Journal of Ecology and Environment

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Published online April 22, 2022
https://doi.org/10.5141/jee.22.003

Journal of Ecology and Environment (2022) 46:12

© The Ecological Society of Korea.

Current methodologies in construction of plant-pollinator network with emphasize on the application of DNA metabarcoding approach

Saeed Mohamadzade Namin1,2* , Minwoong Son3 and Chuleui Jung1,3

1Agricultural Science and Technology Institute, Andong National University, Andong 36729, Republic of Korea
2Department of Plant Protection, Faculty of Agriculture, Varamin-Pishva Branch, Islamic Azad University, Varamin 3381774895, Iran
3Department of Plant Medicals, Andong National University, Andong 36729, Republic of Korea

Correspondence to:Saeed Mohamadzade Namin
E-mail saeedmn2005@gmail.com

Received: January 8, 2022; Revised: March 14, 2022; Accepted: March 16, 2022

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Background: Pollinators are important ecological elements due to their role in the maintenance of ecosystem health, wild plant reproduction, crop production and food security. The pollinator-plant interaction supports the preservation of plant and animal populations and it also improves the yield in pollination dependent crops. Having knowledge about the plant-pollinator interaction is necessary for development of pesticide risk assessment of pollinators and conservation of endangering species.
Results: Traditional methods to discover the relatedness of insects and plants are based on tracing the visiting pollinators by field observations as well as palynology. These methods are time-consuming and needs expert taxonomists to identify different groups of pollinators such as insects or identify flowering plants through palynology. With pace of technology, using molecular methods become popular in identification and classification of organisms. DNA metabarcoding, which is the combination of DNA barcoding and high throughput sequencing, can be applied as an alternative method in identification of mixed origin environmental samples such as pollen loads attached to the body of insects and has been used in DNA-based discovery of plant-pollinator relationship.
Conclusions: DNA metabarcoding is practical for plant-pollinator studies, however, lack of reference sequence in online databases, taxonomic resolution, universality of primers are the most crucial limitations. Using multiple molecular markers is preferable due to the limitations of developed universal primers, which improves taxa richness and taxonomic resolution of the studied community.

Keywords: DNA metabarcoding, pollen, pollination network, taxonomic resolution

Pollination is a key ecological service which is necessary for sustainable food production (Bailes et al. 2015; Klein et al. 2007; Vanbergen et al. 2013). Plant-pollinator interaction is one of the most ecologically important animal-plant relationships, enabling wild flowering plants reproduction and maintaining plant diversity which is crucial for biomass production and cycling of nutrients (Dodd et al. 1999; Isbell et al. 2011; Ollerton et al. 2011). Pollination interaction network reflects the biodiversity of the community and provide ecosystem stability in the face of climate change and environmental stress (Balmaki et al. 2020). Recent honeybee colony collapse disorder as well as the decline in other wild pollinators has raised concerns about the stability of pollination network and ecosystem service (Forup et al. 2008; Tylianakis 2013; Valdovinos et al. 2013; Vanbergen et al. 2017). Understanding the plant-pollinator interaction and pollination network is important to realize the level of generalization of the network and find the exclusive interactions inside the network which is required for decision making about conservation strategies.

