Université du Québec à Rimouski
Ph.D. candidate
Supervisor: Dominique Gravel
Robert Schneider
Start: 2013-01-07
End: 2015-01-01

Project

A Bayesian approach to reduce uncertainty in producing stand level information from an individual level model of tree growth
To reduce uncertainty of predictions of growth under future conditions, I propose here a new model of individual tree growth suitable for management decision making at the stand level. Initially, an individual level model of tree growth will be developed with a focus to incorporate more biological realism at the process level. New components of species specific mortality and individual growth increment will be created. To make this individual level model useful and practical for management objectives, information will be predicted at the stand level with care to reduce uncertainty throughout simulations. An evaluation of the models developed and their ability to accurately predict stand level estimators will be conducted to help guide future management decisions and assess the pragmatism of our models. An optimization will be conducted to explore what mixtures of individuals would maximize productivity in terms of total basal area at the stand level.

Keywords

modelling, decision making tool

Publications

1- Reduction of first-year survival threatens the viability of the Mariana Crow Corvus kubaryi population on Rota, CNMI
HA, JAMES C., ALYSSA BUTLER, RENEE ROBINETTE HA
2010 Bird Conservation International