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Comparing forest and harvested wood product carbon (C) stocks and accumulation among forest management treatments commonly applied in managed forests is needed to inform planning and policy decisions for C objectives. Therefore, pre- and post-harvest C stocks were quantified and C accumulation was projected over a 31-year period (to ∼2050) among forest management treatments that were applied on a subset (n = 3) of the Maine Adaptive Silviculture Network installations in northern Maine, USA. These installations included mature, second-growth forests composed of northern hardwood and hardwood-dominated mixedwood stands. Before treatments were initiated, average aboveground live tree C stocks ranged from 67.1 to 99.7 Mg ha−1. For the aboveground portions of live trees, dead wood and harvested wood products, the projected average annual net change in C (AAC) was 0.232 ± 1.164 Mg ha−1 year−1 (mean ± standard deviation). Models of projected AAC indicated that less biomass removal during harvests and greater representation of tree species with low tolerance of shade were associated with positive AAC values. The results emphasize the importance of leveraging multiple harvesting strategies to achieve C objectives, including consideration of forest reserves and using targeted yet operationally feasible silvicultural treatments that promote forest resilience relative to climate change. [ABSTRACT FROM AUTHOR]

Tree diameter increment (∆DBH) and total tree height increment (∆HT ) are key components of a forest growth and yield model. A problem in complex, multi-species forests is that individual tree attributes such as ∆DBH and ∆HT need to be characterized for a large number of distinct woody species of highly varying levels of occurrence. Based on more than 2.5 million ∆DBH observations and over 1 million ∆HT records from up to 60 tree species and genera, respectively, this study aimed to improve existing ∆DBH and ∆HT equations of the Acadian Variant of the Forest Vegetation Simulator (FVS-ACD) using a revised method that utilize tree species as a random effect. Our study clearly highlighted the efficiency and flexibility of this method for predicting ∆DBH and ∆HT. However, results also highlighted shortcomings of this approach, e.g., reversal of plausible parameter signs as a result of combining fixed and random effects parameter estimates after extending the random effect structure by incorporating North American ecoregions. Despite these potential shortcomings, the newly developed ∆DBH and ∆HT equations outperformed the ones currently used in FVS-ACD by reducing prediction bias quantified as mean absolute bias and root mean square error by at least 11% for an independent dataset and up to 41% for the model development dataset. Using the revised ∆DBH and ∆HT estimates, greater prediction accuracy in individual tree aboveground live carbon mass estimation was also found in general but performance varied with dataset and accuracy metric examined. Overall, this analysis highlights the importance and challenges of developing robust ∆DBH and ∆HT equations across broad regions dominated by mixed-species, managed forests. [ABSTRACT FROM AUTHOR]

The Hyrcanian forests of Iran contain many species-rich communities that can only be maintained through an understanding of the renewal and development of these forests. Located in the Jojadeh section of the Farim forest in northern Iran, individual tree growth of five distinct species [(Oriental beech (Fagus orientalis Lipsky), chestnut-leaved oak (Quercus castaneifolia Coss. ex J.Gay), Persian maple (Acer velutinum Boiss.), common hornbeam (Carpinus betulus L.) and Caucasian alder (Alnus subcordata C.A.Mey.)] were measured on 313 permanent sample plots (0.1 ha) over a 10-year period (2003–2013). In this analysis, various tree-level predictions were investigated using the available data with application of parametric models and two artificial neural networks [i.e., the multilayer perceptron (MLP) and radial basis function (RBF) networks]. Individual tree diameter growth models showed a robust negative relationship with basal area in larger trees, which was relatively consistent across species. A total height model indicated that the examined species did not differ for a given set of covariates. In the survival model, the survival probability of Oriental beech was lower than the other species, while the ingrowth model revealed sapling density of all species increased with greater basal area. The artificial neural network based on the MLP was superior for all models and predicted more accurately than the RBF. Furthermore, the models based on the MLP were also superior to the parametric individual tree models developed using mixed-effect regression. The use of these developed models in forest planning and management is imperative, particularly for uneven-aged stands, but assessment of long-term projection behavior across the contrasting statistical approaches used is warranted despite the general superiority of the nonparametric models. [ABSTRACT FROM AUTHOR]

Stem taper equations, which predict the change in stem form from ground to tip, have become the primary means for estimating bole volume. Stem taper equations can provide predictions with similar levels of accuracy as volume equations, but with greater flexibility, a wider range of potential uses, and consistency between taper and volume. This review is a synthesis of the current state of knowledge on stem taper equations and an assessment of challenges for future model refinement. It includes the history and evolution of stem taper model forms, which have received tremendous attention and focus over the last several decades. Additional focal areas covered are (i) the use of additional covariates beyond tree diameter at breast height (DBH) and total height; (ii) alternative statistical methods for developing stem taper equations such as parametric, semiparametric, and nonparametric approaches; (iii) key considerations for proper development, application, and use of stem taper equations such as sample size requirements, local calibration, and evaluation; and (iv) a synthesis of key findings, future opportunities, and ongoing challenges. Current and developing technologies such as terrestrial laser scanning (TLS) offer an unprecedented opportunity to measure stem form in much greater detail at significantly lower costs and time requirements than traditional methods. Overall, continued development, refinement, and application of stem taper equations will remain important given the critical nature of tree volume for science, accurate inventories, and ultimately, sustainable forest management. [ABSTRACT FROM AUTHOR]

We compared long-term C sequestration in the pools of aboveground portions of live trees, dead wood, and harvested wood products among highly contrasting forest management scenarios on a rotation (30-100 years) and 100-year basis. Average annual net change in C (AAC) and the cumulative sum of net changes in C were calculated using 65 years of data from permanent plots representing contrasting approaches to managing mixed-species stands dominated by shade-tolerant coniferous species on the Penobscot Experimental Forest in Maine, USA. Simulations of tree growth and mortality were used to estimate C pools to 100 years. On a rotation basis and for all pools combined, scenarios with selection cutting had greater AAC than those with shelterwood cutting followed by thinning or with diameter-limit cutting (p < 0.05). For combined pools, the cumulative sum of net changes in C for the unmanaged, selection, and guiding diameter-limit stands was positive for most of the study period. Our results suggest that strategies that maintain overstory stocking levels necessary to regenerate desired species and promote the development of sawlog-sized trees can enhance long-term C sequestration in mixed-species, naturally regenerated northern temperate forests. [ABSTRACT FROM AUTHOR]

To overcome existing knowledge gaps regarding altering harvest activities in the mixed species and heavily forested USA state of Maine, we used continuous forest inventory data to develop statistical models that predict harvest probability of occurrence and intensity. Among the three modeling approaches examined, the first one directly predicted stand-level basal area removal as a percentage of initial total stand basal area. The second approach first predicted stand-level harvest probability and then the stand-level basal area removal of the harvested plots only. The third approach used the stand-level harvest probability equation of the second approach, while subsequently predicting individual tree harvest probability for only the harvested plots. Among the most influential stand-level attributes were quadratic mean diameter, stand density, elevation, and ownership type, while the most influential tree-level attributes were diameter at breast height, basal area in larger trees, and species. Differences in prediction accuracy between modeling approaches were small with the third approach performing slightly better. Our findings suggest that harvesting in Maine might be less opportunistic and short-term driven than generally perceived. Overall, the analysis highlights the complex array of factors that influence harvesting patterns and provides a framework for better representing contrasting harvest behavior in future wood supply projections. [ABSTRACT FROM AUTHOR]

