Plant traits, species pools and the prediction of relative abundance in plant communities: a maximum entropy approach
Bibliographic record
Abstract
Questions: To what extent can Shipley et al.'s original maximum entropy model of trait-based community assembly predict relative abundances of species over a large (3000 km2) landscape? How does variation in the species pool affect predictive ability of the model? How might the effects of missing traits be detected? How can non-trait-based processes be incorporated into the model? Location: Central England. Material and Methods: Using 10 traits measured on 506 plant species from 1308 1-m2 plots collected over 3000 km2 in central England, we tested one aspect of Shipley et al.'s original maximum entropy model of “pure” trait-based community assembly (S1), and modified it to represent both a neutral (S2) and a hybrid (S3) scenario of community assembly at the local level. Predictive ability of the three corresponding models was determined with different species pool sizes (30, 60, 100 and 506 species). Statistical significance was tested using a distribution-free permutation test. Results: Predictive ability was high and significantly different from random expectations in S1. Predictive ability was low but significant in S2. Highest predictive ability occurred when both neutral and trait-based processes were included in the model (S3). Increasing the pool size decreased predictive ability, but less so in S3. Incorporating habitat affinity (to indicate missing traits) increased predictive ability. Conclusions: The measured functional traits were significantly related to species relative abundance. Our results both confirm the generality of the original model but also highlight the importance of (i) taking into account neutral processes during assembly of a plant community, and (ii) properly defining the species pool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".