Comprehensive Global Database Research Articles

Geographic and ecological effects on species richness of liverworts worldwide

Liverworts are one of the oldest lineages of the extant land plants but the geographic patterns and ecological determinants of their species richness have not yet been studied at a global scale until now. Here, using a comprehensive global database, we find that regional species richness of liverworts in general 1) shows a clear latitudinal diversity pattern, 2) is highest in mountains, presumably reflecting the effects of habitat heterogeneity and the occurrence of cloud forest, 3) is more strongly influenced by contemporary climate than by climate change during the Quaternary, 4) is more strongly affected by precipitation‐related than by temperature‐related variables, reflecting the poikilohydric nature of liverworts and hence their water‐dependence, and 5) is more strongly affected by climate extremes than by climate seasonality. However, we find regional deviations from these general patterns, especially in the Southern Hemisphere where the distinct arrangement of land masses leads to different climatic patterns and thus climate–species richness relationships. Compared with other major land plant lineages, liverworts show the same importance of precipitation‐related factors as ferns, whereas in angiosperms temperature also plays an important role, reflecting the different physiological adaptations of these groups to drought and cold stress, and providing insights into the different evolutionary pathways taken by these lineages.

Spatiotemporal Characteristics and Rainfall Thresholds of Geological Landslide Disasters in ASEAN Countries

Spatiotemporal Characteristics and Rainfall Thresholds of Geological Landslide Disasters in ASEAN Countries

Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors

AbstractLand use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha−1) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha−1), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha−1) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha−1) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha−1), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha−1). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha−1) and 75.3% (7.0 ± 0.38 Mg ha−1) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

Sarcoma incidence worldwide: regional differences in histology and molecular subtypes.

There are numerous sarcoma subtypes and vary widely in terms of epidemiology, clinical characteristics, genetic profiles, and pathophysiology. They also differ widely between ethnic groups. This review focuses on the different incidence rates of sarcomas in different regions and the potential explanations for these disparities. In an intercontinental study using national cancer registry databases from France and Taiwan, the French population had a higher risk of liposarcomas, leiomyosarcomas, and synovial sarcomas, whereas the Taiwanese population had a higher incidence of angiosarcomas and malignant peripheral nerve sheath tumors. The anatomical distribution of these sarcomas also varied between these two regions. In France, most angiosarcoma cases occurred in the extremities and trunk, whereas in Taiwan, angiosarcoma cases in the abdomen and pelvis were more common. Another international study showed that in addition to the common known TP53 and NF1 germline mutations, genes involved in centromere and telomere maintenance were also involved in sarcomagenesis. We reviewed factors related to genetics, environmental effects, chemical exposure, and radiation exposure that could explain the differences in sarcoma incidence among different geographical or ethnic regions. Our understanding of the potential cause of sarcomas with different subtypes is limited. Establishing a comprehensive global database for patients with sarcomas from all ethnic groups is essential to deepen our understanding of the potential risk factors and the pathophysiology of all sarcoma subtypes.

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.

A new system of phosphorus and calcium requirements for lactating dairy cows.

Accurately predicting phosphorous (P) and calcium (Ca) dietary requirements is critical for optimizing dairy cattle performance, and minimizing mineral excretions and ecosystems eutrophication. This study provides a new factorial system to determine net and dietary P and Ca requirements for maintenance and lactation, derived from a meta-regression of mineral trials involving lactating dairy cows. A comprehensive global database was constructed from 57 peer-reviewed articles of mineral balance trials, with a wide range of dietary and animal performance data. We estimated the net requirements for maintenance from the intercept of a nonlinear equation between mineral intake and the sum of total fecal and urinary excretions, which is an estimate of endogenous mineral loss. Mineral secreted in milk was used to obtain net requirements for lactation. The mineral metabolizable coefficient was quantified through observed (treatment means) mineral intake and total fecal and urinary excretions, discounting the estimated endogenous excretions from our proposed models. The nonlinear models of total fecal and urinary mineral excretion were evaluated (observed versus predicted values) using a 5-fold cross validation approach. The models to estimate the sum of endogenous fecal and urinary excretions of P (0.135±0.043 g P/kg BW0.75) and Ca (0.360±0.144 g Ca/kg BW0.75) exhibited suitable precision and accuracy; r = 0.89 and 0.79, concordance correlation coefficient = 0.85 and 0.77, and root mean square prediction error = 24.1 and 20.5% observed means, respectively. Dietary variables (forage level, fiber, starch, crude protein, and ether extract) did not affect the metabolizable coefficient (MC) of P and Ca; therefore, an overall dietary MC of P (0.69±0.01) and Ca (0.65±0.02) were proposed. Our new system estimates lower net and dietary P requirements for lactating dairy cows compared to the NASEM-2021 and NRC-2001 models, but slightly higher Ca requirements than NASEM-2021.This proposed system holds potential to reduce the use of phosphorus in diets for dairy cows, and thus to enhance economic efficiency and environmental sustainability of the dairy industry.

