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The formation and failure of landslide dams is an important and understudied, multi-hazard topic. A framework of landslide dam formation susceptibility evaluation was designed for large-scale studies to avoid the traditional dependence on landslide volume calculations based on empirical relationships, which requires comprehensive local inventories of landslides and landslide dams. The framework combines logistic regression landslide susceptibility models and global fluvial datasets and was tested in Italy and Japan based on landslide and landslide dam inventories collected globally. The final landslide dam formation susceptibility index identifies which river reach is most prone to landslide dam formation, based on the river width and the landslide susceptibility in the adjacent delineated slope drainage areas. The logistic regression models showed good performances with area under the receiver operating characteristics curve values of 0.89 in Italy and 0.74 in Japan. The index effectively identifies the probability of landslide dam formation for specific river reaches, as demonstrated by the higher index values for river reaches with past landslide dam records. The framework is designed to be applied globally or for other large-scale study regions, especially for less studied data-scarce regions. It also provides a preliminary evaluation result for smaller catchments and has the potential to be applied at a more detailed scale with local datasets. [ABSTRACT FROM AUTHOR]

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model's output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model's predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model's output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion. [ABSTRACT FROM AUTHOR]

This article examines the influence of wave cycles on major structural changes in the development of natural disasters worldwide from 1900 to 2023. During this period, natural disasters were most common in the latitudinal band between 30° N and 30° S. The results of the analysis show that structural changes in natural disaster types and subtypes coincide with wave cycles, which are characterized by fluctuations in the frequency of natural hazards. Hence, an overall increase in the number of natural disasters linked to solar activity is associated with a rise in the frequency of climatic, hydrological, and biological hazards, while the frequency of geophysical and meteorological hazards gets lower. Conversely, a decrease in the number of natural disasters, such as that from 2001 to 2023, is accompanied by a reduction in climatic, geophysical, and biological hazards, as well as an increase in the frequency of hydrological hazards, but no changes in the frequency of meteorological hazards.

Abstract Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model’s output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model’s predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model’s output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32- years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979--2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability. [ABSTRACT FROM AUTHOR]

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy (paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

Assessing the risk of human pathogens in the environment is crucial for controlling the spread of diseases and safeguarding human health. However, conducting a thorough assessment of low-abundance pathogens in highly complex environmental microbial communities remains challenging. This study compiled a comprehensive catalog of 247 human-pathogenic bacterial taxa from global biosafety agencies and identified more than 78 million genome-specific markers (GSMs) from their 17,470 sequenced genomes. Subsequently, we analyzed these pathogens’ types, abundance, and diversity within 474 shotgun metagenomic sequences obtained from diverse environmental sources. The results revealed that among the four habitats studied (air, water, soil, and sediment), the detection rate, diversity, and abundance of detectable pathogens in the air all exceeded those in the other three habitats. Air, sediment, and water environments exhibited identical dominant taxa, indicating that these human pathogens may have unique environmental vectors for their transmission or survival. Furthermore, we observed the impact of human activities on the environmental risk posed by these pathogens, where greater amounts of human activities significantly increased the abundance of human pathogenic bacteria, especially in water and air. These findings have remarkable implications for the environmental risk assessment of human pathogens, providing valuable insights into their presence and distribution across different habitats.

Microplastics (MPs), microscopic particles originating from plastic products, have emerged as a persistent environmental challenge, posing threats to both ecosystems and human health. Their omnipresence, extending from the highest mountains to the deepest oceans and infiltrating the bodies of humans and animals, requires urgent attention. In the face of escalating annual plastic production and inefficient waste management, where 79% of plastic production ends up in landfill sites or enters the environment, MPs multiply as its consequence. This emphasizes the urgent need for a comprehensive global framework that transcends borders to systematically address and control the growth of MPs. In response, our research conducts an in-depth investigation and proposes a seven-step strategy, providing a global perspective for mitigating microplastic pollution. The proposed approach begins with initial research steps and closes in predicting the remediation of areas impacted by microplastic pollution.

Deforestation poses a global threat to biodiversity and its capacity to deliver ecosystem services. Yet, the impacts of deforestation on soil biodiversity and its associated ecosystem services remain virtually unknown. We generated a global dataset including 696 paired-site observations to investigate how native forest conversion to other land uses affects soil properties, biodiversity, and functions associated with the delivery of multiple ecosystem services. The conversion of native forests to plantations, grasslands, and croplands resulted in higher bacterial diversity and more homogeneous fungal communities dominated by pathogens and with a lower abundance of symbionts. Such conversions also resulted in significant reductions in carbon storage, nutrient cycling, and soil functional rates related to organic matter decomposition. Responses of the microbial community to deforestation, including bacterial and fungal diversity and fungal guilds, were predominantly regulated by changes in soil pH and total phosphorus. Moreover, we found that soil fungal diversity and functioning in warmer and wetter native forests is especially vulnerable to deforestation. Our work highlights that the loss of native forests to managed ecosystems poses a major global threat to the biodiversity and functioning of soils and their capacity to deliver ecosystem services. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Microplastics (MPs), microscopic particles originating from plastic products, have emerged as a persistent environmental challenge, posing threats to both ecosystems and human health. Their omnipresence, extending from the highest mountains to the deepest oceans and infiltrating the bodies of humans and animals, requires urgent attention. In the face of escalating annual plastic production and inefficient waste management, where 79% of plastic production ends up in landfill sites or enters the environment, MPs multiply as its consequence. This emphasizes the urgent need for a comprehensive global framework that transcends borders to systematically address and control the growth of MPs. In response, our research conducts an in-depth investigation and proposes a seven-step strategy, providing a global perspective for mitigating microplastic pollution. The proposed approach begins with initial research steps and closes in predicting the remediation of areas impacted by microplastic pollution. [ABSTRACT FROM AUTHOR]

