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.
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 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 | 0.001 |
| 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) | 0.000 | 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".