Effects Of Spatial Heterogeneity Research Articles

Study on the coupling coordination development of China's multidimensional digital economy and industrial carbon emission efficiency.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China's DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China's ICEE has consistent σ-convergence and β-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition β-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.

The impact of urbanization on ecosystem services: Both time and space are important to identify driving forces

Rapid urbanization is one of the key factors in threatening regional ecological security and undermining human well-being. Understanding of the impacts of urbanization on ecosystem services (ESs) could provide comprehensive information for policy making to support ecological governance. In this study, the spatial and temporal distributions of four ESs, namely water yield (WY), soil conservation (SC), nitrogen export (NE), and habitat quality (HQ), and four factors of urbanization, namely construction land percentage, economic density, population density, and nighttime lighting, were analyzed in the Xiangjiang River Basin (XJRB) from 1990 to 2020. The impacts of urbanization on ESs at the sub-watershed and county level were investigated using the space-for-time and change-over-time methods. The results showed that: (1) WY, SC, and NE fluctuated throughout the study period, while HQ significantly decreased and urbanization factors significantly increased. (2) Each urbanization factor had a significant influence on the spatial heterogeneity of ESs, with the contribution at the county level being 2.88%–56.11% higher than that at the sub-watershed level. Moreover, there were enhanced interactions between factors in general, although spatial heterogeneity effects on NE and HQ were weaker at the county level. (3) Urbanization and ESs had a significant nonlinear relationship, and there was a threshold of relationship change between them, with the impact of urbanization on ESs showing evident spatial heterogeneity in terms of both the driving direction and intensity of change over time. (4) The change-over-time method identified 1992–1995 and 2008–2013 as key periods of change in the relationship between urbanization and ESs in the XJRB, and the method had the advantage of revealing the spatial heterogeneity of the effects of driving factors. These findings provide a reference for decision making related to urban planning.

Combined effects of soil 3D spatial heterogeneity and biotic spatial heterogeneity (plant clumping) on ecosystem processes in grasslands.

Soil heterogeneity has been shown to enhance plant diversity, but its effect on grassland productivity is less clear. Even less is known about the effect of plant clumping (intraspecific aggregation) and its potential interaction with soil heterogeneity. The combined effects of soil 3D spatial heterogeneity and species clumping were experimentally studied in grassland mesocosms consisting of four grassland species. These species were planted in three patterns (i.e. completely mixed, clumped by 9 or 36 individuals of the same species) on soils with heterogeneous cells of alternating nutrient-poor and rich soil differing in size from 0 (mixed soil) to 12, 24, and 48 cm (complete poor or rich mesocosm). Moderate soil cell sizes (12-24 cm) consistently increased whole-mesocosm aboveground productivity by more than 20%, which mainly originated from the increased growth of the plants growing on the poor soil cells. In contrast, total mesocosm productivity was not affected by species clumping although there were some species-specific effects, both of clumping and of the interaction of clumping with soil heterogeneity. Our results show that intermediate soil heterogeneity promotes productivity. Clumping can improve the growth of inferior species, thus promoting coexistence, without affecting overall productivity. We found no interaction effect of clumping and soil heterogeneity on productivity at the community level and some minor species-specific effects.

Effect of altitude and spatial heterogeneity on the host-parasite relationship in anurans from a remnant humid forest in the brazilian semiarid.

In the present study, we investigated the effect of habitat heterogeneity, elevation gradient, and phylogenetic distance of host species on the abundance and richness of anuran endoparasites, assuming that parasites follow the distribution of their hosts independently of environmental variation. We collected 192 anurans distributed in three altitude ranges: 100-200m, 400-500m, and 700-800m. We performed discriminant principal component analysis to analyze the interrelationships between environmental heterogeneity and the distribution of parasite and host species in the formation of species groups in each altitude range. We estimated the niche width and parasite overlap, using host species as a variable, and assessed whether parasite abundance is more influenced by historical (distance host phylogeny) or ecological effects in each altitude category and overall. Finally, we use network analyses to understand how interactions between parasites and hosts are formed along the altitude gradient. We found 22 parasite species, and the overall prevalence of infection was 74%. In our study, we did not identify environmental (altitude gradients and heterogeneity) or phylogenetic effects acting on the parasite species diversity. Overall, our results suggest that the parasites are distributed following the dispersal of their hosts and are dispersed among most anuran species.