Self‐thinning and site maximum carrying capacity are key concepts for understanding and predicting ecosystem dynamics as they represent the outcome of several fundamental ecological processes (e.g., mortality and growth). Relationships are often derived using alternative modeling strategies, depending on the statistical approach, model formulation, and underlying data with unclear implications of these various assumptions. In this analysis, the influence of contrasting modeling strategies for estimating the self‐thinning relationship and maximum carrying capacity in long‐term, permanent plot data (n = 130) from the mixed Nothofagus forests in southern Chile was assessed and compared. Seven contrasting modeling strategies were used including ordinary least squares, quantile, and nonlinear regression that were formulated based on static (no remeasurements) or dynamic data (with remeasurements). Statistically distinct differences among these seven approaches were identified with mean maximum carrying capacity ranging from 1,050 to 1,912 stems/ha depending on the approach. The population‐level static approach based on quantile regression produced an estimate closest to the overall mean with site‐level carrying capacity depending on tree species diversity and climate. Synthesis and applications. Overall, the findings highlight strong variability within and between contrasting methods of determining self‐thinning and site maximum carry capacity, which may influence ecological inferences. Our findings highlight strong variability within and between contrasting methods of determining self‐thinning and site maximum carry capacity, which may influence ecological inferences. [ABSTRACT FROM AUTHOR]

Northern hardwood species display a variety of forms and defects that can reduce stem quality and complicate their timber management. However, for the most part, growth and yield models do not account for the influence of stem form and damage. This study determined the influence of stem form and damage on growth, survival, and projected future sawlog value among several northern commercial hardwood species. To accomplish this, hardwood trees on 112 permanent plots across three long-term research sites in Maine were assigned stem form and risk classes using a tree classification system developed in New Brunswick. A highly significant influence of stem form and risk on annualized individual-tree diameter increment and survival was found. Inclusion of these equations into a regional growth and yield model highlighted the importance of stem form and defects on long-term simulations as projected stand-level future value was significantly reduced by over 17%, on average (range of 13% to 28%), when compared with projections that did not include that tree-level information. The results highlight the importance of stem form and defects, as well as the need to account for them, in growth and yield applications that assess the forecasted value of commercially important hardwood stands. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Forest is a biologically and structurally diverse biome which covers a large area; however, few tree-level biomass models exist for this region. The objective of this study was to evaluate alternative individual tree biomass models for the Atlantic Forest using contrasting prediction approaches, and evaluate their implications for stand-level biomass and carbon. The data comprised 106 destructively sampled trees (dbh; 5.4 - 68.5 cm) from the 16 most abundant species. Different approaches, namely species-specific, functional-trait based, generalized, and previously developed equations, as well as alternative model forms using differing variables including diameter at breast height (dbh), wood specific gravity (wsg), and height to crown base (hcb) were examined. Species-specific models were developed using both ordinary least squares and linear mixed effects with species as a random effect. The various approaches resulted in statistically different estimates of biomass and carbon at both the individual tree- and stand-levels. Compared to a generalized biomass equation for Pan-tropical forests, our results indicate differences of over 46% and 52% for biomass and carbon, respectively. Overall, if no species-specific model is available, we suggest the functional plant trait based on dbh and wsg. [ABSTRACT FROM AUTHOR]

Climate-associated changes in forest composition have been widely reported, particularly where changes in abiotic conditions have resulted in high mortality of sensitive species and have disproportionately favoured certain species better adapted to these newer conditions. In the north-eastern USA and south-eastern Canada, few studies have examined climate-related influences associated with forest composition, and none have considered broad-scale changes over a long temporal (>25 years) period., We used US Forest Service Forest Inventory and Analysis data from 1983 to 2014 across four north-eastern states (Maine, New Hampshire, New York and Vermont) to assess temporal and spatial changes in the occurrence and abundance of American beech Fagus grandifolia Ehrh, sugar maple Acer saccharum L., red maple Acer rubrum L. and birch Betula spp. saplings. We also tested the effects of biotic and abiotic factors on the distribution of the four studied deciduous species over the entire period examined., Occurrence and abundance of American beech have increased substantially over the past three decades, whereas the occurrence and abundance of three other deciduous species have decreased in all ecological provinces of the north-eastern USA, except the Midwest Broadleaf ecological province. Consequently, a clear shift in species composition is currently underway in the beech-maple-birch (BMB) forests of the north-eastern USA, with uncertain consequences for future ecosystem structure and function., In the studied region and over the entire period examined, the distribution of increased occurrence and abundance of beech relative to the three other deciduous species were associated with higher temperature and precipitation as well as higher conspecific basal area and dead tree basal area., Synthesis and applications. The change from beech-maple-birch forests to more beech-dominated forests with beech encroachment to new forest areas across the north-eastern USA may continue if higher intensity harvesting and disturbances (i.e. large-scale canopy openings) do not occur. This would be a significant management concern as beech is associated with a widespread bark disease, is commercially less desirable, and can limit natural regeneration from other species. Our results emphasize the need for management strategies such as higher intensity harvesting methods, vegetation control and limiting browsing pressure to reduce beech dominance. [ABSTRACT FROM AUTHOR]

Defoliation reduces the growth and survival of trees, but this influence can be difficult to evaluate largely because of its interplay with various stand and site factors, especially for the highly dynamic defoliation of spruce-fir ( Picea- Abies) forests caused by spruce budworm (SBW; Choristoneura fumiferana (Clem.)), the primary tree defoliator in North America. In this study, we developed statistical models to evaluate the influence of SBW defoliation, while considering its interaction with various stand and site factors, on spruce-fir stand dynamics of annual volume net growth, mortality, and ingrowth. The data were collected at intervals of 1 to 3 years from 560 permanent sample plots during the last SBW outbreak (1970s-1980s) in Maine, USA, and New Brunswick, Canada. These data comprise a wide range of observations of cumulative defoliation, especially at relatively low levels, that have been largely overlooked in previous studies. Our results strongly demonstrated that even relatively low levels of cumulative defoliation were significantly related to stand-level mortality and ingrowth, while net growth was more competition driven. Additionally, these stand dynamics were found to be not significantly affected by any of the site factors evaluated. These findings were consistent for Maine and New Brunswick despite their differences in forest management and SBW outbreak histories. [ABSTRACT FROM AUTHOR]

