A Shiny R app to solve POMDPs

In the last decade, artificial intelligence (AI) has increasingly been applied to help solve applied ecology problems. Partially observable Markov decision processes (POMDPs) are one such example. POMDPs have been applied in conservation, applied ecology and natural resource management to solve problems such as deciding when to stop managing or surveying threatened species that are difficult to detect. POMDP solvers are useful to find optimal sequential decisions under imperfect detection. However, POMDPs remain inaccessible to most applied ecologists.

We present the shiny r package smsPOMDP that solves the problem of ‘When to stop managing or surveying cryptic threatened species?’ (Chadès et al., 2008). We developed this package to address a common and challenging problem faced by conservation managers.

The smsPOMDP package and documentation are hosted at https://github.com/conservation‐decisions/smsPOMDP

In artificial intelligence, POMDPs are acknowledged as the Swiss army knife of decision models. However, POMDP’s application in applied ecology remains seldom despite repeated evidence of their flexibility. Our package smsPOMDP is fast and provides an entry point to further develop POMDP apps, contributing to further uptake of AI research to solve ecological problems.

Pascal, L, Memarzadeh, M, Boettiger, C, Lloyd, H, Chadès, I. A Shiny r app to solve the problem of when to stop managing or surveying species under imperfect detection. Methods Ecol Evol. 2020; 11: 1707– 1715. https://doi.org/10.1111/2041-210X.13501

Academic conferences urgently need environmental policies

Only 4% of the conferences we assessed offered carbon-offset options for participants, and only 9% advertised any action that reduced the meeting’s environmental impact. You can find out more reading our correspondence article:

Holden, M., Butt, N., Chauvenet, A., Plein, M., Stringer, M. & Chadès, I. (2017). Academic conferences urgently need environmental policies. Nature Ecology & Evolution, 1. doi:10.1038/s41559-017-0296-2 ( http://rdcu.be/uOoO ).

Send me an email if you would like a copy.

 

An efficient formulation to select an optimal set of complementary species

In Tulloch, A. I. T., Chadès, I., Dujardin, Y., Westgate, M. J., Lane, P. W. and Lindenmayer, D. (2016), Dynamic species co-occurrence networks require dynamic biodiversity surrogates. Ecography, 39: 1185–1196. doi:10.1111/ecog.02143, we provide an efficient optimisation formulation to select complementary set of species. This paper was published in December 2016.

My original formulation and solution methods although correct were inefficient for the large datasets that Ayesha was dealing with. Luckily, Yann saved the day with a mathematical programming formulation. This efficient formulation is worth understanding as it can be used to solve many complementary problems in optimal monitoring, conservation planning, project prioritisation, value of information or priority threat management.

Ayesha uses this formulation to show that co-occurence species networks require dynamic set of surrogates.

We also provide the code. As usual, send me an email if you’d like to receive the paper.

 

A review of optimization methods to solve adaptive management problems

 

Do you need to manage a system over time but are uncertain about what will happen in the future? Well! Adaptive management, or learning by doing, might provide a solution. The issue with adaptive management is that no one knows what it is or how to do it, because it has been used in many different fields of research for a different purpose and with a different meaning. In my research, I am interested in the decision theoretic approach of adaptive management (not the resilience approach). What does that mean? That means that I look for ways of providing adaptive decisions that will insure that you would make the best decisions possible over time. That’s right, our approaches account for all the things that could go wrong or right in our decisions before any humans do.

adaptive-management-questions

In our paper “Optimization methods to solve adaptive management problems” (Theoretical Ecology (2016). doi:10.1007/s12080-016-0313-0), we review the methods that would allow you to optimize your adaptive management strategies.

The fun bits: We provide decision trees that would help you choose between different type of approaches available (passive or active adaptive management); We explain the differences and similarities between these approaches;  And, we provide 8 algorithms that will help you understand and write your own code – Have I mentioned a bunch of reference to explore?

This paper concludes the 3 years of my CSIRO Julius Career Award on studying adaptive management problems. I am thankful to all my co-authors for their contributions, but also the adaptive management community more broadly.

