The effect of size and competition on tree growth rate in old-growth coniferous forests

2012 ◽  
Vol 42 (11) ◽  
pp. 1983-1995 ◽  
Author(s):  
Adrian Das
Keyword(s):  
Growth Rate ◽  
Tree Growth ◽  
Large Data ◽  
Common Species ◽  
Tree Size ◽  
Strong Response ◽  
Old Growth ◽  
Potential Growth ◽  
Data Set ◽  
Competition Models

Tree growth and competition play central roles in forest dynamics. Yet models of competition often neglect important variation in species-specific responses. Furthermore, functions used to model changes in growth rate with size do not always allow for potential complexity. Using a large data set from old-growth forests in California, models were parameterized relating growth rate to tree size and competition for four common species. Several functions relating growth rate to size were tested. Competition models included parameters for tree size, competitor size, and competitor distance. Competitive strength was allowed to vary by species. The best ranked models (using Akaike’s information criterion) explained between 18% and 40% of the variance in growth rate, with each species showing a strong response to competition. Models indicated that relationships between competition and growth varied substantially among species. The results also suggested that the relationship between growth rate and tree size can be complex and that how we model it can affect not only our ability to detect that complexity but also whether we obtain misleading results. In this case, for three of four species, the best model captured an apparent and unexpected decline in potential growth rate for the smallest trees in the data set.

scholarly journals The effect of tree size, neighborhood competition and environment on tree growth in an old-growth temperate forest

2016 ◽  
pp. rtw126 ◽  
Author(s):  
Zhaochen Zhang ◽  
Michael J. Papaik ◽  
Xugao Wang ◽  
Zhanqing Hao ◽  
Ji Ye ◽  
...  
Keyword(s):  
Tree Growth ◽  
Temperate Forest ◽  
Tree Size ◽  
Old Growth ◽  
Neighborhood Competition

Tree growth prediction using size and exposed crown area

2005 ◽  
Vol 35 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Peter H Wyckoff ◽  
James S Clark
Keyword(s):  
Growth Rate ◽  
Tree Growth ◽  
Shade Tolerance ◽  
Strong Predictor ◽  
Tree Size ◽  
Crown Area ◽  
Long Term Ecological Research ◽  
Size Growth ◽  
The Relationship

We address the relationships between tree growth rate and growing environment for 21 co-occurring species. Tree growth rates are obtained from mapped plots at the Coweeta Long-Term Ecological Research site in the southern Appalachian Mountains. We employ high-resolution aerial photography to assess the light environment for trees growing in these plots, using exposed crown area (ECA) as a surrogate for light interception. The relationship between growth and ECA is compared with two other growth predictors: tree size and shade-tolerance classification. We find that ECA is an excellent predictor of tree growth (average R2 = 0.69 for nine species). When ECA is combined with tree size, growth rate prediction is improved (average R2 = 0.76). Tree size alone is also a strong predictor of tree growth (average R2 = 0.68). Shade-tolerance classification, by contrast, is a poor predictor of tree growth.

scholarly journals Evaluating tree growth factors into species-specific functional soil maps for improved agroforestry system efficiency

2021 ◽  
Author(s):  
Zhuo-Dong Jiang ◽  
Phillip R. Owens ◽  
Amanda J. Ashworth ◽  
Bryan A. Fuentes ◽  
Andrew L. Thomas ◽  
...  
Keyword(s):  
Growth Rate ◽  
Environmental Factors ◽  
Tree Growth ◽  
Tree Species ◽  
Agroforestry Systems ◽  
Tree Size ◽  
Soil Maps ◽  
Factors Affecting ◽  
Terrain Attributes ◽  
Functional Units

AbstractAgroforestry systems play an important role in sustainable agroecosystems. However, accurately and adequately quantifying the relationships between environmental factors and tree growth in these systems are still lacking. Objectives of this study were to quantify environmental factors affecting growth of four tree species and to develop functional soil maps (FSM) for each species in an agroforestry site. The diameter at breast height, absolute growth rate (AGR), and neighborhood competition index of 259 trees from four species (northern red oak [Quercus rubra], pecan [Carya illinoinensis], cottonwood [Populus deltoides], and sycamore [Platanus occidentalis]) were determined. A total of 51 topsoil samples were collected and analyzed, and 12 terrain attributes were derived from the digital elevation model. The relationships between AGR, soil, topography, and tree size were analyzed using Spearman correlation. Based on correlation analysis, FSM for each species were generated using the k-means cluster method by overlaying correlated soil and terrain attribute maps. Results showed tree size and terrain attributes were driving factors affecting tree growth rate relative to soil properties. The spatial variations in AGR among functional units were statistically compared within tree species and the areas with larger AGR were identified by the FSM. This study demonstrated that FSM could delineate areas with different AGR for the oak, cottonwood, and sycamore trees. The AGR of pecan trees did not vary among functional units. The generated FSM may allow land managers to more precisely establish and manage agroforestry systems.

