Attributing changes in the distribution of species abundance to weather variables using the example of British breeding birds
2017
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
weathervariable 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. We developed a novel approach to distinguish between three types of change in any particular
weather
covariate. We decomposed the
weather
covariateinto three new
covariatesby 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
covariateswere each fitted separately in the models. We illustrate the approach using
generalized additive modelsapplied to
count datafor a selection of species from the UK's
Breeding Bird Survey, 1994–2013. The
weather
covariatesconsidered were the mean temperatures during the preceding winter and temperatures and rainfall during the preceding breeding season. We compare models that include these
covariatesdirectly with models including decomposed components of the same
covariates, considering both linear and smooth relationships. The lowest QAIC values were always associated with a decomposed
weather
covariate model. Different relationships between counts and the three new
covariatesprovided strong evidence that the effects of changes in
covariatevalues 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. Our methods estimate the effect of temporal changes in
weather, while accounting for spatial effects of long-term climate, improving inference on overall and/or localized effects of climate change. With increasing availability of large-scale datasets, need is growing for appropriate analytical tools. The proposed decomposition of the
weathervariables represents an important advance by eliminating the confounding issue often inherent in analyses of large-scale datasets.
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