Environmental Data: Simulating population divergence of Northern chamo...
Description
# General description Genomic data, habitat suitability raster files and scripts to run gen3sis to simulate cumulative divergence over time as approxima...
Citation
Leugger, F., Broquet, T., Karger, D. N., Rioux, D., Buzan, E., Corlatti, L., Crestanello, B., Curt-Grand-Gaudin, N., Hauffe, H. C., Rolečková, B., Šprem, N., Tissot, N., Tissot, S., Valterová, R., Yannic, G., Pellissier, L. (2022). Simulating population divergence of Northern chamois in the Alps based on habitat dynamics. EnviDat. https://www.doi.org/10.16904/envidat.291.
Resources
Genetic data Northern chamois across Alps
ddRADseq data (vcf file and sampling locations) of 449 Northern chamois (Rupicapra rupicapra) sampled across the European Alps
Genetic data Northern chamois across AlpsMetadata
Metadata
MetadataRivers and glaciers
Input rasters required to set up gen3sis simulations
Rivers and glaciersFossil records
Chamois fossil records with estimated time span and coordinates. Please see the sheet "references_data_sets" for the references and read the read_me.txt file prior to using the data.
Fossil recordsChamois occurrences
Coordinates (0.05° resolution) of chamois occurrences across the Alps. See Suppl. mat. Table S1 for more information and citation of the data sets used.
Chamois occurrencesgen3sis simulations
Scripts to run gen3sis simulations, including configs used for the publication. __1.2_gen3sis_input_generation.R__ creates the input files required for __2.2_gen3sis_simulation_chamois.R__, which is running the gen3sis simulations. __ 3_gen3sis_track_simulation_over_time.R__ and __4_gen3sis_divergence_sampling_locations.R__ can be used to start the analysis.
gen3sis simulationsHabitat suitability raster
Habitat suitability raster stacks (.grd) files for different terrain slope scenarios use (see main publication its supporting information for details) for Northern chamois until 20,000 years BP in 100 year time steps. ### Description of layer names: * __prob__: habitat suitability ranging continuously from 0 (unsuitable) to 1 (very well suitable). *__bin__: binary classification of habitat suitability based on TSS threshold optimization (see main publication and supporting information for details). *__glm__: generalized linear model (to model and predict habitat suitability) *__gam__: generalized additive model *__gbm__: generalized boosted regression model *__rf__: random forest *__ensemble__: average of all 4 model types
Habitat suitability raster