Dissertation Research: Disentangling drivers of community structure and composition of a tropical forest

论文研究：解开热带森林群落结构和组成的驱动因素

• 批准号: 1501175
• 负责人: David Wilcove
• 金额: $ 1.63万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1501175&HistoricalAwards=false
• 关键词: Dissertation; Research; Disentangling; drivers; community

This project seeks to disentangle and characterize drivers of plant and animal community structure and composition in one of the world's largest regenerating tropical forests. Determining why species are able to exist in a particular time and place (particularly the role of chance vs. inevitability) is simultaneously one of the most central tasks in ecology and a deeply pressing question for conservation in a rapidly changing world. Understanding these forces in regenerating tropical forest is a particularly timely given (1) the incredible biodiversity of tropical forests, (2) that the proportion of remaining forest classified as regenerating is high and ever-rising, and (3) such knowledge is a prerequisite for deploying ecological restoration as an effective tool for preserving threatened species and ecosystems.The project team will assess three ultimate drivers of community composition and structure: soil quality, landscape composition, and initial vegetative conditions. Using a network of vegetative plots, species composition of terrestrial mammal, bat, bird, and singing-insect assemblages will be assessed. Fundamental to this task is developing a set of tools that will allow for the rapid, cheap, and non-invasive assessment of these animal groups. To this end, ultrasonic and audible-frequency recorders will be deployed, and the research team is developing techniques that will allow for the semi-automated analysis of recordings using machine learning. To further assess vegetative structure and species interactions as proximate drivers of composition, arrays of synced recorders will be used to determine how key forest structural features and the distribution of some species affects the presence of others.

该项目试图在世界上最大的再生热带森林之一中解散和描述动植物群落结构和组成的驱动因素。 确定为什么物种能够在特定的时间和地点存在（尤其是机会的作用与必然性的作用）同时是生态学中最中心的任务之一，也是在迅速变化的世界中为保护而言是一个非常紧迫的问题。 鉴于（1）热带森林的令人难以置信的生物多样性，（2）被归类为再生且不断增长的剩余森林的比例是，（3）这样的知识是将生态恢复的前提，以确保既定的物种和环境的有效工具。景观组成和初始的营养条件。 使用植物图网络，将评估陆生哺乳动物，蝙蝠，鸟和唱歌式组合的物种组成。这项任务的基础是开发一组工具，这些工具将允许对这些动物群体进行快速，便宜和无创的评估。 为此，将部署超声波和声音频率录音机，研究团队正在开发技术，可以使用机器学习对录音进行半自动分析。 为了进一步评估植物结构和物种相互作用作为组成的近端驱动因素，将使用同步记录器的阵列来确定关键的森林结构特征和某些物种的分布如何影响其他物种的存在。