Abstract The fast development of camera technologies opens a breakthrough opportunity for animal ecology, particularly at the marine realm where observing wildlife is challenging. These outstanding technological advances are meeting with the impressive capabilities of artificial intelligence for enabling automatic extraction of relevant information from videos and images. Altogether, this may be a unique opportunity for a qualitative jump in marine wildlife assessment but substantial strengthening of the links between theorists, empiricists and engineers is still required. Specifically, a recent theory proposes that animal density can be estimated from (1) the counted animals per frame, (2) the area surveyed by the camera and (3) the probability of detecting an animal that is actually within the area surveyed by the camera. However, a potential drawback for applying this theory to the real world is that environmental dependencies of camera's detection probability may lead to biased estimates of animal density. Therefore, here we propose a sampling protocol and a statistical model of general application for estimating (and accounting for) the environmental factors affecting fish detectability when estimating fish density with cameras. The method implies one calibration sampling with cameras and with the preferred reference method at the same time and place. The relevance of this method is that, once calibrated, it can be used to obtain unbiased estimates of fish density at new sites and moments using only cameras. Thus, fish density could be estimated at the temporal and the spatial scale needed, but with substantially less cost-effort than any other reference methods (e.g., underwater visual censuses). As a proof of concept, we evaluated the dependence of camera's detection probability on habitat complexity (e.g., cavities, rocks, seagrass, etc.) as a proxy for the hiding capability of a small serranid. In that specific case, probability of detection seems to be independent of habitat complexity. However, the sampling protocol and the statistical model provided here open the opportunity to estimate fish density using underwater cameras at wider temporal and/or spatial scales, which will help to better understanding the ultimate drivers of marine fish population dynamics and further development of science-based management.

中文翻译：

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使用无诱饵相机估算鱼群密度：考虑环境相关的可探测性

摘要 相机技术的快速发展为动物生态学提供了突破性的机遇，特别是在观察野生动物具有挑战性的海洋领域。这些杰出的技术进步与人工智能令人印象深刻的能力相结合，能够从视频和图像中自动提取相关信息。总而言之，这可能是海洋野生动物评估质量飞跃的独特机会，但仍然需要大量加强理论家、经验主义者和工程师之间的联系。具体来说，最近的一个理论提出，动物密度可以通过 (1) 每帧计数的动物，(2) 相机调查的区域和 (3) 检测到实际在调查区域内的动物的概率来估计。相机。然而，将该理论应用于现实世界的一个潜在缺点是，相机检测概率的环境依赖性可能会导致对动物密度的估计有偏差。因此，我们在这里提出了一个采样协议和一个一般应用的统计模型，用于在使用相机估计鱼类密度时估计（和说明）影响鱼类可探测性的环境因素。该方法意味着在同一时间和地点使用相机和首选参考方法进行一次校准采样。这种方法的相关性在于，一旦校准，它就可以用于仅使用相机在新地点和时刻获得鱼类密度的无偏估计。因此，可以在所需的时间和空间尺度上估计鱼类密度，但成本工作比任何其他参考方法（例如 例如，水下视觉普查）。作为概念证明，我们评估了相机检测概率对栖息地复杂性（例如，洞穴、岩石、海草等）的依赖性，作为小型 serranid 隐藏能力的代理。在这种特定情况下，检测概率似乎与栖息地的复杂性无关。然而，这里提供的采样协议和统计模型为使用水下相机在更广泛的时间和/或空间尺度上估计鱼类密度提供了机会，这将有助于更好地了解海洋鱼类种群动态的最终驱动因素和科学的进一步发展-基于管理。) 作为小型 serranid 隐藏能力的代理。在这种特定情况下，检测概率似乎与栖息地的复杂性无关。然而，这里提供的采样协议和统计模型为使用水下相机在更广泛的时间和/或空间尺度上估计鱼类密度提供了机会，这将有助于更好地了解海洋鱼类种群动态的最终驱动因素和科学的进一步发展-基于管理。) 作为小型 serranid 隐藏能力的代理。在这种特定情况下，检测概率似乎与栖息地的复杂性无关。然而，这里提供的采样协议和统计模型为使用水下相机在更广泛的时间和/或空间尺度上估计鱼类密度提供了机会，这将有助于更好地了解海洋鱼类种群动态的最终驱动因素和科学的进一步发展-基于管理。