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Kraberger S, Serieys LEK, Riley SPD, Schmidlin K, Newkirk ES, Squires JR, Buck CB, Varsani A. Novel polyomaviruses identified in fecal samples from four carnivore species. Arch Virol 2023; 168:18. [PMID: 36593361 PMCID: PMC10681122 DOI: 10.1007/s00705-022-05675-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/21/2022] [Indexed: 01/04/2023]
Abstract
Polyomaviruses are oncogenic viruses that are generally thought to have co-evolved with their hosts. While primate and rodent polyomaviruses are increasingly well-studied, less is known about polyomaviruses that infect other mammals. In an effort to gain insight into polyomaviruses associated with carnivores, we surveyed fecal samples collected in the USA from bobcats (Lynx rufus), pumas (Puma concolor), Canada lynxes (Lynx canadensis), and grizzly bears (Ursus arctos). Using a viral metagenomic approach, we identified six novel polyomavirus genomes. Surprisingly, four of the six genomes showed a phylogenetic relationship to polyomaviruses found in prey animals. These included a putative rabbit polyomavirus from a bobcat fecal sample and two possible deer-trophic polyomaviruses from Canada lynx feces. One polyomavirus found in a grizzly bear sample was found to be phylogenetically distant from previously identified polyomaviruses. Further analysis of the grizzly bear fecal sample showed that it contained anelloviruses that are known to infect pigs, suggesting that the bear might have preyed on a wild or domestic pig. Interestingly, a polyomavirus genome identified in a puma fecal sample was found to be closely related both to raccoon polyomavirus 1 and to Lyon-IARC polyomavirus, the latter of which was originally identified in human saliva and skin swab specimens but has since been found in samples from domestic cats (Felis catus).
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Affiliation(s)
- Simona Kraberger
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine and School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.
| | - Laurel E K Serieys
- Panthera, 8 W 40th St, 18th Floor, New York, NY, 10018, USA
- Santa Monica Mountains National Recreation Area, National Park Service, Thousand Oaks, CA, 91360, USA
| | - Seth P D Riley
- Santa Monica Mountains National Recreation Area, National Park Service, Thousand Oaks, CA, 91360, USA
| | - Kara Schmidlin
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine and School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA
| | | | - John R Squires
- U.S. Forest Service, Rocky Mountain Research Station, 800 East Beckwith Avenue, Missoula, MT, 59801, USA
| | - Christopher B Buck
- Lab of Cellular Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Arvind Varsani
- The Biodesign Center for Fundamental and Applied Microbiomics, Center for Evolution and Medicine and School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.
- Structural Biology Research Unit, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa.
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Kraberger S, Serieys LE, Richet C, Fountain-Jones NM, Baele G, Bishop JM, Nehring M, Ivan JS, Newkirk ES, Squires JR, Lund MC, Riley SP, Wilmers CC, van Helden PD, Van Doorslaer K, Culver M, VandeWoude S, Martin DP, Varsani A. Complex evolutionary history of felid anelloviruses. Virology 2021; 562:176-189. [PMID: 34364185 DOI: 10.1016/j.virol.2021.07.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 10/20/2022]
Abstract
Anellovirus infections are highly prevalent in mammals, however, prior to this study only a handful of anellovirus genomes had been identified in members of the Felidae family. Here we characterise anelloviruses in pumas (Puma concolor), bobcats (Lynx rufus), Canada lynx (Lynx canadensis), caracals (Caracal caracal) and domestic cats (Felis catus). The complete anellovirus genomes (n = 220) recovered from 149 individuals were diverse. ORF1 protein sequence similarity network analysis coupled with phylogenetic analysis, revealed two distinct clusters that are populated by felid-derived anellovirus sequences, a pattern mirroring that observed for the porcine anelloviruses. Of the two-felid dominant anellovirus groups, one includes sequences from bobcats, pumas, domestic cats and an ocelot, and the other includes sequences from caracals, Canada lynx, domestic cats and pumas. Coinfections of diverse anelloviruses appear to be common among the felids. Evidence of recombination, both within and between felid-specific anellovirus groups, supports a long coevolution history between host and virus.
