Next Article in Journal
The Effect of Magnesium on Production, Phenology, and Seed Vigor of Cowpea Landrace Varieties (Vigna unguiculata (L.)) Under Salt Stress
Previous Article in Journal
Long-Term Fertilization Mediates Microbial Keystone Taxa to Regulate Straw-Derived 13C Incorporation in Soil Aggregates
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 9
10.3390/agronomy15092117
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modeling the Seasonal and Spatial Dynamics of Epigeic Fauna in the Context of Vineyard Landscape Use Using Machine Learning

by
Vladimír Langraf
1,* and
Kornélia Petrovičová
2
1
Department of Zoology and Anthropology, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, 94974 Nitra, Slovakia
2
Faculty of Agrobiology and Food Resources, Slovak University of Agriculture in Nitra, 94976 Nitra, Slovakia
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(9), 2117; https://doi.org/10.3390/agronomy15092117
Submission received: 10 August 2025 / Revised: 30 August 2025 / Accepted: 2 September 2025 / Published: 3 September 2025
(This article belongs to the Section Pest and Disease Management)
Browse Figures
Versions Notes

Abstract

Epigeic groups play a key ecological role in vineyards, as they represent a significant component of soil and surface communities that directly affect the functioning of the agroecosystem. They act as predators, decomposers of organic matter, and important regulators of pest populations, thereby contributing to the natural biological protection of the vineyard. We conducted research between 2021 and 2023, where we monitored the impact of different types of vineyard landscape habitats on the spatial distribution and abundance of epigeic fauna. Over the study period, 57,964 individuals were recorded, with the highest abundance observed in 2023 and the lowest in 2022. Redundancy analysis confirmed a significant impact of habitat type on community composition, especially in semi-intensive and intensive vineyards, meadows, and abandoned sites, with the differences being statistically significant in all monitored habitats. The interannual changes indicated a significant decrease in biodiversity in 2022, followed by a significant increase in 2023, indicating a positive effect of changing management practices and natural succession on restoring ecological stability. The K-nearest neighbor (KNN) prediction model successfully classified individual years based on the number of individuals and taxa with an accuracy of 97%, with 2021 characterized by lower biodiversity, 2022 by a transitional state, and 2023 by a higher taxa and abundance level. The findings highlight the sensitivity of epigeic fauna communities to management and environmental changes and confirm that the application of gentle agri-environmental measures can significantly contribute to the maintenance and restoration of biodiversity in agricultural landscapes.

1. Introduction

At the heart of the current discourse on sustainable viticulture is the question of the extent to which local management and landscape structure shape communities of epigeic fauna, predators, and decomposers, which fundamentally influence nutrient cycling, biological pest regulation, and the overall ecological stability of the agroecosystem. Meta-analyses and syntheses throughout Europe show that the effects of agri-environmental measures are significantly moderated by the complexity of the surrounding landscape and the chosen farming system; schemes work best in simpler landscapes and with management that supports vegetation cover and diversity of nonproductive elements (e.g., row covers, borders, meadow belts) [1,2]. In the context of vineyards, it has been repeatedly confirmed that organic or more extensive practices and taxa herbaceous cover in the interrows increase the abundance and taxa of key groups and help ensure more stable natural predation [3,4]. However, it is also true that individual taxonomic responses differ and the effects of organic vs. conventional management can manifest themselves in contradictory ways depending on the functional properties of the taxa and the spatial context [5,6].
Research on epigeic fauna from different bioclimatic zones from the Mediterranean to temperate regions has shown that maintaining continuity and diversity in interrow vegetation cover, incorporating green manure, and reducing soil disturbance increase the richness and activity of epigeic predators and enhance natural pest suppression. At the same time, these softer interventions do not necessarily lead to increased herbivore pressure on the vines [7,8]. Scientific works complete the picture by emphasizing the importance of habitat mosaic and proximity to natural vegetation for maintaining Araneae and Coleoptera diversity, even where the vineyards themselves achieve relatively high alpha diversity [9,10]. Finally, recent studies confirm that targeted selection of species-diverse cover mixtures and fine-tuning of intervention intensity can bring synergistic benefits for arthropod diversity and ecosystem services; however, they also point out the need for local calibration according to climate, soil, and landscape contexts [11,12].
The seasonal dynamics of epigeic fauna in vineyards is shaped by a combination of abiotic factors (temperature, precipitation, and soil moisture) and biotic processes (vegetation phenology, food availability, and predator-prey interactions), with individual taxa showing different phenological patterns. In temperate climates, the peak activity for most predator groups typically occurs in spring and summer, when temperatures are favorable and when prey availability is high. Conversely, some groups are more active in early spring or autumn, reflecting their life cycles, diapause strategies, and winter survival mechanisms [13,14,15].
In vineyard interiors, these seasonal peaks can fluctuate depending on management; for example, interrows with stable vegetation cover maintain higher activity throughout the season, while in intensively cultivated vineyards, predator activity is concentrated in shorter time windows. Furthermore, vegetation cover mitigates extreme microclimatic conditions in summer and provides shelter during drier periods, thus extending the period of high activity [16,17]. Seasonal dynamics also affect the interannual stability of communities. Populations that reach high abundance in two to three key periods of the year can better withstand adverse conditions in the following season. Long-term monitoring programs show that seasonal fluctuations are not only the result of climatic conditions, but also the cumulative effects of previous management and the availability of seminatural refuges around vineyards [18,19,20].
The purpose of the paper is to analyze the spatiotemporal dynamics of epigeic taxa in vineyards with different management during different seasons, capturing local effects of vegetation cover and the influence of landscape structure.