Northern hardwood trees display a wide variety of stem forms and defects, which can substantially reduce their financial value and also complicate their silviculture. While attributes of stem form and defect have been incorporated into tree classification systems, their ability to assess product value and recovery in standing trees has not been adequately tested. To address this issue, we classified stem form and risk using a system developed by the Northern Hardwoods Research Institute (NHRI) for four species across several locations in Maine, New Hampshire, and New Brunswick: sugar maple ( Acer saccharum Marsh.), red maple ( Acer rubrum L.), yellow birch ( Betula alleghaniensis Britton), and northern red oak ( Quercus rubra L.). Using these data, we ( i) quantified interspecific and regional variation in stem form and damage, ( ii) related potential sawlog recovery to tree size, form, and risk, and ( iii) compared the efficacy of the NHRI system with a commonly used classification system and a continuous measure of stem quality. High variation in both stem form and damage among the species was found, with red maple showing the largest range. A simplified NHRI system including three form classes proved to be sufficient in differentiating sawlog potential in individual trees, while a model using a continuous measure of stem quality (estimated merchantable sawlog height) performed best. [ABSTRACT FROM AUTHOR]

Measures of forest productivity generally rely on site index, which can be problematic for multicohort and mixed-species stands. Using stand growth and dominant tree height-age (i.e., site tree) measurements from ∼10 900 plot locations from Maine, Nova Scotia, New Brunswick, and Prince Edward Island, a forest productivity model for the Acadian Forest Region was developed as a function of climate, lithology, soils, and topographic metrics. Approximately 65% of variation in observed aboveground dry-biomass growth rate (BG) was explained by a Chapman-Richards function of temperature, bedrock, soil root space, slope, and depth to water in combination with stand structure and species predictors. Productivity was then defined in terms of the predicted asymptote of BG, holding structure and species constant, which was termed biomass growth index (BGI), i.e., the site-influenced component of the BG relationship. BGI was mapped on a 20 m grid throughout the region. BGI explained 0%-30% of the variability in spruce ( Picea sp.) and balsam fir ( Abies balsamea (L.) Mill.) site index and had similar site index predictive performance (±5%) when compared with existing land productivity classifications in each province. BGI provides a direct relationship between site variables and growth and can help guide forest management decisions and future research. [ABSTRACT FROM AUTHOR]

Questions What are the primary biotic and abiotic factors driving composition and abundance of naturally regenerated tree seedlings across forest landscapes of Maine? Do seedling species richness ( SR) and density ( SD) decrease with improved growing conditions (climate and soil), but increase with increased diversity of overstorey composition and structure? Does partial harvesting disproportionately favour relative dominance of shade-intolerant hardwoods ( PIHD) over shade-tolerant softwoods ( PTSD)? Location Forest landscapes across the diverse eco-regions and forest types of Maine, USA. Methods This study used USDA Forest Service Forest Inventory Analysis permanent plots ( n = 10 842), measured every 5 yr since 1999. The best models for each response variable ( SR, SD, PIHD and PTSD) were developed based on AIC and biological interpretability, while considering 35 potential explanatory variables incorporating climate, soil, site productivity, overstorey structure and composition, and past harvesting. Results Mean annual temperature was the most important abiotic factor, whereas overstorey tree size diversity was the most important biotic factor for SR and SD. Both mean annual temperature and overstorey tree size diversity had a curvilinear relationship with SR and SD. Average overstorey shade tolerance and percentage tolerant softwood basal area in the overstorey were the top predictor variables of PIHD and PTSD , respectively. Partial harvesting favoured PIHD but not PTSD. Conclusions This is one of the first studies to comprehensively evaluate a number of factors influencing naturally established tree seedlings at a broad landscape scale in the Northern Forest region of the eastern USA and Canada. Despite limitations associated with relatively small plot size, large seedling size class and lack of direct measurements of light, water and nutrients, this study documents the influence of these factors amid high variability associated with patterns of natural regeneration. The curvilinear relationship between mean annual temperature with SR and SD supports the argument that species richness and abundance usually have unimodal relationships with productivity indicators, whereas the curvilinear relationship between overstorey tree size diversity and SR and SD suggest that moderate overstorey diversity incorporates multiple species as well as higher seedling individuals. [ABSTRACT FROM AUTHOR]

Developing strategies for reducing atmospheric CO2 is one of the foremost challenges facing natural resource professionals today. The goal of this study was to evaluate total ecosystem and harvested wood product carbon (C) stocks among alternative forest management treatments (selection cutting, shelterwood cutting, commercial clearcutting, and no management) in mixed-species stands in central Maine, USA. These treatments were initiated in the 1950s and have been maintained since, and ecosystem C pools were measured in 2012. When compared across managed treatments, the commercial clearcut had the lowest total ecosystem C stocks by 21%, on average ( P < 0.05), while the selection and shelterwood treatments had similar total ecosystem C stocks. Including the C stored in harvested wood products did not influence observed differences in C storage among treatments. Total ecosystem C stocks in the reference stand were 247.0 ± 17.7 Mg·ha−1 (mean ± SD) compared with 161.7 ± 31.3 Mg·ha−1 in the managed stands (171.2 ± 31.7 Mg·ha−1 with products C). This study highlights the impacts of long-term forest management treatments on C storage and indicates that the timing of harvests and the species and sizes of trees removed influence C stored in harvested wood products. [ABSTRACT FROM AUTHOR]

LiDAR has become a common means for providing area-based estimates of forest attributes. An important, yet understudied, factor influencing forest attribute estimation is the prediction cell size. In this analysis, the influence of 6 prediction cell sizes (2-, 4-, 10-, 25-, 50-, and 100-m squares) and 2 prediction methods (parametric vs. nonparametric) on LiDAR-derived, area-based, stand- and forest-level estimates of total volume at 2 separate locations in the Acadian Forest were evaluated and compared to field-based measurements. Statistically significant differences (p< 0.05) were observed between the field-based and some of the LiDAR-derived volumes. The stand-level percent differences between the various cell sizes ranged from < 0.1 to >38%, and were generally greatest at the 2 extreme cell sizes examined (2 m and 100 m). At the forest-level, all LiDAR-estimates were within the 95% confidence interval for the ground-based estimate, except for a few values from smaller cell sizes. There was also a general trend of smaller cell sizes producing lower values of volume, whereas larger cell sizes tended to give higher estimates. Overall, this study highlights the importance cell size influence on stand- and forest-level estimates and indicates that additional work is needed to better understand the trends identified in this analysis. [ABSTRACT FROM PUBLISHER]

