Population Ecology

, Volume 56, Issue 1, pp 41–53 | Cite as

Decision science for effective management of populations subject to stochasticity and imperfect knowledge

  • Hiroyuki Yokomizo
  • Shaun R. Coutts
  • Hugh P. Possingham
Special Feature: Review Mathematical Models for Effective Environmental Management


Many species are threatened by human activity through processes such as habitat modification, water management, hunting, and introduction of invasive species. These anthropogenic threats must be mitigated as efficiently as possible because both time and money available for mitigation are limited. For example, it is essential to address the type and degree of uncertainties present to derive effective management strategies for managed populations. Decision science provides the tools required to produce effective management strategies that can maximize or minimize the desired objective(s) based on imperfect knowledge, taking into account stochasticity. Of particular importance are questions such as how much of available budgets should be invested in reducing uncertainty and which uncertainties should be reduced. In such instances, decision science can help select efficient environmental management actions that may be subject to stochasticity and imperfect knowledge. Here, we review the use of decision science in environmental management to demonstrate the utility of the decision science framework. Our points are illustrated using examples from the literature. We conclude that collaboration between theoreticians and practitioners is crucial to maximize the benefits of decision science’s rational approach to dealing with uncertainty.


Adaptive management Information-gap decision theory Monitoring Stochastic dynamic programming Uncertainty Value of information analysis 



We are grateful to Gaku Takimoto and an anonymous reviewer for their helpful comments. This work was supported by JSPS KAKENHI Grant Number 25281057 and by the Environment Research and Technology Development Fund D-1101 (Leader: K Goka) of the Ministry of the Environment, Japan. SC and HPP are supported by the Australian Government’s National Environmental Research Program. HPP is also supported by several Australian Research Council grants and fellowships, including an ARC Centre of Excellence.