2. Materials and Methods

During the years 2021 to 2023, we conducted research in eight study areas in the cadastral territory of the municipality of Obyc. The studied area has a warm temperate to humid climate and mild winters, and the soil type is brown earth. Geomorphologically, it falls under the Danube upland. Each vineyard study area is approximately 3 hectares in size. During the period under review, we recorded an increase in temperature in 2022 and lower temperatures during 2021 and 2023 from data from the Slovak Hydrometeorological Institute. The total precipitation was balanced over the years (Table 1).
The description of the study areas is as follows (Figure 1):
  • S1—Overgrown, abandoned vineyard, where the vines are overgrown with Prunus spinosa and Rosa rubiginosa species. It is located 282 m above sea level. Geographic coordinates: 48°25′15″ N, 18°26′27″ E.
  • S2—Overgrown, abandoned vineyard, where the vines are overgrown with Prunus spinosa and Rosa rubiginosa species. It is located 281 m above sea level at 48°25′17.8″ N, 18°26′32.4″ E.
  • S3—Meadow, which was originally a vineyard 50 years ago and is mowed twice a year. It is located at an altitude of 282 m above sea level at 48°25′15.8″ N, 18°26′27.6″ E.
  • S4—Meadow, which was originally a vineyard 50 years ago and is mowed twice a year. It is located at an altitude of 284 m above sea level at 48°25′16.9″ N, 18°26′29.5″ E.
  • S5—Intensive vineyard, which, once a year, the grass is mowed, the old vines are removed, and a new one is planted. It is located 262 m above sea level at 48°25′31.7″ N, 18°26′46.0″ E.
  • S6—Intensive vineyard, which, once a year, the grass is mowed, the old vines are removed, and a new one is planted. It is located 274 m above sea level at 48°25′41.5″ N, 18°26′54.2″ E.
  • S7—Semi-intensive vineyard, which, once a year, the grass is cut and the vines are pruned. The old vines are removed, and no new ones are planted. It is located 280 m above sea level at 48°25′35.2″ N, 18°26′48.8″ E.
  • S8—Semi-intensive vineyard, which, once a year, the grass is cut and the vines are pruned. The old vines are removed, and no new ones are planted. It is located 278 m above sea level at 48°25′36.1″ N, 18°26′49.7″ E.
During the years 2021 to 2023, from March to November, we collected epigeic fauna at regular monthly intervals. The spring season includes the months of March, April, and May. The summer season includes the months of June, July, and August, and the autumn season includes the months of September, October, and November. In total, we had 216 samples over all 3 years, i.e., 27 samples for each study area (1–8). We used the pitfall traps method, where, in each study area, we placed five pitfall traps in a line and used formalin (4%) as a fixative. The distance between the two pitfall traps was 10 m; therefore, the total distance between all pitfall traps (1–5) was 40 m. We determined the study material in order according to [21]. A digital microscope (Celestron HDM) was used for taxon identification. The material was preserved in 97% ethanol and stored at the UKF. Mineral (industrial) fertilizers were used in the intensive vineyard, and organic fertilization was used in the semi-intensive vineyard.

Statistical Analysis

The association of taxa with vineyard biotopes (intensive vineyard, semi-intensive vineyard, overgrown, and meadow) and years (2021–2023) during seasons was analyzed by using redundancy analysis (RDA). Statistical significance was tested using the Monte Carlo permutation test in the Canoco5 program [22].
We tested the normality of the distribution of the number of individuals using the Shapiro–Wilks test. We tested the difference in the number of individuals between the years 2021–2023 and between seasons using the Friedman test. Using the K-nearest neighbor (KNN) algorithm (machine learning), we predicted the development of species and the number of individuals during the years 2021–2023. We performed the analyses in Python 3.12 [23].

