Interview: Plants and their fungi to slow down climate change

César Terrer, participant in the IIASA 2016 Young Scientists Summer Program, and PhD student at Imperial College London, recently made a groundbreaking contribution to the way scientists think about climate change and the CO2 fertilization effect. In this interview he discusses his research, his first publication in Science, and his summer project at IIASA.

Conducted and edited by Anneke Brand, IIASA science communication intern 2016.

César Terrer ©Vilma Sandström

César Terrer ©Vilma Sandström

How did your scientific career evolve into climate change and ecosystem ecology?
I studied environmental science in Spain and then I went to Australia, where I started working on free-air CO2 enrichment, or FACE experiments. These are very fancy experiments where you fumigate a forest with CO2 to see if the trees grow faster. In 2014 I moved to London for my PhD project. There, instead of focusing on one single FACE experiment, I collected data from all of them. This allowed me to make general conclusions on a global scale rather than a single forest.

You recently published a paper in Science magazine. Could you summarize the main findings?
We found that we can predict how much CO2 plants transfer into growth through the CO2 fertilization effect, based on two variables—nitrogen availability and the type of mycorrhizal, or fungal, association that the plants have. The impact of the type of mycorrhizae has never been tested on a global scale—and we found that it is huge. I think it’s fascinating that such tiny organisms play such a big role at a global scale on something as important as the terrestrial capacity of CO2 uptake.

How did you come up with the idea? One random day in the shower?
Long story short, researchers used to think that plants will grow faster, and take up a lot of the CO2 we emit. They assumed this in most of their models as well. But plants need other elements to grow besides CO2. In particular, they need nitrogen. So scientists started to question whether the modeled predictions overestimated the CO2 fertilization effect, because the models did not consider nitrogen limitation. To find out, I analyzed all the FACE experiments and indeed I saw that in general plants were not able to grow faster under elevated CO2 and nitrogen limitation. However, in some cases plants were able to take advantage of elevated CO2 even under nitrogen limitation. I grouped together the experiments where plants could grow under nitrogen limitation and after a lot of reading I saw what they had in common: the type of fungi! It turned out that one type of mycorrhizae is really good at transferring large quantities of nitrogen to the plant and the other type is not.

How did that feel?
Awesome! When I saw the graph, I knew: this is going to be important. Of course, after this, my coauthors helped me to polish the story. Without them, the conclusions would not be as robust and clear.

So how does this process work? Where do the fungi get the nitrogen from?
Particular soils might have a lot of nitrogen, but the amount available for plants to absorb might be low. Also, plants have to compete with non-fungal microorganisms for nitrogen. So if there is not much there, the microorganisms take it all. It’s called immobilization. Instead of mineralizing nitrogen, they immobilize it so that plants cannot take it up, at least not in the short term. Some types of fungi are much more efficient in accessing nitrogen, and associated with roots they allow plants to overcome limitations.

Nitrogen mobilization abilities of different types of fungi. Growth of plants associated with fungi not beneficial for nitrogen uptake (illustrated as grass roots on the left) could be limited by low nitrogen availability in soil. Other plants have the advantage of increased nitrogen uptake due to their beneficial association with certain types of fungi (illustrated as yellow mushrooms connected to the roots of the tree on the right). ©Victor O. Leshyk.

Nitrogen mobilization abilities of different types of fungi. Growth of plants associated with fungi not beneficial for nitrogen uptake (illustrated as grass roots on the left) could be limited by low nitrogen availability in soil. Other plants have the advantage of increased nitrogen uptake due to their beneficial association with certain types of fungi (illustrated as yellow mushrooms connected to the roots of the tree on the right). ©Victor O. Leshyk.

What is the impact of your findings?
Plants currently take up 25-30% of the CO2 we emit, but the question is whether they will be able to continue to do so in the long term. Our findings bring good and bad news. On the one hand, the CO2 fertilization effect will not be limited entirely by nitrogen, because some of the plants will be able to overcome nitrogen limitation through their root fungi. But on the other hand, some plant species will not be able to overcome nitrogen limitation.

