Modelling unbiased dispersal kernels over continuous space by accounting for spatial heterogeneity in marking and observation efforts

1. Although a key demographic trait determining the spatial dynamics of wild populations, dispersal is notoriously difficult to estimate in the field. Indeed, dispersal distances obtained from the monitoring of marked individuals typically lead to biased estimations of dispersal kernels as a consequence of i) restricted spatial scale of the study areas compared to species potential dispersal and ii) heterogeneity in marking and observation efforts and therfore in detection probability across space.
2. Here we propose a novel method to circumvent these issues that does not require data on observation effort per se, to correct for the variability in detection of marked individuals across space. Observed dispersal events were weighted by the distribution of departure points and an eroded spatial window approach was applied so as to deal with border effect. We conducted a set of simulations which indicated that our method was successful in correcting the effect of spatially heterogeneous d...

1. 尽管扩散（dispersal）是决定野生种群空间动态的关键种群统计特征，但在野外估算扩散却众所周知地困难。事实上，通过标记个体监测获得的扩散距离通常会导致扩散核（dispersal kernels）的估计存在偏差，其原因在于：i) 研究区域的空间尺度相对于物种潜在扩散范围受限；ii) 标记和观测工作存在异质性，因此空间上的检测概率也存在差异。
2. 在此，我们提出一种无需观测工作本身数据即可规避这些问题的新方法，以校正空间上标记个体检测的变异性。观测到的扩散事件根据出发地点的分布进行加权，并应用侵蚀空间窗口法（eroded spatial window approach）来处理边界效应。我们进行了一系列模拟，结果表明该方法成功校正了空间异质性d...