scholarly journals Can an Exponential Function Be Applied to the Asymptotic Density–Size Relationship? Two New Stand-Density Indices in Mixed-Species Forests

Forests ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 9 ◽  
Author(s):  
Gerónimo Quiñonez-Barraza ◽  
Hugo Ramírez-Maldonado
Keyword(s):  
Mortality Rate ◽  
Initial Density ◽  
Stand Density ◽  
Species Group ◽  
Mixed Species ◽  
Management Objectives ◽  
Density Index ◽  
Density Indices ◽  
Species Groups ◽  
Stand Density Index

This study presents two stand-density indices (SDIs) based on exponential density decline as a function of quadratic mean diameter for all species combined in mixed-species forests with 22 species mix grouped in four species groups. The exponential-based density–diameter relationship, as well the density index corresponding to the slope or instantaneous mortality rate parameters, was compared with those based on power-law density–diameter relationship. A dataset of 202 fully stocked circular plots at maximum density was used for fitting the models, and a dataset of 122 circular plots was used for validation stand density index for all species combined of mixed-species stands. The dataset for validation was independent of dataset for model development. The first stand-density index showed a density management graphic (DMG) with a variable intercept and common instantaneous mortality rate, and the second index showed a DMG with common intercept and variable mortality rate. Additionally, the value of the initial density of the fitted line was more realistic than those generated by the potential model for all species combined. Moreover, the density management diagrams showed a curvilinear trend based on the maximum stand density index in graphical log–log scale. The DMGs could be interpreted as forest scenarios based on variable initial density and common management objectives or the same density and different management objectives for forest-rotation periods involving all species combined in mixed-species stands. The fitting of exponential and potential equations for species or species groups showed that the density–size relationships in mixed-species forests should be modeled for all species combined because the disaggregation of mixture species represented a weak tendency for each species or species group and the resultant fitted equations were unrealistic.

scholarly journals The Ratio of Additive and Traditional Stand Density Indices

2009 ◽  
Vol 24 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Mark J. Ducey
Keyword(s):  
Sampling Methods ◽  
Structural Complexity ◽  
Stand Density ◽  
Experimental Designs ◽  
Diameter Distribution ◽  
Efficient Sampling ◽  
Density Index ◽  
Density Indices ◽  
Stand Density Index

Abstract The ratio between additive and original versions of Reineke's stand density index (SDI) has been used as a descriptor of stand structural complexity. That ratio also can be informative for designing efficient sampling methods and for the design of silvicultural experiments. Previous analyses of this ratio have assumed a diameter distribution without truncation, such that trees from zero to infinite dbh are possible. Truncation of the diameter distribution, e.g., by tallying only trees larger than some minimum dbh, moves the ratio much closer to one when the stand has a classic balanced uneven-aged structure. Minimum values of the ratio are found not with classic reverse-J distributions, but with sharply bimodal distributions that might be typical of a two-cohort stand. The implications for the use of novel sampling methods and for experimental designs to test whether the additive or original SDI provides better prediction in irregular stands are discussed.

Role of species composition in relative density measurement in Allegheny hardwoods

1986 ◽  
Vol 16 (3) ◽  
pp. 574-579 ◽  
Author(s):  
Susan Laurane Stout ◽  
Ralph D. Nyland
Keyword(s):  
Species Composition ◽  
Forest Type ◽  
Density Measurement ◽  
Basal Area ◽  
Stand Density ◽  
Area Ratio ◽  
Index Model ◽  
Density Index ◽  
Species Groups ◽  
Stand Density Index

Two frequently used measures of relative stand density, the tree area ratio and the stand density index, were fitted to data from Allegheny hardwood stands. Both were modified to reflect the influence of species composition, since the proportion of the basal area in different species groups had an important effect on the measurement of relative stand density in this mixed species forest type. The tree area ratio model provided a better fit to the data than the stand density index model, particularly when adapted to include three species groups: one based on Prunusserotina Ehrh. and Liriodendrontulipifera L., one based on Acerrubrum L., and one based on Acersaccharum Marsh, and Fagusgrandifolia Ehrh.

Determining maximum stand density index in mixed species stands for strategic-scale stocking assessments

2005 ◽  
Vol 216 (1-3) ◽  
pp. 367-377 ◽  
Author(s):  
Chris W. Woodall ◽  
Patrick D. Miles ◽  
John S. Vissage
Keyword(s):  
Stand Density ◽  
Mixed Species ◽  
Density Index ◽  
Stand Density Index

scholarly journals Application of stand density indices for Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) plantation management

2018 ◽  
Vol 68 (1) ◽  
pp. 51-63
Author(s):  
Lele Lu ◽  
Bin Zhang ◽  
Jianguo Zhang ◽  
Aiguo Duan ◽  
Xiongqing Zhang
Keyword(s):  
Carrying Capacity ◽  
Initial Density ◽  
Basal Area ◽  
Stand Density ◽  
Chinese Fir ◽  
Cunninghamia Lanceolata ◽  
Likelihood Method ◽  
Nutrient Competition ◽  
Density Index ◽  
Stand Density Index

Abstract The most important issues in Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) management are the quantitative determination of stand density and the selection of appropriate density. Different stand density index models have advantages for special tree species, and this study aimed to estimate the carrying capacity of planted stands of Chinese fir and to select simple and reliable stand density indexes. Based on special experiment of different initial density, the maximum carrying capacity was estimated using Reineke’s self-thinning rule, Nilson’s sparsity theory, Beekhuis’s relative-spacing hypothesis, Zhang’s nutrient-competition rule, Curtis’s maximum stand basal area and Hui’s crowding degree based on mean tree distance. The restricted maximum likelihood method (REML) implemented with ‘nlme’ package in R software was used to refine the parameters of thinning age in Richard’s growth model. The results showed that stand density index models can describe the trends of stand density in response to tree growth: the higher the plantation initial density, the earlier age and stronger self-thinning capacity of stands. Reineke’s SDI and Zhang’s Z model are the most stable and suitable to estimate changes in the density of Chinese fir plantations, competition intensification, and the thinning age. The RD model can also be used, except at low Chinese fir densities. K, which can be affected by the mean crown width of trees, directly reflects the forest area of photosynthesis; this index is easy and simple to apply, but more research is needed to optimize the equation to evaluate whether a forest requires management and to determine the appropriate time for the first thinning and its intensity.

scholarly journals Individuals from different-looking animal species may group together to confuse shared predators: simulations with artificial neural networks

2006 ◽  
Vol 274 (1611) ◽  
pp. 827-832 ◽  
Author(s):  
Colin R Tosh ◽  
Andrew L Jackson ◽  
Graeme D Ruxton
Keyword(s):  
Animal Species ◽  
Empirical Work ◽  
Species Group ◽  
Mixed Species ◽  
Retinotopic Maps ◽  
Species Groups ◽  
Artificial Neural ◽  
Mixed Species Groups ◽  
Species Grouping ◽  
Prey Items

Individuals of many quite distantly related animal species find each other attractive and stay together for long periods in groups. We present a mechanism for mixed-species grouping in which individuals from different-looking prey species come together because the appearance of the mixed-species group is visually confusing to shared predators. Using an artificial neural network model of retinotopic mapping in predators, we train networks on random projections of single- and mixed-species prey groups and then test the ability of networks to reconstruct individual prey items from mixed-species groups in a retinotopic map. Over the majority of parameter space, cryptic prey items benefit from association with conspicuous prey because this particular visual combination worsens predator targeting of cryptic individuals. However, this benefit is not mutual as conspicuous prey tends to be targeted most poorly when in same-species groups. Many real mixed-species groups show the asymmetry in willingness to initiate and maintain the relationship predicted by our study. The agreement of model predictions with published empirical work, the efficacy of our modelling approach in previous studies, and the taxonomic ubiquity of retinotopic maps indicate that we may have uncovered an important, generic selective agent in the evolution of mixed-species grouping.

scholarly journals ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS

2016 ◽  
Vol 40 (5) ◽  
pp. 921-929 ◽  
Author(s):  
Ernani Lopes Possato ◽  
Natalino Calegario ◽  
Gilciano Saraiva Nogueira ◽  
Elliezer de Almeida Melo ◽  
Joyce de Almeida Alves
Keyword(s):  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Genetic Material ◽  
Stand Density ◽  
Model Parameters ◽  
Eucalyptus Urophylla ◽  
Frontier Analysis ◽  
Density Index ◽  
Number Of Stems ◽  
Stand Density Index

ABSTRACT The Reineke stand density index (SDI) was created on 1933 and remains as target of researches due to its importance on helping decision making regarding the management of population density. Part of such works is focused on the manner by which plots were selected and methods for the fit of Reineke model parameters in order to improve the definition of SDI value for the genetic material evaluated. The present study aimed to estimate the SDI value for Eucalyptus urophylla using the Reineke model fitted by the method of linear regression (LR) and stochastic frontier analysis (SFA). The database containing pairs of data number of stems per hectare (N) and mean quadratic diameter (Dq) was selected in three intensities, containing the 8, 30 and 43 plots of greatest density, and models were fitted by LR and SFA on each selected intensities. The intensity of data selection altered slightly the estimates of parameters and SDI when comparing the fits of each method. On the other hand, the adjust method influenced the mean estimated values of slope and SDI, which corresponded to -1.863 and 740 for LR and -1.582 and 810 for SFA.

scholarly journals Use of Modified Reineke’s Stand Density Index in Predicting Growth and Survival of Chinese Fir Plantations

2019 ◽  
Vol 65 (6) ◽  
pp. 776-783 ◽  
Author(s):  
Xiongqing Zhang ◽  
Quang V Cao ◽  
Lele Lu ◽  
Hanchen Wang ◽  
Aiguo Duan ◽  
...  
Keyword(s):  
Predictor Variable ◽  
Basal Area ◽  
Stand Density ◽  
Chinese Fir ◽  
The Self ◽  
Survival Models ◽  
Future Climate Change ◽  
Growth And Survival ◽  
Density Index ◽  
Stand Density Index

Abstract Stand density index (SDI) has played an important role in controlling stand stocking and modeling stand development in forest stands. Reineke’s SDI (SDI_R) is based on a constant slope of –1.605 for the self-thinning line. For Chinese fir plantations, however, it has been reported that the self-thinning slope varied with site and climate, rendering SDI_R questionable. Remeasured data from 48 plots distributed in Fujian, Jiangxi, Guangxi, and Sichuan provinces were used to develop models for prediction of stand survival and basal area, with SDI_R incorporated as a predictor variable. Also included in the evaluation were growth models based on self-thinning slopes estimated from two groups of sites (SDI_S) or from climate variables (SDI_C). Results indicated that models with climate-sensitive SDI (SDI_C) performed best, followed by SDI_S and SDI_R. The control models without SDI received the worst overall rank. Inclusion of climate-sensitive SDI in growth and survival models can therefore facilitate modeling of the relation between stand density and growth/survival under future climate-change conditions.

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