Increases in photosynthetic capacity (A(1500)) after defoliation have been attributed to changes in leaf-level biochemistry, water, and/or nutrient status. The hypothesis that transient photosynthetic responses to partial defoliation are regulated by whole-plant (e.g. sourcesink relationships or changes in hydraulic conductance) rather than leaf-level mechanisms is tested here. Temporal variation in leaf-level gas exchange, chemistry, whole-plant soil-to-leaf hydraulic conductance (K-P), and aboveground biomass partitioning were determined to evaluate mechanisms responsible for increases in A(1500) of Eucalyptus globulus L. potted saplings. A(1500) increased in response to debudding (B), partial defoliation (D), and combined B&D treatments by up to 36% at 5 weeks after treatment. Changes in leaf-level factors partly explained increases in A(1500) of B and B&D treatments but not for D treatment. By week 5, saplings in B, B&D, and D treatments had similar leaf-specific K-P to control trees by maintaining lower midday water potentials and higher transpiration rate per leaf area. Whole-plant source:sink ratios correlated strongly with A(1500). Further, unlike K-P, temporal changes in source:sink ratios tracked well with those observed for A(1500). The results indicate that increases in A(1500) after partial defoliation treatments were largely driven by an increased demand for assimilate by developing sinks rather than improvements in whole-plant water relations and changes in leaf-level factors. Three carbohydrates, galactional, stachyose, and, to a lesser extent, raffinose, correlated strongly with photosynthetic capacity, indicating that these sugars may function as signalling molecules in the regulation of longer term defoliation-induced gas exchange responses.

Early weed control may improve the growth of forest plantations by influencing soil water and nutrient availability. To understand eucalypt growth responses to weed control, we examined the temporal responses of leaf gas-exchange, leaf nitrogen concentration (N) and water status of 7-month-old Eucalyptus globulus L. trees in a paired-plot field trial. In addition, we monitored the growth, leaf N and water status of the competing vegetation in the weed treatment. By the end of the 11-month experiment, complete weed control (WF treatment) of largely woody competitors increased the basal diameter of E. globulus by 14%. As indicated by pre-dawn water potentials of > - 0.05 MPa, interspecies competition for water resources was minimal at this site. In contrast, competition for N appeared to be the major factor limiting growth. Estimations of total plot leaf N (g m(-2) ground) showed that competing vegetation accounted for up to 70% of the total leaf N at the start of the trial. This value fell to 15% by the end of the trial. Despite increased leaf N(area) in WF trees 5 months after imposition of weed control, the photosynthetic capacity (A(1500)) of E. globulus was unaffected by treatment suggesting that the growth gains from weed control were largely unrelated to changes in leaf-level photosynthesis. Increased nutrient availability brought about by weed control enabled trees to increase investment into leaf-area production. Estimates of whole-tree carbon budget based on direct measurements of dark respiration and A(1500) allowed us to clearly demonstrate the importance of leaf area driving greater productivity following early weed control in a nutrient-limited site.

Radiation interception and biomass production by forest stands are the fundamental plant ecosystem processes. Radiation interception depends on the leaf area index and canopy structure, while canopy photosynthesis depends on the photosynthetic characteristics of the foliage and is modified by stomatal behavior. Moreover, the photosynthetic characteristics of leaves are determined by the nitrogen distribution in the canopy, and there is strong observational and theoretical evidence that this distribution tends to optimize whole-canopy production. This chapter considers how canopy structure, leaf area, and photosynthetic properties can be coupled using commonly used and useful radiation interception models to give estimates of tree and canopy biomass production. Canopy production is a complex process because foliage is distributed throughout a canopy, and the main factors that influence the photosynthetic production–radiation, temperature, vapor pressure deficit, leaf nutrient status–vary temporally and spatially. Predicting canopy production thus requires integration of the equations describing photosynthesis over time and space, and the determination of how these environmental factors vary within the canopy in response to transpiration. Complete energy balance and photosynthesis modeling has been accomplished, and is outlined later. However, more simplified models suffice for most practical purposes, and especially when production is required for a stand over an extended period of time. These are the main subject of this chapter. Various factors impose constraints on canopy productivity, operating through both light interception and photosynthesis. This chapter considers how these processes can be coupled through commonly used and useful radiation interception models to give estimates of tree and canopy biomass production. In particular, the extent and distribution of foliage in a canopy clearly affect the amount of light intercepted by the canopy, with shading by the upper canopy and by neighboring trees playing an important role. But despite shading effects, some leaves deep in the canopy experience full sunlight some of the time, and the difference between the sun and shade leaves must be taken into account.

