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This research explores the vegetation compositional changes between the fifth and third millennium BC on the Karelian Isthmus (north-west Russia). Special emphasis is placed on studying the timing and magnitude of the impact of hunter-fisher-gatherers on the vegetation. First, we reconstruct the local vegetation around Lake Bolshoye Zavetnoye by using the Landscape Reconstruction Algorithm modelling. The application of different scenarios to the relevant source area of pollen is used to assess the local vegetation around Lake Bolshoye Zavetnoye (BZL) at specific distances from 500 to 3000 m. The regional vegetation reconstruction is assessed by using the REVEALS model. Second, we present new pollen and charcoal data from adjacent Lake Ikmenlampi (LI). Third, we calculate indices of vegetation change for BZL, LI and the region. We further explore the potential causes for these differences between the spatial scales and compare all these outcomes with local and regional archaeological data. The results show that foragers actively manipulated their environment. A shift towards more heliophilous conditions is shown by ca 4800 BC. Between 4000 and 3000 BC, an increase in archaeological material suggests intensive resource procurement and landscape management activities, particularly near settlements. Around the BZL site a local decrease in birch is observed from 4100 BC, coinciding with an increase in the rate of vegetation changes. Additionally, a decline in tree species (spruce, hazel, Alder) from 3500 to 3300 BC suggests human activities. The high fire frequency recorded between 4090 and 3150 BC further supports the presence of local human disturbances by the deliberate use of fire to create favourable living conditions. The results from the LI site go in the same direction with fluctuating abundances of spruce and the presence of pollen from Cannabis, Hordeum, Urtica and Plantago lanceolata from ca 4000 to 3600 BC, indicating the use of spruce-dominated forests and an early, incipient cultivation. [ABSTRACT FROM AUTHOR]

One of the challenges in global change research is the significant uncertainty in global historical land use and land cover (LUCC) datasets, which are widely used as foundational data. In addition to the regional cropland area reconstructions, improving the grid allocation method is another feasible way to raise the reliability of historical LUCC data. In this study, an integrated reconstruction of the national cropland areas over the past 200 years was developed for 36 European countries. After that, the allocation algorithm was built using physiogeographic variables and historical city sites for accounting for land suitability and cultivation preferences, respectively. Finally, cropland data in Europe with a spatial resolution of 5′×5′ at five time sections from AD 1800 to 2000 were generated using the optimal allocation algorithm in accordance with the stages of the regional history. The results were as follows: (1) The dominant factors governing the distribution of croplands in Europe vary at different agricultural stages, but the results can be merged together. Land suitability was more optimal for allocation during the modern agricultural stage (AD 1950 and 2000); the priority index combined with land suitability and cultivation preference was more reasonable for allocation during the traditional agricultural stage (AD 1800). The average of the allocations by priority index and the land suitability could be adopted as the allocation results during the transitional stage (AD 1850 and 1900) because the grids for absolute differences within ±10 and ±20 percentage points between the results obtained from the above two allocations were above 80% and 95%, respectively, which means the two allocation results could be merged. (2) Over the past 200 years, the total cropland area in Europe first increased to a peak in AD 1900 and then decreased. Spatially, the centre of the higher cropland fraction shifted from the western part of Europe in AD 1800 to the eastern part of the continent after AD 1950. (3) Both the cropland area and the spatial distribution in this study are more reasonable than the global dataset HYDE3.2. [ABSTRACT FROM AUTHOR]

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'. [ABSTRACT FROM AUTHOR]

Since 1950, European rivers have been put under increasing pressure by anthropogenic activities, resulting in changes in climate, land cover, soil properties and channel morphologies. These evolving environmental conditions can translate into changes in hydrological conditions. The availability of consistent estimates of river flow at global and continental level is a necessity to assess and attribute changes in the hydrological cycle. To overcome limitations posed by observations (incomplete records, inhomogeneous spatial coverage), we simulate river discharge for Europe for the period 1950 - 2020 using a state-of-the-art hydrological modelling approach. We use the new European set up of the LISFLOOD model, running at 1 arcminute (-1.8 km) with six-hourly time steps. The hydrological model is forced by climate reanalysis data (ERA5-land) bias-corrected and downscaled to the model resolution with weather observations. The model also ingests 72 surface fields maps representing catchment morphology, vegetation, soil properties, land use, water demand, lakes and reservoirs. Inputs related to human activities are evolving through time to emulate changes in society. The resulting Hydrological European ReAnalysis (HERA), provides sixhourly river discharge for 282 521 river pixels with upstream area > 100km2. We assess its skill using 2901 river gauging stations distributed across Europe. Overall, HERA delivers satisfying results, with a general weak underestimation of observed mean discharge and flow variability. We find that the performance of HERA increases through time between 1950 and 2020. The fine spatial and temporal resolution result in an enhanced performance compared to other reanalysis for small-to-medium-scale catchments (100-10 000 km2), with degraded performance remaining for small catchments. HERA is the first long-term, high resolution hydrological reanalysis for Europe. Despite its limitations, it enables the analysis of hydrological dynamics related to extremes, human influences, and climate change at a continental scale while keeping local relevance. It also creates the opportunity to study these dynamics in ungauged catchments across Europe. [ABSTRACT FROM AUTHOR]

Crop diversification is considered a key element of agroecological transition, whereas current dominant cropping systems are known to rely on only a few crop species – like cereals in Europe. To assess the benefits of crop diversification at a large scale, an accurate description of current crop sequences is required as a baseline. However, such a description is lacking at the scale of Europe. Here, we developed the first map of dominant crop sequences in Europe for the period 2012–2018. We used the Land Use Cover Area frame statistical Survey (LUCAS) dataset that provides temporally incomplete (2012, 2015 and 2018) land cover information from a stable grid of points covering Europe. Eight crop sequence types were identified using hierarchical clustering implemented on LUCAS data and mapped over Europe. We show, in France, that the relative importance of these eight crop sequence types (as estimated from LUCAS data) was highly consistent with those derived from an almost spatially exhaustive temporally complete national dataset (the French Land Parcel Identification System) for the same period, thus validating the method and typology for this country. Land use (i.e. crop production area) derived from our map of dominant crop sequences was also highly consistent with land use reported by official statistics at both national and European levels, validating the approach at a European scale. This first map of dominant crop sequences in Europe should be useful for future studies dealing with agricultural issues that are sensitive to crop rotations. The map of dominant crop sequence types in Europe derived from our work is available at 10.5281/zenodo.7016986 (Ballot et al., 2022). [ABSTRACT FROM AUTHOR]

