Science topic

Phylogeography - Science topic

A field of study concerned with the principles and processes governing the geographic distributions of genealogical lineages, especially those within and among closely related species. (Avise, J.C., Phylogeography: The History and Formation of Species. Harvard University Press, 2000)
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I am researching the geographical distribution of the most common cosmopolitan springtail species. Can you recommend sources related to collembola phylogeography?. It would be nice if we collected a significant number of references on this issue.
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Dear Nikola Z. Grujic . See the following useful link:
Sun, X., Zhang, F., Ding, Y. et al. Delimiting species of Protaphorura (Collembola: Onychiuridae): integrative evidence based on morphology, DNA sequences and geography. Sci Rep 7, 8261 (2017). https://doi.org/10.1038/s41598-017-08381-4
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I am developing a phylogeography workflow and I need PERMUT cpSSR to compute NST and GST for mitochondrial Cytochrome c Oxidase (COI) haplotypes so that I can test for genetic differentiation between populations. Unfortunately, I haven't found any functional Linux OS link to this tool. The links i have tried include those found here:
Even a GAP package - https://www.gap-system.org/Manuals/pkg/permut-2.0.3/doc/chap0.html - which is not a good option.
(III) https://prodinra.inra.fr/?locale=en#!ConsultNotice:255707 - works but only a .exe file is available.
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Hi Hernan,
How you prepared the input file for PERMUT? do you have any example file?
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Aloha,
My research group is embarking on a DNA barcoding effort focusing primarily on COI based phylogeography of Pacific basin species of the genus Apristurus, of which there are around 40 congeneric species! We have downloaded all of relevant COI sequence fragments from Genbank, and are interested in accurate placement of a species collected here in the Hawaiian islands. Happy to chat about potential co-authorship and collaboration with interested researchers.
Cheers,
Brenden
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Dear Brenden S Holland thnak you so much for this collaboration proposition.
Good luck ... I am sorry to tell you that this species is not present in Med sea and this region the shark is very accesory Sp.
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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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Hello all,
I am trying to do phylogeography with discrete traits (likely countries of origin). I am following the protocol "Ancestral Reconstruction/Discrete Phylogeography with BEAST 2.3.x" by Bouckaert & Xie, but with my own data set. 
There are no errors while BEAST is running. Even when I load the location_trait_with_trees file into TreeAnnotator, there are no errors. Regardless, I lose at least 2-5 locations each time, and have not seen anyone ask about this yet. 
Also, some of the locations lost only represent one sequence, while others represent up to 20 sequences. How can I keep all of my locations? Is there a "cut off" value that is imposed somewhere that I can lower? 
Thank you! 
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Good quastion Tessa .
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Need a detailed tutorial on XLSTAT ( Input type format, output file, drawing conclusions etc) Unable to find online.
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As Csaba mentions, I believe there isn't any tool for this. However, if you want to analyze genetic data via Excel, I recommend you GenAlEx: https://biology-assets.anu.edu.au/GenAlEx/Welcome.html Another user-friendly tool that can parse with Excel look-alike genetic data is Genodive: http://www.patrickmeirmans.com/software/GenoDive.html And here are some online tools specifically for phylogenetics: https://ngphylogeny.fr/tools/ www.atgc-montpellier.fr/phyml/
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I recovered an N amount of putative chloroplast contigs (fasta format). I need to align them to a reference genome and there is no reference genome of my species. Fortunately, there is a reference genome of the same genera. So, I think I should align the contigs as if they where homologous sequences of the reference. I have tried using bowtie2 (--very-fast-local option) but I end up loosing a lot of information, and when I use Blastn, almost every contig hits with my reference.
The final goal is to obtain an inferred chloroplast for my species.
May someone suggest me a workflow/article to read in order to achieve this objective?
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Thank you very much, Mark Farman!
I solved this issue using RagTag:
As the description in the git says, "is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome". And it worked fantastically. At this moment I am only trying to recover some contigs to obtain the best from the scaffolding process.
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I am doing a study on phylogeography. An average pairwise difference and Nei's distance matrix was generated in Arlequin. I am having a hard time interpreting the results. As far as I know, higher values would mean higher pairwise difference.
(1) What if I get a negative Nei's distance? (2) What do they mean by "corrected" average pairwise difference? 
Hope someone could help. Thank you.
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Nei's DA is only applicable to frequencies, which inherently have values 0 < p <= 1.
For example, if the value x=12 occurs 30 times among all the loci values in a dataset of 120 profiles, then x has a Loci frequency of 30/120.
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I want to learn R Programming for phylogenetics, phylogeography, and biodiversity studies. I hope it will be free and online! Or Introduce the best books for self studying
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I may suggest you to look for "Cookbooks" for the specific objectives you are trying to solve. As the name says, it's more like a recipe, where you can read workflows that will guide you through every step. (Other tip may be googling for "subject/software tutorial".
Here are some pages that I believe can help you:
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Hi all,
I am currently studying the phylogeography of some Chaetodontid species using Bayesian inference, and I am attempting to reconstruct their ancestral states.
Does anyone here know any reference materials? It would be very helpful if I could get additional information about historical ocean circulation, and paleo climatic data. Thank you!
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80 million years ago the ocean covered more of the earth today and the Atlantic was not as wide as today. North America was bisected by the Western Interior Seaway north to south and Europe and Asia were also separated but a sea. In addition to a circumpolar southern Ocean, the Northern Continents(Laurasia) were separated by ocean from the Southern continents (Gondwana).
60 mya the sea level was lower but still pretty much separated by oceans. All of this had a tremendous impact on the currents at the time.
References: Atlas of Mesozoic and Cenozoic Coastlines. 1995. Smith, Smith and Funnell. Cambridge University Press.
Ancient Landscapes. 2008. Grand Canyon Association. Blakey and Ranney.
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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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I have a dataset of 6 microsats loci from 11 populations of a barnacle. I’d need 100-1000 matrices of pairwise Fst-values, in order to carry out a spatial analysis. Is there any software producing these bootstrapped matrices?
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I had some troubles running diveRsity::writeBoot.
diveRsity::basicStats and diveRsity::diffCalc ran my data. However, I had to make minor adjustments to my data to writeBoot (remove some empty spaces).
