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Aims: Novel ecosystems are self‐maintaining ecosystems that support species assemblages without historical precedent. Despite much interest and controversy around novel ecosystems, it remains poorly understood how they are generated, what their capacity to support biodiversity is and what the implications for society are. Here, we address these issues through a global synthesis of non‐native palms, since palms are likely generators of novel ecosystems because they are introduced widely beyond their native range and have the capacity to act as ecosystem engineers. Location: Global. Methods: We gathered data on non‐native palms from peer‐reviewed literature/papers, grey literature and online databases. We extracted data on the biogeographic context of palm invasions, plant functional traits and anthropogenic drivers to quantify their effects on biodiversity, ecosystem functioning and ecosystem services. Results: Of the 2,557 palm species, 3.4% (86 species) were recorded as naturalized and 1.1% (28 species) as invasive, which exceeds the average invasion success across all woody plants. Naturalized palms are present in most tropical and subtropical regions around the world, often in urban areas, reflecting the use of palms in horticulture. Many naturalized palms were taller and more likely to originate from open habitats or dry forest than non‐naturalized palms. These features likely represent the naturalized palms' competitive ability, high fecundity and dispersal ability along with ecological matching to human‐disturbed environments. Overall, literature on ecological effects of palm invasions was sparse, but we found multiple cases in which palm invasions resulted in strong ecosystem changes or even biome shifts. Main Conclusions: We found strong evidence that palm invasions can generate novel ecosystems. Although there are substantial knowledge gaps on the ecological effects of palm invasion, anthropogenic drivers like urbanization and ongoing global warming will continue to expand palm ranges and promote non‐native palms as generators of novel ecosystems. [ABSTRACT FROM AUTHOR]

Note: Biodiversity and Conservation does not normally publish comments on contributions that have appeared in other journals. Meanwhile, the science of restoration ecology has turned away from literal restoration to past conditions, instead emphasizing future trajectories and the resilience of (socio-)ecological systems (Corlett [1]; Higgs et al. [3]). [Extracted from the article]

Biomes are important constructs for organizing understanding of how the worlds' major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30-year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture-limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function. [ABSTRACT FROM AUTHOR]

The Greater Maasai Mara Ecosystem (GMME) in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage, notably by including the largest remaining seasonal migration of African ungulates and the semi-nomadic pastoralist Maasai culture. Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and space since recent reports suggest dramatic declines in wildlife populations alongside troubling reports of grassland conversion to cropland and habitat fragmentation due to increasing small-holder fencing. Here, we present the first comprehensive vegetation map of GMME at high (10-m) spatial resolution. The map consists of nine key vegetation cover types (VCTs), which were derived in a two-step process integrating data from high-resolution WorldView-3 images (1.2-m) and Sentinel-2 images using a deep-learning workflow. We evaluate the role of anthropogenic, topographic, and climatic factors in affecting the fractional cover of the identified VCTs in 2017 and their MODIS-derived browning/greening rates in the preceding 17 years at 250-m resolution. Results show that most VCTs showed a preceding greening trend in the protected land. In contrast, the semi- and unprotected land showed a general preceding greening trend in the woody-dominated cover types, while they exhibited browning trends in grass-dominated cover types. These results suggest that woody vegetation densification may be happening across much of the GMME, alongside vegetation declines within the non-woody covers in the semi- and unprotected lands. Greening and potential woody densification in GMME is positively correlated with mean annual precipitation and negatively correlated with anthropogenic pressure. Increasing woody densification across the entire GMME in the future would replace high-quality grass cover and pose a risk to the maintenance of the region's rich savanna megafauna, thus pointing to a need for further investigation using alternative data sources. The increasing availability of high-resolution remote sensing and efficient approaches for vegetation mapping will play a crucial role in monitoring conservation effectiveness as well as ecosystem dynamics due to pressures such as climate change. • A deep-learning based high-resolution mapping workflow was proposed. • Vegetation cover types were mapped in 10-m in Greater Maasai Mara Ecosystem (GMME). • Woody vegetation densification is happening across much of the GMME. • A density decline in grass cover was observed in semi- and unprotected lands in GMME. • The woody vegetation densification is correlated with rainfall and human pressure. [ABSTRACT FROM AUTHOR]