Oh, my. I spent Saturday—yes, pretty much all of it—at the Minnesota State Fair with friends. I have to admit, it was impressive. The livestock barns were bio-geeky fun, and the food (sampled strategically, in moderation) was uniformly good, especially the milkshakes at the dairy barn. Deep-fried cheese curds are amazing, and I do not want any more until next year.
Anyway, I’ve finally gotten around to weeding through the photos I took, so here you go. Check out the QR code made out of seeds from the “seed art” competition (which is far from the most peculiar and specific competitive category we encountered) and the sheep in vaguely sinister protective coveralls.
Tetranychus evansi eats a wide range of plants, from tomatoes to potatoes. One female mite can eat enough to lay 50%-70% of her weight in eggs every day, and while that isn’t much on the scale of a single, miniscule red mite, it adds up quickly when colonies build into dense clusters on host plants, sucking them dry and covering them in webs of spun silk.
Most host plants respond to such an onslaught by ramping up production of chemicals that make them unpalatable to herbivores, or that interfere with the mites’ ability to digest plant tissue. However, a team of Dutch and Brazilian biologists recently found that T. evansi somehow short-circuits this response [$a].
The team, whose senior author is the Dutch biologist Arne Jannsen, discovered that mites raised on leaf tissue from tomato plants previously attacked by T. evansi survived longer and laid more eggs than mites raised on tissue from plants that had never been attacked. Analsyis of RNA from tomato leaves attacked by the mites revealed that they were producing fewer of the signalling proteins associated with responding to insect damage than leaves damaged by another, related mite species—and one protein was produced at lower rates than in undamaged leaves!
In other words, the mites were not just preventing the host plant from boosting its defences in response to a mite attack—they were suppressing the defenses below what they would be without an immediate threat. Like a burglar cutting the power to a home security system, T. evansi can somehow prompt a hostile host to become more hospitable.
This raises another problem, however. With its defenses down, the host plant is also more hospitable to other insect herbivores, which could reduce the plant’s value to T. evansi, or even activate the alarms the mites have managed to suppress. A second study by the same team suggests that this may be part of the function of the webs T. evansi spins as it consumes its host.
In this second round of experiments, the group returned to the closely related mite Tetranychus urticae, which was used to stimulate plant defenses in the first study. Earlier work had found that some strains of T. urticae can tolerate or suppress host plant defenses [$a], though not nearly as effectively as T. evansi. That earlier work found that non-suppressing mite strains could benefit from living on the same plant as a suppressing strain, and the new study first demonstrated that this effect is even stronger when T. urticae shares a plant with T. evansi.
A whole lot of (presumably happy) mites. Photo via AgroLink.
In contrast, T. evansi colonies fared worse in the presence of the non-suppressing mites, whether fed leaves that had already been attacked by T. urticae, or placed on a mite-free leaf of a plant with another leaf infested by the non-suppressing species. All else being equal, T. urticae benefits from the defense-suppressing activity of T. evansi, but reduces the value of the host plant to T. evansi.
Faced with this freeloading competitor, T. evansi apparently replaces the disabled plant defenses with webbing. The team found that even though T. urticae thrived when given evansi-chewed tomato leaves, the non-suppressing mites had difficulty colonizing leaves covered in T. evansi webs. Moreover, T. evansi introduced onto a plant with the non-suppressing mites spun more webbing than when introduced onto a mite-free plant; but they didn’t ramp up web-spinning when sharing a plant with another colony of their own species, suggesting that the mites can respond to competition by building up their defenses.
So not only does T. evansi possess the means to turn off its hosts’ biological security system, it erects its own defenses to protect the plant from one competitor that might try to take advantage of the situation. How, exactly, the mites interfere with plants’ defensive responses will be an interesting future line of study. I’d also be very interested to see whether other herbivorous insects—things larger than other mites, and not so easily put off by some silk security fencing—also preferentially attack plants disabled by T. evansi. ◼
Kant, M., Sabelis, M., Haring, M., & Schuurink, R. (2008). Intraspecific variation in a generalist herbivore accounts for differential induction and impact of host plant defences Proc. Royal Soc. B, 275 (1633), 443-52 DOI: 10.1098/rspb.2007.1277
Sarmento, R., Lemos, F., Dias, C., Kikuchi, W., Rodrigues, J., Pallini, A., Sabelis, M., & Janssen, A. (2011). A herbivorous mite down-regulates plant defence and produces web to exclude competitors. PLoS ONE, 6 (8) DOI: 10.1371/journal.pone.0023757
Sarmento, R., Lemos, F., Bleeker, P., Schuurink, R., Pallini, A., Oliveira, M., Lima, E., Kant, M., Sabelis, M., & Janssen, A. (2011). A herbivore that manipulates plant defence. Ecology Letters, 14 (3), 229-36 DOI: 10.1111/j.1461-0248.2010.01575.x
Defining a new adaptive zone for evolutionary ecology. The observation that anoles living in different micro-habitats had different body types, and that those body types might be related to life in the micro-habitats, was first published fifty years ago.
