Soil respiration patterns and rates at three Taiwanese forest plantations: dependence on elevation, temperature, precipitation, and litterfall

Abstract

Background: Soil respiration contributes to a large quantity of carbon emissions in the forest ecosystem. In this study, the soil respiration rates at three Taiwanese forest plantations (two lowland and one mid-elevation) were investigated. We aimed to determine how soil respiration varies between lowland and mid-elevation forest plantations and identify the relative importance of biotic and abiotic factors affecting soil respiration. Results: The results showed that the temporal patterns of soil respiration rates were mainly influenced by soil temperature and soil water content, and a combined soil temperature and soil water content model explained 54–80% of the variation. However, these two factors affected soil respiration differently. Soil temperature positively contributed to soil respiration, but a bidirectional relationship between soil respiration and soil water content was revealed. Higher soil moisture content resulted in higher soil respiration rates at the lowland plantations but led to adverse effects at the mid-elevation plantation. The annual soil respiration rates were estimated as 14.3–20.0 Mg C ha−1 year−1 at the lowland plantations and 7.0–12.2 Mg C ha−1 year−1 at the mid-elevation plantation. When assembled with the findings of previous studies, the annual soil respiration rates increased with the mean annual temperature and litterfall but decreased with elevation and the mean annual precipitation. A conceptual model of the biotic and abiotic factors affecting the spatial and temporal patterns of the soil respiration rate was developed. Three determinant factors were proposed: (i) elevation, (ii) stand characteristics, and (iii) soil temperature and soil moisture. Conclusion: The results indicated that changes in temperature and precipitation significantly affect soil respiration. Because of the high variability of soil respiration, more studies and data syntheses are required to accurately predict soil respiration in Taiwanese forests.