Networking Our Way to Better Ecosystem Service Provision.

The ecosystem services (EcoS) concept is being used increasingly to attach values to natural systems and the multiple benefits they provide to human societies. Ecosystem processes or functions only become EcoS if they are shown to have social and/or economic value. This should assure an explicit connection between the natural and social sciences, but EcoS approaches have been criticized for retaining little natural science. Preserving the natural, ecological science context within EcoS research is challenging because the multiple disciplines involved have very different traditions and vocabularies (common-language challenge) and span many organizational levels and temporal and spatial scales (scale challenge) that define the relevant interacting entities (interaction challenge). We propose a network-based approach to transcend these discipline challenges and place the natural science context at the heart of EcoS research.The QUINTESSENCE Consortium gratefully acknowledges the support of Départment SPE and Métaprogramme ECOSERV of INRA, and the French ANR projects PEERLESS (ANR-12-AGRO-0006) and AgroBioSE (ANR-13-AGRO-0001).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.tree.2015.12.00

Comparison of Evolutionary and Swarm Intelligence-based Approaches in the Improvement of Peach Fruit Quality

The design of peach ideotypes that satisfy the requirement of high fruit quality and low sensitivity to fungal diseases in a given environment is a very challenging problem. In this paper, we propose a model-based design approach to deal with this challenge. First, we formulate it as a multi-objective optimization problem. Two well-known multi-objective optimization algorithms i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Particle Swarm Optimization with the Crowding Distance (MOPSO-CD) were then used to find the best combinations of genetic resources and cultural practices adapted to, and respectful of specific environments. Statistically significant performance measures are employed to compare the two algorithms. The results obtained demonstrate that NSGA-II is able to yield a wide spread of solutions with good coverage and convergence to Pareto fronts