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Spruce–fir (Picea–Abies) forests of the North American Acadian Forest Region are at risk of disappearing from the northeastern United States and Canada due to climate change. Species distribution models (SDMs) have been used to predict changes in this critical transitional ecosystem in the past, but none have addressed how seasonal patterns of temperature and precipitation interact to influence tree species abundance. Inferences have also been limited by contemporary inventory data that could not fully characterize species ranges because they either, (1) only sampled species occurrence after large‐scale human disturbance and settlement, or (2) did not span critical geopolitical boundaries (e.g., the US–Canadian border) that intersect the focal species' range(s). Here, we built new SDM models to better assess the bioclimatic distribution of four spruce–fir species and to test the importance of seasonal climate interactions. We compiled an extensive database of tree occurrence and abundance from recent (~1955–2012) and historical time periods (1623–1869) to model current species distributions and to predict how these might change under future climate. We found that including historical tree data in our SDMs revealed previously unrecognized suitable habitat along the southern edge of species' contemporary ranges. Random forest models predicted occurrence with high accuracy (area under receiver operator curve >0.98), and the seasonal climate variables that emerged as most important for these cold‐adapted species all included interactions that reflected sensitivity to colder temperatures, and preferences for wet weather concentrated in the winter months. Under moderate climate warming (representative concentration pathway 6.0), the northeastern United States retained additional suitable habitat when historical data were included through 2060 for three of the four species: red spruce (Picea rubens), black spruce (Picea mariana), and balsam fir (Abies balsamea), while white spruce (Picea glauca) habitat contracted into Canada. In contrast, future predictions from models that used contemporary data alone forecast extirpation for all four species from the northeastern United States. Overall, these findings highlight that prediction of species ranges in transitional ecosystems that span geopolitical boundaries and gradients of intense land use are improved when historical data and seasonal climate interactions of both temperature and precipitation variables are incorporated. [ABSTRACT FROM AUTHOR]

• 1964–65 harvests were whole-tree, and stem-only with and without prescribed burning. • Stand structure and carbon stock did not differ among treatments 50 years later. • Hardwoods were most prevalent on sites where prescribed burning was conducted. • Pinus strobus was most prevalent on sites that had been whole-tree harvested. • Clearcutting resulted in composition shifts regardless of biomass removal treatment. Whole-tree harvesting (WTH) is increasingly used to extract forest biomass for energy and commercial wood products. Slash burning, which is used for fuels reduction and site preparation, also reduces aboveground biomass. Yet effects of incremental biomass reduction, from either WTH or slash burning, on long-term forest productivity and composition are poorly understood. This research uses data from a 50-year-old study in northern mixedwood (Picea – Abies – hardwood) stands on the Penobscot Experimental Forest in Maine, U.S.A., to address these concerns. Clearcutting was conducted in 1964–65 with WTH, stem-only harvesting (SOH), and SOH with post-harvest prescribed burning of logging residues (SOHB). Growing stock, composition, and soil properties (O horizon thickness and soil drainage) were measured 50 years after treatment. Hardwood composition (percent of total basal area) increased from pre-treatment levels in all treatments but was greater in SOHB than SOH and WTH. Eastern white pine (Pinus strobus), though a minor species, was significantly more abundant in WTH than SOH or SOHB. Results indicated no other significant differences in species composition, or in stand structure or productivity (total basal area, stem density, dominant height, quadratic mean diameter, and total above-ground carbon stock) among treatments. Independent of treatment, we observed relationships between soil properties and stem density and quadratic mean diameter (qmd), such that lower stem density and greater qmd were observed on sites with greater O horizon thickness. These findings suggest that relative to SOH, WTH and SOHB do not degrade northern mixedwood stand productivity as expressed by stand structure and stocking 50 years after a single treatment, even on a site with low to moderate production potential. Nevertheless, species shifts associated with clearcutting (i.e., shade-tolerant conifer to intolerant hardwood composition) and prescribed burning in this forest type should be considered in light of the potential application of either for intensive silviculture treatments. [ABSTRACT FROM AUTHOR]