Also, parallel argument must be set to TRUE. parallel = FALSE causes problems.
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I'm slightly confused as to what inferences can be drawn from these two data of invasive species.
Another set I have is 70% variation is between populations however there is very very high gene flow between the populations.
My initial thought with the data set is that the results are incoherent?
Would appreciate some advice.
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You need to understand the basics of what Fst actually means. Fst is the average inbreeding of the subpopulations relative tot the total population, and it is calculated on the basis of the (average) expected heterozygosity of the subpopulations (Hs) and the expected heterozygosity of the total population (Ht), like Fst=(Ht-Hs)/Ht=1-Hs/Ht. In other words, it is the standardized variance in heterozygosity among subpopulations. Nothing more, nothing less. Under very specific circumstances, it can tell us something about gene flow. Don't make the mistake of interpreting every Fst as a measure of gene flow: only when you a wide range of strict assumptions are met (which they never are), can you tell anything about gene flow from FST. See Whitlock & McCauley 1999 (Heredity). When your studied populations are not in migration-drift equilibrium, FST will strongly mislead you in terms of gene flow.
If 98% of the variation is found within subpopulations, this means there is extreme genetic differentiation, nearly complete fixation among subpopulations.
It would be better to show us the actual data, not your intepretation of gene flow. A good start would be hierarchical F-statistics and basic summary statistics (Fis, Fit, Fst, He per population, Ho per population, He of the total population).
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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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Hi everyone I am new user of BEAST2 software. I worked on population genetics, phylogeny and phylogeography of a complex species in Caryophyllaceae family. I identified haplotypes in cpDNA and nDNA and have phylogeny trees based on haplotypes. Now I want estimate historical demography and divergence time but I cant calibrate it. There isn’t a fossil data or substitution rate. Only before the age of main clade of genus estimated 11 million years. In a similar papers I read “Posterior estimates of the mutation Rate and time of divergence were obtained by Markov Chain Monte Carlo(MCMC)analysis.” Any guidance appreciated Masi
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Following
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Does anyone knows where I can find paleo-oceanographic world maps (sea level, coast lines, shape of continents, ground bridges, seaways, ecc.) dating back to middle miocene through Pliocene and, finally, present day?
Cheers,
Agostino
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Hi Agostina,
You could also check out the free software GPlates: http://portal.gplates.org/
Best, Thomas
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It is common knowledge that internal nodes are ancestral and external nodes are of recent origin in a haplotype network. But most of the median joining haplotype networks published in articles are found to be unrooted. In such cases, how can we infer the internal most frequent haplotypes as ancestral ones?. A similar issue happened in my analysis too.
When I performed dating of those haplotypes and constructed a phylogenetic tree, the internal node which was considered to be ancestral in haplotype network found much recently diverged than the external ones.
May I know your opinions..............
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Hi Rahul,
An unrooted phylogenetic network is a kind of splits graphs, produced by any of several algorithms, including character-based methods: Median Networks and Parsimony Splits, distance-based methods: NeighborNet and Split Decomposition, and tree-based methods: SuperNetworks and Consensus Networks.
Median-Joining Networks and Reduced Median Networks, are two methods popular in population genetics, especially used as haplotype networks.
Haplotype networks represent the relationships among the different haploid genotypes observed in the dataset, usually drawn unrooted, where the root location is often unknown. However, sometimes a root is provided, and the splits graph is interpreted as a directed network, like starting with an unrooted phylogenetic tree and adding a root (via an outgroup), so the rooted tree can be interpreted as a genealogical history. From an unrooted to a rooted tree, each branch acquires a direction (from the root), and the internal nodes become hypothetical ancestors.
This is problematic as each edge acquires an unambiguous direction, as for a tree, but not every internal node can necessarily be interpreted as a hypothetical ancestor.
To avoid misinterpretation (bad usage) you have to recognize that a rooted splits graph does not explicitly represent a phylogeny, because reticulations in the graph represent uncertainty not genealogy.
Please read this article:
Computer programs for population genetics data analysis: a survival guide. Excoffier, Laurent, and Gerald Heckel. Nature Reviews Genetics 7.10 (2006): 745-758 (a good review).
and also these books :
ReCombinatorics: The Algorithmics of Ancestral Recombination Graphs and Explicit Phylogenetic Networks by Dan Gusfield
Phylogenetic Networks: Concepts, Algorithms and Applications by Daniel H. Huson,Regula Rupp,Celine Scornavacca
Hope this could help you.
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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,
I would like to know possible explanations on why DArTseq (http://www.diversityarrays.com/dart-application-dartseq) have been used so little in Population genetics/Phylogeography studies. The technology was proposed in 2001, but since then we have a very reduced number of studies using it (except for studies with crop plants).
I appreciate any thoughts on this issue.
Thanks,
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Hello Manolo,
Basic answer is that DArTseq is mainly used to enrich for active regions of the genome. This is great for doing breeding designs and genome wide selection studies common in agriculture and thus why it is most often used there.
For population genetics and phylogeography you ideally want to use neutral (or nearly-neutral) regions of the genome, so enriching for active regions is not wanted. More often methods for more random reduced representation of the genome are thus used in these fields (i.e RADseq, GBS, ddRADseq, UCE, AHE, etc.).
There is some application for examining functional genes in these fields (although not as common), but these studies are on wild populations that do not have a set breeding design, so application of RNAseq is often preferred over a method like DArTseq.
Best Regards,
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I.e., how many samples per collection location will be needed?
We are transitioning to next-generation techniques from microsatellites.  Microsatellite studies with 8-20 loci typically need 25-30 samples per collection location for population-level studies.  My understanding is that RAD-seq requires fewer because of the number of loci and the amount of data captured using this NG method.
We are somewhat constrained for sample size because we work on rare/endangered species.
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Hi David,
It's really a balance of sample size to sequence coverage. High-throughput sequencers have a fixed number of reads they generate. So you can do some math to estimate how many reads you expect per individual depending on genome size and sequencing instrument (see Puckett 2016 Cons Gen and Andrews et al. 2016 Nature Reviews for some examples). You don't want a large sample size but then poor coverage that limits your ability to call SNPs.
