A study in Forest Ecosystems revealed that Continuous Cover Forestry (CCF) in Europe partly originated in a 17th-century practical agroforestry innovation, and not exclusively in a 19/20th-century academic debate as previously thought. The research into forestry history traced the development of CCF all the way from early agroforestry, through individual-based silviculture, and eventually to the later academic debate, offering historical insights for modern sustainable forest management.

A study in Forest Ecosystems reveals that two closely related evergreen oaks (Quercus aquifolioides and Quercus spinosa) in the Himalayan-Hengduan Mountains adapt to different climates through adjustments in leaf trait integration and modularity, with the high-altitude species having flexible traits for harsh conditions and the lowland one showing tightly coordinated traits for efficiency. It also notes the findings’ value for conservation and understanding species’ responses to climate change.

LIFE SCIENCES

Daniele Canzio, PhD, University of California, San Francisco (Neuroscience)

Kaiyu Guan, University of Illinois Urbana-Champaign (Agriculture & Animal Sciences)

Philip J. Kranzusch, PhD, Dana-Farber Cancer Institute; Harvard Medical School (Microbiology)

Elizabeth Nance, PhD, University of Washington (Biomedical Engineering & Biotechnology)

Tomasz Nowakowski, PhD, University of California, San Francisco (Neuroscience)

Samuel H. Sternberg, PhD, Columbia University/Howard Hughes Medical Institute (Molecular & Cellular Biology)

CHEMICAL SCIENCES

Song Lin, PhD, Cornell University (Organic Chemistry)

Joseph Cotruvo, Jr., PhD, The Pennsylvania State University (Biochemistry & Structural Biology)

Frank Leibfarth, PhD, The University of North Carolina at Chapel Hill (Polymer Chemistry)

Ryan Lively, PhD, Georgia Institute of Technology (Chemical Engineering)

Leslie M. Schoop, PhD, Princeton University (Inorganic & Solid-State Chemistry)

Yogesh Surendranath, PhD, Massachusetts Institute of Technology (Inorganic & Solid-State Chemistry)

PHYSICAL SCIENCES & ENGINEERING

Charlie Conroy, PhD, Harvard University (Astrophysics & Cosmology)

Nathaniel Craig, PhD, University of California, Santa Barbara (Theoretical Physics)

Matthew McDowell, PhD, Georgia Institute of Technology (Materials Science & Nanotechnology)

Prateek Mittal, PhD, Princeton University (Computer Science)

Elaina J. Sutley, PhD, University of Kansas (Civil Engineering)

Zhongwen Zhan, PhD, California Institute of Technology (Physical Earth Sciences)

Birds flock in order to forage and move more efficiently. Fish school to avoid predators. And bees swarm to reproduce. Recent advances in artificial intelligence have sought to mimic these natural behaviors as a way to potentially improve search-and-rescue operations or to identify areas of wildfire spread over vast areas—largely through coordinated drone or robotic movements. However, developing a means to control and utilize this type of AI—or “swarm intelligence”—has proved challenging. In a newly published paper, an international team of scientists describes a framework designed to advance swarm intelligence—by controlling flocking and swarming in ways that are akin to what occurs in nature.

In the human body, stem cells process genetic information in an exceptionally reliable and very fast manner.  To do this, they specifically access certain sections of the DNA in the cell nucleus. Researchers at Karlsruhe Institute of Technology (KIT) have investigated how the DNA-based information processing system works. Their results show that this process is comparable to processes in modern computers and could therefore serve as a model for new types of DNA-based computer chips. Published in “Annals of the New York Academy of Sciences.” (DOI: 10.1111/nyas.15415)