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Plant functional traits have been shown to vary with environmental conditions. However, we lack empirical data on how plant functional traits of different plant life forms respond to environmental factors. We studied the influence of environmental conditions on the distribution of plant functional traits in a Quercus wutaishanica forest with the aim of exploring the patterns of functional traits across different life forms and determining the driving factors of functional trait variation at fine spatial scales. We collected data on environmental factors (soil nutrients and soil moisture, canopy variables, topography) of 70 20 m × 20 m plots. Leaves were harvested from 26 species (4 tree species, 7 shrub species and 15 herbaceous species), and community-weighted mean (CWM) trait values for leaf area (LA), leaf mass per unit area (LMA), leaf carbon content (LCC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), and leaf potassium content (LKC) were calculated. We also measured the height (H) of plants. The importance of biotic and abiotic factors in controlling plant functional traits was quantitatively assessed using redundancy analysis (RCA). Regression was used to determine relationships between CWM trait values and environment variables. We found that plant functional traits varied with life forms of plants. LA, LMA and LCC of trees were greater than those of the understory, whereas LNC, LPC and LKC of herbaceous were greater than those of trees. Responses of plant functional traits to environmental factors varied with different life forms. The combined effect of canopy, topography and soil factors had a greater impact on plant functional traits in understory layer than tree layer. General linear models showed that openness is the main factor affecting various functional traits of undergrowth plants, and the relationship between the element content in leaves of herbaceous plants and environmental factors is greater than that of shrub plants. The strong correlation of plant functional traits and environmental factors at fine spatial scales indicates that Q. wutaishanica forests have high spatial variability. Considering the variation of traits in different life form of plants and their interactions with biotic factors, it provides further insights into ecological mechanisms of shaping plant communities and driving plant community dynamics.

The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region.

Although an increasing number of researchers focus on environmental, infrastructural and cultural dimensions of the Arctic, few efforts have been made to address how these various dimensions coales...

Observing the environment in the vast inaccessible regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest i...

Tundra vegetation productivity and composition are responding rapidly to climatic changes in the Arctic. These changes can, in turn, mitigate or amplify permafrost thaw. In this Review, we synthesize remotely sensed and field-observed vegetation change across the tundra biome, and outline how these shifts could influence permafrost thaw. Permafrost ice content appears to be an important control on local vegetation changes; woody vegetation generally increases in ice-poor uplands, whereas replacement of woody vegetation by (aquatic) graminoids following abrupt permafrost thaw is more frequent in ice-rich Arctic lowlands. These locally observed vegetation changes contribute to regional satellite-observed greening trends, although the interpretation of greening and browning is complicated. Increases in vegetation cover and height generally mitigate permafrost thaw in summer, yet, increase annual soil temperatures through snow-related winter soil warming effects. Strong vegetation–soil feedbacks currently alleviate the consequences of thaw-related disturbances. However, if the increasing scale and frequency of disturbances in a warming Arctic exceeds the capacity for vegetation and permafrost recovery, changes to Arctic ecosystems could be irreversible. To better disentangle vegetation–soil–permafrost interactions, ecological field studies remain crucial, but require better integration with geophysical assessments.

The relict arctic–alpine tundra provides a natural laboratory to study the potential impacts of climate change and anthropogenic disturbance on tundra vegetation. The Nardus stricta-dominated relict tundra grasslands in the Krkonoše Mountains have experienced shifting species dynamics over the past few decades. Changes in species cover of the four competing grasses—Nardus stricta, Calamagrostis villosa, Molinia caerulea, and Deschampsia cespitosa—were successfully detected using orthophotos. Leaf functional traits (anatomy/morphology, element accumulation, leaf pigments, and phenolic compound profiles), were examined in combination with in situ chlorophyll fluorescence in order to shed light on their respective spatial expansions and retreats. Our results suggest a diverse phenolic profile in combination with early leaf expansion and pigment accumulation has aided the expansion of C. villosa, while microhabitats may drive the expansion and decline of D. cespitosa in different areas of the grassland. N. stricta—the dominant species—is retreating, while M. caerulea did not demonstrate significant changes in territory between 2012 and 2018. We propose that the seasonal dynamics of pigment accumulation and canopy formation are important factors when assessing potential “spreader” species and recommend that phenology be taken into account when monitoring grass species using remote sensing.

