Adjustment of estimated tree growth rates in northern California conifers for changes in precipitation levels

1998 ◽  
Vol 28 (8) ◽  
pp. 1241-1248 ◽  
Author(s):  
Lee C Wensel ◽  
Eric C Turnblom
Keyword(s):  
Tree Growth ◽  
Growth Rates ◽  
Initial Conditions ◽  
Growth Parameters ◽  
Forest Growth ◽  
Northern California ◽  
Growth Data ◽  
Short Term ◽  
Individual Tree ◽  
Periodic Growth

Even with similar initial conditions, observed forest growth rates on permanent sample plots in the conifer region of northern California differ for different periods. Thus, individual-tree growth models built with growth parameters estimated from data from one period may not produce accurate estimates for another period unless some allowance is made for this variation in growth rates. Variation in growth rates of northern California conifers through time has been shown to be correlated with precipitation changes. A method is presented that adjusts periodic growth estimates for variation in precipitation between periods. This provides a basis for adjusting short-term growth data for making long-term growth projections. Perhaps more importantly, short-term inventory updates might be made more accurately.

scholarly journals A likelihood perspective on tree-ring standardization: eliminating modern sample bias

2013 ◽  
Vol 9 (4) ◽  
pp. 4499-4551 ◽  
Author(s):  
J. Cecile ◽  
C. Pagnutti ◽  
M. Anand
Keyword(s):  
Tree Ring ◽  
Tree Growth ◽  
Fixed Effects ◽  
Information Criteria ◽  
Systematic Bias ◽  
Growth Data ◽  
Individual Tree ◽  
Regional Curve Standardization ◽  
Sample Bias ◽  
Modern Sample

Abstract. It has recently been suggested that non-random sampling and differences in mortality between trees of different growth rates is responsible for a widespread, systematic bias in dendrochronological reconstructions of tree growth known as modern sample bias. This poses a serious challenge for climate reconstruction and the detection of long-term changes in growth. Explicit use of growth models based on regional curve standardization allow us to investigate the effects on growth due to age (the regional curve), year (the standardized chronology or forcing) and a new effect, the productivity of each tree. Including a term for the productivity of each tree accounts for the underlying cause of modern sample bias, allowing for more reliable reconstruction of low-frequency variability in tree growth. This class of models describes a new standardization technique, fixed effects standardization, that contains both classical regional curve standardization and flat detrending. Signal-free standardization accounts for unbalanced experimental design and fits the same growth model as classical least-squares or maximum likelihood regression techniques. As a result, we can use powerful and transparent tools such as R2 and Akaike's Information Criteria to assess the quality of tree ring standardization, allowing for objective decisions between competing techniques. Analyzing 1200 randomly selected published chronologies, we find that regional curve standardization is improved by adding an effect for individual tree productivity in 99% of cases, reflecting widespread differing-contemporaneous-growth rate bias. Furthermore, modern sample bias produced a significant negative bias in estimated tree growth by time in 70.5% of chronologies and a significant positive bias in 29.5% of chronologies. This effect is largely concentrated in the last 300 yr of growth data, posing serious questions about the homogeneity of modern and ancient chronologies using traditional standardization techniques.

scholarly journals Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1338
Author(s):  
Simone Bianchi ◽  
Mari Myllymaki ◽  
Jouni Siipilehto ◽  
Hannu Salminen ◽  
Jari Hynynen ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Tree Growth ◽  
Cross Validation ◽  
Growth Models ◽  
Forest Growth ◽  
Individual Tree ◽  
Selection Cutting ◽  
Single Tree ◽  
Individual Tree Growth ◽  
Single Tree Selection

Background and Objectives: Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting. Materials and Methods: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting. The models were compared in terms of Akaike Information Criteria (AIC), root mean square error (RMSE), and mean absolute bias (MAB), both with the training data and after cross-validation with a leave-one-out method at stand level. Results: Even though the spatial model had a lower AIC than the nonspatial model, RMSE and MAB of the two models were similar. Both models tended to underpredict growth for the highest observed values when the tree-level random effects were not used. After cross-validation, the aggregated predictions at stand level well represented the observations in both models. For most of the predictors, the use of values based on trees’ height rather than trees’ diameter improved the fit. After single-tree selection cutting, trees had a growth boost both in the first and second five-year period after cutting, however, with different predicted intensity in the two models. Conclusions: Under the research framework here considered, the spatial modeling approach was not more accurate than the nonspatial one. Regarding the single-tree selection cutting, an intervention regime spaced no more than 15 years apart seems necessary to sustain the individual tree growth. However, the model’s fixed effect parts were not able to capture the high growth of the few fastest-growing trees, and a proper estimation of site potential is needed for uneven-aged stands.

