Skip to content

Research at St Andrews

Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds

Research output: Contribution to journalArticle


1. Modelling spatio-temporal changes in species abundance and attributing those changes to potential drivers such as climate, is an important but difficult problem. The standard approach for incorporating climatic variables into such models is to include each weather variable as a single covariate whose effect is expressed through a low-order polynomial or smoother in an additive model. This, however, confounds the spatial and temporal effects of the covariates.
2. We developed a novel approach to distinguish between three types of change in any particular weather covariate. We decomposed the weather covariate into three new covariates by separating out temporal variation in weather (averaging over space), spatial variation in weather (averaging over years) and a space-time anomaly term (residual variation). These three covariates were each fitted separately in the models. We illustrate the approach using generalized additive models applied to count data for a selection of species from the UK’s Breeding Bird Survey, 1994-2013. The weather covariates considered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these covariates directly with models including decomposed components of the same covariates, considering both linear and smooth relationships.
3. The lowest QAIC values were always associated with a decomposed weather covariate model. Different relationships between counts and the three new covariates provided strong evidence that the effects of changes in covariate values depended on whether changes took place in space, in time, or in the space-time anomaly. These results promote caution in predicting species distribution and abundance in future climate, based on relationships that are largely determined by environmental variation over space.
4. Our methods estimate the effect of temporal changes in weather, whilst accounting for spatial effects of long-term climate, improving inference on overall and/or localised effects of climate change. With increasing availability of large-scale data sets, need is growing for appropriate analytical tools. The proposed decomposition of the weather variables represents an important advance by eliminating the confounding issue often inherent in large-scale data sets.


Original languageEnglish
Pages (from-to)1690-1702
JournalMethods in Ecology and Evolution
Issue number12
Early online date15 Jun 2017
StatePublished - Dec 2017

    Research areas

  • Climate change, Decomposition of spatial, temporal and anomaly effects, Generalized additive model, Spatio-temporal modelling, Species abundance, UKCP09 climate projections., Temporal and anomaly effects, Generalized linear models

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Quantifying turnover in biodiversity of British breeding birds

    Harrison, P., Yuan, Y., Buckland, S. T., Oedekoven, C. S., Elston, D. A., Brewer, M., Johnston, A. & Pierce-Higgins, J. 22 Feb 2016 In : Journal of Applied Ecology. 53, 2, p. 469-478 10 p.

    Research output: Contribution to journalArticle

  2. Low tortoise abundances in pine forest plantations in forest-shrubland transition areas

    Rodríguez-Caro, R. C., Oedekoven, C. S., Graciá, E., Anadón, J. D., Buckland, S. T., Esteve-Selma, M. A., Martinez, J. & Giménez, A. 8 Mar 2017 In : PLoS One. 12, 3, 13 p., e0173485

    Research output: Contribution to journalArticle

  3. Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models

    Oedekoven, C. S., King , R., Buckland, S. T., MacKenzie, M. L., Evans, K. O. & Burger Jr., L. W. Jun 2016 In : Computational Statistics and Data Analysis. 98, p. 79-90

    Research output: Contribution to journalArticle

  4. Distance sampling: methods and applications

    Buckland, S. T., Rexstad, E., Marques, T. A. & Oedekoven, C. S. 2015 Cham: Springer. 292 p. (Methods in statistical ecology)

    Research output: Book/ReportBook

Related by journal

  1. Methods in Ecology and Evolution (Journal)

    Gaggiotti, O. E. (Member of editorial board)
    1 Sep 2014 → …

    Activity: Publication peer-review and editorial workEditor of research journal

Related by journal

  1. Analysis of animal accelerometer data using hidden Markov models

    Leos-Barajas, V., Photopoulou, T., Langrock, R., Patterson, T. A., Watanabe, Y. Y., Murgatroyd, M. & Papastamatiou, Y. P. Feb 2017 In : Methods in Ecology and Evolution. 8, 2, p. 161-173 13 p.

    Research output: Contribution to journalArticle

  2. Distance sampling with camera traps

    Howe, E. J., Buckland, S. T., Després-Einspenner, M-L. & Kühl, H. Nov 2017 In : Methods in Ecology and Evolution. 8, 11, p. 1558-1565

    Research output: Contribution to journalArticle

  3. Inference of selection gradients using performance measures as fitness proxies

    Franklin, O. D. & Morrissey, M. B. Jun 2017 In : Methods in Ecology and Evolution. 8, 6, p. 663-677 15 p.

    Research output: Contribution to journalArticle

ID: 249720944