Interspecific interaction network of mites associated with mango trees

Interspecific interaction network of mites associated with mango trees | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interspecific interaction network of mites associated with mango trees André S. Melo, Antônio A. Paz-Neto, José W.S. Melo, Manoel G.C. Gondim-Junior This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4057884/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jun, 2024 Read the published version in Experimental and Applied Acarology → Version 1 posted You are reading this latest preprint version Abstract Direct and indirect ecological interactions, environmental factors, and the phenology of host plants can shape the way mites interact. These relationships interfere with species occurrence and consequently alter the structure and stability of the intraplant community. We evaluated the associations between mite species and mango trees using a complex network approach. Additionally, we observed a correlation between mite population density and plant phenological stage. Environmental variables, such as average monthly temperature, monthly precipitation, and average monthly relative humidity at different times of the year, were used in the correlation analysis. Network analysis revealed that Amblyseius largoensis, Bdella ueckermanni, Parapronematus acaciae, and Tuckerella ornata occupied central positions in the assembly of mites occurring on mango trees. Environmental variables, average monthly temperature, and monthly precipitation were correlated with the occurrence of Brachytydeus formosa, Cisaberoptus kenyae, Oligonychus punicae, T. ornata, and Vilaia pamithus. We also observed a correlation between the plant phenological stage and population densities of Neoseiulus recifenses, O. punicae, P. acaciae, and V. pamithus. Acari Bayesian network ecological interactions Mangifera indica. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Plants harbor various mite species with different feeding habits, including phytophages, predators, and mycophages. These organisms utilize plant vegetative structures (e.g., leaves) and reproductive structures (e.g., flowers and fruits) as resources for feeding, shelter, and reproduction (Walter 1996; Moraes and Flechtmann 2008; Saito 2010). Mite populations form an assembly through continuous intraspecific and interspecific interactions with the host and environment. Interactions between different species (interspecific), such as competition for resources and predation, shape the composition and structure of an assembly (Chesson 2012). This, in turn, affects how the assembly utilizes and shares the host plant (Walter and Proctor 2013; Araújo and Daud 2018). The environment also interferes with the occurrence of arthropod populations, shaping their response to population fluctuations of species over time (Price et al. 2011; Manu et al. 2016). In community ecology, it is essential to study the dynamics of interactions between species for understanding community structure and operation (Poisot et al. 2016). In this context, the application of network theory offers a robust and well-formalized method for handling and interpreting interactions among an arbitrarily large or small number of species (Devictor et al. 2010; Delmas et al. 2019). The abstract model of ecological interaction networks is structured as a set of nodes or vertices (representing species) interconnected via a set of edges or links (representing possible interactions) (Dale and Fortin 2010; Nooy et al. 2011). Recent studies have employed network analyses to observe the association between phytophagous mite species and predators associated with different hosts (Benítez-Malvido and Dáttilo 2015; Calatayud et al. 2016; Araújo and Daud 2018; Araújo et al. 2020). Network analyses can also be used to evaluate interactions among species inhabiting specific locations associated with the same host (Harvey et al. 2017). In this study, we assessed the interspecific relationships within the mite fauna present on mango trees, as well as the population occurrence of these species over time. We employed network analyses to observe the relationships and structures of mite fauna in mango trees. Additionally, we evaluated the effects of environmental variables and plant phenological stages on the population occurrence of these species. To achieve this, we employed three distinct approaches: (1) the use of a Bayesian network approach, (2) correlating the interactions among species with environmental variables (monthly average temperature, monthly precipitation, and monthly average relative humidity), and (3) correlating the interactions among these species with the phenological stage of the mango tree (vegetative and reproductive). Material and methods Network characteristics: The interaction network was constructed using a database compiled in our recent study on the diversity of mite species associated with mango trees (Melo et al. 2023). Abundance data were considered and grouped into a single data matrix of identified species at the genus and species levels using specialized bibliographic materials, such as manuals and identification keys (Keifer 1946, 1966; Sayed 1946; Channabasavanna 1966; Chandrapatya and Boczek 1991; Lofego 1998; Moraes and Flechtmann 2008; Silva et al. 2016; Demite et al. 2021). These data were separated according to species and feeding habits indicated in specialized literature, such as phytophages, predators, and other feeding habits (Moraes and Flechtmann 2008; Krantz and Walter 2009; McMurtry et al. 2013). In addition, we considered the species Oligonychus mangiferus (Rahman and Sapra), as described in other mite studies on mango trees, to be synonymous with Oligonychus punicae (Hirst) (Mushtaq et al. 2022; Migeon and Dorkeld 2024). Environmental data: Environmental data were obtained from the database of the National Institute of Meteorology (INMET), from the climatic station in Recife (Curado) 8°3’33” S, 34°57’33” W and are presented in Fig.1. Data analysis Network estimation: A Gaussian graphical model was estimated and fitted to the L1 LASSO (Tibshirani 1996) and EBIC (Fovgel and Drton 2010) models to zero out parameters with weak links (P > 0.05), to obtain a more parsimonious network. The hyperparameter was set to 0.5 for model selection. Once estimated, the model was presented as an undirected Bayesian network structure: nodes represented mite species occurring on the mango trees, and edges represented the (positive or negative) relationships between these species. The network was evaluated using the following criteria: (1) closeness between two nodes, indicating the average length of the shortest path between two nodes; (2) betweenness of the two nodes, indicating the number of times a node lies on the shortest path between two other nodes; and (3) node strength, which is the sum of all weights connected to a specific node (Opsahl et al. 2010). Solid lines represent positive connections and dashed lines represent negative connections. The line thickness represents the strength of the connection; the thicker the line, the stronger the connection. Indices were standardized and presented as z-scores. Analyses were performed using JASP software (version 0.17.2.1; JASP Team 2023). Population occurrence and ecological interactions: The population occurrence of the central species (nodes) in the interaction network was analyzed using Generalized Linear Models (GLMs), with the data distribution adjusted to the Poisson model, and when necessary, overdispersion was corrected using the quasi-Poisson model. The variables used in ecological interactions, such as phenological stage, monthly average temperature, monthly precipitation, and monthly average relative humidity, were considered to be explanatory variables, and the average number of mites/leaf/plants was considered to be the response variable. Each explanatory variable was analyzed separately in the model to avoid multicollinearity. Contrast analysis using GLM (P ≤ 0.05) was used to check for differences between sampling dates. Analyses were conducted using R statistical software version 4.0.5 for Windows (R Core Team 2023). Results Network estimation: The network displayed 24 positive and 3 negative connections. Among the positive connections, Amblyseius largoensis (Muma) exhibited high closeness and betweenness, whereas Tuckerella ornata (Tucker) demonstrated the greatest strength. Bdella ueckermanni (Hernandes, Daud and Feres) and Brachytydeus formosa (Cooreman) exhibited high values for both closeness and strength. Additionally, Rubroscirus aff. Myabunderensis and Parapronematus acacia e Baker exhibited high closeness and betweenness. Cheletogenes ornatus (Canestrini and Fanzago) and Fungitarsonemus setillus (Souza, Lofego and Gondim Jr.) exhibited high closeness and betweenness, respectively. The highest edge weights, representing the sum of weights connected to the same node, were observed for the following species: T. ornata = 1.55, B. ueckermanni = 1.04, A. largoensis = 0.98, and P. acaciae = 0.89. Negative correlations were identified between the following species: Iphiseiodes zuluagai (Denmark and Muma) and B. formosa = -0.37, Typhlodromina subtropica (Muma and Denmark) and Euseius alatus De Leon = -0.11, T. peregrinus and Neocalacarus mangiferae Channabasavanna = -0.06. Notably, Cunaxatricha aff. Tarsospinosa , Tenuipalpus aff. Lygodii , and Brachytydeus sp. 3 did not exhibit significant relationships with other mite species in this network (Fig. 2). Population occurrence and