How to develop site-specific waterlogging mitigation strategies? Understanding the spatial heterogeneous driving forces of urban waterlogging

Urban waterlogging seriously threatens urban sustainable development and human life. The effects of various landscape elements on urban waterlogging have been extensively documented. However, less attention is deserved to the spatial heterogeneity effects of urban landscape elements on urban waterlogging. The spatial pattern of dominant driving forces and how the interactive effects of landscape elements affect urban waterlogging with different environmental configurations have not been well examined. These shortcomings have hindered the development of target-specific urban waterlogging mitigation strategies. To shed some light on this topic, an innovative method that integrated the boruta algorithm, cubist regression tree, and geographical detector model is presented to investigate the spatial heterogeneous mechanisms of urban waterlogging and map the waterlogging dominant driving forces with different local conditions. The results show that the boruta algorithm proposed in this study introduces shadow variables as a benchmark, thus enabling an unbiased and stable selection of representative waterlogging driving factors based on local conditions. By comparing with two other commonly used regression methods (global regression model, spatial lag model), the cubist regression tree divides the urban waterlogging space into multiple homogeneous subgroups to quantify the spatial non-stationarity relationship and spatially explicit the local driving forces in Guangzhou and Shenzhen, with the adjusted R2 of 0.79 and 0.88. The geographical detector model denotes that waterlogging magnitude within different subgroups is affected by different dominant factors. Even for the same dominant factor, its contribution to waterlogging varies considerably in different subgroups. The independent contribution of the dominant factor in Guangzhou was 23.28%–57.82%, while in Shenzhen it ranged from 25.95% to 53.59%. In addition to the dominant factor of each subgroup, it is noteworthy that in some subgroups the combined effect of different representative factors on waterlogging is significantly stronger than the contribution of their dominant factors. In view of this, urban planners and local authorities need to comprehensively consider the interaction effect between representative factors, which develop urban waterlogging mitigation strategies that integrate multiple factors. The results from this study extend our scientific understanding of the site-specific mechanism of urban waterlogging, which facilitates the implementation of more targeted and effective mitigation strategies, rather than a “one-size-fits-all” policy.

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

With the rapid increase in unmanned aerial vehicles (UAVs), ensuring the safety of airspace operations and promoting sustainable development of airspace systems have become paramount concerns. However, research dedicated to investigating the population exposure risks of UAV operations in urban areas and their spatial pattern is still missing. To address this gap, this study evenly divides the urban space into uniform grids and calculates critical areas for two UAV types within each grid. By integrating geospatial data, including buildings, land use, and population, data-driven risk maps are constructed to assess the spatial distribution patterns and potential population exposure risks of two UAV types and compare them with commonly used census units. The results indicate that the mean time between failures (MTBF) for the selected generic and rotary-type UAVs can be up to 9.04 × 108 h and 1.22 × 108 h, respectively, at acceptable risk levels, considering uncertainties. The spatial pattern of population exposure risk exhibits spatial heterogeneity and multi-scale effects in urban areas, aligning with population distribution. High-risk areas concentrate in regions characterized by high population mobility, such as transport hubs, commercial service areas, residential zones, and business districts. Additionally, the comparation emphasizes the potential bias introduced by using census units in risk assessment, especially in regions with significant urban build-up. This framework enables the evaluation of safety and acceptability across diverse urban land use areas and offers guidance for airspace management in megacities, ensuring the safe integration of UAVs in urban environments.

Analysis of the Spatiotemporal Evolution and Driving Factors of China’s Digital Economy Development Based on ESDA and GM-GWR Model

