Mangrove deforestation threatens to release large stores of carbon from soils that are vulnerable to oxidation. Carbon stored in deep soils is not measured in national carbon inventories. Thus, policies on emission reductions have likely underestimated the contribution of mangrove deforestation to national emissions. Here, we estimate that emissions from deforestation and degradation of mangroves in Mexico are 31 times greater than the values used to determine national emission reduction targets for the Paris Agreement. Thus, Mexico has vastly undervaluated the potential of mangrove protection to reduce its emissions. Accounting for carbon emissions from mangrove soils should greatly increase the priority of mangrove forests to receive funding for protection under carbon trading programs.

We conducted a year-long field experiment to investigate how nitrogen addition affected decomposition of Piscidia piscipula and Gymnopodium floribundum along a precipitation gradient in the Yucatan Peninsula, Mexico. Nitrogen addition did not affect decomposition rates at the drier sites. However, fertilization at the wettest site increased the decomposition of Gymnopodium litter and decreased the decomposition of Piscidia litter. Water-soluble carbon and lignin, and water-soluble carbon and nitrogen concentrations were the best predictors of decomposition for Gymnopodium and Piscidia litters, respectively. We conclude that the effects of nitrogen addition on decomposition will vary from site to site as a function of mean annual precipitation, inherent soil fertility, and species identity.Abstract in Spanish is available in the online version of this article.

Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes.