Measuring Two‐Dimensional Distributional Aggregation Pattern of Species Through One‐Dimensional Approximation

Questions Accurately measuring species distribution patterns has long been a fundamental task in community and spatial ecology. This study tries to address the following ecological problems: (1) Are distance‐based methods powerful and sensitive enough to detect subtle changes of spatial distributional patterns of species in a distributional map, particularly when it comes to complete random distribution? (2) How can spatial distributional aggregation patterns be effectively and rapidly compared when the size of spatial distributional data is large? Location Global. Methods We propose a one‐dimensional aggregation index, quadrat nearest neighboring distance (QNN), integrated with an optimal spatial subdivision protocol. This method was evaluated via numerical simulations and applied to empirical species distribution data and compared against Clark and Evans' R index, which is built upon two‐dimensional nearest neighboring distance (NND). Results QNN is accurate in detecting complete random distribution over different population sizes of species. The accuracy of QNN depended on using an optimal gridding size. The accuracy of detecting the complete random distribution of species is guaranteed when the sampling grain size in QNN is around the optimal gridding size. Furthermore, QNN showed high computational efficiency in extensive numerical simulations. Conclusions QNN offers a powerful, efficient, and scalable tool for detecting and comparing species spatial distribution patterns. Its enhanced sensitivity to random distributions and dependence on optimal gridding make it especially suitable for analyzing large‐scale or high‐resolution spatial data.