Nesting ecology of a naturalized population of Mallards<i>Anas platyrhynchos</i>in New Zealand
Bibliographic record
Abstract
Investigating the reproductive ecology of naturalized species provides insights into the role of the source population's characteristics vs. post‐release adaptation that influence the success of introduction programmes. Introduced and naturalized MallardsAnas platyrhynchosare widely established in New Zealand (NZ), but little is known regarding their reproductive ecology. We evaluated the nesting ecology of female Mallards at two study sites inNZ(Southland and Waikato) in 2014–15. We radiotagged 241 pre‐breeding females with abdominal‐implant transmitters and measured breeding incidence, nesting chronology and re‐nesting propensity. We monitored 271 nests to evaluate nest survival, clutch and egg size, egg hatchability and partial clutch depredation. Breeding incidence averaged (mean ± se) 0.91 ± 0.03, clutch size averaged 9.9 ± 0.1 eggs, 94 ± 2% of eggs hatched in successful nests, partial depredation affected 6 ± 1% of eggs in clutches that were not fully destroyed by predators, and re‐nesting propensity following failure of nests or broods was 0.50 ± 0.003. Nesting season (first nest initiated to last nest hatched) lasted 4.5 months and mean initiation date of first detected nest attempts was 28 August ± 3.3 days. Smaller females were less likely to nest, but older, larger or better condition females nested earlier, re‐nested more often and laid larger clutches than did younger, smaller or poorer condition females. Younger females in Southland had higher nest survival; cumulative nest survival ranged from 0.25 ± 0.007 for adult females in Waikato to 0.50 ± 0.007 for yearling females in Southland. Compared with Mallards in their native range, the nesting season inNZwas longer, clutches and eggs were larger, and nest survival was generally greater. Different predators and climate, introgression with native heterospecifics and/or the sedentary nature of Mallards inNZmay have contributed to these differences.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".