SIMplyBee: an R package to simulate honeybee populations and breeding programs
Jana Obšteter1, Laura K Strachan2, Jernej Bubnič3
1Department of Animal Science, The Agricultural Institute of Slovenia, Ljubljana, Slovenia. jana.obsteter@kis.si.
Genetics, Selection, Evolution : GSE
|May 10, 2023
Summary
SIMplyBee is a new R package simulating honeybee populations and breeding programs, addressing key species-specific traits. This tool aids in testing conservation strategies to improve honeybee genetic gain and variability.
Area of Science:
- * Population Genetics
- * Animal Breeding
- * Apiculture
Background:
- * Western honeybees are vital globally but face population decline, impacting economics and genetic diversity.
- * Breeding and conservation programs are crucial, necessitating efficient simulation tools.
- * Existing simulators lack detailed honeybee population modeling capabilities.
Purpose of the Study:
- * To introduce SIMplyBee, a holistic R package for simulating honeybee populations and breeding programs.
- * To provide a flexible platform for testing diverse breeding and conservation strategies.
- * To enhance research in honeybee genetics and quantitative genetics.
Main Methods:
- * SIMplyBee extends the AlphaSimR package with honeybee-specific classes (SimParamBee, Colony, MultiColony).
- * Incorporates key honeybee reproductive and social characteristics: haplodiploidy, complementary sex determination, and polyandry.
- * Simulates individual genomes, colony events, and quantitative genetics at individual and colony levels.
Main Results:
- * Successfully implemented simulation of honeybee genomes, colonies, and haplodiploid inheritance.
- * Demonstrated capabilities in managing multiple colonies and simulating colony events.
- * Provides tools for obtaining genomic data and quantitative genetics metrics.
Conclusions:
- * SIMplyBee offers a comprehensive platform for simulating honeybee populations and evaluating breeding strategies.
- * Enables modeling of intra-colony interactions for genetic gain and variability assessment.
- * Future work will focus on genome simulation, performance optimization, and spatial awareness.
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