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Scientists Find the Largest Known Genome Inside a Small Plant

A fern from a Pacific island carries 50 times as much DNA as humans do.

Tmesipteris oblanceolata, a fern growing in a forest on an island east of Australia. “It doesn’t catch the eye,” said Jaume Pellicer, who studies it. “You would probably step on it and not even realize it.” Credit... Pol Fernández

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By Carl Zimmer

• May 31, 2024

Last year, Jaume Pellicer led a team of fellow scientists into a forest on Grande Terre, an island east of Australia. They were in search of a fern called Tmesipteris oblanceolata. Standing just a few inches tall, it was not easy to find on the forest floor.

“It doesn’t catch the eye,” said Dr. Pellicer, who works at the Botanical Institute of Barcelona in Spain. “You would probably step on it and not even realize it.”

The scientists eventually managed to spot the nondescript fern. When Dr. Pellicer and his colleagues studied it in the lab, they discovered it held an extraordinary secret. Tmesipteris oblanceolata has the largest known genome on Earth. As the researchers described in a study published on Friday, the fern’s cells contain more than 50 times as much DNA as ours do.

If you find it strange that such a humble plant has such a gigantic genome, scientists do, too. The enigma emerged in the 1950s, when biologists discovered that the double helix of DNA encodes genes. Each gene consists of a series of genetic letters, and our cells read those letters to make corresponding proteins.

Scientists assumed that humans and other complex species must make a lot of different proteins and therefore have bigger genomes. But when they weighed the DNA in different animals, they discovered they were wildly wrong. Frogs, salamanders and lungfish had far bigger genomes than humans did.

It turns out that genomes are much weirder than scientists had expected. We carry about 20,000 protein-coding genes, for example, but they make up only 1.5 percent of the 3 billion pairs of letters in our genome.

Another nine percent or so is made up of stretches of DNA that don’t encode proteins but still carry out important jobs. Some of them, for example, act like switches to turn neighboring genes on and off.

The other 90 percent of the human genome has no known function. Some scientists have an affectionate nickname for this vast quantity of mysterious DNA: junk .

Some species have little junk DNA, whereas others have staggering amounts. The African lungfish , for example, has about the same number of protein-coding genes as we do, but they are scattered in a giant genome that totals 40 billion pairs of DNA letters — 13 times as much DNA as our own genome holds.

In the early 2000s, when Dr. Pellicer trained as a botanist, he was intrigued to learn that a few lineages of plants have massive genomes as well. Onions, for example, have a genome five times as large as ours.

In 2010, when Dr. Pellicer began working at Kew Gardens in London, he got the chance to study a family of plants known as bunchflowers, which were known to have big genomes. He spent months mincing leaves with a razor blade, isolating cells from dozens of species and weighing their DNA.

When he weighed the genome of a plant called Paris japonica, which grows in the mountains near Nagano, Japan, he was shocked at the result. The ordinary flower had a genome containing 148 billion pairs of letters — a world record.

In the years that followed, colleagues sent him fresh samples of ferns from Australia and New Zealand to chop up. He discovered that those plants, too, had massive genomes, although not quite as big as that of Paris japonica.

Dr. Pellicer knew that related fern species grew on a few Pacific islands. In 2016, he began making plans for an expedition to Grande Terre, part of the archipelago known as New Caledonia.

It wasn’t until 2023 that he finally made it to the island. He collected a number of species along with a team that included colleagues from Kew, his graduate student Pol Fernández and local plant experts.

Back in Barcelona, Mr. Fernández was startled to discover that Tmesipteris oblanceolata’s genome contained about 160 billion pairs of DNA letters. Thirteen years after Dr. Pellicer had discovered a record-breaking genome, his graduate student was also experiencing the thrill of breaking the record.

There are two chief ways in which genomes expand over evolutionary time. Many species carry virus-like stretches of DNA. As they make new copies of their genomes, they sometimes accidentally make an extra copy of that viral stretch. Over many generations, a species can accumulate thousands of new copies, causing its genome to swell.

It’s also possible for a species to suddenly end up with two genomes instead of one. One way for an extra genome can arise is for two closely related species to mate. Their hybrid offspring may inherit full sets of DNA from both parents.

Dr. Pellicer and his colleagues suspect that a combination of virus-like DNA and duplicated genomes is responsible for the huge amount of genetic material in Tmesipteris oblanceolata. But they don’t know why this humble fern ended up with a record-setting genome while other species — like us — have so much less DNA.

It’s possible that most species gradually accumulate DNA in their genomes without suffering any harm. “A lot of biology is ‘why not?’ rather than ‘why?’” said Julie Blommaert, a genomicist at the New Zealand Institute for Plant and Food Research who was not involved in the new study.

Eventually, however, genomes may get so big that they become a burden. Cells may have to expand to house all the extra DNA. They also need more time and more nutrients to make new copies of their giant genomes. An organism with an oversized genome may lose out to a rival with a smaller one. So mutations that chop out unneeded DNA may be favored by evolution.

It’s possible that animals and plants can evolve truly giant genomes only in special environments, such as in stable climates where there is little competition. “Maybe that’s why they’re so rare — they get ripped away because they’re not efficient,” Dr. Pellicer said.

Even in the most welcoming home, genomes can’t grow to infinite sizes. In fact, Dr. Pellicer suspects that Tmesipteris oblanceolata may have nearly reached a genome’s physical limit. “I believe we are close,” he said.

Others aren’t so sure.

“I don’t know if we have reached an upper boundary yet,” said Brittany Sutherland, a botanist at George Mason University who was not involved in the study. She noted that botanists have measured the sizes of genomes in only 12,000 species of plants, leaving 400,000 other species to study. “What we have estimates for is a drop in the bucket,” she said.

Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

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Automated plant species identification—Trends and future directions

* E-mail: [email protected]

Affiliation Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Thuringia, Germany

Affiliation Software Engineering for Safety-Critical Systems Group, Technische Universität Ilmenau, Ilmenau, Thuringia, Germany

• Jana Wäldchen, 
• Michael Rzanny, 
• Marco Seeland, 
• Patrick Mäder

Published: April 5, 2018

• https://doi.org/10.1371/journal.pcbi.1005993
• Reader Comments

Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.

Author summary

Plant identification is not exclusively the job of botanists and plant ecologists. It is required or useful for large parts of society, from professionals (such as landscape architects, foresters, farmers, conservationists, and biologists) to the general public (like ecotourists, hikers, and nature lovers). But the identification of plants by conventional means is difficult, time consuming, and (due to the use of specific botanical terms) frustrating for novices. This creates a hard-to-overcome hurdle for novices interested in acquiring species knowledge. In recent years, computer science research, especially image processing and pattern recognition techniques, have been introduced into plant taxonomy to eventually make up for the deficiency in people's identification abilities. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.

Citation: Wäldchen J, Rzanny M, Seeland M, Mäder P (2018) Automated plant species identification—Trends and future directions. PLoS Comput Biol 14(4): e1005993. https://doi.org/10.1371/journal.pcbi.1005993

Editor: Alexander Bucksch, University of Georgia Warnell School of Forestry and Natural Resources, UNITED STATES

Copyright: © 2018 Wäldchen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: We are funded by the German Ministry of Education and Research (BMBF) grants: 01LC1319A and 01LC1319B ( https://www.bmbf.de/ ); the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) grant: 3514 685C19 ( https://www.bmub.bund.de/ ); and the Stiftung Naturschutz Thüringen (SNT) grant: SNT-082-248-03/2014 ( http://www.stiftung-naturschutz-thueringen.de/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

One of the most obvious features of organic life is its remarkable diversity [ 1 ]. Despite the variation of organisms, a more experienced eye soon discerns that organisms can be grouped into taxa. Biology defines taxa as formal classes of living things consisting of the taxon's name and its description [ 2 ]. The assignment of an unknown living thing to a taxon is called identification [ 3 ]. This article specifically focuses on plant identification, which is the process of assigning an individual plant to a taxon based on the resemblance of discriminatory and morphological plant characters, ultimately arriving at a species or infraspecific name. These underlying characters can be qualitative or quantitative. Quantitative characters are features that can be counted or measured, such as plant height, flower width, or the number of petals per flower. Qualitative characters are features such as leaf shape, flower color, or ovary position. Individuals of the same species share a combination of relevant identification features. Since no two plants look exactly the same, it requires a certain degree of generalization to assign individuals to species (or, in other words, assign objects to a fuzzy prototype).

The world inherits a very large number of plant species. Current estimates of flowering plant species (angiosperms) range between 220,000 [ 4 , 5 ] and 420,000 [ 6 ]. Given the average 20,000 word vocabulary of an educated native English speaker, even teaching and learning the "taxon vocabulary" of a restricted region becomes a long-term endeavor [ 7 ]. In addition to the complexity of the task itself, taxonomic information is often captured in languages and formats hard to understand without specialized knowledge. As a consequence, taxonomic knowledge and plant identification skills are restricted to a limited number of persons today.

The dilemma is exacerbated since accurate plant identification is essential for ecological monitoring and thereby especially for biodiversity conservation [ 8 , 9 ]. Many activities, such as studying the biodiversity of a region, monitoring populations of endangered species, determining the impact of climate change on species distribution, payment of environmental services, and weed control actions are dependent upon accurate identification skills [ 8 , 10 ]. With the continuous loss of biodiversity [ 11 ], the demand for routine species identification is likely to further increase, while at the same time, the number of experienced experts is limited and declining [ 12 ].

Taxonomists are asking for more efficient methods to meet identification requirements. More than 10 years ago, Gaston and O’Neill [ 13 ] argued that developments in artificial intelligence and digital image processing will make automatic species identification based on digital images tangible in the near future. The rich development and ubiquity of relevant information technologies, such as digital cameras and portable devices, has brought these ideas closer to reality. Furthermore, considerable research in the field of computer vision and machine learning resulted in a plethora of papers developing and comparing methods for automated plant identification [ 14 – 17 ]. Recently, deep learning convolutional neural networks (CNNs) have seen a significant breakthrough in machine learning, especially in the field of visual object categorization. The latest studies on plant identification utilize these techniques and achieve significant improvements over methods developed in the decade before [ 18 – 23 ].

Given these radical changes in technology and methodology and the increasing demand for automated identification, it is time to analyze and discuss the status quo of a decade of research and to outline further research directions. In this article, we briefly review the workflow of applied machine learning techniques, discuss challenges of image based plant identification, elaborate on the importance of different plant organs and characters in the identification process, and highlight future research thrusts.

Machine learning for species identification

From a machine learning perspective, plant identification is a supervised classification problem, as outlined in Fig 1 . Solutions and algorithms for such identification problems are manifold and were comprehensively surveyed by Wäldchen and Mäder [ 16 ] and Cope et al. [ 17 ]. The majority of these methods are not applicable right away but rather require a training phase in which the classifier learns to distinguish classes of interest. For species identification, the training phase (orange in Fig 1 ) comprises the analysis of images that have been independently and accurately identified as taxa and are now used to determine a classifier's parameters for providing maximum discrimination between these trained taxa. In the application phase (green in Fig 1 ), the trained classifier is then exposed to new images depicting unidentified specimens and is supposed to assign them to one of the trained taxa.

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https://doi.org/10.1371/journal.pcbi.1005993.g001

Images are usually composed of millions of pixels with associated color information. This information is too extensive and cluttered to be directly used by a machine learning algorithm. The high dimensionality of these images is therefore reduced by computing feature vectors, i.e., a quantified representation of the image that contains the relevant information for the classification problem. During the last decade, research on automated species identification mostly focused on the development of feature detection, extraction, and encoding methods for computing characteristic feature vectors. Initially, designing and orchestrating such methods was a problem-specific task, resulting in a model customized to the specific application, e.g., the studied plant parts like leaves or flowers. For example, Wu et al. [ 24 ] employ a processing chain comprised of image binarization to separate background and the leaf, image denoising, contour detection, and eventually extracting geometrical derivations of 12 leaf shape features. The approach was evaluated on 32 species and delivered an identification accuracy of 90%. However, this approach could only deal with species differing largely in their leaf shapes. Jin et al. [ 25 ] propose leaf tooth features extracted after binarization, segmentation, contour detection, and contour corner detection. The proposed method achieved an average classification rate of around 76% for the eight studied species but is not applicable to species with no significant appearances of leaf teeth [ 19 ]. The sole step from an image to a feature vector, however, typically required about 90% of the development time and extensive expert knowledge [ 19 ].

Model-free approaches aim to overcome the described limitations of model-based approaches. They do not employ application-specific knowledge and therefore promise a higher degree of generalization across different classes, i.e., species and their organs. The core concept of model-free approaches is the detection of characteristic interest points and their description using generic algorithms, such as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and histogram of gradients (HOG). These descriptors capture visual information in a patch around each interest point as orientation of gradients and have been successfully used for manifold plant classification studies, e.g., [ 26 – 28 ]. Seeland et al. [ 29 ] comparatively evaluate alternative parts of a model-free image classification pipeline for plant species identification. They found the SURF detector in combination with the SIFT local shape descriptor to be superior over other detector–descriptor combinations. For encoding interest points, in order to form an characteristic image descriptor for classification, they found the Fisher Kernel encoding to be superior.

The next obvious step in automated plant species identification and many other machine learning problems was removing an explicit decision about features to be described entirely. In the last years, deep learning CNNs have seen a significant breakthrough in computer vision due to the availability of efficient and massively parallel computing on graphics processing units (GPUs) and the availability of large-scale image data necessary for training deep CNNs with millions of parameters [ 19 ]. In contrast to model-based and model-free techniques, CNNs do not require explicit and hand-crafted feature detection and extraction steps. Instead, both become part of the iterative training process, which automatically discovers a statistically suitable image representation (similar to a feature vector) for a given problem. The fundamental concept of deep learning is a hierarchical image representation composed of building blocks with increasing complexity per layer. In a similar way, nature is compositional, i.e., small units form larger units, and each aggregation level increases the diversity of the resulting structure ( Fig 2 ). Such hierarchical representations achieve classification performances that were mostly unachievable using shallow learning methods with or without hand-crafted features (see Table 1 ).

https://doi.org/10.1371/journal.pcbi.1005993.g002

https://doi.org/10.1371/journal.pcbi.1005993.t001

Challenges in image-based taxa identification

In providing a reliable and applicable automated species identification process, researchers need to consider the following main challenges: (a) a vast number of taxa to be discriminated from one another; (b) individuals of the same species that vary hugely in their morphology; (c) different species that are extremely similar to one another; (d) specimen or other objects that are not covered by the trained classifier; and (e) large variation induced by the image acquisition process in the field.

Large number of taxa to be discriminated

The world exhibits a very large number of plant species. Distinguishing between a large number of classes is inherently more complex than distinguishing between just a few and typically requires substantially more training data to achieve satisfactory classification performance. Even when restricting the focus to the flora of a region, thousands of species need to be supported. For example, the flora of the German state of Thuringia exhibits about 1,600 flowering species [ 33 ]. Similarly, when restricting the focus to a single genus, this may still contain many species, e.g., the flowering plant genus Dioscorea aggregates over 600 species [ 17 ]. Only a few studies with such large numbers of categories have been conducted so far. For example, the important "ImageNet Large Scale Visual Recognition Challenge 2017" involves 1,000 categories that cover a wide variety of objects, animals, scenes, and even some abstract geometric concepts such as a hook or a spiral [ 34 ].

Large intraspecific visual variation

Plants belonging to the same species may show considerable differences in their morphological characteristics depending on their geographical location and different abiotic factors (e.g., moisture, nutrition, and light condition), their development stage (e.g., differences between a seedling and a fully developed plant), the season (e.g., early flowering stage to a withered flower), and the daytime (e.g., the flower is opening and closing during the day). These changes in morphological characteristics can occur on the scale of individual leaves (e.g., area, width, length, shape, orientation, and thickness), flowers (e.g., size, shape, and color), and fruits but may also affect the entire plant. Examples of visual differences of flowers during the daytime and the season are given in Fig 3 . In addition to the spatial and temporal variation, the shape of leaves and flowers may vary continuously or discretely along a single individual. For example, the leaf shape of field scabious ( Knautia arvensis ), a common plant in grassy places, ranges from large entire or dentate lanceolate ground leafs over deeply lobed and almost pinnate stem leafs to small and again lanceolate and entire upper stem leafs. Furthermore, diseases commonly affect the surface of leaves, ranging from discoloration to distinct marking, while insects often alter a leaf's shape by consuming parts of it. Some of this variation is systematic, particularly the allometric scaling of many features, but much variation is also idiosyncratic, reflecting the expression of individual genotypic and phenotypic variation related to the factors mentioned.

Visual variation of Lapsana communis 's flower throughout the day from two perspectives (left) and visual variation of Centaurea pseudophrygia 's flower throughout the season and flowering stage (right).

https://doi.org/10.1371/journal.pcbi.1005993.g003

Small interspecific visual variation

Closely related species may be extremely similar to one another. Even experienced botanists are challenged to safely distinguish species that can be identified only by almost invisible characteristics [ 35 ]. Detailed patterns in the form of particular morphological structures may be crucial and may not always be readily captured, e.g., in images of specimens. For example, the presence of flowers and fruits is often required for an accurate discrimination between species with high interspecific similarity, but these important characteristics are not present during the whole flowering season and therefore are missing in many images. Furthermore, particular morphological structures which are crucial for discrimination may not be captured in an image of a specimen, even when the particular organ is visible (e.g., the number of stamens or ovary position in the flower).

Rejecting untrained taxa

An automated taxon identification approach not only needs to be able to match an individual specimen to one of the known taxa, but should also be able to reject specimens that belong to a taxon that was not part of the training set. In order to reject unknown taxa, the classification method could produce low classification scores across all known classes for "new" taxa. However, aiming for a classifier with such characteristics conflicts with the goal of tolerating large intraspecific variation in classifying taxa. Finding a trade-off between sensitivity and specificity is a particular challenge in classifier design and training.

Variation induced by the acquisition process

Further variation is added to the images through the acquisition process itself. Living plants represent 3D objects, while images capture 2D projections, resulting in potentially large differences in shape and appearance, depending on the perspective from which the image is taken. Furthermore, image-capturing typically occurs in the field with limited control of external conditions, such as illumination, focus, zoom, resolution, and the image sensor itself [ 2 ]. These variations are especially relevant for an automated approach in contrast to human perception.

In the last decade, research in computer vision and machine learning has stimulated manifold methods for automated plant identification. Existing image-based plant identification approaches differ in three main aspects: (a) the analyzed plant organs, (b) the analyzed organ characters, and (c) the complexity of analyzed images. An extensive overview of studied methods is given by Wäldchen and Mäder [ 16 ] and is briefly summarized below.

Relevant organs for automated identification

Above the ground, plants may be composed of four visible organ types: stem, leaf, flower, and fruit. In a traditional identification process, people typically consider the plant as a whole, but also the characteristics of one or more of these organs to distinguish between taxa. In case of automated identification, organ characteristics were analyzed separately, too. For the following reasons one image alone is typically not sufficient: (a) organs may differ in scale and cannot be depicted in detail along with the whole plant or other organs; and (b) different organs require different optimal image perspectives (e.g., leaves are most descriptive from the top, while the stem is better depicted from the side).

A majority of previous studies solely utilized the leaf for discrimination [ 16 ]. The reason is a more methodological one, rather than meaning that leaves are a more discriminative part of plants from a botanical perspective. On the contrary, manual identification of plants in the vegetative state is considered much more challenging than in the flowering state. From a computer vision perspective, leaves have several advantages over other plant morphological structures, such as flowers, stems, or fruits. Leaves are available for examination throughout most of the year. They can easily be collected, preserved, and imaged due to their planar geometric properties. These aspects simplify the data acquisition process [ 17 ] and have made leaves the dominantly studied plant organ for automated identification methods in the past. In situ top-side leaf images in front of a natural background were shown to be the most effective nondestructive type of image acquisition [ 36 ]. Leaves usually refer only to broad leaves, while needles were neglected or treated separately.

Often, the visually most prominent and perceivable part of a plant is its flower . Traditional identification keys intensively refer to flowers and their parts for determination. In contrast, previous studies on automated identification rarely used flowers for discrimination [ 16 ]. Typically, flowers are only available during the blooming season, i.e., a short period of the year. Due to being complex 3D objects, there is a considerable number of variations in viewpoint, occlusions, and scale of flower images compared to leaf images. If captured in their habitat, images of flowers vary due to lighting conditions, time, date, and weather. All these aspects make flower-based classification a challenging task. However, accurate, automated identification supporting a realistic number of taxa will hardly be successful without the analysis of flowers.

Towards a more mature automated identification approach, solely analyzing one organ will often not be sufficient, especially when considering all the challenges discussed in the previous section. Therefore, more recent research started exploring multi-organ-based plant identification . The Cross Language Evaluation Forum (ImageCLEF) conference has organized a challenge dedicated to plant identification since 2011. The challenge is described as plant species retrieval based on multi-image plant observation queries and is accompanied by a dataset containing different organs of plants since 2014. Participating in the challenge, Joly et al. [ 37 ] proposed a multiview approach that analyzes up to five images of a plant in order to identify a species. This multiview approach allows classification at any period of the year, as opposed to purely leaf-based or flower-based approaches that rely on the supported organ to be visible. Initial experiments demonstrate that classification accuracy benefits from the complementarities of the different views, especially in discriminating ambiguous taxa [ 37 ]. A considerable burden in exploring this research direction is acquiring the necessary training data. However, by using mobile devices and customized apps (e.g., Pl@ntNet [ 38 ], Flora Capture [ 39 ]), it is possible to quickly capture multiple images of the same plant observed at the same time, by the same person, and with the same device. Each image, being part of such an observation, can be labeled with contextual metadata, such as the displayed organ (e.g., plant, branch, leaf, fruit, flower, or stem), time and date, and geolocation, as well as the observer.

It is beneficial if training images cover a large variety of scenarios, i.e., different organs from multiple perspective and at varying scale. This helps the model to learn adequate representations under varying circumstances. Furthermore, images of the same organ acquired from different perspectives often contain complementary visual information, improving accuracy in observation-based identification using multiple images. A structured observation approach with well defined image conditions (e.g., Flora Capture) is beneficial for finding a balance between a tedious observation process acquiring every possible scenario and a superficial acquisition that misses the characteristic images required for training.

Relevant characters for automated identification

A plant and its organs (i.e., objects in computer vision) can be described by various characters, such as color, shape, growing position, inflorescence of flowers, margin, pattern, texture, and vein structure of the leaves. These characters are extensively used for traditional identification, with many of them also being studied for automated identification. Previous research proposed numerous methods for describing general as well as domain-specific characteristics. Extensive overviews of the utilized characteristics, as well as of the methods used for capturing them in a formal description, are given by Wäldchen and Mäder [ 16 ] and Cope et al. [ 17 ].

Leaf shape is the most studied characteristic for plant identification. A plethora of methods for its description can be found in previous work [ 16 , 17 ]. Also, most traditional taxonomic keys involve leaf shape for discrimination, the reason being that, although species' leaf shape differs in detail, general shape types can easily be distinguished by people. However, while traditional identification categorizes leaf shape into classes (e.g., ovate, oblique, oblanceolate), computerized shape descriptors either analyze the contour or the whole region of a leaf. Initially, basic geometric descriptors, such as aspect ratio, rectangularity, circularity, and eccentricity, were used to describe a shape. Later, more sophisticated descriptions, such as center contour distance, Fourier descriptors, and invariant moments, were intensively studied [ 16 , 17 ].

In addition to the shape characteristic, various researchers also studied leaf texture , described by methods like Gabor filters, gray-level co-occurrence matrices (GLCM), and fractal dimensions [ 40 – 42 ]. Although texture is often overshadowed by shape as the dominant or more discriminative feature for leaf classification, it has been demonstrated to be of high discriminative power and complementary to shape information [ 16 , 43 ]. In particular, leaf texture captures leaf venation information as well as any eventual directional characteristics, and more generally allows describing fine nuances or micro-texture at the leaf surface [ 44 ]. Furthermore, leaf texture analysis allows to classify a plant by having only a portion of a leaf available without depending, e.g., on the shape of the full leaf or its color. Therefore, texture analysis can be beneficial for botanists and researchers that aim to identify damaged plants.

The vein structure as a leaf-specific characteristic also played a subordinate role in previous studies. Venation extraction is not trivial, mainly due to a possible low contrast between the venation and the rest of the leaf blade structure [ 45 ]. Some authors have simplified the task by using special equipment and treatments that render images with more clearly identified veins (mainly chemical leaf clarification) [ 45 , 46 ]. However, this defeats the goal of having users get an automated identification for specimens that they have photographed with ordinary digital cameras.

Leaf color is considered a less discriminative character than shape and texture. Leaves are mostly colored in some shade of green that varies greatly under different illumination [ 44 ], creating a low interclass color variability. In addition, there is high intraclass variability. For example, the leaves belonging to the same species or even the same plant can present a wide range of colors depending on the season and the plant's overall condition (e.g., nutrients and water). Regardless of the aforementioned complications, color may still contribute to plant identification, e.g., for considering leaves that exhibit an extraordinary hue [ 44 ]. However, further investigation on the leaf color character is required.

While the shape of the leaves is of very high relevance, flower shape has hardly been considered so far. Interestingly, flower shape is an important characteristic in the traditional identification process. It is dividing plants into families and genera and is thereby considerably narrowing the search space for identification. However, previous attempts for describing flower shape in a computable form did not find it to be very discriminative [ 47 ]. A major reason is the complex 3D structure of flowers, which makes its shape vary depending on the perspective from which an image was taken. Furthermore, flower petals are often soft and flexible, which is making them bend, curl or twist and letting the shape of the same flower appear very differently. A flower's shape also changes throughout the season [ 29 ] and with its age to the extent where petals even fall off [ 48 ], as visualized in Fig 3 .

Flower color is a more discriminative character [ 48 , 49 ]. Many traditional field guides divide plants into groups according to their flower color. For automated identification, color has been mostly described by color moments and color histograms [ 16 ]. Due to the low dimensionality and the low computational complexity of these descriptors, they are also suitable for real-time applications. However, solely analyzing color characters, without, e.g., considering flower shape, cannot classify flowers effectively [ 48 , 49 ]. Flowers are often transparent to some degree, i.e., the perceived color of a flower differs depending on whether the light comes from the back or the front of the flower. Since flower images are taken under different environmental conditions, the variation in illumination is greatly affecting analysis results. This motivated the beneficial usage of photometric invariant color characters [ 29 , 50 ].

Various previous studies showed that no single character may be sufficient to separate all desired taxa, making character selection and description a challenging problem. For example, whilst leaf shape may be sufficient to distinguish some taxa, others may look very similar to each other but have differently colored leaves or texture patterns. The same applies to flowers, where specimens of the same color may differ in their shape or texture. Therefore, various studies do not only consider one type of character but use a combination of characteristics for describing leaves and flowers [ 16 ]. The selection of characteristics is always specific for a certain set of taxa and might not be applicable to others. Meaningful characters for, e.g., flower shape can only be derived if there are flowers of sufficient size and potentially flat structure. The same applies to leaf shape and texture. This reflects a fundamental drawback of shallow learning methods using hand-crafted features for specific characters.

Deep learning

Deep artificial neural networks automate the critical feature extraction step by learning a suitable representation of the training data and by systematically developing a robust classification model. Since about 2010, extensive studies with folded neural networks have been conducted on various computer vision problems. In 2012, for the first time a deep learning network architecture with eight layers (AlexNet) won the prestigious ImageNet Challenge (ILSVRC) [ 51 ]. In the following years, the winning architectures grew in depth and provided more sophisticated mechanisms that centered around the design of layers, the skipping of connections, and on improving gradient flow. In 2015, ResNet [ 52 ] won ILSVRC with a 152 layer architecture and reached a top-5 classification error of 3.6%, being better than human performance (5.1%) [ 34 ]. As for many object classification problems, CNNs produce promising and constantly improving results on automated plant species identification. One of the first studies on plant identification utilizing CNNs is Lee et al.'s [ 53 , 54 ] leaf classifier that uses the AlexNet architecture pretrained on the ILSVRC2012 dataset and reached an average accuracy of 99.5% on a dataset covering 44 species. Zhang et al. [ 55 ] used a six-layer CNN to classify the Flavia dataset and obtained an accuracy of 94,69%. Barre et al. [ 19 ] further improved this result by using a 17-layer CNN and obtained an accuracy of 97.9%. Eventually, Sun et al. [ 31 ] study the ResNet architecture and found a 26-layer network to reach best performance with 99.65% on the Flavia dataset. Simon et al. [ 56 ] used CNNs (AlexNet and VGG19) for feature detection and extraction inside a part constellation modeling framework. Using Support Vector Machine (SVM) as classifier, they achieved 95.34% on the Oxford Flowers 102 dataset. Table 1 contrasts the best previously reported classification results of model-based, model-free and CNN-based approaches on benchmark plant image datasets. A comparison shows that CNN classification performance was unachievable using traditional and shallow learning approaches.

Training data and benchmarks

Merely half of the previous studies on automated plant identification evaluated the proposed method with established benchmark datasets allowing for replication of studies and comparison of methods (see Table 2 ). The other half solely used proprietary leaf image datasets not available to the public [ 16 ].

https://doi.org/10.1371/journal.pcbi.1005993.t002

The images contained in these datasets (proprietary as well as benchmark) fall into three categories: scans, pseudo-scans, and photos. While scan and pseudo-scan categories correspond respectively to leaf images obtained through scanning and photography in front of a simple background, the photo category corresponds to leaves or flowers photographed on natural background. The majority of utilized leaf images are scans and pseudo-scans [ 16 ]. Typically fresh material, i.e., simple, healthy, and not degraded leaves, were collected and imaged in the lab. This fact is interesting since it considerably simplifies the classification task. If the object of interest is imaged against a plain background, the often necessary segmentation for distinguishing foreground and background can be performed in a fully automated way with high accuracy.

Leaves imaged in the natural environment, as well as degraded leaves largely existing in nature, such as deformed, partial, overlapped, and compounded leaves (leaves consisting of two or more leaflets born on the same leafstalk), are largely avoided in the current studies. Segmenting the leaf with natural background is particularly difficult when the background shows a significant amount of overlapping, almost unicolor elements. This is often unavoidable when imaging leaves in their habitat. Interferences around the target leaves, such as small stones and ruderals may create confusion between the boundaries of adjacent leaves. Compound leaves are particularly difficult to recognize and existing studies that are designed for the recognition of simple leaves can hardly be applied directly to compound leaves. This is backed up by the variation of a compound leaf—it is not only caused by morphological differences of leaflets, but also by changes in the leaflet number and arrangements [ 57 ].

The lower part of Table 2 shows benchmark datasets containing flower images. The images of the Oxford Flower 17 and 102 datasets have been acquired by searching the internet and by selecting images of species with substantial variation in shape, scale, and viewpoint. The PlantCLEF2015/2016 dataset consists of images with different plant organs or plant views (i.e., entire plant, fruit, leaf, flower, stem, branch, and leaf scan). These images were submitted by a variety of users of the mobile Pl@ntNet application. The recently published Jena Flower 30 dataset [ 29 ] contains images acquired in the field as top-view flower images using an Apple iPhone 6 throughout an entire flowering season. All images of these flower benchmark datasets are photos taken in the natural environment.

Applicable identification tools

Despite intensive and elaborate research on automated plant species identification, only very few studies resulted in approaches that can be used by the general public, such as Leafsnap [ 61 ] and Pl@ntNet [ 37 ]. Leafsnap, developed by researchers from Columbia University, the University of Maryland, and the Smithsonian Institution, was the first widely distributed electronic field guide. Implemented as a mobile app, it uses computer vision techniques for identifying tree species of North America from photographs of their leaves on plain background. The app retrieves photos of leaves similar to the one in question. However, it is up to the user to make the final decision on what species matches the unknown one. LeafSnap achieves a top-1 recognition rate of about 73% and a top-5 recognition rate of 96.8% for 184 tree species [ 61 ]. The app has attracted a considerable number of downloads but has also received many critical user reviews [ 62 ] due to its inability to deal with cluttered backgrounds and within-class variance.

Pl@ntNet is an image retrieval and sharing application for the identification of plants. It is being developed in a collaboration of four French research organizations (French agricultural research and international cooperation organization [Cirad], French National Institute for Agricultural Research [INRA], French Institute for Research in Computer Science and Automation [Inria], and French National Research Institute for Sustainable Development [IRD]) and the Tela Botanica network. It offers three front-ends, an Android app, an iOS app, and a web interface, each allowing users to submit one or several pictures of a plant in order to get a list of the most likely species in return. The application is becoming more and more popular. The application has been downloaded by more than 3 million users in about 170 countries. It was initially restricted to a fraction of the European flora (in 2013) and has since been extended to the Indian Ocean and South American flora (in 2015) and the North African flora (in 2016). Since June 2015, Pl@ntNet applies deep learning techniques for image classification. The network is pretrained on the ImageNet dataset and periodically fine-tuned on steadily growing Pl@ntNet data. Joly et al. [ 63 ] evaluated the Pl@ntNet application, which supported the identification of 2,200 species at that time, and reported a 69% top-5 identification rate for single images. We could not find published evaluation results on the current performance of the image-based identification engine. However, reviews request better accuracy [ 15 ]. We conclude that computer vision solutions are still far from replacing the botanist in extracting plant characteristic information for identification. Improving the identification performance in any possible way remains an essential objective for future research. The following sections summarize important current research directions.

Open problems and future directions

Utilizing latest machine learning developments.

While the ResNet architecture is still state-of-the-art, evolutions are continuously being proposed, (e.g., [ 64 ]). Other researchers work on alternative architectures like ultra-deep (FractalNet) [ 65 ] and densely connected (DenseNet) [ 66 ] networks. These architectures have not yet been evaluated for plant species identification. New architectures and algorithms typically aim for higher classification accuracy, which is clearly a major goal for species identification; however, there are also interesting advances in reducing the substantial computational effort and footprint of CNN classifiers. For example, SqueezeNet [ 67 ] achieves accuracy comparable to AlexNet but with 50 times fewer parameters and a model that is 510 times smaller. Especially when aiming for identification systems that run on mobile devices, these developments are highly relevant and should be evaluated in this context.

Current studies still mostly operate on the small and nonrepresentative datasets used in the past. Only a few studies train CNN classifiers on large plant image datasets, demonstrating their applicability in automated plant species identification systems [ 68 ]. Given the typically "small" amounts of available training data and the computational effort for training a CNN, transfer learning has become an accepted procedure (meaning that a classifier will be pretrained on a large dataset, e.g., ImageNet, before the actual training begins). The classifier will then only be fine-tuned to the specific classification problem by training of a small number of high-level network layers proportional to the amount of available problem-specific training data. Researchers argue that this method is superior for problems with ≤ 1 M training images. Most previous studies on plant species identification utilized transfer learning, (e.g., [ 54 , 69 ]). Once a sufficiently large plant dataset has been acquired, it would be interesting to compare current classification results with those of a plant identification CNN solely trained on images depicting plant taxa.

Another approach tackling the issue of small datasets is using data augmentation schemes, commonly including simple modifications of images, such as rotation, translation, flipping, and scaling. Using augmentation for improving the training process has become a standard procedure in computer vision. However, the diversity that can be reached with traditional augmentation schemes is relatively small. This motivates the use of synthetic data samples, introducing more variability and enriching the dataset, in order to improve the training process. A promising approach in this regard are Generative Adversarial Networks (GANs) that are able to generate high-quality, realistic, natural images [ 70 ].

Without the complicated and time-consuming process for designing an image analysis pipeline, deep learning approaches can also be applied by domain experts directly, i.e., botanists and biologists with only a basic understanding of the underlying machine learning concepts. Large-scale organizations provide a competing and continuously improving set of openly available machine learning frameworks, such as Caffe2, MXNet, PyTorch, and TensorFlow. Developments like Keras specifically target newcomers in machine learning and provide add-ons to these frameworks that aim to simplify the setup of experiments and the analysis of results. Furthermore, it is mostly common practice that researchers make their models and architectures publicly available (model zoos), increasing visibility in their field but also facilitating their application in other studies.

Creating representative benchmarks

Todays benchmark datasets are limited both in the number of species and in the number of images (see Table 2 ) due to the tremendous effort for either collecting fresh specimens and imaging them in a lab or for taking images in the field. Taking a closer look at datasets, it becomes obvious that they were created with an application in computer vision and machine learning in mind. They are typically created by only a few people acquiring specimens or images in a short period of time, from a limited area, and following a rigid procedure for their imaging. As a result, the plants of a given species in those datasets are likely to represent only a few individual plants grown closely together at the same time. Considering the high variability explained before, these datasets do not reflect realistic conditions.

Using such training data in a real-world identification application has little chance to truly classify new images collected at different periods, at different places, and acquired differently [ 63 ]. Towards real-life applications, studies should utilize more realistic images, e.g., containing multiple, overlapped, and damaged leaves and flowers. Images should have real, complex backgrounds and should be taken under different lighting conditions. Large-scale, well-annotated training datasets with representative data distribution characteristics are crucial for the training of accurate and generalizable classifiers. This is especially true for the training of Deep Convolutional Neural Networks that require extensive training data to properly tune the large set of parameters. The research community working on the ImageNet dataset [ 71 ] and the related benchmark is particularly important in this regard. ImageNet aims to provide the most comprehensive and diverse coverage of the image world. It currently contains more than 14 million images categorized according to a hierarchy of almost 22,000 English nouns. The average number of training images per category is in the range of 600 and 1,200, being considerable larger than any existing plant image collection.

First efforts have been made recently to create datasets that are specifically designed for machine learning purposes—a huge amount of information, presorted in defined categories. The PlantCLEF plant identification challenge initially provided a dataset containing 71 tree species from the French Mediterranean area depicted in 5,436 images in 2011. This dataset has grown to 113,205 pictures of herb, tree, and fern specimens belonging to 1,000 species living in France and the neighboring countries in 2016. Encyclopedia Of Life (EOL) [ 72 ], being the world's largest data centralization effort concerning multimedia data for life on earth, currently provides about 3.8 million images for 1.3 million taxa. For angiosperms, there are currently 1.26 million images, but only 68% of them are reviewed and trusted with respect to the identified taxa [ 73 ].

