Saturday, February 17, 2018

Meet the meat-munching plants

Carnivorous Plants: Physiology, ecology, and evolution edited by Aaron Ellison and Lubomír Adamec, 2017. Oxford University Press.

Famously, Charles Darwin’s opinion of the Venus fly-trap is used to embrace his view of carnivorous plants generally as the ‘most wonderful plants in the world’ (Aaron Ellison and Nicholas Gotelli, Journal of Experimental Botany 60: 19–42, 2009; doi:10.1093/jxb/ern179) and is widely – and oft-repeatedly – used when writing about carnivorous plants*. And he was probably right – after all, he did know a thing or two about biology, especially of plants (e.g. Stephen Hopper and Hans Lambers, Trends in Plant Science 14: 421-435, 2009; And what’s not to be fascinated by with plants that can ‘eat’ animals – and not just tiny insects, but potentially even large rats(!)? Or plants that have trapping mechanisms that use an electrical network – “comparable to the nervous system of vertebrates” [Rainer Hedrich and Erwin Neher, Trends in Plant Science], and which can be anaesthetised – like humans – so they don’t work; or which contain digestive enzymes that may help in the treatment of coeliac disease in humans [Linda Lee et al., J. Proteome Res. 15(9): 3108–3117, 2016; doi: 10.1021/acs.jproteome.6b00224]; or inspire a new generation of biomaterials; or be the model for bizarre plants in movies (e.g. ‘Audrey’ in Little Shop of Horrors. And we know how fascinated people are by these plants – that’s why they are under threat in the wild from collectors and so-called ‘enthusiasts’, who are so enthusiastic about owning – or otherwise profiting from – these animal-capturing plants that their very existence is threatened (e.g. David Jennings and Jason Rohr, Biological Conservation 144: 1356–1363, 2011; doi:10.1016/j.biocon.2011.03.013) in nature.

But, if one is still ignorant of their true biology, it may strike them as entirely odd that anybody could get so excited about carnivorous plants. Aren’t the terrestrial species just plants with slightly odd leaf endings [after all, that’s what the pitchers and folding pads are in pitcher plants and the Venus fly-trap, respectively…]? Well, let’s try and disabuse those who hold such an ill-appreciative view. And that’s easily done: They are wrong to be so dismissive. The leaf endings, which bear the meat-trapping structures, are amazingly engineered (or intelligently designed/created – although to a large extent into doesn’t matter how they came to be, i.e. whether they’ve evolved or not, they are still exquisite examples of structure-and-function). And, in the interests of educating the phytocarnivoro-ignorant amongst us – and to remind those who do know about these plants quite how remarkable they are (and continue to be) – we must be grateful to Aaron Ellison and Lubomír Adamec. These editors have given us Carnivorous Plants: Physiology, ecology, and evolution [hereafter referred to as Phytocarnivoria], which is devoted to those aspects of the biology of these amazing botanics. And a good idea of the scope and coverage of the book can be gleaned from Ellison and Adamec’s Chapter 1, “Introduction: what is a carnivorous plant?”. This is a great overview of the book’s subsequent chapters, and important in setting out what the rest of the tome considers to be true carnivorous plants…

At 510 numbered pages, Phtocarnivoria is a relatively slim volume to try and cover the physiology, ecology, and evolution (aha, so neither created nor intelligently designed…) of these wonderful plant creations. But, it does a very good job of doing just that. And that job is done by the veritable army of scholars who have contributed their phytocarnivorous knowledge to this collection of expert testimonies to the powers that these plants have. Although I claim no expertise in the study of these plants, as an Editor for the Annals of Botany I’ve handled many plant carnivory manuscripts over the last decade or so, and recognise many of the names of those who’ve penned chapters in this collection. And a pretty good idea of the authors’ credentials can be gained by perusing the almost 10 pages of 2-columned contributors’ biographies at the front of the book. So, it certainly looks as if Phytocarnivoria is the combined work of the world’s leading researchers in this area of botanical endeavour, which is about as high an endorsement as you can get for the book’s scholarship. And this 2017 collection edited by Ellison and Adamec proudly carries on the tradition of scholarly works on carnivorous plants, which started with Charles Darwin’s Insectivorous Plants in 1875, via Francis Lloyd’s The Carnivorous Plants (1942), and Barrie Juniper, Richard Robins and Daniel Joel’s The Carnivorous Plants (1989). But, and importantly, Phytocarnivoria not only updates the subject for the advances made in the past nearly 30 years since Juniper et al., it also showcases the latest techniques of study as befits a 21st century text.

