Previous Article in Journal
What the Owls Leave Behind: Pellet Size Variation Reflects Predator Body Size in Israel’s Owls
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Ecologies
Volume 6
Issue 3
10.3390/ecologies6030045
ecologies-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

Role of Agricultural Management in Short-Term Monitoring of Arthropod Diversity at Field Scale

by
Simone Bergonzoli
1,*,
Luca Cozzolino
1,*,
Elio Romano
2 and
Luigi Pari
1
1
CREA Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria Centro di Ingegneria e Trasformazioni, Via della Pascolare, 16, Monterotondo, 00015 Rome, Italy
2
CREA Consiglio per la Ricerca in Agricoltura e L’analisi Dell’economia Agraria Centro di Ingegneria e Trasformazioni, Via Milano, 47, 24047 Treviglio, Italy
*
Authors to whom correspondence should be addressed.
Ecologies 2025, 6(3), 45; https://doi.org/10.3390/ecologies6030045
Submission received: 1 April 2025 / Revised: 15 May 2025 / Accepted: 2 June 2025 / Published: 23 June 2025
Browse Figures
Versions Notes

Abstract

:
In recent decades, a significant decline in arthropods’ abundance and biodiversity, as a consequence of intensive agricultural practices and reductions in their natural environments, has been observed. While landscape-scale biodiversity studies are well documented in the literature, the impact of field-level agricultural management remains less understood. To address this gap, a sampling of diversity was carried out through Malaise traps on five agricultural surfaces with different management schemes: two characterized by the presence of trees (Populus L. spp. and Eucalyptus spp.), two herbaceous fields in different development stages (flowering Carthamus tinctorius L. and stubble of Triticum aestivum), and one mixed system (an agroforestry plantation composed of Populus L. spp. and Carthamus tinctorius L.). Data collection focused on evaluating the total animal biomass (weight and number) and the richness and evenness components of diversity using Shannon and Simpson indices at the Order level. The sampled arthropods belonged to six Orders of Insecta and one Order of Arachnida. The agroforestry system had a higher total animal biomass, in terms of weight, than the other treatments (61.24% higher than in the eucalyptus system, 58.91% higher than in the wheat stubble, 42.63% higher than in the flowering safflower system, and 11.63% higher than in the poplar plantation), with the number of total arthropods following a similar trend. The results demonstrated that the biomass, richness, and evenness of the collected arthropods varied according to the management practices applied, and higher values were recorded in the agroforestry system. Although preliminary, the findings suggest the suitability of mixed systems for sustaining higher diversity than traditional monoculture management schemes.

1. Introduction

Biodiversity encompasses the variety and heterogeneity of organisms or traits at all levels of the hierarchy of life, from molecules to ecosystems [1,2]. It is essential from various perspectives because it is responsible for maintaining the main ecosystem services, represented by (a) ecological, (b) agronomic, and (c) cultural functions, directly related to human well-being. In recent years, attention has been paid to the decline in biodiversity caused by the effects of climate change and environmental pollution. According to the 2019 Conference of the Parties (COP) at the Convention on Biological Diversity (CBD), biodiversity loss encompasses the decline in biomes, habitats, ecosystems, species, populations, and genetic diversity [3,4] and can generally be defined as a reduction in the variety of life on Earth [5]. Regarding agricultural environments, beyond the aspects described above, the intensification of applied management has globally impacted biodiversity and ecosystem services [6,7]. In fact, increased monocultures, higher inputs of fertilizers and pesticides, and reduced within-field heterogeneity affect species diversity and abundance [7,8,9].
Agricultural land is the most extensive habitat for biodiversity in Europe, forming a vast ecological network that includes interacting organisms from both cultivated and uncultivated habitats, thereby creating an ecological framework for food production [6,10,11]. Therefore, a loss of biodiversity at the agricultural level can directly influence biotic and abiotic stress resistance and the productivity of cultivated ecosystems, even when agricultural systems are slated for energy production [1,12,13,14,15]. To hinder insect diversity losses, the composition (the diversity and abundance of different land use or land cover types) and configuration (the size of land cover patches and their spatial arrangement [11,16]) of landscape components should be considered [11,17,18], thereby identifying more sustainable management practices. Mitigation strategies identified in organic farming have been demonstrated to support greater biodiversity compared to conventional and monospecific production systems [13,15,19,20,21]. These agricultural systems often are characterized by more heterogeneity, longer and specific crop rotations, and limited chemical application, emphasizing the impact of management on biodiversity. Another potential strategy involves the availability of nearby natural habitats [12], which can mitigate agricultural disturbances of crop management practices, allowing for more stable environments [9,21]. These concepts, applied and verified in food production contexts, are also applicable to bioenergy production systems [22]. A further possibility is represented by agroforestry, which involves the simultaneous cultivation of tree and shrub species with long production cycles alongside annual herbaceous species, thus generating a higher degree of heterogeneity [23,24,25]. Solutions that balance production and natural systems may offer an alternative to purely managed or naturalistic approaches [26,27,28]. These new agricultural solutions are being studied in several European projects, such as MIDAS [29], with promising results for marginal land or unconventional agricultural systems derived from converting tree plantations for biomass production [22]. While many studies have focused on large-scale biodiversity losses on agricultural surfaces [30], there is still a gap in evaluating the effect of specific components or practices on global trends [6,30]. Moreover, if long-term monitoring can track species richness, composition, and abundance and help to identify causes of changes, including environmental factors [14], short-term monitoring is also important in order to identify in detail the arthropod behaviours linked to seasonal aspects or phenological phases [31,32,33]. For monitoring programs on agricultural surfaces, shortening the sampling period can also be beneficial both from conservation and economic perspectives [34]. In fact, long-term monitoring is very demanding in terms of funding, a factor often linked to research projects and manpower, reducing the applicability and effectiveness of such kinds of biodiversity assessment.
Furthermore, most species at a specific site can be detected by trapping them for just half a month, and sampling at only three to ten sites may be sufficient to capture species that are distributed across a region if proper methods are applied [35].
Effective assessment tools and scientific approaches are crucial for understanding the impact of agricultural practices on different biodiversity components, from the patch to the landscape scale [10,11,36]. In environmental monitoring, certain species are used as indicators (e.g., flagship and reference species) [17,37], as sampling a few species is easier than measuring the overall biological diversity [38]. Other methodologies include index approaches, which consider the interaction of different parameters [39,40,41] to quantify diversity.
The aim of this study is to provide new insights into arthropod diversity in relation to different agricultural management practices in the same geographical area through short-term and highly detailed monitoring assessments. The scope was to understand if (and, if yes, to what extent) agroforestry systems can support higher microarthropod biomass and diversity than monocultures, as examined in other studies [42,43,44]. The experimental question of understanding if agricultural management and a specific crop phenological stage, in this case, the flowering, affect arthropod diversity was also further addressed. Unlike long-term sampling, this sampling was conducted with a much higher degree of attention, with traps being serviced daily. This approach ensured that the traps maintained their maximum attractiveness and allowed for detailed visual identification, which long-term sampling does not permit due to decomposition processes and saturation of the traps.

