Continental Scale Research Articles

Spatial mapping and driving factor Identification for salt-affected soils at continental scale using Machine learning methods

Soil salinization is one of the most serious land-degrading threats globally, particularly in Asia, where there are 21 countries, e.g., Kazakhstan, China, Iran, and Indonesia, lack of accurate spatial information of salt-affected soils currently. Mapping the distribution of salt-affected soils and identifying their main driving factors is critical for sustainable development. Current studies for mapping the spatial distribution of salt-affected soils focused on recognizing the soil salinity by using the indicator of electrical conductivity of saturated soil paste extract (ECe), while cannot distinguish the soil sodicity. Meanwhile, the absence of uncertainty assessment is another problem for large-scale studies. To address these gaps, we developed an effective spatial prediction model that considered both soil salinity and sodicity using machine learning (ML) models and soil salinization data. This study used ECe, pH, and exchangeable sodium percent (ESP) as indicators, with sample sizes of 22,803, 30,821, and 12,961, respectively, to produce salt-affected soil maps with uncertainty assessments in Asia, and conducted the analysis of the drivers of soil salinization. The ML models involved cubist, random forest, quantile regression forest, quantile regression neural network, and a simple multiple linear regression model for comparison. The results showed that among these models, quantile regression forest exhibited the best performance, with mean absolute percentage error values of 2.54 %∼52.06 %, coefficient of determination values of 0.40 ∼ 0.74, and Nash-Sutcliffe efficiency values of 0.36 ∼ 0.71. The predicted maps, with 1 km resolution, demonstrated minimal uncertainty across over 60.4 % of the study area. Approximately 466.36 million hectares of topsoil (0–30 cm) and 377.68 million hectares of subsoil (30–100 cm) in Asia are salt-affected, mainly concentrated in arid, semi-arid, and coastal regions. Climatic factors (the annual precipitation and maximum temperature), topography (digital elevation model and slope), and salinity index V (SI5) were identified as the main drivers of soil salinization. Overall, this study provides important guidelines for soil salinization management and achieving ecological sustainability.

Past fire dynamics in sub-Saharan Africa during the last 25,000 years: Climate change and increasing human impacts

The aim of this study is to provide the drivers of long-term fire dynamics in various regions of Sub-Saharan Africa using a synthesis of updated sedimentary charcoal records, from 25,000 years ago to the present. We used the charcoal data currently available in the Global Paleofire Database, updated with the most recent published charcoal data, to reconstruct past biomass burning across the continent. We analyzed standardized charcoal data grouped by region (central Africa, eastern Africa, southern Africa, and Indian Ocean) and by vegetation type (forest, savanna, and shrubland). Within this framework, we found that quality data were lacking to reconstruct a robust trend in biomass burning before 5000 years at the continental scale. This large spatial scale was indeed masking regional peculiarities. Our results suggest that past changes in biomass burning were nuanced and cannot be simply attributed to either climate- or humans, and that they varied from each sub-regions and vegetation type history. In central Africa, biomass burning increased after the end of the African Humid Period and the first wave of Bantu-speaking people migration, whereas in East Africa and the Indian Ocean islands, it seems that human population growth was the main driver of fire activity. In South Africa, reduced rainfall seemed to offset fire activity due to population growth by reducing potentially flammable biomass. While the diversity of methodological techniques used to produce charcoal quantification made comparisons difficult, regional patterns still emerged from these data. This synthesis thus highlights the need to increase the number of sites with charcoal data and to harmonize charcoal extraction and quantification methods across Africa to improve regional to continental assessments of fire histories.

The impact of fungi on soil protist communities in European cereal croplands.

Protists, a crucial part of the soil food web, are increasingly acknowledged as significant influencers of nutrient cycling and plant performance in farmlands. While topographical and climatic factors are often considered to drive microbial communities on a continental scale, higher trophic levels like heterotrophic protists also rely on their food sources. In this context, bacterivores have received more attention than fungivores. Our study explored the connection between the community composition of protists (specifically Rhizaria and Cercozoa) and fungi across 156 cereal fields in Europe, spanning a latitudinal gradient of 3000 km. We employed a machine-learning approach to measure the significance of fungal communities in comparison to bacterial communities, soil abiotic factors, and climate as determinants of the Cercozoa community composition. Our findings indicate that climatic variables and fungal communities are the primary drivers of cercozoan communities, accounting for 70% of their community composition. Structural equation modelling (SEM) unveiled indirect climatic effects on the cercozoan communities through a change in the composition of the fungal communities. Our data also imply that fungivory might be more prevalent among protists than generally believed. This study uncovers a hidden facet of the soil food web, suggesting that the benefits of microbial diversity could be more effectively integrated into sustainable agriculture practices.

