Open Access

Is ecological speciation a major trend in aphids? Insights from a molecular phylogeny of the conifer-feeding genus Cinara

  • Emmanuelle Jousselin1Email author,
  • Astrid Cruaud1,
  • Gwenaelle Genson1,
  • François Chevenet2, 3,
  • Robert G Foottit4 and
  • Armelle Cœur d’acier1
Frontiers in Zoology201310:56

https://doi.org/10.1186/1742-9994-10-56

Received: 30 April 2013

Accepted: 13 September 2013

Published: 18 September 2013

Abstract

Introduction

In the past decade ecological speciation has been recognized as having an important role in the diversification of plant-feeding insects. Aphids are host-specialised phytophagous insects that mate on their host plants and, as such, they are prone to experience reproductive isolation linked with host plant association that could ultimately lead to species formation. The generality of such a scenario remains to be tested through macroevolutionary studies. To explore the prevalence of host-driven speciation in the diversification of the aphid genus Cinara and to investigate alternative modes of speciation, we reconstructed a phylogeny of this genus based on mitochondrial, nuclear and Buchnera aphidicola DNA sequence fragments and applied a DNA-based method of species delimitation. Using a recent software (PhyloType), we explored evolutionary transitions in host-plant genera, feeding sites and geographic distributions in the diversification of Cinara and investigated how transitions in these characters have accompanied speciation events.

Results

The diversification of Cinara has been constrained by host fidelity to conifer genera sometimes followed by sequential colonization onto different host species and by feeding-site specialisation. Nevertheless, our analyses suggest that, at the most, only half of the speciation events were accompanied by ecological niche shifts. The contribution of geographical isolation in the speciation process is clearly apparent in the occurrence of species from two continents in the same clades in relatively terminal positions in our phylogeny. Furthermore, in agreement with predictions from scenarios in which geographic isolation accounts for speciation events, geographic overlap between species increased significantly with time elapsed since their separation.

Conclusions

The history of Cinara offers a different perspective on the mode of speciation of aphids than that provided by classic models such as the pea aphid. In this genus of aphids, the role of climate and landscape history has probably been as important as host-plant specialisation in having shaped present-day diversity.

Keywords

Ecological speciation Niche shifts Host shift Host race Geographic isolation Phytophagous insect Species delimitation Cladogenesis DNA phylogeny

Introduction

Understanding processes that contribute to reproductive isolation and speciation is one of the most challenging areas in evolutionary biology. Allopatric speciation occurs when populations become spatially separated by regions of unsuitable habitat, which overcomes individual dispersal abilities and interrupts gene flow between those populations. Ecological speciation occurs when a transition in resource and/or habitat within ancestral species triggers gene flow interruption and the formation of new sister species [1, 2]. This process can occur in allopatry (e.g. [3]) but is also supposed to permit sympatric speciation [46]. In the past decade, the role of ecological speciation in the diversification of phytophagous insects has been the focus of many studies and its importance compared to “ordinary” allopatric speciation has been reevaluated [4, 7, 8].

Host plants are considered as the main ecological factor involved in the speciation process of phytophagous insects. Several studies on various plant-feeding insects have clearly demonstrated the existence of host races and suggested that host based selection may underlie or at least speed up the speciation process in these organisms. Among textbook examples, are studies on Rhagoletis pomonella (Walsh) host races [5, 9]. Aphids (Hemiptera, Aphididae) are also considered as model systems for the study of ecological speciation favored by adaptation to different host plants. These insects are often host specific and always mate on their host plants which make them good candidates for host driven speciation. There is much evidence for host shifts in the course of the evolution of aphids and examples of host plant specialisation events in several species have been reported (reviewed in [10]). The classic model for ecological speciation in aphids is the pea aphid (Acyrthosiphon pisum (Harris, 1776)) [11] in which incipient speciation seems to be in progress. Several studies have indeed shown that populations on different host plants (e.g. Vicia spp., Trifolium spp., Medicago spp.) have diverged genetically and exhibit different stages of reproductive isolation [1215]. Other studies have also shown that Aphis gossypii Glover, 1877 consists of several host-associated populations or races with a world-wide distribution [16, 17].

All these studies suggest that host plant association is a major driver of reproductive isolation in aphids. However, explanations involving geographic isolation are seldom explored [10]. This might be because some aphid species are known to disperse over long distances, following aerial currents such as the jet stream, and successfully find their host plants during such events [1820]. This characteristic could limit the influence of geographic barriers on gene flow. Furthermore, many aphid species are associated with economically important host plant species (crops, ornamental plants). Consequently, long range dispersal events associated with human transportation of infested plants are common. These result in many species exhibiting a very large geographic distribution (spreading over several continents): this very recent sympatry (on evolutionary time scale) might sometimes preclude investigating geographic isolation as a cause of species divergence while it might actually be relevant. Short distance dispersal, of 20 km or less, is probably usual in aphids [21] and the success of long range dispersion events is greatly dependent on the availability of host plants: aphids specialised on a few host species or host plants that have a restricted distribution are probably not successful in colonizing distant areas. Hence, allopatric speciation is a possible scenario for the diversification of aphids. Furthermore, host plants are not the only components of the ecological niche of aphids. Temporal shifts in life cycles [22] or changes in feeding habits on the plant can also be involved in reproductive isolation [23]. Hence, alternative scenarios to host-driven speciation probably deserve more attention than currently given in the aphid literature.

The conifer aphid genus Cinara Curtis, 1835 comprises 243 described species [24]. Approximately 154 occur in North America [25], 47 occur in Europe and the Mediterranean area [26] and about 40 occur in the Far East. Cinara species feed exclusively on the two conifer families that are found primarily in temperate and subtropical regions of the Northern hemisphere: Pinaceae and Cupressaceae. They exhibit a diversity of ecological features that make them good models to explore the importance of ecological specialisation in the diversification of aphids. Most species feed on a single or a few species of conifers, while others are less discriminatory, feeding on several species within a genus (e.g. C. pinea (Mordvilko, 1895) and C. pergandei (Wilson, 1919) on Pinus spp.) and sometimes even on several unrelated species of conifers (e.g. C. confinis (Koch, 1856) on Abies spp. and Cedrus spp.). Species of Cinara have specific feeding sites on their host plants. Some species feed only on young shoots, while others feed exclusively on large trunks [27]. However, some of their biological features also make Cinara species susceptible to geographic reproductive isolation. First, they have limited dispersal ability, compared to other aphid species. Their weight/wing length ratio is high [28] and some species are not recorded as producing winged morphs [25, 29], which are the only ones able to disperse over large distances [19]. Then, because of their association with some conifer species living at higher altitude (some Pinus, Picea and. Abies spp.), some species are restricted to mountain ranges. Hence, many Cinara species show disjunct distributions and can encompass several allopatric populations [30].

Speciation processes can be investigated by phylogenetic studies and inferences about the evolution of characters (e.g.[3133]). If speciation is only triggered by ecological changes, lineage splitting in the phylogeny should be accompanied by transitions in ecological characters and closely related species should not overlap in their ecological niche. Conversely, if speciation is not driven by ecological changes, the number of lineage splitting events in the phylogeny should be greater than the number of evolutionary transitions in ecological niche [32] and the phylogenetic reconstruction should reveal more niche conservatism than expected by chance. A phylogenetic study by Favret & Voetglin [30] on twenty-five species of North American Cinara, showed that closely related species used similar feeding sites on different host species. This study does not formally estimate the number of speciation events potentially triggered by ecological changes in Cinara but suggests that shifts in feeding sites followed by host plant specialisation may have driven the diversification of this group [30].

The objectives of this study were to reconstruct the phylogeny of European and North American Cinara species in order to investigate general trends in the diversification of this genus and test the scenario suggested by Favret & Voetglin [30] on a more global scale. More specifically, we aimed at estimating the relative importance of the two ecological characteristics (host plant species and feeding site) studied by Favret & Voetglin [30] versus geographic isolation in the speciation processes. We first investigated how geographic areas, host ranges and feeding sites were distributed along our phylogeny using the recently developed software PhyloType [34]. Given the distributions of characters across a set of taxa and a phylogeny, PhyloType determines if taxa sharing the same character states are more clustered than expected by chance on the phylogeny. If evolutionary transitions in host plant association and feeding sites were recurrently involved in the diversification of Cinara, sister taxa should diverge for these characters and we should not find any significant phylogenetic clusters associated with these characters. Conversely, the occurrence of strong geographic clusters on the phylogeny would be compatible with a scenario in which geographic isolation has not played a predominant role in the diversification of species within these clusters. We then followed the method of Nyman et al.[32] and reconstructed the evolution of a character summarizing the ecological niche (host plants species + feeding site) of each species. We estimated the proportion of lineage splits accompanied by a shift in this character and also tested whether species sharing the same niche were more clustered than expected by chance on our phylogeny. Finally, to give a coarse estimate of the importance of allopatric speciation in Cinara, we plotted geographic overlap among sister clades as a function of time since their divergence. According to theory, if allopatric speciation is prevalent, species that have recently differentiated should not have overlapping geographic ranges, while they may become more sympatric as time since divergence increases and their geographic range expands [33, 35, 36]. Altogether these analyses should give insights into the relative role of geography and ecology in the diversification of this aphid group.

Materials and methods

Taxonomic sampling

In an effort to capture the breadth of the phylogenetic, ecological and geographical diversity of Cinara species, we sampled 246 colonies representing 56 species of the genus Cinara (see Additional file 1). We sampled in both Cinara subgenera (52 species in the subgenus Cinara that encompasses 231 species and 4 species in the subgenus Cupressobium that encompasses 12 species). This represented 24% of the known species diversity. However, about 40 Cinara species listed in Blackman & Eastop [29] are only known from their original description or/and are suspected to be synonyms of other species, which suggests that our sampling was actually close to 30% of Cinara species. Whenever possible, we sampled several colonies per species to check for intraspecific genetic variation (our sampling varied from 1 to 18 colonies per species with an average of 4 colonies per species). Our sampling was focused on the western United States, which encompasses a large part of the species diversity of the Cinara genus, and France which with 26 species encompasses more than half of the European and Mediterranean Cinara fauna. Altogether we sampled in five states in the U.S., throughout six regions in France, and also sampled some specimens from Italy, Greece, Algeria and Kazakhstan. For each oligophagous species, we tried to sample colonies from different host species, in order to investigate the impact of host association on species differentiation, as well as from across their distribution range. Outgroup specimens were collected within the Lachninae subfamily in several genera (Trama, Lachnus, Tuberolachnus). Each colony was given a unique number and was geo-referenced. Host trees were identified to conifer genus and species when possible using local flora. Feeding sites, coloration and patterning in vivo were recorded for each colony and photographs were taken. As a destructive DNA extraction protocol was used, we selected vouchers among specimens from the same colony (i.e. sampled on the same host-plant at the same time) as the individual taken for extraction. Voucher specimens were mounted on microscope slides and deposited in the Aphididae collection of the Center for Biology and Management of Populations (CBGP) at Montferrier-sur-Lez, France. All specimens were identified by ACDA using mainly the keys of Blackman & Eastop [29] and Favret & Voetglin [37]. Collection details, host plant associations and nutrition sites as recorded in the field are given in Additional file 1.

DNA extraction and sequencing

Total genomic DNA was extracted from a single individual per sample with the DNeasy Blood & Tissue Kit (Qiagen) in 120 μl of extraction buffer. We amplified several DNA fragments, two mitochondrial genes [the barcoding gene region (cytochrome oxidase subunit I: COI) and a fragment of the cytochrome b gene (Cytb)], two aphid nuclear DNA fragments [an approximately 770 bp intron corresponding to the para-type gene encoding the IIS2-S6 region of the voltage-gated sodium channel: Aph, and a portion of Elongation Factor (EF)], and two Buchnera aphidicola DNA fragments (GroEL: an approximately 550 bp fragment corresponding to a portion of the GroEL gene, a chaperonin assisting in the folding of proteins and His: an approximately 550 bp fragment of the ATP phosphoribosyltransferase (HisG) gene and histidinol dehydrogenase (HisD) gene and intergenic region). Buchnera aphidicola is the primary endosymbiont of aphids, which is transmitted from mother to offspring. It has been shown to cospeciate with its aphid hosts at several taxonomic levels in many groups of aphids and its genome has been used with success to reconstruct deep and shallow phylogenetic relationships in aphids [3840]. Polymerase chain reactions (PCR) primers (Additional file 2) were designed for Buchnera DNA fragments using published genomes of Buchnera aphidicola in Genbank. Primer fidelity across taxa was not always consistent in Cytb, His and GroEL, we therefore defined several sets of primers for these DNA fragments. Despite our efforts, some specimens have slightly truncated sequence lengths.

PCR were performed in a final volume of 30 μl containing 1× reaction buffer (CoralLoad PCR Buffer, Qiagen), 0.1 mM of each dNTP, 0.7 μM of each primer, 1 U of Taq DNA polymerase and 1 μl of DNA extract. Sequencing reactions were carried out by MWG Operon, (Ebersberg, Germany) using the same primers as for PCR.

Phylogenetic analyses

Sequences were aligned using ClustalW. Alignments of COI, Cytb, His and GroEL were straightforward due to a lack of length variation. Aph and EF comprised intergenic regions with indels, which complicated alignment between distantly related species. To avoid discarding information relevant for resolving shallower nodes of the phylogeny, we first aligned sequences of specimens for which intergenic regions were unambiguously aligned. All sequences were then aligned in one file by inserting gaps in ambiguously aligned regions in sequences that differed too much. This resulted in sequences having blocks of gaps aligned with intergenic regions of specimens that were too phylogenetically distant to assess site homology with confidence.

The alignment resulting from the concatenation of all DNA fragments is given in Additional file 3.

Alignments of the protein coding genes were translated into amino acids using Mega 4.0.2 [41] to detect frameshift mutations and premature stop codons, which may indicate the presence of pseudogenes (i.e. a fragment of nucleotide sequence that resembles a known protein’s domains but with stop codons or frameshifts mid-domain).

Phylogenetic trees were estimated using maximum likelihood (ML) and Bayesian methods. We first conducted ML searches on each DNA fragment. We checked for topological congruence between the trees and then combined all DNA fragments in a single DNA matrix. All analyses were conducted on a 150-core Linux Cluster at CBGP as well as on the CIPRES Science Gateway [42]. The data were partitioned into mitochondrial, nuclear and bacterial gene regions following [38]. The model with best fit for each partition was identified using the Akaike information criterion as implemented in MrAIC.pl 1.4.3 [43]. We performed ML analyses and associated bootstrapping using the MPI-parallelized RAxML 7.2.8-ALPHA [44]. GTRCAT approximation of models was used for ML bootstrapping [44] (1000 replicates). Bootstrap percentage (BP) > 95% was considered as strong support and a BP < 70% as weak support.

Bayesian analyses were conducted using a parallel version of MrBayes v. 3.2.1 [45]. We assumed across-partition heterogeneity in model parameters by unlinking parameters across partitions. Parameter values for the model were initiated with default uniform priors and branch lengths were estimated using default exponential priors. To improve mixing of the cold chain and avoid it converging on local optima, we used Metropolis-coupled Markov chain Monte Carlo (MCMC), with each run including a cold chain and three incrementally heated chains. The heating parameter was set to 0.02 in order to allow swap frequencies from 20% to 70% [46]. We ran two independent runs of 20 million generations. All values were sampled every 2000 generations. For the initial determination of burn-in, we examined the plot of overall model likelihood against generation number to find the point where the likelihood started to fluctuate around a constant value. Convergence was also evaluated using Tracer v1.5 [47]. The first 25% samples from the cold chains were discarded as burn-in. The results were based on the pooled samples from the stationary phases of the two independent runs. Posterior probabilities (PP) > 0.95 were considered as strong support and PP < 0.80 were considered as weak.

Species delimitation

Several studies suggest that species delimitation is sometimes ambiguous within closely related species of aphids for two reasons. First, aphids are often identified based on host association, though such taxonomic treatment is correct only if aphids show a strict specialisation toward their host plants [48]. Second, aphid morphology often shows convergent evolution. Cinara species are no exception. The study by Favret & Voegtlin [30] and Foottit et al.[49] revealed several ambiguities in species delimitation with some mismatches between morphological species and genetic clusters. In our analyses, some species appeared divided into two or several phylogenetic clusters whose level of genetic divergence mirrored usual inter-specific level of genetic distance. To overcome these taxonomic issues, we used the species delimitation method of Pons et al.[50] to identify relevant entities for our study, i.e. genetic clusters of specimens potentially subject to selection and genetic drift.

The method of Pons et al.[50] identifies clusters representing independently evolving entities using a generalized mixed Yule coalescent model (GMYC). The model optimizes the maximum likelihood value of a threshold, such that the nodes before the threshold are identified as species diversification events, while the branches beyond the threshold are clusters following coalescent processes. Two ultrametric trees were constructed from the combined dataset using the uncorrelated lognormal relaxed clock method implemented in BEAST v1.7.4 [47], assuming either a Yule tree prior or a coalescent tree prior with a constant population size back through time. The same modelling strategies as for MrBayes and RAxML were used and clock models for each partition were unlinked. The relative times of divergence events were estimated by fixing the mean rate of molecular clock model to 1.0.

Two runs of 60 million generations with sampling every 6,000 generations were performed for the analysis assuming a coalescent tree prior. Two runs of 100 million generations with sampling every 10,000 generations were performed for the analysis assuming a Yule tree prior. For both analyses, the two separate runs were then combined using LogCombiner 1.7.4. We checked for convergence using Tracer 1.5 [47]. BEAST was also used to compare the goodness of fit of the two models based on Bayes factors (BF) [51, 52] computed from harmonic mean estimators (HME) of the marginal likelihoods (1000 bootstrap replicates) as well as on the Akaike’s information criterion through MCMC (AICM). AICM has been shown to perform better in model selection than HME [53].

Following the removal of 10% burn-in, the sampled posterior trees were summarized using TreeAnnotator 1.7.4 to generate a maximum clade credibility (MCC) tree. The GMYC method as implemented in the R package SPLITS (http://www.rforge.r-project.org/projects/splits/), was then applied to the MCC tree that best fitted our data.

We then derived a “phylogenetic species” tree based on the results of the species delimitation method, by picking (at random) one specimen for each putative species and simply pruning subsequent specimens from the global tree with R using the package APE [54].

Character analyses

We first annotated our specimens with sampling regions (states, province, and country), host plant (conifer genera and species when available) and feeding site(s) (trunk, branches, shoots) as recorded in the field. In a few cases, aphids were obtained by beating the branches of a tree, hence feeding sites could not be recorded for those samples. Character states attributed to each specimen are detailed in Additional file 1.

We then assigned a character state (for continent, host plant genus, host plant species and feeding site) to each cluster defined by the species delimitation method. To do so, we combined information recorded from the field for all specimens assigned to a species cluster with information available for each recognized species of Cinara compiled in the book by Blackman & Eastop [29] (updated in http://www.aphidsonworldsplants.info) and in [55] for species found in North America. Geographic areas were categorized into three character states: Nearctic, Palearctic and cosmopolitan species (the geographic origin of two cosmopolitan species is unknown). Host plant genera were split into seven character states: Picea, Pinus, Abies, Larix, Cedrus, Pseudotsuga (and occasionally Abies) and Cupressaceae as Cinara associated with this family often occurred on plants belonging to different genera (mostly Cupressus and Juniperus). Feeding sites were split into three categories: shoot, branch and trunk. When aphids assigned to a species cluster were found on non-lignified wood only (shoots, young twigs or at the base of new cones) in the field and according to information for the corresponding morphological species available in [29], they were considered as shoot feeders. When they were found capable of feeding on lignified wood (specimens were found on branches, older twigs and small trunks), they were considered as branch feeders. When they were found on trunks only, meaning that they needed a rostrum long enough to reach the sap through thick bark, they were considered as trunk feeders.

Information in Blackman & Eastop [29] is sometimes based on original descriptions or a few taxonomic surveys, therefore adding information from our field campaign generally increased species polymorphism in feeding sites and host plant use. Further, when a morphological species was divided into several clusters, we only took into account information recorded in the field in order to evaluate whether each cluster was associated with a particular character state.

