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If emissions continue at the present-day rate, about 22 years are left until global mean warming reaches the 1.5 °C Paris Agreement target, suggests a new metric based on the observed level and rate of anthropogenic warming.

African savanna ecosystems are home to the world's richest large herbivore (LH) assemblages. However, its landscapes are changing faster than any other region on Earth due to human activities and natural events. Therefore, understanding the factors influencing the distribution of LH in these human‐dominated environments is crucial for decision‐making on wildlife and habitat management. We combined ecological aerial surveys, camera trap, and dung count data to investigate how ecological (habitat types, perennial rivers, and rainfall) and anthropogenic (human settlements and cattle grazing areas) factors influence the distribution of LH species in Limpopo National Park (LNP). We used generalized linear models fitting binary logistic regression models to distinguish 25‐km2 cells occupied (where the species were detected) by elephants (Loxodonta africana), buffalos (Syncerus caffer), zebras (Equus quagga), kudus (Tragelaphus strepsiceros), nyalas (Tragelaphus angasii), and impalas (Aepyceros melampus) from unoccupied regions (where the species were not detected) in the LNP landscape. We found that habitat type and rainfall were the most influential factors shaping the pattern of LH distribution in the LNP, except for elephants, whose prevalence was not associated with rainfall. The prevalence of zebras was positively associated with the proximity to perennial rivers, while kudus avoided these areas. While some species (zebras, kudus, and impalas) tended to avoid settlements, others (elephants, buffalos, and nyala) seemed attracted to settlements. Cattle grazing areas were the worst predictors of the distribution of all study species. Our results disclosed the role of ecological factors for the distribution of LH and showed that anthropogenic disturbances seemed to either (partially) prevent the occurrence of LH or show the potential for human–wildlife conflict risk in the study area. Therefore, the results highlight the need to investigate/quantify the potential human–wildlife conflict risk at finer spatial scales to improve future management.

Mosses and leaves of vascular plants have been used as bioindicators of environmental contamination by heavy metals originating from various sources. This study aims to compare the metal accumulation capabilities of mosses and vascular species in urban areas and quantify the suitability of different taxa for monitoring airborne heavy metals. One pleurocarpous feather moss species, , and two evergreen tree species, , and substrate soil were sampled in the urban area of different land use types in Wuhan City in China. The concentrations of Ag, As, Cd, Co, Cr, Cu, Mn, Mo, Ni, V, Pb, and Zn in these samples were analyzed by inductively coupled plasma mass spectrometry. The differences of heavy metals concentration in the three species showed that the moss species was considerably more capable of accumulating heavy metals than tree leaves (3 times to 51 times). The accumulated concentration of heavy metals in the moss species depended on the metal species and land use type. The enrichment factors of metals for plants and the correlations of metals in plants with corresponding metals in soil reflected that the accumulated metals in plants stemmed mostly from atmospheric deposition, rather than the substrate soil. Anthropogenic factors, such as traffic emissions from automobile transportation and manufacturing industries, were primarily responsible for the variations in metal pollutants in the atmosphere and subsequently influenced the metal accumulation in the mosses. This study elucidated that the moss species is relatively more suitable than tree leaves of and in monitoring heavy metal pollution in urban areas, and currently Wuhan is at a lower contamination level of atmospheric heavy metals than some other cities in China.

The investigation of drivers leading to species extinction is relevant task in global biodiversity conservation. Despite of the large area of Russia, there is a remarkable lack of biodiversity data from this country. In this study, we aimed to evaluate the threats proved for threatened plant taxa according to 59 regional Red Data Books of Russia. For each Red Data Book species, we identified threats, i.e. drivers leading to species extinction, according to sections “Limiting factors” or “Limiting factors and threats” of regional Red Data Books. To identify relation of extinction drivers to natural conditions, we indicated, which biomes are located within each region using scheme of ecoregions. We found that in 59 Russian regions, the total taxonomic list of Red Data Book plants contained of 3390 taxa belonging to 152 families and 869 genera. The biogeographical position of regions was reflected in the similarity of lists of the Red Data Book species on the basis of Jaccard index. In regards of extinction drivers, we found that among 12 of them habitat degradation (16.5%), grazing (14.6%), urbanization (13.5%), and hydrological disturbance (10.8%) were the most impactful factors affecting the Red Data Book plant species in Russian regions. Then, we found that in certain level, the geographical position of a region is correlated with the drivers leading to plant species extinction in Russia. We recommended to continue more detail research to reveal factors affected or led to plant species extinction in Russia as a large and highly diverse country of Eurasia.

Wildfire spread is a stochastic phenomenon driven by a multitude of geophysical and anthropogenic factors. In this study, we propose a spatiotemporal data-driven risk assessment framework to understand the effect of various geophysical/anthropogenic factors on wildfire size, leveraging a systematic machine learning approach. We apply this framework in the state of California–the most vulnerable US state to wildfires. Using county-level annual wildfire data from 2001–2015, and various geophysical (e.g., land cover, wind, surface temperature) and anthropogenic features (e.g., population density, housing type), we trained, tested, and validated a suite of ensemble tree-based learning algorithms to identify and evaluate the key factors associated with wildfire size. The Extreme Gradient Boosting (XGBoost) algorithm outperformed all the other models in terms of generalization performance, categorization of important features, and risk performance. We found that standard deviations of meteorological variables with long-tailed distributions play a key role in predicting wildfire size. Specifically, the top ten factors associated with high risk of larger wildfires include larger standard deviations of surface temperature and vapor pressure deficit, higher wind gust, more grassy and barren land covers, lower night-time boundary layer height and higher population density. Our proposed risk assessment framework will help federal/state decision-makers to adequately plan for wildfire risk mitigation and resource allocation strategies.

The Arctic zone of the Russian Federation is of strategic importance for the country. Considering the fragility of Arctic ecosystems, special attention needs to be paid to the sustainable development of transport and related infrastructure within the framework of the “blue economy” concept, which is relevant for Arctic waters. At the same time, it is necessary to identify the main factors and tasks of creating transport and port infrastructure, building a modern fleet, and organizing fisheries and tourism in an environmentally sound manner. The purpose of the study is to consider the problems of anthropogenic influence for seaport facilities and to create a conceptual model of an environmental risk management system. The existing problems of Arctic ports and infrastructure are analyzed and existing business processes are considered, taking into account the peculiarities of their functioning in Arctic conditions. To systematize environmental assessments and establish dependencies between the main indicators describing the impact of port activities on elements of the natural environment, ontological domain engineering is proposed. It systematizes the basic terminology used within different subject areas of ecology and risks and allows one to visualize the relationships between elements of the natural environment, objects, port systems, their parameters and impact factors to assess the impact of the seaport on the natural environment. The results of ontological engineering (design and development of ontologies) in the field of risk management are presented. Future research will be aimed at developing the applied aspect of applying the results of ontological engineering in terms of specific engineering studies related to the assessment of anthropogenic load on the Arctic territory using simulation modeling.

This study was aimed to determine the patterns as well as the effects of biological, anthropogenic, and climatic factors on plant invasions in China. About 270 volumes of national and regional floras were employed to compile a naturalized flora of China. Habit, life form, origin, distribution, and uses of naturalized plants were also analyzed to determine patterns on invasion. Correlations between biological, anthropogenic and climatic parameters were estimated at province and regional scales. Naturalized species represent 1% of the flora of China. Asteraceae, Fabaceae, and Poaceae are the dominant families, but Euphorbiaceae and Cactaceae have the largest ratios of naturalized species to their global numbers. Oenothera, Euphorbia, and Crotalaria were the dominant genera. Around 50% of exotic species were introduced intentionally for medicinal purposes. Most of the naturalized species originated in tropical America, followed by Asia and Europe. Number of naturalized species was significantly correlated to the number of native species/log area. The intensity of plant invasion showed a pattern along climate zones from mesic to xeric, declining with decreasing temperature and precipitation across the nation. Anthropogenic factor, such as distance of transportation, was significantly correlated to plant invasions at a regional scale. Although anthropogenic factors were largely responsible for creating opportunities for exotic species to spread and establish, the local biodiversity and climate factors were the major factors shaping the pattern of plant invasions in China. The warm regions, which are the hot spots of local biodiversity, and relatively developed areas of China, furthermore, require immediate attentions.

Human influence on the steppe ecosystems of Ukraine caused irreversible loss of biodiversity in the natural zone. Currently, this problem is aggravated by military operations which cover almost half of the steppe zone and are unprecedented in the entire history of their existence. This actualizes the study of vegetation dynamic processes under the influence of the novel anthropogenic factors, and serves as the scientific basis for restoring and preserving steppe vegetation and maintaining its functional stability. The paper highlights anthropogenic changes in vegetation of the Kuyalnik Estuary valley based on long-term comparative phytocenotic surveys and uses of the method of succession series for reconstruction. These changes are representative of the river valleys of the estuaries in the Northern Black Sea region. This work examines vegetation changes induced by runoff overregulation of estuary rivers within the basin, quarrying of sand and limestone, ploughing, grazing, burning, terracing of slopes and their afforestation, excessive mowing of grass stands, and uncontrolled recreation. Subject to the existing anthropogenic impact combined with global climate changes, further vegetation degradation was predicted to occur in the direction of xerophitization and halophitization, reduction in shrubby vegetation areas, degradation of steppe vegetation, and intensification of desertification processes.

A combination of anthropogenic warming and natural variability have led to another record-breaking year of warmth in 2023. Global mean temperatures in 2023 nearly exceeded the 1.5 °C threshold, making it both a scientific and societal imperative to understand the underlying reasons for this warmth.A combination of anthropogenic warming and natural variability led to another record-breaking year of warmth in 2023. Global mean temperatures in 2023 nearly exceeded the 1.5 °C threshold, making it both a scientific and societal imperative to understand the underlying reasons for this warmth.