Nemo
Follow us on twitter @NemoSimul
Latest version:
v2.3.56 [24 Oct 2022] - released 24 October 2022
See the CHANGELOG and the manual
DOWNLOAD
The source code, MacOSX and Windows executables, and documentation are available at the download site, as is the user's manual.
You can subscribe to the nemo-announce mailing list to get news on updates of all Nemo-related projects, and to the nemo-simul mainling list to post your questions, comments, and bug reports.
Development guidelines and code documentation are available here.
Related projects
Nemo-age is the age-/stage-structured version of Nemo. It is available on the git repository at https://bitbucket.org/ecoevo/nemo-age-release
nemoage0.29.0 has been released July 10, 2020.
nemosub is a utility to automatically send job requests from Nemo's init files to a job scheduler on a cluster. Job specification can be set directly from the simulation init files. It works for most types of cluster scheduler: Slurm (sbatch), OAR (oarsub), PBS (qsub) and LSF (bsub).
nemosub is available at https://bitbucket.org/ecoevo/nemosub.
License
Nemo is released under the GNU General Public License version 2+
© 2006-2022 The authors
Authors:
Frédéric Guillaume (main developer)
Jacques Rougemont (original MPI code)
Sam Yeaman
Kimberly Gilbert
Jobran Chebib (variable pleiotropy)
Max Schmid (phenotypic plasticity)
Champak Beeravolu Reddy (epistasis)
Overview – What's in Nemo?
Nemo is a forward-time, individual-based, genetically explicit, and stochastic simulation program designed to study the evolution of quantitative traits and population genetics in a flexible (meta-)population framework.
Nemo implements many life cycle events and evolvable traits with a variety of genetic architectures (see below). Species interaction between a parasite and its host can also be modeled (i.e., Cytoplasmic-Incompatibility inducing endosymbiont: Wolbachia). All this is framed within a flexible meta-population model that allows for patch-specific carrying capacities, dispersal rates, stochastic extinction/harvesting rates, and demographic stochasticity. Populations can be dynamically modified during a simulation, allowing for population bottlenecks, patch fusion/fisson, population expansion, etc. Spatially and temporally heterogenous selection on quantitative traits can also be modelled from the variation of local phenotypic optima.
Nemo's interface is a simple text file containing the simulation parameters and their values. Each parameter can have several argument values, which allows many simulations to be run from a single parameter file. Parameters can also be set with temporal values that will automatically modify the simulation settings during a run.
Main Features:
Genetic elements can be placed on a genetic map:
The following traits define different genotype-phenotype mapping of the genetic elements placed on the genetic map:
Additional evolvable traits can be added to a simulation:
With the availability of a genetic map, Nemo can be used to study the genetics of adaption, model scenarios of adaptation with gene flow, of population expansion into a new environment, adaptation to fluctuating environments, or the joint evolution of dispersal and deleterious mutations in structured populations, among other things. The number of populations, individuals per population or loci to simulate are only restricted by computer physical capacities. Large populations of 10e5-10e6 individuals carrying 10e2-10e3 loci necessitate about 3 to 5GB of RAM on a desktop computer. Nemo is highly optimized to run in batch mode and a parallel computing version is part of the release thus making it a very flexible and powerful simulation tool.
Nemo implements many life cycle events and evolvable traits with a variety of genetic architectures (see below). Species interaction between a parasite and its host can also be modeled (i.e., Cytoplasmic-Incompatibility inducing endosymbiont: Wolbachia). All this is framed within a flexible meta-population model that allows for patch-specific carrying capacities, dispersal rates, stochastic extinction/harvesting rates, and demographic stochasticity. Populations can be dynamically modified during a simulation, allowing for population bottlenecks, patch fusion/fisson, population expansion, etc. Spatially and temporally heterogenous selection on quantitative traits can also be modelled from the variation of local phenotypic optima.
Nemo's interface is a simple text file containing the simulation parameters and their values. Each parameter can have several argument values, which allows many simulations to be run from a single parameter file. Parameters can also be set with temporal values that will automatically modify the simulation settings during a run.
