Potential and limits of modelling to predict the impact of transgenic crops in wild species.

Abstract

Models have become a necessary tool for researchers addressing questions about complex long-term or large-scale processes, such as the impact of GM crops on wild species. They may be used for different purposes such as: (i) accurate quantitative predictions; (ii) ranking of scenarios; (iii) predictions, even inaccurate, of events difficult to observe; and (iv) understanding of relationships between processes. These purposes require different levels of precision, generality or explicative power. We argue that statistical models are the most precise, theoretical models are general, and mechanistic models offer the best explicative power. Intermediate types of models exist, but there is a trade-off between precision, generality and explicative power, and no model can achieve all three. We illustrate this trade-off by detailing a few examples of models for pollen dispersal and we present a non-exhaustive review of the literature on models addressing questions relative to gene flow from crops to related wild species.

Environmental and landscape effects on cross‐pollination rates observed at long distance among French oilseed rape Brassica napus commercial fields

“…decrease of pollination probability with distance and wind) and mechanistic models that account for processes acting on pollen movement. Both approaches have advantages and drawbacks, the former being easier to build, implement and validate but of less use in exploring new situations than the latter (Lavigne et al . 2004).…”

Pollen dispersal of oilseed rape: estimation of the dispersal function and effects of field dimension

“…Despite significant advances recently, these mechanistic approaches are far from being integrated into landscape models because of (i) the ‘cost’ of numerical simulations, (ii) the necessity of many precise data to perform the simulations and (iii) an imperfect understanding of the mechanisms. The second approach models pollen dispersal statistically, by fitting dispersal functions to data measured directly at the temporal and spatial scales of interest (Lavigne et al . 2004).…”

Multicriteria Assessment of the Quality of Waste Sorting Centers—A Case Study

“…Therefore, multicriteria modeling is a key element of decision support, providing a formal structure of existing knowledge or problems related to impacts, identifying gaps, rankings, etc. [8][9][10]. In this light, the authors of [11] proposed a selection of proper waste management systems using the multicriteria analysis (MCA) method ELECTRE.…”