Climate change is leading to increased drought intensity and fire frequency, creating early-successional landscapes with novel disturbance–recovery dynamics. In the Klamath Mountains of northwestern California and southwestern Oregon, early-successional interactions between nitrogen (N)-fixing shrubs (Ceanothus spp.) and long-lived conifers (Douglas-fir) are especially important determinants of forest development. We sampled post-fire vegetation and soil biogeochemistry in 57 plots along gradients of time since fire (7–28 years) and climatic water deficit (aridity). We found that Ceanothus biomass increased, and Douglas-fir biomass decreased with increasing aridity. High aridity and Ceanothus biomass interacted with lower soil C:N more than either factor alone. Ceanothus biomass was initially high after fire and declined with time, suggesting a large initial pulse of N-fixation that could enhance N availability for establishing Douglas-fir. We conclude that future increases in aridity and wildfire frequency will likely limit post-fire Douglas-fir establishment, though Ceanothus may ameliorate some of these impacts through benefits to microclimate and soils. Results from this study contribute to our understanding of the effects of climate change and wildfires on interspecific interactions and forest dynamics. Management seeking to accelerate forest recovery after high-severity fire should emphasize early-successional conifer establishment while maintaining N-fixing shrubs to enhance soil fertility.

Artificial light at night (ALAN) can impact the trophic structure of assemblages of ground-dwelling invertebrates, and changes in such assemblages can affect decomposition in terrestrial systems due to the various functional roles of these invertebrates, including microbial grazing, comminution of litter, and predation of other invertebrates, that can directly or indirectly affect plant-litter breakdown. Despite this, we are unaware of any studies that have evaluated the effects of ALAN on the breakdown of plant litter in a terrestrial ecosystem. We sought to answer whether ALAN affects litter breakdown via its effects on a community of ground-dwelling arthropods using two field experiments. In one experiment, we manipulated the presence of ALAN and the size classes of soil invertebrates that could enter mesh bags containing plant litter (litterbags). We found that the rate of plant-litter breakdown increased with the mesh size of litterbags but was unaffected by presence of ALAN. In a second field experiment carried out to examine the effects of ALAN on the trophic structure of litter-layer invertebrate communities, while controlling for potential effects of ALAN on vegetation, we again found that ALAN did not affect litter breakdown despite the fact that ALAN increased the abundances of secondary and tertiary consumers. Our finding that larger assemblages of ground-dwelling secondary and tertiary consumer invertebrates under ALAN did not slow litter breakdown through increased top-down control of detritivores suggests ALAN may disrupt predator-prey interactions in litter-layer communities.

Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.

Acid deposition is a major biogeochemical driver in forest ecosystems, but the impacts of long‐term changes in deposition on forest productivity remain unclear. Using a combination of tree ring and forest inventory data, we examined tree growth and climate sensitivity in response to 26 years of whole‐watershed ammonium sulfate ((NH4)2SO4) additions at the Fernow Experimental Forest (West Virginia, USA). Linear mixed effects models revealed species‐specific responses to both treatment and hydroclimate variables. When controlling for environmental covariates, growth of northern red oak (Quercus rubra), red maple (Acer rubrum), and tulip poplar (Liriodendron tulipifera) was greater (40%, 52%, and 42%, respectively) in the control watershed compared to the treated watershed, but there was no difference in black cherry (Prunus serotina). Stem growth was generally positively associated with growing season water availability and spring temperature and negatively associated with vapor pressure deficit. Sensitivity of northern red oak, red maple, and tulip poplar growth to water availability was greater in the control watershed, suggesting that acidification treatment has altered tree response to climate. Results indicate that chronic acid deposition may reduce both forest growth and climate sensitivity, with potentially significant implications for forest carbon and water cycling in deposition‐affected regions.

