Climate change has intensified the hydrologic cycle globally, increasing the magnitude and frequency of large precipitation events, or deluges. Dryland ecosystems are expected to be particularly responsive to increases in deluge size, as their ecological processes are largely dependent on distinct soil moisture pulses. To better understand how increasing deluge size will affect ecosystem function, we conducted a field experiment in a native semiarid shortgrass steppe (Colorado, USA). We quantified ecological responses to a range of deluge sizes, from moderate to extreme, with the goal of identifying response patterns and thresholds beyond which ecological processes would not increase further (saturate). Using a replicated regression approach, we imposed single deluges that ranged in size from 20 to 120 mm (82.3rd to >99.9th percentile of historical event size) on undisturbed grassland plots. We quantified pre- and postdeluge responses in soil moisture, soil respiration, and canopy greenness, as well as leaf water potential, growth, and flowering of the dominant grass species (Bouteloua gracilis). We also measured end of season above- and belowground net primary production (ANPP, BNPP). As expected, this water-limited ecosystem responded strongly to the applied deluges, but surprisingly, most variables increased linearly with deluge size. We found little evidence for response thresholds within the range of deluge sizes imposed, at least during this dry year. Instead, response patterns reflected the linear increase in the duration of elevated soil moisture (2-22 days) with increasing event size. Flowering of B. gracilis and soil respiration responded particularly strongly to deluge size (14- and 4-fold increases, respectively), as did ANPP and BNPP (~60% increase for both). Overall, our results suggest that this semiarid grassland will respond positively and linearly to predicted increases in deluge size, and that event sizes may need to exceed historical magnitudes, or occur during wet years, before responses saturate.
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Solid-state light-detection-and-ranging (LiDAR) sensors based on integrated optical phased arrays (OPAs) have shown significant promise to reduce the cost, size, weight, and power consumption associated with LiDAR for autonomous systems. However, these OPA-based LiDAR systems typically operate by rastering a single beam, generating point clouds that constitute a significant amount of data and computational burden in the process. In this paper, we develop and experimentally demonstrate a novel multi-beam solid-state OPA-based LiDAR system capable of detecting and ranging multiple targets simultaneously, passively, and without rastering.
View Article and Find Full Text PDFbioRxiv
November 2023
Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125.
Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the "language of life." However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds.
View Article and Find Full Text PDFbioRxiv
September 2023
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA.
Each new human has an expected = 2 - 10 new deleterious mutations. This deluge of deleterious mutations cannot all be purged, and therefore accumulate in a declining fitness ratchet. Using a novel simulation framework designed to efficiently handle genome-wide linkage disequilibria across many segregating sites, we find that rarer, beneficial mutations of larger effect are sufficient to compensate fitness declines due to the fixation of many slightly deleterious mutations.
View Article and Find Full Text PDFJ Am Soc Mass Spectrom
June 2023
Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics.
View Article and Find Full Text PDFHistochem Cell Biol
September 2023
Australian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, NSW, 2006, Australia.
The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools.
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