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Measuring and modelling the shape of tree stems is a fundamental component of forest inventory systems for both commercial and biological purposes. The change in diameter of the stem along its length (a.k.a. 'taper') is one of the most important and widely used means of predicting tree stem volume. Until recently, the options for obtaining accurate estimates of stem taper and developing stem taper models have been limited to measurements of felled trees or the use of optical dendrometers on standing live trees. Here, we tested both a tripod-mounted terrestrial laser scanner (TLS; a Focus 3D 120 of FARO Technologies, Inc., Lake Mary, FL, USA), and a mobile laser scanner (MLS; the ZEB1 of the GeoSLAM Ltd, Nottingham, UK) to measure tree diameters at various heights along the stem of 20 destructively harvested broadleaf and needleleaf species using the outer hull modelling method, for the purpose of developing individual-tree and species-specific taper models. Laser scanner specifications were a major factor determining stem taper measurement accuracy. The longer-range, low beam divergence TLS could estimate stem diameter to an average of 15.7 m above ground (about 79 per cent of the canopy height), while the shorter-range high beam divergence MLS could estimate an average of 11.5 m above ground (about 45 per cent of the canopy height). Stem taper error increased with respect to height above ground, with the TLS providing more consistent and reliable diameter measurements (root mean square error (RMSE) = 1.93 cm; 9.57 per cent) compared with the MLS (RMSE = 2.59 cm; 12.84 per cent), but both methods were nearly unbiased. We attribute ~60 per cent of the uncertainty in stem measurements to laser beam diameter and point density, showing positive and negative correlations, respectively. MLS was unable to converge on the two tested taper models but was found to be an efficient means of easily sampling diameters at breast height (DBH) and reconstructing stem maps in simple forest stands with trees greater than ~10 cm DBH. TLS provided precision stem diameter measurements that allowed for the creation of similar taper models for three out of the four study species. Future work should focus on evaluating MLS systems with improved specifications (e.g. beam divergence and range), since these instruments will likely lead to dramatic improvements in reliable estimates of forest inventory parameters, in line with the current TLS technology.

Changing forest disturbance regimes and climate are driving accelerated tree mortality across temperate forests. However, it remains unknown if elevated mortality has induced decline of tree populations and the ecological, economic, and social benefits they provide. Here, we develop a standardized forest demographic index and use it to quantify trends in tree population dynamics over the last two decades in the western United States. The rate and pattern of change we observe across species and tree size-distributions is alarming and often undesirable. We observe significant population decline in a majority of species examined, show decline was particularly severe, albeit size-dependent, among subalpine tree species, and provide evidence of widespread shifts in the size-structure of montane forests. Our findings offer a stark warning of changing forest composition and structure across the western US, and suggest that sustained anthropogenic and natural stress will likely result in broad-scale transformation of temperate forests globally., The nature of forest disturbances are changing, yet consequences for forest dynamics remain uncertain. Using a new index, Stanke et al. show the populations of over half of the most abundant tree species in the western US have declined in the last two decades, with grim implications for how temperate forests globally will respond to sustained anthropogenic and natural stress.

‘Analog’ forestry is a novel silvicultural approach for reforestation of degraded land that seeks to establish trees that are analogous in structure and ecological function to the original climax or subclimax vegetation of the region where they are planted. Analog forestry aims to strengthen rural communities, both socially and economically, with tree plantings of high biodiversity, which provide commercial products and resiliency, while avoiding agrochemicals and fossil fuels. The latter links this strategy to climate change mitigation and adaptation strategies, but there has been little analysis comparing the potential for above- and below-ground carbon storage in analog forests, as compared with more typical reforestation efforts via single-species plantations. We present the results of a study to estimate carbon sequestration levels, above- and below-ground, from an experimental analog forest system and a teak (Tectona grandis L.f.) plantation system, in comparison with a degraded pasture system. The study found that the analog and teak plantation forest systems stored higher quantities of total carbon (178 and 141 t C ha−1, respectively), when compared with a degraded pasture system (124 t C ha−1). However, the teak plantation had decreased soil carbon stocks, relative to the degraded pasture. The analog forest had the best balance of both above- and below-ground carbon stored over the 17-year study period and provided a more diverse array of timber and non-timber forest products when compared with the teak plantation. This suggests that analog forestry could be a viable social–ecological approach to carbon storage and reforestation in the study region and other places with large areas of degraded pasture and a good understanding of the structure and function of the original vegetation.

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available. USDA Forest Service Forest Inventory and Analysis Program; National Institute of Food and Agriculture Published version USDA Forest Service Forest Inventory and Analysis Program; National Institute of Food and Agriculture Public domain – authored by a U.S. government employee