We studied latitudinal patterns in the species richness (SR), the phylogenetic diversity (PD), and the functional diversity (FD) of fleas and their mammalian hosts. We asked whether these patterns in either fleas, hosts, or both 1) conform to a classical latitudinal gradient; 2) vary geographically; and 3) differ between fleas and hosts. We also asked whether the patterns of PD and FD follow those of SR. We collected data on the latitudinal distribution of 1022 flea and 900 mammal species from literature sources and calculated the SR, PD, and FD of both groups in 1° latitude bands. Then, we used broken‐stick regression models to analyse separately the latitudinal variation of 1) each diversity facet and 2) fleas and hosts in each geographic quadrant. The classical latitudinal gradient pattern was not found in either fleas or hosts across any facet of diversity or geographic quadrant, except for the PD of fleas in the southeastern quadrant and the FD of hosts in the southwestern quadrant. Latitudinal patterns of the SR, PD and FD of fleas and hosts differed substantially between geographic quadrants. Furthermore, the latitudinal distributions of flea and host SR were similar in three of four quadrants (except the northeastern quadrant), whereas the latitudinal distributions of flea and host PD were similar in the southwestern quadrant only. No similarity in flea versus host FD was revealed. The latitudinal patterns of flea and host PD and FD mostly did not follow those of their SR. We conclude that latitudinal gradients of species richness and phylogenetic and functional diversity appeared not to be universal phenomena. Instead, the latitudinal distributions of these diversity facets represent an interplay of ecological (current and past) and historical processes. For parasites, the processes acting on hosts add another layer of complexity underlying their latitudinal diversity patterns. [ABSTRACT FROM AUTHOR]

Pesticides are widely used in agriculture, but their impact on the environment and human health is a major concern. While much attention has been given to their presence in soil, water, and food, there have been few studies on airborne pesticide pollution on a global scale. This study aimed to assess the extent of atmospheric pesticide pollution in countries worldwide and identify regional differences using a scoring approach. In addition to analyzing the health risks associated with pesticide pollution, we also examined agricultural practices and current air quality standards for pesticides in these countries. The pollution scores varied significantly among the countries, particularly in Europe. Asian and Oceanic countries generally had higher scores compared to those in the Americas, suggesting a relatively higher level of air pollution caused by pesticides in these regions. It is worth noting that the current pollution levels, as assessed theoretically, pose minimal health risks to humans. However, studies in the literature have shown that excessive exposure to pesticides present in the atmosphere has been associated with various health problems, such as cancer, neuropsychiatric disorders, and other chronic diseases. Interestingly, European countries had the highest overall pesticide application intensities, but this did not necessarily correspond to higher atmospheric pesticide pollution scores. Only a few countries have established air quality standards specifically for pesticides. Furthermore, pollution scores across states in the USA were investigated and the global sampling sites were mapped. The findings revealed that the scores varied widely in the USA and the current sampling sites were limited or unevenly distributed in some countries, particularly the Nordic countries. These findings can help global relevant environmental agencies to set up comprehensive monitoring networks. Overall, the present research highlights the need to create a pesticide monitoring system and increase efforts to enhance pesticide regulation, ensure consistency in standards, and promote international cooperation.

Recently, security protection is import in many scenarios. Occluded person re-identification (Re-ID) involves identifying obscured pedestrians from images captured by multiple cameras, even when the images are partially or fully occluded. Many state-of-the-art models for occluded Re-ID utilize auxiliary modules such as pose estimation, feature pyramid, and graph matching to address occlusion challenges. However, this approach results in complex models that struggle to generalize to diverse occlusions and may not effectively handle non-occluded pedestrians. Furthermore, real-world Re-ID applications frequently involve both occluded and non-occluded pedestrians, making it difficult to develop versatile models. To tackle these issues, we introduce a novel Re-ID model that learns discriminative features on both local and global scales for occluded pedestrian identification. Our proposed model, the Local-aware Transformer (LAT) for occluded person Re-ID, comprises three modules: a Discriminative Feature Extraction Module (DFEM), a Local Feature Extraction Module (LFEM), and a Global Feature Extraction Module (GFEM). Our experimental results on three occluded and two general Re-ID benchmarks demonstrate that our model surpasses existing state-of-the-art methods and achieves exceptional performance in both occluded and non-occluded Re-ID tasks.

The main objective of this study is to analyze hazardous elements in nanoparticles (NPs) (smaller than 100 nm) and ultrafine particles (smaller than 1 µm) in Porto Alegre City, southern Brazil using a self-made passive sampler and Sentinel-3B SYN satellite images in 32 collection points. The Aerosol Optical Thickness proportion (T550) identification was conducted using images of the Sentinel-3B SYN satellite at 634 points sampled in 2019, 2020, 2021, and 2022. Focused ion beam scanning electron microscopy analyses were performed to identify chemical elements present in NPs and ultrafine particles, followed by single-stage cascade impactor to be processed by high-resolution transmission electron microscopy. This process was coupled with energy-dispersive X-ray spectroscopy and later analysis via secondary ion mass spectrometry. Data was acquired from Sentinel-3B SYN images, normalized to a standard mean of 0.83 µg/mg, at moderate spatial resolution (260 m), and modeled in the Sentinel Application Platform (SNAP) software v.8.0. Statistical matrix data was generated in the JASP software (Jeffreys's Amazing Statistics Program) v.0.14.1.0 followed by a K-means cluster analysis. The results demonstrate the presence of between 1 and 100 nm particles of the following chemical elements: Si, Al, K, Mg, P, and Ti. Many people go through these areas daily and may inhale or absorb these elements that can harm human health. In the Sentinel-3B SYN satellite images, the sum of squares in cluster 6 is 168,265 and in cluster 7 a total of 21,583. The use of images from the Sentinel-3B SYN satellite to obtain T550 levels is of great importance as it reveals that atmospheric pollution can move through air currents contaminating large areas on a global scale. [ABSTRACT FROM AUTHOR]

