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Related Experiment Video

Updated: Oct 25, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Plotting receiver operating characteristic and precision-recall curves from presence and background data.

Wenkai Li1, Qinghua Guo2

  • 1Guangdong Provincial Engineering Research Center for Remote Sensing and Monitoring of Water Environment School of Geography and Planning Sun Yat-Sen University Guangzhou China.

Ecology and Evolution
|August 9, 2021
PubMed
Summary

A new Presence-Background (PB) approach calibrates species distribution model performance curves using presence and background data. This method offers more reliable Receiver Operating Characteristic (ROC) and Precision-Recall (PR) plots than traditional methods when absence data is unavailable.

Keywords:
area under the curvemodel evaluationprecision–recall curvepresence and background datareceiver operating characteristic curvespecies distribution modeling

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Area of Science:

  • Ecology
  • Computational Biology
  • Statistical Modeling

Background:

  • Receiver Operating Characteristic (ROC) and Precision-Recall (PR) plots are crucial for evaluating species distribution models (SDMs).
  • Traditional methods require presence and absence data (PA approach), which are often unavailable in ecological studies.
  • Presence-only (PO) approaches, using background data as pseudo-absences, can yield misleading performance evaluations.

Purpose of the Study:

  • To introduce a novel Presence-Background (PB) approach for calibrating ROC/PR curves.
  • To address the challenge of evaluating SDMs when only presence and background data are available.
  • To provide a method for estimating the detection probability constant 'c' from PB data.

Main Methods:

  • Developed the PB approach, incorporating a user-defined constant 'c' representing detection probability.
  • Tested the PB approach using five virtual species datasets and one real aerial photography dataset.
  • Compared PB-derived ROC/PR curves against traditional PA and PO approaches using various SDMs and sample sizes.

Main Results:

  • PB-based ROC/PR curves and their areas under the curve (AUC) closely resembled those from the PA approach.
  • The PO approach showed greater deviation from the PA approach compared to the PB method.
  • The PB approach accurately estimated the detection probability constant 'c' in experimental settings.

Conclusions:

  • The proposed PB-based ROC/PR plots offer a valuable alternative for SDM evaluation, especially when absence data is scarce.
  • This method provides a reliable way to estimate species prevalence ('c') from presence and background data.
  • PB plots serve as a significant complement to existing SDM assessment techniques.