Inferring tree growth and allocation from physiological, starvation, and allometric traits.
Ogle, Kiona*,1, Pacala, Stephen1, 1 Princeton University, Princeton, NJ
ABSTRACT- To improve our understanding of how forests are affected by environmental change, a robust framework is needed for linking tree physiology and growth to community and ecosystem dynamics. In this regard, we have developed an approach that integrates models of tree physiology, allocation, and growth; hierarchical Bayesian analyses; forest inventory database of stem diameters for over 200 tree species in the U.S.; and a literature database of species-specific physiology and allometries. Here we focus on the individual-based tree growth model. The model is designed so that all parameters are biologically meaningful and measurable (e.g., photosynthetic traits, respiration rates, sapwood area (SA), specific leaf area, wood density). Allocation of labile carbon to leaves, fine roots, and sapwood is nearly impossible to measure, and it is difficult to identify functional relationships governing allocation. However, our approach does not require explicit allocation functions; allocation is a product of the model whereby labile carbon is allocated in such a way that the structural carbon dynamics are consistent with allometric relationships (e.g., height:diameter, SA:LA). Key assumptions and predictions include: 1) allocation is dynamic, varying with plant age and environmental conditions; 2) labile carbon storage capacity is determined by xylem anatomy; 3) retranslocation of labile carbon to meet excessive respiration demands causes death of living tissues; 4) tree mortality is coupled to starvation (depletion of stored carbon); 5) trade-offs exist such that species with high capacity to store labile carbon are also those that grow fast, have high maintenance requirements, and thus rely on labile carbon to a greater degree than slow growing species that have reduced capacity to store carbon. We illustrate the behavior of the model for two species common to the eastern U.S.: loblolly pine (Pinus taeda) and red maple (Acer rubrum). We conclude by highlighting work that merges the growth model, databases, and Bayesian analyses to estimate species-specific physiological, starvation, morphological, and allometric parameters.
Key words: allocation, retanslocation, forest dynamics, Bayesian
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