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In particular, I'm looking for studies with quantitative spatial analysis of genetic data, with quantitative maps.
For example, predicting genetic diversity using raster data of climate and topographic data.
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That's nice!
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I have 83 samples of wild boar (i.e. large mammal, moderately motile) whose sampling locations were obtained by randomly setting the coordinates in green areas (according to Google Earth imagery) within the municipality of collection. In this sense, when more than one animal was sampled at the same municipality, I have the same coordinates.
Since I want to perform landscape genomics analysis with a population-based approach (because of the uncertainty associated to the individual coordinates) I identified 9 “clusters” by drawing a 10km radius buffer around each unique location (i.e. one record per municipality) and clustering together all the individuals within the areas that would overlap, while discarding the stand-alone samples. In doing so, I verified that the clusters would include only individuals with at least 50% assignment to the same genetic cluster according to ADMIXTURE analysis.
Now, how can I report for each cluster the environmental variables from the Worldclim database? Should I first find the centroid of each cluster and then retrieve the value for that location only, should I retrieve the value for each sample included in a cluster and compute the mean? Any suggestion would be much appreciated!
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Yeah thank you Md. Ashiqul Islam but I've already read a lot of papers and tutorials - included the first two references of yours; I was looking more for a personal opinion
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I'm using the read.structure function in the adegenet package to analyze some genetics data, and need to include spatial coordinates in the genid object so I can run a landscape genetics analysis. Adegent package states that a column/s to be included in the other slot should be referenced using "a vector of integers giving the indexes of the columns containing other informations to be read. Will be available in @other of the created object. " However, so far I have been unsuccessful in my coding to produce the correct genid object.
Can anyone assist me with this?
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Add the spatial coordinates separate from the genotype data. Create the genind with the genotype data first, then import the spatial coordinates into a separate data frame. Then direct those coordinates into the "other" slot. Here is an example (assumes a tab-delimited text file):
Coord<-read.table("Coordinates.txt", header=TRUE, sep="\t")
colnames(Coord)<-c("X", "Y")
Genind@other$xy<-Coord
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I have read that sequence based data are not powerful for analyze isolation by distance between population. However, I want to know if is possible to analyze the effect of environmental variables as elevation, temperature or land use on the genetic differentiation between lineage or cryptic species using sequence based data as mitochondrial DNA and nuclear DNA (ITS2)?
If yes. Which metric should I use as a measure of genetic differentiation between lineage or cryptic species?
If not. I know that ITS data can have microsatellites, it is possible to use these microsatellites as a source of genetic information for landscape genetics?
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selecting the markers for landscape genetics depends on the level of divergence of the putative lineages. For example, cytb of mtDNA can capture the lineages separated around 1 to 20 million years. If you need to capture the differentiation very recently, d-loop could be a better option. Microsatellite can capture the differentiation in very recent times even within a species. I used microsatellite markers for landscape genetics of mosquitofish in coastal Louisiana to understand genetic differentiation due to temperature, salinity and salt water intrusion. . I used cytb for understanding genetic differentiation between 1 to 2o million years.https://scholarworks.uark.edu/etd/3170/.
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Hi. I am revising a paper on landscape genetics between island populations of reindeer. One of the reviewers suggested to perform landscape genetic analyses (IBD, IBR) at the individual level rather than population level, and pointed out that "FST contains mostly historic information, while some individual-based genetic distances might better reflect recent impacts on gene flow."
This could be interesting, but I have a hard time to find a relevant study that would support this claim. Does any of you know of any articles about this?
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Hi all,
Thanks for a very interesting discussion! It is good to hear the viewpoints from others with longer experience on population genetics. I have given it some thought before I read your comments. Thank you Tyler for pointing out that FST indeed is a fixation measure and doesn't quantify the divergence of exchanged alleles.
Overall, I think individual-based statistics can be very useful to pick out contemporary migrants. Pairwise FST's reflect an average genetic distance between populations, but should nevertheless respond quickly to migration. So it is probably, as Thierry pointed out, a question of resolution.
