Adapting AI planning tools to save endangered species

A group of bar-tailed godwits photographed in Songdo, Korea. Photo: Judit Szabo.

The earth is facing an extinction crisis, with some estimates claiming that almost half of the world’s existing species may be extinct within the next 100 years. Conservation of biodiversity has never been more important, but the science of conservation is very much in its infancy, with a lot to learn from other fields, including Artificial Intelligence. Biological systems are stochastic, difficult to observe, and subject to external influences that make precise predictive modelling difficult. The traditional response to uncertainty has been to “collect more data” with the goal of eventually designing a management strategy to protect the species once and for all.

However more recently this approach has been largely rejected, as delaying effective management action while monitoring is completed can mean a species goes extinct before any action is taken. The current best practice is adaptive management (AM), a process that executes the best management action based on current knowledge, observes how the system responds to the action, and uses this feedback to plan the next action. This integrated process of monitoring and management will be familiar to many in AI as the classic exploration/exploitation trade-off encountered in learning algorithms such as reinforcement learning. Indeed, the cutting edge modelling method for adaptive management problems is to use a method drawn from AI, namely Partially Observable Markov Decision Processes (POMDPs) (for details see below, Sidebar: What’s a POMDP?).

Tidal mudflats in northeastern Asia are critical habitat for the shorebirds of the East Asian-Australasian flyway. Major threats to the birds are coastal development (the new city of Songdo, Korea, can be seen being built in the background of this photo) and sea level rise, which threatens to inundate the mudflats permanently, leaving insufficient habitat for the birds. Photo: Judit Szabo.

In our IJCAI paper, we address a limitation of POMDPs that restricts their use in conservation. Biological systems change over time in response to climate change, but current AM methods (including POMDPs) are not able to plan for non-stationary systems. Current methods assume that there is one ‘true’ model of the world, and by collecting enough data, we will eventually converge on the ‘truth’. This is call a stationary model. However, if the world is changing, then collecting more data may never converge to a model. We use a work-around of creating a POMDP that considers a suite of candidate stationary models representing the effects of future change (in our case, sea level rise) on a population, and maintains a belief about the likelihood that a model is the true model at a given time. As the sea level rises, the POMDP planner can ‘switch’ between candidate models once the observations suggest that such a change has occurred.

As a case study, we consider the effects of rising sea level on 10 declining migratory bird species that depend upon low-lying mudflats to provide the food they need to complete their migration. As the sea level rises, these habitats may become inundated so that the birds cannot complete the migration (4) (read more about sea level’s effect on shorebird populations in the East Asian-Australasian flyway here.)  We model the AM problem as a POMDP with the workaround described above, and compute when and where action on sea level rise is required to best protect the birds. We present the paper as a data challenge, and offer flyway network data for 10 species of migratory shorebird for the AI community to develop. We present a data challenge as our method only solves relatively small flyway networks and has high runtimes. Because our workaround and the AM approach are actually simplified POMDPs (they are called hmMDPs), there is room for AI researchers to develop custom solvers that exploit the simplifications, allowing us to find conservation strategies for larger and more realistic networks.


Want to know more? If you’re going to IJCAI in Beijing in August, come and see our poster and talk to us. Our full paper and the files for the data challenge are also available for download here.
Date: Wednesday August 7, 2013
Location and time: Planning and Scheduling session (8:30-9:45am).


Nicol S, Buffet O, Iwamura T, Chadès I (2013). Adaptive management of migratory birds under sea level rise. Proceedings of IJCAI-13, Beijing, China

This is one of our series of posts on the latest research in Computational Sustainability being presented at conferences this summer. This time Sam Nicol, a Postdoc at CSIRO Ecosystem Sciences in Australia tells us about their new paper at IJCAI in Beijing this August.

Sidebar: What’s a POMDP?

For the uninitiated, POMDPs are discrete time stochastic control processes that are used to make decisions in situations where the outcomes are partially controlled by chance and partly by the actions of an agent. In particular, POMDPs have the feature that the managing agent cannot directly observe the true state of the system, and must rely on observations of some other proxy variable to make decisions. The POMDP agent is effectively asking: “what is the best action that I can implement today to achieve my future goal, given that I know that the system is stochastic and the data I’m receiving from observing the system is noisy?” POMDPs appear in a surprising number of places. The most famous real-world example is using POMDPs to assist people with dementia complete simple tasks like hand-washing (2). POMDPs have also been used in conservation, for example for determining how much survey effort should be dedicated to looking for cryptic species like the Sumatran tiger (3).



  1. Chadès, I., Cawardine J., Martin, T., Nicol, S., Sabbadin, R., Buffet, O. (2012). MOMDPs: A solution for modeling adaptive management problems. Proc. of the AAAI-12.
  2. Hoey, Jesse ; Bertoldi, Axel von ; Poupart, Pascal ; Mihailidis, Alex  (2007)  Assisting persons with dementia during handwashing using a partially observable Markov decision process.. The 5th International Conference on Computer Vision Systems, 2007 doi:10.1016/j.cviu.2009.06.008
  3. Chadès, I., McDonald-Madden, E., McCarthy, M.A., Wintle, B., Linkie, M., Possingham, H.P. (2008). When to stop managing or surveying cryptic threatened species. Proc. Natl. Acad. Sci. U.S.A. 105 (37): 13936-40. doi: 10.1073/pnas.0805265105
  4. Iwamura, T., Possingham, H.,  Chadès, I., Minton, C., Murray, N., Rogers, D., Treml, E., Fuller, R. (2013) Migratory connectivity magnifies the consequences of habitat loss from sea-level rise for shorebird populations Proc R Soc B 280: 20130325

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