Automated Author ProfileScholefield, Paul
UK Centre for Ecology & Hydrology
Scholefield, Paul
UK Centre for Ecology & Hydrology
Current S-Index
2.0
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
2.0
Average Dataset Index per dataset
Total Datasets
1
Total datasets for this author
Average FAIR Score
76.9%
Average FAIR Score per dataset
Total Citations
2
Total citations to the author's datasets
Total Mentions
0
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
- Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically-relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies. 2. We assessed whether a novel spatial dataset mapping linear and woody linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites respectively. 3. Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. 4. Synthesis and applications. This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems, and also can potentially be used to assess habitat connectivity. 10-Mar-2017
Authors
- Sullivan, Martin J. P. ;
- Pearce-Higgins, James W. ;
- Newson, Stuart E. ;
- Scholefield, Paul ;
- Brereton, Tom ;
- Oliver, Tom H.
2 Citations0 Mentions77% FAIR2.3 Dataset Index
10.5061/dryad.m5g042017