Agrivoltaics is the dual use of land by combining agricultural crop production and photovoltaic (PV) systems. In this work, we have analyzed three different agrivoltaic configurations: static with optimal tilt, vertically mounted bifacial, and single‐axis horizontal tracking. A model is developed to calculate the shadowing losses on the PV panels along with the reduced solar irradiation reaching the area under them for different PV capacity densities. First, we investigate the trade‐offs using a location in Denmark as a case study and second, we extrapolate the analysis to the rest of Europe. We find that the vertical and single‐axis tracking produce more uniform irradiance on the ground, and a capacity density of around 30 W/m2 is suitable for agrivoltaic systems. Based on our model and a 100‐m‐resolution land cover database, we calculate the potential for agrivoltaic in every region within the European Union. The potential for agrivoltaic is enormous as the electricity generated by agrivoltaic systems could produce 25 times the current electricity demand in Europe. Overall, the potential capacity for agrivoltaic in Europe is 51 TW, which would result in an electricity yield of 71,500 TWh/year. [ABSTRACT FROM AUTHOR]

Climate indices are often used as a climate monitoring tool, allowing us to understand how the frequency, intensity, and duration of extreme weather events are changing over time. Here, based on complex statistical analysis we identify highly correlated significant pairs of compound events at the highest spatial resolution, on a monthly temporal scale across Europe. Continental-scale monthly analysis unleashes information on compound events such as high-risk zones, hotspots, monthly shifts of hotspots and trends, risk exposure to land cover and population, and identification of maximum increasing trends. While there are many studies on single or compound climate extremes there are only a few studies that addresses the relationship between pairs of hazards, the incorporation of bioclimatic indices, the determination of a grid best-fit copula approach, and the outlining relevance of this work of compound event risks with exposures. In this respect, here, using 27-bivariate and 10-trivariate copula models, we show that the different hazard pairs have high combined risks of indices related to radiation, temperature, evapotranspiration, bioclimatic-based indices, such as the universal thermal climate index, wind chill index, and heat index, mainly over the northern and eastern European countries. Furthermore, we show that over the last 7 decades, agricultural and coastal areas are highly exposed to the risks of defined hotspots of compound events. In some of the hotspots of compound events-identified by clusters, there is no monthly shifts of hotspots, leading to higher impacts when compounded. Future work needs to integrate the framework and process to identify other compound pairs. [ABSTRACT FROM AUTHOR]

Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1 ∘ resolution for Europe with annual LULC maps from 1950 to 2100 (10.26050/WDCC/LUC_hist_EU_v1.1, , 10.26050/WDCC/LUC_future_EU_v1.1,), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by. Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25 ∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide. [ABSTRACT FROM AUTHOR]

Understanding the main drivers of biodiversity loss in Europe's agricultural landscapes has been a research priority in the last decades. One of the most important factors promoting biodiversity in farmed landscapes is habitat heterogeneity, which has often proved crucial for avian species and communities. Birds are highly sensitive to environmental changes and make use of a broad range of ecological niches, thus being exceptionally sensitive to the loss of habitat heterogeneity. Remote sensing data are particularly suited to quantify habitat heterogeneity at fine scales over relatively large extents, allowing to consider how different measures of heterogeneity may affect biotic communities at a regional scale. Here, we used airborne LiDAR (Light Detection And Ranging) and satellite multispectral data to derive vegetation canopy height and primary productivity for 118 sites in complex agricultural landscapes in a region in the Central Alps. We computed different bird diversity indices and classified bird species into guilds according to specific traits to analyse the relationship between avian communities and different facets of habitat heterogeneity. Results confirmed that habitat heterogeneity is essential in shaping rich and diverse bird communities, and it is particularly important for several farmland birds. By comparing the effects of canopy height, primary productivity, and specific vegetation features (e.g., cover of grassland, shrub, and tree layers), we showed how different habitat characteristics as well as landscape heterogeneity affected bird richness, diversity, functional entropy, and trait patterns. Landscape and height heterogeneity, estimated by NDVI and LiDAR Rao's Q indices, strongly influenced all response variables, for example, high NDVI values promoted species diversity and ground-understory nesters, and shrub layer was important for ground-understory nesters and forest specialists. Finally, we provide recommendations for conservation practices and mitigation measures to improve bird diversity in agricultural landscapes. [ABSTRACT FROM AUTHOR]

Today societies face an unprecedented challenge to limit global warming and restore agricultural soils. Recent studies show that the introduction of cover crops over Europe could result in a cooling impact due to an increase in soil organic carbon stocks, a decrease in the use of fertilizers, and an increase in surface albedo of the croplands. Based on the use of remote sensing data, land cover database, meteorological data, national agricultural statistics, and ground measurements, a generic model was developed to simulate the radiative forcing following the change in surface albedo. This article analyzes the impact of the introduction of cover crops in Europe during the fallow periods. Compared to previous studies, this work discusses: (i) The maximum greening potential in Europe and the associated indirect surface properties changes (ii) for snowfall episodes, and (iii) due to an increase in organic matter. This study shows that the mitigation potential of cover crops through albedo effects could reach 6.74 MtCO2-eq.yr−1 by extending the periods of the introduction of the cover crops to all possible fallow periods. This mitigation could be limited to 5.68 MtCO2-eq.yr−1 if the impact of snowfalls is considered. This would be equivalent to 9.12 gCO2.m−2.yr−1. Finally, this study investigates the feedback loop due to soil darkening with soil organic carbon content increase when cover crops are introduced, considering two scenarios. The first considers the soil organic carbon content increase following repeated incorporation of cover crop biomass into the soil, simulated with the DayCent model. The second, more conceptual and extreme scenario aims at alerting on the possible impact of a combination of carbon farming practices, such as biochar or organic amendments. Our results show that this effect could lead to a loss of 20% of the climate benefit (i.e., 5.39 MtCO2-eq.yr−1). In conclusion, this study shows that cover crops have a strong potential for climate mitigation through direct albedo effects (soil coverage). However, once introduced, cropland should be permanently covered by vegetation or straws in order to avoid this darkening feedback loop effect. [ABSTRACT FROM AUTHOR]

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas. [ABSTRACT FROM AUTHOR]

This paper studies the impact of land use and land cover change (LULCC) on the climate around 2500 years ago (2.5 ka), a period of rapid transitions across the European landscape. One global climate model was used to force two regional climate models (RCMs). The RCMs used two land cover descriptions. The first was from a dynamical vegetation model representing potential land cover, and the second was from a land cover description reconstructed from pollen data by statistical interpolation. The two different land covers enable us to study the impact of land cover on climate conditions. Since the difference in landscape openness between potential and reconstructed land cover is mostly due to LULCC, this can be taken as a measure of early anthropogenic effects on climate. Since the sensitivity to LULCC is dependent on the choice of climate model, we also use two RCMs. The results show that the simulated 2.5 ka climate was warmer than the simulated pre-industrial (PI, 1850 CE) climate. The largest differences are seen in northern Europe, where the 2.5 ka climate is 2–4 ∘ C warmer than the PI period. In summer, the difference between the simulated 2.5 ka and PI climates is smaller (0–3 ∘ C), with the smallest differences in southern Europe. Differences in seasonal precipitation are mostly within ± 10 %. In parts of northern Europe, the 2.5 ka climate is up to 30 % wetter in winter than that of the PI climate. In summer there is a tendency for the 2.5 ka climate to be drier than the PI climate in the Mediterranean region. The results also suggest that LULCC at 2.5 ka impacted the climate in parts of Europe. Simulations including reconstructed LULCC (i.e. those using pollen-derived land cover descriptions) give up to 1 ∘ C higher temperature in parts of northern Europe in winter and up to 1.5 ∘ C warmer in southern Europe in summer than simulations with potential land cover. Although the results are model dependent, the relatively strong response implies that anthropogenic land cover changes that had occurred during the Neolithic and Bronze Age could have affected the European climate by 2.5 ka. [ABSTRACT FROM AUTHOR]

Wildfires are key drivers of land use/land cover (LULC) dynamics by burning vegetation and affecting human infrastructure. On the contrary, LULC changes (LULCCs) may affect the fire regime by influencing vegetation type, burnable areas, fuel loads and continuity. This study investigates the relationship between LULCC and wildfires. We developed a methodology based on different indicators, which allowed us to quantitatively assess and better understand the transitions between LULC classes and burnt area (BA) in Europe in the last two decades (2000–2019). The assessment was performed for the entire European continent and, independently, for each of the five European countries most affected by wildfires: Portugal, Spain, France, Italy and Greece. The main results are the following: (i) LULCC analysis revealed a net loss in forests and arable land and a net gain in shrubs; (ii) most of the BA occurred in forests (42% for the whole of Europe), especially in coniferous forests; (iii) transitions from BA generally were to transitional woodland/shrub or, again, to BA. Overall, our results confirm the existence of a strong relationship between wildfires and LULCCs in Europe, which was quantified in the present study. These findings are of paramount importance in fire and environmental system management and ecology. [ABSTRACT FROM AUTHOR]

Responses to climate change have often been found to lag behind the rate of warming that has occurred. In addition to dispersal limitation potentially restricting spread at leading range margins, the persistence of species in new and unsuitable conditions is thought to be responsible for apparent time‐lags.Soil seed banks can allow plant communities to temporarily buffer unsuitable environmental conditions, but their potential to slow responses to long‐term climate change is largely unknown. As local forest cover can also buffer the effects of a warming climate, it is important to understand how seed banks might interact with land cover to mediate community responses to climate change.We first related species‐level seed bank persistence and distribution‐derived climatic niches for 840 plant species. We then used a database of plant community data from grasslands, forests and intermediate successional habitats from across Europe to investigate relationships between seed banks and their corresponding herb layers in 2763 plots in the context of climate and land cover.We found that species from warmer climates and with broader distributions are more likely to have a higher seed bank persistence, resulting in seed banks that are composed of species with warmer and broader climatic distributions than their corresponding herb layers. This was consistent across our climatic extent, with larger differences (seed banks from even warmer climates relative to vegetation) found in grasslands.Synthesis. Seed banks have been shown to buffer plant communities through periods of environmental variability, and in a period of climate change might be expected to contain species reflecting past, cooler conditions. Here, we show that persistent seed banks often contain species with relatively warm climatic niches and those with wide climatic ranges. Although these patterns may not be primarily driven by species' climatic adaptations, the prominence of such species in seed banks might still facilitate climate‐driven community shifts. Additionally, seed banks may be related to ongoing trends regarding the spread of widespread generalist species into natural habitats, while cool‐associated species may be at risk from both short‐ and long‐term climatic variability and change. [ABSTRACT FROM AUTHOR]

Wildfire detection and mapping is crucial for managing natural resources and preventing further environmental damage. In this study, we compared two methods of mapping burn scars using Sentinel-2 satellite imagery, a pixel-based approach and an object-based approach, at test sites located in various climatic zones with diverse land cover synthesis. The study aimed to determine the advantages and limitations of each method in terms of accuracy and precision in detecting burn scars. The results showed that both methods could detect burn scars with high accuracy, but with some limitations. The F1 score was in the range of 0.64–0.89 for the object-based approach, and 0.58–0.90 for the pixel-based approach. The pixel-based method produced a more precise delineation of the burnt area, but it was only suitable for detecting burn scars in a limited area of interest. The object-based method, on the other hand, was able to detect burn scars over a larger area accurately but with some commission errors. The results of both methods were also compared to the Copernicus Emergency Management Service (CEMS) rapid mapping product. [ABSTRACT FROM AUTHOR]

Reliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1° × 1°) over the Holocene (last 11.7 ka BP) using the 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity. [ABSTRACT FROM AUTHOR]

Satellite observations have revealed strong land surface "greening" (i.e., increases in vegetation greenness or leaf area index (LAI)) in the Northern Hemisphere over the past few decades. European terrestrial ecosystems are a greening hotspot, but how they respond to land surface greening, climate change, CO2 fertilization, land use and land cover change (LULCC) and other factors is unclear. Here, we assessed how these interacting factors might be combined to alter terrestrial gross primary production (GPP) throughout Europe during the period of 2001 to 2016 using a process-based Farquhar GPP model (i.e., FGM). We found a more productive European terrestrial ecosystem and most of the GPP enhancement in Europe was explained by increases in LAI (62%) and atmospheric CO2 concentration (29%). Spatially, the spatial signature of the LAI and GPP trends both suggested widespread (72–73% of the vegetated area) greening phenomena across Europe, among which 23.7% and 13.3% were statistically significant (p < 0.05). The interannual trend of GPP estimated by the FGM (0.55% yr−1) was reasonable compared with other GPP products (0.47% yr−1 to 0.92% yr−1) and the observed LAI increasing rate (0.62% yr−1). FGM factorial simulations suggested that land surface greening (+35.5 Pg C yr−2, p < 0.01), CO2 fertilization (+16.9 Pg C yr−2, p < 0.01), temperature warming (+3.7 Pg C yr−2, p < 0.05), and enhanced downwards solar radiation (+1.2 Pg C yr−2, p > 0.05) contributed to the GPP enhancement, while the enhanced vapour pressure deficit (−5.6 Tg C yr−2, p < 0.01) had significant negative impacts on GPP, especially in 2006 and 2012, when extreme droughts struck south-eastern Europe. Meanwhile, approximately 1.8% of the total area of Europe experienced LULCC from 2001 to 2016 and LULCC exerted a small but significant (−1.3 Tg C yr−2, p < 0.01) impact on GPP due to decreases in the total number of vegetated pixels (−159 pixels yr−1). Although the LULCC effect was negative, the largest increase occurred in forested land (+0.9% of total area). In addition, the increasing trends for the annual mean LAI (0.01 m2 m−2 yr−1, p < 0.001) and total GPP (22.2 Tg C yr−2, p < 0.001) of forests were more significant and higher than those of other vegetation types, suggesting that European forests may continue to play important roles in combating climate change in the future with long-lasting carbon storage potential. These results provide the first systematic quantitative analysis of the driving force of enhanced gross carbon assimilation by European ecosystems by considering variations in leaf physiological traits with environmental adaptations. [ABSTRACT FROM AUTHOR]

Information regarding the residential status of the built-area is used within several contexts such as disaster management, urban and regional planning, among others. Currently such non-residential built-up information can be extracted for most of Europe from Land Use/Land Cover maps such as CORINE Land Cover (CLC) and Urban Atlas (UA) by harmonizing the class nomenclature into a residential/non-residential nomenclature. However, these have update cycles of several years given their usually costly and lengthy production, which also relies on visual interpretation of ancillary datasets. Given these limitations many methods have been proposed to increase the thematic detail of the built-up environment. More recently, these methods often rely on ancillary datasets such as, e.g., social media and mobile phone networks metadata, which may not be readily available in many areas. In this paper we propose a framework to map non-residential built-up areas by training an image segmentation model with national census information and Sentinel-2 imagery. The non-residential map coming from the segmentation model was compared with public pan-European maps and both of their quality assessed against UA 2018. The results show that using census data to automatically generate training data for a Sentinel-2 image segmentation model of non-residential built-up improves the mapping of non-residential areas when compared with the existing datasets available for most of Europe. [ABSTRACT FROM AUTHOR]

In recent years fires of greater magnitude have been documented throughout northwest Europe. With several climate projections indicating future increases in fire activity in this temperate area, it is imperative to identify the status of fire in this region. This study unravels unknowns about the state of the fire regime in northwest Europe by characterizing one of the key aspects of fire behavior, the rate of spread (ROS). Using an innovative approach to cluster Visible Infrared Imaging Radiometer Suite (VIIRS) hotspots into fire perimeter isochrones to derive ROS, we identify the effects of land cover and season on the rate of spread of 102 landscape fires that occurred between 2012 and 2022. Results reveal significant differences between land cover types, and there is a clear peak of ROS and burned area in the months of March and April. Median ROS within these peak months is approximately 0.09 kmh-1 during a 12 h overpass, and 66 % of the burned area occurs in this spring period. Heightened ROS and burned area values persist in the bordering months of February and May, suggesting that these months may present the extent of the main fire season in northwest Europe. Accurate data on ROS among the represented land cover types, as well as periods of peak activity, are essential for determining periods of elevated fire risk, the effectiveness of available suppression techniques, and appropriate mitigation strategies (land and fuel management). [ABSTRACT FROM AUTHOR]

To ensure the good chemical status of surface water across Europe, it is necessary to increase research on the comprehensive impact of land use and land cover changes, i.e., deforestation, on the natural environment. For this reason, we used data from 9-year environmental monitoring in the Wüstebach experimental catchment of the TERENO (Terrestrial Environmental Observatories) network to determine the impact of partial deforestation on solute fluxes and stream water ionic composition. In 2013, a partial deforestation experiment was conducted in the study area using a cut-to-length logging method. To this end, two headwater catchments were compared: one partially deforested (22% of the catchment area) and one untreated control catchment. The concentrations of ions in stream water, groundwater, and precipitation were analyzed: Ca2+, Mg2+, Na+, K+, Al3+, Fetot, Mn2+, NO3−, SO4−, and Cl−. Most of the ions (Na+, Ca2+, Mg2+, Cl−, and SO4−) showed decreasing trends in concentrations after deforestation, indicating a dilution effect in stream water due to the reduction of the supply of solutes with precipitation in the open deforested area. The fluxes of these ions decreased by 5–7% in the first year after deforestation, although the stream runoff increased by 5%. In the second year, the decrease in ion fluxes was greater, from 6% to 24%. This finding confirms that only limited soil erosion occurred after the deforestation because the soil was well protected during logging works by covering harvester lanes with branches. Only K+ and NO3− ions showed increasing trends in both concentrations and fluxes in the partially deforested catchment in the first two to three years after deforestation. Spruce die-offs, common in Europe, may decrease the concentration and fluxes of base cations in surface water in a nutrient-limited environment. However, the simultaneous planting of young broad-leaved trees with post-harvesting regrowth could create a nutrient sink that protects the catchment area from nutrient depletion. [ABSTRACT FROM AUTHOR]

Land use and cover (LUC) of southern European mountains is dramatically changing, mainly due to observed socioeconomic demands and climatic changes. It is therefore important to understand LUC changes to accurately predict future landscapes and their threats. Simulation models of LUC change are ideal for this task because they allow the in silico experimentation under different socioeconomic and climatic scenarios. In the present study, we employed the trans-CLUE-S model, to predict for 2055 the LUC of a typical southern European sub-mountainous area, which has experienced widespread abandonment until recently. Four demand scenarios were tested, and under each demand scenario, we compared three climatic scenarios, ranging from less to more warm and dry conditions. We found that farmland declined from 3.2% of the landscape in 2015 to 0.4% in 2055 under the business-as-usual demand scenario, whereas forest further increased from 62.6% to 79%. For any demand scenario, differences in LUC between maps predicted under different climatic scenarios constituted less than 10% of the landscape. In the less than 10% that differed, mainly farmland and forest shifted to higher elevation under a warmer and drier climate, whereas grassland and scrubland to lower. Such insights by modelling analyses like the present study's can improve the planning and implementation of management and restoration policies which will attempt to conserve ecosystem services and mitigate the negative effects of socioeconomic and climatic changes in the mountainous regions of southern Europe. [ABSTRACT FROM AUTHOR]

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1° resolution for Europe with annual LULC maps from 1950-2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25? resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide. [ABSTRACT FROM AUTHOR]

Studying landscape heterogeneity (LH) and its changes over time is crucial for understanding ecosystem dynamics and developing effective strategies for biodiversity conservation and climate change adaptation. However, such studies over large areas and long time periods are rare due to the limited availability of land-cover data. Here, we examine two components of LH across the Czech Republic since the 1840s, namely compositional LH representing land cover diversity and configurational LH reflecting spatial landscape structure. Using digitised topographic maps and the Shannon index, we analyse LH distribution and its relationship with land cover changes. Our results show different spatial patterns in LH components, especially in low- and mid-elevation regions compared to mountainous areas. Temporal trends indicate a consistent increase in LH since the 1870s in low- and mid-elevation regions driven by land conversion to urban and agricultural areas. Conversely, mountainous regions show contrasting trends, with compositional LH declining due to forest expansion. We emphasise the importance of considering both the compositional and configurational components of LH in ecological studies and using indices that allow direct comparison between them. [Display omitted] • The spatial patterns of landscape heterogeneity components vary across elevations. • Temporal trends show an increase in heterogeneity below 600 m asl since the 1870s. • The change in compositional heterogeneity is related to the change in arable land. • Afforestation has been an important process in the mountains since the 1840s. [ABSTRACT FROM AUTHOR]

Land cover area estimates can be derived via design-based approaches using a probability (random) reference sample. The collection of samples is usually costly and requires an effective sampling design. Earth-Observation-based mapping approaches do not have this requirement but can be biased in providing area estimates. Combining reference samples with remote sensing products can reduce sampling efforts and provide a more effective method to estimate land cover. The Copernicus High-Resolution Layer (HRL) provides remote-sensing-based data across Europe to support area estimation. Different methods are tested to estimate areas of imperviousness in four selected countries in Europe to demonstrate the use and shortcomings of existing reference information from the LUCAS survey program and the HRL Imperviousness products from 2015 and 2018. [ABSTRACT FROM AUTHOR]

Accurate maps of past land cover and human land use are necessary for studying the impact of anthropogenic land‐cover changes, such as deforestation, on the climate. The maps of past land cover should ideally be separated into naturally occurring vegetation and human‐induced changes, thereby enabling the quantification of the effect of human land use on the past climate. We developed a Bayesian hierarchical model that combines fossil pollen‐based reconstructions of actual land cover with estimates of past human land use. The model interpolates the fractions of unforested land as well as coniferous and broadleaved forest from the pollen data, and uses the human land‐use estimates to decompose the unforested land into natural vegetation and human deforestation. This results in maps of both natural and human‐induced vegetation, which can be used by climate modelers to quantify the influence of deforestation on the past climate. The model was applied to five time periods from 1900 CE to 4000 BCE over Europe. The model uses a latent Gaussian Markov random field (GMRF) for the interpolation and Markov chain Monte Carlo for the estimation. The sparse precision matrix of the GMRF, together with an adaptive Metropolis‐adjusted Langevin step, allows for rapid inference. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data. [ABSTRACT FROM AUTHOR]

Aim: Cities are thought to promote biological invasions because invasive species are more often introduced in urban areas and because they are more successful in disturbed environments. However, the association is not as strongly supported by the literature as is generally assumed and might depend on how urbanization and invasion are measured. In this study, we test if the type of data used to assess the link between urbanization and invasion can affect a study's conclusions. Location: Europe and middle Rhône valley (~5000 km2 in south‐eastern France). Method: We studied the spatial distribution of the invasive garden ant Lasius neglectus in its current introduced range in Europe and tested its association with urbanization using three measures of invasion (presence‐only, presence–absence and population area) and two measures of urbanization (urban/nonurban land cover classification and proportion of impervious surfaces (buildings, road) per spatial unit). Results: Based on presence‐only data across Europe, L. neglectus occurred in urban areas 10 times more often than expected from a random geographical distribution. However, when controlling for spatial bias in sampling effort with presence–absence data (1870 sampling locations in the middle Rhône valley, France), the occurrence of the species was independent of urbanization. Moreover, the surface occupied by L. neglectus populations was negatively correlated with urbanization. Main conclusions: These findings show that the type of occurrence data used to test the link between urbanization and invasion can strongly affect the conclusion of a study. This is particularly concerning because invasion studies often use presence‐only data that are likely biased towards cities. Future urban invasions studies must be carefully designed to avoid this pitfall. [ABSTRACT FROM AUTHOR]

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions. • Complete networks exhibited higher robustness than incomplete ones. • Cropland in summer and forest in autumn and summer exhibited the lowest robustness. • Connectivity of complete networks was controlled mostly by stochasticity than determinism. • Stochasticity was higher in forest (natural soil) than cropland (man-affected soils). • Stochasticity implies the necessity for more proactive management practices and less remedial actions. [ABSTRACT FROM AUTHOR]

Promoting sustainable soil management is a possible option for achieving net-zero greenhouse gas emissions in the future. Several efforts in this area exist, and the application of spatially explicit models to anticipate the effect of possible actions on soils at a regional scale is widespread. Currently, models can simulate the impacts of changes on land cover, land management, and the climate on the soil carbon stocks. However, existing modeling tools do not incorporate the lateral transport and deposition of soil material, carbon and nutrients caused by soil erosion. The absence of these fluxes may lead to an oversimplified representation of the processes, which hinders, for example, a further understanding of how erosion has been affecting the soil carbon pools and nutrient through time. The sediment transport during deposition and the sediment loss to rivers create dependence among the simulation units, forming a cumulative effect through the territory. If, on the one hand, such a characteristic implies that calculations must be made for large geographic areas corresponding to hydrological units, on the other hand, it also can make models computationally expensive, given that erosion and redeposition processes must be modeled at high resolution and over long time scales. In this sense, the present work has a three-fold objective. First, we provide the development details to represent in matrix form a spatially explicit process-based model coupling sediment, carbon, and erosion, transport and deposition processes (ETD) of soil material in hillslopes and valley bottoms (i.e., the CEDYNAM model). Second, we illustrate how the model can be calibrated and validated for Europe, where high-resolution datasets of the factors affecting erosion are available. Third, we presented the results for a depositional site, which is highly affected by incoming lateral fluxes from upstream lands. Our results showed that the benefits brought by the matrix approach to CE-DYNAM enabled the before precluded possibility of applying it to a continental scale. The calibration and validation procedures indicated: i) a close match between the erosion rates calculated and previous works on the literature at local and national scales; ii) the physical consistency of the parameters obtained from the data; and iii) the capacity of the model in predicting sediment discharge to rivers in locations observed and unobserved during its calibration (Model efficiency (ME) = 0.603, R2 = 0.666; and ME = 0.152, R2 = 0.438, respectively). The prediction of the carbon dynamics on a depositional site illustrated the model's ability to simulate the non-linear impact of ETD fluxes on the carbon cycle. We expect that our work advances ETD models' description and facilitates its reproduction and incorporation in land surface models such as ORCHIDEE and DayCent. We also hope that the patterns obtained in this work can guide future ETD models at a European scale. [ABSTRACT FROM AUTHOR]

In recent years, regulatory agencies in the USA and Europe have begun to require documentation that land used to produce crops and biofuels has not been converted from carbon-capturing grasslands or forests. Precise measurement of these land cover changes, however, has proven difficult. Analysis to date has focused primarily on moderate resolution (30 m) satellite imagery, which has not provided the land cover granularity or accuracy needed. These studies have estimated large-scale land conversion to crops in the USA. This study analyzed the satellite datasets but included tabular datasets and aerial imagery of the USA to determine whether the combination of datasets, focusing on more detailed analysis in these locations, could more accurately identify potential locations of land use change. Analyses of satellite imagery data from 1985 to 2020 found that much of the land that 2008 to 2020 satellite datasets classified as natural-to-crop land change was idle cropland. The results indicate a dynamic landscape of marginal land moving in and out of cropland. Approximately as much land was allowed to go fallow (6145 hectares) as land going into crop (7901 hectares) from 1985 to 2020. The results from this study indicate regulatory agencies could more accurately measure the impacts of conversion of natural lands to crop if long-term historical land cover/land use was also analyzed. [ABSTRACT FROM AUTHOR]

This study focuses on changes of biogeochemical parameters related to variable rainfall-runoff conditions and to land cover types for eight headwater catchments in Central Europe. Linear regression, Spearman rank correlation, principal component analysis and C/Q (concentration – discharge) hysteresis loops revealed the main relationships. The presence of peatbogs and waterlogged spruce forests had a decisive influence on the biogeochemistry (COD = chemical oxygen demand measured using the permanganate methodMn, humins, Fe, P-PO43− TP = total phosphorus, N-NO3−). The strongest positive correlation of organic matter and Fe is represented in a catchment with the largest area of damaged forest (70%), but with a smaller proportion of waterlogged areas (8%). High flow rates influence greater release of organic matter and N-NO3−. The catchment with the highest (80%) peatbog cover and the catchment that had undergone revitalization showed similar behaviour in terms of water biogeochemistry. The type of rainfall-runoff event affected the velocity of pH changes, whereas the variability in electric conductivity was also controlled by the catchment characteristics and hydrological preconditions. [ABSTRACT FROM AUTHOR]

The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models (RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset for Europe (LANDMATE PFT Version 1.0; http://doi.org/10.26050/WDCC/LM%5fPFT%5fLandCov%5fEUR2015%5fv1.0 ,). The dataset is based on the high-resolution European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset and is further improved through the additional use of climate information. Within the LANDMATE – LAND surface Modifications and its feedbacks on local and regional cliMATE – PFT dataset, satellite-based LULC information and climate data are combined to create the representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge life zone concept. Through consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European continent. The assessment shows that the dominant LULC types, cropland and woodland, are well represented within the dataset, while uncertainties are found for some less represented LULC types. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as a basis for the Land Use and Climate Across Scales (LUCAS) Land Use Change (LUC) dataset which is available for use as LULC change input for RCM experiment set-ups focused on investigating LULC change impact. [ABSTRACT FROM AUTHOR]

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe. [ABSTRACT FROM AUTHOR]

In recent years fires of greater magnitude have been documented throughout northwest Europe, and with several climate projections indicating future increases in fire activity in this temperate area, it is imperative to identify the status of fire in this region. This study unravels important unknowns about the state of the fire regime in northwest Europe by characterizing one of the key aspects of fire behavior, the rate of spread (ROS). Using an innovative approach to cluster VIIRS hotspots into fire perimeter isochrones to derive ROS, we identify the effects of land cover and season on fire rate of spread of 254 landscape fires that occurred between 2012 and 2020. Results reveal no significant differences between land cover types and there is a clear peak of ROS and burned area in the months of April and May. During this late spring period, 67 % of the burned area occurs and median fire runs are approximately 0.16 km/hr during a 12 hour overpass. Heightened ROS and burned area values persist in the bordering months of March and June suggesting that may present the extent of the fire season in northwestern Europe. Accurate data on ROS among the represented land cover types as well as periods of peak activity are essential for determining periods of elevated fire risk, the effectiveness of available suppression techniques as well as appropriate mitigation strategies (land and fuel management). [ABSTRACT FROM AUTHOR]

Aim: We assessed the spatial distribution of four different types of avian specialization throughout Europe, identifying landscape features associated with specialization and quantifying where the Natura 2000 network intersects with areas of high avian specialization. Location: Europe. Time period: Present day. Taxa studied: European breeding birds. Methods: We used the European Atlas of breeding birds and four avian specialization measures (diet, foraging behaviour, foraging substrate and habitat). We calculated specialization richness and identified geographical hotspots for each of these ecological traits. We tested whether elevational gradient, landscape heterogeneity or dominant land use predicted each type of specialization richness. We determined which types of European protected areas are most associated with higher specialization richness. Results: Diet and foraging substrate specialists increased with elevation, whereas richness of foraging behavioural specialists decreased. There was a greater richness of dietary and habitat specialists in forests than in other environments. The Natura 2000 areas declared under Bird and both directives (Birds and Habitats) intersected with a high dietary, foraging substrate and habitat specialist richness. The richness of foraging behaviour specialists was high in Birds directive areas. Single and multiple hotspots of specialization were greater in protected than non‐protected areas. However, almost 30% of specialization hotspots did not intersect with protected areas. Main conclusions: Our findings suggest that higher levels of avian specialization in Europe are positively associated with elevation and forested land cover. Importantly, we found that the Natura 2000 network supports all types of avian ecological specialization, albeit mainly in areas declared under both directives. [ABSTRACT FROM AUTHOR]

Wildland fire effects are strongly associated with fire regime characteristics. Here, we developed the first European pyrogeography based on different fire regime components to better understand fire regimes across the continent. We identified four large-scale pyroregions: a non-fire-prone (NFP) pyroregion featuring nominal fire activity across central and northern Europe; a cool-season fire (CSF) pyroregion scattered throughout Europe; a fire-prone (FP) pyroregion extending mostly across southern Europe; and a highly fire-prone (HFP) pyroregion spanning across northern Portugal, Sicily, and western Balkans. Land cover analysis indicates that pyroregions were first shaped by vegetation and then by anthropogenic factors. On interannual timescales the spatial extent of pyroregions was found to vary, with NFP showing more stability. Interannual correlations between climate and burned area, fire frequency, and the length of fire period exhibited distinct patterns, strengthening in fire-prone pyroregions (FP and HFP) and weakening in NFP and CSF. Proportion of cool-season fires and large fires were related to fuel accumulation in fire-prone pyroregions. Overall, our findings indicate that such a pyrogeography should allow a more accurate estimate of the effects of climate on fire regimes while providing an appropriate framework to better understand fire in Europe. Pyrogeography provides a level of generalisation that aids in understanding fire regimes at large spatial scales. This study provides insights into current European pyroregions, which may serve as a basis for wildland fire risk management on a continental scale. [ABSTRACT FROM AUTHOR]

Solar energy is increasingly used to produce electricity in Europe, but the environmental impact of constructing and running solar parks (SP) is not yet well studied. Solar park construction requires partial vegetation removal and soil leveling. Additionally, solar panels may alter soil microclimate and functioning. In our study of three French Mediterranean solar parks, we analyzed:(1) effects of solar park construction on soil quality by comparing solar park soils with those of semi‐natural land cover types (pinewood and shrubland) and abandoned croplands (former vineyards); and (2) the effect of solar panels on soil microclimate, CO2 effluxes, and vegetation. We measured 21 soil properties of physical, chemical, and microbiological soil quality in one solar park and its surroundings to calculate integrated indicators of soil quality. We surveyed soil temperature and moisture, CO2 effluxes, and vegetation below and outside solar panels of three solar parks. Soil aggregate stability was reduced by SP construction resulting in a degradation of soil physical quality. Soil chemical quality and a general indicator of soil quality were lower in anthropogenic (SP and abandoned vineyards) than in semi‐natural (pinewood and shrubland) land cover types. However, differences between abandoned vineyards representing the preconstruction land cover type and solar parks were not significant. Solar panels reduced the soil temperature by 10% and soil CO2 effluxes by 50% but did not affect early successional plant communities. Long‐term monitoring is needed to evaluate the effects of solar panels on vegetation. [ABSTRACT FROM AUTHOR]

