The usefulness of a mathematical model of exposure for environmental risk assessment
2011; Royal Society; Volume: 278; Issue: 1708 Linguagem: Inglês
10.1098/rspb.2010.2667
ISSN1471-2954
AutoresJ. N. Perry, Yann Devos, Salvatore Arpaia, Detlef K. Bartsch, Achim Gathmann, Rosemary S. Hails, J. Kiss, K. Lheureux, Barbara Manachini, Sylvie Mestdagh, G. Neemann, Félix Ortego, Joachim Schiemann, Jeremy Sweet,
Tópico(s)Insect and Arachnid Ecology and Behavior
ResumoOpen AccessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article Perry J. N., Devos Y., Arpaia S., Bartsch D., Gathmann A., Hails R. S., Kiss J., Lheureux K., Manachini B., Mestdagh S., Neemann G., Ortego F., Schiemann J. and Sweet J. B. 2011The usefulness of a mathematical model of exposure for environmental risk assessmentProc. R. Soc. B.278982–984http://doi.org/10.1098/rspb.2010.2667SectionOpen AccessComments and invited repliesThe usefulness of a mathematical model of exposure for environmental risk assessment J. N. Perry J. N. Perry Oaklands Barn, Lug's Lane, Broome, Norfolk NR35 2HT, UK [email protected] Google Scholar Find this author on PubMed Search for more papers by this author , Y. Devos Y. Devos European Food Safety Authority (EFSA), GMO Unit, Largo Natale Palli 5/A, 43121 Parma, Italy Google Scholar Find this author on PubMed Search for more papers by this author , S. Arpaia S. Arpaia National Agency for New Technologies, Energy and Sustainable Economic Development, Research Centre Trisaia, 75026 Rotondella, Italy Google Scholar Find this author on PubMed Search for more papers by this author , D. Bartsch D. Bartsch Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Federal Office of Consumer Protection and Food Safety, Mauerstrasse 39-42, 10117 Berlin, Germany Google Scholar Find this author on PubMed Search for more papers by this author , A. Gathmann A. Gathmann Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Federal Office of Consumer Protection and Food Safety, Mauerstrasse 39-42, 10117 Berlin, Germany Google Scholar Find this author on PubMed Search for more papers by this author , R. S. Hails R. S. Hails Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK Google Scholar Find this author on PubMed Search for more papers by this author , J. Kiss J. Kiss Plant Protection Institute, Szent István University, Pater K. 1, 2100 Gödöllő, Hungary Google Scholar Find this author on PubMed Search for more papers by this author , K. Lheureux K. Lheureux European Food Safety Authority (EFSA), GMO Unit, Largo Natale Palli 5/A, 43121 Parma, Italy Google Scholar Find this author on PubMed Search for more papers by this author , B. Manachini B. Manachini Animal Biology Department, University of Palermo, Via Archirafi, 18, 90123 Palermo, Italy Google Scholar Find this author on PubMed Search for more papers by this author , S. Mestdagh S. Mestdagh European Food Safety Authority (EFSA), GMO Unit, Largo Natale Palli 5/A, 43121 Parma, Italy Google Scholar Find this author on PubMed Search for more papers by this author , G. Neemann G. Neemann Büro für Landschaftsökologie und Umweltstudien, Muehlenweg 60, 29358 Eicklingen, Germany Google Scholar Find this author on PubMed Search for more papers by this author , F. Ortego F. Ortego Centro de Investigaciones Biológicas (CSIC), Departamento de Biología de Plantas, Laboratorio Interacción Planta-Insecto, C/ Ramiro de Maeztu 9, 28040 Madrid, Spain Google Scholar Find this author on PubMed Search for more papers by this author , J. Schiemann J. Schiemann Julius Kühn Institute, Federal Research Centre for Cultivated Plants (JKI), Institute for Biosafety of Genetically Modified Plants, Erwin-Baur-Strasse 27, 06484 Quedlinburg, Germany Google Scholar Find this author on PubMed Search for more papers by this author and J. B. Sweet J. B. Sweet Sweet Environmental Consultants, 6 The Green, Willingham, Cambridge CB24 5JA, UK Google Scholar Find this author on PubMed Search for more papers by this author J. N. Perry J. N. Perry Oaklands Barn, Lug's Lane, Broome, Norfolk NR35 2HT, UK [email protected] Google Scholar Find this author on PubMed , Y. Devos Y. Devos European Food Safety Authority (EFSA), GMO Unit, Largo Natale Palli 5/A, 43121 Parma, Italy Google Scholar Find this author on PubMed , S. Arpaia S. Arpaia National Agency for New Technologies, Energy and Sustainable Economic Development, Research Centre Trisaia, 75026 