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 cross-validation 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 height 2 × total height (d 2 h) 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.

No abstract

The ability to precisely describe forest spatial structures, and their modifications through timber harvesting, is of prime importance for sustainable management of complex forest ecosystems, especially regarding uneven-aged, multi-species forests. For this purpose, forest managers require statistical indices that are meaningful descriptors of the spatial structure of a given forest ecosystem. This paper presents a new sensitive permutation test of spatial randomness for solving the classification problem of three nearest neighbour-based indices. The test enables a categorisation of a spatial pattern as a whole into one of three groups: regular, random or cluster, with a sensitivity comparable to that of Ripley's L test, at finer scales. The examples illustrate how the Clark and Evans, the uniform angle, and the mean directional indices can be used for precise detection of departure from spatial randomness. The results show that these three indices should be used simultaneously because they are sensitive to slightly different types of processes.

Introducción: Los sistemas biométricos forestales constituyen las herramientas analíticas más utilizadas para el análisis de la producción y el crecimiento de los bosques.Objetivo: Presentar un nuevo sistema biométrico para los bosques templados y tropicales de México.Materiales y métodos: El área de estudio comprendió los estados de Chihuahua, Guerrero, Jalisco, Oaxaca, Michoacán, Puebla, Estado de México, Hidalgo, Tlaxcala, Veracruz y Quintana Roo. La toma de datos de campo se realizó mediante muestreo destructivo y no destructivo en cada Unidad de Manejo Forestal Regional (UMAFOR) en los estados. La metodología utilizada permitió generar sistemas de ecuaciones para la estimación de atributos de árboles individuales que son aditivos entre componentes, escalables a nivel de árbol completo y consistentes a lo largo de las diversas condiciones forestales del país.Resultados y discusión: El Sistema Biométrico Forestal “SiBiFor” está integrado por más de 6 000 nuevas ecuaciones para 97 especies arbóreas de los bosques templados y tropicales. SiBiFor contiene 2 917 ecuaciones de volumen, 2 868 de ahusamiento-volumen, 341 de índice de sitio y 288 de crecimiento en diámetro.Conclusión: Las ecuaciones desarrolladas mejorarán el manejo de los ecosistemas forestales del país lo que contribuirá a la sustentabilidad de los mismos.

& Context Tree height prediction is an important issue in forest management since tree heights are usually measured only in a sample of trees. Although numerous model approaches have been used for this purpose, no agreement on which one is more appropriate has been achieved. & Aims To analyse the random effects of basic and generalised height-diameter (h-d) models fitted to multi-species unevenaged forest stands, and to establish their ability to explain differences between ecoregions, plots and species. & Methods Height and diameter measurements for 29,084 trees from 187 sample plots located in the state of Durango (Mexico) were used. Basic and generalised h-d models were fitted in a mixed-models framework. The variability between ecoregions, plots and species was considered in the random effects definition. Model calibration for different height sampling designs and sampling sizes was also analysed. & Results Random components performed well in explaining the differences in the h -d relationship between the different plots and species; however, no significant variance for the random effects was found for the different ecoregions. A calibrated basic h -d model produced similar results to a fixed-effects generalised h -d model when a sufficiently large number of trees was used in the calibration process. & Conclusion From a practical point of view, if no calibration is carried out, different models should be used for the different species, so that at least the variation among species is captured.