Human Footprint Research Articles

Tracking data highlight the importance of human-induced mortality for large migratory birds at a flyway scale

Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause (n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation.

Ensemble SDMs reveal the effect of environmental suitability and nature reserves on conserving Chinese pangolins in Guangdong, China

Identifying species distribution patterns and threatened factors is essential for wildlife conservation. For the critically endangered Chinese pangolin, most studies have emphasized negative human interference and ignored the effect of environmental suitability and positive human intervention on altering species distribution patterns. We took Guangdong Province as the research area and obtained census data of the Chinese pangolin. Combined with five types of environmental variables, including climatic, geographical, human, soil and vegetation, we used five algorithms in BIOMOD2 to construct an ensemble species distribution model (SDM) of the Chinese pangolin. The importance and response curves of environmental variables were also obtained. We used the cutoff as a threshold to classify habitat or non-habitat, calculated the habitat coverage of established nature reserves by overlay analysis, and visualized the conservation gaps. The results indicate that the potential distribution range of the Chinese pangolin is wide, fragmented and determined by multiple factors. The average annual temperature (highly correlated with elevation) and the highest temperature in the warmest month were the main influencing factors (contribution rate > 10 %), and the distance from nature reserves and human footprint index were secondary factors (contribution rate > 5 %). The established nature reserve network covers approximately 23 % of the potential habitat of the Chinese pangolin. The proposed Nanling National Park seems incapable of protecting the widely distributed Chinese pangolins. In eastern and northern Guangdong, including Meizhou, Heyuan, Shaoguan and Huizhou and Qingyuan cities, there is still a large area of habitat suitable for the Chinese pangolin that is not well protected. We suggest further expanding the current nature reserve network to absorb suitable habitats of Chinese pangolins from adjacent areas and optimizing the spatial arrangement to improve conservation efficiency.

Interplay of management and environmental drivers shifts size structure of reef fish communities.

Countries are expanding marine protected area (MPA) networks to mitigate fisheries declines and support marine biodiversity. However, MPA impact evaluations typically assess total fish biomass. Here, we examine how fish biomass disaggregated by adult and juvenile life stages responds to environmental drivers, including sea surface temperature (SST) anomalies and human footprint, and multiple management types at 139 reef sites in the Mesoamerican Reef (MAR) region. We found that total fish biomass generally appears stable across the region from 2006 to 2018, with limited rebuilding of fish stocks in MPAs. However, the metric of total fish biomass masked changes in fish community structure, with lower adult than juvenile fish biomass at northern sites, and adult:juvenile ratios closer to 1:1 at southern sites. These shifts were associated with different responses of juvenile and adult fish to environmental drivers and management. Juvenile fish biomass increased at sites with high larval connectivity and coral cover, whereas adult fish biomass decreased at sites with greater human footprint and SST anomalies. Adult fish biomass decreased primarily in Honduran general use zones, which suggests insufficient protection for adult fish in the southern MAR. There was a north-south gradient in management and environmental drivers, with lower coverage of fully protected areas and higher SST anomalies and coastal development in the south that together may undermine the maintenance of adult fish biomass in the southern MAR. Accounting for the interplay between environmental drivers and management in the design of MPAs is critical for increasing fish biomass across life history stages.

Modeling gully initiation by two codeless nonlinear methods: A case study in a small watershed on the Tibetan Plateau