Phytocarnivoria’s back cover blurb states that it is intended to be a text suitable for senior undergraduate and graduate students, and researchers in plant biology, ecology, and evolutionary biology. And it should certainly fit that requirement. It should – as is also hoped – be of relevance to horticulturalists and carnivorous plant enthusiasts. But, given its subject matter, extracts and examples can also be usefully included in courses for more junior undergraduates [one is never too young to be appraised of the strange goings-on as plants take on animals, and win!].

With my botany lecturer’s hat on I recognise that there is much of value in Phytocarnivoria that could be brought into one’s undergraduate teaching. This is important as one acknowledges that plants ‘actually doing something’ is more likely to appeal to today’s plant-averse (if not actually plant-blind) generation. And, if that encourages them to study plants a little more deeply – even if it’s just carnivorous plants! – then that’s a great outcome. So, let’s hope that the contents of Phytocarnivoria are widely read and shared to spread the botanical message. Having been so comprehensibly reminded why carnivorous plants are amongst the most wonderful plants on the planet, maybe Phytocarnivoria will have also gone some way to helping us to protect and conserve these natural curiosities.

My one sentence summary:

Carnivorous Plants: Physiology, ecology, and evolution is a remarkable work of scholarship for a remarkable group of plants (by a remarkable band of enthusiasts).


* Yes, I do realise that here I’m in danger of swelling the ranks of those who can’t write a piece about these plants without mentioning that quote. This tendency is as noticeable as those who write about seeds and feel compelled to add that Thoreau quote about seeds and wonders. But, just because it is so often trotted out, like a prize pony to be showed-off, doesn’t make the Darwinism any less true – or apt!

Heterochronical trends promote labile floral strategies in Eugenia


Comparative ontogeny elucidates subtle changes in developmental rate, known as heterochrony, that discretely alter morphology between species. Vasconcelos et al. show how these trends explain evolution of Eugenia (Myrtaceae) megadiversity in contrast to its apparent flower uniformity.

Eugenia involucrata (Sect. Phyllocalyx)
Eugenia involucrata (Sect. Phyllocalyx)

Selected steps of the floral ontogeny were described and compared between 21 species; trait data were contrasted for correlation analysis. Heterochrony was evident from size differences between structures at similar developmental stages. These differences underlie variable levels of investment in protection and subtle modifications to symmetry and breeding system, producing a wide spectrum of floral display and contributing to fluctuations of fitness in the genus.


Vasconcelos, T. N. C., Lucas, E. J., Faria, J. E. Q., & Prenner, G. (2017). Floral heterochrony promotes flexibility of reproductive strategies in the morphologically homogeneous genus Eugenia (Myrtaceae). Annals of Botany, 121(1), 161–174.

How hard is it to use plant computational models? Ask our students!


A guest post by: Xavier DRAYE, Guillaume LOBET, Brieuc RYELANDT, Antoine RUMMENS, Thomas FERON, Gabriel CARESTIA, Timothée , François DUQUESNE, Nicolas DEFFENSE and Fabio CLAPS

The background

For years, a small proportion of the plant science community has been developing and using plant models. Beware of the confusion here: by saying plant models, we are not referring to any model plant (Arabidopsis, maize or Brachypodium, whichever is your favorite), but to computational models of plants. Virtual plants. Not plants born from ATCG’s, but from 0 and 1.