Excessive fine sediment (particles <2 mm) deposition in freshwater systems is a pervasive stressor worldwide. However, understanding of ecological response to excess fine sediment in river systems at the global scale is limited. Here, we aim to address whether there is a consistent response to increasing levels of deposited fine sediment by freshwater invertebrates across multiple geographic regions (Australia, Brazil, New Zealand and the UK). Results indicate ecological responses are not globally consistent and are instead dependent on both the region and the facet of invertebrate diversity considered, that is, taxonomic or functional trait structure. Invertebrate communities of Australia were most sensitive to deposited fine sediment, with the greatest rate of change in communities occurring when fine sediment cover was low (below 25% of the reach). Communities in the UK displayed a greater tolerance with most compositional change occurring between 30% and 60% cover. In both New Zealand and Brazil, which included the most heavily sedimented sampled streams, the communities were more tolerant or demonstrated ambiguous responses, likely due to historic environmental filtering of invertebrate communities. We conclude that ecological responses to fine sediment are not generalisable globally and are dependent on landscape filters with regional context and historic land management playing important roles. [ABSTRACT FROM AUTHOR]

The worldwide retreat of glaciers is causing a faster than ever increase in ice‐free areas that are leading to the emergence of new ecosystems. Understanding the dynamics of these environments is critical to predicting the consequences of climate change on mountains and at high latitudes. Climatic differences between regions of the world could modulate the emergence of biodiversity and functionality after glacier retreat, yet global tests of this hypothesis are lacking. Nematodes are the most abundant soil animals, with keystone roles in ecosystem functioning, but the lack of global‐scale studies limits our understanding of how the taxonomic and functional diversity of nematodes changes during the colonization of proglacial landscapes. We used environmental DNA metabarcoding to characterize nematode communities of 48 glacier forelands from five continents. We assessed how different facets of biodiversity change with the age of deglaciated terrains and tested the hypothesis that colonization patterns are different across forelands with different climatic conditions. Nematodes colonized ice‐free areas almost immediately. Both taxonomic and functional richness quickly increased over time, but the increase in nematode diversity was modulated by climate, so that colonization started earlier in forelands with mild summer temperatures. Colder forelands initially hosted poor communities, but the colonization rate then accelerated, eventually leveling biodiversity differences between climatic regimes in the long term. Immediately after glacier retreat, communities were dominated by colonizer taxa with short generation time and r‐ecological strategy but community composition shifted through time, with increased frequency of more persister taxa with K‐ecological strategy. These changes mostly occurred through the addition of new traits instead of their replacement during succession. The effects of local climate on nematode colonization led to heterogeneous but predictable patterns around the world that likely affect soil communities and overall ecosystem development. [ABSTRACT FROM AUTHOR]

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America. [ABSTRACT FROM AUTHOR]

Recently, security protection is import in many scenarios. Occluded person re-identification (Re-ID) involves identifying obscured pedestrians from images captured by multiple cameras, even when the images are partially or fully occluded. Many state-of-the-art models for occluded Re-ID utilize auxiliary modules such as pose estimation, feature pyramid, and graph matching to address occlusion challenges. However, this approach results in complex models that struggle to generalize to diverse occlusions and may not effectively handle non-occluded pedestrians. Furthermore, real-world Re-ID applications frequently involve both occluded and non-occluded pedestrians, making it difficult to develop versatile models. To tackle these issues, we introduce a novel Re-ID model that learns discriminative features on both local and global scales for occluded pedestrian identification. Our proposed model, the Local-aware Transformer (LAT) for occluded person Re-ID, comprises three modules: a Discriminative Feature Extraction Module (DFEM), a Local Feature Extraction Module (LFEM), and a Global Feature Extraction Module (GFEM). Our experimental results on three occluded and two general Re-ID benchmarks demonstrate that our model surpasses existing state-of-the-art methods and achieves exceptional performance in both occluded and non-occluded Re-ID tasks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current ecological crisis has risen extinction rates to similar levels of ancient mass extinctions. However, it seems to not be acting uniformly across all species but affecting species differentially. This suggests that species' susceptibility to the extinction process is mediated by specific traits. Since understanding this response mechanism at large scales will benefit conservation effort around the world, we used the IUCN global threat status and population trends of 8281 extant bird species as proxies of the extinction risk to identify the species-specific traits affecting their susceptibility to extinction within the biogeographic regions and at the global scale. Using linear mixed effect models and multinomial models, we related the global threat status and the population trends with the following traits: migratory strategy, habitat and diet specialization, body size, and generation length. According to our results and independently of the proxy used, more vulnerable species are sedentary and have larger body size, longer generation time, and higher degree of habitat specialization. These relationships apply globally and show little variation across biogeographic regions. We suggest that such concordant patterns might be caused either by a widespread occurrence of the same threats such as habitat modification or by a uniform capacity of some traits to reflect the impact of different local threats. Regardless of the cause of this pattern, our study identified the traits that affect species' response capability to the current ecological crisis. Conservation effort should focus on the species with trait values indicating the limited response capacity to overcome this crisis. [ABSTRACT FROM AUTHOR]

The combined effects of natural, anthropogenic, and socioeconomic factors have resulted in significant transformations of land use and landscape pattern in global mountains over the past two centuries, but related studies typically focus on local and regional scales, and analyses on global scale are lacking. Therefore, this study conducts a systematic review of land use and landscape patterns evolution in mountains by selecting relevant case studies to investigate the characteristics and trends at the global scale. The results show that (1) land use change in mountains is different from the global general trend, and there is a bidirectional variation relationship between forest land and agricultural land in global mountains. (2) Land use in mountains roughly experiences the natural period, agricultural expansion period, transition period, and polarization period. The corresponding landscape pattern evolution is reflected in natural mountain landscape, traditional mountain landscape, transitional mountain landscape and modern mountain landscape. (3) Due to disparities in socioeconomic development, different mountain regions are in different stages of land use and landscape pattern evolution at the same time. (4) There are two opposite trends in recent decades of land use and landscape pattern transformation in global mountains. On the positive trend, land use is polarized, and the landscape pattern has distinct heterogeneity in vertical space. On the reverse trend, the mountain landscape pattern is represented by the traditional mountain landscape of the agricultural expansion period. Overall, the results reveal the uniqueness of land use and landscape pattern transformation in global mountains, which has enlightenment for the global sustainable land use. [ABSTRACT FROM AUTHOR]