Crowdsourcing training data

Upcoming trends in crowdsourcing and citizen science offer excellent opportunities to generate and continuously update large repositories of required information. Members of the public are able to contribute to scientific research projects by acquiring or processing data while having few prerequisite knowledge requirements. Crowdsourcing has benefited from Web 2.0 technologies that have enabled user-generated content and interactivity, such as wiki pages, web apps, and social media. iNaturalist and Pl@ntNET already successfully acquire data through such channels [ 37 ]. Plant image collections that acquire data through crowdsourcing and citizen science projects today often suffer from problems that prevent their effective use as training and benchmark data. First, the number of images per species in many datasets follows a long-tail distribution . Thousands of images are acquired for prominent taxa, while less prominent and rare taxa are represented by only a few and sometimes no images at all. The same fact applies to the number of images per organ per taxon. While prominent organs such as the flower of angiosperms are well populated, other organs such as fruits are often underrepresented or even missing. Second, collections contain a high degree of image and tag heterogeneity . As we elaborated in our discussion of identification challenges, the acquisition process is a main contributor of image variability. In a crowdsourcing environment, this fact is even exacerbated since contributors with very different backgrounds, motivations, and equipment contribute observations. Image collections today contain many examples not sufficient for an unambiguous identification of the displayed taxon. They may be too blurry or lack details. Collections also suffer from problems such as heterogeneous organ tags (e.g., "leaf" versus "leaves" versus "foliage"), manifold plant species synonyms used alternatively, and evolving and concurrent taxonomies. Third, nonexpert observations are more likely to contain image and metadata noise . Image noise refers to problems such as highly cluttered images, other plants depicted along with the intended species, and objects not belonging to the habitat (e.g., fingers or insects). Metadata noise refers to problems such as wrongly identified taxa, wrongly labeled organs, imprecise or incorrect location information, and incorrect observation time and date.

These problems show that crowdsourced content deserves more effort for maintaining sufficient data quality. An examination of a small number of randomly sampled images from the Pl@ntNET initiative and their taxa attributions indicated that misclassifications are in the range of 5% to 10%. In a first attempt to overcome these problems, Pl@ntNET introduced a star-based quality rating for each image and uses a community based review system for taxon annotations, whereas EOL offers a "trusted" tag for each taxon that has been identified within an image by an EOL curator. We argue that multimedia data should be based on common data standards and protocols, such as the Darwin Core [ 74 ], and that a rigorous review system and quality control workflows should be implemented for community based data assessment.

Analyzing the context of observations

We argue that it is hard to develop a plant identification approach for the worlds estimated 220,000 to 420,000 angiosperms that solely relies on image data. Additional information characterizing the context of a specimen should be taken into consideration. Today, mobile devices allow for high quality images acquired in well choreographed and adaptive procedures. Through software specifically developed for these devices, users can be guided and trained in acquiring characteristic images in situ. Given that mobile devices can geolocalize themselves, acquired data can be spatially referenced with high accuracy allowing to retrieve context information, such as topographic characteristics, climate factors, soil type, land-use type, and biotope. These factors explaining the presence or absence of species are already used to predict plant distribution and should also be considered for their identification. Temporal information, i.e., the date and the time of an observation, could allow adaptation of an identification approach to species' seasonal variations. For example, the flowering period can be of high discriminative power during an identification. Furthermore, recorded observations in public repositories (e.g., Global Biodiversity Information Facility GBIF) can provide valuable hypotheses as to which species are to expect or not to expect at a given location. Finally, additional and still-emerging sensors built into mobile devices allow for measuring environmental variables, such as temperature and air pressure. The latest cameras can acquire depth maps of specimens along with an image and provide additional characteristics of an observation and its context further supporting the identification.

From taxa-based to character-based training

In automated species identification, researchers solely aim to classify on the species level so far. An alternative approach could be classifying plant characteristics (e.g., leaf shape categories, leaf position, flower symmetry) and linking them to plant character databases such as the TRY Plant Trait Database [ 75 ] for identifying a wide range of taxa. In theory, untrained taxa could be identified by recognizing their characters. So far, it is uncertain whether automated approaches are able to generalize uniform characters from nonuniform visual information. Characters that are shared across different taxa are often differently developed per taxon, making their recognition a particular challenge.

Utilizing the treasure of herbarium specimens

Herbaria all over the world have invested large amounts of money and time in collecting samples of plants. Rather than going into the field for taking images or for collecting specimens anew, it would be considerably less expensive to use specimens of plants that have already been identified and conserved. Today, over 3,000 herbaria in 165 countries possess over 350 million specimens, collected in all parts of the world and over several centuries [ 76 ]. Currently, many are undertaking large-scale digitization projects to improve their access and to preserve delicate samples. For example, in the USA, more than 1.8 million imaged and georeferenced vascular plant specimens are digitally archived in the iDigBio portal, a nationally funded and primary aggregator of museum specimen data [ 76 ]. This activity is likely going to be expanded over the coming decade. We can look forward to a time when there will be huge repositories of taxonomic information, represented by specimen images, accessible publicly through the internet. However, very few previous researchers utilized herbaria sheets for generating a leaf image dataset [ 58 , 69 , 77 – 79 ]. On the other hand, analyzing herbaria specimens may not be suitable for training identification approaches applied in a real environment [ 69 ]. The material is dried, and thereby, original colors change drastically. Furthermore, all herbaria specimens are imaged flattened on a plain homogeneous background, altering their structure and arrangement. In conclusion, more research on the detection and extraction of characteristics from herbaria specimens is required. It is also an open research question (how to train classifiers on herbaria specimens that are applicable on fresh specimens).

Interdisciplinary collaborations

Twelve years ago, Gaston and O’Neill [ 13 ] argued that developing successful identification approaches requires novel collaborations between biologists and computer scientists with personnel that have significant knowledge of both biology and computing science. Interestingly, automated plant species identification is still mostly driven by academics specialized in computer vision, machine learning, and multimedia information retrieval. Very few studies were conducted by interdisciplinary groups of biologists and computer scientists in the previous decade [ 16 ]. Research should move towards more interdisciplinary endeavors. Biologists can apply machine learning methods more effectively with the help of computer scientists, and the latter are able to gain the required exhaustive understanding of the problem they are tacking by working with the former.

A vision of automated identification in the wild

We envision identification systems that enable users to take images of specimens in the field with a mobile device's built-in camera system, which are then analyzed by an installed application to identify the taxon or to at least get a list of candidate taxa. This approach is convenient, since the identification requires no work from the user except for taking an image and browsing through the best matching species. Furthermore, minimal expert knowledge is required, which is especially important given the ongoing shortage of skilled botanists. An accurate automated identification system also enables nonexperts with only limited botanical training and expertise to contribute to the survey of the world's biodiversity. Approaching trends and technologies, such as augmented reality, data glasses, and 3D-scans, give such applications a long-term research and application perspective. Furthermore, large-character datasets can be generated automatically (for instance, by taking measurements from thousands of specimens across a single taxon). We cannot only derive more accurate descriptions of a species and its typical character expressions, but also study the statistical distribution of each character, including variance and skew. Furthermore, image processing provides the possibility to extract not only the linear measurements typical of botanical descriptions (leaf length, leaf width, petal length, etc.), but also more sophisticated and precise descriptions such as mathematical models of leaf shapes.

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Researchers cataloging plant species are trying to decipher what makes some groups so successful

by Trinity College Dublin

Irish researchers involved in cataloging the world's plant species are hunting for answers as to what makes some groups so successful.

One of their major goals is to predict more accurately which lineages of flowering plants—some of which are of huge importance to people and to ecosystems—are at a greater risk from global climate change .

There are about 350,000 species of flowering plants on Earth, and each one is organized into a group called a genus, made up of closely related species with structural similarities. Some genera are small with just a single species (with relatively unique traits), but others are disproportionately large and contain thousands of species.

The "big plant genera" form a significant proportion of both global and Irish plant diversity, and are of disproportionate importance for both human nutrition and planetary health. Roughly 1 in 4 flowering plant species is a member of one of these.

Twenty years after the first assessment of big plant genera, research led by Trinity College Dublin and published today (May 29) in the Proceedings of the Royal Society B: Biological Sciences shows that these big genera are getting bigger and bigger as more and more species are described.

In fact, more than 10,000 species have been described in just 83 big genera since the year 2000, which is about 2.5 times the size of the total flora of Ireland.

Dr. Peter Moonlight, Assistant Professor in Trinity's School of Natural Sciences, led the just-published study. He said, "Until recently, big plant genera were seen as too large to study. But a recent revolution in methods in plant science and the development of global, collaborative networks has allowed us to update our understanding of plant evolution and global plant diversity.

"We now hope to identify common patterns across big plant genera that may explain why they are big when the other 99% of genera are small. Perhaps they have similar distributions, genetics, or morphology—we don't know yet, but this study is a key step to starting to understand this important evolutionary question.

"Big genera represent lineages of flowering plants that have been extremely successful in the game of evolution. Understanding why they became so successful may help us predict how they and other lineages on the tree of life will respond to the ongoing climate and biodiversity crises.

"Given that species in these big genera often have narrow ecological ranges in which they flourish, they may be more likely to be threatened by extinction as and when conditions change. They are a significant proportion of our global biodiversity, so perhaps we need to focus our conservation efforts most keenly on them."

Journal information: Proceedings of the Royal Society B

Provided by Trinity College Dublin

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Diversity and composition of plants species along elevational gradient: research trends

• Original Research
• Published: 29 May 2023
• Volume 32 , pages 2961–2980, ( 2023 )

Cite this article

• Ram Sharan Dani   ORCID: orcid.org/0000-0002-6206-0274 1 , 2 ,
• Pradeep Kumar Divakar   ORCID: orcid.org/0000-0002-0300-0124 3 &
• Chitra Bahadur Baniya   ORCID: orcid.org/0000-0002-8746-7601 2  

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Studies on species richness patterns along elevation gradients are fascinating and gaining attention. We compiled data from 118 studies of elevational gradients in a broad range of organisms of plant species throughout the world between 2001 and 2021 to estimate the patterns of species richness and their determinants. The present study showed that more than half (57%) of studies had found unimodal hump-shaped richness patterns (maximum diversity at the middle and lower edges), followed by a monotonic decline (26%), in different taxa of the plant. Nearly one-fifth of studies (17.5%) followed either monotonic incline, reverse hump-shaped, or non-distinct patterns. A more uniform model as a unimodal hump shape was present in extensive studies in mountainous regions. Despite the latitudinal and elevational variation, both hemispheres showed similar patterns of species richness for all plant taxa. Some taxa showed bimodality, and some others showed multi-model patterns of elevation-species richness patterns with peak diversity due to topographical and climatic factors. Upper and lower elevational ranges may provide temperature and rainfall extremes, reducing species diversity. The mid-domain effect may explain this species richness pattern. However, the current study confirmed the presence of a hump-shaped species richness pattern as a result of the size of the midpoint elevation or elevation range considered for an individual species. The higher the midpoint value, the longer the elevation range that favors a unimodal pattern. Due to inclusion of limited number of species, universal pattern of species richness along elevational gradient and their determinants could not be anticipated. We need more succinct and regional researches focusing specific taxa and delimiting factors.

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Acknowledgements

Thanks are due to the Department of Botany, Trichandra Multiple Campus, and Central Department of Botany, Tribhuvan University. The primary author appreciates and thanks to University Grant Commission (S&T 23-2076/77), Nepal for partial financial support and concerned peoples and journals for providing relevant articles.

This work was supported by University Grant Commission (S&T 23-2076/77). Author R.S.D. has received Faculties Research Grant support from University Grant Commission, Nepal.

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• Technology Feature
• Published: 16 July 2021

Exploring the diverse, intimate lives of plants

• Vivien Marx 1  

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New ways to assess plant–microbe interactions can yield unexpected paths to biodiversity.

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“I’ve invented a new algorithm to predict phenotype from genotype,” says the presenter in a cartoon about a lab meeting. “Brilliant!” “Superb!” exclaim the attendees. Says another meeting attendee, “Someone did it in plants in 1978.” In next cartoon panel, that last commenter is tossed out a window. Scientific findings in animals are sometimes “hailed as breakthroughs” when in fact they were predated by discoveries in plants, as in the case of RNA interference and immune receptors, notes 1 Nick Talbot, who directs the Sainsbury Laboratory in Norwich, UK, where he also runs a lab. Plant blindness, as the underappreciation of plants is sometimes called, is an eye-roll-inducing evergreen. A number of plant labs develop and use new approaches to assess how plants, fungi and microbes interact. There’s more than give and take between plants and microbes; dramatic, intimate strategies are at work 2 , 3 , 4 , 5 , 6 . Given that climate change demands attention, a deep understanding of these interactions offers new ways to address how to sustain ecosystems and biodiversity. “We are in a new era that puts networks in the center of our understanding of biodiversity,” says Toby Kiers, an evolutionary biologist at Vrije Universiteit Amsterdam.

Below ground

“While the spotlight has largely focused on human gut microbiomes, plant roots house one of the most diverse, yet vastly neglected, microbial communities on Earth,” says Kiers. Plants roots are covered by fungi that build mycorrhizal networks underground: long, thready webs that integrate information. The fungi deliver nutrients to plants, receive nutrients in return and connect plants to one another. Biodiversity is often seen as a static measure of species numbers. Instead, Kiers and her colleagues look at the diversity of strategies, such as those that shape the interaction of plants and microbes, especially fungi. “Fungi are incredible in this way: you can expose them to a new situation or environment and track their growth and trade strategies,” she says. “We are just starting to understand how a microbial strategy—for example whether a particular member of a community functions as a ‘mutualist’ or an ‘antagonist’—depends on the presence or absence of other microbes,” says Kiers. When a microbe is the only member of a consortium, it may be more pathogenic and colonize all of a plant or a plant community’s roots. “However, if it is competing with other microbes in a community, it may act more as a commensalist or even a mutualist.”

Fungi can be bad news for plants. Talbot studies the fungus Magnaporthe oryzae , which leads to the rice blast that brings on periodic epidemics and devastating crop loss in China, Korea, Japan and the United States. Regine Kahmann from the Max Planck Institute for Terrestrial Microbiology in Marburg, Germany, has long studied a corn pathogen, the fungus Ustilago maydis . This fungus stunts growth of the corn plants it infects but does not kill. She and her team recently presented findings 7 , 16 years in the making, showing that a complex of seven proteins is critical for the fungus’s virulence. “It really establishes, throughout the life cycle inside the plant, a very intimate relationship in which the fungus needs to make sure that this goddamn plant stays alive, because only then can the plant provide the nutrients the fungus needs,” she says.

Fungi helped ancestral plants launch their trek from Earth’s watery habitat to life on land around 600 million years ago. There are slow-burning disputes about the details of this journey from water by “little more than puddles of green tissue” onto land, as Merlin Sheldrake points out 5 . Sheldrake is a biologist, author and research associate in Kiers’s lab at Vrije University. But there’s agreement that this alliance evolved into an intimate collaboration between fungi and plant root systems. Just about all organisms on land benefit from these relations, as plants form the base of the food chain.

With mycorrhizae, plants have food providers underfoot. Fungi build a dense, branched network that can dwarf the plants’ own roots. The fungi forage for phosphorous, nitrogen, zinc and copper from soil and offer them to plants. The plants give the fungi nutrients such as sugars and lipids produced through photosynthesis. Under the microscope, in samples of roots that have been boiled in dye and squashed onto a glass slide, there’s “an intertwining,” notes Sheldrake. “Fungal hyphae fork and fuse and erupt within plants cells in a riot of branching filaments. Plant and fungus clasp one another. It’s difficult to imagine a more intimate set of poses.” Like most intimate relations, these are entangled, complex and diverse. When I asked Sheldrake how junior scientists building their trajectory might study such plant–microbial relations, he says much depends on the ecosystem and angle a researcher will want to take. But, where possible, plant researchers will want to heed “what’s happening below ground.”

Indeed, says Melanie-Jayne Howes, who leads research in phytochemistry and pharmacognosy at the Royal Botanic Gardens, Kew, there’s huge diversity underground but also in the air, the sea and all around us, which opens up a way to study diversity and seek new solutions to some of our global challenges. Advances such as DNA sequencing technologies enable this quest, she says, helping scientists uncover the vast interactions between organisms.

Herbaria bounty

Planet Earth is home to around 350,000 plant species, 325,000 of which are flowering plants. An estimated two in five plants are threatened with extinction 8 . The UN Sustainable Development Goal No. 15 calls for protecting terrestrial ecosystems and halting biodiversity loss. “However, we can’t assess how threatened a species is until we know it exists,“ note the authors of a Kew report.

Every year around 4,000 new species of plants and fungi are scientifically named for the first time, says Howes. Even as scientific progress is made with known species, “along come another few thousand for us to understand their role in ecosystems,” says Howes. One newcomer species is Artemisia baxoiensis in Tibet, a close relative of the Artemisia annua that is a source of the antimalarial drug artemisinin. Another recent discovery is Galanthus bursanus . A cousin Galanthus species is a source of galantamine, which is used to treat some dementia symptoms. Studying species new to science, she says, could reveal potential new medicines and “could help identify new optimum sources of them, which are potentially less damaging to the environment.”

Of the 350,000 known plant species, seven percent have documented medicinal uses, she says. Conservation biologists have long thought about how to widen this potential. A team from the Royal Botanic Gardens Kew, including Howes and others, and colleagues from several institutions have a proposal 9 : a comprehensive scientific study of biodiversity that could inspire, accelerate and innovate the hunt for pharmaceuticals derived from plants and fungi. The group, says Howes, represents the expertise needed for such an enterprise: chemistry, genomics, taxonomy, drug discovery, botany, mycology.

Before anyone feels ready to pack their bags to help out, reality butts in. Beyond travel limitations in a lingering pandemic, the fieldwork to obtain usable plant and fungal material takes time and money. And one cannot just take plants and fungi from nations and Indigenous peoples. Botanic gardens maintain around one-third of all known land plant species, herbaria hold around 380 million specimens and fungal collections hold around 860,000 strains. Such samples are already being used in genomic research and to inform conservation efforts, given how rich a species record across the globe and through time and space it offers. The group’s idea is to analyze select species from the world’s botanic gardens, herbaria, seed banks and fungal collections and reckon with the heritage of specimens, such as those obtained during times of colonial rule and removed without consent or involvement of local inhabitants.

Chemical analysis of these collections sounds tempting but destructive. But, says Howes, it has become possible to analyze the chemistry of herbarium specimens without damaging them. Only a few milligrams are needed to apply a method that involves liquid chromatography with photodiode array detection and high resolution mass spectrometry. For analyzing the chemical compositions of plants and fungi as they hunt for medicinal molecules, Howes also prizes technical advances such as cryo-electron microscopy and microcrystal electron diffraction, as well as advances in nuclear magnetic resonance spectroscopy and mass spectrometry imaging.

A better understanding of the evolutionary relationships between species comes from phylogenetics research, underpinned by advances in DNA sequencing, says Howes. This in turn helps predict which plants and fungi might produce medicinal or other useful compounds and sheds light on the most sustainable species sources. Insight into biosynthetic pathways in plants and fungi can open up ways of expressing these pathways in organisms such as yeast, which can boost yields and reduce a need for wild harvests of a given plant or fungus. The chemical analysis of this vast number of samples can provide insights into their chemical diversity and unlock a trove of potential medicines and, says Howes, “enable us to identify more sustainable alternatives to source medicinal or other useful chemicals, helping to prevent biodiversity loss.”

Microbial punch

Preventing biodiversity loss and addressing sustainability necessitates a better understanding of plant pathogens. What fascinates Talbot most about destructive plant pathogens such as M. oryzae is how their complex developmental process lets them breach plant cells and gain entry to plant tissue while actively suppressing plant immunity. The fungus uses specialized infection structures called appressoria deployed by many disease-causing microorganisms 10 . In the case of M. oryzae , the fungus adheres to the surface the leaf of a rice plant and, through enzymes and physical force, pushes its way into a plant cuticle, infecting the plant. Push is phrasing it mildly: it punches rice leaves with a pressure wallop of 80 atmospheres, around 40 times a typical car tire pressure. This power stems from the osmotic pressure of accumulated solutes such as glycerol. The infection spreads throughout the plant and spreads from one rice plant to the next through wind and splashing dew drops.

For researchers working on plant disease and plant immunity, microbiome knowledge changes their perspective, says Talbot. The plant microbiome includes consortia of endophytic bacteria and filamentous microbes such as fungi. There’s the mutually beneficial plant–mycorrhizal interaction and, separately, plants have sophisticated immune receptors to prevent disease-causing organisms from invading plant tissue. “The relationships are more complex, however, than previously thought,” he says. In his view, single-cell techniques used in labs to assess gene expression, proteomics and metabolomics are set to dramatically change the understanding of microbial interactions with plants. “By comparing these to existing tissue or whole organism-level datasets, we will begin to appreciate the temporal and spatial dynamics of plant–microbe interactions in unparalleled detail,” he says. “This is a really exciting prospect.”

The seven-protein complex that Kahmann and her team uncovered 7 supplies Ustilago maydis , the corn smut fungus, with its virulence. To infect corn, this fungus also uses appressoria. Rather than a punch, it presses through the plant cuticle and plant cell wall, creating an invagination through which the multi-protein complex can interact with the plant. It suppresses plant immune defenses. The team is still characterizing the stoichiometry of the protein complex, but they have found that all members are expressed during infection and the proteins appear to be co-regulated. While working on this long-term project, Kahmann remembered a paper on the first fungal translocated effector protein. She returned to it and noted similarities to the structures she and her team were seeing in their electron microscopy with immunogold labeling experiments. As it turns out, Ustilago ’s protein complex is also a virulence mediator in other fungi such as rust, which ravages wheat and other crops, too. For a small molecule screen and to avoid the years it would have taken them to carry out 200,000 maize infections, they reconstituted three of the seven complex members in yeast. The yeast were engineered to grow only in the presence of this subcomplex. Then they tested several ‘hits’. Several small molecules stopped virulence not only of Ustilago in maize but also of Uromyces fabae in beans. “Lo and behold, the compounds also work on rust fungi,” she says and bursts out laughing.

After decades of working on a basic research question—understanding how the fungus delivers its effector proteins to the plant—she is now talking to companies about potential licensing agreements for some of the compounds that battle rust. When scientists set out on their trajectory, she advises they “pick something they are really deeply excited about, because otherwise they will not have the strength to go through these valleys where nothing works and where they are simply stuck for a long time.” Overall, she sees a trend in basic and applied plant biology to explore how to tweak a plant’s microbiome to shape plant health. That’s a dive into plant-based decision-making.

Plant strategies

With sequenced plant genomes, researchers usually take a comparative or evolutionary analytical approach. Kiers and her colleagues study organismal decision-making and build experimental setups to enable this. They might force partners to cheat in a trade deal and study how the other partner reacts. “Of course, there is no cognition, no brain involved,” says Kiers. The trade in question is the marketplace of mycorrhizal fungi and plants.

This trade is not one of human economy, but the activities ring familiar: there is exchange, selling and buying, hoarding, begging, borrowing, cheating. Plants supply more carbohydrates to fungal partners that provide more phosphorus to them and vice versa, she says.

Natural selection shapes the microbial strategies that are an overlooked component of biodiversity, she says. Capturing these events is hard. They happen underground and there are loads of confounders. Plants trade only in carbon with fungal partners; fungi can trade multiple resources, such as nitrogen and trace elements. In the lab “it takes achingly precise experiments and careful manipulations to quantify strategies,” she says. The benefit is they can learn how microbes integrate a complex array of chemical, physical and environmental stimuli, which gives insight into the rich social lives of microbes and strategy diversity.

In the lab, Kiers and team capture high-resolution time-lapse videos of fungal trade routes. They developed nanoprobes to track resource exchange: they tag a rock phosphate —hydroxyapatite—with quantum dots. When exposed to ultraviolet light, the tagged nutrients fluoresce. This lets the researchers capture a time series of fluorescent nanoparticles in three colors. Using confocal microscopy they can quantify, for example, how much phosphorus is being stored in hyphae, the fungi’s long branches. With this approach, she says, “we found fungal networks dynamically hoard and transfer resources depending on the nutrient status of the plants connected to network.” The researchers want to link plant productivity to fungal network structure and have begun scaling up imaging to generate three-dimensional images of intact fungal networks.

To measure intrahyphal flow and capture the overall physical architecture of fungal networks as they grow on root cultures in vitro, the team has built a high-throughput imaging and high-resolution video capture platform with Loreto Oyarte Gálvez and Tom Shimizu from the AMOLF physics institute in Amsterdam. The system is a remote-controlled imaging robot that delivers time-resolved datasets from more than 30 fungal networks at the same time. Its optical positioning system helps them collect real-time videos of nutrient flows at high resolution at known physical coordinates across the network. Then they can switch to lower resolution to collect data on fungal topology. Across the fungal network’s different sectors, the videos show flow taking place at different speeds. And it runs in different directions. Along with Shimizu and Princeton University researchers Howard Stone and Philippe Bourrianne, who study microscale fluid dynamics, they built models of fluid flows at different positions in the network. “Given that these fungal flows are responsible for massive amounts of nutrient transfer across ecosystems and can store huge amounts of carbon, we are trying to link underground flows to aboveground ecosystem dynamics, including how flows influence biodiversity,” says Kiers.

In her view, capturing nutrient flow shapes understanding of the biodiversity of microbial strategies. “Our idea is that by oscillating its internal flow, a fungus can compare its nutrient status across and space and time, such that flow can be subsequently redirected,” she says. “Because we can now start to track nutrient flows, we can ask whether oscillations help the fungus to regulate, or actively ‘calculate’, where and when to trade.” Fungi use electrical activity to coordinate their responses to stimuli, a bit like neural networks in animals. As the team explores how a fungus integrates information across an immense number of hyphal tips while connected to multiple plants and extended over tens of meters, they want to also focus on aspects such as voltage changes across cell membranes that help to coordinate such network-level responses. They plan to use neuroscience tools such as microelectrode arrays to quantify electrical activity in symbiotic networks and stimuli-related changes.

Networked capture

“Species interactions are context dependent,” says Kyoto University researcher Hirokazu Toju, who collaborates with Kiers 3 . Changes in biotic and abiotic environments can alter the strength and direction of species interactions through time. To describe biological systems, one might apply standard approaches, but he finds mathematical approaches more promising that focus on time-series changes of species’ interactions in terms of both direction and strength. Theory-based approaches such as empirical dynamic modeling help to provide an understanding of the architecture of species interaction networks and how it changes through time and with environmental conditions.

Machine learning approaches can help to predict growth of fungal networks under different conditions. Kiers and her colleagues use machine learning to analyze the terabytes of data the imaging robot delivers every month. They extract the network skeleton at every node, she says, and follow hundreds of thousands of nodes through time. They can manipulate the network and follow that through those many nodes. Such experiments let the team test big ideas about network formation in nature, under what conditions networks are robust to climate change, how they react to low or high nutrients, and what conditions select for higher efficiencies. “At its core, our research will tell us how microbial strategies are expected to shift under climate change,” says Kiers.

Machine learning can be powerful not only for finding keystone species and but also for designing core microbiomes that can support designed systems in agriculture, says Toju, but one needs sufficient microbiome data to distinguish “benign” from “unfavorable” states of microbial communities. To go beyond case-by-case inference or pattern recognition, “theory-based approaches integrating ecology, microbiology, mathematics, and physics are necessary to reveal basic rules underlying ecological community dynamics.” Some labs build and use synthetic microbial communities, but in Toju’s view, there’s no guarantee these work efficiently if the focus is on functions of respective microbial species. For example, in the case of two plant-growth-promoting microbial species, if these two microbes have different sets of genes, “we may expect that co-inoculation of the two microbes will be highly beneficial to host plants,” he says. “However, this is not necessarily the case.” Co-inoculation effects often diverge greatly from expected effects based on single-inoculation data. Such results mean that, when working on synthetic microbial community approaches, researchers need information on functions of each microbial species and information about mechanisms at the multi-species level, such as the species-interaction network architecture, he says. Given climate change and the emergence of new types of pests and pathogens, scientists can take such knowledge and apply it to support the “entangled webs of interactions” in agroecosystems, where plants, insects and microorganisms including bacteria, archaea, protists and fungi interact, says Toju. He works on plant microbiomes, the human gut microbiome and invertebrate-associated microbiomes. Animals and plants are readily observed in nature and in labs, but ecological studies can reach another level now that high-throughput sequencing datasets reveal the compositions of microbial communities. Within microbial systems, there tends to be redundancy, with countless species fulfilling similar functions, he says. Plant and animal communities have less redundancy, and their dynamics are more likely to be affected by the particular traits of constituent species. It’s why he expects the principles of ecological processes to be uncovered more clearly in species-rich microbial systems than with only animal or only plant communities. This view led him, around a decade ago, to recast his research focus from animals and plants to microbial communities and those involving both microbes and macro-organismal hosts. “With rich, reliable datasets, ecological studies on microbiomes will dramatically promote understandings of basic rules in animal and plant community dynamics,” says Toju.

In ecological research, “microbiomes have another advantage,” he says. They regenerate much faster than animals or plants, which is a trait that makes them ideal for understanding the feedback loops between evolutionary and ecological processes, he says. By targeting microbiomes, one can explore the dynamics of qualitative changes, evolutionary genetic change and quantitative changes that affect the number of individuals or cell numbers 11 .

Such theoretical and informatics frameworks are applicable to diverse types of microbial systems, such as gut microbiomes and plant-associated microbiomes, he says. Toju and colleagues have developed a general algorithm for designing “functional core microbiomes” based on information regarding constituent species’ functions—the genomes—and interspecific interaction networks. “I applied the algorithm to three types of microbiomes: namely, mouse gut, crop-plant-associated, and experimental aquatic microbiomes,” he says. In addition to the core microbiome design algorithms, he and his colleagues also apply mathematical tools used in nonlinear dynamic analysis to highlight key species interactions that have profound effects on the dynamics of entire microbiomes, he says. They explore how to predict drastic changes in microbial community composition such as those involved in human gut dysbiosis. Common ecological theories and community-design algorithms are applicable to diverse microbial communities, and the ability to control microbiomes will vary. He thinks that controlling plant-associated microbiomes will be easier than controlling human gut microbiomes. What matters in such projects is what he calls “priority effects” in species assembly.

Community thinking

In ecological communities of microbes and macro-organisms, says Toju, species that colonize new habitats earlier than others have huge advantages since they can physically block following colonizers. They may defend their habitats by producing antibiotics and can stimulate host plant–animal immune responses, which would indirectly hinder colonization by others. Such historical effects are expected to impede shifts among “alternative stable states” of species compositions.

While it remains challenging to change gut microbiomes from community compositions that cause disease to ones associated with healthy guts, plant seeds are nearly devoid of bacteria and fungi. He sees that as an indication that scientists will be able to control microbiome dynamics and functions in agriculture, says Toju. One approach he explores when he designs core sets of microbes to promote microbiome assembly beneficial to plant hosts is that he introduces those microbiomes into seeds or seedlings. The goal is to promote plant growth and increase plants’ resistance to biotic and abiotic stresses.

Perhaps the same or similar sets of core microbiomes can be applied to diverse crop plant species. That’s possible because the diverse mycorrhizal and endophytic fungi have a broad range of hosts—many of them can form a symbiosis with almost all land plant taxa. In this manner one could alter plant resistance relative to biotic and abiotic environments without modifying plants’ own genomes, he says. Designed microbiomes can be a kind of defense system as a quick and low-cost prevention or treatment related to pathogen outbreaks. And they can complement existing plant breeding efforts. In his view, research efforts on plant-associated and animal-associated microbiomes have much to offer one another and they enhance our understanding of systems consisting of multiple species, says Toju. Species are embedded in complex networks of interactions with other species, and these shape many of their strategies, says Kiers. From nested symbioses with plants, in which plants are colonized by fungi and the fungi in turn are colonized by endosymbiotic bacteria, “biodiversity is best described as layers of interacting strategies.” Given how intense and fast climate change is proceeding, we need to understand how climate shifts will drive changes in these strategies, she says. “What interactions will break down, what networks will become more robust?” The aim should be to conserve network interactions, not just the species. “Microbes are key to our fight against climate change,” says Kiers. They adapt and are faster and more efficient at it than anything humans can do. “What has been missing on the conservation agenda are the organisms we can’t see, and all the fantastic strategies they have evolved.”

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What makes some plant groups so successful?

Irish researchers involved in cataloguing the world's plant species are hunting for answers as to what makes some groups of plants so successful. One of their major goals is to predict more accurately which lineages of flowering plants -- some of which are of huge importance to people and to ecosystems -- are at a greater risk from global climate change.

There are around 350,000 species of flowering plants on Earth and each one is organised into a group called a genus, made up of closely related species with structural similarities. Some genera are small with just a single species (with relatively unique traits), but others are disproportionately large and contain thousands of species.

The "big plant genera" form a significant proportion of both global and Irish plant diversity, and are of disproportionate importance for both human nutrition and planetary health. Roughly one in four flowering plant species is a member of one of these.

Twenty years after the first assessment of big plant genera, research led by Trinity College Dublin and published today in the Proceedings of the Royal Society shows that these big genera are getting bigger and bigger as more and more species are described.

In fact, more than 10,000 species have been described in just 83 big genera since the year 2000, which is about 2.5 times the size of the total flora of Ireland.

Dr Peter Moonlight, Assistant Professor in Trinity's School of Natural Sciences, led the just-published study. He said: "Until recently, big plant genera were seen as too large to study. But a recent revolution in methods in plant science and the development of global, collaborative networks has allowed us to update our understanding of plant evolution and global plant diversity.

"We now hope to identify common patterns across big plant genera that may explain why they are big when the other 99% of genera are small. Perhaps they have similar distributions, genetics, or morphology -- we don't know yet, but this study is a key step to starting to understand this important evolutionary question.

"Big genera represent lineages of flowering plants that have been extremely successful in the game of evolution. Understanding why they became so successful may help us predict how they and other lineages on the tree of life will respond to the ongoing climate and biodiversity crises. Given that species in these big genera often have narrow ecological ranges in which they flourish, they may be more likely to be threatened by extinction as and when conditions change. They are a significant proportion of our global biodiversity, so perhaps we need to focus our conservation efforts most keenly on them."

In collaboration with his co-authors from the Natural History Museum in London, Royal Botanic Garden Edinburgh, and Rio de Janeiro Botanical Garden, Dr Moonlight will be leading a symposium at the International Botanical Congress in Madrid this summer.

• Endangered Plants
• New Species
• Exotic Species
• Environmental Awareness
• Global Warming
• Flowering plant
• Plant sexuality
• Hydroponics
• Climate change mitigation
• Global climate model

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Materials provided by Trinity College Dublin . Note: Content may be edited for style and length.

Journal Reference :

• Peter W. Moonlight, Ludwig Baldaszti, Domingos Cardoso, Alan Elliott, Tiina Särkinen, Sandra Knapp. Twenty years of big plant genera . Proceedings of the Royal Society B: Biological Sciences , 2024; 291 (2023) DOI: 10.1098/rspb.2024.0702

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Research on ant plants in the Peruvian jungle: how a doctoral researcher saved her research project during the pandemic

Andrea Müller from the Max Planck Institute for Chemical Ecology receives the Beutenberg Campus Award for the best doctoral thesis.

Andrea Müller conducting field research on ant plants in the Peruvian Amazon rainforest. In experiments, she showed that the ant plant Tococa quadrialata does not rely solely on symbiotic ants, which serve as bodyguards for the plant, to defend itself against enemies. When attacked by predators, it can also activate its own defense mechanisms. 

Andrea Müller's great curiosity and desire to understand how the world works led her to science. "I found it super exciting what organisms, and plants in particular, can do and how they interact with their environment. I wanted to find out more about this," she says, describing her enthusiasm for research. The ant plant Tococa quadrialata , which grows in a close symbiotic association with ants, became the subject of her doctorate. Both symbiotic partners, plant and ant, benefit from living together: While ants defend the plant against attackers, the plant provides the small insects with an accommodation and food. Andrea Müller wanted to find out whether these plants also use their own defense strategies that are independent of the ants, or whether these have become redundant due to the symbiosis with the ants. She investigated this question not only in the lab, but also directly where the Tococa plants grow in their natural habitat: in the jungle of the Amazon lowlands in south-eastern Peru. Solving logistical problems, such as bringing liquid nitrogen for freezing and transporting plant samples back from the rainforest, were the first hurdles that had to be overcome. However, the young doctoral researcher faced the biggest challenge when the Covid-19 pandemic suddenly turned all plans upside down: "I had just flown back to Peru when the lockdown made all further research activities on site impossible. I couldn't even get my samples from the lab. I waited six weeks in Peru for a return flight, while research was out of the question. The worst thing for me was that my entire project was suddenly on the brink."

Overgrown jungle paths during the pandemic.

© Andrea Müller

As soon as the borders reopened in 2021, Andrea Müller, whose research stay was funded by the German Academic Exchange Service (DAAD), returned to Peru. However, she was still in the midst of a pandemic, which made her research trip a real adventure. "The hostel where I normally stayed was closed. The paths in the jungle that led to my plants were weathered and overgrown," she recalls.

In the end, all the efforts paid off. Andrea Müller was able to conduct further experiments in the Peruvian jungle and show in her studies on the defense strategy of Tococa plants that ant plants benefit twice from the symbiosis with ants: through the protection offered by the ants and through the food waste and excretions of ants, which have a positive effect on the plant's metabolism. Despite the symbiosis with the ants, which have taken on the role of the plants’ bodyguards, the studied species Tococa quadrialata has not completely lost the ability to activate its own defense mechanisms in the course of evolution, even if they are less effective than the ants' protection.

Andrea Müller with a tococa plant.

Andrea Müller discovered that two special plant defense substances are frequently found in the leaves of Tococa plants eaten by caterpillars: Phenylacetaldoxime (PAOx) and the corresponding glucoside (PAOx-Glc). She not only described the previously unknown PAOx-Glc for the first time and elucidated its biosynthetic pathway, she was also able to show its biological function as a defense substance, which surprisingly also occurs in many other plant species.

Last year, Andrea Müller successfully completed her doctorate. Her thesis on the "Physiological and phytochemical aspects of ant-plant mutualism" was awarded "summa cum laude".  On May 30, 2024, she will also be awarded the Beutenberg Campus Award for the best doctoral thesis. Under the motto "Life Sciences meets Physics", this award particularly recognizes the use and establishment of physical measurement methods in the life sciences. "Andrea developed sensitive and reliable protocols for the collection of volatile organic compounds in the rainforest. To do this, she modified the chamber design and airflow of the odor collection devices to improve the recovery of volatiles. She also worked on a new, non-invasive detection system for plant defense reactions that uses specific fluorescent near-infrared polyphenol sensors. This technique enables the rapid quantification of plant defense substances and could be useful for future studies on the analysis of plant defense mechanisms," says her supervisor Axel Mithöfer, summarizing Andreas Müller's scientific achievement.

