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Identification of an unusual tree

Identification of an unusual tree


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Location : Lyon, France

I think it's one of the laziest trees I've ever seen XD


As Sudachi has commented it has been pruned, probably over many years, to take on this form. It is not natural shape.

I would say that it is not possible to identify the exact species of tree without a close up of the leaves. From zooming in it looks like it could be a Lawson Cypress (Chamaecyparis lawsoniana) although it could be a Leyland Cypress (x Cupressocyparis leylandii), or a number of other possibilities.

One way to investigate this further if you want to know for certain, apart from posting a close up of the foliage which may help, would be to find some specimens of cypresses that you can find the name of. Perhaps in a garden centre or an arboretum, then compare the smell of the foliage when you pinch and crush it between your fingers. Different species have a characteristic smell that is a useful method for identification.


Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached

Rapid and reliable identification of insects, either to species or to higher taxonomic groups, is important in many contexts. Insects form a large portion of the biological diversity of our planet, and progress in the understanding of the composition and functioning of the planet’s ecosystems is partly dependent on our ability to effectively find and identify the insects that inhabit them. There is also a need for easy and accurate identification of insects in addressing concerns related to human food and health. Such applications include the detection of insects that are pests of crops ( FAO 2015), disease vectors ( WTO 2014), or invasive species ( GISD 2017).

Identifying insects is hard because of their immense species diversity [more than 1.02 million species described to date ( Zhang 2011)] and the significant variation within species due to sex, color morph, life stage, etc. With some training, one can learn how to distinguish higher taxonomic groups, such as orders, but already at the family level the task becomes quite challenging, even for experts, unless we restrict the problem to a particular life stage, geographic region, or insect order. Generally speaking, the lower the taxonomic level, the more challenging the identification task becomes ( Fig. 1). At the species level, reliable identification may require years of training and specialization on one particular insect taxon. Such expert taxonomists are often in short demand, especially for groups that are not showy and attractive, and their time could be better spent than on routine identifications.

A schematic illustration of the taxonomy of insects. The full tree is organized into hierarchical ranks it contains approximately 1.02 million known species and several millions that remain to be described. Classifying a specimen to a group of higher rank, such as order, is usually relatively easy with a modest amount of training. The challenge and amount of required expertise increases considerably (transition from green to red) as the taxonomic rank is lowered.

A schematic illustration of the taxonomy of insects. The full tree is organized into hierarchical ranks it contains approximately 1.02 million known species and several millions that remain to be described. Classifying a specimen to a group of higher rank, such as order, is usually relatively easy with a modest amount of training. The challenge and amount of required expertise increases considerably (transition from green to red) as the taxonomic rank is lowered.

For these reasons, there has long been an interest in developing automated image-based systems for insect identification ( Schröder et al. 1995 Weeks et al. 1997, 1999a, 1999b Gauld et al. 2000 Arbuckle et al. 2001 Watson et al. 2003 Tofilski 2004, 2007 ONeill 2007 Steinhage et al. 2007, Francoy et al. 2008 Yang et al. 2015 Feng et al. 2016 Martineau et al. 2017). Common to all such systems designed to date is that they depend on handcrafted feature extraction. “Handcrafted” or “hand-engineered” are standard terms in machine learning and computer vision referring to the application of some process, like an algorithm or a manual procedure, to extract relevant features for identification from the raw data (images in our case). Examples of features that have been used for taxonomic identification include the wing venation pattern, the relative position of wing vein junctions, and the outline of the wing or of the whole body. Although many of these systems achieve good identification performance, the need for special feature extraction tailored to each task has limited their use in practice.

In recent years, deep learning (DL) and convolutional neural networks (CNNs) have emerged as the most effective approaches to a range of problems in automated classification ( LeCun et al. 2015 Schmidhuber 2015), and computer vision is one of the fields where these techniques have had a transformative impact. The basic ideas have been around for a long time ( Fukushima 1979, 1980 Fukushima et al. 1983) but a significant increase in the complexity and size of the neural networks and a huge increase in the volume of data used for training have generated spectacular advances in recent years. These developments, in turn, would not have been possible without the extra computational power brought by modern graphical processing units (GPUs).

In contrast to traditional approaches of machine learning, requiring handcrafted feature extraction, DL and CNNs enable end-to-end learning from a set of training data. In end-to-end learning, the input consists of labeled raw data, such as images, nothing else. The images may even represent different views, body parts, or life stages—the CNN automatically finds the relevant set of features for the task at hand. CNNs have been particularly successful in image classification tasks, where large labeled training sets are available for supervised learning. The first super-human performance of GPU-powered CNNs ( Cireşan et al. 2011) was reported in 2011 in a traffic sign competition ( Stallkamp et al. 2011). The breakthrough came in 2012, when a CNN architecture called AlexNet ( Krizhevsky et al. 2012) outcompeted all other systems in the ImageNet Large Scale Visual Recognition Challenge ( Russakovsky et al. 2015), at the time involving 1.3 million images divided into 1000 categories, such as “lion,” “cup,” “car wheel,” and different breeds of cats and dogs. Since then, CNN performance has improved significantly thanks to the development of deeper, more complex neural network architectures, and the use of larger data sets for training. Open-source licensing of DL development frameworks has triggered further methodological advances by attracting a vast developer community.

