Spatio-temporal Distribution Patterns Research Articles

Evolutionary dynamics and adaptive analysis of Seneca Valley virus.

Over the past 20years, the Seneca Valley virus (SVV) has emerged in various countries and regions around the world. Infected pigs display symptoms similar to foot-and-mouth disease and other vesicular diseases, causing severe economic losses to affected countries. In recent years, the number of SVV infections has been increasing in Brazil, China, and the United States. In this study, we comprehensively analyzed SVV genomic sequence data from the perspectives of evolutionary dynamics, phylogeography, and codon usage bias. We aimed to gain further insights into SVV's genetic diversity, spatiotemporal distribution patterns, and evolutionary adaptations. Phylogenetic analysis revealed that SVV has evolved into eight distinct lineages. Based on the results of phylogeographic analysis, it is speculated that the United States might have been the source of SVV, from where it subsequently spread to different countries and regions. Moreover, our analysis of positive selection sites in SVV capsid proteins suggests their potential importance in the process of receptor recognition. Finally, codon preference analysis indicates that natural selection has been a primary evolutionary driver influencing SVV codon usage bias. In conclusion, our in-depth investigation into SVV's origin, dissemination, evolution, and adaptation emphasizes the significance of SVV surveillance and control measures.

Analysis of the spatiotemporal distribution pattern and driving factors of renewable energy power generation in China

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China’s renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China’s renewable energy industry.

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in carbon storage of 0.51 Tg over the 20-year period. (2) From 2020 to 2050, the carbon storage varied significantly among the natural trend scenario (NTS), ecological restoration scenario (ERS), and economic priority scenario (EPS), with values of 2112.05 Tg, 2164.40 Tg, and 2105.90 Tg, respectively. Carbon storage in the ecological restoration scenario exhibited positive growth, mainly due to a substantial increase in forest area. (3) The spatial pattern of carbon storage in Liaoning Province was characterized by “low in the center, high in the east, and balanced in the west”. Therefore, Liaoning Province can consider rationally formulating and strictly implementing the spatial policy of ecological protection in the future land planning so as to control the disorderly growth of construction land, realize the growth of ecological land area, effectively enhance carbon storage, and ensure the realization of the goal of “dual carbon strategy”.

Spatio-temporal distribution pattern and the correlation with key environmental factors of Eurasian otter (Lutra lutra) in Northeast China

Spatio-temporal distribution pattern and the correlation with key environmental factors of Eurasian otter (Lutra lutra) in Northeast China

The evolution of spatiotemporal patterns and influencing factors of high-level tourist attractions in the Yellow River Basin

Introduction: High-level tourist attractions (HLTAs) are essential components of high-quality tourism development in the Yellow River Basin. In the context of holistic tourism and mass tourism, it is necessary to re-examine the spatial pattern of HLTAs.Methods: Selecting the Qing–Gan–Ning region of the Yellow River Basin as a case study site, based on the data on 590 high-level tourist attractions in 2009, 2015, and 2021, and with the help of ArcGIS10.8 spatial analysis tools, the nearest neighbor index, kernel density analysis, and standard deviation ellipse methods, such as ellipse and ESDA spatial exploratory analysis, were used to analyze the spatiotemporal pattern of the spatial distribution of high-level tourist attractions in the study area from the aspects of type, density, and spatial autocorrelation. Overlay analysis, buffer analysis, and other methods were used to select the influencing factors, and finally, the influencing factors were verified with the help of GeoDetector.Conclusions: The conclusions are as follows: the NNI values for the Qing–Gan–Ning area of the Yellow River Basin are 0.699, 0.7, and 0.618, and the spatial structure type was clustered. The distribution density showed an evolutionary trend of point-like agglomeration and linear expansion, with the provincial capital as the core and the Yellow River as the axis. The distribution density of high-level tourist attractions is 27, 44, and 74 per 10,000 km2. In terms of the dynamic distribution direction of the center of mass, there was little interannual variation, showing a northeast–southwest direction, which is consistent with the flow direction of the Yellow River in the region. Furthermore, the analysis of Moran’s I index showed clear spatial autocorrelation at the county scale. HLTAs exhibited clustering and wider distribution in H–H and L–L zones, while the L–H and H–L zones displayed a more dispersed and narrower distribution. The order of factors affecting the spatial distribution of HLTAs was economic factors (0.5257) > social factors (0.5235) > natural factors (0.491), and interactive detection showed that there were two-factor enhancements and nonlinear enhancements in the factors.Dicussions: This study contributes to the conservation development and sustainable development of ecotourism resources in the Yellow River Basin.

