http://onlinelibrary.wiley.com/doi/10.1111/j.1600-0706.2012.20781.x/abstract

The influence of abundance on detectability

Plant and animal survey detection rates are important for ecological surveys, environmental impact assessment, invasive species monitoring, and modeling species distributions. Species can be difficult to detect when rare but, in general, how detection probabilities vary with abundance is unknown. We developed a new detectability model based on the time to detection of the first individual of a species. Based on this model, the predicted detection rate is proportional to a power function of abundance with a scaling exponent between zero and one that depends on clustering of individuals. We estimated the model parameters with data from three independent datasets: searches for chenopod shrub species and coins, experimental searches for planted seedlings, and frog surveys at multiple sites in sub-tropical forests of eastern Australia. Analyses based on the detection time and detection probability suggest that detection rate increases with abundance as predicted. The model provides a way to scale detection rates to cases of low abundance when direct estimation of detection rates is often impractical.

植物和动物的调查发现率对于生态调查、换进影响评估、入侵物种监测和物种分布模拟很重要。当很稀少时物种很难被发现，但是一般来说发现概率如何随物种丰富度变化是不清楚的。我们基于发现某物种的第一个个体的时间开发了一个新的可发现度模型。根据此模型预测的某物种的发现率与其丰富度幂函数成比例，依据该物种的个体之间的聚合度幂函数的指数取值为0-1之间的某个值。我们用3组相互独立的数据（寻找藜科某灌木和硬币的数据，寻找种植苗木的数据和在东澳大利亚的亚热带森林中多个地点进行的蛙类调查数据）估计了该模型的参数。和预测的一样，基于发现时间和发现概率的分析证实了发现率随丰富度升高而增加。该模型提供了一种当丰富度低到无法直接估计发现率时对发现率进行测量的方法。