Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and he...Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.展开更多
Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and compl...Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.展开更多
Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year....Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year.Using resistant genotypes has proven a successful strategy to limit the disease,but resistance selection still relies on visual inspection for symptoms,which can lead to misclassification due to human error and the presence of'escaped susceptibles'(i.e.,susceptible individuals with no visible symptoms due to either an extended asymp-tomatic phase of the disease or the lack of adequate disease pressure to become infected).Here,we propose the use of near-infrared(NIR)spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated.We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S.using a portable,handheld NIR spectrometer.We also used a benchtop Fourier-transformed mid-infrared(FT-IR)spectrometer to analyze phloem phenolic extracts of the same samples,as this phenotyping approach has proved successful in other pathosystems.Our results show a moderate association between the phloem spectra and resistance,and models built with NIR spectra were able to classify extremes(i.e.,very resistant or very susceptible)with up to 69%testing accuracy.This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.展开更多
基金the Natural Sciences and Engineering Research Council of Canada(Discovery Grant RGPIN-2023-05879)the New Brunswick Innovation Foundation(Emerging Projects Grant EP-0000000033)。
文摘Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.
文摘Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.
基金This research was funded by the United States Forest Service,Forest Health Protection Special Technology Development Program(grant number 20-DG-11083150-003)the Southern Pine Health Research Cooperative(SPHRC)at the University of Georgia(Athens,Georgia,United States).
文摘Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year.Using resistant genotypes has proven a successful strategy to limit the disease,but resistance selection still relies on visual inspection for symptoms,which can lead to misclassification due to human error and the presence of'escaped susceptibles'(i.e.,susceptible individuals with no visible symptoms due to either an extended asymp-tomatic phase of the disease or the lack of adequate disease pressure to become infected).Here,we propose the use of near-infrared(NIR)spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated.We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S.using a portable,handheld NIR spectrometer.We also used a benchtop Fourier-transformed mid-infrared(FT-IR)spectrometer to analyze phloem phenolic extracts of the same samples,as this phenotyping approach has proved successful in other pathosystems.Our results show a moderate association between the phloem spectra and resistance,and models built with NIR spectra were able to classify extremes(i.e.,very resistant or very susceptible)with up to 69%testing accuracy.This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.
