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Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

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...

Uncertainty: Overview

Uncertainty: Overview

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.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Conservation of Small Populations

Conservation of Small Populations

Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...

Conservation of Declining Populations

Conservation of Declining Populations

Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.

Habitat Fragmentation

Habitat Fragmentation

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: Jul 9, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Recommendations for quantitative uncertainty consideration in ecology and evolution.

Emily G Simmonds¹, Kwaku P Adjei², Benjamin Cretois³

¹The Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute for Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway; Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FL, UK.

Trends in Ecology & Evolution

|November 29, 2023

Summary

Ecological and evolutionary studies often lack complete reporting of model uncertainty. Addressing this requires better statistical methods, clearer metrics, and considering all uncertainty sources for robust scientific conclusions.

Keywords:

modelling parameter propagation uncertainty

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Last Updated: Jul 9, 2025

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Area of Science:

Ecology and Evolutionary Biology
Ecological Modeling
Evolutionary Modeling

Background:

Inconsistent reporting of model-related uncertainty is prevalent in ecological and evolutionary studies.
Key barriers include a narrow focus on parameter uncertainty, unclear metrics, and insufficient recognition of uncertainty propagation.

Purpose of the Study:

To identify deficiencies in uncertainty reporting within ecological and evolutionary research.
To propose actionable strategies for improving the completeness and consistency of uncertainty reporting.

Main Methods:

Analysis of current practices in ecological and evolutionary modeling.
Review of statistical solutions and best practices from other scientific disciplines.

Main Results:

Identified three primary barriers to comprehensive uncertainty reporting: focus on parameter uncertainty, obscure metrics, and limited propagation awareness.
Highlighted existing statistical methods and cross-disciplinary practices applicable to ecological and evolutionary modeling.

Conclusions:

Uncertainty reporting in ecology and evolution can be significantly enhanced.
Recommendations include broader application of statistical solutions, adopting interdisciplinary best practices, and developing field-specific standards.