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Winnipeg, Canada

Ma Z.,University of Quebec at Montreal | Peng C.,University of Quebec at Montreal | Peng C.,Northwest University, China | Zhu Q.,Northwest University, China | And 4 more authors.

Permanent forest plots (PSP) were used to investigate long-term basal-area tree growth rates across the boreal forests in Canada. The objectives were to discern whether or not these rates i) are similar across the boreal zone and ii) correlate to change in climate from 1970 to 2010. The results show that rates vary by region, with decreasing growth rates for about 60% of individual trees in western Canada (Alberta, Saskatchewan, Manitoba) but increasing rates for about 70% of individual trees in eastern Canada (Ontario and Quebec). These changes are interpreted from an overall carbon sequestration perspective and within the context of available precipitation and air temperature data and an annual climate moisture index. This study provides long-term plot-based evidence for the ecological variability and regional differences in tree growth detected by satellite-based remote-sensing and tree-ring studies in Canada's boreal forests. Source

Ma Z.,University of Quebec at Montreal | Peng C.,University of Quebec at Montreal | Peng C.,Northwest University, China | Li W.,Northwest University, China | And 4 more authors.
Natural Resource Modeling

Abstract Developing models to predict tree mortality using data from long-term repeated measurement data sets can be difficult and challenging due to the nature of mortality as well as the effects of dependence on observations. Marginal (population-averaged) generalized estimating equations (GEE) and random effects (subject-specific) models offer two possible ways to overcome these effects. For this study, standard logistic, marginal logistic based on the GEE approach, and random logistic regression models were fitted and compared. In addition, four model evaluation statistics were calculated by means of K-fold cross-valuation. They include the mean prediction error, the mean absolute prediction error, the variance of prediction error, and the mean square error. Results from this study suggest that the random effects model produced the smallest evaluation statistics among the three models. Although marginal logistic regression accommodated for correlations between observations, it did not provide noticeable improvements of model performance compared to the standard logistic regression model that assumed impendence. This study indicates that the random effects model was able to increase the overall accuracy of mortality modeling. Moreover, it was able to ascertain correlation derived from the hierarchal data structure as well as serial correlation generated through repeated measurements. Copyright © 2012 Wiley Periodicals, Inc. Source

Li C.,Natural Resources Canada | Barclay H.J.,Pacific Forestry Center | Hans H.,Natural Resources Canada | Liu J.,Forestry Branch | And 2 more authors.
Ecological Complexity

Accuracy in population estimation from individual measurement has been traditionally a research focus in both theoretical and applied ecology. In forest sciences, estimation of productivity and value recovery of forest products is essential for decision-making to achieve the goal of sustainable forest management. In this paper, we review the basic structure of data in forest sciences, describe commonly used statistical procedures in obtaining population estimates, and examine the accuracy associated with the forest products value estimation using forest inventory data of Manitoba, Canada. Our results suggested that simplified statistical procedures could bring about a wide range of bias in estimating lumber value recovery at the stand level, and improved understanding of stand structure and its reconstruction through computer simulation could be essential in reducing the bias involved in the estimation. © 2010. Source

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