Intensity of grass invasion negatively correlated with population density and age structure of an endangered dune plant across its range

2021 ◽  
Author(s):  
Scott F. Jones ◽  
Anna Kennedy ◽  
Chase M. Freeman ◽  
Karen M. Thorne
Keyword(s):  
Population Density ◽  
Age Structure ◽  
Dune Plant ◽  
Grass Invasion

Population age structure of the spotted tree frog (Litoria spenceri): insights into population declines

2010 ◽  
Vol 37 (1) ◽  
pp. 19 ◽  
Author(s):  
Graeme Gillespie
Keyword(s):  
Population Density ◽  
Age Structure ◽  
Amphibian Conservation ◽  
First Year ◽  
Tree Frog ◽  
Population Demography ◽  
Population Declines ◽  
Relative Population ◽  
Population Age Structure

Context. Despite increased scientific attention on amphibian conservation in recent years, knowledge of population demography of amphibians remains scarce, hampering evaluation of population declines and development of appropriate management responses. Aims. The aims of this research were to examine variation in population demography of the spotted tree frog (Litoria spenceri), a critically endangered species in Australia, and to evaluate the role of various factors potentially responsible for population declines such as introduced trout, chytridiomycosis and habitat changes. Methods. Skeletochronology combined with mark–recapture sampling were undertaken in two different river systems, Bogong Creek and Taponga River, to determine population age structure. Age-specific survival estimates were derived from each population and were then used to examine variance in age-specific mortality. Key results. Relative population density per 200 m of stream was 67.7 adults and 131.3 juveniles at Bogong Creek and 10.7 adults and 33.8 juveniles at Taponga River. Ages were determined for 578 frogs across the two populations. Age-specific survival was lowest in the first year of life compared to all other age classes, and highest in sexually mature adults. Differences in age-specific survival were similar between the populations, with the exception of first-year survivorship, which averaged 1.9% at Bogong Creek and 0.4% at Taponga River. This difference was large enough to explain most of the marked difference in population density between the two streams. Key conclusions. The difference in first-year age-specific survival is consistent with trout predation as the most parsimonious explanation for the large differences in population density between the populations, and lends further weight to the role of introduced trout in the decline of this species. Implications. This study has contributed to informing management actions for conservation of this species, and demonstrates that population age structure data may provide valuable insights into demographic variability within and between populations and species. This may have important implications for interpretation of population declines, and conservation and management responses.

Population density and age structure of the freshwater pearl mussel, Margaritifera margaritifera, in two Iberian rivers

2008 ◽  
Vol 53 (3) ◽  
pp. 485-496 ◽  
Author(s):  
ADOLFO OUTEIRO ◽  
PAZ ONDINA ◽  
CARLOS FERNÁNDEZ ◽  
RAFAELA AMARO ◽  
EDUARDO SAN MIGUEL
Keyword(s):  
Population Density ◽  
Age Structure ◽  
Margaritifera Margaritifera ◽  
Pearl Mussel ◽  
Freshwater Pearl Mussel ◽  
Iberian Rivers

Population Density and Age Structure of Three Species of Hylid Tadpoles

1975 ◽  
Vol 9 (3) ◽  
pp. 287 ◽  
Author(s):  
Glyn Turnipseed ◽  
Ronald Altig
Keyword(s):  
Population Density ◽  
Age Structure

scholarly journals A New Index to Assess Vulnerability to Regional Shrinkage (Hollowing out) Due to the Changing Age Structure and Population Density

2021 ◽  
Vol 13 (16) ◽  
pp. 9382
Author(s):  
Jimin Lee ◽  
Kyo Suh
Keyword(s):  
Rural Communities ◽  
Population Density ◽  
Correlation Analysis ◽  
Age Structure ◽  
Population Distribution ◽  
Local Population ◽  
Vulnerability Index ◽  
Population Changes ◽  
Population Indices ◽  
Using Data

In South Korea, there is an awareness of the risks of regional shrinkage and depopulation due to demographic changes and unbalanced population distribution. With concerns about the extinction of local cities and the hollowing out of rural communities, scholars have increasingly called for new population indices or indicators to evaluate the current state of the local population. The purpose of this study was to develop a vulnerability index to effectively analyze the age structure and population changes associated with regional shrinkage (i.e., hollowing out). This study applied ranking and correlation analysis results using data for population density and the population structure by age to develop a new index to assess a region’s vulnerability to the regional shrinkage effect. The new vulnerability index identified vulnerable regions by evaluating regional vulnerability using 2019 data. We also conducted a correlation analysis to validate the new index and found that the proposed index was significantly correlated with population growth and all other demographic indicators. The index developed in this study can be used to assess and compare the vulnerability of areas to regional shrinkage following population changes.

scholarly journals COVID-19 and socioeconomic development in Africa: The first 6 months (February 2020-August 2020)

2020 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
M. Michel Garenne
Keyword(s):  
Population Density ◽  
Age Structure ◽  
Socioeconomic Development ◽  
Case Fatality ◽  
World Health ◽  
Gdp Per Capita ◽  
Reproduction Rate ◽  
African Countries ◽  
The World ◽  
Per Capita

The study covers the first 6 months of the coronavirus disease 2019 (COVID-19) epidemics in 56 African countries (February 2020-August 2020). It links epidemiological parameters (incidence, case fatality) with demographic parameters (population density, urbanization, population concentration, fertility, mortality, and age structure), with economic parameters (gross domestic product [GDP] per capita, air transport), and with public health parameters (medical density). Epidemiological data are cases and deaths reported to the World Health Organization, and other variables come from databases of the United Nations agencies. Results show that COVID-19 spread fairly rapidly in Africa, although slower than in the rest of the world: In 3 months, all countries were affected, and in 6 months, approximately 1.1 million people (0.1% of the population) were diagnosed positive for COVID-19. The dynamics of the epidemic were fairly regular between April and July, with a net reproduction rate R0 = 1.35, but tended to slow down afterward, when R0 fell below 1.0 at the end of July. Differences in incidence were very large between countries and were correlated primarily with population density and urbanization, and to a lesser extent, with GDP per capita and population age structure. Differences in case fatality were smaller and correlated primarily with mortality level. Overall, Africa appeared very heterogeneous, with some countries severely affected while others very little.

scholarly journals Influence of Socio-Economic, Demographic and Climate Factors on the Regional Distribution of Dengue in the United States and Mexico

2020 ◽  
Author(s):  
Matthew Watts ◽  
Panagiota Kotsilla ◽  
P Graham Mortyn ◽  
Victor Sarto i Monteys ◽  
Cesira Urzi Brancati
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Population Density ◽  
Age Structure ◽  
Regional Distribution ◽  
The United States ◽  
Economic Indicators ◽  
Climate Factors ◽  
Demographic Indicators ◽  
The Impact

Abstract Background: Dengue is one of the important vector-borne diseases in the world today; it infects tens of millions of people each year and has been on the rise since the 1950s. In this study, we develop a set of indicators that help us examine the impact of socio-economic and demographic factors on the occurrence of dengue in regions of the United States and Mexico. Methods: We assess the relationship between dengue occurrence in humans, climate factors (temperature and minimum quarterly rainfall), socio-economic factors (such as household income, regional rates of education, housing overcrowding, life expectancy, and medical resources), and demographic factors (such as migration flows, age structure of the population, and population density). Areas at risk of dengue are first selected based on the predicted presence of at least one of the two mosquito vectors responsible for dengue’s transmission: Aedes aegypti and Aedes albopictus. In those regions where the vectors had a high probability of presence, we assess the impact of the composite socio-economic indicators (derived through factor analysis to account for collinearity), and three composite demographic indicators (also derived from factor analysis) on the regional distribution of dengue cases, controlling for climate and spatial correlation. Results: We found that an increase of one unit in one of our socio-economic indicators representing labour force with at least secondary education, better broadband access, and rooms per inhabitant, a higher proportions of active physicians is related to a drop in the occurrence of dengue, whereas the demographic indicators such as population density, age structure of the population and population growth showed no significant impact after taking climate into account. More importantly, our socio-economic indicator can also explain differences in the occurrence of dengue across Mexico, whereas simpler measures, such as regional GDP could not. Conclusions: These results suggest that the set of indicators developed is a better indicator than GDP at predicting the distribution of dengue, by capturing information that is much more tailored to poverty related conditions which aid dengue transmission. Given that data for these indicators are available at a sub-national scale for OECD countries and selected OECD non-member economies, these indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations.

scholarly journals Unsupervised machine learning and pandemics spread: the case of COVID-19

2020 ◽  
Author(s):  
Roberto Silva ◽  
Fernando Xavier ◽  
Antonio Saraiva ◽  
Carlos Cugnasca
Keyword(s):  
Machine Learning ◽  
Population Density ◽  
Age Structure ◽  
Disease Spread ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Number Of Clusters ◽  
Unsupervised Machine Learning ◽  
Over Time

Epidemics have severe impacts on people's health. The COVID-19 has infected more than 3 million people in 3 months. In this work, we explore the use of unsupervised machine learning to evaluate and monitor the disease spread worldwide in three points in time: January, February, and March of 2020. Besides the features related to the disease spread, we consider HDI, population density, and age structure. We define the number of clusters using the elbow and agglomerative clustering methods, then implement and evaluate the k-means algorithm with 3, 4, and 5 clusters. We conclude that four clusters better represent the data, analyze the clusters over time, and discuss the impacts on each depending on the measures adopted.

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