  1. Ball IR, Possingham HP, Watts M (2009) Marxan and relatives: software for spatial conservation prioritisation. In: Moilanen A, Wilson KA, Possingham HP (eds) Spatial conservation prioritisation: quantitative methods and computational tools. Oxford University Press, Oxford, pp 185–195Google Scholar
  2. Baxter PWJ, Possingham HP (2011) Optimizing search strategies for invasive pests: learn before you leap. J Appl Ecol 48:86–95CrossRefGoogle Scholar
  3. Bellman RE (1957) Dynamic programming. Princeton University Press, PrincetonGoogle Scholar
  4. Ben-Haim Y (2006) Info-gap decision theory: decisions under severe uncertainty, 2nd edn. Academic Press, LondonGoogle Scholar
  5. Ben-Haim Y (2012) Doing our best: optimization and the management of risk. Risk Anal 32:1326–1332PubMedCrossRefGoogle Scholar
  6. Bogich TL, Liebhold AM, Shea K (2008) To sample or eradicate? A cost minimization model for monitoring and managing an invasive species. J Appl Ecol 45:1134–1142CrossRefGoogle Scholar
  7. Bunnefeld N, Hoshino E, Milner-Gulland EJ (2011) Management strategy evaluation: a powerful tool for conservation? Trends Ecol Evol 26:441–447PubMedCrossRefGoogle Scholar
  8. Burgman MA (2005) Risks and decisions for conservation and environmental management. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  9. Butchart SHM, Walpole M, Collen B, van Strien A, Scharlemann JPW, Almond REA, Baillie JEM, Bomhard B, Brown C, Bruno J, Carpenter KE, Carr GM, Chanson J, Chenery AM, Csirke J, Davidson NC, Dentener F, Foster M, Galli A, Galloway JN, Genovesi P, Gregory RD, Hockings M, Kapos V, Lamarque JF, Leverington F, Loh J, McGeoch MA, McRae L, Minasyan A, Morcillo MH, Oldfield TEE, Pauly D, Quader S, Revenga C, Sauer JR, Skolnik B, Spear D, Stanwell-Smith D, Stuart SN, Symes A, Tierney M, Tyrrell TD, Vie JC, Watson R (2010) Global biodiversity: indicators of recent declines. Science 328:1164–1168PubMedCrossRefGoogle Scholar
  10. Carrasco LR, Baker R, MacLeod A, Knight JD, Mumford JD (2010) Optimal and robust control of invasive alien species spreading in homogeneous landscapes. J R Soc Interface 7:529–540PubMedCentralPubMedCrossRefGoogle Scholar
  11. Chades I, McDonald-Madden E, McCarthy MA, Wintle B, Linkie M, Possingham HP (2008) When to stop managing or surveying cryptic threatened species. Proc Natl Acad Sci USA 105:13936–13940PubMedCentralPubMedCrossRefGoogle Scholar
  12. Chee YE, Wintle BA (2010) Linking modelling, monitoring and management: an integrated approach to controlling overabundant wildlife. J Appl Ecol 47:1169–1178CrossRefGoogle Scholar
  13. Clark CW (1985) Bioeconomic modelling and fisheries management. Wiley, New YorkGoogle Scholar
  14. Coutts SR, Yokomizo H, Buckley YM (2013) The behavior of multiple independent managers and ecological traits interact to determine prevalence of weeds. Ecol Appl 23:523–536PubMedCrossRefGoogle Scholar
  15. D’Evelyn ST, Tarui N, Burnett K (2008) Learning-by-catching: uncertain invasive-species populations and the value of information. J Environ Manag 89:284–292CrossRefGoogle Scholar
  16. Dorn MW (2002) Advice on West Coast rockfish harvest rates from Bayesian meta-analysis of stock-recruit relationships. N Am J Fish Manag 22:280–300CrossRefGoogle Scholar
  17. Drechsler M (2004) Model-based conservation decision aiding in the presence of goal conflicts and uncertainty. Biodivers Conserv 13:141–164CrossRefGoogle Scholar
  18. Duca C, Yokomizo H, Marini MA, Possingham HP (2009) Cost-efficient conservation for White-banded tanagers (Neothraupis fasciata) in the Cerrado, central Brazil. Biol Conserv 142:563–574CrossRefGoogle Scholar
  19. Field SA, Tyre AJ, Jonzen N (2004) Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecol Lett 7:669–675CrossRefGoogle Scholar
