Assessing the accuracy of paired and random sampling for quantifying plant–plant interactions in natural communities
Bibliographic record
Abstract
ABSTRACT Interactions among plant species in extreme ecological systems are often inferred from spatial associations and quantified by means of paired sampling. Yet, this method might be confounded by habitat-sharing effects, in particular when microenvironmental heterogeneity and stress are high. Here, we address whether paired and random sampling methods provide similar results at varying levels of environmental heterogeneity. Furthermore, we investigate how the relationship between species preferences and abiotic severity influences the outcome of these two methods. We quantified spatial associations with the two methods at three sites that encompass different micro-environmental heterogeneity and stress levels: semi-arid 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 our model simulations. At each site, there were strong differences between beneficiary species in their spatial association with benefactor species, and associations became more positive with increasing stress in Spain. Most importantly, there were no differences in the 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. We conclude that the paired sampling method can provide significant insights into net, long-term effects of plant interactions in spatially conspicuous environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".