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Acidic deposition has been well studied for its impacts on forest soils and plants and is thought to detrimentally affect sugar maple (Acer saccharum) in northeastern North America. The physiological mechanism driving reduced tree growth on acid impaired sites is not well understood, but has been suggested to involve increased autotrophic respiration rates. We measured foliar respiration and leaf elemental composition on two species with contrasting acid tolerances (sugar maple and American beech, Fagus grandifolia) across a naturally occurring soil base saturation (BS) gradient in the Adirondack Mountains of New York. Foliar chemistry varied strongly as anticipated across the BS gradient, with decreases in base cations and increases in phytotoxic metals (e.g., Mn) on sites with highly acidified soils. However, foliar respiration rates were not correlated with most measures of acidity (e.g., BS, foliar concentrations of Ca, Mg, Al content). Respiration rates did correlate with other leaf traits (N content, leaf mass per unit area) reflective of leaf morphological variation in response to variable light availability. After accounting for multivariate trait covariation, mass-based respiration rates were associated with foliar Mn content, suggesting a positive relationship between the concentration of this phytotoxic metal and foliar respiration rates. Soluble foliar Ca, Mg, and Mn were closely correlated with total foliar concentrations of these elements. Overall these results demonstrate that soil acidification and loss of base cations is largely unrelated to foliar respiration rates, although the accumulation of foliar Mn on acidified soils may contribute to the metabolic burden of both sugar maple and American beech trees. [ABSTRACT FROM AUTHOR]

Sustaining recent progress in mitigating acid pollution could require lower emissions caps that will give rise to real or perceived tradeoffs between healthy ecosystems and inexpensive energy. Because most impacts of acid rain affect ecosystem functions that are poorly understood by policy-makers and the public, an ecosystem services (ES) framework can help to measure how pollution affects human well-being. Focused on the Adirondack region (USA), a global ‘hot-spot’ of acid pollution, we measured how the chronic acidification of the region's forests, lakes, and streams has affected the potential economic and cultural benefits they provide to society. We estimated that acid-impaired hardwood forests provide roughly half of the potential benefits of forests on moderate to well-buffered soils – an estimated loss of ∼ $10,000 ha −1 in net present value of wood products, maple syrup, carbon sequestration, and visual quality. Acidic deposition has had only nominal impact – relative to the effects of surficial geology and till depth – on the capacity of Adirondack lakes and streams to provide water suitable for drinking. However, as pH declines in lakes, the estimated value of recreational fishing decreases significantly due to loss of desirable fish such as trout. Hatchery stocking programs have partially offset the pollution-mediated losses of fishery value, most effectively in the pH range 4.8–5.5, but are costly and limited in scope. Although any estimates of the monetary ‘damages’ of acid rain have significant uncertainties, our findings highlight some of the more tangible economic and cultural benefits of pollution mitigation efforts, which continue to face litigation and political opposition. [ABSTRACT FROM AUTHOR]

We estimated the potential economic value of recreational fisheries in lakes altered by acid pollution in the Adirondack Mountains (USA). We found that the expected value of recreational fisheries has been diminished because of acid deposition but may improve as lakes recover from acidification under low emissions scenarios combined with fish stocking. Fishery value increased with lake pH, from a low of $4.41 angler day-1 in lakes with pH < 4.5, to a high of $38.40 angler day-1 in lakes with pH > 6.5 that were stocked with trout species. Stocking increased the expected fishery value by an average of $11.50 angler day-1 across the entire pH range of the lakes studied. Simulating the future long-term trajectory of a subset of lakes, we found that pH and expected fishery value increased over time in all future emissions scenarios. Differences in estimated value among pollution reduction scenarios were small (<$1 angler day-1) compared to fish stocking scenarios (>$4 angler day-1). Our work provides a basis for assessing the costs and benefits of emissions reductions and management efforts that can hasten recovery of the economic and cultural benefits of ecosystems degraded by chronic pollution. [ABSTRACT FROM AUTHOR]