2. Materials and Methods

2.1. Experimental Area

The experiment was performed in July 2024 at the farm of the Research Center for Engineering and Agro-Food Processing of the Council for Agricultural Research and Economics (CREA IT), Rome, Italy (WGS84-UTM33T 42°06′09″ N 12°37′43″ E), 25 m above sea level.
The various analysed surfaces had the soil characteristics in common (clay 58%; sand 22%; silt 20%; organic matter 2.1%; N 0.13%; pH 6.1; P2O5 Olsen 7 mg kg−1; K2O 317 mg kg−1) and were free from pesticide use.
The sampling of insect diversity was carried out through Malaise traps on five agricultural surfaces (Figure 1) with different management regimes: two characterized by tree presence (poplars and eucalyptus), two herbaceous fields in different development stages (flowering safflower and wheat stubble) and one mixed system (agroforestry plantation).
  • Stubble: an agricultural field previously cultivated with durum wheat (Triticum durum) harvested on 14th of July. At the time of sampling, the field was characterized by the presence of stubble, in terms only of stems of around 10–12 cm because the straw was collected on 16th of July.
  • Poplar: A 12-year-old medium rotation forestry plantation for biomass production, currently in its second harvest cycle (last utilization in 2022) with 3-year-old plants. The plantation is managed by shredding the herbaceous component in the inter-row during the autumn–winter months. During the time of sampling, natural vegetation was flowering in the inter-row.
  • Agroforestry: An agroforestry field of poplars was established through the conversion of a 12-year-old poplar plantation for biomass production, currently in its second harvest cycle, with 3-year-old plants. This field differs from the poplar plantation by having an inter-row spacing of 6 m (compared to 3 m in the poplar field) and has been cultivated with safflower under. At the time of sampling, it was flowering.
  • Eucalyptus: A medium rotation forestry plantation of eucalyptus, with 12-year-old plants, managed similarly to the poplar plantation (inter-row shredding in autumn-winter), and with natural vegetation flowering in the inter-row at the time of sampling.
  • Safflower: An agricultural field cultivated with safflower that was flowering at the time of sampling.
Meteorological data were acquired during the week of the data collection from 21 to 28 July 2024 using a weather cab “Davis Vantage pro-2” (Davis Instruments, 3465 Diablo Avenue, Hayward, CA 94545-2778, USA) placed in the experimental farm and connected to wireless net. The five locations fell in the same climatic area and can be defined as hot summer Mediterranean, corresponding to the “Csa” in the Köppen climate classification, characterized by relatively mild winters and warm, sunny summers [45,46].
Detailed weather data concerning air temperature, moisture, wind speed and direction of the sampling period in the study area are reported in Table 1 and Figure 2 and Figure 3. Rainfall was totally absent during the sampling period.

2.2. Collection Method and Order Identification

Insects were collected using five Malaise traps (standard SLAM trap, MegaView Science Co., Ltd., Taichung, Taiwan), one for each location. A Malaise trap is a large, tent-like structure used for trapping, killing, and preserving flying insects, particularly Hymenoptera and Diptera [47,48,49]. The used Malaise traps measured 100 cm in width, 170 cm in length, and 150 cm in height and were equipped with a 500 mL collection bottle containing 85% ethanol and glycerine. In each of the five locations, outlined with rectangles in Figure 1, 5 plots were randomly identified. The traps were deployed in each plot for one day (24 h), for a total of five days of sampling. Therefore, 25 plots in the study area were analysed in total. The sampling period of 5 days was selected in the flowering period of safflower and natural herbaceous vegetation, from 21 to 28 July in our environment, according to the results of a previous study on agricultural systems [35]. For each plot and location, insects were counted and weighed and preserved in plastic tubes containing 80% ethyl alcohol [50]. Dry weight of the samples was estimated according to EN ISO 18134-2:2017 [51]. Order identification and classification were carried out, for each sample and location, morphologically via visual detection.

2.3. Diversity Analysis

Taxonomic diversity and evenness, at Order level, for each location was estimated using the Shannon–Weiner diversity index (SHDI) and Simpson index (SIDI), respectively. These indices are considered amongst the most popular and frequently employed indices [52]. The Shannon–Weiner index of diversity is defined as [53,54]:
S H D I = − ∑ i = 1 N p i × l n   p i  
where pi is the proportion of each species in the sample, and ln pi is the natural logarithm of this proportion [52,53]. The parameter ranges from 1 to 5 but typically falls between 1.5 and 3.5, with values exceeding 4.5 being rare.
The Simpson index [55,56] is defined as:
S I D I = 1 − ∑ i = 1 N p i × p i  
where pi is the proportion of each species in the sample values. Producing values from 0 to 1, Simpson’s index defines the probability that two equal-sized sub-units of the diversity, selected at random, belong to different types.

2.4. Statistical Analysis

The descriptive analysis was performed to calculate the mean and standard deviation of each item analysed. A correlation function was used to study the relationship between unitary dry weight and total number of arthropods collected in each location. To better explain the variance among the Order collected in the five agricultural surfaces studied, Principal Component Analysis (PCA) was utilized as statistical approach. The standardized values of the arthropods collected daily at each location, categorized at the Order level, were used as input for the PCA. This resulted in a total of five values per Order and location, as shown in the Supplementary Materials Table S4.
The free software PAST 4 [57] released by the University of Oslo was used for all statistical analysis.