Made available in DSpace on 2020-12-10T19:47:50Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-10-01 The aim of this study was to explore methods to: (1) enhance coarse-scale estimates of wood volume from National Forest Inventories (NFIs) data and (2) map them at finer scales. The 'standard' coarse-scale estimation extrapolates wood volume from clusters to the grid cell they represent, using the cluster's represented forested area (RFA) to predict the cell's forested area. Data from a subset of Brazil's NFI clusters were combined with Landsat-8 imagery to explore a new coarse-scale method, where forested area derived from image classification (FADIC) is used instead of RFA. The RFA- and FADIC-derived estimates of total volume were, respectively, 197.4 million m(3) and 116.3 million m(3). For fine-scale methods, volume was estimated and mapped at pixel level using: (i) surface reflectance-based models (SRMs), and (ii) regression-kriging (RK) and a RK model (RKM) whose inputs were latitude and longitude of pixels. The SRM-based method captured the mean and the general spatial trend of the volume well. The RK-based method also estimated the mean well, but it failed to predict higher and lower volumes. The SRM- and RK-based estimates of total volume were 211.7 million m(3) and 203.3 million m(3), an overestimate of 7 per cent and 3 per cent, respectively, of the 'standard' NFI estimate (197.4 million m(3)), though both estimates were within the 95 per cent confidence interval, meaning that both fine-scale methods yield total volume statistically similar to the 'standard' coarse-scale method. Rural Fed Univ Amazonia, Dept Forestry, Presidente Tancredo Neves Ave 2501, Belem, PA, Brazil Michigan State Univ, Dept Forestry, 480 Wilson Rd, E Lansing, MI 48824 USA Univ Fed Parana, Dept Forestry, Pref Lothario Meissner Ave 900, BR-80210170 Curitiba, PR, Brazil Fed Univ South Bahia, Dept Sci & Agroforestry Technol, Highway 415,Km 22, BR-45653919 Ilheus, BA, Brazil Embrapa Forests, Rd Ribeira,Km 111, BR-83411000 Colombo, PR, Brazil Mato Grosso State Univ, Dept Forestry, Perimetral Deputado Rogerio Silva Ave, BR-78580000 Alta Floresta, MT, Brazil State Univ Sao Paulo, Dept Cartog, Roberto Simonsen St 305, BR-19060900 Presidente Prudente, SP, Brazil State Univ Sao Paulo, Dept Cartog, Roberto Simonsen St 305, BR-19060900 Presidente Prudente, SP, Brazil

Wind is a major force of disturbance in forests throughout the world, causing trees to break or uproot and topple over, depending on tree morphology and growing conditions. Previous research has suggested that trees that are more massive or which have lower height–diameter ratios are less at risk to wind throw, but the influence of crown size and branching architecture has been more difficult to elucidate. In general, there has been more research on coniferous trees, which have more simplified and symmetrical crown structures, relative to broad-leaved, deciduous trees. Here, we modelled the probability of wind throw for American beech (Fagus grandifolia Ehrh.), a broad-leaved, deciduous species common in the eastern USA, for trees covering a broad range of sizes (5.6–79.8 cm, diameter at breast height), in two nearby stands, in an experimental forest in southwestern Michigan, USA, where a wind event occurred. After a major blow-down of more than 200 trees in one of the stands, a subset of trees was destructively sampled after the storm. Analysis of the characteristics of the wind-thrown versus non-wind-thrown, residual trees, indicated that a very large branch fraction of total tree mass was a key factor leading to toppling of the wind-thrown trees. This study provides new empirical data related to the characteristics of wind-thrown American beech trees and suggests a potentially important role for branch to stem mass ratios as a predictor of wind throw risk for this species and possibly other broad-leaved, deciduous tree species, with a deliquescent branching architecture.

The evolution of form and function of trees of diverse species has taken place over hundreds of millions of years, while urban environments are relatively new on an evolutionary time scale, representing a novel set of environmental constraints for trees to respond to. It is important to understand how trees of different species, planted in these anthropogenically-structured urban ecosystems, are responding to them. Many theories have been advanced to understand tree form and function, including several that suggest the fractal-like geometry of trees is a direct reflection of inherent and plastic morphological and physiological traits that govern tree growth and survival. In this research, we analyzed the "fractal dimension" of thousands of tree crowns of many different tree species, growing in different urban environments across the United States, to learn more about the nature of trees and their responses to urban environments at different scales. Our results provide new insights regarding how tree crown fractal dimension relates to balances between hydraulic- and light-capture-related functions (e.g., drought and shade tolerance). Our findings indicate that trees exhibit reduced crown fractal dimension primarily to reduce water loss in hotter cities. More specifically, the intrinsic drought tolerance of the studied species arises from lower surface to volume ratios at both whole-crown and leaf scales, preadapting them to drought stress in urban ecosystems. Needle-leaved species showed a clear trade-off between optimizing the fractal dimension of their crowns for drought vs. shade tolerance. Broad-leaved species showed a fractal crown architecture that responded principally to inherent drought tolerance. Adjusting for the temperature of cities and intrinsic species effects, the fractal dimension of tree crowns was lower in more heavily urbanized areas (with greater paved area or buildings) and due to crowns conflicting with utility wires. With expectations for more urbanization and generally hotter future climates, worldwide, our results add new insights into the physiological ecology of trees in urban environments, which may help humans to provide more hospitable habitats for trees in urbanized areas and to make better decisions about tree selection in urban forest management.

The occurrence (probability) and extent (proportion) of tree internal stem decay are important attributes influencing potential wood quality and value, but variation in decay by species, tree size and geographic range are rarely evaluated and modelled. In this analysis, we used 1246 destructively sampled trees across 33 species in the northeastern United States and New Brunswick, Canada to determine the factors influencing the combined probability and proportion of decay. In the process, we evaluated three modelling approaches including a two-part conditional model, multinomial model and generalized additive model for location, scale and shape (gamlss) that simultaneously predicted both probability and proportion of decay. Predictive capability for all three methods were nearly identical when classifying decay occurrence. Compared with the other methods, the gamlss model had a lower mean bias and root mean square error (RMSE) when predicting decay extent. Tree diameter to height ratio (ratio of diameter at breast height to total height), height, crown ratio, species tolerance to flooding and drought, leaf longevity, and an assessment of perceived tree risk of mortality (risk class) were selected as predictors in the best overall model for decay occurrence. For predicting decay extent, the best model included risk class, crown ratio and the last freezing date of spring. Further analysis identified significant species differences, which we used to develop functional species groupings based on decay occurrence and extent. Despite these observed relationships, a high degree of unexplained variation remained, highlighting the challenges of modelling decay in trees of different species across a range of growing environments.