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.

scholarly journals Examining Changes in Stem Taper and Volume Growth with Two-Date 3D Point Clouds

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 382 ◽  
Author(s):  
Ville Luoma ◽  
Ninni Saarinen ◽  
Ville Kankare ◽  
Topi Tanhuanpää ◽  
Harri Kaartinen ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Volume Growth ◽  
Strong Support ◽  
Forest Growth ◽  
Stem Volume ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
3D Point Clouds

Exact knowledge over tree growth is valuable information for decision makers when considering the purposes of sustainable forest management and planning or optimizing the use of timber, for example. Terrestrial laser scanning (TLS) can be used for measuring tree and forest attributes in very high detail. The study aims at characterizing changes in individual tree attributes (e.g., stem volume growth and taper) during a nine year-long study period in boreal forest conditions. TLS-based three-dimensional (3D) point cloud data were used for identifying and quantifying these changes. The results showed that observing changes in stem volume was possible from TLS point cloud data collected at two different time points. The average volume growth of sample trees was 0.226 m3 during the study period, and the mean relative change in stem volume was 65.0%. In addition, the results of a pairwise Student’s t-test gave strong support (p-value 0.0001) that the used method was able to detect tree growth within the nine-year period between 2008–2017. The findings of this study allow the further development of enhanced methods for TLS-based single tree and forest growth modeling and estimation, which can thus improve the accuracy of forest inventories and offer better tools for future decision-making processes.

scholarly journals Tropical tree growth sensitivity to climate is driven by species intrinsic growth rate and leaf traits

2021 ◽  
Author(s):  
David Bauman ◽  
Claire Fortunel ◽  
Lucas A. Cernusak ◽  
Lisa P. Bentley ◽  
Sean M. McMahon ◽  
...  
Keyword(s):  
Climate Change ◽  
Tree Growth ◽  
Tree Species ◽  
Forest Dynamics ◽  
Species Traits ◽  
Growth Responses ◽  
Growth Data ◽  
Short Term ◽  
Evaporative Demand

A better understanding of how climate affects growth in tree species is essential for improved predictions of forest dynamics under climate change. Long-term climate averages (mean climate) and short-term deviations from these averages (anomalies) both influence tree growth, but the rarity of long-term data integrating climatic gradients with tree censuses has so far limited our understanding of their respective role, especially in tropical systems. Here, we combined 49 years of growth data for 509 tree species across 23 tropical rainforest plots along a climatic gradient to examine how tree growth responds to both climate means and anomalies, and how species functional traits mediate these tree growth responses to climate. We showed that short-term, anomalous increases in atmospheric evaporative demand and solar radiation consistently reduced tree growth. Drier forests and fast-growing species were more sensitive to water stress anomalies. In addition, species traits related to water use and photosynthesis partly explained differences in growth sensitivity to both long-term and short-term climate variations. Our study demonstrates that both climate means and anomalies shape tree growth in tropical forests, and that species traits can be leveraged to understand these demographic responses to climate change, offering a promising way forward to forecast tropical forest dynamics under different climate trajectories.

scholarly journals Deriving a Tree Growth Model from Any Existing Stand Growth Model

2021 ◽  
Author(s):  
Quang V. Cao
Keyword(s):  
Growth Model ◽  
Tree Growth ◽  
Poor Performance ◽  
Tree Level ◽  
Growth Data ◽  
Tree Model ◽  
Individual Tree ◽  
Tree Survival ◽  
Tree Models ◽  
Level Model