Rotondella, Italy Google Scholar Find this author on PubMed , D. Bartsch D. Bartsch Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Federal Office of Consumer Protection and Food Safety, Mauerstrasse 39-42, 10117 Berlin, Germany Google Scholar Find this author on PubMed , A. Gathmann A. Gathmann Bundesamt für Verbraucherschutz und Lebensmittelsicherheit (BVL), Federal Office of Consumer Protection and Food Safety, Mauerstrasse 39-42, 10117 Berlin, Germany Google Scholar Find this author on PubMed , R. S. Hails R. S. Hails Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, UK Google Scholar Find this author on PubMed , J. Kiss J. Kiss Plant Protection Institute, Szent István University, Pater K. 1, 2100 Gödöllő, Hungary Google Scholar Find this author on PubMed , K. Lheureux K. Lheureux European Food Safety Authority (EFSA), GMO Unit, Largo Natale Palli 5/A, 43121 Parma, Italy Google Scholar Find this author on PubMed , B. Manachini B. Manachini Animal Biology Department, University of Palermo, Via Archirafi, 18, 90123 Palermo, Italy Google Scholar Find this author on PubMed , S. Mestdagh S. Mestdagh European Food Safety Authority (EFSA), GMO Unit, Largo Natale Palli 5/A, 43121 Parma, Italy Google Scholar Find this author on PubMed , G. Neemann G. Neemann Büro für Landschaftsökologie und Umweltstudien, Muehlenweg 60, 29358 Eicklingen, Germany Google Scholar Find this author on PubMed , F. Ortego F. Ortego Centro de Investigaciones Biológicas (CSIC), Departamento de Biología de Plantas, Laboratorio Interacción Planta-Insecto, C/ Ramiro de Maeztu 9, 28040 Madrid, Spain Google Scholar Find this author on PubMed , J. Schiemann J. Schiemann Julius Kühn Institute, Federal Research Centre for Cultivated Plants (JKI), Institute for Biosafety of Genetically Modified Plants, Erwin-Baur-Strasse 27, 06484 Quedlinburg, Germany Google Scholar Find this author on PubMed and J. B. Sweet J. B. Sweet Sweet Environmental Consultants, 6 The Green, Willingham, Cambridge CB24 5JA, UK Google Scholar Find this author on PubMed Published:05 January 2011https://doi.org/10.1098/rspb.2010.2667We respond to the Comment of Lang et al. [1] regarding our mathematical model [2] of exposure of non-target Lepidoptera to Bt-maize pollen expressing Cry1Ab within Europe. Lang et al. remark on the degree to which the model was subject to uncertainty. Perry et al. [2] did indeed emphasize precaution: they made four separate decisions to model worst-case scenarios; identified six distinct sources of variability to which their results might be sensitive; and emphasized six different bases for the uncertainty of predictions. Lang et al. rightly emphasize the importance of identifying to which parameters the results of a model are most sensitive; Perry et al. should perhaps have emphasized more that the parameter to which their estimates of mortality were most sensitive was undoubtedly the variable measuring the rate of change of mortality with concentration/dose of the Cry1Ab protein (i.e. the slope of the probit/logit regression line; see below).Regarding the relationship between the toxicity of MON810 and Bt176 pollen, Lang et al. imply that the relationship between mortality and dose may be nonlinear. Regressions from bioassay should always be checked for nonlinearity, but there was no evidence of this in any of the extensive number of regressions of Saeglitz et al. [3,4] upon which our slope estimate was based. Of course, the standard transformation of mortality to probits (or logits) and the logarithmic transformation of concentration [5,6] are designed to achieve a linear regression; both papers cited by Lang et al. use this method. Data from one of these papers [7] were tested for nonlinearity; none was found, and no disproportionally higher mortality at low Cry1Ab concentrations could be verified (figure 1). Figure 1. Logit-transformed observed percentage mortality from fig. 1 of [7] plotted against logarithmically transformed (base 10) dose (Bt-maize pollen consumed) from table 1 of [7], with fitted linear regression for 2 days (filled circles, solid line), 7 days (open circles, dashed line) and 14 days (stars, dotted line). All three regressions are highly significant (p < 0.001) with no indication of nonlinearity. The addition of a nonlinear term for curvature to the linear regressions was not significant for any of the three periods (F1,3 = 0.20, F1,4 = 1.83, F1,4 = 