Now Assistant Professor at Agr Campus Ouest, see her webpage

Research in the MORSE team until 2017

Hierarchical modeling is a powerful framework which allows wide flexibility in model conception.
The studies related to environmental issues mostly occur in some uncontrolled design. Therefore classical statistical models are not pertinent to study the obtained data (spatial dependence, multi scales, zero inflated, large p small n ….). Furthermore, the ecological processes of interest are mostly hidden and data are just a truncated observation of those processes (tuna declared captures as an indicator of the tuna biomass, number of prey in stomach as a measure of tuna feeding, acceleration data for trajectories of fur seals …) Hierarchical modeling is a useful framework to propose adequate models to link observations and latent processes of interest. I work in collaboration with biologists and ecologists in order to develop and analyze statistical models which a strong interest in highlighting the links between biological and mathematical assumptions. Hierarchical Bayesian Model are then a powerful tool for modelling ecological process but the counterpart is the potential difficulty of estimations of such models. The limits of classical algorithms for the estimation of hidden variables models like EM, MCEM in a frequentist framework or Hastings-Metropolis, Gibbs sampler in a Bayesian framework are reached and put some limitations on the modelling process.

Current Focus on :

• Abundance indices : Managing a fish population requires to have accurate information of the actual level of this popupaltion (Bluefin Tuna is one of the most emblematic example), how can me help Fisheries Scientist to define reliable abundance indices ?
• Trajectories Analysis : A population is not only described by its size but also by the spatial and temporal movements. Telemetry is the measure of individual movements using various Tags. It's more and more common in ecology to study fine scale behaviour of animals, from cervids, bovine animals to fish using individuals tags. The resulting data can be very different in term of density (1 GPS position every day to one position every 15mn, measure of depth every second) but also the nature of the data highly depends of the kind of tags used. Available data can be GPS positions, 3D accelerations, 3D compas, depths, …

Some Preprints

Random effects compound Poisson model to represent data with extra zeros

Extracting abundance indices from longline surveys : method to account for hook competition and unbaited hooks

Some talks

• 2010 June : TIES Conference, Venezuela : Accounting For Interspecific Competition And Empty Hooks When Building Relative Abundance Indices From Longline Catch.
• 2009 December : Abundance indices and empty hooks
  
  Pacific Halibut Commission Presentationpdf - 2,4 MB
• 2009 August :  Coalition : a simple and useful tool to distribute R-works on a set of computers. Coalition is available on Google Code
  
  useR 2009pdf - 664,4 kB

Teaching

• Coordinator of regular courses at AgroParistech (since 2011) on Linear Models 

• Coordinator of regular courses in Master degree in Ecological science EBE AgroParisTech (since 2006) 
  • Introduction to Statistics,
  • Advanced Statistics for Ecological Science 
  • Random Model in ecology and environment 

• Coordinator of Hierarchical and Bayesian Modeling in forest science using OpenBugs for Amazonian doctoral formation program (AgroParisTech-Kourou). 
  Some notes on Bayesian modelling, R and openBugs in frenchpdf - 1,3 MB

• Regular courses at AgroParisTech-ENGREF
  • Statistical Modeling for risk assessment and management
  • Simulation and Prediction using random modeling 

• Fall 2007 : Hierarchical and Bayesian Modeling in marine science using OpenBugs, professional formation for Fisheries science center (Centre de Ressourece halieutique ,CRH, Sete)

Some useful things

• Coalition : a Task Scheduler. This simple tool is very useful to schedule tasks with R (by instance simulations study).
  Presentation of Coalition at AgroParisTech http://www.agroparistech.fr/mmip/maths/essaimia/accueil:intranet:coalition
  
  useR 2009pdf - 664,4 kB
• INLA : Integrated Nested Laplace Approximation (INLA) is a method which approximates posterior distribution in a Gaussian Markovian Random Fields framework. The program inla and all the documentations are available at Havard Rue Homepage . How to install R-INLA under Ubuntu distribution for newbie.
  
  Installation Notes for R -INLApdf - 222 kB
• Latex package Installation

Scientific animation

Member of Environmental Groupe at SFDS

Leader of the groupe Statistics for trajectories (funded by INRA, MIA research division)

Latest papers

Pierre Gloaguen,Marie-Pierre Etienne, Sylvain Le Corff . Stochastic differential equation based on a Gaussian potential field to model fishing vessels trajectories  http://arxiv.org/abs/1509.09103

Wege, Mia, Etienne, Marie-Pierre, Oosthuizen, W. Chris, Reisinger, Ryan, Bester, Marthan and de Bruyn, Nico. Trend changes in sympatric Subantarctic and Antarctic fur seal pup populations at Marion Island, Southern Ocean" to appear in Marine Mammal Science

N. C. Friggens, C. Duvaux-Ponter, M. P. Etienne, T. Mary-Huard and P. Schmidely. Characterizing individual differences in animal responses to a nutritional challenge: towards improved robustness measures to appear in Journal of Dairy Science

Ophélie SAUZET, Cécilia CAMMAS, Pierre BARBILLON, Marie-Pierre ETIENNE and David MONTAGNE Illuviation intensity and land use change: Quantification via micromorphological analysis to appear in Geoderma.