Monitoring Water Diversity and Water Quality with Remote Sensing and Traits

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.

A dataset of gridded phenology of woody plants in Europe from 1951 to 2021

Plant phenology records the timing of plant cyclic growth events, and stand as one of the most important indicators of climate change. The monitoring and research of plant phenology is of great significance for understanding the response of ecosystems to global changes and simulating the material and energy balance of terrestrial ecosystems. Based on the ground observation phenological data of six representative woody plants compiled by the Pan European Phenology Project (PEP725) in the past 70 years, this paper employed three phenology models (Unichill, Unified and TSC) to predict and upscale the phenology data on the continental scale, and prepared a dataset of gridded phenology of woody plants in Europe. This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1 ° and a temporal resolution of one day. The quality assessment of the grid phenology data shows that the average error for FLD and FFD is 7.9 and 7.6 days respectively, which are close to simulation errors of spring phenology observed in other regional-scale studies, revealing the high accuracy of the data. This dataset can better characterize the temporal and spatial patterns of plant phenology at a continental scale in Europe, and provide an effective verification approach for plant phenology products from other sources. Furthermore, it can offer valuable data support for research on global change and terrestrial ecosystem simulation.

Developing a remote-sensing-based indicator for peat soil vertical displacement. A case study in the Biebrza Valley, Poland

Peatlands play a crucial role in carbon storage, but drainage and climate change-induced hydrological changes drivers degrade peat soils, followed by negative vertical displacement of the soil surface, referred to in the literature as peat subsidence. Assessing peatland subsidence is an important indicator of peatland status that allows the development of peatlands to be revealed over a given period. However, traditional methods are limited in their applicability to large and inaccessible peatlands. In this study, we introduce a remote sensing framework for easily indicating peat subsidence at a large scale. Our framework utilizes the Alaska Satellite Facility (ASF) Interferometry Synthetic Aperture Radar (InSAR) with on-demand cloud computing, employing an optimization process including a Small Baseline Set technique and seasonal-annual search approach. By implementing this approach in the Biebrza Valley, Poland, covering the period from April 2015 to April 2022, we unveiled a tragedy, an annual subsidence rate of 1.44 cm. It means a peat loss between 58.1 and 89.6 million cubic meters over seven years, an annual loss of 86.4 to 132.5 (average 109) m3, approximately 46 tons, of dry matter peat per hectare. This finding was verified through field surveys, with high accuracy. An R2 value of 0.7, and an RMSE value of 0.23 cm determine the reliability of this approach in estimating the vertical displacement. The potential of this tool as a robust method to assess large-scale peatlands would allow for the assessment of average water levels as well as greenhouse gas emissions over large areas of peatlands, even at the continental scale.

Framework for mapping large-scale nature-based solutions for drought mitigation: Regional application in Flanders

Droughts are classified as the most expensive climate disasters as they leave long-term and chronic impacts on the ecosystem, agriculture, and human society. The intensity, frequency, and duration of drought events have increased in the past and are expected to continue rising at global, continental, and regional scales. Nature-based solutions (NBS) are highlighted as effective solutions to cope with the future impacts of these events. Despite this, there has been limited comprehensive research on the effectiveness of NBS for drought mitigation, and existing suitability mapping frameworks often overlook drought-specific criteria. To address this gap, a new framework is proposed to identify areas suitable for two drought-coping NBS types at a regional scale: detention basins and managed aquifer recharge. Two multi-criteria decision-making techniques (MCDM), i.e. Boolean logic and Analytic- Hierarchy Process (AHP), were used to map suitable large-scale NBS. The new framework accounts for unique criteria to specifically address drought conditions. By incorporating climate change scenarios for both surface and groundwater, recharge, and different groundwater characteristics, it identifies suitable and sustainable locations capable of managing extreme drought events. Executed through Boolean logic at a regional scale in Flanders (Belgium), the framework's strict approach yields significant potential areas for detention basins (298.7 km²) and managed aquifer recharge (867.5 km²). Incorporating AHP with the same criteria introduces a higher degree of flexibility for decision-makers. This approach shows a notable expansion across Flanders, varying with the level of suitability. The results underscore the highly suitable potential for detention basins (2552.2 km²) and managed aquifer recharge (2538.7 km²), emphasizing the adaptability and scalability of the framework for addressing drought in the region. The comparison between potential recharge volume due to detention basin and groundwater use in the region indicated that the detention basins could partially compensate for the high water demand. Therefore, creating a framework targeting drought is vital for the sustainable management of water scarcity scenarios.

