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 appear to offer the most 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.Comment: 21 pages, 3 figures, 5 tables. New table added in response to reviewer comment

Publication patterns of award-winning forest scientists and implications for the ERA journal ranking

Publication patterns of 79 forest scientists awarded major international forestry prizes during 1990-2010 were compared with the journal classification and ranking promoted as part of the 'Excellence in Research for Australia' (ERA) by the Australian Research Council. The data revealed that these scientists exhibited an elite publication performance during the decade before and two decades following their first major award. An analysis of their 1703 articles in 431 journals revealed substantial differences between the journal choices of these elite scientists and the ERA classification and ranking of journals. Implications from these findings are that additional cross-classifications should be added for many journals, and there should be an adjustment to the ranking of several journals relevant to the ERA Field of Research classified as 0705 Forestry Sciences.Comment: 12 pages, 4 figures, 3 tables, 49 references; Journal of Informetrics (2011

Planning horizons and end conditions for sustained yield studies in continuous cover forests

The contemporary forestry preoccupation with non-declining even-flow during yield simulations detracts from more important questions about the constraints that should bind the end of a simulation. Whilst long simulations help to convey a sense of sustainability, they are inferior to stronger indicators such as the optimal state and binding conditions at the end of a simulation. Rigorous definitions of sustainability that constrain the terminal state should allow flexibility in the planning horizon and relaxation of non-declining even-flow, allowing both greater economic efficiency and better environmental outcomes. Suitable definitions cannot be divorced from forest type and management objectives, but should embrace concepts that ensure the anticipated value of the next harvest, the continuity of growing stock, and in the case of uneven-aged management, the adequacy of regeneration.Comment: 8 pages, 1 figure, 54 references, Ecological Indicators (2014

Effects of Selection Logging on Rainforest Productivity

An analysis of data from 212 permanent sample plots provided no evidence of any decline in rainforest productivity after three cycles of selection logging in the tropical rainforests of north Queensland. Relative productivity was determined as the difference between observed diameter increments and increments predicted from a diameter increment function which incorporated tree size, stand density and site quality. Analyses of variance and regression analyses revealed no significant decline in productivity after repeated harvesting. There is evidence to support the assertion that if any permanent productivity decline exists, it does not exceed six per cent per harvest

Compatible Deterministic and Stochastic Predictions by Probabilistic Modeling of Individual Trees

A single growth model can provide both deterministic and stochastic predictions which are compatible. Change may be expressed using probabilistic functions which can represent proportions of populations or probabilities for individuals. The former represents determinism while the latter enables the stochastic implementation. The same functional relationships may thus be used to generate compatible deterministic and stochastic predictions. All components of forest growth and change, including diameter increment, can be expressed as probabilistic functions, enabling construction of a single model which provides compatible stochastic and deterministic outcomes. Users may specify the minimum expansion factor corresponding to the simulated plot size and thus control the granularity of predictions. Such a model may facilitate numerical estimation of confidence intervals about yield forecasts and sustained yield estimates

Refining the H-Index

Braun and colleagues recently examined the utility of the h-index (the number h of papers, each of which is cited at least h times) for assessing the impact of journals, and drew attention to some differences between the top 21 journals ranked according to the h-index and the journal impact factor. Their 4-year window, however, is inadequate. Data from the Web of Science suggest that the h-index for journals increases more-or-less linearly with time until it plateaus at about the twice the cited half-life, so it may be possible to base comparisons on a standard window (e.g., 3 years to be comparable with the journal impact factor), standardized by multiplying by the cited half-life divided by the width of the window (e.g., 3 years). Such an adjustment to the top 21 journals in Braun's table would promote the Journal of the American Chemical Society several places (from rank 20 to rank 6, if no external candidates are considered) and demote Nature Medicine (because of its youth, it has a short cited half-life). The use of a standard interval, without regard for the publication frequency of the journal or the nature of the discipline, introduces bias into both the journal impact factor and the h-index when applied to journals

A Stand Growth Model for Cypress Pine

A deterministic growth model for uneven-aged monospecific stands of cypress pine is presented. It is implemented as a cohort model and comprises equations to (1) predict stand basal area increment, (2) distribute stand increment among component trees, (3) estimate potential diameter increment to check for excessive distributed increments, (4) predict mortality, and (5) predict regeneration

An introduction to Simile

This module provides an introduction to Simile, a powerful modelling language with some innovative features. Simile is being developed by Dr Robert Muetzelfeldt, at the University of Edinburgh, with suggestions and testing by a number of users. The package is still under development, so it is getting better all the time, but it is already a sophisticated and stable package. Good documentation is available at http://www.ierm.ed.ac.uk/simile, including some that was prepared when Simile was known by its previous name, AME (Agroforestry Modelling Environment). The latest version of the software can also be downloaded without charge from this Web site, for PCs with Windows 9x or computers running Linux. Dr Muetzelfeldt has long had an interest in efficient representation of ecological information, and his development of Simile was due in part because of his concern that with many models, the documentation, diagrams and computer implementation diverge. So he set out to build a modelling platform where the diagram was the model and the documentation, and he's close to achieving this goal. He's also provided the possibility to create a model that runs on a computer without the need for computer code or mathematical equations. However, there's no free lunch, and it is necessary to learn some of the standard notation used in systems dynamics to be able to use this package. This module provides introduces the systems dynamics notation, and then provides a worked example of developing a Simile model to represent a personal bank account. Some of the unique features of simile are then examined with reference to a simple forestry model. Finally, two other examples of forestry models are presented briefly

Management Advice from Tree Measurements

The ultimate objective of permanent plots and growth models is to provide management advice. Foresters are often too preoccupied with getting the data and building a model to think too much about providing practical management advice, but it is an important issue that should not be neglected, or postponed until the model is finished, because it should have an influence on model design and implementation. There are 8 steps that are critical to turning tree measurements into management advice: (1) Tree measurements, (2) Data management, (3) Data analysis, (4) Model construction, (5) Model testing, (6) Building a system, (7) Making predictions, and (8) Management advice. Each of these steps is equally important in providing an objective basis for management advice, and those involved in any of these steps should bear in mind the ultimate objective, and the links between these steps