Reflecting back, I really needed this paper when I started, and I hope this will help others. I studied adaptive management methods by accident 6 years ago. At the time I was overwhelmed. I couldn’t understand why there were so many different terminology to define a problem. I couldn’t understand why there were so many approaches available that did not seem to be efficient. Inspired by Darryl MacKenzie’s paper, I took a POMDP perspective, how would I model this problem? One thing, leading to another, and some precious collaborations, led to a best paper award at the 2012 AAAI conference. The power of using POMDP is illustrated in Sam’s Proc B paper (you can read about it here). In my opinion, much more could be done to exploit this result and we are just scratching the surface, so stay tuned!

The utlimate reference:

Chadès, I., Nicol, S., Rout, T.M., Peron M., Dujardin Y., Pichancourt JB., Hastings A., Hauser C. Optimization methods to solve adaptive management problems. Theoretical Ecology (2016). doi:10.1007/s12080-016-0313-0 (request PDF)

The ultimate abstract:

Determining the best management actions is challenging when critical information is missing. However, urgency and limited resources require that decisions must be made despite this uncertainty. The best practice method for managing uncertain systems is adaptive management, or learning by doing.
Adaptive management problems can be solved optimally using decision-theoretic methods; the challenge for these methods is to represent current and future knowledge using easy-to-optimize representations. Significant methodological  advances have been made since the seminal adaptive management work was published in the 1980s, but despite recent active-or-passive-adaptive-managementadvances, guidance for implementing these approaches has been piecemeal and study-specific. There is a need to collate and summarize new work. Here, we classify methods and update the literature with the latest optimal or near-optimal approaches for solving adaptive management problems. We review three mathematical concepts required to solve adaptive management problems: Markov decision processes, sufficient
statistics, and Bayes’ theorem.We provide a decision tree to determine whether
adaptive management is appropriate and then group adaptive management approaches based on whether they learn only from the past (passive) or anticipate future learning (active).We discuss the assumptions made when using existing models and provide solution algorithms for each approach. Finally, we propose new areas of development that could inspire future research. For a long time, limited by the efficiency of the solution methods, recent techniques to efficiently solve partially observable decision problems now allow us to solve more realistic adaptive management problems such as imperfect detection and non-stationarity in systems.

If extinction could be reversed, how would you decide which species to manage?

We have published an opinion piece on how decision science can provide guidance if de-extinction was an option. It has been very stimulating to work on that piece led by Gwen Iacona (UQ). I was particularly interested in the consequences of switching our thought process from managing a non-renewable resource to a renewable resource … it’s fascinating and it works for so many applications e.g. coal generated power vs solar power.

Iacona, G., Maloney, R. F., Chadès, I., Bennett, J. R., Seddon, P. J. and Possingham, H. P. (2016), Prioritising revived species: What are the conservation management implications of de-extinction?. Funct Ecol. Accepted Author Manuscript. doi:10.1111/1365-2435.12720

Abstract:

“De-extinction technology that brings back extinct species, or variants on extinct species, is becoming a reality with significant implications for biodiversity conservation. If extinction could be reversed, there are potential conservation benefits and costs that need to be carefully considered before such action is taken.

Here, we use a conservation prioritization framework to identify and discuss some factors that would be important if de-extinction of species for release into the wild were a viable option within an overall conservation strategy.

We particularly focus on how de-extinction could influence the choices that a management agency would make with regards to the risks and costs of actions, and how these choices influence other extant species that are managed in the same system.

We suggest that a decision science approach will allow for choices that are critical to the implementation of a drastic conservation action, such as de-extinction, to be considered in a deliberate manner while identifying possible perverse consequences.”

Timing of critical habitat protection matters (open access)

 

 

The latest addition to my research interest on how time influences our decision-making process just came out in Conservation Letters (Martin et al, 2016, Free access). We demonstrate once again, that time spent gathering more information to make better decision is beneficial to a point. Aside from the massive modelling effort we had to go through (see lessons learned below), our conclusion summarizes our main point:

It may be tempting to assume that more information is of value for its own sake, in a decision-making context information has value only when it leads to a change in actions taken, specifically, a change with enough benefit to species protection to outweigh the cost of obtaining the information. In the often contentious environment of endangered species decision making, parties who benefit from delay in taking action often lobby strategically for more information, not because they are concerned for the efficacy of protective actions but because their interests are best served by delaying protection as long as possible. In this environment, reminding everyone that more information does not always translate into more efficacious action may help strike a better balance between action and research. When it comes to species conservation, time is the resource that matters most. It is also the resource we cannot get more of.