Quantifying global colonization pressures of alien vertebrates from wildlife trade

The global trade in live wildlife elevates the risk of biological invasions by increasing colonization pressure (the number of alien species introduced to an area). Yet, our understanding of species traded as aliens remains limited. We created a comprehensive global database on live terrestrial vertebrate trade and use it to investigate the number of traded alien species, and correlates of establishment richness for aliens. We identify 7,780 species involved in this trade globally. Approximately 85.7% of these species are traded as aliens, and 12.2% of aliens establish populations. Countries with greater trading power, higher incomes, and larger human populations import more alien species. These countries, along with island nations, emerge as hotspots for establishment richness of aliens. Colonization pressure and insularity consistently promote establishment richness across countries, while socio-economic factors impact specific taxa. Governments must prioritize policies to mitigate the release or escape of traded animals and protect global biosecurity.

Does non‐native diversity mirror Earth's biodiversity?

AbstractAimHuman activities have introduced numerous non‐native species (NNS) worldwide. Understanding and predicting large‐scale NNS establishment patterns remain fundamental scientific challenges. Here, we evaluate if NNS composition represents a proportional subset of the total species pool available to invade (i.e. total global biodiversity), or, conversely, certain taxa are disproportionately pre‐disposed to establish in non‐native areas.LocationGlobal.Time periodPresent day.Major taxa studiedGlobal diversity.MethodsWe compiled one of the most comprehensive global databases of NNS (36,822 established species) to determine if NNS diversity is a representative proportional subset of global biodiversity.ResultsOur study revealed that, while NNS diversity mirrors global biodiversity to a certain extent, due to significant deviance from the null model it is not always a representative proportional subset of global biodiversity. The strength of global biodiversity as a predictor depended on the taxonomic scale, with successive lower taxonomic levels less predictive than the one above it. Consequently, on average, 58%, 42% and 28% of variability in NNS numbers were explained by global biodiversity for phylum, class and family respectively. Moreover, global biodiversity was a similarly strong explanatory variable for NNS diversity among regions, but not habitats (i.e. terrestrial, freshwater and marine), where it better predicted NNS diversity for terrestrial than for freshwater and marine habitats. Freshwater and marine habitats were also greatly understudied relative to invasions in the terrestrial habitats. Over‐represented NNS relative to global biodiversity tended to be those intentionally introduced and/or ‘hitchhikers’ associated with deliberate introductions. Finally, randomness is likely an important factor in the establishment success of NNS.Main conclusionsBesides global biodiversity, other important explanatory variables for large‐scale patterns of NNS diversity likely include propagule and colonization pressures, environmental similarity between native and non‐native regions, biased selection of intentionally introduced species and disparate research efforts of habitats and taxa.