Land and soil resources are scarce in the Tibetan Plateau, and the region is facing ecological pressure from climate warming and increasing human activities. As a major ecological problem, gully erosion is destroying land and soil resources on the Tibetan Plateau, but related research is limited, and susceptibility areas and influencing factors are unclear. Machine learning methods are often applied to study gully initiation susceptibility, but they require a programming foundation. Therefore, the Redui watershed on the southern Tibetan Plateau with severe gully erosion was selected to evaluate the susceptibility and influencing factors of gully initiation through 12 influencing factors including topography, human activity, and underlying surface conditions, and all 2310 gully headcut sites. Two non-code nonlinear modeling methods, the categorical Regression (CATREG) and geographical detector (Geodetector) methods, were first used in the spatial modeling of gully initiation susceptibility. The results showed that the gully initiation susceptibility of the hillslope around the alluvial fan was highest. The very high susceptibility areas of the CATREG model and Geodetector model account for 18.2% and 16% of the total, respectively. The main influencing factors of gully initiation were elevation, relief, and soil type recognized by CATREG, and elevation, human footprint, and soil type recognized by Geodetector. Elevation is the primary factor controlling downstream susceptibility in both models. The primary factors in the upper and middle reaches are soil type and relief identified by CATREG. Human footprint, soil type, and distance to road are primary factors in the upper and middle reaches identified by Geodetector. The explanatory power of elevation, elevation-relief interaction, Geodetector model and CATREG model were 39%, 54%, 46.4% and 73.8%, respectively, at extremely significant levels (P < 0.001), which means that the influencing factors were well considered and that the methods have great application potential in the future.

Habitat Suitability and Determinants for Anatidae in Multi-Watershed Composite Wetlands in Anhui, China.

Habitat suitability analysis is essential in habitat and species conservation. Anatidae are known for their migratory behaviour, high population density, and wide distribution range. Understanding their habitat utilzation and influencing factors is crucial in targeted conservation and management. In this study, we collected Anatidae diversity data, including the number of species, through field surveys from October 2021 to March 2022 and thirty habitat variables through an online database in Anhui Province, China. By using MaxEnt, we simulated the habitat suitability of twenty-one Anatidae species, revealing potential distribution sites in Anhui Province. Generalized linear mixed models (GLMM) were employed to identify factors affecting the distribution of geese and ducks. The results showed that high-suitability habitats were predominantly located in the large lakes of the Yangtze River floodplain. The GLMM analysis showed significant correlations between Anatidae richness and altitude, distribution of farmland, and human footprint. In addition, ducks were more sensitive to the human interference factor than geese. In summary, the lakes in the Yangtze River floodplain emerged as the most important Anatidae habitats in Anhui Province due to their abundant wetland resources, flat terrain, and high distribution of farmlands. These findings provide a scientific basis for the development of relevant conservation strategies and measures, aiding in wildlife epidemic monitoring, prevention, and control.

Distribution and Change Characteristics of Ecosystem Services in Highly Urbanized Areas along Gradients of Human Activity Intensity: A Case Study of Shenzhen City, China

Comprehensively and objectively revealing the spatial relationship between ecosystem services (ESs) and human activity intensity (HAI) is vital for achieving sustainable development goals. However, existing studies still have an incomplete understanding of how ESs change with HAI gradients. Therefore, taking Shenzhen City, China, as an example region experiencing rapid urbanization, the distribution of ESs and HAI in 2010 and 2020 were quantified using the InVEST model and the human footprint index method; at the same time, the gradient perspective was introduced and the spatial and temporal correlation characteristics of ESs along 10 HAI gradient bands, from weak to strong, were captured by applying multiscale geographically weighted regression (MGWR) and the bivariate spatial autocorrelation model. The findings showed that (1) the HAI demonstrated an increasing trend (20.63 (2010) and 23.36 (2020)), and the area with high values of HAI (the 10th gradient band) was distributed in the western part of the study area; meanwhile, the area with low HAI values (the 1st gradient band) was more distributed in the eastern part of the study area. (2) On the whole, the average levels of water conservation, soil conservation, carbon storage, and habitat quality decreased from 2010 to 2020; the spatial distribution characteristics of these parameters were similar. (3) In general, ESs were negatively correlated with HAI, and the negative correlation ratio was more than 65%. At the same time, the spatial and temporal correlations between ES patterns and HAI under different gradient bands were significant. These findings can effectively alleviate the pressure on the ecosystem caused by human activities, which is of great significance for the sustainable development of highly urbanized regions.

