Abstract
Uncovering the roles of biotic interactions in assembling and maintaining species‐rich communities remains a major challenge in ecology. In plant communities, interactions between individuals of different species are expected to generate positive or negative spatial interspecific associations over short distances. Recent studies using individual‐based point pattern datasets have concluded that (a) detectable interspecific interactions are generally rare, but (b) are most common in communities with fewer species; and (c) the most abundant species tend to have the highest frequency of interactions. However, it is unclear how the detection of spatial interactions may change with the abundances of each species, or the scale and intensity of interactions. We ask if statistical power is sufficient to explain all three key results.We use a simple two‐species model, assuming no habitat associations, and where the abundances, scale and intensity of interactions are controlled to simulate point pattern data. In combination with an approximation to the variance of the spatial summary statistics that we sample, we investigate the power of current spatial point pattern methods to correctly reject the null model of pairwise species independence.We show the power to detect interactions is positively related to both the abundances of the species tested, and the intensity and scale of interactions, but negatively related to imbalance in abundances. Differences in detection power in combination with the abundance distributions found in natural communities are sufficient to explain all the three key empirical results, even if all pairwise interactions are identical. Critically, many hundreds of individuals of both species may be required to detect even intense interactions, implying current abundance thresholds for including species in the analyses are too low. Sy n thesis. The widespread failure to reject the null model of spatial interspecific independence could be due to low power of the tests rather than any key biological process. Since we do not model habitat associations, our results represent a first step in quantifying sample sizes required to make strong statements about the role of biotic interactions in diverse plant communities. However, power should be factored into analyses and considered when designing empirical studies.
Highlights
Understanding the contribution of biological interactions to the assembly and regulation of natural communities remains a key goal in ecology
A particular focus on the role of competition can be found in plant ecology, not least because plants seem to require the same few nutrients (Silvertown, 2004), and because their sessile nature might permit an understanding of processes from the spatial pattern of individuals (Murrell, Purves, & Law, 2001), and allow for easier experimental manipulation (Goldberg & Barton, 1992)
Technical details are left to Supporting Information Appendix A, but in brief we assume that the data generating mechanisms can be described by some processes X1 and X2, and the goal of statistical analysis is to draw conclusions about the processes using the observed set (x1, x2)
Summary
Understanding the contribution of biological interactions to the assembly and regulation of natural communities remains a key goal in ecology. We are unaware of any study that investigates the statistical power of the tests for spatial independence between pairs of species that are commonly used and there are no guidelines for the lower abundance threshold As such care is required when interpreting failures to reject the null hypothesis, and we argue it is hard to make strong statements about the relative roles of stochastic‐ and niche‐based processes across different communities until we gain a better understanding of the power of the methods to detect departures from spatial independence. Our results are a useful first guide to understanding the sample sizes required to detect pairwise interactions With this caveat in mind, our analyses will suggest previous abundance thresholds for species inclusion are likely too low to detect even very strong interactions in the most species‐rich communities being tested, questioning the previously derived conclusion of a lack of dependence between species. Since power can be estimated from Monte Carlo simulations, we hope our results will motivate ecologists to think more about the issue of sample size in future studies and help to resolve the debate over the relative importance of biotic interactions in species‐rich communities
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