Title: Global patterns and predictors of stem <scp>CO</scp><sub>2</sub> efflux in forest ecosystems
Abstract: Abstract Stem CO 2 efflux ( E S ) plays an important role in the carbon balance of forest ecosystems. However, its primary controls at the global scale are poorly understood and observation‐based global estimates are lacking. We synthesized data from 121 published studies across global forest ecosystems and examined the relationships between annual E S and biotic and abiotic factors at individual, biome, and global scales, and developed a global gridded estimate of annual E S . We tested the following hypotheses: (1) Leaf area index ( LAI ) will be highly correlated with annual E S at biome and global scales; (2) there will be parallel patterns in stem and root CO 2 effluxes ( R A ) in all forests; (3) annual E S will decline with forest age; and (4) LAI coupled with mean annual temperature ( MAT ) and mean annual precipitation ( MAP ) will be sufficient to predict annual E S across forests in different regions. Positive linear relationships were found between E S and LAI , as well as gross primary production ( GPP ), net primary production ( NPP ), wood NPP , soil CO 2 efflux ( R S ), and R A . Annual E S was correlated with R A in temperate forests after controlling for GPP and MAT , suggesting other additional factors contributed to the relationship. Annual E S tended to decrease with stand age. Leaf area index, MAT and MAP , predicted 74% of variation in E S at global scales. Our statistical model estimated a global annual E S of 6.7 ± 1.1 Pg C yr −1 over the period of 2000–2012 with little interannual variability. Modeled mean annual E S was 71 ± 43, 270 ± 103, and 420 ± 134 g C m 2 yr −1 for boreal, temperate, and tropical forests, respectively. We recommend that future studies report E S at a standardized constant temperature, incorporate more manipulative treatments, such as fertilization and drought, and whenever possible, simultaneously measure both aboveground and belowground CO 2 fluxes.
Publication Year: 2016
Publication Date: 2016-02-29
Language: en
Type: article
Indexed In: ['crossref', 'pubmed']
Access and Citation
Cited By Count: 51
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot