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

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van der Zee R., Gray A., Holzmann C., Pisa L., Brodschneider R., Chlebo R., ...Daha Fazla

JOURNAL OF APICULTURAL RESEARCH, cilt.52, sa.4, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 52 Sayı: 4
  • Basım Tarihi: 2013
  • Doi Numarası: 10.3896/ibra.1.52.4.18
  • Dergi Adı: JOURNAL OF APICULTURAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: COLOSS BEEBOOK, estimating colony losses, Apis mellifera, surveys, questionnaire, random / randomized sampling, non-random sampling, generalized linear models (GZLMs), WINTER LOSSES, INCREASE, SCOTLAND
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.