Tyler O'Neal, Staff Editor ACADEMIA

University of Cincinnati geographers find tipping point in deforestation

Global satellite imagery shows transitions occur quickly after blocks of forest are cut in half

University of Cincinnati geography researchers have identified a tipping point for deforestation that leads to rapid forest loss.

Geography professor Tomasz Stepinski used high-resolution satellite images from the European Space Agency to study landscapes in 9-kilometer-wide blocks across every inch of the planet between 1992 and 2015. He found that deforestation occurs comparatively slowly in these blocks until about half of the forest is gone. Then the remaining forest disappears very quickly. A University of Cincinnati land-use map shows changing landscapes in North and South America between 1992 and 2015. White indicates little or no change. Darker shades indicate the highest rate of change in each category. Forest loss was the most noticeable category in Central and South America.{module INSIDE STORY}

The study was published in the journal Geophysical Research Letters.

Stepinski and former UC postdoctoral researcher Jakub Nowosad, the lead author, discovered something surprising and fundamental: nature abhors mixed landscapes, at least on a scale of 81 square kilometers. The study showed that mixed landscapes (like agriculture and forest) are comparatively few and, more surprisingly, do not stay mixed for long. These mixed blocks tend to become homogeneous over time, regardless of the landscape type.

"I think it's very intuitive. It corresponds to the different climatic zones. The Earth before people was certainly like that. You had forests and mountains and wetlands and deserts," Stepinski said. "You would expect people would create more fragmentation, but as it turns out, people never stop. They convert the entire block on a large scale."

Stepinski said landscapes are always changing through natural or anthropological causes. Human causes are both direct, like clear-cutting, or indirect like climate change.

Last year, Stepinski used the same data to demonstrate that 22% of the Earth's habitable surface was altered in measurable ways between 1992 and 2015. The biggest change: forest to agriculture.

For the new study, Stepinski examined nearly 1.8 million blocks covering Earth's seven continents. Blocks were categorized by 64 landscape combinations. Researchers observed transitions in these blocks from predominantly one type to predominantly another in nearly 15% of the blocks between 1992 and 2015.

"The data we have covers 23 years. That's a relatively short period of time. But from that we can calculate change in the future," Stepinski said.

Deforestation was the most pronounced example of human-caused landscape change, researchers found. They used probability modeling known as Monte Carlo methods to determine the likelihood of different types of landscape change over time (in this case hundreds of years).

The result? Researchers found that the most likely trajectory of change was from one homogeneous type to another.

"Planet Earth wants to be homogeneous. The land wants to be the same in all these patches. And when they start to change, they don't stop until they convert everything into another homogeneous block," he said.

The authors did not examine why blocks change so quickly once a transition begins. But Stepinski said it's possible that development such as logging roads or drainage required to clear forest makes continued change that much easier.

"I can only speculate because that was not part of the study, but I would imagine two things are happening," he said. "If you are cutting forest, you have the infrastructure to finish it. It's so much easier to cut the rest. Second, the forest is more vulnerable to change when there has been a disturbance." 

Wildlife managers often try to preserve larger intact blocks to prevent fragmentation, said Martin McCallister, the Appalachian Forest project manager for the Edge of Appalachia Nature Preserve in southern Ohio. The preserve is managed by the Nature Conservancy, one of the world's largest wildlife conservation organizations.

"You'd be hard pressed to find land managers who wouldn't be strongly in favor of protecting larger tracts because they're more resilient to a variety of challenges, including invasive species and climate change," McCallister said. "Once a property gets fragmented by roads, it's easier to extract resources. It's also easier for invasive species and pests to get a foothold."

McCallister said woodlands can be fragmented on paper, too.

"In Ohio, 96 percent of our woodland owners have less than 50 acres. They represent a lot of small parcels," he said.

The UC study found that mixed land types don't stay mixed for long. 

"I think it is interesting that this property applies both to natural and human landscapes," said co-author Nowosad, a former UC postdoctoral researcher who now works as an assistant professor at the Adam Mickiewicz University in Poland.

Nowosad said the study provides a data-driven model of long-term landscape change. While researchers only looked at changes between forest and agriculture, Nowosad said it would be worthwhile to examine whether tipping points exist for other landscape transitions.

"This model can be used to help understand how landscapes evolved and are going to evolve in the future," Nowosad said.

Stepinski, a physicist who worked for NASA before coming to UC, said the principle borrows from other disciplines, particularly astrophysics.

"If you look at the evolution of stars, the principle is you predict a long-term path statistically from short-term knowledge," Stepinski said. "It's an idea that has been used elsewhere but never for environmental study."

While it's only a theory, it's one that could be borne out by time, he said.

"It's thought-provoking. My hope is that people will criticize it and come up with different ideas," Stepinski said.

University of Minnesota produces study on molecular interactions to improve development of new medicines

Tyler O'Neal, Staff Editor ACADEMIA

A first-of-its-kind study on molecular interactions by biomedical engineers at the University of Minnesota’s College of Science and Engineering will make it easier and more efficient for scientists to develop new medicines and other therapies for diseases such as cancer, HIV and autoimmune diseases.

