Science topic

Phylogeography and Phylogenetic Biogeography - Science topic

Study of taxa relationships and their distributions
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The length to be read depends on the number of variable positions, the latter is defined by the number of OTUs. If you have N OTU to put on the tree, the theoretic minimal number of variable informative (!) sites is N-1 to have a trace of hope for obtaining a fully resoled tree. Otherwise you will depend on the (possibly wrong) coefficients of your model of molecular evolution. Safe approach to at least triple that number. But if your herd of variable sites is crowded on a short stretch of DNA, your alignment and model are doubtful. It is safe to have less than ca. 15% of variable positions.
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I am try to find out the main differences between haplotype and DTE analyses. To my knowledge, the outer Haplotypes are newly created, while the inner ones are evolutionary older than the outers. What happen if we find out different results in DTE?
I mean I found that some taxa are evolutionary young according to DTE, while haplotype analysis showed me the opposite results.
Would you please let me know, how I must interpret this difference?
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Dear Atena,
You should bare in mind that relationships among taxa in haplotype networks are not necessarily equivalent to their relationships in phylogenetic trees (such as Bayesian, maximum likelihood or parsimony). This is because a haplotype network is usually calculated based on a distance matrix, thus it clusters individuals according to their genetic distances. This may or may not reflect the 'true' phylogeny.
So the short answer is that the two are not easily comparable.
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Hello, I would like to create 100 parsimonious trees through the command line. I am looking for the best software to do this. I have 25,000 tips for the trees, so it would not be possible to use ML methods. The software does not have to run 100 of them in one step. I would be happy to use just one script that makes one maximum parsimony tree and run this script 100 times using a workflow management tool like Snakemake. Also, the software has to take a multiple sequence alignment as the input file. I wanted to use TNT, but I cannot use my MSA fa file with TNT. Thank you in advance.
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Thank you for your answering. I am running 25,000 tips since I am trying to reconstruct the phylogenetics for a virus. I need the starting tree for an additional analysis. The sequences range from 200 to 15,000 bp in length. I don't think using a bayesian method here like mrbayes would work for this since that would be far too computationally intensive. All I need is a MP tree, which should be less intensive than any ML method.
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???
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No, you can't delete a question once posted on RG. However, you can edit it.
Thanks!
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That's probably a beginner question, but, after I run the BBM analysis and go to the graphic tree view how interpretation I give to the symbols in event matrix on Information tab? An example:
NODE93:
EVENT MATRIX:
Dispersal:2
Vicariance:1
Extinction:0
Event Route:
->^C->C^C->C|C
PROBABILITY:
0.0000
 What's mean the numbers in Dispersal, Vicariance and Extinction? And the simbols in event route between the area C, they have the same significance which have in mathemarical equations?
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Dear Caio,
thanks a lot for the answer!
Best regards
Emanuele
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I'm currently learning the how's and why's of bioinformatics, this is the third three I build and I noticed that the more sequences I add, the lower is the bootstrap.
My sequences were trimmed using trimAl I choose to obtain them with no gaps. The evolutionary model was chosen using the Jmodeltest software and the phylogenetic tree was generated on Mega.
This is a Maximum Likelihood tree, my Log Likelihood is -24465.21, the three were built using the General Time Reversible model, G+I, gamma 6, NNI, 25 threads, and 500 Boostrap reps.
Can anyone elucidate why this is happening and what I should have in mind when constructing the next three?
Thank you very much
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Keep in mind that for a molecular phylogenetic tree to make sense, homologous nucleotide positions (those that descend from a common ancestor) need to be aligned with each other. While we can't directly "see" homology, the degree of nucleotide similarity is the best guide, and is the tool that alignment programs such as Clustal or Muscle use. If you constructed your tree with no gaps, as you put it, it sounds like you've made no attempt to do this.
You don't describe what locus (or loci) your sequences are from or the length of the sequences. However, I'm guessing these are coronavirus sequences. If a lot of these are SARS-CoV-2, the number of nucleotide differences between correctly aligned sites is still pretty small, so a high-confidence support for most branches simply can't be achieved.
Finally, the cladogram display you're using throws away most of the information from maximum-likelihood tree construction on just how much change has taken place on each branch. A phylogram display would give you that information. I suspect that an aligned set of sequences would show a lot of very short branches, consistent with the things I mention in the previous paragraph.
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I'm trying to carry out the Mantel test using Arlequin, has anyone here done it before?
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For the mantel test, you need to use the arp file attached here
Thanks
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WANTED
We are looking for a frozen or in ethanol-preserved Cerambyx paludivagus for barcoding
SCIENTIFIC REWARD
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Dear Derradj,
Thank you very much for your collaboration offer.
Since the last records of C paludivagus that I know date from several decades ago, I had even thought that this species was extinct.
From your address I suposse that the material will be from the mountains near Constantine.
Best
Luismi
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Hello Everyone,
The past few years have seen a lot of new cyanobacterial taxa being described using a polyphasic approach. It will be interesting to know that what are the various good things about using this approach and importantly, are there some particular taxonomic groups/clusters that are still unresolved where the polyphasic approach is still to give any proper answer?
What further developments do we anticipate in the coming years? What are the new techniques/methods that can be further incorporated for a better understanding of cyanobacterial taxonomy?
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Although it is not my specialty, I advise you to read the following paper:
A polyphasic approach for the taxonomy of cyanobacteria: principles and applications, by Jiří Komárek (2016).
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Hello Everyone, I am in need of performing phylogenetic analysis using PhyML for Maximum Likelihood and MrBayes for Bayesian analysis. I have done the Multiple sequence alignment using MAFFT (online) and then performed the JModelTest (on console) following the manual. I have obtained the BIC results as follows:
* BAYESIAN INFORMATION CRITERION (BIC) *
Sample size: 375.0
Model selected:
Model = K80+G
partition = 010010
-lnL = 1210.4717
K = 116
kappa = 2.4055 (ti/tv = 1.2027)
gamma shape = 0.2970
A) My question would be how to use these values in PhyML ( http://www.atgc-montpellier.fr/phyml/). I cannot make use of the command version of the PhyML program.
B) For MrBayes, m block in the nexus file is as follows:
begin mrbayes;
log start filename=************;
Prset statefreqpr=dirichlet(1,1,1,1);
Lset nst=6 rates=invgamma;
mcmc ngen=3000000 printfreq=100 samplefreq=100;
log stop;
end;
How do I modify the block according to the results above?
Thank you,
Ritesh
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For ML analysis, I highly recommend the program IQ-TREE. It performs model testing and phylogenetic inference in one go so you don't have to perform them separately. The program also has a webserver so you can upload your sequence matrix in phylip format. It is incrincredibly fast, easy, and produces great results.
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I am studying two types of Ophidiid fishes having features morphologically similar and would like to genetically classify them.
I have already investigated COI region of mtDNA, but the genetic difference is quite small (2 to 7 mutational steps between nearest and farthest haplotypes), so I am considering of conducting experiments using nuclear DNA.
However, I am not sure which genetic marker will be suitable for analysis to make a marked difference than COI.
So far, I am considering of using the RAG1 region, which has been studied in related species, or ITS, where base substitution is likely to occur because of non-coding region.
