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.1. 对物种种群丰度的时空变化进行建模，并将其归因于气候等潜在驱动因子，是一项兼具重要性与挑战性的研究课题。将气候变量纳入此类模型的标准方法，是将每个气象变量作为单一协变量（covariate）纳入模型，其效应通过可加模型（additive model）中的低阶多项式（low-order polynomial）或平滑器（smoother）加以表达。然而，该方法会混淆协变量的空间与时间效应。 2. 本研究提出一种全新方法，可区分单一气象协变量的三类变化。我们将原始气象协变量分解为三个新的协变量：分别提取气象的时间变异（空间平均后的时间变化）、空间变异（年份平均后的空间变化）以及时空异常项（residual variation，剩余变异）。随后将这三个协变量分别纳入模型进行拟合。我们以1994-2013年英国繁殖鸟类调查（Breeding Bird Survey, BBS）获取的若干物种种群计数数据（count data）为案例，应用广义可加模型（Generalized Additive Model, GAM）对该方法进行了演示验证。本研究选取的气象协变量包括越冬前期的平均气温，以及繁殖季前期的气温与降雨量。我们将直接纳入原始气象协变量的模型，与纳入同一协变量分解分量的模型进行对比，同时考量线性与平滑关联两种形式。 3. 所有实验中，准赤池信息准则（Quasi-Akaike Information Criterion, QAIC）值最低的模型始终为气象协变量分解模型。种群计数与三个新协变量之间的关联模式存在显著差异，这有力证明了协变量数值变化的效应，取决于该变化是发生在空间维度、时间维度，还是时空异常维度。上述结果提示我们：若基于主要由空间环境变异所决定的关联关系来预测未来气候下的物种种群分布与丰度，需格外谨慎。 4. 本研究提出的方法可在考量长期气候的空间效应的同时，估算气象条件的时间变化所带来的影响，从而提升了对气候变化整体效应与局地效应的推断准确性。随着大规模数据集的可获取性不断提升，学界对适配性分析工具的需求日益增长。本研究所提出的气象变量分解方法，解决了大规模数据集普遍存在的混淆效应问题，因此是一项重要的方法学进展。