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Soil microbiome diversity in selected broadleaf forests of Belasitsa Mountain: A High-Throughput Sequencing approach
No full textSoil is the most species-rich habitat on Earth (Anthony et al. 2023). Soil biota play a key role in providing a wide range of ecosystem services essential for the sustainable functioning of natural ecosystems. Recent rapid advancements in molecular techniques have significantly improved our ability to study soil biodiversity. High-throughput sequencing (HTS) has greatly enhanced targeted eDNA analysis, enabling comprehensive assessments and long-term monitoring of belowground diversity and community composition.In this study, we investigated the diversity and community structure of the soil microbiome (fungi and bacteria) along an altitudinal gradient in non-managed forests dominated by two economically and ecologically significant species – sweet chestnut (Castanea sativa) and European beech (Fagus sylvatica) in Belasitsa Mountain, Bulgaria. As part of the Bulgarian Long-Term Ecological Research network, Belasitsa Mountain provides a valuable framework for studying the long-term forest dynamics related to global changes. The study area is characterised by steep terrain, predominantly northern exposures, and an altitudinal gradient ranging from 400 to 1500 m a.s.l. The region provides unique environmental conditions to monitor biodiversity changes in response to complex spatial heterogeneity influenced by tree species composition, wide altitude range, and specific climate conditions. These forest ecosystems represent ecologically significant and unique habitats, with C. sativa populations forming the largest chestnut stands in Bulgaria and F. sylvatica occurring near its southernmost distribution limit. In Belasitsa Mountain, sweet chestnut forests cover approximately 20% of the total forested area at elevations up to 1000 m, whereas European beech forests predominantly occupy higher elevations. Given the growing threats to C. sativa forests - such as fungal pathogens (Cryphonectria parasitica), climate change-induced droughts, and unsustainable forest management (Zlatanov et al. 2015) – studying soil microbiome dynamics in these forests is crucial for planning conservation and management strategies.A total of 24 composite soil samples were collected from 12 permanent sampling plots during two sampling periods (October 2023 and June 2024). Each sample consisted of 10 sub-samples taken from a depth of 0–5 cm. Soil samples from C. sativa-dominated forests were collected at elevations ranging from 455 to 853 m, while those from F. sylvatica forests were obtained from higher elevations (1080–1434 m), mainly within the Kongura Reserve. Microbial DNA was extracted from the samples, and high-throughput sequencing was applied to target bacterial 16S rRNA (regions bV18-A) and fungal ITS2 (region fITS2-C). Additionally, comprehensive soil physico-chemical analyses were conducted, including assessments of soil texture, pH, organic carbon content, nutrient concentrations (nitrogen, phosphorus, potassium, calcium, magnesium), and heavy metal concentrations (Zn, Cu, Cd, Pb).Taxonomical profiles for both bacteria and fungi showed no significant differentiation at the phylum level. Actinobacteriota was the most abundant bacterial phylum (mean relative abundance 54%), followed by Proteobacteria (31%), and Planctomycetota (6%). Several core bacterial genera, including Bradyrhizobium, Acidothermus, Nocardioides, Conexibacter, Mycobacterium, and Solirubrobacter, dominated the bacterial communities. Notably, a substantial proportion of bacterial sequences remained unassigned at the genus level, highlighting the need for further exploration and characterisation of soil microbiota. Fungal communities were dominated by Basidiomycota (mean 64%), followed by Ascomycota (32%), with Mucoromycota and Mortierellomycota present in lower abundances. Mycorrhizal fungi, including Tomentella, Russula, Sebacina, Inocybe, Piloderma, and Cortinarius, were the most abundant genera, forming core fungal communities in both forest types.Alpha diversity indices showed no significant differences in fungal diversity between the two tree species. In contrast, bacterial alpha diversity varied significantly, with F. sylvatica forests exhibiting higher bacterial diversity than C. sativa forests. Beta diversity analysis identified tree species as a key factor shaping both bacterial and fungal community composition, whereas seasonal variation had no significant effect.Our findings provide the first data on the diversity and community composition of soil microbiomes in both forest types, highlighting the significant effect of tree species on microbial assemblages. By integrating advanced molecular techniques with comprehensive soil analyses, eDNA studies will establish a framework for long-term microbiome monitoring, enhancing our understanding of changes in microbial diversity. Understanding these patterns is crucial for long-term ecosystem monitoring and evaluating microbial responses to environmental changes
