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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Biodiversity describes the variety of living things at multiple organizational levels: genetic, species and ecosystem diversity. Species diversity includes all branches of the evolutionary tree from single-celled prokaryotic organisms, bacteria, and archaea, to the eukaryotic kingdoms: plants; animals; fungi; and protists. To date, there have been about 1.75 million species identified, and new species are discovered every week.
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There have been five major extinction events throughout geological history, resulting in the elimination of biodiversity, followed by a rebound of species that adapted to the new conditions. In the current geological epoch, the Holocene, there is a sixth extinction event in progress. This mass extinction has been attributed to human activities and is thus provisionally called the Anthropocene. In 2019 the human population reached 7.7 billion people and is projected to comprise 10 billion by...
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
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Updated: Nov 20, 2025

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
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A guide to representing variability and uncertainty in biodiversity indicators.

Jessica A Rowland1, Lucie M Bland1,2, Simon James3

  • 1Centre of Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, 3216, Australia.

Conservation Biology : the Journal of the Society for Conservation Biology
|January 24, 2021
PubMed
Summary

Communicating uncertainty in biodiversity indicators is crucial for reliable decision-making. This study provides a guide and decision tree to select appropriate methods for representing data variability and uncertainty in biodiversity metrics.

Keywords:
incertidumbre estadísticaintervalosintervalsreproducibilidadreproducibilitystatistical uncertaintytransparenciatransparencyvariabilidadvariability

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Area of Science:

  • Ecology and Conservation Biology
  • Environmental Science and Policy

Background:

  • Biodiversity indicators are essential for tracking progress toward global targets like the UN Sustainable Development Goals.
  • Current indicators often oversimplify complex data, potentially losing crucial information on variability and uncertainty.
  • Effective communication of uncertainty is vital for robust decision-making and preventing misinterpretation of biodiversity trends.

Purpose of the Study:

  • To develop a practical guide for representing uncertainty and variability in biodiversity indicators.
  • To evaluate the suitability and interpretation of various interval methods for quantifying uncertainty and variability.
  • To provide a decision tree to aid in selecting appropriate methods based on data characteristics and objectives.

Main Methods:

  • Reviewed common methods for representing uncertainty (e.g., standard error, bootstrapping) and variability (e.g., quantiles, standard deviation).
  • Applied and assessed interval methods using three prominent biodiversity indicators: Red List Index, Living Planet Index, and Ocean Health Index.
  • Developed a decision tree to guide the selection of interval methods based on data type and indicator goals.

Main Results:

  • Different interval methods revealed distinct information, with suitability influenced by indicator formulation and data distribution.
  • Methods assuming normal or symmetrical data distributions were unsuitable due to non-normal data underpinning the tested indicators.
  • Quantiles, bootstrapping, and jackknifing proved effective in conveying underlying variability and uncertainty.

Conclusions:

  • A standardized approach to representing uncertainty and variability is needed for biodiversity indicators.
  • The developed guide and decision tree facilitate the transparent and effective communication of indicator trends.
  • Accurate interpretation of biodiversity indicator trends by decision-makers is enhanced through clear uncertainty communication.