The young scientist has now left basic research behind and is working at the interface between production and quality assurance at a biotech company in Laupheim in Upper Swabia. She is delighted with the award, which brings her back to Jena, but also emphasizes: "Without all my colleagues here and in Peru, I wouldn't have been able to do all this. My project is an excellent example of what is possible with good interdisciplinary and international cooperation."

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Wastewater from Tyson meat processing plants is polluting U.S. waterways, report says

John Yang John Yang

Harry Zahn Harry Zahn

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Tyson Foods is one of the world’s biggest meat and poultry producers. According to the Union of Concerned Scientists, it’s also a major polluter in the United States. A new report from the group says Tyson plants dumped more than 371 million pounds of pollutants into U.S. waterways between 2018 and 2022. John Yang speaks with UCS research director Stacy Woods about the report’s findings.

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Tyson Foods is one of the world's biggest meat and poultry producers and according to the Union of Concerned Scientists, it's also a major polluter in the United States. A report entitled waist deep says that in the five years between 2018 and 2022, Tyson plants dumped more than 371 million pounds of pollutants into U.S. waterways. More than half of that was in three states, Nebraska, Illinois and Missouri.

In response, Tyson defends its wastewater treatment program, which it says complies with regulations.

Stacy Woods is Research Director for the Union of Concerned Scientists and one of the authors of the report. Stacy, what pollutants are we talking about here? And what effect do they have on the environment on wildlife and on humans.

Stacy Woods, Union Of Concerned Scientists:

Our report found that Tyson Foods dumped over 25 different pollutants into waterways in 17 states. This pollution included nitrogen, including ammonia, and phosphorus. And we're particularly concerned about those because when there's too much nitrogen and phosphorus in our waterways, it can cause harmful algal blooms that can kill fish and other aquatic wildlife. And also, when people live close to these harmful algal blooms, they can experience things like asthma attacks, and bronchitis.

And these are all byproducts of meat processing?

Stacy Woods:

It comes from the wastewater in the meat processing. Wastewater is produced in these meat processing plants, when folks working in these plants rinse off dead animal carcasses when they clean meat products, and when they rinse down these industrial equipment, and so that wastewater contains things like blood and feces and in bacteria like E. coli.

So there's a lot of wastewater that is produced when meat processing plants create meat and poultry products. The Tyson Foods plants that we looked at produced over 87 billion gallons of wastewater in those five years.

I want to read you part of a statement that a Tyson spokesperson gave us about your report. It says Tyson Foods uses a robust management system to mitigate environmental risks and impact. This report does not acknowledge our ongoing compliance with EPA regulations and certification by the Water Alliance where our strong water management practices. What do you say to that?

The most shocking thing that we found in our investigation was that when Tyson Foods dumped these millions and millions of pounds of pollutants directly into our waterways, they were pretty much following the rules.

Now, there were a few instances where a couple of plants exceeded the rules for a few pollutants some of the time. But by and large, they were in fact following the rules. And we think that's a problem. Those rules need to change.

Luckily, the EPA is actually right now working on updating those rules. The Union of Concerned Scientists and other groups, along with other citizens submitted comments in support of strengthening those rules so that these industrial polluters, like Tyson Foods, will be forced to clean up their act and stop dumping so much pollution directly into our waterways.

Why is it that the these levels would sound high when you describe them are within the APS regulations?

That's a great question. And I can't speak to the reasons why EPA has the regulation set right now. But I can tell you is that the regulations that are in effect right now and during our study period, were enacted over 20 years ago.

In that time, there's been tremendous gains and technologies that can allow these polluters to clean up their wastewater before releasing it out into our environment, which is why it's definitely time for updated regulations that will reduce the amount of water pollution that's allowed to come from these kinds of plants.

The report said that Tyson operates 123 plants in the United States, but that the data you analyze, only came from 41. So could this actually be low?

Yes, our estimate is indeed an under estimate. And the reason is that the current regulations only apply to a small sliver of the meat and poultry plants in the United States.

The EPA estimates that under the current regulations, only about 500 of the roughly 5,000 meat and poultry plants are required to report their water pollution. So, for Tyson Foods, we had reportable water pollution for only 41 of their 123 plants. And we would assume that the remaining plants are also creating wastewater.

Is this a problem specifically to Tyson? Or is this a problem industry wide for meat and chicken processing?

Stacy Woods The meat and chicken processing industry is a known water polluter across the United States. This is not specific to Tyson Foods, but we decided to investigate Tyson Foods because they are one of the largest meat and poultry processors in the US. So we anticipate that their influence in the overall industry pollution would be pretty high.

Stacy Woods of the Union of Concerned Scientists. Thank you very much.

Thank you so much for this opportunity to talk about our report.

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John Yang is the anchor of PBS News Weekend and a correspondent for the PBS NewsHour. He covered the first year of the Trump administration and is currently reporting on major national issues from Washington, DC, and across the country.

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Invasive alien plant species: Their impact on environment, ecosystem services and human health

Prabhat kumar rai.

a Phyto-technologies and Invasion Lab, Department of Environmental Science, School of Earth Sciences and Natural Resources Management, Mizoram University, Aizawl, Mizoram, India

b Ecosystem Analysis Lab, Centre of Advanced Study in Botany, Banaras Hindu University (B.H.U.), Varanasi, 221005, India

Graphical abstract

Ecological perturbations caused by biotic invasion have been identified as a growing threat to global sustainability. Invasive alien plants species (IAPS) are considered to be one of the major drivers of biodiversity loss and thereby altering the ecosystem services and socio-economic conditions through different mechanisms. Although the ecological impacts of IAPS are well documented, there is a dearth of studies regarding their economic quantification, livelihood considerations, biotechnological prospects (phytoremediation, bioenergy, phyto-synthesis of nanoparticles, biomedical, industrial applications etc.) and human health risk assessments of IAPS. In this context, the current panoramic review aimed to investigate the environmental, socio-ecological and health risks posed by IAPS as well as the compounded impact of IAPS with habitat fragmentation, climate and land use changes. To this end, the need of an integrated trans-disciplinary research is emphasized for the sustainable management of IAPS. The management prospects can be further strengthened through their linkage with geo-spatial technologies (remote sensing and GIS) by mapping and monitoring the IAPS spread. Further, the horizon of IAPS management is expanded to ecological indicator perspectives of IAPS, biosecurity, and risk assessment protocols with critical discussion. Moreover, positive as well as negative implications of the IAPS on environment, health, ecosystem services and socio-economy (livelihood) are listed so that a judicious policy framework could be developed for the IAPS management in order to mitigate the human health implications.

1. Introduction

Biodiversity is inextricably linked with the ecosystem services and human welfare. Globally, biodiversity is known to produce food and ensure nutritional security ( Aerts et al., 2018 , Stone et al., 2018 , Jones, 2019 ), provide herbal medicines that cure diseases like cardiovascular, pulmonary, digestive, dermal and even dreaded cancer ( Rai and Lalramnghinghlova, 2011 , Aerts et al., 2018 ) and safeguard the environment/ecosystem services ( Kannan et al., 2016 , Jones and McDermott, 2018 ). Phyto-synthesis of nanoparticles is another facet of biodiversity, which has recently revolutionized biomedical, sanitation, hygiene, food safety, environment, energy and agriculture sectors ( Rai et al., 2018 ). These positive implications of biodiversity are essential for achieving the sustainable development goals (SDGs).

However, for the past several decades, the invasive alien plant species (IAPS) have posed severe threats to the local biodiversity, ecosystem services, environmental quality ( Pejchar and Mooney, 2009 , Kueffer, 2017 , Jones and McDermott, 2018 , Bartz and Kowarik, 2019 ) and human health ( Pysˇek and Richardson, 2010 , Stone et al., 2018 , Jones and McDermott, 2018 , Jones, 2019 ). To this end, United Nation’s (UN) Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES) projected that about one fifth of the Earth’s surface, including the global biodiversity hotspots, are at risk due to biotic invaders ( IPBES, 2019 ). In this context, high income countries recorded 30 times greater numbers of IAPS, in comparison to the low income countries ( Seebens et al., 2018 ). Therefore, IAPS hotspots are often confined to high income countries of European Union, Australasia and North America than Asia Pacific/African regions ( Seebens et al., 2018 , IPBES, 2019 ). This trend can be attributed to higher trade and transport activities in countries with high per capita income.

Emergence of new IAPS in novel ecosystems can impose threats to the environment and human health ( Seebens et al., 2018 ). Advancement in the biomedical sector, to safeguard human health risks, is being impeded by the recent global environmental changes, especially, land-use/climate change-induced biotic invasions of flora and fauna ( Ebi et al., 2017 ).

Habitat destruction, environmental pollution and anthropogenic global changes (e.g. climate change) are other threats to native biodiversity, besides, invasion. Interestingly, it has been a matter of debate among invasion ecologists whether IAPS are the first/second-most severe threat (only 27.3% are in favour of this) or they should be ranked further below ( Young and Larson, 2011 ). It is worth mentioning here, that these rankings in relation to biodiversity threats/extinctions may be region-specific. However, most common view among the invasion ecologists in this respect is that this global problem of IAPS is being accelerated by the anthropogenic perturbations ( Young and Larson, 2011 ). Notably, in this respect Global Assessment Report on Biodiversity and Ecosystem Services of UN, recently declared the IAPS/alien invaders as major driver of biodiversity loss ( IPBES, 2019 )

Human mediated transport, migration, and commerce are continuing to disperse an ever-increasing array of IAPS across previously insurmountable environmental barriers such as fresh and marine aquatic ecosystems, mountain ranges and even inhospitable climate zones ( Rai, 2015 , Kueffer, 2017 ) . Modern intensive agriculture managed for food security, however, has remarkably increased the spread of IAPS ( Mack et al., 2000 , Gilbert and Levine, 2013 , Pimentel et al., 2005 , Simberloff et al., 2013 , Dudley et al., 2014 , Rai et al., 2018 ).

IAPS are not only linked with the environment, but also, to the human well-being, often in negative and sometimes, positive manner. These evaluations are needed in investigating the IAPS impacts in socio-ecological and socio-economic perspectives. Invasion induced biodiversity loss, drastically alters the meteorology/temperature and other climatic variables, which, indirectly exert the negative public health impacts ( Jones, 2019 ). The ornamental and multi-purpose IAPS, which were deliberately or accidentally introduced subsequently spread to impose adverse effects on human and the ecosystem health. In brief, IAPS transmogrifies the global environment and the human health, in a highly intricate fashion, which, must be elucidated, to formulate integrated eco-restoration strategies ( Rai, 2015 ).

Native plants can act as sink for air pollutants and contribute significantly to carbon sequestration ( Pejchar and Mooney, 2009 , Shackleton et al., 2019. ). Therefore, loss of native plant diversity through invasive plant pathogens may indirectly affect human health through perturbations in the environmental quality ( Jones and McDermott, 2018 ). Interestingly, it has been demonstrated that certain IAPS may act as ecological indicators of environmental pollution ( Rai, 2016 ). For example, spread of a plant pathogen, the invasive emerald ash borer (EAB), resulted in massive destruction of dominant ash trees in the United States (US), which otherwise acted as an effective sink for air pollutants ( Jones and McDermott, 2018 ). Exposure to increased concentrations of hazardous air pollutants resulted in cardiovascular/pulmonary problems in human populations. Extreme pollution stress is reported to result in mortality and economic loss is reported ( Jones and McDermott, 2018 ).

Success of the IAPS is not decided by merely a single environmental factor and ecological attribute. Here, it is worth mentioning that the plant invasion, anthropogenic disturbances, climate change, biodiversity and human health may have complex and intricate relationship ( Rai and Kim, 2019 ). Thus, invasion ecology is now increasingly being considered as trans-disciplinary subject, intimately linked with the global change biology, land-use change, health science, restoration and conservation biology ( Pysˇek and Richardson, 2010 , Heshmati et al., 2019 ).

Adverse impacts of the IAPS on human health have been elucidated elsewhere ( Pysˇek and Richardson, 2010 ). The human health impacts of invasion are further exacerbated by the rapid spread of vector-borne pathogens ( Clow et al., 2017 , Schindler, et al., 2018 ). Further, IAPS tends to reduce the global agriculture productivity, by acting as weeds, besides, hampering the forest diversity ( Haines, 2016 ). Interestingly, SDGs, which address sustainable agriculture, water sanitation, food safety/security, poverty, human well-being/health etc., are adversely affected through concerted impact of current environmental disturbances ( Haines, 2016 ), linked with the invasion biology ( Pysˇek and Richardson, 2010 ). These environmental issues are global in nature and adversely affect public health (toxic chemicals, allergic, and the vectors of emerging diseases), this has led to the term ‘planetary health’ ( Haines, 2016 , Ebi et al., 2017 ) ( Fig. 1 ).

An interrelation framework, among anthropogenic factors/global environmental changes (biotic invasions, habitat destruction/fragmentation, land-use/climate change, environmental pollution), impacting socio-economy/livelihood and human health.

Spread of the IAPS at global scale, particularly in disturbed areas such as landfills/dumps (which may form invasive plant epicentres), can profoundly affect human health through their pollen and toxins ( Plaza et al., 2018 ). It has been noted that global terrestrial as well as aquatic environments are being invaded by numerous IAPS ( Table 1 , Table 2 ). Therefore, unravelling the mechanisms, that make the replacement of indigenous climax communities originating through natural succession by IAPS-dominated communities ( Blumenthal, 2006 , Rai, 2015 , Zuppinger et al., 2016 , Chen et al., 2017 , Slingsby et al., 2017 ), is of paramount importance.

Invasive alien plant species (IAPS) documented globally for their adverse environmental as well as socio-economic impacts.

Invasive alien plant species (IAPS) from terrestrial and aquatic environment and their impacts on human health [on direct exposure, as pathogens, as vector, as toxins (chemic release/biological toxins), through contamination of edible foodstuffs, through their morphological/vegetation attributes, allergens and indirect implications].

Global biotic invasions are also among the prime agenda of Convention on Biological Diversity (CBD) for the control of IAPS impacts on the ecosystems and public health, which is further emphasized through Biosafety and Cartagena Protocol ( Pysˇek and Richardson, 2010 ). Earth Summit in Rio de Janeiro, 1992 has further recognised IAPS in the forestry/ agroforestry systems as one of the key causes of the harm to global environment, biodiversity and human health. IPBES (2019) in their deliverables 3(b)(ii) explicitly listed the threats of IAPS to the global biodiversity, ecosystem services, human health and livelihoods.

The IAPS issue should be addressed as an integrated/trans-disciplinary approach, bridging together the biology, bio-medical science, and socio-ecological/economy prospects on common platform ( Hulme, 2017 , Ebi et al., 2017 , Vaz et al., 2017 ). In light of the above, the present paper reviews the progress made during the recent past on the ecological mechanisms of IAPS and their multi-faceted impacts on environmental/ecosystem services and human health. These issues are raised in an environmental management and human well-being perspective with the sustainability paradigm. Further, the quest of phyto-technological implications, associated with IAPS biomass management, further give an impetus to mitigating the associated human health hazards. Therefore, understanding the invasion ecology of such species is of paramount important for developing suitable management strategies.

In this review, the relevant cited literatures covered the progress made in IAPS science over the past couple of decades. However, the majority of cited articles covered the recent progress in invasion science, but particularly from 2012 to 2019. Exhaustive literature survey was done to provide the readers an explicit and panoramic view of the IAPS biology. In this context, we used the search engine like SCOPUS, Web of Science, Science Direct and Google Scholar for the exhaustive literature survey. We collected 650 articles (covering academic/grey literatures) on multifaceted impacts of the IAPS on environment, economy and health. Since there is paucity of critical evaluation on the health impacts of IAPS, the articles covering bio-medical aspects were analysed in-depth. To this end, we used the key words “Plant invasion human health OR well-being”, “Invasive alien plants health risks”, “Alien OR biotic invaders human health”, “Invasive alien plants health impact quantification” in the said search engines. We varied the several IAPS terms as keywords e.g. “invasive”, “exotic”, “alien”, “non-native”, and “introduced” to widely address the state of the art in the plant invasion science. In this respect, we initially confined to the abstract of the articles and if found relevant to the theme, read thoroughly/critically and then extracted the scientific information, with citations in present review. To provide a concise, but explicit overview of the subject, other search terms included “plant invasion economic impacts OR quantification’, “Plant invasion socio-economy OR livelihood impact”, “Invasive plants ecological OR bio indicators” ‘‘plant invasion management’’, ‘’Role of remote sensing OR GIS in plant invasion mapping OR management’’. To study the IAPS interactions keywords comprised “Plant invasion climate OR land-use change”, “Plant invasion habitat fragmentation OR destruction”. In addition, we also accessed the important scientific databases available on the IAPS for extracting the useful information. We also tried to cover the recent efforts carried out at international forum to address the IAPS issue for environmental restoration.

2. How plant invasions occur? Associated terms and mechanisms

In literature, there are several conceptually different ambiguous terms used in context of invasion ecology. Therefore, before starting the discussions on the causes of IAPS success, we will briefly introduce the terms, associated with the invasion ecology.

2.1. Basic terms in the invasion science

‘ Invasion ecology ’ is actually the study of human-mediated introduction of IAPS to regions/area outside their potential native range through transport, establishment, colonization and landscape spread ( Fig. 2 ). In addition to anthropogenic IAPS introductions, several other alien species have been introduced by other ways. For example, Limnocharis flava in Kerala is believed to be introduced by ocean currents ( Abhilash et al., 2008 ). Similarly, the Coconut has been dispersed to various places of the world, even islands, through waves and ocean currents ( Harries and Clement, 2014 ). Thus, the horizon of IAPS ecology is gradually expanding.

The basic mechanisms/hypothesis associated with different Invasive alien plant species (IAPS), corresponding to their spread in varying environment and landscapes.

A frequently cited term i.e. ‘ alien/non-native/exotic/introduced species’ also refers to such species, which exist in a new region (away from the place of origin). Anthropogenic factors enable IAPS to overcome different bio-geographical barriers. Interestingly, these species may or may not be invasive, depending on their status along the naturalization-invasion continuum. Nevertheless, ‘invasive alien plant species (IAPS) are the alien species of plants, with efficient ‘reproductive strategies (i.e. both vegetative and through seed) enabling them to sustain self-replacing populations, capable to produce offspring, even in the remote areas. IAPS can significantly affect the native plants of the invaded region, which are those species which have evolved in a particular area without the human intervention and thriving by natural means.

2.2. Mechanisms involved in the IAPS spread

Anthropogenic disturbances have not only led to the global expansion of IAPS, but also have drastically shaped the invasion mechanisms ( Kueffer, 2017 ). The basic mechanisms behind the IAPS success and impacts should be adequately understood for the ecological/health risk assessment of IAPS ( Stohlgren and Schnase, 2006 ). The reason of success of the IAPS in diverse environments, is complex, and needs to be investigated in the context of specific IAPS. In this respect, species specific mechanisms for elucidating the spread of alien plants is necessary as they show differential invasive potential in tune with their ecosystem attributes ( Ehrenfeld, 2008 ).

