found 23% of animal species surveyed were reconstructed as paraphyletic or polyphyletic based on mitochondrial DNA sequences. That gene genealogies do not always reflect species’ histories is a product of the population genetic 67 processes underlying the transmission of genes through time (Tajima, 1983). Methods accommodating those population genetic processes responsible for the discordance between gene trees and species trees would allow more informed inferences of species phylogenies. One such source of discordance, incomplete lineage sorting, often confounds our ability to infer species phylogenies (Hudson and Coyne, 2002; Funk and
Omland, 2003), so it would be desirable for inference methods to incorporate the processes leading to this phenomenon.
To allow for the possibility of incomplete lineage sorting, one can use the number of ‘deep coalescences’ as an objective function, and find the set of species trees which minimizes the number of deep coalescences for a sample of gene trees (Maddison, 1997).
Deep coalescences occur when alleles from a lineage coalesce deeper in time than the splitting of that lineage from another lineage (Figure 1). The number of deep coalescences can be counted by fitting gene genealogies within a species tree and counting the number of ‘extra’ gene lineages present at each divergence event.
Incorporating the population genetic process of lineage sorting, by using the parsimony based deep coalescence criterion, provides a more inclusive means of estimating species’ histories.
In a thorough exploration of parameter space, Maddison and Knowles (2006) demonstrated that the true evolutionary history of species’ may be inferred minimizing deep coalescences with moderate numbers of loci and copies sampled. However, this approach could be greatly improved in two ways. First, there is almost always some degree of uncertainty in inferred gene genealogies. A method which could accommodate 68 the uncertainty in gene trees would better reflect our confidence in species tree relationships. This accommodation of uncertainty leads easily to the second improvement, which would provide some means of support for relationships in the inferred species trees. This second improvement would resample the gene genealogies to provide measures of support for relationships in the species tree.
In this paper, I explicitly describe the method of inferring species trees minimizing the number of deep coalescences criterion by resampling distributions of gene genealogies. Following the description, I evaluate the method with simulated data and compare the results to other methods of species tree inference. Finally, I provide a worked example on two closely related genera lycaenid butterflies, Cupido (Schrank) and
Everes (Hübner). I use the method to determine if recent taxonomic treatments
(Gorbunov, 2001; Warren, 2005), synonymizing Everes with Cupido , are supported by evolutionary relationships.
The Method
The goal of this method is to infer species relationships based on gene genealogies while accommodating (1) gene tree uncertainty and (2) species tree uncertainty. I first present an informal description of the method, followed by the explicit description of the current implementation. Multiple loci are sampled to generate gene sequence data for specimens; data from each locus are analyzed separately to create a distribution of gene trees. This distribution of gene trees will ideally reflect the confidence in individual partitions by frequency of their occurrence in the distribution. 69
To estimate a species tree, a single gene genealogy for each locus is drawn, at random with replacement, and used to infer the species tree based on an optimality criterion, in this case, the minimum number of deep coalescences. Topological variation among gene genealogies represents uncertainty in the gene tree, and is incorporated into analyses by re sampling these gene genealogies to generate a distribution of species trees. The distribution of species trees can be summarized, for example, as a consensus of all
‘optimal’ species trees recovered in the re sampling procedure. The consensus should reflect the frequency at which partitions in the species tree were encountered during the re sampling procedure, and thus the uncertainty of species tree topology. Figure 2 provides a schematic illustration of the method.
In the study presented here, I sampled multiple loci, and generated a distribution of gene genealogies using Bayesian MCMC analyses (see ‘Data’ below for specific conditions of Bayesian analyses) for each locus separately. Trees sampled before log likelihood scores stabilized and runs converged were discarded. A single gene genealogy was selected from the distribution of genealogies for each locus, and a species tree was inferred using the Tree Search procedure in Mesquite (W.P. Maddison and D.R.
Maddison, 2007), minimizing the number of deep coalescences for multiple loci. The species tree inference step was repeated 100 times, drawing a new gene genealogy for each locus during each species tree search. All inferred species trees were saved to a file and summarized in a single tree, using the majority rule consensus tree function in PAUP
(Swofford, 2001); the frequency of a partition recovered in the species tree inference step is used as a measure of uncertainty for relationships in the inferred species tree. Two 70 additional modules were written for the Mesquite software package to accomplish the resampling of gene genealogies; they are available on request from the author, along with instructions for use.
The Data
Simulated Data
All data were simulated in the Mesquite software version 2.0 beta (W.P.
Maddison and D.R. Maddison, 2007), using the Genesis package (Maddison and
Maddison, 2005) for character data simulation. For each of 10 replicates, I simulated a species tree with 12 terminal taxa under Uniform Speciation, with a total tree depth of
40N e, where N e=100,000. For each species tree, I simulated three gene genealogies, one corresponding to a haploid, mitochondrial marker (N e=25,000) and two corresponding to diploid, nuclear markers (N e=100,000), sampling two alleles from each species for each locus (24 alleles total per locus). Each gene genealogy was used to simulate a single
DNA character matrix of 1000 characters. All loci used an HKY+G model of evolution, with a transition/transversion ratio= 3.0 and a discrete, 4 category gamma with shape parameter=0.8. The equilibrium character states values were: A=0.3, C=0.2, G=0.2,
T=0.3. To reflect differences in mutation rates between mitochondrial and nuclear loci, I used a scaling factor=1.15 ×10 8 for the first (mitochondrial) locus, and a scaling factor=2.875 ×10 9 for the first and second (nuclear) loci. (See Maddison and Knowles
[2006] for a discussion of parameter values and scaling rates). 71
Each character matrix was then used to generate a distribution of gene genealogies by sampling trees from two MCMC runs of four chains each in MrBayes
3.1.1 (Huelsenbeck and Ronquist, 2001). The MCMC sampling was run for 2,000,000 generations, sampling trees every 1,000 generations, with an HKY+G model of evolution, and only those trees sampled after the burnin phase were used for subsequent analyses. The burnin phase was determined to end when the two runs had converged and the log likelihood scores did not increase in subsequent generations. In all 10 replicates, likelihood scores for each run plateaued by 1.5 million generations, and the measure of convergence, the standard deviation of the split frequencies (Huelsenbeck and Ronquist,
2001), dropped below 0.05. This resulted in the final 5 million generations, or 5000 trees, for each locus being included in subsequent analyses for each replicate. The standard deviation of split frequencies after 2 million generations ranged from 0.006 to
0.027.
To sample gene trees for species tree inference, I created new tree blocks, one corresponding to each locus, from post burnin trees from both runs. I performed two species tree inferences; the first sampled the mitochondrial locus and only one of the nuclear loci (two loci total), while the second sampled the mitochondrial locus and both nuclear loci (three loci total). For species tree inference, a single gene tree was drawn from each tree block, and the species tree was inferred using the Minimize Deep
Coalescences (multiple loci) criterion in Mesquite, employing the NNI branch swapping algorithm. This was repeated 100 times, and the results from the 100 searches were summarized using the consensus tree function in PAUP 4.0b10 (Swofford, 2001). The 72 frequency of each partition in the species tree is used as measure of uncertainty in species tree inference. For species tree inference procedure, I saved the 10 best trees
(MAXTREES=10) during NNI branch swapping for each replicate. Additionally, for each replicate I performed inferences based on only two loci (one nuclear and one mitochondrial), saving only a single tree (MAXTREES=1) during branch swapping. For each of the three analyses, I performed one tailed t tests, to determine if the correct partitions supported by the species tree inference procedure (observed at a frequency of
0.5 or higher) were characterized by longer branches in the true species tree than those unsupported partitions (observed at a frequency less than 0.5). The categorization of the partition frequency (a continuous variable) for t tests is relevant because those partitions encountered at a frequency less than 0.5 will not be represented in a standard majority rule consensus tree.
I compare this method to two additional approaches to species tree inference, based on the consensus gene genealogies. For each locus, I computed a consensus genealogy using the ‘SUMT’ command in MrBayes, including only trees sampled after the burnin phase. Using these consensus gene genalogies, I (1) inferred the species tree as the majority rule consensus tree of the Bayesian consensus gene genealogies and (2) used a modified version of the method presented by Maddison and Knowles (2006).
Briefly, the consensus gene genealogies were used to infer a species tree using Minimize
Deep Coalescences criterion in Mesquite, performing SPR branch swapping, keeping only a single best tree (MAXTREES=1). I performed these two analyses on a set of two loci (one mitochondrial and one nuclear) and on a set of all three loci. 73
Empirical Data
As a worked example, I investigated the relationships among the members of two genera of lycaenid butterflies. Everes (the tailed blues) and Cupido (the cupids) have historically been distinguished from one another on the basis of a single character: Everes species have a hair like ‘tail’ projecting from the posterior end of the hind wing, while all
Cupido lack this character. Recent treatments of these taxa (Gorbunov, 2001; Warren,
2005) question the distinction based on a single character, and have re classified all
Everes species under the genus Cupido . I use the method presented here to determine if there is significant evidence for or against monophyly of both genera. Six species of
Everes and five species of Cupido were sampled, with most species represented by at least two specimens; the closely related species, Tongeia fischeri , was included as the outgroup taxon.
To generate distributions of gene trees, I sampled one mitochondrial locus and one nuclear locus. For the mitochondrial locus, I sequenced a portion of cytochrome oxidase subunits I and II (COI and COII, respectively), using the primers Ron, Nancy,
Tonya, and Hobbes for COI, and Pierre and Eva for COII (Caterino and Sperling, 1999;
Monteiro and Pierce, 2001). Although this locus represents two coding genes, it is not appropriate to consider the genes as separate, independent unlinked markers. Because
COI and COII are physically adjacent on the mitochondrial genome, they almost certainly share an evolutionary past with little to no recombination between the two subunits. For the nuclear marker, I sequenced elongation factor 1 alpha (EF 1a), using 74 the primers ef44f and efrcM4r (Monteiro and Pierce, 2001). Both loci were sequenced in both directions on an Applied Biosystems 3730XL DNA Analyzer by the Genomic
Analysis and Technology Core (University of Arizona, Tucson, AZ), consensus sequences generated with the aid of phred/phrap (Green, 1999; Green and Ewing, 2002) and the Chromaseq package for Mesquite (D.R. Maddison and W.P. Maddison, 2007), and alignments were made by eye with the aid of Mesquite.
