Application of multilayer perceptron neural networks in classifying management effects based on morphometric traits of Carabus granulatus

V. Langraf; K. Petrovičová; Z. Lehká; V. V. Brygadyrenko

doi:10.15421/012607

V. Langraf Constantine the Philosopher University in Nitra
K. Petrovičová Constantine the Philosopher University in Nitra
Z. Lehká Constantine the Philosopher University in Nitra
V. V. Brygadyrenko Constantine the Philosopher University in Nitra

Keywords: Carabidae, Carabus granulatus, management, Danube floodplains, Slovakia.

Abstract

Morphometric traits are essential tools for understanding the variability of organisms and their responses to env i ronmental conditions. They provide valuable insights into ecological processes, as changes in body size and proportions often reflect adaptations to habitat characteristics and environmental pressures. During the years 2020 – 2023, we co n ducted research in 6 study areas falling under three habitats ( willow -poplar floodplain forest, poplar nursery, alluvial meadow) in the Danube Floodplains Protected Landscape Area (CHKO Dunajské Luhy ). This study examined the variability of nine morphometric characters (body length, body height, body width, head lenght , head width, pronotum lenght , pronotum width, right elytron , left elytron ) in the ground beetle Carabus granulatus Linnaeus, 1758 in relation to management, habitat, and sex. A total of 992 individuals (440 males, 552 females) were analyzed. Individuals from the managed sites showed slightly higher values in body width, head width, and pronotum width, whereas those from u n managed habitats exhibited greater body length, body height, and elytral length. Generalized linear models revealed that habitat and sex were the main factors influencing morphometric variability, while management had a weaker effect. Habitat significantly affected multiple traits, with lower values observed in more disturbed environments. Sex had a strong influence on most traits, with males consistently smaller than females, confirming sexual dimorphism. A Mult i layer Perceptron model demonstrated high classification performance (accuracy = 0.925), indicating that morphometric traits effectively distinguish individuals based on management. Overall, morphometric variability is primarily driven by habitat conditions and sex, with management playing a secondary role. Morphometric traits can be applied in enviro n mental monitoring as sensitive indicators of habitat quality and ecological changes. Their analysis enables more effe c tive assessment of land management practices and supports conservation strategies aimed at maintaining biodiversity and ecosystem stability.

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