Peatlands have been drained for land use for a long time and on a large scale, turning them from carbon and nutrient sinks into respective sources, diminishing water regulation capacity, causing surface height loss and destroying biodiversity. Over the last decades, drained peatlands have been rewetted for biodiversity restoration and, as it strongly decreases greenhouse gas emissions, also for climate protection. We quantify restoration success by comparing 320 rewetted fen peatland sites to 243 near-natural peatland sites of similar origin across temperate Europe, all set into perspective by 10k additional European fen vegetation plots. Results imply that rewetting of drained fen peatlands induces the establishment of tall, graminoid wetland plants (helophytisation) and long-lasting differences to pre-drainage biodiversity (vegetation), ecosystem functioning (geochemistry, hydrology), and land cover characteristics (spectral temporal metrics). The Paris Agreement entails the rewetting of 500,000 km2 of drained peatlands worldwide until 2050-2070. A better understanding of the resulting locally novel ecosystems is required to improve planning and implementation of peatland rewetting and subsequent management. Whether rewetting leads to effective restoration of drained peatlands is unclear. Here the authors analyse a large number of near-natural and rewetted fen peatland sites in Europe, finding persistent differences in plant community composition and ecosystem functioning, and higher variance in the restored sites. [ABSTRACT FROM AUTHOR]

Aim: Quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land‐cover types. Location: Europe. Time period: 2018. Major taxa studied: Microbial community (fungi and bacteria). Methods: We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20 ºC and microbial biomass (substrate‐induced respiration) using an O2‐microcompensation apparatus. Soil and climate data were obtained from the same LUCAS survey and online databases. Structural equation models (SEMs) were used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Results: Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Main conclusions: Soil microbial communities in grasslands and croplands are likely carbon‐limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already‐degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in future management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [ABSTRACT FROM AUTHOR]

Many recent studies have sought to characterize variations of the annual maximum flood discharge series over time and across space in Europe, including some that have elucidated different process controls on different statistical properties of these series. To further support these studies, we conduct a pan-European assessment of process controls on key properties of this series, including the mean annual flood (MAF) and coefficients of variation (CV) and skewness (CS) of flood discharges. These annual maximum flood discharge series consist of instantaneous peaks and daily means observed in 2370 catchments in Europe without strong human modifications covering the period 1960–2010. We explore how the estimated moments MAF, CV and CS vary due to catchment size, climate and other controls across Europe, where their averages are 0.17 m 3 s -1 km -2 , 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, in the high-rainfall areas of the Mediterranean coast and in mountainous regions, while it is smallest in the sheltered parts of the East European Plain. The CV is largest in southern and eastern Europe, while it is smallest in the regions subject to strong Atlantic influence. The pattern of the CS is similar, albeit more erratic, in line with the greater sampling variability of CS. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area, suggesting that floods in small catchments are relatively very large, while in eastern Europe this dependence is much weaker, mainly due to more synchronized timing of snowmelt over large areas. The process controls on the flood moments in five predetermined hydroclimatic regions are identified through correlation and multiple linear regression analyses with a range of covariates, and the interpretation is aided by a seasonality analysis. Precipitation-related covariates are found to be the main controls of the spatial patterns of MAF in most of Europe except for regions in which snowmelt contributes to MAF, where air temperature is more important. The Aridity Index is, by far, the most important control on the spatial pattern of CV in all of Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, to provide a performance baseline for more local studies, we assess the estimation accuracy of regional multiple linear regression models for estimating flood moments in ungauged basins. [ABSTRACT FROM AUTHOR]

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC Version 1.0 at 0.1° resolution for Europe Hoffmann et al. (2021b,c). The plant functional type distribution for the year 2015 (i.e. LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method based on a cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (submitted). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive realistic LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to remotely-sensed PFT time series. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the next generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide. [ABSTRACT FROM AUTHOR]

In Europe, forest area is increasing. These recently established forests can be classified into regenerating after disturbances or expanding into agricultural areas after abandonment. We used European Space Agency land cover maps and MODIS data to investigate which socioenvironmental drivers influenced recent forest expansion and regeneration in Europe from 1992 to 2015 and to compare their productivity by means of the Enhanced Vegetation Index (EVI). Our results showed that forest area increased in Europe by 1.4% from 1992 to 2015. The 66% of this forest area increase corresponded to forest expansion mostly in Mediterranean and temperate regions, while regeneration (34%) dominated in boreal areas. Forest area and land cover diversity in 1992 were the main drivers of local forest area increase from 1992 to 2015. Forest expansion occurred on the warmer zones far from urban areas in the boreal region while it was the opposite in temperate and Mediterranean areas. On the other hand, forest regeneration showed mostly a positive relation with the distance to urban areas and water availability but no relation with temperature. The EVI values in 2015 were higher in expanding than in regenerating forests except in the warmer and drier bioclimates of Europe. These EVI trends suggest a higher productivity of expanding forests, except in areas where they cannot benefit from biological and physicochemical legacies of abandoned agricultural soils for tree growth, owing to water shortage. In sum, our results highlight that recent forest area increase in Europe is mostly caused by forest expansion into former agricultural areas but this is mostly occurring in less productive (warmer and drought-prone) bioclimates where advantages of agricultural legacies may not occur. Ultimately, this casts doubts whether there may be a limit for the role of forest expansion into agricultural areas for carbon sequestration in the long term. [ABSTRACT FROM AUTHOR]

Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence ( J S D), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms. [ABSTRACT FROM AUTHOR]

Context: While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. Objectives: We developed a comprehensive and high-resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. Methods: Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classification approach and identified land systems that are common in Europe and meaningful for studying biodiversity. We tested the benefits of using this map as compared to land cover information to predict the distribution of bird species having different vulnerability to landscape and land use change. Results: Next to landscapes dominated by one land cover, mosaic landscapes cover 14.5% of European terrestrial surface. When using the land system map, species distribution models demonstrate substantially higher predictive ability (up to 19% higher) as compared to models based on land cover maps. Our map consistently contributes more to the spatial distribution of the tested species than the use of land cover data (3.9 to 39.1% higher). Conclusions: A land systems classification including essential aspects of landscape and land management into a consistent classification can improve upon traditional land cover maps in large-scale biodiversity assessment. The classification balances data availability at continental scale with vital information needs for various ecological studies. [ABSTRACT FROM AUTHOR]