I analyzed each gene separately, running, for each locus, two independent MCMC replicates of 4 chains each. In hierarchical likelihood ratio tests (Sullivan and Swofford,
1997), both loci fit a model of GTR+I, which was used in MrBayes MCMC of
10,000,000 generations, sampling trees every 1000 generations. Trees sampled before the log likelihood scores stabilized and the standard deviation of split frequencies
(Huelsebeck and Ronquist, 2001) dropped below 0.005 (burnin phase) were discarded.
The standard deviation of the split frequencies dropped below 0.005 after 1.5 million generations, so only trees sampled in the latter 8.5 million generations were used in subsequent analyses. To infer the species tree from the two distributions of the sampled loci, I created a new tree block with post burnin trees for each locus, and sampled these distributions for 100 search replicates using the Minimize Deep Coalescences (multiple loci) criterion in Mesquite. For each search, I used the NNI branch swapping algorithm, and summarized the trees using the consensus tree function in PAUP 4.0b10 (Swofford,
2001) to infer a species phylogeny. This process was performed twice, saving either one best tree (MAXTREES=1) or ten best trees (MAXTREES=10) per search replicate. 75
In addition to the inference procedure described above, I assessed support for monophyly in Everes and Cupido by performing constraint search permutations. In this process, a set of gene genealogies is sampled and used to perform two tree inferences: (1) an unconstrained search, as described above and (2) a constrained search, where the inferred species tree is constrained by some pre defined topology. If the data are consistent with monophyly of the taxa being investigated, the constraint search should not produce worse optimal trees (i.e. trees with higher deep coalescence cost) than the unconstrained search. I first performed 20 paired searches to generate an observed distribution of δ, the difference in the number of deep coalescences between trees from unconstrained searches and trees from searches where Everes and Cupido are both constrained to be monophyletic.
To evaluate conditions which could lead to the observed distribution of δ, I used coalescent simulations of gene trees. On a model species tree of the six Everes species, five Cupido species, and one outgroup species, I simulated gene trees using Mesquite’s
Contained Coalescence module; one gene tree corresponded to a nuclear gene (effective population size = N e), and one gene corresponded to a mitochondrial gene (effective population size = ¼N e). In the model species tree, Everes and Cupido are both monophyletic, and the number of alleles sampled in simulated gene trees matched the actual number sampled in empirical data. I tested three models of evolution, which differed only in TD, or the time between divergence of the ancestors of two genera and the diversification within each genus (Figure 3). The three models corresponded to T D values of 10 N e, 1 N e, and 0.1 N e; in all models, the total tree depth was 20 N e. Simulated 76 distributions of δ were generated with 20 replicate paired searches, and models were rejected if the 95% confidence intervals of the observed and simulated distributions did not overlap. For all paired searches, a maximum of 10 trees were saved during each SPR branch swapping replicate.
Results & Discussion
Simulated data
The method presented here successfully reconstructed a majority of the true partitions in a species tree (74% for 2 loci, MAXTREES=1; 78% for 2 loci,
MAXTREES=10; and 83% for 3 loci, MAXTREES=10). Those partitions which were recovered in less than 50% of the species trees (and thus not present in the consensus species tree), were characterized by shorter branches in the true species tree (t test: 2 loci,
MAXTREES=1: t 88 =6.89, p<0.001; 2 loci, MAXTREES=10: t 88 =7.23, p<0.001; 3 loci,
MAXTREES=10, t 88 =7.08, p<0.001). This result is not unexpected, as short internodes are more likely to produce gene genealogies that are not representative of species’ history than are long internodes (Pamilo and Nei, 1988). Figure 4 shows the relationship between the length of a branch for a partition, and the frequency at which that partition was recovered in the species tree inference step.
We encountered two types of errors in species tree inferences in this study: missed partitions and false positives. Missed partitions are those partitions in the true species tree that occur in less than 50% of trees recovered during the species tree inference procedure. False positives are those partitions that did not exist in the true 77 species tree, but occur in at least 50% of the trees recovered in species tree inference.
Figure 5 shows the error count for the three methods presented here. Species trees based on a majority rule consensus encountered no false positives, but had the highest occurrence of missed partitions. The method presented by Maddison and Knowles
(2006) had the lowest missed partition rate, but the highest false positive rate (on average,
1.45 false partitions per species tree). The new method presented in this study had a lower false positive error rate than the Maddison and Knowles approach, and a missed partition error rate intermediate between the consensus gene tree and Maddison &
Knowles (2006) approach. The method presented here provides a reasonable compromise between the other two methods: the recovery rate for true partitions is higher than for species trees inferred as consensus trees of gene genealogy consensus trees, while the number of positively misleading inferences is lower than when a single species tree is inferred using the deep coalescence criterion based on consensus gene genealogies.
A more detailed discussion of conditions resulting in positively misleading inferences under this method can be found below.
Empirical Data
Figure 6 shows consensus gene genealogies and inferred species trees. The placement of two taxa, Everes lacturnus and Cupido prosecusa , suggest that Everes and
Cupido are not reciprocally monophyletic. In the mitochondrial tree (Figure 6 a), neither is placed within the clade containing the remaining members of their respective genus: E. lacturnus is reconstructed as sister to a clade containing all remaining Everes and Cupido , 78 while C. prosecusa is nested within the clade of remaining Everes species. In the inferred species trees, there is little evidence for or against the monophyly of either genus. One weakly supported clade, containing all Cupido species, of the species tree from inferred from MAXTREES=1 tree searches is likely a false positive. Two lines of evidence cast doubt on this inference: first, the mitochondrial tree is in direct conflict
(non monophyletic Cupido is well supported) and second, the species trees inferred from a more thorough sampling of gene genealogies (Figure 6 d) does not support this relationship. In fact, Cupido was only recovered as monophyletic at a frequency of 0.14 in the MAXTREES=10 species tree searches. The species tree inferences alone do not provide significant support for or against monophyly of Everes and Cupido .
In paired search permutations, searches constraining both Everes and Cupido to be monophyletic consistently produced worse trees than unconstrained searches. On average, species trees constrained to recover monophyletic Everes and Cupido had 12.2 more deep coalescences than species trees from unconstrained searches (δobs = 12.2 ±
3.9). In two of the simulated models (T D = 10 N e and T D = 1 N e), constraint searches did not consistently produce worse trees than unconstrained searches (Figure 7) and are not consistent with observed data. However, one model, in which there was relatively little time between divergence of the genera and within genus diversification (TD = 0.1 N e), constrained searches consistently produced trees with higher deep coalescence cost than unconstrained searches ( δ0.1Ne = 4.6 ± 3.3), albeit to a lesser degree than the empirical data. These results indicate the conditions under which the two genera may have evolved if they are monophyletic: a relatively short interval between the divergence of the Everes 79 and Cupido lineages and the diversification within each genus may have led to the observed low level of support for monophyly. Estimates of within and between lineage divergence times are necessary to assess support for the reclassification of Everes species under the genus Cupido (Gorbunov, 2001; Warren, 2005) and understand the evolution of the ‘tails’ distinguishing Everes from Cupido .
Conditions resulting in positively misleading inferences under the deep coalescence criterion
It is essential to understand the reasons for, and if possible, reduce the chances of, misleading inferences in phylogenetic biology. There are at least three scenarios which can lead to false positives when inferring species trees from deep coalescences. The first condition occurs when a majority of the resolved gene genealogies are in direct conflict with the true species tree; this often occurs when time between divergence events is too short to allow for lineage sorting to occur (Pamilo and Nei, 1988). This problem is common to all methods of phylogenetic inference based on gene genealogies, not just those based on deep coalescences, and the chance of such misleading inferences may decrease as the number of loci sampled increases (Pamilo and Nei, 1988; but see Degnan and Rosenberg, 2006). This type of error was the most commonly encountered in our simulated data sets. For the searches based on 2 loci, these false positives occurred four and six times for MAXTREES=1 and MAXTREES=10, respectively. For the searches based on 3 loci, all four false positives encountered were of this type. 80
False positives may also arise due to sampling effects: if the consensus species tree is based on too few inferred species trees, it may support partitions that do not reflect the true species tree. This is most likely to occur when there is uncertainty in the gene genealogy and too few species trees have been sampled. The rate of these false positives can be decreased by a more thorough sampling of tree space: three of the eight false positives in the MAXTREES=1 searches of simulated data reflected this type of error, while setting MAXTREES=10 resulted in no errors of this type being encountered, for both 2 loci and 3 loci searches.
The third condition resulting in positively misleading inferences is inherent in methods using the number of deep coalescences as the optimality criterion. The problem arises when gene genealogies are in direct conflict with each other, and one of the gene genealogies reconstructs a species as paraphyletic (Figure 8). In this case, although the evidence is equivocal, one resolution of the species tree will be chosen over another, due to the optimality criterion. In the example presented in Figure 8, Species Tree 1 has a higher deep coalesce cost than an alternative resolution, Species Tree 2. If Species Tree
1 reflects the true history, it will be recovered at a lower frequency than Species Tree 2, which has a “better” deep coalescence score. If locus 2 was monophyletic in species 1, both species tree resolutions would have a deep coalescence cost of 1, and would be recovered at equal frequency in species tree searches. The uncertainty in species tree inference that would result is desired – if evidence from different loci conflicts, inferences should reflect that uncertainty. Admittedly, for two loci, the incorrect species tree will only be chosen if the gene genealogy displaying the paraphyletic species is the 81 incorrect species tree. In the example shown in Figure 8, if Species Tree 2 actually did reflect the true history, it would be represented at a higher frequency in tree searches than
Species Tree 1, and the consensus species tree would not be misleading for this partition.
