Advancing and Applying a Maximum Entropy Theory of Ecology

了解物种的空间分布、丰度、能量和网络结构的模式是生态学的核心目标。 随着生态学理论的发展，对相对原始的生态系统进行模式预测的目标已经取得了进展，该理论基于严格的推理过程，即最大熵方法，该方法起源于半个世纪前的信息理论的开创性工作。 该项目将通过扩大生态学最大熵理论的分类范围及其预测种群区域空间结构的能力，并通过描述和了解其在高度干扰的生态系统中的预测性能，来推进现有的生态学最大熵理论。由此产生的预测能力是制定实用的地方，国家和全球保护和土地利用政策的关键。 因此，本研究的一个高度优先事项将是应用该理论来解决令人烦恼的保护问题，例如改善对栖息地丧失和气候变化下物种损失的估计，提高生物多样性普查策略的效率和准确性，以及改善对生物群落的生物丰富度的估计。 将开发免费的、用户友好的软件包，使非学术管理人员能够将最大熵方法应用于空间上明确的守恒问题。将为妇女和代表性不足的少数民族提供数量科学方面的培训机会，并为寻求连接物理和生物科学的学生提供培训机会。

Understanding patterns in the spatial distribution, abundance, energetics, and network structure of species are central goals of ecology. Progress toward the goal of pattern prediction has been achieved recently for relatively pristine ecosystems with development of ecological theory based on a rigorous inference procedure, the method of maximum entropy, that originated from pioneering work in information theory half a century ago. This project will advance the existing Maximum Entropy Theory of Ecology by extending its taxonomic range and its ability to predict the regional spatial structure of populations, and by characterizing and understanding its predictive performance in highly disturbed ecosystems. The resulting predictive ability is key to formulating practical local, national, and global conservation and land use policies. Hence, a high priority of this research will be applying the theory to vexing conservation problems, such as improving estimation of species loss under habitat loss and climate change, improving the efficiency and accuracy of biodiversity census strategies, and improving estimation of the biological richness of biomes too large to census directly. Free, user-friendly software packages will be developed, allowing non-academic managers to apply maximum entropy methods to spatially explicit conservation questions. There will be training opportunities for women and underrepresented minorities in the quantitative sciences, and for students seeking to bridge the physical and biological sciences.