The advent of abundant Earth observation data enables the development of novel predictive methods for forecasting climate impacts on the state and health of terrestrial ecosystems. Here, we predict the spatial and temporal variations of land surface reflectance and vegetation greenness, measuring the density of green vegetation and active foliage area, conditioned on current and past weather and the local topography. We train two alternative recurrent deep learning models that combine Long Short-Term Memory cells with convolutional layers (ConvLSTM) for forecasting the spatially resolved deviation of surface reflectance across a heterogeneous landscape from a specified initial state. Using data from diverse ecosystems and land cover types across Europe and following a standardized model evaluation framework (EarthNet2021 Challenge), our results indicate increased performance in predicting surface greenness during extreme drought events of the models presented here, compared to currently published benchmarks. This demonstrates how deep learning methods for optical Earth observation time series enable an early-warning of vegetation responses to the impacts of climatic extreme events, such as the drought-related loss of green foliage. • ConvLSTM models can forecast vegetation, conditioned on climate and topography. • Our models outperform published benchmarks for drought impact predictions. • This enables weather forecast-informed early warning of vegetation water stress. [ABSTRACT FROM AUTHOR]

In the context of the first phase of the Euro-CORDEX Flagship Plot Study (FPS) Land Use and Climate Across Scales (LUCAS), we investigate the afforestation impact on the seasonal cycle of soil temperature over the European continent with an ensemble of ten regional climate models (RCMs). For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) over all European regions, although this not a robust feature among the models. In Mediterranean, the simulated AAST response to afforestation is between -4 K and +2 K while in Scandinavia the inter-model spread ranges from -7 K to +1 K. We then examine the role of changes in the annual amplitude of ground heat flux (AAGHF) and summer soil moisture content (SMC) in determining the effect of afforestation on AAST response. In contrast with the diverging results in AAST, all the models consistently indicate a widespread AAGHF decrease and summer SMC decline due to afforestation. The AAGHF changes effectively explain the largest part of the inter-model variance in AAST response in most regions, while the changes in summer SMC determine the sign of AAST response within a group of three simulations sharing the same land surface model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Such perturbations can be of societal relevance as afforestation is proposed as a climate change mitigation strategy. [ABSTRACT FROM AUTHOR]

Aim: The recent recovery of large carnivores in Europe has been explained as resulting from a decrease in human persecution driven by widespread rural land abandonment, paralleled by forest cover increase and the consequent increase in availability of shelter and prey. We investigated whether land cover and human population density changes are related to the relative probability of occurrence of three European large carnivores: the grey wolf (Canis lupus), the Eurasian lynx (Lynx lynx) and the brown bear (Ursus arctos). Location: Europe, west of 64° longitude. Methods: We fitted multi‐temporal species distribution models using >50,000 occurrence points with time series of land cover, landscape configuration, protected areas, hunting regulations and human population density covering a 24‐year period (1992–2015). Within the temporal window considered, we then predicted changes in habitat suitability for large carnivores throughout Europe. Results: Between 1992 and 2015, the habitat suitability for the three species increased in Eastern Europe, the Balkans, North‐West Iberian Peninsula and Northern Scandinavia, but showed mixed trends in Western and Southern Europe. These trends were primarily associated with increases in forest cover and decreases in human population density, and, additionally, with decreases in the cover of mosaics of cropland and natural vegetation. Main conclusions: Recent land cover and human population changes appear to have altered the habitat suitability pattern for large carnivores in Europe, whereas protection level did not play a role. While projected changes largely match the observed recovery of large carnivore populations, we found mismatches with the recent expansion of wolves in Central and Southern Europe, where factors not included in our models may have played a dominant role. This suggests that large carnivores' co‐existence with humans in European landscapes is not limited by habitat availability, but other factors such as favourable human tolerance and policy. [ABSTRACT FROM AUTHOR]

The real estate cadastre is the primary source of information on land use. It records information related to the division of land into types based on the actual way of land use or development. The distinguished types of land use depend on many geographical factors, as well as historical and economic conditions. The study presents a comparison of the detail of land use classification registered in the real estate cadastre in areas functionally related to the urban areas of 9 European countries: Austria, Bulgaria, the Czech Republic, Estonia, Spain, Lithuania, Luxembourg, Germany and Poland. The research concerned the determination of the degree to which the classification of land use in urbanized areas is detailed, whether the studied European countries are characterized by the same number of distinguished classes of objects at different levels of detail, and what percentage are the distinguished classes of land use objects in urbanized areas in relation to all of the distinguished classes of objects land use at different levels of detail of classification. The study used legal acts regulating land use issues which have been made available in national languages by government institutions. [ABSTRACT FROM AUTHOR]

Peatlands are lands with a peat layer at the surface, containing a large proportion of organic carbon. Such lands cover ≈1 000 000 km2 in Europe, which is almost 10% of the total surface area. In many countries, peatlands have been artificially drained over centuries, leading to not only enormous emissions of CO2 but also soil subsidence, mobilization of nutrients, higher flood risks, and loss of biodiversity. These problems can largely be solved by stopping drainage and rewetting the land. Wet peatlands do not release CO2, can potentially sequester carbon, help to improve water quality, provide habitat for rare and threatened biodiversity, and can still be used for production of biomass ("paludiculture"). Wisely adjusted land use on peatlands can substantially contribute to low‐emission goals and further benefits for farmers, the economy, society, and the environment. [ABSTRACT FROM AUTHOR]

Aim: Global declines in the populations of migratory species have been attributed largely to climate change and anthropogenic habitat change. However, the relative contribution of these factors on species' breeding and non‐breeding ranges is unclear. Here, we present the first large‐scale assessment of the relative importance of climatic conditions and land cover on both the breeding and non‐breeding grounds in driving the long‐term population trends of migratory species. Location: Europe and Africa. Methods: We use data on the long‐term population trends of 61 short‐ and 39 long‐distance migratory species of European breeding birds. We analyse these population trends in relation to changes in climate and land cover across species' breeding and non‐breeding ranges over a 36‐year period, along with species' migratory behaviour. Results: The population trends of European migratory birds appear to be more closely related to changes in climate than changes in land cover on their breeding grounds, but the converse is true on their non‐breeding grounds. While improvements in climate suitability across the breeding ranges of short‐distance migrants led to increasing population trends, the same was not true for long‐distance migrants. The combined effects of changes in climate and land cover account for approximately 40% of the variation in migratory species' population trends, suggesting that factors other than climate and land cover as we have measured them, such as habitat quality, also affect the population trends of migrant birds. Main Conclusions: Over recent decades, population trends of most migrant species are most strongly related to climatic conditions on the breeding grounds but land cover change on the non‐breeding grounds. This suggests that management to stem the declines of migrant birds requires an integrated approach that considers all processes affecting migrant birds across their dynamic distributions throughout the year. [ABSTRACT FROM AUTHOR]