Dead wood pools are strongly influenced by natural disturbance events, stand development processes, and forest management activities. However, the relative importance of these influences can vary over time. In this study, we evaluate the role of these factors on dead wood biomass pools across several forest management alternatives after 60 years of treatment on the Penobscot Experimental Forest in central Maine, USA. After accounting for variation in site quality, we found significant differences in observed downed coarse woody material (CWM; ≥7.6 cm small-end diameter) and standing dead wood biomass among selection, shelterwood, and commercial clear-cut treatments. Overall, total dead wood biomass was positively correlated with live tree biomass and was negatively correlated with the average wood density of nonharvest mortality. We also developed an index of cumulative harvest severity, which can be used to evaluate forest attributes when multiple harvests have occurred within the same stand over time. Findings of this study highlight the dynamic roles of forest management, stand development, and site quality in influencing dead wood biomass pools at the stand level and underscore the potential for various outcomes from the same forest management treatment applied at different times in contrasting stands. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tree and log diameters are usually measured outside bark, but inside-bark diameters are of greater economic interest and are often derived with local or regional bark thickness equations. To date, the influence of measurement method, sampling design, and sample size on bark thickness equation accuracy and precision has received limited attention. The objectives of this study were to use an extensive regional bark thickness dataset for Norway spruce ( Picea abies (L.) Karst) in southwestern Germany to (1) quantify the accuracy and precision of bark thickness measurements with a Swedish bark gauge, (2) determine the required number of measurements to assess the within-tree variation, and (3) estimate the required sample sizes per plot and per region to develop an accurate bark thickness prediction equation. Bark gauge readings were validated with measurements derived from X-ray computed tomography (CT) and indicate that Swedish bark gauges generally overestimated bark thickness by 13.6% ± 28.4% (mean ± standard deviation). Results suggested having at least one measurement location every 2 m along a tree bole and at least five bark thickness measurements per each of these locations to achieve an allowable error of <15%. For the study area, Monte Carlo simulations indicated that a total sample size of 50-250 trees was needed, depending on the complexity of the desired bark thickness model. Overall, this analysis indicated that there was relatively high within- and between-tree variation in bark thickness, but adequate sampling methods and sample sizes produced highly accurate bark thickness equations. [ABSTRACT FROM AUTHOR]

Thinning is believed to accelerate the development of late-successional attributes, thereby enhancing stand structural heterogeneity in young, secondary forests. By making use of a large-scale experiment implemented in 40- to 60-year-old coastal Douglas-fir ( Pseudotsuga menziesii (Mirbel) Franco) forests, we addressed the following objectives: ( i) determine the effect of three thinning treatments on the temporal dynamics (first 11 years after thinning) of key forest structure measures, ( ii) evaluate the relationships between spatially explicit structural diversity measures and spatially nonexplicit stand metrics, and ( iii) test the relationships between stand structure and observed periodic stand volume growth, ingrowth, and mortality. Treatments consisted of high-density, moderate-density, and variable-density thinnings-from-below, as well as a control. Differences in stand structural heterogeneity between treatments were mostly nonsignificant. However, our results suggest that variable-density stands displayed structural enrichment as tree size and tree species diversity increased throughout the study period as a result of continuous ingrowth of species other than Douglas-fir. Simple spatially nonexplicit metrics could not be used to reliably model spatially explicit structural diversity measures. The inclusion of structural and species diversity measures only rarely improved accuracy of sample plot level growth, ingrowth, and mortality prediction models. Despite the short-term nature of this study, we conclude that variable-density thinning shows promise in increasing structural heterogeneity in young even-aged stands. The inclusion of structural diversity measures in growth and mortality models may be beneficial, but further work is needed to clarify the underlying relationships, particularly at the individual-tree level. [ABSTRACT FROM AUTHOR]

The advancement of science requires the timely and effective communication of important findings, which often takes the form of peer-reviewed journal articles. In the past decade, there has been significant changes in the world of scientific publishing with the rise of e-journals, open-access articles, and a greater volume of manuscript submissions. However, the overwhelming majority of journals (particularly in forestry) rely on a traditional peer-review model, which is often ineffcient and ineffective. In this discussion, I evaluate the core assumptions of traditional peer-review processes, assess current alternatives to traditional peer-review, and provide recommendations for authors, reviewers, Associate Editors, and Editors. Overall, the intent of the discussion is to raise the importance of this issue and provide some suggestions for change. [ABSTRACT FROM AUTHOR]

Various methods for predicting annual tree height increment (∆ht) and height-to-crown base increment (∆hcb) were developed and evaluated using remeasured data from permanent sample plots compiled across the Acadian Forest of northeastern North America. Across these plots, 25 species were represented upon which total height (ht) measurements were collected from mixed-species stands displaying both single- and multi-cohort structures. For modeling ∆ht, development of a unified equation form was found to result in higher accuracy and less bias compared to a maximum-modifier approach. Incorporating species as a random effect resulted in predictions that were not significantly different compared to predictions from species-specific equations for nine of the ten most abundant species examined. For ∆hcb, equations that modeled changes in hcb over two time periods (i.e., an incremental approach) were either not significantly different from or significantly closer to zero compared to predictions that estimated hcb at two time periods (i.e., a static approach). Results highlight the advantages of incorporating species as a random effect in individual-tree models and demonstrate the effectiveness of modeling tree crown recession directly for application in mixed-species forest growth and yield models. [ABSTRACT FROM AUTHOR]

Numerous models are available in northeastern North America to estimate aboveground tree biomass, yet most have focused on trees ≥12.5 cm diameter, and these models are often poor predictors of small tree biomass (<12.5 cm diameter). Additionally, models available to estimate small tree biomass often lack independent evaluation with field data. We developed a new set of additive biomass component (foliage, branch, and bole) models for five naturally regenerated hardwood species, four hybrid poplar (Populus sp.) clones and white spruce (Picea glauca (Moench.), from trees in eastern Maine. Biomass measurements were used to evaluate national and regional models for naturally regenerated species, and species specific models for planted stock. Results showed that the regional and national models provided similar fits for total biomass across all species. Moreover, the small tree biomass models currently used to predict woody biomass substantially underestimated biomass from 19.0% to 36.6% for all of the naturally regenerated hardwood species at our site. This substantial underestimation of small tree biomass may have contributed to the recent 34% decrease in estimated sapling woody biomass, and 15% decrease in aboveground biomass of all living trees in Maine, where nearly one-quarter of the 70,000 km2 forestlands are dominated by saplings. This problem may exist in other regions of the United States if substantial proportions of forestlands are dominated by small trees. Further model evaluation is warranted to assess the performance of the current models. [ABSTRACT FROM AUTHOR]

Forest inventory is vital to all aspects of forest management and inventory methods can vary greatly in their accuracy, precision, efficiency and cost. In Maine, much of the forestland base has been managed using partial harvesting methods over the past two decades. These partial harvesting methods generally produce highly heterogeneous stand structures and composition. Consequently, it is currently unclear which inventory methods are best given the distinct spatial and structural heterogeneity that is created. We compared efficiency and stand-level inventory estimates using horizontal point, fixed area and horizontal line sampling measurement methods in 16 partially harvested stands across northern and central Maine. Some stand-level variables were sensitive to measurement method (e.g., volume, quadratic mean diameter and small stem density and basal area), while others were less sensitive (e.g., overall basal area and stem density). Efficiency, defined as a combination of precision of volume estimates and measurement time, varied among measurement methods at lower stand basal area values but was similar at higher basal area, with the exception of the fixed area method. Overall, horizontal line sampling proved to be a viable method in post-partial harvest stand conditions. Our results illustrate the trade-offs between precision and time costs involved in several measurement methods under a range of heterogeneous stand conditions. [ABSTRACT FROM AUTHOR]