3. Results

The results of meteorological data of the study area during the sampling period highlighted quite stable values. No adverse weather conditions that might alter the functioning of Malaise traps or movement of insects were experienced. The main results obtained for each location during sampling are reported in Table 2, and the complete dataset is available as Supplementary Materials.
As shown in Table 2, in all the agricultural systems studied, the collected arthropods from the class Insecta belonged to the following Orders: Diptera, Lepidoptera, Hymenoptera, Coleoptera, and Heteroptera. Arthropods belonging to the Order Ephemeroptera were collected in all systems except for stubble. During sampling, in each location, arthropods belonging to the Order Opiliones of the class Arachnida were also collected. In each system studied, the daily number of arthropods collected for the different Orders varied with large fluctuations among the sampling dates but was not defined as a common trend over time (Supplementary Table S3). Regarding the system daily mean, stubble and eucalyptus systems recorded values lower than 100 arthropods per day (58.6 and 73.6, respectively), while safflower, poplars and agroforestry registered values higher than 200 arthropods per day (253.6, 395.6 and 617.8, respectively). The value of the daily mean of arthropods collected in agroforestry, which had the highest value, was more than 10-times higher than stubble, highlighting the lowest value with an overall coefficient of variation equal to 74%.
Interestingly, in the overall environment studied, intended as the sum of each system, 6.996 arthropods were collected during the sampling period. The most abundant Order, expressed as the percentage of arthropods belonging to the total number of arthropods recorded for each system, was Diptera, followed by Hymenoptera in all systems studied, except for safflowers, where the situation was reversed (Figure 3). Diptera and Hymenoptera together accounted for more than 70% of the total diversity in all systems, with maximum values recorded in the stubble (90.4%) and minimum in the poplars (70.6%). Instead, Ephemeroptera and Opiliones accounted for less than 2% of the total diversity each (1.04 and 1.5%, respectively). The least abundant Order was Ephemeroptera in the stubble, poplars and eucalyptus, while in agroforestry and safflower, it was Opiliones. For eucalyptus only, the Opiliones’ contribution to total arthropod abundance was higher than 2% of the total, with a value of 6.2%.
To simplify the analysis of the multivariate measures of agricultural systems and the collected arthropod orders, we applied Principal Component Analysis (PCA) to reduce the number of variables.
The PCA (Figure 4), emphasizing the results described in Table 2, gave two components with eigenvalues > 1 as a result, and these explained the variance for an overall probability of 74.86% (PC1 = 56.46% + PC2 = 18.40%). The loading matrix and the eigenvalue table are available as Supplementary Materials Tables S1 and S2. The biplot showed a very different distribution of stubble and eucalyptus with respect to the other systems due to the low number of arthropods collected in those locations. The results of the stubble system were influenced by the low total number of arthropods and the absence of Ephemeroptera. The eucalyptus group was characterized by a low number of collected arthropods, including Ephemeroptera, Hymenoptera, and Lepidoptera, along with a high number of Opiliones. The position of safflower is explained by the low presence of Diptera, Coleoptera and Heteroptera and the high level of Hymenoptera. The group of poplars was influenced mainly by the high number of arthropods belonging to Opiliones, Diptera and Heteroptera. The position of the agroforestry group is influenced by the highest number of arthropods collected with respect to the other locations and mainly by the values of Diptera, Heteroptera and Hymenoptera but also by Coleoptera and Epheromeptera.
Detailed results about the quantity and diversity of arthropods collected are reported in Table 3.
The results depicted in Table 3 highlight how the highest number of arthropods collected was recorded in the agroforestry system, followed by poplar and safflower (i.e., 2089, 1978 and 1268 units, respectively). Lower values were registered in eucalyptus and stubble systems, where 368 and 293 arthropods were collected, respectively. The total number of arthropods collected in the agroforestry system was 35.97% higher than poplar, 58.95% than safflower, 88.09% than eucalyptus, and 90.51% than wheat stubble, with an overall coefficient of variation equal to 74.9%. A similar trend of the total arthropods was followed by the value of total dry weight. A higher number of arthropods collected corresponded to a higher value of weight, except for stubble; even though it recorded the lowest value for total arthropods, the measured total dry weight was slightly higher than that from the eucalyptus. Therefore, the values of dry weight were higher in the agroforestry system compared to the other treatments (61.24% of eucalyptus, 58.91% of wheat stubble, 42.63% of flowering safflower, 11.63% of poplar plantation). The trend for the unitary dry weight, calculated as the ratio between the total dry weight and the total number of arthropods, was the opposite of the trend for the total dry weight. It is evident that the higher the total weight is, the lower the unitary weight. The correlation between the number of arthropods and the unitary dry weight is depicted in Figure 5.
The correlation showed an exponential trend, with R2 = 0.86 (p = 0.06). This result can be attributed to the variation in the Orders to which the collected arthropods belong. Concerning the Shannon index, the highest value was recorded in the eucalyptus system and the lowest in the stubble, with a 17% coefficient of variation. The lowest value of the Simpson index was 0.3 in agroforestry and the highest was 0.55 in stubble, with a 61% coefficient of variation. Considering the values of both indices, the best results were obtained in the agroforestry system and the worst in the stubble system.

