Biotic filtering and mass effects in small shrub patches: is arthropod community structure predictable based on the quality of the vegetation?
Bibliographic record
Abstract
1. Community assembly is affected by four processes: dispersal, filtering effects (selection), ecological drift and evolution. The role of filtering relative to dispersal and drift should decline with patch size, hampering possibilities to predict which organisms will be observed within small‐sized patches. However, vegetation structure is known to have a marked impact on species assemblages, and plant quality may act as a biotic filter. This challenges the assumption of unpredictable species assemblages in small‐sized vegetation patches. 2. Using 32 stands of five shrub species in south‐west Finland, this study investigated whether biotic filtering effects caused by patch‐forming plants are strong enough to overcome the mixing of mobile arthropod assemblages across small patches. 3. Stochastic variation did not hide the signals of biotic filtering and dispersal in the small shrub patches. Habitat richness around the patches explained a three times larger share of variation in the species composition than did the identity of the patch‐forming plant, but it had less effect on the abundance of arthropods. A radius of 50–100 m around a patch explained the species composition best. 4. Abundance patterns varied between the feeding guilds; the patch‐forming shrub influenced the abundances of detritivores and leaf‐feeding herbivores, whereas the abundances of flower‐visiting herbivores appeared to track the flowering phenology of the plants. Shrub identity had little effect on omnivores or predators. Predator abundances were correlated with the abundance of potential prey. 5. The results of this study suggest that community composition within a vegetation patch may be predictable even if dispersal overrides local filtering effects, as suggested by the mass‐effects paradigm.
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 | 0.001 | 0.001 |
| 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".