Characterization of Arabidopsis thaliana regrowth patterns suggests a trade-off between undamaged fitness and damage tolerance |
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Authors: | Daniel?R.?Scholes author-information" > author-information__contact u-icon-before" > mailto:scholesd@uindy.edu" title=" scholesd@uindy.edu" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author,Erika?N.?Rasnick,Ken?N.?Paige |
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Affiliation: | 1.Department of Biological Sciences,Virginia Tech,Blacksburg,USA;2.Institute of Arctic and Alpine Research,University of Colorado,Boulder,USA;3.School of Forest Resources and Conservation,University of Florida,Gainesville,USA |
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Abstract: | Metacommunity ecology has rapidly become a dominant framework through which ecologists understand the natural world. Unfortunately, persistent misunderstandings regarding metacommunity theory and the methods for evaluating hypotheses based on the theory are common in the ecological literature. Since its beginnings, four major paradigms—species sorting, mass effects, neutrality, and patch dynamics—have been associated with metacommunity ecology. The Big 4 have been misconstrued to represent the complete set of metacommunity dynamics. As a result, many investigators attempt to evaluate community assembly processes as strictly belonging to one of the Big 4 types, rather than embracing the full scope of metacommunity theory. The Big 4 were never intended to represent the entire spectrum of metacommunity dynamics and were rather examples of historical paradigms that fit within the new framework. We argue that perpetuation of the Big 4 typology hurts community ecology and we encourage researchers to embrace the full inference space of metacommunity theory. A related, but distinct issue is that the technique of variation partitioning is often used to evaluate the dynamics of metacommunities. This methodology has produced its own set of misunderstandings, some of which are directly a product of the Big 4 typology and others which are simply the product of poor study design or statistical artefacts. However, variation partitioning is a potentially powerful technique when used appropriately and we identify several strategies for successful utilization of variation partitioning. |
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