Stratigraphic architecture and distribution patterns of submarine fan-reservoir elements: insights derived from the Pliocene and Pleistocene Bengal Fan

ABSTRACT 3-D seismic data from the Bengal Fan along with spectral decomposition and RGB color blending techniques display stratigraphic architectures and spatiotemporal distribution patterns of submarine fan-reservoir elements in stark detail. Seven reservoir elements are recognized in Pliocene and Pleistocene channel–lobe complexes (i.e., subfans) developed on the northeastern fringe of the Bengal Fan. Among them, crevasse, overbank, and avulsion splays are not well acknowledged by standard models of submarine fan-reservoir elements. Crevasse splays decrease in thicknesses towards ancestral channels, and are capped by overlying levees, whereas overbank and avulsion splays increase in thicknesses towards ancestral channels and cap underlying levees. Crevasse and avulsion splays exhibit lobate planform morphology and are linked updip to ancestral channels by feeder channels, whereas overbank splays display tongue-like planform morphology and lack feeder channels. Fills of laterally migrated channel-complex sets (CCSs) appear only in early stage of subfan evolution, whereas fills of vertically stacked CCSs can appear either in middle or late stages of subfan evolution. The inner segment of the documented subfans fostered infills of both laterally migrated and vertically stacked CCSs, whereas the outer segment of the studied subfans contains terminal lobe complexes and distributary-channel fills. Crevasse, overbank, and avulsion splays can appear either in middle or late stages of subfan evolution, and mainly occur in overbank environments of middle segments of the documented subfans, which are dominated by muddy facies as predicted by the standard model of submarine fan-reservoir elements. Pliocene and Pleistocene subfans demonstrate the importance of splay processes in submarine-fan evolution, and this has implications for understanding the evolution of the volumetrically largest sediment accumulations on Earth and the distribution of submarine fan-reservoir components. The downlap of subsequent levees onto crevasse splays and overbank splays created stratigraphic traps with the potential for large hydrocarbon accumulations.

Geostatistical assessment of the built environment and spatio-temporal distribution patterns of COVID-19 cases in Istanbul, Türkiye

This study aims to investigate the geostatistical relationships between the built environment at the neighborhood level and the spatio-temporal distribution patterns of COVID-19 cases. Istanbul, Türkiye, one of the most highly populated metropolitan cities in the world, was chosen as the study area. Clustering and Outlier Analysis (Anselin Local Moran's I), correlation analysis, and Geographically Weighted Regression (GWR) are utilized for testing the relationships between land use types, land use diversity, and net population density, representing the built environment and the intensity of COVID-19 cases. Spatial patterns of selected regions with high exposure to COVID-19 are also interpreted, and place-based outcomes are presented. Results are supported by and discussed with descriptive statistics. Land-use diversity, net population density, green areas, commercial areas, and commercial-residential areas are determined to be the predominant determinants of the spatio-temporal distribution of COVID-19 cases in Istanbul. Based on the GWR model, green areas are found to be the variable having the highest effect on the neighborhoods. Land use diversity and commercial areas are the other two prominent variables that affect the high exposure to COVID-19 cases in a neighborhood and its surroundings. Relevantly, it can be concluded that the neighborhood effect plays a crucial role in the spread of and high exposure to COVID-19. The findings of the study will guide the development of design and planning criteria to overcome risk factors such as pandemics in spatial planning decisions at the neighborhood level for cities.