Modelling decisions and density dependence in monarch butterflies: A comment on Meehan and Crossley (2023)

Abstract Decisions about which processes to include in a population model can have substantial impact on estimates of population trends and extinction risk. This is particularly important for species of conservation concern, whose conservation status is decided in part on the basis of these models. In their recent paper, Meehan and Crossley (2023) Insect Conservation and Diversity, 16, 566–573 revisit a time series of overwintering monarch butterfly abundances, which previous assessments had characterised as rapidly declining and at risk of extinction. The authors, in contrast, reported no evidence for declines in the past 10 years and characterised extinction risk as low. A primary reason for the difference between these conclusions was that Meehan and Crossley used a more complex model that included a parameter for density dependence. While negative density dependence may play a role in monarch population regulation, there are a variety of unresolved issues with how and if density dependence should be included in models of monarch populations, including widely known issues with separating observation error from density dependence in noisy time series and taxon‐specific issues with fitting models to data surveyed every fourth generation and pooled at continental scales. These issues make the conclusions of Meehan and Crossley about monarch population viability much less robust than implied in their article. They do not provide convincing evidence that density dependence reduces extinction risk in monarch butterflies. Our commentary supports their general conclusion that population viability projections depend on model assumptions, but the ways in which monarch butterfly populations are regulated remains an open question.

Non-native common snapping turtle Chelydra serpentina (Linnaeus, 1758) in Western Europe: a focus on Central Italy showing evidence of a hot spot of introduction

The common snapping turtle, Chelydra serpentina (Linnaeus, 1758), is a robust aquatic turtle native to southeast North America. However, it has also been introduced to other countries through the exotic pet trade, where it has gained popularity due to its distinctive appearance and impressive size. Over the past decade, a considerable number of individuals, abandoned by their owners, have been recorded and captured in Western Europe. Here, we report the first comprehensive summary of records at continental scale, showing that France and Italy represent the countries with the highest number of sightings. In France, exhibition, exchange, or sale events of exotic animals (until 2022 legal; now illegal) have been located near large cities, although records of C. serpentina were significantly higher outside the core of urban areas. We also recorded a significant increase in total number of records at European scale when comparing the periods of 2010-2015 and 2016-2020. Finally, we focused the current study on a peculiar spatial pattern at local scale (Central Italy) near an important site of exhibition and sale events of exotic animals, suggesting the presence of a hot spot of introduction. Popular events may represent critical colonization hubs from where these freshwater turtles can disperse into the surroundings.

Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach

The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil spectroscopy, they have ignored the rarity of As-contaminated soil samples, leading to an imbalanced learning problem. A novel ML framework was thus designed herein to solve the imbalance issue in identifying soil As contamination from soil visible and near-infrared spectra. Spectral preprocessing, imbalanced dataset resampling, and model comparisons were combined in the ML framework, and the optimal combination was selected based on the recall. In addition, Bayesian optimization was used to tune the model hyperparameters. The optimized model achieved recall, area under the curve, and balanced accuracy values of 0.83, 0.88, and 0.79, respectively, on the testing set. The recall was further improved to 0.87 with the threshold adjustment, indicating the model's excellent performance and generalization capability in classifying As-contaminated soil samples. The optimal model was applied to a global soil spectral dataset to predict areas at a high risk of soil As contamination on a global scale. The ML framework established in this study represents a milestone in the classification of soil As contamination and can serve as a valuable reference for contamination management in soil science.

