Avian nest parasites and parental feeding behaviour in urban and rural mountain chickadees
Bibliographic record
Abstract
Urbanization is considered one of the greatest human-caused threats to biodiversity. Human development and encroachment on native habitats and its impacts on species, however, is nuanced; while it has been found to create detrimental impacts on some species, certain aspects of urbanization may benefit others. This interaction of advantages vs constraints offered by urban landscapes is especially relevant to species that show sufficient behavioural plasticity to settle in this rural/urban interface. The objective of this thesis is to investigate some of these potential costs/benefits of urbanization in mountain chickadees. I first assess a potential positive effect of urban settlement noted in other systems – a decrease in the incidence of nest ecotparasites found in urban landscapes - in mountain chickadees nesting in Kamloops, BC, Canada. I enumerated the blowflies and fleas found in collected nests in 2019 and 2020 to determine whether nest parasitism differs between urban and rural habitats in the region, and whether this in turn influences nest success (Chapter 2). I found that abundance of blow fly puparia was higher in rural nests, but that flea abundance was associated with temperature and not urbanization. Additionally, I observed that urban nests fledged approximately one additional chick per nest. This suggests urban habitats could lift some constraints that would normally decrease nestling condition. I then compared rates of adult chickadees feeding nestlings to determine whether differences in ectoparasitism levels between habitats results in compensatory feeding by parents, and if this affected the growth rate of nestlings (Chapter 3). I did not find evidence that urbanization or ectoparasite abundance influenced parental feeding or growth rate. I did find that feeding rate was lower and that growth rate was higher in warmer years. Other studies showing potential differences in prey availability between habitats, with rural sites having potentially greater abundance of prey, may help explain my results – while prey might be more abundant in one habitat, nestling condition may be less affected by parasite infestation in the other, helping balance the costs/benefits of settlement between habitats. While my results provide some evidence that reduction of parasites in urban areas can benefit urban settling species, further research will be required to determine the mechanism that causes this phenomenon.,
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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 | 0.000 | 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 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".