It also depends on if you have an available reference genome or plan on de novo assembly of contigs. If going de novo, you need higher coverage. We will often select a few individuals, sequence them at a high depth using RADseq, assemble reference contigs, and then go back and re-sequence more individuals at a lower depth.
I am use to population genetics: you can get away with larger sample sizes and lower coverage using probabilistic genotyping approaches like ANGSD. For phylogenetics you may want higher coverage to have confidence in your haplotypes (but see Saglam et al. 2016 Molecular Ecology).
Personally, I have a hard time with studies using RADseq that have 1-2 individuals per population. I think you can get away with a minimum of ten. There are a few papers out there that talk about appropriate sample size to generate pop gen stats.
Hope this helps.
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If one has to work in future with phylogeny what are the supposed prospects where we can use Phylogeny ?
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If you are asking about the broader applicability of the methodology and modes of thinking that go into phylogenetics, the answer is very much yes. My favourite example is phylolinguistics, which traces the evolution and spread of languages.
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I'm working with phylogeography and at a loco cpDNA I found haplotypes associated with an SSR region. I would like to know how to analyze, because the evolution rate is different from universal cpDNA
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Thanks Eduardo for your answer
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Basically, I'm exploring new methods for analyzing genetic data of individuals and populations across landscapes, but before applying them to real world data, I'd like to see how they work when applied to simulated data in certain idealized scenarios to help ensure that they work as expected. To that end, I'm interested in finding any software that can be used to generate genetic data (microsatellites and/or SNPs) for individuals across a landscape. Thanks.
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Hi,
See the PDF.
Good Luck.
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Dear all,
I'm having trouble on producing binary trees in PAUP*. It seems I can only do it by Trees/ Generate Trees. But when I do this it seems that I cannot 'use' the already produced trees but that I have to re-generate them (or am I getting all these wrong?). Any thoughts?
Many thanks,
Toni
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I should add that when you use generate trees, you are producing a set of random trees that are not based on the data at all. that s whether are binary. This method does not find a best or optimal trees from your data. The command is used to help you look at the distribution of tree scores to make sure that only one or a few trees have support from the data. Let me know if you do not understand this.
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What is the best genes used to determine the phylogeography of tenebrionidae–darkling beetles‬‏?
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Maybe you can take a look at these papers done in the Canary Islands for genus Pimelia, Nesotes and Hegeter:
Rees et al. (2001). Mitochondrial DNA, ecology and morphology: Interpreting the phylogeography of the Nesotes (Coleoptera: Tenebrionidae) of Gran Canaria (Canary Islands). Molecular Ecology. 10(2):427-34
Rees et al. (2001). The Diversification of the Genus Nesotes (Coleoptera: Tenebrionidae) in the Canary Islands: Evidence from mtDNA. Molecular Phylogenetics and Evolution. 21(2):321-326
Contreras-Díaz et al. (2003). Phylogeography of the endangered darkling beetle species of Pimelia endemic to Gran Canaria (Canary Islands). Molecular Ecology. 12(8):2131 - 2143
Moya et al. (2006). Using statistical phylogeography to infer population history: Case studies on Pimelia darkling beetles from the Canary Islands. Journal of Arid Environments. 66(3):477-497
Juan et al. (1996). Phylogeny of the genus Hegeter (Tenebrionidae, Coleoptera) and its colonization of the Canary Islands deduced from Cytochrome Oxidase I mitochondrial DNA sequences. Heredity. 76(4):392-403
Juan et al. (1997). Molecular phylogeny of darkling beetles from the Canary Islands: Comparison of inter island colonization patterns in two genera. Biochemical Systematics and Ecology. 25(2):121-130
Best regards.
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I generated xml in older version and uploaded that in new Beauti but still it does not take it. What to do?
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Dar Ashwini,
BEAST does not  accept xml files generated with previous versions. In Beast 2 you can generate xml with the proper BEAUti version from nexus using the Import Alignment scroll-down menu option. It accepts well formulated nexus files. If it does not work something is wrong with your nexus file. Alternatively you can use fasta file as well.
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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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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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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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Iam doing research on the molecular phylogeography of the giant african snail in India. How can I calculate the mutation rate/time (time from which the change has occurred in the sequence)? Is there some specific software which could be used for that?
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As an alternative you can try to use known mutation rates of closely related species (e.g. other terrestrial snails) to calculate the time since two alleles diverged. This may lead to wrong estimates if the number of mutations is very low. The calculation can be done in BEAST or as an estimate by hand (knowing the number of mutations and the rate of mutations/unit of time).
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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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Dear colleagues,
I'm looking for a software which can place haplotypes of each geographical locality on a map. I make haplotype network by POPART and plot haplotypes of each locality use add.pie in R. But, when number of haplotype is large, I feel it is a little time-consuming to make consistent of haplotype color generated by the two way.
Are there any other way to place haplotype of each geographical locality on a map?
Thank you in advance.  
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Deat Yue,
You can use the web based application PhyloGeoViz. It was designed to create geographic visualizations of DNA haplotype data that are often used in the course of phylogeographic analysis. These visualizations demonstrate the spatial distributions of each haplotype, the frequency of each haplotype in each population, and the number of samples included per population. 
PhyloGeoViz is available at the following site: 
Please find also attached the tutorial that may help you with inputting data set and generating a map using PhyloGeoViz.
Best of 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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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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Primarily, I am now doing a phylogeography of tide-pool copepod (Tigriopus japonicus) in Taiwan with my colleagues. As far as I know only copepod from Tigriopus spp. could survive in the tide-pools but during my sampling in Penghu Islands and Green Island, we also found this species in the tide-pools that we are not sure that it is Tigriopus japonicus.
The first photo was the unknown copepod species and the second was the Tigriopus japonicus.
Best,
Steve 
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Hi! S/o Cyclopoida, fam. Oncaeidae, m.b., Oncaea sp. For identification need more detail and size.
Sorry! It is my vistake!
