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Provencher JF, Wilcox AAE, Gibbs S, Howes LA, Mallory ML, Pybus M, Ramey AM, Reed ET, Sharp CM, Soos C, Stasiak I, Leafloor JO. BAITING AND BANDING: EXPERT OPINION ON HOW BAIT TRAPPING MAY INFLUENCE THE OCCURRENCE OF HIGHLY PATHOGENIC AVIAN INFLUENZA (HPAI) AMONG DABBLING DUCKS. J Wildl Dis 2023; 59:590-600. [PMID: 37578749 DOI: 10.7589/jwd-d-22-00163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/09/2023] [Indexed: 08/15/2023]
Abstract
A Eurasian lineage highly pathogenic avian influenza virus (HPAIV) of the clade 2.3.4.4b (Goose/Guangdong lineage) was detected in migratory bird populations in North America in December 2021, and it, along with its reassortants, have since caused wild and domestic bird outbreaks across the continent. Relative to previous outbreaks, HPAIV cases among wild birds in 2022 exhibited wider geographic extent within North America and higher levels of mortality, suggesting the potential for population-level impacts. Given the possible conservation implications of HPAIV in wild birds, natural resource managers have sought guidance on actions that may mitigate negative effects of disease among North American bird populations, including modification of existing management practices. Banding of waterfowl is a critical tool for population management for several harvested species in North America, but some banding techniques, such as bait trapping, can lead to increased congregation of waterfowl, potentially altering HPAIV transmission. We used an expert opinion exercise to assess how bait trapping of dabbling ducks in Canada may influence HPAIV transmission and wild bird health. The expert group found that it is moderately likely that bait trapping of dabbling ducks in wetlands will significantly increase the transmission of HPAIV among individual ducks, but there is a low probability that this will result in significant population-level effects on North American dabbling ducks. Considering the lack of empirical work studying how capture and handling methods may change transmission of HPAIV among waterfowl, as well as the importance of bait trapping for waterfowl management in North America, future work should focus on filling knowledge gaps pertaining to the influence of baiting on HPAIV occurrence to better inform banding procedures and management decision making.
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Affiliation(s)
- Jennifer F Provencher
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel By Dr., Ottawa, Ontario K1S 5B6, Canada
| | - Alana A E Wilcox
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel By Dr., Ottawa, Ontario K1S 5B6, Canada
| | - Samantha Gibbs
- Wildlife Health Office, U.S. Fish and Wildlife Service, Lower Suwannee National Wildlife Refuge, 16450 NW 31st Place, Chiefland, Florida 32626, USA
| | - Lesley-Anne Howes
- Canadian Wildlife Service, Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel By Dr., Ottawa, Ontario K1S 5B6, Canada
| | - Mark L Mallory
- Acadia University, 33 Westwood Ave., Wolfville, Nova Scotia B4P 2R6, Canada
| | - Margo Pybus
- Alberta Fish and Wildlife, Government of Alberta, 6909-116 St., Edmonton, Alberta T6H 4P2, Canada
| | - Andrew M Ramey
- U.S. Geological Survey Alaska Science Center, 4210 University Dr., Anchorage, Alaska 99508, USA
| | - Eric T Reed
- Canadian Wildlife Service, Environment and Climate Change Canada, 5019 52nd St., PO Box 2310, Yellowknife, Northwest Territories X1A 2P7, Canada
| | - Chris M Sharp
- Canadian Wildlife Service, Environment and Climate Change Canada, Environmental Science and Technology Centre, 335 River Rd, Ottawa, Ontario K1V 1C7, Canada
| | - Catherine Soos
- Ecotoxicology and Wildlife Health Division, Environment and Climate Change Canada, Prairie and Northern Wildlife Research Centre, 115 Perimeter Rd, Saskatoon, Saskatchewan S7N 0X4, Canada
| | - Iga Stasiak
- Ministry of Environment, Government of Saskatchewan, 112 Research Dr., Saskatoon, Saskatchewan S7N 3R3, Canada
| | - Jim O Leafloor
- Canadian Wildlife Service, Environment and Climate Change Canada, Unit 510, 234 Donald St., Winnipeg, Manitoba R3C 1M8, Canada
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Jeliazkov A, Gavish Y, Marsh CJ, Geschke J, Brummitt N, Rocchini D, Haase P, Kunin WE, Henle K. Sampling and modelling rare species: Conceptual guidelines for the neglected majority. GLOBAL CHANGE BIOLOGY 2022; 28:3754-3777. [PMID: 35098624 DOI: 10.1111/gcb.16114] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 11/18/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.
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Affiliation(s)
| | - Yoni Gavish
- School of Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - Charles J Marsh
- Department of Plant Sciences, University of Oxford, Oxford, UK
- Department of Ecology and Evolution & Yale Center for Biodiversity and Global Change, Yale University, New Haven, Connecticut, USA
| | - Jonas Geschke
- Institute of Plant Sciences, University of Bern, Bern, Switzerland
| | - Neil Brummitt
- Department of Life Sciences, Natural History Museum, London, UK
| | - Duccio Rocchini
- BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy
- Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha - Suchdol, Czech Republic
| | - Peter Haase
- Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany
- Faculty of Biology, University of Duisburg-Essen, Essen, Germany
| | | | - Klaus Henle
- Department of Conservation Biology & Social-Ecological Systems, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
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Bower DS, Mengersen K, Alford RA, Schwarzkopf L. Using a Bayesian network to clarify areas requiring research in a host-pathogen system. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2017; 31:1373-1382. [PMID: 28464282 DOI: 10.1111/cobi.12950] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 03/24/2017] [Accepted: 04/17/2017] [Indexed: 06/07/2023]
Abstract
Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease-driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host-pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious-disease systems.