This error only occurred once in each of the two 2 loci searches (ca. 1% error rate) and never in the 3 loci searches; in the two 2 loci cases, the same false partition was well supported (recovered in 95% and 90% of the MAXTREES=1 and MAXTREES=10 searches, respectively). The avoidance of this false positive in searches based on 3 loci demonstrates the potential utility of additional markers, but additional genealogies may also conflict with the true history and contribute to misleading inferences (Degnan and
Rosenberg, 2006).
Implications and Extensions
The method presented here provides a means to infer species trees from multiple loci, without concatenation of data sets. Additionally, it offers the flexibility of sampling multiple alleles from a single species, and allows inferences even in cases when different numbers of alleles are sampled among loci. The method presented here, as well as other recently developed inference procedures (Edwards et al., 2007; Carstens and Knowles,
2007; Liu and Pearl, 2007) allow species’ relationships to be estimated without constraining gene genealogies to be reciprocally monophyletic. As evidenced in the lower recovery rate of true partitions using a consensus of gene genealogies to estimate the species tree (61% and 73% for 2 loci and 3 loci consensuses, respectively), reciprocal 82 monophyly is not expected for most loci for recently diverged taxa (Hudson and Coyne,
2002).
Other inference procedures using multiple gene genealogies to infer a species tree include Bayesian estimation of species trees (“BEST”, Edwards et al., 2007; Liu and
Pearl, 2007) and estimation of species phylogeny based on gene tree coalescence probability (“ESP”, Carstens and Knowles, 2007). Although the latter does not incorporate gene tree uncertainty (Carstens and Knowles, 2007), both provide a means of assessing the likelihood of a species tree for a given set of gene genealogies. These two methods do not constrain gene trees to be perfectly concordant with species trees, and by using the coalescent probability as the objective function, incorporate the population genetic framework essential to inferring species phylogenies from multiple loci.
Inference of species trees from gene trees face two important challenges, based primarily on the optimality criterion used for evaluating alternative species trees. For approaches based on the degree of discordance between species trees and gene trees
(Maddison, 1997; Maddison and Knowles, 2006; this study), conditions leading to
“anomalous gene trees” may prevent accurate species tree inference (Degnan and
Rosenberg, 2006). Short internodes in the true species phylogeny can lead to discordance between the true species tree and gene trees, and increase the probability of incorrect inferences (Pamilo and Nei, 1988). More importantly, for species trees with five or more taxa, this does not simply result in phylogenetic “noise”, because discordant gene trees are actually more likely than gene trees that are congruent with the true species phylogeny (Degnan and Rosenberg, 2006). Thus, any species tree inference approach 83 based on species tree gene tree discordance may lead to the “wicked forest” of Degnan and Rosenberg (2006), where increased gene tree sampling leads to increasing support for an incorrect species tree. The parallels between these positively misleading inferences and the phenomenon of long branch attraction (Felsenstein, 1978) merit future attention.
The second challenge is faced by species tree inferences based on coalescent likelihoods of gene trees (Rannala and Yang, 2003; Edwards et al., 2007; Carstens and
Knowles, 2007). The population size parameter, θ, is critical in calculating gene genealogy coalescence probabilities (Rosenberg, 2002; Rannala and Yang, 2003); however, estimates of this parameter are often accompanied by wide confidence intervals
(Beerli and Felsenstein, 2001; Rannala and Yang 2003; Edwards et al., 2007), and it remains to be seen how uncertainty in θ affects species tree inferences which rely on this parameter for likelihood calculations. The dependence on parameter estimates is a challenge to all model based inferences, and additional evaluation of the sensitivity of species tree inferences to variation in θ are necessary to determine appropriate conditions for gene tree likelihood approaches.
There are ample opportunities for improvement of this method. The inference of the species tree topology in this method does not result in estimates of divergence times on the species tree. Divergence times could be estimated in the manner currently used to date divergence times of supertrees: molecular divergences among taxa are used to estimate relative branch lengths of the tree, and absolute dates are assigned using those relative branch lengths and nodes calibrated with fossil data (Purvis, 1995; Bininda 84
Emonds et al., 2007). Additionally, more extensive branch swapping algorithms, such as subtree pruning and regrafting (SPR) and tree bisection and reconnection (TBR) could be employed. As the number of species increases, the number of possible tree arrangements increases rapidly, so a more thorough examination of treespace would be necessary to improve the probability of finding the most optimal tree(s). Finally, the search procedure used to infer species trees in Mesquite can use rooted gene trees, if a taxon can be reliably considered as an outgroup. By treating gene trees as rooted, the number of possible rooted species trees is greatly reduced, and searches can be completed on a considerably shorter timescale. This approach merits caution however, and should only be employed if the gene trees can be reliably rooted.
Phylogeny estimation based on multiple loci is experiencing a period of exciting discovery. The development of methods to accommodate multiple loci has accelerated in recent years as genetic data become more available and computing power continues to increase. Future method development should consider some important assumptions of current methods, and if possible, accommodate violations of such assumptions. For example, migration is assumed to be low or non existent in most models, and the degree to which this assumption is violated, and the effect it has on inferences, has been little studied. Additionally, inferences which assign individuals to species a priori may suffer when cryptic lineages are sampled, or misidentification of specimens has occurred.
Estimation procedures which make no a priori judgments, or allow flexibility in species assignment, may provide a means to accommodate uncertainty in specimen assignment. 85
Recognizing the uncertainty associated with data used for phylogenetic estimation will only increase our confidence when making such inferences.
Current gene tree species tree estimates of phylogeny are hierarchical, involving some degree of separation between gene tree inferences and species tree inferences
(Carstens and Knowles, 2007; Liu and Pearl, 2007; this study). An ideal approach would integrate gene tree and species tree inferences in a Bayesian importance sampling procedure, incorporating all parameters involved in gene tree estimation into the combined model, as outlined, but not implemented, in Liu and Pearl (2007). Gene trees evolve within species trees, so some consideration of the non independence of the two should be incorporated into such models. Additionally, as more genetic data become available, and genome scale analyses become possible, the non independence of linked loci should be taken into account, and ideally included, in estimations of species’ histories. With these considerations in mind, current species tree inference approaches are alternative partial solutions to the challenge of phylogenetic estimation. These current alternatives should be eclipsed by future analytical methods explicitly dealing with challenges inherent in gene tree species tree inferences, with the goal of a more complete understanding of evolutionary relationships among lineages.
Acknowledgments
This work benefited greatly from discussions with D.R. Maddison, M.J.
Sanderson, and M.W. Nachman, all of whom provided invaluable insight and advice.
Specimens were generously provided by E. Balleto, G.C. Bozano, and A.M. Shapiro; 86
L.R. Stein assisted in DNA sequencing. This work was funded in part by the Center for
Insect Science at the University of Arizona and an NSF DDIG (#0412447).
87
References Beerli, P. and J. Felsenstein. 2001. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Nat. Acad. Sci. 98:4563 4568. Bininda Emonds, O. R. P., M. Cardillo, K. E. Jones, R. D. E. MacPhee, R. M. D. Beck, R. Grenyer, S. A.Price, R. A. Vos, J. L. Gittleman, and A. Purvis. 2007. The delayed rise of present day mammals. Nature 446:507 512. Carstens, B. C. and L. L. Knowles. 2007. Estimating species phylogeny from gene tree probabilities despite incomplete lineage sorting: an example from Melanoplus grasshoppers. Syst. Biol. 56: 400 411. Caterino, M. S. and F. A. H. Sperling. 1999. Papilio phylogeny based on mitochondrial cytochrome oxidase I and II genes. Mol. Phylogent. Evol. 11:122 137. Degnan, J. H. and N. A. Rosenberg. 2006. Discordance of species trees with their most likely gene trees. PLoS Genetics 2:762 768. Edwards, S. V., L. Liu, and D. K. Pearl 2007. High resolution species trees without concatenation. Proc. Nat. Acad. Sci. 104:5936 5941. Felsenstein, J. 1978. Cases in which parsimony and compatibility methods will be positively misleading. Syst. Zool. 27:401 410. Funk, D. J. and K. E. Omland 2003. Species level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Ann. Rev. Ecol. Evol. Syst. 34:397 423. Gorbunov, P.Y. 2001. The butterflies of Russia: Classification, Genitalia, Keys for Identification. Russian Academy of Sciences. Ekaterinburg, Russia. Green, P. 1999. Phrap . http://phrap.org. Green, P. and Ewing, B. 2002. Phred . http://phrap.org Hudson, R. R. and J. A. Coyne 2002. Mathematical consequences of the genealogical species concept. Evolution 56:1557 1565. Huelsenbeck, J. P. and F. Ronquist. 2001. MrBayes: Bayesian inference of phylogeny. Bioinformatics. 17:754 755. 88
Kubatko, L.S. and J. H. Degnan. 2007. Inconsistency of phylogenetic estimates from concatenated data under coalescence. Syst. Biol. 56:17 24. Liu, L. and D. K. Pearl. 2007. Species trees from gene trees: reconstructing Bayesian posterior distribution of a species phylogeny using estimated gene tree distributions. Syst. Biol. 56:504 514. Maddison, D. R. and W. P. Maddison. 2005. Genesis: Models of character evolution. A package of modules for Mesquite. Version 1.01. http://mesquiteproject.org. Maddison, D. R. and W. P. Maddison. 2007. Chromaseq: a Mesquite module for analyzing sequence chromatograms. Version 0.91. http://mesquiteproject.org/packages/chromaseq. Maddison, W. P. 1997. Gene trees in species trees. Syst. Biol. 46:523 536. Maddison, W. P. and L. L. Knowles. 2006. Inferring phylogeny despite incomplete lineage sorting. Syst. Biol. 55:21 30. Maddison, W. P. and D. R. Maddison. 2007. Mesquite: a modular system for evolutionary analysis. Version 2.0bi44. http://mesquiteproject.org . Monteiro, A. and N. E. Pierce. 2001 Phylogeny of Bicyclus (Lepidoptera: Nymphalidae) inferred from COI, COII and EF 1alpha gene sequences. Mol. Phylogenet. Evol. 18, 264 281. Pamilo, P. and M. Nei. 1988. Relationships between gene trees and species trees. Mol. Biol. Evol. 5:568 583. Purvis, A. 1995. A composite estimate of primate phylogeny. Phil. Trans. R. Soc. Lond. B. 348:405 421. Rannala, B. and Z. Yang. 2003. Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. Genetics 164: 1645 1656. Rosenberg, N. A. 2002. The probability of topological concordance of gene trees and species trees. Theor. Pop. Biol. 61:225 247. Rosenberg, N. A. 2003. The shapes of neutral gene genealogies in two species: Probabilities of monophyly, paraphyly, and polyphyly in a coalescent model. Evolution 57:1465 1477. 89
Sullivan, J. and D. L. Swofford. 1997. Are guinea pigs rodents? The importance of adequate models in molecular phylogenetics. J. Mam. Evol. 4:77 86. Swofford, D. L. 2001. PAUP*: Phylogenetic Analyses Under Parsimony (*and other methods) , version 4.0b10. Sinauer Associates, Sunderland, MA. Tajima, F. 1983. Evolutionary relationship of DNA sequences in finite populations. Genetics 105:437 460. Warren, A.D. 2005. Butterflies of Oregon: Their Taxonomy, Distribution and Biology. Contributions of the C.P. Gillette Museum of Arthropod Diversity, Colorado State University, Fort Collins, CO. 90
Figures
Figure Legends
Figure 1: Example of deep coalescences. Two gene genealogies (locus 1 and locus 2) are shown resolved in the species tree (grey). Deep coalescences are counted as the number of ‘extra’ gene lineages (i.e. those beyond the first) per locus that a species contributes to its immediate ancestral lineage. In this example, there are no extra lineages at the time species 2 and species 3 diverge ( t2,3 ) – alleles sampled from each species coalesce for both loci more recently than t2,3 . However, at the divergence of species 1 from the immediate ancestor of species 2 and species 3 ( t1,(2,3) ), there are two extra lineages, one from each locus (circles).
