Realizing Opportunities in Forest Growth Modelling

The world is continually changing: the emergence of new technology and new demands for pertinent information pose new challenges and possibilities for forest management. Are forest growth models keeping up with client needs? To remain relevant, modelers need to anticipate client needs, gauge the data needed to satisfy these demands, develop the tools to collect and analyze these data efficiently, and resolve how best to deliver the resulting models and other findings. Researchers and managers should jointly identify and articulate anticipated needs for the future, and initiate action to satisfy them. New technology that offers potential for innovation in forest growth modelling include modelling software, automated data collection, and animation of model outputs. New sensors in the sky and on forest machines can routinely provide data previously considered unattainable (e.g., tree coordinates, crown dimensions), as census rather than sample data. What does this revolution in data availability imply for forest growth models, especially for our choice of driving variables

Improving tree mortality models by accounting for environmental influences

Tree-ring chronologies have been widely used in studies of tree mortality where variables of recent growth act as an indicator of tree physiological vigour. Comparing recent radial growth of live and dead trees thus allows estimating probabilities of tree mortality. Sampling of mature dead trees usually provides death-year distributions that may span over years or decades. Recent growth of dead trees (prior to death) is then computed during a number of periods, whereas recent growth (prior to sampling) for live trees is computed for identical periods. Because recent growth of live and dead trees is then computed for different periods, external factors such as disturbance or climate may influence growth rates and, thus, mortality probability estimations. To counteract this problem, we propose the truncating of live-growth series to obtain similar frequency distributions of the "last year of growth" for the populations of live and dead trees. In this paper, we use different growth scenarios from several tree species, from several geographic sources, and from trees with different growth patterns to evaluate the impact of truncating on predictor variables and their selection in logistic regression analysis. Also, we assess the ability of the resulting models to accurately predict the status of trees through internal and external validation. Our results suggest that the truncating of live-growth series helps decrease the influence of external factors on growth comparisons. By doing so, it reinforces the growth-vigour link of the mortality model and enhances the model's accuracy as well as its general applicability. Hence, if model parameters are to be integrated in simulation models of greater geographical extent, truncating may be used to increase model robustness

Estimates and forecasts of forest biomass and carbon sequestration in North America and Australia: a forty-five year quest

A half-century of forest inventory research involving statistically-valid fieldmeasurements (using statistically representative sample size and showing confidence limits) and well-validated forecasting methods are reviewed in this paper. Some current procedures overestimate global and large-scale forest biomass, carbonstorage, and carbon sequestering rates because they are based on statistically-invalid methods (errors in estimates are unavailable and unreported), or they fail to consider key dynamic characteristics of forests. It is sometimes assumed that old-growth forests can serve as fixed, steady-state storage of biomass and carbon for indefinitely long periods, but it is shown by both modelling and remote sensing that forests are dynamic systems, the state of which can change considerably over as shorta time as a decade. Forecasting methods show that maximum biomass and carbon storage in some important forest types occurs in mid-succession, not in old-growth. It is proposed, therefore, that realistic biomass and carbon storage estimates used for carbon credits and offsets be determined as the statistical mean minus the confidence interval and that practical carbon sequestering programs include specific timeframes, not indefinitely long periods of time