Spatial-temporal heterogeneity and built environment nonlinearity in inconsiderate parking of dockless bike-sharing

Although previous studies have shed light on the travel behaviour of dockless bike-sharing (DBS) users, little research focused on their inconsiderate parking behavior. Unlike the travelling behavior, the choice of parking location is closely linked to the different built environments surrounding the parking locations. Therefore, to improve the efficiency of governance, it is vital to explore the parking patterns and heterogeneous influences of the built environment on inconsiderate parking and formulate targeted measures. This paper measures the coordinates of prohibited parking areas in the field to identify inconsiderate parking. Based on big data from Mobike DBS and data on the built environment, the paper empirically analyzes the heterogeneous spatiotemporal distribution patterns of inconsiderate parking with clustering and decision trees. The influencing factors of inconsiderate parking and their nonlinear effects are further analyzed using random forest and partial dependence plots (PDP). The results show that there is significant spatiotemporal heterogeneity in inconsiderate parking, in which different clusters reflect various characteristics of the built environment. Furthermore, marginal effect analysis finds that influencing factors such as riding distance, catering service places, lifestyle services, sports and leisure places, and hotels and hostels have a strong effect on inconsiderate parking behavior, and show nonlinear effects with optimal allocation intervals. Therefore, targeted strategies should be carried out in terms of dynamic temporal adjustment, precise spatial layout, differential management according to time and zone, and cause-assisted administration. The paper’s results provide important decision-making support for inconsiderate parking.

Integration of empirical and modelled data unravels spatiotemporal distribution and connectivity patterns of Fuegian sprat early life stages

The distribution patterns of fish early life stages are critical to recruitment success and closely related to major oceanographic circulation patterns. We explored the spatiotemporal distribution of early life stages of Fuegian sprat Sprattus fuegensis, a key trophic species in the Southwestern Atlantic Ocean (SWAO), in a complex oceanographic setting. Samples were collected during austral spring, summer, and autumn, from 2014 to 2017, across areas with distinct biophysical properties between Tierra del Fuego (TDF) and the marine protected area ‘Namuncurá’ at Burdwood Bank (BB) (ca. 54°S). Results revealed significant seasonal fluctuations in abundance, distribution, and ontogenetic composition across habitats. High egg and early larval abundances at Isla de los Estados (IE) suggest it is an additional spawning ground to those previously identified at TDF and BB. However, only the latter appear to be suitable nursery areas. Particle-tracking simulations based on egg abundance and spawning dates were conducted for the first time using results from a high-resolution hydrodynamic model. Particle transport provides evidence of connectivity between IE and neighbouring areas—enhanced when horizontal diffusivity is incorporated into the model—but not between TDF and BB. Simulated distributions closely resembled empirical patterns from this and other studies, allowing the integration of empirical and modelled data to schematize the species’ dispersal pathways in the study area. These results offer new insights into distribution and connectivity patterns among spawning grounds and highlight the potential use of hydrodynamic models for future assessments of Fuegian sprat and other planktonic species’ dispersal and recruitment in the SWAO.

Potential Distribution of Bryophyte, Entodon challengeri (Entodontaceae), under Climate Warming in China

Entodon challengeri (Paris) Cardot has important environmental monitoring and medical value. It is critical we inspect the influence of climate warming on its spatiotemporal distribution pattern. Based on actual geographical distribution records and environmental datasets, a MaxEnt model coupled with ArcGIS was executed to display the potential suitable habitats of E. challengeri in China under future climate warming scenarios. We showed the following. (i) The simulation accuracy of the MaxEnt model was excellent, with an AUC value of 0.918. (ii) Annual precipitation and precipitation during the wettest month were the critical factors that restricted the distribution range of E. challengeri. (iii) Current suitable habitats were concentrated in the northern temperate zone in eastern China. (iv) Under climate warming scenarios, on the spatial scale, the distributional pattern presented a shrinkage in the south and expansion in the north, which was more obvious in the RCP8.5 than in the RCP2.6 scenario. On the time scale, shrinkage of the potential distributional range was greater in the 2070s than in the 2050s. (v) The distributional centroids shifted to the northeast. In general, future climate warming will have a great negative effect on the suitability of habitats of E. challengeri.

Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China

Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink.

Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors: A case study of Shandong Peninsula urban agglomeration

Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors—advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value—that affected the distribution of RECC. Other factors, including the interactions between residents’ consumption level and environmental regulation, residents’ consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.