  20. Firn J (2009) African lovegrass in Australia: a valuable pasture species or embarrassing invader? Trop Grass 43:86–97Google Scholar
  21. Firn J, Rout T, Possingham H, Buckley YM (2008) Managing beyond the invader: manipulating disturbance of natives simplifies control efforts. J Appl Ecol 45:1143–1151Google Scholar
  22. Gerber LR, Beger M, McCarthy MA, Possingham HP (2005) A theory for optimal monitoring of marine reserves. Ecol Lett 8:829–837CrossRefGoogle Scholar
  23. Gerber LR, Wielgus J, Sala E (2007) A decision framework for the adaptive management of an exploited species with implications for marine reserves. Conserv Biol 21:1594–1602PubMedCrossRefGoogle Scholar
  24. Giljohann KM, Hauser CE, Williams NSG, Moore JL (2011) Optimizing invasive species control across space: willow invasion management in the Australian Alps. J Appl Ecol 48:1286–1294CrossRefGoogle Scholar
  25. Grantham HS, Wilson KA, Moilanen A, Rebelo T, Possingham HP (2009) Delaying conservation actions for improved knowledge: how long should we wait? Ecol Lett 12:293–301PubMedCrossRefGoogle Scholar
  26. Hake M, Mansson J, Wiberg A (2010) A working model for preventing crop damage caused by increasing goose populations in Sweden. Ornis Svecica 20:225–233Google Scholar
  27. Hall JW, Lempert RJ, Keller KR, Hackbarth A, Mijere C, McInerney S (2012) Climate policies under uncertainty: a comparison of robust decision making and info-gap methods. Risk Anal 32:1657–1672PubMedCrossRefGoogle Scholar
  28. Halpern BS, Regan HM, Possingham HP, McCarthy MA (2006) Accounting for uncertainty in marine reserve design. Ecol Lett 9:2–11PubMedCrossRefGoogle Scholar
  29. Halpern BS, Klein CJ, Brown CJ, Beger M, Grantham HS, Mangubhai S, Ruckelshaus M, Tulloch VJ, Watts M, White C, Possingham HP (2013) Achieving the triple bottom line in the face of inherent trade-offs among social equity, economic return, and conservation. Proc Natl Acad Sci USA 110:6229–6234PubMedCentralPubMedCrossRefGoogle Scholar
  30. Hauser CE, Possingham HP (2008) Experimental or precautionary? Adaptive management over a range of time horizons. J Appl Ecol 45:72–81CrossRefGoogle Scholar
  31. Hauser CE, Pople AR, Possingham HP (2006) Should managed populations be monitored every year? Ecol Appl 16:807–819PubMedCrossRefGoogle Scholar
  32. Iwasa Y, Uchida T, Yokomizo H (2007) Nonlinear behavior of the socio-economic dynamics for lake eutrophication control. Ecol Econ 63:219–229CrossRefGoogle Scholar
  33. Iwasa Y, Suzuki-Ohno Y, Yokomizo H (2010) Paradox of nutrient removal in coupled socio-economic and ecological dynamics for lake water pollution. Theor Ecol 3:113–122CrossRefGoogle Scholar
  34. Joseph LN, Maloney RF, Possingham HP (2009) Optimal allocation of resources among threatened Species: a project prioritization protocol. Conserv Biol 23:328–338PubMedCrossRefGoogle Scholar
  35. Kangas AS, Horne P, Leskinen P (2010) Measuring the value of information in multicriteria decisionmaking. Forest Sci 56:558–566Google Scholar
  36. Lee JH, Iwasa Y (2014) Modeling socio-economic aspects of ecosystem management and biodiversity conservation. Popul Ecol 56:27–40Google Scholar
  37. Lee DJ, Adams DC, Kim CS (2009) Managing invasive plants on public conservation forestlands: application of a bio-economic model. Forest Policy Econ 11:237–243CrossRefGoogle Scholar
  38. Lohr CA, Cox LJ, Lepczyk CA (2013) Costs and benefits of trap-neuter-release and euthanasia for removal of urban cats in Oahu, Hawaii. Conserv Biol 27:64–73PubMedCrossRefGoogle Scholar
  39. Ludwig D, Brock WA, Carpenter SR (2005) Uncertainty in discount models and environmental accounting. Ecol Soc 10:13Google Scholar
  40. Macfadyen S, Cunningham SA, Costamagna AC, Schellhorn NA (2012) Managing ecosystem services and biodiversity conservation in agricultural landscapes: are the solutions the same? J Appl Ecol 49:690–694CrossRefGoogle Scholar