Such plant models have been used to describe the formation, growth and development of plant organs (for instance, root, shoot, fruits or leaves), but also how these organs influence and are influenced by their environment. Scale wise, plant models were create from organ to field scales, making them extremely broad and their potential application numerous.

However, the plant science community as a whole is not a heavy user of plant models. Why is that?

One reason might be that plant models might be scary (although we do not have any hard data to support this claim). Indeed, as soon as you talk to a plant modeller, you might be facing a frightful list of Python eggs, C++ libraries and other Java dependencies. There is no better way to get a computer novice to run the other way. But is it really that hard to use plant models?

The actors

Our story took place during the first semester (Sept-Dec) of the academic year 2017-2018, at the University of Louvain (Belgium), in the Faculty of bioscience engineering. The actors were eight master students (Brieuc, Antoine, Thomas, Gabriel, Timothée, François, Nicolas and Fabio) following the lecture “Systems Biology Modelling” given by Xavier and Guillaume. The students did not have a strong background in programming or computer sciences. They only followed two introductory course to the programming environment MatLab.

The structure of the course was the following: it started with a short general introduction to systems biology, then the students were asked to form 3 groups for the rest of the semester. During the remaining lectures (approx. 20h), each group had the following assignment:

  1. choose an existing, published, biological model;
  2. learn the biological theory on which the model was built;
  3. Learn how to install and run the models and;
  4. Answer a simple question using the model.

In addition to these tasks, each group also had to:

  1. Use Github to store their codes and collaborate
  2. Use Twitter to communicate about biological models

What we did

Three different model were chosen by the different groups: OpenSimRoot (Postma et al. 2017), LPy (Boudon et al. 2012) and RootBox (Leitner et al. 2010).


OpenSimRoot is a functional-structural root model that is combined with a soil model to simulate water and nutrients uptake. It allows the implementation of mini models. Our experience with this model was at first difficult, because we needed to understand how to run a simulation, how to include the mini models, what we can easily change in the parameters. But with a few hours of testing, we were able to play with it and perform basic simulations, with contrasting nitrogen levels.

More results are available on the wiki page of the group:

The twitter page of the group was: @biomodelisation

Example of outputs obtained using OpenSimRoot
Figure 1: Example of outputs obtained using OpenSimRoot


RootBox is a model coded in Matlab. It is designed to easily create time dependent branched geometries of growing plant root systems. It has been a real pleasure to work on a model like this one. The entire code was freely available, and the creators took time to answer our questions. A graphical user interface allowed us to take our first steps with the model. The comments in the code helped us to enter deeper into the program.

As future agronomists, we are very interested in plant parasites. This project has allowed us to understand how certain resistance to pathogens could work. We were able to use Rootbox to model a nematode tolerant root beet system, and use the model as a proof of concept to explain this resistance.

Results from RootBox
Figure 2: Example of outputs obtained using RootBox. A. Root length density map for all roots. B. Root length density map for root younger than 3 days-old.

More results are available on the wiki page of the group:

The twitter page of the group was: @BioModelling


L-py is a model based on the L-System (a re-writing language, well suited for fractal structures) and coded in Python.

Our goal was to use L-py to simulate light interception of a hop plant and get more insight on the functional importance of shoot traits. Our first task was to learn the L-System-based language. Once we got a feeling of how the software worked, it was pretty easy to create basic plant model. The next step in our project was to simulate a Hop plant. We were thus confronted with the difficulties to represent the complex physiology of a plant with simple programing rules. Finally, the idea was to integrate our plant in a light simulating module, so we could evaluate the effect of light on the hop plant.

Results from LPy
Figure 3: Example of outputs obtained using LPy. Left: Visual output of the simulated hop plant. Right: LPy coding interface.

The challenges we faced

The three groups successfully managed to run the model of their choice, and to make some basic simulation. However, not everything was easy and smooth. Over the course of the project, the different groups had to face different challenges. We can summarize these in three main categories: installing the model, using the model and parameterizing the model.

