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EcoHealth

, Volume 14, Supplement 1, pp 144–155 | Cite as

A Model to Inform Management Actions as a Response to Chytridiomycosis-Associated Decline

  • Sarah J. Converse
  • Larissa L. Bailey
  • Brittany A. Mosher
  • W. Chris Funk
  • Brian D. Gerber
  • Erin Muths
Original Contribution

Abstract

Decision-analytic models provide forecasts of how systems of interest will respond to management. These models can be parameterized using empirical data, but sometimes require information elicited from experts. When evaluating the effects of disease in species translocation programs, expert judgment is likely to play a role because complete empirical information will rarely be available. We illustrate development of a decision-analytic model built to inform decision-making regarding translocations and other management actions for the boreal toad (Anaxyrus boreas boreas), a species with declines linked to chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd). Using the model, we explored the management implications of major uncertainties in this system, including whether there is a genetic basis for resistance to pathogenic infection by Bd, how translocation can best be implemented, and the effectiveness of efforts to reduce the spread of Bd. Our modeling exercise suggested that while selection for resistance to pathogenic infection by Bd could increase numbers of sites occupied by toads, and translocations could increase the rate of toad recovery, efforts to reduce the spread of Bd may have little effect. We emphasize the need to continue developing and parameterizing models necessary to assess management actions for combating chytridiomycosis-associated declines.

keywords

Anaxyrus boreas boreas boreal toads decision analysis disease resistance evolutionary rescue translocation 

Notes

Acknowledgments

We thank the Zoological Society of London, and especially JG Ewen, for inviting SJC to take part in the symposium on health and disease in translocated wild animals that prompted development of this model. The useful input of JG Ewen, S Canessa, and JD Nichols contributed to the symposium presentation and the evolution of this modeling effort. We thank the members of the Southern Rocky Mountain Boreal Toad Conservation Team, especially H Crockett, for their enthusiastic participation in the SRM boreal toad conservation planning effort. We also appreciate the contribution of RD Scherer, who provided us with the model output necessary to include CMR-based toad survival in our model. Finally, 2 anonymous reviewers and AW Sainsbury made useful comments on the original manuscript. This is contribution number 527 of the USGS Amphibian Research and Monitoring Initiative (ARMI).

