Identify Plant Species Research Articles

Automatic Classification of Medicinal Plants Using State-Of-The-Art Pre-Trained Neural Networks

Now a days every mankind is suffering due to infections. Ayurveda, the science of life helped to take preventive measures which boost our immunity. It is plant-based science. Many medicinal plants found useful in daily life of common people for boosting immunity. Identifying the plant species having medicinal plant is challenging, it requires botanical expert. In the process of manual identification, botanical experts use various plant features as the identification keys, which are examined adaptively and progressively to identify plant species. The shortage of experts and trained taxonomist created global taxonomic impediment problem which is one of the major challenges. Various researchers have worked in the field of automatic classification of plants since the last decade. The leaf is considered as primary input as it is available throughout the whole year. The research paper mainly focuses on the study of transfer learning approach for medicinal plant classification, which reuse already developed model at the starting point for model on a second task. Transfer learning approach is a black box approach used for image classification and many more applications by extracting features from an image. Some of the transfer learning models are MobileNet-V1, VGG-19, ResNet-50, VGG-16. Here it uses Mendeley dataset of Indian medicinal plant species which is freely available. Output layer classifies the species of leaves. The result provides evaluation and variations of above listed features extracted models. MobileNetV1 achieves maximum accuracy of 98%.

Machine Learning As Seed Image Identification Using Principal Component Analysis (PCA)

Plants can be identified using several variables, such as seeds’ shapes, colors, and sizes. However, several types of plants have close similarities to seed shapes. Therefore, additional characteristics are necessary to support the identification process. This study applied machine learning with the PCA method to identify plant species from seed shapes. The PCA simplifies the observed variables by reducing data dimensions and storing 75% of the information. The procedure did not eliminate too much important information while reducing data size and processing time. We collected 100 images of plant seeds similar to one another, such as sapodilla seeds, soursop, cucumber, star fruit, grape, melon, apple, lime, watermelon, and chili. A measurement system was designed using the K-Fold Cross Validation, and 10 tables of experimental results discovered a good level of accuracy of 83%. The Omission error occurred in the seeds of soursop, starfruit, grape, apple, lime, and watermelon while the most commission errors occurred in apple seeds (8 times).

Pollen DNA metabarcoding reveals cryptic diversity and high spatial turnover in alpine plant-pollinator networks.

Alpine plant-pollinator communities play an important role in the functioning of alpine ecosystems, which are highly threatened by climate change. However, we still have a poor understanding of how environmental factors and spatiotemporal variability shape these communities. Here, we investigate what drives structure and beta diversity in a plant-pollinator metacommunity from the Australian alpine region using two approaches: pollen DNA metabarcoding (MB) and observations. Individual pollinators often carry pollen from multiple plant species, and therefore we expected MB to reveal a more diverse and complex network structure. We used two gene regions (ITS2 and trnL) to identify plant species present in the pollen loads of 154 insect pollinator specimens from three alpine habitats and construct MB networks, and compared them to networks based on observations alone. We compared species and interaction turnover across space for both types of networks, and evaluated their differences for plant phylogenetic diversity and beta diversity. We found significant structural differences between the two types of networks; notably, MB networks were much less specialized but more diverse than observation networks, with MB detecting many cryptic plant species. Both approaches revealed that alpine pollination networks are very generalized, but we estimated a high spatial turnover of plant species (0.79) and interaction rewiring (0.6) as well as high plant phylogenetic diversity (0.68) driven by habitat differences based on the larger diversity of plant species and species interactions detected with MB. Overall, our findings show that habitat and microclimatic heterogeneity drives diversity and fine-scale spatial turnover of alpine plant-pollinator networks.

FLORISTIC COMPOSITION AND PLANT DIVERSITY ANALYSIS OF ANBESSA FOREST IN WESEREN ETHIOPIA AND ITS CONRTIBUTION TO BIODIVESRITY CONSERVATION

The study was conducted at Anbessa forest in Western Ethiopia with the objectives to make an assessment of plant diversity and plant community. A systematic sampling technique was used and a total of 30 sample plots each with 20 x 20 m were laid along the altitudinal gradient. A total of 118 plant species were identified which belong to 93 genera in 46 families. Out of which 14.41 % were Fabaceae (17 species), followed by Asteraceae 11.86 % (14 species), Poaceae 8.47 % (10 species) and Lamiaceae 5.93 % (seven species). Out of the 118 identified plant species, 33.05 % were trees, 11.86 % shrubs, 11.86 % climbers and the rest 43.22 % were herbs. The present study reported nine endemic plant species are present in Anbessa forest. Moreover, 29 plant species were recorded as new records for Wellega floristic region from Anbessa forest. Four plant communities were identified. In Anbessa forest Combretum collinum - Dombeya quinqueseta -Securidaca longepedunculata community was the most diverse community, whereas Oxytenanthera abyssinica - Combretum molle - Syzygium guineense subsp. macrocarpum community was the lease diverse with H′ 0.16. The result showed that, Anbessa forest can play a significant role in biodiversity conservation.

Antioxidant and antimicrobial activities of plants grown in the Mediterranean region

AbstractBackgroundThe main objective of this research was to identify plant species with possible bioactivities based on their total phenol content, antioxidant, and antimicrobial properties. Therefore, different parts of 42 plant species grown in the Mediterranean region were extracted with aqueous ethanol solutions to prepare extracts with antioxidant and antimicrobial activities, mainly resulting from their total phenol contents. No detailed laboratory data on the flora of this area exists regarding their total phenol contents and total antioxidant activities.ResultsYields of extraction for each plant material were determined. Extracts were characterized based on their total phenol contents, total antioxidant (both hydrophilic and lipophilic), and antimicrobial activities using Folin–Ciocalteu, Photochemiluminescence, disc diffusion, and microdilution methods, respectively. The extract of Hypericum empetrifolium had the relatively highest total water‐soluble and lipid‐soluble antioxidant activities. Sarcopoterium spinosum extract had relatively high total phenol content. Preliminary screening study was conducted with the disc diffusion method to evaluate the extracts' antimicrobial activities. 26 out of 42 plant species showed significant antimicrobial activities against the growth of microorganisms. Microdilution assays were performed to evaluate the most active plant species with their minimum inhibition concentrations. H. empetrifolium, Pistacia terebinthus, Arbutus unedo, and Cistus parviflorus were the most antimicrobial plant species among those investigated.CONCLUSIONThe new potential sources for the isolation of bioactive natural compounds from specific plant species could be possible with the help of this present screening study. Isolated bioactive natural compounds can be utilized as raw materials in cosmetics, nutraceuticals, food supplements, and pharmaceutical industries.

Identifying plant species in kettle holes using UAV images and deep learning techniques

AbstractThe use of uncrewed aerial vehicle to map the environment increased significantly in the last decade enabling a finer assessment of the land cover. However, creating accurate maps of the environment is still a complex and costly task. Deep learning (DL) is a new generation of artificial neural network research that, combined with remote sensing techniques, allows a refined understanding of our environment and can help to solve challenging land cover mapping issues. This research focuses on the vegetation segmentation of kettle holes. Kettle holes are small, pond‐like, depressional wetlands. Quantifying the vegetation present in this environment is essential to assess the biodiversity and the health of the ecosystem. A machine learning workflow has been developed, integrating a superpixel segmentation algorithm to build a robust dataset, which is followed by a set of DL architectures to classify 10 plant classes present in kettle holes. The best architecture for this task was Xception, which achieved an average F1‐score of 85% in the segmentation of the species. The application of solely 318 samples per class enabled a successful mapping in the complex wetland environment, indicating an important direction for future health assessments in such landscapes.

Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models-A Case Study from Borneo Region.

The identification of plant species is fundamental for the effective study and management of biodiversity. In a manual identification process, different characteristics of plants are measured as identification keys which are examined sequentially and adaptively to identify plant species. However, the manual process is laborious and time-consuming. Recently, technological development has called for more efficient methods to meet species’ identification requirements, such as developing digital-image-processing and pattern-recognition techniques. Despite several existing studies, there are still challenges in automating the identification of plant species accurately. This study proposed designing and developing an automated real-time plant species identification system of medicinal plants found across the Borneo region. The system is composed of a computer vision system that is used for training and testing a deep learning model, a knowledge base that acts as a dynamic database for storing plant images, together with auxiliary data, and a front-end mobile application as a user interface to the identification and feedback system. For the plant species identification task, an EfficientNet-B1-based deep learning model was adapted and trained/tested on a combined public and private plant species dataset. The proposed model achieved 87% and 84% Top-1 accuracies on a test set for the private and public datasets, respectively, which is more than a 10% accuracy improvement compared to the baseline model. During real-time system testing on the actual samples, using our mobile application, the accuracy slightly dropped to 78.5% (Top-1) and 82.6% (Top-5), which may be related to training data and testing conditions variability. A unique feature of the study is the provision of crowdsourcing feedback and geo-mapping of the species in the Borneo region, with the help of the mobile application. Nevertheless, the proposed system showed a promising direction toward real-time plant species identification system.

ETHNOBOTANICAL STUDY OF MEDICINAL PLANTS IN KORE DISTRICT, SOUTHEASTERN ETHIOPIA

Across the world throughout the age, plants have been an important source of both defensive and curative traditional medicine preparation for human and livestock diseases. However, in the Kore district no ethnobotanical exploration had previously been conducted. Therefore, the present study was conducted to identify plant species that are used as traditional medicines for the treatment of human and livestock diseases and to determine and document traditional medicinal plant parts used, their mode of preparation, dosage, route of administration of remedies and associated traditional knowledge in the study area. The result of the survey revealed that a total of 97 medicinal plants belonging to 91 genera and 53 families were identified. From the total plant species (64.9%) were collected from wild vegetation and (15.4%) are from home gardens and the rest of 12.4% are from roadsides, fences, farmlands and villages. In the study area (62.9%) species were recorded for the treatment of human health problems,(20.6%) for livestock and(16.5%) for the treatment of both human and livestock. Leaves were the most frequently used part in preparing herbal remedies. Crushing and oral route of administration were commonly used method of herbal medicine preparation and administration respectively. The most common condition of preparation was fresh form of preparation. Preference ranking analysis showed that Olea europaea ranked first for the most effective medicinal plant to cure wound. Pair wise comparison of medicinal plant shows that Lepidium sativum is the most preferred in treating stomachache. Informant consensus agreement for Febrile illness, Headache, Fever and Dizziness have high Informant consensus factor (0.85). The analysis result of FL shows that Allium sativum scored the highest FL (80%).

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality-A Review.

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

Texture image classification based on a pseudo-parabolic diffusion model.

This work proposes a novel method based on a pseudo-parabolic diffusion process to be employed for texture recognition. The proposed operator is applied over a range of time scales giving rise to a family of images transformed by nonlinear filters. Therefore each of those images are encoded by a local descriptor (we use local binary patterns for that purpose) and they are summarized by a simple histogram, yielding in this way the image feature vector. Three main novelties are presented in this manuscript: (1) The introduction of a pseudo-parabolic model associated with the signal component of binary patterns to the process of texture recognition and a real-world application to the problem of identifying plant species based on the leaf surface image. (2) We also introduce a simple and efficient discrete pseudo-parabolic differential operator based on finite differences as texture descriptors. While the work in [26] uses complete local binary patterns, here we use the original version of the local binary pattern operator. (3) We also discuss, in more general terms, the possibilities of exploring pseudo-parabolic models for image analysis as they balance two types of processing that are fundamental for pattern recognition, i.e., they smooth undesirable details (possibly noise) at the same time that highlight relevant borders and discontinuities anisotropically. Besides the practical application, the proposed approach is also tested on the classification of well established benchmark texture databases. In both cases, it is compared with several state-of-the-art methodologies employed for texture recognition. Our proposal outperforms those methods in terms of classification accuracy, confirming its competitiveness. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image at the same time that it preserves discontinuities that convey critical information for the object description. Such results also confirm that model-based approaches like the proposed one can still be competitive with the omnipresent learning-based approaches, especially when the user does not have access to a powerful computational structure and a large amount of labeled data for training.

