Can the Spatial Point Patterns of Animal Distributions Be Detected Using Sparse Samples? A Case Study of Four Soricomorpha (Mammalia) Species in Poland / Czy Przestrzenny Wzorzec Rozk?adu Punktów W Dystrybucji Zwierz?t Mo?e Zosta? Okre?lony Na Podstawie R

摘要

In the present study, Riley's K function and alternative spatial point process models are calculated and compared for the hybrid distributional records of four Soricomorpha species (Talpa europaea, Sorex araneus, Sorex minutus, and Neomys fodiens) in Poland over different sampling sizes. The following spatial point process models are fitted and compared: homogeneous Poisson process (HPP) and inhomogeneous Poisson process (IPP) models. For IPP models, the covariates explaining the trend are latitude and longitude. Spatial process models and true distributional aggregation status (using K function) of the four species are also calculated based on the full observed data set for the purpose to check how many grids are required to sample so as to reflect the true spatial distributional point patterns. When performind tha sampling, the sanpling size 5, 10, 30, 60 and 100 are considered. For each sampling size, 500 replicates are performed to keep consistence and reduce uncertainty. The results showed that, for the full observed data set over the whole territory of Poland, IPP models were much better than the null HPP model for explaining the distribution of Soricomorpha species. For every sample size, the true aggregation status and the associated spatial point process models of each species over the studied area can be perfectly identified when using the information derived from limiting samples only. Based on the results, it is found that around 20% of grid cells should be used as the minimum threshold for accurately detecting the true spatial point patterns.