4. Discussion

The results of this study revealed significant variation in key indicators, such as the total number, weight, and diversity of arthropods collected, despite all systems examined belonging to the same farm [58,59]. Several studies have already stated that arthropod diversity is associated with the quantity, type of available plants, and their physiological state [60,61,62,63]. Despite these system-level findings, there are few studies in the literature that focus on the contribution of specific factors at the farm level on diversity. This study was conducted specifically to address this gap in the literature in terms of detailed quantification of a factor influencing diversity.
Similar to the approach of Viterbi et al. [64], who analysed a single system factor to explain the distribution of mountain taxa, our study investigated the impact of agricultural management on arthropod diversity during peak flowering of the vegetation [64]. Our findings confirmed the hypothesis that agroforestry systems can host higher species richness than monocultures [65,66,67,68,69,70]. The agroforestry system analysed, which generally yielded better results, represents a structural intermediate between the poplar and safflower systems. It is characterized by a greater inter-row distance and the presence of cultivated vegetation compared to the poplar system, while differing from the safflower system due to its arboreal component. In agroforestry, the total number of arthropods collected increased by 35.97% and 58.95%, and total weight increased by 11.30% and 42.18%, compared to the poplar and safflower systems, respectively. Additionally, the Shannon index increased, while the Simpson index decreased in comparison to these systems.
The safflower and stubble systems differed only in the cultivated crop (safflower or wheat) and its growth stage (flowering or post-harvest). Despite this, the safflower system recorded a 76.89% increase in the total number of arthropods collected, a 28.51% increase in total weight, a higher Shannon index, and a lower Simpson index compared to wheat stubble.
In the medium rotation forestry systems (poplar and eucalyptus), the primary differences were species and age. The eucalyptus system had 81.39% fewer arthropods and 56.28% lower total weight than the poplar system but exhibited higher Shannon and Simpson indices.
PCA confirmed the influence of agricultural management on arthropod Order diversity, as evidenced by the variation in group positioning within the biplot. The arthropod distribution analysis showed that most specimens belonged to the class Insecta, primarily from the Orders Diptera, Lepidoptera, Hymenoptera, Coleoptera, Heteroptera, and Ephemeroptera. A smaller fraction belonged to the Order Opiliones (class Arachnida). Notably, Ephemeroptera was absent from stubble systems during sampling.
Among insect Orders, Diptera and Hymenoptera were the most abundant, together accounting for over 70% of total diversity, with coefficients of variation of 25% and 38%, respectively. This highlights the high variability in the results.
Overall, the findings indicate that agricultural management significantly impacts both arthropod abundance and diversity, even within the same climatic and geographic region. Differences in species distribution were also reflected in unitary weight, with systems hosting fewer arthropods exhibiting higher individual weights.
Moreover, diversity indices confirmed these patterns. Systems dominated by herbaceous species (stubble and safflower) had lower Shannon and higher Simpson indices, whereas systems with trees (poplar, eucalyptus, and agroforestry) and additional vegetation supported greater diversity. Systems combining trees and herbaceous species exhibited higher richness and evenness, indicating a more diverse and balanced arthropod community. It is important to acknowledge the limitations of this study. Our research was conducted in a single study area, making broad generalizations challenging, as is often the case with natural environment studies. However, case studies serve as a foundation for more complex research and meta-analyses. When carefully designed, even preliminary studies can offer valuable insights, this study being a case in point. Nonetheless, we recognize the need for further research to validate these findings on a larger temporal and spatial scale.

5. Conclusions

This study, conducted as part of a multi-year research project, aimed to assess the influence of different agricultural management practices on arthropod diversity at the field level within the same geographical and climatic area. A key objective was to determine whether agroforestry systems can effectively support greater diversity than monocultures. Our findings emphasize the need for a holistic approach to biodiversity assessment, incorporating multiple parameters and indices. However, due to the study’s limitation in not accounting for long-term biodiversity changes, we adopt a cautious approach in drawing conclusions.
The agroforestry system, combining poplars and safflower, exhibited higher arthropod abundance and greater diversity richness and evenness, as reflected in the Shannon and Simpson indices. These results suggest that, under the same environmental conditions, changes in agricultural management alone can significantly influence arthropod diversity.
This evidence aligns with broader concerns about declining arthropod populations at both local and global scales, threatening the sustainability of essential ecosystem services. Given the vital role of insects in agriculture, such as pollination, pest control, decomposition, and serving as a food source for other species, their conservation is crucial for human well-being. To address this, appropriate European-level incentives should be considered to promote small-scale agricultural practices, such as specific crop rotations and dedicated cultivations, which can help maintain or enhance animal biodiversity while preserving essential ecosystem functions.
Future research will focus on long-term studies of soil (macro- and microfauna) and plant diversity to assess the overall impact of different agricultural management practices on ecosystem richness and evenness at the field level.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ecologies6030045/s1, Table S1: Shannon index; Table S2: Simpson Index; Table S3: Sample location & collect data; Table S4: PCA analysis.