Several hypotheses, like enemy release (ERH), novel weapon (NWH) and empty niche (EN), have been proposed to explain the invasion of IAPS in new habitats ( Fig. 2 ). But, any single hypothesis is insufficient to explain the invasion of the IAPS. Sharma et al. (2005) are of the view that the most applicable hypothesis is always IAPS specific. In this respect, ERH hypothesis posits that some IAPS become more successful in the novel habitats when they are away from natural enemies like pathogens and herbivores found in their native habitats ( Blumenthal, 2006 , Rai, 2015 ). For example, the seeds of Impatiens glandulifera , in newly invaded regions are free from fungal pathogens ( Najberek et al., 2018 ).

Allelopathy is basically the ecological process in which biotic interference occurs through bio-active molecules ( Singh et al., 2014a , Singh et al., 2014b ). Allelochemicals are interestingly identified as novel weapon (NW), which dramatically suppress the native species and pave the way for IAPS colonisation in the new habitat ( Pinzone et al., 2018 ). Allelochemicals are basically secondary metabolites; mostly phenolics, terpenoids and sesquiterpenes that affect native plant species adversely ( Singh et al., 2014a , Singh et al., 2014b , Uddin and Robinson, 2017 , Pinzone, et al., 2018 ).

Among these allelochemicals, phenolic compounds are ubiquitous and often result in allelopathy. Several IAPS like Fallopia japonica (Japanese knotweed), in United Kingdom, releases allelochemicals, which act as a novel weapon that drastically alters the food webs ( Smith et al., 2007a , Smith et al., 2007b , Abgrall et al., 2018 ). Likewise, Chromolaena odorata secretes odoratin, a novel allelochemical, which imparts the ability to defend against enemies, especially soil borne pathogens, and thus provides the IAPS a competitive edge over the native species ( Zheng et al., 2015a , Zheng et al., 2015b ).

Propagule pressure is reported as another potential IAPS strategy; it has been found to help Ardisia elliptica to invade the new habitats in south Florida ( Brooks and Jordan, 2013 , Rai, 2015 ). Zheng et al., 2015a , Zheng et al., 2015b are of the view that ERH and EICA together enable Ageratina adenophora to allocate more energy towards growth and resource utilization, so as to outcompete the native species. However, in the case of several IAPS (like Phragmites australis, Melaleuca ericifolia, and Rumex conglomeratus ), allelopathy and resource competition are found to act in unison to make the invasion more successful ( Uddin and Robinson, 2017 ).

Also, studies on the role of plant-microbe/insect interactions (both mutualist and antagonist) are necessary for elucidating the mechanisms of IAPS spread ( Jack et al., 2017 ). Nutrient enrichment in both the terrestrial and aquatic ecosystems plays a vital role in the success of IAPS in new habitats ( Aragón et al., 2014 , Uddin and Robinson, 2018 ); for example, an increased level of nitrogen in soils is found to help Bromus tectorum , (annual cheat grass), to outcompete the native flora ( Morris et al., 2016 ). Further, an interesting research observed that the IAPS impacted soil carbon pool/local climate mirrored differences in the traits of the IAPS and the natives ( Martin et al., 2017 ).

2.3. Molecular tools in elucidating the associated IAPS mechanisms

IAPS tend to affect the biotic (plant-microbe interactions ranging from genomics and proteomics to ecosystem levels) and abiotic (the physicochemical soil attributes) components in a variable spatial and temporal manner ( Song et al., 2015 , Gibbons et al., 2017 ). Molecular tools (16S rRNA gene sequencing) reveal that the success of an IAPS like Ageratina adenophora ( Eupatorium adenophorum ) over native species in a new habitat, lies in their close association with the microbial diversity and the increased levels of nitrate in soils ( Yu et al., 2014 , Kong et al., 2017 ). Transcriptomics has revealed that the rapid invasion of Mikania micrantha in North East India and China over its congeners (non-IAPS i.e. M. cordata , and M. cordifolia ) was in accordance with the environmental adaptations ( Rai, 2015 , Guo et al., 2018 ).

The invasive Impatiens glandulifera increases the diversity of soil fungal and bacterial populations in its newly invaded habitats ( Gaggini et al., 2018 ). Some IAPS, like Fallopia japonica (Japanese knotweed) survives in extreme environment of acute salts demonstrating their potential to tolerate the stressed environmental conditions ( Rouifed et al., 2012 ). Further, several IAPS (e.g., Centaurea stoebe and Bromus tectorum ) facilitate the colonization of ammonia-oxidizing bacteria, which alters the ecosystem functioning ( McLeod et al., 2016 ).

In the natural environment, complementarity among several proposed invasion mechanisms/hypothesis is an enigma which needs to be resolved for the sustainable management and hence, mitigating the adverse human health impacts imposed through the IAPS toxins.

3. Plant invasion in diverse environment, protected areas and diversity hotspots

Invasive plants are found to spread even in the ice-free Islands of Antarctica despite the Antarctica treaty ( Hughes and Convey, 2010 ). This seems to have occurred due to the deliberate movement of people and cargo for scientific explorations, industry and tourism, which may transport alien invaders of fungi, microbes, flora and fauna ( Pysˇek and Richardson, 2010 ). Invasive plants affect the habitats adversely, reduce diversity and ecosystem attributes, which is further compounded by the climate change occurring in the pristine ice-free Islands ( Frenot et al., 2005 ). Cinchona pubescens , known as the red quinine tree, is a model tree species in the treeless ecosystems of Galápagos highland, but recently it has been found to have turned invasive; thereby reducing the incoming solar radiation which affected the endemic herbaceous species more adversely than non-endemic native species ( Ja¨ger et al., 2009 ).

It is generally assumed that the well-managed protected areas, particularly those located on mountain hotspots, are resistant to plant invasion as evident from the notable areas in the Kruger Natonal Park of South Africa ( Jarošík et al., 2011 , Foxcroft et al., 2017 ). Now there is a growing literature which reveals that the plant invasion is a major threat to forest biodiversity in protected areas also as is demonstrated in Gros Morne National Park in boreal Canada ( Rose and Hermanutz, 2004 , Foxcroft et al., 2017 ). The Nature Conservancy (TNC) studies have further reinforced that protected areas across the world are also prone to the alien invaders ( Randall, 2011 ).

SCOPE (Scientific Committee on Problems of the Environment) assessed about 2000 invasive alien vascular plant species from 24 protected natural reserve areas in 1980s ( Usher, 1988 , Foxcroft et al., 2017 ). Global Invasive Species Program (GISP) has also reported that 487 protected areas may be invaded with alien plants that may pose a serious threat to the native forest biodiversity ( Foxcroft et al., 2017 ).

Climate change together with other anthropogenic disturbances are expected to cause the upward movement of invasive plant species from plains to mountain regions especially in the protected forested areas ( Diez et al., 2012 , Dainese et al., 2017 ); and this upward movement is projected to happen at a rapid rate ( Dainese et al., 2017 ). Such studies have also been carried out on the spread of the IAPS in protected forested landscapes of the Himalayan Mountains ( Adhikari et al., 2015 , Carboni et al., 2018 , Lamsal et al., 2018 ).

Globally, the emergence of new IAPS is predicted due to the continuous anthropogenic disturbances ( Seebens et al., 2018 ). To this end, modelling can assist in investigating/predicting the future success of IAPS, as demonstrated through maximum entropy modelling, in the case of Acacia nilotica ( Dermawan et al., 2018 ). Studies have proved that the vegetation ecology, forest community composition, litter decomposition and soil nutrient status in protected areas are drastically affected by the spread of IAPS ( Aragón et al., 2014 , Uddin and Robinson, 2018 ). Several environmental (e.g. solar radiation, soil variables/physico-chemical characteristics) and geographical attributes may act in concert, determining the invasion success, as demonstrated in the temperate forests of Korean Peninsula ( Cerný et al., 2013 ).

In addition to terrestrial environment, aquatic ecosystems, particularly wetlands are also threatened with IAPS. Recently, 40% of Ramsar Parties had developed a comprehensive national inventory of IAPS impacting the wetlands. Nevertheless, only 26% documented the concrete policy framework for their management ( Convention, 2018 , IPBES, 2019 ).

4. Impacts of plant invasion on environment, ecosystem services and economy

Biotic invaders resulted in the homogenization of biota at a global scale and thereby affected the environment and ecosystem services indirectly ( Pejchar and Mooney, 2009 , Shackleton et al., 2019. , Bartz and Kowarik, 2019 ). Socio-economic impacts of invasion are mainly visualized through human health assessment ( Rumlerová et al., 2016Rumlerová et al., 2016Rumlerová et al., 2016 , Fu et al., 2018 , Bartz and Kowarik, 2019 ). The IAPS, particularly 100 flora and fauna invaders as per GISD (2013) , which affect the environment and economy of both terrestrial and aquatic ecosystems, are listed in Table 1 .

4.1. Environmental impacts of the IAPS

Ecosystem functioning is perturbed due to IAPS to a greater extent in the Islands than in the mainland ( Pysˇek et al., 2012 ). It has been demonstrated that IAPS affect the ecosystem functioning through three basic mechanisms, (a) reduction in the diversity of native plants and animals, (b) remarkable changes in physico-chemical characteristics of soils (mostly through allelopathy), and (c) enhancement in ecosystems response towards altered fire regimes ( Pysˇek et al., 2012 ).

One well documented impact of IAPS is to reduce the biodiversity of native plants, which may have adverse implications for environment functioning, ecosystem services and global climate change ( Richardson et al., 2000 , Hulme, 2007 , Winter et al., 2009 , Vilà et al., 2009Vilà et al., 2009Vilà et al., 2009 , Vilà et al., 2011Vilà et al., 2011Vilà et al., 2011 , Pysˇek and Richardson, 2010 , Pysˇek et al., 2012 , Fu et al., 2018 , Schindler, et al., 2018 , Heshmati et al., 2019 ). Interestingly, the role of IAPS, in native biodiversity loss is widely acknowledged. However, their assumed role in extinction is debated among invasion ecologists and in order to negate it or confirm it uniform dataset across the diverse habitats especially in the islands is needed ( Gurevitch and Padilla, 2004 , Sax and Gaines, 2008 ).

Intense competition between IAPS and native flora for critical resources regulating ecosystem functioning may lead to the ‘invasion melt down’ ( Simberloff and Von Holle, 1999 , Pysˇek and Richardson, 2010 ). The invasion meltdown hypothesis states that the establishment of one invasive species in a new environment makes it easier for other non-native species to invade ( Simberloff and Von Holle, 1999 ). It is also worth mentioning that the first impact of IAPS, i.e., reduction in biodiversity is quite uniform across the globe.

Alien invaders are also known to adversely affect the wildlife ( Gan et al., 2009 ). For example, Spartina alterniflora replaces native macrophytes ( Phragmites australis and Scirpus mariqueter ) in wetlands of China, which eventually leads to the decline in avian populations due to the movement and feeding restrictions ( Gan et al., 2009 ).

Nutrient enrichment/eutrophication in the oligotrophic lakes leads to increase in the numerical strength of IAPS ( Vitousek et al., 1987 , Vitousek and Walker, 1989 , Pysˇek et al., 2012 ). Similarly, IAPS tend to spread at rapid rate, consequent upon the expansion of natural fire regime ( D’Antonio and Vitousek, 1992 , Pysˇek et al., 2012 ), which may also have adverse impacts on the ecosystem functioning ( Fig. 1 ). IAPS have also been found to alter the fire regimes in several terrestrial ecosystems that result in a huge socio-economic loss ( D’Antonio and Vitousek, 1992 , D’Antonio, 2000 , Chambers et al., 2007 , Pejchar and Mooney, 2009 ).

The IAPS can invade the aquatic systems through certain novel physiological characteristics (e.g. high biomass, deep roots and high evapo-transpiration) and can thus impede water flow, making it un-fit for drinking and irrigation ( van Wilgen et al., 1998 , Pejchar and Mooney, 2009 ). IAPS also tend to increase the flood frequency by narrowing the stream channels and altering soil attributes (e.g. decreased water holding capacity and increased soil erosion), which eventually harms the riparian native plant communities, besides having the human health implications. Plant invaders like Tamarisk , lead to economic loss around US$52 million annually ( Zavaleta, 2000 ). Lizarralde (1993) has reported that the IAPS, Castor canadensis (beavers) also perturbs water quality and increases the flood risk.

IAPS are also known to affect quantity of surface and ground water ( Shackleton et al., 2019 ). Prosopis pallida , a N-fixing IAPS in arid regions of Hawaii Island exploits groundwater resources to a level that alters the soil’s environment ( Dudley et al., 2014 ). Some IAPS exploit an enormous amount of water, which can compound the impact of water scarcity and bring a paradigm shift in socio-ecological regimes ( Gaertner et al., 2014 , Shackleton et al., 2019. ).

IAPS are also reported to alter the soil stability resulting in soil erosion ( Pejchar and Mooney, 2009 ). Invasions by noxious IAPS, like spotted knapweed ( Centaurea stoebe ), leafy spurge ( Euphorbia esula ) and cheat grass ( Bromus tectorum ) may have profound impact on the soil quality of the grassland ecosystems ( Gibbons et al., 2017 ). Acacia dealbata , an IAPS of Mediterranean ecosystem, reduces the native plant diversity by adversely affecting the soil chemistry and microbial functioning ( Lazzaro et al., 2014 ). Enhanced soil N favoured the IAPS Flaveria bidentis , over the competing non-native Amaranthus retroflexus and the native Bidens sp ( Huangfu and Li, 2019 ). Flaveria bidentis was assumed to modulate the elevated soil N for its growth while interacting with the other non-native/native plants.

4.2. Impacts of the IAPS on ecosystem services

Many IAPS are well known for their influence on ecosystem services viz, aesthetic, recreational, cultural and regulatory ( Pejchar and Mooney, 2009 ). Since IAPS tend to impede the water navigation, they are known to impact adversely the recreation and tourism services ( Eiswerth et al., 2005 ). Restrictions on sale of ornamental IAPS to avoid their harmful effects on environment have been reported to impact the aesthetic services of ecosystems ( Reichard and White, 2001 , Knowler and Barbier, 2005 , Pejchar and Mooney, 2009 ). Many IAPS are also known to impact the regulatory ecosystem services [such as hazards mitigation (e.g. landslide), water treatment, pest management, pollination, climate change, etc.)], which are inextricably linked with agriculture and forestry ( Colautti et al., 2006 , Pejchar and Mooney, 2009 ).

The invasion of Opuntia stricta in African region adversely affected the environment and economy. It has also affected the livelihood of local people through reduction in fodder and livestock health ( Shackleton et al., 2017 ). Since the cultural values are confined to a specific community, their economic quantification is difficult ( Pejchar and Mooney, 2009 ).

The cultivation of multi-purpose trees and shrubs is encouraged widely in order to boost bioenergy and industrial sectors ( Rai et al., 2018 ). Although, multi-purpose plants provide several benefits to humans, the introduction of IAPS as a multipurpose species [e.g. introduction of Prosopis sp. (mesquite) in South Africa] can profoundly affect the ecosystem services ( Rejmánek and Richardson, 2013 , Shackleton et al., 2015 , Shiferaw et al., 2019 ).

4.3. Economic impacts of the IAPS

Several IAPS, introduced for human welfare are known to create environmental and economic havoc ( Souza et al., 2018 ). Therefore, people’s perception about IAPS as well as their local ecological knowledge can be an effective approach to categorize the IAPS impacts. In this context, Acacia mangium , an IAPS in northern Brazilian Amazon, is noted for its harmful effects to economy, environment and indigenous people through alteration of the water quality ( Souza et al., 2018 ).

The invasion of aquatic macrophytes like Eichhornia crassipes (water hyacinth) in Lake Victoria has become a havoc for human welfare as it reduces fish production and eco-tourism potential ( Kasulo, 2000 , Pejchar and Mooney, 2009 ). Furthermore, the ecological niche models (CLIMEX), and Global Climate Models have predicted a shift of water hyacinth, under climate change regime, towards European and Mediterranean regions indicating the serious economic implications of such invasion ( Kriticos and Brunel, 2016 ). The invasion of Tamarix ramosissima has resulted in huge loss of water (1.4–3.0 billion cubic meters worth US$26.3–67.8 million) in USA that deprives various human needs ( Zavaleta, 2000 , Mooney et al., 2005 , Pejchar and Mooney, 2009 ). Similarly, Melaleuca quinquenervia in Florida, and Eucalyptus species in California, with their deep tap roots, use a huge quantity of the ground water ( Schmitz et al., 1997 ).

Myriophyllum spicatum (water milfoil), an aquatic macrophyte, in Lake Tahoe of Sierra Nevada (United States), caused a recreational loss by 1%, which in monetary terms amounts to US$500 000 annually ( Eiswerth et al., 2005 , Pejchar and Mooney, 2009 ). A few IAPS, like Euphorbia esula (leafy spurge) and pathogenic IAPS Xanthomonas campestris (citus canker) are known to cause economic loss of ca US $200 million dollar annually ( Andersen et al., 2004 ). It has been estimated that about US $ 600 million goes to minimize the loss caused by IAPS to environment and agriculture ( Andersen et al., 2004 ).

Office of Technology Assessment (1993) quantified the loss to the tune of US$97 billion (between 1906 and 1991) due to 79 invasive species. In United States alone, the loss due to pathogenic invaders (>50,000 in number) was evaluated to be US $120–138 billion ( Pimentel et al., 2005 ); the economic loss due to pathogenic invaders was further higher at global scale i.e. US$1.5 trillion per annum ( Pimentel et al., 2005 ). In China, an economic loss of the US$ 14.45 billion resulted from 283 flora/fauna invaders, hampering the forest, agriculture, wetland, grassland ecosystems linked with the human well-being ( Xu et al., 2006 ).

In African context, an IAPS of high risk i.e. Opuntia stricta , was evaluated to cause the economic loss of US$ 500–1000 per household per year through participatory rural appraisal (PRA) technique ( Shackleton et al., 2017 ). Further, in the agriculture sector of African countries, alien invaders were evaluated to result in an economic annual loss of US$ 1 billion by causing damage to agriculture crops ( Sileshi et al., 2019 ) ( Fig. 3 a; b).

a. Quantification of IAPS impacts in terms of economic loss driven by environmental alterations in terms of socio-ecological/economic aspects of human well-being of different countries e,g. United states, China, Africa European Union, South East (SE) Asia. Source [( Office of Technology Assessment (1993 ); Duncan et al., 2004 , Xu et al., 2006 , McGeoc, et al., 2010 , Nghiem et al., 2013 ; Shackleton (2017); Sileshi et al. (2019) ] ; Fig. 3 b . An IAPS Ambrosia artemisiifolia common ragweed) of (high risk in European Union (EU) with tremendous pollen production potential, causing human health hazards through allergy; the economic quantification of treatment costs are presented in relation to evaluated data from certain countries of EU; management perspectives tends bring trans-disciplinary researchers on common platform as its pollen biology, invasive potential in context of climate change, restoration aspects, public health hazards are tightly linked with each other.

In Southeast Asian context, human health sector alone suffered economic loss of US $1.85 billion from disease-spreading alien invaders ( Nghiem et al., 2013 ). The agriculture and health sectors together suffered an economic loss of US$33.5 billion due to the alien invasive species. Thus economic loss due to invaders was more pronounced in agriculture (ca 90% of monetary loss) than human health sector ( Fig. 3 a; b).