Spruce budworm (Choristoneura fumiferana; SBW) outbreaks in the northeastern USA and Canada are recurring phenomena leading to large-scale mortality of spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.) forests as susceptibility to SBW is primarily determined by the availability of host species and their maturity. Our study examined several satellite remote sensing (Sentinel-1 C-band synthetic aperture radar (SAR), PALSAR L-band SAR, and Sentinel-2 multispectral) and site variables over space and time to develop a method to produce large-scale SBW stand impact types and susceptibility maps in Maine, USA. We used two machine-learning algorithms (Random Forest, RF; Multi-Layer Perceptron, MLP) to map SBW host species where RF produced better results than MLP. Our best model with site (elevation and aspect) and Sentinel-2 data attained an overall accuracy (OA) of 83.4%. However, the addition of SAR variables did not improve the model further. Combining host species data with age data retrieved from Land Change Monitoring, Assessment, and Projection (LCMAP) products, we demonstrated that SBW susceptibility map (based on stand impact types) could be produced with an OA of 88.3%. The fine spatial resolution (20 m) maps derived from our study provide reliable products for landscape-level SBW interventions in the region. [ABSTRACT FROM AUTHOR]

Leaf traits are expressed either as area-based content (g m−2) or mass-based concentration (mg/g or %) and subsequently scaled to canopy level using leaf area index (LAI) and foliage biomass, respectively. Despite their wide use, it remains unclear whether the two trait expressions and scaling options (by LAI or foliage biomass) yield the same prediction accuracy using multispectral remote sensing data and additional site variables. In this study, we used LAI (m2 m−2) and foliage biomass (kg) to upscale area-based content [leaf mass per area (LMA), equivalent water thickness (EWT), nitrogen (N area) and potassium (K area)] and mass-based concentration [leaf dry matter content (LDMC), leaf water content (LWC), N mass , and K mass)], respectively in two contrasting study sites (coastal vs inland) for an ecologically important yet distinct regional species (red spruce , Picea rubens Sarg.) in Maine, USA. We used partial least squares regression (PLSR) and random forest (RF) to examine the effect of these metrics on model performance and prediction accuracy at both leaf- and canopy-scales using Sentinel-2 satellite bands, vegetation indices, as well as several topographical variables and depth to water table (DWT). The best-performing models (based on cross-validated normalized root mean square error: nRMSE) were used to generate high-resolution canopy trait maps. Results demonstrated that prediction accuracies of area-based leaf traits were generally higher (R2 = 0.41–0.65, nRMSE = 0.14–0.20) than the mass-based concentration (R2 = 0.42–0.65, nRMSE = 0.15–0.25) for both sites and modelling techniques. We also observed that canopy traits upscaled by LAI yield better prediction accuracies (R2 = 0.44–0.70, nRMSE = 0.17–0.20) compared to a foliage biomass upscaling approach (R2 = 0.41–0.57, nRMSE = 0.19–0.24). Among site variables, DWT was a significant variable in modeling canopy traits and showed robust behavior across the contrasting study sites. Overall, our study highlights the importance of trait expression and how it affects foliar trait retrieval accuracy at both leaf- and canopy-scale using satellite multispectral data and site variables. These findings have implications on leaf trait modelling especially in the context of monitoring dynamics in forest health and productivity from space. [ABSTRACT FROM AUTHOR]

Tree basal area (ba) or diameter at breast height (dbh) are universally used to represent tree secondary growth in individual tree based growth models. However, the long-term implications of using either ba or dbh for predictions are rarely fully assessed. In this analysis, Δba and Δdbh increment equations were fit to identical datasets gathered from six conifer and four hardwood species grown in central Maine. The performance of Δba and Δdbh predictions from nonlinear mixed-effects models were then compared with observed growth measurements of up to 29 years via a Monte Carlo simulation. Two evaluation statistics indicated substantial improvement in forecasting dbh using Δdbh rather than Δba. Root mean squared error (RMSE) and percentage mean absolute deviation (MAD%) were reduced by 14% and 15% on average, respectively, across all projection length intervals (5-29 years) when Δdbh was used over Δba. Differences were especially noted as projection lengths increased. RMSE and MAD% were reduced by 24% when Δdbh was employed over Δba at longer projection lengths (up to 29 years). Simulations found that simulating random effects rather than using local estimates for random effects performed as well or better at longer interval lengths. These results highlight the implications that selecting a growth model dependent variable can have and the importance of incorporating model uncertainty into the growth projections of individual tree based models. [ABSTRACT FROM AUTHOR]

Forest tree ingrowth is a highly variable and largely stochastic process. Consequently, predicting occurrence, frequency, and composition of ingrowth is a challenging task but of great importance in long-term forest growth and yield model projections. However, ingrowth data often require different statistical techniques other than traditional Gaussian regression, because these data are often bounded, skewed, and non-normal and commonly contain a large fraction of zeros. This study presents a set of regression models based on discrete Poisson and negative binomial probability distributions for ingrowth data collected from permanent sample plots in the Acadian Forest Region of North America. Models considered here include regular Poisson, zero-inflated Poisson (ZIP), zero-altered Poisson (ZAP; hurdle Poisson), regular negative binomial (NB), zero-inflated negative binomial (ZINB), and zero-altered negative binomial (ZANB; hurdle NB). Plot-level random effects were incorporated into each of these models. The ZINB model with random effects was found to provide the best fit statistics for modeling annualized occurrence and frequency of ingrowth. The key explanatory variables were stand basal area per hectare, percentage of hardwood basal area, number of trees per hectare, a measure of site quality, and the minimum measured diameter at breast height of each plot. A similar model was developed to predict species composition. All models showed logical behavior despite the high variability observed in the original data. [ABSTRACT FROM AUTHOR]

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA's MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited ( R2 of 36%-56%), while the relationship between GPP and climate ( R2 of 76%-91%) was stronger than the one between SI and climate ( R2 of 68%-78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0-5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively. [ABSTRACT FROM AUTHOR]

In many situations, information on stem diameters inside bark (dib) are more desirable than on diameters outside bark (dob). However, obtaining dib measurements is usually expensive, time-consuming, and prone to significant measurement errors when done on standing trees. Many bark thickness equations have been proposed to estimate the dibs of standing trees. In this study, we compared several commonly used bark thickness equations for seven conifer species in the Acadian Region of North America. Mixed-effects modeling techniques were employed to fit linear and non-linear bark thickness equations. We found the equation proposed by Cao and Pepper (South J Appl Forestry 10:220-224, 1986; Eq. 5) performed significantly better than other equations for most of our study species. The Cao and Pepper (South J Appl Forestry 10:220-224, ) equation is a function of dob, relative height in the stem, tree height, and the ratio of dib to dob at breast height. The mean absolute bias was found to be reduced up to 74% compared with using a fixed ratio approach employed in the widely used Northeastern variant of the Forest Vegetation Simulator (FVS-NE) growth and yield model. Leave-one-out cross validation was further performed to determine the location of suitable prior measurements in the prediction process for three of the most well-behaved equations. Results show that no unified prior measurement can provide best predictive abilities across all species as the choice of prior dib measurements depends on both species and bark thickness equations. [ABSTRACT FROM AUTHOR]