Martin T.G., Camaclang A.E., Possingham H.P., Maguire L., Chadès I. (2016) Timing of critical habitat protection matters. Conservation Letters In Press, DOI: 10.1111/conl.12266 (OPEN ACCESS, PDF)

Lessons learned: 

This paper was about 5 years in the making, along the way I have learnt a big deal about using AI reinforcement learning tools for this problem. Once more I had to give up using RL and opted for an exhaustive search to find the optimal stopping time – which was really disappointing considering the amount of time I spent on it. As painful as it sounds, I was using the wrong approach. On top of my head, the hurdles were:

1) the matrix population model of the northern abalone species exhibit some time lag, making the process non-Markovian;

2) the Q-Learning approach took way too long to find the optimal stopping time considering the amount of different configurations I had to go through;

3) the near optimal strategies of the Q-Learning approach were not consistent due to lack of convergence;

4) it was way faster to perform an exhaustive search, and this should have been my first solution for a decision problem that was quite simple to solve.

I am glad this paper is out in Conservation Letters for everyone to enjoy. Well done to all my co-authors for their support and hard work on this piece – especially Tara, for pushing it through the line.

Simple interactive networks to represent complex strategies

I am fascinated by networks, what a great way of graphically representing anything you can think about that interacts somehow! In decision-making, networks can represent complex decision trees (or strategy), i.e. they graphically tell you what to do under specific conditions. The issue is, these networks can become too large to make sense of! I’ve investigated interactive ways of representing decision trees, and I was fortunate to stumble upon Gephi. Few days of programming led me to some interesting results, that I happily share with you. Here the nodes represent springs, that can be occupied or empty. There are about 10 springs, so if you follow a path from the root (bottom node) to a top node, you will find the optimal action to perform. It is a work in progress, and more can be done. I believe this is the future of publication by the way, interactive figures/graph/networks, they make communication so effective.

In summary, don’t be shy, use network! I have noticed that network theory communities can be protective of their tools. With a tendency of making simple things such as networks and their metrics seem overly complex. But really networks are quite simple and fun to play with and they can be whatever you wish, you just have to define them. 

Artificial Intelligence to the rescue of migratory shorebirds

Adaptive management or learning by doing, is praised as the best practice method to manage natural systems under uncertainty (see ESA’14 talk). Limited for a long time by our ability to solve adaptive management problems, our research now allows us to find the best adaptive management strategies when networks change over time. This was made possible thanks to our research in Artificial Intelligence (AI) and Conservation science.

What have we discovered in 2 steps?

1) Unlocking the beast. Being strategic about adaptive management means finding the best management strategy when we don’t know exactly what will happen in the future (structural uncertainty). Until very recently, finding the optimal strategy to such decision problems was possible for very small size problems, limiting the application of adaptive management principles. In 2012, we published a fundamental paper that demonstrates that adaptive management problems can be solved using a simplified POMDP (Partially Observable Markov Decision Process, see tiger paper). This is an important finding because modelling an adaptive management problem as a POMDP means we can use very fast algorithms from AI and solve very large adaptive management problems. On a side note, this paper was published at the top AI Conference (AAAI) and received “best paper award” (Computational Sustainability track, thanks for the support!).

2) Boldly go where no one has gone before. Our second step was to demonstrate the power of our findings on the most complex problem we could imagine. Thinking about it, the most difficult problems to solve in ecology are spatial problems (migratory networks) with changing dynamics over time (non stationarity, climate change) for which the consequences on species management are unknown (structural uncertainty, population dynamics). Well, we did it! Check our splendid paper in Proceedings B led by Sam Nicol that brings it all together. This work is amazing for so many good reasons: the shorebird application, the fundamental AI research, the writing, the figures, the authors, the journal and the 20-page supplementary information!

I hope you will enjoy it as much as we did.

Nicol, S., R.A. Fuller, T. Iwamura and I. Chadès (2015). Adapting environmental management to uncertain but inevitable change. Proceedings of the Royal Society B, 282(1808).

Sam also wrote a fantastic conversation article on the topic that explains the impact of our Proc B paper: We need to get smarter to save shorebirds from rising seas.

