Selecting traits that explain species–environment relationships: a generalized linear mixed model approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Question Quantification of the effect of species traits on the assembly of communities is challenging from a statistical point of view. A key question is how species occurrence and abundance can be explained by the trait values of the species and the environmental values at the sites. Methods Using a sites × species abundance table, a site × environment data table and a species × trait data table, we address the above question using a novel generalized linear mixed model ( GLMM ) approach. The GLMM overcomes problems of pseudo‐replication and heteroscedastic variance by including sites and species as random factors. The method is equally applicable to presence–absence data as to count and multinomial data. We present a tiered forward selection approach for obtaining a parsimonious model and compare the results with alternative methods (the fourth corner method and RLQ ordination). Results We illustrate the approach on a presence–absence version on two data sets. In the D une M eadow data, species presence is parsimoniously explained by moisture and manure on the meadows in combination with seed mass and specific leaf area ( SLA ). In the G razed G rassland data, species presence is parsimoniously explained by the grazing intensity and soil phosphorus in combination with the C : N ratio and flowering mode. Conclusions Our GLMM approach can be used to identify which species traits and environmental variables best explain the species distribution, and which traits are significantly correlated with environmental variables. We argue that the method is better suited for providing an interpretable and predictive model than the fourth corner method and RLQ .
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it