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Pandey A, Wojan C, Feuka A, Craft ME, Manlove K, Pepin KM. The influence of social and spatial processes on the epidemiology of environmentally transmitted pathogens in wildlife: implications for management. Philos Trans R Soc Lond B Biol Sci 2024; 379:20220532. [PMID: 39230447 DOI: 10.1098/rstb.2022.0532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 09/05/2024] Open
Abstract
Social and spatial structures of host populations play important roles in pathogen transmission. For environmentally transmitted pathogens, the host space use interacts with both the host social structure and the pathogen's environmental persistence (which determines the time-lag across which two hosts can transmit). Together, these factors shape the epidemiological dynamics of environmentally transmitted pathogens. While the importance of both social and spatial structures and environmental pathogen persistence has long been recognized in epidemiology, they are often considered separately. A better understanding of how these factors interact to determine disease dynamics is required for developing robust surveillance and management strategies. Here, we use a simple agent-based model where we vary host mobility (spatial), host gregariousness (social) and pathogen decay (environmental persistence), each from low to high levels to uncover how they affect epidemiological dynamics. By comparing epidemic peak, time to epidemic peak and final epidemic size, we show that longer infectious periods, higher group mobility, larger group size and longer pathogen persistence lead to larger, faster growing outbreaks, and explore how these processes interact to determine epidemiological outcomes such as the epidemic peak and the final epidemic size. We identify general principles that can be used for planning surveillance and control for wildlife host-pathogen systems with environmental transmission across a range of spatial behaviour, social structure and pathogen decay rates. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.
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Affiliation(s)
- Aakash Pandey
- Department of Fisheries and Wildlife, Michigan State University , East Lansing, MI 48824, USA
| | - Chris Wojan
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul , MN 55108, USA
| | - Abigail Feuka
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
| | - Meggan E Craft
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul , MN 55108, USA
| | - Kezia Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, 5200 Old Main Hill , Logan, UT 84322, USA
| | - Kim M Pepin
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
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Dolfi AC, Kausrud K, Rysava K, Champagne C, Huang YH, Barandongo ZR, Turner WC. Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs. Proc Biol Sci 2024; 291:20232568. [PMID: 38320613 PMCID: PMC10846954 DOI: 10.1098/rspb.2023.2568] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/09/2024] [Indexed: 02/08/2024] Open
Abstract
An important part of infectious disease management is predicting factors that influence disease outbreaks, such as R, the number of secondary infections arising from an infected individual. Estimating R is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated R for Bacillus anthracis infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations and simulation models, we show that R is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within 2 years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (R < 1) to explosive outbreaks (R > 10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogen interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.
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Affiliation(s)
- Amélie C. Dolfi
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | | | - Kristyna Rysava
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Celeste Champagne
- College of Veterinary Medicine, University of Minnesota, Saint Paul, MN 55108, USA
| | - Yen-Hua Huang
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Institute for Biospheric Studies, Yale University, New Haven, CT 06511, USA
| | - Zoe R. Barandongo
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Wendy C. Turner
- US Geological Survey, Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA
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Wittemyer G, Ryan SJ. Introduction to the Wayne Getz Festschrift. MOVEMENT ECOLOGY 2024; 12:9. [PMID: 38287406 PMCID: PMC10823688 DOI: 10.1186/s40462-023-00441-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 12/09/2023] [Indexed: 01/31/2024]
Affiliation(s)
- George Wittemyer
- Department of Fish, Wildlife and Conservation Biology, Colorado State University, 80523, Fort Collins, CO, USA.
| | - Sadie J Ryan
- Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography and the Emerging Pathogens Institute, University of Florida, 32611, Gainesville, FL, USA
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
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McClintock BT, Lander ME. A multistate Langevin diffusion for inferring behavior-specific habitat selection and utilization distributions. Ecology 2024; 105:e4186. [PMID: 37794831 DOI: 10.1002/ecy.4186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/29/2023] [Accepted: 09/07/2023] [Indexed: 10/06/2023]
Abstract
The identification of important habitat and the behavior(s) associated with it is critical to conservation and place-based management decisions. Behavior also links life-history requirements and habitat use, which are key to understanding why animals use certain habitats. Animal population studies often use tracking data to quantify space use and habitat selection, but they typically either ignore movement behavior (e.g., foraging, migrating, nesting) or adopt a two-stage approach that can induce bias and fail to propagate uncertainty. We develop a habitat-driven Langevin diffusion for animals that exhibit distinct movement behavior states, thereby providing a novel single-stage statistical method for inferring behavior-specific habitat selection and utilization distributions in continuous time. Practitioners can customize, fit, assess, and simulate our integrated model using the provided R package. Simulation experiments demonstrated that the model worked well under a range of sampling scenarios as long as observations were of sufficient temporal resolution. Our simulations also demonstrated the importance of accounting for different behaviors and the misleading inferences that can result when these are ignored. We provide case studies using plains zebra (Equus quagga) and Steller sea lion (Eumetopias jubatus) telemetry data. In the zebra example, our model identified distinct "encamped" and "exploratory" states, where the encamped state was characterized by strong selection for grassland and avoidance of other vegetation types, which may represent selection for foraging resources. In the sea lion example, our model identified distinct movement behavior modes typically associated with this marine central-place forager and, unlike previous analyses, found foraging-type movements to be associated with steeper offshore slopes characteristic of the continental shelf, submarine canyons, and seamounts that are believed to enhance prey concentrations. This is the first single-stage approach for inferring behavior-specific habitat selection and utilization distributions from tracking data that can be readily implemented with user-friendly software. As certain behaviors are often more relevant to specific conservation or management objectives, practitioners can use our model to help inform the identification and prioritization of important habitats. Moreover, by linking individual-level movement behaviors to population-level spatial processes, the multistate Langevin diffusion can advance inferences at the intersection of population, movement, and landscape ecology.
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Affiliation(s)
- Brett T McClintock
- Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service, Seattle, Washington, USA
| | - Michelle E Lander
- Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA, National Marine Fisheries Service, Seattle, Washington, USA
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Dougherty ER, Seidel DP, Blackburn JK, Turner WC, Getz WM. Correction: A framework for integrating inferred movement behavior into disease risk models. MOVEMENT ECOLOGY 2022; 10:36. [PMID: 36008831 PMCID: PMC9414177 DOI: 10.1186/s40462-022-00334-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Eric R Dougherty
- Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA.
| | - Dana P Seidel
- Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA
| | - Jason K Blackburn
- Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Wendy C Turner
- U.S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA
| | - Wayne M Getz
- Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA
- School of Mathematical Sciences, University of KwaZulu-Natal, Durban, South Africa
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