That burbling sound is “Thus Spake Zarathustra” playing underwater. Now dolphins are using conch shells as tools.
Yes, I’ve actually kept busy enough this week to make it all the way to Friday without compiling the weekly linkfest. So, er, here’s the best thing not related to next-generation sequencing I did manage to see all week: Julia Child explaining the Miller-Urey experiment, then making her own “primordial soup.”
Biologists are about to have access to all the genetic data we could ever want. Unfortunately, once we have that data, we have to figure out where to put it—and some way to sift out the bits that answer the questions we want to answer.
That’s the first day of the NESCent workshop in next-generation sequencing methods in a nutshell.
Brian O’Connor, who gave the morning lectures, framed the immediate future of biology as a race between technologies for collecting genetic sequence data and technologies for storing and analyzing that data. Moore’s Law is that computer processor speed (really, the number of transistors packed into a single processor chip) doubles about every two years; Kryder’s Law is that computer storage capacity roughly quadruples in the same amount of time. But in the last few years, and for the foreseeable future, DNA sequence collection capacities are growing on the order of ten times every couple years.
In other words, there may very well come a day when the cost of storing and using a genome (or genomes!) belonging your favorite study organism will exceed the cost of obtaining those data.
O’Connor suggests that one major way to stave off the point where computing capacity limits data collection and analysis will be to use more “cloud” systems—remote servers and storage. Lots of institutions have their own servers and computing clusters. I’m already working with data sets too big to carry, much less process, on my laptop; I filter out the subset of sites I want on the server where the data is stored, and download (some) of that smaller data set for local work.
However, high-capacity computing facilities need a lot of lead time, and infrastructure investment, to scale up. That isn’t practical for individual projects. In such situations, and for researchers at institutions that don’t have their own high-capacity computing resources, commercial services may become a major alternative.
In the afternoon, we got started with one such alternative, Amazon EC2, or “elastic cloud computing.” Yes, that’s Amazon as in Amazon.com, the place where you buy used textbooks. It’s possible to rent processing capacity and storage from Amazon, and the services are provided in such a way that when you need more, you can just request it. “Instances” running on Amazon’s computing facilities can run Unix or Windows—you can interact with an instance via a remote desktop-type interface such as NoMachine’s NX system—and will run any program you’d care to have chew its way through your data.
All of this, of course, assumes you have the budget. It’s not clear to me how easy it’d be to estimate computing needs ahead of time for grant-writing purposes; but on the other hand, you can probably expect that whatever estimate you come up with will likely go that much further when you finally start working a year later. Over beer at the end of the day, Karen Cranston, the Informatics Project Manager for NESCent, told me that Amazon’s pricing is close enough to that of the high-capacity computing facility at Duke University that it’s often worthwhile to use EC2 for short-term, high-volume projects simply because it’s so quick and easy to bring new resources to bear.
As a not-yet faculty member, the cloud means I can plan to do genome-scale work even if I end up at an institution without the on-campus resources to build its own cluster. That’s potentially pretty liberating. ◼
Sighted in the woods near Northgate Park, Durham. For real. Photo by jby.
I’m spending the next two weeks in Durham, North Carolina, for the NESCent workshop on next-generation sequencing. Which is to say, a workshop about collecting great big genetic datasets, and what you can do with them once you have them. I’ll be stretching my programming skills to the maximum, and hopefully getting a head start on some ideas I’ve had for good old Medicago truncatula.