Urban forests are part of the global forest network, providing important benefits to human societies. Advances in remote-sensing technology can create detailed 3D images of trees, giving novel insights into tree structure and function. We used terrestrial laser scanning and quantitative structural models to provide comprehensive characterizations of the woody surface area allometry of urban trees and relate them to urban tree anatomy, physiology, and structural complexity. Fifty-six trees of three species (Gleditsia triacanthos L., Quercus macrocarpa Michx., Metasequoia glyptostroboides Hu & W.C. Cheng) were sampled on the Michigan State University campus. Variations in surface area allocation to non-photosynthesizing components (main stem, branches) are related to the fractal dimension of tree architecture, in terms of structural complexity (box-dimension metric) and the distribution of “path” lengths from the tree base to every branch tip. The total woody surface area increased with the box-dimension metric, but it was most strongly correlated with the 25th percentile of path lengths. These urban trees mainly allocated the woody surface area to branches, which changed with branch order, branch-base diameter, and branch-base height. The branch-to-stem area ratio differed among species and increased with the box-dimension metric. Finally, the woody surface area increased with the crown surface area of the study trees across all species combined and within each species. The results of this study provide novel data and new insights into the surface area properties of urban tree species and the links with structural complexity and constraints on tree morphology.

Trees have a fractal-like branching architecture that determines their structural complexity. We used terrestrial laser scanning technology to study the role of foliage in the structural complexity of urban trees. Forty-five trees of three deciduous species, Gleditsia triacanthos, Quercus macrocarpa, Metasequoia glyptostroboides, were sampled on the Michigan State University campus. We studied their structural complexity by calculating the box-dimension (Db) metric from point clouds generated for the trees using terrestrial laser scanning, during the leaf-on and -off conditions. Furthermore, we artificially defoliated the leaf-on point clouds by applying an algorithm that separates the foliage from the woody material of the trees, and then recalculated the Db metric. The Db of the leaf-on tree point clouds was significantly greater than the Db of the leaf-off point clouds across all species. Additionally, the leaf removal algorithm introduced bias to the estimation of the leaf-removed Db of the G. triacanthos and M. glyptostroboides trees. The index capturing the contribution of leaves to the structural complexity of the study trees (the ratio of the Db of the leaf-on point clouds divided by the Db of the leaf-off point clouds minus one), was negatively correlated with branch surface area and different metrics of the length of paths through the branch network of the trees, indicating that the contribution of leaves decreases as branch network complexity increases. Underestimation of the Db of the G. triacanthos trees, after the artificial leaf removal, was related to maximum branch order. These results enhance our understanding of tree structural complexity by disentangling the contribution of leaves from that of the woody structures. The study also highlighted important methodological considerations for studying tree structure, with and without leaves, from laser-derived point clouds.

Forest ecosystems play an important role in the global carbon cycle and with this there is an increasing need for quantifying carbon at large scales. The aim of this research was to develop a system for classifying tropical forests in Brazil into carbon stock classes, applicable to large areas, emphasizing different sets of stand and climate variables. We used data from forests inventoried in two Brazilian biomes: Atlantic Forest and Savanna. We applied discriminant analysis to generate a classification rule by biome. Three types of variables were used: climatic (mean annual temperature and precipitation, or MAT and MAP), geographical (latitude and longitude), and stand variables (density of trees, mean height or h ¯ , mean square diameter or dg, and basal area or G). We combined these into three scenarios for analysis: (1) all variables; (2) all variables, except h ¯ ; (3) all variables, except h ¯ , dg, and G, to determine their contribution to classifying carbon stocks. We also assessed each set of variables in the presence/absence of MAP and MAT, used simultaneously or not. The best classification rules resulted in 83.9% and 98.5% of correct classifications for Atlantic Forest and Savanna biomes, respectively. Stand variables contributed significantly to successful classification; for the Atlantic Forest biome, dg and G contributed from 36% to 42% and h ¯ from 2% to 5%, yet for the Savanna biome the gains ranged from 31% to 42% and 6%–9%, respectively. For the climate variables, the simultaneous use of MAT and MAP played an important role in the classification in all cases in the Atlantic Forest biome, contributing up to 9.2% for the classification. In the Savanna biome, we found significant positive gains by the simultaneous use in the absence of h ¯ , dg, and G, on the other hand, the simultaneous use exerted negative effects when h ¯ was used. We concluded that climate variables are most helpful when stand variables are not included in the analysis. In terms of carbon stock variation, the Atlantic Forest biome tended to be more sensitive to both MAT and MAP, whereas the Savanna biome had no significant climatic dependence in the classification. The variable h ¯ exerted a greater effect in the Savanna biome than in the Atlantic Forest, however, basal area and mean square diameter were the most important in both biomes.

Background Spatial determinants of malaria risk within communities are associated with heterogeneity of exposure to vector mosquitoes. The abundance of adult malaria vectors inside people’s houses, where most transmission takes place, should be associated with several factors: proximity of houses to larval habitats, structural characteristics of houses, indoor use of vector control tools containing insecticides, and human behavioural and environmental factors in and near houses. While most previous studies have assessed the association of larval habitat proximity in landscapes with relatively low densities of larval habitats, in this study these relationships were analysed in a region of rural, lowland western Kenya with high larval habitat density. Methods 525 houses were sampled for indoor-resting mosquitoes across an 8 by 8 km study area using the pyrethrum spray catch method. A predictive model of larval habitat location in this landscape, previously verified, provided derivations of indices of larval habitat proximity to houses. Using geostatistical regression models, the association of larval habitat proximity, long-lasting insecticidal nets (LLIN) use, house structural characteristics (wall type, roof type), and peridomestic variables (cooking in the house, cattle near the house, number of people sleeping in the house) with mosquito abundance in houses was quantified. Results Vector abundance was low (mean, 1.1 adult Anopheles per house). Proximity of larval habitats was a strong predictor of Anopheles abundance. Houses without an LLIN had more female Anopheles gambiae s.s., Anopheles arabiensis and Anopheles funestus than houses where some people used an LLIN (rate ratios, 95% CI 0.87, 0.85–0.89; 0.84, 0.82–0.86; 0.38, 0.37–0.40) and houses where everyone used an LLIN (RR, 95% CI 0.49, 0.48–0.50; 0.39, 0.39–0.40; 0.60, 0.58–0.61). Cooking in the house also reduced Anopheles abundance across all species. The number of people sleeping in the house, presence of cattle near the house, and house structure modulated Anopheles abundance, but the effect varied with Anopheles species and sex. Conclusions Variation in the abundance of indoor-resting Anopheles in rural houses of western Kenya varies with clearly identifiable factors. Results suggest that LLIN use continues to function in reducing vector abundance, and that larval source management in this region could lead to further reductions in malaria risk by reducing the amount of an obligatory resource for mosquitoes near people’s homes. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-1938-1) contains supplementary material, which is available to authorized users.