Ecological networks study the relationship between living organisms such as food web, symbiosis, parasitism and pollination (Elton 1927; Tylianakis and Morris 2017; Ulanowicz 2004) making it possible to not only estimating the quantitative meaning contained therein (Pocock et al. 2012), but also predicting future situations by simulating with the corresponding data (Frost et al. 2019). Two main methods are available in the visualization of the plant-pollinator interactions. Pollination network which can be shown by a bipartite network (Fig. 1) in which the nodes in the network correspond to plant species and pollinators and the relationship between two groups of species is expressed by connection links between the node groups (Dormann and Strauss 2014). The strength of the connected links between them indicates the frequency of visiting plant species by pollinator (Seo et al. 2018), estimating the magnitude of the effect of interaction partners on each other (Novella-Fernandez et al. 2019). Construction of pollination network is crucial due to the fact that network structure reflects ecosystem functioning and the level of stability (Bascompte et al. 2006; Gómez et al. 2011; Jordano et al. 2006; Kaiser-Bunbury et al. 2010; Novella-Fernandez et al., 2019). The pollination matrix (Fig. 2) is an alternative method for presentation of pollination interactions (Chagnon et al. 2012). Using this method, it is possible to determine whether there is nestedness within the interaction between the two groups, or to separate modularity functional groups (Chagnon et al. 2012). Nestedness is the complexity of the interactions between the two species groups, and it is known that the higher the nestedness, the higher the biodiversity (Staniczenko et al. 2013). Modularity is a representation of subordinate functional groups in the internal structure of the network (Olesen et al. 2007) and in the matrix; those species with high connection relevance can be modular. It is possible to check the hub that connects each module and predict the degree of network collapse that occurs when these hubs are arbitrarily deleted (Danieli-Silva et al. 2012). So that, pollination network structure shows the co-evolutionary processes in the communities (Bascompte and Jordano 2007). Therefore, plant-pollinator network allows anticipating the ecosystem responses due to the disturbances such as incorporation of an alien species (Lopezaraiza-Mikel et al. 2007), species extinction (Memmott et al. 2004) climate change (Hegland et al. 2009) or human activities (Weiner et al. 2014).

Figure 1. Bipartite network structure of plant-pollinator interaction (Son et al. 2019) consisting of two groups of species with interactions. The density of species is expressed by the visual size of each node. The relationship between node groups is represented by connected links between nodes. Quantitative values such as interaction intensity, frequency, and density between nodes are expressed in color or thickness.
Figure 2. Network matrix between two species groups. Nestedness is expressed by sorting the two species interaction in the order of high frequency of connection (A). The higher the nestedness, the more complex the connectivity. Grouping between species with high connectivity interaction and functional gathering is called modularity (B). Through the modularity, the importance of subgroups can be compared and hub species can be identified.

Traditional methods

Visual method

Visual method is the most frequently used method in plant-pollinator interaction studies which is based on counting of visitors of specific flowering plant. The frequency of interaction between pollinators and plants is being used to estimate the strength of the interaction (Novella-Fernandez et al. 2019; Son et al., 2019). Since it is impossible to detect all interactions (Seo et al. 2018), every visitor of the flower is considered to interact positively, however, some visitors are notarial or they are negatively interacted (King et al. 2013). Another limitation of this method is to identification of wide range of visitors from different orders and families are not feasible unless being able to work with large network of insect taxonomist (Lucas et al. 2018).

Palynology

Palynology (identification of plants through pollen grains) is another traditional method in studding plant-pollinator interaction (Beattie 1971). Pollen grains collected from different resources such as gut of insects, pollen loads on the body surface, pollen trap which is put at the entrance of the bee hives and honey (Hawkins 2015; Lucas et al. 2018) are reliable evidences indicating the foraging preference, and can be used in discovery of plant-pollinator interactions. By enabling to study the pollen loads of museum specimens, palynology is also helpful to understand the effect of climate change and landscape changes due to human activities over time (Bell et al. 2016; Gous et al. 2019). Specimens collected in the museums usually carrying labels contain comprehensive information about the geographical origin, date of collection, instruments used to collect the samples and sometimes the plants on which the samples were collected (Bell et al. 2016).

The standard method for collecting and slide preparation of pollen grains is available. Identification of pollen grains is being conducted under a light microscope using regional taxonomic keys which is available for each region or country (Beattie 1971). However, identification of plants through pollen is time-consuming, tedious and required expert botanists for accurate identification (Galimberti et al. 2014; Hawkins 2015). Now that the pollen grains from phylogenetically close species of plants are similar morphologically, the taxonomic resolution of identification of plant through this method in species or even sometimes in genus level is infeasible (Galimberti et al. 2014; Hawkins et al. 2015; Khansari et al. 2012; Smart et al. 2017).