Mountain-basin systems (MBS) in Central Asia are unique and complex ecosystems, wherein their elevation gradients lead to high spatial heterogeneity in vegetation and its response to climate change. Exploring elevation-dependent vegetation greenness variation and the effects of climate factors on vegetation has important theoretical and practical significance for regulating the ecological processes of this system. Based on the MODIS NDVI (remotely sensed normalized difference vegetation index), and observed precipitation and temperature data sets, we analyzed vegetation greenness and climate patterns and dynamics with respect to elevation (300–3600 m) in a typical MBS, in Altay Prefecture, China, during 2000–2017. Results showed that vegetation exhibited a greening (NDVI) trend for the whole region, as well as the mountain, oasis and desert zones, but only the desert zone reached significant level. Vegetation in all elevation bins showed greening, with significant trends at 400–700 m and 2600–3500 m. In summer, lower elevation bins (below 1500 m) had a nonsignificant wetting and warming trend and higher elevation bins had a nonsignificant drying and warming trend. Temperature trend increased with increasing elevation, indicating that warming was stronger at higher elevations. In addition, precipitation had a significantly positive coefficient and temperature a nonsignificant coefficient with NDVI at both regional scale and subregional scale. Our analysis suggests that the regional average could mask or obscure the relationship between climate and vegetation at elevational scale. Vegetation greenness had a positive response to precipitation change in all elevation bins, and had a negative response to temperature change at lower elevations (below 2600 m), and a positive response to temperature change at higher elevations. We observed that vegetation greenness was more sensitive to precipitation than to temperature at lower elevations (below 2700 m), and was more sensitive to temperature at higher elevations.

State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.

Permafrost soils in arctic and boreal ecosystems store twice the amount of current atmospheric carbon that may be mobilized and released to the atmosphere as greenhouse gases when soils thaw under a warming climate. This permafrost carbon climate feedback is among the most globally important terrestrial biosphere feedbacks to climate warming, yet its magnitude remains highly uncertain. This uncertainty lies in predicting the rates and spatial extent of permafrost thaw and subsequent carbon cycle processes. Terrestrial ecosystem influences on surface energy partitioning exert strong control on permafrost soil thermal dynamics and are critical for understanding permafrost soil responses to climate change and disturbance. Here we review how arctic and boreal ecosystem processes influence permafrost soils and characterize key ecosystem changes that regulate permafrost responses to climate. While many of the ecosystem characteristics and processes affecting soil thermal dynamics have been examined in isolation, interactions between processes are less well understood. In particular connections between vegetation, soil moisture, and soil thermal properties affecting permafrost conditions could benefit from additional research. In particular, connections between vegetation, soil moisture, and soil thermal properties affecting permafrost could benefit from additional research. Changes in ecosystem distribution and vegetation characteristics will alter spatial patterns of interactions between climate and permafrost. In addition to shrub expansion, other vegetation responses to changes in climate and disturbance regimes will all affect ecosystem surface energy partitioning in ways that are important for permafrost. Lastly, changes in vegetation and ecosystem distribution will lead to regional and global biophysical and biogeochemical climate feedbacks that may compound or offset local impacts on permafrost soils. Consequently, accurate prediction of the permafrost carbon climate feedback will require detailed understanding of changes in terrestrial ecosystem distribution and function and the net effects of multiple feedback processes operating across scales in space and time.