Summary The architecture of trees is the result of constrained, morphologically plastic growth – constrained by an underlying architectural model embedded in their genome, the structure of which can be significantly altered during growth to match the changing environmental conditions to which the tree is exposed. Here, we examined the hypothesis that crowding from neighbours should cause trees to optimize traits for light competition at the expense of wind resistance, with the reverse being true for trees lacking neighbours. Previous studies have examined the influence of light competition or wind resistance on shaping tree architecture, but few, if any, have simultaneously addressed trade-offs for optimizing these traits in response to crowding from neighbouring trees in forests, as compared to open-grown conditions. We studied the response of tree- and branch-level architectural traits of temperate, broad-leaved, deciduous tree species of differing shade tolerance and wood strength from multiple locations across the north-eastern United States. Trees ranged in size (4–83 cm diameter at 1·3 m) and crowding conditions (open- and forest-grown) and occupied different canopy positions. The open-grown trees represented a null condition, where the lack of neighbouring trees to shape architectural traits could be contrasted with the influence of different levels of crowding in forests. Our results show strong evidence for a tree neighbourhood-induced convergence of architectural traits across species and conditions, even when trees are growing in urban rather than natural forest conditions. After accounting for crowding, the effects of species and sample location contributed very little to explaining variation in architectural traits. One exception was crown dimensions, for which species-specific differences explained about 15% of the residual variation. Under open-grown conditions, alleviation of light competition caused trees to develop relatively large crowns and branches and a squat growth form suitable to resist greater wind exposure. By contrast, increasing shading from neighbouring trees caused forest-grown trees to become increasingly more spindly in the main stem, with slender branches sparsely distributed over a disproportionately large crown volume – presumably to maximize light capture. Although the latter is an intrinsically less wind-stable form, it can be adopted to increase light capture because neighbouring trees reduce exposure to the wind, which should greatly reduce the likelihood of stem breakage or uprooting under critical wind pressures. A lay summary is available for this article.

A 16-antenna-element microwave tomographic imaging system using a fast switching matrix is presented. This system uses frequency domain sampling and time-reversal image processing to image its environment. The use of time-reversal provides a more efficient computational method than traditional inverse techniques, creating a faster processing time. This introduces a potential for near-real-time image processing. To reduce the need for precise time-domain equipment, the short pulses required for time-reversal simulations are synthesized using frequency domain sampling over a broad bandwidth. A 1-5 GHz system using 16 planar antennas controlled by a switch matrix is presented. Measured results and reconstructed images are presented for both a PEC and dielectric cylinder as well as a wood sample and compared to their optical images.

When estimating tree-level biomass and carbon, it is common practice to develop generalized models across numerous species and large spatial extents. However, sampling efforts are generally incomplete and trees are not randomly selected. In this analysis, of the more than 1,000 biomass-related articles that were reviewed, trees were destructively sampled in over 300 studies to estimate biomass in the United States. Studies were summarized and past sampling efforts were explored to illuminate where the largest data gaps occurred in terms of tree components sampled, tree size, tree form, tree species, and location. The most prominent gaps were in large trees, particularly in Douglas-fir trees in the Pacific Northwest. In addition, tree roots were notably undersampled. Lastly, trees of poor or unusual form and low vigor were often not sampled, and this may introduce a systematic bias if not dealt with appropriately. More than 200 species did not have a biomass model or a single data point. The gaps presented here can be viewed as suggestions for future destructive sampling efforts, but the magnitude of a gap for a given model will ultimately depend on the selected modeling framework and the user's objectives.

The US National Greenhouse Gas Inventory uses the component ratio method (CRM), a volume conversion approach that incorporates models for tree biomass components, for forest carbon assessments. However, the performance of the CRM relative to other methods, as well as influences on its accuracy and precision, must be evaluated. We constructed a data-driven CRM (n-CRM), used it to predict total tree and component biomass for six US tree species, and compared this approach to a reference allometric model. We also assessed the influence of size, crown dynamics, and stem growth on the performance of both methods. Results show that the n-CRM was more accurate for four species, resulting from the inclusion of more predictor variables. Both methods had high uncertainty, but the precision of n-CRM predictions was two to eight times higher for small diameter trees (< 10 cm) across all species. Accuracy and precision of the crown component models (i. e. branches and foliage) was low, though better for pines than for hardwoods. Species-level analysis suggests that poor precision is influenced by crown traits and the size distribution of fitting datasets. Our results highlight needed improvements to the n-CRM, and motivate further development of data that facilitate predictive evaluation of biomass models. USDA Forest Service Forest Inventory and Analysis Program, Northern Region; Minnesota Agricultural Experiment Station; Michigan AgBioResearch through the USDA National Institute of Food and Agriculture Data compilation for the legacy data and the independent validation datasets, as well as B. Clough's time, were funded by the USDA Forest Service Forest Inventory and Analysis Program, Northern Region. Additional funding and support was available from the Minnesota Agricultural Experiment Station. Part of D.W. MacFarlane's time was supported with funds from Michigan AgBioResearch through the USDA National Institute of Food and Agriculture. Public domain – authored by a U.S. government employee

