Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA.
Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA.
Ecology. 2024 Jun;105(6):e4283. doi: 10.1002/ecy.4283. Epub 2024 May 13.
As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.
近年来，随着数据和计算能力的迅猛增长，统计建模已成为理解生态模式和过程的重要工具。生态统计学面临两大挑战。首先，生态学数据可能不符合传统方法，其次，专业生态学家通常没有接受广泛的统计培训。为应对这些挑战，《生态学杂志》发表了许多创新性的统计生态学论文，介绍了新颖的建模方法，并为统计最佳实践提供了通俗易懂的指南。在本文中，我们回顾了《生态学杂志》的历史及其在统计生态学这一分支学科的出现中所扮演的角色，我们将统计生态学定义为使用数学方程、概率和经验数据研究生态系统的学科。我们展示了过去一个世纪在《生态学杂志》上发表的 36 篇有影响力的统计生态学论文，并对该领域的发展进行了评论。随着数据和计算能力的持续增长，我们预计统计生态学将继续发展，以应对复杂的分析，《生态学杂志》将继续发表创新性和有影响力的论文，推动学科发展，为实践生态学家提供指导。