There was a big debate about this. One group of scientists believed that plants will continue to take up CO2 and the other group said that plants will be limited by nitrogen availability. These were two very contrasting hypotheses. We discovered that neither of the hypotheses was completely right, but both were partly true, depending on the type of fungi. Our results could bring closure to this debate. We can now make more accurate predictions about global warming.

What will you do at IIASA and how will you link it to your PhD?
I want to upscale and quantify how much carbon plants will take up in the future. If we are to predict the capacity of plants to absorb CO2, we need to quantify mycorrhizal distribution and nitrogen availability on a global scale. We are updating mycorrhizal distribution maps according to distribution of plant species. We know for instance that pines are associated with ectomycorrhizal fungi and always will be. To quantify nitrogen availability we use maps of different soil parameters that are available on a rough global scale.

© Adam Edwards | Dreamstime.com

© Adam Edwards | Dreamstime.com

About César Terrer
Prior to his PhD, Terrer studied at the University of Murcia in Spain and the University of Western Sydney in Australia.

Currently he is a member of the Department of Life Sciences at Imperial College London, UK. For this study he collaborated with researchers from the University of Antwerp, Northern Arizona University, Indiana University and Macquarie University.

In the IIASA Young Scientists Summer Program, Terrer works together with Oskar Franklin from the Ecosystem Services and Management Program and Christina Kaiser from the Evolution and Ecology Program.

Further reading

 

Note: This article gives the views of the interviewee, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

How coordination can boost the resilience of complex supply chains

By Célian Colon, PhD student at the Ecole Polytechnique in France & IIASA Mikhalevich award winner

How can we best tackle risks in our complex and interconnected economies? With globalization and information technologies, people and processes are increasingly interdependent. Great ideas and innovations can spread like wildfire. However, so can turbulence and crises. The propagation of risks is a key concern for policymakers and business leaders. Take the example of production disruption: with global supply chains, local disasters or man-made accidents can propagate from one place to another, and generate significant impact. How can this be prevented?

Risk propagation is like a domino effect. Credit: Martin Fisch (cc) via Flickr

Risk propagation is like a domino effect. Credit: Martin Fisch (cc) via Flickr

As part of my PhD research, I met professionals on the ground and realized that supply risk propagation is a particularly tricky issue, since most parts of the chains are out of their control. Supply chains can be very long, and change with time. It is difficult to keep track of who is working with whom, and who is exposed to which hazard. How then can individual decisions mitigate systemic risks? This question directly connects to the deep nature of systemic problems: everyone is in the same boat, shaping it and interacting with each other, but no one is individually able to steer it. Surprising phenomena can emerge from such interactions, wonderfully illustrated by bird flocking and fish schooling.

As an applied mathematician thrilled by such complexities, I was enthusiastic to work on this question. I built a model where firms produce and interact through supply chain relationships. Pen and paper analyses helped me understand how a few firms could optimally react to disruptions. But to study the behavior of truly complex chains, I needed the calculation power of computers. I programmed networks involving a large number of firms, and I analyzed how localized failures spread throughout these networks, and generate aggregate losses. Given the supply strategy adopted by each firm, how could systemic risk be mitigated?

With my collaborators at IIASA, Åke Brännström, Elena Rovenskaya, and Ulf Dieckmann, we have highlighted the key role of coordination in managing risks. Each individual firm affects how risks propagate along the chain. If they all solely focus on maximizing their own profit, significant amounts of risk remain. But if they cooperate, and take into account the impact of their decisions on the risk profile of their trade partners, risk can be effectively mitigated. Reducing systemic risks can thus be seen as a common good: costs are heterogeneously borne by firms while benefits are shared. Interestingly, even in long supply chains, most systemic risks can be mitigated if firms only cooperate with only one or two partners. By facilitating coordination along critical supply chains, policy-makers can therefore contribute to the reduction of risk propagation.

