Postdoctoral research fellow at CSIRO Sustainable Ecosystems.
Iadine’s current research interests focus on determining how to manage cryptic invasive species across multiple landscapes when resources are limited. Iadine’s research background is in Artificial Intelligence. Iadine has developed and applied new decision making techniques to the field of ecology and natural resource management for the last 4 years.
Why should we care about optimising our conservation decisions?
Because resources to protect biodiversity are limited, solutions must be cost-effective (achieve our biodiversity conservation goals at a minimum cost). Being cost-effective means that our management decisions must anticipate what may happen in the future and maximise our chance of success over time. For example when conserving a population of an endangered species we must anticipate future changes in the abundance of the population. Population dynamics of endangered species evolve over time and are highly sensitive to catastrophic events (drought, flood, bush fires) as well as human disturbances (habitat loss, degradation) and changes in climate. Management decisions might help recover our endangered species but their success is not guarantied. If a management action fails to recover a species, we have spent our limited resources on an inefficient action and we have also wasted precious time leading to an increase in the risk of extinction of the species. To avoid such scenario, before deciding on management actions we should account for all potential outcomes and costs in accordance with clearly specified conservation objectives: maximising the chance of survival of endangered species over the next 50 years using our limited budget. In other words to provide informed guidance to managers, we must optimise our decision making process taking into account the uncertainty surrounding the response of the species to management actions as well as their economic costs and ecological benefits over time.
Why is my research useful?
Due to the very nature of ecological problems, uncertainty is an inescapable aspect of natural resource management. While the uncertain dynamic of species and the uncertain management efficiency can be predicted using stochastic processes, finding the best management strategy over time requires an optimisation procedure. Such optimisation procedures are often referred to as stochastic dynamic programming (SDP). It is no secret that classic SDP techniques suffer from computational limitations that reduce their attractiveness to most ecological problems. Artificial intelligence has long provided alternative solutions to tackle such impediments. Indeed optimisation under uncertainty is one of the most dynamic areas of research where applications are mainly driven by mobile robotics or health systems. Similar in some modelling aspects, ecological problems are different from other applications as a result of the specific constraints and solutions ecologists and decision-managers aim for. While an obscure optimal strategy will be suitable for a mobile robot on Mars, only meaningful rules of thumb are useful to ecologists and decision managers. Extracting these rules of thumb is a difficult exercise that requires interdisciplinary skills. First, a strong knowledge and understanding of the ecological problem is required to frame the problem in such a way that the solutions will bring new insights and can be generalised to similar problems. Once the problem is framed into a decision-theory framework the optimisation procedure should provide not only one solution but a set of equivalent solutions to choose from. The solutions should exhibit clear patterns that reflect the ecological problem, e.g. changes in management decisions should be fully explained by changes in the dynamic of the species and the management efficiency.
Chadès, I., E. McDonald-Madden, M. A. McCarthy, B. Wintle, M. Linkie, and H. P. Possingham. (2008) When to stop managing or surveying cryptic threatened species. Proceedings of the National Academy of Sciences 105:13936. Featured in The Australian, ABC Science and TREE by MacKenzie, D. I. 2009. Getting the biggest bang for our conservation buck. Trends in Ecology & Evolution 24:175-177