Intraspecific trait variation improves the detection of deterministic community assembly processes in early successional forests, but not in late successional forests
Bibliographic record
Abstract
Intraspecific trait variation (ITV) has been increasingly recognized to play an important role in understanding the underlying processes influencing community assembly. However, gaps remain in our understanding of how incorporating ITV will influence the relative importance of deterministic (e.g. habitat filtering, limiting similarity) and stochastic processes in driving community assembly at different successional stages. We used data for eight functional traits from 55 woody species in early (24 ha) and late (25 ha) successional temperate forest plot in northeast China. We employed an approximate Bayesian computation approach to assess the relative contribution of stochastic processes, habitat filtering and limiting similarity in driving community structure. We then compared the results with and without intraspecific trait variation to investigate how ITV influences the inferred importance of each process. We found that when analyzing interspecific trait variation only (i.e. without ITV), stochastic processes were observed most frequently in driving community composition, followed by habitat filtering and limiting similarity in both forests. However, ITV analyses showed that the relative importance of both deterministic processes (habitat filtering and limiting similarity) increased in early successional forest, but remained virtually unchanged in late successional forest. Our study reveals the distinctive influence of ITV on the inference of underlying processes in a context of succession and reinforces the need to estimate ITV for making correct inferences about underlying ecological processes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".