Landscape Genetics - Science topic

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.

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!

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?

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?

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?

I have been looking for one that explains how to evaluate effective landscape connectivity, preferentially in Europe.

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.

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?

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

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?

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

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.

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?

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.

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?

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.

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...

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?

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

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?

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.

Does anyone know about courses of spatial ecology, landscape genetics, seascape ecology scheduled for this year? With application period still open.

I need some articles related to this topic and some suggestions to analyze these data together.

I have to cover around 10 sampling sites for answering hybridization between wolves and dogs and the genetic structure of wolves in India.

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?

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.

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?