RESUMO
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., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. 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 analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.
RESUMO
The consequences of deforestation for aboveground biodiversity have been a scientific and political concern for decades. In contrast, despite being a dominant component of biodiversity that is essential to the functioning of ecosystems, the responses of belowground biodiversity to forest removal have received less attention. Single-site studies suggest that soil microbes can be highly responsive to forest removal, but responses are highly variable, with negligible effects in some regions. Using high throughput sequencing, we characterize the effects of deforestation on microbial communities across multiple biomes and explore what determines the vulnerability of microbial communities to this vegetative change. We reveal consistent directional trends in the microbial community response, yet the magnitude of this vegetation effect varied between sites, and was explained strongly by soil texture. In sandy sites, the difference in vegetation type caused shifts in a suite of edaphic characteristics, driving substantial differences in microbial community composition. In contrast, fine-textured soil buffered microbes against these effects and there were minimal differences between communities in forest and grassland soil. These microbial community changes were associated with distinct changes in the microbial catabolic profile, placing community changes in an ecosystem functioning context. The universal nature of these patterns allows us to predict where deforestation will have the strongest effects on soil biodiversity, and how these effects could be mitigated.