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Affiliation(s)
- Simona Kraberger
- The Biodesign Center of Fundamental and Applied Microbiomics, School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, 85287, USA.
| | - Laurel Ek Serieys
- Environmental Studies, University of California, Santa Cruz, CA, 95064, USA; Institute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Private Bag X3, Rondebosch, Cape Town, 7701, South Africa
| | - Cécile Richet
- The Biodesign Center of Fundamental and Applied Microbiomics, School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, 85287, USA
| | | | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Jacqueline M Bishop
- Institute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Private Bag X3, Rondebosch, Cape Town, 7701, South Africa
| | - Mary Nehring
- Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Jacob S Ivan
- Colorado Parks and Wildlife, 317 W. Prospect Rd., Fort Collins, CO, 80526, USA
| | | | - John R Squires
- US Department of Agriculture, Rocky Mountain Research Station, 800 E. Beckwith Ave., Missoula, MT, 59801, USA
| | - Michael C Lund
- The Biodesign Center of Fundamental and Applied Microbiomics, School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, 85287, USA
| | - Seth Pd Riley
- Santa Monica Mountains National Recreation Area, National Park Service, Thousand Oaks, CA, 91360, USA
| | | | - Paul D van Helden
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for TB Research/Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, 7505, South Africa
| | - Koenraad Van Doorslaer
- School of Animal and Comparative Biomedical Sciences, The BIO5 Institute, Department of Immunobiology, Cancer Biology Graduate Interdisciplinary Program, UA Cancer Center, University of Arizona, Tucson, AZ, 85724, USA
| | - Melanie Culver
- U.S. Geological Survey, Arizona Cooperative Fish and Wildlife Research Unit, University of Arizona, Tucson, AZ, 85721, USA; School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA
| | - Sue VandeWoude
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Darren P Martin
- Computational Biology Group, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, 7925, South Africa
| | - Arvind Varsani
- The Biodesign Center of Fundamental and Applied Microbiomics, School of Life Sciences, Center for Evolution and Medicine, Arizona State University, Tempe, AZ, 85287, USA; Structural Biology Research Unit, Department of Integrative Biomedical Sciences, University of Cape Town, 7925, Cape Town, South Africa.
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3
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Tabak MA, Norouzzadeh MS, Wolfson DW, Newton EJ, Boughton RK, Ivan JS, Odell EA, Newkirk ES, Conrey RY, Stenglein J, Iannarilli F, Erb J, Brook RK, Davis AJ, Lewis J, Walsh DP, Beasley JC, VerCauteren KC, Clune J, Miller RS. Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2. Ecol Evol 2020; 10:10374-10383. [PMID: 33072266 PMCID: PMC7548173 DOI: 10.1002/ece3.6692] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/29/2020] [Accepted: 07/31/2020] [Indexed: 11/24/2022] Open
Abstract
Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.
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Affiliation(s)
- Michael A Tabak
- Quantitative Science Consulting, LLC Laramie WY USA.,Department of Zoology and Physiology University of Wyoming Laramie WY USA
| | | | - David W Wolfson
- Minnesota Cooperative Fish and Wildlife Research Unit Department of Fisheries, Wildlife and Conservation Biology University of Minnesota St. Paul MN USA
| | - Erica J Newton
- Wildlife Research and Monitoring Section Ontario Ministry of Natural Resources and Forestry Peterborough ON Canada
| | - Raoul K Boughton
- Range Cattle Research and Education Center, Wildlife Ecology and Conservation University of Florida Ona FL USA
| | | | | | | | | | | | - Fabiola Iannarilli
- Conservation Sciences Graduate Program University of Minnesota St. Paul MN USA
| | - John Erb
- Forest Wildlife Populations and Research Group Minnesota Department of Natural Resources Grand Rapids MN USA
| | - Ryan K Brook
- Department of Animal and Poultry Science University of Saskatchewan Saskatoon SK Canada
| | - Amy J Davis
- National Wildlife Research Center United States Department of Agriculture Fort Collins CO USA
| | - Jesse Lewis
- College of Integrative Sciences and Arts Arizona State University Mesa AZ USA
| | - Daniel P Walsh
- US Geological Survey National Wildlife Health Center Madison WI USA
| | - James C Beasley
- Savannah River Ecology Laboratory Warnell School of Forestry and Natural Resources University of Georgia Aiken SC USA
| | - Kurt C VerCauteren
- National Wildlife Research Center United States Department of Agriculture, Animal and Plant Health Inspection Service Fort Collins CO USA
| | | | - Ryan S Miller
- Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins CO USA
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Shores CR, Dellinger JA, Newkirk ES, Kachel SM, Wirsing AJ. Mesopredators change temporal activity in response to a recolonizing apex predator. Behav Ecol 2019. [DOI: 10.1093/beheco/arz080] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Apex predators can influence ecosystems through density and behaviorally mediated effects on herbivores and mesopredators. In many parts of the world, apex predators live in, or are returning to, landscapes that have been modified by people; so, it is important to understand their ecological role in anthropogenic landscapes. We used motion-activated game cameras to compare the activity patterns of humans and 2 mesopredators, coyotes (Canis latrans) and bobcats (Lynx rufus), in areas with and without an apex predator, the gray wolf (Canis lupus), in a multiuse landscape of the northwestern United States. In areas with wolves, there was a significant increase in temporal niche overlap between the mesopredators owing to higher levels of coyote activity at all time periods of the day. Temporal overlap between mesopredators and humans also increased significantly in the presence of wolves. Coyotes exposed to wolves increased their activity during dawn, day, and dusk hours. The increase in coyote activity was greatest during the day, when wolves were least active. The direction of change in bobcat activity in areas with wolves was opposite to coyotes, suggesting a behaviorally mediated cascade between wolves, coyotes, and bobcats, although these findings would need to be confirmed with further research. Our findings suggest that mesopredators in human-dominated systems may perceive humans as less dangerous than apex predators, that humans may be more likely to encounter mesopredators in areas occupied by top predators, and that behaviorally mediated effects of apex predators on mesopredators persist in human-dominated landscapes.
