ECOLOGICAL RESEARCH AND RISK ASSESSMENT OF GENETICALLY ENGINEERED CROPS
Alan Raybould

In a 1995 paper, "Assessing weediness of transgenic crops: industry plays plant ecologist," Purrington and Bergelson¹ criticized the quantity and quality of data used to determine the weediness potential of genetically engineered (GE) crops then under regulatory review. The implication of the title was that risk assessments need some serious plant ecology that was not being provided by regulatory studies. Purrington and Bergelson's critique is an example of a widely held view that data obtained by basic ecological research is essential for environmental risk assessment; however, while basic research and risk assessment are similar, there are important differences that, if not recognized, can be detrimental to effective risk assessment².

Similarities between basic research and risk assessment
The idea that science derives true statements ("laws") inductively from accumulated observations is intuitive and widespread. Nevertheless, philosophers since David Hume in the eighteenth century have pointed out that induction is logically flawed—any number of events agreeing with a law cannot guarantee that subsequent events will be in agreement. The impossibility of proving empirical laws is the so-called problem of induction. In the 1930s, Karl Popper offered a solution to the problem for science by proposing that science does not verify laws but postulates theories that are tested by comparing predictions with observations. A theory that has withstood rigorous testing may appear to be a law, but further testing may show the theory to be false.

In later works, Popper proposed that his logic of scientific discovery applied to the development of all objective knowledge: a problem is identified; a trial solution to the problem is proposed; the solution is tested to eliminate errors; and corroboration or falsification of the trial solution provides new knowledge with associated new problems. The process can be represented schematically³:

In science, problems are inconsistencies or inaccuracies of existing theory about nature; trial solutions are testable hypotheses; error elimination is the testing of those hypotheses; and new problems are improved theories with different inconsistencies or inaccuracies.

Effective risk assessment fits Popper's scheme for the development of objective knowledge: it cannot prove something is safe, but it can increase knowledge of risk by testing hypotheses⁴. A risk assessment begins with the identification of protection goals, the desired state of environmental variables, which are often specified by law; they are usually general objectives, such as protection of the environment, which are difficult to analyze scientifically. From general goals, specific targets for protection are identified; for example, if the protection goal is maintenance of biodiversity, a specific target may be the population size of certain species in a given area. The targets are called assessment endpoints, and their unambiguous specification is crucial to focus the risk assessment. Identification of protection goals and derivation of assessment endpoints defines the initial problem [P₁].

Next, one develops a conceptual model that describes how what is proposed, such as the cultivation of a GE crop, may adversely affect the assessment endpoints. From the conceptual model, specific risk hypotheses are derived. Risk hypotheses postulate the absence of phenomena necessary for harm (unacceptable adverse effects)⁴; for example, if adverse effects could arise because of toxicity of a transgenic protein, one could test the conservative hypothesis that the assessment endpoints will not be exposed to concentrations of the protein in excess of the lowest concentration that could have an adverse effect. The risk hypotheses are trial solutions [TS].

Risk hypotheses are tested with data acquired from the literature or from new studies: this is error elimination [EE]. New studies should be required only if existing data do not test the risk hypotheses with sufficient rigor to adequately characterize the risk (the probability and magnitude of potential adverse effects to the assessment endpoints); for example, regulatory risk assessments usually do not require new data on horizontal gene flow because sufficient data exist to satisfactorily corroborate the hypothesis that harm will not arise by this route.

Tests of a risk hypothesis characterize risk and lead to a new problem [P₂]. In general, this problem is whether to stop testing because risk is adequately characterized, or to require further studies: new data may be required to test an existing risk hypothesis more rigorously, or to test a new risk hypothesis⁴. A decision to stop testing does not mean that the risk hypothesis is proven; new information, or a new definition of harm, may trigger further testing.

