Magali San Cristobal

Rural forests, i.e. farm forests and trees outside forests (TOF), are part of traditional agroforestry systems in many European regions. Yet, the industrialization of agriculture has induced the decline of rural forests and promoted a physical and functional separation between trees and agriculture. Despite the recent promotion of TOF in the Common Agriculture Policy (CAP), most farmers do not reinforce them in their farms. In order to understand farmers’ attitudes towards rural forests, we conducted 19 face-to-face interviews in southwestern France. Farmers identified 32 positive contributions, including 29 ecosystem services (ES), associated with rural forests. Similarly, they emphasized 25 negative contributions, including 21 ecosystem disservices (EDS). Contributions varied with the type of forested area. For instance, hedgerows had high levels of positive and negative contributions, while woods had high levels of positive and low levels of negative contributions. Finally, farmers identified 19 stakeholders and institutions, especially the CAP, that influenced rural forest management. In focusing on the balance between positive and negative contributions, our study enabled us to uncover the complex rationale of local rural forest management. Ecosystem disservices and CAP policies tended to discourage farmers to reinforce rural forests in their farms. Taking into account farmers’ rationale and perceptions may give invaluable information to better target public policies.

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Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)

Genetics Selection Evolution (2007)

Mick Watson Mónica Pérez-Alegre Michael D. Baron Céline Delmas Peter Dovč

Abstract Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.

False Discovery Rate

False positive rate

Multiple comparisons problem

Data set

10.1186/1297-9686-39-6-669

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Inférence relative a l'hétérogénéité des composantes de la variance en modèle linéaire mixte gaussien

Magali San Cristobal Jean Louis J. L. Foulley

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