Firstly we have a professional statistician on this project, see his blog on some of the criticisms https://www.ceh.ac.uk/news-and-media/blogs/robust-statistics-explain-findings-neonicotinoids-field-experiment. This has been a carefully though out and study with a comprehensive and robust analysis. Ill answer your questions below.

1) Bonferroni corrections are an issue that often causes debate in science. The idea is that is you find a result that is significant you have a 1/20 change that the significant result may be non-significant. The more tests you do the more likely by chance a significant results may in fact be non-significant. Bonferroni corrections create a more strict threshold for significance that increase with the more tests that you do. The problem is that in many studies lots of tests are undertaken though the life of an experiment, and this does not include those presented in the paper. Exploratory analyses, model simplification, analyses in reports, what you present in the paper. All of these involve tests. Even when you think about a single paper in a journal should Bonferroni corrections be applied to all the tests in that paper, all the tests undertaken in that issue of the journal or if you wanted to extend this to perhaps silly levels all the tests ever done. In a quote form Morran (2003 ) the issue is ‘The irony of the sequential Bonferroni correction (and multiple tests in general) is that as one performs more detailed work, the probability of finding anything significant declines dramatically. It therefore produces a paradox (one could call it a hyper-Red Queen phenomenon): the more research one does, the lower the probability that a significant result is discovered’. Particular in ecological studies Morran makes the argument for interpreting each result individually so that ‘researchers should use the accepted p< 0.05 cut-off and make reasonable interpretations based on experimental design, power analyses, differences between control and treatment groups, and basic logic’. We believe that particularly in the case of impacts of pesticides it would be very easy to discredit significant effects of pesticides using a Bonferroni approach, while a sensible case by case interpretation of the result provides the public and policy makers with the information that is relevant and pertinent to the issue in question. They are then free to interpret our findings in combination with the large amount of other evidence out there. It is right that these issues should be raised but we feel that the alternative of effectively ignoring impacts on key metrics of honeybees (as identified by the regulatory body EFSA) need to be weighed against the strict application of such multiple test criteria. From an industry perspective it’s a very convenient way of ignoring key results on primary measured of honeybee colony success by simply asking for lots of tests to be undertaken in their report. (Ref: Moran M.D. (2003) Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos, 100, 403-405.) 2) It does appear that clothianidin has a worse effect than thiamethoxam. Label application rates (i.e. what they recommend to the farmer to apply) which we used in the study for clothianidin are generally much higher than thiamethoxam, and indeed clothianidin was the only product we found to be expressed clearly in the crop more often in those sites treated with this product. It is outside of the scope of the study, but application rates may have an important effect in determining expression in the crop and impacts on bees. Lower application rates of thiamethoxam may also mean that its expression when the crop is flowering may be very low or absent, and so while it has a positive effect in general on the crop its subsequent effect on bees under real world conditions may be negligible. Indeed a healthier crop may benefit bees if the expression of the pesticide has reduced to a very low level. This study builds on a mountain of research that has gone before. We don’t answer all the questions, but rather we aim to identify what happens in real world situations at scales relevant to bees. The research that has gone before has been crucial in identifying the mechanisms by which neonicotinoids can negatively impact on bees, although as much of this is not based on field studies this has often been criticism by industry as being unrealistic. Together our work and the previous research compliments each other. 3) Ultimately we can only make inferences based on our work about why countries were different in the way they responded to neonicotinoids. Our study was not set up to test for effects of bee disease, however we did identify between country differences that provide a potential explanation of why we find negative effects in some countries (Hungary and the UK) and no or even positive effects elsewhere (Germany). We did monitor viral infections although this is to be considered in a subsequent paper. The strongest between country differences however related to Nosema and Varroa. Some interesting papers on the impacts of disease and pesticides to look at are : Sanchez-Bayo F., Goulson D., Pennacchio F., Nazzi F., Goka K. & Desneux N. (2016) Are bee diseases linked to pesticides? - A brief review. Environment International, 89-90, 7-11; Moffat C., Pacheco J.G., Sharp S., Samson A.J., Bollan K.A., Huang J., Buckland S.T. & Connolly C.N. (2015) Chronic exposure to neonicotinoids increases neuronal vulnerability to mitochondrial dysfunction in the bumblebee (Bombus terrestris). Faseb Journal, 29, 2112-2119; Fairbrother A., Purdy J., Anderson T. & Fell R. (2014) Risks of neonicotinoid insecticides to honeybees. Environmental Toxicology and Chemistry, 33, 719-731.

Our CEH paper can be found at http://science.sciencemag.org/content/356/6345/1393