Population Density Research Articles

Air cargo transport and economic development in emerging economies: A case study of Brazil with panel data analysis

Air cargo transportation accounts for less than 1% of the global trade volume, yet it represents approximately 35% of the total value of goods transported, highlighting its strategic importance in trade and economic development. This study investigates the relationship between domestic air cargo transport in Brazil and key macroeconomic variables, focusing on how regional economic dynamism, logistical infrastructure, and population density impact the country’s development. Using a panel data regression model covering the period from 2000 to 2020, the study analyzes the evolution of air cargo transportation and its role in redistributing economic growth across Brazil’s regions. The findings emphasize the key factors influencing the air cargo sector and demonstrate how these factors can be leveraged to optimize public policies and business strategies. This research provides valuable insights into the relevance of air cargo transportation for regional and national development, particularly in emerging economies like Brazil, offering guidance for the formulation of strategies that promote balanced economic growth across regions.

Dünn in der Fläche? Regionale Disparitäten in der Hospiz- und Palliativversorgung in Deutschland

IntroductionOutpatient and inpatient hospice and palliative care services have been significantly expanded in Germany in recent decades and are increasingly being supplemented by day care services. However, the availability of these services varies greatly from region to region. The extent to which the availability of these care structures is matched by a regional need based on local population structures is as yet unknown. MethodsIn four Poisson regression models, the relationship between population indicators from the areas of demographics, employment, income, education and health and the number of offers of outpatient and inpatient hospice work and palliative care is exploratively examined. The cumulated data at district level is drawn from the INKAR database of the Federal Institute for Research on Building, Urban Affairs and Spatial Development on living conditions in Germany and from the guide of the German Association for Palliative Medicine. By means of logistic regression, factors influencing the establishment of day care services will also be identified. ResultsThe analysis included 401 districts and cities in Germany. The number of inhabitants, settlement density, and the average age of inhabitants are the strongest predictors of the number of palliative care services. In metropolitan regions, both outpatient palliative care services and palliative care units tend to be more frequently available, while the number of outpatient hospice services and inpatient hospices increases in districts with a higher number of inhabitants regardless of settlement density. The regression model was unable to demonstrate a significant influence on the emergence of semi-inpatient care facilities, neither for the population indicators nor for the existing care structures. DiscussionRegional population structures can only partially explain the geographically uneven distribution of hospice and palliative care services in districts and cities in Germany. Despite an increase in hospice and palliative care services, fewer hospice and palliative care structures are available in low population density regions that tend to have a higher share of older inhabitants. ConclusionFuture health care planning should give more consideration to other population characteristics than to population size alone in order to improve care in regions with higher care needs that are, for example, due to a higher proportion of older residents. Subsequent studies should investigate which population characteristics can best describe the actual care needs.

URBAN HEAT ISLAND ANALYSIS USING GIS AND REMOTE SENSING IN KOSHOFE LAGOS STATE

Urban heat islands (UHIs) are a significant concern in urban areas, particularly in tropical regions like Lagos, Nigeria. This study employs geographic information systems (GIS) and remote sensing (rs) techniques to analyze the urban heat island effect in Koshofe, a rapidly urbanizing area in Lagos. The research aims to identify the spatial distribution, intensity, and factors influencing UHI in koshofe. Using Landsat 8 and sentinel-2 satellite imagery, land surface temperature (LST) was retrieved and analyzed. GIS spatial analysis and mapping techniques were applied to examine the relationship between lst, land use/land cover (LULC), population density, and urban morphology. The study also investigated the impact of urban planning strategies on UHI mitigation. Results show that Koshofe experiences significant UHI effects, with lst varying between 28°c and 38°c. Built-up areas, particularly commercial and industrial zones, exhibited higher (LST) values compared to vegetated areas. Population density and urban density were found to be positively correlated with (LST). The study identified areas with high UHI risk, including major road networks and informal settlements.

