Search Results (214)

The Cao’e-Jiaojiang-Oujiang River Basin possesses abundant vernacular architectural heritage with significant historical–cultural value. However, challenges like dispersed distribution and inconsistent conservation hinder its systematic protection and utilization within territorial spatial planning, necessitating a deeper understanding of its spatiotemporal patterns. Utilizing 570 identified heritage sites, this study employed ArcGIS spatial analysis (Kernel Density Estimation, Nearest Neighbor Index), correlation analysis with DEM data, and suitability analysis (Minimum Cumulative Resistance model, Gravity Model) to systematically examine spatial distribution characteristics, their evolution, and relationships with the geographical environment and historical context. Results revealed a distinct “four cores and three belts” spatial pattern. Temporally, distribution evolved from “discrete” (Song-Yuan) to “aggregated” (Ming-Qing) and then “diffused” (Modern era). Spatially, heritage showed density in plains, preference for low slopes, and settlement along waterways. Suitability analysis indicated higher corridor potential in the northern section (Cao’e-Jiaojiang) than the south (Oujiang), leading to the identification of a “Northern Segment (Shaoxing-Ningbo-Shengzhou-Taizhou)” and “Southern Segment (Wenzhou-Lishui)” corridor structure. This research provides a scientific basis for systematic conservation and integrated heritage corridor construction of vernacular architectural heritage in the basin, supporting Zhejiang’s Poetry Road Cultural Belt initiatives and cultural heritage protection within territorial spatial planning. Full article

The blue-eared pheasant (Crossoptilon auritum), a Near Threatened (NT) species endemic to China, is primarily distributed across the northeastern region of the Qinghai–Tibetan Plateau. To bridge the fine-scale spatiotemporal gap in blue-eared pheasant behavioral ecology, this study combines satellite telemetry, movement modeling, and field-based habitat assessments (vegetation, topography, human disturbance). This multidisciplinary approach reveals detailed patterns of their behavior throughout the breeding season. Using satellite-tracking data from six individuals (five males tracked at 4 h intervals; one female tracked hourly) in Wanglang National Nature Reserve (WLNNR), Sichuan Province during breeding seasons 2018–2019, we quantified their home ranges via Kernel Density Estimation (KDE) and examined the female movement patterns using a Hidden Markov Model (HMM). The results indicated male core (50% KDE: 21.93 ± 16.54 ha) and total (95% KDE: 158.30 ± 109.30 ha) home ranges, with spatial overlap among individuals but no significant temporal variation in home range size. Habitat selection analysis indicated that the blue-eared pheasants favored shrub-dominated areas at higher elevations (steep southeast-facing slopes), regions distant from human disturbance, and with abundant animal trails. We found that their movement patterns differed between sexes: the males exhibited higher daytime activity yet slower movement speeds, while the female remained predominantly near nests, making brief excursions before returning promptly. These results enhance our understanding of the movement ecology of blue-eared pheasants by revealing fine-scale breeding-season behaviors and habitat preferences through satellite-tracking. Such detailed insights provide an essential foundation for developing targeted conservation strategies, particularly regarding effective habitat management and zoning of human activities within the species’ range. Full article

The grey-headed woodpecker (Picus canus) (GHW) is one of the least-studied European woodpeckers, listed in Annex I of the Birds Directive. We examined the key environmental characteristics that determine the possibility of GHW occurrence in vast forests in northeast Poland. Woodpeckers were inventoried in spring on 54 study plots (4 km²) covering 20% of the forest area. Active territories were detected and mapped using the playback experiment of territorial voices and drumming. The generalized linear model GLM, random forest RF, and Boosting were used for modeling. GLM was used to indicate the most critical factors affecting the abundance of GHW. The number of territories in a single study plot ranged from 0 to 3; the most frequent were areas without woodpeckers. The probability of the nesting of the GHW was increasing at plots with watercourses, a bigger share of mixed forest area, and a proportion of stands over 120 years old. The calculation for all 400 quadrats allowed us to estimate the population size at approximately 180–200 breeding pairs. The overall density of GHW in the study area was assessed at 0.13/km², while at the optimal quadrats, it increased to about 0.75/km². Preference for watercourses was linked to alders growing along water banks. Near the water, there are often small meadows where the GHW can prey on ants. In turn, old-growth forests above 120 years old increased the probability of the presence of the GHW. There are more dead and dying trees in older forests, which are the ones the GHW chooses to excavate cavities. To effectively protect the habitats of the GHW, it is necessary to maintain a larger area of stands over 120 years old, mainly on wet sites. Full article