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Affiliation(s)
- Carolyn R Shores
- School of Environmental and Forest Sciences, University of Washington, Anderson Hall, West Stevens Way NE, Seattle, WA, USA
- Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Fish and Wildlife, Borland St, Williams Lake, BC, Canada
| | - Justin A Dellinger
- California Department of Fish and Wildlife, Nimbus Rd., Suite D, Rancho Cordova, CA, USA
| | | | - Shannon M Kachel
- School of Environmental and Forest Sciences, University of Washington, Anderson Hall, West Stevens Way NE, Seattle, WA, USA
| | - Aaron J Wirsing
- School of Environmental and Forest Sciences, University of Washington, Anderson Hall, West Stevens Way NE, Seattle, WA, USA
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Tabak MA, Norouzzadeh MS, Wolfson DW, Sweeney SJ, Vercauteren KC, Snow NP, Halseth JM, Di Salvo PA, Lewis JS, White MD, Teton B, Beasley JC, Schlichting PE, Boughton RK, Wight B, Newkirk ES, Ivan JS, Odell EA, Brook RK, Lukacs PM, Moeller AK, Mandeville EG, Clune J, Miller RS. Machine learning to classify animal species in camera trap images: Applications in ecology. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13120] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Michael A. Tabak
- Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado
- Department of Zoology and Physiology University of Wyoming Laramie Wyoming
| | | | - David W. Wolfson
- Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado
| | - Steven J. Sweeney
- Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado
| | - Kurt C. Vercauteren
- National Wildlife Research Center United States Department of Agriculture Fort Collins Colorado
| | - Nathan P. Snow
- National Wildlife Research Center United States Department of Agriculture Fort Collins Colorado
| | - Joseph M. Halseth
- National Wildlife Research Center United States Department of Agriculture Fort Collins Colorado
| | - Paul A. Di Salvo
- Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado
| | - Jesse S. Lewis
- College of Integrative Sciences and Arts Arizona State University Mesa Arizona
| | | | - Ben Teton
- Tejon Ranch Conservancy Lebec California
| | - James C. Beasley
- Savannah River Ecology Laboratory Warnell School of Forestry and Natural Resources University of Georgia Aiken South Carolina
| | - Peter E. Schlichting
- Savannah River Ecology Laboratory Warnell School of Forestry and Natural Resources University of Georgia Aiken South Carolina
| | - Raoul K. Boughton
- Range Cattle Research and Education Center Wildlife Ecology and Conservation University of Florida Ona Florida
| | - Bethany Wight
- Range Cattle Research and Education Center Wildlife Ecology and Conservation University of Florida Ona Florida
| | | | | | | | - Ryan K. Brook
- Department of Animal and Poultry Science University of Saskatchewan Saskatoon SK Canada
| | - Paul M. Lukacs
- Wildlife Biology Program Department of Ecosystem and Conservation Sciences W.A. Franke College of Forestry and Conservation University of Montana Missoula Montana
| | - Anna K. Moeller
- Wildlife Biology Program Department of Ecosystem and Conservation Sciences W.A. Franke College of Forestry and Conservation University of Montana Missoula Montana
| | - Elizabeth G. Mandeville
- Department of Zoology and Physiology University of Wyoming Laramie Wyoming
- Department of Botany University of Wyoming Laramie Wyoming
| | - Jeff Clune
- Computer Science Department University of Wyoming Laramie Wyoming
| | - Ryan S. Miller
- Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado
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