Colon's model analyzes how firms produce and interact through supply chain relationships. Credit: Jan Buchholtz (cc) via Flickr

Colon’s model analyzes how firms produce and interact through supply chain relationships. Credit: Jan Buchholtz (cc) via Flickr

Drawing robust conclusions from such models is a real challenge. On this matter, I benefited from the experience of my IIASA supervisors. Their scientific intuitions greatly helped me focusing on the most fertile ground. It was particularly exciting to borrow techniques from evolutionary ecology and apply them to an economic context. Conceptually, how economic agents co-adapt and influence each other shares many similarities with the co-evolution of individuals in an ecological environment. To address such complex issues, I strongly believe in the plurality of approaches: by illuminating a problem from different angles, we can hope to see it more clearly!

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

The mathematics of love

By Sergio Rinaldi, IIASA Evolution and Ecology Program and Politecnico di Milano, Italy

Is it possible to predict how love stories develop, progress, and end using mathematical models? I have studied this question over the past 20 years with a group of researchers at IIASA and at the Politecnico di Milano, and as we show in our new book Modeling Love Dynamics (World Scientific, 2016), the answer is yes. The emerging message is that prediction is possible, if we can describe in formulas the way each individual reacts to the love and to the appeal of the partner.

Consider a standard love story, which develops like those described in a classical Hollywood movie such as Titanic. This story can be easily modeled, if one considers reasonably appealing individuals who increase their reaction with the partner’s love – so called secure individuals. Starting from the state of indifference, where the individuals are at their first encounter, their feelings continuously grow and tend toward a positive plateau.

Mala Powers & José Ferrer in Cyrano de Bergerac, 1950. Credit: Public Domain

Mala Powers and  José Ferrer in Cyrano de Bergerac, 1950. – Public Domain

Love stories become more intriguing when one individual is not particularly appealing, if not repelling, as in the fairy tale “Beauty and The Beast.” Indeed, in these cases, there exists also a second romantic regime, which is negative and can therefore entrain, in the long run, marital dissolution. In order to avoid that trap, people who are not very charming, or believe to be so, do all they can to look more attractive to the partner. At the first date, she wears her nicest dress and he shows up with his best fitting T-shirt. However, after a while, the bluffing can be interrupted, because the couple has entered the safe basin of attraction of the positive regime. Needless to say, the model also supports much more sophisticated behavioral strategies, like that described by Edmond Rostand in his “Cyrano de Bergerac,” the masterpiece of the French love literature.

Not all individuals are secure. Indeed, some people react less and less strongly when the love of the partner overcomes a certain threshold. These individuals, often very keen to flirtation, are incapable of becoming one with their partner. The model shows that couples composed of insecure individuals tend, with almost no exception, toward an unbalanced romantic regime in which the most insecure is only marginally involved and is therefore prone to break up the relationship at the first opportunity. This is why after just 20 minutes of the very long “Gone with the Wind,” when one realizes that Scarlett and Rhett are both insecure, the model can already predict the end of the film, where he quits her with the lapidary “Frankly, my dear, I don’t give a damn.” The same conclusion is expected if only one of the two individuals is insecure. This explains the numerous failures in the romantic life of some individuals, like the beautiful star Liz Taylor, who is described as very insecure in all her biographies, and went, indeed, through eight marriages.

Clark Gable and Vivien Leigh in "Gone with the Wind."

Clark Gable and Vivien Leigh in Gone with the Wind, 1939 – MGM Pictures | Public Domain

Mathematical models can also be used to interpret more complex romantic behaviors. Particularly important is the case of individuals who overestimate the appeal of the partners when they are more in love with them (like parents who have a biased view of the beauty of their own kids). Interestingly, if insecurity is also present, biased couples can have romantic regimes characterized by recurrent ups and downs. In other words, the theory says that bias and insecurity is an explosive mix that triggers turbulence in the life of a couple.

