What makes trait–abundance relationships when both environmental filtering and stochastic neutral dynamics are at play?
Bibliographic record
Abstract
A major objective in ecology is to determine how local species abundances relate to their functional trait values (i.e. trait–abundance relationship), under a combined influence of 1) environmental filters affecting local species performance conditionally to trait values, 2) neutral demographic and immigration dynamics affecting abundances independently from these trait values, and 3) varying availability and frequency of species at regional level. We examined the nature and strength of the trait–abundance relationship in 30 000 simulated communities covering a gradient of the relative importance of niche‐based environmental filtering and neutral stochastic processes, with heterogeneous regional species frequencies. We explored scenarios of directional, stabilizing and disruptive filtering differently affecting the success of species in communities, depending on their relative trait values. We evaluated how the four first moments of the trait distribution in a local community (i.e. abundance‐weighted mean, variance, skewness and kurtosis) were influenced by immigration, environmental filtering and neutral dynamics. Then we determined whether including constraints related to these moments in a Bayesian maximum entropy regression improved the prediction of the trait–abundance relationships. First, we found pervasive influence of regional frequencies on local species abundances, related to regular input of immigrants. Second, the first four moments of the local trait distribution were affected by environmental filtering, the shape of the response depending on the type of filtering. Third, with decreasing immigration rate, the imprint of local demographic stochasticity overrode the impact of environmental filtering on trait–abundance relationships. Lastly, accounting for the mean and variance of local trait distribution appeared sufficient to explain the trait–abundance relationships in our regression framework, although their contribution differed depending on the type of environmental filtering. Therefore, the mean and variance of trait values in communities, two pillars of trait‐gradient analyses in functional ecology, can capture the key influence of environmental filtering on local trait–abundance relationships.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.001 |
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; both teacher heads agree on what is shown here.
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".