ecological interactions: Neoseiulus recifensis (Gondim Jr. and Moraes), which is intricately connected with the central species in the network ( A. largoensis ), exhibited a notable correlation with both the phenological stage (χ² (1, 117) = 14.05; P = 0.038) and sampling date (χ² (1, 118) = 78.32; P = 0.002). The peak occurrence of N. recifensis (0.5 ± 0.4 mites/leaf/plant) was recorded on sampling date 5, which corresponded to the vegetative stage of the mango tree (Fig. 3). Notably, no significant correlations were identified between the other species and variables analyzed within this group. Vilaia pamithus (Chandrapatya and Boczek), which is intricately linked with the central species B. ueckermanni , showed significant correlations with the monthly average temperature (Fig. 4a; χ² (1, 115) = 13227; P = 0.002) and mango tree phenological stage (χ² (1, 117) = 15600; P = 0.035). The peak occurrence of V. pamithus (220 ± 105 mites/leaf/plant) was recorded on sampling date 7, aligning with the vegetative stage of the mango tree. On the other hand, Cisaberoptus kenyae Keifer exhibited a correlation solely with the monthly average temperature (χ² (1, 115) = 21359; P = 0.003). The date of the highest abundance of C. kenyae (1624 ± 723 mites/leaf/plant) coincided with sampling date 12, corresponding to one of the periods of highest temperature in the year. Notably, no significant correlations were found between the other species and variables analyzed in this group (Fig. 4b). Oligonychus punicae exhibited a correlation with monthly precipitation (χ² (1, 116) = 9.96; P = 0.001) and mango tree phenological stage (Fig. 5; χ² (1, 117) = 6098; P < 0.001), reaching its highest occurrence on sampling date 1 (90 ± 51 mites/leaf/plant). This date corresponds to the reproductive stage of the mango trees and coincides with one of the periods of lower annual precipitation. T. ornata correlated with monthly precipitation (χ² (1, 116) = 184.69; P = 0.013) and sampling date (χ² (1, 118) = 199.14; P < 0.001), with the peak occurrence observed on sampling date 12 (6.8 ± 4.0 mites/leaf/plant). This date aligns with the period of lowest precipitation during the year. Additionally, P. acaciae showed a correlation with the phenological stage (χ² (1, 117) = 309.19; P < 0.001), attaining its highest occurrence on sampling date 12 (3 ± 1.8 mites/leaf/plant), corresponding to the reproductive stage of the mango. Notably, no significant correlation was found between F. setillus and variables analyzed in this group. The species B. formosa displayed a correlation with monthly average temperature (Fig. 6; χ² (1, 115) = 1509; P < 0.001), reaching I highest occurrence on sampling date 12 (17 ± 16 mites/leaf/plant). This date corresponded to one of the periods with the highest temperatures in the year. On the other hand, I. zuluagai correlated solely with the sampling date (χ² (1, 118) = 167.6; P < 0.001), with the highest occurrence observed on sampling date 12 (1.4 ± 1.0 mites/leaf/plant). Discussion The findings of the current study highlight the pivotal role of A. largoensis , B. ueckermanni , P. acaciae , and T. ornata as central connectors in the mite communities associated with mango trees. Amblyseius largoensis , B. ueckermanni , and P. acaciae are generalist predators that act as significant natural enemies of other mites and are encountered in various ecosystems (McCoy et al. 1969; Hernandes et al. 2016; Demite et al. 2021). The prevalence of these predator mites as the central species in the network was anticipated in this study, given the minimally disturbed nature of the environment and the absence of acaricide application. Amblyseius largoensis is associated with various mite species, including invasive species, and is the most frequent and abundant predator of coconut palms (Gondim Jr. et al. 2012; Moraes et al. 2012; Barros et al. 2020). This species plays a crucial role in stabilizing communities of phytophagous mites and other predators through indirect interactions such as predation and apparent competition (Barros et al. 2020). The significance of B. ueckermanni in the structure of this assembly may be associated with its adaptability to diverse environments, considering that bdelloid mites are potential bioindicators in studies on environmental imbalance (Hernandes et al. 2016). Other studies have also noted elevated populations of B. ueckermanni and other bdelloids in undisturbed natural environments, wherein they play relevant roles in population regulation (Wallace 1974; Mejía-Recamier and Cutz-Pool 2007; Souza et al. 2012; Chaires-Grijalva et al. 2021). Iolinid mites feed on pollen, fungi, and other mites such as eriophids, tenuipalpids, and tetranychids (Hernandes et al. 2015). The importance of P. acaciae in the structure of this mite assembly is probably due to the diversification of its feeding habits. This species is also frequently associated with fungi. For example, P. acaciae has been reported to feed on Colletotrichum gloeosporioides Penzig and Penicillium digitatum Saccardo (Fadamiro et al. 2009; Childers and Ueckermann 2020). There are records of more than 50 fungal species in mango trees worldwide (Khaskheli 2020). Parapronematus acaciae is also probably associated with some of these fungi. However, further studies are required to verify this association in mango trees. Tuckerella ornata is poorly studied in Brazil and has been recorded in Barbados cherry ( Malpighia emarginata A.DC.; Malpighiaceae) (Barbosa et al. 2003) and cacao ( Theobroma cacao L.; Malvaceae), with reports of damage (Brito et al. 2023). In the present study, no apparent damage to mango trees associated with T. ornata attacks was observed, probably owing to its low population density. In the network, an association of T. ornata with the predator mites P. acaciae and I. zuluagai was observed. The maintenance of the predator I. zuluagai on mango leaves likely depends on the presence of this phytophagous species. In contrast, a positive association was observed between P. acaciae , O. punicae , and F. setillus , indicating that variation in the population of T. ornata will not have a direct effect on I. zuluagai . The predator mites P. acaciae and I. zuluagai appear to be important biological control agents against T. ornata in mango trees, and laboratory studies are required to assess their potential use. Ecological networks are dynamic structures that are shaped not only by changes in species composition but also by how species assembly interacts over time in their co-occurring location, where environmental factors play a crucial role in this process (Poisot et al. 2012, 2015). Higher temperatures may shorten the developmental cycle of most species, thereby reducing the development time of immature stages and favoring population increases (Devi and Challa 2019). In this study, we found that the monthly average temperature and monthly precipitation affected the population occurrence of some mite species on mango trees. Other studies have found that higher temperatures in Egypt favor mite development in mango trees that do not live in sheltered environments (Abou-Awad et al. 2011; Marei et al. 2020). However, the monthly average relative humidity had no effect on mite occurrence. The effective environment for small arthropods, such as mites, may be very different from the macroscale environmental conditions, being more subject to leaf microenvironmental conditions (Pincebourde and Woods 2012). Intraplant mite interactions can lead phytophagous species to develop adaptive mechanisms that specialize in different vegetative structures or phenological stages of the certain plants, occupying niches with less competition for resources (Saito 2010). In the present study, the population occurrence of N. recifenses , V. pamithus , P. acaciae , and O. punicae was affected by the phenological stage of the mango tree, with higher occurrence of N. recifenses and V. pamithus in the vegetative stage and P. acaciae and O. punicae in the reproductive stage. This indicates a greater tendency for these species to occur at different phenological stages of the plant. Phytophagous mites show distinct levels of specialization to their hosts, indirectly favoring the associated predator mites. These predators depend on this specialization for their occurrence in hosts (Krantz and Lindquist 1979; Skoracka 2006; McMurtry et al. 2013). In addition, the behavior of predator mites depends on the interactions between them. For example, intraguild predation can shape the distribution of mites on host plants (Choh et al. 2015). In this study, we present a network of interactions among mites occurring in mango trees for the first time, emphasizing the importance of some central species within this assembly. Although we did not observe a significant effect of phytophagous mites on the evaluated leaves, some species, such as A. mangiferae , C. kenyae , and O. punicae , have been considered important mango pests. We showed that these species are often positively associated with the predator mite species E. alatus , B. ueckermanni , and P. acaciae , respectively. These predator mites are probably the natural enemies of these phytophagous species. However, further laboratory studies are required to investigate these interactions. Additionally, we found that environmental variables play a crucial role in the regulatory mechanisms affecting the occurrence of mite fauna in mango trees, potentially acting directly on these populations or indirectly through the regulation of plant phenological stages. Notably, future