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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]

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Using periodic measurements from permanent plots in non-thinned and thinned Norway spruce (Picea abies (L.) H. Karst.) stands in Norway, individual-tree growth models were developed to predict annual diameter increment, height increment, and height to crown base increment. Based on long-term data across a range of thinning regimes and stand conditions, alternative approaches for modeling response to treatment were assessed. Dynamic thinning response functions in the form of multiplicative modifiers that predict no effect at the time of thinning, a rapid increase followed by an early maximum before the effect gradually declines to zero could not be fitted to initially derived baseline models without thinning related predictors. However, alternative approaches were used and found to perform well. Specifically, indicator variables representing varying time periods after thinning were statistically significant and behaved in a robust manner as well as consistent with general expectations. In addition, they improved overall prediction accuracy when incorporated as fixed effects into the baseline models for diameter and height to crown base increment. Further, more simply, including exponentially decreasing multiplicative thinning response functions improved prediction accuracy for height increment and height to crown base increment. Irrespective of studied attribute and modelling approach, improvement in performance of these extended models was relatively limited when compared to the corresponding baseline models and more pronounced in trees from thinned stands. We conclude that the largely varying and often multi-year measurement intervals of the periodic data used in this study likely prevented the development of more sophisticated thinning response functions. However, based on the evaluation of the final models’ overall performance such complex response functions may not to be necessary to reliably predict individual tree growth after thinning for certain conditions or species, which should be further considered in future analyses of similar nature. [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]

Highlights • Data harmonized from several structurally complex, mixed-species experimental forests. • Distance-dependent competition metrics included sophisticated light interception indices. • No general superiority of distance-dependent competition metrics over distance-independent counterparts. • Modest improvements for ΔDBH in shade-tolerant trees of lower crown classes post-harvest. • Model complexity strong factor affecting distance-dependent competition metric performance. Abstract Most studies comparing the predictive performance of distance-independent and -dependent competition metrics have been conducted in even-aged, single species stands. In addition, past studies have generally not considered more sophisticated distance-dependent competition metrics such as open sky-view or light interception indices. Using data from fully stem mapped inventory plots (n = 260) established in structurally complex, mixed-species experimental forests (n = 5) in the Northeastern US, we evaluated prediction performance of one- and two-sided distance-dependent as well as more conventional distance-independent competition metrics in both annualized diameter increment (n = 1735) and probability of survival models (n = 1953) developed across 26 contrasting softwood and hardwood species. Prediction accuracy of non-spatial and spatial models was further assessed by classifying observations by species shade-tolerance, relative tree height, time since harvest, and tree clumping. Our analyses revealed no general superiority of distance-dependent competition metrics over their distance-independent counterparts for either diameter increment or survival, which was particularly true for the latter variable. Distance-dependent metrics tended to outperform distance-independent ones for observations from shade-tolerant species, trees of lower crown classes, and immediately after harvest. However, results on the comparative analysis of the predictive power of the different types of competition metrics under varying stand conditions and situations were often inconsistent and not always conclusive. In addition, model complexity, i.e. number of non-spatial explanatory variables in the base model, strongly affected the performance of distance-dependent competition metrics. Our findings thus appear to support the critical assumption that distance-independent competition metrics are sufficient for most operational growth and yield applications, even in managed, naturally-regenerated and species rich forests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tree height can be a time-consuming measurement to obtain accurately in the field. Thus, height-diameter equations are frequently used to minimize costs associated with inventories and to reduce problems associated with height measurement errors. In this analysis, we compare three methods for estimating height from diameter data for a variety of species: (1) a nonlinear mixed-effects (NLME) model; (2) k nearest neighbour (KNN) imputation; and (3) a copula model. Predicted height values were compared with field height measurements for 922 trees across 24 species using paired point-wise and distribution-based goodness-of-fit criteria. All approaches performed very well, with the NLME and KNN imputation having better point-wise goodness-of-fit measures. Copula models, although generally poorer in terms of the paired point-wise goodness-of-fit, adequately predicted height values, maintained variances observed in field data, and showed the least loss of functionality when applied to species with sparse data or data with atypical parameters. Overall, the copula approach is flexible and may be more appropriate for estimating heights where paired point estimate accuracy is less important such as for tree lists that are subsequently used as inputs into growth and yield models. [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]

