Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
收藏资源简介：
Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition. We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement. The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing. Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.
诸多野生动物种群捕获重捕（capture-recapture）调查采用连续时间框架开展，但即便可获取精确的检测时刻，研究人员通常仍会将检测数据汇总至各抽样时段以开展分析。此种做法会丢弃有效信息，且因需人为定义抽样时段而引入主观性偏差。我们针对可记录精确检测时刻的连续时间空间显式捕获重捕（spatially explicit capture-recapture, SECR）调查，构建了时空泊松过程（spatio-temporal Poisson process）模型。我们证明，除部分特殊情形（包括单时段内检测概率保持不变的情况）外，时间汇总数据无法提供种群密度及相关参数的充分统计量；且当检测概率随时间保持恒定时，我们提出的连续时间（CT）模型与基于检测频率的现有模型等价。我们利用该模型基于某相机陷阱调查数据估算美洲豹种群密度，并开展模拟研究，以分析CT估计量与不同时间汇总程度下的离散时段估计量的统计特性。该模拟研究同时探究了动物运动引发的时空相关性对各类估计量的影响。结果表明，在无时空相关性的情形下，CT估计量具备无偏性，且相较于基于二元捕获数据（而非检测频率）的离散时段估计量具有更高的精度；同时，当存在动物运动引发的时空相关性时，CT估计量仅存在轻微偏差，且对检测器布设不足的情形具备更强的稳健性；而基于二元数据的离散时段估计量则对时段长度较为敏感，尤其在检测器布设不足的场景下更为明显。我们提出的模型将基于检测频率的离散时段估计量作为其特殊情形纳入其中，同时也为更复杂的CT模型与估计量的开发奠定了基础。该模型支持对单时段内可检测性的变化进行建模，可灵活适配检测器采样强度的差异，消除了因人为定义抽样时段带来的主观性，并充分利用了连续时间检测数据。我们认为，亟需开发可纳入检测过程时空依赖性的CT方法，同时也看好将CT模型与基于遥测的动物运动模型相结合，以构建更完善的推断框架的发展前景。
