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.


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.


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.


• 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.