Research on the geographical aspects of the digital economy is valuable. We base our study on 10 consecutive years of panel data from 2011–2020 for 31 Chinese provinces. First, we measure the Digital Economy Index using the entropy weight method and analyze its spatiotemporal heterogeneity characteristics using the Exploratory Spatial Data Analysis (ESDA) method. Next, the Grey Model (GM) is utilized to conduct time series predictions of each geographical unit. Finally, we use the GM predicted values and Geographic Weighted Regression (GWR) model to explore the spatial heterogeneity effects of external factors. This study finds that: (1) The overall development shows a trend of vigorous growth, with significant spatial heterogeneity. The gradient difference shows a decreasing trend from the eastern coastal areas to the western inland areas. (2) There is an obvious “digital divide” and a “Matthew effect” in regional development, with agglomeration and spillover effects gradually increasing. (3) Considering the influencing factors, technological progress has a positive impact, and the technology-oriented spatial spillover is obvious, showing a pattern of high in the south and low in the north. The industrial structure is significantly positive, and increases year by year, showing a distribution characteristic of high in the north and low in the south in general, with a clear effect of reducing the “bipolar” distribution. The marginal effects of government support and foreign investment are reduced and there is spatial non-stationarity. This study provides a scientific basis for further research on the spatial development of the digital economy.

Dynamics of a diffusion-advection Lotka-Volterra competition model with stage structure in a spatially heterogeneous environment

In this paper, we study a diffusion-advection Lotka-Volterra competition model with stage structure in a spatially heterogeneous environment. The existence and local asymptotic stability of spatially non-homogeneous semi-trivial steady-state solutions and spatially non-homogeneous positive steady-state solutions are obtained by the implicit function theorem and spectral analysis. We show that three scenarios can occur: if random diffusion rates of two species are sufficiently large, both species go extinct; if random diffusion rates of two species are relatively small, two competing species coexist; if one species has a large random diffusion rate and the other has a small random diffusion rate, the species with a large random diffusion rate are driven to extinction. An interesting finding is that a large delay does not lead to Hopf bifurcation at the spatially non-homogeneous steady-state solution, but makes this steady-state solution approach zero. We numerically demonstrate the effects of spatial heterogeneity on spatiotemporal dynamics.

Performance evaluation of ecological transformation of mineral resource-based cities: From the perspective of stage division

Ecological transformation is important for the sustainable development of China's Mineral Resource-based Cities (MRCs). The ecological transformation performance evaluation will help improve the accuracy and pertinence of transformation policy formulation. Based on the innovative division of ecological transformation stages of MRCs (transformation restoration, transformation adjustment, and transformation innovation periods), this empirical study takes 109 MRCs in China as the object and 2013–2019 as the time window and constructs the corresponding evaluation index system of China's MRC ecological transformation and the evaluation model of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Exploratory Spatial Data Analysis methods (ESDA) based on the Rough Set-Entropy Weight method. First, our results show that the ecological transformation performance of China's MRCs was poor. By 2019, 98% of the cities were in the adjustment stage. The transformation performance of cities with different resource types differed; from high to low, they were oil cities, ferrous metal cities, coal cities, and non-ferrous metal cities. Second, in terms of time, from 2013 to 2019, China's MRC ecological transformation performance showed a trend of first rising and then slightly fluctuating (from 0.3339 to 0.4158), of which the largest and smallest increases were the oil city (0.09) and black metal city (0.0803), respectively; Third, in terms of space, the ecological transformation of MRC in China had obvious spatial heterogeneity, spatial autocorrelation, and aggregation effect. On this basis, relevant policies such as dynamic monitoring, identification of transformation stage and regional cooperation are put forward. This study effectively expands the research on the ecological transformation of China's MRC and provided evidence for the precise formulation of relevant policies. It also provides a new perspective for the ecological transformation research of MRCs in other developing countries.

Spatial heterogeneity in summer phytoplankton communities shaped by anthropogenic and natural effects in typical coastal bay-river systems in South China