Maximum stand density index (SDImax) is an important factor controlling stand dynamics that varies by species and region, but less is understood how it differs within a region for a given species. In this analysis, linear quantile mixed modeling (LQMM) and an extensive network of permanent plots were utilized to examine regional variation in the SDImax of 15 species (7 softwoods and 8 hardwoods) across the complex forests of the Acadian Region in North America. Observed plot-level SDImax was then linked to various stand, plant trait, site, and climatic factors and the spatial patterns throughout the region examined. Results indicated high variability of SDImax for a given species with northern hardwood species like yellow birch ( Betula alleghaniensis Britt.), sugar maple ( Acer saccharum Marsh.), and American beech ( Fagus grandifolia Ehrh.) having the lowest values (555 – 627), while softwood species such as northern white cedar ( Thuja occidentalis L.; 1014), eastern hemlock ( Tsuga canadensis (L.) Carr.; 1026), and white pine ( Pinus strobus L.; 967) had the highest values. Compared to the other stand and site factors examined, climate showed the strongest relationship with SDImax with R 2 ranging from 82 to 98%. Of the climatic variables examined, those representing growing season length and the timing of precipitation were most influential. For the majority of the species examined, reductions in SDImax were forecasted due to changing climatic conditions. Across species, mean SDImax was found to linearly decline with wood specific gravity and increase with leaf longevity, but showed limited relationships with other species-level functional traits. Overall, the analysis highlights the strong variability of SDImax within and between species as well as the important role that climate has on this attribute within a region. [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]

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

Background: Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods: In this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results: Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator (FVS-NE) was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions: Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there. [ABSTRACT FROM AUTHOR]

Introduced invasive pests are perhaps the most important and persistent catalyst for changes in forest composition. Infestation and outbreak of the hemlock woolly adelgid ( Adelges tsugae ; HWA ) along the eastern coast of the USA, has led to widespread loss of hemlock ( Tsuga canadensis (L.) Carr.), and a shift in tree species composition toward hardwood stands. Developing an understanding of the geographic distribution of individual species can inform conservation practices that seek to maintain functional capabilities of ecosystems. Modeling is necessary for understanding changes in forest composition, and subsequent changes in biodiversity, and one that can be implemented at the species level. By integrating the use of remote sensing, modeling, and Geographic Information Systems (GIS) coupled with expert knowledge in forest ecology and disturbance, we can advance the methodologies currently available in the literature on predictive modeling. This paper describes an approach to modeling the spatial distribution of the less common but foundational tree species eastern hemlock throughout the state of Maine (∼84,000 km 2 ) at a high resolution. There are currently no published accuracy assessments on predictive models for high resolution continuous distribution of eastern hemlock relative basal area that span the geographic extent covered by our model, which is at the northern limit of the species’ range. A two stage mapping approach was used where presence/absence was predicted with an overall accuracy of 85% and the continuous distribution (percent basal area) was predicted with an accuracy of 84%. Overall, these findings are quite good despite high variability in the training dataset and the general minor component that eastern hemlock represents in the primary forest types in Maine. Eastern hemlock occurs along the southern half of the state stretching the east-west span with little to no occurrence in the northern regions. Several environmental and site characteristics, particularly average yearly maximum and minimum temperatures, were found to be positively correlated with hemlock occurrence. Eastern hemlock dominated stands appeared predominantly in the southwest corner of the state where HWA monitoring efforts can be focused. Given the importance of climate variables in predicting eastern hemlock, forecasts of future range shifts should be possible using data generated from climate scenarios. [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]

The objective of this study was to evaluate the applicability of using a low-density (1-3 points m-2) discrete-return LiDAR (Light Detection and Ranging) for predicting maximum tree height, stem density, basal area, quadratic mean diameter and total volume. The research was conducted at the Penobscot Experimental Forest in central Maine, where a range of stand structures and species composition is present and generally representative of northern Maine's forests. Prediction models were developed utilizing the random forest algorithm that was calibrated using reference data collected in fixed radius circular plots. For comparison, the volume model used two sets of reference data, with one being fixed radius circular plots and the other variable radius plots. Prediction biases were evaluated with respect to five silvicultural treatments and softwood species composition based on the coefficient of determination (R²), root mean square error and mean bias, as well as residual scatter plots. Overall, this study found that LiDAR tended to underestimate maximum tree height and volume. The maximum tree height and volume models had R² values of 86.9% and 72.1%, respectively. The accuracy of volume prediction was also sensitive to the plot type used. While it was difficult to develop models with a high R², due to the complexities of Maine's forest structures and species composition, the results suggest that low density LiDAR can be used as a supporting tool in forest management for this region. [ABSTRACT FROM AUTHOR]

