NATURE BIOTECHNOLOGY VOLUME 23 NUMBER 12 DECEMBER 2005 1475

Reassessing the environmental risks of GM cropsTo the editor:A decade has now passed since the first commercial releases of genetically modified (GM) crops, a period marked by great controversy over the potential ecological effects of this technology. As a result, the procedures for environmental risk assessment and monitoring have developed rapidly to become formalized within scientific1–3 and regulatory frameworks, such as the Cartagena Protocol and the European Commission’s (EC’s) Directive 2001/18. In practice, these have tended to focus upon risks that can be researched at the small scale, at the expense of assessing the much larger scale risks and benefits of changes to the farming system.

Ecological risk assessments of GM crops have tended to focus upon gene flow to wild relatives and upon effects on species that use the crop as food, either directly or indirectly. Such trials are essential for novel traits. For traits already widely commercialized, studies of gene flow to wild relatives confirm again and again that, given the opportunity, genes will indeed flow, albeit at low frequencies. Yet the ecological consequences of such gene flow are not obvious, especially when compared with the effects of gene flow from conventional crops. One potential consequence is that the gene may make wild populations more invasive, especially should the GM trait confer a selective advantage. However, all the evidence we are aware of suggests that this does not happen4. In any case, it is very difficult to detect rare events using a screening program5; decision theory suggests that it may be a waste of effort trying unless the potential for beneficial effects is very low and the risks are very high and can be identified very accurately6. Indirect effects of GM crops on wild species through changes to the diet are also widely researched. Certainly, there are effects of Bacillus thuringiensis (Bt) toxin crops on the crop pests themselves. Although effects on nontarget organisms can be observed in the laboratory, such experiments are not suitable for extrapolation to the field7, where evidence for effects is much less

widespread2. Indeed, we have surprisingly little evidence of environmental risks to nontarget organisms arising directly from the use of current GM crops.

By contrast, much larger environmental effects of GM crops have been observed when mediated through indirect, larger changes to the farming system. Thus, in the UK’s Farm Scale Evaluations (FSEs) of herbicide-tolerant crops, no direct effect of the GM trait was observed on weed and invertebrate abundance, whereas the different herbicide regimes associated with the GM and conventional varieties often resulted in fivefold treatment effects on individual taxa. Yet the overall effects on biodiversity were less than those resulting from the choice of crop species8. At the landscape level, there is the potential for large-scale ecological impacts of GM crops through changes to rotations and cultivations, and to the area and distribution of the crop9.

It seems to us that the risks of irreversible harm to ecosystems resulting from gene flow from GM crops with well-researched traits of herbicide-tolerance and Bt are most likely to be confined to situations where a wild relative found in the receiving environment is of conservation value. In other cases, the risk of a harmful direct ecological effect is so low, and so hard to detect, that the additional information gained by screening may not be worth the effort.

We suggest that case-by-case assessments drawing on small plot and laboratory experiments are unlikely to provide useful data for traits that have already been widely

commercialized in other crops and/or other receiving environments. To have gained approval, such crops will have been studied in great detail in a multitude of small-scale trials. Instead, there is a strong case to re-assess environmental risks using a small number of large-scale, appropriately powered field studies on a trait-by-trait basis, to determine which signals can be tested for in the laboratory and can show whether the new crop conforms to the typical behavior of its trait.

What would such a trial look like? Like the FSEs, it might focus on a single trait in a variety of crop species under a realistic range of field management conditions. Measurements would be taken of variables that help relate crop characteristics to larger scale effects, for example, expression of Bt toxicity in the plant to changes in invertebrate populations. Such studies would be both cost effective and scientifically rigorous as they would show under what circumstances the introduction of the trait might trigger large-scale effects, enabling more effective regulation and mitigation.

Large environmental effects are more likely to be triggered through changes to land use (e.g., cropping versus grazing) and to farm management systems (e.g., tillage practice). Such processes are not irreversible and can be easily monitored in the early post-commercialization phase by collecting data on crop distribution and management and appropriate indicators of biodiversity and landscape at an appropriate scale. Such systems could be designed for any potential change in land use, whether technology or policy driven. Monitoring systems should engage stakeholders throughout, partly to build trust and legitimacy, but also to ensure that the goals and trigger points of monitoring are consistent with their needs3,10.

In short, for well-known traits, we need to move away from a model of assessing risk to one of assessing the degree to which the new technology improves, or detracts from the delivery of social, economic and environmental aspirations.

…there is a strong case tore-assess environmental risks using a small number oflarge-scale, appropriately powered field studies on atrait-by-trait basis…

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1476 VOLUME 23 NUMBER 12 DECEMBER 2005 NATURE BIOTECHNOLOGY

Relationships between characters of the traits and landscape-scale impacts need to be informed by appropriate large-scale monitoring, experimentation and modeling, whereas impacts need to be interpreted on the basis of a clear understanding of what people want from the agricultural landscape. The use of environmental risk assessment (ERAs) which involve the development of conceptual models outlining assessment and measurement endpoints could offer a powerful framework3 for us to move forward and allow larger scale studies to start receiving the attention they deserve.

