Using statistical phylogeography to infer population history: Case studies on Pimelia darkling beetles from the Canary Islands

Size: px
Start display at page:

Download "Using statistical phylogeography to infer population history: Case studies on Pimelia darkling beetles from the Canary Islands"

Transcription

1 Journal of Arid Environments Journal of Arid Environments 66 (2006) Using statistical phylogeography to infer population history: Case studies on Pimelia darkling beetles from the Canary Islands O. Moya a, H.G. Contreras-Dı az a,b, P. Oromı b, C. Juan a, a Departament de Biologia, Universitat de les Illes Balears, Palma de Mallorca, Spain b Departamento de Biología Animal, Facultad de Biología, Universidad de La Laguna, La Laguna, Tenerife, Spain Available online 3 March 2006 Abstract Sequence data from a 200 bp fragment of the Cytochrome Oxidase I mitochondrial gene was derived from endemic populations of the darkling beetle Pimelia laevigata (Coleoptera, Tenebrionidae) from the volcanic islands of La Gomera, La Palma and El Hierro and from three related congeneric species of Tenerife (Canary Islands). Statistical phylogeographic methods and estimates of demographic parameters suggest that there is a higher genetic variation and geographical structure in two of the Tenerife nominal species than in populations of P. laevigata in the western islands. In La Gomera, La Palma and El Hierro, the patterns are consistent with relatively recent colonizations, followed by range expansions. The results show that hypotheses based on coalescent theory can be useful to reconstruct historic biogeographical events of oceanic islands in a range of different organisms provided that the sample design is adequate and enough genetic resolution is present. However, some specific problems arise when interpreting the inference key applied to the volcanic islands populations. r 2006 Elsevier Ltd. All rights reserved. Keywords: Mitochondrial DNA; Nested clade phylogeographic analysis; Colonization; Population expansion; Arid regions; Darkling beetles Corresponding author. Tel.: ; fax: address: cjuan@uib.es (C. Juan) /$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi: /j.jaridenv

2 478 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Introduction The application of phylogenetic methods to understand the extent of genetic variation in space and time among populations or related taxa has emerged as a major issue in evolutionary and conservation biology. Different theoretical approaches exist in relation to this topic, but the more common and recent uses coalescent theory (Kingman, 1982a, b) as a conceptual framework to estimate different population parameters and understand demographic histories (see Emerson et al., 2001; Knowles, 2004). The inferences obtained by these methods are based on statistical tests of historical hypotheses and estimates of demographic parameters, in contrast with the classical intuitive phylogeographic methodology developed by Avise (1998), which is based on inferring the causes of an association between observed patterns of genetic variation and the geographical distribution of populations (Knowles, 2004). Nested clade phylogeographic analysis (NCPA) is one of the statistical approaches applied to intraspecific or closely related species gene genealogies. It combines genealogical data through haplotype networks obtained using statistical parsimony and geographical information to infer historical range distribution of haplotypes and range movements (Templeton et al., 1995; Templeton, 1998, 2004; Posada and Crandall, 2001). The Hawaiian, Gala pagos and Canary volcanic island chains have become models in evolutionary studies, as they are true microcosms of evolutionary processes (Emerson, 2002). Recently, the ease in obtaining DNA sequence data, and the improvement of phylogenetic methods, have promoted their application to many different speciose endemic organisms from the Canary and other Macaronesian archipelagos, including vertebrate and arthropod groups (e.g. Juan et al., 1995, 1998, 2000; Brown and Pestano, 1998; Emerson et al., 1999, 2000a c; Carranza et al., 2001; Rees et al., 2001a, b; Contreras-Dı az et al., 2003). The molecular phylogenies provide a hypothesis of relationships of taxa, that can be used for testing the mono- or polyphyly of a particular group of taxa on the islands, understanding colonization sequences, disentangling extinction, hybridization and lineage sorting effects, or even comparing island speciation rates with the corresponding continental relatives (Emerson, 2002). Several studies have used Canary Island beetles and mitochondrial DNA sequences at the population or the species level, to study the geographical distribution of genetic diversity (phylogeography). They generally have found consistencies with the known geographical volcanic evolution of the island chain or different volcanic regions within a single island. For example, two clear divergent mitochondrial lineages in Pimelia (Coleoptera, Tenebrionidae) from Tenerife were deduced to relate to expansion from the older isolated areas of the island (Juan et al., 1996). In the Fuerteventura and Lanzarote endemic darkling beetle Hegeter deyrollei (formerly H. politus), a sequential expansion of the populations concomitant with the cessation of volcanism was inferred (Juan et al., 1998). Emerson et al. (2000a) showed for the weevil Brachyderes rugatus that the mitochondrial haplotypes of populations from the four subspecies occurring in Gran Canaria, Tenerife, La Palma and El Hierro belong to two different monophyletic clades and suggested a recent origin and a possible colonization sequence for this group in the Canary Islands. Darkling beetles (Coleoptera, Tenebrionidae) constitute important elements of the arid and semi-arid terrestrial ecosystems, because of their high biomass and role as detritivores (De los Santos et al., 2000, 2002a, b). They show adaptations such as diurnal and seasonal

3 Ó. Moya et al. / Journal of Arid Environments 66 (2006) rhythms of activity, resistance to desiccation and adaptation to high temperatures and ultra-violet light (Cloudsley-Thompson, 1964) as well as tolerance to low temperatures on high mountains (Ottesen and Sømme, 1987). The study of genetic diversity within darkling beetle species and populations can help not only to understand adaptive trends in these insects in relation to environmental factors, but also to give a historic perspective to the biogeography of these key organisms in arid/semi-arid environments. For example, in a previous study we analysed the phylogeography of the Pimelia endemic species of the island of Gran Canaria (Contreras-Dı az et al., 2003) and the results helped to establish evolutionary units for conservation purposes. Here we have studied populations of Pimelia species endemic to four islands of the Canary archipelago. New samples of three species (Pimelia canariensis, P. ascendens and P. radula) occurring on Tenerife are added to a previous mitochondrial sequence data set published elsewhere (Juan et al., 1996). Using these more extensive population and species samples, a re-analysis is performed using new phylogeographic approaches (Templeton, 2004). Pimelia canariensis is included in the List of Threatened Species in the Canaries as a species sensitive to habitat disturbance. We also analyse populations of P. laevigata, a species endemic to the three westernmost Canary islands, which according to immunological and numerical taxonomy studies (Oromí, 1979) and in agreement with a previous morphological taxonomy (Espan ol, 1961) includes three different subspecies. Pimelia l. ssp. validipes is present in La Gomera (another Pimelia species occurs in this island but it derives from an independent colonization event, see Contreras-Dı az et al., 2003), Pimelia l. ssp. laevigata in La Palma and P. l. ssp. costipennis in El Hierro. The three subspecies of P. laevigata are broadly distributed on their correspondent islands, occurring from arid and semi-arid lowland environments to high altitude alpine dry areas, but always avoiding forests. In a previous study based on mitochondrial DNA sequences, the three P. laevigata subspecies were shown to form a monophyletic group apparently derived from Tenerife ancestors, following the east to west and older to younger island general colonization pattern (Juan et al., 1995). The Canary Islands are of independent volcanic origin, so colonization has to be performed across the deep waters separating them. Although we do not have a priori knowledge of when colonization of La Gomera took place, this island has been available for a much longer period to support founders than the two other islands. La Gomera has been estimated to be of a maximum age about 10 million years ago (Ma), while La Palma has been dated at 2 Ma and El Hierro less than 1 Ma (Ancochea et al., 1990; Fuster et al., 1993; Carracedo et al., 1998). We have sampled P. laevigata populations on the above three islands, to include a representation of the entire geographic range of the species, and we obtained DNA sequences for a short fragment of about 200 bp of Cytochrome Oxidase I (COI) mitochondrial gene. This fragment showed enough variation to resolve the major mitochondrial lineages of closely related species and populations of Tenerifean Pimelia in a previous study (Juan et al., 1996), so its use here allows a comparison of P. laevigata sequence diversity with that previously obtained for their Tenerife relatives. The main questions we want to address are: (i) What is the genetic differentiation among P. laevigata subspecies? (ii) Is there geographical structuring among P. laevigata populations within each of the three islands where they occur? and (iii) Can NCPA and demographic inferences help to elucidate the population history processes of relatively recent colonizers, such as the diversification of Pimelia beetles in Tenerife and their relation to western Canary Islands taxa?

4 480 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Material and methods 2.1. Sampling Beetles were collected in Tenerife, La Gomera, La Palma and El Hierro islands of the Canary archipelago. Fig. 1 shows the locations and codes of the samples obtained in this study along with those sampled by Juan et al. (1996). Details of collection localities, geographic co-ordinates, haplotype denominations and sample sizes are given in Appendix A. Samples were stored in absolute ethanol until DNA extractions were performed DNA extractions, PCR amplifications and sequencing A leg or head from each specimen was used for DNA purification with a standard protocol (see Contreras-Dı az et al., 2003). In some cases, the DNeasy Tissue extraction kit (Qiagen) was used following the manufacturer s recommendations. Pellets were resuspended in ml of Tris-EDTA buffer and 1 ml of the dilution was used for PCR amplification of the fragment of COI with one unit of Taq DNA polymerase (Ecogen). The primers used were: (5 0 -ACAGGAATTAAAGTTTTTAGATGATTAGC-3 0 ) and (5 0 -ATAGGGGGAATCAGTGAACTAGTCC-3 0 )(Juan et al., 1996). PCR conditions Fig. 1. Map indicating the sampling design and the areas successfully surveyed in Tenerife, La Gomera, La Palma and El Hierro. Codes included in rectangles correspond to samples obtained in Tenerife in the previous study (Juan et al., 1996). Symbols refer to the different species/subspecies as follows: filled squares P. ascendens, empty squares P. canariensis, empty circle P. r. radula, filled circle P. r. oromii, and filled triangles for P. laevigata. The population codes correspond to the ones listed in Appendix A.

