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.

photo

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

Adaptive management of migratory birds

We have a new paper published at IJCAI (top Artificial Intelligence conference, ranked A*, probably the most selective conference, congratulations to Sam and team!).

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

Or read the blog version on the computational sustainability website.

In this paper we are posing an adaptive management challenge to the AI community:

  • Why do we care? Because solving adaptive management problem is a complex optimisation problem and efficient methods are lacking!
  • What are we hoping? We hope that future AI research will account for the specific description of adaptive management problems using our problem as a classic benchmark problem.
  • How can you help, what’s next? If you have a complex problem feel free to submit a challenge to the AI community!

 

Sam at IJCAI 2013

Sam at IJCAI 2013 – Photo: O. Buffet

The conservation decisions team has a new team leader!

That is now official, I am the new team leader of the conservation decisions team and we will be hiring this year! So stay tuned if you are looking for a wicked postdoc position in adaptive management/computational sustainability!

IMG_1113