  41. Mangel M, Clark CW (1988) Dynamic modeling in behavioural ecology. Princeton University Press, PrincetonGoogle Scholar
  42. Mansson J, Hauser CE, Andren H, Possingham HP (2011) Survey method choice for wildlife management: the case of moose Alces alces in Sweden. Wildl Biol 17:176–190CrossRefGoogle Scholar
  43. Martin TG, Nally S, Burbidge AA, Arnall S, Garnett ST, Hayward MW, Lumsden LF, Menkhorst P, McDonald-Madden E, Possingham HP (2012) Acting fast helps avoid extinction. Conserv Lett 5:274–280CrossRefGoogle Scholar
  44. Matsuda H, Kaji K, Uno H, Hirakawa H, Saitoh T (1999) A management policy for Sika deer based on sex-specific hunting. Res Popul Ecol 41:139–149CrossRefGoogle Scholar
  45. Matsuda H, Uno H, Tamada K, Kaji K, Saitoh T, Hirakawa H, Kurumada T, Fujimoto T (2002) Harvest-based estimation of population size for Sika deer on Hokkaido Island, Japan. Wildl Soc Bull 30:1160–1171Google Scholar
  46. McCarthy MA (1996) Extinction dynamics of the helmeted honeyeater: effects of demography, stochasticity, inbreeding and spatial structure. Ecol Model 85:151–163CrossRefGoogle Scholar
  47. McCarthy MA, Possingham HP (2007) Active adaptive management for conservation. Conserv Biol 21:956–963PubMedCrossRefGoogle Scholar
  48. McCarthy MA, Andelman SJ, Possingham HP (2003) Reliability of relative predictions in population viability analysis. Conserv Biol 17:982–989CrossRefGoogle Scholar
  49. McDonald-Madden E, Bode M, Game ET, Grantham H, Possingham HP (2008a) The need for speed: informed land acquisitions for conservation in a dynamic property market. Ecol Lett 11:1169–1177PubMedGoogle Scholar
  50. McDonald-Madden E, Baxter PWJ, Possingham HP (2008b) Subpopulation triage: how to allocate conservation effort among populations. Conserv Biol 22:656–665PubMedCrossRefGoogle Scholar
  51. McDonald-Madden E, Baxter PWJ, Possingham HP (2008c) Making robust decisions for conservation with restricted money and knowledge. J Appl Ecol 45:1630–1638CrossRefGoogle Scholar
  52. McDonald-Madden E, Probert WJM, Hauser CE, Runge MC, Possingham HP, Jones ME, Moore JL, Rout TM, Vesk PA (2010a) Active adaptive conservation of threatened species in the face of uncertainty. Ecol Appl 20:1376–1489Google Scholar
  53. McDonald-Madden E, Baxter PWJ, Fuller RA, Martin TG, Game ET, Montambault J, Possingham HP (2010b) Monitoring does not always count. Trends Ecol Evol 20:547–550CrossRefGoogle Scholar
  54. McDonald-Madden E, Chades I, McCarthy MA, Linkie M, Possingham HP (2011) Allocating conservation resources between areas where persistence of a species is uncertain. Ecol Appl 21:844–858PubMedCrossRefGoogle Scholar
  55. Meir E, Andelman S, Possingham HP (2004) Does conservation planning matter in a dynamic and uncertain world? Ecol Lett 7:615–622CrossRefGoogle Scholar
  56. Moilanen A, Runge MC, Elith J, Tyre A, Carmel Y, Fegraus E, Wintle BA, Burgman M, Ben-Haim Y (2006) Planning for robust reserve networks using uncertainty analysis. Ecol Model 199:115–124CrossRefGoogle Scholar
  57. Moilanen A, Kujala H, Leathwick JR (2009) The zonation framework and software for conservation prioritization. In: Moilanen A, Wilson KA, Possingham HP (eds) Spatial conservation prioritisation: quantitative methods and computational tools. Oxford University Press, Oxford, pp 196–210Google Scholar
  58. Moore JL, Runge MC (2012) Combining structured decision making and value-of-information analyses to identify robust management strategies. Conserv Biol 26:810–820PubMedCrossRefGoogle Scholar
  59. Moore JL, Runge MC, Webber BL, Wilson JRU (2011) Contain or eradicate? Optimizing the management goal for Australian acacia invasions in the face of uncertainty. Divers Distrib 17:1047–1059CrossRefGoogle Scholar
