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The ability to quantify spatial patterns and detect change in terrestrial vegetation across large landscapes depends on linking ground-based measurements of vegetation to remotely sensed data. Unlike non-overlapping categorical vegetation types (i.e., typical vegetation and land cover maps), species-level gradients of foliar cover are consistent with the ecological theories of individualistic response of species and niche space. We collected foliar cover data for vascular plant, bryophyte, and lichen species and 17 environmental variables in the Arctic Coastal Plain and Brooks Foothills of Alaska from 2012 to 2017. We integrated these data into a standardized database with 13 additional vegetation survey and monitoring data sets in northern Alaska collected from 1998 to 2017. To map the patterns of foliar cover for six dominant and widespread vascular plant species in arctic Alaska, we statistically associated ground-based measurements of species distribution and abundance to environmental and multi- season spectral covariates using a Bayesian statistical learning approach. For five of the six modeled species, our models predicted 36% to 65% of the observed species-level variation in foliar cover. Overall, our continuous foliar cover maps predicted more of the observed spatial heterogeneity in species distribution and abundance than an existing categorical vegetation map. Mapping continuous foliar cover at the species level also revealed ecological patterns obscured by aggregation in existing plant functional type approaches. Species-level analysis of vegetation patterns enables quantifying and monitoring landscape-level changes in species, vegetation communities, and wildlife habitat independently of subjective categorical vegetation types and facilitates integrating spatial patterns across multiple ecological scales. The novel species-level foliar cover mapping approach described here provides spatial information about the functional role of plant species in vegetation communities and wildlife habitat that are not available in categorical vegetation maps or quantitative maps of broadly defined vegetation aggregates.

Dzharylhach Island is the largest one in the Black Sea. It is the part of the “Dzharylhatskyi” National Nature Park, which located in the Southern Ukraine. A 1 : 10000 scale vegetation map of Dzharylhach Island has been developed. The main unit for mapping is a complex of associations. In total 28 of such complexes were identified. The map shows the territorial differentiation of vegetation. It has also been used to reconstruct the island vegetation changes over the past 90 and 20 years. A comparison of cartographic materials revealed that the predominant processes in vegetation cover are halophytization and xerophytization of communities. The most distributed types of communities on the island are aquatic – , halophytic – and psammophytic – . Due to specific hydrological and soil conditions, the northern spit and shores of the island represent natural vegetation types only.

Vegetation maps are fundamental for regional-scale ecological research. However, information is often not sufficiently up to date for such research. The Loess Plateau is a key area for vegetation restoration projects and a suitable area for regional ecological research. To carry out regional vegetation mapping based on the principles of hierarchical classification, object-oriented methods, visual interpretation, and accuracy assessment, this study integrated land cover, high-resolution remote sensing images, background environmental data, bioclimate zoning data, and field survey data from the Loess Plateau. To further clarify the implications of vegetation mapping, we compared the deviation of the 2015 vegetation map of the Loess Plateau (VMLP) and the widely used vegetation map of China (VMC) (1 : 1 000 000) for the expressed vegetation information and the evaluation of ecosystem services. The results indicated that 1) the vegetation of the Loess Plateau could be divided into 9 vegetation type groups and 18 vegetation types with classification accuracies of 87.76% and 83.97%, respectively; 2) the distribution of vegetation had obvious zonal regularity; 3) a deviation of 29.56 × 10 4 km 2 occurred when the vegetation coverage area was quantified with the VMC; 4) the vegetation classification accuracy affected the ecosystem service assessment, the total water yield of the Loess Plateau calculated by the VMC and other required parameters was overestimated by 2.2 × 10 6 mm in 2015. Because vegetation mapping is a basic and important activity, that requires greater attention, this study provides supporting data for subsequent multivariate vegetation mapping and vegetation management for conservation and restoration.