Figure 2: Illustration of species tree inference method. Gene genealogy distributions for n loci were generated by MrBayes. A single gene genealogy was sampled from these distributions for each locus, and the set of n genealogies then used to infer species tree(s) minimizing the number of deep coalescences. This process was repeated m times, and summarized in a consensus species tree, shown in black, with node values reflecting frequency at which a partition was recovered. See text for details of gene genealogy distribution generation and species tree inference conditions.
Figure 3: Model species tree used for gene tree coalescent simulations. T D is the time between divergence of Everes and Cupido and diversification within in each genus.
91
Figure 4: Branch lengths in true species tree for simulated data and frequency at which partition was recovered in species tree inference. White circles represent frequencies for searches based on two loci with MAXTREES=1, closed circles represent frequencies encountered for searches based on two loci with MAXTREES=10, and crosses represent frequencies for searches based on three loci with MAXTREES=10. Branch lengths are scaled to effective population size (N e=100,000). Note X axis has a logarithmic scale.
Figure 5: Comparisons in errors of different species tree inference methods. Circles show average number of missed partitions (out of nine possible); Crosses show average number of false positives (partitions that did not exist in the true species tree). Error bars are ±1 standard error. (a) Inference based on majority rule consensus of Bayesian consensus gene trees. (b) Method presented by Maddison & Knowles (2006), saving one tree
(MAXTREES=1) during SPR branch swapping. This method produces fully resolved species trees, so the number of false positives = number of missed partitions. (c) New method presented in this study, using NNI branch swapping during tree search inference.
Figure 6: Gene trees and inferred species trees for Everes & Cupido . (a) Gene genealogy for COI and COII. (b) Gene genealogy for EF 1a. Bayesian consensus branch lengths shown for each gene genealogy; branches are labeled with Bayesian posterior probability
(*=1.00). (c) Consensus species tree, keeping a single best tree per search replicate. (d)
Consensus species tree, keeping at most 10 best trees per search replicate. For species 92 trees, branch labels indicate frequency at which clade occurred in the distribution of species trees.
Figure 7: Results of constraint test permutations comparing observed results to values based on gene trees simulated on model species trees (Figure 3). δ is the difference in the number of deep coalescences between species trees from unconstrained and species trees from constrained searches. T D is the time from divergence between the genera to diversification within each genus. See text and Figure 3 for test details.
Figure 8: Example of deep coalescence inferences being positively misleading. (a) Gene genealogies of two loci conflict in relationships among three species. (b) The two gene genealogies resolved within the true species tree (grey). There are three deep coalescent events in this resolution of the species tree. (c) The two gene genealogies resolved within an alternative species tree, with only two deep coalescent events. In this case, the ‘best’ tree under the deep coalescence criterion (tree shown in c), is not the true species tree. 93
Figure 1 Species 1 Species 2 Species 3
t2,3
t1,(2,3)
Locus 1 Locus 2 94
Figure 2
Species Tree 1 Gene 1 Gene Tree Inference Matrix Inference
Gene 1 Gene Tree Distribution Species Tree 2 Inference Gene 2 Gene Tree 0.67 Matrix Inference
Gene 2 Gene Tree Distribution Species Tree with Support Values
Gene n Gene Tree Species Matrix Inference Tree m Gene n Inference Gene Tree Distribution 95
Figure 3
Everes Cupido fischeri T.
20 N e
TD
96
Figure 4
1 0.9 0.8 0.7 0.6 0.5 0.4 Frequency 0.3 0.2 0.1 0 0.01 0.1 1 10 100 Branch Length (scaled to Ne)
97
Figure 5
4 a b c
3.5
3
2.5
2
1.5 Average Occurrance Average
1
0.5
0 2 Loci 3 Loci 2 Loci 3 Loci 2 Loci, 2 Loci, 3 Loci, MAXTREES=1 MAXTREES=10 MAXTREES=10 Gene Tree Consensus Maddison & Knowles Present Study
98
Figure 6 a b Tongeia fischeri Tongeia fischeri * * Tongeia fischeri Tongeia fischeri Everes lacturnus * Everes alcetas
Everes lacturnus Everes alcetas Everes argiades * Everes amyntula Everes argiades argiades Everes amyntula Everes amyntula * * Everes comyntas Everes amyntula Everes comyntas Everes comyntas * Everes decoloratus 0.78 Everes comyntas Everes decoloratus 0.51 Everes alcetas 0.57 Everes alcetas 0.98 Everes argiades * 0.79 Everes decoloratus Everes argiades argiades * Everes decoloratus Everes lacturnus 0.93 * Cupido prosecusa * Everes lacturnus Cupido prosecusa 0.73 Cupido prosecusa
Cupido osiris Cupido prosecusa *
* Cupido osiris Cupido buddhista buddhista Cupido buddhista buddhista Cupido buddhista buddhista 0.61 Cupido buddhista buddhista * Cupido minimus noguerae * Cupido tusovi 0.64 Cupido minimus Cupido minimus 0.95 Cupido osiris Cupido minimus 0.88 Cupido osiris Cupido minimus noguerae 0.1 0.1
c d Tongeia fischeri Tongeia fischeri
Everes lacturnus Everes lacturnus
Everes amyntula Everes amyntula
Everes argiades Everes argiades
Everes comyntas Everes comyntas
0.52 Everes alcetas Everes alcetas 0.67 0.62 Everes decoloratus Everes decoloratus
Cupido prosecusa Cupido prosecusa
0.60 Cupido buddhista Cupido buddhista
Cupido osiris Cupido osiris 1.00 0.96 Cupido minimus Cupido minimus 0.95 0.98 Cupido tusovi Cupido tusovi 99
Figure 7
TD = 10 N e 20 TD = 1 N e
15 TD = 0.1 N e Observed
10 Count
5
0 1 1 3 5 7 9 11 13 15 17 19 21
100
Figure 8 a Species 1 Species 2 Species 3 Species 1 Species 2 Species 3
Locus 1 Locus 2
b c Species 1 Species 2 Species 3 Species 1 Species 2 Species 3
Species Tree 1 Species Tree 2 3 deep coalescences 2 deep coalescences
101
APPENDIX C
EVOLUTION OF EXPLOITATION: ILLICIT SIGNALING IN A LYCAENID- ANT INTERACTION 102
Evolution of Exploitation: Illicit Signaling in a Lycaenid Ant Interaction Jeffrey C. Oliver 1 and Laura R. Stein 2 1Interdisciplinary Program in Insect Science and Department of Entomology, and
2Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ
85721, USA. 103
Abstract
Many phytophagous insects participate in interspecific interactions to gain defense from predators and parasitoids. Lycaenid butterfly caterpillars are often involved in mutualisms with ant hosts: these larvae provide carbohydrate rewards from the dorsal nectary organ (DNO) to associated ants, in return for protection from natural enemies.
These interactions also depend on volatile signals emitted from the lycaenid larvae.
However, larvae of some lycaenid species do not possess the reward producing organ, yet are still found in association with ants. Members of the Lycaena xanthoides species group are one such example: larvae lack the DNO, yet are almost always found associated with the ant Formica francoeuri . Here we test the hypothesis that L. xanthoides larvae function as illicit signalers, manipulating ant behavior when faced with a simulated predator attack, while providing no reward to ant associates. We evaluate the relationship in a phylogenetic framework and show that L. xanthoides likely evolved from a non ant associated ancestor. This suggests that L. xanthoides has ‘cracked the code’ that other, honest signaling lycaenid larvae use to communicate to ants. Evolution of mutualisms between honest signaling larvae and ants will likely be affected by the impact of illicit signaling larvae.
104
Introduction
Insects have evolved an array of intrinsic physical, behavioral, and chemical defenses to reduce mortality from predators and parasitoids (Pasteels et al. 1983; Gross
1993). Some insects have also evolved indirect defenses: by participating in a mutualism with a top predator, insects may shift the burden of defense to their partner species (Nault et al. 1976; Pierce & Mead 1981; Weeks 2003). Although living in close contact with top predators seems like a risky defensive strategy, vulnerable partners are equipped with a diverse set of adaptations to keep from becoming prey to their defenders. As incentive to their protective partners, phytophagous insects often provide nutritional rewards in return for defense from natural enemies (Pierce & Mead 1981; Stadler & Dixon 2005).