Gender gap in life expectancy in Europe and the United Kingdom in 2019

Abstract Background Life expectancy (LE) is an important metric for overall population health and well-being. The gender gap in LE can be used as a population health metric to capture progress and monitor health inequality in a specific setting. Health inequality is an expression of universal health care coverage and unequal access to health care and the quality of health care - otherwise known as ‘amenable’ mortality. Methods The Global Burden of Disease (GBD) Study- a comprehensive global epidemiologic database has developed and validated two population health metrics across 200+ countries- the Universal Health Coverage (UHC) Index (0-100) and the Healthcare Access and Quality (HAQ) Index (0-100) to monitor progress in these two specific domains for each country. We set out to examine the association of gender gap in LE with UHC and HAQ indices across 27 EU countries and the UK combined for year 2019 employing correlation and linear regression analyses. Results Overall, LE ranged from 73.3 years in Bulgaria to 83.1 years in Italy; UHC index was worst in Bulgaria (62.6), while Luxembourg (91.5) was the best performing nation; HAQ index had the highest score in the Netherlands (91.1), while Bulgaria had the lowest score (64.9). Lithuania had the largest gender gap in LE (9.2 years; M:71.5; F: 80.7), while the Netherlands had the narrowest gender gap (3.4 years; M:80.0; F: 83.4). On multivariable linear regression, gender gaps in LE were significantly associated with both HAQ (beta: -0.17; R2=0.66), and UHC (beta: -0.14; R2=0.52) across 27 EU countries and the UK combined. Conclusions Gender gap in LE can be a proxy measure to monitor progress in health inequality in terms of universal health care coverage, as well as health care access and quality of health care for a specific population setting. The findings suggest that gender gap in LE can be significantly reduced through expansion of universal health care and improving both access and quality of health care in the EU. Key messages • Gender gap in life expectancy can be a good proxy to monitor progress in health inequality for EU countries and the UK combined, which has 5.5 years of gender gap in life expectancy on average in 2019. • A 10% increase in both universal health care coverage and health care access and quality, can reduce gender gap in life expectancy by 1.4 and 1.7 years, respectively, across EU and the UK combined.

Assessing the Reliability of Global Carbon Flux Dataset Compared to Existing Datasets and Their Spatiotemporal Characteristics

Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R2 (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes.

Impact of Traffic Sign Diversity on Autonomous Vehicles

Traffic sign classification is indispensable for road traffic systems, including automated ones. There is a fundamental difference in the visual appearance of traffic signs from one country to another. Each dataset has its design standards and regulations based on shape, color, and information content, making implementing classification and recognition techniques more difficult. This paper aims to assess the influence of traffic sign diversity on autonomous vehicles (AVs) by reviewing several previous studies, comparing, summarizing their results, and focusing on classifying and detecting traffic sign datasets based on color, shape, and deep learning spaces using various methods and applications. Furthermore, it covers the main challenges facing road designers and planners considering changes to road safety infrastructure. It will be argued that compiling and standardizing a comprehensive global database of traffic signs is very difficult because it is costly and complex in application. However, it is still one of the possible solutions for the coming decades. Recommendations for future developments are also presented in this study.

A review of studies on mass-movements on the Moon

The study of the lunar surface is a significant field in exploring the Moon. As a non-negligible geological process shaping the lunar surface, however, the mass movements on the Moon have not been deeply studied regarding all available datasets. In this paper, we report the results of the literature review and quantitative analysis of 965 articles retrieved from the Scopus, WoS, and Astrophysics Data System databases using keyword search terms between the years 1872 and 2022. The main findings show that the lunar mass movements are a lasting but recent prosperous research topic (since 2009). The top three influential journals in this area are Icarus, JGR, and P&SS. Robinson, Lucchitta, and Carr are the most three productive authors. California Institute of Technology, Arizona State University, and Johns Hopkins University are active institutions leading the lunar mass movements research, and the related institutions are mainly located in the United States, Russia, and China. Articles are primarily published in journals specializing in the fields of astronomy and physics. The index analysis of citation indicates the growth of the academic impact of articles about the lunar mass movements. This article summarizes the datasets, methods, and relevant parameters of lunar mass movements to facilitate future research, as well as discussing the limitations and trends in the field. In addition, four unsolved issues are proposed, including: 1) the lack of a comprehensive global database that records all types of landslides incidents; 2) the need for definite classification indicators to quantify the classification criteria for lunar mass movements; 3) the absence of a mathematical model to explain the triggering mechanism of mass movements on the Moon; and 4) the lack of quantitative indexes to evaluate the modification effect of mass movements on the lunar surface morphology. With the support of big data, the combination of artificial intelligence and traditional GIS methods is expected to become the main approach for addressing these issues such a bibliometric analysis can inspire future researchers by exploring the overall characteristics of the published literature on lunar mass movements.