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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 have a dataset of COI sequences and I'd like to obtain Bayesian Skyline Plots (BSPs) with BEAST for my populations. I made 5 replicates runs obtaining 5 .log and 5 .trees files. I used LogCombiner 2.2.0 to obtain single .log and .trees files from the 5 replicates, in order to construct the BSPs with Tracer. LogCombiner was able to construct a combo file for trees, but it did not for .log files. The program stops without producing any file and without giving any error message. Actually it made nothing....! Any suggestion or hint?
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Thanks everybody! LogCombiner seems to work when I use an old version (1.7).
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Is is legitimate to use geographic occurrence, specifically altitudinal/bathymetric range of species, as character states to use in an ancestral-states reconstruction? Can origins be inferred this way? Are there examples in the literature?
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I think very interesting placement of Crother. However, I worry how this is being done today. I think that current approaches are too simplistic. Do not take into account a number of empirical knowledge that we have about the distribution of species. Thus, what I mean is: It may even be possible to estimate an ancestral area, but it is not a simple task as rebuilding an ancestral state of a character in a phylogeny (at least in the most cases).
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I'm working on comparative phylogeography of an alpine habitat, and i'm looking for a foundation that support my project, is there any international foundation for supporting works on alpine plants phylogeography?
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I know it's not international but if you are willing to cooperate with a German institute and like to go there, the German Academic Exchange Service (DAAD) supports a lot of research! 
Junior researcher
Senior researcher
Good luck with your funding!
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I'm working with two species using the mitochondrial cytb sequences from several localities and I want to obtain a continuous measurement of species assignation (i.e., one value for specimen/haplotype). I thought in use the program STRUCTURE to get the values of Q. But as far I know this approach could have several assumptions problems about LD and loci independence. Do you know another approach to get some estimator as Q from Structure? What about Geneland?
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If you have a mono-locus dataset (for DNA sequences), I would recommend to try the SAMOVA clustering method of Dupanloup et al. (2002; doi:10.1046/j.1365-294X.2002.01650.x). This procedure assigns populations to groups based on geographical vicinity and sequence similarity. The most likely structure corresponds to the partition of populations that maximized among-group variation measured by the AMOVA ΦCT statistic (Excoffier et al. 1992; in Genetics).
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Hi
I am working on identifying the origin of an invasive anuran using mtDNA genetic markers and have found only two similar papers. These are the phylogeography of Bufo marinus (=Rhinella marina) (Slade and Moritz 1998) and a more recent paper by Kuraishi et al 2009 looking at the origin of Polypedates leucostymax in Japan. Does anyone know of similar studies on other invasive anurans such as Lithobates catesbeianus or Eleutherodactylus coqui?
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I thought this review article was useful:
Estoup, A., & Guillemaud, T. (2010). Reconstructing routes of invasion using genetic data: why, how and so what?. Molecular Ecology, 19(19), 4113-4130.
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I want to calculate implementation for nested clade for some birds' species mtDNA dataset. Due to mentioned paper here:
ANeCA is a fully automated implementation of Nested Clade Phylogeographic Analysis and accessible via this link and free of charge:
BTW, I can not download anything from that. Maybe under maintenance for several days. Have you any access or downloaded package for ANeCA? Also, any code for nested clade in R?
Regards,
Mahmoud
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Dear Seyed,
I send you the zip file for ANeCA and also for GEODIS, the software for manual nested clade analysis (you have more overview what you are doing). R code I dont have.
Have fun
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Thank you in advance for your answers.
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Interesting question! Basically there should be no fundamental difference in utility for terrestrial vs. marine systems. However, keep in mind the long - standing notion (assumption) that there *tends* to be more gene flow, fewer obvious physical barriers to gene flow in the three dimensional marine ecosystem, especially for vagile and/or broadcast spawning taxa. 
But recently, the role of mitochondrial DNA (mtDNA) sequences in taxonomy and phylogenetic inference has become quite contentious:  two extreme viewpoints have
emerged. One which criticizes the use of mtDNA because the marker suggests 'misleading patterns of variation'; specifically, phylogenies that
are inconsistent with those derived from nuclear gene
sequences in the context of species relationships among
closely related taxa (Ballard and Whitlock, 2004; Shaw,
2002). The other extreme, the DNA “barcode” movement,
espouses the sole use of small fragments of a single
mtDNA gene, cytochrome c oxidase I (COI), to identify
most of life (Hebert et al., 2003).
In a review paper my colleague Dan Rubinoff and i published a few years back we present the disadvantages of these two extreme
viewpoints and argue for an integrated role for
mtDNA, one that takes advantage of mtDNA’s strengths
but also accounts for its shortcomings by using it in concert
with other independent data sources (e.g., nuclear
DNA, cytosystematic, morphological, behavioral). We
are against the abolition of the use of mtDNA in phylogenetics
but also against its narrow use in barcoding as currently
defined. We try to show why neither viewpoint
is particularly productive and emphasize how analysis
of mtDNA can be an important tool in the context of both
taxonomic and phylogenetic studies, and I would say equally for marine versus terrestrial organism systematics.  (for our review on this debate in general see:
Rubinoff, D. & B.S. Holland. 2005.  Between the two extremes: Mitochondrial DNA is neither the panacea nor the nemesis of phylogenetic and taxonomic inference. Systematic Biology, 54(6): 952-961). 
For a couple of additional papers that used mtDNA markers to examine and evaluate  systematic boundaries in closely related marine taxa:
Bird, C.E., B.S. Holland, B.W. Bowen & R.J. Toonen. 2011.  Diversification in broadcast-spawning sympatric Hawaiian limpets (Cellana spp.). Molecular Ecology. 20: 2128-2141
Bird, C.E., B.S. Holland, B.W. Bowen & R.J. Toonen. 2007. Contrasting phylogeography in three endemic Hawaiian limpets (Cellana spp.) with similar life histories. Molecular Ecology, 16(15): 3173-3187
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We are having data on Bluethroats from its breeding and wintering grounds and would like to study their spatio-temporal migratory connectivity.
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In addition to the the PLoS ONE paper above, see also
Ruegg, Kristen, Eric C. Anderson, Kristina L. Paxton, Vanessa Apkenas, Sirena Lao, Rodney B. Siegel, David F. DeSante, Frank Moore, and Thomas B. Smith. "Mapping migration in a songbird using high‐resolution genetic markers." Molecular ecology (2014).  http://onlinelibrary.wiley.com/doi/10.1111/mec.12977/abstract
and
Rundel, Colin W., Michael B. Wunder, Allison H. Alvarado, Kristen C. Ruegg, Ryan Harrigan, Andrew Schuh, Jeffrey F. Kelly et al. "Novel statistical methods for integrating genetic and stable isotope data to infer individual‐level migratory connectivity." Molecular ecology 22, no. 16 (2013): 4163-4176.
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I am working on the molecular phylogeography of giant african snail in S. India. I would like to know how can I design the primers for the ISSR regions of the snail? Can I use a general primer which has been used for many snails? Is there are some softwares for that?
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hi
ISSRs arn non species specific so the easiest way to find polymorphic ones is checking the articles were related species were analyzed.
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I am working on some plants from Valerianaceae family(angiosperms). I have already sequenced rbcl, matK and two inter-genic spacer regions. I want to amplify ITS1 and ITS 2 regions. Here, previously, I have used primer originally developed by White et al. They didn't work because after sequencing I use to get fungal and bacterial sequence in BLAST hit. So, I am now using ITS2-S2F developed by Chen et al. and ITS 4 primer to amplify ITS 2 region (referred by CBOL working group). Now, I am not getting fungal/bacterial sequences, but getting sequences from Chlorophyta (like Chlorella etc). Every time I used negative control there was no bands present. 
If there is any contamination in my samples how can I get all the sequences and nice BLAST hit?
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I guess you could design your own primers. Download from Genbank what you can find of ITS1/5.8S/ITS2 sequences for Valerianaceae/Caprifoliaceae, and align them. Then look for conserved regions suitable for primer design. I would design (a couple of) forward primers at the beginning of ITS1 and at the beginning of 5.8S, and a (couple of) reverse primers at the end of ITS2 and at the end of 5.8S.
You can then test PCRs using different primer combinations, including also the already published primers that you have tried. Hopefully this will allow you to obtain at least partial sequences for ITS.
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Spatial interpolation of single locus, two allele data. The goal is a contour map (heat map) of allele frequencies from 0.0 to 1.0.  We have the data in GENELAND but Geneland is trying to do much more with the data than we want.  We simply want to map the raw frequency data of two haplogroups. Thanks.
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If you like to work with the R environment the Gstudio package has some nice features for data exploration and one of those is the fact that you can plot allele frequencies onto a map. I use it quite a lot for data exploration with nuclear microsatellites. Here you have the link to Rodney's tutorial. You can download the package from his github or I think it is already update and ready to be installed from the CRAN.
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Why can the results be negative in r coefficient in social insects? What is the explanation? In a study of mine I had negative results for a colony, but the mtDNA of all individuals were the same. What is the explanation for this? Can anyone help me? Send articles for me, please!
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A negative regression relatedness in general means that your tested individuals are less related to each other than the population average (ourbreeding). Which individuals did you test, workers or queens/males (if the colony is polygynous/polyandrous)?
To calculate population average relatedness the software takes your input allele frequencies of the microsatellites. How did you calculate them? They have to be calculated from queens of your sampled nests. Not from workers (because they all share the queen allele, biasing allele frequencies).
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Also who are the leading experts in the area?
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Dear John thanks for the info which was  very useful. How about the phylogeography of freshwater amphipods?
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Does anybody know if it would be possible to build RAD-seq libraries on leaves that have been preserved in Silicagel? I'm wondering if the DNA quality would be high enough to get (enough) markers or if we should consider another conservation method (Silicagel is indeed quite convenient when sampling in the field).
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I'm sure you would have enough DNA of nice quality if you use zip-bags and silica-gel. Please provide enough silica-gel per bag to quickly dehidrate the tissue (e.g. 50 g silica-gel / 5 g leaf f.w.). It that way you'll keep the DNA undamaged and prevent the accumulation of polyphenolics.
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I am interested in performing the PCO-MC (principal coordinates – modal clustering) on population genetic data obtained from an alleged species complex, in order to infer on genetic structure and species delimitation. Freeware software exists for Mac OS X (http://lamar.colostate.edu/~reevesp/PCOMC/PCOMC.html). Does anyone know if software is available for Windows operating system or R environment?
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You can download the R program (http://www.r-project.org) first and install the mmSAR package within it.
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Can you recommend the best program for reconstruction of the continuous distributional ranges having discrete records and meteorological data for A DILETANT, please? I am a beginner, and, unfortunately, I have a limited time to learn all these sophisticated programs. I need to test basic tests, and I need as simple a method as possible. If the results will be promising, I will try to study much deeper.
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Thank you!
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Hi,
I am interested in studying the phylogenetic relations of a C. elgans enzyme that is conserved from yeast to mammals. I have generated a phylogentic tree representing over 100 eukaryotes and found the C. elegans enzyme at the base of the tree. It seems as though there are not enough sequences of species between yeast and C. elegans , i.e. I found no homologous sequences in Cnidaria, Porifera, Placozoa and other lower eukaryotes. I wonder if my enzyme is situated at the base of the tree due to the lack of annotation of early eukaryotes. Is this a known problem in phylogeny?
Thanks for your help!
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well what i say is there may be a homologous sequence in platyhelminthes or in other trematodes try to look for it. It must not be the case that all of sudden the sequence via mutation or gene transmission got into nematode. if you are right annotation is a problem in doing phylogency if you are working in something new.
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Molecular Clock estimation has been used frequently in phylogeographic studies in order to determine divergence time of specific taxon within group of taxa, exploring phylogegeraphical scenarios. How can I do this? I'm not able to run BEAST software. Do I have to use this software? Are there any other tools to do this?
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Dear Gonzalez
Thank you so much for your answer.
Regards
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I've been scouring the literature for objective means to select "good" outgroups in phylogenetic studies, but without success. I've never seen an explanation why a given outgroup was selected. It seems as though it's a realm that deserves study, unless I'm overlooking something.
