Yee Thomas W
Department of Statistics, University of Auckland, New Zealand.
Ecology. 2006 Jan;87(1):203-13. doi: 10.1890/05-0283.
For several decades now, ecologists have sought to determine the shape of species' response curves and how they are distributed along unknown underlying gradients, environmental latent variables, or ordination axes. Its determination has important implications for both continuum theory and community analysis because many theories and models in community ecology assume that responses are symmetric and unimodal. This article proposes a major new technique called constrained additive ordination (CAO) that solves this problem by computing the optimal gradients and flexible response curves. It allows ecologists to see the response curves as they really are, against the dominant gradients. With one gradient, CAO is a generalization of constrained quadratic ordination (CQO; formerly called canonical Gaussian ordination or CGO). It supplants symmetric bell-shaped response curves in CQO with completely flexible smooth curves. The curves are estimated using smoothers such as the smoothing spline. Loosely speaking, CAO models are generalized additive models (GAMs) fitted to a very small number of latent variables. Being data driven rather than model driven, CAO allows the data to "speak for itself" and does not make any of the assumptions made by canonical correspondence analysis. The new methodology is illustrated with a hunting spider data set and a New Zealand tree species data set.
几十年来，生态学家一直试图确定物种响应曲线的形状，以及它们如何沿着未知的潜在梯度、环境潜在变量或排序轴分布。其确定对于连续统理论和群落分析都具有重要意义，因为群落生态学中的许多理论和模型都假设响应是对称且单峰的。本文提出了一种名为约束加法排序（CAO）的重要新技术，该技术通过计算最优梯度和灵活的响应曲线来解决这个问题。它使生态学家能够看到响应曲线的真实形态，相对于主导梯度而言。对于一个梯度，CAO是约束二次排序（CQO；以前称为典范高斯排序或CGO）的推广。它用完全灵活的平滑曲线取代了CQO中的对称钟形响应曲线。这些曲线使用诸如平滑样条等平滑器进行估计。粗略地说，CAO模型是拟合到极少数潜在变量的广义加法模型（GAM）。CAO是由数据驱动而非模型驱动的，它让数据“自己说话”，并且不做典范对应分析所做的任何假设。本文用一个狩猎蜘蛛数据集和一个新西兰树种数据集对这种新方法进行了说明。
