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Related Concept Videos

Ecological Niches02:02

Ecological Niches

All organisms have a position within an ecosystem. The complete set of living and nonliving factors—including food resources, climate, and terrain—that define the position of a given organism are collectively referred to as the organism’s ecological niche.
Ecological Niche01:12

Ecological Niche

Microorganisms occupy diverse habitats and perform essential ecological functions that are defined by their ecological niches. A microbial niche encompasses the organism’s mode of survival, including resource acquisition, reproduction, and interactions with other species in its environment. This concept is vital for understanding microbial community dynamics, biogeography, and ecosystem functionality.The fundamental niche of a microorganism includes the full spectrum of environmental...
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Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Related Experiment Video

Updated: May 28, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

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

Published on: October 11, 2016

Spatial Suitability of Peste des Petits Ruminants in North Africa Using Machine-Learning Ecological Niche Modeling.

Dinara Imanbayeva1, Moh A Alkhamis2, John M Humphreys3

  • 1Center for Animal Health and Food Safety (CAHFS), College of Veterinary Medicine, University of Minnesota, Saint Paul, MN 55108, USA.

Pathogens (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

Peste des Petits Ruminants (PPR) suitability in North Africa is highest along coastal areas, driven by sheep density and climate. Inland arid regions show lower suitability, requiring targeted surveillance strategies for this contagious animal disease.

Keywords:
North AfricaPPRRandom Forestecological niche modelingextreme gradient boostingmachine-learningpeste des petits ruminantsspatial cross-validationspatial suitabilitysupport vector machine

Related Experiment Videos

Last Updated: May 28, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

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

Published on: October 11, 2016

Area of Science:

  • Veterinary Epidemiology
  • Disease Ecology
  • Machine Learning Applications in Animal Health

Background:

  • Peste des Petits Ruminants (PPR) is a highly contagious viral disease threatening small ruminants, food security, and livelihoods in Africa, the Middle East, and Asia.
  • Identifying regions prone to PPR outbreaks is challenging due to uneven reporting and spatial clustering in the Mediterranean.

Purpose of the Study:

  • To characterize the spatial suitability of Peste des Petits Ruminants (PPR) in North Africa using an interpretable machine-learning ecological niche modeling framework.
  • To identify key environmental and anthropogenic drivers influencing PPR occurrence in the region.

Main Methods:

  • Compiled a merged outbreak dataset (n=744) from FAO EMPRES-i and WAHIS (2005-2026).
  • Linked outbreak locations to environmental/anthropogenic predictors, performed spatial thinning, and paired with pseudo-absences (1:1 ratio).
  • Compared four machine-learning classifiers (GLM, SVM, RF, XGBoost) using spatial block cross-validation, with RF and XGBoost showing the highest performance (AUC=0.94).

Main Results:

  • Sheep density, mean diurnal temperature range, temperature seasonality, and human population density were identified as dominant drivers of PPR suitability.
  • Predicted PPR suitability was concentrated along the North African coastal belt, with lower suitability in arid inland regions.
  • Random Forest (RF) model exhibited the highest specificity, while XGBoost showed the highest sensitivity.

Conclusions:

  • PPR surveillance efforts in North Africa should prioritize coastal production systems where host density, environmental suitability, and reporting opportunities converge.
  • Areas with lower predicted suitability are not disease-free and may necessitate active surveillance strategies to complement passive reporting.
  • Machine learning models provide valuable insights into the ecological niche of PPR, aiding in targeted disease management and control.