Stress and the City: Mental Health in Urbanized vs. Rural Areas in Salzburg, Austria

Background: Living in the city is associated with a higher risk of suffering from stress, anxiety, and depression. Due to an increase of migration to the city, the association between mental health and city life is highly relevant to society. Methods: We analyzed data of 9573 participants (Ø 55.3 years, SD = 7.4) of the Paracelsus 10,000 study (Salzburg, Austria) who were classified into having or not having depressive symptoms. Population density, green space, and noise around the home address of the participants were collected and tested for correlations with mental health defined by depressive symptoms. We additionally tested whether migration status influenced the effect of urbanization on mental health. Results: There is a positive correlation between degree of urbanization and the probability of suffering from depressive symptoms (p = 0.011), yet this effect is independent of the migration background (p = 0.581). Participants in areas with high residential density were significantly more likely to suffer from poor mental health (p = 0.006 unadjusted). No significant association could be found between mental health and noise (p = 0.126 unadjusted) or green space neither regarding distance to closest green space (p = 0.549 unadjusted), nor size of green space (p = 0.549 unadjusted). Conclusions: In the Paracelsus 10,000 cohort, living in the city is associated with lower mental health, especially in participants with a high population density in the direct neighborhood. This might be due to social stress yet does not reflect minority stress in migrants. However, the influence of noise pollution and green space on mental health is limited in this cohort.

An investigation of regional public transport spend in Australia

This paper describes the outcome of a study to investigate the regional public transport spend in each state of Australia. The objective is to derive a reliable figure for comparison and the core metric used is spend per capita. The study assessed publicly available data on public transport spend, followed by a series of consultations with key staff from state bus associations and governments. The analysis was conducted separately for metropolitan (metro) and regional areas and then the regional data were further examined. While the Northern Territory (NT), Tasmania (TAS) and regional South Australia (SA) completely rely on bus services, Victoria (VIC) stands out as the state with the highest public transport spend per head in regional areas with robust train and bus networks which deliver the highest patronage. Western Australia (WA) is the state with the highest bus transport spend per head ensuring relatively high patronage despite the low population density. The study identifies low public transport patronage in regional areas as being influenced by low spending per capita and poor connectivity. It recommends prioritizing improvements in connectivity, multi-modal networks, and opening school buses to the general public. Additionally, funding strategies should focus more on patronage trends linked to spend rather than overemphasizing population density. Further, the study reveals that publicly available data seldom clearly segregates public transport spending in terms of regional and metro expenditure, which would lead to inefficiencies when assessing spatial equity in public transport provision.

Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach

The rapid urbanization occurring globally has significantly intensified the challenges of waste management in densely populated metropolitan areas. A growing amount of waste has become a major concern for municipal authorities and local governments due to the limited availability of suitable land. Geospatial techniques, such as Geographic Information Systems (GISs) and remote sensing, combined with machine learning, play a crucial role in identifying suitable sites for urban waste management. These techniques assist planners in making well-informed decisions that strike a balance between environmental preservation and urban expansion by examining spatial data on land use, population density, and environmental concerns. Geospatial tools provide a data-driven basis for policy and urban planning, ensuring effective land use, reducing ecological hazards, and promoting sustainable urban growth for municipalities such as English Bazar and Old Malda. It can also pose serious threats to the environment, public health, and communities. Focusing on the English Bazar and Old Malda Municipalities in India, this paper examines the use of geospatial technologies to identify suitable sites for waste disposal. The research aims to address the complex processes of waste generation, collection, and disposal in urban environments. Using GIS and a Multi-Criteria Decision Analysis (MCDA) approach, the study employs the Analytic Hierarchy Process (AHP) alongside the Random Forest (RF) model and a machine learning (ML) technique to identify potential waste disposal sites within the English Bazar and Old Malda Municipalities in the Malda district. Eight key criteria were considered in the site selection process: land elevation; distances from surface water, roads, railways, and urban areas; groundwater depth; land use and land cover; and distance from sensitive and restricted areas. AHP analysis showed that 8%, 26%, and 27% of the sites were categorized as very highly suitable, moderately suitable, and unsuitable, respectively. Meanwhile, 38%, 17%, and 13% of the areas were classified as unsuitable, moderately suitable, and very highly suitable according to the RF model. The overall accuracy and Kappa coefficient indicated that the AHP method (overall capacity of 83.83% and Kappa coefficient of 0.7894) was slightly better than the RF model (overall capacity of 80.61% and Kappa coefficient of 0.7474) for site suitability analysis. This research underscores the broad relevance of geospatial technology in creating resilient and environmentally sustainable cities while offering valuable guidance on effectively allocating waste disposal sites. The findings provide crucial insights for urban planners and decision-makers, facilitating the identification of optimal locations for sustainable waste management in urban settings.