The volume increment of individual trees is often inferred from a volume or taper equation and predicted or observed diameter and height increments. Prediction errors can be compounded with this type of approach because of the array of equations used and differences in their accuracy. The consequences of several alternative approaches for indirectly or directly estimating individual tree volume increment were examined using an extensive stem analysis data set of Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in southwest Oregon. The data were used to construct new stem volume, taper, and volume increment equations, which were then used to compare predicted and observed 5-year volume increments. The results of this analysis suggest that the indirect prediction of volume increment is sensitive to both the approach used for estimating stem volume and the use of actual versus predicted diameter and height increment, especially diameter increment. In addition, using the indirect method of volume and taper equations was found to have a slightly lower level of accuracy in predicting stem volume increment than the direct method. It was found that the use of local calibration procedures could help to mitigate possible problems with the bias incurred by using predicted rather than actual diameter increment. [ABSTRACT FROM AUTHOR]

Swiss needle cast (SNC) causes premature loss of foliage and subsequent growth decline in Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). Although the mechanisms leading to this growth decline include loss of photosynthetic surface area and physiological disruption of surviving foliage, estimating the relative contribution of these two primary sources requires precise quantification of SNC effects on total foliage mass, foliage age-class structure, and vertical foliage distribution. The effect of SNC severity on these crown structural attributes was tested across a range of stand densities and site qualities in 10- to 60-year-old plantations in north coastal Oregon. Foliage mass in each age-class was sampled at the branch level, and the resulting equations were applied to all live branches on intensively measured sample trees. Vertical distribution of each foliage age-class was described by a beta distribution fitted to each sample tree, and sources of variation in vertical distribution were tested by regressing beta parameter estimates on SNC intensity and other covariates representing tree, stand, and site attributes. Distribution of foliage mass by age-class and by relative height in the crown was significantly affected by SNC severity, in addition to other covariates such as crown size and tree social position. SNC caused a reduction in the amount of foliage in each age-class and greater relative representation of younger needles. SNC also shifted the mode of relative vertical distribution toward the top of the tree for the three youngest foliage age-classes, but toward the base of the crown for 4- and 5-year-old foliage. Quantification of foliage age-class structure and vertical distribution across a range of SNC severity has helped to establish diagnostic criteria for assessing changes in crown structure that precede declines in growth and vigor. The induced changes in crown structure will also help to identify the relative contribution of several mechanisms causing growth losses in diseased trees. [ABSTRACT FROM AUTHOR]

[Display omitted] • Climate zone shifts of northeastern US and maritime Canada delineated and characterized. • Based on differing spatial resolutions, optimal zones of 3, 9, 15, and 21 were determined. • Current high diversity of distinct climate zones in region is anticipated to decrease. • Projected shifts have important implications for regional forest structure and productivity. Climate zones play a significant role in shaping the forest ecosystems located within them by influencing multiple ecological processes, including growth, disturbances, and species interactions. Therefore, delineation of current and future climate zones is essential to establish a framework for understanding and predicting shifts in forest ecosystems. In this study, we developed and applied an efficient approach to delineate regional climate zones in the northeastern United States and maritime Canada, aiming to characterize potential shifts in climate zones and discuss associated changes in forest ecosystems. The approach comprised five steps: climate data dimensionality reduction, sampling scenario design, cluster generation, climate zone delineation, and zone shift prediction. The climate zones in the study area were delineated into four different orders, with increasing subzone resolutions of 3, 9, 15, and 21. Furthermore, projected climate normals under Shared Socioeconomic Pathways 4.5 and 8.5 scenarios were used to predict the shifts in climate zones until 2100. Our findings indicate that climate zones characterized by higher temperatures and lower precipitation are expected to become more prevalent, potentially becoming the dominant climate condition across the entire region. Thes changes are likely to alter regional forest composition, structure, and productivity. In short, such shifts in climate underscore the significant impact of environmental change on forest ecosystem dynamics and carbon sequestration potential. [ABSTRACT FROM AUTHOR]

The pulp and paper industry in Indonesia is the tenth largest producer in the world, with Acacia and Eucalyptus as the main genera used for production; however, limited publications exist related to Eucalyptus growth models in Indonesia compared with other regions. Time-based models have been developed in which height, stand density, and basal area are predicted based on initial conditions and age. In contrast, a state–space approach utilizes the rate of change of these three state variables. Previous direct comparisons of these two approaches are generally limited. Consequently, the objective of this study was to compare two stand-level growth modeling approaches for Eucalyptus hybrid species on Sumatera (Sumatra) Island using both time-based and state–space methods. Our results indicate that dynamic models using either time-based or state–space approaches are adequate for predicting stand parameters to rotation age. A modified Bazukis matrix indicated that the behavior of both methods produced reliable predictions that were biologically reasonable in terms of stand development; however, the time-based approach provided better performance than the state–space approach on a variety of equivalence tests and goodness-of-fit statistics. Overall, the analysis highlights the advantages and disadvantages of these two commonly used, yet highly contrasting, stand-level growth modeling approaches, which need further consideration and evaluation. [ABSTRACT FROM AUTHOR]

Spruce budworm (Choristoneura fumiferana ; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K : 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region. [ABSTRACT FROM AUTHOR]

This study investigates the combined effects of climate and socioeconomic change on fiber supply and forest carbon in Maine, USA, for broad alternative futures. We conduct an econometric analysis to project forest resource use over the next 80 years under a range of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). Results show that continued forest successional dynamics – without any harvesting – contribute the most to Maine's aboveground carbon (AGC) accumulation, with 2100 AGC potentially increasing by 140% compared to 2020. On this basis, climate change could result in 2.44–2.64 times greater AGC in 2100 compared to today. Harvest activities are major drivers of forest C dynamics, resulting in 2100 AGC being only 16% >2020. Socioeconomic factors (SSPs) had much larger effects on total harvest and carbon stocks than climate change (RCPs). Harvest volume were projected to increase by 9–29% between 2020 and 2100 for favorable socioeconomic development scenarios (SSP1/SSP2/SSP5) and decrease by 3–29% for unfavorable socioeconomic development scenarios (SSP3/SSP4). Overall, Maine's forest C pools were projected to increase by end-century for RCPs x SSP1/SSP2. This study offers valuable insight on possible methods for region-specific socioeconomic and climate change assessments, particularly in areas with extensive and diverse working forests with mixed ownership. • Maine's future forest carbon is driven primarily by successional and harvest dynamics. • Climate change influences future carbon dynamics up to 16% for most severe conditions. • Socioeconomic factors are more important than climate change on Maine's future carbon. • Forest carbon only increases in medium and high socioeconomic development scenarios. [ABSTRACT FROM AUTHOR]

This research compares the capabilities of various Sentinel-2-derived spectral vegetation indices (SVIs) in particular red-edge SVIs to detect and classify spruce budworm (Choristoneura fumiferana) (SBW) defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The results showed the superiority of RF in model building for defoliation detection and classification into three classes (nil, light, and moderate) with overall errors of 17% and 32%, respectively. The most important variables for the best model were Enhanced Vegetation Index 7 (EVI7), Modified Chlorophyll Absorption in Reflectance Index (MCARI), Inverted Red-Edge Chlorophyll Index (IRECI), Normalized Difference Infrared Index 11 (NDII11) and Modified Simple Ratio (MSR). Red-edge SVIs were more effective variables for light defoliation detection compared to traditional SVIs such as Normalized Difference Vegetation Index (NDVI) and EVI8. These findings can help improve current remote sensing-based SBW defoliation detection and monitoring. [ABSTRACT FROM AUTHOR]