5. Impacts of the IAPS on human health

Biodiversity and its changes are inextricably linked with the human health, both in positive and negative sense ( Daszak et al., 2000 , Pysˇek and Richardson, 2010 , Young et al., 2017 , Stone et al., 2018 , Aerts et al., 2018 ). Positive implications of IAPS include their applications in vector borne control and ethno-medicinal uses ( Rai and Lalramnghinghlova, 2011 , Rai et al., 2018 ). For instance, a mosquito repellent is extracted from Lantana camara ( Mng’ong’o et al., 2011 , Stone et al., 2018 ).

Some biotic invasive species affect the human health through environmental contamination ( Kueffer, 2017 , Jones and McDermott, 2018 ). For example, invasive plant pathogens such as emerald ash borer, which causes a massive devastation to ash trees in United States, the ash trees earlier acted as a sink to air pollutants ( Jones and McDermott, 2018 ). The elevated levels of air pollutants can elevate regional losses in the tree diversity, which results in severe health implications, including mortality ( Jones and McDermott, 2018 ). Losses of host plants are known to cause a spurt in the growth of pathogen population facilitating outbreak of several diseases like Tick-borne diseases, Tuberculosis (multidrug-resistant), severe acute respiratory syndrome (SARS), acquired immunodeficiency syndrome (AIDS) and virulent Malaria ( Daszak et al., 2000 , Pysˇek and Richardson, 2010 , Hulme, 2014 , Young et al., 2017 , Stone et al., 2018 ). These severe human diseases and their sudden outbreak across continents are akin to biotic invasions themselves ( Pysˇek and Richardson, 2010 ). Increase in pathogen population owing to host loss caused by either the land use change or global warming have led to emergence of new diseases like Dengue and Yellow fever by Aedes aegypti , Lyme disease, African horse sickness, Chikungunya fever, Nipah virus disease etc. ( Daszak et al., 2000 , Hulme, 2014 , Young et al., 2017 , Stone et al., 2018 ).

Though, bees are known pollinators rendering remarkable beneficial ecosystem services ( Morse and Calderone, 2000 ), hybrid invasive bees, however, are hazardous to the human health ( Kenta et al., 2007 , Pejchar and Mooney, 2009 ). Similarly, invasive mosquitoes spread several infectious diseases like yellow fever and dengue fever especially in American and Asian continents. Even in the pristine ecosystems of Antarctica, penguins are found to suffer from microbial pathogens such as avian paramyxoviruses (APMV), Salmonella, Clostridium perfringens , Newcastle (NDV) and Lyme diseases ( Frenot et al., 2005 ).

Besides public health, IAPS also affect health of plants ( Beckstead et al., 2010 , Pysˇek and Richardson, 2010 , Young et al., 2017 ). Several IAPS like cheat grass increase the outbreak of fungal pathogens, which adversely affect the health of native plants ( Beckstead et al., 2010 ). In certain instances, the pathogenic IAPS (i.e. blight fungus i.e. Cryphonectria parasitica ) completely eliminates the existing dominant native life forms (e.g., Castanea dentate or American chestnut in eastern deciduous forest of US) ( Parker et al., 1999 , Andersen et al., 2004 ). Further, a high risk plant invader Parthenium hysterophorus is demonstrated to spread phytoplasmas a vegetable pathogen, which is characterized by using molecular tools (16S rRNA and lineage-specific immune-dominant membrane protein genes). Interestingly, Cai et al. (2016) observed that phytoplasmas infecting vegetables belong to the same genetic lineage as Parthenium.

We have tried to prioritize/document the IAPS, and their impacts on human health, through direct exposure, as vectors or through transfer of toxins in edibles ( Table 2 ). Lantana camara is one of the top ranking invaders, which provides a favourable habitat to tse-tse fly ( Glossina spp.) which causes sleeping sickness ( Leak, 1999 ). Likewise, brushtail possum transmits bovine tuberculosis to live-stock and deer in New Zealand, affecting human health indirectly through food-chain ( Clout et al., 1999 ); whereas Parthenium hysterophorus serves as a vector of Malaria ( Nyasembe et al., 2015 , Stone et al., 2018 ). Similarly, Ixodes scapularis is a vector of Borrelia burgdorferi , which causes the Lyme disease in humans ( Clow et al., 2017 ). In United states, a ‘National Invasive Species Council’ (NISC) is set up, which addresses the multifaceted environmental and human health risks in reference to IAPS spread ( Andersen et al., 2004 ). The ‘Lancet Commission on Planetary Health’ ( Whitmee et al., 2015 , Martin et al., 2016 ) and Universities for Global Health (CUGH) are some agencies which deal with human health from the mutifaceted environmental issues ( Martin et al., 2016 ).

IAPS in the aquatic environments also have human health implications ( Plaza et al., 2018 , Stone et al., 2018 ). The prominent aquatic IAPS like Phragmites australis and Typha assist in the colonization and multiplication of vector-borne pathogens, particularly West Nile virus ( MacKay et al., 2016 ) ( Table 2 ). Eichhornia crassipes (water hyacinth) is also a high risk IAPS, helping in the spread of schistosomiasis ( Mazza et al., 2014 , Gezie et al., 2018 , Stone et al., 2018 ). Likewise, another top ranked IAPS posing severe threats to the global environment and health is Arundo donax ( Plaza et al., 2018 ). Trade of these aquatic plants facilitates the spread of disease causing vectors across the continents and increase the health risks from vector borne diseases ( Mazza et al., 2014 , Stone et al., 2018 ).

Water blooms which belong to invasive cyanobacteria that release the cyano-toxins like microcystin, hepatotoxins, saxitoxins, lynbyatoxin and anatoxins are teratogenic (embryotoxic), carcinogenic, and promote tumours ( Fig. 4 ). These bio-toxins enter into food chain through the edible components of aquatic ecosystems like water chestnut, fishes etc. ( Streftaris and Zenetos, 2006 , Funari and Testai, 2008 , Wu et al., 2012 , Mazza et al., 2014 , Lee et al., 2017 ). Besides the algal invaders, there are several other IAPS, which release diverse chemical toxins; these IAPS prominently include Rhododendron ponticum , which contaminates honey with hazardous toxins (grayanotoxins), and causes health problems in humans ( Koca and Koca, 2007 , Daisie, 2009 , Pysˇek and Richardson, 2010 ).

Chemical structure of hazardous biological/bio-toxins released by several algal IAPS, in invaded aquatic ecosystems exerting carcinogenic, teratogenic and dermatitis impacts on human health, besides negative impacts on other components of food chain.

Ambrosia artemisiifolia, Parthenium hysterophorus, Ailanthus altissima, Acacia, Acer, Casuarina, Eucalyptus, Helianthus, Platanus and Xanthium are some of the IAPS which cause allergy in humans ( Belmonte and Vilà, 2004 , Mazza et al., 2014 , Nyasembe et al., 2015 , Lake et al., 2017 , Müller-Schärer, et al., 2018 , Chen et al., 2018 , Stone et al., 2018 ) ( Table 2 ). European continent is the most severely affected area from the allergic immune responses in the form of asthma and other respiratory and skin diseases ( Schindler et al., 2015 , Bayliss et al., 2017 , Müller-Schärer, et al., 2018 ). Among the various species, Ambrosia artemisiifolia , is reported as the most allergy inducing IAPS in Europe ( Xu et al., 2006 , Pysˇek and Richardson, 2010 , Daisie, 2009 , Schindler et al., 2015 , Lake et al., 2017 , Müller-Schärer, et al., 2018 , Chen et al., 2018 ).

Some studies have linked the IAPS aided allergy with the global climate change ( Storkey et al., 2014 , Lake et al., 2017 ) ( Fig. 1 , Fig. 4 ). The very basis of the pollen allergy from Ambrosia artemisiifolia (ragweed) is due to its 11 allergens reactivity towards IgE; and Amb a 1 and Amb a 11 are recognised as the major allergens ( Chen et al., 2018 ) ( Table 2 ). Parthenium is also considered as an allergy inducing IAPS, which is known to cause respiratory asthma and eczematous dermatitis ( Reaser et al., 2007 , Mazza et al., 2014 ), and Acacia dealbata too provokes allergic problems ( Daisie, 2009 , Pysˇek and Richardson, 2010 ).

Besides causing allergy, sap of Ailanthus altissima upon direct contact effectuates myocarditis ( Daisie, 2009 , Pysˇek and Richardson, 2010 ). Opuntia stricta contains glochids in the fruit, which cause the eye irritations ( Shackleton et al., 2017 ). Likewise, Senecio inaequidens also causes adverse health impacts as it contains retrorsine, an alkaloid of pyrrolizidine group ( Eller and Chizzola, 2016 ). IAPS like Cortaderia selloana ( GISD, 2013 , Mazza et al., 2014 ), Spartina anglica , Caesalpinia decapetala, and Rosa rugosa ( Pysˇek and Richardson, 2010 ) cause skin cuts and injuries owing to their sharp edge and silicate crystal depositions on leaves ( Mazza et al., 2014 ). Several ornamental IAPS also pose health issues as they emit toxins in the environment ( Celesti-Grapow et al., 2010 , Mazza et al., 2014 ).

Allergen-specific immunotherapy (AIT) is considered the most effective tool ( Chen et al., 2018 ) for managing human health issues due to such allergic IAPS, whereas the adoption of ecological breeding measures like cross-breeding, and understanding the invasion biology of the IAPS can be useful for reducing their health impacts ( Müller-Schärer et al., 2018 ). However, species specific focused studies are warranted to provide an insight of health hazards, emanating from the exposure to IAPS for developing better mitigation strategies.

The quantification of economic loss in mitigating the IAPS induced disease is also important for their threat and risk assessment. The treatment costs of common ragweed imposed health risks are expensive in the countries of European Union (EU) and a diagrammatic representation of the economic cost is presented in Fig. 3 b. Management initiatives and health risks mitigation measures are being taken in EU e.g. through SMARTER (“Sustainable Management of Ambrosia artemisiifolia in Europe”) under the framework of EU COST Action-FA1203. ( Müller-Schärer et al., 2018 ) ( Fig. 3 b). Interestingly, besides hazardous health implication of this IAPS (common ragweed), it act as an ideal ‘bridge species’ in management perspectives to bring trans-disciplinary researchers on common platform ( Müller-Schärer et al., 2018 ) ( Fig. 3 b).

Since, the economic impacts of invasive species is not fully quantified uniformly at global scale, the monetary loss is considered as an ‘invisible tax’ ( Pejchar and Mooney, 2009 ). Evidently more precise and adequate economic indicators of IAPS are warranted for the impact assessment and sustainable management.

Among the 128 alien species in Europe, Rumlerová et al. (2016) recorded negative environmental impacts of 83% of species. Interestingly, the socio-economic impacts were manifested through human health, as observed in 78% of IAPS. In Cyprus, 225 non-native alien species were assessed for their health risks and among them, 100 were identified for causing medium to high and very high health risks ( Peyton et al., 2019 ). The rest 125 invaders in Cyprus were of very low human health risks ( Peyton et al., 2019 ). Importantly, Cyprus being an integral portion of Mediterranean biodiversity hotspot deserves immediate restoration measures to safeguard endemic natives and human health. The management of IAPS is predicted to create important improvements in public health alongside biodiversity ( de Wit et al., 2017 ).

6. Whether all IAPS are nuisance? Quest of management implications

The invasion biologists are now realizing that not all the IAPS impose threats to environment ( Young and Larson, 2011 ). It has been well known that almost 99% of the selected IAPS are used globally as food crops ( Pejchar and Mooney, 2009 ). Even, certain IAPS ( Lantana camara and Ageratum conyzoides ) are reported to have some ethno-pharmacological applications in primary health care. ( Rai and Lalramnghinghlova, 2011 , Rai, 2012 ).

IUCN’s V th World Parks Congress (2003) clearly states that ‘‘management of IAPS is a priority issue and must be mainstreamed into all aspects of managements of forests and protected areas”. This issue in context of the protected areas was highlighted during IUCN World Conservation Congress of 2012 and IUCN World Parks Congress held during 2014. Both positive as well as negative ecosystem services of IAPS must be clearly identified to elucidate their cost-benefit to guide the stakeholders and policy makers ( Zengeya et al., 2017 , Shackleton et al., 2019. , Everard et al., 2018 ).

In biodiversity conservation, identifying/prioritizing IAPS has been given the top priority ( IPBES, 2019 ). In this respect, 10% of coastal/marine areas and 17% of terrestrial and inland water areas are conserved through the diverse targets/action plans. Further, attention is needed for the IAPS management of global protected areas, which cover the 14.9%, of the terrestrial realm ( IPBES, 2019 ).

It is well known that mutualistic microbial diversity of arbuscular mycorrhizal fungi (AMF) remarkably assists in the sustenance of forests. Recently, a few IAPS have been reported to promote the diversity of AMF in Hawaii forests ( Gomes et al., 2018 ). Further, it has been observed that certain IAPS ( Centaurea stoebe and Euphorbia esula ) increased the abundance as well as diversity of mycorrhizal fungi, which have immense role in ecosystem functioning ( Lekberg et al., 2013 ).

There is an urgent need to compare the economic cost incurred for the management of IAPS, with their positive ecosystem services, both before and after the eradication of IAPS, in order to have a sustainable approach. For an instance, the eradication of Phragmites australis from the managed wetland systems resulted in the increased emission of NH 4 + while reducing the denitrification ( Alldred et al., 2016 ). Therefore, the aforesaid eradication strategy was not found environmentally suitable. However, the eradication of Lantana camara and Chromolaena odorata in South Africa was found economically suitable as predicted through the cost-benefit models on ecosystem services ( Nkambule et al., 2017 ).

6.1. Eco-technological prospects linked with sustainable management of invasive plants

Several hyper-accumulator IAPS can be wisely used in phytoremediation of organics like poly aromatic hydrocarbons, heavy metals and particulate matter (PM). Thus, utilization of the waste IAPS biomass for phytoremediation can assist in their sustainable restoration and management ( Rai and Kim, 2019 , Prabakaran et al., 2019 ). Sustainable management of the IAPS often leads to environmental and biotechnological implications (through their use in phytoremediation) ( Singh and Rai, 2016 ).

The stress tolerance against pollutants, resistance to herbivores/pathogens and allelopathy ( Rai, 2009 , Prabakaran et al., 2019 ) are common to both the IAPS and the hyperaccumulators. Thus most of the high risk IAPS can be used as environmental remediation tools. A few IAPS like E. crasspes (water hyacinth), Lantana camara , Pistia stratiotes (water lettuce) and Arundo donax (giant reed) are potent bio-systems for phytoremediation ( Prabakaran et al., 2019 , Rai and Kim, 2019 ). As stated in the beginning, these IAPS can also act as an economically feasible tool for environment friendly genesis (phytosynthesis) of nanoparticles (for details see Rai et al., 2018 ). Interestingly, these IAPS are listed within the top 100 global invaders of Invasive Species specialist Group (ISSG; New Zealand) (Lowe et al., 2000). Therefore, our approach should focus on the eco-technological prospects of IAPS by turning nuisance waste into the pollution ameliorating resource.

Three IAPS ( Chromolaena odorata, Bidens pilosa and Praxelis clematidea ) are also recognized as hyper-accumulators for the phytoremediation of health hazardous heavy metal i.e. cadmium ( Wei et al., 2018 ). The IAPS, Pistia stratiotes has been demonstrated to accumulate silver (Ag) nanoparticles from the environment ( Hanks et al., 2015 ). Phragmites australis is observed to remediate the health hazardous organic contaminants (Benzimidazole anthelmintics) from the environment ( Podlipná et al., 2013 )

Several IAPS act as sources for bio-energy, animal feed, bio-polymers, and in augmenting the green economy ( Rai and Kim, 2019 ). Spartina alterniflora , an IAPS, has been demonstrated to act as a potent tool for carbon sequestration in bio-energy industry, besides acting as the bio-agent for heavy metals phytoremediation ( Liao et al., 2007 , Prabakaran et al., 2019 ).

Aquatic ecosystems also comprise numerous IAPS ( Table 1 , Table 2 ) with potential phyto-technological implications that need special attention for the biotechnological innovation through phytoremediation technologies ( Rai, 2008 , Rai, 2009 , Rai, 2010 , Rai, 2009 , Singh and Rai, 2016 , Hussner et al., 2017 , Rai, 2018a , Rai, 2018b , Rai, 2019 , Rai et al., 2019 , Rai et al., 2020 ).

Prosopis juliflora , besides acting as multi-purpose IAPS can assist in phytoremediation of fluoride, in conjunction with iron oxide nanoparticles ( Kumari and Khan, 2018 ). Detailed studies on eco-technological prospects of certain IAPS, may assist in the formulation of suitable rehabilitation and restoration strategies. Melaleuca quinquenervia and Lygodium microphyllum introduced in the environment of southern Florida, were used for investigating the ecosystem attributes in invasion success and conceptualizing an eco-restoration model (Doren et al., 2009).

6.2. Environmental/ecosystem services

In this section we describe the environmental and ecosystem services, taking urban environment as classical example of invasion. In fragmented urban ecosystems, there is an urgent need to document the beneficial environmental/ecological services ( Potgieter et al., 2017 , Potgieter et al., 2019 ). Further, the uneven and unequal diversion of economic funds for urban and city planning can also limit the management aspects of the urban vegetation (Irlich et al., 2017). Urban environment can be ideal model system for plant invasions as they face the problem of habitat fragmentation, shift in land use, climate change, resources as well as hydraulic alterations and pollution and geological disturbance which are congenial to the IAPS colonization and spread ( Klotz and Kühn, 2010 , Cadotte et al., 2017 , Potgieter et al., 2017 , Potgieter et al., 2019 ).

In urban landscapes, air filtration and pollution control, managing noise pollution, microclimatic regulation, etc. can be considered ( Costanza et al., 2007 , Potgieter et al., 2017 ). Acer platanoides, the most widely recorded global IAPS, removes CO 2 and thus contributes to climate change mitigation, but it also contributes to the emission of biogenic volatile organic compounds (BVOCs) ( Millward and Sabir, 2011 , Bogacki and Syguła, 2013 , Potgieter et al., 2017 , Potgieter et al., 2019 ). IAPS drastically alter soil attributes, however, certain IAPS like Kudzu ( Pueraria montana ), have been found to have potential to reduce soil erosion ( Forseth and Innis, 2004 ).

6.3. Energy options

The utilization of IAPS e.g. E. crassipes and Phragmites sp. for the bioenergy production may serve the twin purposes of sustainable renewable energy and alien weed management ( Gizínska-Górna et al., 2016Gizínska-Górna et al., 2016Gizínska-Górna et al., 2016 , Kriticos and Brunel, 2016 , Rai et al., 2018 , Stabenau et al., 2018 ). Other IAPS like Fallopia japonica, Solidago gigantean, Impatiens glandulifera and Heracleum mantegazzianum produce huge biomass with high calorific value, providing a great opportunity for bioenergy production ( Van Meerbeek et al., 2015 , Van Meerbeek et al., 2019 ). Utilization of IAPS biomass with an effective strategy for lingo-cellulose digestion can be a potential option for bio-energy, assisting in the climate change mitigation ( Van Meerbeek et al., 2019 ).