3. Results

Across the study period, a total of 57,964 individuals were recorded. The highest abundance was observed in 2023 (54.39%), while the lowest was in 2022 (18.31%). In 2021, individuals accounted for 27.30% of the total. Eudominant representation (>10%) was found during 2021 in the following taxa: Hymenoptera (36.23%), Coleoptera (15.82%), Oniscidea (11.52%), and Araneae (11.33%). In 2022, we confirmed such a representation in the taxa Hymenoptera (18.87%), Coleoptera (15.96%), Glomerida (15.12%), Julida (13.96%), and Oniscidea (12.34%). In 2023, the eudominant representation was Hymenoptera (48.08%), Coleoptera (20.09%), and Araneae (11.92%) (Table 2).
The redundancy analysis (SD = 0.8 on the first ordination axis) revealed the influence of different biotopes (semi-intensive vineyard, intensive vineyard, abandoned vineyard, and meadow) on the spatial distribution of epigeic groups across seasons and years (2021–2023). The values of the explained cumulative variability of the taxa data were 40.28% on the first ordination axis and 54.71% on the second ordination axis. Due to the influence of land use, the variability on the first ordination axis increased to 55.71%, and on the second cumulative axis, there was an increase to 75.67%. A significant influence on the spatial distribution of epigeic fauna was confirmed in semi-intensive vineyards (p = 0.011), intensive vineyards (p = 0.018), meadow (p = 0.002) and overgrown (p = 0.024).
The ordination graph shows the largest cluster of taxa in 2023 across all habitats and seasons. Another cluster was seen in 2021 during spring and summer. During 2022 and autumn 2021, there is a link with only two taxa, Julida and Oniscidea. These differences point to significant interannual changes. The first occurred in autumn 2021, when the number of taxa decreased compared to spring and summer (2021), and lasted throughout 2022. The second change occurred in 2023, when the number of taxa increased significantly. These changes may be related to habitat management, such as vineyard management or a more intensive overgrown succession (Figure 2).
Using the Shapiro–Wilks test, we confirmed the broken normality of the distribution of the number of individuals for all habitats, intensive vineyard (p = 0.0007), meadow (p = 0.0001), semi-intensive vineyard (p = 0.0001), and overgrown (p = 0.0001). Based on the broken normality of the data distribution, we used the nonparametric Friedman test, which tested the difference in the number of individuals between years (2021–2023) and seasons. We confirmed a statistically significant difference in the number of individuals between years (2021–2023) (Figure 3) and seasons in all types of vineyard habitats: intensive vineyard (p = 0.0112), semi-intensive vineyard (p = 0.0052) (Figure 4), overgrown (p = 0.0314) (Figure 5), and meadow (p = 0.0075) (Figure 6).
From the results, we can say that a significant decrease in individuals occurred in 2022, but the following year, 2023, had an even greater increase in individuals compared to 2021. We recorded this trend in the intensive vineyard habitat. In the semi-intensive vineyard, the years 2021 and 2022 were balanced in terms of the number of individuals, and a high increase occurred during 2023. In the meadow and overgrown habitats, we found a greater number of individuals only in summer during 2021; the following year had a reduced number of individuals, which was more balanced with 2021 during the spring and autumn seasons. In 2023, there was also a significant increase in the number of individuals.
Using the K-nearest neighbors (KNN) algorithm, we first predicted the development of taxa and the number of individuals in the years 2021–2023 in four management types (intensive vineyard, semi-intensive vineyard, meadow, and overgrown). The model accuracy was 20%, which means that 20% of the test examples were correctly classified by the model. This value is very low for interpreting the model results, and we cannot interpret it. This low value is due to the large number of classes, i.e., 12. Subsequently, we reduced the classes to 3, i.e. the years 2021, 2022, and 2023. By adjusting the classes, the model accuracy improved and increased to 97%, which means that 97% of the test examples were correctly classified by the model. Thus, most of the predictions match the actual classes, and the model’s prediction of development is correct. We displayed the performance of the KNN classifier using a confusion matrix. From the results, we see that most cases are correctly classified into classes, as evidenced by the diagonal frequency of cases (Figure 7). Therefore, we subsequently created a KNN model that divided the years (2021–2023) into groups based on the number of taxa and individuals.
The visualization of the results shows how the model divides the space according to the values of the number of individuals and the number of taxa. The areas are colored according to the predicted class. The points (scaled data) show where the training data are located in the space. If they are in the correct area according to the colorr, they are correctly classified. The colored areas show how the KNN model predicts the classification of the space according to the two parameters number of individuals and number of taxa. The year 2021 is classified as an area with a low number of taxa and a medium number of individuals. It indicates low biodiversity, which may be caused by intensive management and ecological degradation of habitats (intensive vineyard and semi-intensive vineyard). The year 2022 is at the middle values of both parameters, which indicates an effort to restore biodiversity. The number of individuals is increasing, but the diversity of taxa is not yet fully restored. The year 2023 belongs to an area with a higher number of individuals and taxa. This indicates greater ecological stability or a return to a natural state (for example, after limiting interventions, introducing conservation management, or natural succession) (Figure 8).

4. Discussion

Based on the results, significant interannual variations in the abundance and taxonomic diversity of epigeic fauna were observed in vineyard habitats, which were influenced by a combination of management practices and natural succession processes. The decline in 2022 and the subsequent sharp increase in 2023 indicate that local conditions and environmental interventions can have a rapid and significant impact on the state of populations, which is also confirmed by similar study in agroecosystems [24]. The high eudominant representation of Hymenoptera, Coleoptera, and Araneae over several years is consistent with the knowledge that these taxa belong to ecologically key groups that respond to changes in the structure of vegetation cover and management intensity [25,26]. Beetles (Coleoptera: Carabidae) are important components of vineyards as a pest control agent, being polyphagous predators, and thus contribute to the maintenance of ecosystem services and support the sustainability of the agroecosystem [27]. Araneae also play an important role in the biological control of pests, such as grape berry moths, mealybugs, leafhoppers, and planthoppers [28]. In vineyards, ants are also used as indicators of ecosystem functioning [29].
The results of the redundancy analysis, which indicate a significant impact of habitats, are consistent with the knowledge of the sensitivity of epigeic communities to environmental heterogeneity and the presence of seminatural habitats in the landscape. The observed spatial aggregation of taxa in 2023 across all seasons indicates favorable conditions for population development, which may be a consequence of reduced disturbances, succession, and temperature changes during 2022 and 2023.The marked seasonal changes in community composition, particularly the contrast between spring and summer activity and the fall decline in 2021, and the persistent low state in 2022, are consistent with phenological patterns known from other agroecosystems. These differences reflect the sensitivity of individual taxa to microclimatic conditions and resource availability during the season [30,31,32]. Several studies described a decline in biological diversity and abundance of taxa in high managed vineyards compared to less managed ones, such as Carabidae and Araneae. Collembola are more sensitive to intensive management [33], as are mites [34]. Ant assemblages were negatively affected by soil tillage in vineyards [35].
In terms of seasonal dynamics, our observations point out that spring and summer represent the peak of activity for most taxa, while an autumn abundance is lower, with the exception of some eurytopic taxa of Carabidae and Oniscidea. In areas with similar climates, the presence of seminatural landscape elements (woodlands and meadows) has been shown to reduce the amplitude of seasonal fluctuations, thus supporting the year-round stability of communities [36,37].
We confirmed significant differences in the number of individuals between years and seasons in all habitats, indicating the universality of responses between different types of vineyards. This supports the theory that the abundance of epigeic groups can be effectively supported by careful management also in production vineyards [16,38].
The high precision of the KNN prediction model (97%) confirms that the combination of taxa and individual number data has a high discriminatory power to distinguish the ecological status of habitats between years. This is of practical importance for monitoring and managing agroecosystems, as it allows for rapid and reliable assessment of trends and the effectiveness of management measures [39]. The classifications of 2021 as a period of lower biodiversity, 2022 as a transitional period, and 2023 as a period of restored ecological stability are consistent with models of habitat succession and regeneration after anthropogenic pressure is reduced [4,8].
Epigeic communities show sensitivity to management intensity and vegetation cover structure [40]. Similar to our study, a decrease in abundance and taxa was recorded in years with more intensive interventions, especially with repeated mechanical soil cultivation and the absence of vegetation cover [41]. Vineyards can also serve as a habitat for rare species such as Erigonoplus globipes, which is rare in Central Europe [42].
Combined agri-environmental measures, including contiguous vegetation cover in between rows and reduced herbicide use, have shown significant increases in the activity and diversity of epigeic fauna, with responses being most pronounced in semi-intensive systems. It has been confirmed that reduced mechanical tillage not only promotes higher predator abundance, but also a more uniform distribution of predators throughout the season, which has a direct impact on the stability of pest control [43]. In Romania, differences in taxa and abundance of some invertebrate taxa (e.g., Coleoptera, Araneae, and Formicidae) were confirmed between intensive and semi-intensive vineyard management. The average density of epigeic invertebrate activity was higher in semi-intensive than in intensive management [44].
These results confirm that the diversity and abundance of epigeic fauna in vineyards are the result of a complex interaction between local management, landscape context, and seasonal dynamics. Therefore, to preserve and promote biodiversity, it is crucial to integrate management measures that respect these relationships, minimize negative interventions during periods of maximum activity of beneficial taxa, and take into account the long-term goals of maintaining ecological stability of vineyard ecosystems.

5. Conclusions

Based on the results achieved, it can be stated that during the monitored period from 2021 to 2023, there were significant interannual changes in the abundance and taxa of epigeic fauna, which were clearly influenced by the type of biotope and its management method. The lowest abundance values were recorded in 2022, while this decrease was noticeable in several biotopes, which may be related to adverse conditions or inappropriate management. On the contrary, 2023 was characterized by a sharp increase in the number of individuals and taxa, which indicates an improvement in ecological conditions, restoration of biodiversity, and increased ecological stability, probably due to changes in management practices, natural succession, and a combination of these factors. The redundancy analysis confirmed a significant influence of habitats on the spatial distribution of taxa, with the most pronounced differences occurring in semi-intensive and intensive vineyards, as well as in meadows and abandoned sites. The results also showed that the differences in abundance between years and seasons are statistically significant in all monitored habitats. The KNN prediction model confirmed with high precision (97%) the ability to distinguish individual years based on the number of taxa and individuals, with 2021 being characterized by lower biodiversity, 2022 by a transitional state, and 2023 by a return to a higher taxa and abundance level. Based on the results, it can be recommended to introduce semi-intensive management, which combines production goals with environmental protection, seasonally adapting interventions as needed. In a broader context, this knowledge can be used in the development of sustainable management strategies, environmental plans, and agro-environmental measures that take into account the preservation and promotion of biodiversity as a key indicator of the ecological health of the landscape.

