Species Overlap and Phylogenetic Relatedness Result in Community Statistical Non‐Independence (and What to Do About It)
Bibliographic record
Abstract
Statistical autocorrelation between sampling units violates independence assumptions in many analyses. Here, we used simulations and empirical analyses to demonstrate how shared evolutionary history between species and species overlap among communities leads to an insidious form of autocorrelation, termed compositional autocorrelation. We simulated compositionally autocorrelated ecosystem functions across communities and assessed the type I error, statistical power and accuracy of slope estimates from naïve linear regression models and mixed models accounting for compositional autocorrelation. Mixed models maintained lower type I error, similar or higher statistical power, and more accurate slope estimates compared to linear regression. Re-analysing an empirical dataset, we found linear regression underestimated uncertainty in species richness effects for eight of 10 ecosystem functions. As species overlap and shared evolutionary history are common features in community data, our results highlight the need to explicitly consider compositional autocorrelation in statistical analyses to ensure correct inferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".