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Emerald ash borer (EAB; Agrilus planipennis Fairmaire, 1888) has been a persistent disturbance for ash forests in the United States since 2002. Of particular concern is the impact that EAB will have on the ecosystem functioning of wetlands dominated by black ash (Fraxinus nigra Marsh.). In preparation, forest managers need reliable and complete maps of black ash dominated stands. Traditionally, forest survey data from the United States Forest Inventory and Analysis (FIA) Program have provided rigorous measures of tree species at large spatial extents but are limited when providing estimates for smaller management units (e.g., stands). Fortunately, geospatial data can extend forest survey information by generating predictions of forest attributes at scales finer than those of the FIA sampling grid. In this study, geospatial data were integrated with FIA data in a randomForest model to estimate and map black ash dominated stands in northern Minnesota in the United States. The model produced low error rates (overall error = 14.5%; area under the curve (AUC) = 0.92) and was strongly informed by predictors from soil saturation and phenology. These results improve upon FIA-based spatial estimates at national extents by providing forest managers with accurate, fine-scale maps (30 m spatial resolution) of black ash stand dominance that could ultimately support landscape-level EAB risk and vulnerability assessments. Key words: compound topographic index (CTI), remote sensing, black ash, emerald ash borer, forest inventory. L'agrile du frene (AF; Agrilus planipennis Fairmaire, 1888) est une source de perturbation persistante dans les forets de frene depuis 2002 aux Etats-Unis. L'impact que l'AF aura sur le fonctionnement de l'ecosysteme des milieux humides domines par le frene noir (Fraxinus nigra Marsh.) est particulierement preoccupant. Pour se preparer, les gestionnaires forestiers ont besoin de cartes exhaustives et fiables des peuplements domines par le frene noir. Traditionnellement, les donnees d'inventaire forestier du programme d'analyse et d'inventaire forestier (AIF) des Etats-Unis ont fourni des mesures rigoureuses concernant les especes arborescentes pour de vastes etendues mais elles sont limitees lorsqu'il s'agit de fournir des estimations pour de plus petites unites d'amenagement (p. ex des peuplements). Heureusement, les donnees geospatiales peuvent etendre la portee des informations de l'inventaire forestier en generant des predictions des attributs forestiers a des echelles plus fines que la grille d'echantillonnage du programme d'AIF. Dans cette etude, des donnees geospatiales ont ete integrees avec des donnees du programme d'AIF dans une foret d'arbres decisionnels pour estimer et cartographier les peuplements domines par le frene noir dans le nord du Minnesota, aux Etats-Unis. Le modele a produit de faibles taux d'erreur (erreur globale = 14,5 %; surface sous la courbe (AUC) = 0,92) et etait fortement informe par des predicteurs ayant trait a la saturation du sol et a la phenologie. Ces resultats ameliorent les estimations spatiales fondees sur le programme d'AIF a l'echelle nationale en fournissant aux gestionnaires forestiers des cartes precises et a petite echelle (resolution spatiale de 30 m) des peuplements domines par le frene noir qui pourraient en fin de compte servir de base a l'evaluation des risques et de la vulnerabilite a l'AL. [Traduit par la Redaction] Mots-cles : indice topographique compose (CTI), teledetection, frene noir, agrile du frene, inventaire forestier., 1. Introduction A variety of exotic flora, fauna, and pathogens induce ecological changes to forest ecosystems worldwide, ultimately disrupting ecosystem processes and functions, including nutrient cycling, productivity, and wildlife habitat [...]