Ants are commonly recruited defenders for phytophagous insects, including aphids, lycaenid butterfly caterpillars, and cynipid wasps (Gross 1993). Interactions between ants and these phytophagous insect species represent a classic signaling system between an emitter and a receiver (Otte 1974). A majority of lycaenid species are involved in mutualistic interactions with ants (Osborn & Jaffé 1997; Pierce et al. 2002): ants respond to signals emitted by larvae, and are provided with a nutritional reward from the dorsal nectary organ (Pierce & Mead 1981; Fiedler and Maschwitz 1989; Agarwal &
Fordyce 2000; Saarinen 2006). However, these systems, in which a larva (signaler) advertises a reward to an ant (receiver), are susceptible to invasion by illicit signalers
(Otte 1974; Haynes & Yeargan 1999). In the case of ant lycaenid mutualisms, illicit signalers would simulate the signals of a reward providing lycaenid larva and gain the protection of ants without providing a reward. There are numerous examples of 105 commensal lycaenid larvae which gain access to enemy free (ant patrolled) space without providing nutritional rewards, but there is no evidence to date of lycaenid larvae acting as illicit signalers taking advantage of the honest signals passed between ants and other lycaenid larvae (Osborn & Jaffe 1997; Pierce et al. 2002). If a lycaenid larva can produce signals which result in manipulating ant behavior without providing a reward, it will be essential to incorporate the existence of such illicit signaling in studies of ant lycaenid mutualisms (Stanton 2003).
In this study, we investigate a system in which the larvae of a lycaenid species lacking the reward producing organ (dorsal nectary organ, or DNO) are associated with ants. Lycaena xanthoides (Boisduval) (Lepidoptera: Lycaenidae) larvae are known to associate with two ant species, Formica francoeuri Bolton and Liometopum occidentale
Emery, and face strong pressure from natural enemies (Ballmer & Pratt 1991; Oliver et al. 2007). Formica francoeuri also tends other lycaenid larvae, most of which possess
DNOs and thus can offer rewards for protection. Because F. francoeuri ants tend other lycaenid species offering rewards, they are susceptible to illicit signals from L. xanthoides , which may emit such signals and manipulate the behavior of F. francoeuri , without providing a nutritional reward. The first goal of this study is to determine if L. xanthoides larvae can function as illicit signalers and manipulate F. francoeuri behavior.
As a second goal, we also investigated the evolution of the association between L. xanthoides larvae and F. francoeuri , by reconstructing a phylogeny of the genus Lycaena .
If L. xanthoides are illicit signalers, there are two likely scenarios for the evolution of this association: (1) the ancestor of L. xanthoides once provided nutritional rewards to ants, 106 but lost the reward producing DNO in the lineage leading to L. xanthoides ; or (2) the ancestor of L. xanthoides was not associated with ants, but evolved a signaling mechanism to exploit other lycaenid ant mutualisms. The former scenario could be considered ‘permanent cheating’, where a once honest signaler was able to gain rewards without ever providing a reward. The second scenario suggests that L. xanthoides larvae have ‘cracked the code’ between honest signaler and receiver (Letourneau 1990). By inferring the phylogeny of related species and reconstructing character evolution on this phylogeny, we will have a better understanding of the conditions leading to the evolution of such associations between lycaenid larvae and ants.
Materials & Methods
Study system
Lycaena xanthoides occupies mesic habitats in low to middle elevations of
California, northern Baja, Mexico, and southern Oregon. Larvae feed on 4 5 species of
Rumex (Polygonaceae) (Scott 1986; Ballmer & Pratt 1988) and are usually associated with ants (Ballmer & Pratt 1991; Oliver et al. 2007). Formica francoeuri occupies the mountains of northern Baja, Mexico, and the Transverse and southern Coast ranges of
California (Francoeur 1973). Formica francoeuri is known to tend at least six species of lycaenids in California: Lycaena xanthoides , L. heteronea (Boisduval), Plebejus acmon
(Westwood & Hewitson), P. lupini (Boisduval), Glaucopsyche piasus (Boisduval), and
Plebulina emigdionis (Grinnell) (Ballmer & Pratt 1991). Neither L. xanthoides nor L. heteronea (subfamily Lycaeninae) larvae possess a dorsal nectary organ; the larvae of the 107 latter four species (all in the subfamily Polyommatinae) all possess a DNO (Ballmer &
Pratt 1988).
Laboratory test for illicit signaling
Fourth (final) instar Lycaena xanthoides larvae and F. francoeuri ants were collected in the field from two populations and brought into the laboratory. We collected
5 larvae from Pine Creek (San Diego County, California, 32.8548 °N, 116.5228 °W) and
4 larvae from Lake Hemet (Riverside County, California, 33.6702 °N, 116.6993 °W).
Each larva was housed singly with host plant material ( Rumex salicifolius ) from respective collection site until trials. For trials, a single larva was placed in an arena with
9 (Pine Creek) or 14 (Lake Hemet) ants from the same location the larva was collected.
After a two minute acclimation period, the larva was randomly given one of two treatments: a pinch with forceps on the dorsal thorax (‘attack’) or no pinch (‘control’). In the control treatment, forceps were introduced into the arena, above the larva, but no pinch was applied. The interaction between the larva and ants was video recorded for 5 minutes. After this trial, the larva was removed from the arena and placed in a container with Rumex salicifolius for four hours before receiving the alternate treatment; ants were alone in the arena for at least 20 minutes following the end of the last trial before another trial began.
To determine if ants responded differently to the different treatments, we recorded the total ant seconds for each trial. Ant seconds reflect the amount the larva was tended by ants. For example if a larva was tended for 10 seconds by one ant, and 15 second by 108 another ant, the total for that trial would be 25 ant seconds. Note that if multiple ants tended a larva at a single time, each of those ants’ tending times was included. We tested for within larvae differences between treatments using a paired t test to determine if larvae were tended more when in attack treatments than control treatments. Additionally, we measured the total time each larva was moving during the trial and performed paired t tests to determine if simulated predation events resulted in greater movement by the larvae. Greater movement by the larva may have afforded greater opportunity to come into contact with ants, producing an artificial positive relationship between a simulated predation event and our measure of ant tending. To test for this relationship, we performed repeated measures linear regression testing for an effect of larvae movement on ant tending. All statistical analyses were performed in R 2.6.0 (R Development Core
Team 2007).
Evolution of association
To investigate the evolutionary history of L. xanthoides ’ association with ants, we reconstructed a molecular phylogeny of the genus Lycaena . We sequenced three genes: a portion of the mitochondrial genes cytochrome oxidase subunits I and II (COI and COII, respectively) and the nuclear gene elongation factor 1 alpha (EF1α). Using the primers
Ron, Nancy, Tonya, Hobbes (COI), Pierre, Eva (COII), ef44f, and efrcM4r (EF1 α)
(Caterino & Sperling 1999, Monteiro & Pierce 2001), all genes were sequenced in both directions on an Applied Biosystems 3730XL DNA Analyzer by the Genomic Analysis and Technology Core (University of Arizona, Tucson, AZ). Consensus sequences were 109 generated and aligned with the aid of phred/phrap (Green 1999; Green & Ewing 2002) and the Chromaseq package of Mesquite (D.R. Maddison & W.P. Maddison 2007). In addition to specimens sequenced for this study, we also included representatives of six other subfamilies of Lycaenidae, as well as two outgroup species (Table 1; Wahlberg et al. 2005). In all subsequent analyses, COI and COII data were analyzed as single locus, because they are tightly linked on the mitochondrial genome.
To reconstruct this phylogeny, we used the method of Oliver (Appendix B), which uses gene genealogies to reconstruct a species phylogeny based on a criterion of deep coalescences (Maddison 1997; Maddison & Knowles 2006). We first generated gene genealogies for the combined COI/COII data and EF1α data using MCMC in
MrBayes (Huelsenbeck & Ronquist 2001). For each locus, we sampled trees from two independent MCMC runs of four chains each. Each MCMC analysis was run for 5 million generations, and trees were sampled every 1000 generations. Only trees sampled after the burnin phase were used in subsequent analyses; the burnin phase lasted until log likelihood scores stabilized and the two runs converged. Convergence was reached when the average standard deviation of the split frequencies was less than 0.02 (Huelsenbeck &
Ronquist 2001). For both loci, the burnin phase ended after 2 million generations, so only trees sampled after 2 million generations were used for species tree inference.
To infer the relationships of the species included in this study, we first pruned gene genealogies of specimens for which data from only one locus were available.
Although species tree inferences can accommodate gene genealogies with differing numbers of taxa, the taxa missing from some gene trees added considerable uncertainty to 110 our species tree inference. From the gene genealogy distributions created in MCMC, we randomly sampled one gene tree for each locus, and used those two genealogies to infer a species tree. Using Mesquite’s Tree Search function (W.P. Maddison & D.R. Maddison
2007), we searched for the species tree that minimized the number of deep coalescences of the two contained gene trees. For each search, we employed the nearest neighbor interchange (NNI) branch swapping algorithm, saving a maximum of 100 most optimal trees per search and treating the contained trees as rooted (outgroup = Danaus plexippus ).
We repeated this search procedure 100 times, sampling two new gene genealogies for each search. We then generated a consensus species tree using PAUP’s consensus tree function (Swofford 2001). The frequency at which a clade occurred in the species tree searches is used as a measure of clade support.
To determine whether L. xanthoides ’ association with ants evolved from an ant associated or non ant associated ancestor, we reconstructed the history of ant association on each of the Bayesian consensus gene trees and on the consensus species tree. We coded taxa as being myrmecophilous (ant associated) or myrmecoxenous (non ant associated), based on published records (Table 1). Here we use Pierce et al.’s (2002) broad definition of myrmecoxeny as non ant associated, as opposed to the definition originally offered by Kitching & Luke (1985), which defines taxa as myrmecoxenous if they lack the DNO. For those taxa lacking records regarding ant association, characters were coded as missing data. For ancestral reconstructions of ant association on the two consensus gene trees, we used the “Trace Over Trees” function in Mesquite, which reconstructs ancestral history on multiple phylogenies, to incorporate topological 111 uncertainty in ancestral reconstructions of character states (W.P. Maddison & D.R.