Highlights: [•] We partition the variation in natural regeneration composition and abundance. [•] Contribution of biotic, abiotic, and disturbance explanatory factors were examined. [•] Overall explanation by regulatory factors was relatively low accounting for 26%. [•] Regeneration composition was structured by biotic factors, which accounted for 16%. [•] Controlling overstory composition is conducive to regeneration composition control. [ABSTRACT FROM AUTHOR]

Abstract: Growth and yield studies of mixed-species stands lack generality, though mixture effects appear to be most likely in stands of species with contrasting traits and/or with vertical stratification. The northern red oak (Quercus rubra L.) – eastern white pine (Pinus strobus L.) forest type of the US Northeast is dominated by species of intermediate shade tolerance, often with a spatially heterogeneous lower canopy of shade-tolerant red maple (Acer rubrum L.) and eastern hemlock (Tsuga canadensis (L.) Carr.). We examined neighborhood scale plots (0.02ha) with variable mixtures of these species and a range of age structure (44–140years mean age) in southern Maine. Linear mixed effects regression was used to model canopy surface area, stem biomass growth, and stem biomass yield across gradients of composition. With stocking, age, and site quality held constant, more species-diverse plots had denser canopies and faster growth, but lower yield than compositionally simpler plots. For a variety of age structures, mixtures of oak and pine produced more canopy surface area and more stem biomass growth than would be expected if interspecific competition were equal to intraspecific, and more than would be expected on plots dominated by one species or the other. Lower canopy red maple and hemlock contributed to higher plot-level values of all variables in mixed plots, but there was no evidence to suggest that any species contributed to stocking without displacing other species to some degree. This study demonstrates that white pine and red oak, along with other species, can grow in fine-scale mixtures in a variety of even- and two-aged structures without sacrificing productivity, and in some conditions enhancing it. [Copyright &y& Elsevier]

Abstract: Snags are an important component of forest structure and a key carbon pool, but there is currently a limited understanding of their survival and decay in the Acadian region of North America. Using standing and fallen snag observations collected from permanent sample plot (PSP) remeasurements at a long-term, replicated silviculture methods experiment in central Maine, USA, the objective of this analysis was to develop quantitative models to better understand snag survival and decay dynamics. Species examined included the conifers Abies balsamea, Picea rubens, Thuja occidentalis, and Tsuga canadensis, and the hardwoods Acer rubrum and Betula papyrifera. Proportional hazards regression models that estimate the 5-year probability (Pr) of snag survival indicated that observed decay class (a measure of snag decomposition) could be used as a surrogate for years since death given the difficulties and errors associated with determining years since death for standing snags. Averaged across all species, Pr (5-year snag survival) estimated as a function of snag diameter at breast height (dbh) in decay classes 1, 2, 3, and 4 were predicted as 0.69±0.09 (mean±standard deviation), 0.36±0.09, 0.16±0.05, and 0.06±0.02, respectively. Where snag dbh was significant in estimating survival (3 of 6 species), hazard ratios indicated that snag survival would increase by as much as 24% for each 10cm increase in dbh, but was dependent on species. Probability matrices predicting the 5-year likelihood of snags transitioning to advanced decay classes or falling showed that T. canadensis and T. occidentalis snags were slowest to decay, as measured by the average time it takes for a snag to progress from decay class 1–4. Probability of snag height-loss, predicted using snag dbh and the harvest interval of the stand, was estimated to be highest for larger dbh snags found in stands with frequent harvest intervals (e.g. 40cm dbh and every 5years). Models predicting the amount of volume reduced in standing snags through time were related to the estimated total volume of a live tree with the same dbh, mode of death, and general decay stage (i.e. soft versus hard). When used together, these models quantifying snag dynamics and associated species and size differences can serve as tools for managers to estimating standing deadwood stocks in mixed-species stands found throughout the Acadian region. [Copyright &y& Elsevier]

