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Does NPP decline because of fundamental physiological constraints, or can intelligent carbon allocation sustain it?
Buckley, Tom*,1,2, Roberts, Dave1, 1 Department of Forest Resources and Ecology Center, Logan, UT2 Environmental Biology Group and CRC for Greenhouse Accounting, Canberra, ACT, Australia
ABSTRACT- We modeled the growth of lodgepole pine (Pinus contorta) stands by assuming that carbon allocation is driven by a simple directive: to maximize whole-tree net carbon gain (NPPt). Our model is based on (a) standard mechanistic descriptions of photosynthesis, resource capture, and resource transport, (b) two different hydraulically-driven models for stomatal conductance (isohydric and anisohydric), (c) a simple treatment of density-dependent mortality to scale NPP to the stand level (NPPs), and (d) the assumption that soil resource availability is unaffected by trees. Our goal was to determine whether the observed age-related declines in NPPs and NPPt are caused by insurmountable constraints of physiology and/or allometry (which our model includes), or instead by less-fundamental constraints on carbon allocation or soil resource availability (which our model excludes). Our simulations predict that lodgepole stands should be able to sustain high NPP indefinitely, in the absence of arbitrary constraints on carbon allocation or soil resource availability. This prediction is robust to broad variation in sapwood and fine-root respiration coefficients, sapwood cavitation vulnerability, and mortality rates, and the prediction holds for isohydric and anisohydric conductance models. These results suggest that the physiological and demographic mechanisms posited to explain the age-related decline in NPPs or NPPt (hydraulic limitation, sapwood maintenance respiration, and mortality) may be causally distal to genetic constraints (e.g., limited plasticity of carbon allocation) or ecological constraints (e.g., resource depletion or competition).
KEY WORDS: age related decline in NPP, hydraulic limitation, sapwood respiration, optimal resource allocation