Related Concept Videos
Threats to Biodiversity
Habitat Fragmentation
Hypothesis: Accept or Fail to Reject?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...
Uncertainty: Overview
What is a Species?
Migration
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
Resilient Antarctic soil bacteria consume trace gases across wide temperature ranges.
Microbial aerotrophy enables continuous primary production in diverse cave ecosystems.
Related Experiment Video
Updated: May 21, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
Published on: July 11, 2025
Uncertainty in invasive alien species listing.
Melodie A McGeoch1, Dian Spear, Elizabeth J Kleynhans
1Cape Research Centre, South African National Parks, P.O. Box 216, Steenberg 7947, South Africa. melodiemcgeoch@gmail.com
Errors in invasive alien species (IAS) lists can lead to underestimations of IAS numbers. Understanding and addressing these listing errors is crucial for effective biodiversity conservation and biosecurity management.
Area of Science:
- Ecology
- Conservation Biology
- Environmental Management
Background:
- Lists of invasive alien species (IAS) are critical tools for managing biological invasions.
- Existing IAS lists contain various errors that compromise their scientific and policy applications.
Purpose of the Study:
- To classify and quantify errors in IAS listing.
- To improve the understanding, communication, and management of these errors.
Main Methods:
- Collated and classified IAS listing errors using a taxonomy of uncertainty.
- Estimated the magnitude of these errors using data from a completed listing exercise.
Main Results:
- Identified ten distinct errors, primarily stemming from epistemic uncertainty (lack of knowledge/measurement error) and linguistic uncertainty (vagueness/context dependence).
- Substantial error effects were observed, often leading to underestimation of IAS numbers, with some effects remaining poorly understood.
- Specialist disagreement can significantly impact IAS lists, especially in data-poor situations, without a consistent directional effect.
Conclusions:
- Five key tactics were identified to reduce uncertainty in IAS lists, enhancing transparency, repeatability, and comparability.
- Addressing listing errors and uncertainties is vital for improving the utility of IAS lists for conservation and biosecurity.
- Improved understanding of IAS listing errors is increasingly important due to rising biological invasion trends and associated risks.
