Here’s the reason of my long silence from this blog. The report of our 2-year project is now available online (PDF, 10Mo)(The Conversation). The report assesses the cost-effectiveness of 17 feasible strategies for managing threats to the 53 most threatened Pilbara species. Key outcomes are that management likely to provide all species with a >50% chance of persistence costs less than $5 million/year. Amongst the most cost-effective strategies are managing introduced species and fire regimes.
Apart from being the project manager – responsible to deliver on time and on budget – I’ve had a lot of fun developing a method to find the complementary strategies that would minimize the biodiversity loss and the cost (manuscript under review). I hope you will enjoy it! It has been a true collaborative project across multiple organizations and has required the involvement of the whole team from start to the end.
Carwardine J, Nicol S, van Leeuwen S, Walters B, Firn J, Reeson A, Martin TG, Chades I (2014) Priority threat management for Pilbara species of conservation significance, CSIRO Ecosystem Sciences, Brisbane.
I’ve recently been contacted to talk about our paper in Conservation Biology (v.26/6), that forced me to read again our paper and assess what information was really valuable in this study. I thought that Jessica Jonhson – science writer for The Wildlife Society’s magazine The Wildlife Professional – did a great job at explaining our findings:
Recovery targets for endangered species often ignore interactions between species. For ones that are tightly linked in a predator-prey relationship—such as the northern sea otter (Enhydra lutris kenyoni) and the northern abalone (Haliotis kamtschatkana), both endangered— management plans that focus on only one species can sometimes put the other at risk of extinction. As reported in Conservation Biology (v.26/6), Iadine Chadès of CSIRO Ecosystem Sciences in Australia and colleagues with Fisheries and Oceans Canada developed a computer model to predict the outcome of proposed recovery strategies that address both sea otters and abalone at once. To make its predictions, the model incorporates the population dynamics of each species, their interactions, and how management decisions affect their abundance. For example, sea otter populations are recovering well, and abalone could be in danger of overpredation by sea otters. However, the model revealed that even if sea otter predation somehow ceased, the abalone populations would not rebound. Instead, the model identified poaching of abalone as the most significant threat to that species, and calculated that poaching would have to be cut in half in order for populations to grow again. Such models cannot guarantee that a management action will succeed, but can help managers make more informed decisions when complex species interactions are involved.
Chadès, I., Curtis, J.M.R., and Martin, T.G., 2012. Setting realistic recovery targets for interacting endangered species. Conservation Biology 26, 1016-1025. (PDF)
I’m delighted to announce that our 3-year postdoctoral fellow position on optimizing adaptive management is now open for application!
We are seeking a highly motivated and dynamic postdoctoral research fellow to join CSIRO Ecosystem Sciences’ conservation decisions team led by Dr Iadine Chades, to undertake research on optimizing adaptive management decisions under imperfect detection. The postdoctoral research fellow will be supervised by Iadine Chades, Andy Sheppard (CSIRO) and Pr Tom Dietterich (Oregon State University).
Resources to halt global biodiversity decline are still inadequate. Managers of threatened species therefore need guidance on how to best invest their scarce resources to maximise the chance of saving species in the long term. Decision theory is now helping decision-makers prioritise biodiversity threat management across time and space but a major drawback with current decision approaches is their need for “data-hungry” models that simulate how a species or system will behave in the future under different management decisions.
Specifically you will:
Develop innovative concepts, theories and techniques to facilitate optimal adaptive management over time for hard to detect invasive and threatened species populations.
Contribute to the development of adaptive management recommendations to help practitioners protect biodiversity.
Publish findings in high impact journals, present finding at both national and international conferences and participate in interdisciplinary working groups.
Contribute to a dynamic, innovative and effective research team working with CSIRO Ecosystem Sciences.
Participate in CSIRO’s postdoctoral training program.
Location: Dutton Park, Brisbane, QLD, Australia Salary: AUD$81K – AUD$88K plus up to 15.4% superannuation Tenure: 3 year specified term Reference: Q13/03434
To be successful in this position you will need:
A PhD in artificial intelligence, ecology, conservation, computational sustainability or related field of decision theory (e.g. applied mathematics, computer science, economics or related discipline).
Note: Owing to the terms of CSIRO Postdoctoral Fellowships, you must not have more than 3 years relevant post doctoral experience.
Demonstrated research achievement in decision theory, optimal resource allocation, adaptive management or ecological modelling. In particular, demonstrated research achievement in one or more of Markov decision processes (MDP), partially observable Markov decision processes (POMDP), stochastic dynamic programming, reinforcement learning and adaptive management.
Demonstrated ability to initiate research characterised by originality, creativity and innovation. Publish the findings from research in international peer reviewed journals or selective conference proceedings.
Enthusiasm for applying advanced computational and decision theoretic tools to ecological problems.
High-level written, oral and interpersonal communication skills, including demonstrated experience in preparing briefings for a range of audiences, and ability to work effectively in a team.
I’ve been asked to provide the slides of my talk today at the Global Change Institute. Feel free to send me your feedback/comments. Here they are! Thanks for coming to the seminar today, and thanks to Chris Brown for inviting me.
At the forefront of linking conservation science with quantitative tools from the field of artificial intelligence (AI), Dr Iadine Chadès will introduce the process of making smart conservation decisions under imperfect knowledge and resource constraints. During her PhD, Dr Chadès developed new methods to tackle complex optimisation problems for mobile robots using Markov decision processes (MDP). She discovered that these models can also be used to improve decision-making in modern conservation science – teaching a robot to navigate utilizes the same mathematics as choosing the best conservation actions to save threatened species under uncertainty. Eager to contribute to conservation science, she changed career and turned towards decisions in ecology. Combining expertise in AI with ecological and economic models, this seminar will look at complex applied conservation problems and the solutions that can be applied to efficiently eradicate invasive weeds, control mosquito-borne diseases and protect threatened species from extinction.
Congratulations to Ayesha Tulloch. We have a new paper in Conservation Biology that addresses how to monitor management actions. I really like that paper and I hope it will become a good reference, check it out! Note that we also provide the Matlab code.
One challenge faced by researchers and conservation practitioners is designing and implementing effective monitoring programs particularly when funds are limited. Decisions about how to monitor are hindered by uncertainty in management outcomes. This research demonstrates a new framework for addressing the uncertainties in selecting species for monitoring change due to a management action or policy, using network theory and decision analysis.
Tulloch A.I.T., Chadès I., Possingham H.P. (2013) Accounting for Complementarity to Maximize Monitoring Power for Species Management. Conservation Biology27, 988-999. Abstract