If time permits, I may take a page from Carl Boettinger’s literally open lab notebook and post some notes and thoughts here as the workshop progresses, but it’s looking likely to be a full two weeks, and time may very well not permit. ◼
So, I’ve known for some time that On the Media co-host Brooke Gladstone has a new book out, and that it’s a meditation on media in quirky graphic-novel form, but I didn’t really know I needed to read it until I saw this trailer. ◼
A honeybee explores the depths of a dandelion, one of the species used in Fründ et al.‘s experiments. Photo by je-sa.
If you’ve ever stopped to admire morning glory flowers opening first thing in the morning, then noticed they’ve closed by evening, you’re at least dimly aware of one of the longest-established ideas in plant biology: that flowers open and close on a reliable daily schedule. Different species are open at different times of day, of course, but each flowering plant has its preferred open period, and it sticks to that schedule during its flowering season.
This idea led Carolus Linneaus, the father of modern biological taxonomy, to propose an Horologium florae, or “floral clock” using plantings of species with known flowering times to mark the hours. You can find his table of proposed species in the online version of Linneaus’ 1783 treatise Philosophia Botanica, if you’re not averse to Latin. Studies of flowers’ daily schedules go back to well before English was the language of international science, and continue to the present day [$a].
Yet no one seems to have spent much time considering how flowers’ schedules might respond to the activity of their very reason for being: pollinators. Flowers don’t open just to be open in a particular kind of sunlight—they’re open to attract animals that can carry pollen to another plant, and maybe leave some, too. If a flower receives enough pollen to make seeds by noon, why would it stay open until two o’clock?
According to some new experimental results, the answer to that question is that they don’t [$a].
Jochen Fründ, Carsten F. Dormann, and Teja Tscharntke set out to see whether a selection of European wildflowers adjusted their opening schedules in response to pollination, with two major experiments and a broader-scale observation project. The experiments address whether pollinator activity could change flowers’ schedules; the observations help determine how important those changes might be in studies of plant-pollinator interaction.
A floral clock in Geneva—not quite what Linneaus had in mind. Photo by aranmanoth.
In the first experiment, the team planted wildflowers—Crespis capillaris, a close relative of common dandelions—in experimental plots spaced across a field. Plots were either caged or left open to insect visitors, and Fründ et al introduced bees into some of the caged plots. So some plots had a controlled set of pollinators, some had none at all, and some had whatever pollinators were already active in the field.
The team then watched the flowers’ daily opening and closing in the experimental plots. (They had a lot of help—a long list of names in the paper’s Acknowledgements section ends with “and many others.”) Over the same period of time, flowers in the un-caged plots received more insect visitors than flowers in either other treatment, and had mostly closed by midafternoon; flowers in the caged plots with bees introduced received fewer visitors and closed hours later; and flowers in the plots with no pollinators at all stayed open till evening.
So flowers experiencing the same daylight pattern closed earlier if they received more pollinator visits. The team followed up this result by hand-pollinating flowers of C. capillaris and a handful of closely related species growing in the same field, including dandelions—and flowers of three out of four species closed more rapidly when hand pollinated. Dandelions didn’t respond to hand pollination, a result the authors explain by noting that dandelions often self-pollinate, and so don’t need to wait for animal pollinators.
Finally, the team compiled observations of plant-pollinator interactions from sites similar to their study field located across Germany, and divided them into observations taken before solar noon, when the focal flower species from the experiments above tend to be open, and after solar noon. Which pollinator species visited which flowering plants depended significantly on when the observations were made—to the extent that the apparent importance of C. capillaris and its relatives is entirely different before and after noon.
Of course, these results apply directly to only a handful of species representing a particular group of flowering plants—but it’s a group with a lot of widespread and abundant members, and the result is straightforward and striking. Animal-pollinated plants may not behave much like clocks at all. Instead, they’re more like the patrons of a singles bar: they show up at about the same time and hang around until they find someone to buy them a drink. That’s a dynamic worth keeping in mind for studies of plant-pollinator interaction, since it suggests that the partners a pollinator chooses will depend, at least in part, on whether or not it’s out after closing time. ◼
The annual meeting of the Ecological Society of America is underway in Austin, Texas, this week. If, like me, you’re not anywhere near Austin, do not despair. There are people who will use the Internet to tell you what is going on at the meetings anyway, out of sheer enthusiasm for ecology! Here are the ones I’m following: