Tropical forests are converted at an alarming rate for agricultural use and pastureland, but also regrow naturally through secondary succession. For successful forest restoration, it is essential to understand the mechanisms of secondary succession. These mechanisms may vary across forest types, but analyses across broad spatial scales are lacking. Here, we analyse forest recovery using 1,403 plots that differ in age since agricultural abandonment from 50 sites across the Neotropics. We analyse changes in community composition using species-specific stem wood density (WD), which is a key trait for plant growth, survival and forest carbon storage. In wet forest, succession proceeds from low towards high community WD (acquisitive towards conservative trait values), in line with standard successional theory. However, in dry forest, succession proceeds from high towards low community WD (conservative towards acquisitive trait values), probably because high WD reflects drought tolerance in harsh early successional environments. Dry season intensity drives WD recovery by influencing the start and trajectory of succession, resulting in convergence of the community WD over time as vegetation cover builds up. These ecological insights can be used to improve species selection for reforestation. Reforestation species selected to establish a first protective canopy layer should, among other criteria, ideally have a similar WD to the early successional communities that dominate under the prevailing macroclimatic conditions.

This paper presents new equations for estimating above-ground biomass (AGB) and biomass components of seventeen forest species in the temperate forests of northwestern Mexico. A data set corresponding to 1336 destructively sampled oak and pine trees was used to fit the models. The generalized method of moments was used to simultaneously fit systems of equations for biomass components and AGB, to ensure additivity. In addition, the carbon content of each tree component was calculated by the dry combustion method, in a TOC analyser. The results of crossvalidation indicated that the fitted equations accounted for on average 91%, 82%, 83% and 76% of the observed variance in stem wood and stem bark, branch and foliage biomass, respectively, whereas the total AGB equations explained on average 93% of the total observed variance in AGB. The inclusion of total height (h) or diameter at breast height² × total height (d²h) as a predictor in the d-only based equations systems slightly improved estimates for stem wood, stem bark and total above-ground biomass, and greatly improved the estimates produced by the branch and foliage biomass equations. The predictive power of the proposed equations is higher than that of existing models for the study area. The fitted equations were used to estimate stand level AGB stocks from data on growing stock in 429 permanent sampling plots. Three machine-learning techniques were used to model the estimated stand level AGB and carbon contents; the selected models were used to map the AGB and carbon distributions in the study area, for which mean values of respectively 129.84 Mg ha-¹ and 63.80 Mg ha-¹ were obtained.

In Mexico and in the world, the incorporation of the scientific community (among them ecologists) to the generation of knowledge that contributes to solving the serious environmental problems, and advancing towards the sustainability, requires a different research vision. Moreover, innovative tools are needed to couple the work of the scientific community with the rest of the society. Ecologists are challenged to become innovators and creators of new models, collaborative processes, methods and research tools, basic and applied, in topics such as agriculture, forestry, livestock andthe use of natural resources. To meet this challenge, ecologists in Mexico need to amalgamate their work with other academic and non-academic actors to jointly promote environmental improvement processes with social benefits. This contribution examines challenges and perspectives of ecotechnological innovation in Mexico. We suggest to foster the research work in this field in laboratories without walls, in which flexible groups of actors design, test, and evaluate ecotechnological innovations that respond to specific local and regional socio-ecological and environmental problems.

Introduction: Forest biometric systems are the most widely used analytical tools for analysis of forest growth and yield. Objective: To present a new biometric system for temperate and tropical Mexican forests. Materials and methods: The study area included the states of Chihuahua, Guerrero, Jalisco, Oaxaca, Michoacán, Puebla, State of Mexico, Hidalgo, Tlaxcala, Veracruz and Quintana Roo. Field data collection was done by using destructive and non-destructive sampling in each Regional Forest Management Unit (RFMU) in the states. The methodology generated equation systems for the estimation of individual-tree attributes that are additive between components, scalable at totaltree level and consistent throughout the country’s different forest conditions. Results and discussion: The Forest Biometric System (SiBiFor for its initials in Spanish) consists of more than new equations for tree species in temperate and tropical forests. SiBiFor contains volume, volume-taper, site index and diameter growth equations. Conclusion: The developed equations will improve the management of the country’s forest ecosystems, which will contribute to their sustainability.