Foliage regeneration and longevity in old-growth trees. Multiple objective optimization, ecological models and problem solving.

We are interested in the characteristics of long-lived trees that allow them to continue to persist in forests when they have reached maximum height increment and crown expansion. At the Wind River Canopy Crane Research Facility (WRCCRF), Ishii and Ford (2001) observed ubiquitous epicormic growth on the interior of Pseudotsuga menziesii crowns, and proposed that foliage regeneration through proleptic reiteration is a way in which P. menziesii survives at maximum crown expansion. I explored this postulate through a geometric simulation model of branch growth in P. menziesii, in contrast with Abies grandis, a species that lacks characteristic proleptic reiteration.

Kennedy, M.C. (2002). A geometric simulation model of foliage regeneration in Abies grandis and Pseudotsuga menziesii. Master of Science Thesis. University of Washington. Seattle, WA

Kennedy, M.C.; Ford, E.D. and Ishii, H. (2004). Model analysis of the importance of reiteration for branch longevity in Pseudotsuga menziesii compared with Abies grandis. Canadian Journal of Botany. 87(7):892-909

Abstract:
It is well-known that as coniferous trees in the Pacific Northwest age they experience a decline in growth. Despite this decline, individual Pseudotsuga menziesii trees can survive for centuries. How can an individual tree continue to survive when its annual growth increment is minimal? To begin to answer this question a stochastic geometric simulation model of branch growth was developed and written for old Pseudotsuga menziesii and Abies grandis trees. Epicormic shoot growth and reiteration were shown to be the sole factors that increased the longevity of branches in old P. menziesii, and when included in the simulation model had a dominating effect on branch growth. It was determined that upper and lower bounds on branch complexity were necessary to stabilize stochastic variability, but those limitations had consequences in long-term branch development; when the model was run for up to 400 years the limitations imposed on branch complexity led to eventual branch death for both species.



Ishii HT, Ford ED, Kennedy MC. (2007). Physiological and ecological implications of adaptive reiteration as a mechanism for crown maintenance and longevity. Tree Physiology. 27:455.462.

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Here is an example of a simulation for Douglas fir. Just click on the slide show button on the bottom right hand side, and press the space bar once. You will see the development of a P. menziesii branch for 200 years. This works best in Internet Explorer (sorry about that!).
Good SCU photo
Good individual SCU
Here's an image of stages of SCU development from Roaki Ishii.
Below is a picture of the Wind River Canopy Crane from which most of the data I'm working with has been gathered.

Ecological phenomena are defined by more than one feature, yet we lack the tools to evalute multiple features simultaneously. We assert that process models are valuable tools to aid in theory development, and to realize the full potential of these models we require multiple objectives to be optimized simultaneously as a vector objective function. Performance of the model is measured through the concept of non-dominance, or Pareto optimality. This is also a valuable tool in management problems, where there are multiple objectives of the management project that likely conflict. The Pareto optimal frontier allows decision-makers to visualize the full range of trade-offs among competing objectives.



For my dissertation, I use Pareto optimality and multi-objective optimization to assess a process model of branch development in old-growth Douglas-fir. I use a set of theoretical objectives to quantify how the branch development pattern may compensate for size-related constraints in old-growth forests.

Dissertation (with link to pdf version) completed March 2008:
Kennedy MC. (2008). Multi-objective Optimization for Ecological Model Assessment and Theory Development. Ph.D. Dissertation. University of Washington. Seattle, WA.


Example for fire and fuels management:
Kennedy MC, Ford ED, Singleton P, Finney M, Agee JK. (2008) Informed multi-objective decision-making in environmental management using Pareto optimality. Journal of Applied Ecology. 45(1):181-192.

Lehmkuhl J, Kennedy M, Ford ED, Singleton PH, Gaines WL, Lind RL. (2007). Seeing the forest for the fuel: Integrating ecological values and fuels management. Forest Ecology and Management. 246: 73-80.