Agricultural impacts: Big data insights into pest spread

news & views a process called translation — are positively correlated with temperature. Experimental studies in the laboratory show that the translation apparatus of two diatom species worked most efficiently when grown at temperatures close to average equatorial surface waters, and were less efficient at Arctic temperatures as the cold slowed down the molecular machinery. However, Toseland et al. observed that actual cellular productivity in the Arctic and Antarctic was not as repressed as it should be, despite the colder water. They attribute this to a considerable increase in abundance of the cellular translation machinery that helps to build protein, so called ribosomes, which are bound in P-rich RNA (Fig. 1). Hence, to overcome the low water temperatures (average of 2 °C) and concomitant reduction in efficiency, these cells just make more protein factories to maintain their productivity. As this requires more P, the N:P ratio in their cells is reduced. This information led to the development of a physiological model of the phytoplankton cell that described how much available P and N the cell would use for creating protein, versus how much it would put into creating RNA. The problem is that RNA uses more P, which is often a limiting nutrient in the world’s oceans3; therefore if the cell diverts its resources to create more RNA-laden ribosomes to overcome their reduced efficiency, it needs more P than cells found in warmer water at the equator. The authors placed their model cell in a computer-generated model ocean that replicates the changing temperature, nutrient availability and amount of light that real phytoplankton cells would experience across the global ocean. The model validated the hypothesis that under low temperatures the cells invested more in their cellular machinery to overcome the inefficiency of their factories; whereas under higher temperatures the cells invested in photosynthesis and hence biomass. In further work they artificially raised the average sea surface temperature by 5 °C, and observed what happened to the phytoplankton cell. As the polar sea warmed up, the phytoplankton cell reduced the production of P-rich ribosomal RNA, changing the cellular N:P ratio, which by definition fundamentally alters this ratio in organic matter. Why does this matter? If the N:P ratio increases then the cell has an increased N requirement, which will cause N to become a limiting resource. Nitrogen limitation could reduce photosynthetic productivity causing an increase in carbon flux from the surface ocean to the atmosphere, thereby resulting in a significant reduction in carbon sequestration by the ocean. Potentially this could result in a catastrophic positive feedback loop, as more atmospheric carbon equals more warming9. Although this model represents one of the most sophisticated methods for capturing and predicting the result of rising temperature on global oceanic primary productivity, it still has limitations. For example, it doesn’t take into consideration the changes in atmospheric carbon levels, which could bolster photosynthetic efficiency and inflate predictions. The model also doesn’t account for cyanobacteria, the other major phytoplankton group in the ocean, nor the interactions with other non-photosynthetic bacteria. Future work should focus on the integration of these efforts to create a comprehensive model that will enable us to predict the real outcome of climate change and global warming in this essential system. ❐ Jack A. Gilbert is at Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, USA and University of Chicago, 9700 South Cass Avenue, Lemont, Illinois 60439, USA. e-mail: References 1. Toseland, A. et al. Nature Clim. Change 3, 979–984 (2013). 2. Jorgensen, B. B. & Boetius, A. Nature Rev. Microbiol. 5, 770–781 (2007) 3. Falkowski, P. G., Barber, R. T. & Smetacek, V. Science 281, 200–206 (1998). 4. Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. G. Science 281, 237–240 (1998). 5. Bopp, L., Aumont, O., Cadule, P., Alvain, S. & Gehlen, M. Geophys. Res. Lett. 32, L19606 (2005). 6. Shuter, B. J. Theor. Biol. 78, 519–552 (1979). 7. Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Science 315, 1843–1846 (2007). 8. Weber, T. S. & Deutsch, C. Nature 467, 550–554 (2012). 9. Martiny, A. C. et al. Nature Geosci. 6, 279–283 (2013) AGRICULTURAL IMPACTS Big data insights into pest spread Pests and diseases reduce agricultural yields and are an important wildcard in the evaluation of future climate impacts. A unique global record of pests and diseases provides evidence for poleward expansions of their distributions. Karen A. Garrett F ood security depends on our ability to effectively manage crop pests (arthropods and pathogens). Because of the important effects of weather variables such as temperature and precipitation on crop pests, scientists have for some time hypothesized that where climate change results in a more (less) favourable environment for pest establishment, losses to unmanaged pests are likely to increase (decrease)1. But evidence that ranges have shifted under climate change is often anecdotal, and the availability of long-term data sets of pest occurrence is limited2,3. In this issue of Nature Climate Change, Bebber and colleagues4 present an analysis of decades of reported pest distributions, concluding that pests have moved towards the poles over the past fifty years, in line with expectation under climate change. One of the interesting aspects of this analysis is its reliance on ‘big data’. The data set that Bebber and colleagues4 analysed, although not challenging in terms of sheer storage and computational requirements, has been assembled over some time as NATURE CLIMATE CHANGE | VOL 3 | NOVEMBER 2013 | www.nature.com/natureclimatechange © 2013 Macmillan Publishers Limited. All rights reserved many, many individuals reported where and when they found particular pests. In their popular book, Mayer-Schönberger and Cukier 5 discuss three aspects of big data that present challenges for scientists. The first is a shift towards using large amounts of data from different sources, often collected for different purposes. The second is an acceptance of ‘messiness’, where having large amounts of data may make up for introducing increased sources of variability, and potentially even for introducing bias (more on that later). The 955 news & views Past Present ... Reported pest distribution C Challenges for interpreting pest observations . Problem of lack of zeroes: People rarely carefully evaluate and report the absence of a pest . Many factors determine the distribution of pests, and the reported distribution of pests . There is the potential for unknown forms of bias in observations ... Actual pest distribution A, B ... Climate favourability for a pest A Weather variables affecting pest establishment B Other factors determining pest establishment C Oth (...truncated)