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Affiliation(s)
- D S Bower
- College of Science and Engineering, James Cook University, 1 James Cook Drive, Douglas, QLD, 4811, Australia
| | - K Mengersen
- Faculty of Science and Engineering, Mathematical Sciences, Statistical Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - R A Alford
- College of Science and Engineering, James Cook University, 1 James Cook Drive, Douglas, QLD, 4811, Australia
| | - L Schwarzkopf
- College of Science and Engineering, James Cook University, 1 James Cook Drive, Douglas, QLD, 4811, Australia
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Udevitz MS, Jay CV, Taylor RL, Fischbach AS, Beatty WS, Noren SR. Forecasting consequences of changing sea ice availability for Pacific walruses. Ecosphere 2017. [DOI: 10.1002/ecs2.2014] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Mark S. Udevitz
- Alaska Science Center, U.S. Geological Survey 4210 University Drive Anchorage Alaska 99508 USA
| | - Chadwick V. Jay
- Alaska Science Center, U.S. Geological Survey 4210 University Drive Anchorage Alaska 99508 USA
| | - Rebecca L. Taylor
- Alaska Science Center, U.S. Geological Survey 4210 University Drive Anchorage Alaska 99508 USA
| | - Anthony S. Fischbach
- Alaska Science Center, U.S. Geological Survey 4210 University Drive Anchorage Alaska 99508 USA
| | - William S. Beatty
- U.S. Fish and Wildlife Service, Marine Mammals Management 1011 East Tudor Road Anchorage Alaska 99503 USA
| | - Shawn R. Noren
- Institute of Marine Science University of California, Santa Cruz 100 Shaffer Road Santa Cruz California 95060 USA
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Haig SM, Miller MP, Bellinger R, Draheim HM, Mercer DM, Mullins TD. The conservation genetics juggling act: integrating genetics and ecology, science and policy. Evol Appl 2015; 9:181-95. [PMID: 27087847 PMCID: PMC4780381 DOI: 10.1111/eva.12337] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 09/27/2015] [Indexed: 01/08/2023] Open
Abstract
The field of conservation genetics, when properly implemented, is a constant juggling act integrating molecular genetics, ecology, and demography with applied aspects concerning managing declining species or implementing conservation laws and policies. This young field has grown substantially since the 1980s following the development of polymerase chain reaction and now into the genomics era. Our laboratory has ‘grown up’ with the field, having worked on these issues for over three decades. Our multidisciplinary approach entails understanding the behavior and ecology of species as well as the underlying processes that contribute to genetic viability. Taking this holistic approach provides a comprehensive understanding of factors that influence species persistence and evolutionary potential while considering annual challenges that occur throughout their life cycle. As a federal laboratory, we are often addressing the needs of the U.S. Fish and Wildlife Service in their efforts to list, de‐list, or recover species. Nevertheless, there remains an overall communication gap between research geneticists and biologists who are charged with implementing their results. Therefore, we outline the need for a National Center for Small Population Biology to ameliorate this problem and provide organizations charged with making status decisions firmer ground from which to make their critical decisions.
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Affiliation(s)
- Susan M Haig
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center Corvallis OR USA
| | - Mark P Miller
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center Corvallis OR USA
| | - Renee Bellinger
- Department of Biology, Tropical Conservation Biology and Environmental Science University of Hawaii Hilo HI USA
| | - Hope M Draheim
- Pacific States Marine Fisheries Commission Eagle Fish Genetics Laboratory Eagle ID USA
| | - Dacey M Mercer
- Hatfield Marine Science Center Oregon State University Newport OR USA
| | - Thomas D Mullins
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center Corvallis OR USA
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McDonald KS, Ryder DS, Tighe M. Developing best-practice Bayesian Belief Networks in ecological risk assessments for freshwater and estuarine ecosystems: a quantitative review. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2015; 154:190-200. [PMID: 25733196 DOI: 10.1016/j.jenvman.2015.02.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 01/10/2015] [Accepted: 02/17/2015] [Indexed: 05/23/2023]
Abstract
Bayesian Belief Networks (BBNs) are being increasingly used to develop a range of predictive models and risk assessments for ecological systems. Ecological BBNs can be applied to complex catchment and water quality issues, integrating multiple spatial and temporal variables within social, economic and environmental decision making processes. This paper reviews the essential components required for ecologists to design a best-practice predictive BBN in an ecological risk assessment (ERA) framework for aquatic ecosystems, outlining: (1) how to create a BBN for an aquatic ERA?; (2) what are the challenges for aquatic ecologists in adopting the best-practice applications of BBNs to ERAs?; and (3) how can BBNs in ERAs influence the science/management interface into the future? The aims of this paper are achieved using three approaches. The first is to demonstrate the best-practice development of BBNs in aquatic sciences using a simple nutrient model. The second is to discuss the limitations and challenges aquatic ecologists encounter when applying BBNs to ERAs. The third is to provide a framework for integrating best-practice BBNs into ERAs and the management of aquatic ecosystems. A quantitative review of the application and development of BBNs in aquatic science from 2002 to 2014 was conducted to identify areas where continued best-practice development is required. We outline a best-practice framework for the integration of BBNs into ERAs and study of complex aquatic systems.
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Affiliation(s)
- K S McDonald
- Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
| | - D S Ryder
- Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
| | - M Tighe
- Agronomy and Soil Science, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
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