In the second part of the book we focus on  the effects of the social environment and to the consequences of extra-emotional compartments. In this context, our analysis of the 20-years long relationship between Laura and the famous Italian poet Francis Petrarch shows that poetic inspiration is an important destabilizing factor, responsible for transforming a quiet relationship into a turbulent one.

Finally, we studied triangular relationships, with emphasis on the effects of conflict and jealousy. In all these cases the dynamics of the feelings can be very wild, up to the point of being chaotic and, hence, unpredictable. When this occurs, the life of the couple becomes unsustainable, because painful periods of crisis can virtually start at any moment: a heavy permanent stress. The model can thus explain why the relationship is often interrupted, sometimes even tragically, as in the famous film by François Truffaut “Jules et Jim”, where Kathe’s suicide is perceived as a real relief.

CaptureMore information: Watch a video of Sergio Rinaldi’s talk at the 2015 Systems Analysis Conference.

Reference
Rinaldi S, Della Rossa F, Dercole F, Gragnani A, Landi P, (2015). Modeling Love Dynamics. World Scientific, Singapore [January 2016]  http://www.worldscientific.com/worldscibooks/10.1142/9656

 

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Don’t dope – run the code!

By Andrey Krasovskii, IIASA Ecosystems Services and Management Program

During his workout in the IIASA gym, my colleague Pekka Lauri often runs on a treadmill. He adjusts the velocity of running using the control panel, and it indicates the distance and approximate calories burnt. While Pekka may not be thinking about mathematical models during his workout break, running and other athletic performance can be modeled using some of the same techniques that we use for other questions at IIASA.

Outside of my academic work at IIASA, I am highly interested in sports, and athletics in particular. My wife, Katy Kuntsevich, has won Austrian championships in high jump several times, and my brother, Nikolay, was on his university team as a 400 meter runner.

Visiting my hometown Ekaterinburg, Russia, back in 2014, I got into a dispute with my father, who is a university professor in theoretical mechanics. Namely, my argument was that the sprinters’ acceleration at the finish line of the 100 meter race should be negative. Later on, during the Christmas holidays, I decided to mathematically support my statement.

Left: Prof. Krasovskii with a page from his Lectures in Theoretical Mechanics (Chapter 2, Kinematics, in Russian) featuring Valeriy Borzov on finish. This page was a reason for my study. Top right: Portrait of Isaac Newton.

Left: Prof. Krasovskii with a page from his Lectures in Theoretical Mechanics (Chapter 2, Kinematics, in Russian) featuring Valeriy Borzov on finish. This page was a reason for my study. Top right: Portrait of Isaac Newton.

When athletes run a race, their horizontal velocity can be estimated by modern technologies such as high resolution cameras and lasers. Knowing the horizontal instantaneous velocity, one can calculate acceleration. According to Newton’s law, one can introduce the forces applied to the body center mass of the athlete. Outside a gym treadmill it gets a bit more complicated, with air resistance and headwind or tail-wind. Against these aerodynamic forces the runner applies horizontal force, which drags him forward. In reality it is an “aggregate” of impulsive normal forces generated by feet and the stroke frequency.

The dynamics of an athlete have been described by an ordinary differential equation studied in papers on sprint modeling, first published by physicist J. Keller. This equation has been considered in numerous papers, which have shown that the equation fits the real data for short-distance running (100 and 200 meters). The indicated studies are devoted to the calibration of parameters, to the wind impact analysis, and to the choice of force functions such that the solution satisfies the actual motions, e.g. the running records of Usain Bolt.

