A New Approach to Modelling the Relationship Between Annual Population Abundance Indices and Weather Data

A - Papers appearing in refereed journals

Elston, D. A., Brewer, M. J., Martay, B., Johnston, A., Henrys, P. A., Bell, J. R., Harrington, R., Monteith, D. T., Brereton, T. M., Boughey, K. L. and Pearce-Higgins, J. W. 2017. A New Approach to Modelling the Relationship Between Annual Population Abundance Indices and Weather Data. Journal of Agricultural Biological and Environmental Statistics. 22 (4), pp. 427-445. https://doi.org/10.1007/s13253-017-0287-4

AuthorsElston, D. A., Brewer, M. J., Martay, B., Johnston, A., Henrys, P. A., Bell, J. R., Harrington, R., Monteith, D. T., Brereton, T. M., Boughey, K. L. and Pearce-Higgins, J. W.
Abstract

Weather has often been associated with fluctuations in population sizes of species; however, it can be difficult to estimate the effects satisfactorily because population size is naturally measured by annual abundance indices whilst weather varies on much shorter timescales. We describe a novel method for estimating the effects of a temporal sequence of a weather variable (such as mean temperatures from successive months) on annual species abundance indices. The model we use has a separate regression coefficient for each covariate in the temporal sequence, and over-fitting is avoided by constraining the regression coefficients to lie on a curve defined by a small number of parameters. The constrained curve is the product of a periodic function, reflecting assumptions that associations with weather will vary smoothly throughout the year and tend to be repetitive across years, and an exponentially decaying term, reflecting an assumption that the weather from the most recent year will tend to have the greatest effect on the current population and that the effect of weather in previous years tends to diminish as the time lag increases. We have used this approach to model 501 species abundance indices from Great Britain and present detailed results for two contrasting species alongside an overall impression of the results across all species. We believe this approach provides an important advance to the challenge of robustly modelling relationships between weather and species population size.

KeywordsAbundance index; Climate change impacts; Distributed lag models; Population abundance models; Population change ; Weather variables
Year of Publication2017
JournalJournal of Agricultural Biological and Environmental Statistics
Journal citation22 (4), pp. 427-445
Digital Object Identifier (DOI)https://doi.org/10.1007/s13253-017-0287-4
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeThe Rothamsted Insect Survey - National Capability [2017-2022]
The Rothamsted Insect Survey [2012-2017]
Output statusPublished
Publication dates
Online29 Jun 2017
Publication process dates
AcceptedJun 2017
PublisherSpringer
Copyright licensePublisher copyright
ISSN1085-7117

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