Data from: Should scientists be required to use a model-based solution to adjust for possible distance-based detectability bias?

The most popular method used to gain an understanding of population trends or of differences in bird abundance among land condition categories is to use information derived from point counts. Unfortunately, various factors can affect one’s ability to detect birds, and those factors need to be controlled or accounted for so that any difference in one’s index among time periods or locations is an accurate reflection of differences in bird abundance and not differences in detectability. Avian ecologists could use appropriately sized fixed-area surveys to minimize the chance that they might be deceived by distance-based detectability bias, but the current method of choice is to use a modeling approach that allows one to account for distance-based bias by modeling the effects of distance on detectability or occupancy. I challenge the idea that modeling is the best approach to account for distance-based effects on the detectability of birds because the most important distance-based modeling assumptions can never be met. The use of a fixed-area survey method to generate an index of abundance is the simplest way to control for distance-based detectability bias and should not be universally condemned or be the basis for outright rejection in the publication process.

用于探究种群动态变化或不同土地状况类别间鸟类丰度差异的最常用方法，是依托点计数法（point counts）获取的观测数据。遗憾的是，诸多因素会影响鸟类的检测概率，因此必须对这些因素加以控制或纳入考量，确保不同时段或地点间的鸟类丰度指数差异，能够准确反映鸟类丰度本身的差异，而非检测概率的差异。鸟类生态学家本可采用规模适配的固定面积调查法（fixed-area surveys），尽可能规避基于距离的检测偏差带来的观测误差，但当前主流研究选择借助建模手段：通过构建距离对检测概率或物种占用率的影响模型，来校正基于距离的检测偏差。笔者对“建模是校正鸟类检测距离效应的最优方案”这一观点提出质疑，因为此类基于距离的建模所依赖的核心假设，在实际研究中永远无法满足。采用固定面积调查法生成丰度指数，是校正基于距离的检测偏差的最简方案，该方法不应被全盘否定，也不应成为学术出版流程中直接拒稿的依据。