| 英文摘要
| Fine root, with diameter ≤ 2 mm, takes responsibility for water and nutrient transport. Besides being a carbon pool, it also provides a carbon flux pathway through plant canopy and soil, to contribute 30% to 70% of total net primary production (NPP) in the forest ecosystem, thus serving a dynamic role in its carbon cycling. Therefore, quantifying fine root productivity is necessary to study terrestrial carbon budget. However, compared to abundant studies on aboveground leaf and canopy, less is known about belowground root system due to its difficulty on observation and quantification. Based on the phenological correlation, we aimed to model fine root production by aboveground parameters.
The study was conducted in Chilan Mountain in northeast Taiwan, a near tropical moist forest. A study plot was set, with 15 minirhizotron tubes installed and root densities measured. We took fine root photographs by an interval of three weeks; presence and absence of fine roots were delineated by image processing algorithms to derive fine-root production (g C ha-1 d-1) through time.
As to the aboveground, we took biological and environmental factors into account, including litterfall production (branches and leaves), canopy density (leaf area index, LAI), meteorological (air temperature, air humidity, precipitation, solar radiation, wind speed), and soil physical conditions (soil temperature, soil moisture). As a result, the variables Leaf Area Index (LAI), △LAI, solar radiation, precipitation, wind speed, and soil temperature were particularly significant to root production, with adjusted R2 of 0.42 (n = 36, p-value u003c 0.005) by multiple linear regression. Synchrony existed in leaf flush and fine root emergence; in addition, environmental restrictions were more evident in soil than air conditions, where air temperature and precipitation were not limiting factors, which added the uniqueness to near tropical wet forests under global warming.
This study demonstrated the feasibility of utilized aboveground variables to indirectly assess fine root growth. The models could reveal fine root dynamics through time, or be further applied on the regional scale mapping with aid of remote sensing, where more validation need to be done to fill the intra- and inter-annual, plot- and region-scale gaps. |