Assessment of Conflict between Humans and the Sclater’s Guenon (Cercopithecus sclateri Pocock 1904) in relation to Plant Species Utilization in Aboh-Mbaise Communal Forest, Imo State, Nigeria

Aims: Assessment of conflict between humans and sclater’s guenon in relation to plant species utilization was carried out to determine the conflict between humans and monkey, and identify the plant species parts utilized by the monkeys in area.
 Place and Duration of Study: The study was conducted in Lagwa community forest, Aboh-Mbaise Local Government Area, Imo State, between December 2019 and November 2021.
 Study Design: The Point transect and visual observation methods were used to collect data on population and food availability.
 Methodology: The research was conducted during the dry and wet seasons. Questionnaire were used to collect information on conflict between humans and guenons, while visual observation and faecal examination were used to identify plant species and part utilized.
 Results: Habitat loss, raiding of farms, destruction of roof tops, and food scarcity were the causes of conflicts between monkeys and humans, while awareness creation and establishment of wildlife sanctuary were ways of reducing conflicts between monkeys and humans. Twenty six plant species were utilized by the monkeys for cover. Pear (21%), oil palm (16%), and mango (13%) were the most utilized during the dry season, while Pterocarpus erinaceous (15%), Dacryodes edulis and Treculia africana (11%) were utilized mostly during the wet season. The monkeys consumed more seeds (63%), less fruits (25%), and leaves (13%) during the wet season, and fruits (71%), leaves and seeds (14%) during the dry season.
 Conclusion: The findings of this research is a guide to understanding the conflict that exist between humans and the sclater’s guenon, as well as communicate the type of plants and parts consumed by the animals. Awareness creation on the need to regenerate the remaining fragmented habitat and establishment of a rehabilitation centre for the animals can be a conflict reduction measure, and can provide alternative food sources for the animals.

The Analysis of Plants Recognition Based on Deep Learning and Artificial Neural Network

Abstract: Classification and identification of plants are helpful for people to effectively understand and safeguard plants. The leaves of plants are the most important identifying organs. With the development of artificial intelligence and machine vision technology, plants leaf recognition technology based on image analysis is used to ameliorate the knowledge of plant classification and protection. Deep learning is the condensation of deep neural network learning method and belongs to neural network structure. The main content of this paper is to extract plant leaf features and identify plant species based on image analysis.

Wild Medicinal Plants of South 24 Parganas District, West Bengal, India

The present study deals with the observation, identification and documentation of medicinal uses of wild plants by the inhabitants of villages in the blocks of South 24 Parganas district, West Bengal. The various medicinal plants are used by the local communities of the study area to treat a variety of health-related challenges and heal different pains and ailments. The study is therefore, undertaken from April 2017 to October 2019, to gather the knowledge and create a record related to important and unexplored wild medicinal plants used by the local population. Data were collected by interviewing local inhabitants or knowledgeable persons and medicine men with the help of a semi-structured and open-ended questionnaire. It provides a comprehensive account of 64 wild medicinal plant species with their botanical name, family, vernacular name(s), parts used and uses. The plant species belonging to 60 different genera and 34 different families were enlisted and found to be used in effective therapies. Among the identified plant species, most belong to the dicotyledonous families. It is observed that out of the surveyed species, 53%, 31%, 11%, 3% and 2% are herbs, trees, shrubs, climbers, and creepers, respectively. The study showed that the area is rich in curative flora. The plants and their parts are used in the treatment of numerous diseases like bronchitis, cut, wound, asthma, jaundice, headache, fever, ear and skin diseases, snake bites, dog bites, vomiting, stomach problems, etc. In this study, 8 species of the family Fabaceae were recorded and it is reported to be the most dominant one. It was found that many diseases are cured by multiple plants, e.g., asthma can be cured by Vachellia nilotica, Boerhaavia diffusa, Calotropis procera, Datura metel, Ficus religiosa, Solanum torvum; burns/boils can be cured by V. nilotica, C. gigantea, F. benghalensis. Sometimes, a single plant is used for curing multiple diseases e.g., B. diffusa can cure abscess, anemia, asthma, cough and cold. Bioassay of all these preparations should be approved for further scientific validation. In West Bengal, the uses of all wild plant taxa have not yet been recorded. The study recommends growing these plants under organized agriculture, as crops, for large scale production, utilization in daily meals in the form of various delicacies and creating awareness meant for conservation and hence, ensuring food and health security for the local growing population.