Author Contributions

L.P.; methodology, L.P., S.B. and L.C.; validation, L.P., S.B. and L.C.; formal analysis, L.P., S.B., L.C. and E.R.; investigation, L.P., S.B. and L.C.; resources, L.P.; data curation, L.P., S.B., L.C. and E.R.; writing—original draft preparation, S.B. and L.C.; writing—review and editing, L.P., S.B., L.C. and E.R.; supervision, L.P.; project administration, L.P.; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was carried out within the MIDAS project that has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No. 101082070. The APC was funded by MIDAS Project.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors would like to thank Paolo Mattei for the valuable help provided in the weather data collection and processing, and Francesca Di Placidi, Italo Colagrossi, and Livia Mariani for the valuable help provided in the insect counting and trap placement.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Duru, M.; Therond, O.; Martin, G.; Martin-Clouaire, R.; Magne, M.A.; Justes, E.; Journet, E.P.; Aubertot, J.N.; Savary, S.; Bergez, J.E.; et al. How to Implement Biodiversity-Based Agriculture to Enhance Ecosystem Services: A Review. Agron. Sustain. Dev. 2015, 35, 1259–1281. [Google Scholar] [CrossRef]
  2. Schmeller, D.S.; Mihoub, J.B.; Bowser, A.; Arvanitidis, C.; Costello, M.J.; Fernandez, M.; Geller, G.N.; Hobern, D.; Kissling, W.D.; Regan, E.; et al. An Operational Definition of Essential Biodiversity Variables. Biodivers. Conserv. 2017, 26, 2967–2972. [Google Scholar] [CrossRef]
  3. Wirth, D.A. The Sixth Session (Part Two) and Seventh Session of the Conference of the Parties to the Framework Convention on Climate Change. Am. J. Int. Law. 2002, 96, 648–660. [Google Scholar] [CrossRef]
  4. UN IRB. Convention on Biological Diversity. In Treaty Collection; United Nations: New York, NY, USA, 1992. [Google Scholar]
  5. Chivian, M.D.E.; Bernstein, M.D. How Our Health Depends on Biodiversity; Center for Health and Global Environment: Boston, MA, USA, 2010. [Google Scholar]
  6. Zimmerer, K.S. Biological Diversity in Agriculture and Global Change. Annu. Rev. Environ. Resour. 2010, 35, 137–166. [Google Scholar] [CrossRef]
  7. Tscharntke, T.; Klein, A.M.; Kruess, A.; Steffan-Dewenter, I.; Thies, C. Landscape Perspectives on Agricultural Intensification and Biodiversity–Ecosystem Service Management. Ecol. Lett. 2005, 8, 857–874. [Google Scholar] [CrossRef]
  8. Tscharntke, T.; Bommarco, R.; Clough, Y.; Crist, T.O.; Kleijn, D.; Rand, T.A.; Tylianakis, J.M.; van Nouhuys, S.; Vidal, S. Conservation Biological Control and Enemy Diversity on a Landscape Scale. Biol. Control 2007, 43, 294–309. [Google Scholar] [CrossRef]
  9. Carvalheiro, L.G.; Veldtman, R.; Shenkute, A.G.; Tesfay, G.B.; Pirk, C.W.W.; Donaldson, J.S.; Nicolson, S.W. Natural and Within-farmland Biodiversity Enhances Crop Productivity. Ecol. Lett. 2011, 14, 251–259. [Google Scholar] [CrossRef]
  10. Swift, M.J.; Anderson, J.M. 2 Biodiversity and Ecosystem Function in Agricultural Systems. In Biodiversity and Ecosystem Function; Springer: Berlin/Heidelberg, Germany, 1994; pp. 15–41. [Google Scholar]
  11. Fahrig, L.; Baudry, J.; Brotons, L.; Burel, F.G.; Crist, T.O.; Fuller, R.J.; Sirami, C.; Siriwardena, G.M.; Martin, J. Functional Landscape Heterogeneity and Animal Biodiversity in Agricultural Landscapes. Ecol. Lett. 2011, 14, 101–112. [Google Scholar] [CrossRef] [PubMed]
  12. Outhwaite, C.L.; McCann, P.; Newbold, T. Agriculture and Climate Change Are Reshaping Insect Biodiversity Worldwide. Nature 2022, 605, 97–102. [Google Scholar] [CrossRef]
  13. Raven, P.H.; Wagner, D.L. Agricultural Intensification and Climate Change Are Rapidly Decreasing Insect Biodiversity. Proc. Natl. Acad. Sci. USA 2021, 118, e2002548117. [Google Scholar] [CrossRef]
  14. Crist, T.O.; Peters, V.E. Landscape and Local Controls of Insect Biodiversity in Conservation Grasslands: Implications for the Conservation of Ecosystem Service Providers in Agricultural Environments. Land 2014, 3, 693–718. [Google Scholar] [CrossRef]
  15. Duelli, P.; Obrist, M.K. In Search of the Best Correlates for Local Organismal Biodiversity in Cultivated Areas. Biodivers. Conserv. 1998, 7, 297–309. [Google Scholar] [CrossRef]
  16. Concepción, E.D.; Díaz, M.; Baquero, R.A. Effects of Landscape Complexity on the Ecological Effectiveness of Agri-Environment Schemes. Landsc. Ecol. 2008, 23, 135–148. [Google Scholar] [CrossRef]
  17. Duelli, P. Biodiversity Evaluation in Agricultural Landscapes: An Approach at Two Different Scales. Agric. Ecosyst. Environ. 1997, 62, 81–91. [Google Scholar] [CrossRef]
  18. Gallé, R.; Császár, P.; Makra, T.; Gallé-Szpisjak, N.; Ladányi, Z.; Torma, A.; Ingle, K.; Szilassi, P. Small-Scale Agricultural Landscapes Promote Spider and Ground Beetle Densities by Offering Suitable Overwintering Sites. Landsc. Ecol. 2018, 33, 1435–1446. [Google Scholar] [CrossRef]
  19. Williams, J.J.; Newbold, T. Local Climatic Changes Affect Biodiversity Responses to Land Use: A Review. Divers. Distrib. 2020, 26, 76–92. [Google Scholar] [CrossRef]
  20. Zapparoli, M. Urban Development and Insect Biodiversity of the Rome Area, Italy. Landsc. Urban Plan. 1997, 38, 77–86. [Google Scholar] [CrossRef]
