Over the past few decades, the use of camera-traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually-identifiable animals are well-established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space-To-Event (STE) model—originally designed to be used with time-lapse images—on motion-triggered camera-trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion-trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1-s sampling window was important to avoid the positive bias that results from violating the instantaneous-sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse-based STE to motion-trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion-triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non-instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate.

中文翻译：

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使用空间到事件模型和运动触发相机陷阱数据的引导重采样来估计动物密度

在过去的几十年里，相机陷阱的使用彻底改变了我们监测野生陆生哺乳动物种群的能力。虽然估计可单独识别的动物丰度的方法已经很成熟，但它们大多仅限于具有清晰自然标记的物种，否则就需要进行侵入性且往往成本高昂的动物标记活动。估计未标记动物的丰度或密度仍然具有挑战性。最近为解决这个问题而开发的几种模型很有前景，但并未被野外生态学家广泛使用。在这里，我们开发了一个应用空间到事件的框架(STE) 模型——最初设计用于延时图像——基于运动触发的相机陷阱数据。我们的方法包括对摄影数据集执行引导重采样以生成多个数据集，然后将其用作 STE 模型的输入。我们在 29 个数据集上测试了我们的方法，其中包括来自 6 个不同国家和不同生态系统的 8 个地点的 17 种有蹄类动物。然后，我们进行了回归分析，以评估研究中生态和采样条件的变化如何影响我们 STE 密度估计的偏差和精度。我们的研究表明，通过引导重采样方法和有关动物活动的信息以及与动物的有效检测距离，STE 模型可用于分析运动触发数据集并提供与其他方法类似的人口密度估计。我们发现测量相机视域对于防止密度估计中出现重大负面偏差至关重要。此外，使用 1 秒采样窗口对于避免因违反瞬时采样假设而导致的正偏差非常重要。我们发现，随着采样工作量的加大和群体密度的增加，精确度也会提高。基于这些结果，我们强调了过去研究中的几个问题，这些研究将原始的基于延时的 STE 应用于运动触发数据集，我们的引导重采样的问题方法地址。我们提醒您，STE 模型无论是应用于延时数据集还是运动触发数据集，都依赖于严格的假设。任何违反这些假设的行为，例如非瞬时采样或应用相机制造商提供的检测角度和距离，都可能导致多个方向上的偏差，而这些偏差可能难以区分。