Forest dynamics from tissues to biomes

Tree demography is an important way to think about forest dynamics.  There are mechanisms that drive demography operating on the scale of cells and tissues, and these operate continuously to define the acquisition and allocation of carbon within the plant.  There are also emergent properties of demography at the popualtion, community, and biome levels.  Further, there are patterns in these properties, such as seasonality or larger trends towards greater growth and greater vulnerabilities. Using micro-cores, sap flow, and remote sensing data, we are interested in examining and linking these scales.  Here are a few of the themes the QEL is pursuing.


Tree growth is fundamental to the carbon cycle, strongly correlates with survival, and reflects evolutionary tradeoffs in allocation strategies. Yet growth, whether bole growth, root growth, or leaf turnover is remarkably difficult to measure.

We are exploring growth using a number of tools at several scales, from cell addition through the year, dendrometer band increments over the year, annual surveys of thousands of trees, to the CTFS-ForestGEO database which offers millions of growth measurements taken every five years.


Survival data generally suffers from different challenges than growth. Although it is binary (and not very finely incremental like bole growth), the largest trees that contain most biomass, are reproductive, and generally tell the story of what it takes to make it in a forest (their allocation strategies have presumably succeeded at some level) are rare compared to the total stem density in a site. Mortality events are rarer (the order of a percentage a year). This makes modeling, and interpretting models of mortality difficult.

We are using inverse models to infer large-tree senescence, and looking at cycles of turnover by monitoring dead wood as well as live stems. Physiological models and Evolutionary Stable Strategies will provide some additional approaches to this complex component of forest dynamics.

Integral Projection Models

IPMs are a modeling platform that can take growth and survival models and integrate them to produce inference on populations and communities. There are challenges to translating individual data to population scales, but by using inverse modeling and Individual Based Models with IPMs, we can include spatial information, densitiy dependence, and individual effects in our simulations.

We are currently working on using IPMs for the analysis of tree disease dynamics, deer browsing, phylogenetic links to life expectancy, and the shifts in a monodominant forest in Central Africa.  We also maintain the IPMpack R-package.


The temporal pattern of allocation can have an important link to fitness.  This is especially important as climates are changing, potentially threatening species with very constrained seasonality in allocation.  We have found that bole growth in temperate forests is highly constrained across species, and that the timing of summer droughts in a window of several months has a huge influence on the impact on forest carbon allocation to stems.