Molecular methods

PCR-free method

Plant identification using high-throughput sequencing of genetic material extracted from environmental samples is a recently developed approach which can be used in the discovery of plant-pollinator interactions. The DNA extract from environmental samples is sequenced directly without requirement of PCR (Garrido-Sanz et al. 2020). Since Metagenomics is a PCR-free approach, it is much more quantitative in compare with metabarcoding (Elbrecht and Leese 2015; Garrido-Sanz et al. 2020; Taberlet et al. 2012; Yu et al. 2012; Zhou et al. 2013). Shotgun sequencing likewise high-throughput sequencing approaches including 454-pyrosequencing, Nanopore, Illumina, and SOLiD provides consensus sequence from short fragments of DNA (Parducci et al. 2020). Metagenomics has been used recently in identification of plant species within environmental samples such as pollen loads (Peel et al. 2019), fecal samples (Srivathsan et al. 2016), lake sediment (Parducci et al. 2020) and honey (Bovo et al. 2020) and there is a possibility of using this method in plant-pollinator interaction studies.

PCR-based method

DNA barcoding is a method of identification of organisms based on the sequence of short fragment or fragments of DNA (de Vere et al. 2012; Hebert et al. 2003; Hebert et al. 2004; Mohamadzade Namin et al. 2021; Mohamadzade Namin and Jung 2020). Since pollen loads collected from the body of insect or bee hives can be originated from different flowering plants, PCR cloning can be used in combination with DNA barcoding for identification of mixed PCR product (Galal-Khallaf et al. 2016; Leontidou et al. 2017; Leontidou et al. 2018). Escherichia coli-based cloning vector is used to isolate DNA amplicons from different species in the mixed sample and the amplicons can be used for sequencing and identification throw DNA barcoding (Galal-Khallaf et al. 2016). This method successfully was used for molecular identification of the bee-collected mixed species pollen loads from Italian Alpine habitats (Galimberti et al. 2014).

DNA metabarcoding is another high throughput sequencing approach, which allows studying taxa richness, and quantify taxa abundance in mixed origin environmental samples such as pollen (Keller et al. 2015; Kraaijeveld et al. 2015; Richardson et al. 2015a; Sickel et al. 2015). This technique is well-established and alternatively has been used in the construction of pollination network and understanding the pollinator-plant interactions (Bell et al. 2017a; Gous et al. 2019; Lucas et al. 2018; Pornon et al. 2016; Pornon et al. 2017) and foraging activities of managed (such as honeybees and bumblebees) or wild pollinators based on the study of pollen loads on the surface of body (Lucas et al. 2018; Potter et al. 2019), pollen traps (Richardson et al. 2015a) or honey (de Vere et al. 2017; Hawkins et al. 2015; Khansaritoreh et al. 2020).

Advantages of using DNA metabarcoding

DNA metabarcoding is time saving and accurate method, allowing analysis of huge number of samples in a shorter period of time without requirement of a palynologist (Bruni et al. 2015; Galimberti et al. 2014; Gous et al. 2019; Hawkins et al. 2015). Using DNA metabarcoding, Lucas et al. (2018) reported lower level of exclusive interaction in the pollen transport network of hoverflies (Diptera: Syrphidae) in compare to the network made based on traditional methods (Fründ et al. 2010; Weiner et al. 2011) suggesting DNA metabarcoding is able to detect a greater number of taxa and useful to trace infrequently detected species (Keller et al. 2015; Kraaijeveld et al. 2015; Pornon et al. 2017). Using honey DNA metabarcoding, Mohamadzade Namin et al. (unpublished) compared the foraging activities of A. cerana and A. mellifera in a mixed apiary in Korea and the results showed clear partitioning in floral resources between these two coexisting honeybees (Fig. 3). Like palynology, DNA metabarcoding is applicable to understand the level of landscape alteration and changes in pollination network over time using pollen loads of the museum samples (Gous et al. 2019).