Basic research and risk assessment cannot inductively verify laws or prove safety. In both domains, experiments should test hypotheses, not seek unattainable proofs by accumulating data. While basic research and risk assessment have the same logic, it is important to recognize their differences to maximize the effectiveness of studies for risk assessment.

Differences between basic research and risk assessment
Basic research and risk assessment produce objective knowledge; however, the production of objective knowledge has subjective elements. In basic research, selection of problems depends upon subjective personal interests of scientists, and societal interests mediated through allocation of research grants; in risk assessment, problem selection depends on what society regards as harmful. A mistaken idea that problem selection is objective may not matter for basic research; however, attempts at objective problem selection are detrimental for risk assessment because it must focus on protecting things of value, which cannot be deduced objectively².

A second important difference between basic research and risk assessment is the nature of the hypotheses under test. All scientific hypotheses seek to be accurate and effective. In basic research, effective hypotheses are interesting, which is judged by their testability and precision³; for example, a hypothesis that predicts rain will fall in London tomorrow at 3pm is more testable (more at risk of being shown to be false) and interesting than a hypothesis that predicts rain will fall somewhere in Europe in the next month. In risk assessment, the effectiveness of a hypothesis comes from its value for decision-making. A hypothesis that accurately predicts no harm is more effective than a hypothesis that makes precise predictions of undefined consequence; for instance, a hypothesis that predicts no hybrids will form between a GE crop and a wild relative in a given area is more effective than a hypothesis that predicts 30,000 hybrids will form. The first is straightforward to test, and it is easy to see how corroboration or falsification would lead to different decisions; the second is more interesting scientifically, but is difficult to test, and unless 30,000 hybrids is a threshold for harm, it is difficult to see how testing this hypothesis could help decision-making.

A similar problem arises when basic research tests a null hypothesis of no difference. Many ecological studies compare the abundance and diversity of insects in fields of GE and non-GE crops. In these studies, no precise prediction is made; however, precision of prediction is replaced by precision of measurement. As many variables as practicable are measured to test the null hypothesis or no difference, whereas a risk assessment study should concentrate on variables that indicate potential harm².

A final contrast between basic research and risk assessment is the preferred method for testing hypotheses. In general, ecology has been wary of laboratory testing: laboratory studies may…"magnify incidental or trivial factors…indeed, laboratory experiments can likely show some effect of any factor by using sufficiently extreme conditions. Laboratory studies are effective in isolating the response to a factor, but the response may not be ecologically relevant"⁵. Over-estimation of the importance of an effect is problematic in basic ecology because one may waste time developing theories with no predictive power in the field. On the other hand, over-estimation of effects is valuable for testing risk hypotheses of no harm, because if the effect cannot be detected in the laboratory, one has high confidence of no effect under field conditions⁴. Tests of a risk hypothesis in the field are less rigorous, and conclusions may apply only to the particular conditions of the test. Thus, laboratory studies are usually more rigorous tests of risk hypotheses, whereas field experiments are more rigorous tests of basic research hypotheses.

Effective studies for environmental risk assessment of GE crops
Studies for environmental risk assessment of GE crops should follow three principles. First, studies should test risk hypotheses that postulate the absence of harm. Secondly, studies should test risk hypotheses rigorously, which usually means under strictly controlled laboratory conditions. Finally, studies must have the potential to improve risk characterization and thereby increase confidence in decisions: if existing tests have not falsified the risk hypothesis, but there is still unacceptable uncertainty about the amount of risk, additional studies are warranted should they provide a more rigorous test of the hypothesis. If laboratory tests have falsified a risk hypothesis, additional "higher tier" studies can test a new risk hypothesis that harm will not be realized under conditions of greater realism; however, confidence in, and the generality of, the conclusions will be less than from corroboration of the original hypothesis in the laboratory⁴.

Basic ecological research is driven by different principles (Table 1) and is characterized by attempts to describe nature in detail⁵, not to predict the likelihood of harm². Failure to address harm means that basic ecological research is a source of what Craig et al.⁶ call, "the risk of competent authorities being submerged by excessively large amounts of data that may be of questionable pertinence to verifiable safety questions."