References

  1. Armstrong DP, Seddon PJ (2008) Directions in reintroduction biology. Trends in Ecology and Evolution 23:20–25.CrossRefPubMedGoogle Scholar
  2. Bataille A, Cashins SD, Grogan L, Skerratt LF, Hunter D, McFadden M, et al. (2015). Susceptibility of amphibians to chytridiomycosis is associated with MHC class II conformation. Proceedings of the Royal Society B 282:20143127.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Berger L, Speare R, Daszak P, Green DE, Cunningham AA, Goggin CL, et al. (1998). Chytridiomycosis causes amphibian mortality associated with population declines in the rain forests of Australia and Central America. Proceedings of the National Academy of Sciences 95:9031-9036.CrossRefGoogle Scholar
  4. Brannelly LA, Hunter DA, Skerratt LF, Scheele BC, Lenger D, McFadden MS, et al. (2015). Chytrid infection and post-release fitness in the reintroduction of an endangered alpine tree frog. Animal Conservation DOI: 10.1111/acv.12230.Google Scholar
  5. Burgman M (2005). Risks and Decisions for Conservation and Environmental Management. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
  6. Burgman M, Carr A, Godden L, Gregory R, McBride M, Flander L, et al. (2011). Redefining expertise and improving ecological judgment. Conservation Letters 4:81-87.CrossRefGoogle Scholar
  7. Canessa S, Converse SJ, West M, Clemann N, Gillespie G, McFadden M, et al. (In Press). Planning for ex situ conservation in the face of uncertainty. Conservation Biology DOI:  10.1111/cobi.12613 Google Scholar
  8. Chandler RB, Muths E, Sigafus BH, Schwalbe CR, Jarchow CJ, and Hossack BR (2015). Spatial occupancy models for predicting metapopulation dynamics and viability following reintroduction. Journal of Applied Ecology 52:1325-1333.CrossRefGoogle Scholar
  9. Clobert J, Baguette M, Benton TG, and Bullock JM (2012). Dispersal ecology and evolution. Oxford University Press.CrossRefGoogle Scholar
  10. Converse SJ, Moore CT, and Armstrong DP (2013a). Demographics of reintroduced populations: estimation, modeling, and decision analysis. Journal of Wildlife Management 77:1081-1093.CrossRefGoogle Scholar
  11. Converse SJ, Moore CT, Folk MJ, and Runge MC (2013b). A matter of tradeoffs: reintroduction as a multiple objective decision. Journal of Wildlife Management 77:1145-1156.CrossRefGoogle Scholar
  12. Ewen JG, Sainsbury AW, Jackson B, and Canessa S (2015). Disease risk management in reintroduction. Pages 43-57 in D. P. Armstrong, M. W. Hayward, D. Moro, and P. J. Seddon, editors. Advances in Reintroduction Biology of Australian and New Zealand Fauna. CSIRO Publishing, Collingwood, Victoria, Australia.Google Scholar
  13. Fisher MC, Garner TWJ, and Walker SF (2009). Global emergence of Batrachochytrium dendrobatidis and amphibian chytridiomycosis in space, time, and host. Annual Review of Microbiology 63:291-310.CrossRefPubMedGoogle Scholar
  14. Gonzalez A, Ronce OFR, and Hochberg ME (2013). Evolutionary rescue: an emerging focus at the intersection between ecology and evolution. Philosophical Transactions of the Royal Society B 368:1610Google Scholar
  15. Gregory R, Failing L, Harstone M, Long G, McDaniels T, and Ohlson D (2012). Structured Decision Making: a Practical Guide to Environmental Management Choices. Wiley-Blackwell, Oxford, UK.CrossRefGoogle Scholar
  16. IUCN/SSC (2013) Guidelines for Reintroductions and Other Conservation Translocations. Version 1. Gland: IUCN Species Survival CommissionGoogle Scholar
  17. Keeney RL (1992). Value-Focused Thinking: a Path to Creative Decisionmaking. Harvard University Press, Cambridge, USA.Google Scholar
  18. Kuhnert PM, Martin TG, and Griffiths SP (2010). A guide to eliciting and using expert knowledge in Bayesian ecological models. Ecology Letters 13:900-914.CrossRefPubMedGoogle Scholar
  19. Lanier WE (2015) Investigating direct and indirect effects of greenback cutthroad trout on boreal toad recruitment. M.S. Thesis. Colorado State University, Fort Collins,Google Scholar
  20. Lips KR (2008). Decline of a tropic montane amphibian fauna. Conservation Biology 12:106-117.CrossRefGoogle Scholar
  21. Longcore JE, Pessier AP, and Nichols DK (1999). Batrachochytrium dendrobatidis gen et sp nov, a chytrid pathogenic to amphibians. Mycologia 91:219-227.CrossRefGoogle Scholar
  22. MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL, and Hines JE (2006). Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, Amsterdam.Google Scholar
  23. MacMillan DC, and Marshall K (2006). The Delphi process-an expert-based approach to ecological modelling in data-poor environments. Animal Conservation 9:11-19.CrossRefGoogle Scholar
  24. Martin TG, Burgman MA, Fidler F, Kuhnert PM, Low-Choy S, McBride MF, et al. (2011). Eliciting expert knowledge in conservation science. Conservation Biology 26:29-38.CrossRefGoogle Scholar
  25. Maslo B, and Fefferman NH (2015) A case study of bats and white-nose syndrome demonstrating how to model population viability with evolutionary effects. Conservation Biology. 29:1176-1185CrossRefPubMedGoogle Scholar
  26. McBride MF, Fidler F, and Burgman MA (2012). Evaluating the accuracy and calibration of expert predictions under uncertainty: predicting the outcomes of ecological research. Diversity and Distributions 2012:782-794.CrossRefGoogle Scholar
  27. McCarthy MA, Armstrong DP, and Runge MC (2012). Adaptive management of reintroduction. Pages 256-289 in J. G. Ewen, D. P. Armstrong, K. A. Parker, and P. J. Seddon, editors. Reintroduction Biology: Integrating Science and Management. Wiley-Blackwell, Oxford, UK.CrossRefGoogle Scholar
