UPLAND-NESTING DUCKS IN THE PARKLANDS OF SASKATCHEWAN: THE EFFECT OF PREDATOR REDUCTION ON NEST SUCCESS AND REPRODUCTIVE INVESTMENT
Bibliographic record
Abstract
I examined the effect of predator reduction on nest success and reproductive investment of upland-nesting ducks in the parklands of Saskatchewan during 2007-09. I used nest defense behaviors as a measure of a hen's reproductive investment and looked at a possible mechanism for adaptively modified reproductive investment. I hypothesized that ducks use ultra-violet (UV) light reflecting off predator urine to evaluate predator densities, and then select nests sites and modify reproductive investment accordingly. Professional trappers removed an average of 191 predators off each treatment block. In 2007 nest success on treatment blocks averaged 33.6% (SE = 4.5) and control blocks averaged 5.6% (SE = 0.2). With the apparent higher likelihood of success on treatment blocks in 2007, hens increased their nest defense, but I found no significant difference in the number of nests on sites with low and high simulated predator densities. In 2008 and 2009 we transposed the treatments and as a result, nest success on treatment blocks averaged only 3.0% (SE = 0.7), while nest success on control blocks averaged 3.6% (SE = 2.3), indicating no effect of predator reduction on the threat of predation. Similarly, as would be expected, I found nest defense behaviors did not differ between treatment and control blocks in 2008 and 2009. However in 2008, significantly more ducks nested in plots where simulated predator densities were lower, providing support for my hypothesis that hens utilize UV cues to determine predator densities. My results also support previously hypothesized relationships between nest defense and incubation stage, visit number, and life history traits. Although results of my study indicate predator reduction does not always increase nest success in the parklands, hens do appear to increase reproductive investment when likelihood of reproductive success is higher, potentially positively influencing waterfowl populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".