Automated Author ProfileSmiley, Tara
Indiana University Bloomington0000-0001-5940-1755
Smiley, Tara
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: 3.4 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This repository contains the model simulation results used in the study “Direct effects of mountain uplift and topography on biodiversity” by Eyal Marder, Tara M. Smiley, Brian J. Yanites, and Katherine Kravitz, published in Science (2025). The dataset includes 3D mountain-building simulations spanning 20 million years, coupled with biological population dynamics(NetCDF files). Additionally, it provides final dataframes detailing individuals’ traits and phylogenetic data (pkl files).For the simulation codes, modified modules, and plotting scripts, please refer to Version 2, available at https://doi.org/10.5281/zenodo.15033903
Authors
- Marder, Eyal ;
- Smiley, Tara ;
- Yanites, Brian ;
- Kravitz, Katherine
Aim: We investigate geographic patterns across taxonomic, ecological, and phylogenetic diversity to test for spatial (in)congruency and identify aggregate diversity hotspots in relation to present land-use and future climate. Simulating extinctions of imperiled species, we demonstrate where losses across diversity dimensions and geography are predicted. Location: North America Time period: Present-day, future Major taxa studied: Rodentia Methods: Using geographic range maps for rodent species, we quantified spatial patterns for eleven dimensions of diversity: taxonomic (species, range-weighted), ecological (body size, diet, habitat), phylogenetic (mean, variance, and nearest-neighbor patristic distances, phylogenetic distance, genus-to-species ratio) and phyloendemism. We tested for correlations across dimensions and used spatial residual analyses to illustrate regions of pronounced diversity. We aggregated diversity hotspots in relation to land-use and climate-change predictions and recalculated metrics following extinctions of IUCN-listed imperiled species. Results: Topographically-complex western North America hosts high diversity across multiple dimensions: phyloendemism and ecological diversity exceed predictions based on taxonomic richness and phylogenetic variance patterns indicate steep gradients in phylogenetic turnover. While an aggregate diversity hotspot emerges in the west, spatial incongruence exists across diversity dimensions at the continental scale. Notably, phylogenetic metrics are uncorrelated with ecological diversity. Diversity hotspots overlap with land-use and climate change, and extinctions predicted by IUCN status are unevenly distributed across space, phylogeny, or ecological groups. Main conclusions: Comparison of taxonomic, ecological, and phylogenetic diversity patterns for North American rodents clearly shows the multifaceted nature of biodiversity. Testing for geographic patterns and (in)congruency across dimensions of diversity facilitates investigation into underlying ecological and evolutionary processes. The geographic scope of this analysis suggests that several explicit regional challenges face North American rodent fauna in the future. Simultaneous consideration of multidimensional biodiversity allows us to assess what critical functions or evolutionary history we might lose with future extinctions and maximize the potential of our conservation efforts.
Authors
- Smiley, Tara ;
- Title, Pascal ;
- Zelditch, Miriam ;
- Terry, Rebecca