Dissertation Research: Prediction and understanding the recovery of communties

论文研究：预测和理解社区的恢复

• 批准号: 0808012
• 负责人: Peter Raimondi
• 金额: $ 1.19万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0808012&HistoricalAwards=false
• 关键词: Dissertation; Research; Prediction; understanding; recovery

Predicting and understanding the way natural communities change over time and recover from disturbance remains one of the great challenges to the field of ecology. Much of the research seeking to predict ecological community dynamics assumes that processes measured in intact communities can be used to predict the response to a disturbance. Preliminary results suggest that for the rocky shore seaweed community dominated by the red turf alga, this assumption does not hold true. This project combines empirical studies of variables that may have profound effects on the rate and trajectory of succession (species interactions, dispersal, life history characteristics and biogeographic region) with the predictive capabilities of modeling. Understanding the processes by which a disturbed area recovers is also a key to understanding the long-term consequences of human impacts to ecosystems. Such information can aid in the placement of reserves and other resource-use decisions. Having the ability to predict recovery rates based on empirical data will allow access to information that would otherwise be impossible to obtain without experimental disturbances. This research takes a multi-faceted approach to understanding intertidal community dynamics that will be useful information to coastal resource managers regarding recovery potential for intertidal organisms. This project also supports the thesis research of a doctoral student.

预测和理解自然群落随时间的变化以及从干扰中恢复的方式仍然是生态学领域面临的巨大挑战之一。 许多试图预测生态群落动态的研究都假设在完整群落中测量的过程可以用来预测对干扰的反应。 初步结果表明，对于以红草藻为主的岩石海岸海藻群落，这一假设并不成立。 该项目结合了对可能对演替速度和轨迹（物种相互作用、扩散、生活史特征和地理区域）产生深远影响的变量的实证研究，并具有建模的预测能力。 了解受干扰地区的恢复过程也是了解人类对生态系统影响的长期后果的关键。 这些信息可以帮助确定储量和作出其他资源使用决定。 有能力预测回收率的基础上的经验数据将允许访问的信息，否则将不可能获得没有实验干扰。 这项研究采取了多方面的方法来了解潮间带群落动态，这将是有用的信息，沿海资源管理人员对潮间带生物的恢复潜力。 该项目还支持博士生的论文研究。