Canadian Forest Service Publications

The importance of tree species and soil taxonomy to modeling forest soil carbon stocks in Canada. 2015. Shaw, C.H.; Bona, K.A.; Kurz, W.A.; Fyles, J.W. Geoderma Regional 4(2015):114-125.

Year: 2015

Available from: Northern Forestry Centre

Catalog ID: 35898

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1016/j.geodrs.2015.01.001

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Abstract

Accurate initialization of soil and dead organic matter carbon (C) stocks in forest ecosystemmodels is challenging but critical to forest C estimation, assessing current and future responses to climate change, and evaluation of management options for climate change mitigation strategies. We identified opportunities to improve the accuracy of soil C estimates from the Carbon Budget of the Canadian Forest Sector (CBM-CFS3) — a model of forest C dynamics used to support greenhouse gas emission reporting. Accuracy of soil C stocks estimated by models is very dependent on the initialization process. Here, we used redundancy analysis (RDA) and ordinations in an exploratory analysis to compare the variance structures of soil C estimates determined by model variables used in the initialization process, in two different soil C datasets; one derived from the model, the other obtained from 2391 ground plots. We also used the ground plot data to determine if soil taxonomy (information currently not used in the CBM-CFS3) could be used to explain variation in addition to that already accounted for by variables in the model. Total variance of the plot C dataset was about twice as large as the variance of the model C dataset confirming that currently the model does not represent all factors that control variation in soil C stocks. Soil C stocks in the mineral soil were highly correlated with C stocks in soil organic horizons in the model dataset but not in the plot dataset, suggesting that the variables included in our assessment controlling C stocks in the mineral soil horizons are different than in the organic soil horizons. Tree productivity (maximum yield curve volume per hectare) explained a much larger proportion of the total variation in the model dataset than in the plot dataset, whereas the leading tree species explained more variation in the plot dataset than in the model, suggesting that accuracy of initialization of soil C stocks could be improved by including leading tree species to stratify soil C modeling parameters. Leading species that are in greatest need of improved representation were identified by ordination. The results from the RDA showed that soil taxonomy explained 4 (order) to 13% (subgroup) of plot soil C variance, in addition to that explained by variables currently used in the model that determine initial soil C stocks. Soil taxonomy and leading species can compensate for one another to explain variance in soil C stocks. Our results suggest the potential of using the combination of leading tree species and soil taxonomy to improve soil C stocks initialized by forest C models, but this remains to be tested.

Plain Language Summary

Most forest ecosystem carbon (C) computer models are run in two stages. The first stage is called the initialization where the starting values, especially for C in soil and deadwood, are determined. The second stage is called the simulation where different ideas about how forest C changes in response to management or climate change can be examined. Soil C is important because more than half of the C in Canada’s forests is stored in the soil. The initial amount of C in soil can differ a lot from one location to the next in Canada’s large forested area, but getting the computer model to express those differences is very difficult. In order to identify what we could use to improve how computer models express those differences in the initial amount of soil C, we compared how the initial amount of soil C calculated by a forest C accounting computer model differed across Canada’s managed forest area to how the amount of soil C measured in the ground differed across Canada’s managed forest area. Factors such as tree species, how much trees grow, and the average temperature are used in the model to determine soil C. We looked at how differences in the amounts of C from the model and from the ground measurements varied across Canada’s forested area due to these kinds of factors. We also conducted analyses to see if using the classification system for soils, which is not currently used in the computer model, could be used to explain more about how soil C differs across Canada’s forests. We found that using tree species, soil classification, or combinations of tree species and soil classification helped to explain differences in soil C. This helps researchers do a better job of reporting on the amount of C in our forests, and it helps improve decision making about how to manage and retain C in our forests.

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