Abstract：
Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R (mgcv and ggplot2) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins (Fratercula cirrhata) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (Canis lupus ligoni) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.
摘要：
饮食分析集成了各种各样的视觉，化学,和生物识别猎物。样品通常被视为成分数据，其中每个猎物都被分析为总数的连续百分比。然而,分析成分数据导致分析挑战，例如,高度参数化的模型或先前的数据转换。这里,我们提出了一种新的近似方法，涉及Tweedie广义线性模型(GLM)。我们首先回顾了这种近似值是如何将捕食者的觅食视为一个稀疏且标记的点过程（标记代表猎物物种和单个猎物的大小）而产生的。这种推导可以激发未来的理论和应用发展。然后，我们使用新的软件包mvweetedie为TweedieGLM提供实用教程，该软件包通过转换输出以计算猎物成分来扩展R（mgcv和ggplot2）中广泛使用的软件包的功能。我们用两个例子演示了这种方法和软件。簇绒的海雀（Fraterculacirrhata）在阿拉斯加湾北部的一个殖民地上提供了它们的小鸡，显示出年代的猎物在沙长矛和长尾鱼（1980-2000）之间切换，然后在太平洋鲱鱼和毛鳞鱼（2000-2020）之间切换，阿拉斯加东南部的狼（Canislupusligoni）在北部高地的山羊和土拨鼠以及向海岛屿海岸线的海洋哺乳动物上觅食。