Plantation management practices may influence carbon dioxide (CO2) uptake dynamics; however, the responses of carbon losses, such as soil respiration, to management (e.g. thinning) have not been sufficiently studied. We evaluated the effects of forest thinning (no thinning (NT), low thinning (LT), medium thinning (MT), and high thinning (HT) and organic matter manipulation (intact soil (untreated control, UC), exclusion of aboveground litter (EL), and exclusion of both aboveground litter and roots (ELR)) on soil respiration in a Larix principis-rupprechtii plantation. During the period from 2015–2018, soil respiration was continuously monitored from May to October (growing season). Meanwhile, soil temperature and soil moisture at a depth of 5 cm were also measured. In addition, three soil samples were collected to measure biochemical properties of soil. We found that mean soil respiration (Rs) was significantly greater in MT than that in NT (by 21.5 %). RS in HT was significantly greater than NT in 2015 (by 25.0 %) and then finally lower than NT (14.8 %) in 2018. However, RS in LT compared with NT showed no response to thinning during our observation period. The organic matter manipulation also affected soil respiration. Mean reduction of soil respiration in EL (REL) and in ELR (RELR) compared to Rs ranged from 25.9%–39.2% and 40.8%–53.6% across all thinning regimes, respectively. The temperature sensitivity (Q10) values of RS, REL, RELR ranged from 2.23 to 2.72, 2.21–2.60, and 2.31–2.90, respectively. The model with the best fitting temperature and moisture factors explained 63.8 % - 75.4 % changes in RS, 65.9 % - 74.7 % changes in REL and 70.4 % - 74.1 % changes in RELR. Stepwise regression analyses showed that soil temperature, ratio of soil carbon and nitrogen in the topsoil explained the pattern of RS across the thinning scenarios. REL showed a high sensitivity to changing in some soil chemical properties and hydrolases activities, whereas RELR showed a high sensitivity to changing in microbial activity combined with temperature and pH of soil. Overall, our data show that the response of organic matter inputs to thinning has an important impact on soil CO2 fluxes of plantation, and emphasize the difference of the response between aboveground and underground organic matter inputs to soil CO2 fluxes.

Terrestrial ecosystem responses to temperature and precipitation have major implications for the global carbon cycle. Case studies demonstrate that complex terrain, which accounts for more than 50% of Earth's land surface, can affect ecological processes associated with land-atmosphere carbon fluxes. However, no studies have addressed the role of complex terrain in mediating ecophysiological responses of land-atmosphere carbon fluxes to climate variables. We synthesized data from AmeriFlux towers and found that for sites in complex terrain, responses of ecosystem CO2 fluxes to temperature and precipitation are organized according to terrain slope and drainage area, variables associated with water and energy availability. Specifically, we found that for tower sites in complex terrain, mean topographic slope and drainage area surrounding the tower explained between 51% and 78% of site-to-site variation in the response of CO2 fluxes to temperature and precipitation depending on the time scale. We found no such organization among sites in flat terrain, even though their flux responses exhibited similar ranges. These results challenge prevailing conceptual framework in terrestrial ecosystem modeling that assumes CO2 fluxes derive from vertical soil-plant-climate interactions. We conclude that the terrain in which ecosystems are situated can also have important influences on CO2 responses to temperature and precipitation. This work has implications for about 14% of the total land area of the conterminous US. This area is considered topographically complex and contributes to approximately 15% of gross ecosystem carbon production in the conterminous US.

Vegetation, active-layer soils, and snow cover regulate energy exchange between the atmosphere and permafrost. Therefore, interactions between changes to tundra vegetation and soil thermal regime will fundamentally affect permafrost in a warmer world. We recorded soil temperatures for approximately 1 year in a Siberian Low Arctic landscape with a known history of alder (Alnus) shrub expansion on disturbed microsites in patterned ground. We recorded near-surface soil temperatures and measured physical properties of soils and vegetation on sorted-circle microsites in four stages of shrubland development: (1) tundra lacking tall shrubs; (2) shrub colonization zones; (3) mature shrublands; and (4) paludified, long-established shrublands with thick soil organic layers. Summer soil temperatures declined with increasing shrub cover and soil organic thickness; shrub colonization suppressed cryoturbation, facilitating the development of continuous vegetation and a surface organic mat on circles. Compared to open tundra, mature shrubs cooled soils by up to 9 °C during summer, but warmed soils by greater than 10 °C in winter presumably because they developed highly insulative snowpacks. Paludified shrublands had the coldest summer active layers, but winter soil temperatures were much colder than mature shrublands and were similar to earlier stages. Our results indicate that although tall shrub establishment dramatically warms winter soils within decades, much of this warming is transient at paludification-prone sites because the buildup of wet peat favors cooling in winter and the stature and snow-trapping capacity of shrubs diminish over time. In the ecosystem we studied, shrub expansion has contrasting effects on active-layer temperatures both seasonally and over longer timescales due to successional processes.

