New Zealand Institute for Advanced Study (NZIAS), Massey University, Auckland, New Zealand.
PRIMER-e (Quest Research Limited), Auckland, New Zealand.
Ecol Lett. 2022 Dec;25(12):2739-2752. doi: 10.1111/ele.14121. Epub 2022 Oct 21.
Species' responses to broad-scale environmental or spatial gradients are typically unimodal. Current models of species' responses along gradients tend to be overly simplistic (e.g., linear, quadratic or Gaussian GLMs), or are suitably flexible (e.g., splines, GAMs) but lack direct ecologically interpretable parameters. We describe a parametric framework for species-environment non-linear modelling ('senlm'). The framework has two components: (i) a non-linear parametric mathematical function to model the mean species response along a gradient that allows asymmetry, flattening/peakedness or bimodality; and (ii) a statistical error distribution tailored for ecological data types, allowing intrinsic mean-variance relationships and zero-inflation. We demonstrate the utility of this model framework, highlighting the flexibility of a range of possible mean functions and a broad range of potential error distributions, in analyses of fish species' abundances along a depth gradient, and how they change over time and at different latitudes.
物种对大范围环境或空间梯度的响应通常是单峰的。目前，沿梯度的物种响应模型往往过于简单化（例如，线性、二次或高斯广义线性模型），或者足够灵活（例如，样条、广义可加模型），但缺乏具有直接生态可解释性的参数。我们描述了一种用于物种-环境非线性建模的参数框架（'senlm'）。该框架有两个组成部分：（i）一种非线性参数数学函数，用于沿梯度模拟物种的平均响应，该函数允许不对称、平坦/峰形或双峰；（ii）一种针对生态数据类型量身定制的统计误差分布，允许存在内在的均值-方差关系和零膨胀。我们演示了该模型框架的实用性，强调了一系列可能的均值函数和广泛的潜在误差分布的灵活性，在对鱼类物种在深度梯度上的丰度进行分析时，以及它们随时间和不同纬度的变化情况。