Ecological niche modeling analysis (Cx. pipiens), potential risk and projection of Dirofilaria spp. infection in Greece

Vector-borne diseases continue to increase worldwide. Dirofilariosis is one of the most common vector-borne zoonotic diseases, mainly caused by Dirofilaria spp. (D. immitis and D. repens) and spread by culicid mosquitoes of different species. Greece is one of the countries in southern Europe where it is traditionally endemic, and its distribution is not homogeneous. The aim of this study was to develop an environmental model for Greece that reflects the suitability of the ecological niche for Dirofilaria spp. infection risk and its projection until 2080. For this purpose, we used the potential distribution of suitable habitats for Culex pipiens calculated using an ecological niche model (ENM) and the potential number of generations of Dirofilaria spp. The ecological niche model of Cx. pipiens in Greece showed good predictive power (AUC=0.897) with the parasite at a resolution of 1 km2. The variables that contributed most to the model were mean annual temperature, rivers and human footprint. The highest risk of infection was found in coastal areas and in riverside areas of the main river basins, as well as in irrigated areas of the mainland and peninsular regions and in the whole territory of island areas, and the lowest risk was found in areas of higher altitude. A positive relationship was found between the risk of dirofilariosis and the location of infected dogs, with 86.65% located in very high and high risk areas. In 2080, the percentage of territory gained by Cx. pipiens will increase by 261.52%. This model provides a high predictive value, predicted presence, and risk of Dirofilaria spp. infection and can serve as a tool for the management and control of this disease.

Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

Environmental drivers of global variation in home range size of terrestrial and marine mammals.

As animal home range size (HRS) provides valuable information for species conservation, it is important to understand the driving factors of HRS variation. It is widely known that differences in species traits (e.g. body mass) are major contributors to variation in mammal HRS. However, most studies examining how environmental variation explains mammal HRS variation have been limited to a few species, or only included a single (mean) HRS estimate for the majority of species, neglecting intraspecific HRS variation. Additionally, most studies examining environmental drivers of HRS variation included only terrestrial species, neglecting marine species. Using a novel dataset of 2800 HRS estimates from 586 terrestrial and 27 marine mammal species, we quantified the relationships between HRS and environmental variables, accounting for species traits. Our results indicate that terrestrial mammal HRS was on average 5.3 times larger in areas with low human disturbance (human footprint index [HFI] = 0), compared to areas with maximum human disturbance (HFI = 50). Similarly, HRS was on average 5.4 times larger in areas with low annual mean productivity (NDVI = 0), compared to areas with high productivity (NDVI = 1). In addition, HRS increased by a factor of 1.9 on average from low to high seasonality in productivity (standard deviation (SD) of monthly NDVI from 0 to 0.36). Of these environmental variables, human disturbance and annual mean productivity explained a larger proportion of HRS variance than seasonality in productivity. Marine mammal HRS decreased, on average, by a factor of 3.7 per 10°C decline in annual mean sea surface temperature (SST), and increased by a factor of 1.5 per 1°C increase in SST seasonality (SD of monthly values). Annual mean SST explained more variance in HRS than SST seasonality. Due to the small sample size, caution should be taken when interpreting the marine mammal results. Our results indicate that environmental variation is relevant for HRS and that future environmental changes might alter the HRS of individuals, with potential consequences for ecosystem functioning and the effectiveness of conservation actions.

Energy input, habitat heterogeneity and host specificity drive avian haemosporidian diversity at continental scales.

