scholarly journals Growth, proportion, and distribution pattern of longleaf pine across southeastern forests and disturbance types: A change assessment for the period 1997-2018

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245218
Author(s):  
Santosh K. Ojha ◽  
Luben D. Dimov ◽  
Wubishet Tadesse
Keyword(s):  
Aboveground Biomass ◽  
Interpolation Method ◽  
Longleaf Pine ◽  
Basal Area ◽  
Area Ratio ◽  
Fire Disturbance ◽  
Importance Value ◽  
Basal Area Growth ◽  
Area Growth ◽  
Biomass Ratio

The long-term decline of longleaf pine-dominated forests has received considerable attention among land managers and conservation professionals in the last few decades. The objective of this study was to investigate the change in and the variation of the proportion, density, growth, and dominance of longleaf pine across the longleaf pine ecosystems for the 1997–2018 period. We used two sets of measurements of 1,432 plots from the Forest Inventory and Analysis (FIA) dataset covering the entire current longleaf pine range. The relationship between disturbances and longleaf pine basal area ratio and basal area growth were analyzed using linear mixed modeling. Change detection maps were produced using the Inverse Distance Weighted (IDW) interpolation method. The total basal area and aboveground biomass per hectare increased in 64% and 72%, but decreased in 30% and 28% of the study area, respectively, between the first and last inventory intervals. Species richness and diversity generally decreased across the studied plots. Longleaf pine tree density and importance value percent increased during the period. However, longleaf basal area ratio and aboveground biomass ratio in the stands decreased on average by 5% during the period, although these ratios increased in some locations in southwest Georgia and near the west coast of Florida. The longleaf pine basal area ratio and aboveground biomass ratio decreased equally in 37%, and increased in 19% and 21% of the study area, respectively. There was about 79% variation in the ratio of longleaf pine basal area among plots. When compared to the natural control of no disturbance, fire disturbance was significantly associated with greater longleaf pine basal area ratio and basal area growth. Understanding the change in growth and distribution patterns of longleaf pine across its range over time is vital to restore these critical ecosystems.

Crown and basal area relationships of open-grown southern pines for modeling competition and growth

1992 ◽  
Vol 22 (3) ◽  
pp. 341-347 ◽  
Author(s):  
W.R. Smith ◽  
R.M. Farrar Jr. ◽  
P.A. Murphy ◽  
J.L. Yeiser ◽  
R.S. Meldahl ◽  
...  
Keyword(s):  
Loblolly Pine ◽  
Longleaf Pine ◽  
Basal Area ◽  
Growth And Yield ◽  
Predictive Equations ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Crown Width ◽  
Basal Area Growth ◽  
Area Growth

Data were collected on open-grown loblolly pine (Pinustaeda L.), longleaf pine (Pinuspalustris Mill.), and shortleaf pine (Pinusechinata Mill.) and analyzed to provide predictive equations of crown width and maximum potential basal area growth for crown competition and growth and yield models. The measurements were taken on 115 open-grown loblolly pine trees and 76 shortleaf pines in southeastern Arkansas. The longleaf pine data consisted of 81 open-grown trees from southern Alabama, Georgia, and Florida. A circle and an ellipse were tested as geometric models of the vertically projected crown. No significant differences between the tree shapes were found based on analyses of length and azimuth of the largest crown diameter, and the circle was chosen as an appropriate model. This indicated that only the distance between trees, not their orientation to one another, need be included in models of crown competition based on crown contact. Predictive equations of mean crown width based on diameter at breast height were fitted for each species for use in models of crown competition. A Chapman–Richards growth rate function with an intercept term was fit to periodic annual inside-bark basal area growth based on initial inside-bark basal area to provide empirical estimates of maximum basal area growth rates for growth and yield modeling of the given species. Additionally, equations to predict double bark thickness as a function of diameter at breast height were fit for each species to facilitate the use of the equations with outside-bark measurements of diameter.

Field Notes: Predicted Vs. Actual Basal Area Growth in West Virginia

1988 ◽  
Vol 5 (3) ◽  
pp. 221-222
Author(s):  
Arlyn W. Perkey ◽  
Kenneth L. Carvell
Keyword(s):  
West Virginia ◽  
Basal Area ◽  
Basal Area Growth ◽  
Area Growth ◽  
Field Notes

A New Index Representing Individual Tree Competitive Status

1973 ◽  
Vol 3 (4) ◽  
pp. 495-500 ◽  
Author(s):  
James A. Moore ◽  
Carl A. Budelsky ◽  
Richard C. Schlesinger
Keyword(s):  
Spatial Distribution ◽  
Strong Correlation ◽  
Basal Area ◽  
Tree Size ◽  
Competition Index ◽  
Individual Tree ◽  
Silvicultural Practices ◽  
Basal Area Growth ◽  
Hardwood Species ◽  
Area Growth

A new competition index, modified Area Potentially Available (APA), was tested in a complex unevenaged stand composed of 19 different hardwood species. APA considers tree size, spatial distribution, and distance relationships in quantifying intertree competition and exhibits a strong correlation with individual tree basal area growth. The most important characteristic of APA is its potential for evaluating silvicultural practices.

scholarly journals Regional Variability of the Romanian Main Tree Species Growth Using National Forest Inventory Increment Cores

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 409
Author(s):  
Gheorghe Marin ◽  
Vlad C. Strimbu ◽  
Ioan V. Abrudan ◽  
Bogdan M. Strimbu
Keyword(s):  
Mixed Models ◽  
Forest Inventory ◽  
Basal Area ◽  
National Forest Inventory ◽  
Forest Growth ◽  
National Forest ◽  
Sessile Oak ◽  
Basal Area Growth ◽  
Temporal Grouping ◽  
Area Growth