Photoacoustic CO2 sensor collocation method at SMEAR Estonia
No full textThe research project aims to develop and install environmental monitoring systems in hemi-boreal ecosystems, utilizing low-cost technologies and artificial intelligence (AI). The proposal integrates sensors to measure atmospheric CO2 by the collocation method, calibration and analysis methods are based on machine learning.The initiative seeks to understand atmospheric dynamics in forests (Järvselja Experimental Forest, Estonia), evaluating carbon responses to environmental changes. The systems are field-calibrated using certified reference equipment and enhanced with AI models, such as Random Forest and XGBoost, to ensure data precision and reliability. Sensors will be used to measure gas exchanges and carbon dynamics, contributing to understanding these ecosystems' role in carbon sequestration.Several correction techniques were used to verify the ability of machine learning models to improve measurements, Fig. 1 shows the Taylor Diagram of the comparison between AI models. The calibration was done for the sensors at 30 meters on the main tower of SMEAR Estonia, which were installed next to the pipe that sucks air for analysis in the reference equipment. The CO2 concentration in ppm, temperature and relative humidity (3 variables measured by the SCD41 sensor) were used as input data and the objective was to train the algorithms to estimate the CO2 concentration in ppm measured by the Los Gatos equipment at 30 meters high. The analysis indicates that for this case the Random Forest (RF) and XGBoost non-linear regression models performed better than the multiple linear regression (MLR) model. Fig. 2 shows the time series of CO2 reference concentration and SCD41 sensor corrections using Multiple Linear Regression (MLR) and Random Forest (RF).With an interdisciplinary focus, the proposal collaborates with national and international institutions, such as SMEAR Estonia, and promotes human resource training, the generation of long-term data, and the publication of scientific results. The project also aims to influence public policies and democratize environmental monitoring, strengthening awareness of air quality and climate change
A Long-term Speleothem-Based Record of Permafrost Presence-Absence in the Mountain Critical Zone
No full textCaves provide unique access to deeper levels of the Critical Zone (CZ), and speleothems formed within caves preserve dateable records of past CZ process. This study used a combination of modern cave climate monitoring, dripwater sampling, and geochemical investigation of speleothem records to consider how the mountain CZ in part of Utah, USA has changed over time. The study site, known as Whiterocks Cave, is a solution cave 800 m long developed in Mississippian-age Madison Limestone in the Uinta Mountains. At its deepest accessible point, the cave is ~150 m below the ground surface. Air temperature and relative humidity have been monitored in the cave for many years, and custom-built sequential samplers have been deployed to collect dripwater. Results reveal that air temperature in interior parts of the cave averages 5.5 °C, varies only a few tenths of a degree over the course of a year, and reaches an annual minimum in late winter/early spring. Relative humidity is steady at 100%. Drip rates are low and stable for much of the year, with values ~20 drips per hour, but exhibit a dramatic increase at the beginning of May before dropping back down in mid-summer. The timing of this annual peak in drip rate matches the main pulse of snowmelt on the upload above the cave. Values of δ18Ovsmow in dripwater average -14.6‰ (d-excess of 8.2‰) and exhibit only minor variability through the year, implying a well-mixed aquifer in the epikarst above the cave. These results are useful in considering stable isotope records developed for 11 speleothems collected from the cave. Dating of these features yielded ages (n=78) ranging from ~80 ka to the limit of U/Th dating (~600 ka). Notably, ages are strongly clustered within interglacial stages, implying that speleothems only grow in Whiterocks Cave under interglacial climate conditions. Given the elevation of the cave, a plausible explanation for this pattern is that permafrost existed in the CZ above the cave for most of the past 600,000 years, blocking water infiltration and prohibiting speleothem growth. Only under peak interglacial conditions, such as those characterizing the Holocene, is this permafrost absent, allowing snowmelt to recharge the aquifer and deliver dripwater to the cave passages. This study provides an example of how cave science can inform understanding of past CZ process