HONEY BEES, Bt CROPS, AND THE ROLE OF META-ANALYSIS IN RISK ASSESSMENT
Michelle Marvier

As the world's most abundant and widespread pollinator, honey bees (Apis mellifera) play a critical role in our food security and make an important contribution to the human economy. Because of their global importance to agriculture, there has been a great deal of consternation surrounding the widespread recent decline of honey bee populations¹. Amid the sometimes wild speculation about what might be causing these declines, it has been suggested in several popular media outlets that pollen from genetically engineered Bt crops might be poisoning honey bees^{2, 3}. It should be emphasized, however, that there are not any scientific publications indicating that Bt crops have anything to do with honey bee declines. All studies performed to date support the contention that Bt toxins, called Cry proteins, are in fact toxic to only a narrow range of insect groups. For example, the Bt transgenes that have so far been incorporated into crops such as corn and canola are toxic to Lepidoptera (butterflies and moths) or Coleoptera (beetles). Although there is the possibility that these Cry proteins are toxic to certain taxa, such as caddisflies (Trichoptera), that are closely related to the target group⁴, hymenopterans (bees, wasps, and ants) are not closely related to beetles or butterflies and moths, and in the laboratory no toxicity test has ever shown Cry proteins to cause any harm to hymenopterans.

Nonetheless scare stories about Bt crops and bees keep circulating. The question is whether, given the absence of evidence implicating Bt crops in honey bee declines, is there anything more that can be done to eliminate this worry once and for all? In order to fully lay to rest the worry about Bt pollen and honey bee declines, there is indeed an additional analysis that needs to be done—one that in a quantitative way "adds up" all evidence from independent experiments that have assessed impacts of Bt toxins on honey bees. This additional step, called meta-analysis, has gained prominence in clinical trials and the medical arena, where one has to be very careful before deciding that a treatment is relatively risk-free. The crux of meta-analysis is the realization that an absence of significant effects in a collection of individual studies is not necessarily as convincing as it might first seem. The problem is that the individual risk assessment or toxicity studies may be poorly replicated and thus have low statistical power. For example, a study might expose three groups of honey bees to a Cry protein incorporated into a standard diet and three groups of honey bees to a control diet, lacking the Cry protein. No matter how many honey bees are in each "group," the replication of the study is only n = 3. With such low replication, only a large and very consistent difference between the two treatments in the survival, development, or growth of the honey bees could be detected as statistically significant. The weak statistical power of these studies means that a finding of no significant effect is not very convincing.

A sample size of just three replicates per treatment might sound unrealistically low, but the reality is that three replicates meets the EPA standards for assessing risks to honey bees and other nontarget invertebrate species. Many studies do use more than three replicates, but in general the level of replication used in industry studies for nontarget invertebrates such as honey bees is only n = 2 – 6 replicates per treatment⁵. However, wouldn't it be reassuring if there were a dozen or so of these poorly replicated studies, all indicating no significant effect of Bt pollen? The answer is no. In fact, a simple tally of the results (the number that found, versus didn't find, significant effects) from a collection of weak studies is not much more convincing than the findings of each individual study on its own. Among statisticians, such tallies are called "vote counts" and if one thinks about it a bit, it is pretty obvious that even a dozen studies, all with poor replication, finding no effect would not constitute convincing evidence that no effect actually exists.

Meta-analysis to the rescue
Fortunately, meta-analysis provides an alternative to vote counting. By statistically combining the observed differences between treatments and controls across a group of independent studies, and weighting the results of each experiment by the variance in the data, one comes up with an estimate of the general effect size across experiments. This resultant effect size is much richer than simply stating that 9 of 11 experiments or even 11 of 11 experiments found no significant impact on honey bee survival. It is possible that, by applying meta-analysis to a set of poorly replicated studies, a more reassuring picture may emerge. Of course, it is also possible that a meta-analysis will draw out small but potentially biologically important effects that went undetected by any individual study.

