The relative importance of dispersal and local processes in structuring phytoplankton communities in a set of highly interconnected ponds
Bibliographic record
Abstract
Summary 1. The recognition that both local and regional processes act together in shaping local communities makes determining their relative roles in natural communities central to understanding patterns in community structure. 2. We investigated the relative influence of these processes on the phytoplankton communities of a highly interconnected pond system. We sampled the phytoplankton communities of 28 ponds concurrently with 20 local environmental variables. 3. We found that phytoplankton community variation, in terms of both phytoplankton community composition (PCC) and diversity, was only significantly explained by local environmental variables. These were mainly associated with the contrasting clear‐water and turbid ecological states of the shallow ponds studied. Clear‐water conditions favoured only a few taxa, resulting in a significantly lower taxon diversity and richness under these conditions. 4. The failure to explain variation in PCC by a dispersal model based on the water flow between ponds points at very effective species sorting. This is attributed to the high population turn‐over rates and sensitivity to environmental conditions of phytoplankton communities. Some evidence was found, however, that dispersal influences local communities through mass effects between neighbouring ponds. 5. Overall, our results emphasize both the strong selection pressure that components of the food web exert on phytoplankton communities and the high potential of these communities to respond to such environmental change, thereby effectively opposing the homogenizing effects of continuous dispersal.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".