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

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.