Geologic map of the lower and middle Russian River valley areas, Sonoma County, California, showing location of wells

The complete mitochondrial genome of Aegialites californicus (Motchoulsky, 1845) (insecta: coleoptera: salpingidae) by Marianne Nilsen Haugen, Vladimir I. Gusarov, Torsten Hugo Struck

“…We here present the first complete mitochondrial genome of this genus, represented by Aegialites californicus (Motchoulsky, 1845) from Sonoma County, California, U.S.A. The complete mitochondrial genome of A. californicus is 15,899 bp long and comprises 13 protein-coding (PCG), two ribosomal RNA (rRNA) and 22 transfer RNA (tRNA) genes. …”
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Sonoma County Complex Fires of 2017: Remote sensing data and modeling to support ecosystem and community resiliency by Kass Green, Mark Tukman, Dylan Loudon, Allison Schichtel, Karen Gaffney, Matthew Clark

“…In October of 2017, Sonoma County, California experienced three significant fires that resulted in loss of life and property, as well as impacts to natural systems. …”
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Incorporating canopy structure from simulated GEDI lidar into bird species distribution models by Patrick Burns, Matthew Clark, Leonardo Salas, Steven Hancock, David Leland, Patrick Jantz, Ralph Dubayah, Scott J Goetz

“…In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. …”
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Soundscape components inform acoustic index patterns and refine estimates of bird species richness by Colin A. Quinn, Patrick Burns, Christopher R. Hakkenberg, Leonardo Salas, Bret Pasch, Scott J. Goetz, Matthew L. Clark

“…We used an acoustic dataset of over 725,000 min of recordings across 1,195 sites in Sonoma County, California, to evaluate the relationship between 15 established acoustic indices and sonic conditions summarized using five soundscape components classified using a convolutional neural network: anthropophony (anthropogenic sounds), biophony (biotic sounds), geophony (wind and rain), quiet (lack of emergent sound), and interference (ARU feedback). …”
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MAPPING FIRE SEVERITY FROM RECENT CALIFORNIA WILDFIRES USING SATELLITE IMAGERY by C. Y. Apraku, Y. A. Twumasi, Z. H. Ning, M. Anokye, P. M. Loh, R. N. D. Armah, J. Oppong

“…This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. …”
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