Yu. G. Bespalov

This paper examines the relationship between diversity and evenness in protective coloration of animals and their adaptive properties, using colorimetric analysis and machine learning for image recognition.

The paper builds mathematical models of animal protective coloration based on diversity and evenness parameters to improve remote detection technologies.

The paper uses mathematical models to compare protective coloration strategies of Taurotragus oryx in different environments, finding that natural selection increases the diversity of quasi-adaptations.

The paper models systemic colorimetric parameters to process images of toxic cyanobacteria clumps for boundary detection.

The paper develops a discrete dynamical model to explore biodiversity-stability relationships across organizational levels, using case studies of zooplankton and microalgae communities.