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Geolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Data integration is especially problematic when geolocation error is not well quantified. We propose a general model that uses airborne laser scanning data to quantify and correct geolocation error in spaceborne sampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDAR Hyperspectral and Thermal Imager (G‐LiHT) was used with a subset of LiDAR data from NASA's Global Ecosystem Dynamics Investigation (GEDI). The model accommodates multiple canopy height metrics derived from a simulated GEDI footprint kernel using spatially coincident G‐LiHT, and incorporates both additive and multiplicative mapping between the canopy height metrics generated from both datasets. A Bayesian implementation provides probabilistic uncertainty quantification in both parameter and geolocation error estimates. Results show a systematic geolocation error of 9.62 m in the southwest direction. In addition, estimated geolocation errors within GEDI footprints were highly variable, with results showing a ∼$$ \sim $$0.45 probability the true footprint center is within 20 m. Estimating and correcting geolocation error via the model outlined here can help inform subsequent efforts to integrate spaceborne LiDAR data, like GEDI, with other georeferenced data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]

Core Ideas: Chronic N enrichment increased N exports, although ecosystem N retention was still high.Soil was the largest ecosystem N pool, but it was not significantly altered by chronic N enrichment.Chronic N enrichment increased biomass N accumulation in hardwood but not in softwood stands. We examine the temporal trend of input–output N fluxes and net ecosystem N retention, and estimate a mass balance for ecosystem soil and vegetation pools, after 25 yr of chemical manipulation at the Bear Brook Watershed in Maine (BBWM). The BBWM is a paired whole watershed manipulation experiment designed to study the effects of elevated N and S deposition on forest ecosystem function. Starting in 1989, 25.2 kg N ha−1 was added annually to the West Bear (treated) watershed. The N additions in West Bear stimulated N loss through stream exports, and West Bear retained 81% of the annual N inputs, compared to 94% retention in the reference, East Bear. After 25 yr of N additions, the West Bear watershed had accumulated ∼700 kg ha−1 more N than East Bear in soils and vegetation, with ∼10% of the accumulated N stored in forest biomass. The treatment increased recent biomass N accumulation rates in the hardwood stands, but not in the softwoods. Soils did not show detectable differences in total N content between watersheds, although the organic soils had greater N in West Bear. This paper presents a unique set of findings from one of the few long‐term whole forest ecosystem N enrichment studies in the world. While N dynamics were clearly altered in West Bear, with evidence of accelerated N cycling, the treated watershed did not attain an advanced stage of N saturation during the study period, based on the evidence from forest growth and stream N exports. [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]

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]

Abstract: Population responses to climate were assessed using 3–7 years height growth data gathered for 266 populations growing in 12 common gardens established in the 1980s as part of five disparate studies of Pinus contorta var. latifolia. Responses are interpreted according to three concepts: the ecological optimum, the climate where a population is competitively exclusive and in which, therefore, it occurs naturally; the physiological optimum, the climate where a population grows best but is most often competitively excluded; and growth potential, the innate capacity for growth at the physiological optimum. Statistical analyses identified winter cold, measured by the square root of negative degree‐days calculated from the daily minimum temperature (MINDD01/2), as the climatic effect most closely related to population growth potential; the colder the winter inhabited by a population, the lower its growth potential, a relationship presumably molded by natural selection. By splitting the data into groups based on population MINDD01/2 and using a function suited to skewed normal distributions, regressions were developed for predicting growth from the distance in climate space (MINDD01/2) populations had been transferred from their native location to a planting site. The regressions were skewed, showing that the ecological optimum of most populations is colder than the physiological optimum and that the discrepancy between the two increases as the ecological optimum becomes colder. Response to climate change is dependent on innate growth potential and the discrepancy between the two optima and, therefore, is population‐specific, developing out of genotype‐environment interactions. Response to warming in the short‐term can be either positive or negative, but long term responses will be negative for all populations, with the timing of the demise dependent on the amount of skew. The results pertain to physiological modeling, species distribution models, and climate‐change adaptation strategies. [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]

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]

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]

A data set of common forest metrics was prepared using inventory data from Ecological Reserves in Maine, northeastern USA. An Ecological Reserve is generally defined as an area where timber harvesting does not occur and natural disturbance events are allowed to proceed without significant human influence. Beginning in the early 2000s, permanent, long term monitoring plots were established in Reserves across Maine. To date, 50 Reserves occupying approximately 70,820 ha with a total of 1,103 monitoring plots comprise Maine's Ecological Reserve System. A goal of the Ecological Reserve Monitoring program is to remeasure plots every 10 years and about half of the plots have been remeasured since the initial inventory. Stand metrics were calculated for both monitoring rounds and include: live tree basal area, live tree density, large (diameter at breast height, dbh ≥40 cm) and very large (dbh ≥51 cm) live tree density, standing dead tree density, large (dbh ≥40 cm) and very large (dbh ≥51 cm) standing dead tree density, total and large (diameter at transect intersect ≥40 cm) downed coarse woody debris volume, as well as various stand dynamic metrics. For comparison, the same metrics were computed for managed forests in Maine using permanent plot data from the US Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program. Information on Ecological Reserve monitoring plots includes Ecological Reserve name, forest‐type group, geographic location, elevation, slope, aspect, and harvest history. This data should prove invaluable for assessing and evaluating long‐term changes in Ecological Reserves across the broad ecological/climate zones that are present in Maine. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper. These data are freely available for non‐commercial scientific use. [ABSTRACT FROM AUTHOR]