Predator Exclusion Grids Protect Common Terns from Avian Nest Predators at Presqu’ile Provincial Park, Lake Ontario
Bibliographic record
Abstract
Common Tern (Sterna hirundo) breeding populations in inland areas of North America have experienced significant declines, particularly in the Canadian waters of the lower Great Lakes. Once home to two-thirds of the Great Lakes population, Presqu’ile Provincial Park maintained only a small breeding colony of Common Terns from the 1970s to the early 2010s. In 2008, a research program was started to understand the population status of this colony. This involved close collaboration between faculty at Penn State University and Ontario Parks staff, and identified poor reproductive success at this site. Although many factors contributed to this poor reproductive success for terns at this colony, regular nest predation in June and July by Black-crowned Night Herons (Nycticorax nycticorax) appeared to be pushing this colony towards abandonment. Based on this finding, innovative management began in 2013 with the development and implementation of predator exclusion grids. These structures are designed to eliminate avian nest predation, as well as competition for nesting space from gulls. Although straightforward in concept, the success of predator exclusion grids for tern management requires careful implementation, attention to detail and timely responses based on regular monitoring. Here, we provide design and construction details, implementation requirements and discuss applicability for other tern colonies. We hope that this new tool allows managers to eliminate avian predation threats faced by Common Terns elsewhere in their range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".