Installing and running the model

The first step, and the first opportunity for head scratching, is the installation of the model on your own computer. Each model, regardless on its complexity, has its own programming language and running environment. While a fair proportion of plant biologists will find their way around R or Python, things can get quickly messy when it comes to Java (which version again?), C++ (which compiler should I use?) or Matlab (…I do not have a licence!). Then comes the loading of libraries, tuning of running environment, loading of more libraries and incompatible version. And, let’s be honest, the documentation for these types of issues is usually lacking and often confusing.

Understanding the model

Once you passed the first step and the model is running, then comes the point where you need to understand it… Which variable, in which file should we change? How should I format my parameter file? Where are the outputs and how to access them? How can I run the model in batch mode to make thousands of simulation? As for the installation, user guides for plant models are often too light and do not allow users to fully explore their capabilities.

That said, for all three projects we contacted the authors of the models, and all were very helpful.

Parametrizing the model

Finally, once you installed the model and know how to use it, it is time to ask a biological, question. And with that question comes the need for solid experimental data. While the literature is full of qualitative data and figures, raw quantitative data needed for modelling are most of the time missing. Knowing that nematodes are moving down in the soil as the season advances is not enough. To use such information in the model, we need to know when that migration starts, where, and how fast. We need numbers to feed the simulations. And these numbers are often missing.

What we learned

The main thing we learned is probably that modelling is not as hard as it seems. Although we encountered some difficulties along the way, each group managed to do some basic simulations and to answer some basic biological questions – and all of this without having a strong computational background. Maybe a taste for computers, but that is all.

We also learned that most of the difficulties we encountered could be addressed (at least partially) by contacting the model developers themselves. They were all happy to take our questions and quick to answer them.

Finally we conclude: once we passed the initial steep learning curve, we learnt that modelling plants can be fun.

About the Authors

Nicolas DeffenseNicolas Deffense
Nicolas is passionate about nature and medicine. He is currently finishing his bioengineering studies with a specialization in modeling at UCL (Louvain-la-Neuve,Belgium). His desire is to use computer tools to improve biomedical techniques. In the same way as climate change, the use of models seems to be a good way to better understand the human body.

Fabio ClapsFabio Claps
Fabio is a master student of agricultural science at Università di Torino. He is currently on an Erasmus exchange at Université catholique de Louvain.

Antoine RummensAntoine Rummens
Antoine is currently studying agronomic sciences at the UCL in Belgium. His main centers of interest are pictorial arts, especially the modern period but also the Italian Renaissance, economics and field hockey. In his spare time, reading occupies his mind.

Thomas FeronThomas Feron
Thomas is a 22-year-old student passionate about applied mathematics and modeling. He started studying bioengineering at UCL to understand how nature works and to get the tools to protect it

Francois DuquesneFrancois Duquesne
François is a student at Université catholique de Louvain mastering in Bioengineering. He is passionate about the environment and computer sciences. His goal is to contribute to the comprehension of natural processes as well as the protection and conservation of Nature.

Brieuc ReylandtBrieuc Reylandt
Brieuc is a student in bioscience engineering at UCLouvain (Belgium). He’s interested in the sciences in general, but also in society’s problems like energy and climate change. He plays guitar during his free time.

Gabriel CarestiaGabriel Carestia
Gabriel is a first-year master student in bioengineering at the University of Louvain-la-Neuve. He have always been passionate about life sciences and mathematics. He naturally chose studies focused on system modeling in agronomy and the interpretation of these models to solve various problems.

Timothée ClémentTimothée Clément
Timothée is a student in bioengineering in agronomic sciences, option “analysis and management of information” at UCL (Belgium). He is an animator in youth movements and is part of a colocation in Louvain-la-neuve leading local projects for sustainable development (“kot planete terre”).

Guillaume LobetGuillaume Lobet
Guillaume is an Assistant Professor between the Forschungszentrum Jülich and the Université catholique de Louvain. The aim of his research is (i) to understand how various signals that carry information are interacting and being conveyed and integrated at the plant level and (ii) to amplify discrete physiological knowledge into functional plant processes. All of that using Functional Structural Plant Models.