Abstract Drought monitoring systems are real‐time information systems focused on drought severity data. They are useful for determining the drought onset and development and defining the spatial extent of drought at any time. Effective drought monitoring requires databases with high spatial and temporal resolution and large spatial and temporal coverage. Recent reanalysis datasets meet these requirements and offer an excellent alternative to observational data. In addition, reanalysis data allow better quantification of some variables that affect drought severity and are more seldom observed. This study presents a global drought dataset and a monitoring system based on the Standardized Precipitation Evapotranspiration Index (SPEI) and ERA5 reanalysis data. Computation of the atmospheric evaporative demand for the SPEI follows the FAO‐56 Penman‐Monteith equation. The system is updated weekly, providing near real‐time information at a 0.5° spatial resolution and global coverage. It also contains a historical dataset with the values of the SPEI at different time scales since January 1979. The drought monitoring system includes the assessment of drought severity for dominant crop‐growing areas. A comparison between SPEI computed from the ERA5 and CRU datasets shows generally good spatial and temporal agreement, albeit with some important differences originating mainly from the different spatial patterns of SPEI anomalies, as well as from employing long‐term climate trends for different regions worldwide. The results show that the ERA5 dataset offers robust results and supports its use for drought monitoring. The new system and dataset are publicly available at the link https://global‐drought‐crops.csic.es/.

ABSTRACTCarbon dioxide (CO2) is one of the main greenhouse gases and has become a major concern as its concentration has been growing in recent years. Satellite remote sensing is an efficient way to monitor CO2 in the atmosphere, and several satellites are already used for CO2 monitoring. It is imperative to investigate the spatial coverage and spatio-temporal trends of satellite products, as well as identify the satellites with higher levels of accuracy. Additionally, examining the disparities between the older and new generations of satellites would be meaningful. Therefore, this paper provides a comprehensive evaluation and inter-comparison for the commonly used satellite column-averaged dry-air mole fraction of CO2 (XCO2) products. Specifically, the temporal trends and monthly coverage of the Greenhouse Gases Observing SATellite (GOSAT), Greenhouse Gases Observing SATellite-2 (GOSAT-2), Orbiting Carbon Observatory-2 (OCO-2), Orbiting Carbon Observatory-3 (OCO-3), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) are investigated. The accuracy of these satellite products is evaluated and analyzed based on Total Carbon Column Observing Network (TCCON) data. The results indicate that the XCO2 of all the satellite products show a year-by-year increase, with seasonal periodicity. In terms of overall accuracy, the OCO series satellites exhibit a slightly higher level of accuracy compared to the GOSAT series. The products of the new generation of satellites are less stable than those of the older generation, probably due to the impacts of the inversion algorithm and platforms.

Abstract Floods are among the most devastating natural hazards worldwide. While rainfall is the primary trigger of floods, human activities and climate change can exacerbate the impacts of floods and lead to more significant economic and social consequences. In this research, fluvial flood fatalities in the 1951–2020 period have been studied, analyzing the information reported in the Emergency Database (EM‐DAT). The EM‐DAT data were classified into five categories in terms of the number of events and fatalities connected with riverine floods, considering only events that caused more than 10 fatalities. The results show that the severity of flood‐related fatalities is not equally distributed worldwide, but presents specific geographical patterns. The flood fatality coefficient, which represents the ratio between the total number of fatalities and the number of flood events, calculated for different countries, identified that the Southern, Eastern, and South‐Eastern regions of Asia have the deadliest floods in the world. The number of flood events has been increasing since 1951 and peaked in 2007, following a relative decline since then. Though, the resulting fatalities do not follow a statistically significant trend. An analysis of the number of flood events in different decades shows that the 2001–2010 decade saw the highest number of events, which corresponds to the largest precipitation anomaly in the world. The lethality of riverine floods decreased over time, from 412 per flood in 1951–1960 to 67 in the 2011–2020 decade. This declining trend is probably a consequence of a more resilient environment and better risk reduction strategies. Based on the presented data and using regression analysis, relationships between flood fatalities and the number of flood events with population density and gross domestic product are developed and discussed.

Spatial information of the natural abundance of soil nitrogen stable isotope (δ15N) is beneficial for deeply understanding the terrestrial nitrogen (N) cycle. However, to date, the precise global map of soil δ15N still lacks. In this study, based on the measured soil δ15N data by Craine et al. (2015) (https://doi.org/10.1038/srep08280) and related environmental variables including soil, topography, vegetation, and climate, we constructed the optimal relationship model between soil δ15N and environmental variables, and mapped the global pattern of soil δ15N at 0.1° × 0.1° resolution (in natural terrestrial ecosystem). Results indicated that satisfied performance was achieved (R2 = 0.68 and RMSE = 1.26‰) by separately building the optimal relationship models for soil δ15N in each of five climate zones (Tropical, Arid, Temperate, Cold and Polar) using the random forest regression algorithm. In addition, critical controls of the soil δ15N in different climate zones were thus identified based on the variable importance calculated by each random forest regression model. In the Tropical zone, soil δ15N might be primarily regulated by microbial N loss, and soil pH and organic matter were identified as two most important factors. In the Arid zone, abiotically gaseous N loss regulated by solar radiation would be the critical controls of soil δ15N. In the Temperate zone, temperature-related variables were identified as the critical controlling factors, and in the Cold zone, soil water and heat conditions had the equally greater importance, and bulk density was the dominated factor in the Polar zone. Furthermore, the predicted global soil δ15N ranged from −0.44‰ to 12.59‰, with the mean value of 5.06‰, and the standard deviation of 1.74‰. Significantly higher soil δ15N (P