Meta-analysis of clinical trials has revolutionized health care, and in ecology, meta-analysis has produced some of the clearest general evaluations of predation, competition, and herbivory. In conservation of endangered species, meta-analysis is just now being adopted as a way to assess the effectiveness of alternative management actions. Risk assessment of genetically modified crops should similarly embrace this paradigm.

To facilitate meta-analysis of risk assessments that have examined the nontarget effects of Bt crops, my colleagues and I have created a searchable database, publically available at http://delphi.nceas.ucsb.edu/btcrops/. Between April 2007 and February 2008, this database was queried by visitors from 215 unique IP addresses, and we hope scientists around the world will use the data to ask questions that summarize all of the evidence available about the nontarget effects of Bt crops. Initial meta-analyses of the field (as opposed to laboratory) studies included in this database have recently been reported⁶, but to date there have been very few field studies that have recorded the abundance of honey bees.

How about those honey bees?
The question of whether Bt crops might be contributing to honey bee declines was clearly in need of a more definitive answer. To provide that answer, and also to demonstrate the general utility of a meta-analysis approach for risk assessment, my coauthors and I assembled and analyzed a collection of 39 independent assessments (appearing in a total of 25 separate publications or unpublished industry reports) that examined the direct effects of Bt Cry proteins on the survival on honey bee larvae and adults in a laboratory setting⁷. Our meta-analysis of the data from these studies revealed no adverse direct effects of Bt Cry proteins on the survival of either larval or adult honey bees. I should note, however, that the studies synthesized in our meta-analysis were all laboratory experiments, so there is still some possibility that different results might be seen in the field, where the stresses of weather, disease, and so forth might alter the susceptibility of honey bees to Bt toxins. On the other hand, the studies from which we drew these data were all so-called Tier I studies that exposed honey bees to extremely high concentrations of Bt toxins—at least an order of magnitude greater than the concentrations that bees would encounter in nature. Given these caveats, we believe that our meta-analysis strongly supports the conclusion that the Cry proteins expressed in the current generation of Bt crops are unlikely to have adverse direct effects on honey bees.

Evidence-based risk assessment
We often hear about all the experience and trials and tests that have been done to assess the safety of genetically modified crops, but there is no single place for concerned citizens or even scientists to turn to in order to see if they are themselves convinced by the accumulated evidence. We believe that creating large open-access databases that include data from all relevant risk assessment studies is the future of risk assessment. With such databases at hand, the power of meta-analysis and of a global community of scientists can be turned loose.

A formal synthesis of what scientists had learned about the effects of Cry proteins for honey bees had been lacking until recently. Once we put together the database, the meta-analysis was straightforward. However, there are many other questions about impacts on other groups of organisms, or concerning how effects might correlate with differences in everything from body size and growth rates to the experimental methods used to assess the risks. The construction of databases detailing the methods and results of all sorts of risk assessment studies, followed by the creative application of meta-analyses to these data, offers the clearest path to the sort of transparent cost-benefit analyses that society deserves.

Meta-analyses have the potential to move the debate about the safety of genetically modified crops beyond a situation in which competing sides argue that "study X shows this" only to be countered with "yes, but studies y and z show the opposite." Indeed, no single study should, by itself, be taken too seriously until other studies have confirmed the findings. Yet there are so many scientists doing so many different experiments and risk assessments that the information has the potential to overwhelm decision makers or cause the debate to zig-zag around. If meta-analyses and large databases of completed studies were to become a routine part of risk assessment, then there would not be the distraction of single experiments capturing media attention and inappropriately alarming or comforting the public and policy makers. An investment in the creation and maintenance of risk assessment databases will have high payoff in terms of improved transparency, increased public confidence in the process, and more rapid advancement of scientific understanding.

