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.
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07.0.PresenceRFAnalysisOperational.R
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
R Syntax - 11,5 KB - MD5: 0718267e390517f780a8ac165924c2ac
For the operational version of the model, train the random forest analysis and test on the sustainable ndvi and lst products (we chose CGLS for ndvi and VIIRS for lst)
07.1.PresenceRFAnalysis.R
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
R Syntax - 13,3 KB - MD5: ff59b89b35b785f4dae015f37b5969f4
Runing the random forest analysis chronologically splitting the data set into train (80%) and test (20%) data 5 different times.
07.2.0.FitSelectedOperationalModonFulldata.R
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
R Syntax - 4,5 KB - MD5: 4d00dc94bc0974372b74e944acc55840
Final step consisting in fitting the best operational model to the full data set (using CGLS and Viirs products representing a more sustainable alternative to MODIS, which is nearing the end of its operational lifespan) This will give the final model outcome saved in .RData used...
07.2.1FitSelectedModonFullMODISdata.R
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
R Syntax - 4,5 KB - MD5: 24244b0032b30234421e37dd723c33ca
Same as for 07.2.1 but for the models trained on MODIS data
08.1.forecast.yaml
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
Unknown - 11,1 KB - MD5: 4e457dbebd10b56c359821ea062853bc
Allows to create a mamba environment necessary to run R and Python scripts used for forecasting. Every files saved in PythonCodes path are used for the forecasting step 8 # Mamba installation To use python and R: install mambaforge Guidelines: https://mamba.readthedocs.io/en/l...
09.webmap.yaml
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
Unknown - 8,0 KB - MD5: 711e96f39ca13714491f6ef83508f787
create a mamba environment to define a different set of packages and functions that will operate together to visualize forecasting maps in an html interface. this final step use files in mppcpro-main folder and can be found also on github https://github.com/pioucyril/mppcpro
backup_cron
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
Plain Text - 579 B - MD5: 7c649a6fff8fd2b8b4ab1a14af1da692
backup_cronjob
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
Plain Text - 925 B - MD5: dfde1e3cee082808ceb25559335bc4d8
clcproRegion.R
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
R Syntax - 2,9 KB - MD5: ac8dd6ca92d8737d883dbb7e9abad610
compute_static_map.py
27 févr. 2025 - A forecasting model for desert locust presence in West and North Africa
Python Source Code - 3,1 KB - MD5: 0f489c885ed9757c8bcc57d7c9957db3
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