Exploring the Freshwater Cladocera (Pleuroxus spp.) as New Potential Live Feed in Aquaculture

In freshwater lakes, cladocera play a crucial role in the food chain, as they are the primary source of nutrition for nearly all aquatic species. Pleuroxus spp. are very suitable for aquaculture due to their ease of handling, rapid growth, non-selective filter feeder, high nutritional value, and remarkable ability to adapt to many environmental conditions. Silver barbs are in high demand in the aquaculture business due to their excellent flavours and rapid development rate. In this study, Pleuroxus spp. was cultured in a control condition with water quality maintained at the room temperature of 26 ± 1 °C and pH of 7.0 ± 0.5. Five treatments of Pleuroxus spp. were prepared, which comprised of T1 (control) = unfed, T2 = coffee grounds, T3 = corn flour, T4 = wheat flour, and T5 = sugarcane waste. Pleuroxus spp. were fed with a selected diet solutions at a concentration of 0.5 g/L daily. The result shows that Pleuroxus spp. given T3 (24.88 ± 5.48 ind/mL) had a greater population density than T4 (22.13 ± 4.83 ind/mL), T2 (21.06 ± 5.27 ind/mL), and T5 (20.56 ± 4.92 ind/mL) dietary treatments. Pleuroxus spp. given T3 had the greatest survival rate (97.9 ± 9.8%) when compared to other diets, T4 (86.81 ± 19.07%), T2 (82.71 ± 16.56%), and T5 (75.00 ± 21.08%). Silver barb showed the highest amount of daily weight gain in treatment T3 (0.11 ± 0.001 mg/days; p < 0.05) compared with other dietary treatments. The maximum specific growth rate (SGR) (%) was achieved when fed with T3 (13.37 ± 0.02%; p < 0.05). The feed conversion ratio (FCR) was shown to be significantly different (p < 0.05). The FCR of larvae fed with different nutritional treatments ranged from 1.22 to 4.12, depending on the treatment. In comparison to other diets, the T3 treatment (1.22 ± 0.03) achieved the highest FCR ratio. In short, pre-feeding of zooplankton with prospective diets has built a baseline of improved aquaculture feeding procedures that may be employed in the future aquaculture businesses.

Analyzing urban water metabolism of Adama city using water mass balance method for advancing water sensitive interventions

Urban water metabolism focuses on measuring water inflows and outflows within a defined urban system. As an emerging concept, it provides valuable understandings into water flow dynamics, supporting evidence-based decision-making. One approach to quantify these flows is the urban water mass balance method, which accounts for both human-induced and natural water resources. By equating these flows, it identifies whether water movement within the system is linear or circular. The primary goal of water mass balance analysis is to assess how closely a city aligns with water-sensitive management approaches. However, urban metabolism studies are rare in developing countries, where cities often lack the experience to estimate water inflows and outflows for informed water-sensitive interventions. This study addresses this gap by analyzing Adama city in Ethiopia using the water mass balance method to measure its water metabolism. The result revealed that the city faces a negative water balance with outflows exceeding inflows by 46.89 million cubic meters annually. The results indicated that Adama’s water flow follows a linear “take-make-use-dispose” model. The imbalance in Adama’s water cycle is driven by urbanization, impervious surfaces, and climate change, which increase runoff and evaporation. The study found that 61.3% of the city’s water comes from a centralized system, with 90% sourced from distant rivers through a telecoupling system. In the city, inadequate water harvesting, high population density and intensive water use are worsening water scarcity. Urban water metabolism indicators reveal significant losses and indicating the need for water conservation efforts. Despite the reliance on centralized systems, the study identifies strong potential for decentralized solutions and alternative water harvesting. To tackle these challenges, the research recommends adopting water-sensitive strategies such as low-impact development, sustainable urban drainage systems, and water-sensitive urban design and planning. These approaches can reduce the negative effects of urbanization, mitigate urban water scarcity risks and improve water management through water sensitive management approach. The study also emphasizes the need for collaborative learning, community involvement, and innovative technologies, supported by legal frameworks to ensure effective water wise interventions. Shifting toward circular water management and decentralized water systems will boost Adama’s resilience and promote sustainable water resource management, making the city more internally self-sufficient.