Phytoplankton community spatial scales vary in typical coastal bay-river systems under natural and anthropogenic effects. However, their relative importance in shaping phytoplankton spatial heterogeneity has been largely uncharacterized in the literature. In this study, we performed comprehensive analyses using field monitoring, meteorological, and remote sensing datasets from a coastal bay-river system in South China (the Guangdong-Hong Kong-Macao Greater Bay-Pearl River tributaries) to examine phytoplankton spatial heterogeneity and main effects. We showed that water quality in the four water bodies of the typical coastal bay-river system in South China were relatively poor. Phytoplankton community abundance, taxonomic grouping, dominant species, and species similarity varied significantly in response to environmental gradients. We showed that Cyanophyta, Bacillatiophyta, and Chlorophyta were dominant phyla, and we observed regional variations in taxonomic groups and dominant species. Phytoplankton abundance was significantly higher in Dongjiang River and water bodies in branched bays when compared with other regions due to nutrient input from extensive agricultural and urban development, where the dominant phylum was Cyanophyta. Cyclotella (Bacillariophyta) had relatively high abundance and was widely distributed throughout the coastal bay-river system, except in the Dongjiang River. Melosira (Bacillariophyta) had relatively high abundance in Dongjiang and Xijiang Rivers, Merismopedia (Cyanophyta) dominated in the Dongjiang River and highly urbanized branched bay areas, while Microcystis (Cyanophyta) only dominated in Beijiang and Xijiang Rivers. Dominant species distributions showed apparent spatial heterogeneity in phytoplankton community composition. Species similarities between different water bodies were generally low, but phytoplankton species similarities between the Dongjiang River and water bodies in the branched bay area were relatively high, which indicated that water exchange associated with freshwater and nutrient inputs may have profoundly influenced species dispersal and dominant phytoplankton species. Redundancy analyses (RDA) demonstrated that pH, elevation, photosynthetically active radiation (PAR), dissolved active phosphorus (SRP), total dissolved phosphorus (TP), total nitrogen (TN), and nitrate nitrogen (NO3−-N) values - associated with nutrient and climate indicators were the main factors explaining structured phytoplankton spatial distributions. Variance partitioning analyses (VPA) showed that phytoplankton community variations were explained by anthropogenic factors (eutrophication) in summer. Our study improves our understanding of the natural and anthropogenic effects on phytoplankton spatial heterogeneity and provides basic study data for long-term phytoplankton evolution from river to bay areas.

An optimization model of on-demand mobility services with spatial heterogeneity in travel demand

We propose an optimization model that predicts the required fleet size of on-demand mobility (ODM) services at various service levels for a range of demand patterns. Both customer waiting time and spatial heterogeneity in demand are considered when formulating the flow conservation equation for the system’s steady state. The demand–supply imbalance problem is solved through vehicle relocation. Vehicle relocation is reformulated into a simple linear programming problem, and the objective function is set to minimize the number of relocation vehicles. Furthermore, indicators were defined to more effectively explain the effects of spatial heterogeneity of travel demand on the required fleet size. The analysis results show that even among regions with the same average demand rate, the required fleet size may vary depending on the spatial demand pattern of origins and destinations. The higher the ratio of long-distance travel and the greater the need for vehicle relocation, the larger the fleet size required. Furthermore, the user travel time reduction effect is maximized at the minimum fleet size and then steadily decreases as fleet size increases. Cost minimization requires more vehicles than fleet size minimization. Cost minimization may be an advantageous goal for operation of an ODM service as a driverless vehicle service. The simultaneous consideration of both fleet size minimization and cost minimization provides a required fleet size range. Although the proposed model is simple and includes approximations, it helps provide insight into ODM service design and investment decision-making. Thus, the proposed model can be used by governments or agencies to design policy or operationally optimized services. Moreover, the model can serve as the basis for the practical implementation of planned services.

Land Surface Temperature Estimation from Landsat-9 Thermal Infrared Data Using Ensemble Learning Method Considering the Physical Radiance Transfer Process

Accurately estimating land surface temperature (LST) is a critical concern in thermal infrared (TIR) remote sensing. According to the thermal radiance transfer equation, the observed data in each channel are coupled with both emissivity and atmospheric parameters in addition to the LST. To solve this ill-posed problem, classical algorithms often require the input of external parameters such as land surface emissivity and atmospheric profiles, which are often difficult to obtain accurately and timely, and this may introduce additional errors and limit the applicability of the LST retrieval algorithms. To reduce the dependence on external parameters, this paper proposes a new algorithm to directly estimate the LST from the top-of-atmosphere brightness temperature in Landsat-9 two-channel TIR data (channels 10 and 11) without external parameters. The proposed algorithm takes full advantage of the adeptness of the ensemble learning method to solve nonlinear problems. It considers the physical radiance transfer process and adds the leaving-ground bright temperature and atmospheric water vapor index to the input feature set. The experimental results show that the new algorithm achieves accurate LST estimation results compared with the ground-measured LST and is consistent with the Landsat-9 LST product. In subsequent work, further studies will be undertaken on developing end-to-end deep learning models, mining more in-depth features between TIR channels, and reducing the effect of spatial heterogeneity on accuracy validation.