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]

The overall goal of this study was to evaluate the correlation of multiple abiotic and biotic factors with organic-horizon (O-horizon) carbon (C) content on the Penobscot Experimental Forest in central Maine, USA. O-horizon samples were collected and their associated depths were recorded from stands managed with a range of silvicultural and harvesting treatments (i.e., selection, shelterwood, and commercial clearcut) and an unmanaged control. The overall mean for O-horizon C content from all samples was 25.6 ± 16.1 Mg ha −1 (mean ± SD). The samples were used to develop a pedotransfer function for predicting O-horizon C content from O-horizon depth ( R 2 = 0.47, RMSE = 1.6 Mg ha −1 ) so that an average of O-horizon C content could be calculated for permanent sample plots on which abiotic and biotic factors were quantified. O-horizon depth measurements recorded along transects on permanent sample plots were used to calculate plot average O-horizon C content. There were no significant differences in average predicted O-horizon C content among selection, shelterwood, and commercial clearcut treatments. However, variation in predicted O-horizon C content between stands where the same treatment was applied was statistically significant and was likely due to the timing of harvests and abundance of dead wood buried within O horizons. Depth to redoximorphic features, cartographic depth to water table or saturated zone, drainage class, fine woody debris mass, downed woody debris volume, tree basal area, and the relative basal area of conifer species were not significant predictors of predicted O-horizon C content at the plot level. When the individual predicted values of O-horizon C content were modeled, within-plot variation accounted for 83.8% of the variance. Bryophyte mass, which was predicted from bryophyte cover, only explained 1.2% of the variation in O-horizon C content at the microsite level. These results highlight the sizeable variability in O-horizon C content within and among these mixed-species stands with various forest management and natural disturbance histories. [ABSTRACT FROM AUTHOR]

Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner’s management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of ‘harvest readiness’ and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior. [ABSTRACT FROM AUTHOR]

Many forest management planning decisions are based on information about the number of trees by species and diameter per unit area. This information is commonly summarized in a stand table, where a stand is defined as a group of forest trees of sufficiently uniform species composition, age, condition, or productivity to be considered a homogeneous unit for planning purposes. Typically, information used to construct stand tables is gleaned from observed subsets of the forest selected using a probability-based sampling design. Such sampling campaigns are expensive, and hence, only a small number of sample units are typically observed. This data paucity means that stand tables can only be estimated for relatively large areal units. Contemporary forest management planning and spatially explicit ecosystem models require stand table input at higher spatial resolution than can be affordably provided using traditional approaches. We propose a dynamic multivariate Poisson spatial regression model that accommodates both spatial correlation between observed diameter distributions and also correlation between tree counts across diameter classes within each location. To improve fit and prediction at unobserved locations, diameter specific intensities can be estimated using auxiliary data such as management history or remotely sensed information. The proposed model is used to analyze a diverse forest inventory dataset collected on the United States Forest Service Penobscot Experimental Forest in Bradley, Maine. Results demonstrate that explicitly modeling the residual spatial structure via a multivariate Gaussian process and incorporating information about forest structure from Light Detection and Ranging (LiDAR) covariates improve model fit and can provide high spatial resolution stand table maps with associated estimates of uncertainty. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the southwestern United States, there are currently no regeneration models for the growth and recruitment of ponderoso pine (Pinus ponderosa C. Lawson var. scopulorum Engelm.) from seedlings to the sapling size class. The oblectives of this study were to (1) develop a height-age relationship for ponderosa pine seedlings on soils derived from sedimentary parent material following a shelterwood establishment cut and (2) use the model and existing regeneration inventory data to estimate seedling and sapling recruitment rates. Ages of 80 cross sections from 19 seedlings collected on the Mollogon Rim Ranger District in northern Arizona were used to construct the model. The model generally fit well and indicated that it takes ponderosa pine seedlings approximately 20 years to reach breast height. Using regeneration inventory data from four stands used to construct the model, estimated seedling recruitment was nearly continuous during the 1st decade following an establishment cut and, in most stands, recruitment declined exponentially. Based on these findings, we recommend that management activities, such as the use of prescribed fire, be conducted within at least 20 years after tree cutting to maintain a forest structure that is not vulnerable to high-intensity crown fire. [ABSTRACT FROM AUTHOR]

