{"data":{"id":"10.6084/m9.figshare.706347","type":"dois","attributes":{"doi":"10.6084/m9.figshare.706347","prefix":"10.6084","suffix":"m9.figshare.706347","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Vincenzi, Simone","nameType":"Personal","givenName":"Simone","familyName":"Vincenzi","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events"}],"publisher":"figshare","container":{},"publicationYear":2013,"subjects":[{"subject":"Ecology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Evolutionary Biology"}],"contributors":[],"dates":[{"date":"2013-05-23","dateType":"Created"},{"date":"2015-12-02","dateType":"Updated"},{"date":"2013","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["294334775 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Data and code for \"Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events\" ######### The simulation model in in the file Monte.Carlo.topub.r. Within the file, all input and output are described in detail. You can simply source the file: source(\"Monte.Carlo.topub.r\") ########################### the file ext.RDS can be loaded in R by typing ext.df = readRDS(\"ext.RDS\") after having moved the file to you current directory ######## In the data set ext.RDS, I included the following variables and outcomes of simulations: sel - strength of selection cat.freq - probability of occurrence of point extremes pop.ext10.rand - mean population size in the \"sampling window\" addvar.mean.10.rand. - mean additive genetic variance in the \"sampling window\" meanopt - directional trend mut - mutation amplitude mean.pheno - mean phenotype at the end of simulation time (interesting for replicates in which ext = 1) cat.bef - number of point extremes in the five years before extinction (interesting for replicates in which ext = 1) ext_year - extinction time (299 for replicates that persisted up to the end of simulation time) ext - extinct (1) or not (0) ############# For some of the analyses and plots in Vincenzi Point Extremes.r, the full dataset is required Please download sum.ris1.RDS and sum.ris2.RDS and move them to the current directory of R Then: sum.ris1 = readRDS(\"sum.ris1.RDS\") sum.ris2 = readRDS(\"sum.ris2.RDS\") sum.ris = c(sum.ris1,sum.ris2) rm(list=c(\"sum.ris1\",\"sum.ris2\")) The list sum.ris contains some of the measured variables for all the 25 600 replicates. In particular: extinct - pop going extinct or not\u003cbr\u003eaddvar - additive genetic variance at each time step\u003cbr\u003ephenvar - phenotypic variance at each time step\u003cbr\u003eyearextinct - year of extinction (299 if not extinct)\u003cbr\u003ephenomean - phenotypic mean at each time step\u003cbr\u003esdopt - increase in variability of the optimum\u003cbr\u003emeanopt - directional trend\u003cbr\u003eselstrength - strength of selection\u003cbr\u003emutalfa - mutation amplitude\u003cbr\u003efitness.mean - mean fitness at each time step\u003cbr\u003eoptimum - optimum at each time step\u003cbr\u003epopsize.post - population size after mortality at each time step Please write to simon.vincenz@gmail.com in the case help or clarifications are needed","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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