The correct identification of variables affecting parasite diversity and assemblage composition at different spatial scales is crucial for understanding how pathogen distribution responds to anthropogenic disturbance and climate change. Here, we used a database of avian haemosporidian parasites to test how the taxonomic andphylogenetic diversity and phylogenetic structure of the genera Plasmodium, Haemoproteus and Leucocytozoon from three zoogeographic regions are related to surrogate variables of Earth's energy input, habitat heterogeneity (climatic diversity, landscape heterogeneity, host richness and human disturbance) and ecological interactions (resource use), which was measured by a novel assemblage-level metric related to parasite niche overlap (degree of generalism). We found that different components of energy input explained variation in richness for each genus. We found that human disturbance influences the phylogenetic structure of Haemoproteus while the degree of generalism explained richness and phylogenetic structure of Plasmodium and Leucocytozoon genera. Furthermore, landscape attributes related to human disturbance (human footprint) can filter Haemoproteus assemblages by their phylogenetic relatedness. Finally, assembly processes related to resource use within parasite assemblages modify species richness and phylogenetic structure of Plasmodium and Leucocytozoon assemblages. Overall, our study highlighted the genus-specific patterns with the different components of Earth's energy budget, human disturbances and degree of generalism.

Plant size traits are key contributors in the spatial variation of net primary productivity across terrestrial biomes in China

Understanding the spatial variability of ecosystem functions is an important step forward in predicting changes in ecosystems under global transformations. Plant functional traits are important drivers of ecosystem functions such as net primary productivity (NPP). Although trait-based approaches have advanced rapidly, the extent to which specific plant functional traits are linked to the spatial diversity of NPP at a regional scale remains uncertain.Here, we used structural equation models (SEMs) to disentangle the relative effects of abiotic variables (i.e., climate, soil, nitrogen deposition, and human footprint) and biotic variables (i.e., plant functional traits and community structure) on the spatial variation of NPP across China and its eight biomes. Additionally, we investigated the indirect influence of climate and soil on the spatial variation of NPP by directly affecting plant functional traits.Abiotic and biotic variables collectively explained 62.6 % of the spatial differences of NPP within China, and 28.0 %–69.4 % across the eight distinct biomes. The most important abiotic factors, temperature and precipitation, had positive effects for NPP spatial variation. Interestingly, plant functional traits associated with the size of plant organs (i.e., plant height, leaf area, seed mass, and wood density) were the primary biotic drivers, and their positive effects were independent of biome type. Incorporating plant functional traits improved predictions of NPP by 6.7 %–50.2 %, except for the alpine tundra on the Qinghai-Tibet Plateau.Our study identifies the principal factors regulating NPP spatial variation and highlights the importance of plant size traits in predictions of NPP variation at a large scale. These results provide new insights for involving plant size traits in carbon process models.

Extinction risk predictions for the world's flowering plants to support their conservation.

More than 70% of all vascular plants lack conservation status assessments. We aimed to address this shortfall in knowledge of species extinction risk by using the World Checklist of Vascular Plants to generate the first comprehensive set of predictions for a large clade: angiosperms (flowering plants, c. 330 000 species). We used Bayesian Additive Regression Trees (BART) to predict the extinction risk of all angiosperms using predictors relating to range size, human footprint, climate, and evolutionary history and applied a novel approach to estimate uncertainty of individual species-level predictions. From our model predictions, we estimate 45.1% of angiosperm species are potentially threatened with a lower bound of 44.5% and upper bound of 45.7%. Our species-level predictions, with associated uncertainty estimates, do not replace full global, or regional Red List assessments, but can be used to prioritise predicted threatened species for full Red List assessment and fast-track predicted non-threatened species for Least Concern assessments. Our predictions and uncertainty estimates can also guide fieldwork, inform systematic conservation planning and support global plant conservation efforts and targets.