Xavier DrayeXavier Draye
Xavier Draye is Professor of Crop Physiology and Plant Breeding at the Université catholique de Louvain. He uses a combination of experimental and modelling (FSPM) strategies, from the organ to the plant scales, to understand the dynamics of root system architecture and hydraulics. He interacts closely with soil hydrologists and molecular biologists to develop novel views of crop water use that integrate notions of growth, development, root hydraulic properties and soil water dynamics. Xavier is also active in the development of root image analysis tools and standards (RSML) and in the development of phenotyping systems. He contributed to the DROPs project and is involved in the modelling section of the EMPHASIS infrastructure.


Postma, J. A., Kuppe, C., Owen, M. R., Mellor, N., Griffiths, M., Bennett, M. J., … Watt, M. (2017). OpenSimRoot: widening the scope and application of root architectural models. New Phytologist, 215(3), 1274–1286.

Boudon, F., Pradal, C., Cokelaer, T., Prusinkiewicz, P., & Godin, C. (2012). L-Py: An L-System Simulation Framework for Modeling Plant Architecture Development Based on a Dynamic Language. Frontiers in Plant Science, 3.

Leitner, D., Klepsch, S., Bodner, G., & Schnepf, A. (2010). A dynamic root system growth model based on L-Systems. Plant and Soil, 332(1-2), 177–192.

Within-plant epigenetic mosaicism is related to subindividual heterogeneity in seed size and production


Homologous organs produced by individual plants are not identical, and within-plant phenotypic variance often exceeds variance between plants. Alonso et al. hypothesised that epigenetic mosaicism, caused by subindividual heterogeneity in DNA methylation levels, may account for within-plant variation in seed size and seed production in the evergreen Mediterranean shrub Lavandula latifolia (Lamiaceae).

Lavandula latifolia
Flowering Lavandula latifolia shrub (left) and idealized drawing of one of its modules, consisting of a single inflorescence plus its subtending leaves (right).

They found that DNA samples from leaves located in different parts of the same shrub differed in global DNA cytosine methylation, and that such subindividual heterogeneity was nonlinearly related to variation in number and size of seeds produced per inflorescence. They propose that variation in global DNA methylation within L. latifolia shrubs may result from the concerted action of plant sectoriality and differential exposure of different plant parts to some environmental factor(s) with a capacity to induce durable epigenetic changes.


Alonso, C., Pérez, R., Bazaga, P., Medrano, M., & Herrera, C. M. (2017). Within-plant variation in seed size and inflorescence fecundity is associated with epigenetic mosaicism in the shrub Lavandula latifolia (Lamiaceae). Annals of Botany, 121(1), 153–160.

The Return of the Rainbow Rose


One of our most popular posts is Roses are red – but they don’t need to be, if you know how to use food dyes and Fibonacci. In it, Anne Osterrieder explains how to make your own rainbow rose. Not everyone is convinced the method works.

I gave it a go and in my house today there’s this.

A rainbow rose
The petals draw their colours from whatever dye was traveling up their part of the stem.

Sadly there’s no connection between me trying to make a rainbow rose and having one in my house. The local shop was selling them for Valentine’s Day. When I tried making one I got nothing.

I thought that could be because I’d mucked up cutting the stem. So what I then did was try to make a green rose. That way I could at least see that the food dye was working to dye the petals. After a day there was a slight green tinge at the edge of the petals, but there was no way it was going to be mistaken for a green rose.

Stained rose stem
If you look at where the stem is peeling away you can see the different colours of the xylem.

So is it all nonsense? Chronica Horticulturae, a Publication of the International Society for Horticultural Science mentions the invention of rainbow roses on page 16, and dyes are the method they describe. The science Anne describes is sound. Also looking at the stem, it’s clear that there’s something going on in the xylem. The cross cross-section is evidence of there being dyes going up the stem.

