Does dispersal limitation impact the recovery of zooplankton communities damaged by a regional stressor?
Bibliographic record
Abstract
The acidification and ongoing pH recovery of lakes in Killarney Provincial Park, Canada, provide a unique opportunity to increase our understanding of the role of dispersal as communities respond to environmental change. Time lags in community recovery following pH increases in acidified lakes have typically been attributed to local factors; however, no studies have been conducted to determine if colonist availability could also play a role. Moreover, the rates and mechanisms of dispersal to recovering lakes are poorly understood. In this study, we sought to determine if dispersal limitation could impede the recovery of zooplankton communities affected by a regional stressor. To achieve this objective, we used a combination of empirical data collection along with spatial modeling and variation partitioning techniques. Data were collected by measuring dispersal to four recovering lakes in Killarney Park. Dispersal traps were placed next to lakes to measure immigration overland, drift nets were used to measure immigration via streams, and in situ emergence traps were used to quantify immigration from historically deposited resting eggs. Documented dispersal levels were then compared with the theoretical critical density required for reproduction (N(c)) to determine if adequate numbers were dispersing to establish populations of acid-sensitive species in recovering lakes. Spatial modeling and variation partitioning were conducted using community and physical/chemical data for 45 park lakes that were collected in 1972-1973, 1990, and 2005. Field data demonstrated that a variety of zooplankton species were dispersing to recovering lakes through streams and the egg bank, but few individuals were collected dispersing overland. Although we identified 24 species of zooplankton dispersing, only six species absent from the communities of our study lakes were identified from our traps, and two of these species did not disperse in high enough numbers to surpass N(c). Local environmental variables explained the largest proportion of the variation in zooplankton communities (18-37%); however, spatial variables were also important (7-18%). The significant spatial patterns we found in the park's zooplankton communities, combined with the low overland dispersal levels we documented, suggest that dispersal limitation may be a more important impediment to recovery than was previously thought.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".