Accurate estimation of forest biomass and carbon stocks at regional to national scales is a key requirement in determining terrestrial carbon sources and sinks on United States (US) forest lands. To that end, comprehensive assessment and testing of alternative volume and biomass models were conducted for individual tree models employed in the component ratio method (CRM) currently used in the US' National Greenhouse Gas Inventory. The CRM applies species-specific stem volume equations along with specific gravity conversions and component expansion factors to ensure consistency between predicted stem volumes and weights, and additivity of predicted live tree component weights to match aboveground biomass (AGB). Data from over 76 600 stem volumes and 6600 AGB observations were compiled from individual studies conducted in the past 115 years - what we refer to as legacy data - to perform the assessment. Scenarios formulated to incrementally replace constituent equations in the CRM with models fitted to legacy data were tested using cross-validation methods, and estimates of AGB were scaled using forest inventory data to compare across 33 states in the eastern US. Modifications all indicated that the CRM in its present formulation underestimates AGB in eastern forests, with the range of underestimation ranging from 6.2 to 17 per cent. Cross-validation results indicated the greatest reductions in estimation bias and root-mean squared error could be achieved by scenarios that replaced stem volume, sapling AGB, and component ratio equations in the CRM. A change in the definitions used in apportioning biomass to aboveground components was also shown to increase prediction accuracy. Adopting modifications tested here would increase AGB estimates for the eastern US by 15 per cent, accounting for 1.5 Pg of C currently unaccounted for in live tree aboveground forest C stock assessments. Expansion of the legacy data set currently underway should be useful for further testing, such as whether similar gains in accuracy can be achieved in estimates of regional or national-scale C sequestration rates. U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis National Program; U.S. Department of Agriculture National Institute of Food and Agriculture, McIntire-Stennis Cooperative Forestry Program The U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis National Program, and the U.S. Department of Agriculture National Institute of Food and Agriculture, McIntire-Stennis Cooperative Forestry Program. Public domain – authored by a U.S. government employee

Understanding variation in tree stem form is fundamental to both ecological and economic assessments of forest ecosystem structure and function. Stem taper models (STMs) are widely used to describe tree form, but it can be challenging to apply them to trees with stems that diverge from an idealized norm, often leading to the exclusion of many trees from stem taper studies. Here, new “whole-tree” form type classes are advanced as simple and useful groupings for capturing stem form variation of trees of diverse morphological types and tested with a large tree data set without exclusion criteria. New form type classes explained much more of main stem form variation than knowledge of tree species, while “merchantable” form types explained the most variation between trees and stands. Broad-leaved species were much more likely to have complex stem forms than needle-leaved species, but species “evergreenness” was a very weak predictor of stem form variation when tree- and stand-level form variation was accounted for. A new, generalized framework for stem taper modeling is demonstrated, using both species and merchantable form types to capture tree-level random effects. New form types and the STM approach are relatively easy to apply and should be relatively simple to integrate into any conventional forest inventory system. Overall, the study demonstrates the importance of including and accounting for the diversity of observed stem forms in developing STMs.

Tree leaf mass is a small, highly variable, but critical, component of forest ecosystems. Estimating leaf mass on standing trees with models is challenging because leaf mass varies both within and between tree species and at different locations and points in time. Typically, models for estimating tree leaf mass are species specific, empirical models that predict intraspecific variation from stem diameter at breast height (dbh). Such models are highly limited in their application because there are many other factors beyond tree girth and species that cause leaf mass to vary and because such models provide no way to predict leaf mass for species for which data are not available. We conducted destructive sampling of 17 different species in Michigan, covering multiple life history traits and sizes, to investigate the potential for using a single, "trans-species" model for predicting leaf mass for all the trees in our study. Our results show the most important predictors of tree leaf mass are dbh, five-year basal area increment, crown class, and competition index, none of which are species specific. Species-specific variation could be captured by leaf longevity and shade tolerance. Wood specific gravity was a statistically significant, but marginally important predictor. Together, these variables describing tree size, life-history traits, and competitive environment allowed us to develop a generalized leaf mass model applicable to a diverse set of species, without having to develop species-specific equations.

Accurate estimates of forest biomass stocks are critical for scientists, policymakers and forest managers trying to address an increasing array of demands on forests, to sustain human well-being and a broader diversity of life forms on Earth. Thus, it is important that forest biomass estimates are translatable into both biologically and economically meaningful components. Here, a new variable-form, variable-density tree mass component model is presented. The model decomposes a tree into a system of tree component-specific equations that: (a) reflect variation in scaling relationships between major portions of the tree body that define variation in whole-tree growth form and (b) relate to commercially relevant portions of the tree. When tested using data collected from felled and dissected hardwood trees of different size and species, growing over a range of stand conditions, the variable-form, variable-density models gave superior predictions for all components of tree mass, when compared to standard fixed-form, fixed-density models that predict tree mass components only from stem diameter at breast height (DBH). The results demonstrated why the standard approach of estimating mass components from DBH with a power function is fairly limited, because base-, trunk-, crown- and main stem-DBH relationships are all variable within and between tree species. Species-specific models were generally superior, but a mixed-species model gave equivalent and sometimes better results than equations fitted to each species individually. The results provide a theoretical basis for biologically-meaningful, robust estimation of tree biomass components over a range of species and forest conditions and may offer new flexibility in producing ecologically and economically relevant biomass inventories.

