Sterfte onder bijenvolken 2002-2003

Enquêt

Results of international standardised beekeeper surveys of colony losses for winter 2012-2013 : analysis of winter loss rates and mixed effects modelling of risk factors for winter loss.

This article presents results of an analysis of winter losses of honey bee colonies from 19 mainly European countries, most of which implemented the standardised 2013 COLOSS questionnaire. Generalised linear mixed effects models (GLMMs) were used to investigate the effects of several factors on the risk of colony loss, including different treatments for Varroa destructor, allowing for random effects of beekeeper and region. Both winter and summer treatments were considered, and the most common combinations of treatment and timing were used to define treatment factor levels. Overall and within country colony loss rates are presented. Significant factors in the model were found to be: percentage of young queens in the colonies before winter, extent of queen problems in summer, treatment of the varroa mite, and access by foraging honey bees to oilseed rape and maize. Spatial variation at the beekeeper level is shown across geographical regions using random effects from the fitted models, both before and after allowing for the effect of the significant terms in the model. This spatial variation is considerable

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals

Divinity and maximal greatness

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Percentage of colonies lost, with 95% confidence intervals, for colonies with/without acetamiprid or thiacloprid present.

<p>Percentage of colonies lost, with 95% confidence intervals, for colonies with/without acetamiprid or thiacloprid present.</p

Percentage of colonies lost, with 95% confidence intervals, for colonies with/without plant species present in bee bread.

<p>Percentage of colonies lost, with 95% confidence intervals, for colonies with/without plant species present in bee bread.</p

Risk factors for colony loss: results from the best explaining model for the 75 colonies used.

<p>⁽¹⁾ Number of positive cases,</p><p>⁽²⁾ Odds Ratio with 95% confidence interval,</p><p>⁽³⁾ Slope parameter,</p><p>⁽⁴⁾ Standard Error,</p><p>⁽⁵⁾ Z-test statistics</p><p>Risk factors for colony loss: results from the best explaining model for the 75 colonies used.</p