Katelin D. Pearson

802 total citations
11 papers, 471 citations indexed

About

Katelin D. Pearson is a scholar working on Ecological Modeling, Ecology, Evolution, Behavior and Systematics and Ecology. According to data from OpenAlex, Katelin D. Pearson has authored 11 papers receiving a total of 471 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Ecological Modeling, 9 papers in Ecology, Evolution, Behavior and Systematics and 5 papers in Ecology. Recurrent topics in Katelin D. Pearson's work include Species Distribution and Climate Change (11 papers), Plant and animal studies (9 papers) and Ecology and Vegetation Dynamics Studies (4 papers). Katelin D. Pearson is often cited by papers focused on Species Distribution and Climate Change (11 papers), Plant and animal studies (9 papers) and Ecology and Vegetation Dynamics Studies (4 papers). Katelin D. Pearson collaborates with scholars based in United States, France and Poland. Katelin D. Pearson's co-authors include Elizabeth R. Ellwood, Pamela S. Soltis, Susan J. Mazer, Gil Nelson, Jenn Yost, Charles C. Davis, Richard B. Primack, Charles G. Willis, Amanda S. Gallinat and Tim H. Sparks and has published in prestigious journals such as Trends in Ecology & Evolution, BioScience and American Journal of Botany.

In The Last Decade

Katelin D. Pearson

11 papers receiving 465 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Katelin D. Pearson United States 7 301 273 161 151 126 11 471
Jenn Yost United States 9 171 0.6× 229 0.8× 100 0.6× 152 1.0× 130 1.0× 19 413
Bipin Charles India 12 260 0.9× 100 0.4× 157 1.0× 159 1.1× 65 0.5× 18 397
Laura Brenskelle United States 7 221 0.7× 131 0.5× 145 0.9× 85 0.6× 51 0.4× 13 339
Reed S. Beaman United States 14 206 0.7× 299 1.1× 115 0.7× 124 0.8× 174 1.4× 19 578
L. Alan Prather United States 13 160 0.5× 299 1.1× 90 0.6× 93 0.6× 234 1.9× 25 517
Kamil Konowalik Poland 12 149 0.5× 170 0.6× 77 0.5× 89 0.6× 119 0.9× 27 351
Dmitry Mozzherin United States 5 155 0.5× 180 0.7× 84 0.5× 191 1.3× 82 0.7× 13 457
Vanessa Graziele Staggemeier Brazil 14 224 0.7× 617 2.3× 177 1.1× 353 2.3× 222 1.8× 29 834
Fangyuan Yu China 8 173 0.6× 136 0.5× 87 0.5× 120 0.8× 45 0.4× 16 298
Hum Kala Rana China 10 151 0.5× 132 0.5× 34 0.2× 98 0.6× 78 0.6× 17 318

Countries citing papers authored by Katelin D. Pearson

Since Specialization
Citations

This map shows the geographic impact of Katelin D. Pearson'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 Katelin D. Pearson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katelin D. Pearson more than expected).

Fields of papers citing papers by Katelin D. Pearson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katelin D. Pearson. 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 Katelin D. Pearson. The network helps show where Katelin D. Pearson may publish in the future.

Co-authorship network of co-authors of Katelin D. Pearson

This figure shows the co-authorship network connecting the top 25 collaborators of Katelin D. Pearson. A scholar is included among the top collaborators of Katelin D. Pearson 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 Katelin D. Pearson. Katelin D. Pearson is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Gilbert, Edward, et al.. (2022). Taxonomic Curation in a Multi-taxa Symbiota Portal. Biodiversity Information Science and Standards. 6. 1 indexed citations
2.
3.
Pearson, Katelin D., Myla F. J. Aronson, Pierre Bonnet, et al.. (2020). Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research. BioScience. 70(7). 610–620. 78 indexed citations
4.
Lorieul, Titouan, Katelin D. Pearson, Elizabeth R. Ellwood, et al.. (2019). Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences. 7(3). e01233–e01233. 47 indexed citations
5.
Pearson, Katelin D.. (2019). Spring- and fall-flowering species show diverging phenological responses to climate in the Southeast USA. International Journal of Biometeorology. 63(4). 481–492. 42 indexed citations
6.
Ellwood, Elizabeth R., Katelin D. Pearson, & Gil Nelson. (2019). Emerging frontiers in phenological research. Applications in Plant Sciences. 7(3). 7 indexed citations
7.
Pearson, Katelin D. & Austin Mast. (2019). Mobilizing the community of biodiversity specimen collectors to effectively detect and document outliers in the Anthropocene. American Journal of Botany. 106(8). 1052–1058. 4 indexed citations
8.
9.
Pearson, Katelin D.. (2019). A new method and insights for estimating phenological events from herbarium specimens. Applications in Plant Sciences. 7(3). e01224–e01224. 27 indexed citations
10.
Pearson, Katelin D.. (2018). Rapid enhancement of biodiversity occurrence records using unconventional specimen data. Biodiversity and Conservation. 27(11). 3007–3018. 5 indexed citations
11.
Willis, Charles G., Elizabeth R. Ellwood, Richard B. Primack, et al.. (2017). Old Plants, New Tricks: Phenological Research Using Herbarium Specimens. Trends in Ecology & Evolution. 32(7). 531–546. 235 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.

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