Spatio-temporal interaction and constraint effects between ecosystem services and human activity intensity in Shaanxi Province,China

Identifying spatial heterogeneity in ecosystem services and understanding the spatial and temporal correlations between ecosystem service exploration and human activity intensity are vital for achieving regional harmony between human activities and the environment, ultimately fostering sustainable development. Previous studies primarily concentrated on the impact of human activities on ecosystem services while overlooking the intricate spatiotemporal dynamics and nonlinear relationships. In this study, which leveraged multi-source data, we found increasing trends in Human Footprint Index (HFI), most pronounced in the central region of Shaanxi Province. From 2000 to 2020, Shaanxi Province consistently exhibited an ecosystem service distribution pattern characterized by a 'high in the central-south and low in the northwest' trend. Habitat Quality (HQ) consistently maintained high values, signifying favorable habitat conditions, while Food Production (FP) remained relatively stable. Carbon Sequestration (CS) and Soil Conservation (SC) displayed diverse temporal patterns, whereas Water Yield (WY) showed noticeable fluctuations.This analysis revealed a negative correlation between HFI indicators and Total Ecosystem Services (TES). Constraint lines delineated hump-shaped relationships between TES and human activity intensity across various geomorphic areas. Notably, the differences between regions were significant, and threshold dynamics were identified. HFI negatively correlated with TES, moderate HFI growth improved services, yet exceeding ecological thresholds caused decline. Overall,this study informs necessary ecosystem management in Shaanxi, highlighting the need for balanced policies and predictive models that ensure sustainable development without crossing critical ecological thresholds. Future work should concentrate on developing predictive models that incorporate environmental, technological, and socio-economic variables, to better anticipate and mitigate the ecological impacts of human activities.

Food webs: Where is the uniqueness?

Conservation planning relies on mapping areas of unique biological diversity and high vulnerability. A new study has found that unique trophic networks are threatened by warming climate in the Arctic and high human footprint in southern Europe.

Evaluating dynamics in human‐bear interactions within social‐ecological system

Abstract Over the past few decades, social‐ecological transformations have contributed to the restoration of numerous carnivores, presenting new challenges for human‐wildlife coexistence. Evaluating and understanding human‐wildlife interaction dynamics, particularly for conflict‐prone species, is crucial for conservation and management strategies. Our study focuses on Asiatic Black Bear (Ursus thibetanus) in Western Yunnan, China, a region with favourable bear conditions and significant environmental changes due to conservation policies. This study employed a multifaceted approach to gather data on bear occurrences and human‐bear conflicts. Utilising species distribution models, we predicted the potential bear habitat in 2000 and 2020. We proposed a methodology integrating bear habitat with human footprint to delineate the human‐bear interface. Additionally, we evaluated the spatial–temporal variations in bear habitat and human‐bear interface between 2000 and 2020. Semi‐structured interviews were conducted to capture the perceptions of local communities regarding the bear population, human‐bear interactions and conservation policies. Our findings indicated that the potential habitats of bears were strongly influenced by forest cover. We noticed improvements in bear habitat from 2000 to 2020, notwithstanding localised losses in specific areas. Most respondents attributed the increasing bear population to forest restoration and reduced human impact during this period. Notably, a significant proportion of respondents supported the conservation policies, previously participating in their formulation and implementation. Our analysis further revealed increases in the human‐bear interface and human‐bear interactions from 2000 to 2020. Remarkably, these changes did not significantly diminish local communities' endorsement of conservation policies. This resilience can be attributed to the net positive impacts of conservation policies on the livelihoods of these communities. Our study highlights the complexity and dynamic nature of human‐wildlife interactions within the social‐ecological system and provides new insights to facilitate sustainable conservation and management measures. Read the free Plain Language Summary for this article on the Journal blog.

Human disturbance in riparian areas disrupts predator-prey interactions between grizzly bears and salmon.