  60. Murdoch W, Polasky S, Wilson KA, Possingham HP, Kareiva P, Shaw R (2007) Maximizing return on investment in conservation. Biol Conserv 139:375–388CrossRefGoogle Scholar
  61. Nicholson E, Possingham HP (2006) Objectives for multiple-species conservation planning. Conserv Biol 20:871–881PubMedCrossRefGoogle Scholar
  62. Nicholson E, Possingham HP (2007) Making conservation decisions under uncertainty for the persistence of multiple species. Ecol Appl 17:251–265PubMedCrossRefGoogle Scholar
  63. Nicol S, Chades I (2012) Which states matter? An application of an intelligent discretization method to solve a continuous POMDP in conservation biology. PLoS ONE 7:e28993PubMedCentralPubMedCrossRefGoogle Scholar
  64. Nuno A, Nils Bunnefeld, Milner-Gulland EJ (2013) Matching observations and reality: using simulation models to improve monitoring under uncertainty in the Serengeti. J Appl Ecol 50:488–498PubMedCentralPubMedCrossRefGoogle Scholar
  65. Possingham HP, Andelman SJ, Noon BR, Trombulak S, Pulliam HR (2001) Making smart conservation decisions. In: Soule ME, Orians GH (eds) Conservation biology: research priorities. Island Press, Washington, DC, pp 225–244Google Scholar
  66. Post van der Burg M, Tyre AJ (2011) Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover. Ecol Appl 21:303–312CrossRefGoogle Scholar
  67. Probert WJM, Hauser CE, McDonald-Madden E, Runge MC, Baxter PWJ, Possingham HP (2011) Managing and learning with multiple models: objectives and optimization algorithms. Biol Conserv 144:1237–1245CrossRefGoogle Scholar
  68. Regan HM, Ben-Haim Y, Langford B, Wilson WG, Lundberg P, Andelman SJ, Burgman MA (2005) Robust decision-making under severe uncertainty for conservation management. Ecol Appl 15:1471–1477CrossRefGoogle Scholar
  69. Regan TJ, McCarthy MA, Baxter PWJ, Panetta FD, Possingham HP (2006) Optimal eradication: when to stop looking for an invasive plant. Ecol Lett 9:759–766PubMedCrossRefGoogle Scholar
  70. Rout TM, Hauser CE, Possingham HP (2007) Minimise long-term loss or maximise short-term gain? Optimal translocation strategies for threatened species. Ecol Model 201:67–74CrossRefGoogle Scholar
  71. Rout TM, Hauser CE, Possingham HP (2009) Optimal adaptive management for the translocation of a threatened species. Ecol Appl 19:515–526PubMedCrossRefGoogle Scholar
  72. Runge MC, Converse SJ, Lyons JE (2011) Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biol Conserv 144:1214–1223CrossRefGoogle Scholar
  73. Saphores JDM, Shogren JF (2005) Managing exotic pests under uncertainty: optimal control actions and bioeconomic investigations. Ecol Econ 52:327–339CrossRefGoogle Scholar
  74. Schlüter M, Mcallister RRJ, Arlinghaus R, Bunnefeld N, Eisenack K, Hölker F, Milner-Gulland EJ, Müller B, Nicholson E, Quaas M, Stöven M (2012) New horizons for managing the environment: a review of coupled social-ecological systems modeling. Nat Res Mod 25:219–272CrossRefGoogle Scholar
  75. Shea K, Amarasekare P, Kareiva P, Mangel M, Moore J, Murdoch WW, Noonburg E, Parma AM, Pascual MA, Possingham HP, Wilcox C, Yu D (1998) Management of populations in conservation, harvesting and control. Trends Ecol Evol 13:371–375PubMedCrossRefGoogle Scholar
  76. Suzuki Y, Iwasa Y (2009a) The coupled dynamics of human socioeconomic choice and lake water system: the interaction of two sources of nonlinearity. Ecol Res 24:479–489CrossRefGoogle Scholar
  77. Suzuki Y, Iwasa Y (2009b) Conflict between groups of players in coupled socio-economic and ecological dynamics. Ecol Econ 68:1106–1115CrossRefGoogle Scholar
  78. Taylor BL, Gerrodette T (1993) The uses of statistical power in conservation biology: the Vaquita and Northern Spotted Owl. Conserv Biol 7:489–500CrossRefGoogle Scholar
  79. Walters C (1986) Adaptive management of renewable resources. The Blackburn Press, New YorkGoogle Scholar