However, the stem itself is a lot less woody than the usual roses that I’ve bought, and this is likely to make a difference. Also, the how-to videos suggest using food dyes. I can see why, but I wonder if you’re going to have more success with a specialist floral adsorption dye. It’s worth noting that the article in Chronica Horticulturae says that everyone already knows about staining flowers. The way Peter van de Werken has innovated is thinking to stain in multiple colours, and getting those stains fairly uniform on a regularly repeatable basis.

As an experiment to see water traveling up the stem into the petals, I think it’s an interesting demonstration, but I don’t see Peter van de Werken’s business being in trouble from hobbyists any time soon. However, if you do want to make your own multicolour roses there’s another way.

The role of local pH in regulating rhizosphere priming effect


Plant roots can alter the decomposition rate of soil organic carbon (SOC) in the rhizosphere (root-soil interface) by either stimulating or suppressing the activity of microbes and enzymes. Wang and Tang used a 13C natural abundance approach to examine the effect of nitrogen form on wheat and white lupin rhizosphere pH and associated changes in the SOC decomposition.

Conceptual diagram of the effects of N form (NO3-N vs. NH4-N)-induced pH changes on the rhizosphere priming effect (the RPE) and involved N immobilization mechanisms.
Conceptual diagram of the effects of N form (NO3-N vs. NH4-N)-induced pH changes on the rhizosphere priming effect (the RPE) and involved N immobilization mechanisms.

They found that the amount of CO2 derived from SOC in the presence of plants positively correlated with rhizosphere pH. In addition, microbes in the rhizosphere of nitrate-fed plants acted as an immediate sink for nitrogen released through the enhanced SOC decomposition.


Wang, X., & Tang, C. (2017). The role of rhizosphere pH in regulating the rhizosphere priming effect and implications for the availability of soil-derived nitrogen to plants. Annals of Botany, 121(1), 143–151.

The benefits of sleeping in


What is it that switches a seed from dormancy to sprouting? It’s important to time it right, as once the seedling emerges from the ground, it is stuck with whatever environment it finds itself. It’s easy to see that seed will not want to germinate during a harsh winter or deep drought, but even when conditions are favourable, there are other problems. Plants rarely travel alone, and if a seed falls into soil somewhere, it’s likely to have neighbours all competing for the same resources.

Arabidopsis. Photo Vasiliy Koval / 123RF.

It’s tempting to see germination among a group of seeds as a competition to be first. The earliest seedlings get the first light, with this extra energy they can shade out their neighbours and keep a greater share of sunlight to themselves. What has puzzled Lindsay Leverett and her colleagues is something that seems to get this all wrong. Some plants seem to delay germination when they have neighbours. Is this just because the other plants are dominating the soil and gobbling up more nutrients, preventing the late seeds from getting the signals they need to germinate? Or is it that something else is going on? In their paper The fitness benefits of germinating later than neighbors Leverett and colleagues list three ways in which late germination could be an advantage.

When two seedlings germinate around the same time, they’re in direct competition. They compete for the same soil with the same resources, and it’s quite a fight. What happens if one plant is much more massive? Surely the fight is one-sided? Leverett and her co-authors argue that the fight doesn’t happen. The bigger plant has deeper roots and has a different relationship to the soil than its smaller neighbour. Instead of fighting its neighbour, the smaller plant can work around it.

The mismatch in growth can also mean the later plant is competing at a different time to its neighbour. A plant can be growing while its neighbour is dying back. The time delay means that a plant can spend less time competing with its neighbours.

Finally, a larger neighbour can act as a windbreak, protecting smaller seedings from the cold. So while there are costs to later germination, there can also be benefits.

To examine how germination is timed, Leverett and colleagues looked at two questions. They measured how germination was affected by conditions in the seed environments. They also looked at whether the maternal environment made a difference. The authors then asked how late germination influenced fitness, but putting seedlings next to plants of various sizes to simulate early or late germination.

What they found was that seeds were more likely to germinate if the mother plant had more neighbours. They also found that germination would be delayed if there was a canopy over the seed site (simulating a neighbour) when the seed was watered.