Water is a controlling factor in the growth of forests, indeed, forests do not occur in low rainfall regions of the world. The water balance of stands depends on precipitation, interception, run-off, evaporation and drainage with the exception of precipitation all these processes are strongly influenced by tree populations, stand structure, and canopy architecture. The availability of water in the soil at any time, interacting with the evaporative demand of the atmosphere and the hydraulic capacity of the trees, determines canopy conductance and the ability of the trees to absorb CO2 for photosynthesis. Tree–water relations are an excellent and well-documented example of processes at different levels with different response times. The hydrology of forest ecosystems is important not only because of the interactions between the soil water balance and tree growth but also because of the importance of catchments as water supply systems. The first part of this chapter provides an outline of the hydrological balance and its components, including consideration of water in root zones and water movement in soils. Then it considers tree–water relations and concludes with a brief discussion of the consequences of water stress.

Publisher Summary Stem population dynamics are important to forest managers, to modelers, and to those concerned with carbon sequestration, either in relation to climate change or wood production. This chapter discusses both density-dependent mortality, also known as self-thinning, and density-independent mortality induced by environmental factors. The distribution of biomass between the various components of a tree— that is, foliage, stems, roots, etc., is important as this determines the potential for growth (leaves and fine roots), structural stability (stem and coarse roots) and economic products (mainly the stem). It also provides a summary of the statistical relationships between stem height and diameter, and discusses the mathematical expressions generally used to describe stem size distributions. The biomass of the component parts of trees (indeed, of any plants) tend to bear fixed, or at least stable and predictable relationships to one another. These are called allometric relationships, and are routinely used to estimate biomass partitioning within a tree from simple measures, such as its stem diameter. This chapter discusses the use of allometric relationships in growth models to constrain biomass allocation to the components of trees so that the resulting partitioning better mirrors that observed in real stands. Canopies are formed by the crowns of trees. The architecture of a forest canopy is described by the vertical and horizontal arrangement of foliage through the canopy space. This, and the leaf area in a canopy per unit ground area determine how much photosynthetically active radiation (PAR) is intercepted by the canopy, and hence the photosynthetic production by the canopy. Foliage dynamics: the emergence, growth, death and fall of leaves, determine the temporal dynamics of canopies, and are clearly a major determinant of the state of deciduous canopies, where the whole population of leaves on trees grows and is lost each growing season. Modelling this is difficult, since it must involve stored carbohydrates; another area where one's knowledge is uncertain. However, foliage dynamics are also important in evergreen trees: if some leaves did not fall each year, leaf area would rapidly reach very high values. Leaf loss is generally called litterfall, although litterfall strictly includes twigs and dead branches, therefore has to be accounted for in attempts to describe the growth patterns and carbon production of trees. Litterfall is also a factor in the overall carbon balance of trees, although the amounts of carbohydrate involved are small in relation to the amounts consumed by the growth of other organs. Finally, it discusses coarse and fine roots and their distribution, and outlines the distinction between fine roots as active uptake organs and coarse roots as passive/structural anchors.

This chapter undertakes the task to try to identify the areas of the field (forest ecophysiology and modeling, in this case) that require, or are likely to reward, particular attention from the research community concerned with this field. These may include areas where knowledge and understanding are limited, and where the problems derive from lack of scientific understanding, as well as areas where scientific understanding may be quite good, but where there are clear opportunities for practical applications that have not yet been developed. Science is concerned with understanding the way natural systems function, and in the biological and ecological fields it is concerned with understanding the mechanisms that drive processes and the way systems respond to changing external conditions and stimuli. On the basis of that understanding it is the hope to be able to predict the behavior and responses of systems, at the levels of interest, and to manage that behavior. Management involves making decisions and taking actions, with the intention of achieving some specified result or goal. The decisions taken are more likely to lead to the desired results, or achievement of the goal, if they are based on sound knowledge about the (eco) system being managed and the way it will respond to change. In relation to tree physiology, it is possible that knowledge of the factors that determine tree growth and the way trees respond to change may be useful in breeding for improved plantation species, and that process-based models will be used to explore the likely implications of various selection criteria and strategies. In relation to forest ecosystems, it is the concern to predict responses to changes, such as rising atmospheric CO2 concentration, as well as to management actions such as thinning and fertilization. The remainder of this chapter outlines the areas where there is need for particular research focus because the scientific understanding of the processes is inadequate, and the areas where exciting opportunities for the application of available knowledge to the business of predicting the behavior and responses of forest ecosystems is seen.