Author Contributions

Conceptualization, V.L. and K.P.; methodology, V.L.; software, V.L.; validation, V.L. and K.P.; formal analysis, V.L. and K.P.; investigation, V.L.; resources, V.L.; data curation, V.L.; writing—original draft preparation, V.L.; writing—review and editing, V.L. and K.P.; visualization, V.L.; supervision, V.L.; project administration, V.L.; funding acquisition, V.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the grants VEGA 1/0603/25 data integration (Big data) for spatial modeling of biodiversity in different ecosystem conditions, KEGA No. 010UKF-4/2025 data science for biology, and No. 037SPU-4/2024 data integrity in biological and ecological databases.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Batáry, P.; Báldi, A.; Kleijn, D.; Tscharntke, T. Landscape-moderated biodiversity effects of agri-environmental management: A meta-analysis. Proc. R. Soc. B 2010, 278, 1894–1902. [Google Scholar] [CrossRef]
  2. Marja, R.; Albrecht, M.; Herzog, F.; Öckinger, E.; Segre, H.; Kleijn, D.; Batáry, P. Quantifying potential trade-offs and win-wins between arthropod diversity and yield on cropland under agri-environment schemes—A meta-analysis. J. Environ. Manag. 2024, 353, 120277. [Google Scholar] [CrossRef] [PubMed]
  3. Isaia, M.; Bona, F.; Badino, G. Influence of landscape diversity and agricultural practices on spider assemblage in Italian vineyards. Environ. Entomol. 2006, 35, 297–307. [Google Scholar] [CrossRef]
  4. Geldenhuys, M.; Gaigher, R.; Pryke, J.; Samways, M. Diverse herbaceous cover crops promote vineyard arthropod diversity. Agric. Ecosyst. Environ. 2020, 314, 107222. [Google Scholar]
  5. Caprio, E.; Nervo, B.; Isaia, M.; Allegro, G.; Rolando, A. Organic versus conventional systems in viticulture: Comparative effects on spiders and carabids in vineyards and adjacent forests. Agric. Syst. 2015, 136, 61–69. [Google Scholar] [CrossRef]
  6. Geldenhuys, M.; Gaigher, R.; Pryke, J.S.; Samways, M.J. Interplay between natural vegetation and vineyard practices benefits local arthropod diversity. Biodivers. Conserv. 2022, 31, 1231–1248. [Google Scholar] [CrossRef]
  7. Yan, Z.; Xiaolin, L.; Zhao, W.; Xiaorong, W.; Xiaoxu, J.; Shao, M. Synchronous sequestration of organic carbon and nitrogen in mineral soils after conversion agricultural land to forest. Agric. Ecosyst. Environ. 2020, 295, 106866. [Google Scholar] [CrossRef]
  8. Ernst, D.; Kolencik, M.; Šebesta, M.; Ďurišová, Ľ.; Kšiňan, S.; Tomovičová, L.; Kotlárová, N.; Kalúzová, M.; Černý, I.; Kratošová, G.; et al. Significance of Phosphate Nano-Fertilizers Foliar Application: A Brief Real-Field Study of Quantitative, Physiological Parameters, and Agro-Ecological Diversity in Sunflower. Agronomy 2023, 13, 2606. [Google Scholar] [CrossRef]
  9. Franin, K.; Kuštera, G.; Šišeta, F. Fauna of ground-dwelling arthropods in vineyards of Zadar County (Croatia). Poljoprivreda 2016, 22, 50–56. [Google Scholar] [CrossRef]
  10. Elise, B.; Christopher, M.; Buddle, N.J.; Bostanian, C.V. Ground-dwelling spider fauna of two vineyards in southern Quebec. Environ. Entomol. 2005, 34, 635–645. [Google Scholar]
  11. Pornaro, C.; Macolino, S. Selecting species for vineyard inter-row vegetation cover requires context-aware choices. PLoS ONE 2025, 20, e0319848. [Google Scholar] [CrossRef]
  12. Brygadyrenko, V.V.; Reshetniak, D.Y. Trophic preferences of Harpalus rufipes (Coleoptera, Carabidae) with regard to seeds of agricultural crops in conditions of laboratory experiment. Baltic J. Coleopterol. 2014, 14, 179–190. [Google Scholar]
  13. Langraf, V.; Petrovičová, K. Seasonal Dynamics of Epigeic Arthropods under the Conditions of Ecological Management of the Triticum aestivum Crop. Agriculture 2024, 14, 482. [Google Scholar] [CrossRef]
  14. Siewers, J.; Schirmel, J.; Buchholz, S. The efficiency of pitfall traps as a method of sampling epigeal arthropods. Eur. J. Entomol. 2014, 111, 69–74. [Google Scholar] [CrossRef]
