Research Scientist

Applied Ecology - Modeling & Data Analysis
About Me

Sarah Bauduin

I am a postdoc working at the Center of Functional Ecology and Evolution (CEFE) in Montpellier (France). My interests are in applied ecology and conservation topics. I am specialized in ecological modeling (individual-based modeling in particular).

  • Ph.D. Forest Sciences, 2016
    Laval University, Quebec city, QC, Canada
  • M.Sc. Ecology and Evolution, 2011
    University of Montpellier, France
  • B.Sc. Biology, 2009
    University Claude Bernard Lyon 1, France
    Montana State University, Bozeman, MT, USA

Current Projects


From pack dynamics to population responses: an individual-based approach to model wolf demography

Development of an individual-based model to predict wolf demography that precisely mimics pack dynamics. This model focuses on the interactions between the individuals, mostly the dynamic of the status between disperser and resident (i.e., belonging to a pack) and the replacement of the alpha individuals also accounting for individuals’ relatedness.

The aim of this model is to be re-used by ecologists and adapted for their research questions on wolf. The model is coded in R language with the R package NetLogoR to facilitate the implementation of the individual-based model structure. The model was built in a flexible and modular way using sub-models that can be reorganized, removed or new ones can be added. This flexibility allows to mimic the species life-cycle as closely as possible, as well as testing the impact of external processes on the simulated population, such as different management actions that can be added or removed.


Avoid, reduce, and compensate lynx mortality due to vehicle collisions

ITTECOP Project. Development of a predictive tool for transport network management while including lynx population dynamics, collision risk and land use challenges. The goal of this project is to help decision-making to reduce lynx mortality, mainly due to vehicle collision. The tool evaluates the positive or negative consequences on the lynx abundance of defined management actions targeting the road network, the land cover and/or the current lynx populations.

A user-friendly interface allows users to define management scenarios and a spatially explicit individual-based model (SE-IBM) that predicts the lynx populations regarding the specified management actions runs behind the interface. The SE-IBM includes lynx dispersal and population dynamics while accounting for habitat preferences and road mortality. The tool was co-constructed with the stakeholders of the study area to be the most useful and practical for them.

We used the R language with the R package NetLogoR to code the SE-IBM and the R package Shiny to create the interface.

The project is carried jointly by the CEFE, the ONCFS, the CROC, and the CEREMA.


NetLogoR: A package to implement spatially explicit agent-based models in R

NetLogoR is an R package to build and run spatially explicit agent-based models (SE-ABMs) using the R language. SE-ABMs are models that simulate the fate of entities at the individual level within a spatial context and where patterns emerge at the population level. NetLogoR follows the same framework as the NetLogo software (Wilenski 1999). Rather than a call function to use the NetLogo software, NetLogoR is a translation into the R language of the structure and functions of NetLogo. Models built with NetLogoR are written in R language and are run on the R platform; no other software or language has to be involved. NetLogoR provides new R classes to define model agent objects and functions to implement spatially explicit agent-based models in the R environment. Users of this package benefit from the fast and easy coding provided by the highly developed NetLogo framework, coupled with the versatility, power and massive resources of the R language.

NetLogoR is available on CRAN.


Sarah Bauduin

CEFE UMR 5175, Campus du CNRS

1919 route de Mende

34293 Montpellier cedex 5, France