My main concern is that individual-based statistics often have much stronger statistical power (sometimes two orders of magnitude) due to the number of observations. This can be advantageous if you have to make a trade-off between spatial and genetic (individual) sampling effort or in species with continuous distribution (see Luximon et al. 2014 Mol Ecol Resour), but can lead to highly significant results of very low or negligible effect sizes.
I think interpreting the differences in temporal scale of migration/gene flow between individual- and population-based metrics is difficult and should be avoided unless it is explicitly connected with the study aim. A simulation study would indeed be interesting.
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I have been looking for one that explains how to evaluate effective landscape connectivity, preferentially in Europe.
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I second Melanie A Murphy . The landscape genetics dgs is a great course for learning landscape genetics and my group project from the course ended up as a publication. Really no better way to learn the field and meet the experts.
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It is important to understand how genetic Structuring in a cotinuously distributed Species, While IBD & IBR doesn't work?
with using Mantel Test& Partial Mantel , P-Value isn't Significant.
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It would be helpful to think about the situation in context to the study system and your analysis. Are you comparing genetic differences among populations or individuals? In other words, in your IDB tests are you grouping individuals into suspected populations and estimating genetic distance between these groups or estimating genetic distance between the actual individuals themselves? What type of genetic data do you have? How many populations/individuals are there in the dataset? Most importantly, does it make sense given the biology of the species that it could be totally panmictic? It is entirely possibly that there really is no genetic structure if gene flow is that high and/or the markers are not providing enough resolution?
Here are a couple of options to consider. First, you could use the program Migrate to explicit test whether your system displays more of an IDB or panmictic structure. Second, you can conduct a Mantel correlogram to assess autocorrelation between genetic and geographic distance over various distance classes. Third, there are several methods out there that can measure non-linear relationships between genetic and geographic distance. Check out the sPCA analysis in adegenet (https://www.nature.com/articles/hdy200834). Also check out this Bayesian kriging method ( ).
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I am on the periphery of several projects looking at developing SNP genotyping arrays for relatively small numbers of markers, and to be run on relatively small numbers of individuals: potentially hundreds of individuals total, but with runs capable of doing few individuals at a time.
The SNP discovery phase has already been done through GBS.
I am wondering if anyone has any opinions on the best method to do this?
The questions to be answered with the data are things like: 1) matching an individual predator to predation marks, 2) relatedness of invading individuals, 3) landscape genetics.
I am guessing something like SNaPshot would be most cost effective for when just tens of markers are required? But what about when more markers are needed?
Obviously just doing more GBS would be best when thousands of markers are needed - probably landscape genetics questions would be best served by doing this and waiting for individuals to fill the runs. But is there a cost effective option somewhere in the middle in terms of marker number?
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We've had success with MassArray/iPlex chemistry (Agena, formerly Sequenom), and this continues to be our go to platform for pilot studies (fee hundred samples) as well as few thousand when samples are limited (saliva), and when the number of polymorphisms is small to medium. Our designs cover in average 29 SNPs at a time and the PCRs and extensions are done in 384 plates, using small volumes (cost saving). Another advantage is that depe ding on the surrounding sequence one can also test small indels. If you have a set of target polymorphisms, this is great, otherwise sequencing or pyrosequencing, as you get to take a peek at the surrounding nucleotides.
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Hello,
I am preparing to run an individual-based landscape genetics analysis on Eastern Indigo Snakes and was wondering if someone could provide some guidance on how to deal with individuals identified as full- or half-sibs. My study species is more-or-less continuously distributed across my landscape and I am measuring the genetic distance between samples using Bray-Curtis distances and principle components axes. I understand that researchers often randomly remove all but one member of full-sib families for population genetics analyses but I have seen less justification for this practice in the context of individual-based landscape genetics analyses.
I have tested for the presence of family structure using COLONY v1.2 and v2. They generally give similar results and indicate the presence of one 4-sib family, three 3-sib families, and 15-22 2-sib families (all full-sibs) out of 109 samples. I understand that these small family sizes may suggest that family structure is being poorly estimated and that I should be cautious about inferring family structure from these results. I have other biologically-based reasons to suspect some of these results as well (e.g., unrealistically large distances between some full-sibs).
