The allocation of ecosystem net primary productivity in tropical forests

Abstract

The allocation of the net primary productivity (NPP) of an ecosystem between canopy, woody tissue and fine roots is an important descriptor of the functioning of that ecosystem, and an important feature to correctly represent in terrestrial ecosystem models. Here, we collate and analyse a global dataset of NPP allocation in tropical forests, and compare this with the representation of NPP allocation in 13 terrestrial ecosystem models. On average, the data suggest an equal partitioning of allocation between all three main components (mean 34 ± 6% canopy, 39 ± 10% wood, 27 ± 11% fine roots), but there is substantial site-to-site variation in allocation to woody tissue versus allocation to fine roots. Allocation to canopy (leaves, flowers and fruit) shows much less variance. The mean allocation of the ecosystem models is close to the mean of the data, but the spread is much greater, with several models reporting allocation partitioning outside of the spread of the data. Where all main components of NPP cannot be measured, litterfall is a good predictor of overall NPP (r(2) = 0.83 for linear fit forced through origin), stem growth is a moderate predictor and fine root production a poor predictor. Across sites the major component of variation of allocation is a shifting allocation between wood and fine roots, with allocation to the canopy being a relatively invariant component of total NPP. This suggests the dominant allocation trade-off is a 'fine root versus wood' trade-off, as opposed to the expected 'root-shoot' trade-off; such a trade-off has recently been posited on theoretical grounds for old-growth forest stands. We conclude by discussing the systematic biases in estimates of allocation introduced by missing NPP components, including herbivory, large leaf litter and root exudates production. These biases have a moderate effect on overall carbon allocation estimates, but are smaller than the observed range in allocation values across sites.

Figure 1.

An example of the full carbon cycle for a mature tropical forest in Amazonia (Caxiuanã, Brazil). Based on data from Malhi et al. [6] with updated values of canopy and branchfall NPP (A. C. L. Costa, L. E. O. Aragão & Y. Malhi 2011, unpublished data). GPP, gross primary productivity; R_total, total ecosystem respiration; R_aut, autotrophic respiration; R_het, heterotrophic respiration; NPP_total, total net primary productivity (NPP); NPP_Ag, above-ground NPP; NPP_Bg, below-ground NPP; NPP_canopy, canopy NPP; NPP_leaf, leaf NPP; NPP_rep, reproductive NPP; NPP_twigs, twig NPP; NPP_VOC, volatile organic compound NPP; NPP_{branch turnover}, branch turnover NPP; NPP_stem, above-ground stem wood NPP; NPP_{coarse roots}, coarse root NPP; NPP_{fine roots}, fine root NPP; D_{fine litterfall}, canopy litterfall; D_CWD, woody mortality; D_roots, fine root detritus; F_DOC, outflow of dissolved organic carbon; R_{soil het}, soil heterotrophic respiration; R_roots, root respiration, R_CWD, coarse woody debris respiration; R_soil, soil respiration; R_stem, above-ground woody respiration; R_leaf, leaf dark respiration. All units are Mg C ha⁻¹ yr⁻¹.

Figure 2.

Pathway showing the key processes linking photosynthesis and the (woody) biomass of a forest. Much effort in terrestrial ecosystem models has gone into accurate representation of the first process in this pathway (photosynthesis) but three other processes can be equally important: autotrophic respiration (or CUE), allocation of NPP, and mortality (or woody biomass residence time). This paper focuses on the third process in the pathway, the allocation of NPP.

Figure 3.

Impact of allocation scheme of eleven terrestrial ecosystem models on the standing biomass of a typical tropical rainforest site (model 1, aDGVM; model 2, BIOME-BGC; model 3, CASA (original); model 4, CASA (Friedlingstein et al. 1999); model 5, CCM3; model 6, CTEM; model 7, ED1; model 8, Hyland; model 9, IBIS; model 10, JULES/TRIFFID; model 11, ORCHIDEE; model 12, Post et al.; model 13, VISIT). We assume a total annual NPP of 11.6 Mg C ha⁻¹ yr⁻¹ [4], a fine root turnover time of 0.45 years (based on data from Jimenez et al. [52]), a leaf turnover time of 1 year (from Chave et al. 2009 [54]) and a woody biomass turnover time of 50 years (based on data from Malhi et al. [53]).

Figure 4.

Canopy NPP (Mg C ha⁻¹ yr⁻¹) versus stem NPP (Mg C ha⁻¹ yr⁻¹) for the Americas (row 1) (n = 33), Asia (row 2) (n = 21) and Hawaii (row 3) (n = 12), and for lowlands (column 1; less than 1000 m elevation), highlands (column 2; greater than 1000 m elevation), and lowlands and uplands combined (column 3). We plot linear regressions (dashed line) forced through the origin and a reference line of y = 1.75x (solid line) to facilitate comparison across graphs. (a) Americas lowlands: slope = 1.50 ± 0.10; (b) Americas highlands: slope = 1.73 ± 0.14; (c) Americas total: slope = 1.51 ± 0.08; (d) Asia lowlands; (e) Asia highlands; (f) Asia total; (g) Hawaii highlands and (h) Hawaii total. Regression lines are plotted and equations given only when significant (p<0.05).

Figure 5.

Ternary diagram (main figure) for woody NPP (includes branch and coarse root NPP), leaf litter NPP (includes reproductive NPP) and fine root NPP for 35 individual field sites and average among all sites (solid circle) surrounded by standard deviation (grey line is s.d. for fine root NPP, black line is s.d. for canopy NPP, dotted line is s.d. for woody NPP). The colour indicates geographical region, with blue for the Americas, red for Asia and black for Hawaii. (inset) Ternary diagram for the same dataset with labels describing methodology for fine root NPP (i, ingrowth core or rhizotron method (purple); e, estimated with litterfall and soil respiration (cyan); and c, sequential coring (green)).

Figure 6.

Total NPP (y axis) versus (a) canopy NPP, (b) woody NPP and (c) fine root NPP (n = 35) for all sites worldwide; (d) woody and fine root NPP versus canopy NPP. A linear function is a sufficient model to predict total NPP based on canopy NPP (linear fit not forced through origin, slope = 1.87 ± 0.18, r² = 0.88, p < 0.0001; linear fit forced through origin, slope =2.27 ± 0.086, r² = 0.83), woody NPP (linear fit not forced through origin, slope = 2.45 ± 0.57, r² = 0.55, p < 0.001; linear fit forced through origin, slope =3.61 ± 0.27, r² = 0.40) and fine root NPP (linear fit not forced through origin, slope = 1.60 ± 0.42, r² = 0.49, p < 0.01; linear fit forced through origin, slope =2.80 ± 0.26, r² = 0.13). We also regress canopy NPP against woody and fine root NPP (linear fit not forced through origin, slope = 0.87 ± 0.18, r² = 0.61, p < 0.001; linear fit forced through origin, slope = 1.27 ± 0.086, r² = 0.47).

Figure 7.

Ternary diagram for allocation patterns of woody NPP (includes branch and coarse root NPP), canopy NPP (includes reproductive NPP), and fine root NPP according to 13 individual models and average among all models (black circle). The average of the data is shown as an open circle surrounded by standard deviation (solid line polygon). The lines within the polygon indicate the standard deviations of woody NPP allocation (dotted line), canopy NPP allocation (solid black line) and fine root NPP allocation (solid grey line). Numbers refer to models as listed in table 1 and figure 3.

Figure 8.

The sensitivity of allocation patterns to inclusion of the potential missing terms herbivory, decomposition and root exudates (see main text for details). Total canopy NPP correction is A–C; total fine root NPP correction is A–D and woody production correction is A–F. If corrections are applied to all three terms the net correction is A–G.