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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive

Kwaku Peprah Adjei1,2,3, Anders Gravbrøt Finstad2,4, Wouter Koch2,5

  • 1Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway.

Ecology and Evolution
|March 8, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to improve species distribution mapping by accounting for varying misclassification probabilities in biodiversity data. This enhances prediction accuracy for conservation efforts.

Keywords:
Bayesian modelscitizen sciencefalse positivesmachine learningmisclassificationmulti‐species distribution models

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Area of Science:

  • Ecology
  • Biodiversity Science
  • Conservation Biology

Background:

  • Species distribution models (SDMs) are crucial for conservation, but taxonomic misclassifications in biodiversity data pose a significant challenge.
  • Existing methods often assume constant misclassification probabilities, which may not reflect reality and can lead to biased predictions.
  • Heterogeneity in classification probabilities, influenced by covariates, is a key issue in accurately mapping species distributions.

Purpose of the Study:

  • To develop and evaluate a novel multi-species distribution model that explicitly accounts for heterogeneous classification probabilities.
  • To compare the performance of this new heterogeneous model against traditional homogeneous models in terms of parameter estimation and predictive accuracy.
  • To assess the impact of accounting for classification heterogeneity on ecological inference and predictive performance using real-world data.

Main Methods:

  • Developed a multi-species distribution model incorporating a multinomial generalized linear model for the classification confusion matrix to handle heterogeneous misclassification probabilities.
  • Conducted simulation studies to compare the parameter estimation and predictive performance of the heterogeneous model versus a homogeneous model.
  • Applied the models to a dataset of gull occurrences from Norway, Denmark, and Finland, sourced from the Global Biodiversity Information Facility (GBIF).
  • Investigated the use of machine learning predictive scores as weights to inform the classification process in species distribution models.

Main Results:

  • Simulation results demonstrated that accounting for heterogeneity in classification significantly improved the precision of species identity predictions by 30% and accuracy/recall by 6%.
  • No significant impact on ecological process inference was observed, as all models addressed misclassification to some extent.
  • Direct application to the gull dataset showed no improvement with parametric heterogeneous models due to small misclassified sample sizes.
  • Incorporating machine learning scores as weights boosted precision by 70%, particularly effective with smaller misclassified sample sizes.

Conclusions:

  • Multiple multinomial regression is recommended for modeling classification process variations when dealing with substantial amounts of misclassified data.
  • Machine learning prediction scores are highly effective as weights for species distribution models when misclassified samples are relatively few.
  • The developed heterogeneous classification model offers a more robust approach to species distribution modeling, especially when classification errors vary across observations.