The aquatic IAPS Elodea nuttallii, recycles phosphorus efficiently, which may cause nutrient enrichment (eutrophication), adversely affecting the aquatic ecosystems ( Stabenau et al., 2018 ). This biomass of aquatic IAPS is also useful for biogas and P-rich compost formation ( Stabenau et al., 2018 ). We identify certain broad application areas of the IAPS linked with ecosystem services, sustainable energy and socio-economy (livelihood) ensuring their sustainable management ( Fig. 5 , Fig. 6 ).

IAPS ecological indicators and methods recommended by experts for an effective risk-analysis through various risk scoring protocols for management of environmentally/agriculturally hazardous invaders with biosecurity and human health implications.

Interdisciplinary interactions of IAPS, with multifaceted aspects of human well-being e.g. public health, (which is generally negative with rare positive impacts), environmental/socio-economic services, with livelihood implications (both positive and negative); An equitable evaluation of ecological economics in conjunction with associated phytotechnological implications of IAPS in nanotechnology, public health, agriculture and environment can provide an impetus to ‘Sustainable Development Goals’ (SDGs).

6.4. Livelihood

Livelihood is an issue of paramount importance, especially in the economically poor landscapes. Certain IAPS on the one hand act as a source of food while on the other adversely affect productivity of agriculturally important food crops ( Shackleton et al., 2019 ).

Prosopis glandulosa is considered to be a beneficial invader as its wood is marketed as a fuel wood in several countries of Africa and south-east Asia ( Shackleton et al., 2019 ). Australian Acacia, an IAPS in the Africa and Madagascar, are also used as fuel wood that serves as a source of livelihood to low-income people ( Kull et al., 2007 , Shackleton et al., 2019. ). An invader, Trichosurus vulpecula adversely impacts the natural vegetation in New Zealand by causing defoliation, but being eco-friendly for ‘fur’ industries and generating revenue worth US$20 million annually through exports ( Pejchar and Mooney, 2009 ).

Prosopis juliflora and Opuntia ficus-indica also serve as food and fodder in their invaded landscapes ( Mwangi and Swallow, 2008 , Shackleton et al., 2019. ). A cacia dealbata in Eastern Cape region of South Africa serves as an important livelihood resource ( Shackleton et al., 2019 ). Cenchrus ciliaris , an invasive grass, is promoted by the farmers for grazing purposes ( Marshall et al., 2011 , Shackleton et al., 2019. ). L. camara , an IAPS with-in top 100 invaders aid livelihood to several local villagers in India, as they use it for furniture and pulp making ( Kannan et al., 2016 , Kannan et al., 2014 ). Although, Melaleuca quinquenervia tree has a positive role in honey production in Florida, its eradication is almost ten times more economical than honey-production ( Serbesoff-King, 2003 , Pejchar and Mooney, 2009 ).

Though some of the IAPS release allelochemicals, bio-active compounds of Parthenium hysterophorus , however, have been found good for vermicomposting. Fourier Transform Infrared (FTIR) Spectrometry has revealed that Parthenium allelochemicals (sesquiterpene, lactones and phenolic constituents), are destroyed during the process of vermicomposting ( Hussain et al., 2016 ).

6.5. IAPS-pollinator interactions

Healthy plant-pollinator interactions maintain biodiversity, food security, farmer/beekeeper livelihoods, social and cultural values ( Potts et al., 2016 ). However, IAPS and climate change remarkably alter the plant-pollinator interactions, in a very complex manner ( Potts et al., 2016 ). An invader plant Robinia pseudoacacia attracts more insect pollinators when compared with the native plant Cytisus scoparius . Thus, IAPS can attract high numbers of insect pollinators in the disturbed/urbanized landscapes ( Buchholz and Kowarik, 2019 ). Similarly, another high risk IAPS i.e. Parthenium hysterophorus also attracted a greater number of bee pollinators than the indicator native plant ( Ojija et al., 2019 ).

6.6. Geospatial technologies in mapping, monitoring and management of IAPS

Geospatial technologies [Remote Sensing and geographical information system (GIS)] are fast and efficient tool for monitoring, assessment and hence management of the IAPS ( Walsh, 2018 , Khare et al., 2018 , Khare et al., 2019 ). In certain cases these tools can also trace the root cause of invasive spread, as demonstrated in the success of an IAPS ( Limnocharis flava ) attributed to ocean currents ( Abhilash et al., 2008 ). Comparative assessment of several remote sensing tools (e.g. Pléiades 1A, RapidEye and Landsat-8 OLI) assessed the L. camara in an Indian biodiversity hotspot (western Himalayan region) and these sensors observed certain differences in relation to spectral reflectance and density of this IAPS ( Khare et al., 2018 ).

The noxious IAPS [ Heracleum mantegazzianum (giant hogweed) and Fallopia japonica (knotweeds)] in Central Europe were mapped and it was observed that timing of the remote sensing is an important attribute in their invasion ecology. Best timing of mapping the IAPS through geospatial technologies can be intimately linked with their phenology (timing of life cycle events of a plant in relation to the environment e.g. flowering) and physiognomy (external appearance e.g. colour of leaves) ( Mullerova et al., 2017 ).

The luxuriant growth as extensive monospecific stands, greater amount of biomass and rapid spread through unique functional traits enable the IAPS to dominate the community. These ecological attributes, differentiating the IAPS from natives is exploited through remote sensing and GIS ( Mullerova et al., 2017 ). Therefore, the incorporation of functional/eco-physiological IAPS traits in spectral/spatial mapping tools can explicitly differentiate them from native plants ( Niphadkar and Nagendra, 2016 ).

Multi-scale remote sensing tools effectively assist in invasion suitability mapping and sustainability of management practices. For example, through remote sensing, we can assess the possible return of IAPS on eradicated sites, thus judge the efficacy of this management practice ( Mullerova et al., 2017 ). Land suitability and ecosystem process models are being used in conjunction with the geospatial tools for managing the IAPS ( He et al., 2015 , Walsh, 2018 ). In this respect, species distribution models (SDMs) in association with multi/hyperspectral remote sensors and Light Detection and Ranging (LiDAR)/RADAR missions, can better map the habitat suitability characteristics of IAPS ( He et al., 2015 ).

For IAPS mapping, in addition to very high resolution satellites (e.g. Pleiades, QuickBird, Ikonos, MODIS, Landsat), unmanned aircraft vehicles (UAV) are preferred in recent times in view of their high spatial resolution and cost-effectiveness ( Mullerova et al., 2017 ). Considerable future challenges in geospatial tools lie in the mapping of herbaceous IAPS and fragmented/sub-canopy distributions ( Walsh, 2018 ). Further, the low temporal/spatial resolutions of multi/hyperspectral sensors with high cost are concern for the developing countries. For addressing this issue, efforts are on to advance the image classification algorithms (e.g. nonparametric) as well as vegetation/diversity indices (e.g. normalized difference vegetation index-NDVI/beta diversity) for cost-effective efficient monitoring, mapping and modelling of IAPS ( Royimani et al., 2018 , Khare et al., 2019 ).

7. Ecological indicator perspectives of the IAPS

Ecological indicators for IAPS monitoring programs through modelling warrant focused research for accelerating their sustainable management efforts ( Doren et al., 2009a , Doren et al., 2009b ). It is worth mentioning that new arrival of the IAPS [new non-indigenous species ( nNIS )] has been demonstrated to act as ecological indicator ( nNIS indicator), to assess the extent of anthropogenic disturbances ( Olenin et al., 2016 ).

Certain aquatic invasive alien species were observed to adversely impact the Benthic Quality Index (BQI) in marine ecosystems ( Zaiko and Daunys, 2015 ). Thus, these coastal invaders can act as ecological indicators of the marine ecosystem health. IAPS are the major vegetation components in the urban environment. Alien species, especially those initially transported as neophytes, act as indicators of land-use change in the urban environment ( Godefroid and Ricotta, 2018 ).

The IAPS Impatiens glandulifera and Fallopia japonica acted as the ecological indicators for the long-term changes recorded in vegetation composition of riparian habitats ( Pattison et al., 2017 ). Other researches also observed that alien invaders also indicate the riparian habitat quality ( Smith et al., 2007a , Smith et al., 2007b ). Non-native alien plants and land-use change were the prime factors impacting functional plant traits in the wetlands ( Roy et al., 2019 ). Thus IAPS spread near wetland habitat can perturb the aquatic biodiversity and biogeochemical cycling, and hence IAPS can act as functional/ecological indicators of wetland’s health ( Roy et al., 2019 ).

Physiognomy of IAPS and the intracellular elemental composition of their plant parts can remarkably alter the diversity attributes of native species ( Fu et al., 2018 ) . For example, herbs, like Ageratina adenophora and Eupatorium, tend to alter the species diversity of understorey vegetation of pine forests owing to their high specific leaf area and foliar phosphorus and nitrogen contents and thus may act as ecological indicators of species diversity loss ( Fu et al., 2018 ). Fu et al. (2018) further observed that native species with low leaf nitrogen concentration will disappear first from Ageratina adenophora invaded ecosystems.

For an effective management of IAPS, their ecological/human health impact should be included as evaluation measures ( Schmiedel et al., 2016 ). The management programmes and policies of IAPS management also cover the huge economic costs. The DAISIE project, designed for preparing alien invaders database of European Union, involved an economic expenditure of €3.4 million ( McGeoc et al., 2010 ). Nevertheless, this cost was quite less than the environmental and socio-economic damages (exceeding €12 billion/year in Europe) caused by the worst alien invaders ( McGeoc et al., 2010 ).

Every nation must evolve appropriate indicators to manage the IAPS problem. These indicators can be useful to quantify the invasive spread adversely impacting biodiversity and policy responses ( McGeoc et al., 2010 ). Further, the formulated indicators should be linked with the nation’s invasion debt (i.e. indicators based on introduction debt, establishment debt, colonization/spread debt, and impact debt) (Wison et al., 2018) ( Fig. 5 ).

An efficient invasion indicator has been developed using the two high risk IAPS (Australian acacias and eucalyptus), incorporating niche and trait proxies ( Gallien et al., 2019 ). Such ecological indicators assess naturalization and establishment potential of the IAPS, thus, can analyse the imposed ecosystem impact. The Indian Himalayan Region covers the biodiversity hotspots, and therefore, Long-Term Ecological Monitoring (LTEM) in concert with the ecological indicators is devised for IAPS and climate change risk assessments and mitigation measures ( Negi et al., 2019 ). Nevertheless, sustained efforts are needed in such IAPS monitoring/modelling prospects for the ecological restoration.

The ambitious, but vital sustainability targets of SDGs, Convention on Biological Diversity (CBD) and International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA-article 5) in biodiversity conservation need quantitative ecological indicators to implement the restoration measures and meet their targets ( United Nations, 2015 , Convention on Biological Diversity (CBD), 2018 , Khoury et al., 2019 ). An indicator, gap analysis methodology, has been demonstrated to be relevant in implementing the conservation measures of wild food crops, intimately linked with human well-being ( Khoury et al., 2019 ).

8. Risk analysis protocols of IAPS and biosecurity: Implications for management

Economic evaluation of the IAPS is essential to rate their threats and thus formulate an action plan for their management and biodiversity restoration. Generic Impact Scoring System (GISS), an important assessment protocol, demonstrated 149 plants as worst invaders among the 486 investigated IAPS of Europe ( Vila‘ et al., 2019 ). Scoring methods for the impact quantification cannot cover all impacts and further they mostly cover the local and regional aspects only ( Ricciardi et al., 2013 , Jeschke et al., 2014 , Vilà et al., 2019 ). Further limitation in protocol formulations has been demonstrated in occurrence of the cryptogenic/cryptic alien species (species with unclear place of origin). Moreover, the risk analysis of IAPS is still inadequate due to lack of data on ecological impacts, transparency/ repeatability and inclusion of uncertainty factor in the assessments ( Vanderhoeven et al., 2017 ). Therefore, concrete impact assessment protocols (scoring methods) should be framed to quantify the environmental and socio-economic impacts of IAPS ( Vila‘ et al., 2019 ) ( Fig. 5 ).

Several models (e.g. a stochastic bioeconomic model) were developed to quantify the economic impacts of IAPS; however, more efforts are needed for having an inclusive model. Through stochastic bioeconomic model, the invasive Varroa destructor in Australia is predicted to cause an annual economic loss of US$16.4 to 38.8 million ( Cook et al., 2007 ). This economic loss due to invasive mite was due to decline in honeybee/pollinators population and reduced yield of the food crops.

In view of negative implications of many IAPS, there is an urgent need to prioritize and formulate cost-effective and eco-feasible strategies for their management. ‘Biosecurity’ is reported as a management strategy to minimize harmful environmental, economic and human health impacts of IAPS ( Pysˇek and Richardson, 2010 ).

Biosecurity of the crops from IAPS and insect invaders can be formulated in biodiversity conservation policies, as intimately linked to food security ( Sileshi et al., 2019 ). Therefore, sustainable bio-control programmes should be implemented for the IAPS management in both natural and agro-ecosystems. The international community should also unite for an integrated approach to safeguard global biodiversity from IAPS and emerging infectious diseases ( Zhou et al., 2019 ).

An effective optimized biosecurity surveillance of invaders can pave the way for implementing the mitigation measures at the initial invasion stage ( Poland and Rassati, 2019 , Yemshanov et al., 2019 ). Further, optimizing the biosecurity surveillance can prevent economic loss by managing the insect invaders at an early stage of establishment ( Yemshanov et al., 2019 ).

Moreover, international and national biosecurity policies [e.g. International Standards for Phytosanitary Measures (ISPMs); CBD] also included risk assessment as integral component of overall plant/human health risk analysis ( Lindgren, 2012 ). However, ranking the invaders, impacting the agriculture/human health biosecurity by predicting their risk is still inadequate. This must be prioritized by each nation for the effective threat analysis ( Paini et al., 2010 , Yemshanov et al., 2019 ).

9. Plant invasion interactions with climate/other global change and health risks

The changing climatic conditions have been reported to affect the spread of such invasive species in terrestrial ( Heshmati et al., 2019 ) as well as fresh water ecosystems of Africa ( Jackson et al., 2016 ). Drastic adverse impacts in the aquatics can affect several ecosystem services like human health, agriculture and forestry ( Rai, 2015 ) ( Fig. 6 , Fig. 7 ).

A complex interdisciplinary/interrelated cumulative framework of global climate/environmental/land-use changes, with IAPS, human health, biodiversity, forestry, agriculture, fisheries and environmental (water/air resource) degradation, emanating from diverse anthropogenic factors.

Certain IAPS like Ageratum houstonianum, Chromolaena odorata, Lantana camara , Parthenium hysterophorus etc. have invaded different Himalayan regions, which exacerbate the future climate change scenario, as predicted by the species distribution modelling ( Shrestha et al., 2018 ). Climate change interactions with the IAPS are argued to have an increased invasion through complex intricate changes in IAPS physiognomy, anatomy and biochemistry ( Ziska, 2016 ). Reduction in leaf protein levels of the IAPS under unpredictably changing climate can minimize effectiveness and persistence of herbicides in farmlands ( Ziska, 2016 ).

In Great and Amazon Basin of US, the replacement of sagebrush ecosystems with the invaded grasses has been found to reduce the carbon sequestration potential drastically ( Prater et al., 2006 , Pejchar and Mooney, 2009 ). In contrast, when grasses are being replaced with the woody tree IAPS (e.g. Prosopis glandulosa), the carbon sequestration potential tends to increase (15–24 times with 32% increase in C-stocks) in the southern Great Plains of US ( Hughes et al., 2006 ) ( Fig. 1 ). Therefore, invasion of grasslands with the woody IAPS can assist the climate change mitigation efforts.

It has been demonstrated that the impact of IAPS on carbon sequestration in invaded ecosystems is dependent on litter chemistry and microbial priming ( Tamura and Tharayil, 2014 ). Furthermore, link of invasion with climate change and other anthropogenic disturbances are demonstrated in marine meadows through Syringodium isoetifolium, an IAPS, the inter-linkage of these environmental perturbations increased the extent of this seagrass invasion by ca 800 fold ( McKenzie et al., 2014 ).

Blumenthal et al. (2016) observed that warming changes the phenology of cheatgrass ( Bromus tectorum ) and expands its invasion range ( Blumenthal et al., 2016 ). Moreover, even the IAPS sharing same niche can have differential adaptability towards future climate and invasion intensity. Several models predicated that IAPS like L. camara was better adapted under the scenario of climate change than the native Cassia tora ( Panda et al., 2018 ).

Cyanobacterial communities in soils promote the spread of cheatgrass ( Bromus tectorum ), besides increasing soil fertility ( Ferrenberg et al., 2018 ). Several models like Ecological Niche Modelling (ENM) with the assistance of Global Biodiversity Information Facility (GBIF) have been developed to predict invasion trend under various climatic scenarios ( Adhikari et al., 2015 ).

10. Conclusions and future prospects

Although IAPS tend to adversely affect the native biodiversity and ecosystem services, their role in species extinctions is debated among the invasion ecologists. Nevertheless, UN-IPBES recently confirmed the biotic invaders as major drivers of biodiversity depletion. Anthropogenic disturbances are the prime factors responsible for biotic invasions. If such human mediated disturbances will be continued in the long term, there may be emergence of new IAPS, hazardous to environmental/human health. However with the explicit understanding of various mechanisms involved in arrival, spread and establishment of IAPS, we can sustainably manage the IAPS. In invasion ecology, more emphasis should be given on chemical ecology of native-IAPS interactions to elucidate the mechanisms of biodiversity loss. Emerging global issues, like biodiversity erosion, climate change, un-sustainable agriculture and environmental disturbances should be studied in depth to understand their complex interacting impacts on human health. Therefore, future researches should use multiple environmental stressors to address their impacts on environment/ecosystem services, socio-economic (livelihood), and human health.

Also, the future course of IAPS management must address the economic considerations and societal acceptability. Management of the IAPS through their eradication involves a huge cost. Since, IAPS impact on ecosystems is highly variable in respect of their socio-ecological conditions, cost-benefit analysis is essential in future studies to safeguard the livelihood benefits.

Biotechnological innovations for utilizing the biomass of the selected IAPS (through phytoremediation technology) may assist in their sustainable environmental management and concomitantly, restore the environment from diverse hazardous contaminants (e.g. heavy metals and particulate matter). Focused future research in the biomedical sector, especially in molecular medicines/phytosynthesis of nanoparticles is warranted to mitigate the human health implications emanating from the exposure to IAPS.

Future advances in geospatial (remote sensing/GIS) and omics (proteomics, genomics, and metabolomics) tools may also assist in unravelling the concerted impacts of environmental degradations on human health, and elucidate the mechanisms of mitigation for health risks. Further, the ecological indicator perspectives of IAPS and developing concrete risk assessment protocol need further studies. Indeed, the UN-IPBES- global indicators target i.e. 15.8, to achieve SDGs, intensively deals with the need of effective prevention and management strategies to control the IAPS by 2020. Moreover, there is a paucity of the ecological models/indicators to establish interrelationship among global environmental changes, biodiversity and health, warranting future researches.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Authors thank the two anonymous reviewers for helpful comments, to Professor P. C. Abhilash (IESD BHU) for vetting the language. Financial support from Department of Biotechnology (DBT) (vide project no.BT/PR24917/NER/95/907/2017) is acknowledged. Professor J.S. Singh thanks BHU and INSA for support.

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