Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED)
2001578 citationsP. R. Moorcroft, G. C. Hurtt et al.Ecological Monographsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of S. W. Pacala's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by S. W. Pacala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. W. Pacala more than expected).
This network shows the impact of papers produced by S. W. Pacala. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by S. W. Pacala. The network helps show where S. W. Pacala may publish in the future.
Co-authorship network of co-authors of S. W. Pacala
This figure shows the co-authorship network connecting the top 25 collaborators of S. W. Pacala.
A scholar is included among the top collaborators of S. W. Pacala based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with S. W. Pacala. S. W. Pacala is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gerber, Stefan, Lars O. Hedin, S. W. Pacala, Elena Shevliakova, & Melanie Oppenheimer. (2006). The Emergence of Nitrogen Limitation in a Global Dynamic Coupled Terrestrial Carbon Nitrogen Model. AGUFM. 2006.
5.
Tol, Richard S.J., S. W. Pacala, & Robert H. Socolow. (2006). Understanding long-term energy use and carbon emissions in the USA. Data Archiving and Networked Services (DANS).11 indexed citations
6.
Shevliakova, Elena, R. J. Stouffer, Michael J. Spelman, Sergey Malyshev, & S. W. Pacala. (2005). Feedbacks Between Terrestrial Biosphere and Climate in the GFDL Dynamic Land / Slab Ocean Climate model.. AGU Fall Meeting Abstracts. 2005.1 indexed citations
7.
Roy, Somnath Baidya, R. L. Walko, & S. W. Pacala. (2003). Can Large Windfarms Affect Local Meteorology. AGU Fall Meeting Abstracts. 2003.1 indexed citations
8.
Malyshev, Sergey, S. W. Pacala, David W. Keith, et al.. (2003). Climate Response to Large-Scale Wind Farms. AGU Fall Meeting Abstracts. 2003.1 indexed citations
9.
Weaver, Chris, et al.. (2002). Impact of Historical Land-use/Land-cover Change on US Summer Climate. AGUSM. 2002.1 indexed citations
Moorcroft, P. R., G. C. Hurtt, & S. W. Pacala. (2001). A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED). Ecological Monographs. 71(4). 557–586.578 indexed citations breakdown →
Caspersen, John P., John A. Silander, Charles D. Canham, et al.. (1999). Modeling the competitive dynamics and distribution of tree species along moisture gradients.. 14–41.11 indexed citations
15.
Pacala, S. W. & Michael J. Crawley. (1992). Herbivores and Plant Diversity. The American Naturalist. 140(2). 243–260.160 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.