Deterministic and Stochastic Effects on Freshwater Diatom Biodiversity and Community Composition
Bibliographic record
Abstract
Recent research on diatom metacommunities has focused on disentangling the assembly mechanisms driving species and functional composition and biodiversity across space and time, including deterministic (environmental filtering and biotic interactions) and stochastic processes (dispersal and ecological drift). In this chapter, we provide an overview of this research and outline future directions. Environmental filtering and dispersal have received the most attention, while biotic interactions and ecological drift have remained comparatively understudied and require more investigations. We discuss diatom species and functional responses to major environmental factors, operating at local scales, including inorganic and organic acidity, conductivity, and limiting nutrients, and at regional scales, namely land use and climate. Research has shown that both high rates of dispersal (mass effects) and low rates of dispersal (limited dispersal) are responsible for species and guild composition. We recommend further observational but also experimental investigations on the relative importance of assembly mechanisms across spatial and temporal scales and along latitudinal, longitudinal, and elevational gradients. Global change with respect to climate, land use, and dissolved organic matter has been recognized as an important driver of diatom compositional and biodiversity shifts. However, further modeling work and building harmonized global diatom databases that encompass spatial and temporal observations as well as morphological and molecular data may be necessary to forecast possible diatom community and functional responses in the decades ahead.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".