Spatial Capture-recapture with Memory: A New Hidden Markov Model Perspective

空间捕捉-用记忆重新捕捉：新的隐马尔可夫模型视角

• 批准号: EP/W001616/1
• 负责人: Ruth King
• 金额: $ 2.86万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW001616%2F1
• 关键词: Spatial; Capture; recapture; Memory; New

Spatial capture-recapture (SCR) surveys are widely used to answer questions in population ecology, for species ranging from slow worms to snow leopards. They are used to estimate the total size (or density) of the population within the given area, for example, or to estimate relationships between density and environmental drivers of density, or to estimate spatial and temporal trends in abundance. However, existing statistical methods for analysing SCR data ignore the fact that detections of animals that are made close together in time, are also likely to be close together in space. Existing SCR methods neglect this spatio-temporal correlation, and this can result in substantial negative bias in estimates of population abundance, sometimes by as much as 50%.We will develop and test a new spatial capture-recapture method that explicitly takes into account the spatio-temporal dependence in detections, by modelling the probability that an individual is observed in any trap as a function of the distance in both space and time from its last detection. This will provide more robust estimates than can be obtained using current SCR methods. The new method and associated software will be developed in collaboration with the Global Snow Leopard Ecosystem Protection Programme (GSLEP), which is coordinating the world's first range-wide survey of snow leopards. The new methods will be applied to camera trap surveys of snow leopards that form a key part of the GSLEP survey initiative. The new SCR models we develop will, however, have much wider utility than this survey alone. They are applicable across a vast range of species ranging from, for example, large mammals (e.g. tigers, bears) and primates (e.g. gibbons, chimpanzees) to small reptiles and amphibians (e.g. salamanders, frogs, slow worms) and birds (e.g. songbirds, grouse).

空间捕获-再捕获（SCR）调查被广泛用于回答种群生态学中的问题，从缓慢的蠕虫到雪豹。例如，它们被用来估计特定区域内种群的总规模（或密度），或估计密度与密度的环境驱动因素之间的关系，或估计丰度的空间和时间趋势。然而，现有的用于分析SCR数据的统计方法忽略了这样的事实，即在时间上接近的动物的检测也可能在空间上接近。现有的SCR方法忽略了这种时空相关性，这可能导致种群丰度估计的显著负偏差，有时高达50%.We将开发和测试一种新的空间捕获-再捕获方法，该方法明确考虑了检测的时空依赖性，通过模拟一个人在任何陷阱中被观察到的概率，作为距离其最后一次检测的空间和时间的函数。这将提供比使用当前SCR方法可以获得的更稳健的估计。新方法和相关软件将与全球雪豹生态系统保护计划（GSLEP）合作开发，该计划正在协调世界上第一次雪豹范围调查。新方法将应用于雪豹的相机陷阱调查，这是GSLEP调查计划的关键部分。然而，我们开发的新SCR模型将比这项调查具有更广泛的实用性。它们适用于范围广泛的物种，例如，从大型哺乳动物（如老虎、熊）和灵长类动物（如长臂猿、黑猩猩）到小型爬行动物和两栖动物（如蝾螈、青蛙、慢蠕虫）和鸟类（如鸣禽、松鸡）。