An update of the Worldwide Integrated Assessment (WIA) on systemic insecticides. Part 2: impacts on organisms and ecosystems

New information on the lethal and sublethal effects of neonicotinoids and fipronil on organisms is presented in this review, complementing the previous WIA in 2015. The high toxicity of these systemic insecticides to invertebrates has been confirmed and expanded to include more species and compounds. Most of the recent research has focused on bees and the sublethal and ecological impacts these insecticides have on pollinators. Toxic effects on other invertebrate taxa also covered predatory and parasitoid natural enemies and aquatic arthropods. Little, while not much new information has been gathered on soil organisms. The impact on marine coastal ecosystems is still largely uncharted. The chronic lethality of neonicotinoids to insects and crustaceans, and the strengthened evidence that these chemicals also impair the immune system and reproduction, highlights the dangers of this particular insecticidal classneonicotinoids and fipronil. , withContinued large scale – mostly prophylactic – use of these persistent organochlorine pesticides has the potential to greatly decreasecompletely eliminate populations of arthropods in both terrestrial and aquatic environments. Sublethal effects on fish, reptiles, frogs, birds and mammals are also reported, showing a better understanding of the mechanisms of toxicity of these insecticides in vertebrates, and their deleterious impacts on growth, reproduction and neurobehaviour of most of the species tested. This review concludes with a summary of impacts on the ecosystem services and functioning, particularly on pollination, soil biota and aquatic invertebrate communities, thus reinforcing the previous WIA conclusions (van der Sluijs et al. 2015)

On Improving the Performance of Logistic Regression Analysis Via Extreme Ranking

Logistic regression models for dichotomous or ordinal dependent variables is one of the generalized linear models. They have been frequently applied in several fields. In this chapter, we present more efficient and powerful performance of the logistic regression models analysis when a modified extreme ranked set sampling (modified ERSS) or moving extreme ranked set sampling (MERSS) are used and further improving the performance when a modified Double extreme ranked set sampling (modified DERSS) is used. We propose that ranking could be performed based on an available and easy to rank auxiliary variable which is associated with the response variable. Analytically and through simulations, we showed the superiority performance of the logistics regression analysis when modified ERSS, MERSS, and DERSS are used compared with using the simple random sample (SRS). For illustration purposes of the procedures developed, we use a real dataset from 2011/12 National Survey of Children’s Health (NSCH)

On Improving the Performance of Logistic Regression Analysis via Extreme Ranking

Invited presentation given at International Symposium on Statistics and Biostatistics (ISBS) at the University of Pretoria