Effects of water-level management on the abundance and habitat use of waterfowl and marsh birds in the Saskatchewan River Delta, Manitoba, Canada
Bibliographic record
Abstract
Waterbird habitat in the Saskatchewan River Delta (SRD) has been altered by numerous upstream and downstream anthropogenic developments. Wetland water-level management has been used in an attempt to mitigate these changes, but the effects on the local waterbird community are unknown. Using an experimental approach, I examined the effects of wetland water-level management on waterfowl and marsh birds in the SRD. In 2007, three wetland basins in the Summerberry Marsh Complex, Manitoba were partially drawn down and paired with three additional wetlands managed with high water levels. In 2008-09, I surveyed waterfowl within the study wetlands during different life stages using point counts and aerial surveys. Abundances of breeding marsh birds, including American Bitterns, Least Bitterns, Soras, Virginia Rails, Yellow Rails, American Coots, and Pied-billed Grebes were estimated using call-response surveys. Generalized linear and generalized linear mixed models were used to determine relationships between relative bird abundances and the physical characteristics, vegetation characteristics, and forage fish and invertebrate abundances within the wetlands. Dabbler species of waterfowl preferred the partial drawdown wetlands during nearly all life stages, except brooding. Diver species preferred the non-drawdown wetlands, but only during the spring-breeding and fall-staging periods. Invertebrate abundance affected wetland use by dabbler and diver species, particularly during the spring-breeding and brooding periods. Subsequently, wetlands with high forage fish abundances were avoided. In the fall, dabbler densities on the partial drawdown wetlands increased from an average of 0.4 birds per hectare to 1.5 birds per hectare. This influx was likely due to the accessibility and availability of submerged aquatic vegetation. The partial water-level drawdowns did not benefit any species of marsh birds examined. American Bitterns, American Coots, and Pied-billed Grebes chose the deeper, non-drawdown wetlands, due to high abundances of forage fish. Vegetation interspersion, particularly that offered by Schoenoplectus, was preferred by these species. Soras and Virginia Rails were not affected by water depth or vegetation characteristics, but were positively correlated to invertebrate abundances. Subsequently, the Sora avoided wetlands with high forage fish abundance. The conditions created by the partial water-level drawdowns did not meet the requirements of all species present in the SRD. Therefore, future wetland management should focus on providing a wide range of water depths within wetland complexes to accommodate the requirements of different avian species.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".