Abstract: Height growth equations for dominant trees are needed for growth and yield projections, to determine appropriate silvicultural regimes, and to estimate site index. Red alder [Alnus rubra Bong.] is a fast-growing hardwood species that is widely planted in the Pacific Northwest, USA. However, red alder dominant height growth equations used currently have been determined using stem analysis trees from natural stands rather than repeated measurements of stand-level top height from plantations, which may cause them to be biased. A regional dataset of red alder plantations was complied and used to construct a dynamic base-age invariant top height growth equation. Ten anamorphic and polymorphic Generalized Algebraic Difference Approach (GADA) forms were fit using the forward difference approach. The Chapman–Richards anamorphic and Schumacher anamorphic model forms were the only ones with statistically significant parameters that yielded biologically reasonable predictions across a full range of the available data. The Schumacher model form performed better on three independent datasets and, therefore, was selected as the final model. The resulting top height growth equations differed appreciably from tree-level dominant height growth equations developed using data from natural stands, particularly at the younger ages and on lower site indices. Both the rate and shape parameters of the Schumacher function were not influenced by initial planting density. However, this analysis indicates that the asymptote, which is related to site index, may be reduced for plantations with initial planting density below 500treesha−1. The final equation can be used for predictions of top height (and thus) site index for red alder plantations across a range of different growing conditions. [Copyright &y& Elsevier]

Abstract: Static models of individual tree crown attributes such as height to crown base and maximum branch diameter profile have been developed for several commercially important species. Dynamic models of individual branch growth and mortality have received less attention, but have generally been developed retrospectively by dissecting felled trees; however, this approach is limited by the lack of historic stand data and the difficulty in determining the exact timing of branch death. This study monitored the development of individual branches on 103 stems located on a variety of silvicultural trials in the Pacific Northwest, USA. The results indicated that branch growth and mortality were significantly influenced by precommercial thinning (PCT), commercial thinning, fertilization, vegetation management, and a foliar disease known as Swiss needle cast [caused by Phaeocryptopus gaeumannii (T. Rohde) Petr.]. Models developed across these datasets accounted for treatment effects through variables such as tree basal area growth and the size of the crown. Insertion of the branch growth and mortality equations into an individual-tree modeling framework, significantly improved short-term predictions of crown recession on an independent series of silvicultural trials, which increased mean accuracy of diameter growth prediction (reduction in mean bias). However, the static height to crown base equation resulted in a lower mean square error for the tree diameter and height growth predictions. Overall, individual branches were found to be highly responsive to changes in stand conditions imposed by silvicultural treatments, and therefore represent an important mechanism explaining tree and stand growth responses. [Copyright &y& Elsevier]

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]

Abstract: Crown structure is a key variable influencing stand productivity, but its reported response to various stand factors has varied. This can be partially attributed to lack of a unified study on crown response to intensive management or stand health. In this analysis of several coastal Douglas-fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) branch datasets, a significant treatment effect of fertilization, thinning, pre-commercial thinning, varying levels of vegetation control, and intensity of a foliar disease (Swiss needle cast, caused by Phaeocryptopus gaeumannii (T. Rohde) Petr.) were all found to influence several key crown structural attributes. Maximum branch size and total and non-foliated crown radii were found to be the most dynamic and sensitive crown variables to the various stand factors as no treatment effects were found for the number of branches within an annual segment or branch angle. When the data sets were combined and used to develop a single predictive equation, treatment effects were largely accounted for by changes in bole and crown size as mean bias was relatively low despite the large range in tree ages examined (4–450 years at breast height). While crown structure is highly variable and sensitive to a variety of stand factors, general empirical equations perform quite well and should be better integrated into models of forest growth and yield. [Copyright &y& Elsevier]

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Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available., (© 2022 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

Relative frequency distribution of observed annual mortality expressed in aboveground (AG) carbon (C) (Mg CO 2 e ha -1 year -1 ) summarized across supersections by forest type [Hardwood (HW) vs. Softwood (SW)] and site class (Low vs. High) based on approximately 130,000 remeasured USDA Forest Service Forest Inventory and Analysis plots across the US. Top panel summarizes conditions in plots that do and do not meet the California Air Resources Board standards based on total basal area, whereas bottom panel summarizes conditions in plots falling inside and outside of optimum relative density levels. The latter represents a biophysically-informed approach accounting for changes in tree (and carbon) packing over forest development., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)