The problem of running dynamics reminded me of a type of model that we sometimes use at IIASA, called an optimal control model. Optimal control models are used to calculate the best or most efficient way of doing something, for example, driving from one place to another. If I want to drive from Vienna to Laxenburg, I start my car’s engine at my house (point A). I look at the time, and plan to arrive to IIASA (point B) in 30 minutes. In optimal control terms, the car is a control object, and the driver controls it by pushing the gas/brake pedals and steering the wheel. In the driving process the car meets certain constraints (e.g. the engine power, available roads, and speed limits) and disturbances (e.g. traffic jams, lights, and weather conditions). Obviously, there are many ways of controlling the car in order to reach IIASA in 30 minutes. What if among those admissible controls, I wanted to find an optimal control minimizing car’s energy expenditures during the 30-minute trip from A to B? Here energy is an intensity (cost) of control actions, i.e. fuel (petrol/electricity), or corresponding greenhouse gas emissions. Well, in this case one needs to solve the classical minimum energy control problem. The solution to this problem gives an optimal plan that the car driver (or autopilot) needs to implement. Note, that the corresponding time-optimal control problem consists in finding the fastest driving time to IIASA under given fuel reserve. Optimal control theory (OCT) is an efficient tool for solving such dynamic optimization problems.

My research question was: “Can one control his/her running similar to driving a car?” The answer is: “Yes!”

I applied an optimal control model to Usain Bolt’s performance data at the Beijing Olympic Games in 2008, when he ran the 100 meter sprint in 9.69 seconds. According to the model, under the same conditions he could have distributed his energy optimally and run the distance in 9.56 s. It is worth mentioning that this time is close to his current world record, 9.58 s, achieved at the 2009 World Championships in Berlin. In the paper I also provide modeling results for optimal (energy-efficient) running over 100 m: calculation of the minimum energy and trajectories of acceleration, velocity, and distance from start.

In the conclusion, I argue that applying advanced science in the athletic training programs is far better than doping–better in terms of a healthy body, mind and soul.

Here is my hypothesis of what Usain Bolt is doing at his laptop. © Weltklasse Zürich - Marcel Giger

Here is my hypothesis of what Usain Bolt is doing at his laptop. © Weltklasse Zürich – Marcel Giger

Reference:
A. A. Krasovskii, “Application of optimal control to a biomechanics model”, Proceedings of the Steklov Institute of Mathematics, 2015, Vol. 291, pp. 118–126. http://dx.doi.org/10.1134/S0081543815080118

 

I would like to thank Sergey Aseev, Katherine Leitzell, as well as my colleagues in the IIASA Ecosystem Services and Management Program (ESM) for their interest and valuable discussions.

 

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Cross-country skiing in Finland: An endangered tradition?

By Mia Landauer, a Finnish postdoc at IIASA Risk, Policy and Vulnerability Program and Arctic Futures Initiative

When I was a child I did not like cross-country skiing. One reason was that like many other schoolmates in Finland, I had no other option than to ski to school throughout the winter, even when temperatures were below -20 C, and even though my skis were too big because I got them from my sister and so old that they could have broken anytime.

When I decided to write my dissertation in Austria about climate adaptation of winter tourism, I found I still couldn’t get away from skiing. My professor at the University of Natural Resources and Life Sciences (BOKU)   asked me to join a research team investigating this topic. “What a great tradition you have in Finland! My friend and colleague from METLA (now Natural Resources Institute) in Finland would love to do research with us but with somebody who knows about cross-country skiing! You are the perfect match!” I guess I was too shy to admit that I was not excited about having cross-country skiing as a case study—but  I decided to give it a try.

Photo Credit: © Mia Landauer

Cross country skiing in Finland is practiced by all age groups (voluntarily or not). Photo Credit: © Mia Landauer

Cross-country skiing is socially and culturally a very important activity in Finland, with considerable health benefits. Forty-two percent of the population practice skiing annually and 98% have the skills. But cross-country skiing, like other snow-based activities, is affected by climate change: even Nordic countries are now seeing lack of snow, shift of seasons, and extreme weather events. The winter 2015/2016 has been no exception. Many Finns are concerned that losing this activity would lead to reduced well-being and loss of cultural tradition. Furthermore, economic impacts on tourism regions brought about by a decrease in skiing would cause problems to local economies heavily dependent on snow-based tourism.

Although vulnerability indicators of some other tourism sectors such as beach tourism exist, nobody had thought about cross-country skiing. So we decided to develop an index, based on climatic observations together with extensive survey data on skiers living in climatically different regions in Finland.