Nevertheless, I am wondering if there is any utility in removing all but one individual from these full-sib families, even if the family structure is uncertain and if I am conducting an individual-based landscape genetics analysis. I noticed in a 2017 paper by Robin Waples and Eric Anderson in Molecular Ecology where they suggested caution when removing full-sibs in some circumstances, particularly with weak family structure.
Any suggestions would be welcome!
Thank you,
Javan Bauder
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Thank you both for your replies and excellent suggestions. My first choice would indeed to try my analyses with and without full-sibs to see if/how the results differ but I might not have the time for that.
Gregoire, my sampling design is very opportunistic (just because Eastern Indigo Snakes are very hard to find) so there is a large cluster of points (about 60% of my samples) within an area roughly one-quarter the size of my entire study area. Some, but not all, of the full-sibs occur within this cluster. But your second point about needing to meet the assumptions of HWE is what I am most wondering about. In my landscape genetics analyses I am testing for a relationship between individual-based genetic distance and cost distance measured on a resistance surface. If genetic distance is measured using only the mathematical dissimilarity among genotypes (e.g., Bray-Curtis distance, Euclidean distance, or principle components axes) then it seems that it does not matter if HWE is met or not (i.e., the assumption of HWE does not seem to come into play in this approach). However, I think I would want a second opinion if this is indeed the case!
Chrystelle, I really like your suggestion about doing multiple runs in COLONY especially with changing the settings.
Thanks again,
Javan
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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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I have a question about the best way to test for violations of Hardy-Weinberg Equilibrium (HWE) among microsatellite loci for a species that is continuously distributed across a study area and showing IBD. We are looking at how landscape features affect gene flow among Eastern Indigo Snakes across a 25 x 50 km study area. We have 110 samples and about half are clustered in the southern half of the study area. A spatial correlogram of individual genetic distance shows that spatial autocorrelation among samples becomes non-significant at 5-10 km. We have used COLONY to identify full-sibs and found about 15 full-sib families although family size was usually two (max. four). There is significant IBD within our study area. STRUCTURE identifies K=4 with all 110 samples but when we randomly exclude all but one full-sib from each full-sib family STRUCTURE identifies K=1-2. We suspect that these STRUCTURE results are the result of neighborhood effects and IBD, respectively.
When we test for violation of HWE at our 15 loci, four have significant violations of HWE. Estimated null allele frequencies at these four loci are 6-15%. When we randomly excluded all but one member from each full-sib family, these four loci were still significantly out of HWE.
In a situation such as this, is it appropriate to test for HWE using all samples? I know that in systems with discrete populations researchers often test for HWE within each population, since violations may represent a mixture of multiple populations. But any designations of “populations” in our study area seem very arbitrary (e.g., driven by sampling intensity rather than the distribution of individuals).
Does anyone have any suggestions about the appropriate way(s) to test for HWE in a system such as ours?
Thanks,
Javan Bauder
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You expect HWE in a panmictic population. If there is population structure (IBD), why should you test for HWE?
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Hello Everyone,
I have a dataset of ~400 individuals typed at 14 microsatellites (sampling was individual-based), and would like to analyze these using RDA. I initially estimated Smouse and Peakall (1999) genetic distance among individuals, but I’m not sure I could use a matrix of genetic distances as a response variable for RDA analyses.
Alternatively, I thought of using the frequency/count of the most common allele (per locus) in genotypes instead. Ex. with one locus and 2 alleles:
Alleles = ‘A’ and ‘B’, ‘A’ being the most common among the individuals sampled.
Individual—genotype -- ‘A’ allele count in genotype:
Ind1--AA--2
Ind2—AB-- 1
Ind3—BB— 0
Could this be a better/adequate way of representing the response variable?
Thanks in advance for your help.
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Dear Jimena,
You can construct a PCA-based genetic matrix of genotypes as explained in Shirk et al.
(2010). See paper attached for an example of use of RDA in this context.
All the best
Julien
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Good day
I am doing a study on landscape genetics at a fine-scale and need to use the program GESTE however I'm struggling to create the factor input file.
Can anybody please help me with this?