Estimating the current status and future trends of carbon (C) stocks and stock changes in forests of the northeastern United States is desired by policy makers and managers as these forests can mitigate climate change through sequestration of atmospheric carbon dioxide (C[O.sub.2]). We developed C flux matrix models using tree and stand variables by tree diameter class and stand age class to compare size-structured models with age-structured models in their capacity to predict forest C dynamics that are central to policy decisions. The primary control variables for the C flux matrix models (diameter at breast height, stand basal area, stem density, and stand age) were all statistically significant at the [alpha] [less than or equal to] 0.05 level. Through comparing the simulation results and root mean square error of C flux matrix models by tree diameter class and stand age class, we found that tree diameter class more accurately predicted C stock change status across the broad compositional and structural conditions in the spatial and temporal domain. An uncertainty analysis revealed that predictions of aboveground C and soil C would be distinctively different whether using tree diameter class or stand age class with high certainty. Overall, this work may enable better integration of forest inventory data and remotely sensed data for the purpose of strategic-scale forest C dynamic simulations. Key words: forest C dynamic, matrix models, diameter class, age class, uncertainty. Les decideurs politiques et les amenagistes souhaitent avoir une estimation de l'etat actuel et des tendances futures des stocks de carbone (C) ainsi que des variations des stocks dans les forets du nord-est des Etats-Unis etant donne que ces forets peuvent attenuer le changement climatique en sequestrant le C[O.sub.2] atmospherique. Nous avons concu des modeles de matrice du flux de C a l'aide de variables de l'arbre et du peuplement fondes soit sur la classe de diametre des arbres, soit sur la classe d'age des peuplements dans le but de comparer les modeles structures par la taille et ceux structures par l'age quant a leur capacite a predire la dynamique du C forestier qui est au creur des decisions strategiques. Les variables de controle primaires dans les modeles de matrice du flux de C (le diametre a hauteur de poitrine, la surface terriere du peuplement, la densite des tiges et l'age du peuplement) etaient toutes statistiquement significatives ([alpha] [less than or equal to] 0,05). En comparant les resultats de la simulation et l'erreur quadratique moyenne des modeles de matrice du flux de C fondes sur la classe de diametre des arbres et ceux fondes sur la classe d'age des peuplements, nous avons trouve que la classe de diametre des arbres predisait avec plus d'exactitude la variation de l'etat des stocks de C pour l'ensemble des grandes conditions de composition et de structure dans les domaines spatial et temporel. Une analyse d'incertitude a revele que les predictions du C aerien et du C dans le sol seraient avec une grande certitude nettement differentes selon qu'on utilise la classe de diametre des arbres ou la classe d'age des peuplements. Dans l'ensemble cette etude peut aider a mieux integrer les donnees d'inventaire forestier et les donnees de teledetection pour simuler la dynamique du C forestier a une echelle strategique. [Traduit par la Redaction] Mots-cles: dynamique du carbone forestier, modeles de matrice, classe de diametre, classe d'age, incertitude., Introduction As forests are the largest terrestrial carbon (C) sink (Pan et al. 85/72011a) on Earth, nations have developed greenhouse gas (GHG) emission reduction targets inclusive of the land sector, [...]

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage--e.g., CO2 fertilization--and negative drivers that decrease it--e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance--regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~ 1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress. [ABSTRACT FROM AUTHOR]

Large‐scale ecological sampling networks, such as national forest inventories (NFIs), collect in situ data to support biodiversity monitoring, forest management and planning, and greenhouse gas reporting. Data harmonization aims to link auxiliary remotely sensed data to field‐collected data to expand beyond field sampling plots, but outliers that arise in data harmonization—questionable observations because their values differ substantially from the rest—are rarely addressed.In this paper, we review the sources of commonly occurring outliers, including random chance (statistical outliers), definitions and protocols set by sampling networks, and temporal and spatial mismatch between field‐collected and remotely sensed data. We illustrate different types of outliers and the effects they have on estimates of above‐ground biomass population parameters using a case study of 292 NFI plots paired with airborne laser scanning (ALS) and Sentinel‐2 data from Sawyer County, Wisconsin, United States.Depending on the criteria used to identify outliers (sampling year, plot location error, nonresponse, presence of zeros and model residuals), as many as 53 of the 292 Forest Inventory and Analysis plot observations (18%) were identified as potential outliers using a single criterion and 111 plot observations (38%) if all criteria were used. Inclusion or removal of potential outliers led to substantial differences in estimates of mean and standard error of the estimate of biomass per unit area. The simple expansion estimator, which does not rely on ALS or other auxiliary data, was more sensitive to outliers than model‐assisted approaches that incorporated ALS and Sentinel‐2 data. Including Sentinel‐2 predictors showed minimal increases to the precision of our estimates relative to models with ALS predictors alone.Outliers arise from many causes and can be pervasive in data harmonization workflows. Our review and case study serve as a note of caution to researchers and practitioners that the inclusion or removal of potential outliers can have unintended consequences on population parameter estimates. When used to inform large‐scale biomass mapping, carbon markets, greenhouse gas reporting and environmental policy, it is necessary to ensure the proper use of NFI and remotely sensed data in geospatial data harmonization. [ABSTRACT FROM AUTHOR]