Training a complex CNN from scratch to performance levels that are on par with humans requires a huge set of labeled images and consumes a significant amount of computational resources, which means that it is not realistic currently to train a dedicated CNN for most image classification tasks. However, in the last few years, it has been discovered that one can take advantage of a CNN that has been trained on a generic image classification task in solving a more specialized problem using a technique called transfer learning ( Caruana 1995 Bengio 2011 Yosinski et al. 2014 Azizpour et al. 2016). This reduces the computational burden and also makes it possible to benefit from the power of a sophisticated CNN even when the training set for the task at hand is moderate to small.

Two variants of transfer learning have been tried. In the first, fine-tuning, the pretrained CNN is slightly modified by fine-tuning model parameters such that the CNN can solve the specialized task. Fine-tuning tends to work well when the specialized task is similar to the original task ( Yosinski et al. 2014), but it may require a fair amount of training data and computational power. It is also susceptible to overfitting on the specialized task when the data sets are small because it may incorrectly associate a rare category with an irrelevant feature, such as a special type of background, which just happens to be present in the few images of that category in the training set.

The second variant of transfer learning is known as feature transfer, and involves the use of the pretrained CNN as an automated feature extractor ( Donahue et al. 2014 Oquab et al. 2014 Razavian et al. 2014 Zeiler and Fergus 2014 Azizpour et al. 2016 Zheng et al. 2016). The pretrained CNN is exposed to the training set for the specialized task, and information is then extracted from the intermediate layers of the CNN, capturing low- to high-level image features see description of the CNN layer architecture below). The feature information is then used to train a simpler machine learning system, such as a support vector machine (SVM) ( Cortes and Vapnik 1995), on the more specialized task. Feature transfer in combination with SVMs tends to work better than fine-tuning when the specialized task is different from the original task. It is computationally more efficient, works for smaller image sets, and SVMs are less susceptible to overfitting when working with imbalanced data sets, that is, data sets where some categories are represented by very few examples ( He and Garcia 2009).

Sophisticated CNNs and transfer learning have been used successfully in recent years to improve the classification of some biological image data sets, such as “Caltech-UCSD Birds-200-2011” (Birds-200-2011) ( Wah et al. 2011) (200 species, 40–60 images per species) and “102 Category Flower Data set” (Flowers-102) ( Nilsback and Zisserman 2008) (102 flower species commonly occurring in the UK, 40–258 images per species) ( Table 1). Similar but larger data sets contributed by citizen scientists are explored in several ongoing projects, such as Merlin Bird ID ( Van Horn et al. 2015), [email protected] ( Joly et al. 2014) and iNaturalist (web application available at http://www.inaturalist.org). These data sets involve outdoor images of species that are usually easy to separate for humans, at least with some training, and the automated identification systems do not quite compete in accuracy with human experts yet.

Comparison of the performance of some automated image identification systems prior to CNNs and some recent state-of-the-art CNN-based methods on two popular fine-grained data sets (i.e., data sets with categories that are similar to each other), Bird-200-2011 ( Wah et al. 2011), and Flower-102 ( Nilsback and Zisserman 2008)

Methods . Bird . Flower . References .
Pre-CNN methods
Color+SIFT 26.7 81.3 ( Khan et al., 2013)
GMaxPooling 33.3 84.6 ( Murray and Perronnin, 2014)
CNN-based techniques
CNNaug-SVM 61.8 86.8 ( Razavian et al., 2014)
MsML 67.9 89.5 ( Qian et al., 2014)
Fusion CNN 76.4 95.6 ( Zheng et al., 2016)
Bilinear CNN 84.1 ( Lin et al., 2015)
Refined CNN 86.4 ( Zhang et al., 2017)
Methods . Bird . Flower . References .
Pre-CNN methods
Color+SIFT 26.7 81.3 ( Khan et al., 2013)
GMaxPooling 33.3 84.6 ( Murray and Perronnin, 2014)
CNN-based techniques
CNNaug-SVM 61.8 86.8 ( Razavian et al., 2014)
MsML 67.9 89.5 ( Qian et al., 2014)
Fusion CNN 76.4 95.6 ( Zheng et al., 2016)
Bilinear CNN 84.1 ( Lin et al., 2015)
Refined CNN 86.4 ( Zhang et al., 2017)

Note: All CNN-based methods used pretrained VGG16 and transfer learning ( Simonyan and Zisserman 2014). Numbers indicate the percentage of correctly identified images in the predefined test set, which was not used during training.

Comparison of the performance of some automated image identification systems prior to CNNs and some recent state-of-the-art CNN-based methods on two popular fine-grained data sets (i.e., data sets with categories that are similar to each other), Bird-200-2011 ( Wah et al. 2011), and Flower-102 ( Nilsback and Zisserman 2008)

Methods . Bird . Flower . References .
Pre-CNN methods
Color+SIFT 26.7 81.3 ( Khan et al., 2013)
GMaxPooling 33.3 84.6 ( Murray and Perronnin, 2014)
CNN-based techniques
CNNaug-SVM 61.8 86.8 ( Razavian et al., 2014)
MsML 67.9 89.5 ( Qian et al., 2014)
Fusion CNN 76.4 95.6 ( Zheng et al., 2016)
Bilinear CNN 84.1 ( Lin et al., 2015)
Refined CNN 86.4 ( Zhang et al., 2017)
Methods . Bird . Flower . References .
Pre-CNN methods
Color+SIFT 26.7 81.3 ( Khan et al., 2013)
GMaxPooling 33.3 84.6 ( Murray and Perronnin, 2014)
CNN-based techniques
CNNaug-SVM 61.8 86.8 ( Razavian et al., 2014)
MsML 67.9 89.5 ( Qian et al., 2014)
Fusion CNN 76.4 95.6 ( Zheng et al., 2016)
Bilinear CNN 84.1 ( Lin et al., 2015)
Refined CNN 86.4 ( Zhang et al., 2017)

Note: All CNN-based methods used pretrained VGG16 and transfer learning ( Simonyan and Zisserman 2014). Numbers indicate the percentage of correctly identified images in the predefined test set, which was not used during training.

