Data from: Estimating bird detection distances in sound recordings for standardising detection ranges and distance sampling

1) Autonomous sound recorders are increasingly used to survey birds, and other wildlife taxa. Species richness estimates from sound recordings are usually compared with estimates obtained from established methods like point counts, but so far the comparisons were biased: Detection ranges usually differ between the survey methods, and bird detection distance data are needed for standardizing data from sound recordings. 2) We devised and tested a method for estimating bird detection distances from sound recordings, using a reference recording of test sounds at different frequencies, emitted from known distances. We used our method to estimate bird detection distances in sound recordings from tropical forest sites where point counts were also used. We derived bird abundance and richness measures and compared them between point counts and sound recordings using unlimited radius and fixed radius counts, as well as distance sampling modelling. 3) First we show that it is possible to accurately estimate bird detection distances in sound recordings. We then demonstrate that these data can be used to standardize the detection ranges between point counts and sound recordings with a fixed-radius approach, leading to higher abundance and richness estimates for sound recordings. Our distance-sampling approach also revealed that sound recorders sampled significantly higher bird densities than human point counts. 4) We show for the first time that it is possible to standardize detection ranges in sound recordings and that distance sampling can successfully be used too. We revealed that birds were flushed by human observers and that this possibly leads to lower density estimates in point counts, although sound recorders could also have sampled more birds because of their earlier deployment times. Sound recordings are more amenable to distance-sampling modelling than point counts as they do not exhibit an observer-induced avoidance effect, and they can easily collect more replicates for obtaining more accurate bird density estimates. Quantifying bird detection distances was so far one important shortcoming that hindered the adoption of modern autonomous sound recording methods for ecological surveys.

1) 自主声学记录仪（autonomous sound recorder）正日益被用于鸟类及其他野生动物类群的调查。基于声学录音的物种丰富度估算结果，通常会与定点计数法（point count）这类成熟调查方法得到的估算结果进行对比，但截至目前此类对比均存在偏倚：不同调查方法的探测范围往往存在差异，且需获取鸟类探测距离数据以实现声学录音数据的标准化。
2) 本研究设计并验证了一种基于声学录音估算鸟类探测距离的方法，该方法使用不同频率的测试声源在已知距离处录制的参考录音作为参照。我们将该方法应用于同时开展了定点计数法调查的热带森林样地的声学录音数据，以估算其中的鸟类探测距离。随后我们推导得到鸟类多度与丰富度指标，并分别采用无界半径计数法、有界半径计数法以及距离采样建模（distance sampling modelling），对比定点计数法与声学录音法得到的结果。
3) 首先，本研究证实可基于声学录音精准估算鸟类探测距离。随后我们证明，此类数据可通过有界半径方法实现定点计数法与声学录音法之间的探测范围标准化，进而使声学录音法得到的多度与丰富度估算结果更高。本研究的距离采样分析还显示，声学记录仪记录到的鸟类种群密度显著高于人类定点计数法。
4) 本研究首次证实，可实现声学录音数据的探测范围标准化，且距离采样方法同样可成功应用于此场景。我们发现，人类观测者会惊飞鸟类，这可能导致定点计数法的种群密度估算结果偏低；不过声学记录仪也可能因部署时间更早而记录到更多鸟类。相较于定点计数法，声学录音更适配距离采样建模，因为其不存在观察者诱导的回避效应，且可轻松获取更多重复样本以得到更精准的鸟类种群密度估算结果。此前，量化鸟类探测距离一直是阻碍现代自主声学录音方法在生态调查中推广应用的重要短板之一。