We then used the PhyloType software [34] to explore the evolutionary trajectory of host plant genus association and feeding sites, and to give a coarse biogeographic scenario for the diversification of North American and European species. The PhyloType method can be summarized as follows (see [34] for details):
  1. 1)

    ancestral character state reconstruction using parsimony (ACCTRAN and DELTRAN algorithms).

     
  2. 2)

    identification of subsets of taxa having close phylogenetic relationships and common character states. Such subsets of taxa are called potential phylotypes. A phylotype has a unique character state at its root. All taxa in a phylotype share the same character state all along the path from every taxon included in the phylotype to the root of the phylotype.

     
  3. 3)

    identification of relevant phylotypes from among all potential phylotypes with combinatorial and numerical criteria. The choice of criteria and selection thresholds is left to users. Nevertheless, some constraints are imposed to avoid meaningless analyses. For instance, the Size criterion, which checks for a minimal number of taxa in the potential phylotype, is mandatory. Then come, the Different criterion which checks for a maximum number of sub-clades within the phylotype with different ancestral character states from that of the phylotype’s root and Persistence, which measures the extent to which the root character state of the phylotype is conserved in its descendants.

     
  4. 4)

    once phylotypes have been identified with the selected criteria, their significance can be assessed: character states are shuffled among the branches of the tree and the search for phylotypes is reiterated with the same criteria. The p values correspond to the fraction of shuffled data sets in which one finds a phylotype with the character being investigated and at least as large a size as the observed phylotypes.

     

PhyloType can therefore test whether characters are phylogenetically conserved and depict their evolution along the phylogeny.

We mapped the evolution of host plant genera, feeding sites and ancestral areas on our species phylogeny and determined if some significant phylotypes were associated with certain character states. The rationales behind conducting these analyses were to infer the number of transitions for each character and investigate how phylogenetically conserved they were; a character that is conserved is not the main driver of speciation. We also combined some of the characters and described their sequence of evolution throughout the diversification of the Cinara genus. For all analyses, criteria chosen for phylotype selection were as follows: size = 3, size/different=1, persistence=1, only nodes which ML bootstrap values were > 80 were taken into account in the analyses. Both ACCTRAN and DELTRAN optimization were tested and outgroups were excluded from the analyses. Shuffling procedures were performed with 1000 iterations.

We then estimated the proportion of lineage splits accompanied by a shift in resource use. We followed the procedure of Nyman et al.[32] and first identified all distinct ecological niches for the species included in our phylogeny (an ecological niche being defined by the combination of host plant species range and feeding sites). Each niche was coded with a different number. In doing so, we assumed that species ecological niches were overlapping when species shared one or several host plant species plus their feeding sites. Hence species were given a different niche number when there was no simultaneous overlap in host plant species and feeding sites with another species. We had three instances where one species shared part of its ecological niche with a first species and another part of its ecological niche with a second species; we either attributed two niche numbers to those or when these species overlapped in their niche with a sister species, we gave them the number corresponding to the niche of this sister species. Niche evolution was then optimized on the phylogenetic tree using maximum parsimony as implemented in PhyloType and we tested whether significant phylotypes were associated with these numbers (using the same procedure as described above for geography, host genus, and feeding sites). Furthermore, we also compared the parsimony score (number of steps) of the character “ecological niche” to the distribution of parsimony scores obtained from the 1000 shuffling made with PhyloType. This allowed for testing, given a tree and given the distribution of ecological niches across taxa, whether the total number of transitions in ecological niche was lower or higher than expected by chance.

To give a coarse estimate of the prevalence of allopatric speciation in Cinara, we plotted geographic overlap among sister species/ clades as a function of relative time since their divergence [33, 35, 36].

We indicated species geographic localization at a regional scale on our species phylogeny. Again, we compiled information from the literature [29] and our field campaigns to define eight geographic zones: North America (meaning the whole North American continent), Western North America (meaning all western states, from the North west coast of Canada to New Mexico), West of the Rockies (meaning the West coast of the US: California, Oregon, Washington), East of the Rockies (meaning in our sampling places in Colorado and New Mexico located East of the Rocky mountains), Europe (any country on the European continent), Mediterranean area (Algeria, Italy, Greece and the south East of France), Central Asia (Kazakhstan in our sampling), Cosmopolitan (the species were found worldwide). For documented cosmopolitan species that have recently spread over a continent, we used their area of origin. When a morphological species was divided into several clusters, we only took into account information recorded in the field in order to evaluate whether each cluster was restricted to a particular geographic zone.

For each node of the phylogeny, the overlap in present day geographic range of each clade/ species splitting at this node was set to 0 when no overlap existed between their geographic range and 1 when their geographic range overlapped. This variable was plotted against the depth of the node (i.e. the corresponding branch length in the ultrametric species tree). A logistic regression using this binary variable as a response variable and time as a factor was then calculated with R (using AOD and GGPlot packages). The prediction under a scenario where geographic speciation was prevalent was that the probability of overlap should increase with time [33].

Results

Sequence data

The final matrix contained 56 morphological ingroup species (five of those could not be assigned to either of two morphologically similar species, we thus gave them “mixed” names), six specimens for which identification keys did not lead to any known species and four outgroups, representing a total of 246 individuals and 4076 bp (COI + Cytb = 1418 bp, Aph = 257 bp, EF = 1135 bp, His = 719 bp, and GroEL = 547 bp). All sequences have been submitted to Genbank (Additional file 1). 1906 bp were variable and 1705 bp were parsimony informative. Sequences were missing for less than 12% of specimens for each DNA marker (18 COI, 5 Cytb, 8 Aph, 28 EF, 20 His and 25 GroEl). Alignment of protein coding genes revealed no stop codons or frame shifts.

Phylogenetic analyses

Models chosen by MrAIC for each partition were as follows: GTR + Γ (nuclear), GTR + I + Γ (mitochondrial and Buchnera aphidicola fragments). Visual inspection of ML phylogenetic trees obtained with each independent partition showed no strong incongruences validating the use of combined analyses. Given that α and the proportion of invariable sites cannot be optimized independently from each others and following the recommendations provided in the RAxML manual, we used GTR + Γ with 4 discrete rate categories for all partitions.

ML and Bayesian analyses produced similar topologies. We obtained well-resolved phylogenetic trees (Additional files 4 and 5), in which most nodes were supported by high ML bootstraps and Bayesian posterior probability values.

Species delimitation analyses

For each partition of the two analyses (assuming either a Yule tree prior or a coalescent tree prior), BEAST returned a 95% credible interval for the coefficient of variation of rates that was not abutting against zero, suggesting among branch rate heterogeneity (i.e., rejection of the molecular clock) [47]. Furthermore, the covariance statistics showed no strong evidence of autocorrelation of rates in the two combined phylogenies (covariance values spanning zero).

Bayes Factors (BF) and AICM both indicated that a Yule tree prior was a better fit to our data (log BF = 9.0; ∆AICM = −38.5). We therefore chose to use the topology obtained with a Yule prior to conduct the species delimitation method.

For both tree priors, MCC topologies were very similar to ML and Bayes topologies. We mapped node support values (pp and BP) obtained with ML and Bayesian analyses on the MCC topology obtained with a Yule tree prior (Figure 1).
Figure 1

Molecular phylogenetic hypothesis for Cinara species resulting from BEAST analysis using a Yule tree prior. The topology on the left corresponds to the topology obtained with all specimens, it is divided into 3 sub-trees A, B and C, corresponding to the clades indicated in the global tree. Numbers at nodes correspond to ML bootstrap values > 50 and to BEAST posterior probability values > 0.80. Red rhombuses correspond to nodes that were not present in the ML tree. For each specimen, voucher number, species morphological identification, host species, sampling locality are indicated from left to right. The red coloration delimits clusters of specimens recognized as species by the GMYC method.

The GMYC model was preferred over the null model of uniform (coalescent) branching rates (P < 0.001). Using the single-threshold GMYC model, 76 (CI = 70-94) putative species (54 genetic clusters and 22 singletons) were inferred for the MCC tree reconstructed using a Yule prior (T = −0.0097 substitutions/site). We further used the 76 putative species (72 ingroups species and four outgroups) inferred by the single-threshold GMYC model (Figure 1).

In most cases, phylogenetic species inferred by the GMYC model matched morphological species. However, we observed several mismatches. Some morphological species were clearly separated into two or even three genetically differentiated clusters. Among species associated with the genus Pinus, Cinara ponderosae (Williams, 1911) specimens formed two clusters, C. terminalis (Gillette & Palmer, 1924) specimens were split into three clusters, and C. pini (Linnaeus, 1758) specimens were split into two clusters. Among species associated with Picea, C. pilicornis (Hartig, 1841) was divided into three clusters and C. pruinosa (Hartig, 1841) into four clusters. Among species associated with Abies and/or Pseudotsuga, C. pseudotaxifoliae Palmer, 1952 formed three clusters and C. occidentalis (Davidson, 1909) formed four clusters.

In a few cases, our phylogenetic analyses revealed that some specimens were probably misidentified: one specimen (3085) associated with Pinus lambertiana Douglas, 1927 and identified as Cinara moketa Hottes, 1957 clustered with Cinara anelia (Favret & Voegtlin, 2004) (Figure 1, Clade B). The latter was known as being associated with P. monophylla Torrey et Frémont 1845 only while C. moketa was referenced as being associated with P. lambertiana. Our results therefore suggest that C. anelia might occasionally infest Pinus lambertiana.

Groups of specimens associated with Pinus contorta Douglas ex Loudon 1838 did not match morphological species (Figure 1, Clade A): there was one cluster including specimens identified as C. murrayanae (Gillette & Palmer, 1924), C. medispinosa (Gillette & Palmer, 1929) and C. contortae Hottes, 1958, and a second cluster including specimens identified as C. murrayanae only. C. brevispinosa (Gillette & Palmer, 1924) on the other hand formed a monophyletic cluster that was retrieved by all phylogenetic analyses as well as by the species delimitation method.

