Back

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

Quinteros Casaverde, N. L.; Serbin, S. P.; Daly, D. C.

2024-01-24 ecology
10.1101/2024.01.21.576284 bioRxiv
Show abstract

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

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.