  21. Tassoni, S.; Becker, D.; Kasten, M.K.; Morinière, J.; Grass, I. Insect Conservation in Agricultural Landscapes Needs Both High Crop Heterogeneity and Semi-Natural Habitats. Glob. Ecol. Conserv. 2024, 55, e03218. [Google Scholar] [CrossRef]
  22. Pari, L.; Bergonzoli, S.; Cozzolino, L.; Baldi, G.M.; Falce, M.; Alexopoulou, E. Mechanical Harvesting of Marginal Land and Agroforestry Field: New Insights from Safflower for Bio-Product Production. Agronomy 2024, 14, 2268. [Google Scholar] [CrossRef]
  23. Smith, J. Agroforestry: Reconciling Production with Protection of the Environment a Synopsis of Research Literature; Organic Research Centre: Newbury, UK, 2010. [Google Scholar]
  24. Kay, S.; Graves, A.; Palma, J.H.N.; Moreno, G.; Roces-Díaz, J.V.; Aviron, S.; Chouvardas, D.; Crous-Duran, J.; Ferreiro-Domínguez, N.; de Jalón, S.G. Agroforestry Is Paying off–Economic Evaluation of Ecosystem Services in European Landscapes with and without Agroforestry Systems. Ecosyst. Serv. 2019, 36, 100896. [Google Scholar] [CrossRef]
  25. Pardon, P.; Reubens, B.; Mertens, J.; Verheyen, K.; De Frenne, P.; De Smet, G.; Van Waes, C.; Reheul, D. Effects of Temperate Agroforestry on Yield and Quality of Different Arable Intercrops. Agric. Syst. 2018, 166, 135–151. [Google Scholar] [CrossRef]
  26. Peters, M.K.; Hemp, A.; Appelhans, T.; Becker, J.N.; Behler, C.; Classen, A.; Detsch, F.; Ensslin, A.; Ferger, S.W.; Frederiksen, S.B. Climate–Land-Use Interactions Shape Tropical Mountain Biodiversity and Ecosystem Functions. Nature 2019, 568, 88–92. [Google Scholar] [CrossRef] [PubMed]
  27. del Pozo, A.; Brunel-Saldias, N.; Engler, A.; Ortega-Farias, S.; Acevedo-Opazo, C.; Lobos, G.A.; Jara-Rojas, R.; Molina-Montenegro, M.A. Climate Change Impacts and Adaptation Strategies of Agriculture in Mediterranean-Climate Regions (MCRs). Sustainability 2019, 11, 2769. [Google Scholar] [CrossRef]
  28. Füssel, H.-M.; Klein, R.J.T. Climate Change Vulnerability Assessments: An Evolution of Conceptual Thinking. Clim. Change 2006, 75, 301–329. [Google Scholar] [CrossRef]
  29. Midas Eu Project. Available online: https://www.midas-bioeconomy.eu/ (accessed on 25 April 2025).
  30. Shah Habibullah, M.; Haji Din, B.; Tan, S.-H.; Zahid, H. Impact of Climate Change on Biodiversity Loss: Global Evidence. Environ. Sci. Pollut. Res. 2021, 29, 1073–1086. [Google Scholar] [CrossRef]
  31. Daskalova, G.N.; Phillimore, A.B.; Myers-Smith, I.H. Accounting for Year Effects and Sampling Error in Temporal Analyses of Invertebrate Population and Biodiversity Change: A Comment on Seibold et al. 2019. Insect Conserv. Divers. 2021, 14, 149–154. [Google Scholar] [CrossRef]
  32. Lancaster, J. Movement and Dispersion of Insects in Stream Channels: What Role Does Flow Play? In Aquatic Insects: Challenges to Populations; CABI: Egham, UK, 2008; pp. 139–157. [Google Scholar]
  33. Bell, J.R.; Bohan, D.A.; Shaw, E.M.; Weyman, G.S. Ballooning Dispersal Using Silk: World Fauna, Phylogenies, Genetics and Models. Bull. Entomol. Res. 2005, 95, 69–114. [Google Scholar] [CrossRef]
  34. Tscharntke, T.; Tylianakis, J.M.; Wade, M.R.; Wratten, S.D.; Bengtsson, J.; Kleijn, D. Insect Conservation in Agricultural Landscapes. In Insect Conservation Biology; CAB International: Egham, UK, 2007; pp. 383–404. [Google Scholar]
  35. Kaczmarek, M.; Entling, M.H.; Hoffmann, C. Using Malaise Traps and Metabarcoding for Biodiversity Assessment in Vineyards: Effects of Weather and Trapping Effort. Insects 2022, 13, 507. [Google Scholar] [CrossRef]
  36. Clergue, B.; Amiaud, B.; Pervanchon, F.; Lasserre-Joulin, F.; Plantureux, S. Biodiversity: Function and assessment in agricultural areas: A review. In Sustainable Agriculture; Springer: Berlin/Heidelberg, Germany, 2009; pp. 309–327. [Google Scholar]
  37. McGeoch, M.A. The Selection, Testing and Application of Terrestrial Insects as Bioindicators. Biol. Rev. 1998, 73, 181–201. [Google Scholar] [CrossRef]
  38. Oliver, I.A.N.; Beattie, A.J. Designing a Cost-effective Invertebrate Survey: A Test of Methods for Rapid Assessment of Biodiversity. Ecol. Appl. 1996, 6, 594–607. [Google Scholar] [CrossRef]
  39. Magurran, A.E. Measuring Biological Diversity; John Wiley & Sons: Hoboken, NJ, USA, 2003; ISBN 0632056339. [Google Scholar]
  40. Conrad, K.F.; Fox, R.; Woiwod, I.P. Monitoring Biodiversity: Measuring Long-Term Changes in Insect Abundance. In Insect Conservation Biology; CAB International: Egham, UK, 2007. [Google Scholar]
  41. Baczkowski, A.J.; Joanes, D.N.; Shamia, G.M. Properties of a Generalized Diversity Index. J. Theor. Biol. 1997, 188, 207–213. [Google Scholar] [CrossRef]
  42. Doblas-Miranda, E.; Paquette, A.; Work, T.T. Intercropping Trees’ Effect on Soil Oribatid Diversity in Agro-Ecosystems. Agrofor. Syst. 2014, 88, 671–678. [Google Scholar] [CrossRef]
  43. Szigeti, N.; Berki, I.; Vityi, A.; Winkler, D. Soil Mesofauna and Herbaceous Vegetation Patterns in an Agroforestry Landscape. Agrofor. Syst. 2022, 96, 773–786. [Google Scholar] [CrossRef]
  44. Jose, S. Agroforestry for Ecosystem Services and Environmental Benefits: An Overview; Springer: Berlin/Heidelberg, Germany, 2009; ISBN 9048133238. [Google Scholar]
  45. Lohmann, U.; Sausen, R.; Bengtsson, L.; Cubasch, U.; Perlwitz, J.; Roeckner, E. The Köppen Climate Classification as a Diagnostic Tool for General Circulation Models. Clim. Res. 1993, 3, 177–193. [Google Scholar] [CrossRef]
