A New Approach to Modelling the Relationship Between Annual Population Abundance Indices and Weather Data
A New Approach to Modelling the Relationship Between Annual Population Abundance Indices and Weather Data
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DOI：
10.1007/s13253-017-0287-4
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发表时间：
2017-12-01
影响因子：
1.4
通讯作者：
Pearce-Higgins, J. W.
中科院分区：
文献类型：
--
作者：
Elston, D. A.;Brewer, M. J.;Pearce-Higgins, J. W.
气候通常与物种种群规模的波动有关;然而，很难令人满意地估计其影响，因为种群规模自然是通过年度丰度指数来衡量的，而天气变化的时间尺度要短得多。我们描述了一种新的方法来估计一个时间序列的天气变量（如连续几个月的平均温度）对年度物种丰度指数的影响。我们使用的模型对时间序列中的每个协变量都有一个单独的回归系数，并且通过将回归系数约束在由少量参数定义的曲线上来避免过度拟合。受约束的曲线是周期函数的乘积，反映了与天气的关联将在全年中平滑变化并且倾向于跨年重复的假设，以及指数衰减项，这反映了一种假设，即最近一年的天气往往对当前人口产生最大的影响，而前几年的天气影响往往随着时间的推移而减弱。增大我们已经使用这种方法来模拟501物种丰富度指数从英国和两个对比物种的详细结果，以及所有物种的结果的总体印象。我们认为，这种方法提供了一个重要的进步，以强大的天气和物种种群规模之间的关系建模的挑战。
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.