Jerome K. Vanclay

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.

Sustainable forestry in the tropics: panacea or folly?

The profitability of uncontrolled logging can be a significant obstacle to sustainable forest management, especially in the tropics. Rice et al. (1997) have argued that not only does traditional selective logging provide higher returns but also incurs less damage to forests than sustainable forest management systems that involve harvesting of many species and the creation of large gaps in the forest canopy to foster regeneration of light-demanding species. They claimed that protected areas were the only viable way to conserve forest ecosystems and proposed that loggers be allowed to log forests selectively once, after which the forests should become parks. Here we respond to the challenge posed by Rice et al. by exhaustively reviewing the evidence regarding the viability and desirability of sustainable forest management in the tropics. Following Rice et al., we use the term conventional timber harvesting to refer to existing practice, which typically pays little attention to maintaining long-term timber supply. Sustainable timber management implies taking steps to ensure forests continue to produce timber in the longer term, while maintaining the full complement of environmental services and non-timber products of the forest. Empirical studies tend to confirm the conclusion of Rice et al. (1997) that although sustainable timber management sometimes provides reasonable rates of return, conventional timber harvesting is generally more profitable. This implies that without additional incentives, one cannot expect companies to adopt sustainable management. The shortsightedness of many loggers, the slow rise in international timber prices, political uncertainty, and tenure insecurity simply reinforce this tendency. However, we reject the claim that sustainable timber management generally damages forests more than conventional logging. Rice et al. base their conclusion largely on the particular case of mahogany extraction in Bolivia, and even there it may not hold. In many cases, sustainable timber management performs better in terms of carbon storage and biodiversity conservation than conventional logging approaches, as well as producing more timber. If new carbon markets emerge, sustainable forest management might compete effectively with conventional timber harvesting. Timber certification systems may also provide a sufficient incentive for sustainable forest management in certain circumstances.

Impact factor: outdated artefact or stepping-stone to journal certification?

A review of Garfield’s journal impact factor and its specific implementation as the Thomson Reuters impact factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.

Evaluating a growth model for forest management using continuous forest inventory data

Abstract Model evaluation should include qualitative as well as quantitative examinations of the model. The qualitative parts should comprise a critical appraisal of model logic as well as theoretical and biological realism of the model. The quantitative parts should comprise statistical tests and comparisons of predictions with observations independent of those used to fit the model. Comprehensive model evaluation requires several alternative approaches and criteria. Model evaluation is not one simple procedure, but consists of a number of interrelated steps that should not be separated from each other or from model construction. It is stressed that models can only be evaluated in relative terms, and their predictive value is always open to question. Thus, model evaluation is an ongoing process. A case study with the PBRAVO growth model for maritime pine (Pinus pinaster Ait.) in the Leiria forest, Portugal, illustrates the utility of selected criteria and graphical techniques. Based on theoretical examinations and tests with data from continuous forest inventories, we conclude that the Leiria version of the PBRAVO model does not adequately represent reality and that forecasts lack sufficient accuracy for forest management purposes.

On the robustness of the h-index

The h-index (Hirsch, 2005) is robust, remaining relatively unaffected by errors in the long tails of the citations-rank distribution, such as typographic errors that short-change frequently cited articles and create bogus additional records. This robustness, and the ease with which h-indices can be verified, support the use of a Hirsch-type index over alternatives such as the journal impact factor. These merits of the h-index apply both to individuals and to journals.

Aggregating tree species to develop diameter increment equations for tropical rainforests

Pairwise F-tests provided an efficient approach for aggregating large numbers of species into a manageable number of groups for developing diameter increment functions. The first stage of the two-stage procedure identified the number of groups required and the species defining these groups; the second stage aggregated all the remaining species into the most appropriate group. Although there is no guarantee that this leads to an optimal solution, empirical results suggest that the outcome is near optimal. This approach is readily automated and computationally efficient. An analysis of diameter increments of 237 species from the rainforests of north Queensland indicated 41 species groups, each with increment functions significantly different at P<0.01. These provided a substantially better model than the previous model based on subjectively formed groups.

A growth model for north Queensland rainforests

A model to predict the growth of commercial timber in north Queenslands rainforests is described. More than 100 commercial species and several hundred other tree species are aggregated into about 20 species groups based on growth habit, volume relationships and commercial criteria. Trees are grouped according to species group and tree size into cohorts, which form the basis for simulation. Equations for predicting increment, mortality and recruitment are presented. The implications of the model on rainforest management for timber production are examined. The model has been used in setting the timber harvest from these rainforests, and should provide an objective basis for investigating the impact of rainforest management strategies. The approach should be applicable to other indigenous forests.

Ranking forestry journals using the h-index

An expert ranking of forestry journals was compared with Journal Impact Factors and h-indices computed from the ISI Web of Science and internet-based data. Citations reported by Google Scholar offer an efficient way to rank all journals objectively, in a manner consistent with other indicators. This h-index exhibited a high correlation with the Journal Impact Factor (r=0.92), but is not confined to journals selected by any particular commercial provider. A ranking of 180 forestry journals is presented, on the basis of this index.

Structure and floristic composition of flood plain forests in the Peruvian Amazon I. Overstorey

Three Peruvian flood plain forests adjacent to the Ucayali river were sampled using nine 1 ha permanent sample plots in which stems exceeding 10 cm DBH were identified and measured. These plots were measured four times during 1993-1997. Three plots were established in each of the three forest types high restinga, low restinga, and tahuampa, characterised in part by an annual inundation of one, two and four months per year, respectively. Stem density varied from 446 to 601 per hectare, and the basal area ranged between 20 and 29 m2/ha. A total of 321 species were recorded in the nine hectare sample, with 88-141 species in each 1 ha plot. Species composition indicated a relatively low similarity between the forest types. Plots with the longest flooding contained the most species, expressed both as per unit area as well as per 1000 stems. The flood plain forests contained fewer tree species than adjacent non-flooded terra firme forest. Family importance values were calculated for each forest. In all three forests Leguminosae, Euphorbiaceae, Annonaceae and Lauraceae were important. The Moraceae family was conspicuous in both high restinga and low restinga. The Arecaceae and Meliaceae were notable in high restinga, as was Rubiaceae in low restinga. Lecythidaceae, Sapotaceae and Chrysobalanaceae exhibited relatively high values in the tahuampa forest. High species importance values were obtained for Maquira coriacea, Guarea macrophylla, Terminalia oblonga, Spondias mombin, Ceiba pentandra, Hura crepitans, Eschweilera spp., Canipsiandra angustifolia, Pouteria spp., Licania micrantha, Parinari excelsa and Calycophyllum spruceanum. Among the species of smaller stature, Drypetes amazonica, Leonia glycicarpa, Theobroma cacao and Protium nodulosum attained high values.

Assessing site productivity in tropical moist forests: a review

Reliable estimates of site productivity are essential for improved predictions of timber yields and for meaningful simulation studies. Few suitable techniques exist for tropical moist forests. Conventional indices such as site index cannot be estimated reliably for stands with many species or indeterminate ages. Emerging techniques require two steps: calibration and validation with permanent sample plots, and correlation with easily measured stand parameters. One promising index for the tropical moist forest is based on the expected diameter increment of individual trees adjusted for tree size and competition. Measures of stand height such as maximum stand height, canopy height and the height-diameter relationship may also prove useful. Proposed measures should satisfy four criteria: they should be reproducible and consistent over long periods of time; indicative of the site, and not unduly influenced by stand condition or management history; correlated with the sites productive potential; and at least as good as any other productivity measures available.