Publisher Summary This book is concerned with analyzing the growth of forest trees in terms of the physiological processes that underlie and determine growth. The rates of these processes depend on external conditions and interactions with other processes going on in the plants. If the rates could be integrated and the interactions between them quantified, the state of the plant could be determined at any time in terms of its current state as described by its mass, the distribution of that mass among the component parts of the plant, and the condition of the plant. Therefore, it is needed to be able to analyze plant growth in terms of the rates, at which physiological processes operate. The condition of the plant may be specified in terms of the water or nutrient status of the plant. The external conditions that affect the rates of physiological processes include the weather variables: radiant energy, air temperature, humidity, and wind. To provide the background necessary for analyzing plant growth in relation to environmental variables, or for the analysis or prediction of observed responses at a particular level, this chapter presents the information about these weather variables. Also discussed here are the methods of estimating their values where measurements are not available.

This chapter presents and discusses in detail the model known as 3-PG. The acronym is an abbreviation for Physiological Processes Predicting Growth. Besides its simple, general structure, a significant factor in the widespread adoption of 3-PG has been that implementations of the model have been made freely available to all who wanted to use it. This chapter provides an overview of why 3-PG has the structure it does, describes that structure and summarizes the various data and species-specific parameters required to run the model. It discusses the assumptions that underlie the sub-models and functional relationships used in it, and it discusses the manner in which species-specific parameter sets can be established. Any model must consist of a set of statements that constitute hypotheses about the way the system being modeled works. Wherever possible these should be in a form that is testable, either by direct measurements designed to test particular sub-models, or indirectly by measurements that evaluate the model as a whole at the level of its outputs. Accordingly, this chapter provides a description of how a species-specific parameter set and 3-PG as a whole can be tested. Applications of 3-PG across a wide range of environments and species are summarized in this chapter. This allows assessment of the extent to which it fulfills the criteria for evaluation, whether it provides a framework within which one can set and evaluate current knowledge and information about tree physiology and the factors that affect and determine stand growth, and whether it is a useful practical tool. Finally, this chapter considers changes that could be made to various parts of the model and assess the implications of these changes in terms of the number and availability of the parameter values that would be required in relation to possible gains in the accuracy and precision of predictions.

Models are essentially abstractions that should encapsulate the essential features of the system being modeled. They may serve any of a number of purposes. They may be designed as practical tools with which to simulate the behavior of a system in response to change or stimuli, such that managers or decision-makers can assess the probable consequences of those changes or stimuli. They may be developed primarily as research tools designed to provide a framework, within which current knowledge and information can be set and the relative importance of different parts of the system evaluated. Or models may be formulated as statements, or sets of statements, embodying current knowledge or hypotheses about the way systems work. This chapter starts with a discussion on the concepts and principles of models. There are three main types of forest growth model: empirical, process-based, or mechanistic and hybrid. Here, the characteristics of each are outlined in relation to its purpose, and it reviews a small sample of the models of each type that have been developed in forest ecophysiology in recent years, focusing on those of their properties that are of particular interest in relation to their purpose. This chapter gathers together and discusses some points arising from the overview of the various models presented here, such as: reinventing the wheel; parameterisation and calibration, and the reason to use process-based models. Models are tested in various ways during their construction, and the terms verification, validation, and testing are variously used. This chapter provides some rather cursory remarks to outline the main points that need to be considered in relation to modeling the growth of trees and forests.

Calibration of the self-thinning frontier in even-aged monocultures is hampered by scarce data and by subjective decisions about the proximity of data to the frontier. We present a simple model that applies to observations of the full trajectory of stand mean diameter across a range of densities not necessarily close to the frontier. Development of the model is based on a consideration of the slope s = ln(Nt/Nt−1)/ln(Dt/Dt−1) of a log-transformed plot of stocking Nt and mean stem diameter Dt at time t. This avoids the need for subjective decisions about limiting density and allows the use of abundant data further from the self-thinning frontier. The model can be solved analytically and yields equations for the stocking and the stand basal area as an explicit function of stem diameter. It predicts that self-thinning may be regulated by the maximum basal area with a slope of −2. The significance of other predictor variables offers an effective test of competing self-thinning theories such Yoda's −3/2 power rule and Reineke's stand density index.