A Bayesian probabilistic modeling platform was used and evaluated for application in a relatively complex individual-tree growth and yield model for coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco), which was expressed as a mixed discrete and continuous Bayesian Network for annual projections. The modeling platform used a common and open-source Bayesian analysis program (JAGS v3.3.0), and was sufficiently exible to handle a relatively complex model structure; namely, a differential form, highly dynamic, recursive, hierarchical, non-linear system of equations with rather complex error structures. This novel probabilistic modeling platform met certain desirable criteria, including: (1) accurate and tractable projections that included full error propagation; (2) exible and comprehensive analytic capabilities; (3) full consideration of hierarchical and multi-level model structures; (4) capacity for random effects calibration; (5) allowance of hypothesis testing and updating knowledge across different system components, simultaneously with varying sources of information (i.e., new data); (6) computational efficiency; and (7) relatively simple implementation as demonstrated in a compiled scripting language. Probabilistic projections of forest growth and yield included all sources of errors and uncertainty (e.g., estimated parameters, state variables, random effects, and residual errors). Cumulative error projections over a 40-year period for three sample Douglas-fir stands were determined. Projection errors for key metrics summed across all trees, such as total basal area and stem density, had coefficient of variations between 4-6% and 7-8%, respectively. Probabilistic projections were markedly different from deterministic projections made with the same model structure. Overall, this novel probabilistic platform showed strong promise as a general platform for ecological modeling, particularly when tractable and analytically correct error projections are required. In particular, the Bayesian probabilistic modeling approach used provided a natural platform for cross-disciplinary research, particularly between social and ecological research domains. [ABSTRACT FROM AUTHOR]

Tourism is an important industry to many regions around the world and has the potential to substantially impact local communities. Climate change is expected to influence tourism since weather patterns help determine where and when people travel. In this analysis, the effect of weather conditions on tourism-related spending at three geographically distinct locations in Maine, United States, was evaluated. A nonparametric method (boosted regression trees) was used to first identify the relative influence of twenty-two weather variables as predictors of tourism spending. Following this, a parametric model was constructed to statistically evaluate tourism spending across different measures and predict potential spending changes due to a warming climate. Results indicated that warmer temperatures increased tourism spending in the summer and fall, but had more varying results in the winter. Findings suggest tourism businesses in Maine and other relatively colder destinations could capitalize on potential gains in warmer months. [ABSTRACT FROM AUTHOR]

Abstract: A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year‐to‐year variability in growth. Numerous dendrochronological (tree‐ring) studies have identified climate factors that influence year‐to‐year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand‐level (as opposed to species‐level) growth. We argue that stand‐level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed‐species stand at the Howland Research Forest of central Maine, USA, we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand‐level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year‐to‐year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand‐level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species‐ and canopy‐position level. Our climate models were better fit to stand‐level biomass increment than to species‐level or canopy‐position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand‐level growth varies less from to year‐to‐year than species‐level or canopy‐position growth responses. By assessing stand‐level growth response to climate, we provide an alternative perspective on climate–growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate. [ABSTRACT FROM AUTHOR]

Many communities rely on tourism spending, so it is important to understand any potential changes to tourist flows resulting from changing climate and weather patterns. However, tourists are not a homogenous group, as they have different motivations, values, and goals. Therefore, the purpose of this investigation is to better understand potentially varying perceptions and behavior of different tourist types, specifically in regards to their weather sensitivity, climate change concern, and behavioral intention for climate change mitigation. Tourists were randomly surveyed at 20 locations throughout the state of Maine in the United States (n = 704). Segmentation analysis on the activities tourists participated in yielded three segments of Maine tourists: non-nature-based tourists (50.6%), nature-based generalists (16.2%), and nature-based specialists (33.2%). Differences across segments were explored for perceptions of weather impacts, climate change concern, and mitigation intent. Additionally, weather sensitivity was analyzed based on type of overnight accommodations to better understand if this also had a role in differences. Non-nature-based tourists thought that weather variables were less influential during their travels in Maine than the other segments, while nature-based generalists perceived weather to have the highest influence. Additionally, nature-based specialists had the highest level of climate change belief, while nature-based generalists had the highest willingness to engage in climate change mitigation behavior. Results are useful to understand how segments of tourism demand may be altered with a changing climate, such as increased temperatures, precipitation, and storms, and what groups may be the most beneficial to target for marketing or educational efforts to reduce the impact of climate change. [ABSTRACT FROM AUTHOR]

Commercial thinning (CT) is an important silvicultural practice in the northeastern United States. Relatively little is known, however, about the interaction of harvest system and treatment (removal intensity or timing of entry) on the overall profitability of CT. To address this question, 10-year measurements from a controlled CT experiment across six sites in Maine were used to project the long-term effect of removal intensity (33% and 50% relative density reduction) and timing of entry (no delay, 5-year delay, 10-year delay) on ( i) maximum net present value (NPV), ( ii) timing of maximum NPV, and ( iii) the effect of three harvesting systems (cut-to-length, whole-tree, hybrid systems) on maximum NPV. A regional growth and yield model was used to project individual-tree growth and mortality into the future. Harvest costs for the harvesting systems were estimated using regional cycle-time equations. No difference was found in maximum NPV between the CT and non-CT areas or the timing of CT entry. Stand age at time of maximum NPV differed between delays but not between the two removal intensities. Our results indicate that there is no economic benefit in delaying a CT or removing more volume at the time of thinning for the range of stand conditions evaluated. [ABSTRACT FROM AUTHOR]

Intelligent management of power and spectrum is the most important ingredient in creating wireless sensor networks with high reliability and longevity. The main application under study in this paper is accurate monitoring of forest ecosystems using high spatio-temporal resolution. High cost of the current systems and their power consumption limits wide spread use of these systems limiting the accuracy of current models. This project utilizes artificial intelligence and machine learning to learn the changes in the wireless network and environment, producing power efficient systems that are low cost to enable large scale monitoring. The proposed system was built at the University of Maine's Wireless Sensor Networks (WiSe-Net) laboratory in collaboration with University of New Hampshire and University of Vermont researchers for soil moisture measurement with provision to include other sensor types at later stages. [ABSTRACT FROM AUTHOR]