Remaining fragments of East African coastal dry forests contain very high levels of endemic species and are in critical need of conservation and restoration. Little is known about natural regeneration dynamics of these forests, or the potential for human action to aid recovery of lost structures and functions after deforestation/degradation. Here, data and analyses are presented from long-term monitoring plots in a 20 year-old forest restoration project in Gede, Kenya, in a fragment of Zanzibar-Inhambane (ZI) regional forest mosaic. Study results provided previously unavailable indigenous tree species growth rates and human-assisted forest regeneration rates for ZI forests and highlighted issues relevant to conserving and regenerating remnants of coastal dry forest throughout East Africa. Enrichment plantings accelerated recovery of indigenous tree species diversity and increased species density above natural levels. A strategy of inter-planting within existing natural regeneration, including leaving large relic trees, accelerated regrowth of the forest, but the main beneficiary of the strategy was exotic Azadirachta indica, which came to dominate significant areas. Analyses indicated that A. indica, which produces insecticidal compounds, was significantly altering the structure of arthropod communities; flying to ground-dwelling arthropod ratios were higher where A. indica made up a higher proportion of above-ground woody biomass. Management strategies appear to be mostly restoring indigenous forest structures, despite continued casual illegal tree cutting and invasion by A. indica. Analysis of illegally harvested trees highlighted the important role of indigenous tree species as a source of ecosystems services to local people; an important consideration for forest conservation planning worldwide.

Tree biomass is typically estimated using statistical models. This review highlights five limitations of most tree biomass models, which include the following: (1) biomass data are costly to collect and alternative sampling methods are used; (2) belowground data and models are generally lacking; (3) models are often developed from small and geographically limited data sets; (4) simplistic model forms and predictor variables are used; and (5) variation is commonly averaged or grouped rather than accounted for. The consequences of these limitations are highlighted and discussed. Several recommendations for future efforts are presented including the following: (1) collection of field measurements of tree biomass using consistent protocols; (2) compilation of existing data; (3) continued evaluation and improvement of existing models; (4) exploration of new models; and (5) adoption of state-of-the-art analytical and statistical techniques. Given the increasing importance of accurately estimating forest biomass, there is a critical need to understand, evaluate, and improve current tree biomass prediction methods.

The density-integral approach worked well for estimating whole-tree and dominant stem vertical biomass profiles, and performed better than the constant-density approach for biomass prediction. Integrated whole-tree biomass equations are in great demand due to the simultaneous need to improve estimation of forest carbon stocks and to quantify the distribution of wood biomass within trees for estimating whole-tree utilization potential. Two approaches were used for modeling the vertical cumulative biomass profile of a whole tree from the ground to the top of the tree, including the dominant stem and branches: (1) a density-integral approach and (2) a constant-density approach. The models were developed from destructive sampling of 27 trees from a temperate hardwood forest in Michigan, USA. The species sampled were American beech (Fagus grandifolia Ehrh.) and sugar maple (Acer saccharum Marsh.). A mixed-effects modeling framework was used to account for within-tree correlation in the biomass profiles. In general, the density-integral approach worked well for estimating whole-tree and dominant stem biomass profiles, and performed better than the constant-density approach. However, the results indicated that errors in the whole-tree volume estimation often overwhelmed differences in biomass prediction attributable to vertical tree density representation. Mixing species with different forms or density profiles could dilute benefits of the density-integral approach.

Whole-tree volume equations are in great demand due to the need to quantify the distribution of wood volume within trees for estimating whole-tree utilization potential. While main stem volume has been extensively studied, related to computing the merchantable timber volume of trees, the relative volume of branches has received much less attention. It is particularly challenging to quantify branch volume in trees with deliquescent branching architecture (i.e. hardwoods) where branching is complex and not strongly controlled by a dominant stem. Here, new mixed-effects cumulative volume profiles are presented that allow for simultaneous volume estimation of the dominant stem and whole tree from ground to the top of the tree. Cumulative branch volume can be estimated at different relative heights from the whole-tree and dominant stem profiles by simple subtraction. The models were developed from destructive sampling of 32 trees from a temperate hardwood forest in Michigan, US. The species in the sample were primarily American beech (Fagus grandifolia Ehrh.) and sugar maple (Acer saccharum Marsh.). The results produce whole-tree cumulative volume models that include all branches in trees and demonstrates the value of studying the whole tree even when the dominant stem is the object of interest.

Functional branch analysis (FBA) is a promising non-destructive method that can produce accurate tree biomass equations when applied to trees which exhibit fractal branching architecture. Functional branch analysis (FBA) is a promising non-destructive alternative to the standard destructive method of tree biomass equation development. In FBA, a theoretical model of tree branching architecture is calibrated with measurements of tree stems and branches to estimate the coefficients of the biomass equation. In this study, species-specific and mixed-species tree biomass equations were derived from destructive sampling of trees in Western Kenya and compared to tree biomass equations derived non-destructively from FBA. The results indicated that the non-destructive FBA method can produce biomass equations that are similar to, but less accurate than, those derived from standard methods. FBA biomass prediction bias was attributed to the fact that real trees diverged from fractal branching architecture due to highly variable length–diameter relationships of stems and branches and inaccurate scaling relationships for the lengths of tree crowns and trunks assumed under the FBA model.

Standing dead trees (SDTs) are an important component of forest ecosystems, but reliable estimates of SDT population parameters may be costly, due to spatial and temporal variation in dead tree abundance, which often causes an excess number of zero observations (a.k.a. ‘zero inflation’) in survey data. The objective of this study was to compare a new estimation method, the Expected-Zero Hurdle (EZ-Hurdle) method, with a typical fixed-area sampling (FAS) method, as methods for estimating the abundance of SDTs under different forest conditions. This study demonstrated that the EZ-Hurdle method provided more precise estimates than FAS when data describing the distance from the plot centre to the nearest SDT were incorporated into a model-based estimator that reduced estimation error associated with zero-inflation in the data. However, the EZ-Hurdle method was determined to be a less cost-efficient sampling method than FAS with smaller (�7 m radius) plots, because the additional time required to find the nearest dead tree to each plot centre was large, relative to the improvement in precision. On the other hand, the EZ-Hurdle method was a more cost-efficient sampling method than using larger (�18 m radius) plots. The EZ-Hurdle method appears to be a useful method for improving the precision of estimates of SDT abundance whenever there is a restriction on changing the sampling design or adding more plots to a survey.

