Nest Site Selection In Hudsonian Godwits: Effects Of Habitat And Predation Risk
Bibliographic record
Abstract
The choice of where to nest has enormous fitness consequences for individuals and, when scaled up, can even affect population demography. Nest site selection in birds is, therefore, guided by a wide variety of proximate and ultimate factors, including protection from predators, access to favorable microclimates, proximity to food resources, and costs/benefits stemming from interactions with neighboring con- and hetero-specifics. Nevertheless, the features associated with, cues used to identify, and availability of favorable nest sites may be changing rapidly with accelerating human development and climate change, especially in the Arctic and sub-Arctic. I investigated how habitat characteristics, predation risk, and proximity to human disturbance (i.e., roads) influenced nest site selection for the Hudsonian Godwit (Limosa haemastica). First, I identified habitat characteristics most associated with nests of Hudsonian Godwits in two breeding populations - Beluga River, Alaska and Churchill, Manitoba. I was particularly interested in habitat attributes that are (or will be) strongly impacted by climate change and human development. Second, I assessed the spatial pattern of nests in Beluga River, Alaska to evaluate if there was evidence of aggregation and then determined the extent to which nest location and fate were associated with predation risk and habitat features. Hudsonian Godwits displayed strong preferences for certain habitat features when selecting nest sites, with individuals preferring nest sites with less bare ground and more graminoid and tall shrubby cover than random locations. Nest site preferences were relatively similar in the two study populations, though birds in Churchill also preferred to nest near shallow water. Given the demonstrated preferences of nesting godwits, our study suggests that habitat degradation due to grubbing by overabundant Snow Geese (Chen caerulescens) and changing climate will reduce the suitability and availability of habitats for breeding godwits. My study also provides evidence that, despite the importance of nesting habitat attributes, additional factors must be guiding nest site selection because nests of godwits were clustered within bogs. Because the spatial aggregation of nests was not explained by either underlying patchiness in habitat or by predation risk, I suggest that clustering may be related to social cues. Therefore, understanding behavioral processes can be as important as ecological requirements for habitat selection.
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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 | 0.001 |
| 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 | 0.001 |
| 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".