Interactive Effects of Built Environment and Parking Policy on Car Use: Examining Differences Between Work and Non-Work Trips

Considerable interest has been shown in decreasing car use through planning and policy-making efforts. However, little is known about the interactive effects of built environment (BE) and parking policy on car use, and it is unclear whether and how these effects differ across trip purposes. We conducted a web-based survey in Beijing and collected data from 1036 respondents, including 517 male and 519 female respondents. This study estimates the interactive effects of BE and parking policy on car use for home-based work and non-work trips by employing multilevel logit models. The results show that BE variables at trip origins and destinations are important for shaping car use for both home-based work and non-work trips. Specifically, land use mixture (Coeff. = −0.121), bus stop density (Coeff. = −0.006), and population density (Coeff. = −0.009) at residential locations are negative factors affecting car use for work trips, whereas distance to local center (Coeff. = 0.012) and distance to the city center (Coeff. = 0.019) at residential locations are positive factors. Land use mixture (Coeff. = −0.323), bus stop density (Coeff. = −0.008), road density (Coeff. = −0.002), and population density (Coeff. = −0.007) at residential locations are negative factors of car use for non-work trips. Among BE factors at destinations, land use mixture (Coeff. = −0.319), bus stop density (Coeff. = −0.015), road density (Coeff. = −0.008), distance to the local center (Coeff. = −0.018), and population density (Coeff. = −0.012) are negative factors for car use for work trips, whereas the negative factors for non-work trips are land use mix (Coeff. = −0. 218), bus stop density (Coeff. = −0.038), road density (Coeff. = −0.003), distance to the city center (Coeff. = −0.121), and population density (Coeff. = −0.009). The effects of BE variables can be strengthened or weakened by free parking and parking convenience. Moreover, the results identify significant differences in the effects between work and non-work trips. These findings inform planners and policymakers of how to coordinate the BE and parking policies to decrease car dependence.

The types of growing media and plant density on the growth and yield of kailan (Brassica oleracea L.) In a floating raft system hydroponic

This research aimed to know the effect of the type of growing media and population density on the growth and yield of kailan (Brassica oleracea L.) hydroponically. The research was conducted in the greenhouse of the integrated agricultural system area at Curug Serang Banten Province. This research used a Randomized Completely Block Design (RCBD) as factorial with two factors. The first factor was the type of growing media (J) which consisted of 3 levels, J1 (Rockwool), J2 (Zeolite), and J3 (Sponge). The second factor was population density (K), which consisted of 3 treatment levels, K1 (3 plants per tub), K2 (4 plants per tub), and K3 (5 plants per tub). The results showed that the type of growing media had a significant effect on plant height growth 7 Day After Plating (DAP) of (4.67cm), 14 DAP (11.58 cm), 21 DAP (17.09 cm), 28 DAP (22.29 cm), 35 DAP (37.57 cm), number of leaves 14 DAP (4.59), 28 HST (6.96), 35 DAP (8.59) and the root weight of your plants was 35 DAP (3.43gram). Population density treatment had a significant effect on plant height growth 28 DAP (22.26 cm), 35 DAP (37,37 cm) and there was an interaction between media type treatment and population density on plant height parameters 35 DAP.

Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning

Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes.

Incidência de tripes (<i>Thrips tabaci</i>) em populações superadensadas de cebola submetidas a doses de nitrogênio e seu efeito na biomassa dos bulbos

The study aimed to verify the incidence of thrips in higher population densities subjected to doses of nitrogen and its possible effects on the biomass production of onion bulbs. The treatments were four plant densities (400, 600, 800 thousand plants ha-1 and 1 million plants ha-1) subjected to two doses of nitrogen (150 and 200 kg N ha-1). The higher temperatures in the initial development and the absence of rain from the end of August to the end of September lead to an early incidence of thrips above the level of economic damage. The incidence of thrips is reduced linearly with increasing plant density, not being influenced by doses of up to 200 kg N ha-1. The use of nitrogen doses above 150 kg N ha-1, even in populations of one million plants per hectare, does not increase the biomass of onion bulbs. Population densities above 600 thousand plants ha-1 reduce the fresh and dry biomass of onion bulbs.

