Biology subjects

Serbin, S. P.

Publications and source records attributed to Serbin, S. P..

3 recordsLinked to original sources

bioRxiv2025

The uncertainty partitioning of the terrestrial C cycling over CONUS NEON sites using model-data fusion

Accurate inventories of terrestrial carbon pools and fluxes are crucial for understanding ecosystem processes, tracking climate change impacts, and meeting the monitoring, reporting, and verification (MRV) requirements in international treaties and voluntary carbon markets. In meeting this need, the fusion of process-based modeling, field data, and remote sensing observations has the potential to provide more accurate and precise estimates than each alone. However, as the number of data constraints on a system increases, different sources of information can interact with each other in complex ways across space, time, and processes. In this study, we undertake a value-of-information analysis to assess the contribution of different observations to reducing carbon cycle uncertainties across pools, fluxes, and spatial domains within the PEcAn carbon cycle data assimilation system. We used a novel block-based Tobit Gamma Ensemble Filter to assimilate four synergistic data constraints, MODIS leaf area index, Landtrendr aboveground biomass, SMAP soil moisture, and SoilGrids soil organic C, into a process-based ecosystem model (SIPNET) at 39 National Ecological Observatory Network sites across the contiguous U.S. from 2012 to 2021. Results showed that not only did we greatly reduce uncertainty among the directly constrained pools but many observations were able to share information across variables and space. These indirect constraints helped identify synergies and conflicts among data streams and across space, which provides insights for further constraining carbon inventories. Overall, soil carbon remains the largest source of uncertainty in the overall carbon budget due to both its large size and limited observational constraints.

ecology↗

bioRxiv2024

Changes in spectral signature of leaves after desiccation: Implications for the prediction of leaf traits and plant-soil interaction in herbarium samples.

O_LIThe study investigated the impact of specimen desiccation on spectral signatures of plant tissue and its influence on models predicting biochemical and physiological traits, as well as ecosystem function. C_LIO_LIAnalyzing 56 species, this study quantified the impact of leaf desiccation on: (1) the degree of change in the reflectance intensity, and its first and second derivatives within the visible - shortwave infrared spectrum, (2) the difference in change between wavebands used by Radiative Transfer Models (RTMs) for predicting traits in fresh leaves, (3) the prediction of leaf traits using PROSPECT RTM, and (4) the prediction of soil nutrient components from the leaf. C_LIO_LIComparing desiccated specimens to fresh leaves, this study found the highest degree of change within the near infrared for the reflectance intensity, its first and second derivatives. Specific wavebands used for the prediction of leaf traits changed less that others in the VIS-SWIR. The prediction uncertainty for PROSPECT RTM differed for various leaf traits, showing an increase for equivalent water thickness, and carotene content, and a decrease in brown pigments and dry mass. Leaf traits predicted from desiccated leaves were better at predicting relationships with soil components, such as soil chemical properties and macro and micronutrients than fresh leaves. C_LIO_LIDesiccated leaves preserve and improves the capacity to inform us about important aspects of ecosystem functioning, particularly nutrient transfer between leaf and soil. C_LI

ecology↗

bioRxiv2023

Integration of leaf spectral reflectance variability facilitates identification of plant leaves at different taxonomic levels.

Plant identification is crucial for the conservation and management of natural areas. The shortwave spectral reflectance of leaves is a promising tool for rapidly identifying plants at different taxonomic levels. However, leaf spectral reflectance changes in response to biotic and abiotic conditions. Here we assess whether this variability in spectral reflectance affects the accuracy of classification methods currently used to predict plant taxonomy and identify factors that most influence leaf spectral signatures, as proxies for predicted biochemical and structural traits. We used leaf reflectance from 42 woody species from the living collection at the New York Botanical Garden across two sets of pairwise samplings (spring 2019/summer 2020 and spring 2019/winter 2021). We found that classification accuracy was poor when only spring samples were used to train models but improved when natural variation across all seasons was incorporated into classification models. To evaluate the influence of species relatedness or growth conditions (temperature, relative humidity, and daylength) on spectrally predicted traits, we applied Partial Least Squares Regression (PLSR) coefficients derived from NEON data to predict foliar traits including photosynthetic pigments, water content, leaf dry mass per area, and carbon and nitrogen content. Results showed that trait variation was not influenced by phylogeny but was significantly influenced by environment, except for water-related spectral bands for species remeasured in summer 2020. These results demonstrate that classification methods developed to handle datasets with large collinearities, such as leaf spectral reflectance, underperform when individual spectral variability is caused by environmental factors. This issue must be addressed for future development of remotely sensed taxonomy applications for evergreen broadleaf vegetation.

ecology↗