The bucket and the searchlight are metaphors for opposing theories of the growth of scientific knowledge. The bucket theory proposes that knowledge is gained by observing the world without preconceptions, and that knowledge emerges from the accumulation of observations that support a hypothesis. There are many problems with this theory, the most serious of which is that it does not appear to offer a means to distinguish between the many hypotheses that could explain a particular set of observations. The searchlight theory proposes that preconceptions are unavoidable and that knowledge advances through the improvement of our preconceptions – our hypotheses – by continuous criticism and revision. A hypothesis is a searchlight that illuminates observations that test the hypothesis and reveal its flaws, and knowledge thereby increases through the elimination of false hypotheses. Research into the risks posed by the cultivation of transgenic crops often appears to apply the bucket theory; many data are produced, but knowledge of risk is not advanced. Application of the searchlight theory, whereby risk assessments test hypotheses that transgenic crops will not be harmful, seems to offer a better way to characterise risk. The effectiveness of an environmental risk assessment should not be measured by the size of the bucket of observations on a transgenic crop, but by the power of the risk hypothesis searchlights to clarify the risks that may arise from cultivation of that crop. These points are illustrated by examples of hypotheses that could be tested to assess the risks from transgenic crops and their hybrids becoming weeds or invading non-agricultural habitats.

While significant progress has been made on the modification of crops for the benefit of producers, the same cannot be said in regards to eliciting the potential impact that these crops may have on the wider landscape and the diversity of life therein. Management impacts can create difficulties when making policy, regulation and licensing decisions in those countries where agriculture has a significant social and ecological position in the landscape. To begin to gauge the potential impacts of the management of a selection of GM crops on an agricultural landscape, four key biodiversity stressors (Chemicals, Introgression, Nutrients and Management: CINMa) were identified and a grading system developed using published data. Upon application to five selected GM crops in a case study area, CINMa identifies areas in the wider landscape where biodiversity is likely to be negatively or positively impacted, as well as agricultural zones which may benefit from the land use change associated with the management of GM crops and their associated post market environmental monitoring.

The cultivation of genetically modified (GM) Bt maize (Zea mays L.) events MON810 and Bt11 is permitted in Uruguay. Local regulations specify that 10% of the crop should be a non-GM cultivar as refuge area for biodiversity, and the distance from other non-GM maize crops should be more than 250 m in order to avoid cross-pollination. However, the degree of cross-fertilization between maize crops in Uruguay is unknown. The level of adventitious presence of GM material in non-GM crops is a relevant issue for organic farming, in situ conservation of genetic resources and seed production. In the research reported here, the occurrence and frequency of cross-fertilization between commercial GM and non-GM maize crops in Uruguay was assessed. The methodology comprised field sampling and detection using DAS-ELISA and PCR. Five field-pair cases where GM maize crops were grown near non-GM maize crops were identified. These cases had the potential to cross-fertilize considering the distance between crops and the similarity of the sowing dates. Adventitious presence of GM material in the offspring of non-GM crops was found in three of the five cases. Adventitious presence of event MON810 or Bt11 in non-GM maize, which were distinguished using specific primers, matched the events in the putative sources of transgenic pollen. Percentages of transgenic seedlings in the offspring of the non-GM crops were estimated as 0.56%, 0.83% and 0.13% for three sampling sites with distances of respectively 40, 100 and 330 m from the GM crops. This is a first indication that adventitious presence of transgenes in non-GM maize crops will occur in Uruguay if isolation by distance and/or time is not provided. These findings contribute to the evaluation of the applicability of the “regulated coexistence policy” in Uruguay.

Determining the effects of genetically modified (GM) crops on non-target organisms is essential as many non-target species provide important ecological functions. However, it is simply not possible to collect field data on more than a few potential non-target species present in the receiving environment of a GM crop. While risk assessment must be rigorous, new approaches are necessary to improve the efficiency of the process. Utilisation of published information and existing data on the phenology and population dynamics of test species in the field can be combined with limited amounts of experimental biosafety data to predict possible outcomes on species persistence. This paper presents an example of an approach where data from laboratory experiments and field studies on phenology are combined using predictive modelling. Using the New Zealand native weevil species Nicaeana cervina as a case study, we could predict that oviposition rates of the weevil feeding on a GM ryegrass could be reduced by up to 30% without threat to populations of the weevil in pastoral ecosystems. In addition, an experimentally established correlation between feeding level and oviposition led to the prediction that a consistent reduction in feeding of 50% or higher indicated a significant risk to the species and could potentially lead to local extinctions. This approach to biosafety risk assessment, maximising the use of pre-existing field and laboratory data on non-target species, can make an important contribution to informed decision-making by regulatory authorities and developers of new technologies.

Regulators are acutely aware of the need for meaningful risk assessments to support decisions on the safety of GM crops to non-target invertebrates in determining their suitability for field release. We describe a process for developing appropriate, testable risk hypotheses for invertebrates in agroecosystems that might be exposed to plants developed by GM and future novel technologies. An existing model (PRONTI) generates a ranked list of invertebrate species for biosafety testing by accessing a database of biological, ecological and food web information about species which occur in cropping environments and their potential interactions with a particular stressor (Eco Invertebase). Our objective in this contribution is to explore and further utilise these resources to assist in the process of problem formulation by identifying potentially significant effects of the stressor on the invertebrate community and the ecosystem services they provide. We propose that for high ranking species, a conceptual food web using information in Eco Invertebase is constructed, and using an accepted regulatory risk analysis framework, the likelihood of risk, and magnitude of impact for each link in the food web is evaluated. Using as filters only those risks evaluated as likely to extremely likely, and the magnitude of an effect being considered as moderate to massive, the most significant potential effects can be identified. A stepwise approach is suggested to develop a sequence of appropriate tests. The GM ryegrass plant used as the “stressor” in this study has been modified to increase triacylglyceride levels in foliage by 100% to increase the metabolisable energy content of forage for grazing animals. The high-ranking “test” species chosen to illustrate the concept are New Zealand native species Wiseana cervinata (Walker) (Lepidoptera: Hepialidae), Persectania aversa (Walker) (Lepidoptera: Noctuidae), and the self-introduced grey field slug, Deroceras reticulatum (Müller).