5:15 PM - 6:45 PM
[ACG35-P02] Facilitating the estimation of the CO2 sequestration rate of trees through their shade tolerance levels
Keywords:carbon sequestration, climate change, growth rate model
Establishing growth rate models for trees is a crucial approach in response to the rise in CO2 levels. By utilizing growth rate models based on tree species, it is possible to identify species that can rapidly absorb atmospheric CO2. Additionally, these models enable the estimation of carbon sequestration capacity for newly planted trees in the coming years. As trees with greater shade tolerance tend to grow slower, this study will classify tree species into five shade tolerance levels based on previous classifications in Taiwan and aim to develop universal growth rate models for each level.
There are two sources of data for establishing tree growth rate models. The first source is the tree monitoring data collected in low-altitude primary forest plots in Taiwan between 1993 and 2019. During this period, measurements of diameter at breast height (DBH) were taken at 5-7 year intervals for approximately 6000 trees representing 118 species. Our focus is on the tree species that were measured for DBH during the 26 years and had a sample size exceeding ten individuals. The primary forest plots mainly consist of shade-tolerant species, corresponding to shade tolerance levels III, IV, and V. To supplement data for shade-intolerant (shade tolerance levels I and II) species, which are less common in these primary forests, we conducted monitoring in areas with stronger sunlight, such as landslide sites and post-fire forest areas, measuring the DBH growth of over 30 shade-intolerant species at three-month intervals over approximately two years. The data for trees of the two sources will be classified by shade tolerance level and used to establish growth rate models for each level through regression analysis with exponential models.
Preliminary analysis results indicate that trees with shade tolerance level I exhibit a faster growth rate compared to those with shade tolerance levels VI and V. Once the growth rate models for these five shade tolerance levels are established, users of the models only need to determine the shade tolerance level of the tree species of a forest to estimate the future carbon sequestration capacity of that forest accurately. These models will enable us to develop better management strategies for climate change.
There are two sources of data for establishing tree growth rate models. The first source is the tree monitoring data collected in low-altitude primary forest plots in Taiwan between 1993 and 2019. During this period, measurements of diameter at breast height (DBH) were taken at 5-7 year intervals for approximately 6000 trees representing 118 species. Our focus is on the tree species that were measured for DBH during the 26 years and had a sample size exceeding ten individuals. The primary forest plots mainly consist of shade-tolerant species, corresponding to shade tolerance levels III, IV, and V. To supplement data for shade-intolerant (shade tolerance levels I and II) species, which are less common in these primary forests, we conducted monitoring in areas with stronger sunlight, such as landslide sites and post-fire forest areas, measuring the DBH growth of over 30 shade-intolerant species at three-month intervals over approximately two years. The data for trees of the two sources will be classified by shade tolerance level and used to establish growth rate models for each level through regression analysis with exponential models.
Preliminary analysis results indicate that trees with shade tolerance level I exhibit a faster growth rate compared to those with shade tolerance levels VI and V. Once the growth rate models for these five shade tolerance levels are established, users of the models only need to determine the shade tolerance level of the tree species of a forest to estimate the future carbon sequestration capacity of that forest accurately. These models will enable us to develop better management strategies for climate change.