Metabolic scaling theory (MST) is still in debate because observed allometric exponents often deviate from MST predictions, and can change significantly depending on environment, phylogeny, and disturbance. We assembled published scaling exponents from literatures for three allometric relationships linked to biomass allocation: leaf biomass-diameter (L-D), stem biomass-diameter (S-D), and root biomass-diameter (R-D). We used data from natural forests and plantations across China to test the following hypotheses: 1) the allometric relationships of trees support the predictions of MST on a broad scale; 2) the observed deviations from MST predictions are caused by climate, biotic factors, and/or phylogeny; 3) abiotic and biotic factors influence allometric relationships in plantations and natural forests differently, and different allometric relationships (i.e. L-D, S-D, and R-D) are affected differently. We related these scaling exponents to geographic climate gradient, successional stage, stand density, leaf form and phenology, and phylogeny. We used mixed-effect models to examine the major factors affecting tree allometries.

In natural forests, S-D and R-D scaling exponents were consistent with MST predictions in primary forests, but were significantly lower in secondary forests. Both S-D and R-D scaling exponents in plantations had a medium value that fell between those of the secondary and primary forests, despite plantations being similar in species characteristics and age to secondary forests. The S-D and R-D exponents were significantly affected by factors that are not yet considered in MST, including winter coldness which explained 2.76% – 3.24% of variations, successional stage (7.91% – 8.20% of variations), density (a surrogate for competition, 5.86% – 8.54% of variations), and especially phylogeny (45.86% – 56.64% of variations explained). However, the L-D scaling exponents conformed to MST predictions in primary, secondary, and plantation forests, and was not strongly explained by most factors.

MST is only applicable to primary (steady-state) forests, and climate, biotic factors and phylogeny are causes of the observed deviations of allometric relationships from MST predictions. Forest management practices in plantations have a strong influence on tree allometries. L-D allometry is more strongly controlled by biophysical constraints than S-D and R-D allometries, however, the mechanisms behind this difference still need further examinations.

Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index (MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways (RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model.

(1) Future (2017–2100) climate change will leave 7.4% (under RCP 4.5) and 57.4% of (under RCP 8.5) of areas under high or very high vulnerable climate exposure; (2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage (vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability; (3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future; (4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid- and high-vulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively.

Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works.