Case study

Using existing data to predict and quantify the risks of GM forage to a population of a non-target invertebrate species: A New Zealand case study

¹ AgResearch, Lincoln Research Centre, Private Bag 4749, Christchurch, New Zealand
² Ministry of Agriculture and Forestry, PO Box 2526, Wellington, New Zealand
³ AgResearch, Invermay Agricultural Centre, Private Bag 50034, Mosgiel, New Zealand

^∗ Corresponding author: maureen.ocallaghan@agresearch.co.nz

Received: 20 October 2010
Accepted: 11 April 2011

Abstract

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.