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Home > People > Mark Vanderwel
Mark Vanderwel

Overview      |      Projects      |      Students      |      Publications

Mark Vanderwel
POST DOC RESEARCHER
.

+44 (0)1223 479957

mark.vanderwel
    @microsoft.com

Computational Ecology and Environmental Science Group

Microsoft Research Cambridge

Overview

My research seeks to develop and enhance modelling tools for understanding and predicting the dynamics of complex forest ecosystems. Models that both incorporate mechanistic realism and are well constrained by data are necessary for understanding the long-term behaviour of multi-species, multi-cohort forest stands. Such models can provide valuable insights into forest responses to human-induced disturbances and environmental change.

Current projects

CAIN: a scalable model of individudual-based forest dynamics. I have developed a new individual-based forest model that captures essential, general features of tree demography, including size- and competition-dependency of vital rates. Unlike most existing forest models, CAIN can be calibrated using widespread observations of tree growth and mortality available in forest inventory data. This model is currently being used to understand regional forest dynamics in the eastern United States, including how the distribution of different forest types arises from climate-dependent demographic variation.

The importance of tree demography to the terrestrial carbon cycle. As part of the Carbon-Climate Feedback Modelling project, I am seeking to incorporate realistic representations of forest dynamics into a new model of the terrestrial carbon cycle. The rates at which forests uptake and release carbon follow from the growth and mortality of individual trees, but these tree-level processes are not typically represented in models that simulate vegetation dynamics at broad scales. How important are local demographic processes to understanding carbon dynamics for the world's terrestrial ecosystems?

Inferring forest structure and disturbance dynamics with LiDAR remote sensing data. LiDAR is a powerful tool for scaling between plot-based measurements of individual trees and the structure and dynamics of broad forest landscapes. David Coomes and I co-supervise Rebecca Spriggs, a PhD student at the University of Cambridge, who is combining CAIN's canopy sub-model with LiDAR data for a temperate forest landscape in Ontario, Canada. By linking LiDAR data with a model of canopy structure, this project will develop a new mechanistic approach for mapping tree-level forest structure across broad areas.

Students

Publications