Hi Mariano,

I am doing something similar to your work, but with marine species. In my search, I found many methods that make a relation between environment with genetics parameters. One useful software is GenAlex 6.2. But, the best statistical, software or method depends of your hypothesis and needs, a good summary of these techniques are listed in these papers:

Landscape genetics: combining landscape ecology and population genetics. Menl et al 2003. Trends in Ecology & Evolution

Putting the "landscape" in landscape genetics. Storfer et al. 2007. Heredity

Many papers are being released each day. I am following the work of Ian Wang and putting "landscape genetics" in the space of search, both things here in researchgate with good results....

Some results thats works for me are:

Temunović, M., Franjić, J., Satovic, Z., Grgurev, M., Frascaria-Lacoste, N., & Fernández-Manjarrés, J. F. (2012). Environmental heterogeneity explains the genetic structure of Continental and Mediterranean populations of Fraxinus angustifolia Vahl. PloS one, 7(8), e42764. doi:10.1371/journal.pone.0042764

And the work of Carlos Guarnizo: https://www.researchgate.net/publication/259321781_Geographic_Determinants_of_Gene_Flow_in_Two_Sister_Species_of_Tropical_Andean_Frogs

I heard recently about workflows software to make tasks like the relation between environment dataset with genetics parameters matrix, some of them are http://orange.biolab.si/

https://github.com/arborworkflows

Finally, if you know about a course related with this topic here in Colombia please let me know.

Best regards,

Angelica

Article Geographic Determinants of Gene Flow in Two Sister Species o...

It looks like you need to use stochastic modeling probably a Markov process with at least four states in the Markov chain

I've not got a perfect answer for you, but the section of this paper relating to the bubble plot is a nice way of determining if ecological niches (or technically the sum of physical and biological stresses) are significantly different from each other. If they are, then you might be able to determine if genetics and morphometrics differ in the same manner? Currently working on a paper that suggests physiology does differ if niches differ. http://www.currentzoology.org/paperdetail.asp?id=12250

If I understand correctly you want to check if the population structure is influenced by landscape features. For this you need to analyse the population structure (e.g. programmes TESS, structure, Geneland or easier things like pairwise Fst, G'st and D between populations. Once this is done you then can compare the distance matrix based on genetic distances with distance matrices such as your morphometric data or landscape features. Here I would look at work of Landguth and Cushmans work. For the first part there is a recently developed R package (if you are familiar with R) where you can analyse genetic structure (PopGenReport.HTH

ps there is a summer school this March in Germany on this topic run by Landguth, Balkenhol et al.. Not sure if they are booked out already, but I assume so.

http://78.47.85.98/website/

I think you could read something pubblished by Carlos Melian that have recently integrated such variables in an evolutionary contest "Eco-evolutionary Dynamics of

Individual-Based Food Webs"

Bernd thank you very much for the information but unfortunately the application process finished and the course is now full

Thanks so much to all who've responded so far

Gideon Bradurd, a graduate student at UC Davis, is developing very cool methods for dealing with "Landscape genetics" and answering questions like how is genetic variation partitioned over a landscape? What can we infer about the processes that are generating structure? He describes these methods and provides R scripts for them at his web page

http://genescape.ucdavis.edu/research/

It seems like some of these methods might be relevant.

Dear Mariano,

if you can express the factors that you are measuring in differences across a geographic range, one way that could be very useful is to employ Mantel-tests to compare difference-matrices.

Mantel test is perfect to detect "patterns" between matrices. Is used also in statistical to test spatial autocorrelation in dataset. Easy to be applied on R. I am agree with de Jong! Good work,

Federico

Hi Mariano,

Echoing Sebastian, you may want to check out the method I developed last year, which is called BEDASSLE (Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology). It's designed to answer the same kinds of questions that can be addressed with the partial Mantel test (e.g. - accounting for the effect of geographic distance, does ecological or geomorphometric distance explain patterns of genetic differentiation?), but it does so without the type I error problems that the partial Mantel and Mantel tests have. See Guillot & Rousset 2013 "Dismantling the Mantel Tests" (http://onlinelibrary.wiley.com/doi/10.1111/2041-210x.12018/abstract) for a discussion of the Mantel test's weaknesses .

BEDASSLE works by modeling the covariance in allele frequencies between different populations (across loci) as a decreasing function of their pairwise geographic and ecological distances. By comparing the effect sizes estimated for the different distance elements, you can make statements about the contributions of ecological (and/or geomorphometric) distance to genetic differentiation in terms of isolation by distance. For example, you could find that one meter of difference in altitude between two populations is the equivalent of an extra 3 kilometers of pairwise geographic separation. In your case, the ecological variable might be being on the same or opposite side of a hypothesized barrier to gene flow, or it could be distance in geomorphospace, or difference in some environmental trait measured at collection sites, or some combination of the three. The method is implemented in R, and the package ("BEDASSLE") can be downloaded off of CRAN.

You could also check out Duforet-Frebourg & Blum's recent paper in Evolution (http://onlinelibrary.wiley.com/doi/10.1111/evo.12342/abstract), for identifying barriers to gene flow.

Your research sounds really interesting! Good luck!

-Gideon

Hi Mariano,

You may be interested by this paper:

Michael Kleyer, Stephane Dray, Francesco de Bello, Jan Leps , Robin J. Pakeman, Barbara Strauss, Wilfried Thuiller & Sandra Lavorel (2012) Assessing species and community functional responses to environmental gradients: which multivariate methods? Journal of Vegetation Science 23: 805–821

All the best!

Hi Mariano,

I think your question is one of the "complex systems" and the agent-based modeling approach can be a good choice, where all multidisciplinary questions are mentioned. GAMA is an interesting example:

https://www.researchgate.net/publication/237837524_GAMA_A_Spatially_Explicit_Multi-level_Agent-Based_Modeling_and_Simulation_Platform

Cheers

Viet

Conference Paper GAMA: A Spatially Explicit, Multi-level, Agent-Based Modelin...