In forest management and ecological research, consideration of the impacts and risks of climate change or management optimisation is complex. Computer models have long been applied as tools for these tasks. Process-based forest growth models claim to overcome the limitations of empirical statistical models, but the capacity of different process-based models and modelling approaches have rarely been compared directly. This study evaluates stepwise multiple regression models in comparison to four process-based modelling approaches (3-PG, 3-PG+, CABALA and Forest-DNDC) for greenfield predictions of Eucalyptus globulus plantation growth from 2 to 8 years after planting throughout southern Australia.The stepwise multiple regression models could not simulate plantation growth to a satisfactory level of precision over the entire simulation period, although 2 years after planting model efficiency was 0.46, greater than for any of the process-based models. The variables that were statistically important to predict early plantation growth were mean minimum temperature, stocking rate and the amount of applied N fertiliser. For plantations between 4 and 8 years of age, coefficients of model efficiency were between -0.55 and -0.99.Only process-based models provided the flexibility to realistically predict the impacts of the different growth conditions throughout of the simulation period. Amongst the process-based models, Forest-DNDC achieved the greatest model efficiency (0.28) at 2 years after planting and was the most consistent performing model (0.20-0.30), whereas CABALA achieved the greates model efficiency (0.70) after 8 years of plantation growth. Both 3-PG models performed best for 6-year-old plantations, but throughout the simulation period their predictive precision was strongly dependent on estimating site fertility. For 3-PG+, a statistical approach to estimate site fertility resulted in a model efficiency of 0.28 at 6 years after planting, whereas a subjective estimation of site fertility for 3-PG resulted in a model efficiency of 0.58. In general, the process-based models had difficulties in simulating very young plantations at less then 4 years after planting, plantations with high tree mortality rates and plantation response to extreme silvicultural management operations. (C) 2008 Elsevier B.V. All rights reserved.

Forest managers now operate in an information-rich but increasingly challenging environment in which the competing demands of environmental stewardship and sustainable management must counter-balance the demands of increased production and profitability. Management support tools, in particular, decision support systems are essential aids in this operating environment. A dynamic forest growth model, CArbon BALAnce (CABALA), that links carbon, water and nitrogen flows through the atmosphere, trees and soil including soil organic matter is presented here as a central part of a silvicultural decision support system.The strong linkage between stand biomass allocation and external environmental conditions make CABALA a model suitable for exploring stand management options and the effects of factors such as frost and drought on growth.The model performance is verified extensively using fertiliser, spacing and thinning trials. Predictions of nitrogen mineralisation, light interception, plant water stress, and biomass allocation as well as stand growth and stand leaf area index are tested with observed data. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

A simple forest growth model, ProMod, was developed to assess productivity by plantation grown Eucalyptus globulus. It is based on a sound understanding of the basic physiology of tree growth and predicts leaf area index (LAI) following canopy closure, annual net photosynthetic production and water use, soil water balance, and stemwood production, all in response to climatic and site factors. Minimum inputs are site latitude, monthly means of daily maximum and minimum temperatures, solar irradiance, rainfall and open-pan evaporation, mean monthly rain-days, and a simple classification of soil depth, texture, stoniness, drainage and fertility. Output from ProMod is affected by processes included as part of the model structure, physiological parameters characterising these processes, and variables characterising site climate and soil. A sensitivity analysis based on predicted net total annual production (G(a)) and annual water use efficiency (omega(alpha)) was applied to the structure of ProMod and to its parameter values. The structural sensitivity analysis shows that, with the possible exception of the temperature dependence of respiration, simplification of any of the processes in ProMod results in a loss of generality. The parameter sensitivity analysis shows that G(a) and omega(alpha) are highly sensitive to a small subset of parameters: light saturated photosynthetic rate, shape of the light response curve and its dependence on temperature, low temperature response of LAI, and parameters in the relationship between water use efficiency and vapour pressure deficit. There is little or no sensitivity to some parameters which are difficult to measure. In many cases sensitivity varied significantly from site to site. This information is important when ProMod is to be parameterised for different species, and impacts on traits to be selected for in a tree-breeding program. (C) 1998 Elsevier Science B.V. All rights reserved.

A simple model, PROMOD, predicts the growth of a forest following canopy closure, i.e. under conditions in which the foliage biomass has attained a steady state. The principal output from PROMOD is peak mean annual increment. However, additional output available includes the closed-canopy leaf area index, evapotranspiration and water use efficiency. In addition, an indication of biomass partitioning around the time of peak MAI and the relative effects different environmental factors play in limiting production can be obtained. PROMOD is based on a generalisation of a simple forest growth model which predicts biomass production and partitioning at the stand level with a daily or annual time step. The minimum level of inputs required by PROMOD are of a quality and quantity that forest managers can readily and cheaply obtain for screening prospective plantation sites: the latitude, longitude, altitude, slope and aspect of the site and a classification of the soil depth, texture, stoniness, drainage and a rating of site fertility. However, PROMOD can be run using daily inputs of weather data and hence predict the seasonal variation of production. The closed-canopy leaf area index is calculated from the mean annual rainfall and temperature at the site, and a simple rating of site fertility. Annual production is calculated as the sum of daily production and takes diurnal temperature variation and possible seasonal photosynthetic acclimation into account. A simple soil water balance model is included in which water use is based on a crop factor which is a function of soil water content and a water use efficiency which is a function of vapour pressure deficit. The model was developed on the basis of data from nine plots of Eucalyptus globulus in south-eastern Tasmania and in Western Australia, and was validated using data from 19 plots in northern Tasmania.