Development of plants morphology characteristics textbook to improve students’ ability in identifying plants

The selection and use of appropriate textbooks in the lecture process is an important factor for improving science process skills. This study aims to develop textbooks on plant morphological characteristics that are valid, practical, and effective in increasing students' ability to identify plants. The method used is research and development with a design Plomp model which consists of five stages in the form of (1) initial investigation, (2) design, (3) realization, (4) testing, evaluation, and revision, (implementation). The subjects of this study were students who took plant anatomy and morphology courses, the academic year 2020/2021 of the STKIP Soe Biology education study program. Data collection techniques in the form of observation, tests, and questionnaires. The product in the form of a textbook developed was tested for validity based on the research results of the expert team as a validator. The practicality of the product was tested using a student and lecturer response questionnaire analysis. The textbook effectiveness test was carried out through quasi-experimental research with analytical techniques using an independent sample T-test and normalized gain scores. The results showed that the textbooks developed were valid, practical, and effective to improve students' ability to identify plant species.

Ethno-botanical Survey and Chemical Study of Medicinal Plants Traditionally used to Treat Anemia in Yakoma Territory (Nord Ubangi), Democratic Republic of the Congo

Aims of the Study: To identify plant species traditionally used to treat anemia in Yakoma territory and to evaluate their chemical composition.
 Place and Duration: Yakoma Territory (survey) and University of Kinshasa (Phytochemical study), from August and October 2019.
 Methods: Ethnobotanical survey according to the "snowball" sampling technique among traditional healers (based on the free consent of the respondents), chemical analyses of plant materials (chemical screening, TLC, phytomarkers content, minerals composition) according to standard methods. ED-XRF was used for mineral analysis. Microsoft Excel version 2010, Origin version 8.5 Pro and IBM SPSS statistics version 20 software packages were used for data processing and analysis.
 Results and Discussion: The survey showed that 18 plant species are traditionally used by Ngbandi traditional healers to treat anemia in Yakoma territory. They belong to 16 families and 17 genera. The most used organs are the leaves (68.4%) and the roots (10.5%). Decoction and infusion are the most used mode of preparation (33.3% each), followed by cooking (22.2%) and maceration (11.1%). The oral route (77.8%) is the most used mode of administration followed by the enema (16.7%) and the anal route (5.6%). Morphological types consist of herbs and trees (33.3% each) and shrubs and lianas (16.7% each). The biotope types consist of forest plants (44.4%), cultivated plants/Crops (38.9%), marshy ground plants (11.1%) and ruderal plants (5.6%). These plants belonging to five biological types: erected therophytes (44.4%), mesophanerophytes (27.8%), microphanerophytes and climbing phanerophytes (11.1% each) and lianescent phanerophytes (5.56%). These anti-anemic plant species are mostly Pantropical and Afro-tropical (39% of species each) followed by Guineo-Congolese (17% of species) and American (5%). Chemical analysis revealed the presence of alkaloids, anthocyanins, flavonoids, anthraquinones and terpenoids and various minerals including iron, zinc, copper, calcium, magnesium and manganese.
 Conclusion: In the current state of knowledge, the survey of anemic plants from this part of the Democratic Republic of the Congo is reported for the first time.

Identification of the Most Important Weed Species in A Barley Crops in Beir Bullerjam Region – Soloq, Libya

A field survey was carried out to highlight the most important and problematic weeds of barley crop at District Beir Bullerjam, Soloq Region, Libya for two seasons (2020 to 2021). The current study is based on field research where recorded most weed the grown species by survey followed by using quadrats and finally calculating the weed density and their frequency. samples from the fields of the barley crop using a quadrat of size 1 m × 1 m randomly thrown at different points in the fields. The plants were identified with the help of available literature and through comparison with the already identified plant species. Data inventory has been documented in the form of family, Botanical name, vernacular name and life cycle. A total of 67 weed species belonging to 54 genera and 21 families of angiosperms. The dominant families according to the number of species were Asteraceae (14 species), Fabaceae (11 species), Poaceae (9 species), and Brassicaceae (7 species). The most dominant life cycle was annuals having 57 followed by perennials having nine species and biennials represented by only two species.The results further revealed that the highest density (43.48 mˉ²) and (30.36 mˉ²) were recorded for Lolium rigidum and Melilotus indicus respectively, while the highest frequency were recorded for Melilotus indicus and Eruca sativa with 84% and 80 respectively.