Poverty rate as an indicator of disproportions in the spatial development of territories

Subject. The article addresses the relationship between the poverty rate of the population and sustainable and balanced spatial development of territories. Objectives. The study aims at scientific substantiation of the need to reduce the rate of poverty of the population as an imperative to achieve balanced spatial development of territories; indicative analysis of spatial development of territories in the projection "poverty rate of the population" based on statistical data from the Rosstat. Methods. I employed the indicative analysis, index method, and "zone theory". Results. The study demonstrated that poverty is one of the key indicators of disproportions in spatial development of territories, which, in accordance with the concept of "geography of poverty", is due to a lack of investment, remoteness from sources of natural resources, population density, and insufficient spread of innovations. An indicative analysis of the poverty rate in two regions of the Volga Federal District showed the relationship between the spatial development of the regions and the factors of the "geography of poverty". Conclusions. To ensure sustainable and balanced spatial development of regions characterized by a high rate of poverty, the paper offers a number of strategic directions.

Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops.

Accurate identification and estimation of the population densities of microscopic, soil-dwelling plant-parasitic nematodes (PPNs) are essential, as PPNs cause significant economic losses in agricultural production systems worldwide. This study presents a comprehensive review of emerging techniques used for the identification of PPNs, including morphological identification, molecular diagnostics such as polymerase chain reaction (PCR), high-throughput sequencing, meta barcoding, remote sensing, hyperspectral analysis, and image processing. Classical morphological methods require a microscope and nematode taxonomist to identify species, which is laborious and time-consuming. Alternatively, quantitative polymerase chain reaction (qPCR) has emerged as a reliable and efficient approach for PPN identification and quantification; however, the cost associated with the reagents, instrumentation, and careful optimisation of reaction conditions can be prohibitive. High-throughput sequencing and meta-barcoding are used to study the biodiversity of all tropical groups of nematodes, not just PPNs, and are useful for describing changes in soil ecology. Convolutional neural network (CNN) methods are necessary to automate the detection and counting of PPNs from microscopic images, including complex cases like tangled nematodes. Remote sensing and hyperspectral methods offer non-invasive approaches to estimate nematode infestations and facilitate early diagnosis of plant stress caused by nematodes and rapid management of PPNs. This review provides a valuable resource for researchers, practitioners, and policymakers involved in nematology and plant protection. It highlights the importance of fast, efficient, and robust identification protocols and decision-support tools in mitigating the impact of PPNs on global agriculture and food security.

MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.

In order to assess risk of mosquito-vector borne disease and to effectively target and monitor vector control efforts, accurate information about mosquito vector population densities is needed. The traditional and still most common approach to this involves the use of traps along with manual counting and classification of mosquito species, but the costly and labor-intensive nature of this approach limits its widespread use. Numerous previous studies have sought to address this problem by developing machine learning models to automatically identify species and sex of mosquitoes based on their wingbeat sounds. Yet little work has addressed the issue of robust classification in the presence of environmental background noise, which is essential to making the approach practical. In this paper, we propose a new deep learning model, MosquitoSong+, to identify the species and sex of mosquitoes from raw wingbeat sounds so that it is robust to the environmental noise and the relative volume of the mosquito's flight tone. The proposed model extends the existing 1D-CNN model by adjusting its architecture and introducing two data augmentation techniques during model training: noise augmentation and wingbeat volume variation. Experiments show that the new model has very good generalizability, with species classification accuracy above 80% on several wingbeat datasets with various background noise. It also has an accuracy of 93.3% for species and sex classification on wingbeat sounds overlaid with various background noises. These results suggest that the proposed approach may be a practical means to develop classification models that can perform well in the field.

Evaluating wetland ecosystem services value and dominant functions: Insights from the Pearl River Delta

Weakening wetland ecosystem services (ES) significantly hinders the achievement of the Sustainable Development Goals (SDGs). It is essential to combine multiple methods for evaluating wetland ecosystem services value (ESV) and to clearly depict the spatial distribution of ESVs. Based on the local conditions of the Pearl River Delta (PRD), this study proposed a monetary evaluation system for wetland ESV, developed a method for spatially allocating individual ESVs, and identified the dominant wetland functions across different cities and types of wetlands. The results yielded the following findings: (1) The wetland ESV system effectively identifies differences in ESV across cities and wetland types. The PRD's ESV increased by 23.29% between 2000 and 2020. (2) The new spatial allocation method analyzed individual ESVs to provide spatial references for improving wetland ESV. Fishery products and flood control and water storage are the two largest wetland functions in the PRD. All ESVs increased except for temperature regulation and water purification. (3) The identification and classification of dominant wetland functions provide insights into site-specific management of urban wetlands informed by ESV. These results provide a reference for assessing wetland ESV in other delta regions facing high population density and wetland degradation pressures. Understanding the role of wetland ESs in supporting the SDGs and how they interconnect and contribute to their achievement will be a key future research topic.

