Using functional dissimilarity among species pools and communities to predict establishment of native and alien species
Bibliographic record
Abstract
Abstract Aims Predicting plant establishment and growth is a fundamental goal of community ecology, especially when studying invasion. Functional traits can infer a species’ environmental tolerances and competitive abilities and may help improve predictions, yet such models are limited by a lack of data on failed establishment. Filtering of species from regional pools to site‐specific pools to local communities provides abundant evidence of failed establishment represented by species excluded at each level of the hierarchy. We tested whether trait differences between observed and absent species among these pools can help predict species establishment. Location Southern Estonia. Methods For 31 semi‐natural grassland sites, we collected plant composition data at multiple spatial scales to estimate the regional species pool, site‐specific pool, and local community. We compared traits among these pools to model community assembly and predict which species could join the site‐specific pool and local community. We tested these models using an experiment in which we added seed and transplants of 15 species (9 native and 6 alien) to intact and disturbed plots at each site. We considered differences in plant performance between intact and disturbed sites to be indicative of competition. Results For all performance metrics, we found significant positive relationships with model predictions. These relationships were stronger for seed establishment than growth and were generally weak when predicting competition. Seed establishment was also best predicted in sites with high local diversity and low root biomass, potentially because of more accurate estimates of species pool and community composition. Seed establishment was better predicted for alien than native species and, although predictive power remained low, the opposite was true for competitive effects on establishment. Conclusions Multi‐scale models of community assembly may predict future establishment but must include other characteristics within such models. These models may perform poorly, however, when predicting biotic interactions.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".