The basic idea is to select an outgroup that is related, but at an ancestral/basal level, relative to the taxa of interest. Typically it appears as though researchers use an outgroup because another also used it. There should be rigor applied to objectively determine the outgroup choice.
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Outgroups often don't get the attention they merit. To my students I recommend two things. Use multiple outgroups, to test the monophyly of the ingroup (you assume it with the use of a single outgroup), and include among the outgroups all taxa thought to be related to the ingroup, i.e. exemplars from its sister group, or taxa thought to represent putative sister groups. Dense sampling of relevant outgroups will avoid assumptions of ingroup monophyly and improve the likelihood of your ingroup network being rooted at an appropriate branch
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I
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Dear Joanna
I agree with Ferruccio Maltagliati
Colin Hawes
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I am working on an alpine medicinal plant collected from all over its geographical distribution around the world. Now I want to do a phylogeographical study as I have samples (10-12/site). Can I proceed with ITS2/psbA-trnH or do I have to take a gene? I am doing phylogenetic studies of the group to understand the speciation as there are some identical species associated with the plant species. How many samples/individuals do I have to take for this studies? I have all the coordinates of the geographical region.
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Hi Biswajit,
That`s depends on the geographical coverage of your samples and the main question of your study. How many species are you intending to use? If your main question is about speciation of these related-species, you may use small samples and just few points distant from each other to have a general idea. Then you can use molecular clock approaches to define time of these speciations, such as it is implemented in Beast software.
If you want to study just this species, it is a totally different approach. You may need more points of collection and more individuals per point to make population genetics and history of these populations. A good start-point is to analyze 10-20 individuals/site.
It is not a good idea to make a phylogenetic analysis using ITS-2, as this marker is probably under a concerted evolution (i.e. is not a neutral marker), and usually it is present in multiple copies in the genome.
As Niladri said, multiple genes can provide better results, but unfortunately I don`t know which markers are frequently used in plants. Maybe you can make a literature revision and look at the markers that are commonly used to answer scientific questions related to yours.
If you need anything else, mail me marciopavan@gmail.com
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I open the program under Windows 95 or 98 as recommended, but it still does not work. Have you had the same problem?
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Alternatively if you're capable of writing R scripts you could use the adegenet package to calculate barrier to geneflow.
Monmoniers algorithm has been incorporated into recent versions
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In the case that variation in molecular markers is not correlated with variation in quantitative traits within populations (as has been widely discusses), what other useful information do molecular markers provide? Imagine an experiment with several populations of a plant species where you estimate genetic variation using microsatellite markers and then use a greenhouse experiment with a classic quantitative genetics design (families within populations). What different information do both assessments provide?
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The microsatellites will tell you how diverse each of your populations are and in how far they are related.
The greenhouse experiment will tell you if / which populations respond similarly to a certain environmental stimulus (at least that's how I understand your question).
Now, if the two experiments are correlated, i.e. two most closely related populations (as measured by microsatellites) react similarly in your greenhouse experiment, you can conclude that this trait has developed once and your related populations are descendants of this founder population. In contrast, if you don't see correlation, the trait might have been developed independently several times.
I can give you an example from malaria parasites (the field where I work): resistance to commonly used drugs has been observed in different places. Genotyping of neutral markers showed that these populations are not related (resistant parasites are more closely related to non-resistant parasites from the same geographical region). Thus, the mutations leading to resistance must have developed independently several times (or, as an alternative explanation, they might always have been present and then selected for by the drug).
See for example this study: Nat Genet. 2013 Jun;45(6):648-55. doi: 10.1038/ng.2624. Epub 2013 Apr 28.
I hope this answer helped.
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It is now standard practice to make not only your results, but also your data and metadata for a project freely accessible to the world. This is the best practice for the scientific enterprise, hands down. Personally, in addition to GenBank, I have had good experiences with TreeBASE in using their services to publish data (although formatting your datasets and getting everything right in their system can be a bit tricky). I want to know which database/repository do you prefer, particularly if you had to choose between Dryad or TreeBASE, for accessioning your phylogenetic results, DNA alignments, metadata, or analysis input files? In considering this question, you may want to give your opinion about which service is least expensive in time or funding, which online interface is nicest and easiest to use, which data types are accepted, or how difficult it is to format your files for upload. One thing that is not clear sometimes is whether you will have to pay a fee for Dryad services or not. Any opinions?
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Thanks to everyone for responses on this. And, Hey Brittany! Sorry for the delayed response, but it was great hearing from you on this! So, the general consensus seems to be that Dryad is preferable to TreeBASE, particularly for population genetics or phylogeographic datasets.
Regarding my experiences, I admit that using TreeBASE was a bit of a pain for just the reasons that Brittany mentioned. Indeed, (1) I found properly formatting the tree tips to be easy but time-consuming and a bit annoying since you can envision a system in which you could keep all the tip labels in their original format and still be able to link all the corresponding species and locality info. (2) The TreeBASE staff never even replied to multiple emails requesting assistance, until after I had completed my submission and finalized it on my own; and of course their email was just like "Sorry." (3) TreeBASE was also sort of tricky to get some of the taxonomic designations (e.g. linking tip names to NCBI Taxonomy etc.) correct to clear the submission. And because of these issues I had to re-do my submission several times to get it right.
In defense of TreeBASE, they provide a great free service to which metadata can be linked, and I was able to figure out their system and complete my submission. So, I would go the TreeBASE again, despite the downfalls, although more likely for phylogenetic data.
But I am listening to the group wisdom here, and so I will likely first attempt to use Dryad for my next data submission, especially since you guys said it was free for Mol Ecol (thus it might be free for other evolutionary biology journals).
One thing that people haven't mentioned is that the Dryad submissions get a doi and a publication reference, whereas it seems the TreeBASE ones do not. This is beneficial because others can cite your dataset if they use it, regardless of whether they cite your study or not (although we might argue that they should cite both).
To Gabriele, thanks so much for this info!! I had never heard of the Global Genome Biodiversity Network before, and this seems like a really great option for preserving physical samples. Honestly, it kind of blew me away. Take care.
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It seems that many controversies exist on the use of different softwares or "R" Packages (Maxent, Garp, Biomod2). Is there a better strategy; abiotic criterias versus ecological features?
