Kearney Michael, Porter Warren
Department of Zoology, The University of Melbourne, Melbourne, Vic. 3010, Australia.
Ecol Lett. 2009 Apr;12(4):334-50. doi: 10.1111/j.1461-0248.2008.01277.x.
Species distribution models (SDMs) use spatial environmental data to make inferences on species' range limits and habitat suitability. Conceptually, these models aim to determine and map components of a species' ecological niche through space and time, and they have become important tools in pure and applied ecology and evolutionary biology. Most approaches are correlative in that they statistically link spatial data to species distribution records. An alternative strategy is to explicitly incorporate the mechanistic links between the functional traits of organisms and their environments into SDMs. Here, we review how the principles of biophysical ecology can be used to link spatial data to the physiological responses and constraints of organisms. This provides a mechanistic view of the fundamental niche which can then be mapped to the landscape to infer range constraints. We show how physiologically based SDMs can be developed for different organisms in different environmental contexts. Mechanistic SDMs have different strengths and weaknesses to correlative approaches, and there are many exciting and unexplored prospects for integrating the two approaches. As physiological knowledge becomes better integrated into SDMs, we will make more robust predictions of range shifts in novel or non-equilibrium contexts such as invasions, translocations, climate change and evolutionary shifts.
物种分布模型(SDMs)利用空间环境数据对物种的分布范围界限和栖息地适宜性进行推断。从概念上讲,这些模型旨在通过空间和时间来确定并绘制物种生态位的组成部分,并且它们已成为理论生态学、应用生态学和进化生物学中的重要工具。大多数方法都是相关性的,因为它们通过统计学方法将空间数据与物种分布记录联系起来。另一种策略是将生物体功能性状与其环境之间的机制联系明确纳入物种分布模型。在此,我们回顾生物物理生态学原理如何用于将空间数据与生物体的生理反应及限制因素联系起来。这提供了一个基本生态位的机制性观点,进而可以映射到景观上以推断分布范围限制。我们展示了如何针对不同环境背景下的不同生物体开发基于生理的物种分布模型。与相关性方法相比,机制性物种分布模型有不同的优缺点,并且将这两种方法整合有许多令人兴奋且尚未探索的前景。随着生理知识更好地融入物种分布模型,我们将能够在诸如入侵、物种迁移、气候变化和进化转变等新的或非平衡背景下对分布范围变化做出更可靠的预测。