Spatial-temporal characteristics of carbon emissions corrected by socio-economic driving factors under land use changes in Sichuan Province, southwestern China

The spatial-temporal distribution characteristics of carbon emissions under land use changes can fully reflect the impact of socio-economic development caused by human activities on terrestrial ecosystems. However, previous studies just focused on the traditional carbon emission coefficient method which was applied to calculate carbon emission amounts from different land use types at a large spatial scale over a long-time period. This approach did not consider the effects of spatial heterogeneity of socio-economic factors on carbon emissions, which can lead to overestimating and underestimating carbon emissions in intra-study areas. Therefore, it is urgent to build a corrected method integrating socio-economic factors into carbon emission calculation which can make up for this shortcoming. Firstly, this study calculated the carbon emissions under land use changes through the traditional method based on spatial maps of land uses and fossil energy consumption during 2000–2018 in 21 cities (states) in Sichuan Province. From 2000 to 2018, the overall carbon emissions increased by 43.14%, and the high and low carbon emission values occurred in the east and west of the study site, respectively. Chengdu had the largest carbon emissions, and its maximum value appeared in 2015. Only the Tibetan Autonomous Prefecture of Garz (Garz) had a negative carbon emission value. Furthermore, the total carbon emissions were significantly correlated with Gross Domestic Product (GDP) and population. This study then proposed a method to correct carbon emissions by considering the spatial heterogeneity of GDP and population. There were some obvious differences between uncorrected and corrected carbon emissions. From 2000 to 2018, the corrected carbon emissions also showed an increasing trend, but their values were much higher than uncorrected carbon emissions. The city (state) with the largest corrected carbon emissions was still in Chengdu but the maximum value occurred in 2018. The city (state) with negative corrected carbon emissions was still in Garz, but its corrected values were much lower than uncorrected carbon emissions. Additionally, the center of gravity of positive carbon emissions shifted from Ziyang before the correction to Chengdu after the correction during 2000–2018. In summary, the corrected carbon emissions proposed in this study by considering socio-economic driving factors can reflect an actual condition of carbon emissions from land use. The results can offer a scientific basis for the local government to construct low-carbon land use patterns in Sichuan Province. This approach can be promoted to calculate carbon emissions in other study sites at different spatial scales.

Spatial Heterogeneity Effects on Meta-Community Stability of Annual Plants from a Coastal Dune Ecosystem.

Spatial heterogeneity affects plant community composition and diversity. It is particularly noticeable in annual plant communities, which vary in space and time over short distances and periods, forming meta-communities at the regional scale. This study was conducted at the coastal dune ecosystem in Nizzanim nature reserve, Israel. This study aimed to analyze the effect of the spatial heterogeneity, which is expressed in differences in the fixation levels of the dunes and patches outside and beneath the dominant Artemisia monosperma shrubs, on the characteristics of the annual plant meta-community and its temporal stability, considering the mechanisms that may affect it. Thirteen dunes were studied: three mobile, seven semi-fixed, and three fixed dunes. Data on the annual plants were collected during the spring seasons of 2006, 2007, 2009, 2014, 2015, and 2016. For each dune, 72 quadrats of 40×40 cm were sampled yearly, with 24 quadrats per slope aspect (windward, leeward, and crest), 12 under the shrub, and 12 in the open. The results indicate that the transition from mobile dunes through semi-fixed to fixed dunes is characterized by an increase in annual plant cover, species richness, species diversity, changes in plant communities, and stability driven by the asynchrony of species population fluctuations. Asynchrony affected the stability of the meta-community of this ecosystem in patches beneath the shrubs but not in the open patches.