We found that exposure to changes in snow conditions have a considerable effect on regional vulnerability. The most vulnerable skiers are in southernmost parts of Finland, which makes sense. But it is not only the amount of snow and length of winter that matter. We also found that skiers in North and East Finland have the highest capacity to adapt, as indicated by their ability to ski: having the necessary skills and equipment, as well as capacity and willingness to travel to be able to ski.

However, the results also show that if it we could enhance these components of adaptive capacity, also the skiers in the south would have a chance. If there are no adaptation options (no artificial snow tracks, no indoor skiing facilities, or simply no interest to use these, or no money or time to travel to be able to ski), in the short term the Finnish cross-country skiing population will face impacts on health, well-being, and quality of life. In the long term, the skiing culture could be lost. Furthermore, decline in demand would lead to regional economic losses in tourism-dependent local economies.

Attempts are being made to maintain the skiing tradition. Nowadays there are a lot of organized activities where kids are introduced to outdoor activities in a playful and educational environment, and ski school and clubs are being established. They play an important role to create a close and pleasant relationship to nature and increase motivation for skiing. But of course the most important element for skiing is snow.

I have always had a very close relationship to nature. Believe me or not, sometimes I do go skiing although it also brings back the unpleasant memories. Despite them, wintery landscapes and nature experience have motivated me to continue skiing as an adult. The gray and rainy winters make me worried and I simply cannot see myself skiing in a ski tunnel… Albeit “you will never know the true value of a moment until it becomes a memory“, I want snow!

Cross country ski track in Ruka, Finland Photo Credit: © Timo Newton-Syms via Flickr

Cross country ski track in Ruka, Finland Photo Credit: © Timo Newton-Syms via Flickr

More information:

Project: “Map Based Assessment of Vulnerability to Climate Change Employing Regional Indicators” (MAVERIC)” http://www.syke.fi/projects/maveric

References

Landauer, M., Sievänen, T., & Neuvonen, M. (2015). Indicators of climate change vulnerability for winter recreation activities: a case of cross-country skiing in Finland, Leisure/Loisir, 39:3-4, 403-440. http://dx.doi.org/10.1080/14927713.2015.1122283

Landauer, M., Haider, W., & Pröbstl, U. (2014). The influence of culture on climate change adaptation strategies: Preferences of cross-country skiers in Austria and Finland. Journal of Travel Research 53(1), pp. 95-109. doi: 10.1177/0047287513481276

Landauer, M., & Sievänen, T. (2011). Suomalaisten maastohiihtäjien sopeutuminen ilmastonmuutokseen. In T. Sievänen & M. Neuvonen (Eds.), Luonnon virkistyskäyttö 2010 (pp. 91–101). Vantaa: Working Papers of the Finnish Forest Research Institute, 212.

Landauer, M., Sievänen, T., & Neuvonen, M. (2009). Adaptation of Finnish cross-country skiers to climate change. Fennia 187 (2), pp. 99–113. http://ojs.tsv.fi/index.php/fennia/article/view/3697

Neuvonen, M., Sievänen, T., Fronzek, S., Lahtinen, I., Veijalainen, N., & Carter, T. R. (2015). Vulnerability of cross-country skiing to climate change in Finland – An interactive mapping tool. Journal of Outdoor Recreation and Tourism, 11, 64–79. doi:10.1016/j.jort.2015.06.010

Neuvonen, M. & Sievänen,T. (2011). Ulkoilutilastot 2010 (Outdoor Recreation Statistics 2010). In: Sievänen, T. & Neuvonen, M. (toim.). Luonnon virkistyskäyttö 2010. Metlan työraportteja / Working Papers of the Finnish Forest Research Institute 212: 133–190

Perch-Nielsen, S. L. (2010). The vulnerability of beach tourism to climate change – An index approach. Climatic Change, 100(3–4), 579–606. doi:10.1007/s10584-009-9692-1

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.