Methane measurements from mature trees in Barro Colorado Island during the dry season. Application to the Gigante Litter Manipulation Project
During the transition to the dry season when the water tables in both the plots were more than 10 m below, Methane flux measurements were done from the mature trees stems at five stem heights. Between 15 and 25 March 2013, there was a certain amount of measured. Methane fluxes were measured using static chambers as described in refs. 3,22, with air samples from the static flux chambers drawn using 30 ml syringes and immediately transferred to a 12 ml exetainer (Exetainer) for later analysis of CH4 using the modified LGR CH4 laser-based analyser3 (LGR UGGA).
Between 18 and 27 November 2015, five control plots of the Gigante Litter Manipulation Project were mapped out in a semi-evergreen tropical forest. A full description of the litter manipulation experiment is given in refs. 36,37. There is a strong dry season from December to mid-April in Barro Colorado Island and the annual precipitation is over 2,400millimetres.
During the dry season of 2021, gas samples were taken from tree woody and soil surfaces at the Negro and Solimes floodplain forests. Samples were taken from air and from flux chambers on the soils surface and tree stem surface at 5 m above the forest floor using gas-tight syringes and then transferred to pre-evacuated 12 ml borosilicate vials fitted with double wadded caps (Exetainer). During transport to the laboratory, the Vials were stiffened to not let in air from pressure or temperature changes. The 13C values of CH4 were analysed using a cavity ring-down spectrometry coupled with a custom built auto-sampler and are reported relative to the Vienna Pee Dee Belemnite standard. The instrument was adjusted using a mixture of reference gases with ratios of 21.9, 54.5 and 66.5. The analytical precision was 0.4.
Our study leveraged TLS technology to develop a new surface area allometry43. TLS provides high-resolution, three-dimensional representations of tree structures, enabling precise surface area estimations. We were able to see a wide range of tree morphologies by scanning a number of trees across 22 plots in the tropics, conifer forests, broadleaf forests, dry eucalyp forests and tropical savannas. We built two woody area index (WAI) allometries (one for tropical forests, one for the rest of the world) to predict woody surface area from tree stem diameter (diameter at breast height, DBH) based on three-dimensional models of trees43. From several angles, each tree was scanned to make sure it was covered. The model were analysed using the treestruct R package45, which showed the surface area and depth of each tree. We then applied hierarchical generalized additive models46 to build the surface area allometric equations. The models were designed to capture the complex relationships between the tree surface areas and their respectiveDBH
We used The Nature Conservancy’s Ecoregions map to determine biome extents and both the 1 km global consensus land cover project48 and the MODIS MOD44B Version 6 Vegetation Continuous Fields (VCF49) 2019 product, as well as the 1 km consensus land cover map, to determine per-pixel forest cover. This dataset provided a global view of vegetation cover, quantified on a continuous scale for each pixel.
To account for climatic variations in our model, we used the ERA5 monthly climate dataset50. We converted the mean monthly temperatures of this dataset to MAT. We applied the CH4 take out versus the MAT regression across the globe, based on the woody surface area in each piece of information (Extended Data Table 4).