Les Firbank1, Mark Lonsdale2 & Guy Poppy3

1Center for Ecology and Hydrology, Lancaster LA1 4AP, UK. 2CSIRO, Australian Commonwealth Scientific and Industrial Research Organization, Entomology, GPO Box 1700, Canberra 2601, Australia. 3School of

Biological Sciences, University of Southampton, Southampton SO16 7PX, UK. e-mail: lgf@ceh.ac.uk

1. Poppy, G. Trends Plant Sci. 5, 4–6 (2000).2. Snow, A.A. et al. Ecol. Appl. 15, 377–404 (2005).3. Poppy, G.M. & Wilkinson, M.J. (eds.) Gene Flow from

GM plants (Blackwell Scientific, Oxford, UK, 2005).4. Crawley, M.J., Brown, S.L., Hails, R.S., Kohn, D.D. &

Rees, M. Nature 409, 682–683 (2001).5. Jorgensen, R.B. & Wilkinson, M.J. in Gene Flow from

GM plants (eds. Poppy, G.M. & Wilkinson, M.J.) 113–142 (Blackwell Scientific, Oxford, UK, 2005).

6. Smith, C.S., Lonsdale, W.M. & Fortune, J. Biological Invasions 1, 89–96 (1999).

7. Lovei, G.L. & Arpaia, S. Entomol. Exp. Appl. 114, 1–14 (2005).

8. Firbank, L.G. et al. The Implications of Spring-sown Genetically Modified Herbicide-tolerant Crops for Farmland Biodiversity: A Commentary on the Farm Scale Evaluations of Spring Sown Crops (Defra, London, 2003).

9. Firbank, L.G. & Forcella, F. Science 289, 1481–1482 (2000).

10. FAO Expert consultation. Genetically Modified Organisms in Crop Production and Their Effects on the Environment: Methodologies for Monitoring and the Way Ahead (FAO, Rome, Italy, 2005).

Bollworm resistance to Bt cottonin IndiaTo the editor:I am writing in response to a letter of correspondence from Govind Gujar in the August issue (Nat. Biotechnol. 23, 927–928, 2005) and a news story by K. Jayaraman, Jeffrey Fox, Hepeng Jia and Claudia Orellana in the February issue of Nature Biotechnology (23, 158, 2005) that refer to research in my group describing a stochastic model to predict the emergence of resistance in the cotton bollworm Helicoverpa armigera to cotton varieties containing the cry1Ac gene encoding Bacillus thuringiensis (Bt) toxin1. The former article contains serious factual errors and the latter misrepresents the implications of our work.

In his letter, Gujar points out that we predicted that Bt cotton will fail in India within the next 3 to 4 years. We never said anything of the sort, either in our paper or elsewhere.

First of all, he presumes (incorrectly) that 70–80% of Gujarat was under Bt cotton cultivation for the past 3 years. On this basis he goes on to ask: “Given that the Bt crop in Gujarat covers the requisite area and is

already in its fourth year of cultivation, why have we not witnessed the failure of Bt cotton due to resistance development in the cotton bollworm, Helicoverpa armigera?” The reason is that Gujar’s calculations and assumptions of the requisite area of Gujarat

under Bt cotton cultivation are simply wrong.

In fact, the 100- to 200-km radius area of Bt cotton cultivation modeled in our paper represents 7.8–31.4 million acres—about 100–200 times greater than the acreage he calculates. He compounds his error by assuming that 70–80% of this area (77,628–155,256 acres by his reckoning) was used to grow Bt cotton. This is subsequently used as a

basis to argue that because resistance has not yet been detected in Gujarat (despite 70–80% of the area being under Bt cotton cultivation), “the effectiveness of insect resistance management strategy is likely just one of several factors that will determine the effectiveness of Bt cotton in suppressing bollworm populations.”

Because his presumptions are wrong his subsequent assumptions are invalid. Realistic estimates of the area in Gujarat cultivating

authorized Bt cotton hybrid are 22,500 acres in 2002, 130,000 acres in 2003 and 330,000 acres in 2004. Even if the area under unapproved illegal Bt cotton were five times (it could actually be only 2–3 times) that of the area cultivating authorized Bt cotton, it would still have been only 4% of 3.75 million acres in 2002, 16% of 4.12 million acres in 2003 and 33% of 4.98 million acres in 2004. Farmers in Gujarat have been cultivating Gossypium herbaceum diploid cotton ‘Wagad’ varieties on saline soils, which are unsuitable for hybrid cotton, in about 50% of the area under cotton cultivation over the past several years. Thus, in all likelihood the area used to grow Bt cotton would not exceed 50% of the total cotton area in the province.

Our surveys in 2004 showed that the area under Bt cotton cultivation in Gujarat was 60–70% of the area under hybrid cotton, which would constitute about 30–35% of the total cotton area. With a scenario such as this, our model predicts insect resistance will evolve under field conditions not for at least another 10 years. And if resistance management strategies are implemented, it will take much longer. On the basis of the model output, our paper suggested strategies appropriate for Indian conditions that have the potential to delay resistance up to 45 years, even with the complete hybrid cotton area converted to Bt cotton. Our statement in the paper (which was misquoted by Gujar) says “it is likely that some regions may develop into ‘hot spots’ of resistance within 3–4 years of introduction of the technology, if the area under Bt cotton hybrids increases beyond 70–80% of the total acreage under cotton.” Development of hot spots contributes to the spread of resistant alleles in the region through migrant moths and does not necessarily cause control failures at the spot itself.

The views of Tabashnik2 support our model predictions and highlight the importance of refuges as a key factor in contributing to the delay in insect resistance development, despite the cultivation of Bt cotton and Bt corn in more than 90 million hectares worldwide since 1996.

Though Jayaraman and his associates interpreted our results to state that “Indian Bt gene monoculture” was like a “potential time bomb,” our aim was never to create panic by predicting resistance through stochastic modeling. We strongly believe that the Bt technology is the best eco-friendly tool available for cotton pest management in India. We wanted to integrate all factors that influence development of insect resistance to the toxin, through stochastic modeling, so that appropriate strategies can be devised to

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