Human disturbance increases spatiotemporal associations among mountain forest terrestrial mammal species.

Spatial and temporal associations between sympatric species underpin biotic interactions, structure ecological assemblages, and sustain ecosystem functioning and stability. However, the resilience of interspecific spatiotemporal associations to human activity remains poorly understood, particularly in mountain forests where anthropogenic impacts are often pervasive. Here, we applied context-dependent Joint Species Distribution Models to a systematic camera-trap survey dataset from a global biodiversity hotspot in eastern Himalayas to understand how prominent human activities in mountain forests influence species associations within terrestrial mammal communities. We obtained 10,388 independent detections of 17 focal species (12 carnivores and five ungulates) from 322 stations over 43,163 camera days of effort. We identified a higher incidence of positive associations in habitats with higher levels of human modification (87%) and human presence (83%) compared to those located in habitats with lower human modification (64%) and human presence (65%) levels. We also detected a significant reduction of pairwise encounter time at increasing levels of human disturbance, corresponding to more frequent encounters between pairs of species. Our findings indicate that human activities can push mammals together into more frequent encounters and associations, which likely influences the coexistence and persistence of wildlife, with potential far-ranging ecological consequences.

Innovative and practical tools for monitoring and assessing biodiversity status and impacts of multiple human pressures in marine systems.

Human activities at sea can produce pressures and cumulative effects on ecosystem components that need to be monitored and assessed in a cost-effective manner. Five Horizon European projects have joined forces to collaboratively increase our knowledge and skills to monitor and assess the ocean in an innovative way, assisting managers and policy-makers in taking decisions to maintain sustainable activities at sea. Here, we present and discuss the status of some methods revised during a summer school, aiming at better management of coasts and seas. We include novel methods to monitor the coastal and ocean waters (e.g. environmental DNA, drones, imaging and artificial intelligence, climate modelling and spatial planning) and innovative tools to assess the status (e.g. cumulative impacts assessment, multiple pressures, Nested Environmental status Assessment Tool (NEAT), ecosystem services assessment or a new unifying approach). As a concluding remark, some of the most important challenges ahead are assessing the pros and cons of novel methods, comparing them with benchmark technologies and integrating these into long-standing time series for data continuity. This requires transition periods and careful planning, which can be covered through an intense collaboration of current and future European projects on marine biodiversity and ecosystem health.

Colonization ability and uniformity of resources and environmental factors determine biological homogenization of soil protists in human land-use systems.

Humans have substantially transformed the global land surface, resulting in the decline in variation in biotic communities across scales, a phenomenon known as "biological homogenization." However, different biota are affected by biological homogenization to varying degrees, but this variation and the underlying mechanisms remain little studied, particularly in soil systems. To address this topic, we used metabarcoding to investigate the biogeography of soil protists and their prey/hosts (prokaryotes, fungi, and meso- and macrofauna) in three human land-use ecosystem types (farmlands, residential areas, and parks) and natural forest ecosystems across subtropical and temperate regions in China. Our results showed that the degree of community homogenization largely differed between taxa and functional groups of soil protists, and was strongly and positively linked to their colonization ability of human land-use systems. Removal analysis showed that the introduction of widespread, generalist taxa (OTUs, operational taxonomic units) rather than the loss of narrow-ranged, specialist OTUs was the major cause of biological homogenization. This increase in generalist OTUs seemingly alleviated the negative impact of land use on specialist taxa, but carried the risk of losing functional diversity. Finally, homogenization of prey/host biota and environmental conditions were also important drivers of biological homogenization in human land-use systems, with their importance being more pronounced in phagotrophic than parasitic and phototrophic protists. Overall, our study showed that the variation in biological homogenization strongly depends on the colonization ability of taxa in human land-use systems, but is also affected by the homogenization of resources and environmental conditions. Importantly, biological homogenization is not the major cause of the decline in the diversity of soil protists, and conservation and study efforts should target at taxa highly sensitive to local extinction, such as parasites.

Bio-Skin-Inspired Flexible Pressure Sensor Based on Carbonized Cotton Fabric for Human Activity Monitoring.

With the development of technology, people's demand for pressure sensors with high sensitivity and a wide working range is increasing. An effective way to achieve this goal is simulating human skin. Herein, we propose a facile, low-cost, and reproducible method for preparing a skin-like multi-layer flexible pressure sensor (MFPS) device with high sensitivity (5.51 kPa-1 from 0 to 30 kPa) and wide working pressure range (0-200 kPa) by assembling carbonized fabrics and micro-wrinkle-structured Ag@rGO electrodes layer by layer. In addition, the highly imitated skin structure also provides the device with an extremely short response time (60/90 ms) and stable durability (over 3000 cycles). Importantly, we integrated multiple sensor devices into gloves to monitor finger movements and behaviors. In summary, the skin-like MFPS device has significant potential for real-time monitoring of human activities in the field of flexible wearable electronics and human-machine interaction.

A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of 'scalograms', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.

Human degradation of tropical moist forests is greater than previously estimated.

Tropical forest degradation from selective logging, fire and edge effects is a major driver of carbon and biodiversity loss1-3, with annual rates comparable to those of deforestation4. However, its actual extent and long-term impacts remain uncertain at global tropical scale5. Here we quantify the magnitude and persistence of multiple types of degradation on forest structure by combining satellite remote sensing data on pantropical moist forest cover changes4 with estimates of canopy height and biomass from spaceborne6 light detection and ranging (LiDAR). We estimate that forest height decreases owing to selective logging and fire by 15% and 50%, respectively, with low rates of recovery even after 20 years. Agriculture and road expansion trigger a 20% to 30% reduction in canopy height and biomass at the forest edge, with persistent effects being measurable up to 1.5 km inside the forest. Edge effects encroach on 18% (approximately 206 Mha) of the remaining tropical moist forests, an area more than 200% larger than previously estimated7. Finally, degraded forests with more than 50% canopy loss are significantly more vulnerable to subsequent deforestation. Collectively, our findings call for greater efforts to prevent degradation and protect already degraded forests to meet the conservation pledges made at recent United Nations Climate Change and Biodiversity conferences.

Elevated levels of antibiotic resistance genes as a factor of human-caused global environmental change.

Antibiotic resistance genes (ARGs) have moved into focus as a critically important response variable in global change biology, given the increasing environmental and human health threat posed by these genes. However, we propose that elevated levels of ARGs should also be considered a factor of global change, not just a response. We provide evidence that elevated levels of ARGs are a global change factor, since this phenomenon is linked to human activity, occurs globally, and affects biota. We explain why ARGs could be considered the global change factor, rather than the organisms containing them; and we highlight the difference between ARGs and the presence of antibiotics, which are not necessarily linked since elevated levels of ARGs are caused by multiple factors. Importantly, shifting the perspective to elevated levels of ARGs as a factor of global change opens new avenues of research, where ARGs can be the experimental treatment. This includes asking questions about how elevated ARG levels interact with other global change factors, or how ARGs influence ecosystem processes, biodiversity or trophic relationships. Global change biology stands to profit from this new framing in terms of capturing more completely the real extent of human impacts on this planet.

A human activity recognition method based on Vision Transformer.

Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human-computer intelligent interaction. It has emerged as a significant research area in computer vision. GCN (Graph Convolutional networks) have recently been widely used in these fields and have made great performance. However, there are still some challenges including over-smoothing problem caused by stack graph convolutions and deficient semantics correlation to capture the large movements between time sequences. Vision Transformer (ViT) is utilized in many 2D and 3D image fields and has surprised results. In our work, we propose a novel human activity recognition method based on ViT (HAR-ViT). We integrate enhanced AGCL (eAGCL) in 2s-AGCN to ViT to make it process spatio-temporal data (3D skeleton) and make full use of spatial features. The position encoder module orders the non-sequenced information while the transformer encoder efficiently compresses sequence data features to enhance calculation speed. Human activity recognition is accomplished through multi-layer perceptron (MLP) classifier. Experimental results demonstrate that the proposed method achieves SOTA performance on three extensively used datasets, NTU RGB+D 60, NTU RGB+D 120 and Kinetics-Skeleton 400.

