Optimal Learning and Management of Threatened Species

Published Online:https://doi.org/10.1287/mnsc.2023.01753

References

  • Bennett JR, Maxwell SL, Martin AE, Chadès I, Fahrig L, Gilbert B (2018) When to monitor and when to act: Value of information theory for multiple management units and limited budgets. J. Appl. Ecology 55(5):2102–2113.CrossrefGoogle Scholar
  • Bertsekas D (2012) Dynamic Programming and Optimal Control: Volume I (Athena Scientific, Belmont, MA).Google Scholar
  • Boakes EH, Rout TM, Collen B (2015) Inferring species extinction: The use of sighting records. Methods Ecol. Evol. 6(6):678–687.CrossrefGoogle Scholar
  • Boulinier T, Nichols JD, Sauer JR, Hines JE, Pollock K (1998) Estimating species richness: The importance of heterogeneity in species detectability. Ecology 79(3):1018–1028.CrossrefGoogle Scholar
  • Buxton RT, Avery-Gomm S, Lin HY, Smith PA, Cooke SJ, Bennett JR (2020) Half of resources in threatened species conservation plans are allocated to research and monitoring. Nature Comm. 11(1):4668.CrossrefGoogle Scholar
  • Camaclang AE, Chadès I, Martin TG, Possingham HP (2022) Predicting the optimal amount of time to spend learning before designating protected habitat for threatened species. Methods Ecol. Evol. 13(3):722–733.CrossrefGoogle Scholar
  • Chadès 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(37):13936–13940.CrossrefGoogle Scholar
  • Chapman A (2023) Can putting a price on a whale save the environment? Sci. Amer. (April 24), https://www.scientificamerican.com/article/can-putting-a-price-on-a-whale-save-the-environment/.Google Scholar
  • He F, Hubbell SP (2011) Species–area relationships always overestimate extinction rates from habitat loss. Nature 473(7347):368–371.CrossrefGoogle Scholar
  • Hemming V, Camaclang AE, Adams MS, Burgman M, Carbeck K, Carwardine J, Chadès I, et al. (2022) An introduction to decision science for conservation. Conservation Biol. 36(1):e13868.CrossrefGoogle Scholar
  • IPBES (2019) Global assessment report on biodiversity and ecosystem services of the intergovernmental science—Policy platform on biodiversity and ecosystem services. Report, IPBES Secretariat, Bonn, Germany.Google Scholar
  • IUCN (2012) IUCN red list categories and criteria: Version 3.1, 2nd ed. Report, IUCN Species Survival Commission, Gland, Switzerland.Google Scholar
  • Jin C, Kakade S, Krishnamurthy A, Liu Q (2020) Sample-efficient reinforcement learning of undercomplete POMDPs. NIPS’20 Proc. 34th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 18530–18539.Google Scholar
  • Joseph LN, Field SA, Wilcox C, Possingham HP (2006) Presence–absence vs. abundance data for monitoring threatened species. Conservation Biol. 20(6):1679–1687.CrossrefGoogle Scholar
  • Leclère D, Obersteiner M, Barrett M, Butchart SH, Chaudhary A, De Palma A, DeClerck FA, et al. (2020) Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585(7826):551–556.CrossrefGoogle Scholar
  • Lin Y, Ren Y, Zhou E (2022) Bayesian risk Markov decision processes. Adv. Neural Inform. Processing Systems 35:17430–17442.Google Scholar
  • Liu G, Lu X, Liu Z, Xie Z, Qi X, Zhou J, Hong X, et al. (2022) The critically endangered Hainan gibbon (Nomascus hainanus) population increases but not at the maximum possible rate. Internat. J. Primatology 43(5):932–945.CrossrefGoogle Scholar
  • MacKenzie DI, Royle JA, Brown JA, Nichols JD, Thompson W (2004) Occupancy estimation and modeling for rare and elusive populations. Thompson WL, ed. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (Island Press, Washington, DC), 149–171.Google Scholar