1.20, respectively).Download figureOpen in new tabDownload PowerPointLang et al. are correct that Perry et al. used the range of published [8,9] values (12.2–78.9) to derive an average of the ratio of the concentration of the Cry1Ab protein expressed in pollen of maize Bt176 relative to that in maize MON810 for which a 31.05-fold difference was assumed. This is already likely to be a worst-case underestimate because, as Sears et al. [8] noted, the value of expression for MON810 was near the current level of detection by immunoassay. We do not agree with Lang et al.'s interpretation of the data from Nguyen [10]: they compared the smallest Bt176 value from 2002 to the largest MON810 value from 2003. The within-year ratio of Bt176 : MON810 was 64.8 in 2002 and 30.5 in 2003. The latter value, very close to that adopted by Perry et al., leads to larger mortality estimates for MON810 than the former, a further example of Perry et al.'s use of worst-case scenarios. Figure 2 shows the importance of the distinction between the intercept and slope of the probit/logit line in this issue. The conclusions of the model for risk management depend on the degree of estimated mortality. The conclusions are clearly highly sensitive to assumptions concerning the slope. They are sensitive, but much less so, to the intercept; it is the intercept that is governed by the Bt176 : MON810 ratio. Figure 2 demonstrates the effect of choosing an alternative worst-case value of the ratio (12.2) at the end of the range. Figure 2. The probit regression line (thick solid line with button ends) for the LC50 of larvae of Inachis io assumed by Perry et al. [2] according to the slope value 1.095 estimated by Saeglitz et al. [3,4]. The assumed concentration of the Cry1Ab protein in pollen of maize MON810 is 31.05-fold less than that in maize Bt176. Shown for comparison are the lines expected if instead the different slope values of 2.25 (average of estimates of Farinós et al. [18]; thick long-dashed line with button ends) or 5.79 (estimated by Felke et al. [11]; thick short-dashed line with button ends) had been assumed. All three lines go through point A, the assumed LC50 of 5800 maize MON810 pollen grains cm−2, for which the mortality rate is 0.5. Within the crop, typical pollen concentrations are 10-fold less than this, and at the crop edge 30-fold less, and less still at distances into the margin, further from the edge. Also shown for comparison are three corresponding lines (thinner lines without button ends) with the same slope but different intercepts to the first three, representing the lines expected if the assumed concentration of the Cry1Ab protein in pollen of maize MON810 was only 12.2-fold less than that in maize Bt176. All three lines of this second set go through point B, for which the mortality rate is 0.5 and the assumed LC50 of 2283 grains cm−2. Estimated mortality is more sensitive to the differences in slopes than to the difference in intercepts.Download figureOpen in new tabDownload PowerPointRegarding the assumption of equal susceptibility for the butterflies Inachis io and Vanessa atalanta, we apologize for the incorrect citation given by Perry et al. We acknowledge that data to compare the sensitivities of I. io and V. atalanta are very limited. We are aware of no evidence that the sensitivities differ; unpublished data from a single field experiment appear to suggest that they may not. Both Perry et al. and Lang et al. remarked on the need for further data on European Lepidoptera of conservation concern.Lang et al. argue that the experimental methodology of Felke et al. [11] was likely to give results that underestimated true mortality. However, in many experiments with MON810, larvae have been exposed to longer time periods than those of Felke et al. [11]: 10 days [12]; 14–22 days [13]; 7 days [14]; 10–14 days [15]. These and other experiments with MON810 pollen have shown no negative effects when lepidopteran larvae were exposed to MON810 pollen alone. Furthermore, susceptibility to Bt toxin declines with age in older instars (e.g. [16]), so any potential for negative impacts of Bt pollen is reduced as the larvae develop. Lang et al. claim that fig. 1 of [7] demonstrates long-term effects (longer than 7 days) following a short acute dose of Bt. However, comparison of treatment and control in that figure appears to contradict rather than support their claim. In the period between 7 and 27 days, the mortality for a dose of 2.5 mg of Bt is only marginally (approx. 