Willow and poplar are both important crops for Short Rotation Intensive Culture (SRIC). Here, we report the results of biomass yield trials at three locations in Michigan, where the average annual yield of twelve willow and two poplar clones where compared over three- or four-year growth periods after coppicing. On average, fast-growing hybrid poplars (NM5 and NM6) were more productive than any of the willow clones tested by about 30%. Poplar and willow revealed different growth patterns after successive harvests; poplars grew quickly and consistently while willow growth was initially slower but increased over time. Willows at the intensively managed site in southern Michigan were usually 2–5 times more productive than at the two less intensively managed sites in southern Michigan and northern Michigan, but poplars at both southern sites were similarly more productive than at the northern site. The annual yield of clones was further standardized to account for differences in growing degree days (GDD) between sites in southern and lower Michigan, which caused differences in the relative ranking of some willow clones to change. The results suggest that GDD-standardized yield rates may be useful for comparisons of clonal performance over larger regions. Decomposition of clonal yield rates into individual stool yield and survival rates revealed a strong positive correlation ( r 2 = 0.94) between them, indicating that faster-growing clones also had higher survival rates. The best performing clones for biomass production were also identified.

Summary As signatories to the United Nation Framework Convention on Climate Change, the US has been estimating standing dead tree (SDT) carbon (C) stocks using a model based on live tree attributes. The USDA Forest Service began sampling SDTs nationwide in 1999. With comprehensive field data now available, the objective of this study was to compare field- and model-based estimates of SDT C stocks across the US to evaluate potential directions for improving National Greenhouse Gas Inventory (NGHGI) reporting and C dynamics research. Field inventory data indicated that most forests have relatively little SDT C stocks ( 25 Mg/ha) are infrequent. Models used for past NGHGIs to predict SDT C stocks do not accurately reflect what was observed in inventory plots, resulting in an overestimation (~100 per cent) of SDT C stocks at the national scale. These results indicate that the current estimate of the Nation’s total forest C stock is overestimated by ~4.2 per cent due to overestimation of SDT C stocks that are a relatively small component of the total forest C stock. A field-based approach is suggested for use in future C reporting efforts to reduce estimation bias.

A novel “varying-centroid” method is presented for predicting whole-tree, aboveground stem volume (i.e., bole plus branch volume) to bole volume ratios from changes in the centroid of tree bole volume associated with branching of the bole. The method was derived from a simple fractal-like tree model based on a conceptualization of tree branching architecture by Leonardo da Vinci. The method recognizes that the centroid of bole volume (the point at which one half of bole volume is above and one half is below) is always lower than the centroid of whole-tree volume and that shifts in the centroid of bole volume should be predictably related to the size of a tree’s crown. The method assumes that branch-displaced bole volume profiles can be compared with reference bole profiles that are not significantly influenced by branching, at the centroid of bole volume, and that the magnitude of bole centroid displacement predicts the branch volume necessary to cause it. When the method was applied to hardwood trees representing diverse species, sizes, and stand conditions across Michigan, the centroid of bole volume was found to vary predictably with measurable tree crown attributes and bole plus branch wood to bole wood volume ratios were generally predicted within 10% of the true value using the new method.

Tree bark provides habitat for many organisms in forest ecosystems, but forest bark structure is typically not considered when important forest structural attributes are discussed. We describe a new metric for quantifying bark structure: a bark-fissure index (BFI). We examined species-specific changes in the frequency and depth of bark fissures caused by both horizontal and vertical splitting of bark layers for trees of different sizes and found that BFI generally scaled exponentially with stem diameter, with distinctively different patterns for 15 different tree species. We found a strong correlation between BFI and tree species preferences of the white-breasted nuthatch, a bark-foraging bird species. We demonstrate how BFI can be scaled up to define forest-scale bark structure using simple stand structural data, such as stand tables. This research contributes a new, objective, and repeatable way of quantifying tree and forest bark structure using simple bark measurements.

Tree and wood biomass from urban areas is a potentially large, underutilized resource viewed in the broader social context of biomass production and utilization. Here, data and analysis from a regional study in a 13-county area of Michigan, U.S.A. are combined with data and analysis from several other studies to examine this potential. The results suggest that urban trees and wood waste offer a modest amount of biomass that could contribute significantly more to regional and national bio-economies than it does at present. Better utilization of biomass from urban trees and wood waste could offer new sources of locally generated wood products and bio-based fuels for power and heat generation, reduce fossil fuel consumption, reduce waste disposal costs and reduce pressure on forests. Although wood biomass generally constitutes a “carbon-neutral” fuel, burning rather than burying urban wood waste may not have a net positive effect on reducing atmospheric CO 2 levels, because it may reduce a significant long term carbon storage pool. Using urban wood residues for wood products may provide the best balance of economic and environmental values for utilization.

We are interested in predicting one or more continuous forest variables (e.g., biomass, volume, age) at a fine resolution (e.g., pixel level) across a specified domain. Given a definition of forest/nonforest, this prediction is typically a two-step process. The first step predicts which locations are forested. The second step predicts the value of the variable for only those forested locations. Rarely is the forest/nonforest status predicted without error. However, the uncertainty in this prediction is typically not propagated through to the subsequent prediction of the forest variable of interest. Failure to acknowledge this error can result in biased estimates of forest variable totals within a domain. In response to this problem, we offer a modeling framework that will allow propagation of this uncertainty. Here we envision two latent processes generating the data. The first is a continuous spatial process while the second is a binary spatial process. The continuous spatial process controls the spatial association structure of the forest variable of interest, while the binary process indicates presence of a possible nonzero value for the forest variable at a given location. The proposed models are applied to georeferenced National Forest Inventory (NFI) data and spatially coinciding remotely sensed predictor variables. Due to the large number of observed locations in this dataset we seek dimension reduction not just in the likelihood, but also for unobserved stochastic processes. We demonstrate how a low-rank predictive process can be adapted to our setting and reduce the dimensionality of the data and ease the computational burden.

