Application of decision theory to conservation management: recovery of Hector's dolphin

Decision theory provides an organised approach to decision making in natural resource conservation. The theory requires clearly stated objectives, decision alternatives and decision-outcome utilities, and thus allows for the separation of values (conservation and other societal objectives) from beliefs. Models express belief in the likely response of the system to conservation actions, and can range from simple, graphical representations to complex computer models. Models can be used to make predictions about likely decision-outcomes, and hence guide decision making. Decision making must account for uncertainty, which can be reduced but never eliminated. Uncertainty can be described via proba- bilities, which in turn can be used to compute the expected value of alternative decisions, averaging over all relevant sources of uncer tainty. Reduction of uncertainty, where possible, improves decision making. Adaptive management involves the reduction of uncertainty via prediction under two or more alternative, structural models, comparison of model predictions to monitoring, and feedback via Bayes' Theorem into revising model weights, which in turn influences deci- sion making. As part of a 3-day workshop on structured decision making (SDM) and adaptive resource management (ARM), w e constructed a prototypical decision model for the recovery for Hector's dolphin (Cephalorynchus hectori), an endangered dolphin endemic to New Zealand coastal waters. Our model captures several steps in the process of building an SDM/ARM framework, and should be useful for managers wishing to apply these principles to dolphin conservation or other resources problems.

[1]  Stephen M. Dawson,et al.  Designing a mark-recapture study to allow for local emigration , 2002 .

[2]  James E. Hines,et al.  Identification and synthetic modeling of factors affecting American black duck populations , 2002 .

[3]  M. Conroy,et al.  Analysis and Management of Animal Populations , 2002 .

[4]  Christopher Dieckmann 7.2.1 Robust Decision Making , 2010 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  Helen M. Regan,et al.  ROBUST DECISION‐MAKING UNDER SEVERE UNCERTAINTY FOR CONSERVATION MANAGEMENT , 2005 .

[7]  C. Walters,et al.  Uncertainty, resource exploitation, and conservation: lessons from history. , 1993, Science.

[8]  Frank Lad,et al.  SURVIVAL RATES OF PHOTOGRAPHICALLY IDENTIFIED HECTOR'S DOLPHINS FROM 1984 TO 1988 , 1992 .

[9]  Helen M. Regan,et al.  Comparing predictions of extinction risk using models and subjective judgement , 2004 .

[10]  R. Clemen,et al.  Soft Computing , 2002 .

[11]  J. Nichols,et al.  Monitoring for conservation. , 2006, Trends in ecology & evolution.

[12]  Stephen M. Dawson,et al.  Conservation of Hector's dolphins: The case and process which led to establishment of the Banks Peninsula Marine Mammal Sanctuary , 1993 .

[13]  Stephen M. Dawson,et al.  CAPTURE‐RECAPTURE ESTIMATES OF HECTOR'S DOLPHIN ABUNDANCE AT BANKS PENINSULA, NEW ZEALAND , 2005 .

[14]  Kenneth H. Williams,et al.  Protocol and Practice in the Adaptive Management of Waterfowl Harvests , 1999 .

[15]  Stephen M. Dawson,et al.  AERIAL SURVEYS FOR COASTAL DOLPHINS: ABUNDANCE OF HECTOR'S DOLPHINS OFF THE SOUTH ISLAND WEST COAST, NEW ZEALAND , 2004 .

[16]  M. Klinowska,et al.  Dolphins, porpoises and whales of the world : the IUCN red data book , 1991, Oryx.

[17]  K. Mengersen,et al.  The power of expert opinion in ecological models using Bayesian methods: impact of grazing on birds , 2005 .

[18]  Ray Hilborn,et al.  Effects of alternative control rules on the conflict between a fishery and a threatened sea lion (Phocarctos hookeri) , 2003 .

[19]  F. Johnson,et al.  Conditions and Limitations on Learning in the Adaptive Management of Mallard Harvests , 2002 .

[20]  Richard J. Barker,et al.  Modelling survival of Hector's Dolphins around Banks Peninsula, New Zealand , 1999 .

[21]  Mark N. Maunder,et al.  A BAYESIAN ANALYSIS TO ESTIMATE LOSS IN SQUID CATCH DUE TO THE IMPLEMENTATION OF A SEA LION POPULATION MANAGEMENT PLAN , 2000 .

[22]  Fred A. Johnson,et al.  BAYESIAN INFERENCE AND DECISION THEORY—A FRAMEWORK FOR DECISION MAKING IN NATURAL RESOURCE MANAGEMENT , 2003 .

[23]  Elisabeth Slooten,et al.  Population viability analysis for Hector's dolphin (Cephalorhynchus hectori): A stochastic population model for local populations , 2003 .

[24]  H. Prosper Bayesian Analysis , 2000, hep-ph/0006356.

[25]  R. McLain,et al.  Adaptive management: Promises and pitfalls , 1996, Environmental management.

[26]  P. Jones Making Decisions , 1971, Nature.

[27]  Carl J. Walters,et al.  Adaptive Management of Renewable Resources , 1986 .

[28]  H. P. Possingham,et al.  State-Dependent Decision Analysis for Conservation Biology , 1997 .

[29]  B. Marcot,et al.  Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement , 2001 .

[30]  David Fletcher,et al.  Accounting for Uncertainty in Risk Assessment: Case Study of Hector's Dolphin Mortality due to Gillnet Entanglement , 2000 .

[31]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[32]  C. T. Moore,et al.  Optimal Regeneration Planning for Old-Growth Forest: Addressing Scientific Uncertainty in Endangered Species Recovery through Adaptive Management , 2006 .

[33]  James O. Berger Statistical Decision Theory , 1980 .