5 were as follows: 4 min at 95 1C followed by 35 cycles of denaturation at 95 1C for 30 s, annealing at 50 1C for 1 min, and extension at 72 1C for 1 min, with a final single extra extension step at 72 1C for 10 min. PCR products were checked in a 1% agarose gel and the products of the expected length were precipitated with ammonium acetate 5 M and isopropanol. The forward, and reverse strands in the cases where sequence ambiguities were detected, were cycle-sequenced using an Applied Biosystems ABI Prism DYE Terminator Cycle Sequencing Reaction Kit and sequenced in an ABI 377 automated sequencer. A common fragment of about 200 bp was sequenced for all samples. An alignment of all the sequences used in this study has been deposited in the EMBL database under accession number ALIGN Phylogenetic analyses Ó. Moya et al. / Journal of Arid Environments 66 (2006) Sequences were aligned using the ClustalX program (Thompson et al., 1997) and no length differences were found in the alignments. ModelTest (version. 3.06; Posada and Crandall, 1998) was used to select the substitution model(s) that best described the data under the Akaike information criterion, and PAUP* vs. 4.0b10 (Swofford, 2002) was used to calculate sequence divergences and obtain phylogenetic trees. Neighbour-joining (NJ) and maximum parsimony trees were obtained, the latter using heuristic searches with treebisection-reconnection as the branch-swapping algorithm and the starting tree was obtained via stepwise addition with random addition of sequences with 100 replicates. The sequence from P. lutaria, endemic to Fuerteventura and basal to all congeneric Canarian species, as was demonstrated by using different mitochondrial and nuclear markers (Pons et al., 2004), was used as outgroup. Bootstrap values were estimated with heuristic searches using 500 pseudo-replicates Molecular diversity and population dynamics Estimates of mean nucleotide and haplotype diversities within each species and populations were obtained with the program DnaSP vs. 4.0 (Rozas et al., 2003). Mismatch analysis of COI mitochondrial sequences (frequency of pairwise differences between haplotypes) was performed to explore the demographic history of the studied populations. This method is based on the assumption that population growth or decline leave distinctive signatures in the DNA sequences compared to constant population size. A recent growth is expected to generate a unimodal distribution of pairwise differences between sequences (Rogers and Harpending, 1992). The distribution is compared to that expected under a model of population expansion (Rogers, 1995) calculating the estimator of the time of expansion (t) and the mutation parameter (y) according to Schneider and Excoffier (1999). The formula t ¼ t=ð2uþ is used to estimate the timing of population expansions. We assume a substitution rate per site per lineage of per million years (equivalent to a pairwise divergence of 2.15%/Ma assumed to be the average for insect mitochondrial DNA) (De Salle et al., 1987; Brower, 1994), so the mutation rate u in our 200 bp COI sequence is per generation, assuming a generation time of 1 year. Confidence intervals for the parameters of the distributions were obtained by parametric bootstrap (1000 replicates) using Arlequin vs (Schneider et al., 2000). If population growth applies, the validity of a stepwise expansion model is tested using the same bootstrap approach by a goodness-of-fit statistic (P), representing the probability that the variance of

6 482 Ó. Moya et al. / Journal of Arid Environments 66 (2006) the simulated data set is equal or greater than the observed data set. We also computed the raggedness index (r) of the distribution and its significance, as implemented in Arlequin vs The mismatch analysis has been shown to be very conservative, having a low statistical power in the case of low sample sizes (Ramos-Onsins and Rozas, 2002). For this reason, other tests have been proposed for detecting past population growth such as Fu s F (Fu, 1997) orr 2 which have been shown to be superior for small sample sizes (Ramos- Onsins and Rozas, 2002). For this reason, we also computed Fu s test of neutrality as implemented in Arlequin vs (Schneider et al., 2000) Network estimation and nested clade phylogeographic analysis The network of mitochondrial Pimelia haplotypes was inferred using statistical parsimony (Templeton et al., 1992) as implemented in the program TCS vs (Clement et al., 2000). The method links haplotypes with the smallest number of differences as defined by a 95% confidence criterion. NCPA was used (Templeton et al., 1995) to infer the population history of studied species of Pimelia. The NCPA first tests the null hypothesis of no association between haplotype variation and geography, and then proceeds to interpret the significant association patterns (Crandall and Templeton, 1993). The NCPA nesting design was constructed by hand using the statistical parsimony network following the rules given in Templeton (1998). In essence, the nesting procedure consists of nesting n-step clades or haplotype groups, where n is correlated with the number of nucleotide mutations separating haplotypes (Crandall, 1996). The procedure begins from the external (tip) clades and proceeds to the interior (clades joined to one or more clades by a single step). The haplotypes are nested in increasing step-levels until all the data become nested into a single clade, the total cladogram. The software GeoDis vs. 2.1 (Posada et al., 2000) was used to calculate the NCPA distance measures and their statistical significance. This method uses geographical distances between the sampled locations and estimates four basic statistics: Dc, Dn, IT-Dc and IT-Dn. Dc or clade distance measures the average distance of all clade members from its geographical centre of distribution. Dn or nested clade distance measures how widespread is a particular clade relative to the distribution of its sister clades in the same nesting group. IT-Dc and IT-Dn constitute similar distances considering tips and interiors differentially. The distinction between tip (with only one connection to the remaining network) and interior (with two or more connections) haplotype groups in the context of the coalescent theory allows testing the hypothesis of random geographical distribution by permutational tests (we performed 10,000 permutations). The predictions from the coalescent can be summarized as follows: (i) on average, haplotypes with higher frequency tend to be older and have a greater probability of being interior, (ii) older haplotypes will be more broadly geographically distributed, (iii) haplotypes with greater frequency will tend to have more mutational connections and (iv) unique haplotypes (singletons) are more likely to be joined to non-unique ones than to other singletons, and to haplotypes from the same population (Posada and Crandall, 2001). Using the updated version of the inference key in Templeton (2004) we can deduce which factor(s) could plausibly account for the spatial and/or temporal significant association of haplotypes. In this way, we can distinguish historical (fragmentation, range expansion) from current (gene flow, genetic drift) processes responsible for the observed pattern of genetic variation (Templeton, 2004).

7 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Results 3.1. Genetic diversity For this study, 32 localities were sampled in the four western Canary Islands and 143 individuals have been analysed (see Fig. 1, Table 1 and Appendix A). COI sequences were obtained for 129 individuals of P. laevigata from 27 localities across the three islands where this species occurs (La Gomera, La Palma and El Hierro). From the sampling made in Tenerife, we obtained 14 further individuals of P. radula and P. canariensis to complete the geographical range of the 61 samples already sequenced in the previous study (see Fig. 1 in Juan et al., 1996). These 75 individuals are now a good representation of the three different nominal species present in Tenerife, one of which is split into two subspecies. Considering that a taxonomy redefinition has been recently proposed, the haplotypes have been arranged differently to the denominations used by Juan et al. (1996) (see Vin olas, 1994; Oromı and Garcı a, 1995). We obtained 83 different haplotypes in our total Pimelia data set (excluding sites with missing data) that showed considerable genetic diversity. Of those, 36 were exclusive to Tenerife beetles and the remaining 47 belonged to P. laevigata from the westernmost islands. Table 1 shows molecular diversity estimates for the sequence data obtained across all Pimelia samples within Tenerife, within each of the Tenerife species, and within the three different island populations of P. laevigata. Haplotype diversities are lower within P. canariensis (Tenerife) (a fact that could be due to the lower sample size in this case), and in P. l. costipennis (El Hierro) and P. l. laevigata (La Palma), suggesting a relatively recent diversification of these populations Phylogenetic analyses Overall, 60 variable positions are present in the ingroup sequence data set (of which 47 are parsimoniously informative). Sequences are AT-biased (65.8%); with most changes Table 1 Summary of mean nucleotide and haplotype diversities of the different Pimelia species sequenced for the mitochondrial COI fragment Species/populations N Haplotypes Nucleotide diversity7s.d. Haplotype diversity7s.d. P. ascendens P. canariensis P. radula Total Tenerife La Gomera P. l. validipes La Palma P. l. laevigata El Hierro P. l. costipennis Total P. laevigata Total

8 484 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Fig. 2. Fifty per cent majority rule consensus phylogenetic topology of the 29 maximum parsimony trees (CI ¼ 0.322; RI ¼ 0.836) obtained based on the COI mitochondrial DNA sequence data set. Bootstrap support (first number) of 500 pseudo-replicates and Bremer support values (second number) are indicated for the relevant nodes. Nodes discussed in the text are labelled with letters (A and B) and numbers. Symbols at the terminal nodes refer to the different species/subspecies as indicated in Fig. 1 from which the haplotypes were obtained.

9 corresponding to synonymous substitutions at third codon positions (81.7%). The consensus maximum parsimony tree obtained shows the haplotypes arranged into two major sister clades (Fig. 2). Clade A includes haplotypes of P. r. oromii (north-east Tenerife) and P. r. radula (north Tenerife), and clade B is formed by P. ascendens (west and central regions), P. canariensis (southern arid coastal areas) plus P. laevigata mitochondrial haplotypes from the three western islands. Haplotypes of P. r. radula from La Matanza (MT in Tenerife) are present in clades A1 and A2. In addition, P. canariensis haplotypes are polyphyletic with P. ascendens haplotypes, despite being the former markedly differentiated in morphology from the high altitude P. ascendens populations (see Espan ol, 1961). In fact, P. ascendens had been considered just a subspecies of P. radula when only morphological characters were used (Espan ol, 1961); later immunological studies showed higher differences between the two taxa and suggested their independent species status (Oromı, 1979), which was finally established (Oromı and Garcı a, 1995) after better supported differences based on mtdna sequences (Juan et al., 1995). Increased sampling of this coastal species and the use of additional molecular markers could clarify if P. canariensis represents an ecotype of P. ascendens. Within clade B, the phylogenetic relationships are not well resolved what is not surprising given the short DNA sequence used and the relatively low divergence observed. Uncorrected distances between different Pimelia species from the easter and central Canary Islands haplotypes ranged from to 0.14, and the mean distance between ingroup taxa and the outgroup P. lutaria was 0.12 ( range). The optimal substitution model for the complete data set (including the outgroup) is the General Time Reversible (GTR, Rodrı guez et al., 1990) with a proportion of invariable sites (I) of and a value for the G distribution of using the Akaike information criterion. The NJ topology using these distances and parameters was similar to the one obtained by maximum parsimony. A likelihood ratio test using the likelihood scores of the original tree with branch lengths estimated implementing the GTR+I+G model assuming no molecular clock and constraining rate constancy indicated that the data is consistent with a molecular clock (2DL ¼ 120:88 at df ¼ 110; p ¼ 0:22) Demographic inferences Ó. Moya et al. / Journal of Arid Environments 66 (2006) The frequency of pairwise differences between haplotypes of each species within Tenerife, or island populations of P. laevigata, showed mismatch distributions consisting of unimodal curves (except for P. radula that shows two modal peaks, Figs. 3a e). Both the variance (SSD) and raggedness index (r) tests suggested that the curves do not significantly differ from the distributions under the model of population expansions (P SSD ¼ 0:1820:94 and P r ¼ 0:1720:91, see legend to Fig. 3) in accordance with the corresponding Fu s F S tests. The P. radula mismatch distribution shows two waves of expansion (Fig. 3b), possibly corresponding to the geographically structured clades A1 and A2. Accordingly, the more stringent Fu s test of neutrality (Fu, 1997) gave a statistically significant negative value indicating sudden population growth (F S ¼ 4:86; p ¼ 0:041). However, when this test was performed for the P. r. radula and P. r. oromii haplotypes separately, the distribution for P. r. oromii was not statistically different than the one expected under a scenario of constant population size (F S ¼ 0:65; p ¼ 0:62). A similar result was obtained for P. canariensis (F S ¼ 1:09; p ¼ 0:08), although in this case the test can be compromised due to the low sample size. Assuming a stepwise expansion model, the

10 486 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Fig. 3. Mismatch distributions for Pimelia COI DNA sequence subsets. The curves represent the observed relative frequencies of nucleotide differences between pairs of individuals and the distribution fitted to the data under the assumption of a model of population expansion. p-value represents the probability that the variance of the simulated data set is equal or greater than the observed one. (a) P. ascendens; (b) P. radula including the two subspecies; (c) P. l. validipes; (d) P. l. laevigata (e) P. l. costipennis. Pimelia canariensis was not included in this analysis due to the low number of individuals of the sample. Table 2 Summary of parameters estimated assuming a stepwise population growth model in several Pimelia species from the Canary Islands. The estimator of the expansion time (t) and the mutation parameters before (y 0 ) and after (y 1 ) the expansions are given according to Schneider and Excoffier (1999). The formula t ¼ t=ð2uþ was used to estimate the time of population expansions taking a generation time of 1 year, where u corresponds to the mutation rate for the DNA sequence used per generation. Confidence intervals for t are based on the maximum minimum nucleotide substitution rate values for arthropod mitochondrial DNA Species y 0 y 1 t Time (Ma) P. ascendens P. radula P. l. validipes P. l. laevigata P. l. costipennis

11 Ó. Moya et al. / Journal of Arid Environments 66 (2006) oldest population growth in Tenerife is estimated at about Ma for P. radula. This estimate is based on a time of the expansion t value of and a mutation rate for the considered sequence of u ¼ per generation (see Material and Methods and Table 2). Similarly, in P. ascendens the expansion could be dated at Ma, while P. laevigata expansions are estimated to be Ma for P. l. validipes (La Gomera) and Ma for P. l. laevigata (La Palma) and Ma for P. l. costipennis (El Hierro) Population history inferences from NCPA The limit of mutational connections (probabilityx95% of being connected in a parsimonious way) for our data set was five nucleotide substitutions using statistical parsimony. The parsimony networks of COI haplotypes were resolved except for some loops of ambiguity (haplotypes with more than one most parsimonious connection to the rest of the network). We present in Fig. 4 the results for the preferred network, based on geographical distribution of the mitochondrial genotypes and haplotype frequency criteria (see Material and Methods). Four major subnetworks were obtained connected by more than five steps. Haplotype group 5.1 includes all P. laevigata haplotypes and 5.2 contain P. ascendens and P. canariensis haplotypes (equivalent to clade B1 in the tree of Fig. 2). Group 5.3 contains P. r. radula and P. r. oromii haplotypes (corresponding to clade A2 in the tree of Fig. 2) and 5.4 P. r. radula (Clade A1) exclusively. We used the maximum parsimony tree (Fig. 2) to compute the steps connecting these higher categories; clades 5.1 and 5.2 are connected by nine steps, 5.3 and 5.4 by 10, and 6.1 and 6.2 are joined by 18 nucleotide substitutions. At the total cladogram level, we explored two nesting alternatives. The more conservative nesting option was considering the four 5-levels as unconnected (nesting I in Fig. 4). The second option (nesting II in Fig. 4) was to build the last two nesting levels connecting the clades and to obtain the higher 6.1 and 6.2 clades, respectively, at the total cladogram level. These connections have a high probability of being affected by homoplastic changes, but still this nesting design is a plausible representation of the phylogenetic relationships if we take into account previous phylogenetic information (Pons et al., 2004) and geographic-paleogeological considerations (i.e. distance between islands and their age of emergence; Juan et al., 1995; Juan et al., 2000). In addition, distinguishing tip and interior clades at this nesting levels can be ambiguous based on the tree of Fig. 2 or in the distance analysis, as the phylogenetic relationships of clades A and B in are not well supported. For example, some haplotypes (GM27) seem to be as close to the outgroup as any haplotype in clades A1 or A2. Instead, we used a more robust Pimelia species-phylogeny using mitochondrial (COI+cytochrome b, 16S RNA) and nuclear (28S RNA+Histone 3) sequences (Pons et al., 2004) to root at clade 5.4 (P. r. radula) in nesting I option, while clade 6.2 was the root (interior) and 6.1 tip in nesting II. The association between genetic and geographic distribution was rejected at most low nesting levels (1step clades) showing haplotypic variation. In the few cases where significant associations were obtained, many gave inconclusive outcomes after following the inference key of Templeton (2004) probably due to an inadequate sample design. Contingency tests showed significant geographical associations of haplotypes contained within some higher nesting levels, for example, 5.1 (P. laevigata), 5.2 (Tenerife clade B1)

12 488 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Fig. 4. Statistically preferred parsimony network and associated nesting design. The numbers included in circles correspond to the haplotypes in each island (codes listed in Appendix A without the island identifying letters for simplicity). The size of the circle is scaled to the number of individuals possessing that haplotype. Numbers with an asterisk correspond to different haplotypes listed in Appendix A that are collapsed by the program TCS due to nucleotide (nt) sequence size differences and consequently with missing data at particular nucleotide sites. Empty circles indicate haplotype intermediates not present in the sample. Each connecting line represents a single mutational step between any two given haplotypes. Dotted lines represent alternative ambiguous connections (loops). The nested design is represented by increasing levels of nested boxes (see Material and Methods for details). Clades for which significant association between genotypes and geography were obtained are labelled with two numbers. The first one refers to the nesting level, and the second number identifies a particular clade at this level (the missing ones gave non-significant results). A1, B, B1 and B2 relate to the particular clades in Fig. 2 and the island-specific haplotypes of P. laevigata are separated by three thick lines, the name of each island from which the haplotypes come is indicated for clarity. The inset shows the two alternative nestings (I and II) at the total cladogram level (see text for details). Connections by broken lines join major subnetworks separated by a number of mutational steps higher than the limit of parsimony in alternative II. Two asterisks indicate root clades deduced from the phylogeny obtained in Pons et al. (2004). and 5.3 (Tenerife clade A2) (see Table 3). Using geographic distance analysis, the oldest inferred event corresponding to the total cladogram was independent of the nesting design. Both I and II nestings point to long distance colonizations from the north-east tip of Tenerife to the west (see Fig. 5) although an alternative explanation of past fragmentation followed by range expansion cannot be completely ruled out (see Discussion). The long distance movement inference is obtained at other nesting levels as well for the clades within 6.1 (Nesting II), that is, Tenerife seems to have been the source of colonizers that populated La Gomera. From the latter, subsequent distance dispersals colonized possibly La Palma first (clade 5.1, inference of long distance colonization from La Gomera to La Palma, see Fig. 5). This inference is reinforced by the result obtained at the 4.2 level,