The physical structure of vegetation is thought to be closely related to ecosystem function, but little is known of its pertinence across geographic regions. Here, we used data from over three million trees in continental North America to evaluate structural diversity – the volumetric capacity and physical arrangement of biotic components in ecosystems – as a predictor of productivity. We show that structural diversity is a robust predictor of forest productivity and consistently outperforms the traditional measure – species diversity – across climate conditions in North America. Moreover, structural diversity appears to be a better surrogate of niche occupancy because it captures variation in size that can be used to measure realized niche space. Structural diversity offers an easily measured metric to direct restoration and management decision making to maximize ecosystem productivity and carbon sequestration. [ABSTRACT FROM AUTHOR]

We used tree ring measurements to investigate the temporal response of basal area increment (BAI) of canopy trees following selection harvests by sampling across a chronosequence of stands with known harvest dates in tolerant hardwood (Great Lakes--St. Lawrence) stands in central Ontario. Seven tree species of various shade tolerances ranged widely in their responses to reduced competition. The more shade-tolerant species responded more positively: shade-tolerant species showed an average increase in BAI of 35% 4-15 years postharvest compared with 16% for mid-tolerant species and -7.5% for intolerant species. All species showed a time-lag in postharvest growth responses, with maximum growth responses occurring between 3 and 15 years postharvest. Tree size was the most important factor determining the magnitude of BAI response, with smaller trees consistently responding more than larger trees. We suggest that higher growth responses to selection harvests among shade-tolerant species may contribute to declines in mid-tolerant species abundance in selectionmanaged stands. More broadly, interspecific variability in canopy tree responses to forest disturbance appears to follow patterns distinct from seedling and sapling responses, with important implications to forest community dynamics in both managed and unmanaged forests. Nous aeons utilise des mesures d'anneau de croissance pour etudier la reaction temporelle de l'accroissement en surface terriere (AST) d'arbres dominants a la suite de coupes de jardinage en echantillonnant une chronosequence de peuplements de feuillus tolerants a l'ombre du centre de l'Ontario (region des Grands-Lacs et du St-Laurent) pour lesquels l' annee de recolte etait connue. La reaction des arbres a une diminution de la competition a grandement varie parmi sept especes d'arbre dont la tolerance a l'ombre est differente. Les especes plus tolerances a l'ombre ont reagi plus positivement avec une augmentation moyenne de PAST de 35 %, de 4 a 15 ans apres la coupe, comparativement a une augmentation moyenne de 16 % pour les especes de tolerance intermediaire et de -7,5 % pour les especes intolerantes. Un delai de reaction en croissance a ete observe pour toutes les especes a la suite de la coupe et la reaction maximale est survenue entre 3 et 15 ans apres la coupe. La taille des arbres etait le facteur le plus important pour determiner l' amplitude de la reaction de l'AST, les plus petits arbres reagissant invariablement davantage que les plus gros arbres. Nous croyons que les plus fortes reactions de croissance aux coupes de jardinage des especes tolerances a l'ombre peuvent contributr au declin de l'abondance des especes de tolerance intermediaire dans les peuplements jardines. Plus generalement, la variation interspecifique de la reaction des arbres dominants aux perturbations forestieres semble suivre un patron distinct de celui des semis et des gaules, ce qui a des implications importantes pour la dynamique des communautes forestieres dans les forets amenagees et non amenagees. [Traduit par la Redaction], Introduction Tree growth response to disturbance is an important aspect of forest dynamic processes in both natural and managed forest ecosystems. Canopy gap formation, in particular, is thought to be [...]

