Accounting for soil respiration variability - Case study in a Mediterranean pine-dominated forest

Area of Science:

• Ecology
• Forest Science
• Soil Science

Background:

• Soil respiration (R_s) monitoring is crucial for understanding forest ecosystems.
• Current methods for selecting monitoring spots are often empirical, potentially leading to inaccurate R_s estimates.
• Variability in R_s necessitates a data-driven approach to determine optimal sampling strategies.

Purpose of the Study:

• To determine the minimum number of sampling spots required for reliable R_s estimation in a Mediterranean pine forest.
• To account for R_s spatial variability within a mixed forest ecosystem.
• To establish a balance between field sampling effort and the precision of R_s measurements.

Main Methods:

• Year-round R_s measurements were conducted on 50 spots per date using a portable gas analyzer.
• Linear mixed-effects models incorporating soil temperature and litter moisture were employed.
• Monte Carlo simulations were used to evaluate R_s estimation accuracy with a decreasing number of sampling spots.

Main Results:

• Estimating R_s with less than 14 spots increased the probability of model non-fit to 10%.
• Utilizing approximately 20 sampling spots reduced this probability to about 5%, offering an optimal compromise.
• A rainfall index was developed to aid in selecting sampling dates during summer drought periods.

Conclusions:

• Approximately 20 sampling spots are recommended for accurate R_s monitoring in Mediterranean pine forests.
• This sampling density balances fieldwork efficiency with the statistical reliability of R_s data.
• The proposed rainfall index can improve sampling strategies during dry seasons.