scholarly journals Validierung des Waldwachstumsmodells MOSES für Schweizer Wälder | Validating the tree growth model MOSES for Swiss Forests (reviewed paper)

2005 ◽  
Vol 156 (5) ◽  
pp. 149-156 ◽  
Author(s):  
Dionys Hallenbarter ◽  
Hubert Hasenauer ◽  
Andreas Zingg
Keyword(s):  
Tree Growth ◽  
Model Calibration ◽  
Large Data ◽  
Forest Growth ◽  
Height Increment ◽  
Data Set ◽  
Growth Simulation ◽  
Permanent Sample Plots ◽  
Tree Growth Model ◽  
Forest Conditions

This work presents the results of a validation study using the forest growth simulation model MOSES 3.0 for Swiss forest conditions. During the latest parameterization of the model a large data set from Austria as well as Switzerland was used. The goal of this work was to validate the diameter and height increment functions using a large and independent data set (not used for model calibration) recorded on permanent sample plots in Switzerland. Tree growth was simulated over several growth periods and analyzed for a possible bias. The main results of this study suggest that no systematic discrepancies exist between the predicted and the observed diameter and height increment.

scholarly journals Problems with models assessing influences of tree size and inter-tree competitive processes on individual tree growth: a cautionary tale

2021 ◽  
Author(s):  
P. W. West ◽  
D. A. Ratkowsky
Keyword(s):  
Tree Growth ◽  
Growth Rates ◽  
Tree Size ◽  
Model Systems ◽  
Data Set ◽  
Individual Tree ◽  
Technical Problems ◽  
Competitive Effects ◽  
Individual Tree Growth ◽  
Eastern Australia

AbstractIn forest growing at any one site, the growth rate of an individual tree is determined principally by its size, which reflects its metabolic capacity, and by competition from neighboring trees. Competitive effects of a tree may be proportional to its size; such competition is termed ‘symmetric’ and generally involves competition below ground for nutrients and water from the soil. Competition may also be ‘asymmetric’, where its effects are disproportionate to the size of the tree; this generally involves competition above ground for sunlight, when larger trees shade smaller, but the reverse cannot occur. This work examines three model systems often seen as exemplars relating individual tree growth rates to tree size and both competitive processes. Data of tree stem basal area growth rates in plots of even-aged, monoculture forest of blackbutt (Eucalyptus pilularis Smith) growing in sub-tropical eastern Australia were used to test these systems. It was found that none could distinguish between size and competitive effects at any time in any one stand and, thus, allow quantification of the contribution of each to explaining tree growth rates. They were prevented from doing so both by collinearity between the terms used to describe each of the effects and technical problems involved in the use of nonlinear least-squares regression to fit the models to any one data set. It is concluded that quite new approaches need to be devised if the effects on tree growth of tree size and competitive processes are to be quantified and modelled successfully.

Plant size, environmental factors and functional traits jointly shape the stem radius growth rate in an evergreen coniferous species across ontogenetic stages

2020 ◽  
Author(s):  
Zhili Liu ◽  
Kouki Hikosaka ◽  
Fengri Li ◽  
Liangjun Zhu ◽  
Guangze Jin
Keyword(s):  
Growth Rate ◽  
Environmental Factors ◽  
Light Intensity ◽  
Soil Nutrients ◽  
Functional Traits ◽  
Tree Growth ◽  
Plant Size ◽  
Tree Size ◽  
Coniferous Species ◽  
Ontogenetic Stages

Abstract Aims Plant size, environmental conditions and functional traits are important for plant growth; however, it is less clear which combination of these factors is the most effective for predicting tree growth across ontogenetic stages. Methods We selected 65 individuals of an evergreen coniferous species, Pinus koraiensis, with diameters at breast height (DBH) from 0.3 to 100 cm in Northeast China. For each individual, we measured the stem radius growth rate (SRGR, μm/year) for the current year, environmental factors (light, soil nutrients and water) and functional traits (leaf, branch and root traits). Important Findings SRGR increased with DBH when the DBH was lower than 58 cm, whereas it decreased with DBH when the DBH was larger than 58 cm. Structural equation modeling analysis suggested that , when the DBH was 0-15 cm, plant size had a direct negative influence on SRGR and an indirect positive influence on SRGR due to the light intensity above the plant. Plant size had direct positive and negative effects when the DBH was 16-58 cm and 59-100 cm, respectively. When the DBH was larger than 15 cm, soil parameters were more important than light intensity for SRGR. The functional traits selected for use in the best model were changed from the specific leaf area and wood density to the root nitrogen concentration with increasing tree size. In summary, plant size, environmental factors and functional traits jointly shaped tree growth, and their relative influence varied with size, suggesting that the resources limiting tree growth may change from light to soil nutrients with increasing tree size.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

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