Sugar maple ( Acer saccharum ) is among the most ecologically and economically important tree species in North America, and its growth and regeneration is often the focus of silvicultural practices in northern hardwood forests. A key stressor for sugar maple (SM) is acid rain, which depletes base cations from poorly-buffered forest soils and has been associated with much lower SM vigor, growth, and recruitment. However, the potential interactions between forest management and soil acidification – and their implications for the sustainability of SM and its economic and cultural benefits – have not been investigated. In this study, we simulated the development of 50 extant SM stands in the western Adirondack region of NY (USA) for 100 years under different soil chemical conditions and silvicultural prescriptions. We found that interactions between management prescription and soil base saturation will strongly shape the ability to maintain SM in managed forests. Below 12% base saturation, SM did not regenerate sufficiently after harvest and was replaced mainly by red maple ( Acer rubrum ) and American beech ( Fagus grandifolia ). Loss of SM on acid-impaired sites was predicted regardless of whether the shelterwood or diameter-limit prescriptions were used. On soils with sufficient base saturation, models predicted that SM will regenerate after harvest and be sustained for future rotations. We then estimated how these different post-harvest outcomes, mediated by acid impairment of forest soils, would affect the potential monetary value of ecosystem services provided by SM forests. Model simulations indicated that a management strategy focused on syrup production – although not feasible across the vast areas where acid impairment has occurred – may generate the greatest economic return. Although pollution from acid rain is declining, its long-term legacy in forest soils will shape future options for sustainable forestry and ecosystem stewardship in the northern hardwood forests of North America. [ABSTRACT FROM AUTHOR]

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980–2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products. [ABSTRACT FROM AUTHOR]

Depletion of Ca from forest soils due to acidic deposition has had potentially pervasive effects on forest communities, but these impacts remain largely unknown. Because snails, salamanders, and plants play essential roles in the Ca cycle of northern hardwood forests, we hypothesized that their community diversity, abundance, and structure would vary with differences in biotic Ca availability. To test this hypothesis, we sampled 12 upland hardwood forests representing a soil Ca gradient in the Adirondack Mountains, New York (USA), where chronic deposition has resulted in acidified soils but where areas of well-buffered soils remain Ca rich due to parent materials. Along the gradient of increasing soil [Ca2+], we observed increasing trends in snail community richness and abundance, live biomass of redback salamanders ( Plethodon cinereus (Green, 1818)), and canopy tree basal area. Salamander communities were dominated by mountain dusky salamanders ( Desmognathus ochrophaeus Cope, 1859) at Ca-poor sites and changed continuously along the Ca gradient to become dominated by redback salamanders at the Ca-rich sites. Several known calciphilic species of snails and plants were found only at the highest-Ca sites. Our results indicated that Ca availability, which is shaped by geology and acidic deposition inputs, influences northern hardwood forest ecosystems at multiple trophic levels, although the underlying mechanisms require further study. [ABSTRACT FROM AUTHOR]

Large-scale government efforts to develop resources for societal benefit have often experienced cycles of growth and decline that leave behind difficult social and ecological legacies. To understand the origins and outcomes of these failures of resource governance, scholars have applied the framework of the adaptive cycle. In this study, we used the adaptive cycle as a diagnostic approach to trace the drivers and dynamics of forest governance surrounding a boom-bust sequence of industrial forest management in one of the largest-scale resource systems in U.S. history: the Tongass National Forest in southeastern Alaska. Our application of the adaptive cycle combined a historical narrative tracing dynamics in political, institutional, and economic subsystems and a longitudinal analysis of an indicator of overall system behavior (timber harvests). We found that federal policies in concert with global market changes drove transformative change in both forest governance (policy making) and forest management (practices), through creation and dissolution of subsidized long-term lease contracts. Evidence of the systemic resilience provided by these leases was found in the analysis of industry responses to market volatility before and after Tongass-specific federal reforms. Although the lease contracts stabilized the Tongass system for a period of time, they fostered a growing degree of rigidity that contributed to a severe industrial collapse and the subsequent emergence of complex social traps. Broader lessons from the Tongass suggest that large-scale changes occurred only when the nested economic and policy cycles were in coherence, and a systemic effort to minimize social and ecological variability ultimately resulted in catastrophic collapse of governance. This collapse resulted in a pervasive and challenging legacy that prevents Tongass reorganization and limits the adaptive capacity of the larger social-ecological system of southeastern Alaska. Although this legacy has inhibited system renewal for two decades, recent trends indicate the emergence of new opportunities for progress toward sustainable governance of the Tongass National Forest. [ABSTRACT FROM AUTHOR]