In this study, a new method was developed to derive a tree survival and diameter growth model from any existing stand-level model, without the need for individual-tree growth data. Predictions from the derived tree model are constrained to match number of trees and basal area per ha as outputted by the stand model. The tree models derived from three different stand models were evaluated against a tree model, in both unadjusted and disaggregated forms. For the same stand-level model, the derived tree model outperformed its counterpart, the disaggregated tree model. Furthermore, except for one stand model with poor performance, the tree models derived from the remaining two stand models delivered comparable results to those obtained from the unadjusted tree model. The tree model derived from one stand model even performed slightly better than the unadjusted tree model. This is significant because the coefficients of the unadjusted and disaggregated tree models had to be estimated from tree-level growth data, whereas the derived tree model required no tree growth data at all. The methodology presented in this study should be applicable when there is no ingrowth or recruitment.

scholarly journals Genetic Variation in Growth Curve Parameters of Konishii fir (Cunninghamia lanceolata (LAMB.) HOOK. var. konishii)

2009 ◽  
Vol 58 (1-6) ◽  
pp. 1-10 ◽  
Author(s):  
Jeng-Der Chung ◽  
Ching-Te Chien ◽  
Gordon Nigh ◽  
Cheng C. Ying
Keyword(s):  
Growth Curve ◽  
Tree Growth ◽  
Curve Fitting ◽  
Repeated Measures ◽  
Variance Component Analysis ◽  
Weibull Function ◽  
Timber Species ◽  
Growth Data ◽  
Individual Tree ◽  
Practical Applications

Abstract Cunninghamia konishii is the island race of the species complex C. lanceolata, and is native to Taiwan. It is a valuable timber species. A comprehensive provenance- family test was established in 1973. Height and diameter were measured periodically until age 26, which was close to the species’ harvest age of about 30. These data offered an opportunity to examine the species’ growth characteristics by fitting asymptotic growth functions. We adopted the concept of repeated measures data analyses, i.e., a combination of variance component analysis and growth curve fitting, the latter involved fitting the individual tree height and diameter data to a Weibull-based function. A severe typhoon in 1996 caused serious damage to the plantation, mostly to tree heights. To prevent this damage from influencing our results, we limited the analyses to those trees judged relatively free of typhoon damage, and focused on the diameter growth data. Fitting a Weibull function with parameters a, b, and c was statistically successful (e.g. the mean R2 for diameter was 0.98). Both analyses indicate substantial variation among provenances and families, and thus opportunities for genetic selection and breeding. We particularly expound on the practical applications of growth curve fitting as an analytical tool for elucidating the mechanistic process of tree growth to assist decisions on the age for selection, even retrospectively, and modeling the response of tree growth to future climate.

Integrating biophysical controls in forest growth and yield predictions with artificial intelligence technology

2013 ◽  
Vol 43 (12) ◽  
pp. 1162-1171 ◽  
Author(s):  
M. Irfan Ashraf ◽  
Zhengyong Zhao ◽  
Charles P.-A. Bourque ◽  
David A. MacLean ◽  
Fan-Rui Meng
Keyword(s):  
Artificial Intelligence ◽  
Nova Scotia ◽  
Soil Water ◽  
Tree Growth ◽  
Forest Growth ◽  
Growth And Yield ◽  
Individual Tree ◽  
Artificial Intelligence Technology ◽  
Growth And Yield Models ◽  
Forest Growth And Yield

Growth and yield models are critically important for forest management planning. Biophysical factors such as light, temperature, soil water, and nutrient conditions are known to have major impacts on tree growth. However, it is difficult to incorporate these biophysical variables into growth and yield models due to large variation and complex nonlinear relationships between variables. In this study, artificial intelligence technology was used to develop individual-tree-based basal area (BA) and volume increment models. The models successfully account for the effects of incident solar radiation, growing degree days, and indices of soil water and nutrient availability on BA and volume increments of over 40 species at 5-year intervals. The models were developed using data from over 3000 permanent sample plots across the province of Nova Scotia, Canada. Model validation with independent field data produced model efficiencies of 0.38 and 0.60 for the predictions of BA and volume increments, respectively. The models are applicable to predict tree growth in mixed species, even- or uneven-aged forests in Nova Scotia but can easily be calibrated for other climatic and geographic regions. Artificial neural network models demonstrated better prediction accuracy than conventional regression-based approaches. Artificial intelligence techniques have considerable potential in forest growth and yield modelling.

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