Preface. Acknowledgements. 1 Introduction. 1.1 Model development and validation. 1.2 Important uses. 1.3 Overview of the book. 2 Indices of competition. 2.1 Introduction. 2.2 Two-sided competition. 2.2.1 Distance-independent. 2.2.2 Distance-dependent. 2.3 One-sided competition. 2.3.1 Distance-independent. 2.3.2 Distance-dependent. 2.4 Limitations. 2.4.1 Low predictive power. 2.4.2 Distance-independent vs. distance-dependent. 2.4.3 Influence of sampling design. 2.5 Summary. 3 Forest site evaluation. 3.1 Introduction. 3.2 Phytocentric measures of site quality. 3.2.1 Site index. 3.2.2 Plant indicators. 3.2.3 Other phytocentric measures. 3.3 Geocentric measures of site productivity. 3.3.1 Physiographic measures. 3.3.2 Climatic measures. 3.3.3 Soil measures. 3.4 Summary. 4 Whole-stand and size-class models. 4.1 Introduction. 4.2 Whole-stand models. 4.2.1 Yield tables and equations. 4.2.2 Compatible growth and yield equations. 4.2.3 Systems of equations. 4.2.4 State-space models. 4.2.5 Transition matrix models. 4.3 Size-class models. 4.3.1 Stand table projection. 4.3.2 Matrix models. 4.3.3 Diameter-class models. 4.3.4 Cohort models. 4.4 Summary. 5 Tree-level models. 5.1 Introduction. 5.2 Single-tree distance-dependent models. 5.2.1 Example models. 5.3 Tree-list distance-independent models. 5.3.1 Example models. 5.4 Summary. 6 Components of tree-list models. 6.1 Introduction. 6.2 Diameter increment. 6.2.1 Potential diameter increment equations with multiplicative modifiers. 6.2.2 Realized diameter increment equations. 6.3 Height increment. 6.3.1 Potential height increment equations with multiplicative modifiers. 6.3.2 Realized height increment equations. 6.4 Crown recession. 6.4.1 Individual-tree crown recession models. 6.4.2 Branch-level crown recession models. 6.5 Summary. 7 Individual-tree static equations. 7.1 Introduction. 7.2 Total height. 7.3 Crown length. 7.4 Crown width and profile. 7.5 Stem volume and taper. 7.6 Biomass. 7.7 Use of static equations to predict missing values. 7.8 Summary. 8 Mortality. 8.1 Introduction. 8.2 Stand-level mortality. 8.3 Individual-tree-level mortality. 8.4 Mechanistic models of mortality. 8.5 Development and application of mortality equations. 8.6 Summary. 9 Seeding, regeneration, and recruitment. 9.1 Introduction. 9.2 Seeding. 9.2.1 Flowering and pollination. 9.2.2 Seed production. 9.2.3 Seed dispersal. 9.2.4 Seed germination. 9.3 Regeneration. 9.4 Recruitment. 9.4.1 Static. 9.4.2 Dynamic. 9.5 Summary. 10 Linking growth models of different resolutions. 10.1 Introduction. 10.2 Linked stand- and size-class models. 10.2.1 Parameter recovery. 10.2.2 Modified stand table projection. 10.3 Linked stand- and tree-models. 10.3.1 Disaggregation. 10.3.2 Constrained. 10.3.3 Combined. 10.4 Summary. 11 Modeling silvicultural treatments. 11.1 Introduction. 11.2 Genetic improvements. 11.2.1 Stand-level. 11.2.2 Tree-level. 11.3 Early stand treatments. 11.3.1 Stand-level. 11.3.2 Tree-level. 11.4 Thinning. 11.4.1 Stand-level. 11.4.2 Tree-level. 11.5 Fertilization. 11.5.1 Stand-level. 11.5.2 Tree-level. 11.6 Combined thinning and fertilization. 11.6.1 Stand-level. 11.6.2 Tree-level. 11.7 Harvesting. 11.7.1 Stand-level. 11.7.2 Tree-level. 11.8 Summary. 12 Process-based models. 12.1 Introduction. 12.2 Key physiological processes. 12.2.1 Light interception. 12.2.2 Photosynthesis. 12.2.3 Stomatal conductance. 12.2.4 Respiration. 12.2.5 Carbon allocation. 12.2.6 Soil water and nutrients. 12.3 Example models. 12.3.1 Forest-BGC. 12.3.2 CenW. 12.3.3 BALANCE. 12.4 Limitations. 12.4.1 Initialization. 12.4.2 Parameterization. 12.4.3 Scale. 12.4.4 Sensitivity. 12.5 Summary. 13 Hybrid models of forest growth and yield. 13.1 Introduction. 13.2 Types of hybrid models. 13.2.1 Statistical growth equations with physiologically derived covariate. 13.2.2 Statistical growth equations with physiologically derived external modifier. 13.2.3 Allometric models. 13.3 Comparison to statistical models. 13.4 Summary. 14 Model construction. 14.1 Introduction. 14.2 Data requirements. 14.2.1 Stem analysis. 14.2.2 Temporary plots. 14.2.3 Permanent plots. 14.3 Model form. 14.4 Parameter estimation. 14.4.1 Regression. 14.4.2 Quantile regression. 14.4.3 Generalized linear regression models. 14.4.4 Mixed models. 14.4.5 Generalized algebraic difference approach. 14.4.6 System of equations. 14.4.7 Bayesian. 14.4.8 Nonparametric. 14.4.9 Annualization. 14.5 Summary. 15 Model evaluation and calibration. 15.1 Introduction. 15.2 Model criticism. 15.2.1 Model form and parameterization. 15.2.2 Variable selection and model simplicity. 15.2.3 Biological realism. 15.2.4 Compatibility. 15.2.5 Reliability. 15.2.6 Adaptability. 15.3 Model benchmarking. 15.3.1 Statistical tests. 15.3.2 Model error characterization. 15.4 Model calibration. 15.5 Summary. 16 Implementation and use. 16.1 Introduction. 16.2 Collection of appropriate data. 16.3 Generation of appropriate data. 16.4 Temporal scale. 16.5 Spatial scale. 16.6 Computer interface. 16.7 Visualization. 16.8 Output. 16.9 Summary. 17 Future directions. 17.1 Improving predictions. 17.2 Improving input data. 17.3 Improving software. 17.4 Summary. Bibliography. Appendix 1: List of species used in the text. Appendix 2: Expanded outline for ORGANON growth and yield model. Index.

