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      Pollen protein and lipid content influence resilience to insecticides in honey bees ( Apis mellifera)

      1 , 2
      Journal of Experimental Biology
      The Company of Biologists

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          ABSTRACT

          In honey bees (Apis mellifera), there is growing evidence that the impacts of multiple stressors can be mitigated by quality nutrition. Pollen, which is the primary source of protein and lipids in bee diets, is particularly critical for generating more resilient phenotypes. Here, we evaluated the relationship between pollen protein to lipid (P:L) ratio and honey bee insecticide resilience. We hypothesized that pollen diets richer in lipids would lead to increased survival in bees exposed to insecticides, as pollen-derived lipids have previously been shown to improve bee resilience to pathogens and parasites. Furthermore, lipid metabolic processes are altered in bees exposed to insecticides. We fed age-matched bees pollen diets of different P:L ratios by altering a base pollen by either adding protein (casein powder) or lipid (canola oil) and simulating chronic insecticide exposure by feeding bees an organophosphate (chlorpyrifos). We also tested pollen diets of naturally different P:L ratios to determine whether the results were consistent. Linear regression analysis revealed that mean survival time for bees fed altered diets was best explained by protein concentration (P=0.04, adjusted R2=0.92), and that mean survival time for bees fed natural diets was best explained by the P:L ratio (P=0.008, adjusted R2=0.93). Our results indicate that higher dietary P:L ratios have a negative effect on bee physiology when combined with insecticide exposure, while lower P:L ratios have a positive effect. These results suggest that protein and lipid intake differentially influence insecticide response in bees, laying the groundwork for future studies of metabolic processes and development of improved diets.

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          Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment.

          Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk-effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework.
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            High Levels of Miticides and Agrochemicals in North American Apiaries: Implications for Honey Bee Health

            Background Recent declines in honey bees for crop pollination threaten fruit, nut, vegetable and seed production in the United States. A broad survey of pesticide residues was conducted on samples from migratory and other beekeepers across 23 states, one Canadian province and several agricultural cropping systems during the 2007–08 growing seasons. Methodology/Principal Findings We have used LC/MS-MS and GC/MS to analyze bees and hive matrices for pesticide residues utilizing a modified QuEChERS method. We have found 121 different pesticides and metabolites within 887 wax, pollen, bee and associated hive samples. Almost 60% of the 259 wax and 350 pollen samples contained at least one systemic pesticide, and over 47% had both in-hive acaricides fluvalinate and coumaphos, and chlorothalonil, a widely-used fungicide. In bee pollen were found chlorothalonil at levels up to 99 ppm and the insecticides aldicarb, carbaryl, chlorpyrifos and imidacloprid, fungicides boscalid, captan and myclobutanil, and herbicide pendimethalin at 1 ppm levels. Almost all comb and foundation wax samples (98%) were contaminated with up to 204 and 94 ppm, respectively, of fluvalinate and coumaphos, and lower amounts of amitraz degradates and chlorothalonil, with an average of 6 pesticide detections per sample and a high of 39. There were fewer pesticides found in adults and brood except for those linked with bee kills by permethrin (20 ppm) and fipronil (3.1 ppm). Conclusions/Significance The 98 pesticides and metabolites detected in mixtures up to 214 ppm in bee pollen alone represents a remarkably high level for toxicants in the brood and adult food of this primary pollinator. This represents over half of the maximum individual pesticide incidences ever reported for apiaries. While exposure to many of these neurotoxicants elicits acute and sublethal reductions in honey bee fitness, the effects of these materials in combinations and their direct association with CCD or declining bee health remains to be determined.
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              Modeling Survival Data: Extending the Cox Model

              This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Experimental Biology
                The Company of Biologists
                0022-0949
                1477-9145
                May 01 2021
                May 01 2021
                May 13 2021
                : 224
                : 9
                Affiliations
                [1 ]Intercollege Graduate Program in Ecology, Center for Pollinator Research, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
                [2 ]Department of Entomology, Center for Pollinator Research, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
                Article
                10.1242/jeb.242040
                33758024
                ab9ecc69-5603-4259-883d-fe49ce773e6a
                © 2021

                http://www.biologists.com/user-licence-1-1/

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