  80. Westphal MI, Pickett M, Getz WM, Possingham HP (2003) The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations. Ecol Appl 13:543–555CrossRefGoogle Scholar
  81. Williams BK (2011) Resolving structural uncertainty in natural resources management using POMDP approaches. Ecol Model 222:1092–1102CrossRefGoogle Scholar
  82. Wilson KA, McBride MF, Bode M, Possigham HP (2006) Prioritizing global conservation efforts. Nature 440:337–340PubMedCrossRefGoogle Scholar
  83. Wilson KA, Carwardine J, Possingham HP (2009) Setting conservation priorities. Ann N Y Acad Sci 1162:237–264PubMedCrossRefGoogle Scholar
  84. Wintle BA, Runge MC, Bekessy SA (2010) Allocating monitoring effort in the face of unknown unknowns. Ecol Lett 13:1325–1337PubMedCrossRefGoogle Scholar
  85. Wintle BA, Bekessy SA, Keith DA, van Wilgen BW, Cabeza M, Schröder B, Carvalho SB, Falcucci A, Maiorano L, Regan TJ, Rondinini C, Boitani L, Possingham HP (2011) Ecological-economic optimization of biodiversity conservation under climate change. Nat Clim Change 1:355–359CrossRefGoogle Scholar
  86. Yamamura K, Matsuda H, Yokomizo H, Kaji K, Uno H, Tamada K, Kurumada T, Saitoh T, Hirakawa H (2008) Harvest-based Bayesian estimation of sika deer populations using a state-space model. Popul Ecol 50:131–144CrossRefGoogle Scholar
  87. Yemshanova D, Koch FH, Ben-Haim Y, Smith WD (2010) Detection capacity, information gaps and the design of surveillance programs for invasive forest pests. J Environ Manag 91:2535–2546CrossRefGoogle Scholar
  88. Yokomizo H, Yamashita J, Iwasa Y (2003a) Optimal conservation effort for a population in a stochastic environment. J Theor Biol 220:215–231PubMedCrossRefGoogle Scholar
  89. Yokomizo H, Haccou P, Iwasa Y (2003b) Conservation effort and assessment of population size in fluctuating environments. J Theor Biol 224:167–182PubMedCrossRefGoogle Scholar
  90. Yokomizo H, Haccou P, Iwasa Y (2004) Multiple-year optimization of conservation effort and monitoring effort for a fluctuating population. J Theor Biol 230:157–171PubMedCrossRefGoogle Scholar
  91. Yokomizo H, Haccou P, Iwasa Y (2007) Optimal conservation strategy in fluctuating environments with species interactions: resource-enhancement of the native species versus extermination of the alien species. J Theor Biol 244:46–58PubMedCrossRefGoogle Scholar
  92. Yokomizo H, Possingham HP, Thomas MB, Buckley YM (2009) Managing the impact of invasive species: the value of knowing the impact-density curve. Ecol Appl 19:376–386PubMedCrossRefGoogle Scholar
  93. Yokomizo H, Possingham HP, Hulme PE, Grice AC, Buckley YM (2012) Cost-benefit analysis for intentional plant introductions under uncertainty. Biol Invasions 14:839–849CrossRefGoogle Scholar
  94. Yokomizo H, Naito W, Tanaka Y, Kamo M (2013) Setting most robust effluent level against severe uncertainty: application of information-gap decision theory to chemical management. Chemosphere 93:2224–2229PubMedCrossRefGoogle Scholar
  95. Yokota F, Thompson KM (2004) Value of information analysis in environmental health risk management decisions: past, present, and future. Risk Anal 24:635–650PubMedCrossRefGoogle Scholar

Copyright information

© The Society of Population Ecology and Springer Japan 2013

Authors and Affiliations

  • Hiroyuki Yokomizo
    • 1
  • Shaun R. Coutts
    • 2
  • Hugh P. Possingham
    • 3
    • 4
  1. 1.Center for Environmental Risk ResearchNational Institute for Environmental StudiesTsukubaJapan
  2. 2.NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation ScienceThe University of QueenslandBrisbaneAustralia
  3. 3.School of Biological SciencesThe University of QueenslandBrisbaneAustralia
  4. 4.Department of Life SciencesImperial College LondonAscotEngland, UK

Personalised recommendations