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Hi Stephanie
Thank you for your response...
I understand that each row represents a factor and that the values are for each site.
So if I have a factor of altitude, my values would be the altitude itself right? Or is it an arbitrary number that represents each altitude?
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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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We had already started a research, based on landscape genetics for a resistance animal species. In this study, we used SSR markers for genetic distance parameters such as Fst, Nei distance etc,. Now, is it logic using OWA scenarios to determine habitat suitability between two centroid of animal population?
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Yes, this paper used weighted averages of landscape variables to compare least cost path distances between sites: Rangewide landscape genetics of an endemic Pacific northwestern salamander, Molecular Ecology (2013) doi: 10.1111/mec.12168
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I'm working on a fish species with populations distributed across a river network, using microsatellite markers to investigate population genetics and landscape genetics in a conservation context.  I would like to:
1) Tease apart which landscape-based distance variables (distance between, elevation change, count of anthropogenic structures, etc.) are contributing most to fragmentation of identified populations; and
2) If the top contributing types of features are anthropogenic (e.g., dams or road crossings), I would like to identify the individual features within that type that are functioning as the most restrictive barriers.
Any ideas of potentially useful programs, analyses, or references would be appreciated.  Thank you.
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I would suggest looking at the new Bedassle package in R, from Gideon Bradburg, Peter Ralph, and Graham Coop.  It does many of the things you are looking to.  It uses a fairly complex Bayesian framework, and getting it running in R is not trivial.  But it might outperforming other platforms like genalex, spagedi, arlequin, or genodive, that will also do some of the analyses you want.
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Landscape genetics is combination of landscape ecology and conservation genetics. Diversity is generated by cross pollination which may be mediated by wind or any biological agent (pollinator). What will be difference in landscape genetics of species if it is pollinated by insect or wind...
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Hi,
you might be interested in looking to literature on Salix species: Salix flowers are usually wind-pollinated, but some species are also pollinated by bumble bees.
best regards
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I would like to perform a path analysis but I only have 4 distance matrices. First, can I really do it? And what software should I use?
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They also have a LISREL package in R "lisrelToR"
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I'm looking for a database that has landscape genetics data (genetic data + landscape features corresponding to the sampled locations). Does such a database exist?
Thans
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Hello Hamid,
Here's a few places to start. These government sites are a mix of data types so its best to start up a conversation regarding what data types exist and what can be shared. Definitely the genetic data will need to be asked for directly but as government employees, most agencies are obliged to share upon request. The nice thing about using the stream GIS layers as a framework for genetic data is that individuals are highly constrained in the direction they travel and its easier to model expectations for gene flow.
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When you run a simple mantel test, do the distance matrices have to be in the same order and to correspond the rows to the same individual or register?
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Not sure I fully understand your question, but almost all spatial analysis is going to require that you keep track CONSISTENTLY of the entities. So I'd say yes, you need to preserve the ordering. In fact, one of the ways that we might do a simple simulation test, would be to compute for the OBSERVED labels, and then scramble them (say reorder them 100 times) and see how DIFFERENT the observed value is from the distribution of values from the variations? Maybe this does not make sense from my poor explanation, but take a look at  a book like EXPLORATORY SPATIAL DATA ANALYSIS by Bailey and Gatrell. I think it does a good job on the idea of simulating random variations on the observed data.
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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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Does anyone know about courses of spatial ecology, landscape genetics, seascape ecology scheduled for this year? With application period still open.
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Did you see, the uicn courses?
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I need some articles related to this topic and some suggestions to analyze these data together.
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Hi Patric. Thanks so much!
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I have to cover around 10 sampling sites for answering hybridization between wolves and dogs and the genetic structure of wolves in India.
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Hi Srinivas,
I agree with the previous comments above regarding sampling size, but especially with that of Nickolay Markov. As you have packs, your study design is nested and you have to modify your sampling design to this feature. From that point of view I would suggest to reduce the number of samples per location and increase the number of locations. Given the large terretories wolves could inhabit, you might have problems to come up with 30 samples at one location when there is just one pack of wolfes around and you probably have to sample each wolf twice or more often to get the n = 30, which makes of course no sense at all.
