Published April 16, 2026 | Version v4

Dispersal ability modulates fluctuating asymmetry in carabid beetle populations across solar parks and dry grasslands

  • 1. ROR icon HUN-REN Centre for Ecological Research
  • 2. 'Lendület' Landscape and Conservation Ecology, Institute of Ecology and Botany, HUN-REN Centre for Ecological Research, 2163 Vácrátót, Alkotmány út 4, Hungary

Contributors

  • 1. Mendel University in Brno, Faculty of AgriSciences
  • 2. Department of Biostatistics, University of Veterinary Medicine Budapest
  • 3. HUN-REN-DE Anthropocene Ecology Research Group, University of Debrecen

Description

Sampling focused on four dry-grassland specialist species across temperate lowlands in southern Hungary. The geographic coordinates of all study sites (from Google Earth), together with standardized site IDs, are provided in the third attachment to ensure full spatial reproducibility of the sampling design. Habitat types included solar parks and extensive grasslands. 

The dataset comprises multiple Excel sheets corresponding to four ground beetle species: Ophonus cribricollis, Harpalus subcylindricus, Harpalus flavicornis, and Harpalus picipennis. For each species, three paired morphological structures were measured: the second, third, and fourth antennal segments (hereafter a2, a3, and a4). These antennomeres were selected because they are bilaterally symmetrical, easily measurable, and not prone to regeneration or breakage under normal field conditions, making them reliable indicators of subtle developmental instability.

Wing morphotypes were classified based on the degree of wing and flight-muscle development into three categories: (i) macropterous (M)—fully developed wings with functional flight muscles, enabling long-distance dispersal; (ii) brachypterous (B)—reduced wings with functional flight muscles, or fully developed wings lacking functional muscles, precluding sustained flight; and (iii) apterous (A)—absence of wings and flight muscles, restricting mobility to ground locomotion. Wing morphotype was subsequently recoded as a three-point numeric index of dispersal ability (A = 1, B = 2, M = 3), assuming equal step sizes between morphotypes. For species represented by only two morphotypes, only the available dispersal codes were retained. This coding imposes an a priori monotonic increase in dispersal capacity (1 < 2 < 3) and facilitates estimation of linear trends and habitat interactions that would otherwise be lost if morphotype were treated categorically.

The dataset additionally includes dorsal photographs of all four species, comprising a total of 810 specimens collected during May and July cohorts. Each specimen was measured twice from the same photograph, with measurements conducted blind to the habitat of origin. Antennomeres a2, a3, and a4 were measured alongside the right elytron, which served as a proxy for body size to account for allometric relationships. These antennomeres were chosen for their high measurement precision and repeatability, thereby minimizing measurement error. Each row of the dataset contains paired left–right measurements for the same individual, enabling direct comparison of sides and rigorous estimation of measurement error for fluctuating asymmetry analyses.

Fluctuating asymmetry can be influenced by multiple sources of bias, including genetic stress, additive environmental stress, allometry, directional asymmetry, and antisymmetry. After applying the established filtering protocol (see https://doi.org/10.1002/ece3.70793), the dataset provides estimates of unbiased, true fluctuating asymmetry.

Future research on fluctuating asymmetry would benefit from integrating epigenetic mechanisms such as DNA methylation, direct quantification of flight-muscle development as a more precise proxy for dispersal ability, and measurements of fat reserves as indicators of physiological condition and survivability.

The published paper for these datasets can be found here: https://doi.org/10.1016/j.gecco.2026.e04256
The statistical approach and R scripts used in our study can be found at: DomecStoces/Deeplearning

Files

Measuring_in_xlsx.zip

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Additional details

Funding

National Research, Development and Innovation Office
NKFIH-OTKA-FK-142929

Dates

Updated
2026-02-08
Added background info

Software

Repository URL
https://github.com/DomecStoces/Deeplearning.git
Programming language
Python , R
Development Status
Active