Related Experiment Video
Updated: Jul 4, 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
Estimating the functional form for the density dependence from life history data.
T Coulson1, T H G Ezard, F Pelletier
1Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, United Kingdom. t.coulson@imperial.ac.uk
Ecology
|July 1, 2008
Summary
This study integrates demographic and time series analyses for population dynamics, revealing density dependence as the primary driver of Soay sheep population fluctuations, followed by climate and age structure.
Area of Science:
- Ecology
- Population Biology
- Quantitative Biology
Background:
- Traditional population dynamics studies use either demographic or time series approaches.
- Demographic approaches link population statistics to demographic rates.
- Time series approaches link population dynamics to density and environmental factors.
Purpose of the Study:
- To develop and apply a combined demographic and time series approach for analyzing population dynamics.
- To investigate the influence of density dependence and climate on population size through age- and sex-specific demographic rates.
- To understand how demographic structure fluctuations impact population dynamics.
Main Methods:
- Combined demographic and time series analysis.
- Application to detailed Soay sheep (Ovis aries) population data.
- Development of a simplified density-dependent, stochastic, age-structured demographic model.
- Derivation of a new phenomenological time series model.
Main Results:
- Density dependence was the most significant factor influencing population size fluctuations.
- Climatic variation, age structure fluctuations, and their interactions with density also contributed.
- The derived time series model better captured population dynamics than previous models.
- The integrated approach provided insights into individual- and population-level processes.
Conclusions:
- Integrating demographic and time series methods offers a powerful framework for understanding population dynamics.
- Density dependence plays a crucial role in regulating population size in Soay sheep.
- The developed simplified model enhances the analysis of populations in fluctuating environments.
More Related Videos
Related Concept Videos
01:29Life Histories
Constrained by limited energy and resources, organisms must compromise between offspring quantity and parental investment. This trade-off is represented by two primary reproductive strategies; K-strategists produce few offspring but provide substantial parental support, whereas r-strategists produce much progeny that receives little care. These strategies are related to an organism’s survival likelihood across its lifespan, which is represented by a survivorship curve. Three general types of...
01:14Parametric Survival Analysis: Weibull and Exponential Methods
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...
01:25Modeling with Differential Equations
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:22Life Tables
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,...
00:57Population Growth
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.However, realistic environmental conditions limit the number of...