Foraging Behaviours in Urban Wildlife: Squirrel Route Choices and Wildlife Trash Foraging
Bibliographic record
Abstract
Urbanization impacts wildlife survival by altering foraging decisions and introducing anthropogenic food sources, like waste. Foraging behaviours are critical to animal survival, and understanding these decisions provides insights into species adaptation and resilience, which can inform biodiversity conservation strategies. While the physical impacts of ingesting waste are well-documented, the nuanced behavioural aspects are often overlooked. This thesis explored foraging route choice among Eastern gray squirrels (Sciurus carolinensis) through an experimental field study on wild urban squirrels, compared these data with Japanese macaques (Macaca fuscata), and investigated how anthropogenic food sources alter foraging strategies in urban vertebrates through a literature review. In Chapter 2, I used a 1mx2m multi-destination, Z-shaped foraging array to collect 62 foraging trials on squirrels at Mount Royal Park in Montreal. Comparing these data with macaque data from Joyce et al. (2021), I found that squirrels (1) foraged more slowly, (2) exhibited a higher rate of platform revisiting, and (3) chose routes consistent with heuristic use at a similar rate to macaques. Observations also indicated that garbage and human food waste were an important part of the squirrels’ diet, leading to Chapter 3's exploration of how urban trash affects wildlife behaviour. I found that urban species foraging on waste modify their behaviours in various ways, including adopting new behaviours, changes in foraging methods, timing, energy budgets and social behaviours. My research enhances our understanding of urban mammal foraging behaviour and my literature review highlights the specific behaviours urban species adopt to thrive in urbanized settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".