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Environmental Science and Pollution... Aug 2021Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is...
Currently, 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. We collected data from official webpages of cities in China and in the USA. The data were organized on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between morbidity and other factors. Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the USA, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the USA have no association, with an r value of - 0.1288. Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.
Topics: COVID-19; China; Humans; Pandemics; Physical Distancing; Population Density; SARS-CoV-2
PubMed: 33442802
DOI: 10.1007/s11356-021-12364-4 -
Annual Review of Animal Biosciences Feb 2023Deleterious mutations decrease reproductive fitness and are ubiquitous in genomes. Given that many organisms face ongoing threats of extinction, there is interest in... (Review)
Review
Deleterious mutations decrease reproductive fitness and are ubiquitous in genomes. Given that many organisms face ongoing threats of extinction, there is interest in elucidating the impact of deleterious variation on extinction risk and optimizing management strategies accounting for such mutations. Quantifying deleterious variation and understanding the effects of population history on deleterious variation are complex endeavors because we do not know the strength of selection acting on each mutation. Further, the effect of demographic history on deleterious mutations depends on the strength of selection against the mutation and the degree of dominance. Here we clarify how deleterious variation can be quantified and studied in natural populations. We then discuss how different demographic factors, such as small population size, nonequilibrium population size changes, inbreeding, and gene flow, affect deleterious variation. Lastly, we provide guidance on studying deleterious variation in nonmodel populations of conservation concern.
Topics: Animals; Genetics, Population; Models, Genetic; Inbreeding; Mutation; Population Density; Genetic Variation
PubMed: 36332644
DOI: 10.1146/annurev-animal-080522-093311 -
The Science of the Total Environment Jun 2024Accurate prediction of fluctuations of wildlife local number of individuals is crucial for effective population management to minimise human-wildlife conflicts. Climate,...
Accurate prediction of fluctuations of wildlife local number of individuals is crucial for effective population management to minimise human-wildlife conflicts. Climate, habitat, food availability, and density dependence are among the main factors influencing mammalian population dynamics. In southern Europe, precipitation and temperature, particularly during summer have been suggested as key factors affecting wild boar (Sus scrofa L.). However, there is uncertainty regarding the role of these factors and the mechanisms driving population fluctuations. This study utilized long-term data of wild boar populations from 14 study sites collected for 23 years in Catalonia, Spain, to analyse the factors that drive population density and growth rate. Generalized Additive Mixed Models (GAMM) explained respectively, 94 % and 65 % of the density and growth rate variability. Spring precipitation in both current and previous year, female weight, and forest cover (particularly above 60 %) were directly associated with higher wild boar densities and population growth rates. The interaction between crop cover and total annual precipitation also played a significant role in determining population density. Higher densities were linked to lower population growth in the following year, likely due to a density-dependent process. These results suggest that the expected decrease in rainfall linked with global warming may limit the availability of natural resources and potentially slow wild boar population growth. Nevertheless, wild boar can exploit alternative anthropogenic food sources, potentially leading to an increase of human-wildlife conflicts. Therefore, incorporating management policies aimed at restricting wild boar access to human food sources is key for controlling their reproductive output. Additionally, landscape management strategies targeted at diminishing refuge and resource availability in regions experiencing high wild boar impact are essential for contributing to sustainable coexistence between wild boars and human populations.
Topics: Animals; Sus scrofa; Spain; Population Density; Population Growth; Ecosystem; Population Dynamics; Animals, Wild; Conservation of Natural Resources
PubMed: 38697537
DOI: 10.1016/j.scitotenv.2024.172739 -
Ecology Letters Feb 2023Although it is well established that density dependence drives changes in organismal abundance over time, relatively little is known about how density dependence affects...
Although it is well established that density dependence drives changes in organismal abundance over time, relatively little is known about how density dependence affects variation in abundance over space. We tested the hypothesis that spatial trade-offs between food and safety can change the drivers of population distribution, caused by opposing patterns of density-dependent habitat selection (DDHS) that are predicted by the multidimensional ideal free distribution. We addressed this using winter aerial survey data of northern Yellowstone elk (Cervus canadensis) spanning four decades. Supporting our hypothesis, we found positive DDHS for food (herbaceous biomass) and negative DDHS for safety (openness and roughness), such that the primary driver of habitat selection switched from food to safety as elk density decreased from 9.3 to 2.0 elk/km . Our results demonstrate how population density can drive landscape-level shifts in population distribution, confounding habitat selection inference and prediction and potentially affecting community-level interactions.
Topics: Animals; Deer; Ecosystem; Population Density; Predatory Behavior; Seasons; Wolves; Parks, Recreational; Northwestern United States
PubMed: 36573288
DOI: 10.1111/ele.14155 -
Ecological Applications : a Publication... Oct 2022Information about how animal abundance varies across landscapes is needed to inform management action but is costly and time-consuming to obtain; surveys of a single...
Information about how animal abundance varies across landscapes is needed to inform management action but is costly and time-consuming to obtain; surveys of a single population distributed over a large area can take years to complete. Surveys employing small, spatially replicated sampling units improve efficiency, but statistical estimators rely on assumptions that constrain survey design or become less reasonable as larger areas are sampled. Efficient methods that avoid assumptions about similarity of detectability or density among replicates are therefore appealing. Using simulations and data from >3500 black bears sampled on 73 independent study areas in Ontario, Canada, we (1) quantified bias induced by unmodeled spatial heterogeneity in detectability and density; (2) evaluated novel, design-based estimators of average density across replicate study areas; and (3) evaluated two estimators of the variance of average density across study areas: an analytic estimator that assumed an underlying homogeneous spatial Poisson point process for the distribution of animals' activity centers, and an empirical estimator of variance across study areas. In simulations where detectability varied in space, assuming spatially constant detectability yielded density estimates that were negatively biased by 20% to 30%; estimating local detectability and density from local data and treating study areas as independent, equal replicates when estimating average density across study areas using the design-based estimator yielded unbiased estimates at local and landscape scales. Similarly, detectability of black bears varied among study areas and estimates of bear density at landscape scales were higher when no information was shared across study areas when estimating detectability. This approach also maximized precision (relative SEs of estimates of average black bear density ranged from 7% to 18%) and computational efficiency. In simulations, the analytic variance estimator was robust to threefold variation in local densities but the empirical estimator performed poorly. Conducting multiple, similar SECR surveys and treating them as independent replicates during analyses allowed us to efficiently estimate density at multiple scales and extents while avoiding biases caused by pooling spatially heterogeneous data. This approach enables researchers to address a wide range of ecological or management-related questions and is applicable with most types of SECR data.
Topics: Animals; Data Collection; Ontario; Population Density; Ursidae
PubMed: 35441452
DOI: 10.1002/eap.2638 -
Proceedings. Biological Sciences Nov 2023Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called...
Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity and the fixation of maladaptive alleles, hindered adaptation and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.
Topics: Animals; Biological Evolution; Population Dynamics; Population Density; Probability; Extinction, Biological
PubMed: 37989246
DOI: 10.1098/rspb.2023.1228 -
International Journal of Environmental... Nov 2021Urban population density distribution contributes towards a deeper understanding of peoples' activities patterns and urban vibrancy. The associations between the...
Urban population density distribution contributes towards a deeper understanding of peoples' activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of "rapid growth-gentle decline-rapid growth-rapid decline". Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.
Topics: China; Cities; Humans; Population Density; Spatial Analysis; Urban Population; Urbanization
PubMed: 34831916
DOI: 10.3390/ijerph182212160 -
BMC Genomics Nov 2022Inferring the demographic history of a population is essential in population genetic studies. Though the inference methods based on the sequentially Markov coalescent...
BACKGROUND
Inferring the demographic history of a population is essential in population genetic studies. Though the inference methods based on the sequentially Markov coalescent can present the population history in detail, these methods assume that the population size remains unchanged in each time interval during discretizing the hidden state in the hidden Markov model. Therefore, these methods fail to uncover the detailed population history in each time interval.
RESULTS
We present a new method called Beta-PSMC, which introduces the probability density function of a beta distribution with a broad variety of shapes into the Pairwise Sequentially Markovian Coalescent (PSMC) model to refine the population history in each discretized time interval in place of the assumption that the population size is unchanged. Using simulation, we demonstrate that Beta-PSMC can uncover more detailed population history, and improve the accuracy and resolution of the recent population history inference. We also apply Beta-PSMC to infer the population history of Adélie penguin and find that the fluctuation in population size is contrary to the temperature change 15-27 thousand years ago.
CONCLUSIONS
Beta-PSMC extends PSMC by allowing more detailed fluctuation of population size in each discretized time interval with the probability density function of beta distribution and will serve as a useful tool for population genetics.
Topics: Animals; Population Density; Computer Simulation; Likelihood Functions; Research Design; Spheniscidae
PubMed: 36451098
DOI: 10.1186/s12864-022-09021-6 -
Biology Letters Feb 2023Most small rodent species display cyclic fluctuations in their population density. The mechanisms behind these cyclical variations are not yet clearly understood....
Most small rodent species display cyclic fluctuations in their population density. The mechanisms behind these cyclical variations are not yet clearly understood. Density-dependent effects on reproductive function could affect these population variations. The fossorial water vole ecotype, , exhibits multi-year cyclical dynamics with outbreak peaks. Here, we monitored different water vole populations over 3 years, in spring and autumn, to evaluate whether population density is related to male reproductive physiology. Our results show an effect of season and inter-annual factors on testis mass, plasmatic testosterone level, and androgen-dependent seminal vesicle mass. By contrast, population density does not affect any of these parameters, suggesting a lack of modulation of population dynamics by population density.
Topics: Animals; Male; Population Density; Seasons; Population Dynamics; Arvicolinae
PubMed: 36815586
DOI: 10.1098/rsbl.2022.0441 -
Molecular Biology and Evolution Aug 2021The multispecies coalescent model provides a natural framework for species tree estimation accounting for gene-tree conflicts. Although a number of species tree methods...
The multispecies coalescent model provides a natural framework for species tree estimation accounting for gene-tree conflicts. Although a number of species tree methods under the multispecies coalescent have been suggested and evaluated using simulation, their statistical properties remain poorly understood. Here, we use mathematical analysis aided by computer simulation to examine the identifiability, consistency, and efficiency of different species tree methods in the case of three species and three sequences under the molecular clock. We consider four major species-tree methods including concatenation, two-step, independent-sites maximum likelihood, and maximum likelihood. We develop approximations that predict that the probit transform of the species tree estimation error decreases linearly with the square root of the number of loci. Even in this simplest case, major differences exist among the methods. Full-likelihood methods are considerably more efficient than summary methods such as concatenation and two-step. They also provide estimates of important parameters such as species divergence times and ancestral population sizes,whereas these parameters are not identifiable by summary methods. Our results highlight the need to improve the statistical efficiency of summary methods and the computational efficiency of full likelihood methods of species tree estimation.
Topics: Computer Simulation; Models, Genetic; Phylogeny; Population Density; Probability
PubMed: 33492385
DOI: 10.1093/molbev/msab009