The study resulted in a mathematical framework that simulates the effects of the key parameters that control interactions between molecules that have multiple binding sites, as is the case for many medicines. Researchers plan to use the supercomputer model to develop a web-based app that other researchers can use to speed the development of new therapies for diseases. The research is published in an educational journal.

“The big advance with this study is that usually researchers use a trial-and-error experimental method in the lab for studying these kinds of molecular interactions, but here we developed a mathematical model where we know the parameters so we can make accurate predictions using a computer,” said Casim Sarkar, a University of Minnesota biomedical engineering associate professor and senior author of the study. “This computational model will make research much more efficient and could accelerate the creation of new therapies for many kinds of diseases.” This illustration highlights a small sampling of the 78 unique binding configurations that arise when molecule chains with three binding sites interact. The research team developed a computational model that can predict how key parameters can be “dialed up” or “dialed down” to control how such molecules with more than one binding site interact with one another. This should accelerate biological research and discovery of new medicines. Image credit: Errington et al., University of Minnesota {module INSIDE STORY}

The research team studied three main parameters of molecular interactions—binding strength of each site, the rigidity of the linkages between the sites, and the size of the linkage arrays. They looked at how these three parameters can be “dialed up” or “dialed down” to control how molecule chains with two or three binding sites interact with one another. The team then confirmed their model predictions in lab experiments.

“At a fundamental level, many diseases can be traced to a molecule not binding correctly,” said Wesley Errington, a University of Minnesota biomedical engineering postdoctoral researcher and lead author of the study. “By understanding how we can manipulate these ‘dials’ that control molecular behavior, we have developed a new programming language that can be used to predict how molecules will bind.”

The need for a mathematical framework to decode this programming language is highlighted by the researchers’ finding that, even when the interacting molecule chains have just three binding sites each, there are a total of 78 unique binding configurations, most of which cannot be experimentally observed. By dialing the parameters in this new mathematical model, researchers can quickly understand how these different binding configurations are affected, and tune them for a wide range of biological and medical applications.

“We think we’ve hit on rules that are fundamental to all molecules, such as proteins, DNA, and medicines, and can be scaled up for more complex interactions,” said Errington “It’s really a molecular signature that we can use to study and to engineer molecular systems. The sky is the limit with this approach.”

In addition to Sarkar and Errington, the research team included Bence Bruncsics from the Budapest University of Technology and Economics who was a visiting masters’ student in the Sarkar lab at the University of Minnesota. The team also partnered with the Institute for Therapeutics Discovery & Development (ITDD) at the University of Minnesota’s College of Pharmacy for the lab experiments to test the computational model. The research was funded by the National Institutes of Health.

Researchers at The University of Tokyo Institute of Industrial Science gaze into crystal balls to advance understanding of crystal formation

Tyler O'Neal, Staff Editor ACADEMIA

Crystallization is the physical phenomenon of the transformation of disordered molecules in a liquid or gas phase into a highly ordered solid crystal through two stages: nucleation and growth. Crystallization is very important in materials and natural sciences because it occurs in a wide range of materials, including metals, organic compounds, and biological molecules, so it is desirable to comprehensively understand this process. 

Colloids consisting of hard spheres suspended in a liquid are often used as a model system to study crystallization. For many years, a large discrepancy of up to ten orders of magnitude has been observed between the computationally simulated and experimentally measured nucleation rates of hard-sphere colloids. This discrepancy has typically been explained by the simulations not taking hydrodynamic interactions--the interactions between solvent molecules--into account. Researchers at The University of Tokyo Institute of Industrial Science, the University of Oxford, and the Sapienza University recently teamed up to further explore this explanation for the discrepancy between actual and calculated nucleation rates. {module INSIDE STORY}

The collaboration first developed a hard-sphere colloidal model that could reliably simulate the experimental thermodynamic behavior of real hard-sphere systems. Next, they conducted simulations of crystallization of the model system considering and neglecting hydrodynamic interactions to clarify the effect of these interactions on crystallization behavior.

"We initially designed a simulation model that accurately reproduced the real thermodynamics of hard-sphere systems," says study lead author Michio Tateno. "This confirmed the reliability and suitability of the model for use in further simulations."

The simulation results obtained using the developed model neglecting and considering hydrodynamic interactions revealed that hydrodynamic interactions did not affect nucleation rate, which was contrary to the prevailing consensus. Plots of nucleation rate against the proportion of hard spheres in the system were the same for calculations both with and without hydrodynamic interactions and also agreed with results reported by another research group.

"We performed calculations using the developed model with and without considering hydrodynamic interactions," explains senior author Hajime Tanaka. "The calculated rates of crystal nucleation were similar in both cases, which led us to conclude that hydrodynamic interactions do not explain the hugely different nucleation rates obtained experimentally and theoretically."

The research team's findings clearly illustrated that hydrodynamic interactions are not the origin of the large discrepancy between experimental and simulated nucleation rates. Their results further our understanding of crystallization behavior but leave the origin of this large discrepancy unexplained.

The article "Influence of hydrodynamic interactions on colloidal crystallization" was published in Physical Review Letters.