Biological and Environmental Factors Associated with the Autumn Migration Timing of North American Raptors
Bibliographic record
Abstract
Migration is an energetically expensive and risky event in the life cycles of many organisms, and migration impacts survival and reproductive success through direct and carry-over effects. The timing of migration departure from the summer breeding grounds is important for determining the probability of survival and physical condition upon arrival at wintering sites. Many internal and external factors contribute to departure timing. It is important to study these factors across species, populations, and individuals to determine which traits and conditions are most important for shaping departure timing and its variability. Improving our understanding of the factors that affect organisms’ phenological cycles, particularly the understudied events in those cycles, is necessary to predict how populations, and the communities they are a part of, may be affected by environmental change. I studied the life history correlates and weather conditions associated with the timing of departure for North American raptor species. In the first chapter, I used data from eBird to calculate spatially-explicit autumn departure timing for 15 migratory raptor species across the United States and Canada from 2011 to 2021. Using these departure dates as a response variable and accounting for the uncertainty involved in these estimates, I then examined the relative importance of five life history traits and latitude in explaining departure timing between species and the effect of three life history traits on variation in departure timing within species across space and over time. I found that an interaction between migration distance and latitude affected departure timing. The negative effect of latitude (northern birds departing before southern birds) was stronger in short-distance migrants compared to long-distance migrants. Further, socially migrating species tended to depart earlier than species that migrate alone, and larger-bodied species tended to depart later than smaller species. Finally, long-distance migrants, diet-specialists, and social migrants had more consistent departure timing compared to short-distance migrants, diet generalists, and solitary migrants who were more variable in timing. In the second chapter, I selected the four raptor species (Broad-winged Hawk, Osprey, Swainson’s Hawk, and Turkey Vulture) with the greatest eBird data availability and assessed their departure timing within a latitudinal band across the northern US from 2002 to 2021. Using weather data from Daymet and a sliding window analysis, I determined the most likely periods of sensitivity to four weather variables for each species' departure timing. I then combined these season-specific weather variables to build a full weather model for each species. Maximum temperature was important for determining the departure timing of all species, but the direction of effect varied (some advancing or delaying departure) depending on the time of year and species. Minimum temperature and precipitation was important for determining the departure time of several species, and snow-water-equivalent only explained the departure of Broad-winged Hawks. Finally, I used the full weather models for each species and data from NA-CORDEX to forecast departure timing of all four species to 2075. The forecast analysis showed that over the next 50 years, Swainson’s Hawks will advance their departure timing (depart earlier in the year) and Turkey Vultures will delay departure (depart later in the year). There were no significant temporal trends for the timing of Broad-winged Hawk autumn migration, and Osprey departure timing is expected to advance very slightly. Combined, these analyses provide insight into the biological and environmental factors affecting raptor autumn migration timing in North America. This information furthers our understanding of the relationships between life history traits and departure timing and provides new insight into factors associated with annual variation in departure timing, which may underlie vulnerability to changing environmental conditions. Additionally, it identifies unique environmental factors related to the timing of autumn departure for four common species and provides estimates of future changes in an important part of their full annual cycles.
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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 | 0.001 |
| 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".