In the context of ongoing climatic warming, certain landscapes could be near a tipping point where relatively small changes to their fire regimes or their postfire forest recovery dynamics could bring about extensive forest loss, with associated effects on biodiversity and carbon-cycle feedbacks to climate change. Such concerns are particularly valid in the Klamath Region of northern California and southwestern Oregon, where severe fire initially converts montane conifer forests to systems dominated by broadleaf trees and shrubs. Conifers eventually overtop the competing vegetation, but until they do, these systems could be perpetuated by a cycle of reburning. To assess the vulnerability of conifer forests to increased fire activity and altered forest recovery dynamics in a warmer, drier climate, we characterized vegetation dynamics following severe fire in nine fire years over the last three decades across the climatic aridity gradient of montane conifer forests. Postfire conifer recruitment was limited to a narrow window, with 89% of recruitment in the first 4 years, and height growth tended to decrease as the lag between the fire year and the recruitment year increased. Growth reductions at longer lags were more pronounced at drier sites, where conifers comprised a smaller portion of live woody biomass. An interaction between seed-source availability and climatic aridity drove substantial variation in the density of regenerating conifers. With increasing climatic water deficit, higher propagule pressure (i.e., smaller patch sizes for high-severity fire) was needed to support a given conifer seedling density, which implies that projected future increases in aridity could limit postfire regeneration across a growing portion of the landscape. Under a more severe prospective warming scenario, by the end of the century more than half of the area currently capable of supporting montane conifer forest could become subject to minimal conifer regeneration in even moderate-sized (10s of ha) high-severity patches.

Red spruce (Picea rubens) is an evergreen tree with a range from Canada to North Carolina that provides habitat for multiple rare, endemic species. Red spruce-dominated forests once covered over 600,000 ha in the southeastern US, yet currently occupy a small fraction of their historical range due largely to logging that began in the nineteenth century. To combat this loss, restoration groups have emerged to actively improve the health and areal extent of red spruce. This study was conducted to (1) predict how suitable habitat for red spruce in the southeastern US is expected to change by the year 2100 in response to increasing global temperatures and (2) illustrate how these predictions can be used, in concert with local-scale information, to support efforts to restore red spruce in this region. Red spruce currently occupies a small fraction of the area indicated by our model to be suitable. The area of habitat supportive of red spruce was projected to decline from present day to the year 2100, but the magnitude of this decline depended on the level of carbon emissions, and there was considerable variability between climate models. In our case-study watershed, suitability for red spruce is predicted to decline by 2100, but may still support red spruce under optimistic to moderate emissions scenarios. At this scale, restoration strategies should also take into account locally varying conditions such as the current distribution of red spruce and competitive shrubs that may inhibit growth.

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change. acceptedVersion

Questions How do plant communities on zonal loamy vs. sandy soils vary across the full maritime Arctic bioclimate gradient? How are plant communities of these areas related to existing vegetation units of the European Vegetation Classification? What are the main environmental factors controlling the transitions of vegetation along the bioclimate gradient? Location 1700‐km Eurasia Arctic Transect (EAT), Yamal Peninsula and Franz Josef Land (FJL), Russia. Methods The Braun‐Blanquet approach was used to sample mesic loamy and sandy plots at 14 total study sites at six locations, one in each of the five Arctic bioclimate subzones and the forest‐tundra transition. Trends in soil factors, cover of plant growth forms (PGFs), and species diversity were examined along the summer‐warmth‐index (SWI) gradient and on loamy and sandy soils. Classification and ordination were used to group the plots and to test relationships between vegetation and environmental factors. Results Clear, mostly nonlinear, trends occurred for soil factors, vegetation‐structure, and species diversity along the climate gradient. Cluster analysis revealed seven groups with clear relationships to subzone and soil texture. Clusters at the ends of the bioclimate gradient (forest‐tundra and polar desert) had many highly diagnostic taxa, whereas clusters from the Yamal Peninsula had only a few. Axis 1 of a Detrended Correspondence Analysis was strongly correlated with latitude and summer warmth; Axis 2 was strongly correlated with soil moisture, percentage sand, and landscape age. Conclusions Summer temperature and soil texture have clear effects on tundra canopy structure and species composition, with consequences to ecosystem properties. Each layer of the plant canopy has a distinct region of peak abundance along the bioclimate gradient. The major vegetation types are weakly aligned with described classes of the European Vegetation Checklist indicating a continuous floristic gradient rather than distinct subzone regions. The study provides ground‐based vegetation data for satellite‐based interpretations of the western maritime Eurasian Arctic, and the first vegetation data from Hayes Island, Franz Josef Land, which is strongly separated geographically and floristically from the rest of the gradient and most susceptible to ongoing climate change. This article is protected by copyright. All rights reserved.

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Gross primary production (GPP) estimation usually involves a priori assumptions about biome-specific rules or climate controls, which hampers an objective analysis of driving mechanisms. Observation-based methods that are biome-invariant and globally uniform are thus highly desirable. To facilitate this, a reflectance index representing the ratio of chlorophyll to total pigments (R chl) was proposed to consider the variation of energy conversion efficiency driven by different pigment contents in the canopy. Experiments based on simulated reflectance spectra showed that R chl could explain over 83% of chlorophyll ratio dynamics. A model was then developed which approximates GPP as the product of R chl, the normalized difference vegetation index, the near-infrared reflectance, and the photosynthetically active radiation. The model is simple, fast, with definite physical meaning and independent of climatic parameters such as temperature and humidity. Validated with over one hundred thousand field measurements, the model exhibited comparable accuracy to biome- and climate-based GPP models (r = 0.74 for both types of models), demonstrating satisfactory performance. It also achieved significantly better results compared with a regression model excluding R chl, which emphasizes the important role of R chl. By avoiding circular analyses in mechanism studies on GPP variations, this model may extend our previous understanding of global terrestrial carbon uptake.

Overgrazing has caused serious grassland degradation, and grazing exclusion (GE) has been considered an effective and economic approach for restoring vegetation and soils in degraded grassland. In this study, we evaluated the ecosystem restoration of two types of degraded spring and autumn pasture in arid regions of northwest China, based on field surveys (community structure characteristics, soil C and N) and Landsat images (greenness and wetness). In temperate typical steppe (TS), our findings indicated that GE significantly increased plant height, coverage, biomass, soil C and N, as well as greenness and wetness values from Landsat images, however GE decreased plant density and species diversity. Restoration on greenness and wetness peaked in the fourth and fifth years, respectively, following GE. The restoration of greenness and wetness showed bimodal curves during growing season. In temperate desert (TD), results revealed that community structure characteristics, greenness, and wetness improved after GE, and the restoration of greenness and wetness increased with duration of GE. Soil C and N however, did not recover significantly after six years of GE. The restoration of greenness and wetness remained stable during growing-season months. Our findings suggest that longer GE might be considered in the restoration of TD, whereas short-term (not more than 4–5 years) GE, reduction in the livestock load or mowing regularly may be more appropriate for TS. Decision to adopt measures of GE and the duration of GE should be reasonably determined according to the specific grassland type. This research can provide guidance for the restoration of degraded grassland ecosystems in arid and semi-arid regions all over the world.

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems.