  15. Litavský, J.; Prokop, P. Coverings on Pitfall Traps Influence the Abundance of Ground-Dwelling Arthropods. Diversity 2024, 16, 19. [Google Scholar] [CrossRef]
  16. Zanettin, G.; Bullo, A.; Pozzebon, A.; Burgio, G.; Duso, C. Influence of Vineyard Inter-Row Groundcover Vegetation Management on Arthropod Assemblages in the Vineyards of North-Eastern Italy. Insects 2021, 12, 349. [Google Scholar] [CrossRef]
  17. Blaise, C.; Christophe, M.; Armin, B.; Alexandre, M.; Philippe, P.; Olivier, B. Vegetation increases abundances of ground- and canopy arthropods in Mediterranean vineyards. Sci. Rep. 2022, 12, 3680. [Google Scholar] [CrossRef]
  18. Pérez-Bote, L.J. Epigeic soil arthropod abundance under different agricultural systems. Span. J. Agric. Res. 2012, 10, 794–802. [Google Scholar] [CrossRef]
  19. Gaigher, R.; Pryke, S.J.; Kratschmer, S.; Zaller, G.J.; Winter, S.; Michael, J.; Samways, B.P.; Geldenhuys, M.; Schalkwyk, V.J. Area and configuration of both semi-natural habitats and vineyards influence arthropods in South African and central European agricultural landscapes. Insect Conserv. Divers. 2024, 17, 702–718. [Google Scholar] [CrossRef]
  20. Dobrovodská, M.; Kanka, R.; Gajdoš, P.; Krištín, A.; Kollár, J.; Stašiov, S.; Lieskovsky, J. Factors affecting the biodiversity of historical landscape elements: Detailed analyses from three case studies in Slovakia. Environ. Monit. Assess. 2023, 195, 674. [Google Scholar] [CrossRef]
  21. Schierwater, B.; DeSalle, R. Invertebrate Zoology: A Tree of Life Approach; CRC Press: London, UK, 2021; 644p. [Google Scholar]
  22. Ter Braak, C.J.F.; Šmilauer, P. Canoco Reference Manual and User’s Guide: Software for Ordination, version 5.0; Microcomputer Power: Ithaca, NY, USA, 2012. [Google Scholar]
  23. Python Software Foundation Legal Statements Privacy Notice, Python version 3.12; Python Software Foundation: Beaverton, OR, USA, 2023.
  24. Bruggisser, O.T.; Schmidt-Entling, M.H.; Bacher, S. Effects of vineyard management on biodiversity at three trophic levels. Biol. Conserv. 2010, 143, 1521–1528. [Google Scholar] [CrossRef]
  25. Rocher, L.; Melloul, E.; Blight, O.; Bischoff, A. Effect of spontaneous vegetation on beneficial arthropods in Mediterranean vineyards. Agric. Ecosyst. Environ. 2024, 359, 108740. [Google Scholar] [CrossRef]
  26. Bale, J.; van Lenteren, J.C.; Bigler, F. Biological control and sustainable food production. Philos. Trans. R. Soc. B Biol. Sci. 2007, 363, 761–776. [Google Scholar] [CrossRef]
  27. Kromp, B. Carabid beetles in sustainable agriculture: A review on pest control efficacy, cultivation impacts and enhancement. Agric. Ecosyst. Environ. 1999, 74, 187–228. [Google Scholar] [CrossRef]
  28. Gaigher, R.; Samways, M.J. Landscape mosaic attributes for maintaining ground-living spider diversity in a biodiversity hotspot. Insect Conserv. Divers. 2014, 7, 470–479. [Google Scholar] [CrossRef]
  29. Chong, C.S.; D’Alberto, C.F.; Thomson, L.J.; Hoffmann, A.A. Influence of native ants on arthropod communities in a vineyard. Agric. For. Entomol. 2010, 12, 223–232. [Google Scholar] [CrossRef]
  30. Beaumelle, L.; Auriol, A.; Grasset, M.; Pavy, A.; Thi´ery, D.; Rusch, A. Benefits of increased cover crop diversity for predators and biological pest control depend on the landscape context. Ecol. Solut. Evid. 2021, 2, e12086. [Google Scholar] [CrossRef]
  31. Roschewitz, I.; Gabriel, D.; Tscharntke, T.; Thies, C. The effects of landscape complexity on arable weed species diversity in organic and conventional farming. J. Appl. Ecol. 2005, 42, 873–882. [Google Scholar] [CrossRef]
  32. Lu, Z.X.; Zhu, P.Y.; Gurr, G.M.; Zheng, X.S.; Read, D.M.Y.; Heong, K.-L.; Yang, Y.-J.; Xu, H.X. Mechanisms for flowering plants to benefit arthropod natural enemies of insect pests: Prospects for enhanced use in agriculture. Insect Sci. 2014, 21, 1–12. [Google Scholar] [CrossRef] [PubMed]
