This book attempts to make growth models more accessible to foresters and others interested in mixed forests, whether planted or natural. There is an increasing interest in,
 and controversy surrounding the use of mixed plantations and natural forests, and rational discussion and resolution of management options require reliable growth models linked to other
 information systems. It is my hope that this book will help researchers to build better models, and will help users to understand how the models work and thus to appreciate their strengths
 and weaknesses. During recent years, vast areas of natural forest, especially in the tropics, have been logged or converted to other uses. Well-meaning forest managers have often been
 over-optimistic in estimating forest growth and yields, and this has contributed to over-cutting in some forests. Growth models can provide objective forecasts, offering forest managers the
 information needed to maintain harvests within the sustainable capacity of the forest, and providing quantitative data for land use planners to make informed decisions on land use
 alternatives. In this way, I hope that this book will contribute to the conservation and sustainable management of natural forests in the tropics and elsewhere. This is not a "How to do it"
 manual with step-by-step instructions to build a growth model for mixed forests. Unfortunately, modelling these forests isn't that easy. There is no single "best" way to build a model for
 these forests. Rather, many approaches can be used, and the best one depends on the data available, the time and expertise available to build the model, the computing resources, and the
 inferences that are to be drawn from the model. So instead of writing a "cookbook" with one or two recipes, I review and illustrate some of the many approaches available, indicate the
 requirements of and output from each, and highlight their strengths and limitations. The book emphasizes empirical-statistical models rather than physiological-process type models, not
 because they are superior, but because they have proven utility and offer immediate benefits for forest management. A more comprehensive treatment of all the options is beyond the scope of
 this book, which is intended to serve as a ready reference manual for those building growth models for forest management. Because of my limited linguistic ability, the material covered is
 more-or-less restricted to English-language material. I have not attempted to review all the published work on growth modelling (it would be a huge task), but have tried to highlight
 examples that may be applicable to mixed forests in tropical areas. I hope that the language and terminology used in this book will be accessible to all readers, especially those for whom
 English is a second language. The glossary may help to clarify some terms, and those that have a specific technical meaning are printed in italics the first time they are used. Readers
 should consult the glossary to clarify the meaning of these words unless they are sure of the meaning. Exercises are given at the end of each chapter to reinforce points made in the
 chapter. These are simple exercises, deliberately chosen so that they can be completed quickly with pen and paper or PC and spreadsheet, but within these constraints, I have tried to keep
 them realistic. Some exercises (e.g. 9.1 and 10.3) require more specialized statistical analyses, but many commercial statistical packages (e.g. GLIM) are suitable. Where possible, these
 exercises draw on real data, but some data were simulated to create interesting exercises with few data. Whilst my approach places more responsibility on the reader to choose and develop a
 suitable modelling methodology, I hope it will help readers gain a better understanding of modelling, which should in turn lead to better models and more reliable predictions. And I hope
 that better models will provide better information, greater understanding, and better management of mixed forests.

https://espace.library.uq.edu.au/view/UQ:8211/ModellingForestG.pdf

Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests

In recent decades, Mediterranean landscapes have been affected by human-induced drivers, such as land use and climate change. Forest ecosystems and landscapes have been particularly affected in mountainous regions due to limited management and stewardship, especially in remote areas. Therefore, there is a need to set up new strategies to enhance ecosystem services in forested areas which, in turn, will benefit local communities and economies. In this study, we implemented a new approach—Multiscale Mapping of Ecosystem Services (MIMOSE)—to assess ecosystem services in Mediterranean forests located in a mountainous region of Italy. We spatially assessed timber provision and carbon sequestration according to three forest management strategies: business-as-usual, maximizing economic values, and prioritizing conservation. Sustainable strategies for forest planning were identified at the landscape scale. We found that (i) timber provision is a conflicting service, especially when adaptation strategies are promoted; (ii) the most balanced set of forest ecosystem services is achieved through prioritizing conservation; and (iii) the ecosystem services availability is enhanced by optimizing the spatial allocation of different management strategies. Our approach is suitable to support landscape planning for balancing forest ecosystem potentialities while respecting local community needs and promoting sustainable development goals in the Mediterranean area.

The MIMOSE Approach to Support Sustainable Forest Management Planning at Regional Scale in Mediterranean Contexts

The ecotourism literature is focused on market segmentation, ecological impacts of wildlife viewing, and community-based ecotourism, but there has been minimal attention to critical areas such as quality control, the industry, external environments or institutions even as the components and parameters of ecotourism are being extended. This imbalance, combined with the fragmentation and lack of integration within the literature, suggest that ecotourism, as a field of academic inquiry, is still in a state of adolescence. r 2007 Elsevier Ltd. All rights reserved.

Progress in tourism management Twenty years on: The state of contemporary ecotourism research

1. Introduction.- 2. Tree Form and Stem Taper.- 3. Tree-stem Volume Equations.- 4. Tree Weight and Biomass Estimation.- 5. Quantifying Tree Crowns.- 6. Growth Functions.- 7. Evaluating Site Quality.- 8. Quantifying Stand Density.- 9. Indices of Individual-tree Competition.- 10. Modeling Forest Stand Development.- 11. Whole-stand Models for Even-aged Stands.- 12. Diameter-distribution Models for Even-aged Stands.- 13. Size-class Models for Even-aged Stands.- 14. Individual-tree Models for Even-aged Stands.- 15. Growth and Yield Models for Uneven-aged Stands.- 16. Modeling Response to Silvicultural Treatments.- 17. Modeling Wood Characteristics.- 18. Model Implementation and Evaluation.-

Modeling Forest Trees and Stands

http://www.wildonesniagara.org/images/twentyyearsofecotourism.pdf

Twenty years on: The state of contemporary ecotourism research

Adjacency constraints in harvest scheduling models prevent the harvest of adjacent management units within a given time period Two mixed integer linear programming (MILP) harvest scheduling formulations are presented that include adjacency constraints, yet allow the simultaneous harvest of groups of contiguous management units whose combined areas are less than some predefined limit These models are termed Area Restriction Models, orARMs, following Murray (1999) The first approach, the Path Algorithm, generates a set of constraints that prevent concurrent harvesting of groups of contiguous stands only when the combined area of a group exceeds the harvest area restriction The second approach defines the set of Generalized Management Units (GMUs) that consist of groups of contiguous management units whose combined areas do not exceed the maximum harvest area limit This formulation of the model can recognize direct cost savings-such as sale administration costs or harvest costs-or higher stumpage prices that may be realized by jointly managing stands Example problems are formulated and solved using both ARM approaches and compared with models that restrict concurrent harvests on all adjacent units, regardless of area [termed Unit Restriction Models, or URMs, again following Murray (1999)] The ARM formulations usually result in larger models and take longer to solve, but allow for higher objective function values than otherwise similar URM formulations While the proposed ARM approaches should be applicable to more general problems, the examples are constructed so that the largest number of contiguous stands that can be harvested jointly is three Strategies for reducing the size of the ARM formulations are discussed and tested

https://faculty.washington.edu/toths/Presentations/Lecture%2010/McDill_etal_2002.pdf

Harvest scheduling with area-based adjacency constraints

Measuring Forest Site Quality Using the Parameters of a Dimensionally Compatible Height Growth Function

Three types of adjacency constraint formulations-pairwise, Type I ND (nondominated), and NOAM (new ordinary adjacency matrix)-were compared on 900 hypothetical, randomly generated, spatially explicit forest management problems with between 50 and 350 stands. Forests were generated using four age-class distributions, nominally called immature, regulated, overmature, and old-growth. Management planning problems with adjacency constraints were formulated for these forests in Model 1 format based on a planning horizon consisting of three 20 yr periods. The problems also included flow constraints and minimum average age requirements for the ending forest. For all age-class distributions, the Type I ND constraint type resulted in significantly lower solution times than either the Pairwise or the NOAM constraint types. NOAM constraints performed better than Pairwise constraints for immature forest problems, but Pairwise constraints performed better than NOAM constraints for overmature and old-growth forest problems. There was no difference between these two constraint types for regulated forest problems. Results also show that the age-class distribution of the forest is one of the most important factors determining the time needed to solve forest management problems with adjacency constraints. In general, the more mature the forest, the harder the problem is to solve. In particular, problems based on the old-growth age-class distribution typically took much longer to solve than comparable problems based on the other age-class distributions.

Comparing adjacency constraint formulations for randomly generated forest planning problems with four age-class distributions.

The practice of multicriteria forest management planning is often complicated by the need to explicit a priori goals and preferences of the decisionmaker. This manuscript aims at describing an approach that may take advantage of a posteriori preference modeling to facilitate the specification of the levels of achievement of various objectives in a typical forest management planning framework. The goal is to provide information about nondominated points in the feasible set in the criteria space (FSCS) so that decisionmakers may take advantage of trade-off information. The emphasis is on demonstrating the potential of adaptive search methods to enhance decisions when three or more criteria are considered. The approach combines the use of mathematical programming and interactive decision maps techniques. It is shown how the estimation refinement method may be used to approximate the Pareto frontier of a typical model I linear programming model. It is further shown how the feasible goals method/interactive decision maps method may be used to retrieve a solution selected by stakeholders from interactive decision maps depicting the Pareto frontier. Results are discussed for a large-scale test application encompassing over 1 million ha of cork and holm oak forest ecosystems in southern Portugal. FOR .S CI. ❚(❚):000-000.

/pdf/addressing-multicriteria-forest-management-with-pareto-1pvjhcg1nz.pdf

Addressing Multicriteria Forest Management With Pareto Frontier Methods: An Application in Portugal

This article evaluates the performance of five traditional methods and one new method of generating the efficient frontier for a bi-criteria, spatially explicit harvest scheduling problem. The problem is to find all possible efficient solutions, thus defining the trade-offs between two objectives: (1) maximizing the net present value of the forest and (2) maximizing the minimum area over the planning horizon in large, mature forest patches. The methods for generating the efficient frontier were tested using a hypothetical forest consisting of 50 stands. The methods were compared based on the number of efficient solutions each method can identify and on how quickly the solutions were identified. The potential to generalize these algorithms to 3- or n-criteria cases is also assessed. Three of the traditional approaches, the - constraining; the triangles method, the decomposition algorithm based on the Tchebycheff metric; and the new, proposed method are capable of generating all or most of the efficient solutions. However, the triangles and the new method far outperformed the other approaches in terms of solution time. The new method, called alpha-delta, appears to be the simplest to generalize to the tri-criteria case. FOR .S CI. 52(1):93-107.

https://faculty.washington.edu/toths/Publications/Toth_etal_2006_pre-print.pdf

Marc E. McDill

Papers

Harvest scheduling with area-based adjacency constraints

Measuring Forest Site Quality Using the Parameters of a Dimensionally Compatible Height Growth Function

Comparing adjacency constraint formulations for randomly generated forest planning problems with four age-class distributions.

Addressing Multicriteria Forest Management With Pareto Frontier Methods: An Application in Portugal

Finding the efficient frontier of a bi-criteria, spatially explicit, harvest scheduling problem