Maddison 2007). For each Bayesian consensus gene genealogy, we reconstructed ancestral states on a sample of 3000 post burnin gene trees using an Mk1 likelihood model of evolution. We focused on nodes within the Lycaeninae clade that were ancestral to the L. xanthoides clade. Likelihood reconstructions are reported as (1) the average likelihood of each state reconstruction and (2) the number of trees containing the node in question in which a particular state was reconstructed as the uniquely best state.
To reconstruct the history of ant association on the species tree, we again used
“Trace Over Trees” in Mesquite. We reconstructed the history of ant association in
Lycaenidae on all species trees used to generate the consensus species tree, using an unordered parsimony model of character change. These reconstructions were then summarized on the consensus species tree, where the frequency of each state is reported for each ancestral node. We focused on the ancestral state of the subfamily Lycaeninae, which, in our species tree reconstructions, is the direct ancestor of the clade leading to L. xanthoides . If the ancestor of all Lycaeninae is reconstructed as myrmecoxenous, we can infer that L. xanthoides ’ association with ants evolved from a myrmecoxenous ancestor.
Ancestral character reconstructions indicate a second, separate origin of myrmecophily within the Lycaeninae (see Results & Discussion). Lycaena heteronea is also associated with ants (Ballmer & Pratt 1991), but is not closely related to L. xanthoides (Pratt & Wright 2002; this study). Ancestral reconstructions of ant association indicate L. heteronea evolved myrmecophily independently of the association observed in L. xanthoides . To test if these are two independent origins of myrmecophily, 112 we performed paired tree searches as described in Oliver (Appendix B), comparing species trees from unconstrained searches to species trees constrained to reflect an exclusive clade of all myrmecophilous Lycaena . If L. xanthoides and L. heteronea evolved myrmecophily independently, constrained searches should produce consistently less optimal trees than unconstrained searches. We calculated the difference in the number of deep coalescences of optimal trees from constrained and unconstrained searches based on the same set of gene genealogies. We performed 20 replicates, saving a maximum of ten most optimal trees (MAXTREES=10) during SPR branch swapping, treating the contained gene trees as rooted (outgroup = Danaus plexippus ). If myrmecophilous Lycaena form a clade, we would not expect constrained searches to consistently recover less optimal trees than unconstrained searches.
Results & Discussion
Laboratory test for illicit signaling
For eight of nine larvae tested, we found higher rates of ant tending in attack treatments than control treatments (Figure 1). Ant tending was typically characterized by antennal palpation, often at the posterior end of the larva, where the DNO is located on other lycaenid larvae. On average, individual larvae were tended more when subjected to a simulated attack than in control treatments (t 8 = 3.92, p = 0.004). Larvae moved more during attack treatments than control treatments, but the difference between treatments was only marginally significant (t 8 = 2.24, p = 0.055). In a linear regression model, we found no relationship between the time a larvae spent moving and the amount of ant 113
tending (F1,7 = 0.049, p = 0.8312). These results demonstrate the ability of L. xanthoides larvae to manipulate ant behavior without providing a nutritional reward.
The mechanism of this manipulation remains unknown, due in large part to the lack of many ant associated organs within the subfamily Lycaeninae and the lack of functional knowledge about those that are present in ant associated Lycaena . In addition to the absence of the dorsal nectary organ, all surveyed Lycaena species lack eversible tentacle organs (Ballmer & Pratt 1988); these are used by many ant associated lycaenid species to emit volatiles that elicit an alarm response in ants (Henning 1983; Pierce et al.
2002). Almost all Lycaenidae, including all surveyed Lycaena species, possess pore cupola organs, although these are presumed to be used for suppressing ant aggression, not manipulating ant behavior (Pierce et al. 2002). All ant associated Lycaena species possess dendritic setae, while all non ant associated Lycaena do not. The function of these setae remains unknown, but the correlation of their presence with ant association suggests a relationship (Ballmer & Pratt 1991). Regardless of the signaling mechanism, the lack of any rewarding mechanism argues for exploitation of ant defense by a non rewarding lycaenid species.
We hypothesize this interaction between L. xanthoides larvae and F. francoeuri represents a case of aggressive chemical mimicry (Wickler 1968; Dettner & Liepert
1994). In aggressive mimicry, a deceiver gains access to resources by mimicking a rewarding model (Wickler 1968; Vane Wright 1976; Ruxton et al. 2004). In this case, L. xanthoides larvae are able to mimic the signals produced by other, rewarding lycaenid larvae that F. francoeuri tends in nature. This exploitation may be possible due to F. 114 francoeuri ’s generalist strategy of tending: in addition to associations with at least six species of California lycaenid caterpillars, laboratory trials indicate that F. francoeuri workers will tend novel lycaenid species, including at least one species from Asia
(Ballmer & Pratt 1991). However, in order to confirm this hypothesis of mimicry, it will be necessary to compare the responses of F. francoeuri to other, rewarding species of lycaenid larvae, in experiments analogous to those presented in this study.
Evolution of association
Phylogenetic reconstructions of gene trees revealed little resolution among the subfamilies of Lycaenidae (Figure 2), and for one gene (EF1a), the monophyly of the family Lycaenidae was not supported. Additionally, among those subfamilies represented by more than one species, only Lycaeninae was reconstructed as monophyletic. The consensus inferred species tree, based on 8668 trees recovered in the tree searching procedure, reflects uncertainty in early lycaenid divergences, as well as the monophyly of subfamily Lycaeninae (Figure 3). The species tree shows some resolution of relationships within the genus Lycaena , including strong support for a ( L. xanthoides +
L. editha + L. dione + L. rubidus ) clade. These four species, taxonomically treated as subgenus Gaeides , are all associated with ants in nature (Ballmer & Pratt 1991; Allen et al. 2005), but lack the rewarding structure, the dorsal nectary organ (Ballmer & Pratt
1988). For the purposes of ancestral reconstruction, we consider myrmecophily of these taxa to be homologous, and assume the most recent common ancestor of the four taxa was also associated with ants. 115
Ancestral state reconstructions indicate the association observed in L. xanthoides and other members of the subgenus Gaeides evolved from a myrmecoxenous ancestor.
Figure 4 and Table 2 show the results of reconstructing the evolution of ant association on the two consensus gene trees. For each gene tree, the nodes within Lycaeninae that are ancestral to the subgenus Gaeides are reconstructed as myrmecoxenous with high likelihood. In parsimony reconstructions on inferred species trees, 8496 of the 8668 trees contained a node corresponding to the ancestor of all Lycaeninae (indicated by arrow in
Figure 3). This ancestor was reconstructed as myrmecoxenous in 6370 trees (75.0%), ant associated in 26 trees (0.3%), and equivocal in 2100 trees (24.7%). The reconstructions of ant association on gene trees and species trees support the hypothesis that the current association between ants and Gaeides species arose from a myrmecoxenous ancestor.
We assessed support for a single origin of myrmecophily within the genus
Lycaena by comparing species trees from searches topologically unconstrained to trees constrained to reflect a clade of myrmecophilous Lycaena ( Gaeides + L. heteronea ). We found no support for an exclusive myrmecophilous clade within the Lycaeninae: constraining trees to reconstruct an exclusive Gaeides + L. heteronea clade consistently produced less optimal trees than unconstrained searches (difference in number of deep coalescences: average = 7.1, minimum = 5, maximum = 9). Although L. heteronea has not been assayed to measure its ability to manipulate ant behavior (as we assayed L. xanthoides in this study), Ballmer & Pratt (1991) found that F. francoeuri would tend fourth instar L. heteronea larvae in the laboratory and in the wild. This independent evolution of association between ants and a non rewarding species of lycaenid 116 demonstrates the possibility of ant lycaenid interactions evolving from a myrmecoxenous ancestor lacking the rewarding DNO. Future studies should test whether L. heteronea larvae are able to manipulate ant behavior as observed in L. xanthoides larvae.
Illicit signaling and the evolution of exploitation
This study demonstrates that L. xanthoides can manipulate ant behavior and the association between L. xanthoides and relatives in the subgenus Gaeides likely evolved from a myrmecoxenous ancestor. By ‘cracking the code’ of communication passed between honest signaling larvae and ants, L. xanthoides may enjoy protection from natural enemies without the cost of providing a nutritional reward. Illicit signaling by lycaenids is not uncommon: many species mimic ant pheromones to gain access to ant nests, where larvae feed on ant brood or other ant provided resources (Fiedler 1991;
Pierce et al. 2002); however, the type of illicit signaling described here has not been demonstrated in previous studies of lycaenids. This phenomenon may be more common among ‘myrmecoxenous’ larvae than previously thought, and other potential examples involving lycaenid larvae lacking the rewarding DNO include Lycaena dispar
(Lycaeninae) and Myrmica laevinodis (Myrmicinae) (Hinton 1951), Curetis regula
(Curetinae) and Anoplolepis longipes (Formicinae) (DeVries 1984), and Aloeides dentatis
(Theclinae) and Lepisiota ( Acantholepis ) capensis (Formicinae) (Henning 1983).
A most promising avenue of research lies in the evaluation of signals passed between members of these interactions. What are the signals passed from larvae to ant?
The volatile components of signals produced by lycaenids under simulated attacks remain 117 undescribed. Although it is hypothesized (and likely) that volatiles emitted from the tentacle organs mimic ant alarm pheromones, data supporting this claim are lacking
(Pierce et al. 2002). Future work should also consider other potential components of signals produced by larvae, such as acoustic signaling (DeVries 1990; Travassos &
Pierce 2000), and how signal complexity may affect interactions among ants and the various signalers (Rowe 1999). In addition to pairwise analyses between ants and lycaenids, a more inclusive approach is warranted: the communication underlying the interactions between one (honest) species of lycaenid and the ant protector will likely effect the interaction between the other (illicit) species of lycaenid, and vice versa
(Bronstein 2001; Stanton 2003). Of course in many cases it may also be necessary to consider other illicit members of lycaenid ant interactions, such as illicit ant receivers, which may exploit rewards provided by honest signaling lycaenids without providing protection in return (Fraser et al. 2001).