The main purpose of the current article is to explore the extent to which CNN feature transfer can be used in developing accurate diagnostic tools given realistic-size image sets and computational budgets available to systematists. The article represents one of the first applications of CNN feature transfer to challenging and realistic taxonomic tasks, where a high level of identification accuracy is expected. In contrast to previous studies, all independent identifications used here for training and validation have been provided by taxonomic experts with access to the imaged specimens. Thus, the experts have been able to examine characters that are critical for identification but that are not visible in the images, such as details of the ventral side of specimens imaged from above. The experts have also had access to collection data, which often facilitates identification.

We examined two types of challenging taxonomic tasks: 1) identification to higher groups when many specimens are likely to belong to subgroups that have not been seen previously and 2) identification of visually similar species that are difficult to separate even for experts. For the first task, we assembled two data sets consisting of diverse images of Diptera faces and the dorsal habitus of Coleoptera, respectively. For the second task, we used images of three closely related species of the Coleoptera genus Oxythyrea, and of nine species of Plecoptera larvae ( Lytle et al. 2010). Training of the automated identification system was based entirely on the original images no preprocessing was used to help the computer identify features significant for identification.

In all our experiments, we utilized the CNN architecture VGG16 with weights pretrained on the ImageNet data set ( Simonyan and Zisserman 2014) for feature extraction, and a linear SVM ( Cortes and Vapnik 1995) for classification. Our work focused on optimizing feature extraction techniques to reach high levels of identification accuracy. We also analyzed the errors made by the automated identification system in order to understand the limitations of our approach. Finally, to validate the generality of our findings, we tested our optimized system on several other biological image classification tasks studied in the recent literature on automated identification.


Strange Organism Has Unique Roots in the Tree of Life

Talk about extended family: A single-celled organism in Norway has been called "mankind's furthest relative." It is so far removed from the organisms we know that researchers claim it belongs to a new base group, called a kingdom, on the tree of life.

"We have found an unknown branch of the tree of life that lives in this lake. It is unique! So far we know of no other group of organisms that descend from closer to the roots of the tree of life than this species," study researcher Kamran Shalchian-Tabrizi, of the University of Oslo, in Norway, said in a statement.

The organism, a type of protozoan, was found by researchers in a lake near Oslo. Protozoans have been known to science since 1865, but because they are difficult to culture in the lab, researchers haven't been able to get a grip on their genetic makeup. They were placed in the protist kingdom on the tree of life mostly based on observations of their size and shape.

In this study, published March 21 in the journal Molecular Biology Evolution, the researchers were able to grow enough of the protozoans, called Collodictyon, in the lab to analyze its genome. They found it doesn't genetically fit into any of the previously discovered kingdoms of life. It's an organism with membrane-bound internal structures, called a eukaryote, but genetically it isn't an animal, plant, fungi, algae or protist (the five main groups of eukayotes). [Extreme Life on Earth: 8 Bizarre Creatures]

"The microorganism is among the oldest currently living eukaryote organism we know of. It evolved around one billion years ago, plus or minus a few hundred million years. It gives us a better understanding of what early life on Earth looked like," Shalchian-Tabrizi said.

Mix of features

What it looked like was small. The organism the researchers found is about 30 to 50 micrometers (about the width of a human hair) long. It eats algae and doesn't like to live in groups. It is also unique because instead of one or two flagella (cellular tails that help organisms move) it has four.

The organism also has unique characteristics usually associated with protists and amoebas, two different branches. This left researchers wondering where the microorganism fits into the tree of life. They analyzed its genetic code to see how similar it is to organisms that have already been genetically catalogued.

"We are surprised," said study researcher Dag Klaveness, also of the University of Oslo, because the species is unique. They compared its genome with those in hundreds of databases around the world, with little luck. In all that looking they "have only found a partial match with a gene sequence in Tibet."

The researchers think this organism belongs in a new group on the tree of life. Researchers can't say for certain if other organisms previously classified as protozoans are in this same branch without their genetic information. Its closest known genetic relative is the protist Diphylleia, though other organisms that haven't been analyzed genetically may be closer relatives.

"It is conceivable that only a few other species exist in this family branch of the tree of life, which has survived all the many hundreds of millions of years since the eukaryote species appeared on Earth for the first time," Klaveness said.

Because it has features of two separate kingdoms of life, the researchers think that the ancestors of this group might be the organisms that gave rise to these other kingdoms, the amoeba and the protist, as well. If that's true, they would be some of the oldest eukaryotes, giving rise to all other eukaryotes, including humans.

You can follow LiveScience staff writer Jennifer Welsh on Twitter, on Google+ or on Facebook. Follow LiveScience for the latest in science news and discoveries on Twitter and on Facebook.

Editor's Note: This article has been updated to correct the fact that it stated amoebas and protists were two kingdoms when in fact they are just two different branches within eukaryotes.


Species Biology of Sagebrush Steppe

SPECIES BIOLOGY of SAGEBRUSH STEPPE:
Species Biology can be described by a Species’ life strategies. Life strategies are how an organism allocates energy and materials to be able to compete in an environment, to survive and reproduce. Evolving through natural selection, developing tradeoffs of growth/survival/reproduction life strategies are a sum of a species’ morphology, physiology, environmental responses, resource requirements, energy acquisition, storage and allocation, reproduction strategy, and life cycle. The main life strategies of Sagebrush Steppe Species evolved to be adaptations to heat and aridity (drought).