The effects of neighbours were a bit more complicated.

If you want to survive as a young seedling, then the best you can hope for is to have a large neighbour. Growing alone was more likely to lead to an early death than growing with a large neighbour. However, get past that early stage, and you’re doing well as a sole plant. In contrast plants with neighbours were more likely to die as they aged. They also grew smaller and their ability to reproduce was diminished. However, the plants with larger neighbours did better than the plants with smaller neighbours. The results are consistent with there being benefits to late germination, and this might explain why seeds with neighbours could delay their germination.

The result that surprised me the most was the influence of the maternal environment on how seeds germinate – and it really shouldn’t have. Two of the authors of the AmJBot paper were also authors on an earlier Annals of Botany paper that showed the importance of the maternal environment.

The paper, Contrasting germination responses to vegetative canopies experienced in pre- vs. post-dispersal environments, looked at seeds maturing under vegetative canopies, simulated by adding a green filter to light.

The Annals paper shows that when the seed is maturing it’s being loaded with baggage from the maternal plant. In the case of the AmJBot paper, the seeds are growing on the mother plant which has the stresses of its neighbours. That’s how the mother plant influences the seeds germination when they’re independent in the soil themselves.

As each plant tackles a slightly different micro-environment, it helps show how variation in responses is being passed along to the next generation. But a seed isn’t merely preprogrammed to germinate. There’s a definite response to neighbours in an organism that appears to be dormant.

If you’d like to read more, the Annals paper is free access now. The AmJBot paper will be free access from January 2019, if you don’t have access to a subscription. Wiley has done an excellent job with the new AmJBot site in showing which issues of the journal are free to access.


Leverett, L. D., Schieder IV, G. F., & Donohue, K. (2018). The fitness benefits of germinating later than neighbors. American Journal of Botany.

Leverett, L. D., Auge, G. A., Bali, A., & Donohue, K. (2016). Contrasting germination responses to vegetative canopies experienced in pre- vs. post-dispersal environments. Annals of Botany, 118(6), 1175–1186.

What makes New Caledonian rainforests so different?


The biodiversity hotspot of New Caledonia is globally renowned for the diversity and endemism of its flora. Ibanez et al. compare the composition, the diversity and the structure of nine 1-ha plots of New Caledonian rainforests with fourteen 1-ha plots located across other tropical rainforests in Australia, Fiji, Papua New Guinea and the Solomon Islands.

Location of the nine 1-ha plots in the North Province of New Caledonia (SW Pacific)
Location of the nine 1-ha plots in the North Province of New Caledonia (SW Pacific). Protected areas are ‘wilderness areas’ (IUCN category Ib). Am. = Amoss, Ao. = Aoupinié, Ar. = Arago, At. = Atéu, Bo. = Bouirou, F. P. = Forêt Plate, Ji. = Jiève, Gu. = La Guen and Ti. = Tiwaé.

This provides the first such overview for the Southwest Pacific region. Rainforests of this region are highly diverse, even on a global scale. High stem densities, endemism and abundance of tree ferns are characteristics of New Caledonian rainforests.


Ibanez, T., Blanchard, E., Hequet, V., Keppel, G., Laidlaw, M., Pouteau, R., … Birnbaum, P. (2017). High endemism and stem density distinguish New Caledonian from other high-diversity rainforests in the Southwest Pacific. Annals of Botany, 121(1), 25–35.

Molecular mimicry modulates plant host responses to pathogens


Pathogens often secrete molecules that mimic those present in the plant host. Recent studies indicate that some of these molecules mimic plant hormones required for development and immunity. Ronald and Joe review the literature on microbial molecules produced by plant pathogens that functionally mimic molecules present in the plant host.

Plant pathogens produce molecular mimics that modulate plant signalling pathways.
Plant pathogens produce molecular mimics that modulate plant signalling pathways. Pseudomonas syringae pv. tomato (Pst) produces coronatine, a structural and functional mimic of jasmonoyl-L-isoleucine (JA-Ile). Coronatine binds the plant JA receptor, COI1/JAZ, to activate the JA signalling pathway, which suppresses salicylic acid (SA)-mediated signalling and inhibits the immune response. Nematodes secrete a mimic of a plant peptide called CLAVATA3/ENDOSPERM SURROUNDING REGION-related (CLE), which is perceived by the plant CLAVATA1 (CLV1)/CLV2 heterodimer or a tracheary element differentiation inhibitory factor (TDIF) receptor (TDR). It is hypothesized that nematode CLE peptides subvert plant CLE-mediated shoot and root meristem development to instead produce feeding cells for the nematode. Another class of nematode effectors mimics the plant C-TERMINALLY ENCODED PEPTIDEs (CEPs). Plant CEPs are produced in the roots under nitrogen starvation and then move through xylem vessels to shoots, where they are recognized by two receptors, CEPR1 and CEPR2. Activation of CEPRs induces nitrogen-demand signals, which increase expression of nitrogen transporters, inhibit primary root elongation and initiate lateral root development to take up nitrogen. The benefit to the nematode is that nematode CEPs induce more nitrogen uptake and keep the size of the feeding site small for biotrophic interaction with plants. Nematodes need to maintain small feeding sites to prevent excessive nutrient drainage and allow host plants to survive. The fungal pathogen Fusarium oxysporum secretes a mimic of the plant rapid alkalinization factor (RALF) peptide. Plant RALF targets the FERONIA (FER) receptor to activate a plasma membrane H(+)-ATPase 2 (AHA2) and thus alkalinizes the extracellular space in planta. RALF-induced extracellular alkalinization regulates the plant cell expansion required for plant growth and development. Fungal RALF-induced alkalinization in the plant apoplast is beneficial to fungal infection and multiplication but the underlying mechanism remains unclear. The sulphated RaxX (RaxX-sY) peptide from Xanthomonas oryzae pv. oryzae (Xoo) mimics the plant peptide hormone PSY (plant peptide containing sulphated tyrosine). RaxX-sY activates PSY signalling and promotes plant growth. Rice XA21 recognizes and responds specifically to microbial RaxX to activate the immune response. Straight lines indicate the secretion of pathogen molecules. Dashed lines indicate products of the endogenous factor from the plant. Question marks indicate pathways that have not yet been fully elucidated.

They include examples from nematodes, bacteria, and fungi with a particular emphasis on RaxX, a microbial protein produced by the bacterial pathogen Xanthomonas oryzae pv. oryzae. RaxX mimics a plant peptide hormone, PSY (plant peptide containing sulphated tyrosine). The rice immune receptor XA21 detects sulphated RaxX but not the endogenous peptide PSY. Studies of the RaxX/XA21 system have provided insight into both host and pathogen biology and offered a framework for future work directed at understanding how XA21 and the PSY receptor(s) can be differentially activated by RaxX and endogenous PSY peptides.


Ronald, P., & Joe, A. (2017). Molecular mimicry modulates plant host responses to pathogens. Annals of Botany, 121(1), 17–23.

Hydraulic architecture of Eucalyptus grandis


A comprehensive understanding of the systems behind vertical transport of water in tall trees is crucial when predicting the susceptibility of these long-lived organisms to drought.

Pfautsch et al. use detailed physiological and wood anatomical analyses of 20m tall Eucalyptus grandis (Myrtaceae) trees to unveil that – contrary to widespread assumptions – the widest xylem vessels were guarded by the thickest pit membranes, located several meters above the ground. The results explain how ultrastructural traits of xylem help improve the efficiency and apical dominance of water transport and stress the importance of studying hydraulic architecture at the whole-tree scale.


Pfautsch, S., Aspinwall, M. J., Drake, J. E., Chacon-Doria, L., Langelaan, R. J. A., Tissue, D. T., … Lens, F. (2018). Traits and trade-offs in whole-tree hydraulic architecture along the vertical axis of Eucalyptus grandis. Annals of Botany, 121(1), 129–141.