Readings

Readings by day and topic

For each topic, there will be a required reading (or pre-class activity) and several optional readings.

We expect you to have read the required reading before the day. There will not be time during the week of the course to read new materials and therefore we highly suggest that you do these readings in advance of the course starting.

Sunday

Basic Population Genetics

No background reading is required for this module.

Basic R

If you are new to R, please attempt the following tutorials on your own. If you are experienced in R, skim through the tutorials and make sure that all the topics are familiar to you.

Through this course, most of the examples you are likely to encounter will predominately use base R. However, learning Tidyverse syntax will make your life easier in the long run, especially if you are going to be manipulating spatial data.

Basic Bash

As with R, you need to have some basic comfort and familiarity with bash. Either ensure you can complete the tutoirals on your own or attend this special Sunday session.

See activities and links on bash tutorial webpage.

Monday

Land, river, sea

Required
  • Storfer, A., Patton, A., & Fraik, A. K. (2018). Navigating the interface between landscape genetics and landscape genomics. Front Genet, 9, 68. doi:10.3389/fgene.2018.00068
Optional

  • Manel, S., Schwartz, M. K., Luikart, G., & Taberlet, P. (2003). Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology and Evolution, 18(4), 189-197. doi:papers3://publication/doi/10.1016/S0169-5347(03)00008-9 This is the original paper defining landscape genetics as a field.

  • Riginos, C., & Liggins, L. (2013). Seascape genetics: populations, individuals, and genes marooned and adrift. Geography Compass, 7(3), 197-216. doi:papers3://publication/doi/10.1111/gec3.12032 Discusses many ways that marine landscape genetics is distinct from terrestrial

  • Selkoe, K. A., Scribner, K. T., & Galindo, H. M. (2016). Waterscape genetics – applications of landscape genetics to rivers, lakes, and seas. In N. Balkenhol, S. A. Cushman, A. Storfer, & L. P. Waits (Eds.), Landscape Genetics: Concepts, Methods, Applications (pp. 1-27): John Wiley & Sons, Ltd. Includes overviews on some of the contrasting features of marine and freshwater environments

  • Liggins, L., Treml, E. A., & Riginos, C. (2019). Seascape genomics: contextualizing adaptive and neutral genomic variation in the ocean environment. Overviews on issues particular to marine studies

  • Blanchet, S., Prunier, J. G., Paz-Vinas, I., Saint-Pe, K., Rey, O., Raffard, A., Mathieu-Begne, E., Loot, G., Fourtune, L., & Dubut, V. (2020). A river runs through it: The causes, consequences, and management of intraspecific diversity in river networks. Evolutionary Applications, 13(6), 1195-1213. doi:10.1111/eva.12941 Features and emerging conclusions from freshwater systems

Maps & spatial data

No readings are required.

  • The best website explaining how to use spatial data in R is [Geocomputation with R, a book on geographic data analysis, visualization and modeling](https://r.geocompx.org)

  • Leempoel, K., Duruz, S., Rochat, E., Widmer, I., Orozco-terWengel, P., & Joost, S. (2017). Simple rules for an efficient use of geographic information systems in molecular ecology. _Frontiers in Ecology and Evolution, 5_. doi:10.3389/fevo.2017.00033 *Reviews various approaches and tools, good list of terrestrial environmental data sources included in appendix*

Tuesday

Genetic differentiation and genetic structuring

Required
  • Waples, R. S., & Gaggiotti, O. E. (2006). What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Molecular Ecology, 15(6), 1419-1439. doi:papers3://publication/doi/10.1111/j.1365-294X.2006.02890.x This study gives a nice example of what a population is.
Optional
  • Leslie, S., Winney, B., Hellenthal, G., Davison, D., Boumertit, A., Day, T., Hutnik, K., Royrvik, E. C., Cunliffe, B., Wellcome Trust Case Control, C., International Multiple Sclerosis Genetics, C., Lawson, D. J., Falush, D., Freeman, C., Pirinen, M., Myers, S., Robinson, M., Donnelly, P., & Bodmer, W. (2015). The fine-scale genetic structure of the British population. Nature, 519(7543), 309-314. doi:10.1038/nature14230 This study is a nice example of how clustering can be used to study population structure.
  • Linck, E., & Battey, C. J. (2019). Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Molecular Ecology Resources, 19(3), 639-647. doi:10.1111/1755-0998.12995

RDA as a general toolkit

Required
  • Capblancq, T., & Forester, B. R. (2021). Redundancy analysis: A Swiss Army Knife for landscape genomics. Methods in Ecology and Evolution. doi:10.1111/2041-210x.13722 Clear explanations and worked examples that we will follow in class
Optional

  • Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M., & Holderegger, R. (2015). A practical guide to environmental association analysis in landscape genomics. Molecular Ecology, 24(17), 4348-4370. doi:papers3://publication/doi/10.1111/mec.13322 Highly recommend reading as a general overview to GEA/EEA analyses and concepts

  • Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L., & Lasky, J. R. (2016). Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Molecular Ecology, 25(1), 104-120. doi:papers3://publication/doi/10.1111/mec.13476 Evaluates various methods for finding genotype-by-environment associations and finds that RDA methods often best

  • Lasky, J. R., Josephs, E. B., & Morris, G. P. (2023). Genotype-environment associations to reveal the molecular basis of environmental adaptation. Plant Cell, 35, 125-138. doi:10.1093/plcell/koac267 Focusing on plants, reviews the strengths and shortcomings of GEA approaches

Wednesday

Simulations and demographic analyses

Required

  • Sousa, V., Hey, J. Understanding the origin of species with genome-scale data: modelling gene flow.Nat Rev Genet 14, 404–414 (2013). https://doi.org/10.1038/nrg3446

  • Kelleher, J., Wong, Y., Wohns, A.W. et al. Inferring whole-genome histories in large population datasets. Nat Genet 51, 1330–1338 (2019). https://doi.org/10.1038/s41588-019-0483-y

Optional
  • Meier, J.I., Sousa, V.C., Marques, D.A., Selz, O.M., Wagner, C.E., Excoffier, L. and Seehausen, O. (2017), Demographic modelling with whole-genome data reveals parallel origin of similar Pundamilia cichlid species after hybridization. Mol Ecol, 26: 123-141. https://doi.org/10.1111/mec.13838

Thursday

Resistance surfaces

Required

  • Beninde, J., Wittische, J., & Frantz, A. C. (2023). Quantifying uncertainty in inferences of landscape genetic resistance due to choice of individual-based genetic distance metric. Molecular Ecology Resources. doi:10.1111/1755-0998.13831

  • Peterman, W. E. (2018). ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms. Methods in Ecology and Evolution, 9(6), 1638-1647. doi:10.1111/2041-210X.12984

Future projections with GDM & GFs

Required
  • Fitzpatrick, M. C., & Keller, S. R. (2015). Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett, 18(1), 1-16. doi: 10.1111/ele.12376
Optional

  • Mokany, K., Ware, C., Woolley, S. N. C., Ferrier, S., Fitzpatrick, Matthew C., & Bahn, V. (2022). A working guide to harnessing generalized dissimilarity modelling for biodiversity analysis and conservation assessment. Global Ecology and Biogeography, 31(4), 802-821. doi:10.1111/geb.13459. A worked example for GDM

  • Ferrier, S., Manion, G., Elith, J., & Richardson, K. (2007). Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13(3), 252-264. doi:papers3://publication/doi/10.1111/j.1472-4642.2007.00341.x Original method for GDM

  • Ellis, N., Smith, S. J., & Pitcher, C. R. (2012). Gradient forests: calculating importance gradients on physical predictors. Ecology, 93(1), 156-168. doi:papers3://publication/uuid/06CCD6BA-6A4B-49EB-AE26-736E8A04B9C8 Original method for GF

Biophysical models

Required
  • Jahnke, M., Jonsson, P. R., Moksnes, P. O., Loo, L. O., Nilsson Jacobi, M., & Olsen, J. L. (2017). Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak-Kattegat region of the eastern North Sea. Evolutionary Applications, 1-46. doi:10.1111/eva.12589 Comprehensive review of studies pairing seascape genetic data with biophysical models
Optional
  • Boulanger, E., Dalongeville, A., Andrello, M., Mouillot, D., & Manel, S. (2020). Spatial graphs highlight how multi‐generational dispersal shapes landscape genetic patterns. Ecography. doi:10.1111/ecog.05024

Friday

More GEA: genetic offsets and maladaptation

Required
  • Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M., & Keller, S. R. (2020). Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annual Review of Ecology, Evolution, and Systematics, 51(1), 245-269. doi:10.1146/annurev-ecolsys-020720-042553 Review of maladaptation and genomic offset concepts
Optional

  • Gougherty, A. V., Keller, S. R., & Fitzpatrick, M. C. (2021). Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat Clim Change, 11(2), 166-171. doi:10.1038/s41558-020-00968-6 Interesting conceptual example that integrates genomic offsets with dispersal capacity

  • Fitzpatrick, M. C., V. E. Chhatre, R. Y. Soolanayakanahally and S. R. Keller, 2021 Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol Ecol Resour 21: 2749-2765. One of the best examples of an empirical test of GEA predictions

Landscape genomics and genetic architectures

No readings are required.

Optional

  • Laruson, A. J., Fitzpatrick, M. C., Keller, S. R., Haller, B. C., & Lotterhos, K. E. (2022). Seeing the forest for the trees: Assessing genetic offset predictions from gradient forest. Evol Appl, 15(3), 403-416. doi:10.1111/eva.13354

  • Lotterhos, K. E. (2019). The effect of neutral recombination variation on genome scans for selection. G3 (Bethesda), 9(6), 1851-1867. doi:10.1534/g3.119.400088

  • Bierne, N., Gagnaire, P.-A., & David, P. (2013). The geography of introgression in a patchy environment and the thorn in the side of ecological speciation. Current Zoology, 59(1), 72-86. doi:papers3://publication/doi/10.1093/czoolo/59.1.72