Plants
Photosynthesis is the foundation of the food-chain, providing energy for all trophic levels. Solar radiation is used to convert H20 and C20 into carbohydrates that produce energy for plants and animals. There are three photosynthetic pathways that evolved/adapted and thrive in different environments: C3, C4, and CAM. Plants are Primary producers, in that they produce energy by using sunlight to synthesize water and carbon dioxide into carbohydrate, for all upper trophic levels of the food chain.
C3 pathway produces 3-Carbonic acid. There is a one step carbon fixation process in which CO2 is fixed by Rubisco directly in the chloroplasts of a plant. C3 plants have the most ancient pathway because they evolved first, during a time period of high CO2 concentration and low O2. Therefore C3 plants can be inhibited by high levels of O2, an issue called photorespiration: where O2 binds to Rubisco instead of CO2. They are cool season plants, sensitive to warm and dry climates (thriving in temperatures 65-75 degrees F).
C4 pathway produces 4-carbonic acid. It can perform the one step function of the C3 pathway or it can use ATP as energy for a two step process that reduces photorespiration. This two step process involves PEPcase acting as the initial receptor of CO2, not Rubisco. PEPcase has high affinity for CO2 and none for oxygen. Temperature ranges from 90-95 degrees F, so they are warm season plants. C4 plants evolved after C3, during a period with high O2 concentration.
CAM plants have evolved adaptations that conserve water in hot and arid environments, with high evapotranspiration. Stomata open in the nighttime (dark) instead of daytime (light), when CO2 enters the plant. CAM plants start photorespiration with PEPcase without solar radiation, and continue in the daytime when light is available. CAM plants are most closely related to C4 pathway, the most recently evolved pathway.
The dominant types of plants in a Sagebrush Steppe ecosystem are shrubs and grasses including Basin Big Sagebrush, Antelope Bitterbrush, Idaho Fescue, Bluebunch Wheatgrass, Rubber Rabbitbrush, Green Rabbitbrush, Cheatgrass, Ventenata, Sandberg Bluegrass, and Basin Wildrye. The general adaptations are to drought (aridity) and heat, with abundant vegetation in areas with enough precipitation to support shrubs and grasses, but not trees. They survive in the system by lasting through snowy winters and hot, dry summers. The dominant vegetation is plants that can survive in a semi-arid environment. The adaptations to heat and drought include mechanisms to survive the low precipitation, low temperature, heavy winds, and high salinity of semi-arid environments. Sagebrush Steppe ecosystem include plant species adapted for wind-dispersed seed pollination. Soil quality involves clusters of bacteria, algae, moss, and lichen growth. These soil features are heat and arid resistant, as well as fix their own nitrogen. This influences soil stability and erosion control, water infiltration, nitrogen fixation, facilitate seed germination, and nutrient cycling. Whether adaptations of Avoidance (dependent on precipitation) or Tolerance (leaf polymorphism, stem photosynthesis, and phreatophytes to reduce transpiration/photosynthesis) or Resistance (many CAM plants resistant to heat and aridity), plants have evolved to survive in a variety of different environments of heat and drought.


Novel Insights into Tree Biology and Genome Evolution as Revealed Through Genomics

Reference genome sequences are the key to the discovery of genes and gene families that determine traits of interest. Recent progress in sequencing technologies has enabled a rapid increase in genome sequencing of tree species, allowing the dissection of complex characters of economic importance, such as fruit and wood quality and resistance to biotic and abiotic stresses. Although the number of reference genome sequences for trees lags behind those for other plant species, it is not too early to gain insight into the unique features that distinguish trees from nontree plants. Our review of the published data suggests that, although many gene families are conserved among herbaceous and tree species, some gene families, such as those involved in resistance to biotic and abiotic stresses and in the synthesis and transport of sugars, are often expanded in tree genomes. As the genomes of more tree species are sequenced, comparative genomics will further elucidate the complexity of tree genomes and how this relates to traits unique to trees.


Acknowledgements

We thank J. Cate and S. Moore for input into the ribosomal protein analysis, J. Doudna and E. Nawrocki for suggestions on the rRNA insertion analysis, and M. Markillie and R. Taylor for assistance with RNA sequencing. Research was supported by the US Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research under award number DE-AC02-05CH11231 (Sustainable Systems Scientific Focus Area and DOE-JGI) and award number DE-SC0004918 (Systems Biology Knowledge Base Focus Area). L.A.H. was partially supported by a Natural Sciences and Engineering Research Council postdoctoral fellowship. DNA sequencing was conducted at the DOE Joint Genome Institute, a DOE Office of Science User Facility, via the Community Science Program. RNA sequencing was performed at the DOE-supported Environmental Molecular Sciences Laboratory at Pacific Northwest National Laboratory.


Horizontal Gene Transfer

Horizontal gene transfer (HGT) is the passing of genetic material between species by mechanisms other than from parent to offspring.

Learning Objectives

Explain how horizontal gene transfer can make resolution of phylogenies difficult

Key Takeaways

Key Points

  • It is thought that HGT is more prevalent in prokaryotes than eukaryotes, but that only about 2% of the prokaryotic genome may be transferred by this process.
  • Many scientists believe that HGT and mutation appear to be (especially in prokaryotes) a significant source of genetic variation, which is the raw material for the process of natural selection.
  • HGT in prokaryotes occurs by four different mechanisms: transformation, transduction, conjugation, and via gene transfer agents.
  • HGT occurs in plants through transposons (jumping genes), which transfer between different species of plants.
  • An example of HGT in animals is the transfer (through consumption) of fungal genes into insects called aphids, which allows the aphids the ability to make carotenoids on their own.