To understand how a highly contentious policy process influenced a major conservation effort, I examined the origins, compromises, and outcomes of the Alaska National Interest Lands Conservation Act of 1980 (ANILCA) for the Tongass National Forest. Tongass wilderness designation was among the most controversial issues in the ANILCA debate, and it faced strong opposition from influential lawmakers, land managers, and Alaska residents. To investigate the influence of this opposition on Tongass conservation outcomes, I conducted a gap analysis of Tongass reserves and a policy analysis of the ANILCA debate and traced the influence of specific interests through the amendments, negotiations, and resulting compromises needed to enact ANILCA. Overall, I found that Tongass reserves comprise a broadly representative cross-section of ecosystems and species habitats in southeastern Alaska. Redrawn reserve boundaries, industry subsidies, and special access regulations reflected compromises to minimize the impact of wilderness conservation on mining, timber, and local stakeholder interests, respectively. Fragmentation of the Admiralty Island National Monument—the most ecologically valuable and politically controversial reserve—resulted from compromises with Alaskan Native (indigenous peoples of Alaska) corporations and timber interests. Despite language to accommodate “reasonable access” to wilderness reserves, ongoing access limitations highlight the concerns of Alaska residents that opposed ANILCA several decades ago. More broadly, the Tongass case suggests that early and ambitious conservation action may offset strong political opposition; compromises needed to establish key reserves often exacerbate development impacts in unprotected areas; and efforts to minimize social conflicts are needed to safeguard the long-term viability of conservation measures. [ABSTRACT FROM AUTHOR]

Managed ecosystems experience vulnerabilities when ecological resilience declines and key flows of ecosystem services become depleted or lost. Drivers of vulnerability often include local management actions in conjunction with other external, larger-scale factors. To translate these concepts to management applications, we developed a conceptual model of feedbacks linking the provision of ecosystem services, their use by society, and anthropogenic change. From this model we derived a method to integrate existing geodata at relevant scales and in locally meaningful ways to provide decision-support for adaptive management efforts. To demonstrate our approach, we conducted a case study assessment of southeast Alaska, where managers are concerned with sustaining fish and wildlife resources in areas where intensive logging disturbance has occurred. Individual datasets were measured as indicators of one of three criteria: ecological capacity to support fish/wildlife populations ( provision); human acquisition of fish/wildlife resources ( use); and intensity of logging and related land-use change ( disturbance). Relationships among these processes were analyzed using two methods—a watershed approach and a high-resolution raster—to identify where provision, use and disturbance were spatially coupled across the landscape. Our results identified very small focal areas of social-ecological coupling that, based on post-logging dynamics and other converging drivers of change, may indicate vulnerability resulting from depletion of ecosystem services. We envision our approach can be used to narrow down where adaptive management might be most beneficial, allowing practitioners with limited funds to prioritize efforts needed to address uncertainty and mitigate vulnerability in managed ecosystems. [ABSTRACT FROM AUTHOR]

Decline of yellow-cedar (Chamaecyparis nootkatensis ((D. Don) Spach) has occurred on 200 000 ha of temperate rainforests across southeastern Alaska. Because declining forests appeared soon after the Little Ice Age and are limited mostly to low elevations (whereas higher elevation forests remain healthy), recent studies have hypothesized a climatic mechanism involving early dehardening, reduced snowpack, and freezing injury. This hypothesis assumes that a specific suite of microclimatic conditions occurs during late winter and declining cedar populations across the region have responded similarly to these conditions. Based on the first geographically extensive tree ring chronologies constructed for southeastern Alaska, we tested these assumptions by investigating regional climatic trends and the growth responses of declining cedar populations to this climatic variation. Warming winter trends were observed for southeastern Alaska, resulting in potentially injurious conditions for yellow-cedar due to reduced snowfall and frequent occurrence of severe thaw–freeze events. Declining cedar forests shared a common regional chronology for which late-winter weather was the best predictor of annual growth of surviving trees. Overall, our findings verify the influence of elevational gradients of temperature and snow cover on exposure to climatic stressors, support the climatic hypothesis across large spatial and temporal scales, and suggest cedar decline may expand with continued warming. [ABSTRACT FROM AUTHOR]