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs. Significance Models on vegetation dynamics are indispensable for our understanding of climate change impacts. These models contain variables describing vegetation attributes, so-called traits. However, the direct impacts of trait variation on global vegetation distribution are unknown. We derived global trait maps based on information on environmental drivers. Subsequently, we characterized nine globally representative vegetation types based on their trait combinations and could make valid predictions of their global occurrence probabilities based on trait maps. This study provides a proof of concept for the link between plant traits and vegetation types, stimulating enhanced application of trait-based approaches in vegetation modeling. We envision that our approach, our observation-driven trait maps, and vegetation maps may inspire a new generation of powerful traits-based vegetation models.

As the Earth's third pole, the Tibetan Plateau has experienced a pronounced warming in the past decades. Recent studies reported that the start of the vegetation growing season (SOS) in the Plateau showed an advancing trend from 1982 to the late 1990s and a delay from the late 1990s to 2006. However, the findings regarding the SOS delay in the later period have been questioned, and the reasons causing the delay remain unknown. Here we explored the alpine vegetation SOS in the Plateau from 1982 to 2011 by integrating three long-term time-series datasets of Normalized Difference Vegetation Index (NDVI): Global Inventory Modeling and Mapping Studies (GIMMS, 1982-2006), SPOT VEGETATION (SPOT-VGT, 1998-2011), and Moderate Resolution Imaging Spectroradiometer (MODIS, 2000-2011). We found GIMMS NDVI in 2001-2006 differed substantially from SPOT-VGT and MODIS NDVIs and may have severe data quality issues in most parts of the western Plateau. By merging GIMMS-based SOSs from 1982 to 2000 with SPOT-VGT-based SOSs from 2001 to 2011 we found the alpine vegetation SOS in the Plateau experienced a continuous advancing trend at a rate of ~1.04 d·y⁻¹ from 1982 to 2011, which was consistent with observed warming in springs and winters. The satellite-derived SOSs were proven to be reliable with observed phenology data at 18 sites from 2003 to 2011; however, comparison of their trends was inconclusive due to the limited temporal coverage of the observed data. Longer-term observed data are still needed to validate the phenology trend in the future.

Question: What are the composition and spatial patterns of native woody plant communities in the southern Great Chaco and Espinal? Location: Córdoba Province, central Argentina, an area of ca. 161,000 km2. Methods: We collected 351 geo-referenced relevés representative of the geographic, topographic and ecological variation of the Chaco and Espinal woody vegetation in central Argentina. The relevés were classified into vegetation types using the hierarchical ISOPAM method. Forest and shrubland types were described on the basis of diagnostic species occurrences and their distribution in relation to environmental factors. A map of the actual vegetation derived from remote-sensed images (Landsat) and field data was used to describe the current distribution and abundance of the different vegetation types. Results: The classification of the 351 plots × 837 species matrix revealed two major clusters comprising seven woody vegetation types corresponding to Chaco lowland and mountain forests and shrublands, Espinal forests and edaphic vegetation. The most important gradients in woody vegetation types are related to elevation, temperature and rainfall variables. Conclusions: Subtropical seasonally dry woody plant communities from the southern extreme of the Great Chaco and Espinal forests were described for the first time based on complete floristic data. Our results show that lowland Chaco native forests, as well as replacement communities, are still present in its southern distribution range and are well distinguishable from other vegetation types such as the Espinal and mountain forests. Overall, extensive Espinal forests have almost disappeared while Chaco vegetation is highly fragmented and degraded.

Subalpine-alpine vegetation of Himalayan global biodiversity hotspot forms the highest and unique ecosystem of the world. These ecosystems inhabit diverse cold adapted plants, which are currently threatened by global warming. Deciphering vegetation forms and their ecological niches is pre-requisite for evolving conservation strategies. Emerging remote sensing datasets, processing techniques and platforms offer potential to map fine-scale vegetation patterns at ecoregion level. We conceptualised a four-fold classification scheme considering climate, vegetation physiognomy, floristics, and gregarious formations for the subalpine-alpine vegetation of the Western Himalaya spanning over 45,202 km2. Sentinel-2 satellite images were classified using a combination of rule-based and machine learning approach i.e. Random Forest in Google Earth Engine to generate vegetation map at regional scale. Reflectance bands alone provided an overall classification accuracy of 76.46% (kappa 0.78), while, the addition of vegetation indices improved the accuracy to 84.43%. (kappa 0.79). When topographical variables were also considered, the accuracy increased to 91.71% (kappa 0.81). The vegetation map at 10 m resolution represents in total 23 vegetation classes covering subalpine zone (9 coniferous forests, 4 broad-leaved forests, 2 scrubs, bamboo brake and grassland) and alpine zone (2 scrubs and 4 herbaceous). Study enhances knowledge on the coverage, distribution, abundance, diversity of subalpine and alpine vegetation and ecological amplitudes with respect to temperature, precipitation, elevation and aspect. The study outcomes are useful for developing landscape as well as species specific conservation planning and bioresource utilization.