Forest landscape models (FLMs) have become a valuable tool for projecting broad-scale forest dynamics, but incomplete knowledge about model behavior can make parameterization challenging and outcomes unreliable. FLMs generally model forest growth as a set of interacting processes, and, consequently, predictions can be influenced by process or parameter uncertainty. A sensitivity analysis can potentially help identify sources of uncertainty, but if it does not use global measures of sensitivity nor consider that sensitivity in a process-based model is likely time-dependent, results could be misleading. Our aim was to evaluate the sensitivity of nine key parameters when predicting live aboveground biomass (AGB) with the widely used FLM, LANDIS-II. To fully explore parameter interactions and nonlinear model behavior, we selected a range of parameter values based on LANDIS-II applications in North America that was considerably wider than in previous local sensitivity analyses. Our results showed commonalities with previous studies, which concluded the maximum allowable biomass and maximum annual net primary productivity specified for a species were most influential when predicting AGB. In contrast to earlier work, we also clearly demonstrated how relative importance was time-dependent for all but the least important parameters. Interactions between parameters and with simulation duration generated substantial variability in AGB and number of cohorts established. Results will improve future calibration efforts and may offer insight into opportunities for possible model refinements. This study also suggests, however, that parameters which cannot be calibrated based on empirical data will continue to be a major source of model uncertainty. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) has become a common means for predicting key forest structural attributes, but comparisons of alternative statistical methods and the spatial extent of LiDAR metrics extraction on independent datasets have been minimal. The primary objective of this study was to assess the performance of local and non-local LiDAR aboveground biomass (AGB) prediction models at two locations in the Acadian Forest. Two common statistical techniques, nonlinear mixed e ects (NLME) and random forest (RF), were used to t the prediction models and compared. Finally, this study evaluated the inuence of alternative plot radii for LiDAR metrics extraction on model t and prediction accuracy. AGB models were independently developed at each forest and tested both locally (model applied to same forest used for development) and non-locally (model applied to di erent forest) using an extensive network of ground-based plots. In general, RF was found to outperform NLME when applied locally, but the di erences between the approaches were negligible when applied to the non-local dataset. NLME was found to perform equally well locally and non-locally. LiDAR extraction radius had very little inuence on model performance as well. Minimal di erences between models developed using xed- and variable-radius methods were found, while the optimal LiDAR extraction radius was not consistent among forests, statistical technique, or local vs. non-local. Overall, the results highlight the importance of a robust calibration dataset that covers the full range of observed variation for developing accurate prediction models based on remote sensing data. [ABSTRACT FROM AUTHOR]

Climate change is impacting forest ecosystems, which support key ecosystem services and the general well-being of natural resource-dependent communities in Northeastern, USA. Understanding the determinants of climate change risk perceptions among forest resource stakeholders is critical to eliciting broad support for adaptation. We examined social-psychological drivers of climate change risk perceptions using hierarchical regression based on an online survey of 211 forest stakeholders, representing a wide range of subsectors, in Maine, USA. Using the climate change risk perceptions model (CCRPM), we explained 70% of the variance in risk perception. Political orientation, belief in climate change, social norms, affect, and experience with weather-related impacts were all significant predictors of perceived risk. Mediation results demonstrate that experience with weather-related impacts influences risk perceptions indirectly via attribution and holistic affect. This study advances our understanding of the social-psychological determinants of climate change risk perceptions, with implications for communication and outreach strategies. [ABSTRACT FROM AUTHOR]

Despite the importance of financial outcomes to stand-level forest management decision-making, long-term assessments are rare. We compared the quality and value of mixed, northern conifer (Picea–Abies–Tsuga) stands in Maine, USA, treated with a range of silvicultural systems and exploitive cuttings for 65 years. Ten replicated treatments — single-tree selection and uniform shelterwood systems, commercial clearcutting, fixed and guiding diameter-limit cutting, and no cutting — were assessed for quality and value of standing and harvested trees. Selection systems resulted in good tree quality and high stand value, particularly when applied on a short cutting cycle, but small harvest volumes were not always commercially operable. Shelterwood system resulted in good tree quality, but changes in species values over time influenced financial outcomes. In contrast, commercial clearcutting and fixed diameter-limit cutting resulted in poor tree quality and low residual stand value after multiple harvests. Guiding diameter-limit cutting resulted in high stand value and is more appropriately characterized as a silvicultural system than exploitive harvesting when applied with harvest volume limits and seed tree retention. Overall, treatments focused on short-term financial gain led to degraded stand conditions, while those that sought to grow high-quality trees resulted in desirable outcomes over the long term. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) affects forest-atmosphere fluxes and light interception rates and thus influences forest productivity. Early silvicultural treatments affect LAI partitioning among species of different shade tolerance in natural stands due to changes in composition and structure. We examined effects of species compositional objectives (conifer, hardwood, and mixedwood) and management intensity (low: thinning and/or release to 2,500 crop trees ha, medium: thinning and/or release plus planting to 2,500 crop trees ha, and a second year of vegetation control) on LAI and its vertical distribution in different shade tolerance groups over a 7-year period. Hardwood LAI increased by 242 and 318 % in the low and medium hardwood treatments, respectively, compared to a 123 % increase in the untreated control. Thinning possibly increased resource availability for upper stratum shade intolerant hardwoods, while also increasing light penetration through the canopy for midstory shade tolerant hardwoods. Conifer treatments substantially reduce overtopping hardwood LAI, facilitating an increase in conifer LAI by 281 and 378 % in the low and medium treatments, respectively. The height from the canopy base where LAI peaked increased between 29 and 80 % in the low mixedwood and medium hardwood treatments from pre-treatment through 7 years post-treatment. Comparatively, LAI at the peak increased by 16 and 36 % in the low and medium conifer treatments, respectively, with a minimal change in the height of the LAI peak. These contrasting responses were likely due to slower conifer reaction following treatment than hardwoods and stacking of conifer foliage within the middle portion of the canopy. [ABSTRACT FROM AUTHOR]

As concerns rise over potential effects of greenhouse gas related climate change on terrestrial ecosystems, forest managers require growth and yield modeling capabilities responsive to changing climate conditions. Our goal was to develop prediction models of site index for eastern US forest tree species with climate and soil properties as predictors for use in predicting potential responses of forest productivity to climate change. Species-specific site index data from the USDA Forest Service Forest Inventory and Analysis (FIA) program were linked to contemporary climate data and soil properties mapped in the USDA Soil Survey Geographic (SSURGO) database. Random forest regression tree based ensemble prediction models of site index were constructed based on 37 climate-related and 15 soil attributes. In addition to a species-specific site index, aggregate models were developed for species grouped into two broad categories: conifer (softwood) and hardwood (broadleaved) species groups. Species-specific models based on climate and soil predictors explained the most variation in site index of any models tested ( R2 = 62.5%, RMSE = 3.2 m). Comparable results were found when grouping species into conifer and hardwood groups ( R2 = 63.9%, RMSE = 4.6 m for conifers; R2 = 35.9%, RMSE = 4.2 m for hardwoods). Model predictions based on multiple global circulation models (GCMs) and Intergovernmental Panel on Climate Change (IPCC) development scenarios were tested for statistical significance using bootstrap resampling methods. Results showed significant increases over the 21st century in mean site index for conifers between +0.5 and +2.4 m. Over the same time period, mean hardwood site index showed decreases of as much as −1.7 m for the scenarios tested. The results demonstrate the utility of using climate and soils data in predicting site index across a large geographic region, and the potential of climate change to alter forest productivity in the eastern US. Additional investigation is needed to interpret spatial patterns and ecological relationships related to predictions from this type of model. [ABSTRACT FROM AUTHOR]