Spatio-Temporal Distribution Characteristics of Intangible Cultural Heritage and Tourism Response in the Beijing–Hangzhou Grand Canal Basin in China

The Beijing–Hangzhou Grand Canal is renowned for being one of the longest and largest canals in the world. Running from Beijing to Hangzhou (north to south), it connects China’s five major water systems and has an important impact on the ecological environment and economy of northern and southern China. It also boasts a large quantity of intangible cultural heritage (ICH). Clarifying the spatio-temporal distribution pattern of ICH in the Beijing–Hangzhou Grand Canal Basin and its influencing factors is essential for the protection and utilization of heritage resources and the formulation of management policies. In this study, 977 national ICH items in the Beijing–Hangzhou Grand Canal Basin are analyzed with the help of ArcGIS spatial analysis technology, SPSS regression analysis, and human geography research methods. The results show that the national ICH in the Beijing–Hangzhou Grand Canal Basin has complete categories but varies in provincial scale, particularly between the north and south parts. According to the analysis using tools such as kernel density estimation, standard deviation ellipse, and the center-of-gravity model, it is clear that the ICH in the Beijing–Hangzhou Grand Canal Basin shows different degrees of sub-type aggregation, varying directional characteristics of each batch of ICH, and a centre of gravity of ICH with a tendency to shift in multiple directions. The main factors affecting the spatio-temporal distribution pattern of ICH in the Beijing–Hangzhou Grand Canal Basin are natural geographical factors, socioeconomic factors, and policy environment factors. Moreover, there is a significant positive correlation between ICH resources and the tourism industry that cannot be ignored. This study provides an important reference for planning the reuse of ICH resource systems in northern and southern China.

Physical meaning of temperature and evaluation of distribution laws (in the area of the Lake Sevan basin)

Formulation of the problem. In the work, the most general definition of temperature was discussed and presented, the temporal probability distribution of air temperature was analyzed and evaluated. Temperature has no specific definition. Thus it will be difficult to study the nature of any geophysical phenomena, including the characteristics of temperature distribution, without understanding the modern scientific definition and adjustment of temperature. The aim of the work is to give the most modern reasonable definition of temperature or what the thermometer measures, the answer to which requires a more specific scientific justification, as well as to evaluate the patterns of possible spatiotemporal distribution of air temperature in the territory of Armenia and in the area of the Lake Sevan basin. Methods. To solve the set tasks, the authors used corresponding research and published works as a theoretical basis in the work. In the work, the average monthly data of actual observations of the temperature of the ground surface air layer and the amount of precipitation of the «Center for Hydrometeorology and Monitoring» SNCO of the Ministry of Environment of the Republic of Armenia were used. It was found that temperature is a quantity characterizing the thermal state and radiation of the terrestrial and celestial bodies and, in general, the environment, which is strongly related to the entropy change of the system; temperature is the main thermodynamic characteristic of thermal equilibrium; the thermodynamic and microscopic concepts of temperature coincide; the temperature-heat (energy) difference becomes known in the sense that a system can have high energy but low temperature. The authors applied the following research methods in the article: mathematical and statistical, extrapolation, analysis, analogy, correlation, cartographic. Results. Energy depends on the geometry (dimensions) of the system, but temperature does not. A trend of increasing air temperature is observed in the RA territory, which is also a result of the entropy change of the system. It can change very quickly, depending on the environmental factors in the given area (the growth rate of greenhouse farms, the artificial filling of valleys, which are wave carriers of air flow, the relentless use of green spaces for the purpose of public buildings, and other factors) in the process of disrupting the excessively permissible norms, which are currently separate needs serious research and prevention. The long-term variability of surface air temperature in the area of the Lake Sevan basin is analyzed. The analysis carried out made it possible to give a quantitative assessment characterizing the climate change in this region over the past 98 years. An analysis of the observational data showed that the trend of climate warming is confirmed by an increase in air temperature both in winter and over a long period of time. The change in surface air temperature occurs at a rate of 0.002 ºС/year to 0.012 ºС/year and is generally 0.008 ºС/year (or 0.08 ºС/10 years) for all analyzed stations. The results obtained confirm the presence of two periods of warming observed in 1927–1970 and 1971–2021. All the results obtained in the course of the work testify to the trend of climate mitigation in the area of the Lake Sevan basin at the end of the 20th - beginning of the 21st century.