Summary: First principles predict that diversity at one trophic level often begets diversity at other levels, suggesting plant and mycorrhizal fungal diversity should be coupled. Local‐scale studies have shown positive coupling between the two, but the association is less consistent when extended to larger spatial and temporal scales. These inconsistencies are likely due to divergent relationships of different mycorrhizal fungal guilds to plant diversity, scale dependency, and a lack of coordinated sampling efforts. Given that mycorrhizal fungi play a central role in plant productivity and nutrient cycling, as well as ecosystem responses to global change, an improved understanding of the coupling between plant and mycorrhizal fungal diversity across scales will reduce uncertainties in predicting the ecosystem consequences of species gains and losses. [ABSTRACT FROM AUTHOR]

Most existing functional diversity indices focus on a single facet of functional diversity. Although these indices are useful for quantifying specific aspects of functional diversity, they often present some conceptual or practical limitations in estimating functional diversity. Here, we present a new functional extension and evenness (FEE) index that encompasses two important aspects of functional diversity. This new index is based on the straightforward notion that a community has high diversity when its species are distant from each other in trait space. The index quantifies functional diversity by evaluating the overall extension of species traits and the interspecific differences of a species assemblage in trait space. The concept of minimum spanning tree (MST) of points was adopted to obtain the essential distribution properties for a species assembly in trait space. We combined the total length of MST branches (extension) and the variation of branch lengths (evenness) into a raw FEE0 metric and then translated FEE0 to a species richness‐independent FEE index using a null model approach. We assessed the properties of FEE and used multiple approaches to evaluate its performance. The results show that the FEE index performs well in quantifying functional diversity and presents the following desired properties: (a) It allows a fair comparison of functional diversity across different species richness levels; (b) it preserves the essence of single‐facet indices while overcoming some of their limitations; (c) it standardizes comparisons among communities by taking into consideration the trait space of the shared species pool; and (d) it has the potential to distinguish among different community assembly processes. With these attributes, we suggest that the FEE index is a promising metric to inform biodiversity conservation policy and management, especially in applications at large spatial and/or temporal scales. [ABSTRACT FROM AUTHOR]

The US National Greenhouse Gas Inventory uses the component ratio method (CRM), a volume conversion approach that incorporates models for tree biomass components, for forest carbon assessments. However, the performance of the CRM relative to other methods, as well as influences on its accuracy and precision, must be evaluated. We constructed a data-driven CRM (n-CRM), used it to predict total tree and component biomass for six US tree species, and compared this approach to a reference allometric model. We also assessed the influence of size, crown dynamics, and stem growth on the performance of both methods. Results show that the n-CRM was more accurate for four species, resulting from the inclusion of more predictor variables. Both methods had high uncertainty, but the precision of n-CRM predictions was two to eight times higher for small diameter trees (<10 cm) across all species. Accuracy and precision of the crown component models (i.e. branches and foliage) was low, though better for pines than for hardwoods. Species-level analysis suggests that poor precision is influenced by crown traits and the size distribution of fitting datasets. Our results highlight needed improvements to the n-CRM, and motivate further development of data that facilitate predictive evaluation of biomass models. [ABSTRACT FROM AUTHOR]