  • McCarthy M, Possingham H (2012) The public should help decide which species to save and which to let go. Conversation (June 5), https://theconversation.com/the-public-should-help-decide-which-species-to-save-and-which-to-let-go-7331.Google Scholar
  • McDonald-Madden E, Chadès I, McCarthy MA, Linkie M, Possingham HP (2011) Allocating conservation resources between areas where persistence of a species is uncertain. Ecological Appl. 21(3):844–858.CrossrefGoogle Scholar
  • Mei Y (2006) Sequential change-point detection when unknown parameters are present in the pre-change distribution. Ann. Statist. 34(1):92–122.CrossrefGoogle Scholar
  • Monahan G (1982) State of the art—A survey of partially observable Markov decision processes: Theory, models and algorithms. Management Sci. 28(1):1–16.LinkGoogle Scholar
  • O’Grady JJ, Reed DH, Brook BW, Frankham R (2004) What are the best correlates of predicted extinction risk? Biol. Conservation 118(4):513–520.CrossrefGoogle Scholar
  • Osogami T (2015) Robust partially observable Markov decision process. Proc. 32nd Internat. Conf. Machine Learn., vol. 37 (JMLR, Lille, France), 106–115.Google Scholar
  • Poor HV, Hadjiliadis O (2009) Quickest Detection (Cambridge University Press, Cambridge, UK).Google Scholar
  • Poupart P, Vlassis N (2008) Model-based Bayesian reinforcement learning in partially observable domains. Proc Internat. Sympos. Artificial Intelligence Math. (Fort Lauderdale, FL), 1–2.Google Scholar
  • Pyke GH, Ehrlich PR (2014) Conservation and the holy grail: The story of the night parrot. Pacific Conservation Biol. 20(2):221–226.CrossrefGoogle Scholar
  • Regan TJ, McCarthy MA, Baxter PW, Dane Panetta F, Possingham HP (2006) Optimal eradication: When to stop looking for an invasive plant. Ecology Lett. 9(7):759–766.CrossrefGoogle Scholar
  • Ross S (1971) Quality control under Markovian deterioration. Management Sci. 17(9):587–596.LinkGoogle Scholar
  • Ross S, Pineau J, Chaib-draa B, Kreitmann P (2011) A Bayesian approach for learning and planning in partially observable Markov decision processes. J. Machine Learn. Res. 12(5):1729–1770.Google Scholar
  • Silvestro D, Goria S, Sterner T, Antonelli A (2022) Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5(5):415–424.CrossrefGoogle Scholar
  • Turvey ST, Traylor-Holzer K, Wong M, Bryant J, Zeng X, Hong X, Long Y (2015) International Conservation Planning Workshop for the Hainan Gibbon: Final report. Report, Zoological Society of London, London/IUCN SSC Conservation Breeding Specialist Group, Apply Valley, MN.Google Scholar
  • Turvey ST, Bryant JV, Duncan C, Wong MH, Guan Z, Fei H, Ma C, et al. (2016) How many remnant gibbon populations are left on Hainan? Testing the use of local ecological knowledge to detect cryptic threatened primates. Amer. J. Primatology 79(2):e22593.CrossrefGoogle Scholar
  • United Nations (2023) The sustainable development goals: Report 2023. Report, United Nations, New York.Google Scholar
  • White C (1977) A Markov quality control process subject to partial observation. Management Sci. 23(8):843–852.LinkGoogle Scholar
  • Williams BK (2011) Resolving structural uncertainty in natural resources management using POMDP approaches. Ecological Model. 222(5):1092–1102.CrossrefGoogle Scholar
  • Williams BK, Szaro RC, Shapiro CD (2009) Adaptive management: The US Department of the Interior technical guide. Report, Department of the Interior, Adaptive Management Working Group, Washington, DC.Google Scholar
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