Strong Spatial Influence on Colonization Rates in a Pioneer Zooplankton Metacommunity
Bibliographic record
Abstract
The magnitude of community-wide dispersal is central to metacommunity models, yet dispersal is notoriously difficult to quantify in passive and cryptic dispersers such as many freshwater invertebrates. By overcoming the problem of quantifying dispersal rates, colonization rates into new habitats can provide a useful estimate of the magnitude of effective dispersal. Here we study the influence of spatial and local processes on colonization rates into new ponds that indicate differential dispersal limitation of major zooplankton taxa, with important implications for metacommunity dynamics. We identify regional and local factors that affect zooplankton colonization rates and spatial patterns in a large-scale experimental system. Our study differs from others in the unique setup of the experimental pond area by which we were able to test spatial and environmental variables at a large spatial scale. We quantified colonization rates separately for the Copepoda, Cladocera and Rotifera from samples collected over a period of 21 months in 48 newly constructed temporary ponds of 0.18-2.95 ha distributed in a restored wetland area of 2,700 ha in Doñana National Park, Southern Spain. Species richness upon initial sampling of new ponds was about one third of that in reference ponds, although the rate of detection of new species from thereon were not significantly different, probably owing to high turnover in the dynamic, temporary reference ponds. Environmental heterogeneity had no detectable effect on colonization rates in new ponds. In contrast, connectivity, space (based on latitude and longitude) and surface area were key determinants of colonization rates for copepods and cladocerans. This suggests dispersal limitation in cladocerans and copepods, but not in rotifers, possibly due to differences in propagule size and abundance.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".