Forecasting tropical cyclone tornadoes and impacts: Report from IWTC-X

This report synthesizes global tropical cyclone (TC) tornado research and operational practices to date. Tornadoes are one of the secondary (and lesser researched) hazards contributing to the devastation TCs leave in their wake. While gale-force winds and storm surge produce the majority of damage and fatalities globally, TC tornadoes also pose a fatal threat, complicating evacuation plans and protective actions as the storm moves inland. Climatological studies characterize TC-spawned tornadoes as usually weak and short-lived, primarily originating from miniature supercells in the outer rainbands. These tornadic features pose challenges to forecasting and radar detection. Additionally, TC tornadoes can pose a threat to communities 12 h prior to and beyond 48 h after a TC makes landfall.Research, both basic and operational, has increased globally over the last few years in efforts to move from a climatological to ingredients-based approach to detect and forecast TC tornadoes. While the United States has led the charge, given the increased exposure to tornadoes year round, other nations such as China, Japan, and Australia have increased their efforts to record and detect TC tornadoes. Despite these advancements, more work needs to be done globally to understand the TC environment conducive for tornadic activity. Recommendations for future forecasting and research for TC tornadoes include i) develop a comprehensive global tornado database to improve research and forecasting efforts; ii) apply innovative technology to detect tornadoes; and iii) conduct field campaigns to thoroughly sample TC tornado environments, particularly along coastlines.

A machine learning led investigation to understand individual difference and the human-environment interactive effect on classroom thermal comfort

The availability of the global thermal open database means that machine learning models have been increasingly applied in thermal comfort studies in order to understand the factors and mechanisms that affect human thermal sensation. Previous global database analyses focused less on classroom thermal comfort, however, and more on model accuracy, while model interpretation was usually ignored, and individual differences and interaction effects are particularly poorly explained. This study screened 4527 related records about classrooms from the ASHRAE Global Thermal Comfort Database II, and used the cleaned data to train a hybrid model of extreme gradient boosting (XGBoost) and Bayesian optimisation (BO). SHAP values were used to interpret the machine learning model. The results identified ten key influencing factors that are associated with thermal comfort, although their importance varies among individuals. The effects of the factors can also be divided into main effects (80%) and interactive effects (20%), and some interactive effects are more potent than the main effect. Three typical types of interactive effects are concluded: two-way interaction, one-way interaction, and cross-interaction. This study was based on a comprehensive global database and an innovative machine learning method, and will lead to a more robust personal comfort model (PCM) that guides HVAC design and regulation development in order to meet thermal environment and energy-saving requirements.

Institutional quality, asset specificity, and foreign direct investment

This article demonstrates that MNC's foreign direct investment decision is linked with institutional quality and hinges on the asset-specificity intensity of FDI. Using a comprehensive global FDI database between 2003 and 2019, our results show that MNCs tend to engage in FDI of high asset-specificity in countries with superior institutional quality, while they launch FDI of low asset-specificity in countries with inferior institutional quality. Moreover, when engaging in FDI of high asset-specificity, MNCs take property rights institutions more seriously, and prefer the sole proprietorship model to the joint venture model there.

Above- and belowground net-primary productivity: A field-based global database of grasslands.

Net primary productivity (NPP) over global grasslands is crucial for understanding the terrestrial carbon cycling and for the assessments of wild herbivores food security. During the past few decades, numerous field investigations have been conducted to estimate grassland NPP since the measuring criterion released by the International Biological Program. However, a comprehensive NPP database, particularly for belowground NPP (BNPP), in global grasslands is rare to date. Here, field NPP measurements from 438 publications (1957-2018) in global grasslands were collected, critically filtered, and incorporated in a comprehensive global database with observations for aboveground NPP (ANPP), BNPP, total NPP (TNPP), and BNPP fraction (fBNPP ). Associated information on geographical locations, climatic records, grassland types, land use patterns, manipulations subjected to manipulative experiments, sampling year of study sites, as well as NPP measurement methods are also documented. This database included 2985 entries from 1785 study sites. Among them, 806 entries contained paired data of ANPP and BNPP, resulting in the 806 fBNPP data. The study sites encompassed global grasslands with latitudinal range of 54.5° S~78.9° N, longitudinal range of 157.4° W~175.8° E, and altitudes from 0 to 5168 m above sea level, covering broad climatic gradients (-17.6 to 28.8°C in mean annual temperature and 63-2052 mm in mean annual precipitation). This global database is the world's largest paired data of ANPP and BNPP field measurements in grasslands. It can be used to study the spatio-temporal patterns of NPP and its allocation, evaluate the responses of above- and below-ground carbon components to future global changes, and validate the NPP estimation by empirical or process-based models in global grasslands. The database can be freely used for noncommercial applications. We kindly request users cite this data paper when using the database, respecting all the hard work during data compilation.

Trends and rhythms in carbonatites and kimberlites reflect thermo-tectonic evolution of Earth

Abstract Earth's thermo-tectonic evolution determines the way the planet's interior and surface interact and shows temporal changes in both trends and periodic rhythms. By sampling the subcontinental lithospheric mantle that represents the interface between the convecting mantle and the crust, carbonatite and kimberlite should be ideal rock types for documenting this evolution. The first-order secular rise of kimberlites over time has been noted by researchers, but there is much debate over how to interpret this trend, and their second-order variability has received less attention. We compiled a comprehensive global carbonatite database and compared it with an existing kimberlite one. We find that the numbers of carbonatites and kimberlites have similar increasing secular trends, with accelerated growth after ca. 1 Ga, and show the same periodic rhythms that have been synchronized to the supercontinent cycle since ca. 2.1 Ga. We link these trends and rhythms to the long-term change of Earth and the supercontinent cycle, both of which have altered the temperature of, and the subduction-recycled volatile flux into, the subcontinental lithosphere. Such consistent records in carbonatite and kimberlite behavior provide critical evidence for the synchronous thermo-tectonic evolution of the entire subcontinental lithosphere.

Global economic costs of mammal invasions

Invasive alien mammals cause huge adverse ecological impact on human society and natural ecosystems. Although studies have estimated economic costs of mammal invasions at regional scales, there is lacking the large-scale comprehensive assessment of currency costs for this taxon. Here, we estimated the economic cost of invasive alien mammals on a global scale using the most comprehensive global database compiling economic costs of invasive species (InvaCost). From 1960 to 2021, mammal invasions caused costs (summing damage costs and management costs) of US$ 462.49 billion to the global economy, while the total amount of robust costs reached US$ 52.49 billion. The majority of the total economic costs corresponded to damage costs (90.27 %), while only 7.43 % were related to management cost. Economic costs showed an increasing trend over time. The distribution of costs was uneven among taxonomic groups and regions, with the global total cost highly biasing toward to 5 species (European rabbit, Domestic cat, Black rat, Wild boar and Coypu), and North America reporting much higher costs (60.78 % of total economic costs) than other regions. The total costs were borne by agriculture, environment, authorities stakeholders and other sectors. Geographic and taxonomic biases suggested that total economic costs caused by invasive alien mammals were underestimated. Integrated research efforts are needed to fill in knowledge gaps in the economic costs generated by mammal invasions and to identify the drivers of the economic costs.

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

The Foraminiferal Response to Climate Stressors Project: Tracking the Community Response of Planktonic Foraminifera to Historical Climate Change

Planktonic Foraminifera are ubiquitous marine protozoa inhabiting the upper ocean. During life, they secrete calcareous shells, which accumulate in marine sediments, providing a geological record of past spatial and temporal changes in their community structure. As a result, they provide the opportunity to analyze both current and historical patterns of species distribution and community turnover in this plankton group on a global scale. The FORCIS project aims to unlock this potential by synthesizing a comprehensive global database of abundance and diversity observations of living planktonic Foraminifera in the upper ocean over more than 100 years starting from 1910. The database will allow for unravelling the impact of multiple global-change stressors acting on planktonic Foraminifera in historical times, using an approach that combines statistical analysis of temporal diversity changes in response to environmental changes with numerical modeling of species response based on their ecological traits.