Sampling the soil in long-term forest plots: the implications of spatial variation

Area of Science:

• Environmental Science
• Soil Science
• Ecology

Background:

• Long-term forest soil monitoring is crucial for detecting environmental change across Europe.
• Forest soils exhibit significant spatial variability, complicating accurate mean property determination.
• Existing monitoring networks require optimized sampling strategies to account for this heterogeneity.

Purpose of the Study:

• To explore and quantify spatial variability of soil properties at British forest monitoring plots.
• To determine an optimal soil sampling strategy for accurate mean property estimation.
• To inform future sampling designs for pan-European forest soil monitoring networks.

Main Methods:

• Detailed soil sampling across three forest monitoring plots.
• Analysis of chemical data using classical statistics and geostatistics (variogram analysis).
• Optimal sampling analysis based on multivariate variograms.

Main Results:

• Analysis of variance revealed no consistent positional effects of sample sites relative to trees.
• Variogram analysis indicated spatial dependence for several soil variables, with some showing periodic variation.
• Optimal sampling analysis suggested a bulked sample of 36 cores would achieve acceptable error levels.

Conclusions:

• Stratified random sampling, avoiding specific features like trees or disturbed ground, is recommended for future forest soil monitoring.
• Understanding and accounting for spatial soil variability is essential for reliable environmental change detection.
• Optimized sampling strategies enhance the accuracy and efficiency of long-term ecological monitoring programs.