Wildlife must increasingly balance trade-offs between the need to access important foods and the mortality risks associated with human-dominated landscapes. Human disturbance can profoundly influence wildlife behavior, but managers know little about the relationship between disturbance-behavior dynamics and associated consequences for foraging. We address this gap by empirically investigating the consequences of human activity on a keystone predator-prey interaction in a region with limited but varied industrial disturbance. Using stable isotope data from 226 hair samples of grizzly bears (Ursus arctos horribilis) collected from 1995 to 2014 across 22 salmon-bearing watersheds (88,000 km2) in British Columbia, Canada, we examined how human activity influenced their consumption of spawning salmon (Oncorhynchus spp.), a fitness-related food. Accounting for the abundance of salmon and other foods, salmon consumption strongly decreased (up to 59% for females) with increasing human disturbance (as measured by the human footprint index) in riparian zones of salmon-bearing rivers. Declines in salmon consumption occurred with disturbance even in watersheds with low footprints. In a region currently among the least influenced by industrial activity, intensification of disturbance in river valleys is predicted to increasingly decouple bears from salmon, possibly driving associated reductions in population productivity and provisioning of salmon nutrients to terrestrial ecosystems. Accordingly, we draw on our results to make landscape-scale and access-related management recommendations beyond current streamside protection buffers. This work illustrates the interaction between habitat modification and food security for wildlife, highlighting the potential for unacknowledged interactions and cumulative effects in increasingly modified landscapes.

Mapping roadless areas in regions with contrasting human footprint.

In an increasingly human- and road-dominated world, the preservation of functional ecosystems has become highly relevant. While the negative ecological impacts of roads on ecosystems are numerous and well documented, roadless areas have been proposed as proxy for functional ecosystems. However, their potential remains underexplored, partly due to the incomplete mapping of roads. We assessed the accuracy of roadless areas identification using freely available road-data in two regions with contrasting levels of anthropogenic influence: boreal Canada and temperate Central Europe (Poland, Slovakia, Czechia, and Hungary). Within randomly selected circular plots (per region and country), we visually examined the completeness of road mapping using OpenStreetMap 2020 and assessed whether human influences affect mapping quality using four variables. In boreal Canada, roads were completely mapped in 3% of the plots, compared to 40% in Central Europe. Lower Human Footprint Index and road density values were related to greater incompleteness in road mapping. Roadless areas, defined as areas at least 1 km away from any road, covered 85% of the surface in boreal Canada (mean size ± s.d. = 272 ± 12,197 km2), compared to only 0.4% in temperate Central Europe (mean size ± s.d. = 0.6 ± 3.1 km2). By visually interpreting and manually adding unmapped roads in 30 randomly selected roadless areas from each study country, we observed a similar reduction in roadless surface in both Canada and Central Europe (27% vs 28%) when all roads were included. This study highlights the urgent need for improved road mapping techniques to support research on roadless areas as conservation targets and surrogates of functional ecosystems.

Accelerated human-induced extinction crisis in the world's freshwater mammals

Human activities have significantly impacted natural habitats and wildlife worldwide, particularly emphasizing repercussions for freshwater habitats and associated species. These negative impacts on freshwater fish are well known, but for mammal species that regularly use and dependend on freshwater systems, there is an incomplete understanding. Here, we assessed the status of freshwater and semi-aquatic mammal species inhabiting and dependent on freshwater ecosystems (hereafter referred to as freshwater mammals) and evaluated the impact of human activities on species richness both globally and by biogeographical regions. We used structural equation modeling and simultaneous autoregressive models to assess the direct and indirect effects of seven anthropogenic-related variables on overall freshwater mammal richness. Specifically, we examined the effects of anthropogenic disturbance on the richness of threatened and non-threatened species, as well as species with decreasing and stable/increasing populations. Forty-three percent of all freshwater mammal species are classified as globally threatened by the IUCN Red List, with 50% experiencing population declines. Furthermore, 48% are affected by domestic or international trade, while 75% face threats from geographically expanding human activities. Aridity, pesticide pollution, human footprint, and croplands had the strongest negative effects on freshwater mammal richness. In contrast, the coverage of freshwater habitats (FWs), Indigenous Peoples' lands (IPLs), and protected areas (PAs) helped to sustain freshwater mammal species globally, with an even stronger positive effect at individual biogeographical regions. We conclude that FWs, IPLs, and PAs play a critical role in the conservation of freshwater mammal species, helping safeguard these species from extinction, however, freshwater ecosystems are seldom the focal point of conservation management strategies. The ongoing adverse anthropogenic impacts on these natural habitats present a potentially catastrophic and irreversible threat to global freshwater environments and the species, including humans, reliant upon them. We strongly advocate for the implementation of more robust national and international policy frameworks that endorse alternative and sustainable livelihoods. Such frameworks can play a crucial role in alleviating anthropogenic pressures, thereby aiding in the mitigation of the extinction risk faced by these vital ecosystems and the world's freshwater mammal species.

Integrating patch stability and network connectivity to optimize ecological security pattern

ContextDesigning and optimizing ecological security pattern (ESP) is an effective solution to formulate landscape planning. The commonly used network analysis for ESP optimization abstracts ecological sources and ecological corridors as homogeneous nodes and edges, ignoring the differentiated landscape pattern and patch stability.ObjectivesIt is aimed to construct ESP considering pattern and function, to explore the relationship of patch stability and network connectivity conservation objectives, and to optimize ESP with the integration of the two objectives.MethodsWe proposed a framework for constructing ESP based on ecosystem health and human footprint, and optimizing ESP with network robustness analysis. Land use conflict analysis and node/ edge removal method were further used to assess patch stability and network connectivity, respectively.ResultsESP of Ningxia was composed of 71 ecological sources covering an area of 10970.25 km2, and 150 ecological corridors covering an area of 3950.88 km2. The ecological sources and ecological corridors along the Yellow River had high patch stability but low network connectivity, while the largest ecological source had both high values of the two indicators. With the removal of nodes and edges, connectivity robustness, global efficiency, and equivalent connectivity of the ESP decreased from 1, 0.29, and 342.80 to 0, respectively. The variation trends of the three indicators under different removal scenarios were inconsistent. There was a distinct trade-off between the conservation objectives of patch stability and network connectivity.ConclusionsThis study highlighted how to balance different conservation objectives in landscape planning. Our framework can provide guidance for conservation planners to construct and optimize ESP without losing information due to the element abstraction in network analysis.

Spatio-Temporal Dynamics of Normalized Difference Vegetation Index and Its Response to Climate Change in Xinjiang, 2000–2022

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, and the coefficient of variation. Meanwhile, the persistence of the NDVI distribution was analyzed by combining the trend results and Hurst index. Finally, partial correlation analysis was used to deeply explore the response mechanisms of interannual and seasonal-scale NDVI and climatic factors in Xinjiang, and the characteristics of multi-year vegetation distribution were comprehensively analyzed with the help of human footprint data. The findings indicate the following: (1) The NDVI of interannual and seasonal vegetation in Xinjiang showed a significant increasing trend during the 23-year period, but the spatial distribution was heterogeneous, and the improvement of the vegetation condition in the southern part of the region was remarkable. (2) The NDVI is relatively stable across the region. Unlike in other regions, in general, it is difficult to maintain the existing trend in NDVI in the study area for a long period of time, and the reverse trend is more persistent. (3) On the interannual scale, both precipitation and temperature are positively correlated with the NDVI, and the influence of temperature (80.94%) is greater than that of precipitation (63.82%). Precipitation was dominantly positively correlated with the NDVI in spring, summer, and the growing season, while it was negatively correlated with it in autumn. Temperature and NDVI were positively correlated, with the greatest influence in the spring. (4) Human activities had the greatest impact on the areas with low vegetation cover and areas with medium–low vegetation cover, and there was a high degree of overlap between the areas where the interannual human footprints and NDVI showed an increasing trend. The percentage of human footprints that significantly correlated with interannual NDVI was 34.79%. In the future, the protection and management of ecologically fragile areas should be increased to increase desert-vegetation cover.