Figure 3. Bipartite foraging network of A. mellifera and A. cerana to 56 plant taxa present in honey, based on ribulose bisphosphate carboxylase large chain (rbcL) metabarcoding.

In addition, wet lab technical issues such as sample preparation, DNA extraction, number of PCR cycle and bioinformatics pipelines influencing the results have been optimized (Bell et al. 2017a; Bell et al. 2017b; de Vere et al. 2017; Lucas et al. 2018; Sickel et al. 2015; Swenson and Gemeinholzer 2021) to land a trustful qualitative (taxa richness) and quantitative (relative abundance of each taxa) results. A large dataset composed of huge number of reads per sample is generated. These sequences need to be analyzed through bioinformatics pipelines which is consist of trimming based on their quality score and length, merging forward and reverse reads, chimera checking, clustering and taxonomy assignment of the representative of each cluster (Fig. 4) (Dufresne et al. 2019; Ghosh et al. 2021). Although different bioinformatics software is available for each step of this analysis, some convenient and rapid pipelines are also can be used for analysis of mixed-species sequences (Bell et al. 2017b; Sickel et al. 2015).

Figure 4. Schematic diagram representing the methods involved in plant-pollinator analysis.
Limitations of DNA metabarcoding

Although DNA metabarcoding has been used successfully in plant-pollinator studies, likewise other methods have some limitations. Lacking of reference sequences in the databases with which the resulted sequences are being compared is the most crucial limitation (Gous et al. 2019; Laha et al. 2017). The ideal molecular marker contains low intraspecific variation while providing enough interspecific variation allowing differentiation of different species (Galtier et al. 2009). Unlike animals which cytochrome oxidase I is accepted universally and is being used as a barcoding region (Hebert et al. 2003), several molecular markers such as ribulose bisphosphate carboxylase large chain (rbcL), MaturaseK (matK), trnL, trnH, ITS2, and trnH-psbA intergenic spacer, are suggested for molecular identification of plants (Bell et al. 2016; Richardson et al. 2019). Recently, pipelines become available for analysis of ITS2 (Sickel et al. 2015) and rbcL (Bell et al. 2017b) sequences. However, some bioinformatics software such as OBITools (Boyer et al. 2016), RDP (Wang et al. 2007), Metaxa2 (Bengtsson-Palme et al. 2018), UTAX (Edgar 2015) and RESCRIPt (Robeson et al. 2021) give the opportunity of making reference libraries for analysis although they require high quality computers which are able to deal with huge number of sequences.

With exception of ITS2 marker which has enough interspecific variations (Chen et al. 2010; Moorhouse-Gann et al. 2018) the species level identification using other molecular markers is not feasible. Construction of a local database with limited number of sequences is a useful method to improve taxonomic resolution depending on the availability of reference sequences in GenBank and local databases (de Vere et al. 2017). BLAST (McGinnis and Madden 2004) allows preparation of local databases based on the provided information. Furthermore, using blastn or megablast, we are able to manually remove the taxa which are not available in the region from where samples have been collected. Based on our analysis on 1,255 Korean plant rbcL sequences available in GenBank, 2.07 % of the sequences are deposited in reverse instead of forward direction (Table S1) making automated pipelines unable to use such reference sequences. Interestingly both online Blastn software and offline megablast are able to use such reverse sequences in taxonomy assignment resulted in their better performance (Mohamadzade Namin and Jung, unpublished data).

In addition, most of available universal primers are not really universal due to the some level of mismatch between the primers and sequence of the target region of DNA in some species, makes them unable to amplify during PCR (Chen et al. 2010; Cheng et al. 2016; de Vere et al. 2012; Moorhouse-Gann et al. 2018).

