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

Woodall, Christopher W.; Miles, Patrick D.; Vissage, John S.

doi:10.1016/j.foreco.2005.05.050

Cited by 137 publications

(129 citation statements)

References 22 publications

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“…The relative density of the plots was calculated by dividing the current stand density index (SDI) by the maximum SDI (Woodall et al, 2005). SDI is based on tree density and size for fully stocked pure stands (Reineke, 1933).…”

Section: Estimation Of Relative Densitymentioning

confidence: 99%

“…To estimate stand specific maximum SDI of mixed species stands, we used a published regression equation (Woodall et al, 2005) based on wood specific gravity (Miles & Smith, 2009) of each tree species in the stand. Relative density was used to characterize comparative stocking level of each plot and its values range from 0 to 1 representing understocked to overstocked plots.…”

Section: Estimation Of Relative Densitymentioning

confidence: 99%

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Linking hypothesized causal factors to aboveground biomass growth in forests of Alabama and the eastern United States

Ojha

Dimov

2018

For. syst.

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Aim of study: We analyzed the hypothesized causal effects of relative density, density, height, species richness, species diversity, temperature, precipitation, and slope on above ground biomass growth (AGBG).Area of study: Eastern region of the USA. Materials and methods:We used the USDA Forest Service's Forest Inventory and Analysis (FIA) database. A total of 2554 plots from all stand ages, regardless of disturbance history, were selected from the state of Alabama and 967 plots of stand age under 30 years and no prior disturbance were selected from the eastern US. We analyzed the data using descriptive statistics and structural equation modeling.Main results: Relative stand density exhibited a strong positive direct effect on AGBG, especially in the young forests (path coefficient 0.79), but a weaker indirect effect through species richness/diversity. Tree height influenced positively AGBG directly and indirectly through relative density and species richness. The effect of temperature and slope was greater than the effect of species richness/diversity on AGBG in the young forests of the eastern US.Research highlights: For the forests of the eastern US, greater tree species diversity did not appear to result in neither greater nor lower productivity. The diversity-productivity relationship was negative in forests of Alabama, however, where prior management likely resulted in removal of select dominant trees from valuable species (i.e., high-grading).Additional keywords: FIA; productivity; path analysis; relative stand density; species richness; Shannon's diversity index; temperature.Abbreviations used: AGB (above ground biomass); AGBG (above ground biomass growth); CCR (compacted crown ratio); DBH (diameter at breast height); IVP (importance value percent); FIA (Forest Inventory and Analysis); SDI (stand density index); SEM (structural equation modeling).Authors´ contributions: LDD and SKO conceived and designed the research. Both authors read and approved the final manuscript. SKO acquired national forest inventory data from the USDA, Forest Service, Forest Inventory and Analysis (FIA), Knoxville Tennessee for the study. SKO extracted climatic variables from spatial data sources; he conducted statistical analysis and made figures and tables and prepared the first draft of the manuscript. LDD contributed to all parts and drafts of the manuscript.Data acquisition and accuracy of analysis: The national forest inventory dataset was obtained with the help of the USDA Forest Service, Forest Inventory and Analysis (FIA) personnel. FIA data is publicly available at FIA DataMart (https://apps.fs.usda.gov/fia/ datamart/CSV/datamart_csv.html).

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Section: Estimation Of Relative Densitymentioning

confidence: 99%

Section: Estimation Of Relative Densitymentioning

confidence: 99%

Linking hypothesized causal factors to aboveground biomass growth in forests of Alabama and the eastern United States

Ojha

Dimov

2018

For. syst.

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“…Dean and Baldwin (1996) introduced the idea that that maximum SDI is negatively correlated with specific gravity for conifers in single-species forests, so mechanistically a site can be occupied by a maximum density of a species based on the wood properties of the species. Woodall et al (2005) extended the idea to mixed forests by using an average wood density for the mixture. More recently, Ducey & Knapp (2010) modif ied the approach to correspond more closely with the additive SDI for single-species uneven-aged stands developed by Long & Daniel (1990).…”

Section: Conversion Of Monocultures and Stand Density In Mixed-speciementioning

confidence: 99%

European Mixed Forests: definition and research perspectives

et al. 2014

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Aim of study: We aim at (i) developing a reference definition of mixed forests in order to harmonize comparative research in mixed forests and (ii) review the research perspectives in mixed forests.Area of study: The definition is developed in Europe but can be tested worldwide.Material and Methods: Review of existent definitions of mixed forests based and literature review encompassing dynamics, management and economic valuation of mixed forests.Main results: A mixed forest is defined as a forest unit, excluding linear formations, where at least two tree species coexist at any developmental stage, sharing common resources (light, water, and/or soil nutrients). The presence of each of the component species is normally quantified as a proportion of the number of stems or of basal area, although volume, biomass or canopy cover as well as proportions by occupied stand area may be used for specific objectives. A variety of structures and patterns of mixtures can occur, and the interactions between the component species and their relative proportions may change over time.The research perspectives identified are (i) species interactions and responses to hazards, (ii) the concept of maximum density in mixed forests, (iii) conversion of monocultures to mixed-species forest and (iv) economic valuation of ecosystem services provided by mixed forests.Research highlights: The definition is considered a high-level one which encompasses previous attempts to define mixed forests. Current fields of research indicate that gradient studies, experimental design approaches, and model simulations are key topics providing new research opportunities.Keywords: COST Action; EuMIXFOR; mixed-species forests; admixtures of species.

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“…In fact, Jacob et al (2010) found no over-or under-yielding effects within stands differing in tree species diversity. Accordingly, no general patterns of maximum stand density of mixed stands versus pure stands were found by Woodall et al (2005). Unfortunately, the results on the effect of mixtures on stand stability are even more inconsistent than for productivity.…”

Section: Discussionmentioning

confidence: 94%

How to quantify forest management intensity in Central European forests

2013

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Existing approaches for the assessment of forest management intensity lack a widely accepted, purely quantitative measure for ranking a set of forest stands along a gradient of management intensity. We have developed a silvicultural management intensity indicator (SMI) which combines three main characteristics of a given stand: tree species, stand age and aboveground, living and dead wooden biomass. Data on these three factors are used as input to represent the risk of stand loss, which is a function of tree species and stand age, and stand density, which is a function of the silvicultural regime, stand age and tree species. Consequently, the indicator consists of a risk component (SMI r ) and a density component (SMI d ). We used SMI to rank traditional management of the main Central European tree species: Norway spruce (Picea abies [Karst.] L.), European beech (Fagus sylvatica L.), Scots pine (Pinus sylvestris L.), and oak (Quercus robur L. and Quercus petraea L.). By analysing SMI over their whole rotation period, we found the following ranking of management intensity: oak\beech\pine(spruce. Additionally, we quantified the SMI of actual research plots of the German Biodiversity exploratories, which represent unmanaged and managed forest stands including conifer forests cultivated outside their natural range. SMI not only successfully separate managed from unmanaged forests, but also reflected the variability of forest management and stand properties across the entire sample and within the different management groups. We suggest using SMI to quantify silvicultual management intensity of stands differing in species composition, age, silvicultural system (even-aged vs. uneven-aged), thinning grade and stages of stand conversion from one stand type into another. Using SMI may facilitate the assessment of the impact of forest management intensity on biodiversity in temperate forests.

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

Cited by 137 publications

References 22 publications

Linking hypothesized causal factors to aboveground biomass growth in forests of Alabama and the eastern United States

Linking hypothesized causal factors to aboveground biomass growth in forests of Alabama and the eastern United States

European Mixed Forests: definition and research perspectives

How to quantify forest management intensity in Central European forests

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