This study demonstrates that lycaenid larvae may gain protection from ants even in the absence of providing a reward, and this exploitation likely evolved from a myrmecoxenous ancestor. Exploiters of mutualisms usually arise from non mutualistic ancestors, and are often not closely related to members of the mutualism they are exploiting (Bronstein 2001). Additional studies are necessary to resolve the relationships within Lycaenidae, but associations between Lycaeninae and ants have evolved at least twice, once in the lineage leading to Gaeides and once leading to L. heteronea . A more thorough sampling of taxa and genetic loci will provide better understanding of the evolution of myrmecophily within Lycaeninae, as well as the evolution of myrmecophily 118 in general. A broader sampling of taxa and loci will allow researchers to answer questions essential to the study of mutualisms. Did ant lycaenid mutualisms evolve from parasitic lycaenids? How does the presence or absence of illicit signalers affect signal receiver communication? How does geographical variation in illicit signaler distribution affect mutualisms? This study highlights the need for additional behavioral, chemical, and phylogenetic studies in order to understand the evolution of ant associations in
Lycaenidae.
Acknowledgments
We thank D.R. Papaj, Y. Carrière, K.L. Prudic, E.C. Snell Rood, and J.M. Davis for discussions concerning behavioral analyses and species interactions. D.R. Maddison and
M.J. Sanderson provided insight on analyses of character evolution. We would also like to thank those individuals who provided specimens: G. Anweiler, G.C. Bozano, A.M.
Shapiro, and E. Weingartner. This work was funded by the Center for Insect Science and an NSF DDIG (# 0412447) to JCO. 119
References
Agarwal, A. A. & Fordyce, J. A. 2000 Induced indirect defense in a lycaenid ant association: the regulation of a resource in a mutualism. Proc. R. Soc. Lond. B 67 , 1857 1861. Allen, T. J., Brock, J. P. and Glassberg, J. 2005 Caterpillars in the Field and Garden: A Field Guide to the Butterfly Caterpillars of North America . Oxford University Press. Ballmer, G. R. & Pratt, G. F. 1988 A survey of the last instar larvae of the Lycaenidae (Lepidoptera) of California. J. Res. Lepid. 27 , 1 81. Ballmer, G. R. & Pratt, G. F. 1991 Quantification of ant attendance (myrmecophily) of lycaenid larvae. J. Res. Lepid. 30 , 95 112. Bernays, E. A. 1997 Feeding by lepidopteran larvae is dangerous. Ecol. Entomol. 22 , 121 123. Bronstein, J. L. 2001 The exploitation of mutualisms. Ecol. Lett. 4, 277 287. Caterino, M. S. & Sperling, F. A. H. 1999 Papilio phylogeny based on mitochondrial cytochrome oxidase I and II genes. Mol. Phylogenet. Evol. 11 , 122 137. Dettner, K. & Liepert, C. 1994 Chemical mimicry and camouflage. Annu. Rev. Entomol. 39 , 129 154. DeVries, P. J. 1984 Of crazy ants and Curetinae: are Curetis butterflies tended by ants? Zool. J. Linnean Soc. 79 , 59 66. DeVries, P. J. 1990 Enhancement of symbioses between butterfly caterpillars and ants by vibrational communication. Science 248 , 1104 1106. Fiedler, K. 1991 Systematic, evolutionary, and ecological implications of myrmecophily within the Lycaenidae (Insecta: Lepidoptera: Papilionoidea). Bonn. Zool. Monogr. 31 , 1 210. Fiedler, K. & Maschwitz, U. 1989 Functional analysis of the myrmecophilous relationships between ants (Hymenoptera: Formicidae) and Lycaenids (Lepidoptera: Lycaenidae). Ethology 80 , 71 80. Francoeur, A. 1973 Revision taxonomique des especes nearctiques du group fusca , genre Formica (Hymenoptera: Formicidae). Mem. Soc. Entomol. Québec 3, 1 316 120
Fraser, A. M., Axén, A. H. & Pierce, N. E. 2001 Assessing the quality of different ant species as partners of a myrmecophilous butterfly. Oecologia 129 , 452 460. Green, P. 1999 Phrap. http://phrap.org. Green, P. & Ewing, B. 2002 Phred. http://phrap.org Gross, P. 1993 Insect behavioral and morphological defenses against parasitoids. Ann. Rev. Entomol. 38 , 251 273. Haynes, K. F. & Yeargan, K. V. 1999 Exploitation of intraspecific communication systems: illicit signalers and receivers. Ann. Entomol. Soc. Am. 92 , 960 970. Henning, F. F. 1983 Chemical communication between lycaenid larvae (Lepidoptera: Lycaenidae) and ants (Hymenoptera: Formicidae). J. Entomol. Soc. S. Afr. 46 , 341 366. Hinton, H. E. 1951 Myrmecophilous Lycaenidae and other Lepidoptera – a summary. Proc. Trans. S. London Entomol. Nat. Hist. Soc. 1949-50 , 111 175. Huelsenbeck, J. P. & Ronquist, F. 2001 MrBayes: Bayesian inference of phylogeny. Bioinformatics 17 , 754 755. Kitching, R. L. & Luke, B. 1985 The myrmecophilous organs of the larvae of some British Lycaenidae (Lepidoptera): a comparative study. J. Nat. Hist. 19 , 259 276. Letourneau, D. K. 1990 Code of ant plant mutualism broken by parasite. Science 248 , 215 217. Maddison, D. R. & Maddison, W. P. 2007 Chromaseq: a Mesquite module for analyzing sequence chromatograms. Version 0.91. http://mesquiteproject.org/packages/chromaseq. Maddison, W. P. 1997 Gene trees in species trees. Syst. Biol. 46 , 523 536. Maddison, W. P. & Knowles, L. L. 2006 Inferring phylogeny despite incomplete lineage sorting. Syst. Biol. 55 , 21 30. Maddison, W. P. & Maddison, D. R. 2007 Mesquite: a modular system for evolutionary analysis. Version 2.0bi44. http://mesquiteproject.org. Monteiro, A. & Pierce, N. E. 2001 Phylogeny of Bicyclus (Lepidoptera: Nymphalidae) inferred from COI, COII and EF 1alpha gene sequences. Mol. Phylogenet. Evol. 18 , 264 281. 121
Nault, L. R., Montgomery, M. E. & Bowers, W. S. 1976 Ant aphid association: role of aphid alarm pheromone. Science 192 , 1349 1351 Oliver, J. C. (in preparation) Inferring species trees from deep coalescences while accommodating gene tree uncertainty. Oliver, J. C., Prudic, K. L. & Pauly, G. B. 2007 Parasitism rates in larval Lycaena xanthoides (Godart) (Lepidoptera: Lycaenidae) and a new host record for Cotesia theclae (Riley) (Hymenoptera: Braconidae). Pan Pac. Entomol. 83 , 255 257. Osborn, F. & Jaffé, K. 1997 Cooperation vs. exploitation: interactions between Lycaenid (Lepidoptera: Lycaenidae) larvae and ants. J. Res. Lepid. 34 , 69 82. Otte, D. 1974 Effects and functions in the evolution of signaling systems. Ann. Rev. Ecol. Syst. 5, 385 417. Pasteels, J. M., Grégoire, J. C. & Rowell Rahier, M. 1983 The chemical ecology of defense in arthropods. Ann. Rev. Entomol. 28 , 263 289. Pierce, N. E., Braby, M. F., Heath, A., Lohman, D. J., Mathew, J., Rand, D. B. & Travassos, M. A. 2002 The ecology and evolution of ant association in the Lycaenidae (Lepidoptera). Ann. Rev. Entomol. 47 , 733 771. Pierce, N. E. & Mead, P. S. 1981 Parasitoids as selective agents in the symbiosis between lycaenid butterfly larvae and ants. Science 211 , 1185 1187. Pratt, G. F. & Wright, D. M. 2002 Allozyme phylogeny of North American coppers (Lycaeninae: Lycaenidae). The Pan Pacific Entomologist 78 , 219 229. R Development Core Team (2007). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R project.org. Rowe, C. 1999 Receiver psychology and the evolution f multicomponent signals. Anim. Behav. 58 , 921 931. Ruxton, G. D., Sherratt, T. N. & Speed, M. P. 2004 Avoiding Attack: The Evolutionary Ecology of Crypsis, Warning Signals and Mimicry . Oxford University Press. Saarinen, E. V. 2006 Differences in worker caste behaviour of Oecophylla smaragdina (Hymenoptera: Formicidae) in response to larvae of Anthene emolus (Lepidoptera: Lycaenidae). Biol. J. Linnean Soc. 88 , 391 395 122
Scott, J. A. 1986 The Butterflies of North America: A Natural History and Field Guide . Stanford, CA: Stanford University Press. Stadler, B. & Dixon, A. F. G. 2005 Ecology and evolution of aphid ant interactions. Ann. Rev. Ecol. Evol. Syst. 36 , 345 372. Stanton, M. L. 2003 Interacting guilds: moving beyond the pairwise perspective on mutualisms. Am. Nat. 162 , S10 S23. Swofford, D. L. 2001 PAUP*: Phylogenetic Analyses Under Parsimony (*and other methods) , version 4.0b10. Sunderland, MA: Sinauer Associates. Travassos, M. A. & Pierce, N. E. 2000 Acoustics, context and function of vibrational signalling in a lycaenid butterfly ant mutualism. Anim. Behav. 60 , 13 26. Vane Wright, R. I. 1976 A unified classification of mimetic resemblances. Biol. J. Linnean Soc. 8, 25 56. Wahlbert, N., Braby, M. F., Brower, A. V. Z., de Jong, R., Lee, M. M., Nylin, S., Pierce, N. E., Sperling, F. A. H., Vila, R., Warren, A. D. & Zakharov, E. 2005 Synergistic effects of combining morphological and molecular data in resolving the phylogeny of butterflies and skippers. Proc. R. Soc. B 272 , 1577 1586. Weeks, J. A. 2003 Parasitism and ant protection alter the survival of the lycaenid Hemiargus isola . Ecol. Entomol. 28 , 228 232. Wicker, W. 1968 Mimicry in Plants and Animals. New York: McGraw Hill Book Company. 123
Tables and figures Table 1 Lycaenidae and outgroups included in this study. Family Subfamily Species Ant associated Reference Lycaenidae Curetinae Curetis bulis No Fiedler 1991 Miletinae Miletus ancon ? Pierce et al. 2002 Liphyra brassolis Yes Braby 2000 Poritiinae Poritia erycinoides No Fiedler 1991 Lipteninae Baliochila minima No Fiedler 1991 Theclinae Araragi enthea No Fiedler 1991 Lucia limbaria Yes Braby 2000 Thecla coelicolor ? Callipsyche behrii ? Polyommatinae Anthene emolus Yes Saarinen 2006 Plebejus acmon Yes Ballmer & Pratt 1991 Echinargus isola Yes Weeks 2003 Lycaeninae Heliophorus eventa ? Lycaena margelanica ? Lycaena phlaeas No Ballmer & Pratt 1991 Lycaena cupreus No Ballmer & Pratt 1991 Lycaena dispar No Fiedler 1991 Lycaena li ? Lycaena arota No Fiedler 1991 Lycaena alpherakyi ? Lycaena solskyi ? Lycaena tityrus No Fiedler 1991 Lycaena virgaureae No Fiedler 1991 Lycaena dione Yes Allen et al 2005 Lycaena editha Yes Ballmer & Pratt 1991 Lycaena rubidus Yes Ballmer & Pratt 1991 Lycaena xanthoides Yes Ballmer & Pratt 1991, Oliver et al. 2007 Lycaena hyllus No Fiedler 1991 Lycaena dorcas No Fiedler 1991 Lycaena dospassosi No Fiedler 1991 Lycaena helloides No Fiedler 1991 Lycaena nivalis No Fiedler 1991 Lycaena mariposa No Fiedler 1991 Lycaena gorgon No Ballmer & Pratt 1991 Lycaena heteronea Yes Ballmer & Pratt 1991 Lycaena hermes No Fiedler 1991 Nymphalidae Danainae Danaus plexippus NA Riodinidae Riodininae Riodina lysippus NA
124
Table 2 Likelihood ancestral state reconstructions of myrmecophily on select nodes of gene trees. Nodes correspond to labels in Figure 4. Values for trees in which particular node was present: (a) is the average likelihood of node reconstructed as myrmecoxenous (non ant associated), indicated as white in charts in Figure 4; (b) is the proportion of trees in which myrmecoxeny is the uniquely best state at node; and (c) is the proportion of trees in which myrmecophily is the uniquely best state at node. T is the number of sampled gene genealogies in which node was present. See text for details of ancestral state reconstruction. Node a b c T COI/COII M1 0.979 0.391 0 3000 M2 0.973 0.931 0 1637 M3 0.970 0.603 0 1717 M4 0.073 0 0.899 3000 EF1α N1 0.978 0.598 0 2920 N2 0.983 0.987 0 1559 N3 0.982 0.993 0 2998 N4 0.884 0.619 0 2496 N5 0.002 0 1 3000 125
Figure Legends
Figure 1: Individual larval responses to attack and control treatments in laboratory test of ant manipulation.
Figure 2: Consensus gene genealogies for (a) COI and COII data and (b) EF1α data.
Branch labels indicate clade posterior probability (*=1.00). Branch lengths are consensus branch lengths calculated by MrBayes (Huelsenbeck and Ronquist 2001); see text for conditions of Bayesian estimation.
Figure 3: Inferred phylogeny of Lycaenidae included in this study. Branch labels indicate frequency at which a clade was recovered in species tree searches. Boxes indicate character states of ant association for Lycaenidae taxa: black = myrmecophilous (ant associated), white = myrmecoxenous (not ant associated). For taxa lacking data on ant association, no box is shown. Arrow indicates ancestor of subfamily Lycaeninae. Chart on node indicates proportion of species trees containing monophyletic Lycaeninae in which the ancestor of Lycaeninae was reconstructed as myrmecophilous (black, 26 trees), myrmecoxenous (white, 6370 trees), or equivocal (grey, 2100 trees) in parsimony ancestral state reconstructions.
Figure 4: History of ant association of L. xanthoides ’ ancestors reconstructed on the
Lycaeninae clade of (a) COI and COII and (b) EF1α Bayesian consensus gene trees. 126
Clades shown have Bayesian posterior probability greater than 0.5. Charts on nodes indicate average likelihood reconstructions of myrmecoxenous (white) and myrmecophilous (black) lifestyle. See Table 2 for likelihood values. 127
Figure 1
1600
1400
1200 ) 3 1000
800
600 Ant-seconds(10 400
200
0 Control Attack Treatment
128
Figure 2 (a) Danaus plexippus Riodina lysippus 0.65 Anthene emolus Curetis bulis Araragi enthea Lucia limbaria 0.82 Liphyra brassolis * Miletus ancon 0.99 Baliochila minima Poritia erycinoides 0.63 Callipsyche behrii 0.68 Thecla coelicolor * Echinargus isola 0.56 Plebejus acmon Heliophorus eventa 0.70 Lycaena cupreus 0.68 Lycaena dispar Lycaena virgaureae * Lycaena tityrus * 0.91 Lycaena solskyi * Lycaena solskyi Lycaena alpherakyi * Lycaena phlaeas 0.64 Lycaena phlaeas Lycaena arota Lycaena li Lycaena rubidus * Lycaena dione * Lycaena xanthoides 0.65 Lycaena editha Lycaena xanthoides 0.91 * Lycaena margelanica Lycaena margelanica Lycaena hermes 0.70 Lycaena hyllus Lycaena nivalis 0.63 * Lycaena mariposa Lycaena helloides * Lycaena helloides * Lycaena dorcas 0.53 Lycaena dorcas Lycaena dospassosi * Lycaena gorgon Lycaena heteronea 0.1 129
Figure 2 (b) Danaus plexippus 0.56 Curetis bulis * Echinargus isola Plebejus acmon Anthene emolus Liphyra brassolis Lucia limbaria Riodina lysippus 0.56 0.74 Araragi enthea Miletus ancon 0.56 * Callipsyche behrii Thecla coelicolor 0.76 Baliochila minima 0.63 Poritia erycinoides Heliophorus eventa Lycaena li 0.76 Lycaena cupreus 0.61 Lycaena dispar Lycaena virgaureae 0.69 Lycaena tityrus 0.83 Lycaena alpherakyi * * * Lycaena solskyi 0.68 Lycaena solskyi * Lycaena phlaeas Lycaena phlaeas Lycaena hermes 0.83 Lycaena arota Lycaena rubidus
0.97 Lycaena xanthoides * Lycaena editha Lycaena dione 0.52 Lycaena xanthoides Lycaena hyllus Lycaena nivalis 0.97 * Lycaena mariposa Lycaena helloides 0.90 * Lycaena dorcas * Lycaena dorcas Lycaena dospassosi * Lycaena gorgon Lycaena heteronea 0.1 130
Figure 3 Danaus plexippus Nymphalidae Riodina lysippus Riodinidae Curetis bulis Curetinae Miletus ancon Miletinae Liphyra brassolis 0.77 Poritia erycinoides Poritiinae Baliochila minima Lipteninae Araragi enthea Lucia limbaria Theclinae 0.70 Thecla coelicolor Callipsyche behrii Anthene emolus 1.00 Plebejus acmon Polyommatinae Echinargus isola Heliophorus eventa Lycaena li Lycaena arota Lycaena phlaeas 0.72 Lycaena cupreus 0.67 Lycaena dispar 1.00 Lycaena alpherakyi Lycaena solskyi 0.98 0.84 0.79 Lycaena tityrus Lycaena virgaureae Lycaena dione Lycaeninae 0.96 Lycaena rubidus 0.51 Lycaena editha Lycaena xanthoides Lycaena hermes 0.64 Lycaena hyllus Lycaena nivalis 0.92 0.98 Lycaena mariposa 0.65 Lycaena dorcas 1.00 0.66 Lycaena dospassosi Lycaena helloides 1.00 Lycaena gorgon Lycaena heteronea 131
Figure 4 Lycaena phlaeas Lycaena phlaeas Lycaena Heliophorus eventa cupreus Lycaena dispar Lycaena virgaureae Lycaena tityrus Lycaena alpherakyi Lycaena solskyi Lycaena solskyi Lycaena li Lycaena hermes Lycaena arota Lycaena rubidus Lycaena xanthoides Lycaena editha Lycaena dione Lycaena xanthoides Lycaena hyllus Lycaena nivalis Lycaena mariposa Lycaena helloides Lycaena dorcas Lycaena dorcas Lycaena dospassosi Lycaena gorgon Lycaena heteronea Lycaena 5 N 4 N 3 N 2 N 1 N b Heliophorus eventa phlaeas Lycaena phlaeas Lycaena cupreus Lycaena disparLycaena festivus virgaureae Lycaena tityrus Lycaena solskyi Lycaena solskyi Lycaena alpherakyi Lycaena arota Lycaena li Lycaena rubidus Lycaena dione Lycaena xanthoides Lycaena editha Lycaena xanthoides Lycaena margelanica Lycaena margelanica Lycaena hermes Lycaena hyllus Lycaena nivalis Lycaena mariposa Lycaena helloides Lycaena helloides Lycaena dorcas Lycaena dorcas Lycaena dospassosi Lycaena gorgon Lycaena heteronea Lycaena 4 M 3 M 2 M 1 M
a 132
APPENDIX D PERMISSION TO REUSE BLACKWELL CONTENT FOR THIS DISSERTATION
133
Date: Tue, 16 Oct 2007 09:24:14 +0100 [Tuesday October 16, 2007 01:24:14 AM MST] From: Journals Rights