Drought monitoring systems are real‐time information systems focused on drought severity data. They are useful for determining the drought onset and development and defining the spatial extent of drought at any time. Effective drought monitoring requires databases with high spatial and temporal resolution and large spatial and temporal coverage. Recent reanalysis datasets meet these requirements and offer an excellent alternative to observational data. In addition, reanalysis data allow better quantification of some variables that affect drought severity and are more seldom observed. This study presents a global drought dataset and a monitoring system based on the Standardized Precipitation Evapotranspiration Index (SPEI) and ERA5 reanalysis data. Computation of the atmospheric evaporative demand for the SPEI follows the FAO‐56 Penman‐Monteith equation. The system is updated weekly, providing near real‐time information at a 0.5° spatial resolution and global coverage. It also contains a historical dataset with the values of the SPEI at different time scales since January 1979. The drought monitoring system includes the assessment of drought severity for dominant crop‐growing areas. A comparison between SPEI computed from the ERA5 and CRU datasets shows generally good spatial and temporal agreement, albeit with some important differences originating mainly from the different spatial patterns of SPEI anomalies, as well as from employing long‐term climate trends for different regions worldwide. The results show that the ERA5 dataset offers robust results and supports its use for drought monitoring. The new system and dataset are publicly available at the link https://global‐drought‐crops.csic.es/. [ABSTRACT FROM AUTHOR]

The first attempts to identify amorphous organic substances in archaeology date to the end of the 19th century and the beginning of the 20th century. The 1960s saw the development of infrared spectrometry, and then separative and mass spectrometry analyses were implemented in the 1980s. But it is only since the 1990s that extended and systematic research programmes were devoted to these substances. The number of publications has not stopped growing and is becoming exponential. To get an overview of the lipid studies in archaeology, we conceived the ARchaeological Organic residues Literature Database (AROLD) as a first structured and collaborative research tool. This paper describes the challenges of setting up such a database, details its architecture, presents the choices involved in its implementation, and discusses the possibilities of sharing and evolving this tool. [ABSTRACT FROM AUTHOR]

Antibiotic overuse and the subsequent environmental contamination of residual antibiotics poses a public health crisis via an acceleration in the spread of antibiotic resistance genes (ARGs) through horizontal gene transfer. Although the occurrence, distribution, and driving factors of ARGs in soils have been widely investigated, little is known about the antibiotic resistance of soilborne pathogens at a global scale. To explore this gap, contigs from 1643 globally sourced metagnomes are assembled, yielding 407 ARG‐carrying pathogens (APs) with at least one ARG; APs are detected in 1443 samples (sample detection rate of 87.8%). The richness of APs is greater in agricultural soils (with a median of 20) than in non‐agricultural ecosystems. Agricultural soils possess a high prevalence of clinical APs affiliated with Escherichia, Enterobacter, Streptococcus, and Enterococcus. The APs detected in agricultural soils tend to coexist with multidrug resistance genes and bacA. A global map of soil AP richness is generated, where anthropogenic and climatic factors explained AP hot spots in East Asia, South Asia, and the eastern United States. The results herein advance this understanding of the global distribution of soil APs and determine regions prioritized to control soilborne APs worldwide. [ABSTRACT FROM AUTHOR]

Here, I create a dataset of fronts in mid- and high latitudes by applying an objective front detection method to the JRA-55 reanalysis and try climate classification based on dynamic climatology from temperate to polar regions. Additionally, I describe the interannual variations and long-term trends in the frontal zone. The unique feature of this study lies in the methods used for frontal data creation. This includes adding the geopotential height condition at 500-hPa to the conventional thermal-based objective method with equivalent potential temperature, including incorporating latitude-dependent parameters. The former increased the similarity between fronts created by the objective method and manually counted fronts on surface weather maps, while the latter enabled an examination of climate classification based on dynamic climatology by increasing the frontal frequency at high latitudes. The areas where climatic zones can be clearly defined are limited to the east of the great mountains in the midlatitudes and the region where the Siberia-Canada Arctic frontal zone exists due to the obscuration or unclear seasonal movement of the frontal zones in other areas. The interannual variability in frontal zones is generally consistent with the characteristics of the regional climate variability associated with the El Niño Southern Oscillation, Pacific Decadal Oscillation, and Arctic Oscillation, as reported by previous studies. This study also reveals significant trends in some frontal zones since 1979, such as the northward shift in the eastern part of the North Pacific polar frontal zone during boreal autumn and winter and the decreasing frontal frequency on the northern coast of Norway in the European Arctic frontal zone from boreal winter to summer, including around the Beaufort Sea in the Siberia-Canada Arctic frontal zone in boreal summer. [ABSTRACT FROM AUTHOR]

Wind erosion represents one of the major land degradation forms that significantly threatens sustainable development across the globe. A series of conservation practices have been widely developed and tested in order to effectively reduce wind erosion rates in non‐irrigated dry and semi‐dry lands. However, a comprehensive investigation regarding the efficacy of conservation practices and potential impacting factors on erosion control at the global scale has not been undertaken. In this study, a synthesis was conducted, based on data from global experimental plots, to comprehensively examine the efficacy of varying conservation practices, and their effectiveness at reducing erosion, in different geographical regions, under varying climatic conditions, and with different soil properties. The results showed that conservation practices were able to reduce wind erosion rates by over 52% globally. Straw use and soil stabilizer were the most effective measures in wind erosion control, which could reduce erosion rates by over 70%. Wind erosion control efficacy of conservation practices differed among climatic zones, continents, and countries. Notably, the lowest efficacy was observed in dry climatic zones. Erosion control efficacy highly depended on climatic conditions, such as mean annual precipitation (MAP), mean annual temperature (MAT), wind speed, and soil properties, such as water content and clay content. We further observed a positive correlation between wind erosion reduction rates and straw mulching rates, suggesting a straw mulching rate of at least 40% was necessary in order to reach a potential erosion reduction rate of 70%. To sum up, this study illustrates that the overall efficacy of conservation practices in wind erosion control is highly effective at the global scale, but the specific effects vary among different regions, and depend on conservation practice type, climatic condition, and soil property. The lower erosion reduction rate of dry regions at high erosion risk suggests the high erosion control pressure, and more effective measures should be considered and developed. The results of this study are, importantly, complementary to existing wind erosion control guidelines. [ABSTRACT FROM AUTHOR]