Integrating Data into Hydrogeophysical Models to Unveil Fluxes and River Interactions: Insights from the Orgeval Critical Zone Observatory
No full textQuantifying water and heat fluxes at the surface water (SW)–groundwater (GW) interface is crucial for ensuring sustainable water management and quality. However, direct field-based quantification remains challenging due to the dynamic nature of SW-GW interactions, which are influenced by poorly constrained boundary conditions and spatial hydrofacies distributions. Traditionally, these parameters are inferred through model calibration using conventional data, such as hydraulic heads and river discharge. Many regional studies have treated rivers as curvilinear GW divides, with flow either converging toward or diverging from the river center—an assumption rooted in Tóth’s theory, which correlates surface and subsurface drainage boundaries. However, this oversimplification fails to account for geological heterogeneity, river morphology, variable hydraulic conditions, and anthropogenic influences like withdrawals.While regional-scale studies commonly examine SW-GW exchanges, their coarse resolution limits the ability to resolve localized hydraulic gradients. Understanding flow dynamics in heterogeneous environments, such as alluvial plains, requires a more detailed, integrated approach. Here, we present a multi-method framework to strengthen numerical simulations and improve hydrodynamic and thermal parameter calibration in both space and time. Applied to the Orgeval Critical Zone Observatory (France), our approach estimates SW-GW fluxes using a combination of long-term hydrological data (10 years), time-lapse seismic imaging, and numerical modeling.We demonstrate how high-resolution geophysical imaging, combined with geotechnical data, enables a detailed characterization of hydrofacies and provides valuable prior constraints on hydrodynamic properties. Time-lapse seismic acquisitions offer a high-resolution view of groundwater table (WT) dynamics, with each seismic snapshot carefully inverted to capture spatial WT variations. By integrating these geophysical insights with long-term hydrogeological observations (hydraulic head and temperature), we refine parameterization within the hydrogeological modeling domain, leading to improved estimates of transient stream-aquifer exchanges. Finally, we outline future steps toward achieving a fully coupled hydrogeophysical model to further enhance SW-GW interaction predictions
Intercropping in Europe: a meta-analysis
No full textStudies around the world indicate a range of advantages of intercrops compared to monocultures, such as increased land use efficiency (Paul et al. 2023), higher yield stability (Martin-Guay et al. 2018), weed suppression (Nelson et al. 2021), and improved grain quality (Peoples et al. 2009). However, in Europe, food crops are not widely grown as intercrops by farmers, though mixtures are widely used in sown grassland and cover crops. A range of challenges of intercropping for food production have been identified, especially the complexity of the management and harvesting of mixtures (Timaeus et al. 2022). Another challenge is a lack of knowledge on the advantages that might accrue to farmers when adopting intercrops under European conditions. Farmers need guidance on which species combinations and management might be most advantageous under their growing conditions, but this insight is simply not available because the available information is too fragmented and has not been synthesized. Intercropping is complex. There is a vast amount of possible crop species combinations and for each there are multiple design options (cultivar combinations, sowing time and density, spatial pattern) and management practices (fertilization, irrigation, disease and weed management). For monocultural arable cropping systems there are well-studied substantial interactions among genotypes (G), environment (E) and management (M, such as sowing density or fertilization) on yields, yield components and quality. Understanding these GxExM interactions enables evidence-based optimization of cropping systems. In intercrops, we have to deal with more complex interactions (G1xG2xExM), hugely complicating the issue of finding the best combination of species, varieties, design and management.A large number of experimental studies investigating the yield of intercropping systems has been conducted over the last decades, globally. A simple search in web of science core collection in for intercrop* OR "crop mixture" OR "species mixture" OR "relay crop" OR "strip crop" yielded 10,276 articles (search conducted 12.06.2023 in title/abstract/keywords). Several global meta-analyses of these data have been conducted, e.g Yu et al. (2015), Yu et al. (2016), Martin-Guay et al. (2018), Li et al. (2020) and Li et al. (2023). Furthermore, some meta-analyses of productivity of intercrops have been conducted for specific crop species combinations (e.g. maize/soybean; Xu et al. (2018); or maize/peanut; Feng et al. (2021)) or for specific regions such as China (Li et al. 2020, Mudare et al. 2022) or Africa (Mudare et al. 2022). However, apart from a synthesis of studies in France, there is no meta-analysis on the productivity of intercropping under European conditions. It is important to conduct a study specifically for Europe because growing conditions and growing objectives and constraints in Europe differ from those in China or Africa. While production systems in China are characterized by very high and sometimes excessive inputs to reach high yields, those in Africa are often characterized by lack of fertilizer input. Systems in Europe vary from low input systems on poor soils with limiting water, often with organic management, to higher input systems under more favourable growing conditions, either with organic or conventional management. Due to a lack of synthesis, there is currently no good overview of the comparative advantageousness of these European systems and the variation in performance according to species choice, intercrop design, growing conditions and management.We are working on the first meta-analysis of the relative performance of intercropping systems under European growing conditions. We focus on grain producing intercropping systems such as cereal/legume combinations because these have received the most attention in research because of their potential to provide grain production at low environmental costs. We ask the following questions:What is the average land equivalent ratio (LER) of European intercropping systems?How does the LER vary over European regions, and between conventional and organic farming?How is the relative performance of intercrop vs sole crop systems affected by the climatic conditions and the soils?How is the LER affected by temporal complementarity between species, nutrient inputs, and the comparative plant density in intercrops?A literature search was conducted on the Web of Science Core Collection (WoSCC) on 24th of November 2023 without any restriction on time or document type. The final search was conducted and resulted in 2070 articles after removal of duplicates.Data extraction and analysis is ongoing, and the intermediate results will be presented
Luminescent Sensors for Continuous Monitoring of Important Analytes in Ecosystems
No full textLast decades have witness significant progress in optical sensing technology. Within this group of methods, luminescence-based sensing attracted much attention. Here an optically silent analyte interacts with the sensing material and reversibly changes its luminescent properties. Luminescent sensors benefit from highest flexibility of formats (planar sensors, microsensors, nanoparticles) and applications. A unique feature of such optical sensors (optodes) is their suitability for imaging of analyte distribution on surface (planar sensors) or in 3D (nanoparticles). Although optical sensors have been widely applied since several decades, for instance in (marine) biology, environmental monitoring etc., their even wider adoption is hindered by several factors. In order to achieve this purpose, both the sensing materials and the dedicated read-out equipment have to reach the needed robustness and fulfil the required characteristics, which may differ significantly from application to application. Furthermore, the number of analytes that can be reliably quantified by means of luminescence is currently rather limited. In this talk we will first highlight development of robust oxygen optodes for various applications (Wang and Wolfbeis 2014). These rely on dynamic quenching of luminescence of an indicator dye by molecular oxygen. Among numerous indicator dyes, platinum(II) and palladium(II) complexes with benzoporphyrins (Borisov et al. 2008) demonstrated an unmatched combination of desired photophysical properties: intense absorption in the blue and red parts of the electromagnetic spectrum, high brightness and photostability. They additionally benefit from straightforward synthesis and are now commercially available. To obtain an oxygen-sensing material, these indicator dyes are immobilized in suitable matrices, usually polymers like polystyrene. Importantly, high brightness of the indicators made it possible to minimize the thickness of the sensing layer and thus to