Ticks carry a range of pathogens, the best known of which causes Lyme disease, prevalent in the northeastern United States. Emerging diseases do not yet consist of a wide range of Lyme diseases, raising the question of how long it takes for a newly introduced tick-borne disease to establish itself. The aim of this study was to address this question, with the agent of Lyme disease used as the test case. A prior process-based model of the Ixodes scapularis (Say 1821) life cycle and the transmission of Borrelia burgdorferi (Burgdorfer 1982) between this tick and its various hosts was used to predict the dynamics of disease introduction into a new area. The importance of temperature, infection probabilities, and tick host populations, relative to that of other factors, was established by a global sensitivity analysis using Latin hypercube sampling. The results of those samples were analyzed to determine the time to near-equilibrium. Eight locations in New Hampshire were chosen for high/low temperature, high/low mouse, and high/low deer values. Mammal abundance was estimated by relating the known mammal density from previous studies to a MaxEnt analysis output. The time required to reach Borrelia endemicity in the ticks of New Hampshire ranged from 8 to 20 years in regions where the tick population is viable, with a strong dependency on susceptible tick host populations. Full article

The dipteran family Chironomidae is the most widely distributed and, frequently, the most abundant group of insects in freshwater environments. Ecologically, the group plays an important role in trophic cycles and detritus processing in aquatic ecosystems and can be the most productive group of secondary producers in running waters. The annual secondary production of Chironomidae was estimated using emergence trap data from riffles and pools from two stations on the middle reaches of the Shinano River: Tokida Bridge Area (slope 1/180) and Iwano Bridge Area (1/1000). Emergence traps were set up for 24 h on the river’s water surface at three points each in the riffles and pools monthly from April 2015 to March 2019. Five subfamilies of Chironomidae were recorded during the investigation period: Chironominae, Orthocladiinae, Tanypodinae, Diamesinae, and Prodiamesinae. In the Shinano River (Tokida + Iwano area), Orthocladiinae and Diamesinae were the dominant subfamilies in terms of both density (63.2% and 19.0%, respectively) and biomass (62.2% and 25.2%, respectively). Each year’s annual secondary production measured as ash free dry weight (AFDW) was 11.33–55.04 g/m²/year in Tokida and 5.83–38.43 g/m²/year in Iwano. The average annual secondary production of detritus feeder Chironomidae (all except for Tanypodinae) was 24.46 ± 20.38 (ranging from 11.13 in 2015 to 54.67 in 2018) in the Tokida area and 19.61 ± 16.38 (ranging from 5.41 in 2016 to 37.53 in 2017) in the Iwano area. On the other hand, that of carnivorous Chironomidae (Tanypodinae) was 0.22 ± 0.10 (ranging from 0.13 in 2016 to 0.37 in 2018) in the Tokida area and 0.66 ± 0.24 (ranging from 0.42 in 2016 to 0.90 in 2017) in the Iwano area, and overall secondary production averaged 22.48 g/m²/year in the middle reaches of the Shinano River. These values were higher than in previous reports. Full article

