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This dataset consists of pulse-amplitude modulation (PAM) fluorometry data collected using six Micro-PAM (Walz GmbH, Effeltrich, Germany) measurement heads installed on two black spruce trees at the Caribou-Poker Creeks Research Watershed National Ecological Observatory Network (NEON) site (site abbreviation: BONA) near Fairbanks, Alaska. These measurements provide information on the physiological status of photosystem II and photosynthesis more broadly for the needles and trees observed. Three PAM measurement heads were installed on each tree at low, intermediate, and high heights qualitatively within the canopy. Five measurement heads (second intermediate position measurement excluded) were installed in March 2022, with the sixth measurement head being installed in July 2022. Data was collected semi-continuously, including the harsh winter season, until July 2024. PAM fluorescence measurements were collected in a ramped scheme centered around noon to maximize measurement frequency while still allowing for adequate dark acclimation for the nighttime measurement. Saturation pulses were performed by each measuring head every fifteen minutes from 9:00AM – 5:00PM, every thirty minutes from 7:00AM-9:00AM and 5:00PM-7:00PM, and every hour from 2:00AM-7:00AM and 7:00PM-9:00PM. 

This dataset consists of tower-based remote sensing measurements collected using a pair of Fluospec2 (FS2; Yang et al., 2018) instruments, each consisting of two QEPro spectrometers (Ocean Optics, Orlando, USA), installed at the Caribou-Poker Creeks Research Watershed National Ecological Observatory Network (NEON) site (site abbreviation: BONA) near Fairbanks, Alaska. Remote sensing observations include solar-induced fluorescence, derived via a spectral fitting method, near-infrared reflectance of vegetation, and several vegetation indices that can collectively be used to evaluate canopy productivity, photosynthetic phenology, and physiology. Data was collected from two subsites, one FS2 (frequency-synthesizer) instrument installed at each site, located roughly one kilometer apart along a topographic gradient. The evergreen site consists of a black spruce canopy and a lichen and moss understory, with a few larch trees interspersed. The FS2 instrument here was installed on an observation tower (latitude/longitude: 65.15401/-147.50258) installed by NEON that also hosts NEON's meteorological and flux instruments, with the FS2 instrument observing a cluster of black spruce (Picea mariana) trees. At the deciduous site (latitude and longitude: 65.15686833, -147.50367667), a mast was installed within a deciduous canopy consisting of alaskan paper birch (Betula neoalaskana), black spruce, and quaking aspen (Populus tremuloides) trees located roughly one kilometer (km) uphill from the evergreen site. The FS2 instrument installed here observed a paper birch tree located near-nadir from the instrument. Literature Citation: Yang, X., Shi, H., Stovall, A., Guan, K., Miao, G., Zhang, Y., Zhang, Y., Xiao, X., Ryu, Y., & Lee, J. E. (2018). FluoSpec 2—an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors (Switzerland), 18(7). https://doi.org/10.3390/s18072063 

ABSTRACT Conventional covalent and supramolecular crosslinks impose inherent trade‐offs between mechanical robustness and environmental adaptability in polymer networks. Mechanically interlocked junctions offer an alternative route by enabling topological motion without compromising structural integrity. Here, a modular approach is reported to construct topology‐engineered gels by copolymerizing 2‐hydroxyethyl acrylate (HEA) with doubly threaded (dt‐) pseudo[4]rotaxane crosslinkers, formed via the complexation of γ‐cyclodextrin (γ‐CD) and acrylate‐end‐capped pyrene derivatives. By tuning the HEA/water ratio in pre‐gel formulations, the supramolecular assembly can be shifted from dt‐ to singly threaded (st‐) entities, enabling access to gels with the same chemical composition but different architectures—dt‐slide ring network versus st‐polyrotaxane covalent network. The dt‐network exhibits solvent‐dependent fluorescence, swelling, and viscoelasticity that is not observed in either the st‐network or a covalent control gel. Mechanical testing of the bulk networks reveals that the dt‐network exhibits high extensibility (>1050% strain at break), strain rate‐dependent stiffening and rupture, and a favorable combination of high toughness and comparatively low hysteresis under cyclic deformation that is not observed in the networks lacking the dt‐architecture. Overall, these results establish a structure–property relationship dictated by crosslinking topology, demonstrating how dt‐interlocked motifs can encode both mechanical performance and environmental responsiveness in soft materials. 

Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis. 

We present novel material-level countermeasures in the form of watermarking and machine learning-based solutions that biochip companies can effectively utilize to secure their products against malicious and intellectual property (IP) theft attacks.