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As alien invasive species are a key driver of biodiversity loss, understanding patterns of rapidly changing global species compositions depends upon knowledge of invasive species population dynamics and trends at large scales. Within this context, the Ponto-Caspian region is among the most notable donor regions for aquatic invasive species in Europe. Using macroinvertebrate time series collected over 52 years (1968–2020) at 265 sites across 11 central and western European countries, we examined the occurrences, invasion rates, and abundances of freshwater Ponto-Caspian fauna. We examined whether: (i) successive Ponto-Caspian invasions follow a consistent pattern of composition pioneered by the same species, and (ii) Ponto-Caspian invasion accelerates subsequent invasion rates. In our dataset, Ponto-Caspian macroinvertebrates increased from two species in 1972 to 29 species in 2012. This trend was parallelled by a non-significant increasing trend in the abundances of Ponto-Caspian taxa. Trends in Ponto-Caspian invader richness increased significantly over time. We found a relatively uniform distribution of Ponto-Caspian macroinvertebrates across Europe without any relation to the distance to their native region. The Ponto-Caspian species that arrived first were often bivalves (46.5% of cases), particularly Dreissena polymorpha, followed secondarily by amphipods (83.8%; primarily Chelicorophium curvispinum and Dikerogammarus villosus). The time between consecutive invasions decreased significantly at our coarse regional scale, suggesting that previous alien establishments may facilitate invasions of subsequent taxa. Should alien species continue to translocate from the Ponto-Caspian region, our results suggest a high potential for their future invasion success highly connected central and western European waters. However, each species’ population may decline after an initial ‘boom’ phase or after the arrival of new invasive species, resulting in different alien species dominating over time. This study was supported by the Grant Agency of the University of South Bohemia, project number 065/2022/Z. R.N.C. acknowledges funding from the Leverhulme Trust Early Career Fellowship (ECF-2021-001). We also acknowledge Flanders Environment Agency for providing the publicy available data. P.H. and P.J.H. received funding from the EU Horizon 2020 project eLTER PLUS (Grant Agreement No. 871128). NB and MC were supported by the Serra-Hunter programme (Generalitat de Catalunya). MC was also supported by a Ramón y Cajal contract funded by the Spanish Ministry of Science and Innovation (RYC2020-029829-I). NB was also supported by the H2020 European Research and Innovation action Grant Agreement no. 869226 (DRYvER). The FEHM (Freshwater Ecology, Hydrology and Management) research group (to which NB and MC belong) was funded by the “Agència de Gestió d’Ajuts Universitaris i de Recerca” (AGAUR) at the "Generalitat de Catalunya” (2017SGR1643). BG was funded through the 2017–2018 Belmont Forum and BIODIVERSA joint call for research proposals, under the BiodivScen ERANet COFUND programme, funded through the Spanish State Research Agency (MCI/AEI/FEDER, UE, PCI2018-092986). Peer reviewed

Globalization has led to the introduction of thousands of alien species worldwide. With growing impacts by invasive species, understanding the invasion process remains critical for predicting adverse effects and informing efficient management. Theoretically, invasion dynamics have been assumed to follow an “invasion curve” (S-shaped curve of available area invaded over time), but this dynamic has lacked empirical testing using large-scale data and neglects to consider invader abundances. We propose an “impact curve” describing the impacts generated by invasive species over time based on cumulative abundances. To test this curve's large-scale applicability, we used the data-rich New Zealand mud snail Potamopyrgus antipodarum, one of the most damaging freshwater invaders that has invaded almost all of Europe. Using long-term (1979–2020) abundance and environmental data collected across 306 European sites, we observed that P. antipodarum abundance generally increased through time, with slower population growth at higher latitudes and with lower runoff depth. Fifty-nine percent of these populations followed the impact curve, characterized by first occurrence, exponential growth, then long-term saturation. This behaviour is consistent with boom-bust dynamics, as saturation occurs due to a rapid decline in abundance over time. Across sites, we estimated that impact peaked approximately two decades after first detection, but the rate of progression along the invasion process was influenced by local abiotic conditions. The S-shaped impact curve may be common among many invasive species that undergo complex invasion dynamics. This provides a potentially unifying approach to advance understanding of large-scale invasion dynamics and could inform timely management actions to mitigate impacts on ecosystems and economies. Kuwait Foundation for the Advancement of Sciences http://dx.doi.org/10.13039/501100003286 Gulf University for Science and Technology (GUST) http://dx.doi.org/10.13039/100016753 Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347 Alexander von Humboldt Foundation http://dx.doi.org/10.13039/100005156 Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661 Leverhulme Trust http://dx.doi.org/10.13039/501100000275 Leibniz Competition Peer Reviewed

Abstract Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co‐occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open‐access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local‐to‐regional datasets to openly release data. We thus present sPlotOpen, the largest open‐access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co‐occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot‐level data also include community‐weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot‐level records. Software format Three main matrices (.csv), relationally linked. Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung http://dx.doi.org/10.13039/501100001711 National Research, Development and Innovation Office, Hungar http://dx.doi.org/10.13039/501100018818 German Centre for Integrative Biodiversity Research http://dx.doi.org/10.13039/501100020056 Russian Foundation for Basic Research http://dx.doi.org/10.13039/501100002261 Grantová Agentura České Republiky http://dx.doi.org/10.13039/501100001824 Agence Nationale de la Recherche http://dx.doi.org/10.13039/501100001665 Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659 Brazil’s National Council of Scientific and Technological Development Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661 U.S. National Science Foundation http://dx.doi.org/10.13039/100000001 Akademie Věd České Republiky http://dx.doi.org/10.13039/501100004240 H2020 European Research Council http://dx.doi.org/10.13039/100010663 Spanish Research Agency http://dx.doi.org/10.13039/501100011033 Narodowe Centrum Nauki http://dx.doi.org/10.13039/501100004281 Volkswagen Foundation http://dx.doi.org/10.13039/501100001663 Basque Government http://dx.doi.org/10.13039/501100003086 Fundación BBVA http://dx.doi.org/10.13039/100007406 Villum Fonden http://dx.doi.org/10.13039/100008398 NSF http://dx.doi.org/10.13039/100003187 Latvia grant

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes. ispartof: GLOBAL CHANGE BIOLOGY vol:26 issue:11 pages:6616-6629 ispartof: location:England status: published