The creation of the "Nontarget effects of Bt Crops" database was supported by EPA grant CR-83214701 awarded to Michelle Marvier and Peter Kareiva of Santa Clara University.

Introduction
Public debate about the ethical acceptability of genetically engineered (GE) plants in general, and food in particular, is highly polarized. Proponents of GE products passionately argue that novel gene technologies have the potential to contribute to more efficient agricultural processes, promote environmental sustainability, and curb malnutrition in the developing world. Opponents of novel plant biotechnologies counter with equal vehemence that GE products are unnatural, potentially harmful to humans, and capable of catastrophic injury. As more uses for GE products are realized and investment in plant biotechnologies continues to grow, both groups have stepped up advocacy of their respective views in academic and public forums. Both sides continue to be at loggerheads with one another, in trying to force the public to choose between their two opposing views.

At times, an unfortunate casualty in the debate has been well-reasoned and independent argument. Recent advances in novel gene technologies across several disciplines, most notably medicine and agriculture, have fuelled optimism about the potential benefits of genetic engineering and have led some biotechnologists to make ambitious projections about its potential impact. In response, various green lobby groups have wasted no time in marshalling public support for anti-GE campaigns that have tended to overstate the potential risks of GE products in their support for organic farming practices. Such campaigns have consequently influenced governments in continuing moratoriums in Australia and elsewhere.

Several food-related health scares in Europe and Britain in the 1980s and 1990s have also provided the public with a reason to be suspicious of regulatory authorities who are responsible for assessing food safety. Given this climate, it is not surprising that the debate about the ethics of genetic engineering has not advanced beyond the usual emotive and ill-informed objections typically offered in opposition to novel biotechnologies.

This article provides an overview of the most prominent arguments put forward in favor of, and in opposition to, the development and use of novel plant biotechnologies. In doing so, it summarizes the findings of a study conducted by the author, in which the quality of arguments used in the debate was tested. The conclusions made here offer some practical points of departure for moving past the traditional impasse that ethical and policy debates about GE often find themselves.

The debate so far
For the most part, the potential benefits postulated by advocates of genetic engineering are at least theoretically conceivable. Genetic modification of plants (particularly, crops) confers several advantages over traditional agricultural methods. Improvement in agronomic traits such as pest resistance, herbicide tolerance, salinity tolerance, and viral and fungal resistance have the potential to improve or increase yield quantities while significantly limiting the amount of toxic pesticides currently used in conventional agricultural practices worldwide.

A primary aim for biotechnologists and agriculturalists alike is to enhance a plant's phenotypic characteristics for the purpose of improving a plant's ability to withstand biotic and abiotic stressors. Examples of desirable traits in plants include increased growth rate, plant architecture, and stress tolerance. More advanced applications of genetic engineering include the fortification of food crops through the introduction of novel genes to enhance nutrient content, and the development of plants that express proteins that can be harvested for the manufacture of oral plant vaccines.

Proponents of genetic engineering have long argued that products derived using this technology have the capacity to provide malnourished populations with sufficient nutrients to prevent many of the secondary diseases that commonly lead to death in poverty-stricken countries. Malnutrition in the developing world is a very complex problem and genetic engineering may only provide part of the solution¹. I have argued elsewhere that provided certain conditions are met, we have a moral duty to facilitate the diffusion of genetic engineering technology to developing countries on the grounds that doing nothing might be worse for the current situation².

There are, however, significant political and legal obstacles to overcome before such benefits are realized. Beta-carotene enriched Golden Rice, for example, has been demonstrated to contain significant output potential, yet the widespread dissemination of fortified rice to malnourished populations in Southeast Asia and Africa remains problematic. A lack of basic infrastructure and poor or non-existent regulatory controls continue to hamper access to genetic engineering technology and its benefits. The impact of intellectual property rights regimes on developing world agriculture and the presence of cultural and social inequities are also obstacles to the global diffusion of genetic engineering technology. These impediments reduce the prospect of this technology of being of any real use, irrespective of a moral duty to provide aid. Such issues have not received the attention they deserve in the debate about the ethics of genetic engineering.

