Person: Duquesne, Sabine
Lade...
E-Mail-Adresse
Geburtsdatum
Forschungsvorhaben
Organisationseinheiten
Berufsbeschreibung
Nachname
Duquesne
Vorname
Sabine
Name
3 Ergebnisse
Suchergebnisse
Gerade angezeigt 1 - 3 von 3
Veröffentlichung The role of behavioral ecotoxicology in environmental protection(2021) Ford, Alex; Ågerstrand, Marlene; Brooks, Bryan W.; Duquesne, Sabine; Sahm, René; Gergs, René; Jacob, Stefanie; Maack, Gerd; Mohr, SilviaFor decades, we have known that chemicals affect human and wildlife behavior. Moreover, due to recent technological and computational advances, scientists are now increasingly aware that a wide variety of contaminants and other environmental stressors adversely affect organismal behavior and subsequent ecological outcomes in terrestrial and aquatic ecosystems. There is also a groundswell of concern that regulatory ecotoxicology does not adequately consider behavior, primarily due to a lack of standardized toxicity methods. This has, in turn, led to the exclusion of many behavioral ecotoxicology studies from chemical risk assessments. To improve understanding of the challenges and opportunities for behavioral ecotoxicology within regulatory toxicology/risk assessment, a unique workshop with international representatives from the fields of behavioral ecology, ecotoxicology, regulatory (eco)toxicology, neurotoxicology, test standardization, and risk assessment resulted in the formation of consensus perspectives and recommendations, which promise to serve as a roadmap to advance interfaces among the basic and translational sciences, and regulatory practices. © 2021 The AuthorsVeröffentlichung Better define beta-optimizing MDD (minimum detectable difference) when interpreting treatment-related effects of pesticides in semi-field and field studies(2020) Alalouni, Urwa; Duquesne, Sabine; Egerer, Sina Elisabeth; Frische, Tobias; Gergs, René; Gräff, Thomas; Sahm, René; Pieper, Silvia; Wogram, JörnThe minimum detectable difference (MDD) is a measure of the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. It is a measure at a defined level of probability and a given variability of the data. It provides an indication for the robustness of statistically derived effect thresholds such as the lowest observed effect concentration (LOEC) and the no observed effect concentration (NOEC) when interpreting treatment-related effects on a population exposed to chemicals in semi-field studies (e.g., micro-/mesocosm studies) or field studies. MDD has been proposed in the guidance on tiered risk assessment for plant protection products in edge of field surface waters (EFSA Journal 11(7):3290, 2013), in order to better estimate the robustness of endpoints from such studies for taking regulatory decisions. However, the MDD calculation method as suggested in this framework does not clearly specify the power which is represented by the beta-value (i.e., the level of probability of type II error). This has implications for the interpretation of experimental results, i.e., the derivation of robust effect values and their use in risk assessment of PPPs. In this paper, different methods of MDD calculations are investigated, with an emphasis on their pre-defined levels of type II error-probability. Furthermore, a modification is suggested for an optimal use of the MDD, which ensures a high degree of certainty for decision-makers. © 2020 Springer Nature Switzerland AGVeröffentlichung Effects of a realistic pesticide spraying sequence for apple crop on stream communities in mesocosms: negligible or notable?(2023) Duquesne, Sabine; Feibicke, Michael; Frische, Tobias; Gergs, René; Meinecke, Stefan; Sahm, René; Mohr, SilviaBackground Several large-scale studies revealed impacts and risks for aquatic communities of small rural lakes and streams due to pesticides in agricultural landscapes. It appears that pesticide risk assessment based on single products does not offer sufficient protection for non-target organisms, which are exposed repeatedly to pesticide mixtures in the environment. Therefore, a comprehensive stream mesocosm study was conducted in order to investigate the potential effects of a realistic spraying sequence for conventional orchard farmed apples on a stream community using pesticides at their regulatory acceptable concentrations (RACs). Eight 74-m-long stream mesocosms were established with water, sand, sediment, macrophytes, plankton and benthic macroinvertebrates. In total, nine fungicidal, four herbicidal and four insecticidal pesticides were applied in four of the eight stream mesocosms on 19 spraying event days in the period from April to July while the remaining four stream mesocosms served as controls. The community composition, the abundance of benthos, periphyton and macrophytes, the emergence of insects, physico-chemical water parameters, and drift measurements of aquatic invertebrates were measured. Results The pesticide spraying sequence induced significant effects on invertebrates, periphyton, and macrophytes as well as on the water ion composition especially in the second half of the experiment. It was not possible to relate the observed effects on the community to specific pesticides applied at certain time points and their associated toxic pressure using the toxic unit approach. The most striking result was the statistically significant increase in variation of population response parameters of some taxa in the treated mesocosms compared to the controls. This inter-individual variation can be seen as a general disturbance measure for the ecosystem. Conclusions The pesticide spraying sequence simulated by using RAC values had notable effects on the aquatic stream community in the conducted mesocosm study. The results indicate that the current risk assessment for pesticides may not ensure a sufficient level of protection to the field communities facing multiple pesticide entries due to spraying sequences and other combined stress. Hence, there is still room for improvement regarding the prospective risk assessment of pesticides to further reduce negative effects on the environment. © The Author(s) 2023