Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS) active fire hotspots-expressed as a Fire Hotspot Density index (FHD)-from an Accumulated Fuel Dryness Index (AcFDI), for 17 main vegetation types and regions in Mexico, for the period 2011-2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI), which was developed after the structure of the Fire Potential Index (FPI). Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R 2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.

Background: Dissimilarity in community composition is one of the most fundamental and conspicuous features by which different forest ecosystems may be distinguished. Traditional estimates of community dissimilarity are based on differences in species incidence or abundance (e.g. the Jaccard, Sørensen, and Bray-Curtis dissimilarity indices). However, community dissimilarity is not only affected by differences in species incidence or abundance, but also by biological heterogeneities among species.Methods: The objective of this study is to present a new measure of dissimilarity involving the biological heterogeneity among species. The "discriminating Avalanche" introduced in this study, is based on the taxonomic dissimilarity between tree species. The application is demonstrated using observations from five stem-mapped forest plots in China and Mexico. We compared three traditional community dissimilarity indices (Jaccard, Sørensen, and Bray-Curtis) with the "discriminating Avalanche" index, which incorporates information, not only about species frequencies, but also about their taxonomic hierarchies.Results: Different patterns emerged for different measures of community dissimilarity. Compared with the traditional approaches, the discriminating Avalanche values showed a more realistic estimate of community dissimilarities, indicating a greater similarity among communities when species were closely related.Conclusions: Traditional approaches for assessing community dissimilarity disregard the taxonomic hierarchy. In the traditional analysis, the dissimilarity between Pinus cooperi and Pinus durangensis would be the same as the dissimilarity between P. cooperi and Arbutus arizonica. The dissimilarity Avalanche dissimilarity between P. cooperi and P. durangensis is considerably lower than the dissimilarity between P. cooperi and A. arizonica, because the taxonomic hierarchies are incorporated. Therefore, the discriminating Avalanche is a more realistic measure of community dissimilarity. This main result of our study may contribute to improved characterization of community dissimilarities.

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Tropical forests are some of the most biodiverse ecosystems in the world, yet their functioning is threatened by anthropogenic disturbances and climate change. Global actions to conserve tropical forests could be enhanced by having local knowledge on the forests functional diversity and functional redundancy as proxies for their capacity to respond to global environmental change. Here, we create estimates of plant functional diversity and redundancy across the tropics by combining a dataset of 16 morphological, chemical and photosynthetic plant traits sampled from 2461 individual trees from 74 sites distributed across four continents, together with local climate data for the last half century. Our findings suggest a strong link between climate and functional diversity and redundancy with the three trait groups responding similarly across the tropics and climate gradient. We show that drier tropical forests are overall less functionally diverse than wetter forests and that functional redundancy declines with increasing soil water and vapour pressure deficits. Areas with high functional diversity and high functional redundancy tend to better maintain ecosystem functioning, such as aboveground biomass, after extreme weather events. Our predictions suggest that the lower functional diversity and lower functional redundancy of drier tropical forests, in comparison to wetter forests, may leave them more at risk of shifting towards alternative states in face of further declines in water availability across tropical regions.

Background: Understanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming. At present, no system for prediction of fire occurrence risk based on fuel dryness conditions is available in Mexico. As part of an ongoing national-scale project, we developed an operational fire risk mapping tool based on satellite and weather information. Results: We demonstrated how differing monthly temporal trends in a fuel greenness index, dead ratio (DR), and fire density (FDI) can be clearly differentiated by vegetation type and region for the whole country, using MODIS satellite observations for the period 2003 to 2014. We tested linear and non-linear models, including temporal autocorrelation terms, for prediction of FDI from DR for a total of 28 combinations of vegetation types and regions. In addition, we developed seasonal autoregressive integrated moving average (ARIMA) models for forecasting DR values based on the last observed values. Most ARIMA models showed values of the adjusted coefficient of determination (R 2 adj) above 0.7 to 0.8, suggesting potential to forecast fuel dryness and fire occurrence risk conditions. The best fitted models explained more than 70% of the observed FDI variation in the relation between monthly DR and fire density. Conclusion: These results suggest that there is potential for the DR index to be incorporated in future fire risk operational tools. However, some vegetation types and regions show lower correlations between DR and observed fire density, suggesting that other variables, such as distance and timing of agricultural burn, deserve attention in future studies.