Accurate information on species distributions and population sizes is essential for effective biodiversity conservation, yet such data are often lacking at national scales. This study addresses this gap by assessing the distribution and abundance of 111 bird species across Croatia, including breeding, wintering, and migratory flyway populations. We combined Species Distribution Models (SDMs) with expert-based population estimates to generate spatially explicit predictions. The modeling framework incorporated high-resolution Earth observation (EO) data and advanced spatial analysis techniques. Environmental variables, such as land cover, were derived from satellite datasets, while climate variables were interpolated from ground measurements and refined using EO-based co-variates. Model calibration and validation were based on species occurrence records and EO-derived predictors. This integrative approach enabled both national-scale population estimates and fine-scale habitat assessments. The results identified critical habitats, population hotspots, and areas likely to experience distribution shifts under changing environmental conditions. By integrating EO data with expert knowledge, this study enhances the robustness of population estimates, particularly where species monitoring data are incomplete. The findings support conservation prioritization, inform land use and resource management, and contribute to long-term biodiversity monitoring. The methodology is scalable and transferable, offering a practical framework for ecological assessments in diverse regions. We integrated expert-based population estimates with species distribution models (SDMs) by applying expert-derived density values to areas of suitable habitat predicted by SDMs. This approach enables spatially explicit population estimates by combining ecological modeling with expert knowledge, which is particularly useful in systems with limited data. Experts provided species-specific density estimates stratified by habitat type, seasonality, behavior, and detectability, aligned with habitat suitability classes derived from SDM outputs. Full article

As a pivotal industrial hub in southwestern China, Kunming City has accumulated abundant industrial heritage resources. Investigating the spatial distribution characteristics and influencing factors of industrial heritage across different districts in Kunming is critical for understanding its historical evolution and current status, and for providing scientific guidance for conservation and sustainable development. From a sustainability perspective, this study selected 80 industrial heritage sites in Kunming as research subjects. Utilizing ArcGIS spatial analysis techniques combined with kernel density estimation, standard deviational ellipse, and Geographical Detector analysis, we systematically visualized the spatial distribution patterns and driving factors. Key findings include that (1) industrial heritage exhibits significant spatial heterogeneity, concentrating primarily in the city center and surrounding areas, forming high-density clusters in Wuhua District, Panlong District, and Haikou Subdistrict, while showing marked disparities among regions; (2) distinct spatial distribution patterns emerge across heritage types—manufacturing heritage clusters in central urban zones, whereas mining heritage disperses in peripheral mountainous areas; and (3) historical preservation policies are identified as the dominant factor shaping the current distribution, whereas terrain and natural environmental impacts remain secondary. These findings offer actionable insights for optimizing the conservation and adaptive reuse of Kunming’s industrial heritage. Full article

Lake Maggiore is a deep subalpine lake that has been well studied since the last century thanks to a monitoring program funded by the International Commission for the Protection of Italian–Swiss Waters. The monitoring program comprises both abiotic and biotic parameters, including zooplankton pelagic organisms. In this study, we present a dataset of 15,563 records of population densities and standing stock biomass for zooplankton pelagic taxa recorded over 43 years (1981–2023). The long-term dataset is valuable for tracing changes in trophic conditions experienced by the lake during the last century (eutrophication and its reversal) and the impact of global warming. Zooplankton samples (Crustacea and Rotifera Monogononta) were collected within 0–50 m depth by vertical hauls with an 80 µm light plankton sampler. The sampling frequency was monthly, with the exception of the 2009–2012 period, which employed seasonal frequency. The estimation of zooplankton taxon abundance and of its standing stock biomass is crucial in order to quantify the flux of matter, energy, and pollutants up to the upper trophic levels of the food web. The dataset provided is also suitable for food web analysis because the zooplankton taxa have been classified according to their ecological roles (microphagous organisms; primary and secondary consumers). Full article

Microplastics (MPs) have been extensively studied in the marine environment, but reliable data on their sources and pathways in freshwater ecosystems, which are the main sources of such pollutants, are still limited. In this study, we investigated the spatiotemporal variations, characteristics, and sources of MPs in Nakivubo catchment, which drains waste and stormwater from Kampala city (Uganda) and empties it into Lake Victoria through the Nakivubo channel. Surface water samples (n = 117) were collected from thirteen sites in the Nakivubo catchment (S1 to S13) during the dry and wet seasons in 2022. The MPs were recovered by wet peroxide oxidation protocol, followed by salinity-based density separation, stereomicroscopy, and micro-attenuated total reflectance Fourier-transform infrared spectroscopy. All the samples had MPs, with mean concentrations ranging from 1568.6 ± 1473.8 particles/m³ during the dry season to 2140.4 ± 3670.1 particles/m³ in the wet season. Nakivubo catchment discharges an estimated 293.957 million particles/day into Lake Victoria. A Two-Way ANOVA revealed significant interactive effects of seasons and sampling sites on MPs abundance (p < 0.05). Spatially, the highest mean concentrations of MPs (5466.67 ± 6441.70 particles/m³) were in samples from site S3, which is characterized by poor solid waste and wastewater management practices. Filaments (79.7%) and fragments (17.9%) made of polyethylene (75.4%) and polyethylene/polypropylene co-polymer (16.0%) were the most common MPs. These are likely from single-use polyethylene and polypropylene packaging bags, water bottles, and filaments shed from textiles during washing. These results highlight the ubiquity of MPs in urban drainage systems feeding into Lake Victoria. To mitigate this pollution, urban authorities need to implement strict waste management policies to prevent plastic debris from entering drainage networks. Full article

Airborne hyperspectral LiDAR (AHSL) is a technology that integrates the spectral content collected using hyperspectral imaging and the precise 3D descriptions of observed objects obtained using LiDAR (light detection and ranging). AHSL detects the spectral and three-dimensional (3D) information on an object simply using laser measurements. Nevertheless, the advantageous richness of spectral properties also introduces novel issues into the scan unit, the mechanical–optical trade-off. Specifically, the abundant spectral information requires a larger optical aperture, limiting the acceptance of the mechanic load by the scan unit at a demanding rotation speed and flight height. Via the simulation and analysis of scan models, it is exhibited that Palmer scans fit the large optical aperture required by AHSL best. Furthermore, based on the simulation of the Palmer scan model, 45.23% is explored as the optimized ratio of overlap (ROP) for minimizing the diversity of the point density, with a reduction in the coefficient of variation (CV) from 0.47 to 0.19. The other issue is that it is intricate to calibrate the scanning geometry using outside devices due to the complex optical path. A self-calibration strategy is proposed for tackling this problem, which integrates indoor laser vector retrieval and airborne orientation correction. The strategy is composed of the following three improvements: (1) A self-determined laser vector retrieval strategy that utilizes the self-ranging feature of AHSL itself is proposed for retrieving the initial scanning laser vectors with a precision of 0.874 mrad. (2) A linear residual estimated interpolation method (LREI) is proposed for enhancing the precision of the interpolation, reducing the RMSE from 1.517 mrad to 0.977 mrad. Compared to the linear interpolation method, LREI maintains the geometric features of Palmer scanning traces. (3) A least-deviated flatness restricted optimization (LDFO) algorithm is used to calibrate the angle offset in aerial scanning point cloud data, which reduces the standard deviation in the flatness of the scanning plane from 1.389 m to 0.241 m and reduces the distortion of the scanning strip. This study provides a practical scanning method and a corresponding calibration strategy for AHSL. Full article

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method. Full article

Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this capacity would require accurate estimation of forest structural attributes for deriving their aboveground biomass (AGB). Traditional field measurements, while precise, are labor-intensive and often spatially limited. Handheld Mobile Laser Scanning (HMLS) offers a rapid alternative for building forest inventories; however, its effectiveness and accuracy in diverse subtropical forests with complex canopy structure remain under-investigated. In this study, we employed both HMLS and traditional surveys within structurally complex subtropical forest plots, including old-growth forests (Fung Shui Woods) and secondary forests. These forests are characterized by dense understories with abundant shrubs and lianas, as well as high stem density, which pose challenges in Light Detection and Ranging (LiDAR) point cloud data processing. We assessed tree detection rates and extracted tree attributes, including diameter at breast height (DBH) and canopy height. Additionally, we compared tree-level and plot-level AGB estimates using allometric equations. Our findings indicate that HMLS successfully detected over 90% of trees in both forest types and precisely measured DBH (R² > 0.96), although tree height detection exhibited relatively higher uncertainty (R² > 0.35). The AGB estimates derived from HMLS were comparable to those obtained from traditional field measurements. By producing highly accurate estimates of tree attributes, HMLS demonstrates its potential as an effective and non-destructive method for rapid forest inventory and AGB estimation in subtropical forests, making it a competitive option for aiding carbon storage estimations in complex forest environments. Full article

Hydrogen energy, characterized by its abundant resources, green and low-carbon attributes, and wide-ranging applications, is a critical energy source for achieving carbon peaking and carbon neutrality goals. The operational efficiency of the hydrogen energy industrial chain is pivotal in determining the security of its supply chain and its contribution to China’s energy transition. This study investigates the efficiency of China’s hydrogen energy industrial chain by selecting 30 listed companies primarily engaged in hydrogen energy as the research sample. A three-stage data envelopment analysis (DEA) model is applied to assess the industry’s comprehensive technical efficiency, pure technical efficiency, and scale efficiency. Additionally, kernel density estimation is utilized to analyze efficiency trends over time. Key factors influencing efficiency are identified, and targeted recommendations are provided to enhance the performance and sustainability of the hydrogen energy industrial chain. These findings offer valuable insights to support the development and resilience of China’s hydrogen energy industry. Full article

The cooling water intake systems of coastal nuclear power plants are frequently clogged by marine organisms in the water intake area. This study uses hydroacoustic (BioSonics DT-X, 199 kHz) and trawling methods (20 mm codend mesh size) to research fish spatiotemporal dynamics. Species composition, seasonal variations in fish abundance, and target strength were analyzed. Pearson’s correlation analysis revealed a significant negative correlation between water depth and resource density. Significant differences in fish abundance and biomass density were observed among seasons and stations. An analysis of dominant species showed that the IRI (index of relative importance) of Collichthys lucidus was highest in February 2023 (5736.54), while Harpadon nehereus had the highest IRI in November 2023 (2309.17). The distribution and abundance estimates of dominant species from acoustic surveys and biological trawling were highly consistent, demonstrating the applicability and reliability of hydroacoustic methods in fish resource assessments. Through the identification of dominant species as well as the distribution patterns of fish within the water intake area, valuable data support can be provided for the confirmation of those organisms that are prone to clogging the cooling water source. Moreover, it lays a solid foundation for the development of relevant protection work. Full article

Recent single-cell experiments that measure copy numbers of over 40 proteins in thousands of individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the statistical time-evolution of protein abundances in single cells, information that could yield insights into the mechanisms influencing the biochemical signaling kinetics of a cell. However, when multiple candidate models (i.e., mechanistic models applied to initial protein abundances) can potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. For example, popular approaches like Kullback–Leibler divergence and Akaike’s Information Criterion are often difficult to implement largely because mathematical expressions for the likelihoods of candidate models are typically not available. To perform model selection, we introduce an entropy-based approach that uses split-sample techniques to exploit the availability of large data sets and uses (1) existing generalized method of moments (GMM) software to estimate model parameters, and (2) standard kernel density estimators and a Gaussian copula to estimate candidate models. Using simulated data, we show that our approach can select the ”ground truth” from a set of competing mechanistic models. Then, to assess the relative support for a candidate model, we compute model selection probabilities using a bootstrap procedure. Full article