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When can species abundance data reveal non-neutrality?
Omar Al Hammal1, David Alonso2, Rampal S Etienne3
1School of Biology, University of Leeds, Leeds, United Kingdom.
Plos Computational Biology
|March 21, 2015
Summary
Detecting deviations from ecological neutrality requires substantial data. Our power analysis shows that even large datasets may not detect subtle non-neutral processes, highlighting the need for careful sampling design in community ecology.
Area of Science:
- Ecology
- Community Ecology
- Theoretical Ecology
Background:
- Species abundance distributions (SADs) are a fundamental pattern in ecology.
- Numerous models exist to explain SADs, but consensus on the correct model is lacking.
- Quantifying the ability to detect non-neutral processes from SADs remains a challenge.
Purpose of the Study:
- To develop a power calculation for detecting deviations from neutrality in species abundance data.
- To rigorously quantify the detectability of non-neutral processes using SAD patterns.
- To provide a framework for determining sampling effort and estimating non-neutral process strength.
Main Methods:
- Utilized non-neutral stochastic community models for simulations.
- Developed a power calculation framework applicable to any computer-simulatable community model.
- Assessed the influence of sample size and effect amplitude on detectability.
Main Results:
- Non-neutral processes are detectable with sufficient sample size and/or strong effect amplitude.
- Existing large datasets, like the Barro Colorado Island forest plot, may be insufficient to detect neutral deviations from competition alone.
- Multiple contrasting non-neutral processes might be detectable even in limited datasets.
Conclusions:
- The detectability of ecological processes from SADs is contingent on sampling effort and effect strength.
- Current ecological datasets may underestimate the prevalence of non-neutral processes.
- A robust framework is needed to guide future ecological sampling and analysis for discerning community assembly mechanisms.
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