Tree-growth responses to environmental change could provide early detection of shifts in forest composition and help facilitate ecosystem management and conservation., We studied forest tree responses to recent trends in climate and acidic deposition using analyses of tree rings and long-term climate, deposition and forest plot data along an elevational climatic gradient in the northeastern United States. We analysed how (i) individual growth of dominant species ( Picea rubens, Abies balsamea) and (ii) spatial distributions of all species, changed with elevation over time due to changing environment., We observed a mean 220 m upslope shift of temperature envelopes since the 1960s, consistent with regional climate warming, but found no evidence of synchronous upslope shifts in species abundance. Species' ranges were stable although some leaned upslope or downslope, suggesting species-specific migration lags or controls on species' ranges., Compared to species distributions, the growth of dominant species was more responsive to environmental change. Although the basal area of P. rubens declined within its range since the 1960s, its growth has increased recently with increasing precipitation pH and to a lesser extent with warming climate. Abies balsamea has gradually increased in both basal area and density since the 1960s, with its growth responding to precipitation pH but not climate. Historically, P. rubens grew better at lower and A. balsamea at higher elevations, but these elevation effects appeared to be mediated primarily by moisture, and have disappeared over time., Synthesis and applications. Mean tree-growth responses to changing climate (temperature, moisture) and precipitation chemistry were more consistent and more clearly detectable than shifts in tree species' ranges, suggesting that monitoring tree growth across climatically controlled species' ranges (e.g. along elevational or latitudinal gradients) may provide a powerful tool for early detection of potential future changes in forest composition in a changing environment. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha-1 and R² : 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha-1 and R² of 0.81 for the combination of optical and SAR data in the GBM model. [ABSTRACT FROM AUTHOR]

Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict shrubland distributions at 30 m resolution across New York State (NYS), using a stacked ensemble combining a random forest, gradient boosting machine, and artificial neural network to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1 m canopy height model (CHM), derived from a patchwork of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally segmented imagery to predict shrubland probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC = 0.893, real-world AUC = 0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. After ground-truthing, we expect these shrubland maps and models will have many research and stewardship applications including wildlife conservation, invasive species mitigation and natural climate solutions. Our results suggest that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve land cover classification of historically rare but increasingly prevalent shrubland habitats across broader areas. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State's forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 106 Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 106 Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 106 Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. [ABSTRACT FROM AUTHOR]

The article reports on evaluation of gastric lavage method for diet analysis of Plethodon cinereus. It states that animals were dissected using stereomicroscope to assess stomach contents while identified prey items were sent to taxonomic resolution possible. It mentions that stomach flushing is a method to collect diet samples removing most of the stomach contents and gastric lavage is an effective technique for studying plethodontid diets without removing individuals from population.

Shrub willow is considered an important dedicated energy crop in temperate climates for the production of bioenergy, biofuels, and bio-based products. A methodology to rapidly and accurately estimate above-ground biomass (AGB) is essential for understanding potential biomass supply, identifying potential growth limitations, and making management decisions. The main objective of this study was to investigate different statistical, machine learning, and deep learning models to estimate shrub willow AGB at a site in Camillus, NY using multi-spectral unmanned aerial system (UAS) imagery. The efficiency of the convolutional neural network (CNN) deep learning algorithm was compared to the well-known methods including linear regression, decision tree (DT), random forest (RF), and support vector regression (SVR). The RF model estimated the AGB with the root mean square error (RMSE) of 1.73 Mg/ha and R2 of 0.95, and outperformed other methods. The next most effective method was CNN with the RMSE of 2.69 Mg/ha and R2 of 0.89. Feature importance analysis indicated that normalized difference vegetation index (NDVI), ratio vegetation index (RVI), and difference vegetation index (DVI) had the greatest contribution to AGB estimation. This study compared shrub willow AGB estimation models using UAS imagery which will streamline bioenergy/biofuel development compared to the existing methods. [ABSTRACT FROM AUTHOR]

Projected changes in temperature and drought regime are likely to reduce carbon (C) storage in forests, thereby amplifying rates of climate change. While such reductions are often presumed to be greatest in semi-arid forests that experience widespread tree mortality, the consequences of drought may also be important in temperate mesic forests of Eastern North America (ENA) if tree growth is significantly curtailed by drought. Investigations of the environmental conditions that determine drought sensitivity are critically needed to accurately predict ecosystem feedbacks to climate change. We matched site factors with the growth responses to drought of 10,753 trees across mesic forests of ENA, representing 24 species and 346 stands, to determine the broad-scale drivers of drought sensitivity for the dominant trees in ENA. Here we show that two factors--the timing of drought, and the atmospheric demand for water (i.e., local potential evapotranspiration; PET)--are stronger drivers of drought sensitivity than soil and stand characteristics. Droughtinduced reductions in tree growth were greatest when the droughts occurred during early-season peaks in radial growth, especially for trees growing in the warmest, driest regions (i.e., highest PET). Further, mean species trait values (rooting depth and w50) were poor predictors of drought sensitivity, as intraspecific variation in sensitivity was equal to or greater than interspecific variation in 17 of 24 species. From a general circulation model ensemble, we find that future increases in earlyseason PET may exacerbate these effects, and potentially offset gains in C uptake and storage in ENA owing to other global change factors. [ABSTRACT FROM AUTHOR]

Large-footprint waveform light detection and ranging (lidar) data have been widely used in above-ground forest biomass estimation. Waveform metrics derived from basic statistics (e.g. percentile of energy) of the lidar waveform, such as canopy height and height of median energy, have been applied to biomass estimation in numerous studies. In this study, a set of metrics based on Gaussian decomposition (GD) results were developed and evaluated for forest above-ground biomass estimation using NASA's laser vegetation imaging sensor (LVIS) data. The GD metrics were designed to explicitly incorporate lidar intensity and vertical structures of canopy layers for biomass estimation. The proposed GD metrics used information related to the above-ground height of each Gaussian centroid and the Gaussian area index (GAI), where GAI is the area covered by a Gaussian function. Two types of novel GD metrics were developed: (1) percentileheight GAI metrics expressing the GAI summation of a subset of Gaussian centroids located within a certain percentile height range; and (2) height-weighted GAI metrics, a summation of GAIs of a waveform weighted by the corresponding heights of their Gaussian centroids. A biomass regression model was built by eight newly developed GD metrics using GAI information and five pre-existing GD-derived metrics that have not previously been used for biomass estimation. The performance of the regression model was then compared to another regression model using 12 previously published metrics (non-GD metrics). The Random Forests (RF) regression algorithm was employed for predicting biomass. The RF out-of-bag results indicated that above-ground biomass estimations using GD metrics achieved significantly better results than those derived from non-GD metrics for deciduous plots (19% lower root mean square error (RMSE), 25% higher coefficient of determination (R²), and marginally better results in coniferous plots (4% lower RSME, 6% higher R²). The combination of GD and non-GD metrics achieved comparable biomass estimation results to the model using exclusively GD metrics. GD metrics also showed strong correlation with forest attributes such as mean diameter at breast height (DBH) and stem density. This study contributes to the usage of GD results for accurate estimation of forest above-ground biomass in large-footprint lidar waveform data in temperate deciduous forests, because temperate deciduous forests have been proved challenging in regard to lidar-derived biomass estimations. [ABSTRACT FROM AUTHOR]

Non-spatial regression models are rarely adequate for exploring ecological phenomena, especially in settings where the processes operate at large spatial scales and when model covariates do not explain all variation present in the outcome variable. Given the complexity of ecological processes, it is often unrealistic to assume a set of stationary regression coefficients can capture space-varying and scale-dependent relationships between covariates and an ecological response. Spatially-varying coefficients (SVC) models fit within a Bayesian inferential framework provide a statistically robust method to explore potential space-varying and scale-dependent impacts of covariates. Our study objective was to assess the utility of SVC models for capturing non-stationary relationships between temporal community dynamics in avian assemblages and variation in environmental factors. We also wanted to compare the inference drawn from SVC models to that obtained from space-varying intercept models and also models that do not acknowledge any spatial structure beyond what is introduced by the covariates. Our analysis examines the temporal turnover, expressed as a proportion, of avian communities across New York State, USA. Given the expected outcome is non-Gaussian, we detail a generalized linear model specification of the proposed model structures. Our results show the SVC model outperformed the spatially-varying intercept and non-spatial models in terms of model fit and model predictive inference. Further, by fitting these models within a Bayesian inferential framework, we were able to make inferences about the spatial impact of covariates and other process parameters, as well as obtain full posterior predictive inference about the rate of turnover at new, unobserved locations. We conclude that SVC models provide a flexible framework for exploring and accounting for non-stationary mechanisms driving ecological patterns. [ABSTRACT FROM AUTHOR]

Ericaceous shrubs can influence soil properties in many ecosystems. In this study, we examined how soil and forest floor properties vary among sites with different ericaceous evergreen shrub basal area in the southern Appalachian mountains. We randomly located plots along transects that included open understories and understories with varying amounts of Rhododendron maximum (rosebay rhododendron) and Kalmia latifolia (mountain laurel) at three sites. The three sites were a mid-elevation ridge, a low-elevation cove, and a high-elevation southwest-facing slope. Basal area of R. maximum was more correlated with soil properties of the forest floor than was K. latifolia. Increasing R. maximum basal area was correlated with increasing mass of lower quality litter and humus as indicated by higher C:N ratios. Moreover, this correlation supports oar prediction that understory evergreen shrubs may have considerable effect on rarest floor resource heterogeneity in mature stands. [ABSTRACT FROM AUTHOR]