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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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Related Experiment Video

Updated: May 8, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Novel three-step pseudo-absence selection technique for improved species distribution modelling.

Senait D Senay¹, Susan P Worner, Takayoshi Ikeda

¹Bio-Protection Research Centre, Lincoln University, Lincoln, New Zealand. senait.senay@lincolnuni.ac.nz

|August 23, 2013

Summary

Selecting pseudo-absence points is crucial for accurate species distribution models (SDMs). This study introduces a novel three-step method balancing spatial and ecological factors for better pseudo-absence selection in SDMs.

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Area of Science:

Ecology
Biogeography
Computational Biology

Background:

Pseudo-absence selection is critical for the accuracy of species distribution models (SDMs).
Current methods for generating pseudo-absences vary in effectiveness and often lack ecological or spatial balance.
There is a need for a robust method that integrates both geographical extent and environmental dissimilarities.

Purpose of the Study:

To develop and present a novel three-step approach for selecting pseudo-absence points for correlative SDMs.
To ensure pseudo-absence points are spatially and ecologically balanced, improving model accuracy.
To provide a method that considers both geographical and environmental criteria for pseudo-absence selection.

Main Methods:

A three-step approach was developed: 1) Defining an ecologically meaningful geographical extent around presence points based on environmental variable importance.
2) Identifying environmentally dissimilar locations within the defined spatial extent.
3) Employing K-means clustering to select representative pseudo-absence points from dissimilar areas.

Main Results:

The novel three-step method effectively generates spatially and ecologically balanced pseudo-absence points.
The method was successfully illustrated by predicting the distribution of Aedes albopictus and Diabrotica virgifera virgifera in New Zealand.
This approach offers an improvement over random or arbitrary pseudo-absence selection techniques.

Conclusions:

The proposed three-step method provides a more appropriate way to select pseudo-absence points for SDMs.
Integrating spatial and environmental data enhances the reliability of pseudo-absence selection.
This method contributes to more accurate species distribution modeling and ecological predictions.