Forest growth and yield modeling

We review the evidence of how organisms and populations are currently responding to climate change through phenotypic plasticity, genotypic evolution, changes in distribution and, in some cases, local extinction. Organisms alter their gene expression and metabolism to increase the concentrations of several antistress compounds and to change their physiology, phenology, growth and reproduction in response to climate change. Rapid adaptation and microevolution occur at the population level. Together with these phenotypic and genotypic adaptations, the movement of organisms and the turnover of populations can lead to migration toward habitats with better conditions unless hindered by barriers. Both migration and local extinction of populations have occurred. However, many unknowns for all these processes remain. The roles of phenotypic plasticity and genotypic evolution and their possible trade-offs and links with population structure warrant further research. The application of omic techniques to ecological studies will greatly favor this research. It remains poorly understood how climate change will result in asymmetrical responses of species and how it will interact with other increasing global impacts, such as N eutrophication, changes in environmental N : P ratios and species invasion, among many others. The biogeochemical and biophysical feedbacks on climate of all these changes in vegetation are also poorly understood. We here review the evidence of responses to climate change and discuss the perspectives for increasing our knowledge of the interactions between climate change and life.

/pdf/evidence-of-current-impact-of-climate-change-on-life-a-walk-346dlyaiht.pdf

Evidence of current impact of climate change on life: a walk from genes to the biosphere

Plant drought tolerance is a complex trait that requires a global view to understand its underlying mechanism. The proteomic aspects of plant drought response have been extensively investigated in model plants, crops and wood plants. In this review, we summarize recent proteomic studies on drought response in leaves to reveal the common and specialized drought-responsive mechanisms in different plants. Although drought-responsive proteins exhibit various patterns depending on plant species, genotypes and stress intensity, proteomic analyses show that dominant changes occurred in sensing and signal transduction, reactive oxygen species scavenging, osmotic regulation, gene expression, protein synthesis/turnover, cell structure modulation, as well as carbohydrate and energy metabolism. In combination with physiological and molecular results, proteomic studies in leaves have helped to discover some potential proteins and/or metabolic pathways for drought tolerance. These findings provide new clues for understanding the molecular basis of plant drought tolerance.

https://www.mdpi.com/1422-0067/17/10/1706/pdf

Drought-Responsive Mechanisms in Plant Leaves Revealed by Proteomics

The plant hormones auxin and abscisic acid may at first sight appear to be a conflicting pair of plant regulators. Abscisic acid content increases during stress and protects plant water status. The content of free auxin in the developing xylem of poplar declines during stress, while auxin conjugates increase. This indicates that specific down-regulation of a signal transduction chain is important in plant adaptation to stress. Diminished auxin content may be a factor that adapts growth and wood development of poplar during adverse environmental conditions. To allow integration of environmental signals, abscisic acid and auxin must interact. Data are accumulating that abscisic acid-auxin cross-talk exists in plants. However, knowledge of the role of plant hormones in the response of trees to stress is scarce. Our data show that differences in the localisation of ABA synthesis exist between the annual, herbaceous plant Arabidopsis and the perennial woody species, poplar.