  33. Buchholz, J.; Querner, P.; Paredes, D.; Bauer, T.; Strauss, P.; Guernion, M.; Scimia, J.; Cluzeau, D.; Burel, F.; Kratschmer, S.; et al. Soil biota in vineyards are more influenced by plants and soil quality than by tillage intensity or the surrounding landscape. Sci. Rep. 2017, 7, 17445. [Google Scholar] [CrossRef]
  34. Seniczak, A.; Seniczak, S.; García-Parra, I.; Ferragut, F.; Xamaní, P.; Graczyk, R.; Messeguer, E.; Laborda, R.; Rodrigo, E. Oribat mites of conventional and organic vineyards in the Valencian Community. Spain Acarol. 2018, 58, 119–133. [Google Scholar] [CrossRef]
  35. Sharley, D.J.; Hoffman, A.A.; Thomson, L.J. The effect of soil tillage on beneficial invertebrates within the vineyard. Agric. For. Entomol. 2008, 10, 233–243. [Google Scholar] [CrossRef]
  36. Yu, X.D.; Luo, T.H.; Zhou, H.-Z. Habitat associations and seasonal activity of carabid beetles (Coleoptera: Carabidae) in Dongling Mountain, North China. Entomol. Fenn. 2006, 17, 174–183. [Google Scholar] [CrossRef]
  37. Bengtsson, J.; Ahnström, J.; Weibull, A.C. The effects of organic agriculture on biodiversity and abundance: A meta-analysis. J. Appl. Ecol. 2005, 42, 261–269. [Google Scholar] [CrossRef]
  38. Gonçalves, F.; Carlos, C.; Aranha, J.; Torres, L. Does habitat heterogeneity affect the diversity of epigaeic arthropods in vineyards? Agric. For. Entomol. 2018, 20, 366–379. [Google Scholar] [CrossRef]
  39. Bianchi, F.J.J.A.; Booij, C.J.H.; Tscharntke, T. Sustainable pest regulation in agricultural landscapes: A review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B Biol. Sci. 2006, 273, 1715–1727. [Google Scholar] [CrossRef] [PubMed]
  40. Burel, F.; Baudry, J. Landscape Ecology: Concepts, Methods, and Applications; Science Publishers, CRC Press: Boca Raton, FL, USA, 2003; p. 394. [Google Scholar]
  41. Duelli, P.; Obrist, M.K. Regional biodiversity in an agricultural landscape: The contribution of seminatural habitat islands. Basic. Appl. Ecol. 2003, 4, 129–138. [Google Scholar] [CrossRef]
  42. Hänggi, A.; Stöckli, E.; Nentwig, W. Habitats of Central European spiders—Characterisation of the habitats of the most abundant spider species of Central Europe and associated species. CNCF Neuchâtel 1995, 4, 460. [Google Scholar]
  43. Moreira, F.; Louvres, C.; Pinto, M.J. Influence of temporal and spatial variation of management on ground beetle diversity in annual crops. Agric. Ecosyst. Environ. 2008, 124, 225–231. [Google Scholar]
  44. Fiera, C.; Ulrich, W.; Popescu, D.; Bunea, C.I.; Manu, M.; Nae, I.; Stan, M.; Markó, B.; Urák, I.; Giurginca, A.; et al. Effects of vineyard inter-row management on the diversity and abundance of plants and surface-dwelling invertebrates in Central Romania. J. Insect Conserv. 2020, 24, 175–185. [Google Scholar] [CrossRef] [PubMed]
Agronomy 15 02117 g001
Figure 1. Map of study areas (1–8).
Figure 1. Map of study areas (1–8).
Agronomy 15 02117 g001
Agronomy 15 02117 g002
Figure 2. RDA analysis of taxa in habitats during seasons and years.
Figure 2. RDA analysis of taxa in habitats during seasons and years.
Agronomy 15 02117 g002
Agronomy 15 02117 g003
Figure 3. Difference in the number of individuals between years (2021–2023) and season in intensive vineyard habitat.
Figure 3. Difference in the number of individuals between years (2021–2023) and season in intensive vineyard habitat.
Agronomy 15 02117 g003
Agronomy 15 02117 g004
Figure 4. Difference in the number of individuals between years (2021–2023) and season in semi-intensive vineyard.
Figure 4. Difference in the number of individuals between years (2021–2023) and season in semi-intensive vineyard.
Agronomy 15 02117 g004
Agronomy 15 02117 g005
Figure 5. Difference in the number of individuals between years (2021–2023) and season in overgrown.
Figure 5. Difference in the number of individuals between years (2021–2023) and season in overgrown.
Agronomy 15 02117 g005
Agronomy 15 02117 g006
Figure 6. Difference in the number of individuals between years (2021–2023) and season in meadow.
