[Forest landscapes' spatial point patterns and associations based on Ripley L and O-ring functions]

Area of Science:

• Forest Ecology
• Spatial Analysis
• Geospatial Statistics

Background:

• Understanding forest landscape patterns is crucial for effective forest management and conservation.
• The Great Xing'an Mountains in northeastern China host diverse forest types with unique spatial characteristics.

Purpose of the Study:

• To analyze the spatial distribution and associations of four major forest landscape types in Pangu Forest Farm.
• To compare the effectiveness of Ripley's L and O-ring functions in assessing spatial patterns and associations.

Main Methods:

• Utilized forest resource inventory data from Pangu Forest Farm.
• Applied spatial point pattern analysis using Ripley's L and O-ring functions.
• Examined four main forest types: natural Larix gmelini forest (NLG), natural Betula platyphylla forest (NBP), natural coniferous mixed forest (NCM), and natural mixed broadleaf-conifer forest (NCB).

Main Results:

• All four forest types exhibited clumped distributions at small scales, transitioning to random distributions as scale increased.
• Spatial associations differed significantly between the Ripley's L and O-ring methods.
• Ripley's L indicated negative correlations between NLG and NCB, and NBP and NCB at smaller scales, while O-ring showed negative associations at small scales and positive at large scales.

Conclusions:

• The spatial distribution and interrelationships of forest types are scale-dependent and method-sensitive.
• Different spatial analysis methods can yield contrasting results, highlighting the need for careful method selection.
• Findings provide insights into the complex spatial dynamics of forest landscapes in northeastern China.