Abstract Antibiotic overuse and the subsequent environmental contamination of residual antibiotics poses a public health crisis via an acceleration in the spread of antibiotic resistance genes (ARGs) through horizontal gene transfer. Although the occurrence, distribution, and driving factors of ARGs in soils have been widely investigated, little is known about the antibiotic resistance of soilborne pathogens at a global scale. To explore this gap, contigs from 1643 globally sourced metagnomes are assembled, yielding 407 ARG‐carrying pathogens (APs) with at least one ARG; APs are detected in 1443 samples (sample detection rate of 87.8%). The richness of APs is greater in agricultural soils (with a median of 20) than in non‐agricultural ecosystems. Agricultural soils possess a high prevalence of clinical APs affiliated with Escherichia, Enterobacter, Streptococcus, and Enterococcus. The APs detected in agricultural soils tend to coexist with multidrug resistance genes and bacA. A global map of soil AP richness is generated, where anthropogenic and climatic factors explained AP hot spots in East Asia, South Asia, and the eastern United States. The results herein advance this understanding of the global distribution of soil APs and determine regions prioritized to control soilborne APs worldwide.

Abstract How much terrestrial precipitation is used by vegetation and how much runs off, represents central issues in hydrologic science, ecology, climate change, and even geopolitics. We present a theory for the water balance to predict the fractional change in streamflow due to given fractional changes in temperature and precipitation. The theory involves a single parameter whose value is derived under the conditions of neither energy‐ nor water‐limitations and, therefore, is not an adjustable parameter. By comparison with extensive data for precipitation elasticity ϵp at global scale, we find that the theory captures the key trends of the variations of the median value of ϵp with the aridity index AI. In contrast to a shortcoming of the classical Budyko phenomenology, namely, convergence to ϵp = 4 for large AI, our theory yields a value of 2 for the median value of ϵp for all AI > 1, in accord with the data for major river basins, as well as with the median value of summaries of global and continental data sets. Incorporating in the theory the effects of annual changes in water storage leads to the ability to predict the range of observed values of the elasticity as a function of the aridity index, or its inverse, the humidity index, as well as the run‐off ratio. When changes in storage are neglected, the theory yields more accurate predictions for major river drainages than for small watersheds, particularly if the large basin spans various climate regimes and, as such, an integration over climates tends to reduce relative changes in the storage.

Accurate high-resolution atmospheric temperature profiles are essential for precisely characterizing the evolution of the atmosphere and developing numerical forecasts. Atmospheric datasets, such as ERA5 (the fifth-generation ECMWF Reanalysis) and MERRA-2 (the Modern-Era Retrospective Analysis for Research and Applications, Version 2), provide global and continuous temperature profiles, with fine vertical distribution and horizontal resolution. RAOB (Radiosonde Observation) sounding data have high confidence and representativeness and are usually used for data accuracy verification. Due to the difficulty of updating existing products, and the scarcity of research on mesospheric temperature profiles, this work maximizes the high observation accuracy of RAOB data, combines the benefits of ERA5's horizontal resolution and MERRA-2's vertical distribution, and employs the optimal interpolation method to combine the data, in order to produce a fused result with high spatial resolution. After converting all of the data to the same spatial distribution, the optimal interpolation method was used to combine the two datasets from separate places and different pressure layers in order to produce the fused results, which had a vertical distribution of 45 layers and a spatial resolution of 0.25°. The fused data's RMSE and MAE were 6.0 K and 5.0 K lower than those of the MERRA-2 temperature profile data, respectively, and 0.3 K and 0.4 K lower than those of the ERA5 temperature profile data, respectively. The validation, using data from 2019, showed that the fused data exhibits better correlation and data accuracy than the other two datasets, which demonstrated that the fused algorithm can potentially be used to generate reliable datasets for future meteorological research. [ABSTRACT FROM AUTHOR]

Founded in the countryside an hour from Bordeaux, France, by Thích Nhät Hcnh, known for his engaged and modern Buddhism, Plum Village is famous throughout the world for its mindfulness meditation retreats. Although many studies have been carried out on Thích Nhất Hạnh's history and spirituality, none has addressed the question of the territorial implantation of Plum Village on a local scale, nor the processes of the worldwide diffusion of its network. After presenting the context of the penetration of Thích Nhất Hạnh's teaching in the West, I explore the establishment of an exogenous religious community in the French countryside in terms of otherness and analyse the communication at play in the process of integration into the local scale in its municipalities in New Aquitaine, and then look at the strategy and means of communication put in place to to federate and expand its network on a global scale. [ABSTRACT FROM AUTHOR]

Residential buildings generate significant greenhouse gas (GHG) emissions and consume energy throughout their life cycle. In recent years, research on GHG emissions and energy consumption of buildings has developed rapidly in response to the growing climate change and energy crisis. Life cycle assessment (LCA) is an important method for evaluating the environmental impacts of the building sector. However, LCA studies of buildings show widely varying outcomes across the world. Besides, environmental impact assessment from a whole life cycle perspective has been undeveloped and slow. Our work presents a systematic review and meta-analysis of LCA studies on GHG emissions and energy consumption in the preuse, use, and demolition stages of residential buildings. We aim to examine the differences among the results of diverse case studies and demonstrate the spectrum of variations under contextual disparities. Results show that residential building emits about 2928 kg GHG emission and consumes about 7430 kWh of energy per m2 of gross building area on average throughout the life cycle. Residential buildings have an average GHG emission of 84.81% in the use phase, followed by the preuse phase and demolition phase; the mean energy consumption in the use stage occupied the largest share of 84.52%, followed by preuse stage and demolition stage. GHG emissions and energy use vary significantly in different regions due to different building types, natural conditions, and lifestyles. Our study stresses the compelling requirement to slash GHG emissions and optimize energy consumption from residential buildings by use of low carbon building materials, energy structure adjustment, consumer behavior transformation, etc. [ABSTRACT FROM AUTHOR]

