Automated Author ProfileFallow, Pamela M.
Australian National University
Fallow, Pamela M.
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
Vertebrates that eavesdrop on heterospecific alarm calls must distinguish alarms from sounds that can safely be ignored, but the mechanisms for identifying heterospecific alarm calls are poorly understood. While vertebrates learn to identify heterospecific alarms through experience, some can also respond to unfamiliar alarm calls that are acoustically similar to conspecific alarm calls. We used synthetic calls to test the role of specific acoustic properties in alarm call identification by superb fairy wrens, Malurus cyaneus. Individuals fled more often in response to synthetic calls with peak frequencies closer to that of conspecific calls, even if other acoustic features were dissimilar to that of fairy-wren calls. Further, they then spent more time in cover following calls that had both peak frequencies and frequency modulation rates closer to natural fairy-wren means. Thus, fairy wrens use similarity in specific acoustic properties to identify alarms and adjust a two-stage antipredator response. Our study reveals how birds respond to heterospecific alarm calls without experience, and together with previous work using playback of natural calls shows that both acoustic similarity and learning are important for interspecific eavesdropping. More generally, this work reconciles contrasting views on the importance of alarm signal structure and learning in recognition of heterospecific alarms.
Authors
- Fallow, Pamela M. ;
- Pitcher, Benjamin J. ;
- Magrath, Robert D.