AIM: For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. LOCATION: Global. METHODS: Using a generalized classification scheme and an accuracy‐based integration approach, we integrated four global land‐cover products. We evaluated the performance of this product compared with inputs for estimating subpixel 30‐m resolution land cover. We also compared the accuracy of deductive and inductive species distribution models built with the different products for modelling the continental distributions of six avian habitat specialists. RESULTS: Our product offers accuracy‐weighted consensus information on the prevalence of 12 land‐cover classes within every nominal 1‐km pixel across the globe (except for Antarctica). Compared with the four base products, it better captures the land‐cover information contained in the fine‐grain validation data for all classes combined and for most individual classes. It also has the highest sensitivity and overall accuracy for detecting the presence of every fine‐grain land‐cover class. Both deductive and inductive models built with the consensus dataset have the highest or second highest accuracy for modelling bird species distributions. MAIN CONCLUSIONS: Our consensus product integrates the four base products and successfully maximizes accuracy and reduces errors of omission. Specifically, the consensus product reduces limitations caused by misclassifications, false absence rates and the categorical format of existing land‐cover products. Consequently, it surpasses single base products in the ability to capture subpixel land‐cover information and the utility for modelling species distributions. Both the presented methodology and the consensus product have multiple applications in biodiversity research and for understanding and modelling of global terrestrial ecosystems.

Aim: To evaluate the biomization technique for reconstructing past vegetation in the Eastern Mediterranean–Black Sea–Caspian-Corridor using an extensive modern pollen data set and comparing reconstructions to potential vegetation and observed land cover data. Location: The region between 28–48°N and 22–62°E. Methods: We apply the biomization technique to 1,387 modern pollen samples, representing 1,107 entities, to reconstruct the distribution of 13 broad vegetation categories (biomes). We assess the results using estimates of potential natural vegetation from the European Vegetation Map and the Physico-Geographic Atlas of the World. We test whether anthropogenic disturbance affects reconstruction quality using land use information from the Global Land Cover data set. Results: The biomization scheme successfully predicts the broadscale patterns of vegetation across the region, including changes with elevation. The technique discriminates deserts from shrublands, the prevalence of woodlands in moister lowland sites, and the presence of temperate and mixed forests at higher elevations. Quantitative assessment of the reconstructions is less satisfactory: the biome is predicted correctly at 44% of the sites in Europe and 33% of the sites overall. The low success rate is not a reflection of anthropogenic impacts: only 33% of the samples are correctly assigned after the removal of sites in anthropogenically altered environments. Open vegetation is less successfully predicted (33%) than forest types (73%), reflecting the underrepresentation of herbaceous taxa in pollen assemblages and the impact of long-distance pollen transport into open environments. Samples from small basins (<1 km2) are more likely to be reconstructed accurately, with 58% of the sites in Europe and 66% of all sites correctly predicted, probably because they sample an appropriate pollen source area to reflect regional vegetation patterns in relatively heterogeneous landscapes. While methodological biases exist, the low confidence of the quantitative comparisons should not be over-emphasized because the target maps themselves are not accurate representations of vegetation patterns in this region. Main Conclusions: The biomization scheme yields reasonable reconstructions of the broadscale vegetation patterns in the Eastern Mediterranean–Black Sea–Caspian-Corridor, particularly if appropriate-sized sampling sites are used. Our results indicate biomization could be used to reconstruct changing patterns of vegetation in response to past climate changes in this region.