Some proponents have also claimed that functional foods derived from genetic engineering can help to address the various problems caused by overnutrition in the developed world. Excessive caloric intake, including a diet high in carbohydrates and saturated fat, has long been associated with the incidence of obesity, cardiovascular disease (CVD), diabetes, and some forms of cancer. Despite the vigorous marketing of such products as 'disease-preventing', there has been little or no direct evidence that consuming functional foods prevents diseases caused by affluence such as CVD or obesity at a population level. This raises serious questions about the ethical acceptability of promoting individual products as being directly beneficial to consumer health. The role of functional foods in the human diet needs to be considered in the context of a healthy lifestyle. Regulators of food derived from both GE and conventional means should be concerned about the continued promotion of putative health benefits of individual food products.

The majority of opponents' arguments to genetic engineering have largely focused on the perception that genetic engineering technology will harm the environment. The potential creation of a so-called "superweed" has been advanced by many opponents of genetic engineering as the most damaging consequence of commercializing GE crops. Pollen travel and subsequent gene flow is dependent on a complex series of factors that impact the likelihood of hybridization and subsequent introgression. Transgene escape can be controlled, monitored, and even planned for³. Before particular GE plants are grown, it is possible to make a relatively confident determination of the likelihood of gene flow. There are a myriad of physical, temporal, and genetic measures currently in various stages of development that may serve to reduce or eliminate the potential for gene flow between GE crops and related non-GE species.

Growers of organic produce have voiced their opposition to genetic engineering on the grounds that pollen flow from GE crops could potentially harm the organic food industry's 'clean-green' image. The last two decades have seen an increase in the demand for both GE and organic crops⁴. Increasingly there has been a need for growers to find mutually acceptable practices in an effort to co-exist successfully. Co-existence is not a new concept and only becomes an issue if there is a distinct demand for non-GE crops, as is the case for the organics industry. The expectation by some organic growers for absolute non-interference from other agricultural growers is unreasonable and unjustifiable given the complexities of modern agriculture and surrounding ecosystems. The principle of justice needs to apply to both systems of agriculture in the spirit of fair enterprise. Conventional farmers have a duty to ensure that the risk of pollution is minimized. Similarly, organic farmers have a right of protection against avoidable gene flow. These expectations should be based on shared norms.

Five years ago the chief argument used against the development and use of GE products was that they offered little in the way of consumer benefit. It was widely held that large biotechnology companies were the primary beneficiaries of genetic engineering. It has now become clear that in some instances, this may in fact be true. The benefits of genetic engineering technology applied to plants are more likely to improve agricultural systems by providing farmers with an opportunity to reduce input and in some cases increase output while improving soil health. These benefits are environmental as well as commercial.

References

Agricultural Biotechnology for a Competitive and Sustainable Future
August 24^th to 27^th, 2008
University College Cork, Ireland

The annual Agricultural Biotechnology International Conference (ABIC), sponsored by Teagasc, provides a unique forum where the latest scientific advances in agricultural biotechnology are presented, and where future directions of agricultural biotechnology are highlighted and discussed.

The theme of ABIC 2008 is Agricultural Biotechnology for a Competitive and Sustainable Future. At a time when global agricultural production (e.g. of food, feed, fibre, fuel) faces very significant challenges due to rising demand, rising prices and population increases, the ABIC 2008 meeting will provide the details on how agricultural biotechnology R & D will continue to significantly impact sustainable social and economic development over the coming years.

The goal of ABIC 2008 is to provide a stimulating forum for delegates to learn about the latest cutting-edge scientific advances in agricultural biotechnology research. The conference facilitates world-leading authorities on agricultural biotechnology research to present their latest research advances and future directions, across 12 parallel and 2 plenary sessions. Session themes include: