Individual-based models (IBMs) of complex systems emerged in the 1960s and early 1970s, across diverse disciplines from astronomy to zoology. Ecological IBMs arose with seemingly independent origins out of the tradition of understanding the ecosystems dynamics of ecosystems from a 'bottom-up' accounting of the interactions of the parts. Individual trees are principal among the parts of forests. Because these models are computationally demanding, they have prospered as the power of digital computers has increased exponentially over the decades following the 1970s.This review will focus on a class of forest IBMs called gap models. Gap models simulate the changes in forests by simulating the birth, growth and death of each individual tree on a small plot of land. The summation of these plots comprise a forest (or set of sample plots on a forested landscape or region). Other, more aggregated forest IBMs have been used in global applications including cohort-based models, ecosystem demography models, etc. Gap models have been used to provide the parameters for these bulk models. Currently, gap models have grown from local-scale to continental-scale and even global-scale applications to assess the potential consequences of climate change on natural forests. Modifications to the models have enabled simulation of disturbances including fire, insect outbreak and harvest.Our objective in this review is to provide the reader with an overview of the history, motivation and applications, including theoretical applications, of these models. In a time of concern over global changes, gap models are essential tools to understand forest responses to climate change, modified disturbance regimes and other change agents. Development of forest surveys to provide the starting points for simulations and better estimates of the behavior of the diversity of tree species in response to the environment are continuing needs for improvement for these and other IBMs.

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Coverage rate, a critical variable for gridded forest area, has been neglected by previous studies in estimating the annual gross primary production (GPP) of global forest ecosystems. In this study, we investigated to what extent the coverage rate could impact forest GPP estimates from 1982 to 2011. Here we show that the traditional calculation without considering the coverage rate globally overestimated the forest gross carbon dioxide uptake by approximately 8.7%, with a value of 5.12 ± 0.23 Pg C yr−1, which is equivalent to 48% of the annual emissions from anthropogenic activities in 2012. Actually, the global annual GPP of forest ecosystems is approximately 53.71 ± 4.83 Pg C yr−1 for the past 30 years by taking the coverage rate into account. Accordingly, we argue that forest annual GPP calculated by previous studies has been overestimated due to the exaggerated forest area, and therefore, coverage rate may be a required factor to further quantify the global carbon cycle.

Mountain forests provide the main water resources and lumber for Northwest China. The understanding of the differences in forests growing among individual slope aspects in mountainous regions is of great significance to the wise management and planning of these natural systems. The aim of this study was to investigate the impacts of slope aspect on forest dynamic succession in Northwest China by using the dynamic forest succession model (FAREAST).First, the simulated forest composition and vertical forest zonation produced by the model were compared against recorded data in three sub-regions of the Altai Mountains. The FAREAST model accurately reproduced the vertical zonation, forest composition, growth curves of the dominant species (Larix sibirica), and forest biomass in the Altai Mountains. Transitions along the forest zones of the Altai Mountains averaged about a 400 m difference between the northern and southern sites. Biomass for forests on north-facing slopes were 11.0, 15.3 and 55.9 t C ha −1 higher than for south-facing slopes in the Northeast, Central and Southeast sub-regions, respectively. Second, our analyses showed that the FAREAST model can be used to predict dynamic forest succession in Northwest China under the influence of slope and aspect. In the Altai Mountains, the north-facing slopes supported the best forest growth, followed by the west-and east-facing slopes. South-facing slopes consistently exhibited the lowest growth, biomass storage and forest diversity.

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