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Simple Summary: The species distribution model has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of bioclimate variables may affect the performance of the species distribution model. Here, we tested a new bioclimate variable dataset (i.e., CMCC BioClimInd) and used it in the species distribution model. We evaluated the predictive performance and explanatory power of WorldClim and CMCC-BioClimInd using AUC and omission rate, and also used the ODMAP protocol to record CMCC-BioClimInd to ensure reproducibility. The results indicate that CMCC BioClimInd can effectively simulate the distribution of invasive plant species. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, we inferred that the modified simplified continentality index and modified Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. Our research provides a new perspective for risk assessment and management of global invasive plant species. Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species' distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species. [ABSTRACT FROM AUTHOR]

Abstract Background Little is known about the global distribution and environmental drivers of key microbial functional traits such as antibiotic resistance genes (ARGs). Soils are one of Earth’s largest reservoirs of ARGs, which are integral for soil microbial competition, and have potential implications for plant and human health. Yet, their diversity and global patterns remain poorly described. Here, we analyzed 285 ARGs in soils from 1012 sites across all continents and created the first global atlas with the distributions of topsoil ARGs. Results We show that ARGs peaked in high latitude cold and boreal forests. Climatic seasonality and mobile genetic elements, associated with the transmission of antibiotic resistance, were also key drivers of their global distribution. Dominant ARGs were mainly related to multidrug resistance genes and efflux pump machineries. We further pinpointed the global hotspots of the diversity and proportions of soil ARGs. Conclusions Together, our work provides the foundation for a better understanding of the ecology and global distribution of the environmental soil antibiotic resistome. Video Abstract

Background and Aims The hart's tongue fern (HTF) complex is a monophyletic group composed of five geographically segregated members with divergent abundance patterns across its broad geographic range. We postulated hierarchical systems of environmental controls in which climatic and land-use change drive abundance patterns at the global scale, while various ecological conditions function as finer scale determinants that further increase geographic disparities at regional to local scales. Methods After quantifying the abundance patterns of the HTF complex, we estimated their correlations with global climate and land-use dynamics. Regional determinants were assessed using boosted regression tree models with 18 potential ecological variables. Moreover, we investigated long-term population trends in the USA to understand the interplay of climate change and anthropogenic activities on a temporal scale. Key Results Latitudinal climate shifts drove latitudinal abundance gradients, and regionally different levels of land-use change resulted in global geographic disparities in population abundance. At a regional scale, population isolation, which accounts for rescue effects, played an important role, particularly in Europe and East Asia where several hot spots occurred. Furthermore, the variables most strongly influencing abundance patterns greatly differed by region: precipitation seasonality in Europe; spatial heterogeneity of temperature and precipitation in East Asia; and magnitudes of past climate change, temperature seasonality and edaphic conditions in North America. In the USA, protected populations showed increasing trends compared with unprotected populations at the same latitude, highlighting the critical role of habitat protection in conservation measures. Conclusions Geographic disparities in the abundance patterns of the HTF complex were determined by hierarchical systems of environmental controls, wherein climatic and land-use dynamics act globally but are modulated by various regional and local determinants operating at increasingly finer scales. We highlighted that fern conservation must be tailored to particular geographic contexts and environmental conditions by incorporating a better understanding of the dynamics acting at different spatiotemporal scales. [ABSTRACT FROM AUTHOR]

As a kind of large-scale connectivity infrastructure, submarine cables play a vital role in international telecommunication, socio-economic development and national defense security. However, the current understanding about the spatial pattern of global submarine cable network is relatively limited. In this article, we analyze the spatial distribution and connectivity pattern of global submarine cables, and identify their strategic pivots and strategic channels. The main conclusions are as follows: (1) The spatial distribution of global submarine cables is significantly unbalanced, which is characterized by the facts that the distribution of submarine cable lines is similar to that of sea lanes, and the agglomerations of landing stations are distributed unevenly along the coastline. (2) The connectivity pattern of global submarine cable network has a significant scale effect. At the micro, meso and macro scales, the connectivity structure presents chain model, cluster model and hub-and-spoke model, respectively. (3) The distribution of strategic pivots and strategic channels shows a pyramidal hierarchical feature. Singapore ranks highest among all the strategic pivots, while the Gulf of Aden and the Strait of Malacca rank highest among the strategic channels. Based on the identification of strategic pivots and channels, six strategic regions have been divided, which face various network security risks and need special attention and vigilance. [ABSTRACT FROM AUTHOR]

Belowground organisms play critical roles in maintaining multiple ecosystem processes, including plant productivity, decomposition, and nutrient cycling. Despite their importance, however, we have a limited understanding of how and why belowground biodiversity (bacteria, fungi, protists, and invertebrates) may change as soils develop over centuries to millennia (pedogenesis). Moreover, it is unclear whether belowground biodiversity changes during pedogenesis are similar to the patterns observed for aboveground plant diversity. Here we evaluated the roles of resource availability, nutrient stoichiometry, and soil abiotic factors in driving belowground biodiversity across 16 soil chronosequences (from centuries to millennia) spanning a wide range of globally distributed ecosystem types. Changes in belowground biodiversity during pedogenesis followed two main patterns. In lower-productivity ecosystems (i.e., drier and colder), increases in belowground biodiversity tracked increases in plant cover. In more productive ecosystems (i.e., wetter and warmer), increased acidification during pedogenesis was associated with declines in belowground biodiversity. Changes in the diversity of bacteria, fungi, protists, and invertebrates with pedogenesis were strongly and positively correlated worldwide, highlighting that belowground biodiversity shares similar ecological drivers as soils and ecosystems develop. In general, temporal changes in aboveground plant diversity and belowground biodiversity were not correlated, challenging the common perception that belowground biodiversity should follow similar patterns to those of plant diversity during ecosystem development. Taken together, our findings provide evidence that ecological patterns in belowground biodiversity are predictable across major globally distributed ecosystem types and suggest that shifts in plant cover and soil acidification during ecosystem development are associated with changes in belowground biodiversity over centuries to millennia.

Belowground organisms play critical roles in maintaining multiple ecosystem processes, including plant productivity, decomposition, and nutrient cycling. Despite their importance, however, we have a limited understanding of how and why belowground biodiversity (bacteria, fungi, protists, and invertebrates) may change as soils develop over centuries to millennia (pedogenesis). Moreover, it is unclear whether belowground biodiversity changes during pedogenesis are similar to the patterns observed for aboveground plant diversity. Here we evaluated the roles of resource availability, nutrient stoichiometry, and soil abiotic factors in driving belowground biodiversity across 16 soil chronosequences (from centuries to millennia) spanning a wide range of globally distributed ecosystem types. Changes in belowground biodiversity during pedogenesis followed two main patterns. In lower-productivity ecosystems (i.e., drier and colder), increases in belowground biodiversity tracked increases in plant cover. In more productive ecosystems (i.e., wetter and warmer), increased acidification during pedogenesis was associated with declines in belowground biodiversity. Changes in the diversity of bacteria, fungi, protists, and invertebrates with pedogenesis were strongly and positively correlated worldwide, highlighting that belowground biodiversity shares similar ecological drivers as soils and ecosystems develop. In general, temporal changes in aboveground plant diversity and belowground biodiversity were not correlated, challenging the common perception that belowground biodiversity should follow similar patterns to those of plant diversity during ecosystem development. Taken together, our findings provide evidence that ecological patterns in belowground biodiversity are predictable across major globally distributed ecosystem types and suggest that shifts in plant cover and soil acidification during ecosystem development are associated with changes in belowground biodiversity over centuries to millennia.

In recent decades, the effect of human activities on freshwater cycle transgressed the catchment scale. In order to solve the global and regional water problems effectively, a new framework is needed to transform from the traditional hydrology which focuses on catchment processes to the global-scale hydrology which focuses on the earth system processes. Based on the recent progress of global hydrology, this paper summarizes the patterns of anthropogenic effect on global freshwater cycle, reviews the framework of Planetary Boundaries for Water (PBW) and the research progress, and analyzes the possible approaches of freshwater management from the catchment scale to the global scale. Directed by the Earth system science, PBW pushes forward water cycle research to the global scale from the local and catchment scale, and evaluates the safe operating space of global freshwater use, with the aim to maintain the functions of the Earth system. The framework provides an important merit to evaluate the global sustainability of water resources. Water management should not only balance off the relations of water resources, ecological environment and socio-economic development locally, but also the relations of water cycle, Earth system functions and global sustainability. PBW is an important supplement to the watershed or aquifer management, and puts forward a new framework for the global and regional water management. In order to meet the demand of water management under the new system of natural resources management in China, the study of PBW should focuses on the following aspects: to establish global hydrological models and simulation and prediction platform on the basis of better understanding of global water cycle; to advance the study of relations between PBW and other planetary boundaries such as land use, biodiversity, and atmospheric CO2; to improve the double evaluation approaches (resource and environment carrying capacity, territorial development suitability) for territorial spatial planning with the framework of PBW.

Desert expansion can cause tremendous damage to human wellbeing. However, the process of shifting from the non‐desert state to the desert state, a representation of a system regime shift, remains unclear on a global scale. Clarifying the underpinning patterns, predictors and signals of this process is of great value in advancing our understanding of both ecosystem resilience and sustainable development. Here, we combine the climate classification map and long‐term observational land cover data to assess the global desert distribution and its changes from 2000 to 2019. The identified desert areas covered approximately 7.53% of the global land in the past two decades. Only approximately 16.03% of these deserts showed expanding trends, especially in countries such as Tunisia, Tajikistan, and Peru. After assessing 26 climatic, ecological, and socioeconomic factors that could potentially influence desert expansion rates, vegetation cover diversity was identified as the strongest predictor in both hot and cold deserts, followed by cattle density in hot deserts and desert size in cold deserts. In addition, we also found that pronouncedly high fluctuations in vegetation productivity could serve as a possible signal for desert conversion. Our results provide not only a long‐term overview of current global desert changing patterns but also possible guidance for constraining desert expansion. [ABSTRACT FROM AUTHOR]

Considering the legacy of plant functional composition can help assess ecosystem functions and ecosystem services across different spatial scales under land cover changes. Woody plants likely respond to natural and anthropogenic perturbations due to historical events (e.g., agricultural development), thus contemporary plant functional composition may be explained by historical woodland change, a type of land cover change. We propose that historical woodland changes may have legacy effects on contemporary plant functional composition. Here, we used partial least squares regression and linear mixed model analyses to test this assumption by coupling data on community weighted means (CWM) and community weighted variance (CWV) of vegetation plots and calculating the time of woodland existence across different periods from AD 0 to 2017. We found that the legacy effects of historical land cover changes on CWM and CWV during the existence time of woodland, particularly from AD 0 to 900, were drivers of contemporary plant functional composition at large spatial scales. Furthermore, historical woodland changes can affect contemporary plant functional composition, depending on the biome type. Particularly, the CWM of plant height, seed mass, and seed length showed the strongest correlations with woodland changes from AD 1910 to 2010 in tropics with year-round rain, and the CWM of leaf traits correlated with woodland changes from AD 0 to 1700 in tropics with summer rain. Our study provides the effective evidence on the legacy of historical woodland changes and the effects on contemporary plant functional composition, which is crucial with respect to effective management of plant diversity and assessing ecosystem functions and services from local to global scales over time. [ABSTRACT FROM AUTHOR]

Hunters' valuations of recreational hunting have been estimated by a large number of location-specific studies since the early 1970s, but to date there has been no systematic assessment of this research at the global scale. The present study performed a meta-analysis of 80 studies with 588 value-per-day estimates. The assessment showed a high concentration of studies pertaining to the valuation of deer and the valuation of hunting in the USA. The average value was USD 69 /hunting day in 2020 prices, but the variation was large, ranging from USD 4 to 325 /hunting day. The statistical performance of alternative mixed-effect models explaining the estimated value differences was tested with different hunting attributes (targeted game animal); context variables (income/capita, population density, year of study, region of application); and study characteristics (valuation method, publication outlet). The results showed that the type of game animal, income per capita, and valuation method had significant effects on estimated values. The predictive power was high for all models, supporting the application of the meta-analysis results to guide the management of hunting where primary valuation studies have not been undertaken, in particular outside the USA. [ABSTRACT FROM AUTHOR]