Dispersal strength influences zooplankton co‐occurrence patterns in experimental mesocosms
Bibliographic record
Abstract
Negative co-occurrence patterns are intriguing because they may reflect the outcome of interspecific interactions and therefore signal how competition shapes communities. However, other factors also contribute to these patterns. For example, theoretical studies as well as two survey-based studies have all suggested that dispersal may also impact these patterns. While natural communities commonly have nonrandom patterns of negative co-occurrence, understanding how different processes drive these patterns requires further research. We tested the influence of dispersal on co-occurrence patterns using a zooplankton mesocosm experiment with four different dispersal treatments varying in the number of dispersers delivered into mesocosms on regular intervals. Our dispersal treatments were intended to adjust the relative importance of dispersal and competition experienced within mesocosms (i.e., high dispersal results in a relatively low influence of competition on species composition and vice versa). Higher dispersal translated into increased zooplankton species richness and inter-mesocosm compositional similarity, and also changed species occupancy patterns such that species occurrences were more even across mesocosms in higher-dispersal treatments. Dispersal treatments also differed markedly in species co-occurrence patterns. Negative co-occurrence patterns were significant for all but the lowest-dispersal treatment, peaked in the intermediate-dispersal treatments, and declined in the highest-dispersal treatment. Stability analyses illustrate that co-occurrence differences are robust to the exclusion of any single mesocosm in null model analyses. Dispersal treatments did not significantly differ with respect to abiotic variation, which has been recognized as a potential driver of negative co-occurrence patterns. These results suggest that not only can dispersal influence patterns of negative co-occurrence via changes to species richness and distribution (occupancy patterns among mesocosms), but the degree to which they do so varies nonlinearly with the strength of dispersal. Critically, because negative co-occurrence patterns were nonsignificant when the contribution of dispersal was lowest, it is possible that dispersal contributes strongly to many observed patterns of negative co-occurrence. Consequently, great care should be taken prior to interpreting significant co-occurrence tests as a product of species interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".