Archive for the ‘Ecology’ Category


The robotic system in action (from Vision Systems Design)

Among scientific disciplines, botany might be considered one of the least tech-minded branches, concerned as it is with the natural world of plant life. But like the rest of biology, botany is quickly moving into the types of large-scale experiments that require more sophisticated and advanced techniques. In many botany labs, high-throughput sequencers generate data at unprecedented rates about plant genomics for many different species. However, this genetic bounty creates a new bottleneck, as the complementary studies examining how those genes control plant traits still proceed at a speed closer to that of old-fashioned fieldwork. That old cliche “watching the grass grow” is not compatible with fast-paced science.

To help bring phenotype closer to the pace of genotype, Nicola Ferrier has equipped botanists with powerful new lab assistants: robots. Ferrier, now an engineer at Argonne National Laboratory, worked with University of Wisconsin botanists to design better equipment for monitoring the growth of the plant species Arabidopsis thaliana. Arabidopsis is a small flowering plant popular as a laboratory model species, in part because of its relatively small genome of roughly 27,000 genes. Eventually, scientists would like to find the role of each of those genes on aspects of the plant’s phenotype, such as root gravitropism, how the roots grow in response to gravity.

But the popular method – a computer-controlled camera to monitor the growth of one Arabidopsis seedling at a time – was far too slow to monitor tens of thousands of mutants. So Ferrier helped the laboratory of Edgar Spalding replace their single-camera system with a “robotic machine vision platform” capable of monitoring up to 144 seedlings simultaneously as their gravity is artificially changed (by rotating the dish 90 degrees).


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Bonanza Creek

Bonanza Creek Experimental Forest, where Fan and colleagues calibrated their high-latitude peatlands model.

Climate change is not an equal opportunity threat. Certain areas of the world are more susceptible than others to the shifts in temperature, precipitation and other effects climate scientists predict to occur over the next century. Even when these vulnerable regions are remote and rarely visited by humans, how climate change affects these ecosystems could have dramatic consequences for the global population.

One such area of vulnerability is the high-latitude peatlands, the bogs, fens and boreal forests found in the northern parts of Canada, Alaska and Russia. Much of this frigid land carries a layer of permafrost year-round, but the vegetation and soil there accounts for the majority of Earth’s biomass and carbon storage – roughly double that of the much better known tropical forests. Climate change is also expected to be particularly dramatic in these areas, with some models predicting temperatures will increase by as much as 7.5 degrees Celsius over the next hundred years.

Because of this vulnerability, Argonne computer scientist Zhaosheng Fan is focusing his attention specifically on how climate will affect this unique ecosystem. In his talk at the Computation Institute on February 14th, Fan (Assistant Biogeochemical Modeler in the Biosciences Division at ANL) described how scientists created a model of these high-latitude peatlands, how they refined the model based on field experiments and the very serious warnings the model produced about how these remote areas might someday affect the rest of the world.


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When more dimensions are added, food webs quickly grow more complex. (From Eklöf et al, 2013)

Ecosystems are a chaotic battle royale, with predators and prey, plants and animals, competitors and allies all fighting it out to eat or be eaten. But the food webs scientists typically put together are deceptively tidy diagrams, with simple arrows connecting diners to their natural food options. Ecologists readily admit that a true representation of an ecosystem’s network would be multi-dimensional, simultaneously taking into account multiple traits for each species involved. But just how many dimensions would such a model need to accurately depict the complexity of a large ecosystem? 10? 100? 1000?

In a new paper published this week in Ecology Letters, a team led by scientists at the Computation Institute and University of Chicago calculate that number – and find that it is surprisingly low. Using data collected by their co-authors on 200 different food webs, ranging from the Caribbean reef to New Zealand grasslands to an Arctic Ocean inlet, Anna Eklöf, Stefano Allesina and colleagues looked for the minimum number of dimensions and traits needed to accurately describe a food network. The findings may save ecologists time and effort in revealing the structure underlying an ecosystem, and also help scientists build computational models that can make predictions about an ecosystem’s future.

“To collect this kind of data takes ages to do,” said Eklöf. “If we can find some common rules about these networks, then we can apply them to larger networks. We can also learn about the function of networks, and what happens to networks when we disturb them in different ways.” (more…)

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