[Characteristics and drivers of vegetation temporal dynamics in Hunan Province of China during 2002-2020]. / 湖南省2002­2020å¹´æ¤è¢«åŠ¨æ€æ¼”å˜ç‰¹å¾åŠå½±å“å› å­.

Understanding the influences of climate change and human activities on vegetation change is the foundation for effective ecosystem management. Based on the 250 m MODIS-NDVI data from 2002 to 2020, we employed Theil-Sen Median trend analysis and the Mann-Kendall test to quantify vegetation change in Hunan Province. By combining with meteorological, nighttime light index, land cover and other data, residual analysis and correlation analysis, we examined the impacts of human activities and climate change on vegetation dynamics at both the pixel level and the county level. The results showed that the normalized difference vegetation index (NDVI) in Hunan Province exhibited a spatial pattern of "overall improvement with localized degradation" during 2002-2020. Approximately 64.9% of the study area experienced significant vegetation improvement, mainly occurring in the western and central-southern parts of Hunan Province. 1.4% of the study area experienced significant vegetation degradation, mostly in the newly developed urban areas and the farmland in the Dongting Lake Plain. Human activities and climate change jointly promoted vegetation improvement in 67.9% of the study area. Human activities and climate contributed to 96% and 4% of the NDVI change, respectively. At the county level, human activities contributed to over 80% of the NDVI change in each district or county. The impacts of human activities on vegetation change exhibited significant spatial heterogeneity. Urban expansion led to vegetation degradation in the newly developed areas, while vegetation growth appeared in the old developed urban areas. The ecological restoration projects promoted vegetation restoration in the western part of Hunan Province. This study could help us better understand the spatiotemporal variations of vegetation and their responses to climate change and human activities, which would offer scientific basis for effective ecological restoration policy.

Position-Aware Indoor Human Activity Recognition Using Multisensors Embedded in Smartphones.

Composite indoor human activity recognition is very important in elderly health monitoring and is more difficult than identifying individual human movements. This article proposes a sensor-based human indoor activity recognition method that integrates indoor positioning. Convolutional neural networks are used to extract spatial information contained in geomagnetic sensors and ambient light sensors, while transform encoders are used to extract temporal motion features collected by gyroscopes and accelerometers. We established an indoor activity recognition model with a multimodal feature fusion structure. In order to explore the possibility of using only smartphones to complete the above tasks, we collected and established a multisensor indoor activity dataset. Extensive experiments verified the effectiveness of the proposed method. Compared with algorithms that do not consider the location information, our method has a 13.65% improvement in recognition accuracy.

Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple-stressor experiments.

Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple-stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co-occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open-source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater-multiple-stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple-stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors.

White-tailed deer population dynamics in a multipredator landscape shaped by humans.

Large terrestrial mammals increasingly rely on human-modified landscapes as anthropogenic footprints expand. Land management activities such as timber harvest, agriculture, and roads can influence prey population dynamics by altering forage resources and predation risk via changes in habitat, but these effects are not well understood in regions with diverse and changing predator guilds. In northeastern Washington state, USA, white-tailed deer (Odocoileus virginianus) are vulnerable to multiple carnivores, including recently returned gray wolves (Canis lupus), within a highly human-modified landscape. To understand the factors governing predator-prey dynamics in a human context, we radio-collared 280 white-tailed deer, 33 bobcats (Lynx rufus), 50 cougars (Puma concolor), 28 coyotes (C. latrans), and 14 wolves between 2016 and 2021. We first estimated deer vital rates and used a stage-structured matrix model to estimate their population growth rate. During the study, we observed a stable to declining deer population (lambda = 0.97, 95% confidence interval: 0.88, 1.05), with 74% of Monte Carlo simulations indicating population decrease and 26% of simulations indicating population increase. We then fit Cox proportional hazard models to evaluate how predator exposure, use of human-modified landscapes, and winter severity influenced deer survival and used these relationships to evaluate impacts on overall population growth. We found that the population growth rate was dually influenced by a negative direct effect of apex predators and a positive effect of timber harvest and agricultural areas. Cougars had a stronger effect on deer population dynamics than wolves, and mesopredators had little influence on the deer population growth rate. Areas of recent timber harvest had 55% more forage biomass than older forests, but horizontal visibility did not differ, suggesting that timber harvest did not influence predation risk. Although proximity to roads did not affect the overall population growth rate, vehicle collisions caused a substantial proportion of deer mortalities, and reducing these collisions could be a win-win for deer and humans. The influence of apex predators and forage indicates a dual limitation by top-down and bottom-up factors in this highly human-modified system, suggesting that a reduction in apex predators would intensify density-dependent regulation of the deer population owing to limited forage availability.

