Related Experiment Video
Updated: Mar 17, 2026

09:23
Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
Published on: November 1, 2017
12.6K
Summary
This study introduces a new two-sex life table model to reconcile demographic differences between males and females. The findings suggest that total marriages in a population are not limited by one-sex demographic rates.
Area of Science:
- Demography
- Mathematical Biology
- Sociology
Background:
- The "problem of the sexes" in demography arises from inconsistent male and female demographic rates.
- Reconciling these differing rates is crucial for accurate population modeling.
Purpose of the Study:
- To address the "problem of the sexes" using a two-sex nuptiality-mortality life table.
- To introduce a "rectangular" population standard for demographic analysis.
- To develop a standardization relationship for age-sex composition.
Main Methods:
- Development of a two-sex nuptiality-mortality life table.
- Introduction of a "rectangular" population as a demographic standard.
- Derivation of a standardization relationship (equation 9) to link population rates with age-sex composition.
Main Results:
- The proposed standardization relationship exhibits desirable properties and generates a realistic two-sex demographic model.
- Application to 1973 Swedish data demonstrates the model's utility.
- The total number of marriages in a two-sex population is not constrained by one-sex nuptiality-mortality tables.
Conclusions:
- The novel standardization approach effectively reconciles demographic inconsistencies between sexes.
- The model provides a more realistic representation of two-sex population dynamics.
- Findings challenge traditional assumptions about marriage rate limitations in demographic analysis.
Related Concept Videos
Life Histories
23.1K
Overview
23.1K
Life Tables
609
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
609
Applications of Life Tables
397
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
397
Population Growth
29.4K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
29.4K
Survival Curves
829
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
829
Parametric Survival Analysis: Weibull and Exponential Methods
1.2K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.2K