13 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Table 3 Results of the nested cladistic phylogeographic analyses for Pimelia samples. Only instances of statistically significant results obtained by geographical distance analysis are shown. The results at the one-step level are not shown due to the low number of individuals (see Results). w 2 statistics and the associated p-values were obtained by permutational contingency tests with 10,000 replications. This test was non-significant only for clades 2.1 and 2.3, being the geographical distance analysis using Dc and Dn distances statistically significant Clade w 2 p-value Inference chain Inference Total cladogram I Total cladogram II No Inconclusive outcome No Contiguous range expansion No Contiguous range expansion No Contiguous range expansion No Inconclusive outcome No Restricted gene flow with isolation by distance No Contiguous range expansion No Restricted gene flow with isolation by distance Too few clades-7- Restricted gene flow/dispersal Yes but with some long distance dispersal No Contiguous range expansion No Allopatric fragmentation Yes Long distance colonisation No Restricted gene flow with isolation by distance Yes Long distance colonisation No Contiguous range expansion No Long distance movements or combined effects of gradual movement during a past range expansion and fragmentation No Long distance movements or combined effects of gradual movement during a past range expansion and fragmentation including haplotypes of P. laevigata from the three islands. Besides these results, two instances of restricted gene flow with isolation by distance (clades 3.10 and 5.2) and one of allopatric fragmentation (clade 4.4) are inferred within Tenerife. Most other nesting levels

14 490 Ó. Moya et al. / Journal of Arid Environments 66 (2006) Fig. 5. Schematic representation of the Pimelia NCPA inferences overlaid on a map of the western Canary Islands. Several instances of contiguous range expansion (CRE) are deduced within each island, and cases of restricted gene flow with isolation by distance (RGF) in Tenerife and El Hierro. At least three long distance colonizations (LDC) are inferred, represented in the figure showing east west direction, although an alternative explanation for the one at the total cladogram level is also possible (see Discussion). The origin of colonization for El Hierro remains unclear. Numbers for each inference refer to nesting groups presented in Fig. 4 and Table 3. with significant associations of genotypes with geography provide the general common inference of contiguous range expansion in Tenerife (clades 6.2 north north-east, and 4.3 in the central region), La Gomera (clades 3.5 and 2.12), La Palma (clade 2.3) and El Hierro (clade 2.9). In the latter, a case of restricted gene flow with isolation by distance is deduced (clade 3.4) between the south-west localities and the rest of the island. Clades 2.1 and 2.15 showed inconclusive results. 4. Discussion In this study we have extended Juan et al. s surveys (1995, 1996) of western Canary Pimelia species by widening the sampling in Tenerife and collecting in the other three western islands, where the related species P. laevigata occurs in unforested areas. If we take into account geography, age of the islands and genetic data, P. laevigata populations in the western islands seem to derive from a Tenerifean ancestor (Juan et al., 1995, 1996, this work). The alternative hypothesis seems more unlikely as it would imply a backcolonization from these islands to Tenerife. Although the DNA fragment sequenced is too short to provide enough phylogenetic information to support each P. laevigata island population as unequivocally monophyletic, other genetic markers point to the independence of the three clades (Pons et al., 2004). The presence in La Gomera of two haplotypes (GM26 and GM27) that are relatively divergent from the remaining haplotypes sampled on the island make it ambiguous to postulate a single colonization event and/or estimate date(s) for them in this island. As pointed out by Emerson et al. (2000c), the effect of existing genetic diversity in the

15 Ó. Moya et al. / Journal of Arid Environments 66 (2006) ancestral population and the possibility of lineage extinction, complicate the inference of colonization events from one island to another in a given phylogeny. In Tenerife, the results obtained elsewhere (Juan et al., 1996) pointing out the existence of two very divergent mitochondrial lineages are confirmed with our present more comprehensive sampling. Lineage A includes P. radula haplotypes, with the current subspecies designation not completely coincidental with lineages A1 and A2 since haplotypes of P. r. radula are in both groups. This can be explained either by retention of ancestral polymorphisms or, more probably, by mitochondrial introgression. Nevertheless, after the analysis of the geographically intermediate, newly discovered population in La Matanza (MT, Tenerife), we can state that the morphological characters on which the two subspecies are based show a clinal variation along the north north-east region of Tenerife. On the other hand, Tenerife mitochondrial lineage B1 is the sister group of P. laevigata, including P. ascendens distributed at relatively high altitude localities, and P. canariensis present along the south-east to south-west coastal arid areas of the island. In addition, the tree topology suggests that colonization to the western islands (being probably La Gomera first to be colonized) was by ancestors of the B1 Tenerife lineage. Pairwise differences between sampled DNA sequences (Fig. 3) show that in all the studied cases, mitochondrial mismatch distributions are compatible with historic population expansions or bottlenecks followed by a return to the original population size (Slatkin and Hudson, 1991; Rogers and Harpending, 1992). Estimates for the corresponding dates of population expansion are in good accordance with the island dates of formation (Table 2). For example, population expansion is inferred to be older for the A lineage of Tenerife compared to one of the remaining species, while the P. laevigata expansion in the El Hierro population is deduced to be more recent and explosive (Table 2). This result is in accordance with the younger age for the subaerial formation of this island and, therefore, within the time frame in which colonization was possible. One objective of this paper was to assess the validity of nested phylogeographical cladistic analysis (Templeton et al., 1995; Templeton, 2004) in order to infer historical population events in an a priori scenario of recurrent long distance dispersal, such as the one assumed to explain colonization and diversification of an oceanic archipelago. The Canary Islands are of independent volcanic origin and have never been connected by the effect of sea level oscillations, with the possible exception of Fuerteventura and Lanzarote that are separated by a very shallow strait, but whose populations are not included in this study. As expected, molecular phylogenies show that in the Canaries the general pattern of colonization in a diversity of terrestrial organisms is consistent with dispersal from older to younger islands, westwards along the island chain (Juan et al., 2000). NCPA infers long distance movements in cases of a large nesting distance (Dn) in a clade with small clade distance (Dc) (though extinction of intermediate populations in organisms with low vagility can also be compatible with this pattern). This inference is expected to occur, for example, if a peripheral population has a unique haplotype, but a reduced geographical distribution with respect to that of the haplotypes from which it derived, while, on the contrary, concordant Dc and Dn values suggest short distance movements (Masta et al., 2003). Assuming an adequate geographical sampling, NCPA should distinguish between the above alternatives, but cases of misinterpretation have been shown to occur in situations of gradual expansions or re-invasions of previous ranges (Masta et al., 2003). In

16 492 Ó. Moya et al. / Journal of Arid Environments 66 (2006) the present data set, the NCPA analysis can detect at least three instances in which geographical association of genotypes could be explained by long distance movements of the species. Alternatively, the pattern could be explained by the combined effects of gradual movement during a past range expansion and a subsequent fragmentation (step 21 in the NCPA updated inference key, Templeton, 2004). The discrimination between the two alternatives has shown to be only possible using outside independent information (Masta et al., 2003; Templeton, 2004). Pimelia species are flightless, so passive dispersal has to be invoked for these organisms, possibly through floating vegetation drifted by the prevailing north-east south-west sea currents in this part of the Atlantic. In fact, the Canary archipelago has been colonized primarily by single or multiple continental dispersal events in terrestrial organisms even in animals with low vagility (Juan et al., 2000; Emerson, 2002). On the other hand, in a scenario of islands of independent volcanic origin, the possibility of range expansions followed by fragmentation has to be in principle discarded to explain haplotype distribution patterns among islands. Because of that, all the instances in which both alternative scenarios were suggested by the inference key for clades including different islands were considered as long distance movements. Nevertheless, colonization events of the western islands of La Gomera, La Palma and El Hierro consistent with a minimum of two eastward long distance dispersals, were recovered by the NCPA analysis, although the origin of colonization for El Hierro remains unclear. Another inference frequently obtained at other nesting levels (6.2, 4.3, 3.5, 2.12, 2.9 and 2.3) is contiguous range expansion, deduced to have occurred more than once in Tenerife and in each of P. laevigata island populations, presumably after the original colonizations. These inferences are consistent with the deduced population growth by mismatch distribution analyses in the same clades (Fig. 3). Finally, in Tenerife a particular case of allopatric fragmentation of the Izan a (IZ) locality, and two instances of restricted gene flow with isolation by distance are deduced. This can be related to the complex geological dynamics of the island, in which geographical barriers and local extinctions due to volcanism are expected to have been particularly frequent. NCPA has indeed limitations inherent to the sampling scheme (size and number of sites) and cases in which there is not enough resolution to detect past events (Templeton, 2004). In addition, more serious limitations of the analysis occur when false inferences or biological misidentifications are obtained. The original inference key (Templeton et al., 1995) has been validated using biological examples for which strong prior evidence existed for particular past events such as range expansion or fragmentation and updated to avoid errors or ambiguities (Templeton, 1998, 2001, 2004). Cross-validation obtaining multilocus data from independently segregating DNA regions, is recommended for obtaining robust inferences (Templeton, 2004). In our case, sampling size is scarce from some of the geographical sites, and although the number of sampled localities is relatively high, a more comprehensive sample scheme would be needed to cover the geographic micro-scale of the high altitude regions in the western islands. These sampling limitations resulted in few cases of significant geographical associations of haplotypes for which inconclusive outcomes are obtained, although the low nesting level in which they occur is irrelevant for the main conclusions of NCPA. Our results also show that there is enough variation in the 200 bp fragment and the pooling of data from a previous study allowed a comprehensive NCPA. However, the relatively high divergence of mitochondrial lineages within Tenerife, and the ones corresponding to island populations of P. laevigata, make the connections

