Regional Dispersal Influences Zooplankton Community Response to Dreissena polymopha Invasion
Bibliographic record
Abstract
Aquatic systems are becoming increasingly susceptible to invasive species whereby local species are reduced in abundance and richness leading to changes in many food webs. Dispersal of species from surrounding lakes may provide a natural mechanism to increase local resistance by providing a diversity of locally adapted species to colonize affected communities. This study examined how zooplankton dispersal could potentially reduce the effects of the invasive zebra mussel, Dreissena polymorpha, on zooplankton community total abundance, species richness and diversity. Field experiments were conducted in 20 large tanks, with five replicates, to observe zooplankton community response to (1) the presence and absence of zebra mussels, and (2) the presence and absence of regional disperser zooplankton. Live regional zooplankton, from six surrounding lakes, were added fortnightly to dispersal treatments, while heat-killed zooplankton were added to no-dispersal treatments. All tanks were sampled for chlorophyll and zooplankton community samples prior to dispersal additions. Zooplankton were counted and identified as cladocerans and copepods (macrozooplankton), and rotifers (microzooplankton) to species. In the presence of mussels, chlorophyll was significantly depleted, reducing nutrient availability. All zooplankton richness and abundance decreased suggesting strong resource competition and direct predation by mussels. Dispersal did not affect macrozooplankton community structure, however, dispersal influenced the effect of zebra mussels on rotifers, further decreasing richness. This suggests species from the surrounding lakes may be highly competitive among local species, further proposing that regional species may influence zooplankton community structure and responses to zebra mussel invasion, but the effect is species dependent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| 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".