Introduction

Many models that simulate the dynamics of forest ecosystems have been developed during the last few decades. The term dynamics is meant to represent the attributes of forest ecosystems that describe the changes occurring over time, including growth, productivity, yield, net primary productivity, biomass turnover, or succession. Forest ecosystem models are used as research tools to better understand the mechanisms that govern tree growth and ecosystem dynamics and as decision-making tools to predict the growth of unmanaged and managed forest ecosystems, plan forest management activities, and predict the effects of disturbances. The majority of forest ecosystem models have a relatively complex structure to describe the complexity of the processes that govern the dynamics of forest ecosystems. Forests are characterized by the presence of complex interactions among processes and environmental factors that are regulated by many nonlinear feedback mechanisms.

Forest ecosystem models can be classified into three broad categories. The first category consists of growth and yield models, also known as empirical growth models, and they are used to predict tree and stand productivity. The second category includes process-based models, also known as mechanistic models, and they focus on the modeling of ecophysiological processes that govern the behavior of forest ecosystems. The third category consists of gap models, also known as forest succession models. In this article, the main characteristics of these three categories of forest models are discussed.

Worm Farming

Worm Farming

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