Variability in Forest Plant Traits Along the Western Ghats of India and Their Environmental Drivers at Different Resolutions

Abstract

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.