Evaluating forest Growth Models

Effective model evaluation is not a single, simple procedure, but comprises several interrelated steps that cannot be separated from each other or from the purpose and process of model construction. We draw attention to several statistical and graphical procedures that may be used both with data used for model calibration and with data used in the evaluation of the model. We emphasize that the validity of conclusions depends on the validity of assumptions. These principles should be kept in mind throughout model construction and evaluation

Estimating Sapling Vitality for Scots Pine (Pinus sylvestris L.) in Russian Karelia

A new method is proposed for estimating vitality or growth potential for saplings of Scots pine (Pinus sylvestris L.), based on height, diameter and height increment. A two-stage process was used to establish the vitality index. The logarithms of height, diameter and height increment were regressed against age, to adjust for the wide range of ages present in our data (c. 10,000 saplings with ages spanning 4-50 years). Then principal component analysis was used to obtain coefficients, which were, in turn, standardized on each axis to provide a vitality index scaled in standard deviations. This standardized scale allows the rank of an individual in the population to be assessed, and draws attention to possible outliers. The use of age-adjusted residuals ensured that the estimator was independent of age, and stable over a wide age range. The first principal component indicates if a sapling is relatively tall (weight = 0.5), thick (w = 0.5) or fast-growing (w = 0.7) for its age. Most of the information is contained in the first principal component, but the second component, which explains about 10% of the variance, appears to offer some utility as an indicator of `acceleration' due to changing conditions. The resulting measures of vitality have been useful for research and management in the dry lichen-moss pine forest in Russian Karelia, but are specific to this species, locality and ecotype. Further research and site-specific data are necessary to adapt the system to other situations

Assessing the Quality of Permanent Sample Plot Databases for Growth Modelling in Forest Plantations

Informed plantation management requires a good database, since the quality of information depends on the quality of data, growth models and other planning tools. There are several important questions concerning permanent plots: how many plots, where to put them, and how to manage them. Plot measurement procedures are also important. This paper illustrates graphical procedures to evaluate existing databases, to identify areas of weakness, and to plan remedial sampling. Two graphs, one of site index versus age, another with stocking versus tree size, may provide a good summary of the site and stand conditions represented in the database. However, it is important that these variables, especially site index, can be determined reliably. Where there is doubt about the efficacy of site index estimates, it is prudent to stratify the database according to geography, soil/geology or yield level (total basal area or volume production). Established permanent plot systems may sample a limited range of stand conditions, and clinal designs are an efficient way to supplement such data to provide a better basis for silvicultural inference. Procedures are illustrated with three data sets: teak plantations in Burma, Norway spruce in Denmark, and a clinal spacing experiment in India

Evaluating Forest Growth Models

Effective model evaluation is not a single, simple procedure, but comprises several interrelated steps that cannot be separated from each other or from the purpose and process of model construction. We draw attention to several statistical and graphical procedures that may assist in model calibration and evaluation, with special emphasis on those useful in forest growth modelling. We propose a five-step framework to examine logic and bio-logic, statistical properties, characteristics of errors, residuals, and sensitivity analyses. Empirical evaluations may be made both with data used in fitting the model, and with additional data not previously used. We emphasize that the validity of conclusions drawn from all these assessments depends on the validity of assumptions underlying both the model and the evaluation. These principles should be kept in mind throughout model construction and evaluation