Time distribution pattern and spatial heterogeneity of hourly scale event-based extreme precipitation in China

Extreme precipitation occurs naturally in the form of “events,” and the spatio-temporal distribution pattern of event-based extreme precipitation is of great significance to flood disaster prevention and water resources protection. This study proposed the concept of hourly scale event-based extreme precipitation (HEEP), referring to a precipitation event with at least an hourly precipitation extreme (exceeding the 99th percentile threshold). Then we identified the dominant time distribution pattern (TDP), analyzed the seasonal proportion and distribution, and investigated the spatial distribution of three basic features (total frequency, amount, and duration) of HEEP from 2008 to 2017 in China and its nine major river basins. Results showed that over 90% of the areas in China were dominated by short-duration HEEP (S-HEEP, less than eight hours). While the rest areas were led by long-duration HEEP (L-HEEP, more than eight hours), mainly concentrated on the middle and lower reaches of the Yangtze River and southeast coastal areas of China. Areas dominated by S-HEEP were more than L-HEEP in different seasons except in winter. Three basic features of HEEP showed similar spatial distribution patterns of high in southeastern China and low in northwestern China, indicating HEEP frequently occurred in southeastern China with relatively large amounts and long duration. The concept of HEEP can provide new ideas for regional and global extreme precipitation research.

Quantifying the spatial nonstationary response of influencing factors on ecosystem health based on the geographical weighted regression (GWR) model: an example in Inner Mongolia, China, from 1995 to 2020

The identification of ecosystem health and its influencing factors is crucial to the sustainable management of ecosystems and ecosystem restoration. Although numerous studies on ecosystem health have been carried out from different perspectives, few studies have systematically investigated the spatiotemporal heterogeneity between ecosystem health and its influencing factors. Considering this gap, the spatial relationships between ecosystem health and its factors concerning climate, socioeconomic, and natural resource endowment at the county level were estimated based on a geographically weighted regression (GWR) model. The spatiotemporal distribution pattern and driving mechanism of ecosystem health were systematically analysed. The results showed the following: (1) the ecosystem health level in Inner Mongolia spatially increases from northwest to southeast, displaying notable global spatial autocorrelation and local spatial aggregation. (2) The factors influencing ecosystem health exhibit significant spatial heterogeneity. Annual average precipitation (AMP) and biodiversity (BI) are positively correlated with ecosystem health, and annual average temperature (AMT) and land use intensity (LUI) are estimated to be negatively correlated with ecosystem health. (3) Annual average precipitation (AMP) significantly improves ecosystem health, whereas annual average temperature (AMT) significantly worsens eco-health in the eastern and northern regions. LUI negatively impacts ecosystem health in western counties (such as Alxa, Ordos, and Baynnur). This study contributes to extending our understanding of ecosystem health depending on spatial scale and can inform decision-makers about how to control various influencing factors to improve the local ecology under local conditions. Finally, this study also proposes some relevant policy suggestions and provides effective ecosystem preservation and management support in Inner Mongolia.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020

BackgroundCutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters.MethodsOn the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance.ResultsIn general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country.ConclusionsOur study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.Graphical

Dynamic Evolution and Scenario Simulation of Ecosystem Services under the Impact of Land-Use Change in an Arid Inland River Basin in Xinjiang, China

