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Tao Y, Hastings A, Lafferty KD, Hanski I, Ovaskainen O. Landscape fragmentation overturns classical metapopulation thinking. Proc Natl Acad Sci U S A 2024; 121:e2303846121. [PMID: 38709920 PMCID: PMC11098110 DOI: 10.1073/pnas.2303846121] [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: 03/07/2023] [Accepted: 03/05/2024] [Indexed: 05/08/2024] Open
Abstract
Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally ("residents") were often more resilient to fragmentation than long-ranging "migrants." This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.
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Affiliation(s)
- Yun Tao
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA93117
- Institute of Bioinformatics, University of Georgia, GA30602
| | - Alan Hastings
- Department of Environmental Science and Policy, University of California, Davis, CA95616
- Santa Fe Institute, NM87501
| | - Kevin D. Lafferty
- U.S. Geological Survey, Western Ecological Research Center, CA93106
- Marine Science Institute, University of California, Santa Barbara, CA93117
| | - Ilkka Hanski
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki00014, Finland
| | - Otso Ovaskainen
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki00014, Finland
- Department of Biological and Environmental Science, University of Jyväskylä, JyväskyläFI-40014, Finland
- Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, TrondheimN-7491, Norway
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Ellis J, Brown E, Colenutt C, Schley D, Gubbins S. Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation. Epidemics 2024; 46:100740. [PMID: 38232411 DOI: 10.1016/j.epidem.2024.100740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 12/06/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.
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Affiliation(s)
- John Ellis
- The Pirbright Institute, Pirbright, Surrey, UK.
| | - Emma Brown
- The Pirbright Institute, Pirbright, Surrey, UK
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Nga BTT, Padungtod P, Depner K, Chuong VD, Duy DT, Anh ND, Dietze K. Implications of partial culling on African swine fever control effectiveness in Vietnam. Front Vet Sci 2022; 9:957918. [PMID: 36118335 PMCID: PMC9479321 DOI: 10.3389/fvets.2022.957918] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022] Open
Abstract
The introduction of the African swine fever (ASF) into previously unaffected countries often overwhelms veterinary authorities with the resource demanding control efforts that need to be undertaken. The approach of implementing total stamping out of affected herds is taken as “default” control measure in many countries, regardless of the transboundary animal disease addressed, leading to a variety of challenges when implemented. Apart from the organizational challenges and high demand for human and financial resources, the total stamping out approach puts a high burden on the livelihoods of the affected farmers. After the spread of ASF throughout the country in 2019, Vietnam changed the culling approach enabling partial culling of only affected animals in the herd, in order to save resources, and reduce the environmental impact because of the carcass disposal and allow farmers to protect valuable assets. Until now, field data comparing these disease control options in their performance during implementation has not been evaluated scientifically. Analyzing the effect of the change in a control policy, the present study concludes that partial culling can on average save over 50% of total stock with an 8-day prolongation of the implementation of control measures. With 58% of farms undergoing partial culling scoring high on a time-livelihoods matrix, while total stamping out fails to score on livelihoods, much-needed clarity on the livelihood-protecting effects of alternative culling strategies is given. In the future, this will allow veterinary authorities to adjust control measures according to differing priorities, targeting peculiarities of ASF and acknowledging resource constraints faced.
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Affiliation(s)
- Bui Thi To Nga
- Department of Veterinary Medicine, Faculty of Veterinary Medicine, Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Pawin Padungtod
- Food and Agriculture Organization of the United Nations (FAO), Country Office for Vietnam, Hanoi, Vietnam
| | - Klaus Depner
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institut, Greifswald, Germany
| | - Vo Dinh Chuong
- Department of Animal Health, Ministry of Agriculture and Rural Development, Hanoi, Vietnam
| | - Do Tien Duy
- Department of Infectious Diseases and Veterinary Public Health, Faculty of Animal Science and Veterinary Medicine, Nong Lam University, Ho Chi Minh City, Vietnam
| | - Nguyen Duc Anh
- Department of Veterinary Medicine, Faculty of Veterinary Medicine, Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Klaas Dietze
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institut, Greifswald, Germany
- *Correspondence: Klaas Dietze
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Cardenas NC, Sanchez F, Lopes FPN, Machado G. Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance. Transbound Emerg Dis 2022; 69:e2757-e2768. [PMID: 35694801 PMCID: PMC9796646 DOI: 10.1111/tbed.14627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/25/2022] [Accepted: 06/10/2022] [Indexed: 01/01/2023]
Abstract
Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.
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Affiliation(s)
- Nicolas C. Cardenas
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Felipe Sanchez
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA,Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Francisco P. N. Lopes
- Departamento de Defesa AgropecuáriaSecretaria da AgriculturaPecuária e Desenvolvimento Rural (SEAPDR)Porto AlegreBrazil
| | - Gustavo Machado
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA
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Tao Y, Hite JL, Lafferty KD, Earn DJD, Bharti N. Transient disease dynamics across ecological scales. THEOR ECOL-NETH 2021; 14:625-640. [PMID: 34075317 PMCID: PMC8156581 DOI: 10.1007/s12080-021-00514-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 05/04/2021] [Indexed: 11/25/2022]
Abstract
Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12080-021-00514-w.
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Affiliation(s)
- Yun Tao
- Intelligence Community Postdoctoral Research Fellowship Program, Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA 93106 USA
| | - Jessica L. Hite
- School of Veterinary Medicine, Department of Pathobiological Sciences, University of Wisconsin, Madison, WI 53706 USA
| | - Kevin D. Lafferty
- Western Ecological Research Center at UCSB Marine Science Institute, U.S. Geological Survey, CA 93106 Santa Barbara, USA
| | - David J. D. Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4K1 Canada
| | - Nita Bharti
- Department of Biology Center for Infectious Disease Dynamics, Penn State University, University Park, PA 16802 USA
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