A Critique of the Space-for-Time Substitution Practice in Community Ecology. (May 2019)

• Record Type: Journal Article
• Authors: Damgaard, Christian
• Is Part Of: Trends in ecology & evolution. Volume 34:Number 5(2019)
• Page Start: 416
• Page End: 421
• Publication Date: 2019-05
• Subjects: space-for-time substitution -- community ecology -- interspecific competition -- successional processes -- nonstationary environment; Ecology -- Periodicals; Evolution (Biology) -- Periodicals; 576.8
• DOI: 10.1016/j.tree.2019.01.013
• Languages: English
• ISSNs: 0169-5347

Abstract

Abstract : The space-for-time substitution assumption is often used implicitly for studying ecological processes in static spatial data sets. Since ecological processes occur in time, this practice is problematic, especially in nonstationary environments. More processes might lead to the same spatial pattern, and instead of testing hypotheses on ecological processes by analyzing spatial variation in static data, it is more judicious to report the observed spatial patterns and only discuss which ecological processes are in concordance with the observed spatial pattern. Alternatively, it might be feasible to combine relatively sparse time-series data or experimental data with spatial variation data and analyze such data types in a common statistical framework. Highlights: The implicit use of the space-for-time substitution assumption when using spatial regression analysis on static data with only spatial variation to study processes of community ecology may lead to erroneous conclusions. Credible inferences on ecological processes are preferably done using time-series data from several sites, where the sampled ecological data are collected at a spatial scale and at a frequency that is relevant for the studied ecological process. It may be feasible to combine sparse time-series data with spatial variation data and analyze both data types in a common statistical framework, and in the coming years it will be important to further develop methods for combining sparse time-series data with spatial data.