 

Comparing adaptive management and real options: slides and PDF

Adaptive management and real options approaches for sequential decisions making have undergone significant evolution over the last two decades. Both approaches are based on stochastic optimal control and Markov decision processes. They evolved independently from each other and their developments were motivated by different needs.

Adaptive management was specifically developed to handle decision problems with imperfect knowledge of the dynamics of the system, and is known as ‘learning by doing’. On the other hand, real options analysis was introduced specifically to value the flexibility to change actions over time in response to the evolution of uncertainty, and represents both optimal sequential decisions under uncertainty and a capital budgeting methodology. Because of these different purposes, different analytic and numerical methods were developed to solve these problems.

In our recent MODSIM paper (Chades et al, 2015), we review and compare the concepts, applications and recent advances in the numerical and analytic techniques in adaptive management and real options methodologies. A large body of knowledge accumulated in both fields makes a comprehensive review impractical in the context of this paper. Therefore, our review focuses on the most recent developments, with the purpose to identify potential areas of new developments that would address new challenges in the environmental decision area.

I. Chadès, T. Tarnopolskaya, S. Dunstall, J.Rhodes, and A.Tulloch (2015). A comparison of adaptive management and real options approaches for environmental decisions under uncertainty. In Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2015. ISBN: 978-0-9872143-5-5. (PDF)

When do we need more data? Don’t miss the excel spreadsheet

Stefano Canessa and colleagues have recently published a much needed paper in Methods in Ecology and Evolution to help managers answer: when do we need more data?

Stefano provides an excel spreadsheet and also wrote a blog in MEE! Well done Stefano.

Canessa, S., Guillera-Arroita, G., Lahoz-Monfort, J. J., Southwell, D. M., Armstrong, D. P., Chadès, I., Lacy, R. C., Converse, S. J. (2015), When do we need more data? A primer on calculating the value of information for applied ecologists. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12423

I have found that thinking through what uncertainty really matters a rewarding process. For example, my last talk at ICCB 2015 also showed that critical uncertainty is often limited to a small number of unknown in Conservation. Go ahead fellow scientists, embrace uncertainty. If you were an economist you would say that uncertainty and associated decisions bring opportunities and perhaps flexibility!

On a side note, I’ve been recently thinking about a dynamic version of the Expected Value of Perfect Information – For example you can check out this paper.

 

Complementary strategies double the impact of cost-effective ranked strategies

Our manuscript on how complementarity can help saving more species per dollar spent is available online. If you are interested in cost-effectiveness analysis, PPP (Project Prioritisation Protocol), priority threat management, expert elicitations, or the Pilbara, have a look:

Chades, I., Nicol, S., van Leeuwen, S., Walters, B., Firn, J., Reeson, A., Martin, T. G. . and Carwardine, J. (2014), Benefits of integrating complementarity into priority threat management. Conservation Biology. doi: 10.1111/cobi.12413 (abstract) (request pdf)

In Tulloch et al (2013), we studied how complementarity could help choosing indicator species to improve our monitoring power, Chades et al (2014) explores further this idea in the context of priority threat management. Feel free to download the Pilbara report for details about the study.

Priority threat management process requires a strong collaborative team to be successful. This manuscript is a great example of a work that would not have been possible without the essential contribution of all authors.

Abstract:Figure2_manuscript_R1_with legend_rgb

Conservation decision tools based on cost-effectiveness analysis are used to assess threat management strategies for improving species persistence. These approaches rank alternative strategies by their benefit to cost ratio but may fail to identify the optimal sets of strategies to implement under limited budgets because they do not account for redundancies. We devised a multiobjective optimization approach in which the complementarity principle is applied to identify the sets of threat management strategies that protect the most species for any budget. We used our approach to prioritize threat management strategies for 53 species of conservation concern in the Pilbara, Australia. We followed a structured elicitation approach to collect information on the benefits and costs of implementing 17 different conservation strategies during a 3-day workshop with 49 stakeholders and experts in the biodiversity, conservation, and management of the Pilbara. We compared the performance of our complementarity priority threat management approach with a current cost-effectiveness ranking approach. A complementary set of 3 strategies: domestic herbivore management, fire management and research, and sanctuaries provided all species with >50% chance of persistence for $4.7 million/year over 20 years. Achieving the same result cost almost twice as much ($9.71 million/year) when strategies were selected by their cost-effectiveness ranks alone. Our results show that complementarity of management benefits has the potential to double the impact of priority threat management approaches.