studies must assess the effects of microclimatic conditions inside the plant on the populations of this mite assembly. Declarations Acknowledgments Thanks are to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) for granting a scholarship to the first author and for the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (306092/2021-2) for financial support for this research. Conflict of interest The authors declare no conflict of interest. References Abou-Awad BA, Metwally AS, Al-Azzazy MM (2011) Environmental management and biological aspects of two eriophyid mango mites in Egypt: Aceria mangiferae and Metaculus mangiferae . Acarologia 51:481-497. https://doi.org/10.1051/acarologia/20112030 Araújo WS, Daud RD (2018) Contrasting structures of plant–mite networks compounded by phytophagous and predatory mite species. Exp Appl Acarol 74:335-346. https://doi.org/10.1007/s10493-018-0250-2 Araújo WS, Freitas EVD, Silveira LT, Daud RD (2020) Network structure of interactions between phytophagous mites and their hostplants in natural ecosystems in Brazil. Syst Appl Acarol 25:821-832. https://doi.org/10.11158/saa.25.5.4 Barbosa DGF, Gondim Junior MGC, Barros R, Oliveira JV (2003) Diversidade de ácaros em aceroleira ( Malpighia emarginata A.DC.) na Universidade Federal de Pernambuco em Recife, PE. Neotrop Entomol 32:577-583. https://doi.org/10.1590/S1519-566X2003000400007 Barros MEN, Lima DB, Mendes JA, Gondim Jr MG, Melo JWDS (2020) The establishment of an invasive pest mite, Raoiella indica , affects mite abundance and diversity on coconut plants. Syst Appl Acarol 25:881-894. https://doi.org/10.11158/saa.25.5.9 Benítez-Malvido J, Dáttilo W (2015) Interaction intimacy of pathogens and herbivores with their host plants influences the topological structure of ecological networks in diferent ways. Am J Bot 10:512-519. https://doi.org/10.3732/ajb.1400303 Brito EADS, De Castro IS, Vinhas NAN, Santana RDR, Pinheiro GM, Ferla NJ (2023) Damage caused by Tuckerella ornata (Acari: Tuckerellidae) on cocoa fruits ( Theobroma cacao ) in the Amazon region, state of Pará, Brazil. Syst Appl Acarol 28:11-19. https://doi.org/10.11158/saa.28.1.2 Calatayud J, Hórreo JL, Madrigal-González J, Migeon A, Rodríguez MÁ, Magalhães S, Hortal J (2016) Geography and major host evolutionary transitions shape the resource use of plant parasites. PNAS 113:9840-9845. https://doi.org/10.1073/pnas.1608381113 Chaires-Grijalva MP, Vázquez-Rojas IM, Mejía-Recamier BE, Ruíz-Cancino E, Coronado-Blanco JM (2021) Population Dynamics of the Predator Mite Bdella longistriata Collected by Malaise Trap at Tula, Tamaulipas, Mexico. Outhwest Entomol 46:129-136. https://doi.org/10.3958/059.046.0112 Chandrapatya A, Boczek J (1991) Studies on eriophyid mites (Acari: Eriophyoidea). IV. Bull Pol Acad, Sci Biol Sci 39:427-433 Channabasavanna GP (1966) A contribution to the knowledge of Indian eriophyid mites. Univ Agric Sci, Bangalore Chesson P. (2012) Species competition and predation. In: Leemans, R (ed) Ecological Systems, 1rd edn. Springer, New York, pp 223–256. https://doi.org/10.1007/978-1-4614-5755-8_13 Childers CC, Ueckermann EA (2020) The Tetranychoidea, Tarsonemidae and Tydeoidea mite complex on Florida citrus between 1954 and 2014: Pests or Beneficials? Syst Appl Acarol 25:1257-1278. https://doi.org/10.11158/saa.25.7.8 Choh Y, Sabelis MW, Janssen A. (2015) Distribution and oviposition site selection by predatory mites in the presence of intraguild predators. Exp Appl Acarol 67:477-491. https://doi.org/10.1007/s10493-015-9970-8 Dale M, Fortin MJ (2010) From graphs to spatial graphs. Annu Rev Ecol Evol Syst 41:21–38. https://doi.org/10.1146/annurev-ecolsys-102209-144718 Delmas E, Besson M, Brice MH, Burkle LA, Dalla Riva GV, Fortin MJ, Poisot T (2019) Analysing ecological networks of species interactions. Biol Rev 94:16-36. https://doi.org/10.1111/brv.12433 Demite PR, Moraes GJ, McMurtry JA, Denmark HA, Castilho RC (2021) Phytoseiidae Database. http:// lea. esalq. usp. br/phytoseiidae. Accessed 16 December 2023 Devi M, Challa N (2019) Impact of weather parameters on seasonality of phytophagous mites. J Entomol Zool Stud 7:1095-1100 Devictor V, Clavel J, Julliard R, Lavergne S, Mouillot D, Thuiller W, Venail P, Villeger S, Mouquet N (2010) Defining and measuring ecological specialization. J Appl Ecol 47:15-25. https://doi.org/10.1111/j.1365-2664.2009.01744.x Fadamiro HY, Xiao Y, Nesbitt M, Childers CC (2009) Diversity and seasonal abundance of predacious mites in Alabama Satsuma citrus. Ann Entomol Soc Am 102:617-628. https://doi.org/10.1603/008.102.0406 Foygel R, Drton M (2010) Extended Bayesian information criteria for Gaussian graphical models. https://proceedings.neurips.cc/paper_files/paper/2010/file/072b030ba126b2f4b2374f342be9ed44-Paper.pdf. Accessed 08 March 2024 Gondim Jr MGC, Castro TMMG, Marsaro Jr AL, Navia D, Melo JWS, Demite PR, Moraes GJ (2012) Can the red palm mites threaten the Amazon vegetation? Syst Biodivers 10:527-535. https://doi.org/10.1080/14772000.2012.752415 Harvey E, Gounand I, Ward CL, Altermatt F (2017) Bridging ecology and conservation: from ecological networks to ecosystem function. J Appl Ecol 54:371-379. https://doi.org/10.1111/1365-2664.12769 Hernandes FA, Castro TMM, Venancio R (2015) Prostigmata (Acari: Trombidiformes) as Biological Control Agents. In: Carrillo D, Moraes G, Peña J (ed) Prospects for Biological Control of Plant Feeding Mites and Other Harmful Organisms, 19rd edn. Springer, Cham pp151-184 Hernandes FA, Skvarla MJ, Fisher JR, Dowling AP, Ochoa R, Ueckermann EA, Bauchan GR (2016) Catalogue of snout mites (Acariformes: Bdellidae) of the world. Zootaxa 4152:1-83. https://doi.org/10.11646/zootaxa.4152.1.1 JASP Team (2023). JASP (Version 0.17.2) [Computer software]. https://jasp-stats.org/ Keifer HH (1946) Eriophuid Studies XVI. Bull Calf Dept Agric 35:39-48 Keifer HH (1966) Eriophyid Studies B-18. Bur Entomol Calif Dept Agric Khaskheli MI (2020) Mango Diseases: Impact of Fungicides. In: Noureddine HKB, Kolombia IA (ed) Horticultural Crops, 1rd edn. IntechOpen, London, pp 143 Krantz GW, Lindquist EE (1979) Evolution of phytophagous mites (Acari). Annu Rev Entomol 24:121-158 Krantz GW, Walter DE (2009) A manual of acarology. Texas Tech University Press, Lubbock Lofego AC (1998) Caracterização morfológica e distribuição geográfica das espécies de Amblyseiinae (Acari: Phytoseiidae) no Brasil. Dissertation, University of São Paulo Manu M, Iordache V, Băncilă RI, Bodescu F, Onete M (2016) The influence of environmental variables on soil mite communities (Acari: Mesostigmata) from overgrazed grassland ecosystems Romania. Ital J Zool 83:89-97. https://doi.org/10.1080/11250003.2015.1091897 Marei FA, Negm MW, Nasser MA, Eraky SA (2020) Population Dynamics of Oligonychus mangiferus and Aceria mangiferae (Acari: Tetranychidae, Eriophyidae) on Two Mango Cultivars in Asyut Governorate, with an Annotated Checklist of Mango Mites in Egypt. Int J Entomol Nematol 6:149-155 McCoy CW, Selhime AG, Kanavel RF (1969) The feeding behavior and biology of Parapronematus acaciae (Acarina: Tydeidae). Fla Entomol 52:13-19. https://doi.org/10.2307/3493703 McMurtry JA, Moraes GJ, Sourassou NF (2013) Revision of the lifestyles of phytoseiid mites (Acari: Phytoseiidae) and implications for biological control strategies. Syst Appl Acarol 18:297-320. https://doi.org/10.11158/saa.18.4.1 Mejía-Recamier BE, Cutz-Pool LQ (2007) Diversidad altitudinal de Bdellodea (Prostigmata) en musgos cortícícolas en el Estado de México. Entomol Mex 6:54-59. https://doi.org/10.3958/059.046.0112 Melo AS, Melo JW, Paz-Neto AA, Gondim Jr MG (2023) Mite diversity and intra-plant distribution in mango crop. Syst Appl Acarol 28:864-875. https://doi.org/10.11158/saa.28.5.8 Migeon A, Dorkeld F (2024) Spider Mites Web: a comprehensive database for the Tetranychidae. Available from https://www1.montpellier.inrae.fr/CBGP/spmweb. Accessed 13 January 2024 Moraes GJ, Castro TMMG, Kreiter S, Quilici S, Gondim Jr MGC, Sa LAN (2012) Search for natural enemies of Raoiella indica Hirst in La Réunion Island (Indian Ocean). Acarologia 52:129-134. https://dx.doi.org/10.1051/acarologia/20122043 Moraes GJD, Flechtmann CHW (2008) Manual de acarologia: acarologia básica e ácaros de plantas cultivadas no Brasil. Holos, Ribeirão Preto Mushtaq HMS, Kamran M, Saleh AA, Alatawi FJ (2022) Evidence for Reconsidering the Taxonomic Status of Closely Related Oligonychus Species in punicae Complex (Acari: Prostigmata: Tetranychidae). Insects 14:3. https://doi.org/10.3390/insects14010003 Nooy W, Mrvar A, Batagelj V (2011) Exploratory social network analysis with Pajek. Cambridge University Press, London Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: Generalizing degree and shortest paths. Soc Netw 32:245-251. https://doi.org/10.1016/j.socnet.2010.03.006 Pincebourde S, Woods HA (2012) Climate uncertainty on leaf surfaces: the biophysics of leaf microclimates and their consequences for leaf‐dwelling organisms. Funct Ecol 26:844-853. https://doi.org/10.1111/j.1365-2435.2012.02013.x Poisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecol Lett 15:1353-1361. https://doi.org/10.1111/ele.12002 Poisot T, Stoufer DB, Kéf S (2016) Describe, understand and predict: Why do we need networks in ecology? Funct Ecol 30:1878-1882. https://doi.org/10.1111/1365-2435.12799 Poisot T, Stouffer DB, Gravel D (2015) Beyond species: why ecological interaction networks vary through space and time. Oikos 124:243-251. https://doi.org/10.1111/oik.01719 Price PW, Denno RF, Eubanks MD, Finke DL, Kaplan I (2011) Insect ecology: behavior, populations, and