Collaborative Research/LTREB Successional pathways and rates of change in tropical forests of Brazil, Costa Rica, and Mexico

本项目的重点是农业废弃后的次生林更新过程。目前在亚马孙中部(巴西马瑙斯)、哥斯达黎加东北部和墨西哥恰帕斯进行的独立研究表明，观察到的森林内植被变化往往偏离基于对自被遗弃以来不同年龄的森林的一次性研究得出的预测。该项目结合了拉丁美洲潮湿热带森林的四个现有项目。将在长期研究地块中进行协调的年度植被普查，以测试对一系列因变量的特定时间序列预测，这些变量包括树干密度、大小分布、断面积、生物量、物种丰富度、物种组成和生命形式组成。观察到的因变量的变化率将与每个区域内的时间序列研究的预测率进行比较，这是基于最适合的统计模型。通过了解林分水平的动态如何符合或偏离每个区域内的整体时间序列模式，研究人员将确定影响演替路径的关键生态因素、过程和机制。收集的长期数据将为指导新热带地区的森林恢复、碳固存和生物多样性保护提供必要的信息。最终，这项研究的结果将使在当今复杂的热带景观中开发更具预测性和更广泛应用的演替植被动态模型成为可能。

This project focuses on the process of secondary forest regeneration following agricultural abandonment. Current independent studies conducted in Central Amazonia (Manaus, Brazil), northeastern Costa Rica, and Chiapas, Mexico reveal that observed vegetation changes within forests often deviate from predictions based on single-time studies of forests of different age since abandonment. This project unites four existing projects in wet tropical forests of Latin America. Coordinated annual vegetation censuses in long-term study plots will be conducted to test specific chronosequence predictions for a range of dependent variables including stem density, size distributions, basal area, biomass, species richness, species composition, and life-form composition. Observed rates of change in dependent variables will be compared with predicted rates from chronosequence studies within each region, based on the best-fit statistical models. By understanding how stand-level dynamics conform to or deviate from overall chronosequence patterns within each of these regions, the researchers will identify key ecological factors, processes, and mechanisms that influence successional pathways. The long-term data gathered will provide essential information to guide forest restoration, carbon sequestration, and biodiversity conservation in the Neotropics. Ultimately, results from the study will enable development of more predictive and more widely applied models of successional vegetation dynamics in today's complex tropical landscapes.