17 Ó. Moya et al. / Journal of Arid Environments 66 (2006) and designation of interior-tip status at the higher nesting level ambiguous and dependant on external previous information. Better genetic resolution by longer mitochondrial DNA sequences and independent genetic markers would clarify the above points further. Finally, NCPA and the associated inference key present some difficulties when applied to populations present in several volcanic islands as the ones studied here. Creer et al. (2001) studied the phylogeography of bamboo viper in Taiwan and in the offshore Pacific, Orchid and Green islands. Haplotypes of the two latter populations were omitted from the NCPA analysis because the islands are of volcanic origin and never have been connected to Taiwan and the study focused on historical terrestrial migrations. As mentioned above, in the colonization and diversification of an oceanic archipelago, the a priori hypothesis is long distance dispersal, but if the clades within a nesting clade are found in separate islands (so in separate areas with no overlap by definition) the inference is allopatric fragmentation. In addition, the inferences of long distance colonization with subsequent fragmentation or past fragmentation followed by range expansion are implicitly applied to historical terrestrial situations, being the application to oceanic islands somewhat elusive. In summary statistical phylogeography and more specifically nested phylogeographic cladistic analysis, provides an excellent opportunity to contrast current and historical causes for the genetic and geographical distribution of darkling beetles in the Canary Islands. There is a considerable genetic variation and geographical structure both in low and high altitude arid region populations of the ancient, geologically complex island of Tenerife. In contrast, the younger western islands show evidences of more recent colonizations and subsequent range expansions of Pimelia populations derived from a Tenerife ancestor, and, therefore, limited geographical structuring. Acknowledgements Giulia Paroni helped us with laboratory techniques. Heriberto Lo pez, Antonio J. Pe rez, Antonio Camacho, Rube n Barone and Jesu s Alonso contributed collecting specimens. We thank the suggestions made by Eduard Petitpierre and Jesu sgo mez-zurita. The comments of David Posada and of an anonymous referee on a previous version of the manuscript helped to improve the paper. This study is supported by the Spanish Ministerio de Educacio n y Ciencia including European Union FEDER funds (project REN ). The permits to collect beetles in the protected areas were obtained from the Viceconsejerı a de Medio Ambiente del Gobierno de Canarias and the corresponding Cabildos of the islands, which occasionally provided accommodation. Appendix A Summary of Pimelia sampling in the western Canary Islands and in Tenerife. Species localities and their codes, co-ordinates, haplotype distribution (number of individuals with the same haplotype in parenthesis) and number of individuals collected at a given locality are given. Total number of haplotypes is higher than the figures given in Table 1 because the haplotypes with missing data were excluded from the analysis of nucleotide and haplotype diversities. (Table A1)

Phylogeography of the endangered darkling beetle species of Pimelia endemic to Gran Canaria (Canary Islands)

Phylogeography of the endangered darkling beetle species of Pimelia endemic to Gran Canaria (Canary Islands) Molecular Ecology (2003) 12, 2131 2143 doi: 10.1046/j.1365-294X.2003.01884.x Phylogeography of the endangered darkling beetle species Blackwell August 12 8Original PHYLOGEOGRAPHY H. G. CONTRERAS-DÍAZ 2003

More information

C3020 Molecular Evolution. Exercises #3: Phylogenetics

C3020 Molecular Evolution. Exercises #3: Phylogenetics C3020 Molecular Evolution Exercises #3: Phylogenetics Consider the following sequences for five taxa 1-5 and the known outgroup O, which has the ancestral states (note that sequence 3 has changed from

More information

Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2018 University of California, Berkeley

Integrative Biology 200 PRINCIPLES OF PHYLOGENETICS Spring 2018 University of California, Berkeley Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2018 University of California, Berkeley B.D. Mishler Feb. 14, 2018. Phylogenetic trees VI: Dating in the 21st century: clocks, & calibrations;

More information

Intraspecific gene genealogies: trees grafting into networks

Intraspecific gene genealogies: trees grafting into networks Intraspecific gene genealogies: trees grafting into networks by David Posada & Keith A. Crandall Kessy Abarenkov Tartu, 2004 Article describes: Population genetics principles Intraspecific genetic variation

More information

8/23/2014. Phylogeny and the Tree of Life

8/23/2014. Phylogeny and the Tree of Life Phylogeny and the Tree of Life Chapter 26 Objectives Explain the following characteristics of the Linnaean system of classification: a. binomial nomenclature b. hierarchical classification List the major

More information

Chapter 22: Descent with Modification 1. BRIEFLY summarize the main points that Darwin made in The Origin of Species.

Chapter 22: Descent with Modification 1. BRIEFLY summarize the main points that Darwin made in The Origin of Species. AP Biology Chapter Packet 7- Evolution Name Chapter 22: Descent with Modification 1. BRIEFLY summarize the main points that Darwin made in The Origin of Species. 2. Define the following terms: a. Natural

More information

Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut

Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University-Egypt Phylogenetic analysis Phylogenetic Basics: Biological

More information

Dr. Amira A. AL-Hosary

Dr. Amira A. AL-Hosary Phylogenetic analysis Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University-Egypt Phylogenetic Basics: Biological

More information

POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics

POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics POPULATION GENETICS Winter 2005 Lecture 17 Molecular phylogenetics - in deriving a phylogeny our goal is simply to reconstruct the historical relationships between a group of taxa. - before we review the

More information

Unit 9: Evolution Guided Reading Questions (80 pts total)

Unit 9: Evolution Guided Reading Questions (80 pts total) Name: AP Biology Biology, Campbell and Reece, 7th Edition Adapted from chapter reading guides originally created by Lynn Miriello Unit 9: Evolution Guided Reading Questions (80 pts total) Chapter 22 Descent

More information

"PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2011 University of California, Berkeley

PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION Integrative Biology 200B Spring 2011 University of California, Berkeley "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2011 University of California, Berkeley B.D. Mishler March 31, 2011. Reticulation,"Phylogeography," and Population Biology:

More information

Ph ylogeography. A guide to the study of the spatial distribution of Seahorses. By Leila Mougoui Bakhtiari

Ph ylogeography. A guide to the study of the spatial distribution of Seahorses. By Leila Mougoui Bakhtiari Ph ylogeography A guide to the study of the spatial distribution of Seahorses By Leila Mougoui Bakhtiari Contents An Introduction to Phylogeography JT Bohem s Resarch Map of erectu s migration Conservation

More information

A (short) introduction to phylogenetics

A (short) introduction to phylogenetics A (short) introduction to phylogenetics Thibaut Jombart, Marie-Pauline Beugin MRC Centre for Outbreak Analysis and Modelling Imperial College London Genetic data analysis with PR Statistics, Millport Field

More information

Reconstructing the history of lineages

Reconstructing the history of lineages Reconstructing the history of lineages Class outline Systematics Phylogenetic systematics Phylogenetic trees and maps Class outline Definitions Systematics Phylogenetic systematics/cladistics Systematics

More information

"PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2009 University of California, Berkeley

PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION Integrative Biology 200B Spring 2009 University of California, Berkeley "PRINCIPLES OF PHYLOGENETICS: ECOLOGY AND EVOLUTION" Integrative Biology 200B Spring 2009 University of California, Berkeley B.D. Mishler Jan. 22, 2009. Trees I. Summary of previous lecture: Hennigian

More information

UoN, CAS, DBSC BIOL102 lecture notes by: Dr. Mustafa A. Mansi. The Phylogenetic Systematics (Phylogeny and Systematics)

UoN, CAS, DBSC BIOL102 lecture notes by: Dr. Mustafa A. Mansi. The Phylogenetic Systematics (Phylogeny and Systematics) - Phylogeny? - Systematics? The Phylogenetic Systematics (Phylogeny and Systematics) - Phylogenetic systematics? Connection between phylogeny and classification. - Phylogenetic systematics informs the

More information

Michael Yaffe Lecture #5 (((A,B)C)D) Database Searching & Molecular Phylogenetics A B C D B C D

Michael Yaffe Lecture #5 (((A,B)C)D) Database Searching & Molecular Phylogenetics A B C D B C D 7.91 Lecture #5 Database Searching & Molecular Phylogenetics Michael Yaffe B C D B C D (((,B)C)D) Outline Distance Matrix Methods Neighbor-Joining Method and Related Neighbor Methods Maximum Likelihood

More information

Estimating Evolutionary Trees. Phylogenetic Methods

Estimating Evolutionary Trees. Phylogenetic Methods Estimating Evolutionary Trees v if the data are consistent with infinite sites then all methods should yield the same tree v it gets more complicated when there is homoplasy, i.e., parallel or convergent

More information

Molecular Markers, Natural History, and Evolution

Molecular Markers, Natural History, and Evolution Molecular Markers, Natural History, and Evolution Second Edition JOHN C. AVISE University of Georgia Sinauer Associates, Inc. Publishers Sunderland, Massachusetts Contents PART I Background CHAPTER 1:

More information

GENETICS - CLUTCH CH.22 EVOLUTIONARY GENETICS.

