Michael W. Ackerman

642 total citations
18 papers, 478 citations indexed

About

Michael W. Ackerman is a scholar working on Nature and Landscape Conservation, Genetics and Ecology. According to data from OpenAlex, Michael W. Ackerman has authored 18 papers receiving a total of 478 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Nature and Landscape Conservation, 13 papers in Genetics and 6 papers in Ecology. Recurrent topics in Michael W. Ackerman's work include Fish Ecology and Management Studies (17 papers), Genetic diversity and population structure (13 papers) and Genetic and phenotypic traits in livestock (7 papers). Michael W. Ackerman is often cited by papers focused on Fish Ecology and Management Studies (17 papers), Genetic diversity and population structure (13 papers) and Genetic and phenotypic traits in livestock (7 papers). Michael W. Ackerman collaborates with scholars based in United States and France. Michael W. Ackerman's co-authors include Shawn R. Narum, Matthew R. Campbell, Lisa W. Seeb, Craig A. Steele, Christopher Habicht, Andrew P. Matala, Maureen A. Hess, Eric C. Anderson, Nathan R. Campbell and Timothy Copeland and has published in prestigious journals such as Molecular Ecology, Canadian Journal of Fisheries and Aquatic Sciences and ICES Journal of Marine Science.

In The Last Decade

Michael W. Ackerman

18 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
Michael W. Ackerman United States 11 358 337 168 78 76 18 478
Andrew P. Matala United States 15 494 1.4× 468 1.4× 194 1.2× 121 1.6× 85 1.1× 30 652
Patrick W. DeHaan United States 12 391 1.1× 294 0.9× 259 1.5× 91 1.2× 82 1.1× 27 518
Jeff Stephenson United States 7 313 0.9× 230 0.7× 112 0.7× 65 0.8× 50 0.7× 11 394
Amanda E. Haponski United States 11 232 0.6× 169 0.5× 159 0.9× 43 0.6× 73 1.0× 18 326
Cathy MacConnachie Canada 14 472 1.3× 350 1.0× 223 1.3× 104 1.3× 54 0.7× 16 584
Anna Elz United States 11 186 0.5× 211 0.6× 166 1.0× 122 1.6× 36 0.5× 20 392
Tasha Q. Thompson United States 5 250 0.7× 213 0.6× 134 0.8× 45 0.6× 37 0.5× 7 341
Nicholas M. Sard United States 11 220 0.6× 104 0.3× 248 1.5× 133 1.7× 54 0.7× 28 351
Benjamin C. Hecht United States 6 218 0.6× 279 0.8× 107 0.6× 73 0.9× 58 0.8× 8 412
Alicia Abadía‐Cardoso Mexico 9 272 0.8× 277 0.8× 91 0.5× 86 1.1× 69 0.9× 21 388

Countries citing papers authored by Michael W. Ackerman

Since Specialization
Citations

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

Fields of papers citing papers by Michael W. Ackerman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael W. Ackerman

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

All Works

18 of 18 papers shown
1.
Ackerman, Michael W., et al.. (2021). State-Space Model to Estimate Salmon Escapement Using Multiple Data Sources. North American Journal of Fisheries Management. 41(5). 1360–1374. 1 indexed citations
2.
Ackerman, Michael W., et al.. (2021). Estimating carrying capacity for juvenile salmon using quantile random forest models. Ecosphere. 12(3). 9 indexed citations
3.
Copeland, Timothy, et al.. (2019). Patterns of Iteroparity in Wild Snake River Steelhead. Transactions of the American Fisheries Society. 148(5). 926–937. 7 indexed citations
4.
Copeland, Timothy, et al.. (2017). Life History Diversity of Snake River Steelhead Populations between and within Management Categories. North American Journal of Fisheries Management. 37(2). 395–404. 18 indexed citations
5.
Copeland, Timothy, et al.. (2016). Abundance estimates and confidence intervals for the run composition of returning salmonids. Fishery Bulletin. 115(1). 1–12. 7 indexed citations
6.
Ackerman, Michael W., Brian K. Hand, Ryan K. Waples, et al.. (2016). Effective number of breeders from sibship reconstruction: empirical evaluations using hatchery steelhead. Evolutionary Applications. 10(2). 146–160. 52 indexed citations
7.
Hess, Jon E., Michael W. Ackerman, Daniel J. Hasselman, et al.. (2016). Differential adult migration-timing and stock-specific abundance of steelhead in mixed stock assemblages. ICES Journal of Marine Science. 73(10). 2606–2615. 41 indexed citations
8.
9.
Ackerman, Michael W., et al.. (2016). The Genetic Relationship between Anadromous and Resident Oncorhynchus mykiss at a Putative Barrier with Implications for Habitat Improvement. Transactions of the American Fisheries Society. 145(2). 305–318. 11 indexed citations
10.
Steele, Craig A., Michael W. Ackerman, Matthew R. Campbell, et al.. (2016). Maximum Likelihood Estimation of the Proportion of Hatchery‐Origin Fish on Spawning Grounds Using Coded Wire Tagging and Parentage‐Based Tagging. Transactions of the American Fisheries Society. 145(3). 671–686. 14 indexed citations
11.
Matala, Andrew P., et al.. (2016). What goes up does not come down: the stock composition and demographic characteristics of upstream migrating steelhead differ from post-spawn emigrating kelts. ICES Journal of Marine Science. 73(10). 2595–2605. 8 indexed citations
12.
Hand, Brian K., Clint C. Muhlfeld, Alisa A. Wade, et al.. (2015). Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics. Molecular Ecology. 25(3). 689–705. 36 indexed citations
13.
Matala, Andrew P., Michael W. Ackerman, Matthew R. Campbell, & Shawn R. Narum. (2014). Relative contributions of neutral and non‐neutral genetic differentiation to inform conservation of steelhead trout across highly variable landscapes. Evolutionary Applications. 7(6). 682–701. 45 indexed citations
14.
Steele, Craig A., Eric C. Anderson, Michael W. Ackerman, et al.. (2013). A validation of parentage-based tagging using hatchery steelhead in the Snake River basin. Canadian Journal of Fisheries and Aquatic Sciences. 70(7). 1046–1054. 90 indexed citations
15.
Campbell, Matthew R., et al.. (2012). Estimating Abundance and Life History Characteristics of Threatened Wild Snake River Steelhead Stocks by Using Genetic Stock Identification. Transactions of the American Fisheries Society. 141(5). 1310–1327. 28 indexed citations
16.
Ackerman, Michael W., William D. Templin, James E. Seeb, & Lisa W. Seeb. (2012). Landscape heterogeneity and local adaptation define the spatial genetic structure of Pacific salmon in a pristine environment. Conservation Genetics. 14(2). 483–498. 25 indexed citations
17.
Ackerman, Michael W., Christopher Habicht, & Lisa W. Seeb. (2011). Single‐Nucleotide Polymorphisms (SNPs) under Diversifying Selection Provide Increased Accuracy and Precision in Mixed‐Stock Analyses of Sockeye Salmon from the Copper River, Alaska. Transactions of the American Fisheries Society. 140(3). 865–881. 67 indexed citations
18.

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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