communities. Cambridge University Press, New York R Core Team (2023) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 08 October 2023 Saito Y (2010) Plant Mites and Sociality. Springer, Tokyo Sayed T (1946) Aceria mangiferae new spec. Bull Soc Fouad 1 Enttomol 30:7-10 Silva GL, Metzelthin MH, Silva OS, Ferla NJ (2016) Catalogue of the mite family Tydeidae (Acari: Prostigmata) with the world key to the species. Zootaxa 4135:1-68. https://doi.org/10.11646/zootaxa.4135.1.1 Skoracka A (2006) Host specifcity of eriophyoid mites: Specialists or generalists? Biol Let 43:289-298 Souza IV, Gondim Junior MGC, Ramos ALR, Santos EA, Ferraz MI, Oliveira AR (2012) Population dynamics of Aceria guerreronis (Acari: Eriophyidae) and other mites associated with coconut fruits in Una, state of Bahia, northeastern Brazil. Exp Appl Acarol 58:221-233. https://doi.org/10.1007/s10493-012-9576-3 Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol 58:267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x Wallace MMH (1974) An attempt to extend the biological control of Sminthurus viridis (Collembola) to new areas in Australia by introducing a predatory mite, Neomolgus capillatus (Bdellidae). Aust J Zool 22:519-529. https://doi.org/10.1071/ZO9740519 Walter DE (1996) Living on leaves: mites, tomenta, and leaf domatia. Annu Rev Entomol 41:101-114. https://doi.org/10.1146/annurev.en.41.010196.000533 Walter DE, Proctor HC (2013) Mites: ecology, evolution, and behavior: life at a microscale. Springer, New York Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2024 Read the published version in Experimental and Applied Acarology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4057884","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278911263,"identity":"00b6b155-4771-40b0-b614-12e0d5749bbf","order_by":0,"name":"André S. Melo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHACAxAhw8fA2HBAogLIZGZuIEoLDxsD88EHFmdAWhiJ1sKWbFDZBmIT0MI/u3nbh485Njxs7D1mEjfn1UbztwO1/KjYhlOLxJ1jxTNnbkvjYeM5YyY5c9vx3BmHGRsYe87cxm3NjRxjZt5th3nYJHLMpCW3HcttAGphZmzDrUUepOXvtv8QLX/nHMudT0iLAUgL47YDQC1pyQaSDTW5GwhpMbyRVszYuy0Z6JfDBx9IHDuQuxGo5SA+v8jdSN7M8HObnRw/eyMwKmvqcuedB+r9UYHH+2jgMJg8QLR6IKgjRfEoGAWjYBSMEAAAgGtZrZ6MB38AAAAASUVORK5CYII=","orcid":"","institution":"Universidade Federal Rural de Pernambuco","correspondingAuthor":true,"prefix":"","firstName":"André","middleName":"S.","lastName":"Melo","suffix":""},{"id":278911264,"identity":"6b4a5c1d-0592-4265-bee8-d398359e2d5b","order_by":1,"name":"Antônio A. Paz-Neto","email":"","orcid":"","institution":"Universidade do Vale do Taquari","correspondingAuthor":false,"prefix":"","firstName":"Antônio","middleName":"A.","lastName":"Paz-Neto","suffix":""},{"id":278911265,"identity":"cd757736-097c-450c-bb8d-b7dfdcfd6831","order_by":2,"name":"José W.S. Melo","email":"","orcid":"","institution":"Universidade Federal de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"W.S.","lastName":"Melo","suffix":""},{"id":278911266,"identity":"3d26a614-826c-4b88-91de-848bbf681902","order_by":3,"name":"Manoel G.C. Gondim-Junior","email":"","orcid":"","institution":"Universidade Federal Rural de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Manoel","middleName":"G.C.","lastName":"Gondim-Junior","suffix":""}],"badges":[],"createdAt":"2024-03-09 15:33:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4057884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4057884/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10493-024-00936-1","type":"published","date":"2024-06-18T15:44:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52586763,"identity":"8220125f-9da7-4130-9d44-cf8286bb440f","added_by":"auto","created_at":"2024-03-13 09:12:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1327946,"visible":true,"origin":"","legend":"\u003cp\u003eVariables explaining the population occurrence of mite species on mango trees include monthly precipitation (mm), represented by the gray bars and the letter (A); average monthly temperature (ºC), represented by the dashed line and the letter (B); phenological stage of the mango tree (vegetative and reproductive), represented by the vertical dashed line and the letter (C); and the sampling period over a year, from December 2020 (1) to November 2021 (12), represented by the letter (D)\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/2f867517128e01244e7df990.jpg"},{"id":52586759,"identity":"f57e9fc2-d4f2-4caa-8b93-b3d228c1ad71","added_by":"auto","created_at":"2024-03-13 09:12:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17024336,"visible":true,"origin":"","legend":"\u003cp\u003eInterspecific correlation network structure estimated for 28 species of mites on mango trees (left panel) and corresponding centrality indices (right panel). The network structure is a Gaussian graphical model, consisting of a network of partial correlation coefficients. Centrality indices are displayed as standardized z-scores. Nodes represent predator mite species (black nodes), phytophagous mites (gray nodes), and mites with other feeding habits (white nodes). Solid lines represent positive connections, while dashed lines represent negative connections. The thickness of the line indicates the strength of the connection, with thicker lines indicating stronger connections. Node positioning was based on the adaptive LASSO network for better visualization\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/8e3144366653ba316b0e2cc4.jpg"},{"id":52586760,"identity":"c035c619-5284-447c-b471-54956bdbaf33","added_by":"auto","created_at":"2024-03-13 09:12:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4246475,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation occurrence of mite species [\u003cem\u003eB. ueckermanni\u003c/em\u003e (3), \u003cem\u003eR. \u003c/em\u003eaff.\u003cem\u003emyabunderensis\u003c/em\u003e (7), \u003cem\u003eF. setillus\u003c/em\u003e (10), and \u003cem\u003eN. recifensis\u003c/em\u003e (13)] connected to the central species/node [\u003cem\u003eA. largoensis\u003c/em\u003e (2)] in the interaction network. Letters (C) and (D) represent correlation with the phenological stage and sampling period variables, respectively. Asterisks indicate significance of the number of mites in the sampling periods. Bars represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/868968f12ef2b069f13a1e58.jpg"},{"id":52586767,"identity":"fda90630-89ff-447c-9955-180830fd70b5","added_by":"auto","created_at":"2024-03-13 09:12:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10989602,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation occurrence of mite species [\u003cem\u003eV. pamithus\u003c/em\u003e (24) and \u003cem\u003eC. kenyae\u003c/em\u003e (6) (Panel A); \u003cem\u003eB. ueckermanni\u003c/em\u003e (3) and \u003cem\u003eA. largoensis\u003c/em\u003e (2) (Panel B)]. Letters (B) and (C) represent correlations with the average monthly temperature (ºC) and phenological stage variables, respectively. Asterisks indicate significance of the number of mites in the sampling periods. Bars represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/f599680f4d4e9cf1acbedbec.jpg"},{"id":52586754,"identity":"14a80094-ee42-4de4-8a31-1d197ed55655","added_by":"auto","created_at":"2024-03-13 09:12:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4587532,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation occurrence of mite species [\u003cem\u003eF. setillus\u003c/em\u003e (10), \u003cem\u003eO. punicae\u003c/em\u003e (14), and \u003cem\u003eT. ornata\u003c/em\u003e (21)] connected to the central species/node [\u003cem\u003eP. acaciae\u003c/em\u003e(15)] in the interaction network. Letters (A), (C), and (D) represent correlations with the monthly precipitation (mm), phenological stage, and sampling period variables, respectively. Asterisks indicate significance of the number of mites in the sampling periods. Bars represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/80eb2ee6fe2a7e5430e284c1.jpg"},{"id":52586776,"identity":"f938e116-604d-4cc6-83b4-2ab25904d500","added_by":"auto","created_at":"2024-03-13 09:12:56","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1352750,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation occurrence of mite species [\u003cem\u003eB. formosa\u003c/em\u003e (4), \u003cem\u003eI. zuluagai\u003c/em\u003e (11), and \u003cem\u003eP. acaciae\u003c/em\u003e (15)] connected to the central species/node [\u003cem\u003eT. ornata\u003c/em\u003e (21)] in the interaction network. Letters (A), (B), (C), and (D) represent correlations with the monthly precipitation (mm), average monthly temperature (ºC), phenological stage, and sampling period variables, respectively. Asterisks indicate significance of the number of mites in the sampling periods. Bars represent 95% confidence intervals\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/f8b4fc0c84256239acc25490.jpg"},{"id":58823610,"identity":"c75c5a99-94c7-47e7-bb5c-86f6cb545887","added_by":"auto","created_at":"2024-06-21 17:04:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":39919716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4057884/v1/92b776e4-c138-444a-ace9-f06a0ba6bb00.