Automated Author ProfileDing, Yuan‐Yuan
East China Normal University
Ding, Yuan‐Yuan
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: 2.0 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Density-dependent recruitment is fundamental to understanding species diversity and community dynamics in plants. Although there is compelling evidence that seeds and seedlings die from conspecific negative density dependence (CNDD) as predicted by the Janzen–Connell hypothesis, characterizing adult recruitment remains a challenge for long-living trees. Previous studies have used the decrease of fine-scale spatial genetic structure (FSGS) across life stages to indicate CNDD; however, this has not been tested rigorously. We addressed these challenges by integrating dispersal kernels and FSGS. To establish links between density dependence and FSGS, we simulated seedlings based on the estimated dispersal kernels from parentage analyses, and further simulated adults under various seedling-to-adult recruitment scenarios, using an individual-based spatially explicit model. We tested this method in an isolated Cyclobalanopsis glauca population on the Dajinshan Island of China. We detected significant FSGS in the seedlings and a weaker, though also significant, FSGS in the adults. As expected, the empirical FSGS of seedlings was well predicted by the simulated seedlings, with observations falling inside the 95% confidence envelopes over all distance classes. However, the simulation showed that CNDD enhanced the FSGS and positive density dependence dampened it during the seedling-to-adult transition. The adult FSGS of our population was therefore explained by positive rather than negative density-dependent adult recruitment.
Authors
- Tong, Xin ;
- Nason, John D. ;
- Ding, Yuan‐Yuan ;
- Chen, Xiao-Yong