Unusual migratory strategy a key factor driving interactions at wind energy facilities in at-risk bats
Bibliographic record
Abstract
Abstract Seasonal movement strategies are poorly understood for most animals, impeding broader understanding of processes underlying migration and limiting practical conservation needs. Here we develop and implement a framework for integrating multiple sources of endogenous markers, in particular stable hydrogen isotope data, that capture and scale dynamics from the movements of individuals to that of continental migration. We assembled and integrated thousands of new isotopic measurements from bat fur with existing datasets and applied this framework to reveal migratory patterns of three broadly distributed bat species most at risk for fatalities at wind energy facilities. Our findings show that the species comprising the lowest proportion of wind turbine fatalities (silver-haired bats) exhibits expected movements to lower latitudes in autumn and higher latitudes in spring. Surprisingly, the two species with higher wind turbine fatality rates (hoary and eastern red bats) have more complex movements, including significant movement to higher latitudes during autumn. We term this unique strategy “pell-mell” migration, during which some individuals are as likely to move to higher latitudes as lower latitudes, relative to their individual summering grounds, in early autumn, after which they move to similar or lower latitudes to overwinter. The pell-mell migratory period corresponds with peak fatalities at wind energy facilities, and bats moving northward during autumn are associated with mortality at those facilities. Our results provide direct support for the hypothesis that bat fatalities at wind energy facilities are related to migration and highlight the importance of migratory distance as an ultimate driver of increased interactions with wind energy facilities, which appears significantly greater for species that travel widely across latitudes in the autumn.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".