[Coupling Mechanisms of Eco-environmental Quality and Human Activities in China and Their Influencing Factors].

In the context of sustainable development, it is important to thoroughly investigate the coupling mechanism between China's eco-environmental quality and human activities, as well as identify the influencing factors, in order to provide scientific references for achieving sustainable development goals in China. This study applied trend analysis, coupling coordination degree, LMDI, and optimal parameter geographic detector models to explore and evaluate the coupling mechanism between China's eco-environmental quality and human activities. The findings of the study were as follows:① During the research period, there was a growth trend in China's coupling coordination degree, human activities, and eco-environmental quality. Human activities and coupling coordination degree exhibited a spatial differentiation pattern with the Hu Line as the boundary, showing an "east high, west low" distribution. The eco-environmental quality demonstrated a "south high, north low" differentiation pattern. ② The overall trend of China's coupling coordination type transformation was shifting from lower-level to higher-level coordination types. ③ Based on the geographic detector and LMDI models, the dominant factors influencing the coupling coordination degree in most provinces east of the Hu Line were social and economic factors, as well as the comprehensive coordination index. In contrast, the dominant factors in most provinces west of the Hu Line were natural environmental factors and coupling degree. ④ The evaluation of the impact of changes in human activities on eco-environmental quality revealed that the regions east of the Hu Line were mainly characterized by favorable development and effective protection, whereas the regions west of the line were mainly characterized by destructive development and ineffective protection. It is suggested that the regions on both sides of the Hu Line should prioritize development based on local prerequisites influencing the coupling coordination degree and the relative relationship between human activities and eco-environmental quality. It is crucial to actively adjust development strategies and pursue a sustainable development path towards the high-level coordination between eco-environmental quality and human activities.

The dominant influencing factors of desertification and ecological risk changes in Qinghai Area of Qilian Mountains National Park: Climate change or human activity?

Transitional features of desert environments partially determine the risks associated with ecosystems. Influenced by climate change and human activities, the variability and uncertainty of desertification levels and ecological risks in the Qinghai Area of Qilian Mountain National Park (QMNPQA) has become increasingly prominent. As a critical ecological barrier in northwest China, monitoring desertification dynamics and ecological risks is crucial for maintaining ecosystem stability. This study identifies the optimal monitoring model from four constructed desertification monitoring models and analyzes spatiotemporal changes in desertification. The spatial and temporal changes in ecological risks and their primary driving factors were analyzed using methods such as raster overlay calculation, geographic detector, cloud model, and trend analysis. The main conclusions are as follows: The desertification feature spatial model based on GNDVI-Albedo demonstrates better applicability in the study area, with an inversion accuracy of 81.24%. The levels of desertification and ecological risks in QMNPQA exhibit significant spatial heterogeneity, with a gradual decrease observed from northwest to southeast. From 2000 to 2020, there is an overall decreasing trend in desertification levels and ecological risks, with the decreasing trend area accounting for 89.82% and 85.71% respectively, mainly concentrated in the southeastern and northwestern parts of the study area. The proportion of areas with increasing trends is 4.49% and 7.05% respectively, scattered in patches in the central and southern edge areas. Surface temperature (ST), Digital Elevation Map (DEM), and Green normalized difference vegetation index (GNDVI) are the most influential factors determining the spatial distribution of ecological risks in QMNPQA. The effects of management and climatic factors on ecological risks demonstrate a significant antagonistic effect, highlighting the positive contributions of human activities in mitigating the driving effects of climate change on ecological risks. The research results can provide reference for desertification prevention and ecological quality improvement in QMNPQA.

