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Towards a more predictive community ecology: integrating functional traits and disequilibrium

走向更具预测性的群落生态：整合功能特征和不平衡

基本信息

批准号：
NE/M019160/1
负责人：
Benjamin Blonder
金额：
$ 58.72万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FM019160%2F1
关键词：
Towards more predictive community ecology

项目摘要

Imagine going to a garden store and buying every kind of vegetable seed packet for sale. Sow them all in a patch of bare ground, and then try to predict which particular species will be harvestable at the end of the season. Even the best ecological science is stymied by this simple challenge. An open problem is predicting or controlling the dynamics of communities, whether they are vegetable gardens or tropical forests. The goal of this work is to develop biodiversity theory that can link across scales from individuals to communities to ecosystems. A more predictive ecological science could 1) delineate the processes and mechanisms that operate at or across different spatial scales and 2) generate falsifiable predictions for biodiversity dynamics in specific systems over time. To begin to address these aims, I will test two key ideas: first, that organisms' physiology needs to be built in to models of population growth; and second, that the matching between physiology and environment is not necessarily optimal, because of disequilibrium caused by lagged responses of organisms to rapid change. Over the next five years I will expand and test these ideas in a set of long-term study sites spanning the lowland tropics of Malaysia to the high alpine of Colorado.

想象一下，去一家花园商店，买各种蔬菜种子包装出售。将它们全部播种在一块裸露的土地上，然后尝试预测哪些特定物种将在季节结束时可收获。即使是最好的生态科学也会受到这一简单挑战的阻碍。一个悬而未决的问题是预测或控制群落的动态，无论它们是菜园还是热带森林。这项工作的目标是发展生物多样性理论，将从个人到社区再到生态系统的各个尺度联系起来。更具预测性的生态科学可以1)描述在不同空间尺度上或跨不同空间尺度运行的过程和机制，以及2)对特定系统中的生物多样性动态随时间的变化产生可证伪的预测。为了实现这些目标，我将测试两个关键观点：第一，生物体的生理学需要建立在种群增长模型中；第二，由于有机体对快速变化的反应滞后导致的不平衡，生理和环境之间的匹配不一定是最佳的。在接下来的五年里，我将在一系列长期的研究地点扩展和测试这些想法，这些地点横跨马来西亚的热带低地到科罗拉多州的高山。