Photoperiod alone does not explain the variations of leaf senescence onset across Europe

The onset of autumnal leaf senescence (TLSO) plays a crucial role in understanding and modeling how plants prepare for winter dormancy, but it has remained under-explored. Previous research, based on limited in situ observations, have proposed that the year-to-year invariant photoperiod is the primary trigger of TLSO, indicating potential insensitivity to climate change. This study verifies this hypothesis using high spatial resolution satellite data and ground-based observations throughout the European continent. Surprisingly, our findings reveal a widespread delay in TLSO during 2001–2019 (approximately 73 %, significant in 19 % of the continent, p < 0.05), closely aligned with delayed leaf senescence end (TLSD) as reported in previous studies. Moreover, as the delaying trend was larger for TLSO than for TLSD, it is apparent that climate change does not only postpone the onset of the leaf senescence process but also shortens its duration across the continent. This finding indicates that a reduced duration for plants to get ready for dormancy could potentially undermine their resilience to cold and other winter stresses. Contrary to the hypothesized photoperiodic control, TLSO exhibited opposing inter-annual temporal trends along temperature gradients, despite similar daylengths. Ground based observations failed to support photoperiodic influence either, highlighting the substantial spatial variation in TLSO responses to climate change. Intriguingly, complex climate-TLSO interactions were observed, with radiation dominating TLSO changes in 37.0 % of the study area. This analysis underscores the necessity of considering multiple variables to comprehend the patterns of TLSO at continental and vegetation scales.

Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales.

Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in predicting SOC concentration distribution with terrain, contributing approximately one-third of the overall prediction at the local scale. At the continental scale, climate is only 1.2 times more important than terrain in predicting SOC distribution, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation, respectively. We underscore that terrain attributes, while being integral to the SOC distribution at all scales, are stronger predictors at the local scale with explicit spatial arrangement information. While this observational study does not assess causal mechanisms, our analysis nonetheless presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.

Slip Tendency Analysis From Sparse Stress and Satellite Data Using Physics‐Guided Deep Neural Networks

AbstractThe significant risk associated with fault reactivation often necessitates slip tendency analyses for effective risk assessment. However, such analyses are challenging, particularly in large areas with limited or absent reliable stress measurements and where the cost of extensive geomechanical analyses or simulations is prohibitive. In this paper, we propose a novel approach using a physics‐informed neural network that integrates stress orientation and satellite displacement observations in a top‐down multi‐scale framework to estimate two‐dimensional slip tendency analyses even in regions lacking comprehensive stress data. Our study demonstrates that velocities derived from a continental scale analysis, combined with reliable stress orientation averages, can effectively guide models at smaller scales to generate qualitative slip tendency maps. By offering customizable data selection and stress resolution options, this method presents a robust solution to address data scarcity issues, as exemplified through a case study of the South Australian Eyre Peninsula.

Southern breeding populations drive declining migration distances in Arctic and subarctic geese

Migration is a prevalent strategy among birds used to track seasonal resources throughout the year. Individual and population‐level migratory movements provide insight to life‐history variation, carry‐over effects, and impacts of climate change. Our understanding of how geographic variation in a species' breeding or wintering grounds can impact migration distances is limited. However, changes in migration distances can have important fitness consequences for individuals and conservation implications for populations, particularly if migratory connectivity is altered during the annual cycle. In this study, we use three decades of data from the United States Geological Survey Bird Banding Laboratory for six migratory species of Arctic and subarctic breeding geese. We employ a Bayesian hierarchical framework to test if the distance between breeding and wintering locations has changed over time, while accounting for the latitude of the breeding grounds. A model that included only a temporal trend estimated the average rate of change in migration distance, across all six species, at −3.0 km/year over the period 1990–2019. Five of the six species showed a significant decrease in migration distances. Including an interaction effect with breeding latitude revealed that the reduction in migration distance was strongest in the southernmost populations for four of the six species. For those species, migration distance in northern populations were all either relatively unchanged or increasing. This indicates that southern breeding populations of geese had a stronger association with the observed spatiotemporal changes in wintering ranges, potentially influenced by a combination of climatic and biotic factors (e.g. resource availability or competitive interactions) that uniquely impact these populations. Abundant, long‐term banding data shows promise for use in illuminating changes in migratory patterns under climate change, leading to improved management and conservation outcomes, from regional to continental scales.

