Troy Magney
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troymagney.bsky.social
Troy Magney
@troymagney.bsky.social
1.2K followers 370 following 17 posts
Plant ecophysiology, ecosystem ecology, tinkering | Plant Optics Lab @ University of Montana | https://magneylab.ucdavis.edu
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troymagney.bsky.social
…admittedly, its hard for me to celebrate this moment publicly given the current challenges science and higher ed face….I wrote a little reflection below...

magneylab.ucdavis.edu/news/troy-mo...
Troy is moving to Montana! But the lab will endure
As I wrap up my time at UC Davis this week, I’ve been reflecting on the last five years—what we built, who we built it with, and where my head at was during all of it.
magneylab.ucdavis.edu
June 25, 2025 at 6:00 PM
troymagney.bsky.social
Team included @wwoodgate.bsky.social @drbenrunkle.bsky.social @xiyang.bsky.social @albert-loren.bsky.social @mallorybarnes.bsky.social @mostafaj.bsky.social @jbfisher.bsky.social @stovallatticus.bsky.social and many more not yet on bsky!
January 23, 2025 at 6:23 PM
troymagney.bsky.social
This was a big collaborative effort and was so fun to put together. Stemming from a FLUXNET workshop we led in 2023. @ameriflux.bsky.social @fluxnetecn.bsky.social
January 23, 2025 at 6:23 PM
troymagney.bsky.social
It was impossible to capture the massive complexity of this question given the word/reference count for commentaries.

So we are only able to scratch the surface of the large implications these changes have for climate regulation, biodiversity, and ecosystem services.
January 14, 2025 at 3:39 PM
troymagney.bsky.social
This commentary was on a recently published paper by Wu et al. (nph.onlinelibrary.wiley.com/doi/10.1111/...) - who use hyperspectral reflectance to predict leaf dark respiration. It outperforms traditional trait-based approaches by a lot.

I re-analyzed some of their data to try to explain why
Subtle differences in hyperspectral reflectance curves from leaves representing a range of physiological conditions. (a) Reflectance data for the mean (50th percentile, black dashed), high (90th percentile, green), and low (10th percentile, purple) Rdark samples from the Wu et al. (2024; doi: 10.1111/nph.20267) dataset, highlighting key traits associated with regions of interest in the visible (400–700 nm), near-infrared (c. 700–1400 nm), and shortwave infrared (c. 1400–2500 nm). (b) The percent difference between high- and low-Rdark samples from the mean, annotated with the direction of observed differences for key traits. The high-Rdark spectra show increased absorption in the chlorophyll (Chl) regions (c. 400–470 and c. 630–670 nm), while the low-Rdark spectra show decreased reflectance centered at 531 nm, a prominent xanthophyll absorption feature. Additionally, there is greater reflectance in the near-infrared region for high-Rdark spectra, suggesting higher leaf thickness and leaf mass per area (LMA), and higher absorption (lower reflectance) in the water absorption features in the shortwave infrared.
December 16, 2024 at 4:31 PM