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Abstract: Forest mycorrhizal type mediates nutrient dynamics, which in turn can influence forest community structure and processes. Using forest inventory data, we explored how dominant forest tree mycorrhizal type affects understory plant invasions with consideration of forest structure and soil properties. We found that arbuscular mycorrhizal (AM) dominant forests, which are characterised by thin forest floors and low soil C : N ratio, were invaded to a greater extent by non‐native invasive species than ectomycorrhizal (ECM) dominant forests. Understory native species cover and richness had no strong associations with AM tree dominance. We also found no difference in the mycorrhizal type composition of understory invaders between AM and ECM dominant forests. Our results indicate that dominant forest tree mycorrhizal type is closely linked with understory invasions. The increased invader abundance in AM dominant forests can further facilitate nutrient cycling, leading to the alteration of ecosystem structure and functions. [ABSTRACT FROM AUTHOR]

Biomass production in living trees is the basis of numerous forest ecosystem functions and services. However, rates of and controls on biomass production vary widely across temperate forests, particularly over successional timescales of decades and centuries. Biomass production in temperate forests is most often interpreted within the context of biotic or top-down controls, such as species composition or disturbance. However, there is need to investigate how bottom-up physiographic factors, such as landform attributes, drainage, and soil properties mediate biomass production. In order to investigate patterns, controls, and potentials for biomass production across spatial levels ranging from individual ecosystems, to landscapes, to entire regions, we synthesized long-term forest inventory datasets from the United States Great Lakes region, placed them in the context of a hierarchical ecological unit classification, and tested the influence of physiographic factors on biomass production rates and temporal trajectories across ecological levels. Key findings include: 1) At nearly all ecological levels, physiographic controls ( e.g ., soil texture, drainage class, water table depth) on soil moisture status are significant predictors of variation in biomass production rates, with mesic sites accumulating biomass more rapidly than xeric sites, which, in turn accumulate biomass more rapidly than hydric sites. 2) Aboveground live biomass can apparently continue to accumulate through 2–3 centuries of succession, exceeding 300–400 Mg ha −1 on mesic sites throughout the region. 3) Stand age distributions indicate that hydric sites are harvested least often, while the high production rates of mesic sites suggest they are most appropriate for frequent harvesting. 4) Median, 1st-quartile, and 3rd-quartile growth rates of individual ecosystems, landscapes, and ecoregional subsections and sections reveal ecological units in which forests may vary in their potential for increases or decreases in biomass production, e.g ., due to management interventions, climate change, or disturbances. Specifically, some units have tightly constrained distributions, suggesting little capacity for change in production rates relative to observed medians, while other units have wide variation in biomass production rates, indicating the potential for relatively large increases or decreases in production. Altogether, the results of this analysis show that physiography exerts widespread, bottom-up controls on biomass production across the region of study, and can be used in spatially explicit frameworks to understand ecosystem functioning and inform scientific forest management. [ABSTRACT FROM AUTHOR]

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA--Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)--in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R2) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA. [ABSTRACT FROM AUTHOR]

Forest understories across the northern United States (US) are a complex of tree seedlings, endemic forbs, herbs, shrubs, and introduced plant species within a forest structure defined by tree and forest floor attributes. The substantial increase in white-tailed deer ( Odocoileus virginianus Zimmerman) populations over the past decades has resulted in heavy browse pressure in many of these forests. To gain an objective assessment of the role of deer in forested ecosystems, a region-wide forest inventory across the northern US was examined in concert with white-tailed deer density information compiled at broad scales. Results indicate that deer density may be an additional driver of tree seedling abundance when analyzed along with stand attributes such as aboveground biomass, relative density, and stand age. Tree seedling abundance generally decreased as deer density increased above 5.8 deer km 2 for all forest type groups with the exception of oak-dominated forests. Findings indicate that introduced plant species, of which 393 were recorded in this study, increased in areas with higher deer density. The abundance of white-tailed deer is just as important as forest stand and site attributes in the development of forest understories. Given the complexity of forest and land use dynamics across the northern US, this study provides directions for future research as more data linking forest-dependent wildlife and forest dynamics at regional and national scales become available. [ABSTRACT FROM AUTHOR]