So, I would suggest to sample packs instead of locations. Try to sample ca. 5 individuals per pack (depending on how big packs are in your study region). Given the previous comments (30 samples for 10 locations) would result in 300 samples in total. Changing the design based on packs instead of locations and using 300 samples could allow you to sample 60 packs! Spread this number equally over your study area involving areas close to human settlements (likely more hybridization with feral dogs) and in the wild far apart from human settlements (if possible; hybridization less likely), then you would have a very nice and sound sampling design.
Also think about your marker system. If you work with microsatellites, make sure that you know in advance that you have private alleles, at least in some of the markers, to be able to determine possible hybrids. You should have a minimum of eight (better more than ten) loci that are working. If you haven't done so, make an initial screening on markers - that safes money and time.
Hope that helps.
Good luck,
Jan
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I am trying to figure out the best approach to determine the effect of fragmentation on the standing genetic variation in a population. We have the relatedness data of the individuals in the form of a family pedigree (20-30 years of data, ~500 individuals). This means we can estimate a relatedness matrix for all living individuals. Till now, the population has been allowed to freely mix, and did so often over long distances. However, changes in the environment is going to restrict these movements to a degree. The sub populations are going to be rather variable in size from a less than 10 to more than 150. We will get an estimate of the reduction in these movements. What I want to do is model/estimate what the effect is of these reduced movements on the standing genetic variation of the population as a whole. I am thinking of using some genetic drift principles to model these things. What would others suggest?
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I am not sure you can say much about SGV with only pedigree data. And depending on the degree of a barrier, you might not see very much in such a short period of time in terms of how your relatedness matrix correlates to habitat fragmentation.
But modelling drift is a good start - you could do some coalescence modelling of populations of different sizes with different degrees of migration and see if you can differentiate it from a null model.
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I am having trouble finding information on landscape genomics workshops in the U.S. I have limited funds which is why I'm trying to find something close to where I live. I am specifically interested in learning more about methods to analyze NGS data to identify loci associated with climatic variation, designing a good sampling scheme and how one can use SNPs generated from restriction site associated seq (RAD) data (and other platforms) to address landscape genomics questions.
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Too bad you just missed a deadline for a landscape genetic workshop in Germany!
Otherwise, you could look at the Summer Institute in Statistical Genetics, which has different modules every year but always some on genomics.
Lastly, some good articles on landscape genomics recently, especially on climate variation:
Bierne, N., Roze, D., & Welch, J. J. (2013). Pervasive selection or is it…? Why are FST outliers sometimes so frequent? Molecular Ecology, 22(8), 2061–4. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/23671920
Eckert, A. J., van Heerwaarden, J., Wegrzyn, J. L., Nelson, C. D., Ross-Ibarra, J., González-Martínez, S. C., & Neale, D. B. (2010). Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics, 185(3), 969–82. doi:10.1534/genetics.110.115543
Joost, S., Vuilleumier, S., Jensen, J. D., Schoville, S., Leempoel, K., Stucki, S., … Manel, S. (2013). Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics. Molecular Ecology.
Paz-Vinas, I., Quemere, E., Chikhi, L., Loot, G., & Blanchet, S. (2013). The demographic history of populations experiencing asymmetric gene flow: combining simulated and empirical data. Molecular Ecology, 22, 3279–3291. doi:10.1111/mec.12321
Sork, V. L., Aitken, S. N., Dyer, R. J., Eckert, A. J., Legendre, P., & Neale, D. B. (2013). Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate. Tree Genetics & Genomes. doi:10.1007/s11295-013-0596-x
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I am running Barrier to detect genetic barriers among my populations, and want to use the bootstrap procedure to assess their significance. But for some reason the user needs to feed the program with the bootstrapped matrices. In my case, PhiST distances from mtDNA sequences. Does anybody have any experience with this, or know about e.g. any R-package able to perform this task?
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Hola María,
Puedes usar PHYLIP, para hacer un bootstrap de tu matriz de haplotipos en seqbot y después calcular tus distancias genéticas en dnadist también en PHYLIP.
Puedes explorar!,
Saludos