Ecological determinism increases with organism size
Bibliographic record
Abstract
After much debate, there is an emerging consensus that the composition of many ecological communities is determined both by species traits, as proposed by niche theory, as well as by chance events. A critical question for ecology is, therefore, which attributes of species predict the dominance of deterministic or stochastic processes. We outline two hypotheses by which organism size could determine which processes structure ecological communities, and we test these hypotheses by comparing the community structure in bromeliad phytotelmata of three groups of organisms (bacteria, zooplankton, and macroinvertebrates) that encompass a 10 000-fold gradient in body size, but live in the same habitat. Bacteria had no habitat associations, as would be expected from trait-neutral stochastic processes, but still showed exclusion among species pairs, as would be expected from niche-based processes. Macroinvertebrates had strong habitat and species associations, indicating niche-based processes. Zooplankton, with body size between bacteria and macroinvertebrates, showed intermediate habitat associations. We concluded that a key niche process, habitat filtering, strengthened with organism size, possibly because larger organisms are both less plastic in their fundamental niches and more able to be selective in dispersal. These results suggest that the relative importance of deterministic and stochastic processes may be predictable from organism size.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".