Abstract Context The shore-based survey is a common, non-invasive, and low-cost method in marine mammal science, but its scientific applications are currently limited. Such studies typically target populations whose distributions are not random with respect to nearshore sites and involve repeated scans of the same area from single, stationary platforms. These circumstances prohibit the use of classic distance sampling techniques for estimating animal densities or distributions, particularly the derivation of a detection function that describes the probability of detecting targets at various distances from the observer. Aims Here, we present a technique for estimating land-based detection functions, as well as quantifying uncertainty in their parameterisation, on the basis of the range-specific variability of observations from one scan to the next. Methods This Bayesian technique uses Monte Carlo simulation to determine the likelihood of thousands of candidate detection functions, then conducts weighted sampling to generate a posterior distribution estimate of the detection function parameterisation. We tested the approach with both archival and artificial datasets built from known detection functions that reflect whale and porpoise detectability. Key results When the base distribution of targets was random, the whale detection function was estimated without error (i.e. the difference of the median of the posterior and the true value was 0.00), and the porpoise detection function was estimated with an error equal to 4.23% of the true value. When the target base distribution was non-random, estimation error remained low (2.57% for targets concentrated offshore, 1.14% when associated with nearshore habitats). When applied to field observations of humpback whales and Dall’s porpoises from a land-based study in northern British Columbia, Canada, this technique yielded credible results for humpback whales, but appeared to underestimate the detectability of Dall’s porpoises. Conclusion The findings presented here indicate that this approach to detection function estimation is appropriate for long-running surveys in which scan regularity is high and the focus is on large, slow-moving, low herd-size, and easily detectable species. Implications The derivation of a detection function is a critical step in density estimation. The methodology presented here empowers land-based studies to contribute to quantitative monitoring and assessment of marine mammal populations in coastal habitats.

中文翻译：

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确定固定陆基调查站点的海洋哺乳动物检测功能

摘要 背景 岸基调查是海洋哺乳动物科学中一种常见的、非侵入性的、低成本的方法，但其科学应用目前受到限制。此类研究通常针对其分布在近岸站点方面不是随机的人群，并且涉及从单个固定平台对同一区域进行重复扫描。这些情况禁止使用经典的距离采样技术来估计动物密度或分布，特别是推导检测函数，该函数描述了在距观察者不同距离处检测目标的概率。目标 在这里，我们提出了一种技术，用于估计陆基检测功能，并根据从一次扫描到下一次扫描的观测范围特定可变性来量化其参数化的不确定性。方法 这种贝叶斯技术使用蒙特卡罗模拟来确定数千个候选检测函数的可能性，然后进行加权采样以生成检测函数参数化的后验分布估计。我们使用由反映鲸鱼和海豚可检测性的已知检测函数构建的档案和人工数据集测试了该方法。主要结果 目标基分布随机时，鲸鱼检测函数估计无误差（即后验中位数与真值之差为0.00），海豚检测函数估计误差为4.23真实值的百分比。当目标基数分布非随机时，估计误差保持在较低水平（离岸集中的目标为 2.57%，1. 14% 与近岸栖息地相关）。当应用于加拿大不列颠哥伦比亚省北部陆基研究对座头鲸和达尔氏鼠海豚的实地观察时，该技术为座头鲸产生了可靠的结果，但似乎低估了达尔氏鼠海豚的可探测性。结论 这里提出的结果表明，这种检测函数估计方法适用于扫描规律性高且重点关注大型、移动缓慢、畜群规模小且易于检测的物种的长期调查。含义 检测函数的推导是密度估计的关键步骤。此处介绍的方法使陆基研究有助于对沿海栖息地中的海洋哺乳动物种群进行定量监测和评估。当应用于加拿大不列颠哥伦比亚省北部陆基研究对座头鲸和达尔氏鼠海豚的实地观察时，该技术为座头鲸产生了可靠的结果，但似乎低估了达尔氏鼠海豚的可探测性。结论 这里提出的结果表明，这种检测函数估计方法适用于扫描规律性高且重点关注大型、移动缓慢、畜群规模小且易于检测的物种的长期调查。含义 检测函数的推导是密度估计的关键步骤。此处介绍的方法使陆基研究有助于对沿海栖息地中的海洋哺乳动物种群进行定量监测和评估。当应用于加拿大不列颠哥伦比亚省北部陆基研究对座头鲸和达尔氏鼠海豚的实地观察时，该技术为座头鲸产生了可靠的结果，但似乎低估了达尔氏鼠海豚的可探测性。结论 这里提出的结果表明，这种检测函数估计方法适用于扫描规律性高且重点关注大型、移动缓慢、畜群规模小且易于检测的物种的长期调查。含义 检测函数的推导是密度估计的关键步骤。此处介绍的方法使陆基研究有助于对沿海栖息地中的海洋哺乳动物种群进行定量监测和评估。