Key Terms

  • transformation: the alteration of a bacterial cell caused by the transfer of DNA from another, especially if pathogenic
  • transduction: horizontal gene transfer mechanism in prokaryotes where genes are transferred using a virus
  • conjugation: the temporary fusion of organisms, especially as part of sexual reproduction

Horizontal Gene Transfer

Horizontal gene transfer (HGT) is the introduction of genetic material from one species to another species by mechanisms other than the vertical transmission from parent(s) to offspring. These transfers allow even distantly-related species (using standard phylogeny) to share genes, influencing their phenotypes. It is thought that HGT is more prevalent in prokaryotes, but that only about 2% of the prokaryotic genome may be transferred by this process. Some researchers believe these estimates are premature the actual importance of HGT to evolutionary processes must be viewed as a work in progress. As the phenomenon is investigated more thoroughly, it may be revealed to be more common. Many evolutionists postulate a major role for this process in evolution, thus complicating the simple tree model. A number of scientists believe that HGT and mutation appear to be (especially in prokaryotes) a significant source of genetic variation, which is the raw material for the process of natural selection. These transfers may occur between any two species that share an intimate relationship, thus adding a layer of complexity to the understanding or resolution of phylogenetic relationships.

Mechanisms of prokaryotic and eukaryotic horizontal gene transfer: Horizontal gene transfer is the introduction of genetic material from one species to another species by mechanisms other than the vertical transmission from parent(s) to offspring. These transfers allow even distantly-related species (using standard phylogeny) to share genes, influencing their phenotypes. Examples of mechanisms of horizontal gene transfer are listed for both prokaryotic and eukaryotic organisms.

HGT in Prokaryotes

The mechanism of HGT has been shown to be quite common in the prokaryotic domains of Bacteria and Archaea, significantly changing the way their evolution is viewed. These gene transfers between species are the major mechanism whereby bacteria acquire resistance to antibiotics. Classically, this type of transfer was thought to occur by three different mechanisms:

  • Transformation: naked DNA is taken up by a bacteria.
  • Transduction: genes are transferred using a virus.
  • Conjugation: the use a hollow tube called a pilus to transfer genes between organisms.

More recently, a fourth mechanism of gene transfer between prokaryotes has been discovered. Small, virus-like particles called gene transfer agents (GTAs) transfer random genomic segments from one species of prokaryote to another. GTAs have been shown to be responsible for genetic changes, sometimes at a very high frequency compared to other evolutionary processes. The first GTA was characterized in 1974 using purple, non-sulfur bacteria. These GTAs, which are thought to be bacteriophages that lost the ability to reproduce on their own, carry random pieces of DNA from one organism to another. The ability of GTAs to act with high frequency has been demonstrated in controlled studies using marine bacteria. Gene transfer events in marine prokaryotes, either by GTAs or by viruses, have been estimated to be as high as 10 13 per year in the Mediterranean Sea alone. GTAs and viruses are thought to be efficient HGT vehicles with a major impact on prokaryotic evolution.

HGT in Eukaryotes

Although it is easy to see how prokaryotes exchange genetic material by HGT, it was initially thought that this process was absent in eukaryotes. After all, prokaryotes are only single cells exposed directly to their environment, whereas the sex cells of multicellular organisms are usually sequestered in protected parts of the body. It follows from this idea that the gene transfers between multicellular eukaryotes should be more difficult. Indeed, it is thought that this process is rarer in eukaryotes and has a much smaller evolutionary impact than in prokaryotes. In spite of this fact, HGT between distantly-related organisms has been demonstrated in several eukaryotic species. It is possible that more examples will be discovered in the future.

In plants, gene transfer has been observed in species that cannot cross-pollinate by normal means. Transposons or “jumping genes” have been shown to transfer between rice and millet plant species. Furthermore, fungal species feeding on yew trees, from which the anti-cancer drug TAXOL® is derived from the bark, have acquired the ability to make taxol themselves a clear example of gene transfer.

In animals, a particularly interesting example of HGT occurs within the aphid species. Aphids are insects that vary in color based on carotenoid content. Carotenoids are pigments made by a variety of plants, fungi, and microbes, which serve a variety of functions in animals who obtain these chemicals from their food. Humans require carotenoids to synthesize vitamin A and we obtain them by eating orange fruits and vegetables: carrots, apricots, mangoes, and sweet potatoes. On the other hand, aphids have acquired the ability to make the carotenoids on their own. According to DNA analysis, this ability is due to the transfer of fungal genes into the insect by HGT, presumably as the insect consumed fungi for food. A carotenoid enzyme called a desaturase is responsible for the red coloration seen in certain aphids. Furthermore, it has been shown that when this gene is inactivated by mutation, the aphids revert back to their more common green color.

HGT within the aphid species: (a) Red aphids get their color from red carotenoid pigment. Genes necessary to make this pigment are present in certain fungi. Scientists speculate that aphids acquired these genes through HGT after consuming fungi for food. If genes for making carotenoids are inactivated by mutation, the aphids revert back to (b) their green color. Red coloration makes the aphids much more conspicuous to predators, but evidence suggests that red aphids are more resistant to insecticides than green ones. Thus, red aphids may be more fit to survive in some environments than green ones.


