Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests
Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests
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发表时间：
1994
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通讯作者：
J. Vanclay
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作者：
J. Vanclay
这本书试图使增长模型更容易获得林业和其他感兴趣的混合森林，无论是种植或自然。人们对使用混合人工林和天然林越来越感兴趣，也越来越有争议，合理讨论和解决管理备选方案需要与其他信息系统相联系的可靠增长模型。我希望这本书能帮助研究人员建立更好的模型，并帮助用户了解模型是如何工作的，从而了解它们的优点和缺点。近年来，大面积的天然林，特别是热带地区的天然林，已被砍伐或转作其他用途。善意的森林管理人员在估计森林生长和产量时往往过于乐观，这导致了一些森林的过度砍伐。生长模型可以提供客观的预测，为森林管理人员提供在森林可持续能力范围内保持收获所需的信息，并为土地使用规划人员提供量化数据，以便就土地使用替代办法作出知情的决定。通过这种方式，我希望这本书将有助于热带和其他地区天然林的保护和可持续管理。这不是一本“如何做”的手册，其中有一步一步的说明来建立混交林的增长模型。不幸的是，模拟这些森林并不容易。没有单一的“最佳”方法来为这些森林建立模型。相反，可以使用许多方法，最好的方法取决于可用的数据，建立模型的时间和专业知识，计算资源以及从模型中得出的推论。因此，我不是写一本有一两个食谱的“食谱”，而是回顾和说明许多可用方法中的一些，指出每种方法的要求和输出，并强调它们的优点和局限性。这本书强调化学统计模型，而不是生理过程类型的模型，不是因为他们是上级，但因为他们已经证明的效用，并提供直接的利益，森林管理。对所有选择的更全面的处理超出了本书的范围，本书的目的是为那些建立森林管理增长模型的人提供一个现成的参考手册。由于我的语言能力有限，所涉及的材料或多或少限于英语材料。我没有试图回顾所有已发表的关于生长模型的工作（这将是一项艰巨的任务），但试图强调可能适用于热带地区混交林的例子。我希望本书中使用的语言和术语对所有读者都是容易理解的，特别是那些英语是第二语言的读者。术语表可能有助于澄清某些术语，具有特定技术含义的术语在首次使用时以斜体字印刷。读者应查阅词汇表，以澄清这些词的含义，除非他们确定的含义。每章末尾都有练习，以加强本章中提出的要点。这些都是简单的练习，经过精心挑选，以便用笔和纸或PC和电子表格快速完成，但在这些限制条件下，我努力使它们保持现实。一些练习（例如9.1和10.3）需要更专业的统计分析，但许多商业统计软件包（例如GLIM）都是合适的。在可能的情况下，这些练习利用真实的数据，但有些数据是模拟的，以创建有趣的练习，数据很少。虽然我的方法让读者有更多的责任来选择和开发合适的建模方法，但我希望它能帮助读者更好地理解建模，这反过来会导致更好的模型和更可靠的预测。我希望更好的模型能提供更好的信息，更好的理解，更好的管理混交林。
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.