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

该项目的重点是农业废弃后次生林的再生过程。目前在亚马逊河流域中部（巴西马瑙斯）、哥斯达黎加东北部和墨西哥恰帕斯州进行的独立研究表明，观测到的森林内植被变化往往偏离根据对废弃后不同年龄森林的一次性研究所作的预测。该项目将拉丁美洲潮湿热带森林的四个现有项目结合起来。将在长期研究地块进行协调的年度植被普查，以测试一系列因变量的具体时序预测，包括树干密度、大小分布、断面积、生物量、物种丰富度、物种组成和生活型组成。基于最佳拟合统计模型，将观察到的因变量变化率与各地区时间序列研究的预测率进行比较。通过了解这些地区的林分水平动态如何符合或偏离整体时序模式，研究人员将确定影响演替途径的关键生态因素，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重要信息。最终，研究结果将使发展更具有预测性和更广泛的应用模型的演替植被动态在今天的复杂的热带景观。

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.