significantly improve the response time of the resulting materials. Coating of the sensing material on a tapered glass fiber results in fast-responding sensors suitable for accessing O2 concentration multiple times in a second. These sensors are now commercially available and have been utilized in eddy covariance experiments in marine ecosystems (Merikhi et al. 2021, Berg et al. 2022).Moreover, we designed and optimized sensors for monitoring traces of oxygen in various environments. This was achieved via combination of oxygen indicators with long luminescence lifetime and highly oxygen-permeable polymeric matrices (Lehner et al. 2015). Although optical pH sensors offer a much narrower dynamic range compared to the glass electrode (typically max. 3 pH units), they are still of much interest for many applications, particularly for measurements in seawater. Numerous groups of fluorescent pH indicators have been investigated by us and other researchers over last decades (Steinegger et al. 2020). Aza-BODIPY dyes offer attractive photophysical properties (red light excitation and emission in far-red region, excellent photostability) along with the modular character that allows to easily tune the pKa value (and thus the dynamic range) of the indicator (Jokic et al. 2012, Strobl et al. 2015). Particularly, these dyes were optimized for optical measurement of pH in seawater (Staudinger et al. 2019). We showed that matrix, in which the pH indicator is imbedded, is also of highest importance for performance of the resulting sensing materials. For instance, although some of the sensors showed acceptably fast response at room temperature, they became virtually unsuitable at low temperatures (Staudinger et al. 2019).Monitoring of such important parameters like oxygen and pH is not possible without dedicated devices for the read-out of the sensing material. A submergible prototype equipped with optical feed-through, longer and battery has been developed (Staudinger et al. 2018), and utilized in proof-of-concept studies (profiling and long-term monitoring in seawater). The prototype was also successfully employed to detect potential leakage of carbon dioxide stored offshore via pH change. Further development in collaboration with an industrial partner, PyroScience GmbH (Aachen, Germany), resulted in a family of instruments and dedicated sensors for monitoring oxygen in pH in shallow water and in deep sea (up to 4000 m).The same sensing principle was utilized to sense other environmentally important analytes including ammonia (Strobl et al. 2017), carbon dioxide (Fritzsche et al. 2017) and ions like sodium (Müller et al. 2017). Together with collaboration partners we are currently working on development of compact and affordable system for robust mapping the above palette of analytes with help of planar optodes and water-dispersible particles
Global change experiments in mountain ecosystems: A systematic review
No full textMountains are experiencing climate warming at a faster pace than other terrestrial ecosystems, with temperature increases of up to twice the global average. These rapid changes, combined with shifts in precipitation patterns and increased nitrogen deposition, make mountain ecosystems particularly vulnerable and critical as early warning systems for vegetation responses to global change. To strengthen our mechanistic understanding of how environmental drivers affect mountain vegetation and associated ecosystem processes, we systematically reviewed three decades of manipulation experiments. Among the seven major global change drivers examined (temperature, water availability, nutrient addition, snow manipulation, radiation, atmospheric gases, and disturbance), temperature was most frequently manipulated (45% of studies), followed by nutrient addition (15%) and water availability (14%). Our analysis of 767 studies reveals that temperature manipulation consistently affected plant life history, functional traits, and phenology, with experimental warming generally accelerating phenological events and altering species composition. The review showed strong evidence that changes in water and nutrient availability directly impact plant life history and ecosystem functioning. We found that soil microbial communities respond rapidly to warming, with implications for nutrient cycling and decomposition processes. Long-term datasets demonstrate complex interactions between climate warming and soil processes, where changes in plant functional traits and community composition influence carbon and nutrient cycling. Notably, experiments combining temperature with water manipulation showed that soil moisture often mediates warming effects on plant productivity and biogeochemical cycles. While biotic interactions were understudied (only 2% of responses), evidence suggests that warming can disrupt plant-pollinator relationships and alter competitive dynamics between species. While our synthesis provides evidence for vegetation responses to key global change drivers, we identify critical research gaps, particularly in tropical and boreal regions, and in understanding adult tree responses. We propose that future research should emphasize integrated approaches combining long-term monitoring of vegetation changes with experimental manipulations of multiple drivers to better predict mountain ecosystem responses to accelerating global change
