Automated Author ProfileCamphuysen, C J
University of Amsterdam
Camphuysen, C J
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Birds in flight are proposed to adjust their body orientation (heading) and airspeed to wind conditions adaptively according to time and energy constraints. Airspeeds in goal-directed flight are predicted to approach or exceed maximum-range airspeeds, which minimize transport costs (energy expenditure per unit distance) and should increase in headwinds and crosswinds. Diagnosis of airspeed adjustment is however obscured by uncertainty regarding birds' goal-directions, transport costs, interrelations with orientation strategy and the attainability of predicted behaviour. To address these issues, we tested whether gulls minimized transport costs through adjustment of airspeed and heading to wind conditions during extended inbound flight over water (180–360 km) to their breeding colony, and introduce a methodology to assess transport (energy) efficiency given wind conditions. Airspeeds, heading, flight mode and energy expenditure were estimated using GPS tracking, accelerometer and wind data. Predicted flight was determined by simulating each trip according to maximum-range airspeeds and various orientation strategies. Gulls employed primarily flapping flight (93%), and negotiated crosswinds flexibly to exploit both high altitude tailwinds and coastal soaring opportunities. We demonstrate that predicted airspeeds in heavy crosswinds depend strongly on orientation strategy and presumed preferred direction. Measured airspeeds increased with headwind and crosswind similarly to maximum-range airspeeds based on full compensation for wind drift, yet remained ∼ 30% lower than predicted by all strategies, resulting in slower and 30–35% costlier flight. Interestingly, more energy could be saved through adjustment of airspeed (median 40%) than via orientation strategy (median 4%). Therefore, despite remarkably flexible reaction to wind at sea, these gulls evidently minimized neither time nor energy expenditure. However, airspeeds were possibly over-predicted by current aerodynamic models. This study emphasizes the importance of accounting for orientation strategy when assessing airspeed adjustments to wind and indicates that either the cost or adaptive ‘currency’ of extended flight among gulls may require revision.
Authors
- McLaren, James D. ;
- Shamoun-Baranes, Judy ;
- Camphuysen, C J ;
- Bouten, Willem