  46. Cui, D.; Liang, S.; Wang, D. Observed and Projected Changes in Global Climate Zones Based on Köppen Climate Classification. Wiley Interdiscip. Rev. Clim. Change 2021, 12, e701. [Google Scholar] [CrossRef]
  47. Schmidt, O.; Schmidt, S.; Häuser, C.L.; Hausmann, A.; Van Vu, L. Using Malaise Traps for Collecting Lepidoptera (Insecta), with Notes on the Preparation of Macrolepidoptera from Ethanol. Biodivers. Data J. 2019, 7, e32192. [Google Scholar] [CrossRef]
  48. Uhler, J.; Haase, P.; Hoffmann, L.; Hothorn, T.; Schmidl, J.; Stoll, S.; Welti, E.A.R.; Buse, J.; Müller, J. A comparison of different Malaise trap types. Insect Conserv. Divers. 2022, 15, 666–672. [Google Scholar] [CrossRef]
  49. McCravy, K.W.; Geroff, R.K.; Gibbs, J. Malaise Trap Sampling Efficiency for Bees (Hymenoptera: Apoidea) in a Restored Tallgrass Prairie. Fla. Entomol. 2016, 99, 321–323. [Google Scholar] [CrossRef]
  50. Marquina, D.; Buczek, M.; Ronquist, F.; Łukasik, P. The Effect of Ethanol Concentration on the Morphological and Molecular Preservation of Insects for Biodiversity Studies. Peer J. 2021, 9, e10799. [Google Scholar] [CrossRef]
  51. ISO 18134-2:2017; Solid Biobuels Determination of Moisture Content—Oven Dry Method—Part 2 Total Moisture—Simplified Method. ISO: Geneva, Switzerland, 2017.
  52. Fedor, P.; Zvaríková, M. Biodiversity Indices. Encycl. Ecol. 2019, 2, 337–346. [Google Scholar]
  53. Shannon, C.E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef]
  54. Spellerberg, I.F.; Fedor, P.J. A Tribute to Claude Shannon (1916–2001) and a Plea for More Rigorous Use of Species Richness, Species Diversity and the ‘Shannon–Wiener’Index. Glob. Ecol. Biogeogr. 2003, 12, 177–179. [Google Scholar] [CrossRef]
  55. Nagendra, H. Opposite Trends in Response for the Shannon and Simpson Indices of Landscape Diversity. Appl. Geogr. 2002, 22, 175–186. [Google Scholar] [CrossRef]
  56. Gregorius, H.-R.; Gillet, E.M. Generalized Simpson-Diversity. Ecol. Modell. 2008, 211, 90–96. [Google Scholar] [CrossRef]
  57. Hammer, Ø.; Harper, D.A.T. Past: Paleontological Statistics Software Package for Educaton and Data Anlysis. Palaeontol. Electron. 2001, 4, 1. [Google Scholar]
  58. Favila, M.E.; Halffter, G. The use of indicator groups for measuring biodiversity as related to community structure and function. Acta Zool. Mex. 1997, 72, 1–25. [Google Scholar] [CrossRef]
  59. Noss, R.F. Indicators for Monitoring Biodiversity: A Hierarchical Approach. Conserv. Biol. 1990, 4, 355–364. [Google Scholar] [CrossRef]
  60. Hortal, J.; Roura-Pascual, N.; Sanders, N.J.; Rahbek, C. Understanding (Insect) Species Distributions across Spatial Scales. Ecography 2010, 33, 51–53. [Google Scholar] [CrossRef]
  61. Kuchenbecker, J.; Macedo-Reis, L.E.; Fagundes, M.; Neves, F.S. Spatiotemporal Distribution of Herbivorous Insects Along Always-Green Mountaintop Forest Islands. Front. For. Glob. Change 2021, 4, 709403. [Google Scholar] [CrossRef]
  62. Schweiger, O.; Maelfait, J.P.; Van Wingerden, W.; Hendrickx, F.; Billeter, R.; Speelmans, M.; Augenstein, I.; Aukema, B.; Aviron, S.; Bailey, D.; et al. Quantifying the Impact of Environmental Factors on Arthropod Communities in Agricultural Landscapes across Organizational Levels and Spatial Scales. J. Appl. Ecol. 2005, 42, 1129–1139. [Google Scholar] [CrossRef]
  63. Cushman, S.A.; McGarigal, K. Multi-Scale Decomposition of Species-Environment Relationships. Landsc. Ecol. 2002, 17, 637–646. [Google Scholar] [CrossRef]
  64. Viterbi, R.; Cerrato, C.; Bionda, R.; Provenzale, A. Effects of Temperature Rise on Multi-Taxa Distributions in Mountain Ecosystems. Diversity 2020, 12, 210. [Google Scholar] [CrossRef]
  65. Samways, M.J.; Barton, P.S.; Birkhofer, K.; Chichorro, F.; Deacon, C.; Fartmann, T.; Fukushima, C.S.; Gaigher, R.; Habel, J.C.; Hallmann, C.A. Solutions for Humanity on How to Conserve Insects. Biol. Conserv. 2020, 242, 108427. [Google Scholar] [CrossRef]
  66. Obrist, M.K.; Duelli, P. Rapid Biodiversity Assessment of Arthropods for Monitoring Average Local Species Richness and Related Ecosystem Services. Biodivers. Conserv. 2010, 19, 2201–2220. [Google Scholar] [CrossRef]
  67. Ebeling, A.; Hines, J.; Hertzog, L.R.; Lange, M.; Meyer, S.T.; Simons, N.K.; Weisser, W.W. Plant Diversity Effects on Arthropods and Arthropod-Dependent Ecosystem Functions in a Biodiversity Experiment. Basic. Appl. Ecol. 2018, 26, 50–63. [Google Scholar] [CrossRef]
  68. Gutiérrez-Chacón, C.; Valderrama-A., C.; Klein, A.-M. Biological Corridors as Important Habitat Structures for Maintaining Bees in a Tropical Fragmented Landscape. J. Insect Conserv. 2020, 24, 187–197. [Google Scholar] [CrossRef]
  69. Hernandez-Aguilera, J.N.; Conrad, J.M.; Gómez, M.I.; Rodewald, A.D. The Economics and Ecology of Shade-Grown Coffee: A Model to Incentivize Shade and Bird Conservation. Ecol. Econ. 2019, 159, 110–121. [Google Scholar] [CrossRef]
  70. McDermott, M.E.; Rodewald, A.D. Conservation Value of Silvopastures to Neotropical Migrants in Andean Forest Flocks. Biol. Conserv. 2014, 175, 140–147. [Google Scholar] [CrossRef]
Ecologies 06 00045 g001
Figure 1. View from the satellite of the five studied agricultural surfaces: (a) stubble; (b) poplars; (c) agroforestry; (d) eucalyptus; (e) safflower.