Vegetation properties of arctic tundra vary dramatically across its full latitudinal extent, yet few studies have quantified tundra ecosystem properties across latitudinal gradients with field-based observations that can be related to remotely sensed proxies. Here we present data from field sampling of six locations along the Eurasia Arctic Transect in northwestern Siberia. We collected data on the aboveground vegetation biomass, the normalized difference vegetation index (NDVI), and the leaf area index (LAI) for both sandy and loamy soil types, and analyzed their spatial patterns. Aboveground biomass, NDVI, and LAI all increased with increasing summer warmth index (SWI—sum of monthly mean temperatures > 0 °C), although functions differed, as did sandy vs. loamy sites. Shrub biomass increased non-linearly with SWI, although shrub type biomass diverged with soil texture in the southernmost locations, with greater evergreen shrub biomass on sandy sites, and greater deciduous shrub biomass on loamy sites. Moss biomass peaked in the center of the gradient, whereas lichen biomass generally increased with SWI. Total aboveground biomass varied by two orders of magnitude, and shrubs increased from 0 g m−2 at the northernmost sites to >500 g m−2 at the forest-tundra ecotone. Current observations and estimates of increases in total aboveground and shrub biomass with climate warming in the Arctic fall short of what would represent a ‘subzonal shift’ based on our spatial data. Non-vascular (moss and lichen) biomass is a dominant component (>90% of the photosynthetic biomass) of the vegetation across the full extent of arctic tundra, and should continue to be recognized as crucial for Earth system modeling. This study is one of only a few that present data on tundra vegetation across the temperature extent of the biome, providing (a) key links to satellite-based vegetation indices, (b) baseline field-data for ecosystem change studies, and (c) context for the ongoing changes in arctic tundra vegetation.

Remote sensing can potentially be used to monitor the extent and distribution of invasive species across landscapes and regions, thus aiding conservation efforts. We collected ground-level hyperspectral data of six exotic invasive plant species in abandoned agricultural fields at the Blandy Experimental Farm in northern Virginia to determine the degree to which species could be identified using visible and near-infrared wavelengths. The spectral profile from 350 to 1025 nm was used in support vector machine analysis to determine separability of these species. We used sensitivity analyses to determine which spectral regions were most influential to identifying species by removing 50 nm regions and comparing species identification to that using the full spectral profile. Ailanthus altissima, Carduus acanthoides, and Cirsium arvense had high ability to be identified 75%, 87.5%, and 75%, respectively. Galium verum had low ability to be identified 44.4%, perhaps due to high spectral contamination from soil. Celastrus orbiculatus and Rhamnus davurica had low ability to be identified 27.3% and 30.8%, respectively; however, they were often misclassified as each other, due to their physical overlap in the field. The sensitivity analysis revealed that the 350–399, 500–549, 700–749, and 900–949 nm regions were most useful for species identification, while 550–599 and 650–699 nm regions were detrimental, perhaps due to greater intraspecific variability than interspecific variability in these regions. These most influential regions for identification were similar to those found in other studies. Thus, it is possible to identify species using ground-level hyperspectral data.

Pulmonary veno-occlusive disease (PVOD) is rare condition which can lead to severe pulmonary hypertension, right ventricular dysfunction, and cardiopulmonary failure. The diagnosis of PVOD can be challenging due to its nonspecific symptoms and its similarity to idiopathic pulmonary arterial hypertension and interstitial lung disease in terms of diagnostic findings. This case describes a 57 year old female patient who presented with a 5-month history of progressive dyspnea on exertion and nonproductive cough. Workup at another hospital was nonspecific and the patient underwent surgical lung biopsy due to concern for interstitial lung disease. She subsequently became hemodynamically unstable and was transferred to our hospital where she presented with severe hypoxemia, hypotension, and suprasystemic pulmonary artery pressures. Preliminary lung biopsy results suggested idiopathic pulmonary arterial hypertension and the patient was started on vasodilating agents, including continuous epoprostenol infusion. Pulmonary artery pressures decreased but remained suprasystemic and the patient did not improve. Final review of the biopsy by a specialized laboratory revealed a diagnosis of PVOD after which vasodilating therapy was immediately weaned off. Evaluation for dual heart-lung transplantation was begun. The patient's hospital course was complicated by hypotension requiring vasopressors, worsening right ventricular dysfunction, and acute kidney injury. During the transplantation evaluation, the patient decided that she did not want to undergo continued attempts at stabilization of her progressive multi-organ dysfunction and she was transitioned to comfort care. She expired hours after removing inotropic support.