 

A toolbox to solve stochastic dynamic programming problems in R, Matlab, SciLab or Octave

Our MDPToolbox is now published in Ecography. Thank you for supporting freely available programs. Please spread the word! The MDP/ SDP toolbox is now available in R, Matlab, SciLab and Octave. No excuses!

Stochastic dynamic programming (SDP) or Markov decision processes (MDP) are increasingly being used in ecology to find the best decisions over time and under uncertainty so that the chance of achieving an objective is maximised. To date, few programs are available to solve SDP/MDP. We present MDPtoolbox, a multi-platform set of functions to solve Markov decision problems (MATLAB, GNU Octave, Scilab and R). MDPtoolbox provides state-of-the-art and ready to use
algorithms to solve a wide range of MDPs. MDPtoolbox is easy to use, freely available and has been continuously improved since 2004. We illustrate how to use MDPtoolbox on a dynamic reserve design problem.

Chadès, I., Chapron, G., Cros, M.-J., Garcia, F. and Sabbadin, R. (2014), MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems. Ecography. doi: 10.1111/ecog.00888

Prioritising the management of threat affecting the Pilbara species: conversation article and report available

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.

Pilbara_report_snapshotPilbara_report_snapshot1Pilbara_report_snapshot2

Recovering Interacting Species: Are sea otters a threat for abalone?

And the answer is no, but poaching is.

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.

Thanks Jessica,
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)

How to manage a commercially valuable invasive species – buffel grass?

Cenchrus_ciliaris Have you ever been in a position where you had to make a decision but you were torn by conflicting objectives? Well, that is a very common issue when managing an invasive species that is also valuable for society. Unfortunately such a situation often paralyze the decision making process. In a recent paper to appear in Agricultural Systems led by Isabelle Grechi, we looked at how we could model such decision problem and propose valid solutions based on our so-called conflicting objectives. We introduce happyness curve to do so! We applied our decision framework to buffel grass (it doesn’t get more controversial than this plant in Australia!).

Have a look at the paper:

Grechi, I., I. Chades, Y. Buckley, M. Friedel, A. C. Grice, H. P. Possingham, R. D. van Klinken, and T. G. Martin. 2014. A decision framework for management of conflicting production and biodiversity goals for a commercially valuable invasive species. Agricultural Systems 125:1-11.

Highlights:

• Commercially valuable invasive species present a conflict for management.
• We model buffel grass dynamics with production and biodiversity benefits and costs.
• Management solutions are found that account for production-biodiversity trade-offs.
• Solutions are sensitive to the shape of the buffel cover–biodiversity relationship.
• Solutions are less sensitive to uncertainty about the management effectiveness.

 

 

 

Analyzing, discussing and revising experts’ opinion via an anonymous online forum: video

Today, we are launching our ‘albopictus’ online forum to allow our experts to analyze, discuss and revise their opinion while remaining anonymous. This project is about providing guidance on which pathways to manage in priority to reduce the risk of infestation of mainland Australia by the invasive mosquito aedes Albopictus. Our experts are based in different locations across Australia and Asia. When asked if they would be willing to participate to such ‘online forum’, all experts responded positively. It is now time to check if we can use that tool for future expert elicitation exercises.

The need for anonymous discussion prompted us to use a web forum rather than emails. Setting up the forum has been time consuming and we are still learning how to make it clearer for non-nerdy experts. It’s also easy to assess how many experts actually went to the forum or contributed.

Chrystal has recorded a video to help our experts. Have a look. Fingers crossed. I will report on this experience in 2 weeks time!

 

 

Apply now: 3-year postdoctoral fellow on optimizing adaptive management!

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.

Position Details – Q13/03434

Harnessing artificial intelligence for biodiversity conservation: the slides!

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.

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Seminar at the Global Change Institute, University of Queensland

I’m excited to present my work at the Global Change Institute tomorrow (26/09/2013). A good opportunity to communicate and reflect on my work so far. I can promise that the slides will have no equations :-). Here’s the abstract:IMG_1115

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.

Accounting for complementary to maximize monitoring power for species management

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 Biology 27, 988-999. Abstract