A Bayesian network approach for the identification of relationships between drivers of chlordecone bioaccumulation in plants

Yohan Liber (1, 2) , Denis Cornet (3, 4) , Régis Tournebize (5) , Cyril Feidt (6) , Maurice Mahieu (7) , Francois Laurent (8) , Jean-Philippe Bedell (1)

1 LEHNA - Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés
2 ToxAlim - ToxAlim
3 UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
4 Cirad-BIOS - Département Systèmes Biologiques
5 ASTRO - Agrosystèmes tropicaux
6 URAFPA - Unité de Recherches Animal et Fonctionnalités des Produits Animaux
7 URZ - Unité de Recherches Zootechniques
8 ToxAlim-MeX - Métabolisme et Xénobiotiques

Plants were sampled from four different types of chlordecone-contaminated land in Guadeloupe (West Indies). The objective was to investigate the importance of biological and agri-environmental parameters in the ability of plants to bioaccumulate chlordecone. Among the plant traits studied, only the growth habit significantly affected chlordecone transfer, since prostrate plants concentrated more chlordecone than erect plants. In addition, intensification of land use has led to a significant increase in the amount of chlordecone absorbed by plants. The use of Bayesian networks uncovers some hypothesis and identifies paths for reflection and possible studies to identify and quantify relationships that explain our data.

Mots clés: Bayesian network modeling

Domaines: Sciences agricoles; Méthodologie [stat.ME]

Yohan Liber, Denis Cornet, Régis Tournebize, Cyril Feidt, Maurice Mahieu, et al.. A Bayesian network approach for the identification of relationships between drivers of chlordecone bioaccumulation in plants. Environmental Science and Pollution Research, 2020, 6 p. ⟨10.1007/s11356-019-07449-0⟩. ⟨hal-02623186⟩