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interspecific interaction network of mites associated with mango trees","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlants harbor various mite species with different feeding habits, including phytophages, predators,\u0026nbsp;and\u0026nbsp;mycophages. These organisms utilize plant vegetative structures (e.g.,\u0026nbsp;leaves) and reproductive structures (e.g.,\u0026nbsp;flowers and fruits) as\u0026nbsp;resources\u0026nbsp;for feeding, shelter,\u0026nbsp;and reproduction (Walter 1996; Moraes and Flechtmann 2008; Saito 2010). Mite populations form an assembly through continuous intraspecific and interspecific interactions with the host and environment. Interactions between different species (interspecific), such as competition for resources and predation, shape the composition and structure of an assembly (Chesson 2012). This, in turn, affects how the assembly utilizes and shares the host plant (Walter and Proctor 2013; Ara\u0026uacute;jo and Daud 2018). The environment also interferes with the occurrence of arthropod populations, shaping their\u0026nbsp;response\u0026nbsp;to population fluctuations of species over time (Price et al. 2011; Manu et al. 2016).\u003c/p\u003e\n\u003cp\u003eIn community ecology, it is essential to study the dynamics of interactions between species for understanding community structure and operation (Poisot et al. 2016). In this context, the application of network theory offers a robust and well-formalized method for handling and interpreting interactions among an arbitrarily large or small number of species (Devictor et al. 2010; Delmas et al. 2019). The abstract model of ecological interaction networks is structured as a set of nodes or vertices (representing species) interconnected via a set of edges or links (representing possible interactions) (Dale and Fortin 2010; Nooy et al. 2011). Recent studies have employed network analyses to observe the association between phytophagous mite species and predators associated with different hosts (Ben\u0026iacute;tez-Malvido and D\u0026aacute;ttilo 2015; Calatayud et al. 2016; Ara\u0026uacute;jo and Daud 2018; Ara\u0026uacute;jo et al. 2020). Network analyses can also be used to evaluate interactions among species inhabiting specific locations associated with the same host (Harvey et al. 2017).\u003c/p\u003e\n\u003cp\u003eIn this study, we assessed the interspecific relationships within the mite fauna present on mango trees, as well as the population occurrence of these species over time. We employed network analyses to observe the relationships and structures of mite fauna in mango trees. Additionally, we evaluated the effects of environmental variables and plant phenological stages on the population occurrence of these species. To achieve this, we employed three distinct approaches: (1) the use of a Bayesian network approach, (2) correlating the interactions among species with environmental variables (monthly average temperature, monthly precipitation, and monthly average relative humidity), and (3) correlating the interactions among these species with the phenological stage of the mango tree (vegetative and reproductive).\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eNetwork characteristics:\u0026nbsp;\u003c/strong\u003eThe interaction network was constructed using a database compiled in our recent study on the diversity of mite species associated with mango trees (Melo et al. 2023). Abundance data were considered and grouped into a single data matrix of identified species at the genus and species levels using specialized bibliographic materials,\u0026nbsp;such as manuals and identification keys (Keifer 1946, 1966; Sayed 1946; Channabasavanna 1966; Chandrapatya and Boczek 1991; Lofego 1998; Moraes and Flechtmann 2008; Silva et al. 2016; Demite et al. 2021). These data were separated according to species and feeding habits indicated in specialized literature, such as phytophages, predators, and other feeding habits (Moraes and Flechtmann 2008; Krantz and Walter 2009; McMurtry et al. 2013).\u0026nbsp;In addition, we considered\u0026nbsp;the species\u0026nbsp;\u003cem\u003eOligonychus mangiferus\u003c/em\u003e (Rahman and Sapra),\u0026nbsp;as\u0026nbsp;described in other mite studies on mango\u0026nbsp;trees, to be synonymous with \u003cem\u003eOligonychus punicae\u003c/em\u003e (Hirst)\u0026nbsp;(Mushtaq et al. 2022; Migeon and Dorkeld 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental data:\u003c/strong\u003e Environmental data were obtained from the database of the National Institute of Meteorology (INMET), from the climatic station in Recife (Curado) 8\u0026deg;3\u0026rsquo;33\u0026rdquo; S, 34\u0026deg;57\u0026rsquo;33\u0026rdquo; W and are presented in Fig.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork estimation:\u003c/strong\u003e A Gaussian graphical model was estimated and fitted to\u0026nbsp;the\u0026nbsp;L1 LASSO (Tibshirani 1996) and EBIC (Fovgel and Drton 2010) models to zero out parameters with weak links (P \u0026gt; 0.05), to obtain a more parsimonious network. The hyperparameter was set to 0.5 for model selection. Once estimated, the model was presented as an undirected Bayesian network structure: nodes represented mite species occurring on the mango trees, and edges represented the (positive or negative) relationships between these species. The network was evaluated using the following criteria: (1) closeness between two nodes, indicating the average length of the shortest path between two nodes; (2) betweenness of the two nodes, indicating the number of times a node lies on the shortest path between two other nodes; and (3) node strength, which is the sum of all weights connected to a specific node (Opsahl et al. 2010). Solid lines represent positive connections and dashed lines represent negative connections. The line thickness represents the strength of the connection; the thicker the line, the stronger the connection. Indices were standardized and presented as z-scores. Analyses were performed using JASP software (version 0.17.2.1; JASP Team 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation occurrence and ecological interactions:\u003c/strong\u003e The population occurrence of the central species (nodes) in the interaction network was analyzed using Generalized Linear Models (GLMs), with the data distribution adjusted to the Poisson model, and when necessary, overdispersion was corrected using the quasi-Poisson model. The variables used in ecological interactions, such as phenological stage, monthly average temperature, monthly precipitation, and monthly average relative humidity, were considered to be explanatory variables, and the average number of mites/leaf/plants was considered to be the response variable. Each explanatory variable was analyzed separately in the model to avoid multicollinearity. Contrast analysis using GLM (P \u0026le; 0.05) was used to check for differences between sampling dates. Analyses were conducted using R statistical software version 4.0.5 for Windows (R Core Team 2023).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eNetwork estimation:\u003c/strong\u003e The network displayed 24 positive and 3 negative connections. Among the positive connections, \u003cem\u003eAmblyseius largoensis\u003c/em\u003e (Muma) exhibited high closeness and betweenness, whereas \u003cem\u003eTuckerella ornata\u003c/em\u003e (Tucker) demonstrated the greatest strength. \u003cem\u003eBdella ueckermanni\u003c/em\u003e (Hernandes, Daud and Feres) and \u003cem\u003eBrachytydeus formosa\u003c/em\u003e (Cooreman) exhibited high values for both closeness and strength. Additionally, \u003cem\u003eRubroscirus\u0026nbsp;\u003c/em\u003eaff. \u003cem\u003eMyabunderensis\u003c/em\u003e and \u003cem\u003eParapronematus acacia\u003c/em\u003e\u003cem\u003ee\u003c/em\u003e Baker exhibited high closeness and betweenness. \u003cem\u003eCheletogenes ornatus\u003c/em\u003e (Canestrini and Fanzago) and \u003cem\u003eFungitarsonemus setillus\u003c/em\u003e (Souza, Lofego and Gondim Jr.) exhibited high closeness and betweenness, respectively. The highest edge weights, representing the sum of weights connected to the same node, were observed for the following species: \u003cem\u003eT. ornata\u003c/em\u003e = 1.55, \u003cem\u003eB. ueckermanni\u003c/em\u003e = 1.04, \u003cem\u003eA. largoensis\u003c/em\u003e = 0.98, and \u003cem\u003eP. acaciae\u003c/em\u003e = 0.89. Negative correlations were identified between the following species: \u003cem\u003eIphiseiodes zuluagai\u003c/em\u003e (Denmark and Muma) and \u003cem\u003eB. formosa\u003c/em\u003e = -0.37, \u003cem\u003eTyphlodromina subtropica\u003c/em\u003e (Muma and Denmark) and \u003cem\u003eEuseius alatus\u003c/em\u003e De Leon = -0.11, \u003cem\u003eT. peregrinus\u003c/em\u003e and \u003cem\u003eNeocalacarus mangiferae\u003c/em\u003e Channabasavanna = -0.06. Notably, \u003cem\u003eCunaxatricha\u0026nbsp;\u003c/em\u003eaff.