Using multiple markers has a better performance in taxonomy resolution with most of them recognizable into species level while the species level identification of single barcode is 16 to 42% using chloroplast markers (Hawkins et al. 2015) and up to 90% using ITS2 marker with other limitations (Chen et al. 2010). Batuecas et al. (2022) demonstrated the higher performance of using multi-primer metabarcoding approach to understand trophic interactions in agroecosystems. Although multi-locus metabarcoding clearly give better taxonomic resolution, still lack of reference sequences is problematic. In addition, it is not feasible to join sequences of different markers belongs to same taxa (Bell et al. 2016). Furthermore, multiple markers can be used in qualitative (present/absent) studies where the frequency of sequences is invaluable. The frequency of specific taxa can be slightly differ while using different markers of same type of DNA such as rbcL and trnL of chloroplast DNA (cpDNA) or totally differ while using molecular markers of different type such as rbcL and ITS2 of nuclear ribosomal DNA (nrDNA) (Khansaritoreh et al. 2020).

Another problem about using pollen metabarcoding is the difference in copy number of plastids in pollen grains of different species (Corriveau and Coleman 1988). In addition, results of some studies indicated missing of the ptDNA from the sperm cells in pollen grains of some taxa (Hu et al. 2008; Nagata et al. 1999; Zhang et al. 2003; Zhang and Sodmergen 2010) risen doubt about the possibility of using DNA matabarcoding in quantitative analysis (Bell et al. 2019; Richardson et al. 2015a). Some authors reported the qualitative mismatch between the results of metabarcoding analysis with the ratio of mixed-pollen samples, which was mixed artificially suggesting to use this technique in pollen present/absent studies (Bell et al. 2019). However, recent detailed studies indicated the reliability of quantitative analysis based on plastid markers such as rbcL, trnL and matK (Baksay et al. 2020; Keller et al. 2015; Kraaijeveld et al. 2015; Pornon et al. 2016). In this regard, it is worth considering the “median-based analysis of multi-locus metabarcoding” developed by Richardson et al. (2015b; 2019) to mitigate the biasness of the primer sets of different loci and increase the confidence of detections by excluding taxa which identified using one locus and focus on consensus taxa identified by multiple markers.

DNA metabarcoding is a state-of-the-art technology for analysis of mixed samples like pollen loads however; there is still debate on the possibility of using this technology in qualitative analysis due to the possible biased amplification of during PCR step (Shokralla et al. 2012). Recent studies indicated the efficacy of metagenomics in pollen-based quantitative analysis, however, it is not feasible to detect low abundance taxa based on metagenomics (Peel et al. 2019). Selecting the best method for study is depends on the generated objectives and questions and since in plant- pollinator interaction studies all possible interaction are valuable and due to the fact that DNA metabarcoding is able to detect low abundant taxa (Lucas et al. 2018) the later method over performing metagenomics and even traditional methods. Furthermore, it is crucial to take into consideration either metagenomics or DNA metabarcoding required expertise in data analysis as well as financial support due to the cost of high throughput sequencing technology in compare to traditional methods (Yohe and Thyagarajan 2017).

Pollen-based discovery of the plant-pollinator interaction has been criticized because of the lack of evidence about effective pollination (Armbruster 2017; King et al. 2013) and due to the fact that insects can carry pollen without proper pollination. Further studies are required to evaluate the possibility of translation of pollen transportation to effective pollination (Lucas et al. 2018).

PCR: polymerase chain reaction

DNA: Deoxyribonucleic Acid

matK: MaturaseK

trnL: transfer RNA leucine

ITS2: Ribosomal internal transcribed spacer 2

rbcL: Ribulose bisphosphate carboxylase large chain precursor

ptDNA: plastid DNA

nrDNA: nuclear ribosomal DNA

cpDNA: chloroplast DNA

SMN performed the molecular analyses, interpreted the data, wrote the first draft of the manuscript and last revision and located some key publications. MS wrote some part of introduction and CJ suggested the study, edited and revised the manuscript, located some key publications and provided financial supports.

The research received support from a grant to Professor Chuleui Jung via the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2018R1A6A1A03024862).

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