Does functional trait diversity predict aboveground biomass and productivity of tropical forests? Testing three alternative hypotheses.

FINEGAN, B.; PEÑA- CLAROS, M.; DE OLIVEIRA, A.; ...; DÍAZ, S; ET AL

JOURNAL OF ECOLOGY (PRINT)

WILEY-BLACKWELL PUBLISHING, INC

Lugar: Londres; Año: 2015 vol. 103 p. 191 - 201

0022-0477

1. Tropical forests are globally important, but it is not clear whether biodiversity enhances carbonstorage and sequestration in them. We tested this relationship focusing on components of functionaltrait biodiversity as predictors.2. Data are presented for three rain forests in Bolivia, Brazil and Costa Rica. Initial above-groundbiomass and biomass increments of survivors, recruits and survivors + recruits (total) were estimatedfor trees ≥10 cm d.b.h. in 62 and 21 1.0-ha plots, respectively. We determined relationships of biomassincrements to initial standing biomass (AGBi), biomass-weighted community mean values(CWM) of eight functional traits and four functional trait variety indices (functional richness, functionalevenness, functional diversity and functional dispersion).3. The forest continuum sampled ranged from ?slow? stands dominated by trees with tough tissuesand high AGBi, to ?fast? stands dominated by trees with soft, nutrient-rich leaves, lighter woods andlower AGBi.4. We tested whether AGBi and biomass increments were related to the CWM trait values of thedominant species in the system (the biomass ratio hypothesis), to the variety of functional trait values(the niche complementarity hypothesis), or in the case of biomass increments, simply to initialstanding biomass (the green soup hypothesis).5. CWMs were reasonable bivariate predictors of AGBi and biomass increments, with CWM specificleaf area SLA, CWM leaf nitrogen content, CWM force to tear the leaf, CWM maximum adult heightHmax and CWM wood specific gravity the most important. AGBi was also a reasonable predictor ofthe three measures of biomass increment. In best-fit multiple regression models, CWM Hmax was themost important predictor of initial standing biomass AGBi. Only leaf traits were selected in the bestmodels for biomass increment; CWM SLA was the most important predictor, with the expected positiverelationship. There were no relationships of functional variety indices to biomass increments, andAGBi was the only predictor for biomass increments from recruits.6. Synthesis. We found no support for the niche complementarity hypothesis and support for the greensoup hypothesis only for biomass increments of recruits. We have strong support for the biomass ratiohypothesis. CWM Hmax is a strong driver of ecosystem biomass and carbon storage and CWM SLA,and other CWM leaf traits are especially important for biomass increments and carbon sequestration.