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A lot of great answers here, and I would reiterate many of the common themes (clearly state your question/interests, understand your data, and know the pros/cons of each approach, MaxEnt might be the best if you only have presence, however you should look elsewhere if you have both presence and absence data).
First, I wanted to provide some other citations that might be really helpful.
Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186.
Redfern, J. V, Ferguson, M.C., Becker, E.A., Hyrenbach, K.D., Good, C., Barlow, J., Kaschner, K., Baumgartner, M.F., Forney, K.A., Ballance, L.T., Fauchald, P., Halpin, P., Hamazaki, T., Pershing, A.J., Qian, S.S., Read, A., Reilly, S.B., Torres, L., Werner, F., 2006. Techniques for cetacean – habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295. (I know the title says for cetaceans, but most of it is relevant for all taxa).
Guisan, A., Thuiller, W., 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009.
Valavanis, V.D., Pierce, G.J., Zuur, A.F., Palialexis, A., Saveliev, A., Katara, I., Wang, J.J., 2008. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia 612, 5–20.
For all of the potential approaches off the top of my head, the list includes: MaxEnt, GLM, GAM, GAM, boosted regression trees (BRT), random forests (RF), Biomod. It is important to note that each have their pros and cons. Some are better at fitting the data, others tend to be better at predicting data not included in models. Others are good at providing "response curves" that are highly interpretable and have biological/ecological meaning; others don't. For a balance of all of these, I tend to prefer generalized additive models (GAM), as they provide flexibility for model fit, potential constraints to flexibility to prevent overfitting, and response curves that are interpretable. Decide what is best for your data, however. In R, I prefer the mgcv package for GAM implementation. A couple examples of my use of this framework include:
Furey, N.B., Rooker, J.R., 2013. Spatial and temporal shifts in suitable habitat of juvenile southern flounder (Paralichthys lethostigma). J. Sea Res. 76, 161–169.
Rooker, J.R., Simms, J.R., Wells, R.J.D., Holt, S.A., Holt, G.J., Graves, J.E., Furey, N.B., 2012. Distribution and Habitat Associations of Billfish and Swordfish Larvae across Mesoscale Features in the Gulf of Mexico. PLoS One 7, e34180.
Regardless of the approach used, you need to know your data and covariates (scale of data collected at, collinearity among variables, autocorrelation of data, etc). This has been mentioned by others.
Another thought, that I don't think gets enough discussion, is the ability for organisms to disperse across suitable habitats (ie just because a model predicts an area to be suitability for presence doesn't mean the species is there, or can be there, based on dispersive limitations [on a variety of spatial-temporal scales]). Depending on your aims, you might want to address this; see the following paper/package for an interesting idea in regards to predicting shifts based on climate change, given a starting distribution.
Engler, R., Hordijk, W., Guisan, A., 2012. The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models. Ecography (Cop.). 35, 872–878.
I hope this helps, and best of luck with your data. This is a great interest of mine, and have a soft side for insects even though I mostly work with fish, so definitely post any of your publications in the future!
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We collected this summer in the French Pyrenees (1900 m) flies (Diptera -Muscidae) foraging on flowers. We are looking for someone who can help us with the identification.
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Well, you could DNA barcode them ....
Or maybe find a Diptera systematist who is willing to collaborate with you on your project. Becoming an expert in recognizing species of a diverse group takes years of specialized training and access to a good collection. As resources to support professional systematists and natural history collections are diverted into sexier branches of biology, this expertise is being lost. All biologists need to recognize and advocate for the ongoing fundamental importance of scientists trained in morphological systematics of insects and other poorly-known taxa.
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I have a dataset consisting in COI sequences from several catshark populations with different sample size (7 < N < 24). In order to obtain comparable estimates of genetic diversity, I would like to employ a rarefaction algorithm. Can anyone suggest a rarefaction method similar to those employed with microsatellites and if there is any software performing such analysis on mtDNA data?
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You could try the rarefraction program by Steven Hollond, available from: http://www.uga.edu/strata/software/
This software has been used for COI data before e.g.: Aguilar, Aquatic Sciences (2011) 73:15–20
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I am looking at the phylogeographic pattern of a shallow water rock lobster species along Mozambique, South Africa and Madagascar using a portion of the COI gene. What is an appropriate sample size for a study like this?
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How long is a string of rope? Depends on the level of abstraction and choice of software for analysis. If you believe a single specimen from each population is sufficient to draw any robust conclusion, then go for it (not adviced though). If you wish to make e.g. skyline plots you definitely need more samples to satisfy the coalescent. It also depends on the level of rate variation across populations/locations for COI. If there is no variation it matters little how many specimens you sample in the end. So my initial suggestion would be to screen the locus across the species distribution and check for rate variations. Then, depending on what level you detect, decide how many specimens are worthwile to sample from each population/location. General rule of thumb - the more the merrier.
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I f you are using sequences, DNASP is what you need, but if you are using other sorts of genetic markers such as microsatellites, you should use Arlequin
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Has anybody ever used Geneland package in R? I wonder where I can include zone information in the coordinates input file? I am dealing with samples from New Zealand and South America, which locate in distant zones. However in Geneland, the demanded coordinates file contains only two columns, no place to include zone. I converted the Lon/Lat according to Geneland Guide using convUL function in PBSmapping including zone (otherwise PBSmapping will consider all in one zone).
However when I put the converted UTM value to some UTM to Lon/Lat website to check if the conversion is correct, then it is totally wrong. No matter if it is correct or wrong, I still wonder if Geneland can only deal with samples from a small scale?
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I generally use ArcGIS to convert my coordinates (I typically also use UTM or lat/long) to planar coordinates for Geneland. Geneland seems to be sensitive to the type of coordinates you use, and I have found planar coordinates to produce the best results. If you have your coordinates georeferenced, and you have Arc software, I would use the project (not define projection) tool to convert your coordinates. In the attribute table, you can then add two columns, one for each coordinate, and calculate the x and y coordinates for your dataset. GIS is highly accurate for this purpose because you can visualize your coordinates as you calculate the new coordinates, but if you do not have access to GIS, I would recommend Google Earth (I know, not very satisfactory...).