The role of abscisic acid and auxin in the response of poplar to abiotic stress.

Abiotic stresses, such as drought, salinity and cold, are the major environmental stresses that adversely affect tree growth and, thus, forest productivity, and play a major role in determining the geographic distribution of tree species. Tree responses and tolerance to abiotic stress are complex biological processes that are best analyzed at a systems level using genetic, genomic, metabolomic and phenomic approaches. This will expedite the dissection of stress-sensing and signaling networks to further support efficient genetic improvement programs. Enormous genetic diversity for stress tolerance exists within some forest-tree species, and due to advances in sequencing technologies the molecular genetic basis for this diversity has been rapidly unfolding in recent years. In addition, the use of emerging phenotyping technologies extends the suite of traits that can be measured and will provide us with a better understanding of stress tolerance. The elucidation of abiotic stress-tolerance mechanisms will allow for effective pyramiding of multiple tolerances in a single tree through genetic engineering. Here we review recent progress in the dissection of the molecular basis of abiotic stress tolerance in forest trees, with special emphasis on Populus, Pinus, Picea, Eucalyptus and Quercus spp. We also outline practices that will enable the deployment of trees engineered for abiotic stress tolerance to land owners. Finally, recommendations for future work are discussed.

/pdf/molecular-and-physiological-responses-to-abiotic-stress-in-2nikowqy7a.pdf

Molecular and physiological responses to abiotic stress in forest trees and their relevance to tree improvement.

Proteomic analysis provides a powerful method of studying plant responses to stress at the protein level. In order to study stress-responsive molecular mechanisms for Populus × euramericana cv. ‘74/76’, one of the most important forest plantation tree species in subtropical and temperate regions, we analyzed the response of 2-year-old cuttings of P. × euramericana cv. ‘74/76’ to drought and high temperature using two-dimensional gel electrophoresis. More than 1,000 reproducible leaf proteins were detected in the controls and treatments, and 26 proteins were found to change notably in abundance. We identified 13 proteins affected by drought stress and 11 proteins affected by high temperature. These proteins are mainly involved in photosynthesis such as ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit and putative photosystem I reaction center subunit II precursor, and detoxification (manganese superoxide dismutase and methionine sulfoxide reductase A). Furthermore, the level of the photosynthesis proteins affected greatly by the imposed stress conditions was consistent with the observed noticeable decrease in net photosynthesis rate. These studies provides a fundamental data for future research on responses to drought and high temperature, two major factors limiting the growth of forest trees during summer under recent climatic warming.

Proteins responding to drought and high-temperature stress in Populus × euramericana cv. ‘74/76’

Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as: 1) simultaneous equation methods, 2) difference models, 3) artificial neural network techniques, 4) linear/nonlinear regression models, and 5) matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.

A review of stand basal area growth models

Estimation of the self-thinning boundary line within even-aged Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) stands: Onset of self-thinning

The effect of thinning has not been investigated previously in young even-aged Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] stands in Southern China. In long-term experiments, the effect of thinning on growth and yield of Chinese fir trees at five spacing levels was studied (2 × 3 m, 2 × 1.5 m, 2 × 1 m, 1 × 1.5 m and 1 × 1 m) thinned or left unthinned every 2 years from age 10. Trees in 2 × 3 m spaced unthinned plots were used as controls. Tree growth at the highest planting densities was lower in thinning treatments than in the control. The highest proportions of small, intermediate- and large stems were in plots with initial planting densities of 1 × 1 m, 1 × 1.5 m and 2 × 1 m, respectively. Merchantable volume increment after thinning showed no significant difference but increased with the initial planting density. The proportion of large-diameter trees and merchantable volume were highest in the controls. The total stand volume in thinning treatments increased with increasing initial planting density, but was lower than that of the control. Thinning Chinese fir stands with a planting density greater than 2 × 1 m could increase tree diameter but not merchantable volume. A stand density of less than 2 × 3 m is most profitable for increased tree growth and merchantable volume.

	 

	Key words: Diameter class distribution, thinning, Cunninghamia lanceolata (Lamb.) Hook., merchantable volume

https://www.academicjournals.org/article/article1381504935_Sun et al.pdf

Honggang Sun

Papers

Proteins responding to drought and high-temperature stress in Populus × euramericana cv. ‘74/76’

A review of stand basal area growth models

Estimation of the self-thinning boundary line within even-aged Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) stands: Onset of self-thinning

Effect of pre-commercial thinning on diameter class distribution and total merchantable volume growth in Chinese fir stands in Southern China