Could you tell me if there are better genetic markers that has a fast base substitution rate and is effective in clarifying interspecific or intraspecific differences?
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Hi Mita,
Given your fish species are closely related as revealed by mtDNA, it is preferable to choose hyper variable regions of nuclear genome. Markers occurring to me are microsattlites (STR) and MHC. STRs have faster mutation rate than mtDNA in animals in general. Some regions of MHC also evovled very fast due to selection. Have a look at review paper below might help.
Zhang DX, Hewitt GM (2003) Nuclear DNA analyses in genetic studies of populations: practice, problems and prospects. Molecular Ecology, 12: 563-584.
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I've tried several programs (e.g. TreeView, FigTree, MEGA, Archaeopteryx, Mesquite) but they just display node numbers and branch lengths. I'm using the software SYMMETREE to infer diversification rate shifts on particular nodes within a tree. Results refer to "branch numbers" which, according to the manual, can be displayed using MacClade. However, I´m not using a Mac OS platform.
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Yes, ape in R is a good option
For the sake of providing a direct answer to the SPECIFIC question you asked, the following R code will do exactly what you require, and output the tree to a PDFfile in your current working directory
library(ape)
tree = read.nexus(file="path\to\file")
tree_export = "tree.pdf"
pdf(file=tree_export, width=6, height=6)
plot(tree, cex =0.5, use.edge.length=FALSE)
axisPhylo()
edgelabels(cex = 0.25, width = 0.1)
dev.off()
depending on your tree structure just play around with the "cex"and "width" parameters to make the branch numbers readable, the above peramters are reasonable starting points.
The above code assumes your tree is in Nexus format. For the commonly used Newick format do this:
library(ape)
tree = read.tree(file="path\to\file")
tree_export = "tree.pdf"
pdf(file=tree_export, width=6, height=6)
plot(tree, cex =0.5, use.edge.length=FALSE)
axisPhylo()
edgelabels(cex = 0.25, width = 0.1)
dev.off()
i.e. swap "read.nexus" for "read.tree" function
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I have prepared a nj phylogenetic tree using ape package in R. But due to large number of sequences and taxa, the tips of the tree looks overlapping. How can I increase the distance between the branches, so that my tree can be viewed properly with all the tips?
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You may find it easier to just view the tree in a graphical tree browser: http://tree.bio.ed.ac.uk/software/figtree/
Output using ape write.tree, then import to figtree
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In order to obtain important active compund from cinnamon zylanicum extract which is beter solvent must be used ?
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hi
ethanol is better for extraction
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Citations that appear in the BODY of published papers contribute to metrics (e.g. h-index) that account for the impact of the work of a particular researcher. I acknowledge that the suitability of these indexes is debatable. In the meantime, I wonder why the citations that appear in Supporting Information (particularly those that refers to relevant methods and databases that were used in the papers) are not included in the calculation of such indexes.
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Yes! Hope journals will/are considering this issue.
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Hello,
Can anyone help me to argument wheter I shoul or not remove my outgroup sequences before running a substitution model test (PartitionFinder)? I have heard that it is a best practice to keep only ingroup in such tests, though I am having a hard time finding a refence on that.
Thank you,
Best Regards,
Manolo Perez
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There is no single correct answer for that question. It depends a lot on the diversity of sequences in your ingroup, and how far distant the outgroup is from the ingroup. In my opinion, if the outgroup is so far distant from the ingroup that the model of evolution or the partitions would be altered significantly by including it, you should be using a different outgroup or perhaps using midpoint rooting with no outrgroup. Running ModelTest or a modeltest version within PartitionFinder should not take too long, so you should be able to run it both with and without the outgroup to see if there is any change.
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Project management in research project
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Yes, it is one of the foundations of research
Best Regards Javan Doloei
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Hello everyone!
I have been consulting this webpage http://beast.community/ and http://www.beast2.org/ , but the examples for the use of BEAUti and BEAST are for old versions. The versions of BEAST 2 since 2.4 have more and detailed priors. ¿Some of you have a guide for these versions? It could be nice if you share it to me
Thanks
Angélica
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As some of the samples are incredibly diverse they do not allow for a clear, concise and legible haplotype network.
Would it be appropriate to create a histogram of haplotype frequency.
While this would not show the phylogenetic perspective it would still provide a measure of how haplotypes are distributed.
Suggestions would be welcome.
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You can plot the variants to make a 'heatmap' like thing as in Xu et al 2017 (https://academic.oup.com/mbe/article/34/10/2704/3988100) Figure 2b, or build a phylogeny using VCFtoTree (https://github.com/duoduoo/VCFtoTree_3.0.0).
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I would be grateful for sharing publications dealing with pre-glacial (Tertiary) plant migrations from Asia to Europe, both phylogeographic papers on particular taxa and more general discussions of migration trends.
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Hi
Sorry for the delay, just found your question. For an overview of plant and animal invasions into the Mediterranean region try --
Groves RH & Di Castri F. 1991. Biogeography of Mediterranean Invasions. Cambridge University Press.
For more specific information try --
Pons A, de Beaulieu JL, Coute
aux M & Reille M. 1990. Vegetal invasions in Mediterranean Europe and North Africa from the paleoecological point of view. In: Biological Invasions in Europe and the Mediterranean basin. eds F Di Castri, AJ Hansen & M Debussche. pp51-60. Dordrecht, Kluwer.
Regards
Peter Flint
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Ecological niche modeling is increasingly important for understanding the factors that shape species distributions, as well as testing biogeographical hypotheses about species past, present, and future distributions as well as the role of ecology in speciation. However, most niche modeling work has focused on terrestrial and marine species (sound like conservation biology, in general?). I have previously used MAXENT to develop and project models of fish distributions, and the models we have published exhibited excellent predictive performance. And I am very interested in continuing to do so, particularly through coupling ENMs with phylogeographic analyses, and/or using them to inform phylogeographic hypotheses testing. However, I am skeptical of all models to some degree, and I am wanting to learn whether other techniques exist that would be more suitable for freshwater fish ecological niche modeling and paleoclimatic modeling, other than MAXENT (which is obviously most convenient for me). I am also interested in what the best data layers are for ENM analyses of freshwater habitats. I always want to learn more about these topics, so I figured I would ask here.
So, first, do such 'better' ENM models exist that could/should be used instead of or in combination with MAXENT? And, if so, what is required to run such other models, and how would the assumptions of these potentially 'better' models differ from those of MAXENT in different cases?
Second, it seems that a limitation of ecological niche modeling for freshwater taxa is a lack of sufficiently high resolution data layers for aquatic habitats. However, I am unsure about geospatial data repositories or resources for generating more suitable layers, and I would like specific advice about GIS procedures and data layers for making better data coverages. I am aware that some people are already doing this, but usually at very fine spatial scales. The broader community of ecologists and evolutionary biologists interested in fishes would therefore benefit much more from more comprehensive coverages.
FYI, I should indicate that I am not really interested in using masks over bioclimatic variables to restrict model output to the boundaries of stream and river networks, because many pilot analyses I have run on North American species suggest this does not add much or produce different results relative to running the models without such masks. So, I would prefer to avoid such discussion unless you know or can show me that doing so improves model performance. Thanks in advance for your replies. Take care.
