The role of dispersal levels, <scp>A</scp>llee effects and community resistance as zooplankton communities respond to environmental change
Bibliographic record
Abstract
Summary Worldwide, freshwater zooplankton communities have been subjected to multiple environmental stressors including acidification, invasive species introductions, habitat alteration and climate change. Understanding the factors that affect zooplankton community responses in the face of environmental change is an important goal if ecologists expect to make predictions regarding the impact of current and future stressors. The acidification and recovery of lakes provides a well‐studied example of the impacts of environmental change. For this study, we conducted a field enclosure experiment using zooplankton communities that are recovering from the effects of acidification to determine whether dispersal levels, Allee effects and biotic interactions could influence comˋmunity responses as pH levels increase. The experiment involved the introduction of four acid‐sensitive colonist species ( D aphnia retrocurva , S kistodiaptomus oregonensis , E pischura lacustris and T ropocyclops extensus ) into enclosures stocked with recipient communities. The experiment was a factorial design with colonists introduced at three starting densities (low, medium and high) and enclosures being stocked with either diverse communities typical of neutral lakes or relatively depauperate communities dominated by the acid‐tolerant copepod L eptodiaptomus minutus . Per capita growth rates ( r ) for colonist species were measured by sampling communities in enclosures over a 10‐week period. Factorial anova s were conducted to determine whether r differed according to starting densities, recipient community type or their interaction. Our results indicated that r for the copepods E . lacustris and S . oregonensis were greater when introduced at high initial densities, suggesting an Allee effect based on mate limitation. In addition, r for S . oregonensis was lower when introduced to acid communities, suggesting biotic interactions with the resident community were important for this species. Per capita growth rates for T . extensus and D . retrocurva did not differ among treatments. Synthesis and applications . These results suggest that community response to environmental change can be influenced by dispersal, A llee effects and community interactions. Studies evaluating the ongoing recovery of zooplankton communities from acidification, or their response to other stressors, should take these factors into account. If rapid community responses are desired, dispersal rates for sexual zooplankton species (copepods) may need to be artificially increased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".