Europe as a secondary distribution hub in the worldwide invasion of the potato cyst nematode Globodera rostochiensis

The potato cyst nematode Globodera rostochiensis originates from the Andean Mountain region in South America and has unintentionally been introduced to all inhabited continents. Several studies have examined the population genetic structure of this pest in various countries by using microsatellite markers. However, merging microsatellite data produced from different laboratories is challenging and can introduce uncertainty when interpreting the results. To overcome this challenge and to explore invasion routes of this pest, we have genotyped 22 G. rostochiensis populations from all continents. Within populations, the highest genetic diversity was observed in the South American populations, the European populations showed an intermediate level of genetic diversity and the remaining populations were the less diverse. This confirmed pre-existing knowledge such as a first introduction event from South America to Europe, but the less diverse populations could originate either from South America or from Europe. At the continental scale, STRUCTURE genetic clustering output indicated that North America and Asia have experienced at least two introduction events. Comparing different evolutionary scenarios, the Approximate Bayesian Computation analysis showed that Europe served as a secondary distribution centre for the invasion of G. rostochiensis into all other continents (North America, Africa, Asia and Oceania).

European vineyards and their cultural landscapes exposed to record drought and heat

CONTEXTEuropean vineyards, producing over 50% of the world's wine and hosting significant cultural landscapes, face threats from climate change and related severe weather events. The summer of 2022, especially July, posed significant challenges due to extreme drought and high temperatures. However, this period also provided an opportunity to study how such events might involve viticulture in Europe and to explore mitigation solutions. OBJECTIVEThe objectives are (1) to characterize the severity of the extreme event of July 2022 in European wine regions regarding primary climatic parameters, (2) map vineyards at risk due to agricultural drought and high land surface temperature, and (3) discuss the role of various water-related interventions for mitigating similar events. METHODSAfter identifying the locations of European vineyards using the Corine Land Cover 2018 (CLC2018), open-access satellite data were employed to: (1) assess anomalies in Maximum Air Temperature (NATm), Land Surface Temperature (LST), Precipitation (P), and Soil Moisture (SM) in July 2022 compared to long-term averages; (2) identify regions at higher risk that experienced extreme agricultural drought (Vegetation Health Index, VHI = Extreme) and LST > 35 °C. RESULTS AND CONCLUSIONSIn July 2022, European vineyards experienced an average increase of 11% in NATm, a 9% rise in LST, and a reduction of 47% in P and 30% in SM compared to historical averages. 18% of European vineyards were at risk of drought and excessive heat, particularly in Portugal (31%), France (27%), and Italy (21%), including 10 viticultural cultural landscapes. Findings highlight the urgent need for long-term sustainable water management practices over emergency interventions. This research supports informed decision-making, emphasizing that climate resilience is necessary for preserving the cultural heritage of European wine-growing areas. SIGNIFICANCEThis research provides an overview of the dynamics of extreme events on viticulture at a continental scale, promoting climate-aware viticultural systems and offering insight scalable to global viticulture under changing climatic conditions.

Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine

The advent of cloud computing platforms (e.g., Google Earth Engine (GEE)) and the massive amounts of optical and radar Earth Observation (EO) data hosted by these platforms present new opportunities for mapping soil pH at large scales. However, existing studies generally lack consensus on the effects of satellite and radar sensor parameters on GEE-based soil pH prediction models. In this study, we assessed the suitability of long-term radar (C-band Sentinel-1 and L-band PALSAR-1/2) and optical (Sentinel-2) EO data on GEE for the digital mapping of soil pH on a continental (Europe) scale and determined the most appropriate radar sensor parameters. Thirteen scenarios with different data configurations were simulated and combined with the 2018 LUCAS soil database and two machine learners (boosted regression trees and extreme gradient boosting) to develop soil prediction models. Results showed that the selection of modeling techniques, satellite sensors and radar system parameters largely affected the model output. Models involving a single polarization mode of PALSAR-1/2 data performed the worst (RPD = 1.24). Models based on Sentinel-1 data performed better than those built using PALSAR-1/2 data. The model performance was improved when a model involved more polarization bands, orbital directions, and band frequencies. The multiband model built using the two radar datasets achieved a comparable accuracy to the model based on optical data. Moreover, the model that fused radar-optical data achieved better results, with RPD values of 1.56 and 1.46 for the models with and without radar data, respectively; its performance was comparable to that of models built with commonly used variables (topography and climate). The analysis of importance indicated that long-term optical and radar EO data on GEE were important in our model. The modelling of soil pH at the continental scale largely benefits from GEE. The predicted maps exhibited strong spatial heterogeneity among different biogeographic regions, with similar spatial patterns under different modelling scenarios.