UAV-Based Sampling of Volatile Organic Compounds (VOCs) for Atmospheric Monitoring: from Estonia to Cyprus
No full textVolatile organic compounds (VOCs) emitted by terrestrial vegetation and human activities play a significant role in air quality, human health, and ecosystem functioning. With UAV technology becoming more advanced and accessible, opportunities are opening to deploy such innovative techniques for the assessment of atmospheric VOC composition in remote and sensitive regions. Here, we present the development of a UAV-based VOC sampling system designed to collect samples using VOC-adsorbent cartridges, followed by offline thermal-desorption gas chromatography-mass spectrometric analysis.A single-tube system was initially deployed in Estonia and later expanded into a stand-alone, 4-tube system by the Cyprus Institute within the framework of the ATMO-ACCESS TNA project. This enhanced system enables the collection of multiple samples per flight, allowing for diverse sampling strategies, including vertical and horizontal profiling, with options for single-tube and simultaneous multi-tube sampling. Additionally, meteorological parameters such as temperature, humidity, and wind conditions are recorded alongside VOC sampling, facilitating data interpretation. A field campaign in Cyprus in October 2023 involved 16 UAV flights across different environments, yielding a substantial dataset for further analysis.Interpreting both vertical and horizontal VOC distributions requires an understanding of numerous factors, including wind speed and direction, atmospheric lifetimes and sources of individual VOC species, turbulence, and the characteristics of the underlying surfaces. To address these challenges, we present several tools developed during our initial single-tube flights in Estonia and the Cyprus campaigns, which incorporate micrometeorological and land-cover data, demonstrating the UAV system’s capability for detailed atmospheric measurements
Understanding spatial patterns of elevation-dependent climate change and associated impacts in mountain regions of Europe and the world.
No full textMountain systems in Europe and around the world are known to be experiencing more rapid environmental changes than many other ecosystems, but our knowledge of, and ability to predict, future changes is hampered by lack of integrated long-term monitoring systems at high elevations and in areas of complex terrain. An analysis of the elevational distribution of weather stations in Europe shows bias towards lower elevations. This is unfortunate since physical theory suggests that future climate change will be elevation-dependent, with often faster warming observed and predicted at high elevations. Elevation-dependent climate change (EDCC) occurs for many reasons including the decline of the cryosphere and snow-albedo feedback effects, changes in ecological zonation including migration of treelines, physical changes in the temperature and moisture structure of the free atmosphere which will enhance high altitude warming, and the influence of aerosol deposition on the surface, including on snow. Although the mechanisms and drivers of elevation dependent climate change are quite well known, the exact way in which these currently combine in the mountain systems of Europe is unclear, with many regional differences in observed trends in climate variables being evident. A metanalysis of the literature on climate changes observed within the Greater Alpine Region (GAR) is presented. Examples of research studies in contrasting European mountain regions, from the Alps to the Scandes in northern Finland, are also presented.The effects of enhanced climate changes on the mountain ecosystem include influences on ecological zonation and the migration of species upslope. Changing lapse rates and uneven warming with elevation will alter the elevational extent of ecological zones, either expanding or compressing them. There will be additional impacts on cryospheric and hydrological systems, including rising snowlines, and a change from snow dominated to rain dominated watersheds. The hydrological regime in many mountain systems will become more variable, and