Spatiotemporal Variations and Determinants of Supply–Demand Balance of Ecosystem Service in Saihanba Region, China

Maintaining a supply–demand balance of ecosystem services (ES) is essential for enhancing the effectiveness of ecosystem restoration. However, inappropriate land use and reforestation practices can negatively impact this balance. In this study, the ES balance of the Saihanba region in China was quantified by integrating land use/cover change (LUCC) data, landscape metrics, and ES indicators. The relationship between ES balance and its driving factors was analyzed using spatial panel models. The spatiotemporal changes of landscape patterns from 2002 to 2020 were also explored. The results indicated that the overall ES supply capacity of the study region, especially in the southwestern area, increased during the research period. The ES balance and its determining factors exhibited significant spatial heterogeneity and spillover effects. Large–scale afforestation increased the local ES supply and provided economic benefits, but it also led to ecological issues, including declines in wetland area and landscape fragmentation. Our study emphasized the importance of considering the supply–demand balance in the planning and decision–making of ES, providing insight into multifunctional management and the sustainable development in the Saihanba area.

Effects of Spatial Heterogeneity and Homogeneity on an S-K-T Competition Model

Effects of Spatial Heterogeneity and Homogeneity on an S-K-T Competition Model

Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems

Public transport, especially bus and metro, are fundamental elements of sustainable transport systems. However, a dearth of research has been devoted to exploring the correlation between the built environment and the intermodal transfer modes that link bus and metro. To address this research gap, this study aims to explore the relationship between the built environment and transfer ridership by examining transfer ridership across different modes. First, this study uses Automatic Fare Collection (AFC) and Automatic Vehicle Location (AVL) data collected in the city of Chengdu to identify the ridership of Metro-to-Bus (M-B) and Bus-to-Metro (B-M) transfer passengers using dynamic transfer time thresholds. A multi-scale geographically weighted regression model (MGWR) is employed to examine the impact of the built environment on M-B and B-M transfer modes and their scale effects. The findings demonstrate that the MGWR model is effective in capturing the spatial heterogeneity and scale effects of the interrelationships between different built environment factors in the M-B and B-M modes. Furthermore, the impact of different built environment factors on transfer ridership varies. In particular, the number of bus stops and lines have a more pronounced positive effect on promoting transfer ridership, while the density of non-motorway lanes has a significant negative effect. This research provides valuable insights for public transportation management and supports the seamless integration of bus and metro systems to optimize transfer services.

Spatial non-stationarity effect of determinants regulates variation in amphibian species richness

Despite the variation in amphibian biodiversity being often explained through environmental variables, the effects of spatial non-stationarity factors are too-often ignored as significant geographic characteristics, especially at large scales. Here, using a spatial regression approach through a multiscale analysis, we explored the spatial heterogeneity and scale effects in the impacts of environments on amphibian species richness across China. We showed that the impacts of variables varied with regions, and the individual scales specific to each variable were negatively correlated with effect sizes. We then demonstrated that climate variables, followed by topography, showed a high explanatory power for species richness in most areas, while the effects of precipitation and temperature were characterized by high geographical heterogeneity. In addition, elevation was positively related to local species richness in most plains, while being a main negative variable to species richness in the western highlands. The analysis of geographic heterogeneity showed that the explanatory power of most variables declined with increasing elevation. Although anthropogenic impacts contributed less than climatic variables, they significantly increased the sensitivity of amphibian species to environmental variations. Finally, to measure the aggregation pattern of heterogeneous effects of variables on species richness, we used a neural network to identify ecoregions regulated by similar variables and determined the presence of four ecologically consistent regions. Our findings provide further evidence supporting spatially variables regulators of amphibian diversity.

Sharp profiles for diffusive logistic equation with spatial heterogeneity

Abstract In this article, we study the sharp profiles of positive solutions to the diffusive logistic equation. By employing parameters and analyzing the corresponding perturbation equations, we find the effects of boundary and spatial heterogeneity on the positive solutions. The main results exhibit the sharp effects between boundary conditions and linear/nonlinear spatial heterogeneities on positive solutions.

Transmission dynamics of visceral leishmaniasis with PKDL and periodic delays

We consider the transmission of visceral leishmaniasis in three populations: humans, dogs and sandflies. To investigate the effects of spatial heterogeneity and temporal periodicity on disease transmission, a nonlocal periodic reaction‐diffusion equation model is developed. We introduce the basic reproduction number and establish the global dynamics. Numerical simulations show that (i) is overestimated in the absence of spatial heterogeneity; (ii) is underestimated without considering the effect of temporal seasonality; and (iii) shortening periodic delays of visceral leishmaniasis reduces the risk of disease transmission.