DOI: 10.57760/sciencedb.23658
title: Plot data for determining minimum sampling scale using moving window method: insights from forest structural and functional indicators
title: Plot data for determining minimum sampling scale using moving window method: insights from forest structural and functional indicators

description: Accurate forest ecosystem assessments depend on selecting appropriate sampling scales that reflect spatial heterogeneity. This study aimed to identify the minimum sampling scale required to capture key forest structural indicators using a moving window approach. By systematically increasing window sizes and traversing every location within temperate mixed forest plots, we calculated seven indicators—neighborhood comparison, angular scale, mingling, biomass, basal area, Simpson and Shannon diversity—at each scale and compared them with whole-plot values to identify the minimum spatial scale at which indicator values stabilized within an acceptable deviation threshold. Our results revealed that different indicators exhibit varying sensitivities to sampling scale. Variables such as neighborhood comparison and angular scale stabilized at smaller sampling scales due to their reliance on local structural properties, whereas biodiversity and mingling required larger sampling scales. Biomass and basal area, being driven by large-scale ecological processes, showed the least sensitivity to smaller sampling windows. The analysis indicated that an 85 m window (within a 10% deviation rate from the overall plot) was determined as the minimum necessary to accurately capture the spatial variability of all indicators. To further investigate the drivers of minimum sampling scale variation, a random forest model identified the coefficient of variation as a key factor influencing scale sensitivity for basal area, angular scale, neighborhood comparison, and biomass. This finding underscores the importance of spatial heterogeneity in sampling design. Additionally, partial least squares path modeling (PLS-PM) was used to assess ecological interactions at the minimum scale. Unexpectedly, species diversity showed a negative effect on productivity, potentially due to canopy gaps and asymmetric competition, but positively influenced spatial structure (especially mingling), underscoring its role in enhancing structural complexity. By integrating minimum sampling scale determination with ecological interaction analysis, this study provides a framework for context-specific sampling strategies that consider multiple ecological variables. The findings contribute to improving forest inventory practices and offer practical guidance for large-scale monitoring and conservation efforts in temperate mixed forests.
description: Accurate forest ecosystem assessments depend on selecting appropriate sampling scales that reflect spatial heterogeneity. This study aimed to identify the minimum sampling scale required to capture key forest structural indicators using a moving window approach. By systematically increasing window sizes and traversing every location within temperate mixed forest plots, we calculated seven indicators—neighborhood comparison, angular scale, mingling, biomass, basal area, Simpson and Shannon diversity—at each scale and compared them with whole-plot values to identify the minimum spatial scale at which indicator values stabilized within an acceptable deviation threshold. Our results revealed that different indicators exhibit varying sensitivities to sampling scale. Variables such as neighborhood comparison and angular scale stabilized at smaller sampling scales due to their reliance on local structural properties, whereas biodiversity and mingling required larger sampling scales. Biomass and basal area, being driven by large-scale ecological processes, showed the least sensitivity to smaller sampling windows. The analysis indicated that an 85 m window (within a 10% deviation rate from the overall plot) was determined as the minimum necessary to accurately capture the spatial variability of all indicators. To further investigate the drivers of minimum sampling scale variation, a random forest model identified the coefficient of variation as a key factor influencing scale sensitivity for basal area, angular scale, neighborhood comparison, and biomass. This finding underscores the importance of spatial heterogeneity in sampling design. Additionally, partial least squares path modeling (PLS-PM) was used to assess ecological interactions at the minimum scale. Unexpectedly, species diversity showed a negative effect on productivity, potentially due to canopy gaps and asymmetric competition, but positively influenced spatial structure (especially mingling), underscoring its role in enhancing structural complexity. By integrating minimum sampling scale determination with ecological interaction analysis, this study provides a framework for context-specific sampling strategies that consider multiple ecological variables. The findings contribute to improving forest inventory practices and offer practical guidance for large-scale monitoring and conservation efforts in temperate mixed forests.