dominated by precipitation events rather than by snowmelt. This will make runoff more variable and less easy to manage for human systems. Additional changes in hazard and risk in mountain systems, include increased flooding, periods of low flow, risk of extinction events in high elevation ecosystems, and increased rockfalls and landslides as a result of increased heavy precipitation events. All these will have consequences for human systems in mountain regions. An interdisciplinary approach is required to address such changes and mitigate against future adverse consequences on ecological and human systems. Future recommendations for scientific advances are proposed and discussed, including the identification of Essential Mountain Climate Variables (EMCVs), the development of integrated interdisciplinary Mountain Observatories (MO), and a consistent protocol for the monitoring of long-term change over the elevation gradient (the Unified High Elevation Observation Platform or UHOP). Many such initiatives are already underway, facilitated by the Mountain Research Initiative (MRI)
Biodiversity monitoring of island ecosystems (BioMonI)
No full textOceanic islands contribute disproportionately to global biodiversity and contain many endemic species carrying unique evolutionary and functional adaptations that reflect life in isolation (Schrader et al. 2024). For instance, islands that are part of the European Union contribute significantly to the biodiversity of the EU and are thus essential for reaching European and global biodiversity targets. To give an example, the Canary Islands, representing only 1.5% of Spain’s land area, are home to 50% of its endemic species (Petit and Prudent 2010). Regrettably, islands are also epicenters of biodiversity change, particularly vulnerable to anthropogenic disturbances such as the introduction of non-native species, habitat loss, and climate change (Harter et al. 2015, Fernández-Palacios et al. 2021, Dawson et al. 2017, Bellard et al. 2017). Islands contain the majority of documented species extinctions and threatened species (Fernández-Palacios et al. 2021). Because of their biological uniqueness and high vulnerability, powerful monitoring tools are needed to inform conservation and restoration initiatives, ecosystem managers, policy-makers, and other stakeholders about the status and trends of biodiversity.Thus, in BioMonI, we are building the foundations for a global, long-term, easily accessible monitoring network tailored explicitly to the pressing needs of biodiversity conservation and monitoring on islands. This effort aligns with the concept of Essential Biodiversity Variables (EBVs) developed by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to offer a standardized framework for tracking biodiversity change across spatial and temporal scales. Specifically, in BioMonI, we are:elucidating spatiotemporal biodiversity trends (e.g. Borges (2025)), including elusive dimensions of biodiversity, broadening the spectrum of monitoring and conservation by integrating evolutionary and functional perspectives;mobilizing existing monitoring data, identifying gaps, co-designing work-flows to strengthen (existing) monitoring efforts,developing a harmonized monitoring scheme,and working to make monitoring information easily accessible across archipelagos for stakeholders including researchers, citizen scientists, conservation managers, (non-)governmental organizations and public institutions.To do that, we are reviewing current and past global monitoring schemes on islands (e.g. Borges et al. (2018)); leveraging long-term palaeoecological investigation of natural archives; integrating emerging genetic monitoring tools; assembling BioMonI-Plot, a vegetation plot network to understand biodiversity and ecosystem change; providing biodiversity informatics and developing e-infrastructures; and scaling up the monitoring of biodiversity and ecosystem structure and functioning using remote sensing, macroecological modeling, and future scenarios.The BioMonI team includes Holger Kreft, Nathaly Guerrero, and Wolf Wildpret at the University of Göttingen (Germany), Bernd Lenzner, Franz Essl, and Fabio Mologni at the University of Vienna (Austria), Paulo A. V. Borges, Rosalina Gabriel, Leila Morgado, and Rui Bentos Elias at the University of the Azores (Portugal), Lea de Nascimento, José Maria Fernández-Palacios, and Rüdiger Otto at the University of La Laguna (Spain), Clara Zemp, Samantha Suter, Vladimir Wingate, and Giorgia Camperio at the University of Neuchâtel (Switzerland), Claudine Ah-Peng and Dominique Strasberg at the University of La Réunion (France), Jairo Patiño and Brent Emerson at the Spanish National Research Council (CSIC, Spain) and Patrick Weigelt at Radboud University (Netherlands)