Abstract: Point pattern analysis is a fundamental and widely used method in population pattern research. Population gaps constitutes a critical factor influencing the outcomes of point pattern analysis, and its effect on the outcomes is closely related to the reliability of conclusions drawn from population pattern studies. Therefore, an examination of how these gaps impact point pattern analysis is of paramount importance. Through a simulation experiment within a 5 m×5 m area, three densities (low, medium and high) were set to investigate the effect of gap area changes on the analysis outcomes of point patterns under both non-shielded and shielded conditions. The K(r) function, incorporating cumulative effects, and the pairwise correlation function g(r), without cumulative effects, were analyzed. The results showed that: (1) As gap radius increased, both K(r) and g(r) functions affected the analysis outcomes of point patterns, leading to a transition from the original random distribution of a single pattern type to the coexistence of three pattern types after the transition from two pattern types; (2) The impact of gap radius on the analysis outcomes of point patterns differed between the K(r) function and g(r) function; (3) The influence of gaps on point pattern analysis outcomes varied with different density conditions for the same analysis method; (4) Shielding gaps can mitigate their impact on point pattern analysis outcomes, with the evasion effect of the K(r) function being more pronounced than that of the g(r) function.

Key words: gap, point pattern analysis, K(r) function, pairwise correlation function g(r), shielding