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.

用于解析种群动态，或探究不同土地状况类别间鸟类丰度差异的最主流方法，是依托定点计数法（point counts）获取的相关数据。然而，诸多因素会干扰鸟类检测成功率，因此需对这些因素加以控制或校正，以确保不同时段、不同地点间的鸟类丰度指数差异，能够准确反映鸟类实际丰度的变化，而非检测成功率本身的差异。鸟类生态学家可采用规格适配的固定面积调查法（fixed-area surveys），以降低因距离依赖型检测偏差导致误判的概率，但当前学界首选的校正方案，却是通过构建距离对鸟类检测率或生境占据率（occupancy）的影响模型，来抵消距离依赖型检测偏差的影响。笔者对“建模是校正距离对鸟类检测率影响的最优方案”这一观点提出质疑，因为距离依赖型检测模型的核心假设从根本上无法得到满足。采用固定面积调查法生成鸟类丰度指数，是校正距离依赖型检测偏差的最简方案，因此在学术出版流程中，该方法不应被一概否定，亦不应成为直接拒稿的依据。