Togashi, Henrique Fürstenau, Atkin, Owen K., Bloomfield, Keith J., Bradford, Matt, Cao, Kunfang, Dong, Ning, Evans, Bradley J., Fan, Zexin, Harrison, Sandy P., Hua, Zhu, Liddell, Michael J., Lloyd, Jon, Ni, Jian, Wang, Han, Weerasinghe, Lasantha K., and Prentice, Iain Colin (2018) Functional trait variation related to gap dynamics in tropical moist forests: a vegetation modelling perspective. Perspectives in Plant Ecology Evolution and Systematics, 35. pp. 52-64.

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Abstract

The conventional representation of Plant Functional Types (PFTs) in Dynamic Global Vegetation Models (DGVMs) is increasingly recognized as simplistic and lacking in predictive power. Key ecophysiological traits, including photosynthetic parameters, are typically assigned single values for each PFT while the substantial trait variation within PFTs is neglected. This includes continuous variation in response to environmental factors, and differences linked to spatial and temporal niche differentiation within communities. A much stronger empirical basis is required for the treatment of continuous plant functional trait variation in DGVMs. We analyse 431 sets of measurements of leaf and plant traits, including photosynthetic measurements, on evergreen angiosperm trees in tropical moist forests of Australia and China. Confining attention to tropical moist forests, our analysis identifies trait differences that are linked to vegetation dynamic roles. Coordination theory predicts that Rubisco-and electron-transport limited rates of photosynthesis are co-limiting under field conditions. The least-cost hypothesis predicts that air-to-leaf CO2 drawdown minimizes the combined costs per unit carbon assimilation of maintaining carboxylation and transpiration capacities. Aspects of these predictions are supported for within-community trait variation linked to canopy position, just as they are for variation along spatial environmental gradients. Trait differences among plant species occupying different structural and temporal niches may provide a basis for the ecophysiological representation of vegetation dynamics in next-generation DGVMs.

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