One in Four Tree Deaths in Blue Ridge Mountains Linked to Invasive Species

New research from the Smithsonian Conservation Biology Institute (SCBI) and Shenandoah National Park finds that invasive species of forest insects and pathogens contributed to about a quarter of the tree deaths in Virginia’s Blue Ridge Mountain forests in the past three decades.

According to the authors, this is the first study to evaluate the long-term impact of the multiple invasive species affecting forests. The results, published today in the journal Ecosystems, have implications for the protection of forest health and mitigation of climate change.

“As the world struggles with COVID-19, we are becoming increasingly aware that health is globally interconnected—that a disease agent accidentally transferred to a new host can have devastating consequences,” said Kristina Anderson-Teixeira, forest ecologist at SCBI and the Smithsonian Tropical Research Institute and lead author of the study. “We expect more exotic tree disease agents to arrive in the future, and how we handle that threat will have important consequences for the health and diversity of our forests, along with their ability to help sequester carbon dioxide from the atmosphere and slow climate change.”

Non-native insects and pathogens can cause significant harm when brought to a new environment by human activity. In the Blue Ridge Mountain region alone, invasive species have led to the classification of seven tree species as threatened or endangered.

Beyond individual types of trees, however, scientists have not previously studied how invasive species affect entire forests in the long term. For this research, Anderson-Teixeira and her co-authors studied decades of data from forest plots at Shenandoah National Park and the neighboring SCBI. At SCBI, this includes a plot from the Smithsonian’s Forest Global Earth Observatory (ForestGEO), a worldwide network of forest monitoring sites.

The research plots are distributed across an 80-mile stretch of the Blue Ridge Mountains in Virginia. According to the authors, these plots are by no means unusual among forests of the eastern United States, which have all been subjected to multiple invasive species. Scientists have monitored the plots for years to measure the growth, death, abundance and diversity of tree species present. Combined, the records total more than 350,000 tree observations dating from 1987 to 2019.

Anderson-Teixeira and her team focused on the impact of eight invasive species, including insects like the gypsy moth and emerald ash borer, as well as fungi that cause disease in trees. They found that these eight species contributed to substantial increases in tree mortality over the past three decades. Their findings attribute about 25% of tree deaths to non-native insects and pathogens, with at least 22 tree species affected.

The study also reveals the resilience of these forests, however. Despite significant losses to individual tree species, the total number of species present remained relatively constant, and there was no overall reduction in the number and size of the trees. Other tree species compensated for the losses, making the forests stable over the past several decades.

“Insect and fungal pathogens are continually reshaping the forest composition in Shenandoah National Park, and it is reassuring to know that park forests are demonstrating resilience to these pressures by maintaining tree diversity and abundance,” said Wendy Cass, botanist at Shenandoah National Park and co-author of the study. “Shenandoah National Park is pleased that data from the park's ongoing long-term forest-monitoring program has supported this study.”

This long-term forest data demonstrates how invasive species have shaped entire ecosystems over time. Despite past resilience, invasive species continue to pose a growing threat to forests, and limiting their spread is important to maintaining the health and diversity of these forests.

Trees also play an important role in climate regulation because they absorb carbon from the atmosphere. The authors say that efforts to limit the spread of invasive species will not only protect the health of forests worldwide, but also aid efforts to slow climate change.

This research received grant funding from the Virginia Native Plant Society and Shenandoah National Park Trust.


The Life Story of The Oldest Tree on Earth

Revered for its beauty and its longevity, the ginkgo is a living fossil, unchanged for more than 200 million years. Botanist Peter Crane, who has a written what he calls a biography of this unique tree, talks to Yale Environment 360 about the inspiring history and cultural significance of the ginkgo.

Millions of urban dwellers know the ginkgo primarily as a street tree, with elegant, fan-shaped leaves, foul-smelling fruits, and nuts prized for their reputed medicinal properties. But botanist Peter Crane sees the ginkgo as much more — an oddity in nature because it is a single species with no known living relatives a living fossil that has been essentially unchanged for more than 200 million years and an inspiring example of how humans can help a species survive.

Crane, who is dean of the Yale School of Forestry & Environmental Studies, has written what he describes as a biography of the oldest tree on earth, a living link to the age of the dinosaurs. His new book, Ginkgo, tells the story of a tree that over centuries has made its way from China across Asia and around the world and today is found along streets everywhere from Seoul to New York.

In an interview with Yale Environment 360, Crane explains what makes the ginkgo unique and what makes it smell, how its toughness and resilience has enabled it to thrive, and what the tree’s long history says about human life on earth. The ginkgo, which co-existed with the dinosaurs, “really puts our own species — let alone our individual existence — into a broader context,” says Crane.

Yale Environment 360: You’ve been studying ginkgo trees for a long time. How did you come to develop an interest in them?

Peter Crane: I think that anyone who is seriously interested in plants inevitably comes across ginkgo pretty early in their training, because there are only five living groups of seed plants, and ginkgo is one of them. And ginkgo is the only one that consists of just one species. So it’s an important plant in any botanist’s view of the plant world — you inevitably run across it early in your training. The other thing is that it has such a distinctive leaf — once you see it, you don’t forget it. It’s thoroughly memorable.

e360: You’ve mentioned that ginkgo is something of a biological oddity in that it’s a single species with no living relatives. That’s somewhat unusual in the plant and animal world, isn’t it?

