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Abstract Plant interactions in extreme environments are often inferred from spatial associations and quantified by means of paired sampling. Yet, this method might be confounded by habitat‐sharing effects. Here, we address whether paired and random sampling methods provide similar results at varying levels of environmental heterogeneity. We quantified spatial associations with the two methods at three sites that encompass different micro‐environmental heterogeneity and stress levels: Mediterranean environments in Canary Islands, Spain, and Sardinia, Italy, and a cold alpine environment in Hokkaido, Japan. Then, we simulated plant communities with different levels of species micro‐habitat preferences, environmental heterogeneity, and stress levels. We found that differences in species associations between paired and random sampling were indistinguishable from zero in a homogeneous space. When simulating codispersion over a decreasing abundance gradient, both sampling methods correctly identified facilitation and distinguished it from codispersion. Yet, the pairwise method provided higher facilitation estimates than the random one. At each site, there were strong differences between beneficiary species in their spatial association with nurse species, and associations became more positive with increasing stress in Spain. Most importantly, there were no differences in results yielded by the two methods at any of the different stress levels at the Spanish and Japanese sites. At the Italian site, although micro‐environmental heterogeneity was low, we found weakly significant differences between methods that were unlikely due to habitat‐sharing effects. Thus, the paired sampling method can provide significant insights into net and long‐term effects of plant interactions in spatially conspicuous environments.
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Better Accuracy for Better Science . . . Through Random Conclusions
Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect-size estimates, in turn producing more stringent thresholds for hypothesis testing and for statistical-power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions.
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- Award ID(s):
- 2145308
- PAR ID:
- 10433301
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Perspectives on Psychological Science
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 1745-6916
- Format(s):
- Medium: X Size: p. 223-243
- Size(s):
- p. 223-243
- Sponsoring Org:
- National Science Foundation
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