2%) greater than that of the control, while the mortalities for all other doses (1, 5, 7.5, 10, 20, 30 mg) are at least 5 per cent less than that of the control.We fully agree with Lang et al. that sublethal effects should encompass fecundity and other parameters. However, many studies neglect to test parameters other than larval or pupal weight (e.g. [7]). Perry et al. emphasized that 'our methods are subject to considerable uncertainty', a caveat repeated in the final sentence of the Discussion.Lang et al. stated that 'all publications cited in Perry et al. in support of a possible reduction in exposure through behaviour of the larvae refer to Danaus plexippus', but have perhaps overlooked Perry et al.'s text: 'Both species [V. atalanta and I. io] are somewhat protected under field conditions from pollen deposition; the former species creates "leaf bags", the latter builds webs (e.g. [17])'. We agree that the extent to which exposure is reduced through such behaviour is variable but it is surely not contentious to state that there is evidence for this for neonate larvae within Europe (see e.g. http://tristram.squarespace.com/home/2009/6/9/peacock-butterfly-caterpillars.html).Regarding sensitivity analysis, it is important to allow for the fact that depositions of pollen in the field occur at far lesser concentrations than the LC50s for the three species considered by Perry et al. In consequence, as shown in figure 2, differing assumptions for the slope of the assumed probit (or logit) line will have little effect on the results for concentrations close to the LC50, but result in very large differences at concentrations around those expected within the crop or in the margin. Within the crop, the estimated mortality using the slope estimated by Felke et al. [11] is vanishingly small and such a value would be impossible to measure in field conditions. Even a doubling of the Saeglitz et al. [3,4] slope of close to 1.1 to the moderately small average 2.25 estimated by Farinós et al. [18] would result in roughly 10-fold decrease in estimated mortality. It is for these reasons that we consider that the consequence of this sensitivity completely outweighs any of the several effects claimed by Lang et al. to engender uncertainty and affect mortality estimates. We regard them as minor compared with the 'safety margin' factor of 8 × 10−7 by which Perry et al. inflated the estimated mortality through deliberate choice of a small value of the logit slope, designed to give worst-case mortality.The value of the Perry et al. model is that it provides a transparent, structured and simple approach to exposure analysis that may be followed for other species and taxa in other settings, if sufficient data become available. Further, in its derivation of an integrated mortality–distance relationship, it offers the opportunity for relatively accurate laboratory-based estimation of mortality–dose relationships to supplement relatively inaccurate determinations of mortality in the field. We agree with Lang et al. that species' sensitivity to particular GM events that express different forms of Cry1 proteins is an important determinant of mortality; also that further data would be welcome on the mortality–dose relationships (particularly regarding the slopes) for a range of species, especially those of conservation concern. However, we disagree that we have been incautious regarding the implications of our results for conclusions regarding regulatory policy. 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Dolezel M, Lüthi C and Gaugitsch H (2020) Beyond limits – the pitfalls of global gene drives for environmental risk assessment in the European Union, BioRisk, 10.3897/biorisk.15.49297, 15, (1-29) This Issue07 April 2011Volume 278Issue 1708 Article InformationDOI:https://doi.org/10.1098/rspb.2010.2667PubMed:21208963Published by:Royal SocietyOnline ISSN:1471-2954History: Manuscript received10/12/2010Manuscript accepted13/12/2010Published online05/01/2011Published in print07/04/2011 License:This journal is © 2011 The Royal SocietyThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Citations and impact Subjectsecology Large datasets are available through Proceedings B's partnership with Dryad
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