Intraspecific predator interference promotes biodiversity in ecosystems.

Explaining biodiversity is a fundamental issue in ecology. A long-standing puzzle lies in the paradox of the plankton: many species of plankton feeding on a limited variety of resources coexist, apparently flouting the competitive exclusion principle (CEP), which holds that the number of predator (consumer) species cannot exceed that of the resources at a steady state. Here, we present a mechanistic model and demonstrate that intraspecific interference among the consumers enables a plethora of consumer species to coexist at constant population densities with only one or a handful of resource species. This facilitated biodiversity is resistant to stochasticity, either with the stochastic simulation algorithm or individual-based modeling. Our model naturally explains the classical experiments that invalidate the CEP, quantitatively illustrates the universal S-shaped pattern of the rank-abundance curves across a wide range of ecological communities, and can be broadly used to resolve the mystery of biodiversity in many natural ecosystems.

Peacock mites on cannon-ball tree, Couroupita guianensis (Lecythidaceae) in the Southeast Region of Brazil

Tuckerella ornata, Tuckerella knorri (Acari: Tuckerellidae) and Brevipalpus yothersi (Acari: Tenuipalpidade) were found associated with the native South American Cannon-ball tree, Couroupita guianensis, for the first time in Brazil. The trees were observed throughout their flowering season in spring for two consecutive years. The highest population densities of peacock and flat mites were located on the raceme-type inflorescences. Throughout this survey, no mites were detected on the fruits.

Intraspecific predator interference promotes biodiversity in ecosystems

Explaining biodiversity is a fundamental issue in ecology. A long-standing puzzle lies in the paradox of the plankton: many species of plankton feeding on a limited variety of resources coexist, apparently flouting the competitive exclusion principle (CEP), which holds that the number of predator (consumer) species cannot exceed that of the resources at a steady state. Here, we present a mechanistic model and demonstrate that intraspecific interference among the consumers enables a plethora of consumer species to coexist at constant population densities with only one or a handful of resource species. This facilitated biodiversity is resistant to stochasticity, either with the stochastic simulation algorithm or individual-based modeling. Our model naturally explains the classical experiments that invalidate the CEP, quantitatively illustrates the universal S-shaped pattern of the rank-abundance curves across a wide range of ecological communities, and can be broadly used to resolve the mystery of biodiversity in many natural ecosystems.

Spatiotemporal Variations and Driving Factor Analysis of Aerosol Optical Depth in Terrestrial Ecosystems in Northern Xinjiang from 2001 to 2023

Investigating the spatiotemporal variations in Aerosol Optical Depth (AOD) in terrestrial ecosystems and their driving factors is significant for deepening our understanding of the relationship between ecosystem types and aerosols. This study utilized 1 km resolution AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Mann–Kendall (M-K) trend test to analyze the spatiotemporal variations in AOD in seven ecosystems in Northern Xinjiang from 2001 to 2023. The geographic detector model was employed to investigate the effects of driving factors, including gross domestic product, population density, specific humidity, precipitation, temperature, wind speed, soil moisture, and elevation, on the distribution of AOD in the ecosystems. The results indicate that over the past 23 years, wetlands had the highest annual average AOD values, followed by settlements, farmlands, deserts, grasslands, others, and forests, respectively. Furthermore, the AOD values decrease with increasing ecosystem elevation. The annual mean of AOD in Northern Xinjiang generally shows a fluctuating upward trend. The M-K test shows that the proportion of area with an increasing trend in AOD in the settlement ecosystems is the highest (92.17%), while the proportion of area with a decreasing trend in the forest ecosystem is the highest (21.78%). On a seasonal scale, grassland, settlement, farmland, forest, and wetland ecosystems exhibit peak values in spring and winter, whereas desert and other ecosystems only show peaks in spring. Different types of ecosystems show different sensitivities to driving factors. Grassland and forest ecosystems are primarily influenced by temperature and altitude, while desert and settlement ecosystems are most affected by wind speed and humidity. Farmlands are mainly influenced by wind speed and altitude, wetlands are significantly impacted by population density and humidity, and other ecosystems are predominantly affected by humidity and altitude. This paper serves as a reference for targeted air pollution prevention and regional ecological environmental protection.