Main Features:
Genetic elements can be placed on a genetic map:
The following traits define different genotype-phenotype mapping of the genetic elements placed on the genetic map:
- neutral markers (microsatellites, SNPs)
- deleterious mutations (with locus-specific deleterious and dominance effects)
- quantitative traits (with pleiotropic quantitative loci, additive continuous or di-allelic QTL effects)
- Dobzhansky-Muller incompatibility loci (encoding epistatic interactions b/n pairs of loci)
Additional evolvable traits can be added to a simulation:
- dispersal rate (2 sets of loci with male- and female-specific expression)
- Wolbachia (maternally-inherited Cytoplasmic-Incompatibility inducing endosymbiont)
- breeding (with promiscuity, polygyny, monogamy, selfing, and cloning mating systems)
- disperse (migrant pool/propagule pool island model, 1D & 2D lattice models, etc.)
- aging (with ceiling patch regulation)
- selection (directional on deleterious mutations or stabilizing selection on quantitative traits with a multivariate Gaussian or quadratic selection surface)
- extinction/harvesting
- patch fusion/fission
- crossing design (full-sib/half-sib designs)
- Wright-Fisher population models (removes demographic stochasticity)
- and more...
With the availability of a genetic map, Nemo can be used to study the genetics of adaption, model scenarios of adaptation with gene flow, of population expansion into a new environment, adaptation to fluctuating environments, or the joint evolution of dispersal and deleterious mutations in structured populations, among other things. The number of populations, individuals per population or loci to simulate are only restricted by computer physical capacities. Large populations of 10e5-10e6 individuals carrying 10e2-10e3 loci necessitate about 3 to 5GB of RAM on a desktop computer. Nemo is highly optimized to run in batch mode and a parallel computing version is part of the release thus making it a very flexible and powerful simulation tool.
References
Please cite Nemo as: Guillaume, F., and J. Rougemont. 2006. Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22:2556-2557.
Here is a list of published work using Nemo as a research tool.
Modelling life history trait evolution
- Guillaume, F., and N. Perrin 2006 Joint evolution of dispersal and inbreeding load. Genetics 173:497–509.
- Guillaume, F., and N. Perrin 2009 Inbreeding Load, Bet Hedging, and the Evolution of Sex-Biased Dispersal. The American Naturalist 173:536-541.
Modelling local adaptation and the genetic architecture of complex (polygenic) traits
- Guillaume, F., and M. C. Whitlock 2007 Effects of migration on the genetic covariance matrix. Evolution 61:2398–2409.
- Yeaman, S., and F. Guillaume 2009 Predicting adaptation under migration load: the role of genetic skew. Evolution 63:2926-2938.
- Guillaume, F. 2011 Migration-induced phenotypic divergence: the migration-selection balance of correlated traits. Evolution 65:1723-1738.
- Yeaman, S., and M. C. Whitlock 2011 The genetic architecture of adaptation under migration-selection balance Evolution 65:1897-1911.
- Yeaman, S. 2013 Genomic rearrangements and the evolution of clusters of locally adaptive loci. PNAS 110:E1743-E1751.
- Debarre, F., S. Yeaman, and F. Guillaume 2015 Evolution of quantitative traits under a migration-selection balance: when does skew matter? Am. Nat. 186:S37-S47.
- Yeaman, S. 2015 Local adaptation by alleles of small effect. Am. Nat. 186:S74-S89.
- Gilbert, K. J., and M. C. Whitlock 2017 The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large‐effect alleles can allow range expansion. J. of Evol. Biol. 30:591-602.
- Chebib, J. and F. Guillaume 2017 What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation. Evolution 71:2298-2312.
- Schmid, M. and Guillaume, F. 2017 The role of phenotypic plasticity on population differentiation. Heredity 119:214-225.
- McDonald, T.K., and S. Yeaman 2018 Effect of migration and environmental heterogeneity on the maintenance of quantitative genetic variation: a simulation study. J. Evol. Biol. 31:1386-1399.
