Impact of management treatments on waterfowl use of dense nesting cover in the Canadian parklands
Bibliographic record
Abstract
Abstract Periodic treatment of established stands of dense nesting cover (DNC) is a recommended practice to maintain cover quality, but little information exists on the magnitude and duration of treatment effects on nesting waterfowl. During 1998–2001, we examined the effect of management treatments on vegetative characteristics and waterfowl nest success and density in fields of DNC seeded to introduced and native grass and forb mixes in the parklands of Saskatchewan and Manitoba. We measured vegetation height–density and litter depth within fields and located and monitored 1,927 duck nests within 33–42 fields/yr ranging in size from 6 ha to 62 ha. We considered a series of models examining the influence of grass type and management treatment (GTMT) and years post‐management (YPM) on vegetative characteristics, nest success, and nest density while including covariates potentially affecting these response variables. Visual obstruction and litter depth were lowest in native‐burned fields and greatest in introduced‐hayed fields. Visual obstruction was low the year following management, peaked 2–3 YPM, and remained at intermediate levels through ≥6 YPM. Litter depth remained low for the first 3 YPM and increased thereafter. Nest success and nest density varied little among GTMT. Nest success was high (14.3%) the year following a management treatment, low (6.5%) at 2 YPM, and moderate thereafter. Nest success decreased with percent cropland in the surrounding landscape. Nest density was 0.7 nests/ha the first year following management, increased to approximately 1.3 nests/ha in years 2–3, and declined back to approximately 0.7 nests/ha for ≥6 YPM. Nest density decreased with field size and increased with the area of small wetlands, percent cropland, and percent wetland within surrounding landscapes. Nest density tracked vegetation density as expected and our results indicate a possible trade‐off between nest density and nest success. Given ancillary data on small mammal and insect prey in our study fields, and evidence from other studies, we speculate that DNC fields may act as prey reservoirs during years of peak vegetative density with a consequent reduction in nest survival. Therefore, management to increase waterfowl production based on our results needs to consider the interaction of treatment effects, competing habitats, and surrounding landscape composition. © 2011 The Wildlife Society.
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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) | 0.001 | 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".