Revegetation facilitated by legume species introduction has been used for soil erosion control on the Loess Plateau, China. However, it is still unclear how vegetation and soil resources develop during this restoration process, especially over the longer term. In this study, we investigated the changes of plant aboveground biomass, vegetation cover, species richness and density of all individuals, and soil total nitrogen, mineral nitrogen, total phosphorus and available phosphorus over 11 years from 2003 to 2013 in three treatments (natural revegetation, Medicago sativa L. introduction and Melilotus suaveolens L. introduction) on the semi-arid Loess Plateau. Medicago significantly increased aboveground biomass and vegetation cover, and soil total nitrogen and mineral nitrogen contents. The Medicago treatment had lower species richness and density of all individuals, lower soil moisture in the deep soil (i.e., 1.4-5m), and lower soil available phosphorus. Melilotus introduction significantly increased aboveground biomass in only the first two years, and it was not an effective approach to improve vegetation biomass and cover, and soil nutrients, especially in later stages of revegetation. Overall, our study suggests that M. sativa can be the preferred plant species for revegetation of degraded ecosystems on the Loess Plateau, although phosphorus fertilizer should be applied for the sustainability of the revegetation.

Abandoned agricultural fields have recently become more abundant in the U.S. and remain susceptible to species invasions after cultivation disturbance. As invasive species become more widespread with increases in anthropogenic activities, we need more effective ways to use limited resources for conservation of native ecosystems. Remote sensing can help us monitor the spread and effects of invasive species, and thus determine the species and locations to target for conservation. To examine this potential, we studied plant communities dominated by exotic invasive plant species in secondary successional fields in northern Virginia using ground-level hyperspectral data. Within these communities, ordination analyses of vegetation surveys revealed differences in species compositions among plots and fields. These differences among communities were also observed in the spectral data. Stepwise multiple linear regression analyses to determine which species influenced the ordination axes revealed that many of the influential species are considered invasive, again underscoring the influence of invasive species on community properties. Stepwise regression analyses also revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from the visible to the near-infrared regions. A discriminant analysis using wavelengths selected with a principal components analysis demonstrated that different plant communities were separable using spectral data. These spectrally observable differences suggest that we can use hyperspectral data to distinguish among invasive-dominated successional plant communities in this region.

Permafrost and varying land surface properties greatly complicate modelling of the thermal response of Arctic soils to climate change. The forest-tundra transition near Nadym in west Siberia provides an excellent study area in which to examine the contrasting thermal properties of soils in a forested ecosystem without permafrost and peatlands with permafrost. We investigated the effects of forest shading, snow cover and variable organic soil horizons in three common ecosystems of the forest-tundra transition zone. Based on the year-round temperature profile data, the most informative annual parameters were: (1) the sum of positive average daily temperatures at depths of 10 and 20 cm; (2) the maximum penetration depth of temperatures above 10 °C; and (3) the number of days with temperatures below 0 °C at a depth of 20 cm. The insulative effect of snow cover in winter was at least twice that of the shading and cooling effect of vegetation in summer. In areas with shallow permafrost, the presence of a thick organic horizon, with an extremely low thermal diffusivity, creates a very steep temperature gradient that limits heat penetration to the top of the permafrost in summer. Copyright © 2015 John Wiley & Sons, Ltd.

The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

The accurate estimation of temporally-continuous gross primary production (GPP) is important for a mechanistic understanding of the global carbon budget, as well as the carbon exchange between land and atmosphere. Ground-based PhenoCams can provide near-surface observations of plant phenology with high temporal resolution and possess great potential for use in modeling the seasonal dynamics of GPP. However, due to the site-level empirical approaches for estimating the fraction of absorbed photosynthetically active radiation (fAPAR), a broad application of PhenoCams in GPP modeling has been restricted. In this study, the stage of vegetation phenology (Pscalar) is proposed, which is calculated from the excess green index (ExGI) derived from PhenoCam data. We integrate Pscalar with the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to generate a daily time-series of the fAPAR (fAPARCAM), and then to estimate daily GPP (GPPCAM) with a light use efficiency model in a semi-arid grassland area from 2012 to 2014. Over the three continuous years, the daily fAPARCAM exhibited similar temporal behavior to the eddy covariance–measured GPP (GPPEC), and the overall determination coefficients (R2) were all > 0.81. GPPCAM agreed well with GPPEC, and these agreements were highly statistically significant (p