- Jasper RJ, and S Yeaman 2020 Local adaptation can cause both peaks and troughs in nucleotide diversity within populations. bioRxiv doi:10.1101/2020.06.03.132662.
Evolution of the genotype-phenotype map of complex traits
- Guillaume F, and SP Otto 2012 Gene functional trade-offs and the evolution of pleiotropy. Genetics 192:1389-1409.
- Chebib J, and F Guillaume 2019 The relative impact of evolving pleiotropy and mutational correlation on trait divergence. bioRxiv doi:10.1101/702407.
- Chebib J, and F Guillaume 2019 Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies. bioRxiv doi:10.1101/656413.
Transmission dynamics and population genetic consequences of Wolbachia infections
- Reuter, M., Lehmann, L., and F. Guillaume 2008 The spread of incompatibility-inducing parasites in sub-divided host populations. BMC Evolutionary Biology 8:134.
- Zhang, H., Guillaume, F. and J. Engelstädter 2012 The dynamics of mitochondrial mutations causing male infertility in spatially structured populations. Evolution 66:3179-3188.
- Schmidt, T. L., I. Filipovic, A. A. Hoffmann, and G. Rasic 2018 Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia Heredity 120:386–395.
Testing statistical methods and estimators
- Jaquiéry, J., Guillaume, F., and N. Perrin 2009 Predicting the deleterious effects of mutation load in fragmented populations. Conservation Biology 23:207-218.
- Whitlock, C., and F. Guillaume 2009 Testing for Spatially Divergent Selection: Comparing QST to FST. Genetics 183:1055-1063.
- Hoban, S., J. A. Arntzen, M. W. Bruford, J. A. Godoy, A. Rus Hoelzel, G. Segelbacher, C. Vila, and G. Bertorelle 2014 Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion. Evolutionary Applications 7:984-998.
- Gilbert, K. J., and M. C. Whitlock 2015 QST–FST comparisons with unbalanced half-sib designs. Molecular Ecology Resources 15:262-267.
- Gilbert, K. J., and M. C. Whitlock 2015 Evaluating methods for estimating local effective population size with and without migration. Evolution 69:2154-2166.
- Gompert, Z., and C. A. Buerkle 2016 What, if anything, are hybrids: enduring truths and challenges associated with population structure and gene flow. Evolutionary Applications 9:909-923.
- Nietlisbach, P., Keller, L. F., Camenisch, G., Guillaume, F., Arcese, P., Reid, J. M. and Postma, E. 2017. Pedigree-based inbreeding coefficient explains more variation in fitness than heterozygosity at 160 microsatellites in a wild bird population. Proc. R. Soc. B 284:20162763.
- Yeaman, S., Gerstein, A. C., Hodgins, K. A. and Whitlock, M. C. 2018 Quantifying how constraints limit the diversity of viable routes to adaptation. PLOS Genetics 14:e1007717.
- Nietlisbach, P., Muff, S., Reid, J. M., Whitlock, M. C. and Keller, L. F. 2019 Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evolutionary Applications 12:266-279.
Testing demographic and evolutionary scenarios against observed patterns of genetic variation
- Broquet, T., F. Viard, and J. M. Yearsley 2013 Genetic Drift and Collective Dispersal Can Result in Chaotic Genetic Patchiness. Evolution 67:1660-1675.
- Perrier, C., J.-L. Baglinière, and G. Evanno 2013 Understanding admixture patterns in supplemented populations: a case study combining molecular analyses and temporally explicit simulations in Atlantic salmon. Evolutionary Applications 6:218–230
- Fernandez-Cebrian, R., R. M. Araguas, N. Sanz, and J. L. Garcia-Marin 2014 Genetic risks of supplementing trout populations with native stocks: a simulation case study from current Pyrenean populations. Can. J. Fish. Aquat. Sci. 71:1243-1255.
- Vera-Escalona, I., Senthivasan, S., Habit, E., and D.E. Ruzzante. 2018 Past, present, and future of a freshwater fish metapopulation in a threatened landscape. Conservation Biology 32:849-859.
