Estimating abundance and phenology from transect count data with GLMs
Estimating abundance and phenology from transect count data with GLMs
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DOI：
10.1111/oik.08368
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发表时间：
2021-05-16
期刊：
影响因子：
3.4
通讯作者：
Crone, Elizabeth E.
中科院分区：
文献类型：
--
作者：
Edwards, Collin B.;Crone, Elizabeth E.
估计种群丰度是种群生态学的核心。随着人们对昆虫数量下降的日益关注，估计昆虫数量丰富的趋势变得更加紧迫。与此同时，人们对量化物候模式越来越感兴趣，部分原因是物候变化是气候变化最明显的迹象之一。现有的拟合活动曲线（以及丰度和物候）与昆虫的重复样条计数（这些分类群的一种常见数据形式）的技术经常不能用于稀疏数据，并且通常需要高级的统计计算知识。这些限制使我们无法理解种群趋势和物候变化，特别是对处于危险中的物种来说，这种理解是至关重要的。在这里，我们提出了一种使用线性模型将重复样条计数数据与高斯曲线拟合的方法，并展示了如何使用标准回归工具获得稳健的丰度和物候指标。然后，我们使用广义线性模型（GLMs）将该方法应用于巴尔的摩9年的方格点数据。本案例研究说明了我们的方法在只有少数非零调查计数的情况下拟合偶数年的能力，并确定了人口规模与每年的增长天数（GDD）之间存在显著的负相关关系。我们相信我们的新方法提供了一个关键工具来解锁以前不可用的数据集，并可能在丰度和物候的临时指标和定制编码的机制模型之间提供一个有用的中间位置。
Estimating population abundance is central to population ecology. With increasing concern over declining insect populations, estimating trends in abundance has become even more urgent. At the same time, there is an emerging interest in quantifying phenological patterns, in part because phenological shifts are one of the most conspicuous signs of climate change. Existing techniques to fit activity curves (and thus both abundance and phenology) to repeated transect counts of insects (a common form of data for these taxa) frequently fail for sparse data, and often require advanced knowledge of statistical computing. These limitations prevent us from understanding both population trends and phenological shifts, especially in the at-risk species for which this understanding is most vital. Here we present a method to fit repeated transect count data with Gaussian curves using linear models and show how robust abundance and phenological metrics can be obtained using standard regression tools. We then apply this method to nine years of Baltimore checkerspot data using generalized linear models (GLMs). This case study illustrates the ability of our method to fit even years with only a few non-zero survey counts, and identifies a significant negative relationship between population size and growing degree days (GDD) each year. We believe our new method provides a key tool to unlock previously-unusable data sets, and may provide a useful middle ground between ad hoc metrics of abundance and phenology, and custom-coded mechanistic models.