Environmental factors associated with seasonal variations of night-time plant canopy and soil respiration fluxes in deciduous conifer forest, Western Himalaya, India

Abstract

In situ carbon flux studies are typically rare over the Himalaya but are important to understand carbon (C) balance. We investigated night-time canopy respiration (Rnc) and soil respiration (Rs) of a deciduous coniferous forest in response to environmental factors. A comprehensive investigation has been carried out on C balance indicators by employing systematic and concurrent measurements over an annual growth cycle of pine (Nov 2010–Dec 2011). The study site consists of uniformly distributed young deciduous Pinus roxburghii plantation having understory as Lantana camara (an invasive shrub). Results underlined that both Rnc and Rs fluxes were highest in the post-monsoon season. Evaporative fraction (EF) and temperature explained maximum variability of fluxes during warm-moist monsoon. Our key finding depicts an inverse significant correlation between day-time canopy photosynthesis (Ac) and Rnc across the seasons (r = 0.83–0.99). This can be explained by the mechanistic physiological phase of optimal anabolism (Ac) with favorable environmental conditions and minimum level of catabolism (Rnc). The respiration-photosynthesis ratio (Rnc/Ac) typically ranged from 0.25 ± 0.11 (peak growing season) to 0.71 ± 0.16 (winter season) with mean of 0.26 ± 0.10. The ratio Rs/Ac was highest during the winter season (2.69 ± 0.43), while minimum during peak growing season (0.64 ± 0.29). The Rnc/Ac ratio and night-time temperature (AT) also revealed that the ratio could increase when AT crossed 24 °C. These responses indicate that under climate warming, it may have a significant influence on net plant C uptake. Presence of understory shrub minimizes the Rnc/Ac ratio, and indicative of a more positive C-balance. Nevertheless, the observations could certainly lend useful insight into C-balance and ecological function in the region. Further, it may be useful in parameterizing and validating C-cycle models.