Figure 1. View from the satellite of the five studied agricultural surfaces: (a) stubble; (b) poplars; (c) agroforestry; (d) eucalyptus; (e) safflower.
Ecologies 06 00045 g001
Ecologies 06 00045 g002
Figure 2. Results of: (a) wind direction of the sampling period in the study area; (b) wind speed of the sampling period in the study area.
Figure 2. Results of: (a) wind direction of the sampling period in the study area; (b) wind speed of the sampling period in the study area.
Ecologies 06 00045 g002
Ecologies 06 00045 g003
Figure 3. Percentage distribution of the collected arthropods in each location and in the overall environment studied.
Figure 3. Percentage distribution of the collected arthropods in each location and in the overall environment studied.
Ecologies 06 00045 g003
Ecologies 06 00045 g004
Figure 4. Biplot of the results of Principal Component Analysis. Black: stubble; green: eucalyptus; purple: safflower; blue: poplars; orange: agroforestry.
Figure 4. Biplot of the results of Principal Component Analysis. Black: stubble; green: eucalyptus; purple: safflower; blue: poplars; orange: agroforestry.
Ecologies 06 00045 g004
Ecologies 06 00045 g005
Figure 5. Correlation between unitary dry weight and total number of arthropods collected in each location.
Figure 5. Correlation between unitary dry weight and total number of arthropods collected in each location.
Ecologies 06 00045 g005
Table 1. Meteorological data of the study area during the sampling period.
Table 1. Meteorological data of the study area during the sampling period.
DateMean Temp. (°C)Max Temp. (°C)Min Temp.
(°C)		Mean Wind Speed
(km·h−1)		Max Wind Speed
(km·h−1)		Mean Daily Air Moisture
(%)
21 July 202429.7539.8019.902.908.0041.69
22 July 202429.1038.3019.402.378.0042.35
23 July 202428.3338.4018.702.106.4045.21
24 July 202428.8638.1018.802.408.0043.46
25 July 202428.3837.4019.302.608.0048.29
26 July 202428.0135.1020.503.749.7049.33
27 July 202426.4135.4019.302.178.0052.92
28 July 202427.8137.3019.701.878.0052.92
Table 2. Number of arthropods collected during the sampling period in each location, categorized by Order. The Order mean indicates the average number of arthropods collected per day for each = Order. The system daily mean represents the average number of arthropods collected per day.
Table 2. Number of arthropods collected during the sampling period in each location, categorized by Order. The Order mean indicates the average number of arthropods collected per day for each = Order. The system daily mean represents the average number of arthropods collected per day.
OrderRichnessMean ± St. Dev.
StubbleDiptera21142.20 ± 20.29
Lepidoptera51.00 ± 1.26
Hymenoptera5410.80 ± 6.52
Coleoptera122.40 ± 2.06
Heteroptera91.80 ± 1.94
Ephemeroptera0/
Arachnida20.40 ± 0.49
PoplarsDiptera1006201.20 ± 109.75
Lepidoptera12224.40 ± 8.4
Hymenoptera39178.20 ± 38.67
Coleoptera21142.20 ± 7.19
Heteroptera20841.60 ± 17.72
Ephemeroptera61.20 ± 1.47
Arachnida346.80 ± 1.83
AgroforestryDiptera1325265.00 ± 209.39
Lepidoptera18336.60 ± 15.70
Hymenoptera966193.20 ± 166.38
Coleoptera26152.20 ± 15.56
Heteroptera27855.60 ± 26.45
Ephemeroptera459.00 ± 4.94
Arachnida316.20 ± 3.71
EucalyptusDiptera19238.40 ± 20.51
Lepidoptera214.20 ± 1.72
Hymenoptera7014.00 ± 2.28
Coleoptera275.40 ± 2.06
Heteroptera326.40 ± 3.14
Ephemeroptera30.60 ± 1.20
Arachnida234.60 ± 1.02
SafflowerDiptera42885.60 ± 46.36
Lepidoptera15030.00 ± 12.71
Hymenoptera570114.00 ± 66.65
Coleoptera479.40 ± 6.86
Heteroptera397.80 ± 4.49
Ephemeroptera193.80 ± 2.32
Arachnida153.00 ± 2.28
System daily mean (±st. dev.) of total arthropods: Stubble 56.60 ± 26.20, Poplars 395.30 ± 105.73, Agroforestry 617.80 ± 260.30, Eucalyptus 73.60 ± 22.09, Safflower 253.60 ± 29.71.
Table 3. Arthropod Order results in terms of quantity and diversity of the arthropods collected in each location.
Table 3. Arthropod Order results in terms of quantity and diversity of the arthropods collected in each location.
SystemsTotal
Arthropods (n)		Total
Dry Weight (g)		Unitary
Dry Weight (g)		Shannon
Index		Simpson
Index
Stubble2935.340.01820.910.55
Poplars197811.460.00581.400.32
Agroforestry308912.920.00421.430.30
Eucalyptus3685.010.01361.440.33
Safflower12687.470.00591.320.33
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

Bergonzoli, S.; Cozzolino, L.; Romano, E.; Pari, L. Role of Agricultural Management in Short-Term Monitoring of Arthropod Diversity at Field Scale. Ecologies 2025, 6, 45. https://doi.org/10.3390/ecologies6030045

AMA Style

Bergonzoli S, Cozzolino L, Romano E, Pari L. Role of Agricultural Management in Short-Term Monitoring of Arthropod Diversity at Field Scale. Ecologies. 2025; 6(3):45. https://doi.org/10.3390/ecologies6030045

Chicago/Turabian Style

Bergonzoli, Simone, Luca Cozzolino, Elio Romano, and Luigi Pari. 2025. "Role of Agricultural Management in Short-Term Monitoring of Arthropod Diversity at Field Scale" Ecologies 6, no. 3: 45. https://doi.org/10.3390/ecologies6030045

APA Style

Bergonzoli, S., Cozzolino, L., Romano, E., & Pari, L. (2025). Role of Agricultural Management in Short-Term Monitoring of Arthropod Diversity at Field Scale. Ecologies, 6(3), 45. https://doi.org/10.3390/ecologies6030045

Article Metrics

Back to TopTop