Influence of Climate on Long-Term Recovery of Adirondack Mountain Lakewater Chemistry from Atmospheric Deposition of Sulfur and Nitrogen

In this study, we assessed temporal patterns and long-term trends in nitrate (NO3-), two forms of aluminum (inorganic, Ali, and organic, Alo), and dissolved organic carbon (DOC) concentrations in the water of 29 Adirondack Mountain, New York lakes, and the potential effects of ambient weather conditions (i.e., climatic variation) on these patterns and trends

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.Comment: Manuscript: 24 pages, 7 figures; Supplements: 12 pages, 5 figures; Submitted to Forest Ecology and Managemen

Regional growth decline of sugar maple (Acer saccharum) and its potential causes

Sugar maple (Acer saccharum Marsh) has experienced poor vigor, regeneration failure, and elevated mortality across much of its range, but there has been relatively little attention to its growth rates. Based on a well‐replicated dendrochronological network of range‐centered populations in the Adirondack Mountains (USA), which encompassed a wide gradient of soil fertility, we observed that the majority of sugar maple trees exhibited negative growth trends in the last several decades, regardless of age, diameter, or soil fertility. Such growth patterns were unexpected, given recent warming and increased moisture availability, as well as reduced acidic deposition, which should have favored growth. Mean basal area increment was greater on base‐rich soils, but these stands also experienced sharp reductions in growth. Growth sensitivity of sugar maple to temperature and precipitation was non‐stationary during the last century, with overall weaker relationships than expected. Given the favorable competitive status and age structure of the Adirondack sugar maple populations sampled, evidence of widespread growth reductions raises concern over this ecologically and economically important tree. Further study will be needed to establish whether growth declines of sugar maple are occurring more widely across its range

The Effect of Map Boundary on Estimates of Landscape Resistance to Animal Movement

BACKGROUND: Artificial boundaries on a map occur when the map extent does not cover the entire area of study; edges on the map do not exist on the ground. These artificial boundaries might bias the results of animal dispersal models by creating artificial barriers to movement for model organisms where there are no barriers for real organisms. Here, we characterize the effects of artificial boundaries on calculations of landscape resistance to movement using circuit theory. We then propose and test a solution to artificially inflated resistance values whereby we place a buffer around the artificial boundary as a substitute for the true, but unknown, habitat. METHODOLOGY/PRINCIPAL FINDINGS: We randomly assigned landscape resistance values to map cells in the buffer in proportion to their occurrence in the known map area. We used circuit theory to estimate landscape resistance to organism movement and gene flow, and compared the output across several scenarios: a habitat-quality map with artificial boundaries and no buffer, a map with a buffer composed of randomized habitat quality data, and a map with a buffer composed of the true habitat quality data. We tested the sensitivity of the randomized buffer to the possibility that the composition of the real but unknown buffer is biased toward high or low quality. We found that artificial boundaries result in an overestimate of landscape resistance. CONCLUSIONS/SIGNIFICANCE: Artificial map boundaries overestimate resistance values. We recommend the use of a buffer composed of randomized habitat data as a solution to this problem. We found that resistance estimated using the randomized buffer did not differ from estimates using the real data, even when the composition of the real data was varied. Our results may be relevant to those interested in employing Circuitscape software in landscape connectivity and landscape genetics studies

Histone Deacetylase Inhibitors Impair the Elimination of HIV-Infected Cells by Cytotoxic T-Lymphocytes

Resting memory CD4+ T-cells harboring latent HIV proviruses represent a critical barrier to viral eradication. Histone deacetylase inhibitors (HDACis), such as suberanilohydroxamic acid (SAHA), romidepsin, and panobinostat have been shown to induce HIV expression in these resting cells. Recently, it has been demonstrated that the low levels of viral gene expression induced by a candidate HDACi may be insufficient to cause the death of infected cells by viral cytopathic effects, necessitating their elimination by immune effectors, such as cytotoxic T-lymphocytes (CTL). Here, we study the impact of three HDACis in clinical development on T-cell effector functions. We report two modes of HDACi-induced functional impairment: i) the rapid suppression of cytokine production from viable T-cells induced by all three HDACis ii) the selective death of activated T-cells occurring at later time-points following transient exposures to romidepsin or, to a lesser extent, panobinostat. As a net result of these factors, HDACis impaired CTL-mediated IFN-γ production, as well as the elimination of HIV-infected or peptide-pulsed target cells, both in liquid culture and in collagen matrices. Romidepsin exerted greater inhibition of antiviral function than SAHA or panobinostat over the dose ranges tested. These data suggest that treatment with HDACis to mobilize the latent reservoir could have unintended negative impacts on the effector functions of CTL. This could influence the effectiveness of HDACi-based eradication strategies, by impairing elimination of infected cells, and is a critical consideration for trials where therapeutic interruptions are being contemplated, given the importance of CTL in containing rebound viremia

Characterization of 4-HNE Modified L-FABP Reveals Alterations in Structural and Functional Dynamics

4-Hydroxynonenal (4-HNE) is a reactive α,β-unsaturated aldehyde produced during oxidative stress and subsequent lipid peroxidation of polyunsaturated fatty acids. The reactivity of 4-HNE towards DNA and nucleophilic amino acids has been well established. In this report, using proteomic approaches, liver fatty acid-binding protein (L-FABP) is identified as a target for modification by 4-HNE. This lipid binding protein mediates the uptake and trafficking of hydrophobic ligands throughout cellular compartments. Ethanol caused a significant decrease in L-FABP protein (P<0.001) and mRNA (P<0.05), as well as increased poly-ubiquitinated L-FABP (P<0.001). Sites of 4-HNE adduction on mouse recombinant L-FABP were mapped using MALDI-TOF/TOF mass spectrometry on apo (Lys57 and Cys69) and holo (Lys6, Lys31, His43, Lys46, Lys57 and Cys69) L-FABP. The impact of 4-HNE adduction was found to occur in a concentration-dependent manner; affinity for the fluorescent ligand, anilinonaphthalene-8-sulfonic acid, was reduced from 0.347 µM to Kd1 = 0.395 µM and Kd2 = 34.20 µM. Saturation analyses revealed that capacity for ligand is reduced by approximately 50% when adducted by 4-HNE. Thermal stability curves of apo L-FABP was also found to be significantly affected by 4-HNE adduction (ΔTm = 5.44°C, P<0.01). Computational-based molecular modeling simulations of adducted protein revealed minor conformational changes in global protein structure of apo and holo L-FABP while more apparent differences were observed within the internal binding pocket, revealing reduced area and structural integrity. New solvent accessible portals on the periphery of the protein were observed following 4-HNE modification in both the apo and holo state, suggesting an adaptive response to carbonylation. The results from this study detail the dynamic process associated with L-FABP modification by 4-HNE and provide insight as to how alterations in structural integrity and ligand binding may a contributing factor in the pathogenesis of ALD

Assessing Uncertainty in High-Resolution Spatial Climate Data across the US Northeast

<div><p>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.</p></div

Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast

Context: 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. Objectives: 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 30m resolution across New York State (NYS), using machine learning and model ensembling techniques to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1m 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). Results: 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. Conclusions: 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.Comment: 29 pages (19 main text, 10 supplementary materials); 11 figures (10 main text, 1 supplementary materials), 10 tables (3 main text, 7 supplementary materials). Submitted to Landscape Ecolog