\u003cem\u003e\u0026nbsp;Tarsospinosa\u003c/em\u003e, \u003cem\u003eTenuipalpus\u0026nbsp;\u003c/em\u003eaff. \u003cem\u003eLygodii\u003c/em\u003e, and \u003cem\u003eBrachytydeus\u003c/em\u003e sp. 3 did not exhibit significant relationships with other mite species in this network (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation occurrence and ecological interactions:\u003c/strong\u003e \u003cem\u003eNeoseiulus recifensis\u003c/em\u003e (Gondim Jr. and Moraes), which is intricately connected with the central species in the network (\u003cem\u003eA. largoensis\u003c/em\u003e), exhibited a notable correlation with both the phenological stage (\u0026chi;\u0026sup2; \u003csub\u003e(1, 117)\u003c/sub\u003e = 14.05; P = 0.038) and sampling date (\u0026chi;\u0026sup2; \u003csub\u003e(1, 118)\u003c/sub\u003e = 78.32; P = 0.002). The peak occurrence of \u003cem\u003eN. recifensis\u003c/em\u003e (0.5 \u0026plusmn; 0.4 mites/leaf/plant) was recorded on sampling date 5,\u0026nbsp;which corresponded to the vegetative stage of the mango tree (Fig. 3). Notably, no significant correlations were identified between\u0026nbsp;the\u0026nbsp;other species and\u0026nbsp;variables\u0026nbsp;analyzed\u0026nbsp;within this group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVilaia pamithus\u003c/em\u003e (Chandrapatya and Boczek), which is intricately linked with the central species \u003cem\u003eB. ueckermanni\u003c/em\u003e, showed significant correlations with the monthly average temperature (Fig. 4a; \u0026chi;\u0026sup2; \u003csub\u003e(1, 115)\u003c/sub\u003e = 13227; P = 0.002) and mango tree phenological stage (\u0026chi;\u0026sup2; \u003csub\u003e(1, 117)\u003c/sub\u003e = 15600; P = 0.035). The peak occurrence of \u003cem\u003eV. pamithus\u003c/em\u003e (220 \u0026plusmn; 105 mites/leaf/plant) was recorded on sampling date 7, aligning with the vegetative stage of the mango tree. On the other hand, \u003cem\u003eCisaberoptus kenyae\u003c/em\u003e Keifer exhibited a correlation solely with the monthly average temperature (\u0026chi;\u0026sup2; \u003csub\u003e(1, 115)\u003c/sub\u003e = 21359; P = 0.003). The date of the highest abundance of \u003cem\u003eC. kenyae\u003c/em\u003e (1624 \u0026plusmn; 723 mites/leaf/plant) coincided with sampling date 12, corresponding to one of the periods of highest temperature in the year. Notably, no significant correlations were found between\u0026nbsp;the\u0026nbsp;other species and\u0026nbsp;variables\u0026nbsp;analyzed\u0026nbsp;in this group (Fig. 4b).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOligonychus punicae\u003c/em\u003e exhibited a correlation with monthly precipitation (\u0026chi;\u0026sup2; \u003csub\u003e(1, 116)\u003c/sub\u003e = 9.96; P = 0.001) and mango tree phenological stage (Fig. 5; \u0026chi;\u0026sup2; \u003csub\u003e(1, 117)\u003c/sub\u003e = 6098; P \u0026lt; 0.001), reaching its highest occurrence on sampling date 1 (90 \u0026plusmn; 51 mites/leaf/plant). This date corresponds to the reproductive stage of the mango trees\u0026nbsp;and\u0026nbsp;coincides with one of the periods of lower\u0026nbsp;annual\u0026nbsp;precipitation. \u003cem\u003eT. ornata\u003c/em\u003e correlated with monthly precipitation (\u0026chi;\u0026sup2; \u003csub\u003e(1, 116)\u003c/sub\u003e = 184.69; P = 0.013) and sampling date (\u0026chi;\u0026sup2; \u003csub\u003e(1, 118)\u003c/sub\u003e = 199.14; P \u0026lt; 0.001), with the peak occurrence observed on sampling date 12 (6.8 \u0026plusmn; 4.0 mites/leaf/plant). This date aligns with the period of lowest precipitation\u0026nbsp;during\u0026nbsp;the year.\u0026nbsp;Additionally, \u003cem\u003eP. acaciae\u003c/em\u003e showed a correlation with the phenological stage (\u0026chi;\u0026sup2; \u003csub\u003e(1, 117)\u003c/sub\u003e = 309.19; P \u0026lt; 0.001), attaining its highest occurrence on sampling date 12 (3 \u0026plusmn; 1.8 mites/leaf/plant), corresponding to the reproductive stage of the mango. Notably, no significant correlation was found between \u003cem\u003eF. setillus\u003c/em\u003e and variables\u0026nbsp;analyzed\u0026nbsp;in this group.\u003c/p\u003e\n\u003cp\u003eThe species \u003cem\u003eB. formosa\u003c/em\u003e displayed a correlation with monthly average temperature (Fig. 6; \u0026chi;\u0026sup2; \u003csub\u003e(1, 115)\u003c/sub\u003e = 1509; P \u0026lt; 0.001), reaching I highest occurrence on sampling date 12 (17 \u0026plusmn; 16 mites/leaf/plant). This date corresponded to one of the periods with\u0026nbsp;the\u0026nbsp;highest\u0026nbsp;temperatures\u0026nbsp;in the year. On the other hand, \u003cem\u003eI. zuluagai\u003c/em\u003e correlated solely with the sampling date (\u0026chi;\u0026sup2; \u003csub\u003e(1, 118)\u003c/sub\u003e = 167.6; P \u0026lt; 0.001), with the highest occurrence observed on sampling date 12 (1.4 \u0026plusmn; 1.0 mites/leaf/plant).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe\u0026nbsp;findings of the current study highlight the pivotal role of\u0026nbsp;\u003cem\u003eA. largoensis\u003c/em\u003e, \u003cem\u003eB. ueckermanni\u003c/em\u003e, \u003cem\u003eP. acaciae\u003c/em\u003e, and \u003cem\u003eT. ornata\u0026nbsp;\u003c/em\u003eas central connectors in the mite communities associated with mango trees. \u003cem\u003eAmblyseius largoensis\u003c/em\u003e, \u003cem\u003eB. ueckermanni\u003c/em\u003e, and \u003cem\u003eP. acaciae\u003c/em\u003e are generalist predators that act as significant natural enemies of other mites and are encountered in various ecosystems (McCoy et al. 1969; Hernandes et al. 2016; Demite et al. 2021). The prevalence of these predator mites as\u0026nbsp;the\u0026nbsp;central species in the network was\u0026nbsp;anticipated in this study, given the minimally disturbed nature of the environment and the absence of acaricide application.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAmblyseius largoensis\u003c/em\u003e is associated with various mite species, including invasive species, and is the most frequent and abundant predator of coconut palms (Gondim Jr. et al. 2012; Moraes et al. 2012; Barros et al. 2020). This species plays a crucial role in stabilizing communities of phytophagous mites and other predators through indirect interactions such as predation and apparent competition\u0026nbsp;(Barros et al. 2020).\u003c/p\u003e\n\u003cp\u003eThe significance of \u003cem\u003eB. ueckermanni\u003c/em\u003e in the structure of this assembly may be associated with its adaptability to diverse environments, considering that bdelloid mites are potential bioindicators in studies on environmental imbalance (Hernandes et al. 2016). Other studies have also noted elevated populations of \u003cem\u003eB. ueckermanni\u003c/em\u003e and other bdelloids\u0026nbsp;in undisturbed natural environments, wherein they\u0026nbsp;play\u0026nbsp;relevant roles\u0026nbsp;in population regulation (Wallace 1974;\u0026nbsp;Mej\u0026iacute;a-Recamier and Cutz-Pool 2007; Souza et al. 2012; Chaires-Grijalva et al. 2021).\u003c/p\u003e\n\u003cp\u003eIolinid mites feed on pollen, fungi, and other mites such as eriophids, tenuipalpids, and tetranychids (Hernandes et al. 2015). The importance of \u003cem\u003eP. acaciae\u003c/em\u003e in the structure of this mite assembly is probably due to the diversification of its feeding habits. This species is also frequently associated with fungi. For example, \u003cem\u003eP. acaciae\u003c/em\u003e has been reported to feed on \u003cem\u003eColletotrichum gloeosporioides\u003c/em\u003e Penzig and \u003cem\u003ePenicillium digitatum\u003c/em\u003e Saccardo (Fadamiro et al. 2009; Childers and Ueckermann 2020). There are records of more than 50\u0026nbsp;fungal\u0026nbsp;species\u0026nbsp;in mango trees worldwide (Khaskheli 2020). \u003cem\u003eParapronematus acaciae\u003c/em\u003e is\u0026nbsp;also\u0026nbsp;probably\u0026nbsp;associated with some of these fungi. However, further studies are required to verify this association in mango trees.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTuckerella ornata\u0026nbsp;\u003c/em\u003eis poorly studied in Brazil and has been recorded in Barbados cherry (\u003cem\u003eMalpighia emarginata\u003c/em\u003e A.DC.; Malpighiaceae) (Barbosa et al. 2003) and cacao (\u003cem\u003eTheobroma cacao\u003c/em\u003e L.; Malvaceae),\u0026nbsp;with reports of damage (Brito et al. 2023). In the present study, no apparent damage to mango trees associated with \u003cem\u003eT. ornata\u0026nbsp;\u003c/em\u003eattacks\u0026nbsp;was observed, probably owing\u0026nbsp;to its low population density. In the network, an association of \u003cem\u003eT. ornata\u0026nbsp;\u003c/em\u003ewith the predator mites \u003cem\u003eP. acaciae\u003c/em\u003e and \u003cem\u003eI. zuluagai\u003c/em\u003e was observed. The maintenance of the predator \u003cem\u003eI. zuluagai\u003c/em\u003e on mango leaves likely depends on the presence of this phytophagous species. In contrast, a positive association was observed between \u003cem\u003eP. acaciae\u003c/em\u003e, \u003cem\u003eO. punicae\u003c/em\u003e, and \u003cem\u003eF. setillus\u003c/em\u003e, indicating that variation in the population of \u003cem\u003eT. ornata\u003c/em\u003e will not have a direct effect on \u003cem\u003eI. zuluagai\u003c/em\u003e. The predator mites \u003cem\u003eP. acaciae\u003c/em\u003e and \u003cem\u003eI. zuluagai\u0026nbsp;\u003c/em\u003eappear to be important biological control agents against \u003cem\u003eT. ornata\u003c/em\u003e in mango trees, and laboratory studies\u0026nbsp;are required\u0026nbsp;to assess\u0026nbsp;their potential use.\u003c/p\u003e\n\u003cp\u003eEcological networks are dynamic structures that are shaped not only by changes in species composition\u0026nbsp;but also by how\u0026nbsp;species assembly interacts over time in their co-occurring location, where environmental factors play a crucial role in this process (Poisot et al. 2012, 2015). Higher temperatures may shorten the developmental\u0026nbsp;cycle of most species,\u0026nbsp;thereby\u0026nbsp;reducing the development time of immature stages and favoring population increases (Devi\u0026nbsp;and Challa 2019). In this study, we found that\u0026nbsp;the\u0026nbsp;monthly average temperature and monthly precipitation affected the population occurrence of some mite species on mango trees. Other studies have found that higher temperatures in Egypt\u0026nbsp;favor\u0026nbsp;mite development in mango trees\u0026nbsp;that do\u0026nbsp;not\u0026nbsp;live in sheltered environments (Abou-Awad et al. 2011; Marei et al. 2020). However,\u0026nbsp;the\u0026nbsp;monthly average relative humidity had no effect on mite occurrence. The effective environment\u0026nbsp;for small arthropods, such as\u0026nbsp;mites, may be very different from\u0026nbsp;the macroscale environmental conditions, being more subject to leaf microenvironmental conditions (Pincebourde and Woods 2012).