Estimating acute mortality of Lepidoptera caused by the cultivation of insect-resistant Bt maize – The LepiX model

Article by LorenzFahse, Phillip Papastefanou, and Mathias Otto



The cultivation of Bt maize, genetically modified to be resistant to insect pests, has led to intense scientific and political debate about its possible adverse impacts on biodiversity. To better address this question we developed an individual-based simulation model (LepiX). LepiX considers the temporal dynamics of maize pollen shedding and larval phenology, and pollen deposition on host plants related to distance from the maize field, in order to estimate mortality of lepidopteran larvae exposed to toxic Bt pollen. We employed a refined exposure analysis, comparison to previous approaches, using recent evidence on leaf pollen deposition and accounting for the spatial heterogeneity of pollen on leaves. Moreover, we used a stochastic approach, considering literature data on a minimum dataset for butterfly biology in combination with historic data on temporal pollen deposition to predict the coincidence between larval phenology and pollen deposition. Since conservation management actions may act at the level of the individual for protected species, LepiX, as an individual based spatially explicit model, is suited to assist both risk assessment and management measures based on threshold mortalities. We tested our model using Inachis io (Lepidoptera: Nymphalidae) as butterfly species and the cultivation of insect resistant MON810 maize. In accordance to predictions based on other models we identified mortality risks of I. io larvae for the second larval generation. An analysis of the sensitivity of input parameters stressed the importance of both the slope and the LC50 value of the dose-response curve as well as the earliest day of larval hatching. Using different published data to characterize the dose-response of MON810 pollen to I. io we revealed consequences due to uncertainties in ecotoxicological parameters and thus highlight the importance of key biological parameters for reliable estimates of effects, and decision making (e.g. isolation distances) in risk assessment.

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