Character analyses

Parsimony reconstructions as implemented in PhyloType suggested a Nearctic origin for Cinara, and several shifts to the Palearctic as well as one shift back to the Nearctic (14 transitions in character states for “geographic origin” when geography was defined as broadly as continents, Figure 2A). Using ACCTRAN optimization, a single significant geographic phylotype associated with the character state “Palearctic” was found (Table 1, Figure 2A, filled circles). Hence, lineage splitting events were often accompanied by geographic shifts. Conversely, the reconstruction of the history of host genera association revealed that this character was conserved in our phylogeny. The parsimonious reconstruction of the evolution of host range suggested only 10 evolutionary transitions for this character (Figure 2B). Species were clustered into 6 phylotypes corresponding to the 6 defined host ranges (Figure 2B). These phylotypes included 94% of the species in our phylogeny. This meant that there were very few lineage splits events associated with a shift to a new host genus. Reconstruction of the evolutionary trajectory of feeding sites yielded a single significant phylotype (Figure 2C, filled circle), meaning that there were many evolutionary transitions for this character. The parsimonious reconstruction of the evolution of feeding sites suggested 17 transitions (Figure 2C). No additional significant phylotype was found when combining “host plant genus” with “feeding sites” or “geographic origin” with “feeding site” (e.g. looking for phylotype “feeding on shoots of Pinus” or “feeding on trunks in the Nearctic”). However, combining the two main host plant genera (Picea and Pinus) with geographic origins, i.e. creating four new character states (Picea and Nearctic, Picea and Palearctic, Pinus and Nearctic, Pinus and Palearctic) yielded four significant additional phylotypes (Figure 3, Table 1), meaning that there was some geographic structure within species associated with Pinus on the one hand and with Picea on the other hand. Based on our results, the diversification of the genus can be summarized by Figure 3. Cinara associated with the genus Abies (P16 on Figure 3), indifferently of their geographic origin, formed a group of closely related species from which all species associated with Cupressaceae (P17) but one were derived. This latter phylotype (P17) actually corresponded to the subgenus Cinara group Cupressobium. A Nearctic species collected on Picea, which we failed to identify as either C. engelmanniensis (Gillette & Palmer, 1925) or C. bonica Hottes, 1956 (N° 2908), was found as sister species to the rest of this clade. Further diversification of Cinara stemmed from species associated with the genus Pinus in North America (P9). Within this latter phylotype, were nested two phylotypes associated with Pinus in Europe (P12, P15), one clade associated with the genus Picea, the latter being divided into a Nearctic phylotype (P14) and a Palearctic phylotype (P13). One North American species, C. wahluca Hottes, 1952 associated with a number of Juniperu s species was found as sister to this clade. Species on Larix and species on Pseudotsuga (and occasionally Abies) formed two separate phylotypes (respectively P11 and P10) with no geographic substructure and also derived from P9. Several singleton species were scattered in the phylogeny, namely C. cedri Mimeur, 1936 (a cosmopolitan species, native to North Africa and the South East Mediterranean region, associated with the genus Cedrus), C. curtihirsuta Hottes & Essig, 1954 a species associated with Abies, C. cembrae (Seitner, 1936) a European species associated with pine trees, and two unidentified North American species associated with Picea.
Figure 2

Character history and phylotype identification as inferred by PhyloType (MP reconstruction, ACCTRAN optimization). The topology used is from the ML analysis. Only significant phylotypes (whose sizes have a p- value ≤ 0.05) are indicated (filled circles at nodes with identifying numbers refer to phylotype identification in Table 1). A) Evolution of ancestral areas and phylotypes associated with this character, aphid species names are indicated as leaves of the tree. B) Evolution of host use and associated phylotypes, host species are indicated as leaves of the tree, N = not applicable. C) Evolution of feeding sites and associated phylotypes: aphid species names are indicated as leaves of the tree. Branches are coloured according to MP reconstruction (legends on the figure), and the root node identifiers of phylotypes are provided and refer to Table 1.

Table 1

Detailed table of the significant phylotypes found

Analysis

P-label

Character state

Cov (%)

Sz

Ps

Df

Sz/Df

Geographic origin (Figure 2A)

1

Palearctic

46

12, P = 0.004

2

1

12

Tot cov = 16%

Host genus (Figure 2B)

2

Pinus

100

32, P = 0.008

3

4

8

Tot cov = 90.4%

3

Pseudotsuga

100

5, P < 0.0001

2

0

4

Larix

100

4, P < 0.0001

1

0

5

Picea

82

15, P < 0.0001

2

1

14

6

Cupressaceae

80

4, P = 0.004

2

0

7

Abies

88

7, P < 0.0001

1

1

7

Feeding site (Figure 2C)

8

Shoot & branch

67

4, P = 0.005

2

3

1.3

Tot cov = 5%

Host genus & geographic origin (Figure 3)

9

Nearctic & Pinus

100

20, P < 0.0001

3

11

11

Tot cov = 82.2%

10

Pseudotsuga/ Abies

100

5, P < 0.0001

2

0

11

Larix

100

4, P < 0.0001

1

0

12

Palearctic & Pinus

42

5, P = 0.054

1

0

13

Palearctic & Picea

100

7, P < 0.0001

2

0

14

Nearctic & Picea

70

7, P < 0.0001

1

0

15

Palearctic & Pinus

50

6, P = 0.001

1

0

16

Abies

88

7, P < 0.0001

1

1

7

17

Cupressaceae

80

4, P = 0.002

2

0

Ecological niche (Figure 4)

18

Shoots and branches of Pseudotsuga spp. and/or Abies spp.

100

5, P < 0.0001

2

0

Tot cov = 56.2%

19

Shoots of Pinus spp. subsect. Pinus

100

4, P < 0.0001

1

1

4

20

Shoots of Picea spp.

60

3, P = 0.002

1

0

21

Branches of Picea spp.

70

7, P < 0.0001

1

5

1.4

22

Branches of Pinus contorta

100

3, P = 0.002

1

2

1.5

23

Shoots of Pinus edulis and/or monophylla

100

3, P = 0.003

1

0

24

Branches of Pinus sylvestris and/or P. nigra

100

3, P = 0.003

1

1

3

25

Shoots of Pinus spp. subsection Pinaster

100

3, P < 0.0001

1

0

26

Shoots of Juniperus spp.

100

4, P < 0.0001

2

0

 

27

Branches of Abies spp.

86

6, P < 0.0001

2

1

2

The analyses were run with ACCTRAN option (similar results with DELTRAN option). Abbreviations are: P-label, identifier of phylotype; Cov, coverage, i.e. percentage of taxa annotated with Character state that belongs to the phylotype. The p- value of Sz is given in the corresponding column.

Selection criteria are Size (Sz ≥ 3), Size/Different (Df ≤ 1), Persistence (Ps ≥ 1) and Support (Sp ≥ 0.80).

Figure 3

Evolution of “Host range” combined with “Geographic range” and associated phylotypes. The root node identifiers of phylotypes are provided and refer to numbers in Table 1, leave labels correspond to species names, the phylotype map below the tree summarizes the information in the tree.

The existence of 72 ingroup species in our phylogenetic tree requires at least 71 past speciation events. The reconstruction of the history of ecological niches suggested that there have been 31 transitions for this character (Figure 4). This meant that among the 71 “speciation” events in our tree, less than half of them were accompanied by a shift in resource use. The results of 1000 shufflings, showed that the number of changes in ecological niche was lower than expected by chance (P < 0.001). Phylotype analyses yielded 10 significant phylotypes associated with ecological niche (Table 1) meaning that among species sharing the same ecological niche, there were 10 species groups that were more clustered than expected by chance. To give a different estimate of the importance of shifts in resource use in species differentiation, when only sister species on the ML species tree were considered, only three out of 21 pairs had non overlapping niches (i.e. 15%), and among those three, two had also non overlapping geographic ranges (Figure 4).
Figure 4

Evolution of niche differentiation among species as inferred by MP using Phylotype: branch colors and numbers correspond to a niche (an ecological niche being defined by the combination of host-plant species range and feeding sites). Species names are followed by a number referring to their ecological niche. The root node identifiers of phylotypes are provided and refer to numbers in Table 1. Fine scale (regional) geographic distribution of each species is indicated at the right end of the figure. Green dots indicate species pairs that do not overlap in their ecological niche. ∆ Black triangles indicate species pairs that do not overlap in their geographic distribution.

As a comparison, 8 out of these 21 pairs (38%) had non-overlapping geographic ranges (Figure 4). The results of the logistic regression showed that the probability of clades overlapping in their contemporary geographic range increased significantly with time since their divergence (Coeff =32.82, P= 0.038) (Additional file 6), which followed the prediction of the scenario in which geographical isolation played a significant role in the speciation process.

Discussion

Molecular systematics of the genus Cinara

We obtained a well-resolved phylogeny of the genus Cinara encompassing a quarter of the species diversity. Our results confirmed the monophyly of the subgenus Cupressobium though it appeared nested within a group of species belonging to the subgenus Cinara. These relationships suggest that the subgeneric classification within Cinara needs revision. The data presented here will be useful for future work dealing with the taxonomy of the genus.

Our molecular work on species delimitation was largely concordant with previous taxonomic investigation in the genus [25, 37, 56, 57]. Overall there was a good correspondence between morphological species and genetic clusters inferred by the GMYC species delimitation method. But our analyses revealed more diversity than currently described in some common species with large geographic distributions: C. pilicornis, C. pruinosa, C. ponderosae, C. terminalis, C. pini and C. occidentalis. The subdivision of C. terminalis into several lineages was already suggested by [30]. In addition, species clusters on Pinus contorta did not correspond to morphological identifications. These results confirmed that taxonomic relationships in this group of species require further investigation [29, 58]. The published taxonomic treatments of the genus Cinara are quite extensive. This is probably because the genus encompasses several forest pests that are sometimes considered invasive [59, 60]. Some species are important producers of forest-honey [61] and they show variations in their natural history, which make them attractive to biologists. The concordance of our molecular results with morphological taxonomy on our broad geographical sampling suggests that biological information (patterns of host-plant association, geographic distribution, feeding sites) compiled from the taxonomic literature are reliable and can be used with confidence to infer character history on phylogenetic trees.

Distribution patterns across continents and among conifer genera

Groups of phytophagous insects with large Holarctic distributions offer good models for comparing the effects of host shifts and geographic events on their diversification [62]. We can investigate whether their phylogenetic history reflects host conservatism followed by range expansion and diversification with their host or geographic clustering of closely related species and opportunistic shifts to new unrelated hosts. Our reconstructions of the history of the genus Cinara and PhyloType inferences depict a global diversification scenario where speciation processes are strongly constrained by host genus association. There are few transitions in host plant genera that have occurred early in the diversification of the genus. Each Cinara clade associated with a particular host genus naturally present on both continents encompasses Nearctic and Palearctic Cinara species. In two of the most diverse Pinaceae genera (Pinus and Picea), geographic clustering of species at continental scales is observed. This pattern of distribution suggests repeated independent evolution of Nearctic and Palearctic lineages in each “host genus cluster” via large scale geographic isolation. The recurrent presence of species from two continents in relatively terminal positions on phylogenetic trees is not rare in phytophagous insects [62] and these patterns are always interpreted as reflecting speciation by geographic processes such as vicariance and dispersal (see [63] and [64] for studies on aphids).

Understanding the distribution of Cinara species at continental scales requires more thorough biogeographic analyses (e.g.[65]). This is beyond the scope of this paper as elaborating robust scenarios would require including Asian species (about 40 known species), species restricted to Eastern North America (about 10-15 species) and fossil data to calibrate the phylogeny. In any case, the genus Cinara probably offers a good model to test hypotheses on historical biogeography of the Holarctic fauna [66]. The coarse scenario given by our analyses already suggests that the diversity of the genus has probably arisen from the Nearctic zone. Numerous faunal exchanges between the Palearctic and Nearctic are then highlighted by our analyses. Inferring the relative importance of vicariance versus dispersal events to explain this pattern requires further study. It is noteworthy that many Cinara species are now found worldwide, which clearly reflects the impact of human activities on aphids’ dispersal. For instance, many species associated with Cupressus and Thuja, often used as ornamental hedges, are found across the globe. Some Cinara species (C. pilicornis, C. pruinosa) also probably hitchhike around the world with Christmas trees (several Picea spp. and Abies spp.). Therefore, inferring a proper biogeographical history for the genus will necessitate categorizing species that have been transported around the world relative to locations of natural populations.

Interestingly, the history of host genera association throughout the evolution of Cinara does not parallel conifer phylogeny [6769]. In the classification of conifers, Cupressaceae and Pinaceae form two well-differentiated families. Within Pinaceae, Abies, Tsuga and Cedrus form a sister clade to the other genera hosting Cinara. Within this latter group, Larix + Pseudotsuga are sister to Pinus + Picea[67]. In our phylogeny, most Cinara species associated with Cupressaceae (P6 Figure 2B, and P17 Figure 3) have evolved from an Abies feeding ancestor: this represents a shift to a distant host. The colonisation of all Pinaceae genera then arose from Cinara associated with Pinus (P2 Figure 2B and P9 Figure 3). The only phylogenetic relationships in Cinara that mirrors conifer genera relatedness is the proximity of species feeding on Larix and Pseudotsuga. Therefore, the history of Cinara reflects shifts to available hosts that are not always closely related. Comparing this history with the biogeographic history of associated conifer genera will allow clarification of how constraints linked with host association and Cinara’s biogeographic history are entangled and have shaped present-day diversity.

Previous studies of conifer-feeding insects in the Holarctic region have also rejected the hypothesis of independent radiations of these insects in Europe, Asia and North America [7072]. The lack of resolution and/or incomplete sampling in these phylogenies does not allow for a detailed comparison of the evolution of the association with conifer genera with our results. However in contrast to our study, the history of Megastigmus (Hymenoptera: Torymidae) [71] suggests that species on Cupressaceae form a distinct clade from species feeding on Pinaceae (including Abies-feeding species). On the other hand, the relatedness of species feeding on Pseudotsuga and Larix was also found in a Dendroctonus (Scolytidae) phylogeny [72, 73]. Comparative historical biogeography of these conifer-feeding groups should provide insights on the role of conifer history on the diversification of phytophagous insects with similar ecological requirements in the Holarctic.

Speciation in the genus Cinara

In the last decade, the topic of ecological speciation has fueled many debates in the literature [7] and stimulated many research projects, such as the search for signature of this process in various genomes [7480]. Consequently, studying “ordinary” geographic speciation has almost become an unusual area of research. As Coyne and Orr [81] mention in their book on speciation “allopatric speciation appears so plausible that it hardly seems worth documenting”. Ecological speciation is definitely a plausible scenario for several taxa [8284] and several studies have convincingly demonstrated that this process can occur in sympatry [85, 86]. However, these studies were generally conducted on very closely related species or lineages that have not always achieved complete reproductive isolation [6, 85, 87]. Because of this narrow focus on a few model species, it is difficult to give an estimate of the frequency of ecological speciation even for species belonging to the same taxonomic groups as the focal species. This needs to be approached by broader macro-evolutionary studies. Phylogenetic studies on the patterns of diversification of phytophagous insects have largely focused on the role of host plant shifts in the speciation process (e.g. [8894]). This also applies to aphids (see [10] for a review but see also [63, 64, 95] for other views). However, these studies rarely evaluate the frequency of speciation by host shifts or weight it against alternative modes of speciation.

Our analyses on the conifer-feeding aphid genus, Cinara, reveal a pattern of frequent niche shifts in terms of host plant use and feeding habits. Cinara species show frequent host specialisation events and multiple transitions from branch-feeding to shoot-feeding. We do not find the clustering of species using similar feeding sites observed by Favret and Voegtlin [30] on North American Pinyon pines Cinara on a broader scale. Despite these multiple transitions, mapping the evolution of ecological niche on our phylogeny shows that less than 50% of lineage splits have been accompanied by a shift in resource use. Though parsimony can tend to underestimate the frequency of changes [96], this should not affect our conclusions, as randomization tests clearly show that the probability of observing 31 changes is much lower than expected by chance. If we look more precisely at terminal nodes of our phylogeny, phylogenetic clusters revealed by the species delimitation method do not correspond to host specialised races or lineages specialised on particular feeding sites, and only a few sister species pairs differ in their resource use (15%). Given that there is a broad range of host specificity in Cinara (species range from strictly monophagous to highly polyphagous), this is not that surprising. Such diversity in feeding diets already suggests that different speciation mechanisms have been acting. Ecological speciation via host shifts in generalist lineages (e.g. feeding on several Pinus species or even across conifer families in our biological model) can only occur by shifts to higher plant taxa, i.e. by shifting to a new plant genus, which probably require important physiological and behavioural changes that might constitute rare events (see [97] for a thorough discussion on the importance of niche width in ecological speciation). Our estimate of the number of ecology driven speciation events is similar to the finding of Nyman et al.[32] on sawflies (Hymenoptera: Tenthredinidae), and the conclusions from a broad literature survey of macroevolutionnary studies by Winkler & Mitter [98] that both suggest that the ratio of ecological versus non-ecological speciation in phytophagous insect is at the most “1:1”. This also echoes the results of Imada et al.[99] on a group of 25 phytophagous moth species showing that none of them have speciated via ecological speciation and a study by Roesch Goodman et al.[100] that demonstrates that geography and not ecology is responsible for the diversification of host specific Hawaiian plant hoppers. Furthermore, as underlined by previous authors [31, 35], showing that a shift in ecological resource has accompanied a speciation event does not mean that ecological differentiation has triggered the formation of two species. Resource shifts frequently occur after speciation. Therefore, our estimate of 50% of ecology-based speciation events is an upper limit. This result contrasts with the signature of geographical isolation imprinted in the biogeographical history of the genus, as outlined in the previous paragraph. The comparison of sister-species pairs (there are more sister species that do not overlap in their geographic range than species that do not overlap in their resource use) and the fact that geographic overlap between lineages tends to increase with time elapsed since the speciation event also indicate that geographic isolation has promoted speciation events in Cinara, Geographical barriers such as mountain ranges have probably shaped some of the diversity at the regional scale. Indeed, in some western North American species, we observe a disjunct distribution in sister species pairs, with some species being restricted on either side of the Rocky Mountains [for instance Cinara splendens (Gillette & Palmer, 1924) and Cinara pseudotsugae (Wilson, 1912) (Figure 4)]. C. ponderosae is also subdivided into two sub-clades (as identified by the species delimitation method) found either West or East of the Rockies (Figure 4). Western North America actually contains a large part of the diversity of Cinara (about 40% with more than 85 species [29]). Topographic complexity in western North America [101] and more specifically in the southern Rocky mountains and intermontane plateau has been suggested to be responsible for high diversification rates in several organisms including several insects [102, 103]. It is also probably implicated in the diversification of Cinara. Evidence for geographical isolation linked with long distance is not always obvious in the terminal nodes of our phylogeny. If we compare Central Asian populations with European populations of several species exhibiting a large Palearctic distribution, they do not always show significant divergence. C pinea specimens from Europe and Central Asia appear divided into two separate clades, however these clades are not diverged enough to be recognized as different species (Figure 1, clade A). However, in C. pilicornis, a Kazakh cluster is retrieved that is clearly differentiated from the rest of the specimens (Figure 1, clade A). This east–west divergence can be interpreted as a result of range expansion across the Palearctic and subsequent isolation by distance. Nevertheless, within the European cluster, there is one Kazakh-type individual, which may correspond to a recent long dispersal event associated with human transport. American specimens from C. pinea and C. pilicornis are entangled within European specimens which agrees with the fact that they have been recently introduced (early last century) into North America from Europe [55].

Several issues could bias our conclusions. First our sampling only encompasses a third of the diversity of the genus and missing species might influence the results of our studies. However, our sampling was mainly focused on maximizing the number of species sampled from one geographical location rather than sampling the genus throughout its geographic range (for instance within Colorado (US) we have included 21 species among the 39 that are recorded from this state). The vast majority of species missing from our study is restricted to eastern Nearctic or occurs throughout Asia. Therefore, we believe that adding species in our analyses will add at least as much geographic variation as ecological variation and that it should not significantly affect the proportion of speciation events driven by ecological niche shifts inferred by our analyses. More importantly, taxonomic issues concerning host plants might have influenced our conclusions. Host identification can be difficult and Pinus sub-species or hybrids that we have not managed to identify might occur throughout our sampling. For instance, Pinus ponderosa and P. lambertiana encompass infra-specific diversity [104106]. Some of the species diversity in Cinara attributed to geographic factors might actually reflect specialisation to particular hybrid or subspecies. In the Picea genus, many species are planted as ornamental trees and occur outside their geographic range, which renders identification quite difficult. Cinara feeding on Picea are often indicated as feeding on Picea spp. [29] with no precise definition of their host range. We have also found a lot of “phylogenetic species” within widely distributed species feeding on Picea (e.g. C. pruinosa and C. pilicornis). This suggests that the taxonomic treatment of Cinara species on Picea and determination of their host associations might have been less thorough than the treatment of species associated with Pinus. Hence, patterns of host association in Picea-feeding species in the literature and our study probably lack precision and we might have failed at identifying host specialisation events on Picea species. However, it is also likely that there have also been more vicariance events than suggested by our phylogeny. Present day distributions of many Cinara species are obviously the result of range expansion. Climatic history has probably contributed to repeated instances of range contraction and range expansion in response to glacial cycles. These changes in geographic distributions could have led to sympatry in many species of our study while they had actually separated in allopatric conditions. But there are actually many cases where geographic factors and hosts adaptation are simply confounded. The species diversity and intraspecific variation in Cinara hosts trees have also resulted from geographic events. Pine tree species occurring naturally in the Nearctic region are different from species occurring in Europe. Climatic fluctuations occurring during the last glacial cycles have also probably affected the genetic structure of Cinara’s host plants, especially in the mountainous environments of western North America where conifer diversity is high. Therefore, when the genetic clusters observed in aphids mirror the genetic structure of their hosts and also correspond to isolated geographic zones; it will be difficult to tell apart geographic isolation from host adaptation in the speciation process. Finally, we have limited our definition of ecology-based speciation events to events due to host shifts and/or changes in feeding sites. We believe that these are the two main ecological factors that could lead to species divergence in our system. However, divergence in the timing of reproduction [22] and changes in reproductive modes could also be driven by ecological forces such as competition or escape from parasites [107], and might account for some speciation events in Cinara.

Conclusions

Our broad inference regarding diversification within Cinara suggests that even in this group of specialised phytophagous insects, ecological differentiation linked with host plant and feeding sites shifts is not the sole driver of speciation. In this aphid genus, climatic events and landscape history are probably as important as ecology in having shaped present day diversity. The history of Cinara offers a different view on the processes of speciation in aphids than that provided by models such as the pea aphid.

Declarations

Acknowledgements

This work was funded by ANR Phylospace. Many thanks to Anne-Laure Clamens and J. Piffaretti for help in the field. We also thank Alexandre Dehne Garcia and the CBGP HPC computational platform. We would like to thank three anonymous reviewers for helpful comments on earlier versions of this manuscript.

Authors’ Affiliations

(1)
INRA–UMR 1062 CBGP (INRA, IRD, CIRAD, Montpellier SupAgro), Centre de Biologie pour la Gestion des Populations, Campus International de Baillarguet CS 30 016
(2)
Institut de Biologie Computationnelle, LIRMM
(3)
MIVEGEC, CNRS 5290
(4)
Agriculture and Agri-Food Canada, Canadian National Collection of Insects, K.W. Neatby Building

References

  1. Gavrilets S: Models of speciation: what have we learned in 40 years. Evolution. 2003, 57: 2197-2215.PubMedGoogle Scholar
  2. Schluter D: Evidence for ecological speciation and its alternative. Science. 2009, 323: 737-741. 10.1126/science.1160006.PubMedGoogle Scholar
  3. Linnen CR, Farrell BD: A test of the sympatric host race formation hypothesis in Neodiprion (Hymenoptera: Diprionidae). Proc R Soc B-Biol Sci. 2010, 277: 3131-3138. 10.1098/rspb.2010.0577.Google Scholar
  4. Via S: Sympatric speciation in animals: the ugly duckling grows up. Trends Ecol Evol. 2001, 16: 381-390. 10.1016/S0169-5347(01)02188-7.PubMedGoogle Scholar
  5. Berlocher SH, Feder JL: Sympatric speciation in phytophagous insects: moving beyond controversy?. Annu Rev Entomol. 2002, 47: 773-815. 10.1146/annurev.ento.47.091201.145312.PubMedGoogle Scholar
  6. Drès M, Mallet J: Host races in plant-feeding insects and their importance in sympatric speciation. Philo Transac R Soc London Series B-Biol Sci. 2002, 357: 471-492. 10.1098/rstb.2002.1059.Google Scholar
  7. Rundle HD, Nosil P: Ecological speciation. Ecol Lett. 2005, 8: 336-352. 10.1111/j.1461-0248.2004.00715.x.Google Scholar
  8. Futuyma DJ: Sympatric speciation: norm or exception. Specialization, speciation and radiation: the evolutionary biology of herbivorous insects. Edited by: Tilmon K. 2008, Berkeley: University of California Press, 136-148.Google Scholar
  9. Berlocher SH: Radiation and divergence in the Rhagoletis pomonella species group: inferences from allozymes. Evolution. 2000, 54: 543-557.PubMedGoogle Scholar
  10. Peccoud J, Simon JC, Von Dohlen C, Coeur d’acier A, Plantegenest M, Vanlerberghe-Masutti F, Jousselin E: Evolutionary history of aphid-plant associations and their role in aphid diversification. C R Biol. 2010, 333: 474-487. 10.1016/j.crvi.2010.03.004.PubMedGoogle Scholar
  11. Via S, Bouck AC, Skillman S: Reproductive isolation between divergent races of pea aphids on two hosts: II: selection against migrants and hybrids in the parental environments. Evolution. 2000, 54: 1626-1637.PubMedGoogle Scholar
  12. Hawthorne DJ, Via S: Genetic linkage of ecological specialization and reproductive isolation in pea aphids. Nature. 2001, 412: 904-907. 10.1038/35091062.PubMedGoogle Scholar
  13. Simon JC, Carré S, Boutin M, Prunier-Leterme N, Sabater-Munoz B, Latorre A, Bournoville R: Host-based divergence in populations of the pea aphid: insights from nuclear markers and the prevalence of facultative symbionts. Proc R Soc B-Biol Sci. 2003, 270: 1703-1712. 10.1098/rspb.2003.2430.Google Scholar
  14. Peccoud J, Ollivier A, Plantegenest M, Simon JC: A continuum of genetic divergence from sympatric host races to species in the pea aphid complex. Proc Natl Acad Sci U S A. 2009, 106: 7495-7500. 10.1073/pnas.0811117106.PubMed CentralPubMedGoogle Scholar
  15. Ferrari J, Via S, Godfray HCJ: Population differentiation and genetic variation in performance on eight hosts in the pea aphid complex. Evolution. 2008, 62: 2508-2524. 10.1111/j.1558-5646.2008.00468.x.PubMedGoogle Scholar
  16. Charaabi K, Carletto J, Chavigny P, Marrakchi M, Makni M, Vanlerberghe-Masutti F: Genotypic diversity of the cotton-melon aphid Aphis gossypii (Glover) in Tunisia is structured by host plants. Bull Entomol Res. 2008, 98: 333-341.PubMedGoogle Scholar
  17. Carletto J, Lombaert E, Chavigny P, Brevault T, Lapchin L, Vanlerberghe-Masutti F: Ecological specialization of the aphid Aphis gossypii Glover on cultivated host plants. Mol Ecol. 2009, 18: 2198-2212. 10.1111/j.1365-294X.2009.04190.x.PubMedGoogle Scholar
  18. Hodson AC, Cook EF: Long-range aerial transport of the harlequin bug and the greenbug into Minnesota. J Econ Entomol. 1960, 53: 604-608.Google Scholar
  19. Robert Y: Dispersion and migration. Aphids their biology, natural enemies and control. Edited by: Minks AK, Harrewijn P. 1987, Amsterdam: Elsevier, 299-313.Google Scholar
  20. Zhu M, Radcliffe EB, Ragsdale DW, MacRae IV, Seeley MW: Low-level jet streams associated with spring aphid migration and current season spread of potato viruses in the US northern great plains. Agr Forest Meteorol. 2006, 138: 192-202. 10.1016/j.agrformet.2006.05.001.Google Scholar
  21. Loxdale HD, Hardie J, Halbert S, Foottit R, Kidd NAC, Carter CI: The relative importance of short-and long-range movement of flying aphids. Biological Review. 1993, 68: 291-311.Google Scholar
  22. Abbot P, Withgott JH: Phylogenetic and molecular evidence for allochronic speciation in gall-forming aphids (Pemphigus). Evolution. 2004, 58: 539-553.PubMedGoogle Scholar
  23. Lassaad M, Martínez-Torres D, Monia BHK: Two mitochondrial haplotypes in Pterochloroides persicae (Hemiptera: Aphididae: Lachninae) associated with different feeding sites. Insect Science. 2013, in pressGoogle Scholar
  24. Favret C: Aphid species file. 2013,http://aphid.speciesfile.org/, 10/41,Google Scholar
  25. Voegtlin DJ, Bridges CA: Catalog of the Cinara species of North America (Homoptera: Aphididae). Illinois Nat Hist Survey Special Publ. 1988, 8: 1-55.Google Scholar
  26. Nieto Nafria JM: Fauna Europaea version 13. Fauna Europaea: aphididae. 2007,http://www.faunaeur.org/,Google Scholar
  27. Bradley GA: Feeding sites of aphids of the genus Cinara Curtis (Homoptera: Aphididae) in Northwestern Ontario. The Canadian Entomologist. 1959, 91: 670-671. 10.4039/Ent91670-10.Google Scholar
  28. Kidd NAC: Does wingloading limit flight potential in aphids?. J Appl Entomol. 1991, 112: 27-30. 10.1111/j.1439-0418.1991.tb01024.x.Google Scholar
  29. Blackman RL, Eastop VF: Aphids on the world trees: an identification and information guide. 1994, London, UK: The Natural History MuseumGoogle Scholar
  30. Favret C, Voegtlin DJ: Speciation by host-switching in pinyon Cinara (Insecta: Hemiptera: Aphididae). Mol Phylogenet Evol. 2004, 32: 139-151. 10.1016/j.ympev.2003.12.005.PubMedGoogle Scholar
  31. Barraclough TG, Nee S: Phylogenetics and speciation. Trends in Ecol Evol. 2001, 16: 391-399. 10.1016/S0169-5347(01)02161-9.Google Scholar
  32. Nyman T, Vikberg V, Smith DR, Boeve JL: How common is ecological speciation in plant-feeding insects? A ‘Higher’ Nematinae perspective. BMC Evol Biol. 2010, 10: 266-10.1186/1471-2148-10-266.PubMed CentralPubMedGoogle Scholar
  33. Barraclough TG, Vogler AP: Detecting the geographical pattern of speciation from species-level phylogenies. Am Nat. 2000, 155: 419-434. 10.1086/303332.PubMedGoogle Scholar
  34. Chevenet F, Jung M, Peeters M, De Oliveira T, Gascuel O: Searching for virus phylotypes. Bioinformatics. 2013, 29 (5): 561-570. 10.1093/bioinformatics/btt010.PubMed CentralPubMedGoogle Scholar
  35. Barraclough TG, Vogler AP, Harvey PH: Revealing the factors that promote speciation. Philo Transac R Soci London Series B-Biol Sci. 1998, 353: 241-249. 10.1098/rstb.1998.0206.Google Scholar
  36. Losos JB, Glor RE: Phylogenetic comparative methods and the geography of speciation. Trends in Ecol Evol. 2003, 18: 220-227. 10.1016/S0169-5347(03)00037-5.Google Scholar
  37. Favret C, Voegtlin DJ: A revision of the Cinara species (Hemiptera: Aphididae) of the United States pinyon pines. Ann Entomol Soc Am. 2004, 97: 1165-1197. 10.1603/0013-8746(2004)097[1165:AROTCS]2.0.CO;2.Google Scholar
  38. Jousselin E, Desdevises Y, Coeur d’acier A: Fine-scale cospeciation between Brachycaudus and Buchnera aphidicola: bacterial genome helps define species and evolutionary relationships in aphids. Proc R Soc B Biol Sci. 2009, 276: 187-196. 10.1098/rspb.2008.0679.Google Scholar
  39. Moran NA, Munson MA, Baumann P, Ishikawa H: A molecular clock in endosymbiotic bacteria is calibrated using the insect hosts. Proc R Soc B-Biol Sci. 1993, 253: 167-171. 10.1098/rspb.1993.0098.Google Scholar
  40. Lozier JD, Roderick GK, Mills NJ: Genetic evidence from mitochondrial, nuclear, and endosymbiont markers for the evolution of host plant associated species in the aphid genus Hyalopterus (Hemiptera : Aphididae). Evolution. 2007, 61: 1353-1367. 10.1111/j.1558-5646.2007.00110.x.PubMedGoogle Scholar
  41. Tamura K, Dudley J, Nei M, Kumar S: MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol. 2007, 24: 1596-1599. 10.1093/molbev/msm092.PubMedGoogle Scholar
  42. Miller MA, Pfeiffer W, Schwartz T: Creating the CIPRES science gateway for inference of large phylogenetic trees. 2010, New Orleans,LA: Proceedings of the Gateway Computing Environments Workshop (GCE), 1-8.Google Scholar
  43. Nylander JAA, Ronquist F, Huelsenbeck JP, Nieves-Aldrey JL: Bayesian phylogenetic analysis of combined data. Syst Biol. 2004, 53: 47-67. 10.1080/10635150490264699.PubMedGoogle Scholar
  44. Stamatakis A: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006, 22: 2688-2690. 10.1093/bioinformatics/btl446.PubMedGoogle Scholar
  45. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Hohna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP: MrBayes 3.2: efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012, 61: 539-542. 10.1093/sysbio/sys029.PubMed CentralPubMedGoogle Scholar
  46. Huelsenbeck JP, Bollback JP: Empirical and hierarchical Bayesian estimation of ancestral states. Syst Biol. 2001, 50: 351-366.PubMedGoogle Scholar
  47. Drummond AJ, Rambaut A: BEAST: bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007, 7: 214-10.1186/1471-2148-7-214.PubMed CentralPubMedGoogle Scholar
  48. Coeur d’Acier A, Jousselin E, Martin JF, Rasplus JY: Phylogeny of the genus Aphis Linnaeus, 1758 (Homoptera: Aphididae) inferred from mitochondrial DNA sequences. Mol Phylogenet Evol. 2007, 42: 598-611. 10.1016/j.ympev.2006.10.006.PubMedGoogle Scholar
  49. Foottit RG, Maw HEL, Von Dohlen CD, Hebert PDN: Species identification of aphids (Insecta: Hemiptera: Aphididae) through DNA barcodes. Mol Ecol Resour. 2008, 8: 1189-1201. 10.1111/j.1755-0998.2008.02297.x.PubMedGoogle Scholar
  50. Pons J, Barraclough T, Gomez-Zurita J, Cardoso A, Duran D, Hazell S, Kamoun S, Sumlin W, Vogler A: Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Syst Biol. 2006, 55: 595-609. 10.1080/10635150600852011.PubMedGoogle Scholar
  51. Kass R, Raftery A: Bayes factors. J Am Stat Assoc. 1995, 90: 773-795. 10.1080/01621459.1995.10476572.Google Scholar
  52. Suchard M, Weiss R, Sinsheimer J: Bayesian selection of continuous-time Markov chain evolutionary models. Mol Biol Evol. 2001, 18: 1001-1013. 10.1093/oxfordjournals.molbev.a003872.PubMedGoogle Scholar
  53. Baele G, Lemey P, Bedford T, Rambaut A, Suchard MA, Alekseyenko AV: Improving the accuracy and molecular clock model comparison while accomodating phylogenetic uncertainty. Mol Biol Evol. 2012, 29: 2157-2167. 10.1093/molbev/mss084.PubMed CentralPubMedGoogle Scholar
  54. Paradis E, Claude J, Strimmer K: APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004, 20: 289-290. 10.1093/bioinformatics/btg412.PubMedGoogle Scholar
  55. Foottit RG, Halbert SE, Miller GL, Maw E, Russell LM: Adventive aphids (Hemiptera: Aphididae) of America North of Mexico. Proc Entomol Soc Wash. 2006, 108: 583-610.Google Scholar
  56. Binazzi A: Contributi alla conoscenza degli afidi delle conifere: I: Le specie dei genn: Cinara Curt., Schizolachnus Mordv., Cedrobium Remaud. ed Eulachnus D.Gu. presenti in Italia (Homoptera Aphidoidea Lachnidae). Redia. 1978, 61: 291-400.Google Scholar
  57. Eastop VF: A taxonomic review of the species of Cinara curtis occurring in Britain (Hemiptera: Aphididae). Bull Bri Mus(Nat Hist) Entomol. 1972, 27: 101-186.Google Scholar
  58. Foottit R: Morphometric analysis of character variation and taxonomic discrimination among a complex of species of the genus Cinara (Homoptera, Aphidoidea, Lachnidae). 1987, Simon Fraser University, Biological Sciences, 227-Google Scholar
  59. Watson GW, Voegtlin DJ, Murphy ST, Foottit RG: Biogeography of the Cinara cupressi complex (Hemiptera: Aphididae) on Cupressaceae, with description of a pest species introduced into Africa. Bull Entomol Res. 1999, 89: 271-283.Google Scholar
  60. Fabre JP: Comparative susceptibility of the genus Cedrus to aphids infestation-Cedrobium laportei Rémaudière and Cinara cedri Mimeur (Homoptera, Lachnidae). Annal Des Sci Forestieres. 1988, 45: 125-140. 10.1051/forest:19880203.Google Scholar
  61. Binazzi A, Scheurer S: Atlas of the honeydew producing conifer aphids of Europe. 2009, Rome: BrochetGoogle Scholar
  62. Kolsch G, Pedersen BV: Molecular phylogeny of reed beetles (Col., Chrysomelidae, Donaciinae): the signature of ecological specialization and geographical isolation. Mol Phylogenet Evol. 2008, 48: 936-952. 10.1016/j.ympev.2008.05.035.PubMedGoogle Scholar
  63. Von Dohlen CD, Kurosu U, Aoki S: Phylogenetics and evolution of the eastern Asian-eastern North american disjunct aphid tribe, Hormaphidini (Hemiptera: Aphididae). Mol Phylogenet Evol. 2002, 23: 257-267. 10.1016/S1055-7903(02)00025-8.PubMedGoogle Scholar
  64. Moran NA, Kaplan ME, Gelsey MJ, Murphy TG, Scholes EA: Phylogenetics and evolution of the aphid genus Uroleucon based on mitochondrial and nuclear DNA sequences. Syst Entomol. 1999, 24: 85-93. 10.1046/j.1365-3113.1999.00076.x.Google Scholar
  65. Ronquist F, Sanmartin I, Futuyma DJ, Shaffer HB, Simberloff D: Phylogenetic methods in biogeography. Annu Rev Ecol Evol Syst. 2011, 42: 441-464. 10.1146/annurev-ecolsys-102209-144710.Google Scholar
  66. Sanmartin I, Enghoff H, Ronquist F: Patterns of animal dispersal, vicariance and diversification in the Holarctic. Biol J Linn Soc. 2001, 73: 345-390.Google Scholar
  67. Leslie AB, Beaulieu JM, Rai HS, Crane PR, Donoghue MJ, Mathews S: Hemisphere-scale differences in conifer evolutionary dynamics. Proc Natl Acad Sci U S A. 2012, 109: 16217-16221. 10.1073/pnas.1213621109.PubMed CentralPubMedGoogle Scholar
  68. Gernandt DS, Magallon S, Lopez GG, Flores OZ, Willyard A, Liston A: Use of simultaneous analyses to guide fossil based calibrations of Pinaceae phylogeny. Int J Plant Sci. 2008, 169: 1086-1099. 10.1086/590472.Google Scholar
  69. Wang XQ, Tank DC, Sang T: Phylogeny and divergence times in Pinaceae: evidence from three genomes. Mol Biol Evol. 2000, 17: 773-781. 10.1093/oxfordjournals.molbev.a026356.PubMedGoogle Scholar
  70. Sachet JM, Roques A, Despres L: Linking patterns and processes of species diversification in the cone flies Strobilomyia (Diptera: Anthomyiidae). Mol Phylogenet Evol. 2006, 41: 606-621. 10.1016/j.ympev.2006.06.005.PubMedGoogle Scholar
  71. Auger-Rozenberg MA, Kerdelhue C, Magnoux E, Turgeon J, Rasplus JY, Roques A: Molecular phylogeny and evolution of host-plant use in conifer seed chalcids in the genus Megastigmus (Hymenoptera: Torymidae). Syst Entomol. 2006, 31: 47-64.Google Scholar
  72. Kelley ST, Farrell BD: Is specialization a dead end? The phylogeny of host use in Dendroctonus bark beetles (Scolytidae). Evolution. 1998, 52: 1731-1743. 10.2307/2411346.Google Scholar
  73. Reeve JD, Anderson FE, Kelley ST: Ancestral state reconstruction for Dendroctonus bark beetles: evolution of a tree killer. Environ Entomol. 2012, 41: 723-730. 10.1603/EN11281.PubMedGoogle Scholar
  74. Via S: Natural selection in action during speciation. Proc Natl Acad Sci U S A. 2009, 106: 9939-9946. 10.1073/pnas.0901397106.PubMed CentralPubMedGoogle Scholar
  75. Schwarz D, Robertson H, Feder J, Varala K, Hudson M, Ragland G, Hahn D, Berlocher S: Sympatric ecological speciation meets pyrosequencing: sampling the transcriptome of the apple maggot Rhagoletis pomonella. BMC Genomics. 2009, 10: 633-10.1186/1471-2164-10-633.PubMed CentralPubMedGoogle Scholar
  76. Smadja CM, Canback B, Vitalis R, Gautier M, Ferrari J, Zhou JJ, Butlin RK: Large-scale candidate gene scan reveals the role of chemoreceptor genes in host-plant specialization and speciation in the pea aphid. Evolution. 2012, 66: 2723-2738. 10.1111/j.1558-5646.2012.01612.x.PubMedGoogle Scholar
  77. Jaquiery J, Stoeckel S, Nouhaud P, Mieuzet L, Maheo F, Legeai F, Bernard N, Bonvoisin A, Vitalis R, Simon JC: Genome scans reveal candidate regions involved in the adaptation to host plant in the pea aphid complex. Mol Ecol. 2012, 21: 5251-5264. 10.1111/mec.12048.PubMedGoogle Scholar
  78. Feder JL, Egan SP, Nosil P: The genomics of speciation-with-gene-flow. Trends Genet. 2012, 28: 342-350. 10.1016/j.tig.2012.03.009.PubMedGoogle Scholar
  79. Nosil P, Gompert Z, Farkas TE, Comeault AA, Feder JL, Buerkle CA, Parchman TL: Genomic consequences of multiple speciation processes in a stick insect. Proc R Soc B-Biol Sci. 2012, 279: 5058-5065. 10.1098/rspb.2012.0813.Google Scholar
  80. Fan S, Elmer KR, Meyer A: Genomics of adaptation and speciation in cichlid fishes: recent advances and analyses in African and Neotropical lineages. Philo Transac R Soc B: Biol Sci. 2012, 367: 385-394. 10.1098/rstb.2011.0247.Google Scholar
  81. Coyne JA, Orr HA: Speciation. 2004, Sunderland, MA, USA: Sinauer AssociatesGoogle Scholar
  82. Stireman JO, Nason JD, Heard SB: Host-associated genetic differentiation in phytophagous insects: general phenomenon or isolated exceptions? Evidence from a goldenrod-insect community. Evolution. 2005, 59: 2573-2587.PubMedGoogle Scholar
  83. Funk DJ: Does strong selection promote host specialisation and ecological speciation in insect herbivores? Evidence from Neochlamisus leaf beetles. Eco Entomol. 2010, 35: 41-53.Google Scholar
  84. Egan SP, Hood GR, Feder JL, Ott JR: Divergent host-plant use promotes reproductive isolation among cynipid gall wasp populations. Biology letters. 2012, 8: 605-608. 10.1098/rsbl.2011.1205.PubMed CentralPubMedGoogle Scholar
  85. Thorpe RS, Surget-Groba Y, Johansson H: Genetic tests for ecological and allopatric speciation in Anoles on an island archipelago. PLoS Genet. 2010, 6: e1000929-10.1371/journal.pgen.1000929.PubMed CentralPubMedGoogle Scholar
  86. Bolnick DI, Fitzpatrick BM: Sympatric speciation: models and empirical evidence. Annu Rev Ecol Syst. 2007, 38: 459-487. 10.1146/annurev.ecolsys.38.091206.095804.Google Scholar
  87. Peccoud J, Simon JC: The pea aphid complex as a model of ecological speciation. Eco Entomol. 2010, 35: 119-130.Google Scholar
  88. Lopez-Vaamonde C, Godfray HCJ, Cook JM: Evolutionary dynamics of host-plant use in a genus of leaf-mining moths. Evolution. 2003, 57: 1804-1821.PubMedGoogle Scholar
  89. Farrell B: “Inordinate fondness” explained: why are there so many beetles?. Science. 1998, 281: 555-559.PubMedGoogle Scholar
  90. McLeish MJ, Chapman TW, Schwarz MP: Host-driven diversification of gall-inducing acacia thrips and the aridification of Australia. BMC Biol. 2007, 5: 3-10.1186/1741-7007-5-3.PubMed CentralPubMedGoogle Scholar
  91. Kergoat GJ, Delobel A, Fediere G, Ru BL, Silvain JF: Both host-plant phylogeny and chemistry have shaped the African seed-beetle radiation. Mol Phylogenet Evol. 2005, 35: 602-611. 10.1016/j.ympev.2004.12.024.PubMedGoogle Scholar
  92. Winkler IS, Mitter C, Scheffer SJ: Repeated climate-linked host shifts have promoted diversification in a temperate clade of leaf-mining flies. Proc Natl Acad Sci U S A. 2009, 106: 18103-18108. 10.1073/pnas.0904852106.PubMed CentralPubMedGoogle Scholar
  93. Leppanen SA, Altenhofer E, Liston AD, Nyman T: Phylogenetics and evolution of host-plant use in leaf-mining sawflies (Hymenoptera: Tenthredinidae: Heterarthrinae). Mol Phylogenet Evol. 2012, 64: 331-341. 10.1016/j.ympev.2012.04.005.PubMedGoogle Scholar
  94. Janz N, Nylin S, Wahlberg N: Diversity begets diversity: host expansions and the diversification of plant-feeding insects. BMC Evol Biol. 2006, 6: 4-10.1186/1471-2148-6-4.PubMed CentralPubMedGoogle Scholar
  95. Jousselin E, Genson G, Coeur d’Acier A: Evolutionary lability of a complex life cycle in the aphid genus Brachycaudus. BMC Evol Biol. 2010, 10: 295-10.1186/1471-2148-10-295.Google Scholar
  96. Schluter D, Price T, Mooers AO, Ludwig D: Likelihood of ancestor states in adaptive radiation. Evolution. 1997, 51: 1699-1711. 10.2307/2410994.Google Scholar
  97. Nyman T: To speciate, or not to speciate? Resource heterogeneity, the subjectivity of similarity, and the macroevolutionary consequences of niche-width shifts in plant-feeding insects. Biol Rev. 2010, 85: 393-411.PubMedGoogle Scholar
  98. Winkler IS, Mitter C: The phylogenetic dimension of insect-plant interactions: a review of recent evidence. Specialization, speciation and radiation: the evolutionary biology of herbivorous insects. Edited by: Tilmon K. 2008, University of California Press, 240-263.Google Scholar
  99. Imada Y, Kawakita A, Kato M: Allopatric distribution and diversification without niche shift in a bryophyte-feeding basal moth lineage (Lepidoptera: Micropterigidae). Proc R Soc B Biol Sci. 2011, 278: 3026-3033. 10.1098/rspb.2011.0134.Google Scholar
  100. Roesch Goodman K, Welter SC, Roderick GK: Genetic divergence is decoupled from ecological diversification in the Hawaiian neosydne planthopers. Evolution. 2012, 66: 2798-2814. 10.1111/j.1558-5646.2012.01643.x.PubMedGoogle Scholar
  101. Cracraft J: Biological diversification and its causes. Ann Mo Bot Gard. 1985, 72: 794-822. 10.2307/2399222.Google Scholar
  102. Vogler AP, Welsh A, Barraclough TG: Molecular phylogeny of the Cicindela maritima (Coleoptera: Cicindelidae) group indicates fast radiation in western North America. Ann Entomol Soc Am. 1998, 91: 185-194.Google Scholar
  103. Knowles LL, Otte D: Phylogenetic analysis of montane grasshoppers from western North America (Genus Melanoplus, Acrididae: Melanoplinae). Ann Entomol Soc Am. 2000, 93: 421-431. 10.1603/0013-8746(2000)093[0421:PAOMGF]2.0.CO;2.Google Scholar
  104. Conkle MT, Critchfiled WB: Genetic variation and hybridization of ponderosa pine. Ponderosa pine: the species and its management. Edited by: Baumgartner DM, Lotan EJ. 1988, Pullman: Washington State University Press, 27-43.Google Scholar
  105. Johansen AD, Latta RG: Mitochondrial haplotype distribution, seed dispersal and patterns of postglacial expansion of ponderosa pine. Mol Ecol. 2003, 12: 293-298.PubMedGoogle Scholar
  106. Liston A, Parker-Defeniks M, Syring JV, Willyard A, Cronn R: Interspecific phylogenetic analysis enhances intraspecific phylogeographical inference: a case study in Pinus lambertiana. Mol Ecol. 2007, 16: 3926-3937. 10.1111/j.1365-294X.2007.03461.x.PubMedGoogle Scholar
  107. Nyman T, Bokma F, Kopelke JP: Reciprocal diversification in a complex plant-herbivore-parasitoid food web. Bmc Biology. 2007, 5: 49-10.1186/1741-7007-5-49.PubMed CentralPubMedGoogle Scholar

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