Movement ecology and habitat use of Canada geese using major metropolitan areas in the context of human-wildlife conflicts
Bibliographic record
Abstract
Canada geese (Branta canadensis) are economically and socially important due to their popularity as a game species and association with human-wildlife conflicts. The species’ ability to adapt to human-altered agricultural and urban landscapes has contributed to the success of temperate-breeding Canada goose populations. Differences in habitat resources and predation risks across the rural to urban gradient of the upper Midwest shape the movement and behavioral ecology of Canada geese, requiring unique life history strategies to maximize survival through-out the annual cycle. Winter is an energetically costly period due to food limitation and increased thermoregulatory costs. Urban areas have altered these dynamics and facilitate northward shifts in wintering distributions by providing anthropogenic food sources, reduced predations risk, and thermal refugia for many avian species. Large abundances of Canada geese winter in highly developed, urban areas of Chicago, leading to concerns over risks to air traffic. Previous work indicates that safety due to hunting restrictions are driving these patterns rather than food availability or thermal refugia. We used global positioning system (GPS) transmitters equipped with tri-axial accelerometers to quantify factors influencing use of both urban and rural areas during two energetically costly portions of the annual cycle: winter and remigial molt. Pertaining to winter ecology, my research sought to examine, 1) differences in wintering strategies of geese in rural versus urban areas, 2) environmental and behavioral factors influencing goose movements and subsequent risk to air traffic, and 3) behavioral responses to targeted harassment. Regarding differences in wintering strategies, I found no differences in winter survival between rural- and urban-wintering geese but differences in cause-specific mortality indicating strong effects of temperature on survival in urban areas and alternatively harvest in rural areas. In addition, movements and behavioral time budgets suggest access to high-energy foods in rural areas may ameliorate energetic costs during extreme cold periods while geese in urban areas must rely on energetically conservative behaviors and endogenous reserves. Regarding movements and risks to air traffic, the risk of movements to air traffic varied by the juxtaposition of habitats relative to important air traffic areas but were associated with novel urban goose habitats including rooftops and railyards. In response to harassment, geese left the harassment site more often, were more alert, and flew more, but changes in habitat preferences during cold periods likely reduced discernible effects of harassment on survival or emigration from the area.\n\nRemigial molt is the loss and regrowth of flight feathers and occurs simultaneously in waterfowl, rendering them flightless. Because of the energetic cost of replacing all remiges and risk of predation due to flightlessness, geese should select areas to undergo molt that provide high quality foraging environments and low predation risk. These decisions can occur at the landscape scale, involving the choice to molt near breeding areas or migrate to another area. This molt migratory behavior is common in temperate-breeding Canada goose populations, which undertake molt migrations of thousands of kilometers to the Subarctic. However, the trade-offs associated with molt migration may be altered by increased availability of novel molting habitat in temperate regions, in the form of urban greenspaces, and increased predation risk from hunting during migration. My research sought to determine 1) landscape factors influencing molt migration, 2) trade-offs in foraging environments between subarctic and temperate molting areas, and 3) differences in survival. My results demonstrate that the propensity to molt migrate decreases with the greater proportions of land uses that provide escape from predators (i.e. waterbodies), that foraging and alert behaviors indicate a better quality foraging environment in the subarctic, even when corrected for differences in day length, and that survival of molt migrants is greater than non-molt migrants until September when a large proportion were harvested on return migration. While nest removal to induce molt migration may serve as an important tool to indirectly decrease adult survival of urban-wintering geese. However, increased harvest of molt migrants is likely to affect breeding areas differentially and disproportionately decrease survival of geese nesting in more natural wetland and rural areas.
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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) | 0.000 | 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".