Quantifying Waterfowl Use and Habitat Characteristics Following Wetland Restoration in Lake Ontario Coastal Wetlands at Braddock Bay Wildlife Management Area
Bibliographic record
Abstract
The coastal wetlands of Braddock Bay Wildlife Management Area (WMA) on the southern coast of Lake Ontario provide migratory spring-stopover habitat that is important for a variety of pre-breeding season waterfowl. Cattail (Typha sp.) invasion can degrade these spring-stopover wetlands, and directly decrease the amount of open-water interspersion within a wetland. One of the targets of restoration is to increase the amount of open-water area interspersed within marshes to improve waterfowl habitat. This study aimed to assess the effects that dredging various size and shape ponds within a cattail marsh has on the abundance, diversity, and composition of waterfowl within Braddock Bay WMA during late winter into spring. We used a combination of point-count and trail camera surveys to estimate spring waterfowl abundance and composition of dredged ponds, undredged, natural ponds, and large open-water areas occurring throughout the Braddock Bay WMA. We also assessed differences in vegetation, invertebrates, and physical habitat characteristics among the different habitat types of Braddock Bay WMA. We found that waterfowl area-corrected cumulative use days, species, richness, and species diversity were similar or greater in dredged ponds than natural ponds. We also found that vegetation species richness, percentage vegetated, invertebrate order richness, and invertebrate abundance/cubic meter within dredged ponds was similar or greater than natural ponds in Braddock Bay WMA, in each year of sampling. These findings suggest that dredging treatments to increase interspersion within Braddock Bay WMA provide additional open-water habitat that offers comparable food and cover resources supports similar waterfowl diversity, richness, and use to pre-existing natural \nponds. We found that shoreline development index and interspersion were significant explanatory variables for predicting square root transformed waterfowl cumulative use days per hectare for dredged and natural ponds. Using trail cameras proved to be an efficient, non-nonintrusive method for the long-term monitoring of waterfowl in areas that were inaccessible to point-count surveys. Trail camera data provide an estimate of waterfowl use and diversity within the dredged and natural ponds of Braddock Bay WMA. The multivariate relationships we found between wetland habitat characteristics and waterfowl use and diversity can be used to help influence future wetland restoration and waterfowl management efforts within Braddock Bay WMA and other coastal wetlands throughout the Great Lakes region.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".