Crane: Yes. When we think about flowering plants, there are about 350,000 living species. And in an evolutionary sense, they’re equivalent to that one species of ginkgo. They’re all more closely related to each other than they are to anything else. But the ginkgo is solitary and unique, not very obviously related to any living plant. One of the points I wanted to draw out in the book is that in the past there were a variety of ginkgo-like plants, but this is the only one surviving.

e360: You describe the ginkgo as a “living fossil,” in the sense that in many ways it’s unchanged in more than 200 million years. How do we know that?

Crane: If you look at fossils from more than 200 million years ago, you can see leaves that are very very similar to modern ginkgo leaves. But you have to look more closely to really assess whether those leaves were produced by plants that are identical to modern ginkgo. And that work has been done now, by my colleague [Chinese paleobotanist] Zhou Zhiyan, who has worked on fossil material from China. And what he’s noticed is that there are some differences in the ways that the seeds are attached in these fossil plants — but in the grand scheme of things, they’re not very different.

With the fossils that I’ve worked on myself, from about 65 million years ago, we were able to determine exactly how the seeds were attached to the plant, and they were attached in an identical way to modern ginkgo. If we could go back in a time machine, maybe we would find some differences, but I suspect not.

e360: And the oldest fossil record?

Crane: A little over 200 million years old. So it is a good example of a living fossil, like the coelacanth, which has also changed very little over millions of years.

Ginkgo leaves in the autumn. AJYI/Ko.Yo

e360: Most of us know ginkgo from its very distinctive, fan-shaped leaves, and also from its very distinctive smell. What is with the smell?

Crane: It’s the outer part of the seed that produces the smell, and it smells, to put it bluntly, like vomit. More than likely, it reflects some sort of adaptation or modification in its dispersal biology. Probably either now or in the past the smell has been attractive to animals. You hear stories of dogs, for example, eating ginkgo seeds — sometimes with not a terribly happy outcome in that they don’t feel so good afterward. But it must be part of a dispersal system. The interesting question is, are the things that adapted to disperse it still around? Or are they extinct?

There’s this wonderful idea that [Daniel] Janzen and [Paul] Martin published about how many neo-tropical fruits don’t appear to have any dispersers in the contemporary fauna. And their idea was that as many large mammals went extinct about 10,000 years ago, many plants actually lost their most important dispersal agents. So in a sense, the plants have continued to live on, while the dispersers themselves have already gone extinct.

e360: So their theory would say that the ginkgo smell would have attracted dinosaurs to eat it?

Crane: Yes, or more likely some mammals that died out much more recently. But the idea is that the tree now could be out of phase with its dispersal agents. There are records of the seeds being eaten by badgers and so on, and as I talk to people it’s clear that the seeds do still move around. So something’s moving them. And you know, the seeds are very attractive — once that smell’s gone, they look a bit like a pistachio. And they have a nice nutritious meat in them, so they would attract animals like squirrels.

e360: When are the seeds on the ground? Is that the late fall?

Crane: They’re usually on the ground in the late fall here in temperate North America. So the trees are dropping their seeds in late November, December. And then often, what saves us from the smell is that they all freeze.

e360: When was the ginkgo first cultivated by humans?

Crane: Our best estimate is about 1,000 years ago in China, which is somewhat late for the cultivation of many plants in China. There’s a lot of Chinese literature from before 1,000 years ago, and it doesn’t mention ginkgo, while it does mention a lot of other plants. The evidence points to the fact that ginkgo was probably always a rather rare tree, and that it first attracted the attention of people about a thousand years ago. Probably originally as a nut — a rather unusual nut tree. And then it was moved around and grown for its nuts in China, before eventually — maybe in the 14th or 15th centuries — making its way up the coastal trade routes into Korea and Japan.

e360: And how and when did it appear in the West?

Crane: The first Westerner to encounter ginkgo — or at least the first Westerner to encounter it and write about it — was Engelbert Kaempfer, who was with the Dutch East India Company at their trading station in southern Japan in 1692. When he returned, he wrote his account of his time in Japan. He is the one who first uses the word in the Western literature — ginkgo — and he provides an illustration of it. But probably living plants weren’t introduced into Europe until a few decades after that — perhaps in the 1730s, but I think more likely in the 1750s.

e360: Ginkgoes have long been valued for their healing properties, their medicinal properties, particularly for helping memory. And we see today ginkgo being sold pretty widely in health food stores. Did the medicinal use of ginkgo emerge in China, and if so, how recent is its move to the West?

Crane: That’s a very interesting question, because if you look and see how ginkgo is used medicinally in China, it’s mainly the seeds that are used. Yet, the Ginkgo biloba that you buy in health food stores here is an extract of the leaves. And this is pretty much a Western phenomenon. So this is a use that we’ve invented for it in the West, rather than a use that has come to us from China. The medicinal uses in the East and the supposed medicinal uses in the West have gone in different directions, using two different parts of the plant — mainly the seeds in the East, and mainly the leaves in the West.

e360: Are there any scientific studies that looked at the efficacy of the medicinal properties, like for memory enhancement — either for the leaves or the seeds?

Crane: The most work’s been done on the leaves in the West. And I think it’s true to say the results are equivocal. I don’t think there’s really strong evidence for its efficacy, but on the other hand, there are conflicting results. There’s some evidence that it’s helpful in some ways, but the large-scale trials that we expect from our drugs these days have been unable to be really definitive about that. It’s a bit of an enigma in that respect — it’s difficult to prove its value.

e360: You write in the book about how the ginkgo’s resilience has enabled it to become quite a popular street tree — it can take a lot of abuse. What makes the ginkgo so resilient as a tree?