Figure 6. Difference in the number of individuals between years (2021–2023) and season in meadow.
Agronomy 15 02117 g006
Agronomy 15 02117 g007
Figure 7. Confusion matrix of KNN classification with representation of case classification assignment.
Figure 7. Confusion matrix of KNN classification with representation of case classification assignment.
Agronomy 15 02117 g007
Agronomy 15 02117 g008
Figure 8. KNN analysis expressing the development of abundance and number of taxa during the years 2021–2023.
Figure 8. KNN analysis expressing the development of abundance and number of taxa during the years 2021–2023.
Agronomy 15 02117 g008
Table 1. Average temperature and precipitation during 2021–2023.
Table 1. Average temperature and precipitation during 2021–2023.
MonthAverage Temperature (°C)Average Rainfall (mm)
202120222023202120222023
1022606060
2142555555
3455505050
4898474547
5131715505050
6212119505050
7232222474547
8192321434043
9151519434043
10101210404043
11566605555
12122605555
Table 2. Representation of epigeic fauna during the years 2021–2013.
Table 2. Representation of epigeic fauna during the years 2021–2013.
Biotops/ArthropodsYear∑ Individuals
202120222023
Intensive vineyard
 Araneae41122411701805
 Auchenorrhyncha130114
 Coleoptera92555813722855
 Collembola17001171
 Dermaptera4640126212
 Diptera21012779416
 Geophilomorpha004040
 Glomerida6401255662
 Hemiptera2930223516
 Hymenoptera165949127724922
 Oniscidea630306741010
 Julida111443196750
 Lithobiomorpha4013980
 Lumbricidae15442584
 Opilionida7018196284
 Orthoptera25626990615
 Polydesmida0077
 Scorpionida1001
 Zygentoma0033
meadow
 Acarina8008
 Araneae5382328751645
 Auchenorrhyncha2002
 Blattodea4004
 Coleoptera76039427263880
 Collembola66600666
 Dermaptera21353692
 Diptera1109282284
 Geophilomorpha1012
 Glomerida584357361229
 Hemiptera2070272479
 Hymenoptera246235830205840
 Oniscidea610379791068
 Julida363366281000
 Lithobiomorpha41189131
 Lumbricidae38252891
 Mantodea0011
 Opilionida2102054284
 Orthoptera86810171093
 Polydesmida2002
 Scorpionida2002
 Zygentoma2013
Semi-intensive vineyard
 Acarina4004
 Araneae2572587831298
 Coleoptera32934711821858
 Collembola530053
 Dermaptera194751117
 Diptera2714467238
 Geophilomorpha0077
 Glomerida5310168483
 Hemiptera580212270
 Hymenoptera73368959967418
 Chordeumatida1001
 Oniscidea15728764508
 Julida2134868437
 Lithobiomorpha533745
 Lumbricidae10333376
 Opilionida733172212
 Orthoptera10112577303
 Zygentoma0011
Overgrown
 Acarina0011
 Araneae5873099291825
 Blattodea2002
 Coleoptera48939510551939
 Collembola38100381
 Dermaptera16273275
 Diptera5417571300
 Ensifera0033
 Geophilomorpha2013
 Glomerida93459240792
 Hemiptera2430112355
 Hymenoptera87946533724716
 Chordeumatida1001
 Oniscidea426338138902
 Julida7735486517
 Lithobiomorpha2524976
 Lumbricidae16253172
 Opilionida5627158241
 Orthoptera4711518180
 Zygentoma0011
∑ individuals15,82310,61231,52957,964
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Langraf, V.; Petrovičová, K. Modeling the Seasonal and Spatial Dynamics of Epigeic Fauna in the Context of Vineyard Landscape Use Using Machine Learning. Agronomy 2025, 15, 2117. https://doi.org/10.3390/agronomy15092117

AMA Style

Langraf V, Petrovičová K. Modeling the Seasonal and Spatial Dynamics of Epigeic Fauna in the Context of Vineyard Landscape Use Using Machine Learning. Agronomy. 2025; 15(9):2117. https://doi.org/10.3390/agronomy15092117

Chicago/Turabian Style

Langraf, Vladimír, and Kornélia Petrovičová. 2025. "Modeling the Seasonal and Spatial Dynamics of Epigeic Fauna in the Context of Vineyard Landscape Use Using Machine Learning" Agronomy 15, no. 9: 2117. https://doi.org/10.3390/agronomy15092117

APA Style

Langraf, V., & Petrovičová, K. (2025). Modeling the Seasonal and Spatial Dynamics of Epigeic Fauna in the Context of Vineyard Landscape Use Using Machine Learning. Agronomy, 15(9), 2117. https://doi.org/10.3390/agronomy15092117

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop