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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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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.
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Analyzing dynamic species abundance distributions using generalized linear mixed models.

Erik Blystad Solbu1, Bert van der Veen1,2,3, Ivar Herfindal3

  • 1Department of Landscape and Biodiversity, Norwegian Institute of Bioeconomy Research (NIBIO), Trondheim, Norway.

Ecology
|May 13, 2022
PubMed
Summary

Generalized linear mixed models offer a new way to analyze species abundance, revealing how environmental changes impact ecological communities. Species heterogeneity significantly influences community similarity across space and time.

Keywords:
Poisson lognormalenvironmental variancegeneralized linear mixed modelpopulation dynamicsspatial and temporal correlationspecies abundance distributionspecies heterogeneityvariance partitioning

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Area of Science:

  • Ecology and Evolutionary Biology
  • Population Dynamics
  • Ecological Modeling

Background:

  • Understanding ecological community dynamics and their response to environmental change is crucial.
  • Traditional population dynamic models struggle to incorporate vital ecological parameters like environmental noise and density regulation.
  • Integrating key species characteristics into community dynamics models presents a significant challenge.

Purpose of the Study:

  • To demonstrate the application of generalized linear mixed models (GLMMs) for fitting dynamic species abundance distributions.
  • To provide ecological interpretations for random effects within GLMMs, linking them to environmental stochasticity and species-specific variations.
  • To assess the accuracy of parameter estimation in relation to density regulation strength.

Main Methods:

  • Utilized intercept-only generalized linear mixed models with various random effects to model dynamic species abundance.
  • Interpreted random effects to represent general and species-specific responses to temporal/spatial environmental stochasticity and variations in growth rate/carrying capacity.
  • Employed simulations to evaluate estimation accuracy based on density regulation strength.

Main Results:

  • Successfully fitted dynamic species abundance distributions using GLMMs with ecologically meaningful random effects.
  • Demonstrated the estimation of population dynamic parameters and covariances, including statistical uncertainties, for fish and bat communities.
  • Identified species heterogeneity as the primary driver of spatial and temporal community similarity in both case studies.

Conclusions:

  • GLMMs provide a flexible framework for analyzing complex ecological community dynamics and incorporating key population parameters.
  • Species heterogeneity plays a critical role in shaping community structure and similarity over space and time.
  • The developed modeling approach enhances our ability to detect and understand changes in ecological communities under environmental pressures.