Crane: It’s hard to put a finger on what exactly does it. But the leaves are particularly unattractive to pests, so it doesn’t suffer from the pest problems that some trees do. And it seems to survive in a street setting: its roots aren’t getting much oxygen, they’re getting a lot of salt and goodness knows what else is getting poured on them, and it seems relatively resistant to those problems. So it’s just a good old tough tree, and it is incredibly widely planted.

e360: How widely, and in what places is it most common?

Crane: Well, it’s particularly widespread in the East: you see it all over Tokyo, you see it all over Seoul. But you also see it all over Manhattan. Once you start to recognize ginkgo trees in the urban landscape, you start to see them everywhere.

An early Western botanical illustration of Ginkgo biloba, published in Europe in 1835.

e360: You mentioned in the book that the female seeds are the ones that smell. In New York City, the parks department has a policy of planting only males?

Crane: Yes. I think today most people would plant males. Most reputable nurseries will sell only males.

e360: One of the things you get into in the book is the broader discussion of the importance of street trees. One of the benefits, which I had never thought about before, is how trees along a street make it feel narrower and cause drivers to go more slowly. It makes sense, but I had never thought of it. Can you describe some of the other benefits that street trees bring to a city or an urban setting?

Crane: I think most obviously they help reduce the urban heat island effect. They provide shade they make the place a lot more comfortable. But I think there are a lot of intangible benefits too: people want to walk in the shade, they want to be out in the shade. And so trees create a less sterile environment and encourage people to want to be outside, with all the benefits that come from people being out and about — from having kids playing outside, to having neighbors keep an eye on each other’s houses, to encouraging people to linger in a shopping area that they would otherwise walk right through.

e360: You certainly see ginkgos everywhere, especially in New York City. You tell a story about a Harlem homeowner who has a ginkgo tree in front of her house and finds people in it regardless of the smell. Can you explain?

Crane: Yes, in many places where ginkgo is planted in the West, people who’ve known ginkgo or know about ginkgo through their cultural background, will often seek out the trees in the fall and collect the seeds. Particularly with people from Korea or China or Japan, it’s quite common. You see them in Central Park [in New York]. I’ve seen them in Chicago. You see them all over. And I’m sure none of those seeds are sold into commerce. I’m sure those seeds are used locally because people enjoy eating them. And sometimes people won’t wait for the seeds to fall. They’ll take sticks and bang them up into the branches to try to get the seeds to come down.

e360: I was surprised to learn from your book that the ginkgo nut is potentially toxic?

Crane: Yes, it does have some toxicity to it. It’s generally recommended that people don’t eat too many of these seeds. A small proportion of the population seems to have a bad reaction to ginkgo, but it’s a very small proportion. I’ve eaten ginkgo seeds many times.

e360: You actually have an ancient species of ginkgo, Ginkgo cranei, named after you, right?

Crane: Well, yes, that’s the fossil ginkgo from North Dakota that I worked on as a researcher, which a colleague quite recently very kindly named after me. But in a way it’ll be interesting to see if the name survives, because giving it a separate name implies that it’s actually different from modern ginkgo. And the study did point out a few very subtle differences. However, it remains to be seen whether those differences hold up. So I wouldn’t be surprised to see my name get synonymized back into Ginkgo biloba at some point.

e360: By distributing ginkgo around the planet, humans have, unlike with many other species, helped ensure the ginkgo’s survival. Is that the right way to look at it?

Crane: Yes, I think that’s right. I think by cultivating plants like ginkgo that are very rare in the wild, we’ve sort of taken out insurance for their long-term survival. In China, for a long time there was a lot of discussion about whether there were any native ginkgos at all, or whether all of them had the hand of people in their past. I think the consensus now is that probably a couple of wild, original populations still exist in China. But it’s very difficult to exclude the possibility that even those have been aided by people.

That is another message in the book. Obviously we should try to preserve animals and plants in their native habitats, where they’re part of a functioning integrated ecosystem. But in the same way that we’ve used ex situ methods for conserving large mammals, charismatic animals, I think conservation through cultivation is an important part of the toolkit for preserving plant diversity for the future.

e360: You’ve talked about how one of the things that drew you to learn more about the ginkgo was the sense of timelessness that its history gives you and how that helps us think about our place in the world.

Crane: Obviously, we’re evolved to live in the present, so we’re very focused on the short-term. One of our biggest shortcomings is that we can’t see the long-term, and we see that in the way we respond to all kinds of environmental issues. So reflecting on a plant like ginkgo that was around in very different ecosystems when the dinosaurs were on the planet, that has been around for hundreds of millions of years, really puts our own species — let alone our own individual existence — into a broader context.

It’s a bit like those diagrams that you see, where there’s a picture of the Milky Way and there’s a little sign that says, “You are here.” Well, it’s the same idea. Guess what? We’re not at the center of everything. And guess what? The universe doesn’t revolve around us. And guess what? We’re only here for a short time, whereas some things have been here for a really long time. That ought to encourage us to take the long view as we think about our relationship to the natural world.


Significance to Wildlife

One interesting aspen poplar fact is their importance to beavers. Both kinds of aspens are a principal food of beavers throughout their range. The mammals will eat the bark, leaves and the twigs of these trees, and use the branches to construct dams. Other mammals that depend on the aspen tree for food include deer, moose and elk, which browse the leaves and twigs. Rabbits and muskrats will eat the bark, and birds such as the ruffed grouse will consume the seeds and the flower buds. The yellow-bellied sapsucker and the hairy woodpecker frequently hollow out parts of the tree to create a nesting cavity.



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