W. David Walter

1.9k total citations
86 papers, 1.2k citations indexed

About

W. David Walter is a scholar working on Ecology, Agronomy and Crop Science and Molecular Biology. According to data from OpenAlex, W. David Walter has authored 86 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Ecology, 26 papers in Agronomy and Crop Science and 22 papers in Molecular Biology. Recurrent topics in W. David Walter's work include Wildlife Ecology and Conservation (38 papers), Animal Disease Management and Epidemiology (20 papers) and Prion Diseases and Protein Misfolding (19 papers). W. David Walter is often cited by papers focused on Wildlife Ecology and Conservation (38 papers), Animal Disease Management and Epidemiology (20 papers) and Prion Diseases and Protein Misfolding (19 papers). W. David Walter collaborates with scholars based in United States, Australia and Canada. W. David Walter's co-authors include Justin W. Fischer, Kurt C. VerCauteren, Howard J. Kilpatrick, Scott E. Hygnstrom, David M. Leslie, David P. Onorato, William L. Miller, Michael L. Avery, Charles W. Anderson and Daniel P. Walsh and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

W. David Walter

79 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
W. David Walter United States 20 670 277 254 152 126 86 1.2k
Daniel P. Walsh United States 22 471 0.7× 184 0.7× 311 1.2× 84 0.6× 108 0.9× 74 1.2k
Antoinette Kotzé South Africa 24 827 1.2× 400 1.4× 217 0.9× 141 0.9× 97 0.8× 153 1.9k
Scott E. Hygnstrom United States 19 988 1.5× 58 0.2× 225 0.9× 205 1.3× 150 1.2× 103 1.4k
Timothy R. Van Deelen United States 26 1.6k 2.4× 148 0.5× 240 0.9× 324 2.1× 209 1.7× 102 2.0k
Jiřı́ Kamler Czechia 14 616 0.9× 50 0.2× 195 0.8× 169 1.1× 95 0.8× 50 1.0k
Limin Feng China 21 842 1.3× 159 0.6× 57 0.2× 98 0.6× 129 1.0× 54 1.2k
Frank Carrick Australia 22 762 1.1× 136 0.5× 130 0.5× 164 1.1× 59 0.5× 74 1.4k
Pedro Mayor Spain 20 610 0.9× 44 0.2× 166 0.7× 129 0.8× 163 1.3× 102 1.2k
Ulf Hohmann Germany 13 675 1.0× 55 0.2× 180 0.7× 79 0.5× 100 0.8× 21 1.0k
Nancy E. Mathews United States 20 900 1.3× 98 0.4× 131 0.5× 244 1.6× 191 1.5× 52 1.1k

Countries citing papers authored by W. David Walter

Since Specialization
Citations

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

Fields of papers citing papers by W. David Walter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by W. David Walter. 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 W. David Walter. The network helps show where W. David Walter may publish in the future.

Co-authorship network of co-authors of W. David Walter

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

All Works

20 of 20 papers shown
1.
Walter, W. David, et al.. (2025). Overview of North American Isolates of Chronic Wasting Disease Used for Strain Research. Pathogens. 14(3). 250–250. 1 indexed citations
2.
Rosenberry, Christopher S., et al.. (2024). Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types. Ecological Modelling. 493. 110756–110756. 2 indexed citations
3.
Walter, W. David, et al.. (2024). Large‐scale assessment of genetic structure to assess risk of populations of a large herbivore to disease. Ecology and Evolution. 14(5). e11347–e11347. 1 indexed citations
4.
Walter, W. David, et al.. (2024). Evaluation of DNA yield from various tissue and sampling sources for use in single nucleotide polymorphism panels. Scientific Reports. 14(1). 11340–11340. 3 indexed citations
5.
Montecino‐Latorre, Diego, et al.. (2023). Spatial modeling of two mosquito vectors of West Nile virus using integrated nested Laplace approximations. Ecosphere. 14(1). 5 indexed citations
6.
Diefenbach, Duane R., et al.. (2023). Change-point models for identifying behavioral transitions in wild animals. Movement Ecology. 11(1). 2 indexed citations
7.
Jennelle, Christopher S., et al.. (2022). Movement of white‐tailed deer in contrasting landscapes influences management of chronic wasting disease. Journal of Wildlife Management. 86(8). 5 indexed citations
8.
Rosenberry, Christopher S., et al.. (2022). Variability in prion protein genotypes by spatial unit to inform susceptibility to chronic wasting disease. Prion. 16(1). 254–264.
9.
Brown, Justin D., et al.. (2021). Comparison of sample types from white-tailed deer (Odocoileus virginianus) for DNA extraction and analyses. Scientific Reports. 11(1). 10003–10003. 6 indexed citations
10.
Newman, Robert A., et al.. (2019). Seasonal home ranges and habitat selection of three elk (Cervus elaphus) herds in North Dakota. PLoS ONE. 14(2). e0211650–e0211650. 6 indexed citations
11.
Miller, William L., et al.. (2019). Identification and evaluation of a core microsatellite panel for use in white-tailed deer (Odocoileus virginianus). BMC Genetics. 20(1). 49–49. 17 indexed citations
12.
Walter, W. David, et al.. (2018). Heterogeneity of a landscape influences size of home range in a North American cervid. Scientific Reports. 8(1). 14667–14667. 13 indexed citations
13.
Johnson, Heather E., et al.. (2017). Influence of Precipitation and Crop Germination on Resource Selection by Mule Deer (Odocoileus hemionus) in Southwest Colorado. Scientific Reports. 7(1). 15234–15234. 3 indexed citations
14.
Walter, W. David. (2015). Unusual Documentation of Elk Behaviors Using Automated Cameras. Proceedings of the Oklahoma Academy of Science. 85. 81–83. 2 indexed citations
15.
Walter, W. David. (2015). Harvest Strategies and Number of Elk (Cervus elaphus) in Oklahoma, 1987-2001. Proceedings of the Oklahoma Academy of Science. 82. 89–94. 1 indexed citations
16.
Walter, W. David, David P. Onorato, & Justin W. Fischer. (2015). Is there a single best estimator? Selection of home range estimators using area-under-the-curve. Movement Ecology. 3(1). 10–10. 65 indexed citations
17.
Schuler, Krysten L., et al.. (2014). Surveillance and Monitoring of White-Tailed Deer for Chronic Wasting Disease in the Northeastern United States. Journal of Fish and Wildlife Management. 5(2). 387–393. 27 indexed citations
18.
Walter, W. David, et al.. (2013). Topographic Home Range of Large Mammals: Is Planimetric Home Range Still a Viable Method?. Insecta mundi. 45. 21. 4 indexed citations
19.
Walter, W. David, Charles W. Anderson, & Kurt C. VerCauteren. (2012). Evaluation of Remote Delivery of Passive Integrated Transponder (PIT) Technology to Mark Large Mammals. PLoS ONE. 7(9). e44838–e44838. 4 indexed citations
20.
Walter, W. David, Daniel P. Walsh, Matthew L. Farnsworth, Dana L. Winkelman, & Michael W. Miller. (2011). Soil clay content underlies prion infection odds. Nature Communications. 2(1). 200–200. 63 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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