The National Forestry Commission of Mexico continuously monitors forest structure within the country’s continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., vegetation indexes, surface temperature). Training data is from the 2009-2014 cycle (n>26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r2=0.4 [0.15,0.55] (mean[min, max]) than for tree density r2=0.2[0.10,0.31]. Maximum values of tree height were for coniferous forests, coniferous-broadleaf forests and cloud mountain forest (~36 m, 30 m and 21 m, respectively). Tropical forests had maximum values of tree density (~1370 trees/ha), followed by tropical dry forest (1006 trees/ha) and coniferous forest (988 trees/ha). Although most forests had relatively low values of uncertainty, e.g., values <40%, arid and semiarid ecosystems had high uncertainty in both tree height and tree density predictions, e.g., values >60%. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for technical capabilities aimed to use and resignify all the effort done by the Mexican Forestry Commission in implementing the INFyS.

Coastal vegetated habitats like seagrass meadows can mitigate anthropogenic carbon emissions by sequestering CO2 as “blue carbon” (BC). Already, some coastal ecosystems are actively managed to enhance BC storage, with associated BC stocks included in national greenhouse gas inventories or traded on international markets. However, the extent to which BC burial fluxes are enhanced or counteracted by other carbon fluxes, especially air-water CO2 flux (FCO2) remains poorly understood. To this end, we synthesized all available direct FCO2 measurements over seagrass meadows made using a common method (atmospheric Eddy Covariance), across a globally-representative range of ecotypes. Of the four sites with seasonal data coverage, two were net CO2 sources, with average FCO2 equivalent to 44 - 115% of the global average BC burial rate. At the remaining sites, net CO2 uptake was 101 - 888% of average BC burial. A wavelet coherence analysis demonstrates that FCO2 was most strongly related to physical factors like temperature, wind, and tides. In particular, tidal forcing appears to shape global-scale patterns in FCO2, likely due to a complex suite of drivers including: lateral carbon exchange, bottom-driven turbulence, and pore-water pumping. Lastly, sea-surface drag coefficients were always greater than prediction for the open ocean, supporting a universal enhancement of gas-transfer in shallow coastal waters. Our study points to the need for a more comprehensive approach to BC assessments, considering not only organic carbon storage, but also air-water CO2 exchange, and its complex biogeochemical and physical drivers.