估算海豚对干扰的响应距离：一种带有惩罚阈值隐马尔可夫模型
Estimating the distance at which narwhal respond to disturbance: a penalized threshold hidden Markov model
AI总结：
本文提出一种带惩罚的阈值隐马尔可夫模型，用于量化野生动物对刺激的反应，通过模拟和实证数据展示该方法在估计海豚对船舶响应距离方面的应用。
摘要：
理解野生动物对干扰的反应对于野生动物保护至关重要。例如，在北极，海冰减少开辟了新的航运路线，增加了需要量化海洋哺乳动物对船舶存在反应距离的评估需求。使用轨迹数据确定与行为偏离相关的距离需要高级统计模型，如阈值隐马尔可夫模型（THMMs）。虽然这些模型非常强大，但它们不评估估计的阈值是否反映有意义的行为变化。我们引入了一种带有Lasso惩罚的THMM，基于计算高效的方法对HMMs施加惩罚，并提出了一个新的高效惩罚准限制最大似然估计器。我们的框架能够估计阈值并评估干扰效应是否可区分于基线行为。通过模拟，我们证明了我们的Lasso方法能有效将伪阈值效应缩小到零。当应用于海豚运动数据时，我们的分析表明，海豚在距离船舶4公里范围内通过减少运动持续性和在更深水域（平均最大深度356米）花费更多时间来响应船舶。总体而言，我们提供了一个广泛适用的框架，用于量化对刺激的反应，应用范围从确定反应阈值到干扰，到估算陆地物种如大象检测水源的距离。
英文摘要：
Understanding behavioural responses to disturbances is vital for wildlife conservation. For example, in the Arctic, the decrease in sea ice has opened new shipping routes, increasing the need for impact assessments that quantify the distance at which marine mammals react to vessel presence. This information can then guide targeted mitigation policies, such as vessel slow-down regulations and delineation of avoidance areas. Using telemetry data to determine distances linked to deviations from normal behaviour requires advanced statistical models, such as threshold hidden Markov models (THMMs). While these are powerful tools, they do not assess whether the estimated threshold reflects a meaningful behavioural shift. We introduce a lasso-penalized THMM that builds on computationally efficient methods to impose penalties on HMMs and present a new, efficient penalized quasi-restricted maximum-likelihood estimator. Our framework is capable of estimating thresholds and assessing whether the disturbance effects are distinguishable from baseline behaviour. With simulations, we demonstrate that our lasso method effectively shrinks spurious threshold effects towards zero. When applied to narwhal movement data, our analysis suggests that narwhal react to vessels up to 3.4 kilometres away by decreasing movement persistence and spending more time in deeper waters (average maximum depth of 356m). Overall, we provide a broadly applicable framework for quantifying behavioural responses to stimuli, with applications ranging from determining reaction thresholds to disturbance to estimating the distances at which terrestrial species, such as elephants, detect water. We also provide a tutorial and an open-source implementation to facilitate the application of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