GENETICS - CLUTCH CH.22 EVOLUTIONARY GENETICS. !! www.clutchprep.com CONCEPT: OVERVIEW OF EVOLUTION Evolution is a process through which variation in individuals makes it more likely for them to survive and reproduce There are principles to the theory

More information

Applications of Genetics to Conservation Biology

Applications of Genetics to Conservation Biology Applications of Genetics to Conservation Biology Molecular Taxonomy Populations, Gene Flow, Phylogeography Relatedness - Kinship, Paternity, Individual ID Conservation Biology Population biology Physiology

More information

Constructing Evolutionary/Phylogenetic Trees

Constructing Evolutionary/Phylogenetic Trees Constructing Evolutionary/Phylogenetic Trees 2 broad categories: istance-based methods Ultrametric Additive: UPGMA Transformed istance Neighbor-Joining Character-based Maximum Parsimony Maximum Likelihood

More information

Algorithms in Bioinformatics

Algorithms in Bioinformatics Algorithms in Bioinformatics Sami Khuri Department of Computer Science San José State University San José, California, USA khuri@cs.sjsu.edu www.cs.sjsu.edu/faculty/khuri Distance Methods Character Methods

More information

Geography of Evolution

Geography of Evolution Geography of Evolution Biogeography - the study of the geographic distribution of organisms. The current distribution of organisms can be explained by historical events and current climatic patterns. Darwin

More information

Chapter 27: Evolutionary Genetics

Chapter 27: Evolutionary Genetics Chapter 27: Evolutionary Genetics Student Learning Objectives Upon completion of this chapter you should be able to: 1. Understand what the term species means to biology. 2. Recognize the various patterns

More information

Integrative Biology 200A "PRINCIPLES OF PHYLOGENETICS" Spring 2012 University of California, Berkeley

Integrative Biology 200A PRINCIPLES OF PHYLOGENETICS Spring 2012 University of California, Berkeley Integrative Biology 200A "PRINCIPLES OF PHYLOGENETICS" Spring 2012 University of California, Berkeley B.D. Mishler April 12, 2012. Phylogenetic trees IX: Below the "species level;" phylogeography; dealing

More information

Phylogeny and systematics. Why are these disciplines important in evolutionary biology and how are they related to each other?

Phylogeny and systematics. Why are these disciplines important in evolutionary biology and how are they related to each other? Phylogeny and systematics Why are these disciplines important in evolutionary biology and how are they related to each other? Phylogeny and systematics Phylogeny: the evolutionary history of a species

More information

CHAPTERS 24-25: Evidence for Evolution and Phylogeny

CHAPTERS 24-25: Evidence for Evolution and Phylogeny CHAPTERS 24-25: Evidence for Evolution and Phylogeny 1. For each of the following, indicate how it is used as evidence of evolution by natural selection or shown as an evolutionary trend: a. Paleontology

More information

Biology 211 (2) Week 1 KEY!

Biology 211 (2) Week 1 KEY! Biology 211 (2) Week 1 KEY Chapter 1 KEY FIGURES: 1.2, 1.3, 1.4, 1.5, 1.6, 1.7 VOCABULARY: Adaptation: a trait that increases the fitness Cells: a developed, system bound with a thin outer layer made of

More information

(Stevens 1991) 1. morphological characters should be assumed to be quantitative unless demonstrated otherwise

(Stevens 1991) 1. morphological characters should be assumed to be quantitative unless demonstrated otherwise Bot 421/521 PHYLOGENETIC ANALYSIS I. Origins A. Hennig 1950 (German edition) Phylogenetic Systematics 1966 B. Zimmerman (Germany, 1930 s) C. Wagner (Michigan, 1920-2000) II. Characters and character states

More information

Statistical phylogeography

Statistical phylogeography Molecular Ecology (2002) 11, 2623 2635 Statistical phylogeography Blackwell Science, Ltd L. LACEY KNOWLES and WAYNE P. MADDISON Department of Ecology and Evolutionary Biology, University of Arizona, Tucson,

More information

Mitochondrial DNA evolution and population history of

Mitochondrial DNA evolution and population history of Molecular Ecology (2000) 9, 1061 1067 Mitochondrial DNA evolution and population history of Blackwell Science, Ltd the Tenerife skink Chalcides viridanus R. P. BROWN,* R. CAMPOS-DELGADO and J. PESTANO

More information

Name. Ecology & Evolutionary Biology 2245/2245W Exam 2 1 March 2014

Name. Ecology & Evolutionary Biology 2245/2245W Exam 2 1 March 2014 Name 1 Ecology & Evolutionary Biology 2245/2245W Exam 2 1 March 2014 1. Use the following matrix of nucleotide sequence data and the corresponding tree to answer questions a. through h. below. (16 points)

More information

GIS Applications to Museum Specimens

GIS Applications to Museum Specimens GIS Applications to Museum Specimens Joseph Grinnell (1877 1939) At this point I wish to emphasize what I believe will ultimately prove to be the greatest value of our museum. This value will not, however,

More information

Integrative Biology 200A "PRINCIPLES OF PHYLOGENETICS" Spring 2012 University of California, Berkeley

Integrative Biology 200A PRINCIPLES OF PHYLOGENETICS Spring 2012 University of California, Berkeley Integrative Biology 200A "PRINCIPLES OF PHYLOGENETICS" Spring 2012 University of California, Berkeley B.D. Mishler Feb. 7, 2012. Morphological data IV -- ontogeny & structure of plants The last frontier

More information

Phylogenetic analyses. Kirsi Kostamo

Phylogenetic analyses. Kirsi Kostamo Phylogenetic analyses Kirsi Kostamo The aim: To construct a visual representation (a tree) to describe the assumed evolution occurring between and among different groups (individuals, populations, species,

More information

What is Phylogenetics

What is Phylogenetics What is Phylogenetics Phylogenetics is the area of research concerned with finding the genetic connections and relationships between species. The basic idea is to compare specific characters (features)

More information

PHYLOGENY AND SYSTEMATICS

PHYLOGENY AND SYSTEMATICS AP BIOLOGY EVOLUTION/HEREDITY UNIT Unit 1 Part 11 Chapter 26 Activity #15 NAME DATE PERIOD PHYLOGENY AND SYSTEMATICS PHYLOGENY Evolutionary history of species or group of related species SYSTEMATICS Study

More information

Constructing Evolutionary/Phylogenetic Trees

Constructing Evolutionary/Phylogenetic Trees Constructing Evolutionary/Phylogenetic Trees 2 broad categories: Distance-based methods Ultrametric Additive: UPGMA Transformed Distance Neighbor-Joining Character-based Maximum Parsimony Maximum Likelihood

More information

Classification and Phylogeny

Classification and Phylogeny Classification and Phylogeny The diversity of life is great. To communicate about it, there must be a scheme for organization. There are many species that would be difficult to organize without a scheme

More information

Classification and Phylogeny

Classification and Phylogeny Classification and Phylogeny The diversity it of life is great. To communicate about it, there must be a scheme for organization. There are many species that would be difficult to organize without a scheme

More information

Chapter 19: Taxonomy, Systematics, and Phylogeny

Chapter 19: Taxonomy, Systematics, and Phylogeny Chapter 19: Taxonomy, Systematics, and Phylogeny AP Curriculum Alignment Chapter 19 expands on the topics of phylogenies and cladograms, which are important to Big Idea 1. In order for students to understand

More information

ESS 345 Ichthyology. Systematic Ichthyology Part II Not in Book

ESS 345 Ichthyology. Systematic Ichthyology Part II Not in Book ESS 345 Ichthyology Systematic Ichthyology Part II Not in Book Thought for today: Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else,

More information

Phylogenetic Tree Reconstruction

Phylogenetic Tree Reconstruction I519 Introduction to Bioinformatics, 2011 Phylogenetic Tree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Evolution theory Speciation Evolution of new organisms is driven

More information

Phylogenetic Analysis

Phylogenetic Analysis Phylogenetic Analysis Aristotle Through classification, one might discover the essence and purpose of species. Nelson & Platnick (1981) Systematics and Biogeography Carl Linnaeus Swedish botanist (1700s)

More information

How should we organize the diversity of animal life?

How should we organize the diversity of animal life? How should we organize the diversity of animal life? The difference between Taxonomy Linneaus, and Cladistics Darwin What are phylogenies? How do we read them? How do we estimate them? Classification (Taxonomy)

More information

Phylogenetics. Applications of phylogenetics. Unrooted networks vs. rooted trees. Outline

Phylogenetics. Applications of phylogenetics. Unrooted networks vs. rooted trees. Outline Phylogenetics Todd Vision iology 522 March 26, 2007 pplications of phylogenetics Studying organismal or biogeographic history Systematics ating events in the fossil record onservation biology Studying

More information

Effects of recent population bottlenecks on reconstructing. the demographic history of prairie-chickens

Effects of recent population bottlenecks on reconstructing. the demographic history of prairie-chickens Molecular Ecology (2007) 16, 2203 2222 doi: 10.1111/j.1365-294X.2007.03285.x Effects of recent population bottlenecks on reconstructing Blackwell Publishing Ltd the demographic history of prairie-chickens

More information

Alan R. Templeton, Eric Routman and Christopher A. Phillips *

Alan R. Templeton, Eric Routman and Christopher A. Phillips * Copyright 0 1995 by the Genetics Society of America Separating Population Structure from Population History: A Cladistic Analysis of the Geographical Distribution of Mitochondrial DNA Haplotypes in the

More information

Unit 7: Evolution Guided Reading Questions (80 pts total)

Unit 7: Evolution Guided Reading Questions (80 pts total) AP Biology Biology, Campbell and Reece, 10th Edition Adapted from chapter reading guides originally created by Lynn Miriello Name: Unit 7: Evolution Guided Reading Questions (80 pts total) Chapter 22 Descent

More information

Consensus Methods. * You are only responsible for the first two

Consensus Methods. * You are only responsible for the first two Consensus Trees * consensus trees reconcile clades from different trees * consensus is a conservative estimate of phylogeny that emphasizes points of agreement * philosophy: agreement among data sets is

More information

Integrating Fossils into Phylogenies. Throughout the 20th century, the relationship between paleontology and evolutionary biology has been strained.

