Dynamic Models for Longitudinal Butterfly Data

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• 作者：Emily B. Dennis ; Byron J. T. Morgan…
• 关键词：Abundance indices ; Auto ; regression ; Concentrated likelihood ; Generalised additive models ; Phenology ; Stopover models
• 刊名：Journal of Agricultural, Biological, and Environmental Statistics
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：21
• 期：1
• 页码：1-21
• 全文大小：808 KB
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• 作者单位：Emily B. Dennis (1) (2)
  Byron J. T. Morgan (1)
  Stephen N. Freeman (3)
  David B. Roy (3)
  Tom Brereton (2)
  
  1. School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UK
  2. Butterfly Conservation, Manor Yard, East Lulworth, Wareham, UK
  3. Centre for Ecology & Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, UK
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Agriculture
  Statistics for Life Sciences, Medicine and Health Sciences
  Biostatistics
  Monitoring/Environmental Analysis
  
• 出版者：Springer New York
• ISSN：1537-2693

文摘

We present models which provide succinct descriptions of longitudinal seasonal insect count data. This approach produces, for the first time, estimates of the key parameters of brood productivities. It may be applied to univoltine and bivoltine species. For the latter, the productivities of each brood are estimated separately, which results in new indices indicating the contributions from different generations. The models are based on discrete distributions, with expectations that reflect the underlying nature of seasonal data. Productivities are included in a deterministic, auto-regressive manner, making the data from each brood a function of those in the previous brood. A concentrated likelihood results in appreciable efficiency gains. Both phenomenological and mechanistic models are used, including weather and site-specific covariates. Illustrations are provided using data from the UK Butterfly Monitoring Scheme, however the approach is perfectly general. Consistent associations are found when estimates of productivity are regressed on northing and temperature. For instance, for univoltine species productivity is usually lower following milder winters, and mean emergence times of adults for all species have become earlier over time, due to climate change. The predictions of fitted dynamic models have the potential to improve the understanding of fundamental demographic processes. This is important for insects such as UK butterflies, many species of which are in decline. Supplementary materials for this article are available online. Keywords Abundance indices Auto-regression Concentrated likelihood Generalised additive models Phenology Stopover models