  28. McGowan CP, Runge MC, and Larson MA (2011). Incorporating parametric uncertainty into population viability analysis models. Biological Conservation 144:1400-1408.CrossRefGoogle Scholar
  29. Meyer MA, Booker JM (1990) Eliciting and Analyzing Expert Judgment: A Practical Guide. Office of Nuclear Regulatory Research, Division of Systems Research. U.S. Nuclear Regulatory Commission, Washington DC, USAGoogle Scholar
  30. Moore JL, and Runge MC (2012). Combining structured decision making and value-of-information analyses to identify robust management strategies. Conservation Biology 26:810-820.CrossRefPubMedGoogle Scholar
  31. Murphy PJ, St-Hilaire S, Bruer S, Corn PS, and Peterson CR (2009). Distribution and pathogenicity of Batrachochytrium dendrobatidis in Boreal Toads from the Grand Teton Area of Western Wyoming. EcoHealth 6:109-120.CrossRefPubMedGoogle Scholar
  32. Muths E, Corn PS, Pessier AP, and Green DE (2003). Evidence for disease-related amphibian decline in Colorado. Biological Conservation 110:357-365.CrossRefGoogle Scholar
  33. Pilliod DS, Muths E, Scherer RD, Bartelt PE, Corn PS, Hossack BR, et al. (2010). Effects of amphibian chytrid fungus on individual survival probability in wild boreal toads. Conservation Biology 24:1259-1267.CrossRefPubMedGoogle Scholar
  34. Puschendorf R, Hoskin CJ, Cashins SD, McDonald K, Skerratt LF, Vanderwal J, et al. (2011). Environmental refuge from disease-driven amphibian extinction. Conservation Biology 25:956-964.CrossRefPubMedGoogle Scholar
  35. R Development Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org.
  36. Rachowicz LJ, Knapp RA, Morgan JAT, Stice MJ, Vredenburg VT, Parker JM, et al. (2006). Emerging infectious disease as a proximate cause of amphibian mass mortality. Ecology 87:1671-1683.CrossRefPubMedGoogle Scholar
  37. Regan HM, Colyvan M, and Burgman MA (2002). A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications 12:618-628.CrossRefGoogle Scholar
  38. Richmond OM, Hines JE, and Beissinger SR (2010). Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails. Ecological Applications 20:2036-2046.CrossRefPubMedGoogle Scholar
  39. Roznik EA, Sapsford SJ, Pike DA, Schwarzkopf L, Alford RA (2015) Natural disturbance reduces disease risk in endangered rainforest frog populations. Scientific Reports 5.Google Scholar
  40. Runge MC (2011). An introduction to adaptive management for threatened and endangered species. Journal of Fish and Wildlife Management 2:220-233.CrossRefGoogle Scholar
  41. Runge MC, Converse SJ, and Lyons JE (2011). Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biological Conservation 144:1214-1223.CrossRefGoogle Scholar
  42. Sainsbury AW, and Vaughan-Higgins RJ (2012). Analyzing disease risks associated with translocations Conservation Biology 26:442-452.CrossRefPubMedGoogle Scholar
  43. Sarrazin F, and Barbault R (1996). Re-introductions: challenges and lessons for basic ecology. Trends in Ecology and Evolution 11:474-478.CrossRefPubMedGoogle Scholar
  44. Savage AE, and Zamudio KR (2011). MHC genotypes associate with resistance to a frog-killing fungus. Proceedings of the National Academy of Sciences 108:16705-16710.CrossRefGoogle Scholar
  45. Scheele BC, Hunter DA, Grogan LF, Berger L, Kolby JE, McFadden MS, et al. (2014). Interventions for reducing extinction risk in chytridiomycosis-threatened amphibians Conservation Biology 28:1195-1205.CrossRefPubMedGoogle Scholar
  46. Skerratt LF, Berger L, Speare R, Cashins S, McDonald KR, Phillott AD, et al. (2007). Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth 4:125-134.CrossRefGoogle Scholar
  47. Speirs-Bridge A, Fidler F, McBride M, Flander L, Cumming G, and Burgman M (2010). Reducing overconfidence in the interval judgments of experts. Risk Analysis 30:512-523.CrossRefPubMedGoogle Scholar
  48. Starfield AM (1997). A pragmatic approach to modeling for wildlife management. Journal of Wildlife Management 61:261-270.CrossRefGoogle Scholar
  49. US Fish and Wildlife Service (2012). Endangered and threatened wildlife and plants: 90–day finding on a petition to list the eastern or southern Rocky Mountain population of the boreal toad as an endangered or threatened distinct population segment. Federal Register 77:21920–21936.Google Scholar
  50. Walters C (1986). Adaptive Management of Renewable Resources. MacMillian, New York, New York, USA.Google Scholar
  51. White GC (2000). Population viability analysis: data requirements and essential analyses. Pages 288-331 in L. Boitani and T. K. Fuller, editors. Research techniques in animal ecology: controversies and consequences. Columbia University Press, New York, New York, USA.Google Scholar
  52. Woodhams DC, Bosch J, Briggs CJ, Cashins S, Davis LR, Lauer A, et al. (2011). Mitigating amphibian disease: strategies to maintain wild populations and control chytridiomycosis. Frontiers in Zoology 8:8.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© International Association for Ecology and Health (outside the USA) 2016

Authors and Affiliations

  • Sarah J. Converse
    • 1
  • Larissa L. Bailey
    • 2
  • Brittany A. Mosher
    • 2
  • W. Chris Funk
    • 3
  • Brian D. Gerber
    • 2
  • Erin Muths
    • 4
  1. 1.U.S. Geological SurveyPatuxent Wildlife Research CenterLaurelUSA
  2. 2.Department of Fish, Wildlife, and Conservation BiologyColorado State UniversityFort CollinsUSA
  3. 3.Department of Biology and Graduate Degree Program in EcologyColorado State UniversityFort CollinsUSA
  4. 4.U.S. Geological SurveyFort Collins Science CenterFort CollinsUSA

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