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As an update to this question, I'd like to point out that there are some recently published papers that 1) demonstrate that it remains possible to predict the distributions of freshwater fishes (and other obligate freshwater taxa) using ENM approaches such as MaxEnt (e.g. Cao et al. 2013; Campbell and Hildebrand 2017; and several others), and 2) demonstrate that bioclimatic variables work well as a proxy of stream habitat variables (e.g. McGarvey et al. 2017).
I am particularly encouraged by the McGarvey et al. (2017) paper in Ecography, which shows that at the scale of the Columbia River watershed the performance of building MaxEnt SDMs from climate covariates/layers as proxies for variation in in-stream "environments" inhabited by fishes (which in reality include a variety of in-stream variables such as hydrological parameters) is essentially equivalent to that of SDMs based on "instream covariates" (hydrological data layers).
One common theme in some recent papers is to model only within the hydrological network, at high spatial resolution (e.g. 30 arc-seconds); however, while results from this approach are easily and beautifully visualized over smaller spatial scales (e.g. within states, regions), in my experience doing this at larger spatial scales (e.g. subcontinental scales) gives results that are much harder to visualize. At larger scales, it may be best 1) to use a discrete symbology for displaying prediction/habitat suitability within river networks, if they are used; 2) to model at the HUC rather than network scale (e.g. Cao et al. 2013); or 3) simply not to mask to hydrological network at all (make continuous plots and interpret them in the context of hydrology).
REFERENCES
Campbell, C. A., & Hilderbrand, R. H. (2017). Using maximum entropy to predict suitable habitat for the endangered dwarf wedgemussel in the Maryland Coastal Plain. Aquatic Conservation: Marine and Freshwater Ecosystems, 27(2), 462-475.
Cao, Y., DeWalt, R. E., Robinson, J. L., Tweddale, T., Hinz, L., & Pessino, M. (2013). Using Maxent to model the historic distributions of stonefly species in Illinois streams: the effects of regularization and threshold selections. Ecological Modelling, 259, 30-39.
McGarvey, D. J., Menon, M., Woods, T., Tassone, S., Reese, J., Vergamini, M., & Kellogg, E. (2017). On the use of climate covariates in aquatic species distribution models: are we at risk of throwing out the baby with the bath water? Ecography.
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I imported my SNP data successfully, but as I am trying to make my first scenario I keep getting the above error (At line 11, the number of words is incorrect) and it makes no sense. I'm literally copying what they have in the manual, but adding in 'samples' (populations) to match how many populations I have, but other than that it's exactly the same. No matter how I change it, I get that error. Any help will be appreciated. Below is the text I have in the scenario box. I'm just trying to get a basic scenario to build off. Thanks!
N1 N2 N3 N4 N5 N6 N7
0 sample 1
0 sample 2
0 sample 3
0 sample 4
0 sample 5
0 sample 6
0 sample 7
ta merge 4 5 6 7
ts merge 1 2 3 4
ts varne 1 N8
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For those interested, I figured out the issue. You can only have two pops on each line so the code has to look like this:
N1 N2 N3 N4 N5 N6 N7
0 sample 1
0 sample 2
0 sample 3
0 sample 4
0 sample 5
0 sample 6
0 sample 7
t4 merge 6 7
t2 merge 6 5
t2 merge 6 4
t1 merge 6 3
ta merge 6 2
ts merge 6 1
ts varne 6 N8
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I have two data sets, one chloroplast SSR and one nuclear SSR, both with 20 populations and I would like to run a Barrier analysis (Barrier 2.2) separately once on nuclear and once on chloroplast. as I know I need a Fst or Gst matrix and a bootstrap matrix and GPS coordinates of the pops. Unfortunately, I don't know R software, so I need a something which can create these matrices from my data set. Any suggestions?
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Dear Alfredo López Caamal ,
I attache here the MICROSAT software version 1 and 2. But, I would recommend to generate the distance matrices with a different software called Microsatellite Analyzer (MSA). This software is simple and runs under newer operation systems such as Win 8.1 and 10. I used this to calculate DA the Nei's chord distance, which used in several studies for Barrier analysis. This also capable to generate over 100 bootstrap matrices. If you check my publication Tóth et al. 2017 in Tree Genetics and Genomes I used this software in it. It is available for several platforms, including Mac, Win...etc.:
I hope you find it useful!
Best wishes;
Endre
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Plant Morphology versus Molecular phylogeny
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This question certainly requires specification. The answers depend heavily on what you are actually want to now.
(1) Morphological evidence considered in total evidence studies; A wide range and the selection depends mostly on the morphological disparity of the lineage inferred. Thus, the answer depends on the lineage of taxa inferred.
(2) Morphological evidence considered in the discussion of studies employing molecular phylogenetic: Again, the answer depends on the organisms studied.
(3) Material used to extract DNA for molecular phylogenetic studies: Again, the answer depends on the organisms studied and the question asked.
The hypothetical answers has one thing in common: it depends very much on the organisms studied.
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Hello all.
Does anyone have any good reference/tutorial regarding selecting appropriate distribution models for priors used for dating calibration? I am interested in using three types of priors, a fossil one, a well known biogeographical (geological) and a very loose biogeographical (geological). I believe that each one of these would require a special kind of settings. I've been reading about the lognormal and normal, but I also do believe that for the loose one, having a "traditional" normal prior would not be the "best" setting. 
Kind of related, also any hints on how to make a thoughtful and perhaps more accurate choice of mean and stdev values for these priors?
Thank you very much,
JP
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Hey Curintia,
This article explains the types of callibrations in a way that is very easy to understand:
Ho and Phillips (2009) Accounting for Calibration Uncertainty in Phylogenetic Estimation of Evolutionary Divergence Times. Syt. Biol.
Cheers,
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Here is my question:
I have used several kinds of genetic markers to reconstruct the phylogeographic history of a kind of bats. A geographic relatively isolated population (call it YM population) contained three adjacent phylogeographic clades inference from mtDNA with lowest nucleotide diversity and haplotype diversity in both mtDNA and ncDNA, and firstly isolated from other populations in microsatellite analysis. ncDNA suggested weak geographical pattern of all clades inferred from mtDNA.
I want to figure out the potential origin of YM population, or the possible reasons for low nucleotide diversity and haplotype diversity in both mtDNA and ncDNA. By the way no bottleneck (based on microsatellite) or expansion (based on mtDNA) was detected.
Any suggestion will be appreciated including the question itself.
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Depends on the number of loci and quality of the data. IMA2 and DiYABC requires many loci and high quality of the data. Use different bottleneck tests. To check for contemporary bottlneck you can try Wang's Colony. 
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It is pretty difficult for me to install Lositan and other necessary items such as Fdist2, Batik 1.7. Any advice on using Lositan, or any other methods for finding out loci of outliers?
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Hello Everyone,
I just thought of adding my experience if anyone still can't figure out how to do it.