Abstract: To simulate stand-level impacts of climate change, predictors in the widely used Forest Vegetation Simulator (FVS) were adjusted to account for expected climate effects. This was accomplished by: (1) adding functions that link mortality and regeneration of species to climate variables expressing climatic suitability, (2) constructing a function linking site index to climate and using it to modify growth rates, and (3) adding functions accounting for changing growth rates due to climate-induced genetic responses. For three climatically diverse landscapes, simulations were used to explore the change in species composition and tree growth that should accompany climate change during the 21st century. The simulations illustrated the changes in forest composition that could accompany climate change. Projections were the most sensitive to mortality, as the loss of trees of a dominant species heavily influenced stand dynamics. While additional work is needed on fundamental plant–climate relationships, this work incorporates climatic effects into FVS to produce a new model called Climate–FVS. This model provides for managers a tool that allows climate change impacts to be incorporated in forest plans. [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]

Abstract: Simulating the influence of intensive management and annual weather fluctuations on tree growth requires a shorter time step than currently employed by most regional growth models. High-quality data sets are available for several plantation species in the Pacific Northwest region of the United States, but the growth periods ranged from 2 to 12 years in length. Measurement periods of varying length complicate efforts to fit growth models because observed growth rates must be interpolated to a common length growth period or those growth periods longer or shorter than the desired model time step must be discarded. A variation of the iterative technique suggested by Cao [Cao, Q.V., 2000. Prediction of annual diameter growth and survival for individual trees from periodic measurements. Forest Sci. 46, 127–131] was applied to estimate annualized diameter and height growth equations for pure plantations of Douglas-fir, western hemlock, and red alder. Using this technique, fits were significantly improved for all three species by embedding a multi-level nonlinear mixed-effects framework (likelihood ratio test: p <0.0001). The final models were consistent with expected biological behavior of diameter and height growth over tree, stand, and site variables. The random effects showed some correlation with key physiographic variables such as slope and aspect for Douglas-fir and red alder, but these relationships were not observed for western hemlock. Further, the random effects were more correlated with physiographic variables than actual climate or soils information. Long-term simulations (12–16 years) on an independent dataset using these annualized equations showed that the multi-level mixed effects models were more accurate and precise than those fitted without random effects as mean square error (MSE) was reduced by 13 and 21% for diameter and height growth prediction, respectively. The level of prediction error was also smaller than an existing similar growth model with a longer time step (ORGANON v8) as the annualized equations reduced MSE by 17 and 38% for diameter and height growth prediction, respectively. These models will prove to be quite useful for understanding the interaction of weather and silviculture in the Pacific Northwest and refining the precision of future growth model projections. [Copyright &y& Elsevier]

Spruce budworm (SBW) is the most destructive forest pest in eastern forests of North America. Mapping annual current-year SBW defoliation is challenging because of the large landscape scale of infestations, high temporal/spatial variability, and the short period of time when detection is possible. We used Landsat-5 and Landsat-MSS data to develop a method to detect and map SBW defoliation, which can be used as ancillary or alternative information for aerial sketch maps (ASMs). Results indicated that Landsat-5 data were capable of detecting and classifying SBW defoliation into three levels comparable to ASMs. For SBW defoliation classification, a combination of three vegetation indices, including normalized difference moisture index (NDMI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI), were found to provide the highest accuracy (non-defoliated: 77%, light defoliation: 60%, moderate defoliation: 52%, and severe defoliation: 77%) compared to using only NDMI (non-defoliated: 76%, light defoliation: 40%, moderate defoliation: 43%, and severe defoliation: 67%). Detection of historical SBW defoliation was possible using Landsat-MSS NDVI data, and the produced maps were used to complement coarse-resolution aerial sketch maps of the past outbreak. The method developed for Landsat-5 data can be used for current SBW outbreak mapping in North America using Landsat-8 and Sentinel-2 imagery. Overall, the work highlights the potential of moderate resolution optical remote sensing data to detect and classify fine-scale patterns in tree defoliation. [ABSTRACT FROM AUTHOR]