L’UMR CBGP – Centre de Biologie pour la Gestion des Populations – a pour vocation de comprendre les mécanismes qui régissent l’évolution de populations d'organismes importants pour l’agronomie, les forêts, la santé humaine ou la conservation de la biodiversité. Les recherches portent sur des modèles biologiques et sont développées selon six axes : origine et caractérisation de la biodiversité ; adaptation des phytophages, de leurs ennemis naturels et de leurs symbiontes ; écologie et évolution des zoonoses ; biologie, écologie et évolution des espèces envahissantes ; génomique statistique et évolutive des populations ; approches moléculaires et bioinformatiques haut débit. Nos tutelles sont l'INRAE, le CIRAD, l'IRD et l'Institut Agro-Montpellier.

UMR CBGP – Centre de Biologie pour la Gestion des Populations – aims to understand the mechanisms that govern populations of organisms that are important to agriculture, forest, human health and biodiversity conservation. Studies concern biological models and follow six lines of research: origin and characterization of biodiversity; adaptation of plant eaters, their natural enemies and symbionts; ecology and evolution of zoonoses; biology, ecology and evolution of invasive species; statistical and evolutionary population genomics; and high-throughput molecular and bioinformatics techniques. Our supervising bodies are INRAE, CIRAD, IRD and Institut Agro-Montpellier.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 10 of 133 Results
text/nlogo - 114,5 KB - MD5: c9280ecdba91b7bdb4c6bfeb0a3d9627
Code
A generic model built with NetLogo to explore the insect pest and biocide traits that encourage the success of the boosted Sterile Insect Technique. Source code suitable for Netlogo 6.1.1.
Comma Separated Values - 71,3 KB - MD5: ec74f77b9ce3bea5dcb4a80682528049
Data
t0 estimates (one replicate) associated with the values of the 28 environmental predictors, for each orchard and year. The site of the orchards is also included as it is used as a random effect in the model.
Comma Separated Values - 12,4 KB - MD5: 1479b68b3efb3ea6c5a64a520e379ffe
Data
t0 estimates for each orchard and year (one replicate), the orchard site, as well as the orchard coordinates (X and Y columns, coordinates in WGS84/UTM 28N).
Comma Separated Values - 26,5 MB - MD5: 5b4fd6d6ef0845f0ddef138b290fff50
Data
The 500 replicates of t0 estimates used in the analyses presented in the article, and the associated lc values estimated with Siland.
R Syntax - 16,5 KB - MD5: 04313d70dd47569246569fa98331fb20
Code
From one replicate of the dataset, this script presents the main steps in using of GPboost in the article (i.e. tuning the hyperparameters, training the model and prediction).
Gzip Archive - 180 B - MD5: 716b2584366c138c086aab8c89e486c4
The grid of gpboost hyperparameter values (learning rate, minimum data in leaf, maximum depth) used during the hyperparameter tuning step.
Comma Separated Values - 13,0 KB - MD5: 77b5028a9e3ee52775a1b8fece8038b7
Data
Input data for t0 estimation, i.e. one file per year containing the mean abundance values of B. dorsalis per week for each orchard (average of captures per week and orchard).
Comma Separated Values - 14,7 KB - MD5: d7df444a7be63e0896ae99c777e6d60c
Data
Input data for t0 estimation, i.e. one file per year containing the mean abundance values of B. dorsalis per week for each orchard (average of captures per week and orchard).
Comma Separated Values - 13,5 KB - MD5: 0d4f7fb1767b3b9d4735aadb5fe7a935
Data
Input data for t0 estimation, i.e. one file per year containing the mean abundance values of B. dorsalis per week for each orchard (average of captures per week and orchard).
Add Data

Log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.