1. Download the software from this page - http://popgen.net/soft/lositan/3264.html
2. Add 'http://popgen.net' to the list of sites in Java Exception list, as described in this link - https://java.com/en/download/faq/exception_sitelist.xml
3. Double click on the 'selwb.jnlp' download
This should work for both Windows and Mac, the only difference being where to find Java control panel. Both ways are explained in the link at point 2.
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Attach some journal papers listing the phytogeographical elements leads to speciation and endemism in plants?
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Occurrence of a complex of plant species in any geographic location is an indication of the adaptability of the species in that complex to the prevalent conditions in the location being studied. Adaptability, in turn, is an expression of genes for survival and reproduction  in the location. Initial lot of plants in this population might have come from diverse locations by way of dispersal or migration through various means available. Together, migration and survival make-up the adaptive radiation. At this stage, the change in genes in any species in the complex may not be much different from those prevailing in the parent populations from which they are derived. With advancing time and concurrent changes in climate, the genes for adaptation may change by way of recombining with related species in the vicinity, by mutation or by both. This results in a significant change in the expression of these genes in the next generation that may lead one to consider that new species are emerging. Sometimes, related species may interbreed and the resulting hybrids may tetraploidize as happened in the evolution of several crop species like coffee, wheat etc. In these cases speciation events are more clearly discernible.
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I am working on microsatellite data of two endemic amphibians from western ghats
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@Shafagat Mahmudova: thanks a lot 
@ Rosalia Pineiro: I am not able to open the TESS page from github. Thanks anyways
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There are some examples given in PopArt manual. Below is the geotags table.
seq_2 54.756365N 20.559500E 10
seq_2 43.362073N 85.087465E 5
seq_2 27.743932N 134.852156E 6
seq_7 6.903639N 28.416291E 5
seq_5 53.102734N 14.241575E 4
seq_5 5.671317N 26.812297E 10
seq_4 26.568643N 137.810478E 4
seq_4 10.557455N 69.352084E 2
seq_3 26.903287N 136.965627E 3
seq_3 6.620725N 72.943461E 5
seq_1 26.216272N 135.401545E 3
seq_1 13.450485N 69.562928E 3
seq_6 22.851512N 134.605860E 7
seq_6 8.989081N 74.288348E 3
Then, the traits table.
,Europe,Asia,Africa,Australia,America
seq_2,10,5,0,6,0
seq_7,0,0,5,0,0
seq_5,4,0,10,0,0
seq_4,0,0,0,4,2
seq_3,0,0,0,3,5
seq_1,0,0,0,3,3
seq_6,0,0,0,7,3
However, I do not understand how to create my own one so that my haplotype network is coloured. 
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What exactly are you trying to do? How many traits are you using?
If you need help let me know I can send you one of my own files which works fine. 
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We have sequenced D Loop region of Mitochondrial DNA of three types of horses in Bhutan. However, we happened to have overlooked the Nuclear mitochondrial DNA segments (NUMTs) during the amplification. Is there a method to make the sequences generated usable for analyses? 
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I am also assuming you do not know whether you have NUMTs or not? If you know that you do, then as Artur has said, just dump them unfortunately.
Heterozygous sites do not necessarily indicate NUMTS since heteroplasmy is also a possibility.
briefly discussed in:
Galtier et al. 2009. Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Molecular Ecology 18(22):4541-4550.
The horse genome is full of NUMTs from every part of the mtDNA including CR. If your sequences match to any of these that could help indicate whether you have a true mtDNA copy or not.
Nergadze et al. 2010. Mitochondrial DNA insertions in the nuclear horse genome. Animal genetics 41(s2):176-185.
Alternatively, if there is still tissue for some of the specimens you used you could re-extract the mtDNA only (e.g. using ultracentrifugation), re PCR and compare the sequences to those you originally made:
Triant DA, deWoody JA. 2007. The occurrence, detection, and avoidance of mitochondrial DNA translocations in mammalian systematics and phylogeography. Journal of Mammalogy 88(4):908-920.
Or either follow the post extraction advice given in:
 Calvignac S, Konecny L, Malard F, Douady CJ. 2011. Preventing the pollution of mitochondrial datasets with nuclear mitochondrial paralogs (numts). Mitochondrion 11(2):246-254.
and compare the sequences with those you originally generated.
Good luck!
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Are above phylogenetic markers are Universal...............
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Molecular Markers in Phylogenetic Studies-A Review
Choosing and Using a Plant DNA Barcode
Use of DNA barcodes to identify flowering plants
DNA Barcoding: An Alternative for the Identification of the Medicinal Plants Employed in Mexico
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I wonder whether substitution saturation can influence only the results of phylogeny reconstruction, or whether it can also influence results of DNA-based species delimitation and/or DNA barcoding. Does anyone know? Or what is your opinion? I will appreciate any contribution on the topic.
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Biologically, the probability of mutation occurred at the same site more than once (multiple hits) is small (given the low mutation rate), the lower mutation rates the smaller the likelihood given a time interval. Given a certain mutation rate, the smaller the time interval the less likely of substitution saturation.
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Start Period of rattans evolution in Indian phytogeographical zones?
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A quick look through Saxena and Trivedi's Catalogue of Tertiary spores and pollen from India for Dicolpopollis spp (dispersed pollen of Rattans) reveals many records from the Middle Eocene onward, but virtually no records from the Paleocene or Early Eocene (a couple of records such as 'Dicolpopollis spp, Paleocene-Eocene' for instance need to be checked out for both the actual age of the sediments and the attribution to Calamoidae) .
Rattan pollen first appears in the Maastrictian of the Horn of Africa, and is also present in the Paleocene of Sarawak, so the possibility needs to be raided that Calamoids dispersed into India from Asia following collision, and might therefore be an 'into India' immigrant!!  
The oldest records of Calamoid pollen in India therefore needs to be carefully checked to comfirm or refute the possible Middle Eocene first appearance
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I am trying to run Beast 1.8.4 using two fossil calibrations of clouded leopard and Lion-Jaguar with tiger subspecies sequences. Under strict clock model with Yule process, my priors for clouded leopard and Lion-Jaguar are in normal distribution with specified divergence times and tmrca (tiger subspecies) under "Using tree prior". Kappa also in normal distribution. Unfortunately I am getting good convergence for all ESS but not for tmrca and branchRate. Can anyone suggest what is wrong with this? 
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Dear Abdul Aziz, this is a a very open question, I would try to relax the clock model (uncorrelated lognormal distribution), reduce the number of hypotheses (priors), revise the calibration, use a log normal distribution for tmrca priors if needed, and launch the analysis again... (assuming that chain size and sampling rate are ok given other ESS values) with the correct substitution model selected for your dataset. Hope you will solve your problem!
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I am trying to do some quick species tree generation from multi gene datasets using ASTRAL. I am aiming to use *beast but the 2 week computation time to find out that setting were wrong or that some genes need to be excluded is killing me.
Hence, screening with ASTRAL to get an idea of what the phylogeny will look like. The beast files are .xml, and I can convert these to .nexus using readseq. However ASTRAL needs the newick file format.
Does anyone know of a way to convert these files? Preferably R or python.
Thanks!
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If you need to convert just one tree from Nexus to Newick, the simplest thing might be just to open it up in a tree-viewing program like FigTree (http://tree.bio.ed.ac.uk/software/figtree/), and export as a Newick: File > Save Trees > Select 'Newick' from menu. 
