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.


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.

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.

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.


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.


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)

New paper out: Growing biodiverse carbon-rich forests

Congratulations to JB Pichancourt and team: An excellent work in an excellent journal! Feel free to contact JB directly if you require additional information on our paper.Pichancourt, JB; Firn, J,; Chades, I.; Martin T.G. 2013. Growing Biodiverse Carbon-Rich Forests. DOI: 10.1111/gcb.12345

Regrowing forests on cleared land is a key strategy to achieve both biodiversity conservation and climate change mitigation globally. Maximizing these co-benefits, however, remains theoretically and technically challenging because of the complex relationship between carbon sequestration and biodiversity in forests, the strong influence of climate variability and landscape position on forest development, the large number of restoration strategies possible, and long time-frames needed to declare success.Through the synthesis of three decades of knowledge on forest dynamics and plant functional traits combined with decision science, we demonstrate that we cannot always maximize carbon sequestration by simply increasing the functional trait diversity of trees planted. Continue reading New paper out: Growing biodiverse carbon-rich forests