The Late Pliocene–Middle Pleistocene Large Mammal Faunal Units of Greece

Located at the eastern corner of Mediterranean Europe, Greece occupies a critical position for mammal dispersals to/from Europe, Asia, and Africa and constitutes a potential passageway towards Western Europe. During recent decades, numerous fieldwork campaigns in several Pliocene–Pleistocene sites have greatly enriched the fossil record and provided valuable taxonomic and biostratigraphic data. However, a fully developed reference biochronological unit scheme for the Greek record that could contribute to correlations at a continental scale is still pending. In this article, we provide the updated Late Pliocene to Middle Pleistocene large mammal succession, and we introduce the Faunal Units (FUs) of Greece. We define eight FUs, the Milia, Dafnero, Gerakarou, Tsiotra Vryssi, Krimni, Apollonia, Marathousa, and Apidima FUs (from the oldest to the youngest), which are determined by a set of first and last local occurrences. The results form the basis for discussion of already set turnovers, dispersals, and extinction/immigration events and showcase the importance of the local record for the investigation of the European terrestrial ecosystems. By developing the first detailed biochronological scheme for the Pliocene–Pleistocene of Southeastern Europe, this study comprises the basis for an expanded Balkan faunal unit scale and a reference framework for future investigations.

An Ediacaran orogeny in subglacial East Antarctica is uncovered by detrital garnet geochronology

Detrital minerals provide valuable insights into the tectonic history of continents. Uranium-lead dating of detrital zircon is widely used to characterize the magmatic history of continents but is generally insensitive to metamorphism accompanying the production and reworking of crust during orogenesis. Garnet is the most important mineral for recording prograde and peak orogenic metamorphism and can occur as a common detrital phase. Here, we demonstrate laser-ablation lutetium-hafnium (Lu-Hf) geochronology of detrital garnet as a provenance tool for reconstructing orogenic histories at (super)continental scales. Detrital garnet (n = 557) from modern sands and Permo-Carboniferous glacial strata in South Australia faithfully record local garnet-grade metamorphic events but also include a major population at ca. 590 million-years with no known source in South Australia. We trace the ca. 590 million-year-old detrital garnets to a largely ice-covered orogenic province in East Antarctica, uncovering the inception of convergent margin tectonism along the palaeo-Pacific margin of Gondwana.

Evaluation of flux and fate of plastic in terrestrial–aquatic–estuarine continuum by using an advanced process‐based model

AbstractEnvironmental contamination by plastics has been receiving considerable attention from scientists, policy makers and the public during the last few decades. Though some of the models have succeeded in simulating the transport and fate of plastic debris in freshwater systems, a complete model is now being developed to clarify the dynamic characteristics of the plastic budget on a continental scale. Recently, the author linked two process‐based eco‐hydrology models, NICE (National Integrated Catchment‐based Eco‐hydrology) and NICE‐BGC (BioGeochemical Cycle), to a plastic debris model that accounts for both the transport and fate of plastic debris (advection, dispersion, diffusion, settling, dissolution and biochemical degradation by light and temperature) and applied these models on a regional scale and also for global major rivers. The present study was newly modified to incorporate the plastic dynamics in estuaries by extending the previous studies. The model was employed to conduct a 2‐year global simulation aimed at evaluating changes in plastic dynamics in major rivers including 130 tidal estuaries. The model simulated the impact of estuaries on plastic budget and its seasonal variability caused by settling, resuspension and bedload transport during 2014–2015. The model showed that plastics with smaller particle sizes account for more in the water of estuaries than that of rivers, and plastics with larger particle sizes accumulate more on the riverbed. The simulated result also showed that estuaries trap more plastic than lakes and riverbeds (0.218 ± 0.053 Tg/year) although not as much as reservoirs (0.386 ± 0.103 Tg/year). More than 40% of plastics were retained by lakes, reservoirs, riverbeds and estuaries and the riverine plastic transport to the ocean was revised from 1.749 ± 0.371 Tg/year in the author's previous study to 1.000 ± 0.397 Tg/year in the present study. These results aid the development of solutions and measures for the reduction of plastic input to the ocean and help quantify the magnitude of plastic transport under climate change.