Dissertation Research: Prediction and understanding the recovery of communties

预测和理解自然群落随时间变化和从干扰中恢复的方式仍然是生态学领域的重大挑战之一。许多试图预测生态群落动态的研究假设，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岸海藻群落，这一假设并不成立。该项目将对演替速率和演替轨迹（物种相互作用、扩散、生活史特征和生物地理区域）产生深远影响的变量的实证研究与建模预测能力相结合。了解受干扰地区恢复的过程也是理解人类对生态系统影响的长期后果的关键。这类资料可以帮助确定储量和作出其他资源使用决定。有了根据经验数据预测采收率的能力，就可以获得没有实验干扰就无法获得的信息。本研究采用多方面的方法来了解潮间带群落动态，这将为沿海资源管理者提供有关潮间带生物恢复潜力的有用信息。该项目同时支持一名博士生的论文研究。

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.