Ecosystem services (ESs) are crucial for sustainable development, as they impact human well-being. However, changes in land use/land cover (LULC) caused by climate change and social development can negatively affect ESs, particularly in arid river basins. This study focuses on current and future changes in LULC in the Kaxghar River Basin (KRB) in Xinjiang, China, to determine how these changes will affect the region’s ESs. The integrated PLUS-InVEST model was used to investigate the spatiotemporal distribution and changing patterns of habitat quality (HQ) and carbon storage (CS) under the natural increase scenario (NIS), economic development scenario (EDS), and water protection scenario (WPS). Additionally, the Ecosystem Service Contribution Index (ESCI) was also calculated to evaluate the contribution of LULC changes to ESs. The results show the following: (1) from 2000 to 2020, the average value of HQ in the KRB gradually decreased from 0.54 to 0.49 and CS trended slightly upward, with a total increase of 0.07 × 106 t. Furthermore, the changes in CS were highly consistent with changes in LULC. (2) From 2020 to 2030, the area of low-grade (0–0.2) HQ saw a continuous increase, with the fastest growth occurring in 2030 under the EDS. Meanwhile, under the WPS, HQ significantly improved, expanding by 1238 km2 in area. Total CS under the three test scenarios tended to decline, with the NIS showing the smallest decrease. (3) The expansion of cropland and unused land had a negative impact on ESs, particularly on CS, whereas the conversion to grassland and forestland had a significant positive impact. In conclusion, these insights will enrich our understanding of ESs in the study area and contribute to balancing the relationship between ecological conservation and socioeconomic development in the Kaxghar River Basin, as well as in other parts of China’s arid Northwest and similar regions around the world.

Exploring biophysical links of catch rate from mid-water trawl fishery in the north-west coast of India, Eastern Arabian Sea: A remote sensing approach

Fish frequently shift their distribution ranges as a result of changes in preferred environmental factors. Knowledge on distribution of fish in relation to their environmental optima is crucial for improving the understanding of fishing grounds and planning sustainable exploitation. This study investigated the monthly variability in environmental factors impacting the catch rate and the spatio-temporal distribution patterns of fish along northwest coast of India (NWCI) from 2017 to 2019. The time series images of sea surface temperature (SST), sea surface height anomaly (SSHa), chlorophyll-a concentration (CHL), and euphotic depth (Zeu) indicate close coupling between bio-physical variables in the region. Generalized Additive Model (GAM) applied to the catch rate of mid-water trawlers found that CHL and Zeu were the most influential variables for ribbonfish distribution, while CHL and SST influence squid distribution and SST and SSHa influence cuttlefish distribution. The total deviation explained 37.70 %, 10.70 %, and 22.28 % in the final model for simulating the spatio-temporal distribution of ribbonfish, squid, and cuttlefish, respectively. Environmental factors were significant in the final GAM model (p < 0.05). Spatio-temporal distribution patterns of fishery resources from mid-water trawlers showed that they were related to biophysical changes in the northwest coast of India. The high catch rate was observed in the northern latitudes during the post-monsoon (October), the major fishing season in the region. However, it shrank and shifted to southern latitudes during the winter (January). This study could be adapted to nearby countries in the Arabian Sea region for an effective and useful tool to monitor and manage the fishery resources. Furthermore, it could aid in decision-making for policymakers and resource managers to formulate strategies for holistic marine fishery management and sustainable resource exploitation.

Fine‐scale spatial and temporal distribution patterns of large marine predators in a biodiversity hotspot

AbstractAimLarge marine predators, such as cetaceans and sharks, play a crucial role in maintaining biodiversity patterns and ecosystem function, yet few estimates of their spatial distribution exist. We aimed to determine the species richness of large marine predators and investigate their fine‐scale spatiotemporal distribution patterns to inform conservation management.LocationThe Hauraki Gulf/Tīkapa Moana/Te Moananui‐ā‐Toi, Aotearoa/New Zealand.MethodsWe conducted a replicate systematic aerial survey over 12 months. Flexible machine learning models were used to explore relationships between large marine predator occurrence (Bryde's whales, common and bottlenose dolphins, bronze whaler, pelagic and immature hammerhead sharks) and environmental and biotic variables, and predict their monthly distribution and associated spatially explicit uncertainty.ResultsWe revealed that temporally dynamic variables, such as prey distribution and sea surface temperature, were important for predicting the occurrence of the study species and species groups. While there was variation in temporal and spatial distribution, predicted richness peaked in summer and was the highest in coastal habitats during that time, providing insight into changes in distributions over time and between species.Main ConclusionsTemporal changes in distribution are not routinely accounted for in species distribution studies. Our approach highlights the value of multispecies surveys and the importance of considering temporally variable abiotic and biotic drivers for understanding biodiversity patterns when informing ecosystem‐scale conservation planning and dynamic ocean management.