Our team is much involved in the organisation of the 4th International Statistical Ecology Conference 2014 that will be held in Montpellier (France) the 1-4 July 2014. We have an amazing list of plenary speakers, invited sessions and workshops. Check out the ISEC2014 website for more details!
We have just released a list of frequently asked questions (FAQ) on E-SURGE, a software application that is developed by Rémi Choquet in our team. E-SURGE allows the estimation of demographic parameters based on capture-recapture data. It relies on the multievent modeling framework developed by Roger Pradel to deal with uncertainty in the assignment of states to individuals. We will do our best to keep a list of applications updated. Do not hesitate to get back to us if you have any comments on the FAQ.
A new paper by a former PhD student of the team, Lucile Marescot, now a post-doc at UC Davis.
Marescot, L., Chapron, G., Chadès, I., Fackler, P., Duchamp, C., Marboutin, E. and O. Gimenez (2013). Complex decisions made simple: A primer on stochastic dynamic programming. Methods in Ecology and Evolution. In press. DOI: 10.1111/2041-210X.12082. PDF on request.
This review and tutorial paper (with R code) is about dynamic programming, a powerful mathematical technique to make decisions in presence of uncertainty. This paper would not have been born without the help of Guillaume Chapron, population modeller and large carnivores specialist, as well as Iadine Chadès and Paul Fackler both world experts in the field of decision making.
1. Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most relevant tool for determining an optimal sequence of decisions over time.
2. Despite an increasing number of applications in ecology, SDP still suffers from a lack of widespread understanding. The required mathematical and programming knowledge as well as the absence of introductory material provide plausible explanations for this.
3. Here, we fill this gap by explaining the main concepts of SDP and providing useful guidelines to implement this technique, including R code.
4. We illustrate each step of SDP required to derive an optimal strategy using a wildlife management problem of the French wolf population. Our results show how the determination of optimal policies is sensitive to the incorporation of uncertainty.
5. SDP is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.