\u003c/p\u003e\n\u003cp\u003eIntraplant mite interactions can lead phytophagous species to develop adaptive mechanisms that specialize in different vegetative structures or phenological stages of the certain plants, occupying niches with less competition for resources (Saito 2010). In the present study, the population occurrence of \u003cem\u003eN. recifenses\u003c/em\u003e, \u003cem\u003eV. pamithus\u003c/em\u003e, \u003cem\u003eP. acaciae\u003c/em\u003e, and \u003cem\u003eO. punicae\u003c/em\u003e was affected by the phenological stage of the mango tree, with higher occurrence of \u003cem\u003eN. recifenses\u003c/em\u003e and \u003cem\u003eV. pamithus\u003c/em\u003e in the vegetative stage and \u003cem\u003eP. acaciae\u003c/em\u003e and \u003cem\u003eO. punicae\u003c/em\u003e in the reproductive stage. This indicates a greater tendency for these species to occur at different phenological stages of the plant. Phytophagous mites show distinct levels of specialization to their hosts, indirectly favoring\u0026nbsp;the\u0026nbsp;associated predator mites. These predators\u0026nbsp;depend on this specialization for their occurrence in hosts (Krantz and Lindquist 1979; Skoracka 2006; McMurtry et al. 2013). In addition, the behavior of predator mites depends on\u0026nbsp;the\u0026nbsp;interactions\u0026nbsp;between them. For example, intraguild predation can shape the distribution of mites on host plants\u0026nbsp;(Choh et al. 2015).\u003c/p\u003e\n\u003cp\u003eIn this study, we present a network of interactions among mites occurring in mango trees\u0026nbsp;for the first time, emphasizing the importance of some central species within this assembly. Although we did not observe a significant\u0026nbsp;effect of phytophagous mites on the evaluated leaves, some species, such as \u003cem\u003eA. mangiferae\u003c/em\u003e, \u003cem\u003eC. kenyae\u003c/em\u003e, and \u003cem\u003eO. punicae\u003c/em\u003e, have been considered important mango pests. We showed that these species are often positively associated with the predator mite species \u003cem\u003eE. alatus\u003c/em\u003e, \u003cem\u003eB. ueckermanni\u003c/em\u003e, and \u003cem\u003eP. acaciae\u003c/em\u003e, respectively. These predator mites are probably the natural enemies of these phytophagous species. However, further laboratory studies are required to investigate these interactions. Additionally, we found that environmental variables play a crucial role in the regulatory mechanisms affecting the occurrence of mite fauna in mango trees, potentially acting directly on these populations or indirectly through the regulation of plant phenological stages. Notably, future studies must assess the effects of microclimatic conditions inside the plant on the populations of this mite assembly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks are to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) for granting a scholarship to the first author and for the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (306092/2021-2) for financial support for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbou-Awad BA, Metwally AS, Al-Azzazy MM (2011) Environmental management and biological aspects of two eriophyid mango mites in Egypt: \u003cem\u003eAceria mangiferae\u003c/em\u003e and \u003cem\u003eMetaculus mangiferae\u003c/em\u003e. Acarologia 51:481-497. https://doi.org/10.1051/acarologia/20112030\u003c/li\u003e\n\u003cli\u003eAra\u0026uacute;jo WS, Daud RD (2018) Contrasting structures of plant\u0026ndash;mite networks compounded by phytophagous and predatory mite species. Exp Appl Acarol 74:335-346. https://doi.org/10.1007/s10493-018-0250-2\u003c/li\u003e\n\u003cli\u003eAra\u0026uacute;jo WS, Freitas EVD, Silveira LT, Daud RD (2020) Network structure of interactions between phytophagous mites and their hostplants in natural ecosystems in Brazil. Syst Appl Acarol 25:821-832. https://doi.org/10.11158/saa.25.5.4\u003c/li\u003e\n\u003cli\u003eBarbosa DGF, Gondim Junior MGC, Barros R, Oliveira JV (2003) Diversidade de \u0026aacute;caros em aceroleira (\u003cem\u003eMalpighia emarginata\u003c/em\u003e A.DC.) na Universidade Federal de Pernambuco em Recife, PE. Neotrop Entomol 32:577-583. https://doi.org/10.1590/S1519-566X2003000400007\u003c/li\u003e\n\u003cli\u003eBarros MEN, Lima DB, Mendes JA, Gondim Jr MG, Melo JWDS (2020) The establishment of an invasive pest mite, \u003cem\u003eRaoiella indica\u003c/em\u003e, affects mite abundance and diversity on coconut plants. Syst Appl Acarol 25:881-894. https://doi.org/10.11158/saa.25.5.9\u003c/li\u003e\n\u003cli\u003eBen\u0026iacute;tez-Malvido J, D\u0026aacute;ttilo W (2015) Interaction intimacy of pathogens and herbivores with their host plants influences the topological structure of ecological networks in diferent ways. Am J Bot 10:512-519. https://doi.org/10.3732/ajb.1400303\u003c/li\u003e\n\u003cli\u003eBrito EADS, De Castro IS, Vinhas NAN, Santana RDR, Pinheiro GM, Ferla NJ (2023) Damage caused by \u003cem\u003eTuckerella ornata\u003c/em\u003e (Acari: Tuckerellidae) on cocoa fruits (\u003cem\u003eTheobroma cacao\u003c/em\u003e) in the Amazon region, state of Par\u0026aacute;, Brazil. Syst Appl Acarol 28:11-19. https://doi.org/10.11158/saa.28.1.2\u003c/li\u003e\n\u003cli\u003eCalatayud J, H\u0026oacute;rreo JL, Madrigal-Gonz\u0026aacute;lez J, Migeon A, Rodr\u0026iacute;guez M\u0026Aacute;, Magalh\u0026atilde;es S, Hortal J (2016) Geography and major host evolutionary transitions shape the resource use of plant parasites. PNAS 113:9840-9845. https://doi.org/10.1073/pnas.1608381113\u003c/li\u003e\n\u003cli\u003eChaires-Grijalva MP, V\u0026aacute;zquez-Rojas IM, Mej\u0026iacute;a-Recamier BE, Ru\u0026iacute;z-Cancino E, Coronado-Blanco JM (2021) Population Dynamics of the Predator Mite \u003cem\u003eBdella longistriata\u003c/em\u003e Collected by Malaise Trap at Tula, Tamaulipas, Mexico. Outhwest Entomol 46:129-136. https://doi.org/10.3958/059.046.0112\u003c/li\u003e\n\u003cli\u003eChandrapatya A, Boczek J (1991) Studies on eriophyid mites (Acari: Eriophyoidea). IV. Bull Pol Acad, Sci Biol Sci 39:427-433\u003c/li\u003e\n\u003cli\u003eChannabasavanna GP (1966) A contribution to the knowledge of Indian eriophyid mites. Univ Agric Sci, Bangalore\u003c/li\u003e\n\u003cli\u003eChesson P. (2012) Species competition and predation. In: Leemans, R (ed) Ecological Systems, 1rd edn. Springer, New York, pp 223\u0026ndash;256. https://doi.org/10.1007/978-1-4614-5755-8_13\u003c/li\u003e\n\u003cli\u003eChilders CC, Ueckermann EA (2020) The Tetranychoidea, Tarsonemidae and Tydeoidea mite complex on Florida citrus between 1954 and 2014: Pests or Beneficials? Syst Appl Acarol 25:1257-1278. https://doi.org/10.11158/saa.25.7.8\u003c/li\u003e\n\u003cli\u003eChoh Y, Sabelis MW, Janssen A. (2015) Distribution and oviposition site selection by predatory mites in the presence of intraguild predators. Exp Appl Acarol 67:477-491. https://doi.org/10.1007/s10493-015-9970-8\u003c/li\u003e\n\u003cli\u003eDale M, Fortin MJ (2010) From graphs to spatial graphs. Annu Rev Ecol Evol Syst 41:21\u0026ndash;38. https://doi.org/10.1146/annurev-ecolsys-102209-144718\u003c/li\u003e\n\u003cli\u003eDelmas E, Besson M, Brice MH, Burkle LA, Dalla Riva GV, Fortin MJ, Poisot T (2019) Analysing ecological networks of species interactions. Biol Rev 94:16-36. https://doi.org/10.1111/brv.12433\u003c/li\u003e\n\u003cli\u003eDemite PR, Moraes GJ, McMurtry JA, Denmark HA, Castilho RC (2021) Phytoseiidae Database. http:// lea. esalq. usp. br/phytoseiidae. Accessed 16 December 2023\u003c/li\u003e\n\u003cli\u003eDevi M, Challa N (2019) Impact of weather parameters on seasonality of phytophagous mites. J Entomol Zool Stud 7:1095-1100\u003c/li\u003e\n\u003cli\u003eDevictor V, Clavel J, Julliard R, Lavergne S, Mouillot D, Thuiller W, Venail P, Villeger S, Mouquet N (2010) Defining and measuring ecological specialization. J Appl Ecol 47:15-25. https://doi.org/10.1111/j.1365-2664.2009.01744.x\u003c/li\u003e\n\u003cli\u003eFadamiro HY, Xiao Y, Nesbitt M, Childers CC (2009) Diversity and seasonal abundance of predacious mites in Alabama Satsuma citrus. Ann Entomol Soc Am 102:617-628. https://doi.org/10.1603/008.102.0406\u003c/li\u003e\n\u003cli\u003eFoygel R, Drton M (2010) Extended Bayesian information criteria for Gaussian graphical models. https://proceedings.neurips.cc/paper_files/paper/2010/file/072b030ba126b2f4b2374f342be9ed44-Paper.pdf. Accessed 08 March 2024\u003c/li\u003e\n\u003cli\u003eGondim Jr MGC, Castro TMMG, Marsaro Jr AL, Navia D, Melo JWS, Demite PR, Moraes GJ (2012) Can the red palm mites threaten the Amazon vegetation? Syst Biodivers 10:527-535. https://doi.org/10.1080/14772000.2012.752415\u003c/li\u003e\n\u003cli\u003eHarvey E, Gounand I, Ward CL, Altermatt F (2017) Bridging ecology and conservation: from ecological networks to ecosystem function. J Appl Ecol 54:371-379. https://doi.org/10.1111/1365-2664.12769\u003c/li\u003e\n\u003cli\u003eHernandes FA, Castro TMM, Venancio R (2015) Prostigmata (Acari: Trombidiformes) as Biological Control Agents. In: Carrillo D, Moraes G, Pe\u0026ntilde;a J (ed) Prospects for Biological Control of Plant Feeding Mites and Other Harmful Organisms, 19rd edn. Springer, Cham pp151-184 \u003c/li\u003e\n\u003cli\u003eHernandes FA, Skvarla MJ, Fisher JR, Dowling AP, Ochoa R, Ueckermann EA, Bauchan GR (2016) Catalogue of snout mites (Acariformes: Bdellidae) of the world. Zootaxa 4152:1-83. https://doi.org/10.11646/zootaxa.4152.1.1\u003c/li\u003e\n\u003cli\u003eJASP Team (2023). JASP (Version 0.17.2) [Computer software]. https://jasp-stats.org/\u003c/li\u003e\n\u003cli\u003eKeifer HH (1946) Eriophuid Studies XVI. Bull Calf Dept Agric 35:39-48 \u003c/li\u003e\n\u003cli\u003eKeifer HH (1966) Eriophyid Studies B-18. Bur Entomol Calif Dept Agric \u003c/li\u003e\n\u003cli\u003eKhaskheli MI (2020) Mango Diseases: Impact of Fungicides. In: Noureddine HKB, Kolombia IA (ed) Horticultural Crops, 1rd edn. IntechOpen, London, pp 143\u003c/li\u003e\n\u003cli\u003eKrantz GW, Lindquist EE (1979) Evolution of phytophagous mites (Acari). Annu Rev Entomol 24:121-158\u003c/li\u003e\n\u003cli\u003eKrantz GW, Walter DE (2009) A manual of acarology. Texas Tech University Press, Lubbock\u003c/li\u003e\n\u003cli\u003eLofego AC (1998) Caracteriza\u0026ccedil;\u0026atilde;o morfol\u0026oacute;gica e distribui\u0026ccedil;\u0026atilde;o geogr\u0026aacute;fica das esp\u0026eacute;cies de Amblyseiinae (Acari: Phytoseiidae) no Brasil. Dissertation, University of S\u0026atilde;o Paulo\u003c/li\u003e\n\u003cli\u003eManu M, Iordache V, Băncilă RI, Bodescu F, Onete M (2016) The influence of environmental variables on soil mite communities (Acari: Mesostigmata) from overgrazed grassland ecosystems Romania. Ital J Zool 83:89-97. https://doi.org/10.1080/11250003.2015.1091897\u003c/li\u003e\n\u003cli\u003eMarei FA, Negm MW, Nasser MA, Eraky SA (2020) Population Dynamics of \u003cem\u003eOligonychus mangiferus\u003c/em\u003e and \u003cem\u003eAceria mangiferae\u003c/em\u003e (Acari: Tetranychidae, Eriophyidae) on Two Mango Cultivars in Asyut Governorate, with an Annotated Checklist of Mango Mites in Egypt. Int J Entomol Nematol 6:149-155\u003c/li\u003e\n\u003cli\u003eMcCoy CW, Selhime AG, Kanavel RF (1969) The feeding behavior and biology of \u003cem\u003eParapronematus acaciae\u003c/em\u003e (Acarina: Tydeidae). Fla Entomol 52:13-19. https://doi.org/10.2307/3493703\u003c/li\u003e\n\u003cli\u003eMcMurtry JA, Moraes GJ, Sourassou NF (2013) Revision of the lifestyles of phytoseiid mites (Acari: Phytoseiidae) and implications for biological control strategies. Syst Appl Acarol 18:297-320. https://doi.org/10.11158/saa.18.4.1\u003c/li\u003e\n\u003cli\u003eMej\u0026iacute;a-Recamier BE, Cutz-Pool LQ (2007) Diversidad altitudinal de Bdellodea (Prostigmata) en musgos cort\u0026iacute;c\u0026iacute;colas en el Estado de M\u0026eacute;xico. Entomol Mex 6:54-59. https://doi.org/10.3958/059.046.0112\u003c/li\u003e\n\u003cli\u003eMelo AS, Melo JW, Paz-Neto AA, Gondim Jr MG (2023) Mite diversity and intra-plant distribution in mango crop. Syst Appl Acarol 28:864-875. https://doi.org/10.11158/saa.28.5.8\u003c/li\u003e\n\u003cli\u003eMigeon A, Dorkeld F (2024) Spider Mites Web: a comprehensive database for the Tetranychidae. Available from https://www1.montpellier.inrae.fr/CBGP/spmweb. Accessed 13 January 2024\u003c/li\u003e\n\u003cli\u003eMoraes GJ, Castro TMMG, Kreiter S, Quilici S, Gondim Jr MGC, Sa LAN (2012) Search for natural enemies of \u003cem\u003eRaoiella indica\u003c/em\u003e Hirst in La R\u0026eacute;union Island (Indian Ocean). Acarologia 52:129-134. https://dx.doi.org/10.1051/acarologia/20122043\u003c/li\u003e\n\u003cli\u003eMoraes GJD, Flechtmann CHW (2008) Manual de acarologia: acarologia b\u0026aacute;sica e \u0026aacute;caros de plantas cultivadas no Brasil. Holos, Ribeir\u0026atilde;o Preto\u003c/li\u003e\n\u003cli\u003eMushtaq HMS, Kamran M, Saleh AA, Alatawi FJ (2022) Evidence for Reconsidering the Taxonomic Status of Closely Related \u003cem\u003eOligonychus\u003c/em\u003e Species in \u003cem\u003epunicae\u003c/em\u003e Complex (Acari: Prostigmata: Tetranychidae). Insects 14:3. https://doi.org/10.3390/insects14010003\u003c/li\u003e\n\u003cli\u003eNooy W, Mrvar A, Batagelj V (2011) Exploratory social network analysis with Pajek. Cambridge University Press, London\u003c/li\u003e\n\u003cli\u003eOpsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: Generalizing degree and shortest paths. Soc Netw 32:245-251. https://doi.org/10.1016/j.socnet.2010.03.006\u003c/li\u003e\n\u003cli\u003ePincebourde S, Woods HA (2012) Climate uncertainty on leaf surfaces: the biophysics of leaf microclimates and their consequences for leaf‐dwelling organisms. Funct Ecol 26:844-853. https://doi.org/10.1111/j.1365-2435.2012.02013.x\u003c/li\u003e\n\u003cli\u003ePoisot T, Canard E, Mouillot D, Mouquet N, Gravel D (2012) The dissimilarity of species interaction networks. Ecol Lett 15:1353-1361. https://doi.org/10.1111/ele.12002\u003c/li\u003e\n\u003cli\u003ePoisot T, Stoufer DB, K\u0026eacute;f S (2016) Describe, understand and predict: Why do we need networks in ecology? Funct Ecol 30:1878-1882. https://doi.org/10.1111/1365-2435.12799\u003c/li\u003e\n\u003cli\u003ePoisot T, Stouffer DB, Gravel D (2015) Beyond species: why ecological interaction networks vary through space and time. Oikos 124:243-251. https://doi.org/10.1111/oik.01719\u003c/li\u003e\n\u003cli\u003ePrice PW, Denno RF, Eubanks MD, Finke DL, Kaplan I (2011) Insect ecology: behavior, populations, and communities. Cambridge University Press, New York\u003c/li\u003e\n\u003cli\u003eR Core Team (2023) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed 08 October 2023\u003c/li\u003e\n\u003cli\u003eSaito Y (2010) Plant Mites and Sociality. Springer, Tokyo \u003c/li\u003e\n\u003cli\u003eSayed T (1946) \u003cem\u003eAceria mangiferae\u003c/em\u003e new spec. Bull Soc Fouad 1 Enttomol 30:7-10 \u003c/li\u003e\n\u003cli\u003eSilva GL, Metzelthin MH, Silva OS, Ferla NJ (2016) Catalogue of the mite family Tydeidae (Acari: Prostigmata) with the world key to the species. Zootaxa 4135:1-68. https://doi.org/10.11646/zootaxa.4135.1.1\u003c/li\u003e\n\u003cli\u003eSkoracka A (2006) Host specifcity of eriophyoid mites: Specialists or generalists? Biol Let 43:289-298\u003c/li\u003e\n\u003cli\u003eSouza IV, Gondim Junior MGC, Ramos ALR, Santos EA, Ferraz MI, Oliveira AR (2012) Population dynamics of \u003cem\u003eAceria guerreronis\u003c/em\u003e (Acari: Eriophyidae) and other mites associated with coconut fruits in Una, state of Bahia, northeastern Brazil. Exp Appl Acarol 58:221-233. https://doi.org/10.1007/s10493-012-9576-3\u003c/li\u003e\n\u003cli\u003eTibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol 58:267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x\u003c/li\u003e\n\u003cli\u003eWallace MMH (1974) An attempt to extend the biological control of \u003cem\u003eSminthurus viridis\u003c/em\u003e (Collembola) to new areas in Australia by introducing a predatory mite, \u003cem\u003eNeomolgus capillatus\u003c/em\u003e (Bdellidae). Aust J Zool 22:519-529. https://doi.org/10.1071/ZO9740519\u003c/li\u003e\n\u003cli\u003eWalter DE (1996) Living on leaves: mites, tomenta, and leaf domatia. Annu Rev Entomol 41:101-114. https://doi.org/10.1146/annurev.en.41.010196.000533\u003c/li\u003e\n\u003cli\u003eWalter DE, Proctor HC (2013) Mites: ecology, evolution, and behavior: life at a microscale. Springer, New York\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acari, Bayesian network, ecological interactions, Mangifera indica.","lastPublishedDoi":"10.21203/rs.3.rs-4057884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4057884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Direct and indirect ecological interactions, environmental factors, and the phenology of host plants can shape the way mites interact. These relationships interfere with species occurrence and consequently alter the structure and stability of the intraplant community. We evaluated the associations between mite species and mango trees using a complex network approach. Additionally, we observed a correlation between mite population density and plant phenological stage. Environmental variables, such as average monthly temperature, monthly precipitation, and average monthly relative humidity at different times of the year, were used in the correlation analysis. Network analysis revealed that Amblyseius largoensis, Bdella ueckermanni, Parapronematus acaciae, and Tuckerella ornata occupied central positions in the assembly of mites occurring on mango trees. Environmental variables, average monthly temperature, and monthly precipitation were correlated with the occurrence of Brachytydeus formosa, Cisaberoptus kenyae, Oligonychus punicae, T. ornata, and Vilaia pamithus. We also observed a correlation between the plant phenological stage and population densities of Neoseiulus recifenses, O. punicae, P. acaciae, and V. pamithus.","manuscriptTitle":"Interspecific interaction network of mites associated with mango trees","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 09:10:16","doi":"10.21203/rs.3.rs-4057884/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89fb687d-56e4-476e-bbfb-f9bdba22298b","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T15:44:25+00:00","versionOfRecord":{"articleIdentity":"rs-4057884","link":"https://doi.org/10.1007/s10493-024-00936-1","journal":{"identity":"experimental-and-applied-acarology","isVorOnly":false,"title":"Experimental and Applied Acarology"},"publishedOn":"2024-06-18 15:44:25","publishedOnDateReadable":"June 18th, 2024"},"versionCreatedAt":"2024-03-13 09:10:16","video":"","vorDoi":"10.1007/s10493-024-00936-1","vorDoiUrl":"https://doi.org/10.1007/s10493-024-00936-1","workflowStages":[]},"version":"v1","identity":"rs-4057884","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4057884","identity":"rs-4057884","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}