Newick treefiles are of course not necessarily ultrametric, but you can convert your tree into a cladogram (a tree with all branch lengths = 1, which is what I think you might mean) using the options within FigTree: Trees > Transform Branches > Select 'Cladogram'.
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I used Micro-checker to assess my microsat amplification. I have 17 localities, of which 5 have null alleles. The null alleles are, however, not only in 1 or 2 microsats, but vary according to the locality. Hence, I do not want to discard the microsats in question, but am not sure how to proceed. Do I use the adjusted genotypes calculated by Microchecker to change these localities, and if so, how do I know which new genotype corresponds to which sample number, as Microchecker states "Genotypes are ordered by allele size. Note: Row numbers do not correspond to the original sample numbers". 
Any help will be appreciated.
Regards
Genevieve
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What do you want to do with the genotypes? For parentage analysis, we do calculations using both scenarios: either the observed homozygotes are truly homozygous, or observed homozygotes are heterozygous null, and then take the conservative result for parentage. E.g. If one of these methods gives a match, then consider the genotypes matching (or if using likelihood methods, take the one with the higher likelihood). This is done for each trio or pair being compared.
If you are doing some sort of population genetics, then you might just need allele frequencies (which micro-checker gives?).
I can't sign off without putting a plug in for genotyping by sequencing (GBS) methods which we think are a much better approach, at least for species without SNP chips available.
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I'm currently working on my first Population Genetic project and I'm studying the population structure of a species of Asian tropical snail. These snails are very small (2mm) and live exclusively on calcareous outcrops. Populations on each outcrop are therefore expected to be isolated from each other. One of the goal of my project is to calculate fixation index and genetic differentiation between outcrops. We sampled 25 plots, collected 15 individuals per plot and sequenced 550bp segment from COI (mithocondrial DNA). I analysed sequence data with DNAsp and Arlequin.
Results are more extreme than expected: outcrops seems isolated from each other but most of FST values are between 0.9 and 0.99! It seems like populations have been diverging for very long time and these outcrops are much older than expected. This is strange, especially considering that these outcrops, although isolated, are very close from each other (some only 500 meters). These results look unrealistic to me, I therefor ask your help in finding the flaw.
Am I calculating Fst in the wrong way? Both DNAsp and Arlequin give same results as Fst. DANsp also provide Gst, and these results are much lower. (0.4-0.7) but Gst is not as popular and reliable as Fst. And I didn't expect so much differences between these 2 values.
Shoul I use other estimators? Which softwares do you suggest to calculate ΦST and Dest? It is not possible to compute these metrics with DNAsp or Arlequin. 
Can you suggest any paper that address a similar problem?
I attach here a graph of Fst plotted on distance between plots.
Thank you very much, your help is much appreciated!
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Hi Giacomo:
First of all, Gst is so popular and reliable as Fst. Perhaps you have noted a lot more literature talking about Fst than Gst; but that is not a flaw of the statistic (in fact, for two populations of equal size (sampling size), Gst=Fst). One difference between the two statistics, that could help to explain why Fst is used often than Gst, is that you can easily get the error of Fst, whereas the uncertainty about the actual value of Gst is usually obtained by means of confidence intervals.
Now, regarding your question. As you have noted, COI is a mitochondrial gene. Do you know how is the inheritance of mitochondrias in your snails? Do you know anything regarding the mitochondrial mutation rate?
  In our work with European white oaks cpDNA (which is inherited exclusively through the mother and has a very low mutation rate), we also found very large values of population differentiation (although not as high as yours), which was explained by a very large degree of within populations homozigosity (i.e., all trees in the same pop had the same alleles, suggesting cpDNA undergoes lineage sorting). So, your results could be explained by mitochondrial lineage sorting (causing homozygosity within populations) and a relatively large mutation rate in the fragment you are studying (causing different populations to have different alleles).
You can find those examples regarding the white oaks in my RG profile (the papers were published in For Ecol Manage in 2002).
In order to obtain Phi and D you could use the mmod package for R.
Hope this helps.
Pablo
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In Ecological Niche Modelling, what are the best approaches to select climatic variables before modelling the species distribution for each climatic period?
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Hi Manolo,
Today we have many forms to select environmental varibles to use in ENMs and the difference between each approach depend of your aims and questions.
The correlation between variables are important to minimize the colinearity but, many times, the variable selected are not the best to use on ENMs.
The second form is use PCA. But, the PCA method have a problem. If you predict the build models to other climate (temporal scenarios) you will have a problem because the correlation between variables are different in each climate scenario and the comparation between models will not possible.
I suggest to use Jackniffe approach, based on species physiological tolerances, or PCiA or Factorial Analysis using the more explicative variable assocated to each axis and not the autovalues and/or autovectors values of axis.
If you use one specie in your study and past climate scenario I suggest the Jacknniffe approach or Factorial Analysis.
If you need of help, send me a email.
Bests,
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The posterior probability is mostly (>0.90), ESSs are great, MCMC samplings converge very well but I am getting overlapped HPD for divergence time estimate. I used relaxed molecular clock with log noarmal distribution. I am analyzing a mitochondrial gene with two calibration node, one mid-interior and the other is very recent. 
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Dear Binod Regmi,
I found this post from the BEAST team very helpful when setting the parameters for divergence time estimates: http://beast2.org/2015/06/23/help-beast-acts-weird-or-how-to-set-up-rates/
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Please contact to Girardo Alayon in Natural History Museum in Habana. I remember a case of spider invasion many year ago by winds but I dont know if it was documented. Alayon studied the case
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I´m searching an R package or command line program to calculate heterozygosity-excess (similar to BOTTLENECK, Piry et al. 1999) and m-ratio (Garza and Williamson, 2001) tests for population bottlenecks based on microsatellites. 
Would be great if anybody has an idea about that as I have lots of datasets to work on.
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Hi Martin,
The R package diveRsity may be useful for this, specifically the basicStat function which will do Hardy-Weinberg Equilibrium tests to look at homozygosity/heterozygosity excesses. Alternatively, the Hwxtest R package might also do the job. More information on these is available in the links below.
Cheers,
ML
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Could anyone advise me on creating geographic tree model (.gtm) in GenGIS? I've a phylogenetic tree and I wanted to create GTM from my tree. Could anyone suggest me how to performed it?
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I don't think GenGIS generates this kind of files. What is exactly what you need?
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The enormous geographic extent and the scarcity of mycologists in the region presently limit the comprehension of the Amazonian mycota, but are there solutions we can figure out collectively to increase our knowledge?
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Dear Ricardo most hot spots in poor countries without taxonomist face sort of same issues. Of course the Amazon is bigger, so the challenge is bigger. In my experience in Mexico where most mushrooms are unknown and few taxonomist available (particularly in tropical regions) a good approach is to do good voucher specimens and sequence (ITS region) all your collections. Even in the species is not in public databases, the sequences most of the time give you the genus an closer species.  They also give good clues on new potential taxa and help in focusing on groups of interesting taxa. They also allow you to know many things you have even if you dont have names for them.
sequencing is not very expensive, we expend doing every thing in mexico like 12 american dollars per sample, from DNA extraction to double strand sequencing.
Given, the hot, moisture, insects and molds, I would say your biggest challenge is not collecting or identifying but keeping the materials in good shape por the near future. You may think having DNA collections also.
my best
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In the attached file, I have demographic statistics (Fu's Fs; Tajimas D; and Ramos-Onsins and Rozas's R2 neutrality tests), mismatch distributions (MMD) and related goodness-of-fit statistics for Pooled samples, Clad 1, and Clade 2. For Pooled samples as well as Clade 1, I have significant negative Tajimas D, negative but non-significant Fu's Fs, and positive but non-significant R2 values. For Clade 2, I have non-significant negative Tajimas D, negative and significant Fu's Fs, and negative and significant R2 values. On the other hand, MMD only for Clade 2 is uni-modal. The interpretation of data in each case seems to be complicated. What is the most reasonable demographic explanations for Pooled samples, Clade 1, and Clade 2 based on these evidence?
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Hi Fatah,
  Tajimas D, Fu's Fs and Ramos-Onsins and Rozas's R2 use different information contents of DNA sequences. Tajima's D test is based on the number of segregating sites; Fu’s Fs test use information from the haplotype distribution, while Ramos-Onsins and Rozas’s R2 test use information of the mutation frequency. The sample sizes and number of segregating sites affect these statistics in different ways. You should give report these two parameters in the table. Read Ramos-Onsins & Rozas, 2002 and a later following up paper by the same author (in MBE 2005?) will help you make a judgement. 
BTY. Maybe you can try Bayesian skyline plot to see changes of population size through time.
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Primarily I have attempted to install the MrBayes program. I have entered the MrBayes web site (http://mrbayes.sourceforge.net/download.php) and then I have downloaded it with the link for Macintosh(64bit), but I didn't succeed in running it. I installed the file but it doesn't include any running file (it only includes documentation and examples).
I haven't succeeded in installing BioEdit in the same way either.
Is there anyone who can help me? (I use an iMac- OS X 10.8.2)
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Dear Omer, how were you able to run MrBayes through terminal?
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Please help by clear explanation. Is it fact (proved as Mendel's law) or faith (not proven yet)?
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yes evolution has been proven, your analogy to chopping off hands is more acclimation that adaptation. adaptation means the progeny inherit that trait but of course that doesn't happen if you lose a hand. its nothing to do with faith.
also Darwin did not say an ape became a human and this is not what the scientific community have shown. apes (eg chimps) and humans diverged about 5 million years ago.  this is not the same as one species 'becoming' another. 
for evidence, which you say isn't present, please see 15 examples here:
Not faith. Facts.
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DearScientists,
I am doing estimation of divergence time using BEAST.
However, when running BEST there is error.
The error is "XML document stuctures must start and end within the same entity". the information can be seen in the attached figure.
Could you please give me your advise of the reason of this error and the solution for correcting this error?
Thank you very much for your answers.
Best regards,
Do Hoang Dang Khoa
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Didn't you prepare your xml file into the BEAUTi? I recommend you to use this. In addition, I also recommend you download the last version of BEAUTi/BEAST (2.3.2) that give you more options for priors settings.
Please check the link.
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This is what I have done,
Neighbor-joining (NJ) trees were constructed using MEGA 6.0 and K2P genetic distance model was chosen, and node support was assessed based on 1000 bootstrap replicates. Species with multiple individuals forming a monophyletic clade in phylogenetic trees with a bootstrap value above 60% were considered as successful identifications (Kress et al. 2010).
In accordance to the method discussed above.... I am having number of phylogenetic trees constructed from huge number of sequences belonging to multiple markers assessed through different nucleotide models...
This is what I need...
A software/ online web server/ R code that could cluster these monophyletic successful taxas. For example like Taxon DNA (Standalone Java based Software) or BlastClust (Web sever) does cluster analysis from genetic distances and similarity scores respectively. So, by using phylogeneitc newick trees as input, I need to seggregate these successful ids into clusters having bootstrap value above 60%
Help appericiated...
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paper kasa upload karaycha plz tell me
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I have built the trees in starbeast with the same dataset (three nuclear markers). In one tree it is classified as 14 species and in another 16 species. I would like to know which one of these classification is better. One tree is classified based on GMYC and another based bPTP species delimitation method.
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I would do a bayes factor test to test it. You run the analyses with the only differences are the number of species and d stepping stone sampling to estimate the marginal likelihood used for the Bayes factor test
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Why does few reptiles follow inverse Bergmann's Rule?
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Debaprasad
See Kyle Ashton's research papers
This paper has been cited 262 times, so go to Google Scholar and Web of Science to retrieve those papers.
Best regards,
Gordon
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hello everyone ! I got a question about trait evolution analysis.
I have estimated a continuous trait of several species, which is I interested in. I know those species could be separated into several groups, but I only know the topological relationship of groups, not precisely of every species.
Now, I want to analysis this trait evolution history(eg. the state of most recently common ancestor, tracing this character at every lineage split node et.al.).
So, how could I achieve that goal ? even when I have no branch length to every tips of this phylogeny?
thank you all ! Any comments would be appreciated !
Actually, the main problems in this project is the molecular phylogeny relationship  :
1, the sequence data is not available for most of the species I studied;
2, BUT, my species could clustered into several groups, and I have  sequence data of other species in those groups; 
so, available sequence doesn't match at species level, but group level.
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This is very straightforward to do using parsimony.  You use a downpass/uppass approach and do it by hand (if two terminals have the same state, then their most recent common ancestral node probably had that state, too), or you can probably also use the MacClade package of Mesquite to accomplish the same end.
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The methods for identification(molecular ) of chlorella species and their phylogenetic analysis to confirm the identity, could be suggested by anyone?
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See the most recent paper dealing with new description of a Chlorella species and references therein: Ma et al. (2015) Hydrobiologia 760:81-89
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Hi everybody! I've been tying to construct an haplotype network using TCS software (Molecular Ecology (2000) 9, 1657–1659) but I'm having problems with the input matrix. I realize that TCS was developed to infer haplotype networks from DNA sequences. However a large quantity of studies infer haplotype networks from cpSSR. How can I construct a distance matrix from cpSSR when I only have the allele sizes? or can I use a matrix with the "raw" data from the cpSSR?
Thank you in advance!
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Dear Alfredo:
If you know the mutations involved in your cpSSR allele size variations (and that they are restricted to the microsatellite motif) you can transform then into a virtual sequence and then construct a fasta file to analyse it with TCSconsidering gaps as a fith state.
Example you have three samples with sizes 101, 102, 103 corresponding to a (A)n microsatellite. then you can build a sequence matrix that would be:
Sample 1: A--
Sample 2: AA-
Sample 3: AAA
Hope this is of help.
Best regards
José Gabriel Segarra
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I work on an ancient protein family that exists in both eukaryotes and prokaryotes and my interest is in elucidating its deeper branches. When I pull down the data from NCBI I get about 6000 sequences. If I use an algorithm to help me cluster similar proteins (e.g. 80% similar) it gets reduced, and if I do my clustering with 50% similarity I get about 550.
The question is, in your experience, which one would you choose:
- use the smaller dataset (or even smaller) and do the most sophisticated, yet computationally intensive, analyses you can hoping that if there is a signal, these analyses would pick it up and thus the deeper branches get better support.
- use a lot of data and hope that then the intermediate evolutionary steps could be inferred more easily and the deeper branches get more support?
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The 2nd option is probably more likely to give a quality answer. If you have the time/computing power I think you should look at the larger dataset, too. If your clustering combines particularly disparate sequences you may be thinning out your taxa distribution and inviting more opportunity for long branch attraction. I would consider comparing the topologies and look for places where lots of relative diversity was collapsed in one area but not in other areas of the tree. If it is even throughout, the smaller dataset is probably much more likely to give a fair answer.
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What kind of treatment can be done and what kind of data is needed? And what meaningful implication can we observe? (to plant conservation, etc)
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Climatic change is closely related to the development of new species.  During the last glaciation, 12 new species of alpine plants were evolved on the nunataks in southwest Alberta, only four on Haida Gwai which had a more maritime climate, and over 40 in the North American areas of Beringia.  The latter presumably had the most extreme climate change of the three areas.
On deglaciation, these new species spread rapidly along the available mountain ranges with a suitable climate and substrate until the warming trend confined alpine conditions to the tops of the mountains.  The species from Haida Gwai could not leave the island, while those from southwest Alberta were calciphiles that could only colonize the adjacemt limestone mountain ranges such as the Selkirks.  However, the new Beringian specis spread south in a remarkably short time and are now isolated on the mountaintops along much of the formerly glaciated Canadian Cordillera.
In the case of the Qinghai-Tibet Plateau, it has a distinct endemic flora characterized by the evolution of large numbers of families including Oxytropis of the Leguminosae, the Gentianacae (over 100 species),  Papaveracae (particularly Mecanopsis), Saussurea (Compostae), etc..  The flora that is found across the boreal forest realm of the Northern Hemisphere disappears almost completely south of the Hexi Corridor between Inner Mongolia and the Plateau.  However, on the Plateau, tectonic history appears to be important in the species distribution in many cases, since the individual species are often limited in elevational range to 200 m out of the 6000+ m possible. 
In your case, the development and demise of Sundaland is involved, together with the tectonics resulting from the northward movement of the Indian Plate though it would appear that the area was a refugium for many tropical ans subtropical species during the last glaciation.  Sea level had dropped over 120m, so that the China Sea was largely dry land with lakes until about 18 ka when the climate started to change and sea level started to rise, inundating the China Sea, and causing the East China monsoon to begin again.  Before that, bitterly cold and dry conditions affected the northeastern part of the Plateau and it is thought that permafrost conditions evtended south almost to the Himalayas.  The Indian monsoon would have affected the south coast of Sundaland.
I can give you references if you need them, but more work on the development and demise of Sundaland will be necessary to answer your questions.  Good Luck!
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I was trying to do Bayesian analysis on some of my sequence data using BEAST 1.7.5 to see how closely related they are and their migration patterns. 
The substitution model used was GTR+I+G (strict molecular clock). I did 10 million iterations primarily to have a better ESS thus a rich posterior probability. Well it worked fine and for each run, I had ESS <700.
But once their locations (discrete trait) are added to the analysis, ESS dropped down to <10. Even after combining 4 independent runs, ESS remained low (<75). Trees each run generated were significantly different and location patterns doesn't seem to right. The branch colours were really confusing.
Can anyone help me to get this analysis right with the discrete trait (location)?
I guess if everything goes right, the posterior probability values I got w/o locations should be similar to with locations, right?
My expertise with Bayesian algorithms and BEAST/ beauti is extremely low.
Thanks
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Hi Harindra,
I meant the number states in your trait (e.g. Areas). Also, what trait reconstruction model are you using (symetrical or asymetrical?). Try with longer generations, I think its just a convergence issue. If the model gets more complex, it needs more time to finish. If you are increasing to say 50 M generations, remember also to increase the sampling frequency to 5,000 so you end up with 10,000 states.
cheers,
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Looking to make Haplotype networks in the above mentioned software or any other such freeware. 
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Dear Asif,
In DnaSP (after formatting each of the taxa to their responding localities/population), go to Generate> Haplotype Data File> Roehl Data File (Network Software). After generating the *.rdf file, open in NETWORK, calculate network and lastly, Draw Network by opening the *.out file.
Best of luck,
HongChang
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I am working with closely related species in family Caprifoliaceae. Full ITS amplification is a major problem in this group. I have used several chloroplast regions but not much variation detected.
Could you suggest some potential mitochondrial region for plant or specifically for caprifoliaceae.
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Dear Biswajit,
The amount of genetic variation among plant mitochondrial (mt) markers vary by plant group. Some regions are found to be highly variable in some species, such as Silene and Lobelia but this may not hold true for other species. Note that the aforementioned genera have some gynodioecious species; so it is still unclear whether the mt variation is associated with a particular sexual system or a general nature of mt genome. So, you cannot just rely on other examples. You could try a few universal mt markers listed in the attached paper to see if you find any intraspecific variation.
If you have a luck, you should be aware that mitochondrial genome is highly fluid and there are reports of heteroplasmy and recombination. You need to take into account these things before you use your data for phylogenetic analyses.
Good luck! 
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The software merges geographic, ecological and phylogenetic biodiversity data in a single interactive visualization and analysis environment.
Does anybody know to how build the various layers such as maps, genetic information as well as geographic location?
Any help would be much appreciated. 
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Hello Asif. 
I have been working a little bit with Gengis.  
The files are quite simple to create:
MAPS: download http://kiwi.cs.dal.ca/GenGIS/MapMaker Here you can edit the maps, selecting the specific region you want to plot your samples. Save it and load it as a raster map. 
LOCATION FILE EXAMPLE: 
Site ID, Latitude, Longitude
G10011, 18.8833, -99.1500
.....
....
G10011 is the ID of the sample followed by the coordinates
SEQUENCE ID FILE: 
Site ID, Sequence ID
G10011, G10011
(Normally I put the same name of the ID for both site and sequence
LOAD TREE: 
You have to have a tree with the ID you put for each sequence. Save in the geographic tree format (use MEGA or some other software) and load it on Gengys
Goog luck!
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Difference
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Dear Atena,
Your question is somewhat broad but I believe it can be taclked by a peculiar approach. Diversity is variability in life forms. Phylogeography is evolutionary history of an organism based on geographic colonization. 
For example; Marsupial mammals are endemic to Oceania Islands (Australia and adjacent lands) . It means they originated, colonised and localised there itself. From the present pattern of distribution it can be concluded that phylogeographic affinities for diversity of Marsupial mammals correspond to Oceania Islands.
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I am looking for information about plant DNA barcode as tool for identification of illegal traffic of endangered species. Thank you very much in advance.
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when I try to compare difference of trait among tree\shrub\liana in a big database of China woody species, I konw ANOVA or Kruskal-Wallis rank sum test and Wilcoxon rank sum test could be used in traditional statistics. however, in phylogenetic context, I don't quite clear how or when to use the right technique.
phylogenetic analysis is not easy for me, it seems like a brandnew field to an ordinary ecological researcher. Thus, I am looking for help or potential collaborator. 
thanks for any suggestion.
