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.

 

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.

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)

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. 

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