| Abstrakt: |
Imaging spectroscopy offers great potential to characterize plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least‐squares regression to map seven key foliar chemical and morphological traits using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) for six sites spanning a climatological gradient in the Western Ghats of India. We studied the variation of trait space at spatial resolutions from the plot level (4 m), community level (30 and 100 m) to the ecosystem level (1,000 m). We observed a consistent pattern of trait space across different resolutions, with one axis defined by foliar nitrogen and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment‐trait correlations across resolutions with generally higher predictive capacity of our environment‐traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin were strongly influenced by environmental factors (model R2 > 0.5 at 1,000 m). Calcium, sugars, and nitrogen were significantly affected by site conditions, incorporating site as a fixed effect largely improved model performance. LMA showed little dependence on environmental factors or site conditions, suggesting a stronger influence of species composition and site history on LMA variation. Our results show that reliable trait‐trait relationships can be identified in coarse resolution imagery, but that local scale trait‐trait relationships (resolutions finer than 30 m) are not sensitive to broad‐scale abiotic/biotic factors. Plain Language Summary: We combined field leaf measurements and airborne hyperspectral images to map seven key foliar functional traits across the Western Ghats in India. We analyzed the traits at different spatial resolutions, from plot‐level (4 m), community level (30 and 100 m) to ecosystem‐level (1,000 m). We found that the trait‐trait relationship remains the same at different resolutions, which shows that we can learn a lot about the trait relationships by using images that aren't very detailed. We also looked at how traits are linked to the environment. Traits like chemicals that help plants defend themselves, as well as physical defenses like fiber and lignin, were strongly influenced by broad climate conditions. We could predict these traits well at 1,000 and 100 m. Other traits like calcium, sugars, and nitrogen were more affected by where the plants were growing, and we could predict them well when we considered the specific location. However, leaf mass per area didn't seem to depend much on the environment or location, which means it's probably influenced more by species composition and site history. When we looked at traits on a finer scale (30 m), our predictions weren't as accurate, suggesting that other things might also be affecting traits at those levels. Key Points: We mapped seven key plant functional traits for six sites along the Western Ghats of India‐a diverse but under‐represented areaOur study showed that the trait‐trait relationship (or trait space) is well conserved during the spatial aggregation from 4 to 1,000 mMost traits could be predicted well at 1,000 m and moderately at 100 m by environmental parameters but not at local scales finer than 30 m [ABSTRACT FROM AUTHOR] |
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