- Vera-Escalona, I., Habit, E., and D.E. Ruzzante. 2019 Invasive species and postglacial colonization: their effects on the genetic diversity of a Patagonian fish. Proc. Roy. Soc. B 286:20182567. doi:10.1098/rspb.2018.2567.
- Grossen C, Guillaume F, Keller LF, and D Croll. 2020. Purging of deleterious mutations through severe bottlenecks in Alpine ibex. Nature Communications, 11:1001; doi:10.1038/s41467-020-14803-1.
Eco-evolutionary dynamics and models of species' range evolution
- Gilbert, K. J., N. P. Sharp, A. L. Angert, G. L. Conte, J. A. Draghi, F. Guillaume, A. L. Hargreaves, R. Matthey-Doret, and M. C. Whitlock 2017 Local adaptation interacts with expansion load during range expansion: maladaptation reduces expansion load. Am. Nat.:10.1086/690673
- Cotto, O., Wessely, J., Georges, D., Klonner, G., Schmid, M., Dullinger, S., Thuiller, W. and F. Guillaume. 2017 A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nature Communications 8:15399.
- Chevin, L.-M., Cotto, O. and J. Ashander 2017 Stochastic Evolutionary Demography under a Fluctuating Optimum Phenotype. The American Naturalist 190:786-802.
- Senner, N. R., Stager, M. and Z.A. Cheviron 2018 Spatial and temporal heterogeneity in climate change limits species' dispersal capabilities and adaptive potential. Ecography 41:1428-1440.
- Schmid M, Dallo R, and F Guillaume 2019 Species' range dynamics affect the evolution of spatial variation in plasticity under environmental change. The American Naturalist 193:798-813, doi:10.1086/703171.
- Schmid M, Paniw M, Postuma M, Ozgul A, and F Guillaume 2019 A tradeoff between robustness to environmental fluctuations and speed of evolution. bioRxiv doi:10.1101/834234.
- Postuma M, Schmid M, Guillaume F, Ozgul A, and M Paniw 2020 The effect of temporal environmental autocorrelation on eco-evolutionary dynamics across life histories. Ecosphere 11:e03029; doi:10.1002/ecs2.3029.
Individual-based simulators
- Neuenschwander, S., Hospital, F., Guillaume, F. and J. Goudet 2008 quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation. Bioinformatics 24:1552-1553.
- Neuenschwander, S., Michaud, F., and J. Goudet 2019 QuantiNemo 2: a Swiss knife to simulate complex demographic and genetic scenarios, forward and backward in time. Bioinformatics 35:886-888.
- Cotto O, Schmid M, and F Guillaume 2020 Nemo-age: spatially explicit simulations of eco-evolutionary dynamics in stage-structured populations under changing environments. Methods in Ecology and Evolution doi:10.1111/2041-210X.13460.
As statistical tool
- Kesselring, H., Armbruster, GFJ, Hamann, E., and J. Stöcklin 2015 Past selection explains differentiation in flowering phenology of nearby populations of a common alpine plant. Alpine botany
Note: send us your references and we'll add them here!
Community
Nemo is currently developed and maintained by Fred Guillaume.
The following persons have contributed to its development at some point:
Jacques Rougemont (MPI version)
Samuel Neuenschwander
Alistair Blachford
Sam Yeaman
Kimberly Gilbert
Nemo also benefited from development done on EasyPop by François Balloux, and from some improvements brought to quantiNEMO, an off-shoot based on earlier work done in collaboration with Samuel Neuenschwander and Jérôme Goudet.
Many thanks to all those great people!
Development Guidelines
Nemo's framework has been designed as a programing tool to easily implement new components into the simulation framework. Interfaces are provided to derive new evolvable traits, new life cycle events and their accompanying data handlers. Besides that, the implementer should not worry (or not too much) about how its new components are handled within the population and simulation frameworks. The population framework is designed to give access to the individuals within the different age classes and sub-populations present in the model to the different components, in particular the life cycle events.