If you do not have tons of coordinates and know about your locations, I would use GE. That gives you lat/long coordinates that Geneland takes just fine. I often get locations that are general at best (this road near this tree or creek, etc), and GE calculates very accurate points. This necessitates going one-by-one that is a pain.
As for the scale issue, I have used Geneland for my study area which is over 10,000 km^2 (pretty much all of North America). Although I had ghost clusters due to IBD (typical of Geneland), I obtained concordant results from BAPS, TESS, and Structure. Geneland is particularly sensitive to IBD, so if you suspect you have strong IBD, I would suggest BAPS with spatial clustering of individuals. It was much less noisy for me.
Hope this helps and good luck.
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I wish to create a Haplotype network based on DNA sequence using TCS program. I am following TCS guidelines to run the software, still been unable to get the results. Everytime its coming up with a TCS warning on the screen "You have extra returns in sequence". Does anybody has similar kind of experience? Please let me know. And how to correctly run the program.
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Hi Swaraj,
Don't get fool. It happens quite a lot. It is a problem of the aligment format. TCS reads files in NEXUS or PHYLIP sequential formats. First, be sure TCS is reading an aligment, not just a file of sequences. Secondly, be sure the aligment is in nexus or phylip sequential format. TCS can not open "interleaved" formats. To change the format an useful online tool is: http://hcv.lanl.gov/content/sequence/FORMAT_CONVERSION/form.html
Best,
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I have a very specific question concerning modern human/neanderthal studies. As long as I understand, neanderthal trace in human genome is due to some recombinant loci. That means, neanderthal clonal genes (Y-chromosome, mt-DNA) were completely washed out from the modern human populations due to gene drift, but some recombinant loci still remain in the gene pool. Moreover, they exist in literally any non-African human person.
Discovering presence of neanderthal alleles in the sapiens genome became possible after scientists sequenced neanderthal genome. Thus, the location of the neanderthal alleles in Eurasian genomes is known and, perhaps, even available. That means, primers can be easily designed for these fragments and the "neanderthal" fragments should be relatively easy to sequence for any modern human.
That means, by sequencing these fragments for humans from the different parts of Eurasia one can reconstruct the underlining Neanderthal phylogeny, i.e. one can compare the neanderthals from West Europe, Caucasus, Central and East Asia, whose differences may well be much deeper in time than the differences between respective modern human lineages, which are thought to diverge 100 TY or similar. Should the existing differences between modern human populations be completely attributed to the divergence that started 100,000 TY? Or, perhaps, they at least partly root into the time of divergence among neanderthal geographic populations?
It sounds too simple, that means, most likely is something wrong and stupid in this logic, or evolutionary anthropologists already working on this. Or?
Would appreciate much for the comment.
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There is a very nice paper by L Excoffier who modelised the admixture between Neandertal and Sapiens. One of the results is to show that we expect different parts of Neandertal genome to have been retained up to now in each non-african individuals. So it would be very difficult to find different homologous Neandertal fragments and do population genetics with them. Else your idea would be very nice. You should also look to a paper by Labuda's team on the X chromosome (Dystrophin gene), I do not remember all the details, but he was able to clearly identify some Nenadertal "fragments".
Best regards
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Several types of markers can be used to study population genetics and phylogeography. Markers that evolve relatively rapidly like microsatellites and relatively slow like mitochondrial markers can be technically used to get insights into population structure at different time points. Can these be analysed separately and then contrasted to study geneflow patterns and population structure at different time points for the same spatial scale?
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I thought the same question before,and I find some peers really did this.
please refer to M. Alice Pinto, Genetics 170: 1653–1665 and Irene MUÑOZ, Apidologie, 2013 (DOI: 10.1007/s13592-012-0179-0), and so on.
hope it helps.
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I am studying a riparian plant where both chloroplast and nuclear microsatellite data show high differentiation between two sets of sites. The sites are located in the European Alps, in an area which was glaciated during the LGM. One set of sites has low nuclear, but high chloroplast diversity, and these sites all have an 'eastern' chloroplast lineage. The other set of sites has high nuclear but low chloroplast diversity, and are occupied by a 'western' chloroplast lineage. There are no shared chloroplast alleles between the two site types; each site is either populated by the 'western' or the 'eastern' chloroplast lineage.
Has anyone observed a similar pattern?
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Hi Silke, thank you for your answer. One reason for the lack of nuclear variation in the Eastern block might be self-pollination. Which species are you looking at? As you say, they might be isolated populations in the East vs. connected populations in the West. Try to look at genetic diversity measures and shared/ non shared alleles among pops. In any case I would also look at the genetic relationships based on nuclear and on cp DNA. Maybe there is structuring in the East block with different degrees of isolation among pops. I study Phragmites australis. In this species there are areas in which there is huge microsatellite variation in chloroplast sequences and areas (as big as continents) in which there is no variation at all in the same cp sequences. Although I don´t have a compelling explanation, I have two hypothesis for this: either the cp-microsatellite diverse areas are the center of origin of cp- lineages or, somehow, it has to do with hybridization among distantly related lineages. You are looking at n-microsatellites and at a very small part of the genome. Maybe the microsatellites were developed for the lineage in the west. Maybe there is more variation in the Eastern block, but you are not looking at the variable sites in the eastern group. In your place, I would also look at different markers (AFLPs for example) to make sure that the lack of n-variation in the East is true and not "biased" by the variation used to develop the microsatellites.
I aplologize for the long monolog. I would check three things:
- another n-marker
- selfing
- hybridization among distantly related lineages.
Good luck, Carla
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I have sequences of birds blood parasites (haemoproteus, plasmodium and leucocytozoon), from north and south Caucasus and now I need to see if the parasite communities between this two locations are similar or not. For geographic structuring I use simple statistics to see, but for phylogeographic is unclear. I would say that in case of haemoproteus lineages there is a geographic structuring between these two sites.
Do you have any information that could help me solve my problem?
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I would try to use SAMOVA. You could calculate PhiST using a program like Arlequin. This statistic is similar to FST but takes into account the amount of sequence divergence between haplotypes.