<p><strong>Background:</strong> Accurate estimations of aboveground biomass (AGB) based on allometric models are needed to implement climate-change mitigation strategies. However, allometry can change with tree size.</p><p><strong>Questions:</strong> Does allometry in a tropical dry forest change with tree size? Does combining different allometric equations provide better AGB estimates than using a single equation?</p><p><strong>Study site and dates:</strong> San Agustín Ejido, Yucatán, México, 2016.</p><p><strong>Methods:</strong> Forty-seven trees of 18 species with 2.5 to 41.5 cm in diameter at breast height (DBH) were sampled. Stems and branches were sectioned, and samples were dried and weighed to estimate tree AGB. Segmented linear regression was used to evaluate changes in allometry between DBH, height and AGB. Different equations were tested for each size category identified, and the best models and model-combinations selected.</p><p><strong>Results:</strong> A shift in the AGB-height relationship was found, defining two tree-size categories (2.5-9.9 cm and ≥ 10 cm in DBH), with the inflection point corresponding to the average canopy height (12.2 m). The best models were AGB = exp(-2.769+0.937ln(D²HPw)) for trees &lt; 10 cm DBH and AGB = exp(-9.171+1.591lnD+3.902lnH+0.496lnPw) for trees ≥ 10 cm DBH (<em>R</em>² = 0.85 and <em>R</em>² = 0.92, respectively). The combination of these models produced more accurate AGB estimates than a single model or combinations involving regional models with larger sample sizes.</p><p><strong>Conclusions</strong>: These results highlight the importance of locally-developed models and suggest changes in allometry and resource allocation: towards height growth for small trees, thereby reducing the risk of suppression, <em>versus</em> towards AGB growth for larger trees, thereby maximizing stability and resource acquisition.</p>

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Accounting for small-size tree biomass is critical to improve total stand biomass estimates of secondary tropical forests, and is essential to quantify their vital role in mitigating climate change. However, owing to the scarcity of equations available for small-size trees, their contribution to total biomass is unknown. The objective of this study was to generate allometric equations to estimate total biomass of 22 tree species ≤ 10 cm in diameter at breast height (DBH), in the Yucatan peninsula, Mexico, by using two methods. First, the additive approach involved the development of biomass equations by tree component (stem, branch and foliage) with simultaneous fit. In the tree-level approach, total tree biomass equations were fit for multi-species and wood density groups. Further, we compared the performance of total tree biomass equations that we generated with multi-species equations of previous studies. Data of total and by tree component biomass were fitted from eight non-linear models as a function of DBH, total height (H) and wood density (ρ). Results showed that two models, identified as model I and II, best fitted our data. Model I has the form AGB = β0 (ρ•DBH 2 •H)β1 + ε and model II: AGB = exp(-β0) (DBH 2 •H)β1 + ε, where AGB is biomass (kg). Both models explained between 53% and 95% of the total observed variance in biomass, by tree-structural component and total tree biomass. The variance of total tree biomass explained by fit models related to wood density group was 96%-97%. Compared foreign equations showed between 30% and 45% mean error in total biomass estimation compared to 0.05%-0.36% error showed by equations developed in this study. At the local level, the biomass contribution of small trees based on foreign models was between 24.38 and 29.51 Mg ha -1 , and model I was 35.97 Mg ha -1 . Thus, from 6.5 up to 11.59 Mg ha -1 could be excluded when using foreign equations, which account for about 21.8% of the total stand biomass. Local equations provided more accurate biomass estimates with the inclusion of ρ and H as predictors variables and proved to be better than foreign equations. Therefore, our equations are suitable to improve the accuracy estimates of carbon forest stocks in the secondary forests of the Yucatan peninsula.

La selva mediana superennifolia es la comunidad más extendida y una de las más transformadas en la Península de Yucatán, sin embargo, existen pocos estudios sobre su proceso de recuperación. Este trabajo planteó analizar los patrones de recuperación de la estructura, diversidad y composición de especies en una selva de este tipo. El estudio se realizó en el área de conservación El Zapotal, en Yucatán, México, entre 2011 y 2012. Se muestreó la vegetación leñosa en una cronosecuencia de rodales, de 4 años a 60 años de abandono tras un uso ganadero, y en un remanente de selva conservada (madura). Los patrones de recuperación de la estructura y la diversidad con la edad sucesional se analizaron comparando diferentes modelos no lineales y los de composición mediante análisis de ordenación y de clasificación. Los atributos de la estructura y la diversidad aumentaron con la edad sucesional, excepto la densidad, que alcanzó un máximo en edades tempranas, seguido de una disminución. La diversidad alcanzó los valores observados en la selva conservada, pero la estructura no. La similitud florística con respecto a la selva madura aumentó con la edad de sucesión y se distinguieron tres grupos de especies que indican un recambio en la dominancia durante la sucesión. Los resultados sugieren una recuperación rápida de la diversidad, más lenta de la estructura, y un patrón sucesional de composición más semejante al de las selvas más húmedas que al de selvas más secas, con importantes implicaciones para la conservación, la restauración y la mitigación del cambio climático.

Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in nonlinear ways. Using a multi-scale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density and basal area are less predictable in landscapes (4 km radius) with intermediate (40–60%) forest cover than in landscapes with high (greater than 60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation of key ecological factors for forest recovery (e.g. seed dispersal and seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only species richness is positively related to forest cover in relatively small (1 km radius) landscapes. These findings highlight the importance of using a spatially explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1–4 km radius.