[Influence of climate change and human activities on grassland phenology in Anhui Province]. / 安徽省气候变化和人类活动对草地物候的影响.

To explore the influence of climate change and human activities on grassland phenology in Anhui Pro-vince, and quantify the contribution rate of climate change and human activities to phenology, we extracted the phenology of grassland, including the start of growing season (SOS) and the end of growing season (EOS), based on the normalized difference vegetation index (NDVI) dataset of Anhui Province from 2003 to 2020. The temporal and spatial characteristics and future evolution trends of phenological changes were analyzed using slope trend ana-lysis, Mann-Kendall non-parametric test, and Hurst index. We further conducted correlation analysis and residual analysis based on the datasets of mean annual temperature and mean annual precipitation to explore the responses of phenology to climate change and human activities, and quantify their contribution rate. The results showed that SOS and EOS showed an advancing trend with a rate of 0.8 and 0.7 days per year from 2003 to 2020. SOS in the sou-thern part of the study area was significantly earlier than in the central and northern regions, while EOS gradually advanced from south to north. Both SOS and EOS in the future showed an advancing trend. SOS was negatively correlated with annual average temperature, while positively correlated with annual precipitation. EOS was negatively correlated with both annual average temperature and annual precipitation. The proportion of the area where SOS was advanced driven by both climate change and human activities was 56.9%, and the value was 48.3% for EOS. Human activities were the main driving factor for phenology, and climate change was the secondary driving factor. The relative contributions of human activities and climate change to SOS were 66.4% and 33.6%, and to EOS were 61.2% and 38.8%, respectively. Human activities had stronger impact on SOS and EOS than climate change, resulting in earlier phenology.

Biomonitoring of human activities recovery following lockdown in a highly touristic Mediterranean Island using Mytilus galloprovincialis.

Coastal waters face significant anthropogenic stress, particularly from tourism, exacerbating pollution, especially in areas like touristic islands. Ischia, the largest island in the Gulf of Naples and part of the Regno di Nettuno Marine Protected Area, suffers from pollution due to tourism and maritime traffic. During the initial SARS-CoV-2 lockdown from March to June 2020, Ischia was isolated, providing a unique opportunity to study pollutant release and its impact on coastal ecosystems. Adult Mytilus galloprovincialis mussels were transplanted to three sites on the island for active biomonitoring. Accumulation of chemicals in tissues and biomarkers related to metabolism, detoxification, and oxidative stress were measured. Results indicated that pollutants from daily activities entered the sea, affecting filter feeders. Translocated organisms showed modulated metabolic functions and biochemical changes, highlighting coastal vulnerability and calling for conservation efforts.

Variation in microplastic characteristics among amphibian larvae: a comparative study across different species and the influence of human activity.

Microplastic pollution is a significant global environmental issue, and impacts span from individual organisms to the entire ecosystems. This study investigated the properties of microplastics in amphibian larvae, shedding light on their environmental interactions and potential ecological consequences. We examined microplastics extracted from amphibian larvae of 10 taxa, sampled from sites experiencing different levels of human impact. Our findings revealed a predominance of blue microplastics and fibres, each comprising 53% of the total microplastics in amphibian larvae. Microplastic fibres were also notably longer than other morphological types of microplastics. Furthermore, we observed variations in the surface area of microplastics among different amphibian families. An interesting observation from our research is the apparent positive relationship between the size of amphibian larvae and the length of granular and flake-shaped microplastics. Conversely, we observed a negative relationship between the length of these microplastics and human environmental impact. These insights significantly contribute to the understanding of microplastic pollution in freshwater environments, highlighting its complexities beyond marine ecosystems. Our research emphasises the intricate relationships between microplastics and freshwater organisms, underscoring the need for comprehensive strategies to mitigate microplastic pollution.

Gaps and opportunities in modelling human influence on species distributions in the Anthropocene.

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no 'rule of thumb' for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.