Integrating Fossils into Phylogenies. Throughout the 20th century, the relationship between paleontology and evolutionary biology has been strained. IB 200B Principals of Phylogenetic Systematics Spring 2011 Integrating Fossils into Phylogenies Throughout the 20th century, the relationship between paleontology and evolutionary biology has been strained.

More information

Fields connected to Phylogeography Microevolutionary disciplines Ethology Demography Population genetics

Fields connected to Phylogeography Microevolutionary disciplines Ethology Demography Population genetics Stephen A. Roussos Fields connected to Phylogeography Microevolutionary disciplines Ethology Demography Population genetics Macrevolutionary disciplines Historical geography Paleontology Phylogenetic biology

More information

Likelihood Ratio Tests for Detecting Positive Selection and Application to Primate Lysozyme Evolution

Likelihood Ratio Tests for Detecting Positive Selection and Application to Primate Lysozyme Evolution Likelihood Ratio Tests for Detecting Positive Selection and Application to Primate Lysozyme Evolution Ziheng Yang Department of Biology, University College, London An excess of nonsynonymous substitutions

More information

Rapid speciation following recent host shift in the plant pathogenic fungus Rhynchosporium

Rapid speciation following recent host shift in the plant pathogenic fungus Rhynchosporium Rapid speciation following recent host shift in the plant pathogenic fungus Rhynchosporium Tiziana Vonlanthen, Laurin Müller 27.10.15 1 Second paper: Origin and Domestication of the Fungal Wheat Pathogen

More information

School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom 2

School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom 2 Evolution, 54(3), 2000, pp. 911 923 COLONIZATION AND DIVERSIFICATION OF THE SPECIES BRACHYDERES RUGATUS (COLEOPTERA) ON THE CANARY ISLANDS: EVIDENCE FROM MITOCHONDRIAL DNA COII GENE SEQUENCES BRENT C.

More information

Molecular Phylogenetics and Evolution

Molecular Phylogenetics and Evolution Molecular Phylogenetics and Evolution 49 (2008) 277 291 Contents lists available at ScienceDirect Molecular Phylogenetics and Evolution journal homepage: www.elsevier.com/locate/ympev In situ genetic differentiation

More information

Phylogenies & Classifying species (AKA Cladistics & Taxonomy) What are phylogenies & cladograms? How do we read them? How do we estimate them?

Phylogenies & Classifying species (AKA Cladistics & Taxonomy) What are phylogenies & cladograms? How do we read them? How do we estimate them? Phylogenies & Classifying species (AKA Cladistics & Taxonomy) What are phylogenies & cladograms? How do we read them? How do we estimate them? Carolus Linneaus:Systema Naturae (1735) Swedish botanist &

More information

Homework Assignment, Evolutionary Systems Biology, Spring Homework Part I: Phylogenetics:

Homework Assignment, Evolutionary Systems Biology, Spring Homework Part I: Phylogenetics: Homework Assignment, Evolutionary Systems Biology, Spring 2009. Homework Part I: Phylogenetics: Introduction. The objective of this assignment is to understand the basics of phylogenetic relationships

More information

Lecture 11 Friday, October 21, 2011

Lecture 11 Friday, October 21, 2011 Lecture 11 Friday, October 21, 2011 Phylogenetic tree (phylogeny) Darwin and classification: In the Origin, Darwin said that descent from a common ancestral species could explain why the Linnaean system

More information

Biogeography expands:

Biogeography expands: Biogeography expands: Phylogeography Ecobiogeography Due to advances in DNA sequencing and fingerprinting methods, historical biogeography has recently begun to integrate relationships of populations within

More information

Speciation. Today s OUTLINE: Mechanisms of Speciation. Mechanisms of Speciation. Geographic Models of speciation. (1) Mechanisms of Speciation

Speciation. Today s OUTLINE: Mechanisms of Speciation. Mechanisms of Speciation. Geographic Models of speciation. (1) Mechanisms of Speciation Speciation Today s OUTLINE: (1) Geographic Mechanisms of Speciation (What circumstances lead to the formation of new species?) (2) Species Concepts (How are Species Defined?) Mechanisms of Speciation Last

More information

Chapter 26 Phylogeny and the Tree of Life

Chapter 26 Phylogeny and the Tree of Life Chapter 26 Phylogeny and the Tree of Life Biologists estimate that there are about 5 to 100 million species of organisms living on Earth today. Evidence from morphological, biochemical, and gene sequence

More information

Unfortunately, there are many definitions Biological Species: species defined by Morphological Species (Morphospecies): characterizes species by

Unfortunately, there are many definitions Biological Species: species defined by Morphological Species (Morphospecies): characterizes species by 1 2 3 4 5 6 Lecture 3: Chapter 27 -- Speciation Macroevolution Macroevolution and Speciation Microevolution Changes in the gene pool over successive generations; deals with alleles and genes Macroevolution

More information

Conceptually, we define species as evolutionary units :

Conceptually, we define species as evolutionary units : Bio 1M: Speciation 1 How are species defined? S24.1 (2ndEd S26.1) Conceptually, we define species as evolutionary units : Individuals within a species are evolving together Individuals of different species

More information

STEM-hy: Species Tree Estimation using Maximum likelihood (with hybridization)

STEM-hy: Species Tree Estimation using Maximum likelihood (with hybridization) STEM-hy: Species Tree Estimation using Maximum likelihood (with hybridization) Laura Salter Kubatko Departments of Statistics and Evolution, Ecology, and Organismal Biology The Ohio State University kubatko.2@osu.edu

More information

Classification, Phylogeny yand Evolutionary History

Classification, Phylogeny yand Evolutionary History Classification, Phylogeny yand Evolutionary History The diversity of life is great. To communicate about it, there must be a scheme for organization. There are many species that would be difficult to organize

More information

A comparison of two popular statistical methods for estimating the time to most recent common ancestor (TMRCA) from a sample of DNA sequences

A comparison of two popular statistical methods for estimating the time to most recent common ancestor (TMRCA) from a sample of DNA sequences Indian Academy of Sciences A comparison of two popular statistical methods for estimating the time to most recent common ancestor (TMRCA) from a sample of DNA sequences ANALABHA BASU and PARTHA P. MAJUMDER*

More information

Genetic diversity of beech in Greece

Genetic diversity of beech in Greece Genetic diversity of beech in Greece A.C. Papageorgiou (1), I. Tsiripidis (2), S. Hatziskakis (1) Democritus University of Thrace Forest Genetics Laboratory Orestiada, Greece (2) Aristotle University of

More information

How to read and make phylogenetic trees Zuzana Starostová

How to read and make phylogenetic trees Zuzana Starostová How to read and make phylogenetic trees Zuzana Starostová How to make phylogenetic trees? Workflow: obtain DNA sequence quality check sequence alignment calculating genetic distances phylogeny estimation

More information

Phylogenetic Analysis

Phylogenetic Analysis Phylogenetic Analysis Aristotle Through classification, one might discover the essence and purpose of species. Nelson & Platnick (1981) Systematics and Biogeography Carl Linnaeus Swedish botanist (1700s)

More information

Phylogenetic Analysis

Phylogenetic Analysis Phylogenetic Analysis Aristotle Through classification, one might discover the essence and purpose of species. Nelson & Platnick (1981) Systematics and Biogeography Carl Linnaeus Swedish botanist (1700s)

More information

BINF6201/8201. Molecular phylogenetic methods

BINF6201/8201. Molecular phylogenetic methods BINF60/80 Molecular phylogenetic methods 0-7-06 Phylogenetics Ø According to the evolutionary theory, all life forms on this planet are related to one another by descent. Ø Traditionally, phylogenetics

More information

Chapter 26: Phylogeny and the Tree of Life Phylogenies Show Evolutionary Relationships

Chapter 26: Phylogeny and the Tree of Life Phylogenies Show Evolutionary Relationships Chapter 26: Phylogeny and the Tree of Life You Must Know The taxonomic categories and how they indicate relatedness. How systematics is used to develop phylogenetic trees. How to construct a phylogenetic

More information

Assessing the Effect of Genetic Mutation: A Bayesian Framework for Determining Population History from DNA Sequence Data

Assessing the Effect of Genetic Mutation: A Bayesian Framework for Determining Population History from DNA Sequence Data BAYESIAN STATISTICS 8, pp. 1 26. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West (Eds.) c Oxford University Press, 2007 Assessing the Effect of Genetic

More information

UON, CAS, DBSC, General Biology II (BIOL102) Dr. Mustafa. A. Mansi. The Origin of Species

UON, CAS, DBSC, General Biology II (BIOL102) Dr. Mustafa. A. Mansi. The Origin of Species The Origin of Species Galápagos Islands, landforms newly emerged from the sea, despite their geologic youth, are filled with plants and animals known no-where else in the world, Speciation: The origin

More information

C.DARWIN ( )

C.DARWIN ( ) C.DARWIN (1809-1882) LAMARCK Each evolutionary lineage has evolved, transforming itself, from a ancestor appeared by spontaneous generation DARWIN All organisms are historically interconnected. Their relationships

More information

SPECIATION. REPRODUCTIVE BARRIERS PREZYGOTIC: Barriers that prevent fertilization. Habitat isolation Populations can t get together

SPECIATION. REPRODUCTIVE BARRIERS PREZYGOTIC: Barriers that prevent fertilization. Habitat isolation Populations can t get together SPECIATION Origin of new species=speciation -Process by which one species splits into two or more species, accounts for both the unity and diversity of life SPECIES BIOLOGICAL CONCEPT Population or groups

More information

Gene Genealogies Coalescence Theory. Annabelle Haudry Glasgow, July 2009

Gene Genealogies Coalescence Theory. Annabelle Haudry Glasgow, July 2009 Gene Genealogies Coalescence Theory Annabelle Haudry Glasgow, July 2009 What could tell a gene genealogy? How much diversity in the population? Has the demographic size of the population changed? How?