Monoclonal antibody-based immunoassay for the specific quantification of licochalcone A: an active chalcone in licorice

ABSTRACT Licochalcone A (LicoA), which is found in the root of Chinese licorice (Glycyrrhiza inflata Batalin), has been reported as an effective anti-microbial and anti-inflammation and was approved for the treatment of rosacea and acne in clinical therapeutic. To develop a monoclonal antibody against LicoA, a mannich reaction hapten conjugate was used for immunization, followed by the hybridoma technique. Enzyme-linked immunosorbent assay (ELISA) for the quantitative determination of LicoA was developed using a constructed antibody. The assay validation results were highly specific for the target compound, but minimally cross-reactive with the structure-related substances. After optimal conditions, the detection limit was 4.32 ng/mL and the quantification limit was 6.84–107.21 ng/mL. As a result, the developed ELISA was applied to determine the concentration of LicoA in raw licorice and marketed samples. This assay will aid in the quality control of licorice and derived products, as it can be used to identify plant species and determine bioactive markers.

Host plants and pollination regions for the long-distance migratory noctuid moth, Hadula trifolii Hufnagel in China.

Nocturnal moths are important pollinators of plants. The clover cutworm, Hadula trifolii, is a long‐distance migratory nocturnal moth. Although the larvae of H. trifolii are polyphagous pests of many cultivated crops in Asia and Europe, the plant species pollinated by the adult are unclear. Pollen species that were attached to individual migrating moths of H. trifolii were identified based on pollen morphology and DNA to determine their host plants, geographic origin, and pollination areas. The moths were collected on their seasonal migration pathway at a small island, namely Beihuang, in the center of the Bohai Sea of China during 2014 to 2018. Pollen was detected on 28.60% of the female moths and 29.02% of the male, mainly on the proboscis, rarely on compound eyes and antennae. At least 92 species of pollen from 42 plant families, mainly from Asteraceae, Amaranthaceae, and Pinaceae, distributed throughout China were found on the test moths. Migratory H. trifolii moths visited herbaceous plants more than woody plants. Pollen of Macadamina integrifolia or M. tetraphylla was found on moths early in the migratory season. These two species are distributed in Guangdong, Yunnan, and Taiwan provinces in China, indicating that migratory moths probably traveled about 2000 km from southern China to the Beihuang Island in northern China. Here, by identifying plant species using pollen, we gained a better understanding of the interactions between H. trifolii moths and a wide range of host plants in China. This work provides valuable and unique information on the geographical origin and pollination regions for H. trifolii moths.

Spatial variation of vegetation changes in Sokoto region from 2000 to 2018

Previous researchers reported that vegetation is one of the natural resources that are constantly changing. These changes are mostly found in an area affected by human activities. Those researchers study vegetation using holistic approach without identifying the locations affected and species of plant that withstand the prevailing conditions. The aim of this research was to find out the locations where these changes are mostly affected. This was achieved using Coefficient of Variation analysis on MODIS-EVI dataset, supported by the field observation. The result shows that high CV areas are mostly around farmlands and around the nomadic routes, while low CV areas are around the floodplains of Rivers Sokoto and Rima. The identified plant species that can survive in the high CV areas are Gueira senegalensis (Sabara), Combretum micranthum (Geza) and Piliostigma thonningii (Kalgo). It was recommended that the dominant plant species in the area should be domesticated and used for afforestation programme in the region. Government should provide adequate grazing reserves and animal routes to avoid indiscriminate grazing activities in the area.

Using Metabarcoding to Investigate the Strength of Plant-Pollinator Interactions From Surveys of Visits to DNA Sequences

The ongoing decline in pollinators and increasing concerns about pollination services require a better understanding of complex pollination networks, particularly their response to global climate change. While metabarcoding is increasingly used for the identification of taxa in DNA mixtures, its reliability in providing quantitative information on plant-pollinator interactions is still the subject of debate. Combining metabarcoding and microscopy, we investigated the relationships between the number and composition of sequences and the abundance and composition of pollen in insect pollen loads (IPL) and how the two are linked to insect visits. Our findings confirm that metabarcoding is more effective than microscopy in identifying plant species in IPL. For a given species, we found a strong positive relationship between the amount of pollen in IPL and the number of sequences. The relationship was stable across species even if the abundance of co-occurring species in IPL (hereafter “co-occurring pollen”) tended to reduce the sequence yield (number of sequences obtained from one pollen grain) of a given species. We also found a positive relationship between the sequence count and the frequency of visits, and between the frequency and the amounts of pollen in IPL. Our results demonstrate the reliability of metabarcoding in assessing the strength of plant-pollinator interactions and in providing a broader perspective for the analyses of plant-pollinator interactions and pollination networks.