Ecology of mid-continent migratory shorebirds, performance of tracking devices, and modelling animal social structure and demography
Bibliographic record
Abstract
[EMBARGOED UNTIL 6/1/2023] Migratory birds are threatened by landscape and climate change, and shorebirds are particularly imperiled. Shorebirds undertake some of the most impressive migrations in the world, but in every season, there are threats such as habitat loss, limited prey abundance, overhunting, and climate change contributing to their declines. New technologies and analytical techniques offer opportunities to improve our ability to disentangle drivers of shorebird declines to inform conservation investments. However, it is important to test and evaluate new technologies and methods to move toward the most cost-effective and least invasive ways to gather necessary information. The chapters of my dissertation were: (1) studying environmental drivers of migration decision-making and reproductive success in black-bellied plovers (Pluvialis squatarola); (2) examining spatial and temporal habitat characteristics that influence energy expenditure and behavior in American avocets (Recurvirostra americana); (3) characterizing and comparing migration strategies among three species with contrasting life histories: American avocets, black-bellied plovers, and Hudsonian godwits (Limosa haemastica); (4) testing performance of tracking devices for bird research; and (5) developing and evaluating a modelling approach to combine social structure and survival information. In chapter 1, we found that winter and spring weather conditions influenced migration duration and number of stopovers in black-bellied plovers, and these metrics explained substantial variation in reproductive success, while breeding season weather did not. In chapter 2, we found that in two study areas with contrasting habitat compositions, landcover type, time of day, and tide all explained substantial variation in energy expenditure (i.e., activity) in American avocets, but that relationships were different between the two sites. In chapter 3, we showed that shorebirds with different migration distances varied in both their migration strategies and in their plasticity in strategy. In chapter 4, we found that tracking devices performed well enough to meet most research objectives in standardized tests, however we caution that device performance is likely reduced when deployed on wild birds. We split chapter 5 in two parts: (a) we present current literature and techniques for studying animal social structure and demography, and (b) we developed a simulation study that evaluated performance of a model linking social network analysis and Cormack-Jolly-Seber survival models and found that models performed with minimal bias and good precision, but precision was influenced by data resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".