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Use the recommendation of Vladislav Susoy... also try to introduce λ in the regression analysis (PGLSλ, sensu Revell 2010), where phylogeny is incorporated as a variance- covariance matrix with λ in the error term of the regression equation. The error term is then decomposed into a component that represents the phylogeny and the remaining error term. In this approach, when λ is forced to be equal to 0, it is equivalent to OLS regression (i.e. a species level analysis in which the phylogeny is not considered). On the other hand, when λ is forced to be equal to 1, the results are similar to those obtained with phylogenetically independent contrasts (PIC). However, when the estimation of λ is between 0 and 1, neither OLS nor PIC methods are suitable given that they underestimate or overestimate the influence of phylogeny (see Revell 2010; Hernandez et al., 2013). 
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Dear all,
Maybe it is widely known that there is a rank in informative characters contains in chloroplast regions (Fig.4  in American Journal of Botany 94(3): 275–288. 2007).
Recently, I plan to find genetic variation between two subspecies.  I sequenced some chloroplast regions with  relative more informative characters, such as rpl32-trnL, trnQ-5'rps16, psbJ-petA, 3'rps16-5'trnK and atpI-atpH and other high rank regions.
But, only one indel and one substitution are detected in rpl32-trnL and trnQ-5'rps16,respectively. I feel maybe it is not easy to detect enough variation even if more regions are sequenced.  So, I plan to use others species. 
But I heard that, in some plant species, rank of protential informative characters is not always similar to Fig.4 in the formerly mentioned paper. Some low rank regions may show far more informative sites than high rank regions. If so, I will sequence more regions to find informative sites.
So, is there anyone would like to share you experience in primer screening? Is it possible that a low rank region show far more informative site than high rank regions?
Thanks in advance.
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Hi
The short answer is YES, regions can show huge differences among species.
We have observed such differences in different species of the European oaks (Petit et al., 2002 For Ecol Manage). The polymorphism differences are even greater when you compare to the evergreen European oaks. (http://www.nature.com/hdy/journal/v93/n5/full/6800551a.html). In this latter study there is a region which is highly informative within Q. ilex but presents no variation within Q. suber.
On the other hand, working with sub-species might be tricky. If they still breed and have fertile progeny, then you may need to look at the very genes causing the taxonomic differences in order to find genetic variation among them.
Hope this helps
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Hi,
I am barcoding plant populations of species that are geographically separated. I am getting some genetic divergence between the same species (intraspecific) but, I am not sure if it is significant enough to propose the two varieties. I would appreciate if some one can help me on deciding to criterion to infer the genetic distances.
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Hello Jaspreet, this is just for fun, but ...
My dear Hooker
I return with many thanks Watson's letter which I have had copied: it is a capital one & I am extremely obliged to you for obtaining me such valuable information: Surely he is rather in a hurry when he says intermediate varieties must almost be necessarily rare, otherwise they would be taken as the types of the species; for he overlooks numerical frequency as an element. Surely if A. B C were three varieties & if A were a good deal the commonest (therefore, also, first known) it would be taken as the type, without regarding whether B was quite intermediate or not, or whether it was rare or not.
Charles R Darwin, 18 april 1847
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Kindly could anybody suggest any highly variable markers for discrimination for closely related plant species/lower taxonomic level identification. If DNA barcoding could able to go at that level? I have already used rbcla, matK, ITS2 , trnL-F and psbA-trnH but it didn't able to discriminate. Now, what should I use? any other highly variable intergenic spacers/NGS/RAD seq?
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Hello Biswajit:
interesting question, partly because none of the answers posted thus far have addressed the possibility that you are dealing with a single lineage. Maybe the fact that you have used 5 loci and none of these markers have shown any differentiation, is an important fact that is apparently not being acknowledged!  In other words, perhaps rather than simply continue your quest for faster evolving markers, which suggests that you are coming into this study with a strong, unshakable preconception that there ARE two species, and you are simply trying to confirm with molecular markers a conclusion that you have already made based on other data (morphology, ecology, etc.).  You should consider that it only take a few loci to control certain morphological features.  Genomic approach will be the best way to find these "needles in the haystack", but in the meantime, it sounds to me like you have made an important discovery, i.e. that these two lineages are very closely related indeed and by some species definitions (i.e. "phylogenetic species concept") are as single species!
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I have demonstrated a project, assessing the genetic diversity of breeds of an animal using SSR markers. I've got the allelic data from GeneMapperv.3.7 but i have no idea how to plot the dendrogram to show the genetic distance and what is the software through which I can calculate the Cophenetic Coefficient for my data? Can anyone guide me through this? 
I have also attached an excel file containing the allelic data.
Thank you
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  • If you have the SSR data in binary matrix (so your object is not diploid), then I supose you to use the Treecon software (as I wrote in my last comment).
  • If you have the SSR data for diploids, then you don't need to convert it to binary matrix, just normalize the geneMapper data and use the Powermarker software
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I'll probably use Rag-1 or C-mos, but I don't know which one of these two is the best.
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I think it depends how divergent your species are (seems like not very divergent if there is hybridization). I would switch to more variable ones, not genes, but microsatellites or even RADseq. Nuclear genes can have some mutation steps between closely related taxa, but then you can not exclude incomplete lineage sorting if you find two "parental" alleles in you presumable hybrid population.
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I calculated PSE and PSC in bacterial freshwater communities and get very low values for PSE (< 0.2) but constantly high values for PSC (>0.8). How does that go together? Shouldn't low tip clustering correlate with higher phylogenetic evenness? Sure the species abundance if often quite uneven which drags down PSE, but PSV is also low (<0.5 ). 
Also: does anyone know of some studies that used these metrics in bacterial communities in order to get an idea of the range of values that are normally found?
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I apologize.  In my prior post, rho should equal -0.65 and not positive 0.65.  Thanks again for any thoughts.
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The quartet method is one of the methods for a molecular clock. The method has some advantages over the other methods and some weaknesses against other methods. So, I search about strengths and weakness of the quartet method, if you have any experience or paper, explain about it, please.
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I would like to calculate and compare allelic richness (or similar parameters) of 10 populations with different sample sizes. I thought to use a rarefaction method, in order to adjust the estmates for the different sample sizes. My dataset deals with codominant markers (6 microsat loci with more than 20 alleles/locus). Can anyone indicate a program for this purpose? Thanks.
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One very effective package that has not been mentioned is 'Heirfstat' in R.
The function allelic.richness calculates the rarefied allelic count. In my view, it is easier to use that FSTAT and is faster than GENALEX.
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I generated a phylogenetic reconstruction of protein homologous belongs to different taxa using NJ method with 5000 bootstrap support . However, two sequences which showed relatively high sequence similarity (Obtained by MatGat software) clustered separately in the tree diagram, while there are closely clustering with relatively less similar sequences, respectively.  Is there any reasonable explanation behind this type of observations?
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You might want to use the nucleotide sequences. Nucleotide sequences are less homoplasious and more informative than amino acid ones. If the sequences are much divergente, you may codon-align them (e.g. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896173/). Good luck!