Subject trees included in growth analyses often vary in their initial size, possibly obscuring the effects of growth factors. We compare methods for incorporating size effects into growth models. For four different tree species, red maple (Acer rubrum L.), sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Ehrh.), and red oak (Quercus rubra L.), we compared models of radial growth rate of saplings as a function of light, water, and nitrogen availability that (i) ignored size effects on absolute growth–resource relationships, (ii) related absolute growth rate (AGR) to size and resource availability, (iii) related relative growth rate (RGR) to resource availability, and (iv) related RGR to tree size and resource availability. Size effects explained 13%–14% of variation in growth rates, and failure to account for these effects resulted in a substantial size bias in growth prediction. Overall, AGR-based models that included size as a predictor variable provided the best predictions and clearest interpretation of growth–resource relationships across all growth model types and species examined. Modeling RGR without including size effects resulted in residual size bias. Including size as a predictor of RGR yielded nearly equivalent results to using size to predict AGR. We suggest that investigators evaluate both AGR- and RGR-based approaches and determine which is most appropriate for their study.

The emerald ash borer (EAB) (Agrilus planipennis) is a recently discovered (July 2002) exotic insect pest, which has caused the death of millions of ash trees (Fraxinus spp.) in Detroit, MI, USA and has also spread into other areas of Michigan, isolated locations in Indiana, Ohio, Maryland and Virginia, and nearby Windsor, Ont., in Canada. Ash trees occur in many different forest ecosystems in North America, are one of the more widely planted trees in urban areas, and are a valuable commercial timber species. If emerald ash borer populations are not contained and eventually eradicated, the ash resource in North America could be devastated. The destruction caused by EAB and its rate of spread are likely to be strongly influenced by the spatial distribution and status of the ash tree host, but general information regarding the abundance, health and distribution of ash trees is diffused throughout the literature. Here, we summarize what is currently known regarding the characteristics and potential spatial distribution of various species of Fraxinus in natural and planted ecosystems in North America and evaluate this information with specific regard to assessing the relative risk of ash populations to EAB.

Light capture estimates from models can be related to survival and growth rates and may provide new ways to model forest dynamics. However, relationships between light capture, growth, and survival should vary widely with tree age, site conditions, and stand density, so predictions from light capture models need to be tested over a range of stand conditions. We used the tRAYci stand light model (A. Brunner. 1998. For. Ecol. Manage. 107: 19–46) to estimate weighted leaf area (WLA), an estimate of annual light capture, for every tree, in 36 even-aged loblolly pine (Pinus taeda L.) stands, representing different combinations of site index and planting density, over an 8-year period. We also developed regression equations relating light capture estimates to height growth, basal area growth, stem volume growth, and survival probability for individual trees at different ages, sites, and planting densities. Our results suggest a significant correlation between estimates of WLA and tree growth and survival rates, and that the tRAYci model is robust across a range of stand conditions. An important finding was that WLA was a better predictor of survival probability than measured basal area increment. Effects of site index, age, and planting density on light capture – growth relationships are also discussed.

BACKGROUND: Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. METHODS: We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time. RESULTS: Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models. CONCLUSIONS: This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts.

‘Process-based’ models have been advanced to incorporate current knowledge regarding forest processes explicitly into model structure, yet uncertainty regarding these processes is often omitted from parameter estimation. This problem reflects the fact that parameters have been traditionally viewed as constants. In process models this is often unrealistic, since physiological rates and morphological characteristics, which have known variation, are often parametrized. Reasonable estimates for parameters can, and should be, abstracted from the vast body of forestry literature, and formulated into probability distributions which reflect uncertainty in their potential value. Here probability distributions are estimated for 14 physiological or morphological parameters of Pipestem , a stand-level model of carbon allocation and growth for loblolly pine ( Pinus taeda ), based on an extensive review of published information. Investigation of parameters revealed a wide range of variation in accumulated knowledge regarding their value, and led to the development of generic parameters which may be transferrable to other similar models. Parameter uncertainty also appeared tractable in some cases and might be reduced through reformulation of the model. Some parameters investigated had known co-dependency on model variables or other parameters, and may be better expressed as dependent variables. This study was part of a larger study in which a Bayesian analysis was used to assess the uncertainty in the predictions of a forest growth model.

The Bayesian synthesis method is reviewed and judged to be useful for determining posterior distributions and interval estimates for inputs and outputs of process-based forest models. The method furnishes posterior distributions of the values of a model's parameters and response variables. The method also provides estimates of correlation among the parameters and output variables. Bayesian synthesis is the only type of uncertainty analysis that affords incorporation of all the information available to the investigator, in addition to the information contained in the model itself.

Recent findings regarding the magnitude of the influence of carbon dioxide concentration on the rate of photosynthesis in loblolly pine have been incorporated into Pipestem, a model of carbon allocation and growth. Pipestem translates photosynthetic rates into rates of change in stand basal area, quadratic mean diameter, tree density, average tree height, average crown length, dominant tree height, and woody dry matter. Projections of loblolly pine growth were run under the assumption that the atmospheric concentration of CO2 will continue to increase by 1.6 ppm/yr, the average rate of increase in the last 10 yr. Standing crops of woody dry matter in 20-yr-old loblolly pine stands in Buckingham County, Virginia, are projected to increase, on the average, by 9.8% in 20 yr. It is concluded that the CO2 effect should be accounted for in long-term projections of loblolly pine growth. South. J. Appl. For. 23(4):212-216.

The assumption that congenital rubella is commonly associated with microcephaly and mental retardation was examined. Among a rubella clinic population of 111 children, 92 children had vision sufficient to allow testing by the Leiter International Scale. The mean IQ for this group was 99.46 (SD, 19.5). Head circumference in this group correlated well with stature but poorly with intellect. The authors conclude that children with intrauterine rubella should be viewed as small children rather than children with small heads and that such children are poorly served if mental subnormality is assumed without careful study.