Scale dependency of processes structuring metacommunities of cladocerans in temporary pools of High‐Andes wetlands
Bibliographic record
Abstract
Metacommunity structure can be shaped by a variety of processes operating at different spatial scales. With increasing scale, the compositional variation among local communities (beta diversity) may reflect stronger environmental heterogeneity, but may also reflect reduced exchange of organisms between habitat patches. We analyzed the spatial architecture of a metacommunity of cladoceran zooplankton in temporary pools of High Andes wetlands, with the objective of explaining the spatial dependency of its structure. The spatial distribution of the pools is hierarchical and highly discontinuous: pools are clustered within small wetlands, which lay scattered over valleys that are separated from each other by mountain ridges. We studied a total of 59 pools, belonging to six different wetlands in four different valleys. We assessed pool environmental heterogeneity and sampled active communities and dormant propagule banks of cladoceran zooplankton. Environmental heterogeneity proved very high within wetlands and showed almost no increase with increasing spatial scale. Conversely, diversity partitioning analyses indicated an increase in beta diversity with spatial scale, especially among valleys. Variation partitioning on environmental data and spatial RDA models suggested environmental heterogeneity as the most important generator of beta diversity within wetlands. At the largest spatial scale, beta diversity manifested itself mainly as a differentiation of species occurrence patterns among valleys, which could not be entirely explained by environmental variables. Our study thus presents a case where environmental control seems to be the dominant metacommunity structuring process at the smallest spatial scale, whereas neutral processes and dispersal limitation are the most likely generators of beta diversity at the largest spatial scale.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".