Bayesian inference of natural selection from spatiotemporal phenotypic data.

Spatiotemporal variations of natural selection may influence the evolution of various features of organisms such as local adaptation or specialisation. This article develops a method for inferring how selection varies between locations and between generations from phenotypic data. It is assumed that generations are non-overlapping and that individuals reproduce by selfing or asexually. A quantitative genetics model taking account of the effects of stabilising natural selection, the environment and mutation on phenotypic means and variances is developed. Explicit results on the evolution of populations are derived and used to develop a Bayesian inference method. The latter is applied to simulated data and to data from a wheat participatory plant breeding programme. It has some ability to infer evolutionary parameters, but estimates may be sensitive to prior distributions, for example when phenotypic time series are short and when environmental effects are large. In such cases, sensitivity to prior distributions may be reported or more data may be collected.

SHiNeMaS: a web tool dedicated to seed lots history, phenotyping and cultural practices.

MOTIVATION: In 2005, researchers from the French National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, INRAE) started a collaboration with the French farmers' seed network Réseau Semences Paysannes (RSP) on bread wheat participatory breeding (PPB). The aims were: (1) to study on-farm management of crop diversity, (2) to develop population-varieties adapted to organic and low-inputs agriculture, (3) to co-develop tools and methods adapted to on-farm experiments. In this project, researchers and farmers' organizations needed to map the history and life cycle of the population-varieties using network formalism to represent relationships between seed lots. All this information had to be centralized and stored in a database. RESULTS: We describe here SHiNeMaS (Seeds History and Network Management System) a web tool database. SHiNeMaS aims to provide useful interfaces to track seed lot history and related data (phenotyping, environment, cultural practices). Although SHiNeMaS has been developed in the context of a bread wheat participatory breeding program, the database has been designed to manage any kind and even multiple cultivated plant species. SHiNeMaS is available under Affero GPL licence and uses free technologies such as the Python language, Django framework or PostgreSQL database management system (DBMS). CONCLUSION: We developed SHiNeMaS, a web tool database, dedicated to the management of the history of seed lots and related data like phenotyping, environmental information and cultural practices. SHiNeMaS has been used in production in our laboratory for 5 years and farmers' organizations facilitators manage their own information in the system.