In many countries, National Forest Inventory (NFI) data is used to assess the variability of forest growth across the country. The identification of areas with similar growths provides the foundation for development of regional models. The objective of the present study is to identify areas with similar diameter and basal area growth using increment cores acquired by the NFI for the three main Romanian species: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl.). We used 6536 increment cores with ages less than 100 years, a total of 427,635 rings. The country was divided in 21 non-overlapping ecoregions based on geomorphology, soil, geology and spatial contiguousness. Mixed models and multivariate analyses were used to assess the differences in annual dimeter at breast height and basal area growth among ecoregions. Irrespective of the species, the mixed models analysis revealed significant differences in growth between the ecoregions. However, some ecoregions were similar in terms of growth and could be aggregated. Multivariate analysis reinforced the difference between ecoregions and showed no temporal grouping for spruce and beech. Sessile oak growth was separated not only by ecoregions, but also by time, with some ecoregions being more prone to draught. Our study showed that countries of median size, such as Romania, could exhibit significant spatial differences in forest growth. Therefore, countrywide growth models incorporate too much variability to be considered operationally feasible. Furthermore, it is difficult to justify the current growth and yield models as a legal binding planning tool.

scholarly journals An Individual-Tree Growth and Yield Prediction System for Uneven-Aged Shortleaf Pine Stands

2000 ◽  
Vol 24 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Michael M. Huebschmann ◽  
Lawrence R. Gering ◽  
Thomas B. Lynch ◽  
Onesphore Bitoki ◽  
Paul A. Murphy
Keyword(s):  
Basal Area ◽  
Growth Patterns ◽  
Growth And Yield ◽  
Prediction System ◽  
Pinus Echinata ◽  
Shortleaf Pine ◽  
Individual Tree ◽  
Basal Area Growth ◽  
Area Growth ◽  
Pine Stands

Abstract A system of equations modeling the growth and development of uneven-aged shortleaf pine (Pinus echinata Mill.) stands is described. The prediction system consists of two main components: (1) a distance-independent, individual-tree simulator containing equations that forecast ingrowth, basal-area growth, probability of survival, total and merchantable heights, and total and merchantable volumes and weights of shortleaf pine trees; and (2) stand-level equations that predict hardwood ingrowth, basal-area growth, and mortality. These equations were combined into a computer simulation program that forecasts future states of uneven-aged shortleaf pine stands. Based on comparisons of observed and predicted stand conditions in shortleaf pine permanent forest inventory plots and examination of the growth patterns of hypothetical stands, the simulator makes acceptable forecasts of stand attributes. South. J. Appl. For. 24(2):112-120.

Long-term patterns of diameter and basal area growth of old-growth Douglas-fir trees in western Oregon

2002 ◽  
Vol 32 (7) ◽  
pp. 1232-1243 ◽  
Author(s):  
Nathan J Poage ◽  
John C Tappeiner, II
Keyword(s):  
Large Diameter ◽  
Basal Area ◽  
Douglas Fir ◽  
Slope Aspect ◽  
Site Factors ◽  
Old Growth ◽  
Site Class ◽  
Basal Area Growth ◽  
Area Growth ◽  
Age Range

Diameter growth and age data collected from stumps of 505 recently cut old-growth Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees at 28 sample locations in western Oregon (U.S.A.) indicated that rapid early and sustained growth of old Douglas-fir trees were extremely important in terms of attaining large diameters at ages 100–300 years. The diameters of the trees at ages 100–300 years (D100–D300) were strongly, positively, and linearly related to their diameters and basal area growth rates at age 50 years. Average periodic basal area increments (PAIBA) of all trees increased for the first 30–40 years and then plateaued, remaining relatively high and constant from age 50 to 300 years. Average PAIBA of the largest trees at ages 100–300 years were significantly greater by age 20 years than were those of smaller trees at ages 100–300 years. The site factors province, site class, slope, aspect, elevation, and establishment year accounted for little of the variation observed in basal area growth at age 50 years and D100–D300. The mean age range for old-growth Douglas-fir at the sample locations was wide (174 years). The hypothesis that large-diameter old-growth Douglas-fir developed at low stand densities was supported by these observations.

Individual-tree basal area growth models for jack pine and black spruce in northern Ontario

2004 ◽  
Vol 80 (3) ◽  
pp. 366-374 ◽  
Author(s):  
Lianjun Zhang ◽  
Changhui Peng ◽  
Qinglai Dang
Keyword(s):  
Tree Growth ◽  
Black Spruce ◽  
Basal Area ◽  
Jack Pine ◽  
Mixed Stands ◽  
Northern Ontario ◽  
Individual Tree ◽  
Basal Area Growth ◽  
Wide Range ◽  
Area Growth

Individual-tree models of five-year basal area growth were developed for jack pine (Pinus banksiana Lamb.) and black spruce (Picea mariana (Mill.) BSP) in northern Ontario. Tree growth data were collected from long-term permanent plots of pure and mixed stands of the two species. The models were fitted using mixed model methods due to correlated remeasurements of tree growth over time. Since the data covered a wide range of stand ages, stand conditions and tree sizes, serious heterogeneous variances existed in the data. Therefore, the coefficients of the final models were obtained using weighted regression techniques. The models for the two species were evaluated across 4-cm diameter classes using independent data. The results indicated (1) the models of jack pine and black spruce produced similar prediction errors and biases for intermediate-sized trees (12–28 cm in tree diameter), (2) both models yielded relatively large errors and biases for larger trees (> 28 cm) than those for smaller trees, and (3) the jack pine model produced much larger errors and biases for small-sized trees (< 12 cm) than did the black spruce model. Key words: mixed models, repeated measures, model validation

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