Biology subjects

Desai, A. R.

Publications and source records attributed to Desai, A. R..

3 recordsLinked to original sources

bioRxiv2023

The global distribution of paired eddy covariance towers

The eddy covariance technique has revolutionized our understanding of ecosystem-atmosphere interactions. Eddy covariance studies often use a "paired" tower design in which observations from nearby towers are used to understand how different vegetation, soils, hydrology, or experimental treatment shape ecosystem function and surface-atmosphere exchange. Paired towers have never been formally defined and their global distribution has not been quantified. We compiled eddy covariance tower information to find towers that could be considered paired. Of 1233 global eddy covariance towers, 692 (56%) were identified as paired by our criteria. Paired towers had cooler mean annual temperature (mean = 9.9 {degrees}C) than the entire eddy covariance network (10.5 {degrees}C) but warmer than the terrestrial surface (8.9 {degrees}C) from WorldClim 2.1, on average. The paired and entire tower networks had greater average soil nitrogen (0.57-0.58 g/kg) and more silt (36.0-36.4%) than terrestrial ecosystems (0.38 g/kg and 30.5%), suggesting that eddy covariance towers sample richer soils than the terrestrial surface as a whole. Paired towers existed in a climatic space that was more different from the global climate distribution sampled by the entire eddy covariance network, as revealed by an analysis of the Kullback-Leibler divergence, but the edaphic space sampled by the entire network and paired towers was similar. The lack of paired towers with available data across much of Africa, northern, central, southern, and western Asia, and Latin America with few towers in savannas, shrublands, and evergreen broadleaf forests point to key regions, ecosystems, and ecosystem transitions in need of additional research. Few if any paired towers study the flux of ozone and other atmospherically active trace gases at the present. By studying what paired towers measure - and what they do not - we can make infrastructural investments to further enhance the value of FLUXNET as it moves toward its fourth decade.

ecology↗

bioRxiv2023

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

Identifying key environmental drivers for plant functional traits is an important step to understanding and predicting ecosystem responses to a changing climate. Imaging spectroscopy offers great potential to map 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 NASAs Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for six sites spanning the Western Ghats of India. We studied the variation of trait space using principal components analysis at spatial resolutions from the plot level (4m), community level (30m and 100m) to the ecosystem level (1000m). We observed a consistent pattern of trait space across different resolutions, with one axis representing the traditional leaf economic spectrum defined by foliar nitrogen concentration 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 showed strong environmental dependencies across sites, while calcium, sugar, and nitrogen were significantly affected by site conditions. Models incorporating site as a fixed effect explained more than 50% of the trait variance at 1000m resolution. LMA showed little dependence on both environment and site conditions, implying other factors such as species composition and perhaps site history strongly affect variation in LMA. 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 30m) are not sensitive to broad-scale abiotic/biotc factors.

ecology↗

bioRxiv2021

Capturing site-to-site variability through Hierarchical Bayesian calibration of a process-based dynamic vegetation model

Process-based ecosystem models help us understand and predict ecosystem processes, but using them has long involved a difficult choice between performing data- and labor-intensive site-level calibrations or relying on general parameters that may not reflect local conditions. Hierarchical Bayesian (HB) calibration provides a third option that frees modelers from assuming model parameters to be completely generic or completely site-specific and allows a formal distinction between prediction at known calibration sites and "out-of-sample" prediction to new sites. Here, we compare calibrations of a process-based dynamic vegetation model to eddy-covariance data across 12 temperate deciduous Ameriflux sites fit using either site-specific, joint cross-site, or HB approaches. To be able to apply HB to computationally demanding process-based models we introduce a novel emulator-based HB calibration tool, which we make available through the PEcAn community cyberinfrastructure. Using these calibrations to make predictions at held-out tower sites, we show that the joint cross-site calibration is falsely over-confident because it neglects parameter variability across sites and therefore underestimates variance in parameter distributions. By showing which parameters show high site-to-site variability, HB calibration also formally gives us a structure that can detect which process representations are missing from the models and prioritize errors based on the magnitude of the associated uncertainty. For example, in our case-study, we were able to identify large site-to-site variability in the parameters related to the temperature responses of respiration and photosynthesis, associated with a lack of thermal acclimation and adaptation in the model. Moving forward, HB approaches present important new opportunities for statistical modeling of the spatiotemporal variability in modeled parameters and processes that yields both new insights and improved predictions.

ecology↗