Racing to measure photosynthesis: why fast gas exchange measurements matter

One of the grand challenges of the 21st century is to meet global food demand. The circumstances that lead to this issue are: 1) population growth, 2) climate change, and 3) limited genetic variability. Let’s focus on issues 2 and 3, since these are issues that photosynthesis research can help solve.

Climate change is changing precipitation patterns, increasing temperatures, and increasing the frequency of extreme weather events. Generally, crops will face drier and hotter conditions, and more frequent extreme heat events.

Photosynthesis research can help by allowing us to find plants and crop varieties that are capable of maintaining carbon-fixation (required for growth) while minimizing water use in the face of these climate change-induced stresses. Different crop varieties have different traits, and given that there can be thousands of varieties of a given crop, it can be very time consuming to measure traits of interest (such as water use efficiency) in all varieties.

This is where rapid gas exchange techniques can help. In collaboration with Dr. David T. Hanson at the University of New Mexico and LI-COR Biosciences, we developed a method for rapidly measuring the response of photosynthesis to CO2. Measuring such a response allows us to understand the underlying biochemistry. Our method is up to 12 times faster than the traditional approach.

This is fast enough to be able to measure these CO2 responses in thousands of varieties within a reasonable time frame, which could allow a researcher to correlate the data-rich gas exchange measurements with the underlying genetic variation among plant varieties. In turn, this could enhance crop breeding efforts to improve crop yields, stress tolerance, and yield sustainability.

If you’re interested in learning more about the rapid gas exchange method, you can read the article for free (it is open access) here.

Stay safe and stay informed,


Don’t forget about plant sex: climate change can affect sex ratios in plants

Most climate change studies on plants are focused on how the growth or range of a plant species will response to a changing climate. The sex of the plant is usually ignored, and in some cases this is okay since some plants have both male and female structures on an individual. However, dioecious plants have a particular sex for an individual, much like dogs and cats. Males and females of dioecious plant species can have different physiology, and this may affect male/female responses to climate change in these species.

Petry et al. (2016, Science, 353:69-71) sought to determine whether males and females of a dioecious plant would respond differently to climate change. They looked at how the sex ratio changed across the entire range of the herb, Valeriana edulis, over the past 33 years. They found the proportion of males in the population decrease with elevation, but the proportion of males overall has increased by ~6% over the past 33 years. They attribute these results to differences in water use efficiency (the ability of a plant to conserve water while maintaining growth). The sex with the greater proportion in a population had higher water use efficiency than the other sex. However the reason for these differences is unclear.

The results of Petry et al. have significant implications for modelling. If we assume that both sexes of a dioecious plant species are the same, our models of plant migration or populations could be wrong. Modellers will have to take this into account when trying to predict migration patterns of plants, and potentially even carbon uptake. It boils down to one thing: sex does matter, and it can no longer be ignored in plant modelling efforts.

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“Simply” Modelling Complex Systems

Complex systems are everywhere: living organisms, ecosystems, financial systems, computing systems, and more. The stability of these systems is something that we take for granted, as we assume that living things won’t suddenly die without warning or that ecosystems won’t suddenly collapse. Complex systems are groups of interacting components, however there are enough interacting components that even if we can predict how each component operates by itself (e.g. cells in our body, financial transactions) we cannot easily predict how a system of interacting components will behave (e.g. our bodies, the global economy).

There has been a long standing debate on what makes complex systems such as these stable. One argument is that diversity in the components of a complex system make it more resilient, and therefore more stable, under the influence of disturbances. The second argument is that greater diversity increases the complexity of the system and makes it more likely to collapse, since there are more opportunities for components of the system to break down. Fyodorov and Khoruzhenko (2016, PNAS 113:6827-6832) sought to study the collapse of complex systems using a simplified mathematical model. Through their analysis, Fyodorov and Khoruzhenko find that as the diversity of a system increases, the proportion of stable states decreases.

The model developed by Fyodorov and Khoruzhenko considers only randomly assembled, randomly interacting components. While their model may imply that diversity in complex systems such as ecosystems could introduce instabilities, biological systems and financial systems are not randomly assembled or randomly interacting. Further work on specifying their model for non-random systems may reveal insights into how ecosystems are structured and resist disturbance, and how to predict the dynamics of such complex systems.

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A biologist’s perspective on modelling

Before I came out to Colorado State University, the first thing that jumped to mind when I heard about modelling in science was ‘predicting the future’. Predicting the future – that would be fascinating wouldn’t it? That’s what the Intergovernmental Panel on Climate Change tries to do with Earth using mathematical modelling of the climate system. However mathematical modelling can do more than surmise about the future (which is difficult to do, even with a good model and good data).

I’ve learned that modelling can help us to understand nature in a way that may be inaccessible to experimentation (e.g. take too many experiments to reach the same conclusion), and it can reveal gaps in our knowledge. Modelling can do this because, being virtual, we can run thousands or millions of ‘experiments’ in mere minutes, an enormous task outside of a computer. In biology, we can vary the information that goes into the model to see what happens. For example, if we were interested in what would happen if we made photosynthesis less sensitive to temperature in a plant, we could simply change the numbers that describe the temperature response of photosynthesis and run a simulation. An experiment with real plants would test the limits of genetic engineering, not to mention take countless hours to run.

Now suppose instead that we were confident that we knew everything about photosynthesis and how a plant uses photosynthesis to grow – how would we know if we were right? Well, if a model could accurately describe the results of an experiment with many different environmental conditions, it would suggest that we were correct. However, what if the model just couldn’t quite match the results of a single group in our experiment, but could perfectly describe everything else? This indicates that we are missing something that is fundamentally different in the underlying biology. In this case we can try to figure out what could be different, and test our ideas using the model, or we could use the information that the model gives us to run another experiment.

Modelling then becomes part of the toolkit of science, rather than a goal, that we can use to further our knowledge. As Donald Rumsfeld (the origin of the phrasing is murky) once said, “there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we know”. As I’ve come to realize thus far in my trip, this message describes exactly how we can use mathematical modelling – we construct a model of the known knowns (e.g. the current state of knowledge on photosynthetic biology), when the results of the model are off by a consistent quantity, this tells us the known unknowns (e.g. we know that there is something we don’t understand that determines how photosynthesis contributes to growth), and lastly, if model predictions are off in only a few special cases, then that reveals the unknown unknowns to us. Modelling thus allows us to figure out what we didn’t know we didn’t know so that we can experiment and uncover new knowledge.

And so, if you have ever doubted or misunderstood the use of modelling in science, keep in mind that it can be a powerful tool to further experimentation in a given field and our understanding of science as a whole.

Stay safe and stay informed,

Maple Syrup, Aged to Perfection

Have you ever asked yourself the question, “how old is the sugar in my maple syrup”? Maple syrup is created by boiling down sap from sugar maple trees, concentrating the sugar into a viscous fluid. The sugars are produced through photosynthesis in leaves, and extra sugars are stored in woody parts of the tree. The sugars in maple syrup come from these carbohydrates stores – however the age of the sugars in the sap used to produce maple syrup has never been determined.

In a recent paper, Muhr et al. (Muhr et al., 2015, New Phytologist, in press) investigated the age of sugars in that delicious breakfast syrup. To do this, they used a radiocarbon dating method, similar to the one commonly used to determine the age of archaeological artifacts, which uses a carbon isotope signature to calculate the age of the carbohydrates released during early spring (when sap is harvested for maple syrup). They found that the carbon signature of sugar maple sap matches sugars produced 3 to 5 years previously, and due to the well-mixed nature of these sugars, the sap likely contains sugars that were produced greater than 5 years ago as well as recently produced sugars.

These findings have implications that go far beyond maple syrup. If the trees can use carbohydrate stores produced many years ago, this could make sugar maples resilient to environmental stress, including drought and insect attack, and allow survival of several years under poor growing seasons. It then becomes possible for sugar maples to maintain sap production even if the previous growing season was poor.

So the next time you’re eating maple syrup, think about how those sugars make sugar maples resilient, and how you’re eating a little bit of history.

Stay safe and stay informed,
Joseph Stinziano

Science in the Political Sphere – Time to #ActOnClimate

The Canadian federal election was just called, meaning that campaigning has begun. One thing that is rarely discussed by Canadian politicians to the public is science. We all need science – from the water you drink and the food you eat, to the bicycles, planes, and automobiles that you use to travel long distances – without science we would not have any of that. Humanity faces grand challenges this century due to climate change, and we need to support the science that will help us adapt to our changing planet.

So, what are these grand challenges? The grand challenges include the sustainability and stability of our food and water supply in the face of rising temperatures, increased frequency of extreme weather events (such as hurricanes, drought, and heat waves), and shifting precipitation patterns, all the while producing more food to feed more people. Solving these grand challenges won’t be easy, but is possible – scientists around the world are currently working towards solving these problems. For example, one initiative funded by the Bill and Melinda Gates Foundation is called RIPE, for Realizing Increased Photosynthetic Efficiency, which is about increasing food production by improving upon photosynthesis in crops. (You can read more about it at However, while scientists are working on solving these grand challenges, politicians have been cutting science funding. This reduces the resources available to scientists to solve humanity’s grand challenges, and without breakthroughs in basic and applied science, solving these challenges will remain out of our reach.

So, as the next election cycle begins in your respective countries, let us unite together and make science an important topic in elections worldwide. Let’s finally make politicians #ActOnClimate.

Stay safe and stay informed,
Joseph Stinziano

Very variable climate, very variable yields

Variability. It’s something we tend to forget about, especially when it comes to climate change. Most research into the effects of climate change on crops focuses on changes in the average temperature. In reality, climate change is more complicated than just an increase in average global temperature. It is expected to cause an increase in temperature variation, that is, more frequent extreme temperature events and larger fluctuations in temperature from day to day, season to season, and year to year. This greater variation leads to an increased chance of extreme warming events that can adversely affect crop yields.

So how does variation in temperature affect crop yields exactly? Ray et al. (Ray et al., 2015, Nature Communications, 6:5989) set out to determine how variations in temperature and precipitation have affected crop yields, and more specifically, variation in crop yields. Stable crop yields provide a stable food supply and stable income for farmer, while highly variable crop yields can affect food prices, food security, and the ability of farmers to continue farming. Looking at historical data on climate variability and yields of wheat, rice, soybean and maize, Ray et al. found that approximately one-third of variation in crop yield on a yearly basis was due to variations in climate. In some regions, such as the Midwestern United States, as much as 75% of the variability in crop yield was due to climate variability. Increasing climate variability is thus a serious issue that challenges global food security.

So what is an appropriate solution? As Ray et al. point out, some agricultural regions showed no relationship between crop yield and climate variability, since farmers were already adapting their practices to a changing climate. Therefore, a cursory glance suggests that to ensure food security in the face of a changing climate, global agriculture needs to adapt to the changing climate, or risk catastrophe.

Stay safe and stay informed,