More information

Using phylogeographic analyses of gene trees to test species

Using phylogeographic analyses of gene trees to test species Molecular Ecology (2001) 10, 779 791 Using phylogeographic analyses of gene trees to test species Blackwell Science, Ltd status and processes ALAN R. TEMPLETON Department of Biology, Campus Box 1137, Washington

More information

5/31/17. Week 10; Monday MEMORIAL DAY NO CLASS. Page 88

5/31/17. Week 10; Monday MEMORIAL DAY NO CLASS. Page 88 Week 10; Monday MEMORIAL DAY NO CLASS Page 88 Week 10; Wednesday Announcements: Family ID final in lab Today Final exam next Tuesday at 8:30 am here Lecture: Species concepts & Speciation. What are species?

More information

Postglacial colonizer or cryptic invader? Case of Gammarus roeselii (Crustacea Amphipoda) in Europe

Postglacial colonizer or cryptic invader? Case of Gammarus roeselii (Crustacea Amphipoda) in Europe Postglacial colonizer or cryptic invader? Case of Gammarus roeselii (Crustacea Amphipoda) in Europe Tomasz Rewicz, Paula Krzywozniak, Tomasz Mamos, Karolina Bacela Spychalska, Remi Wattier, Michal Grabowski

More information

Phylogenetic Trees. Phylogenetic Trees Five. Phylogeny: Inference Tool. Phylogeny Terminology. Picture of Last Quagga. Importance of Phylogeny 5.

Phylogenetic Trees. Phylogenetic Trees Five. Phylogeny: Inference Tool. Phylogeny Terminology. Picture of Last Quagga. Importance of Phylogeny 5. Five Sami Khuri Department of Computer Science San José State University San José, California, USA sami.khuri@sjsu.edu v Distance Methods v Character Methods v Molecular Clock v UPGMA v Maximum Parsimony

More information

Bio94 Discussion Activity week 3: Chapter 27 Phylogenies and the History of Life

Bio94 Discussion Activity week 3: Chapter 27 Phylogenies and the History of Life Bio94 Discussion Activity week 3: Chapter 27 Phylogenies and the History of Life 1. Constructing a phylogenetic tree using a cladistic approach Construct a phylogenetic tree using the following table:

More information

Phylogeny 9/8/2014. Evolutionary Relationships. Data Supporting Phylogeny. Chapter 26

Phylogeny 9/8/2014. Evolutionary Relationships. Data Supporting Phylogeny. Chapter 26 Phylogeny Chapter 26 Taxonomy Taxonomy: ordered division of organisms into categories based on a set of characteristics used to assess similarities and differences Carolus Linnaeus developed binomial nomenclature,

More information

PHYLOGENY & THE TREE OF LIFE

PHYLOGENY & THE TREE OF LIFE PHYLOGENY & THE TREE OF LIFE PREFACE In this powerpoint we learn how biologists distinguish and categorize the millions of species on earth. Early we looked at the process of evolution here we look at

More information

Phylogenetic inference

Phylogenetic inference Phylogenetic inference Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, March 7 th 016 After this lecture, you can discuss (dis-) advantages of different information types

More information

Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2016 University of California, Berkeley. Parsimony & Likelihood [draft]

Integrative Biology 200 PRINCIPLES OF PHYLOGENETICS Spring 2016 University of California, Berkeley. Parsimony & Likelihood [draft] Integrative Biology 200 "PRINCIPLES OF PHYLOGENETICS" Spring 2016 University of California, Berkeley K.W. Will Parsimony & Likelihood [draft] 1. Hennig and Parsimony: Hennig was not concerned with parsimony

More information

Biology 2. Lecture Material. For. Macroevolution. Systematics

Biology 2. Lecture Material. For. Macroevolution. Systematics Biology 2 Macroevolution & Systematics 1 Biology 2 Lecture Material For Macroevolution & Systematics Biology 2 Macroevolution & Systematics 2 Microevolution: Biological Species: Two Patterns of Evolutionary

More information

Evaluate evidence provided by data from many scientific disciplines to support biological evolution. [LO 1.9, SP 5.3]

Evaluate evidence provided by data from many scientific disciplines to support biological evolution. [LO 1.9, SP 5.3] Learning Objectives Evaluate evidence provided by data from many scientific disciplines to support biological evolution. [LO 1.9, SP 5.3] Refine evidence based on data from many scientific disciplines

More information

Introduction to characters and parsimony analysis

Introduction to characters and parsimony analysis Introduction to characters and parsimony analysis Genetic Relationships Genetic relationships exist between individuals within populations These include ancestordescendent relationships and more indirect

More information

Bustamante et al., Supplementary Nature Manuscript # 1 out of 9 Information #

Bustamante et al., Supplementary Nature Manuscript # 1 out of 9 Information # Bustamante et al., Supplementary Nature Manuscript # 1 out of 9 Details of PRF Methodology In the Poisson Random Field PRF) model, it is assumed that non-synonymous mutations at a given gene are either

More information

Lecture V Phylogeny and Systematics Dr. Kopeny

Lecture V Phylogeny and Systematics Dr. Kopeny Delivered 1/30 and 2/1 Lecture V Phylogeny and Systematics Dr. Kopeny Lecture V How to Determine Evolutionary Relationships: Concepts in Phylogeny and Systematics Textbook Reading: pp 425-433, 435-437

More information

AP Biology. Cladistics

AP Biology. Cladistics Cladistics Kingdom Summary Review slide Review slide Classification Old 5 Kingdom system Eukaryote Monera, Protists, Plants, Fungi, Animals New 3 Domain system reflects a greater understanding of evolution

More information

Using phylogenetics to estimate species divergence times... Basics and basic issues for Bayesian inference of divergence times (plus some digression)

Using phylogenetics to estimate species divergence times... Basics and basic issues for Bayesian inference of divergence times (plus some digression) Using phylogenetics to estimate species divergence times... More accurately... Basics and basic issues for Bayesian inference of divergence times (plus some digression) "A comparison of the structures

More information

Evolutionary Patterns, Rates, and Trends

Evolutionary Patterns, Rates, and Trends Evolutionary Patterns, Rates, and Trends Macroevolution Major patterns and trends among lineages Rates of change in geologic time Comparative Morphology Comparing body forms and structures of major lineages

More information

Assessing an Unknown Evolutionary Process: Effect of Increasing Site- Specific Knowledge Through Taxon Addition

Assessing an Unknown Evolutionary Process: Effect of Increasing Site- Specific Knowledge Through Taxon Addition Assessing an Unknown Evolutionary Process: Effect of Increasing Site- Specific Knowledge Through Taxon Addition David D. Pollock* and William J. Bruno* *Theoretical Biology and Biophysics, Los Alamos National

More information

Lecture 6 Phylogenetic Inference

Lecture 6 Phylogenetic Inference Lecture 6 Phylogenetic Inference From Darwin s notebook in 1837 Charles Darwin Willi Hennig From The Origin in 1859 Cladistics Phylogenetic inference Willi Hennig, Cladistics 1. Clade, Monophyletic group,

More information

Speciation. Today s OUTLINE: Mechanisms of Speciation. Mechanisms of Speciation. Geographic Models of speciation. (1) Mechanisms of Speciation

Speciation. Today s OUTLINE: Mechanisms of Speciation. Mechanisms of Speciation. Geographic Models of speciation. (1) Mechanisms of Speciation Speciation Today s OUTLINE: (1) Geographic Mechanisms of Speciation (What circumstances lead to the formation of new species?) (2) Species Concepts (How are Species Defined?) Mechanisms of Speciation Last

More information

Project Budget: State Wildlife Grant Requested: $16,412 Project Match (UARK in kind services): $5,744 Total Project Cost: $22,156

Project Budget: State Wildlife Grant Requested: $16,412 Project Match (UARK in kind services): $5,744 Total Project Cost: $22,156 Project Title: Genetic examination of the Ringed Crayfish species group, with special emphasis on the endemic Gapped Ringed Crayfish (Orconectes neglectus chaenodactylus) Project Summary: Morphological

More information