State-and-transition modelling for Adaptive Management of native woodlands

摘要: Adaptive Management (AM) is widely advocated as an approach to dealing with uncertainty in natural resource management it provides explicit framework for motivating, designing and interpreting the results of monitoring. One major factors impeding implementation failure use appropriate process models; a core element AM. Process models represent beliefs about properties dynamics ecological system ecosystem responses management. Quantitative response help resolve ambiguity efficacy facilitate iterative updating knowledge using monitoring data. We report on state-and-transition model (STM) native woodland vegetation south-eastern Australia. The STM implemented Bayesian network, making simple communicate update new data they arise. Application demonstrated case-study simulation show how may be used predict probability achieving desirable state transitions at restoration sites those can (learn) adapt (review strategies). After just one monitoring/learning cycle, 7 years after first investments, we found that updated markedly different transition probabilities compared initial based expert opinion. This has strong implications apparent cost-efficiency strategies. sound theoretical basis decisions, while network workable adaptively.