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Estimation model and its trade-off strategy of Mangifera persiciforma Colletotrichum gloeosporioides degree based on leaf reflection spectrum.

Authors :
Zhu J
Cao Y
Yao J
He W
Guo X
Zhao J
Xu Q
Zhang X
Xu C
Source :
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2021 Aug; Vol. 28 (32), pp. 44288-44300. Date of Electronic Publication: 2021 Apr 13.
Publication Year :
2021

Abstract

Colletotrichum gloeosporioides is one of the most common and serious fungal diseases of the tree Mangifera persiciforma. Yet we lack an effective method to evaluate this ecological interaction accurately. Here, we measured the functional traits and leaf reflectance spectrum of the host plants under different disease degrees. The findings provide a fast and efficient method for large-scale and high-precision monitoring of C. gloeosporioides in M. persiciforma stands. Using the collected leaf reflection data, we set up a prediction model of the optimal disease degree. Firstly, we found that leaf functional traits of M. persiciforma generally consisted of low leaf thickness, low relative chlorophyll content, small specific leaf area, high leaf tissue density, high dry matter content, low stomatal density, and large stomatal area. Secondly, leaf reflectivity increases with damage of C. gloeosporioides, which corresponds to five main reflection peaks and five absorption valleys in the spectral reflectance curve of leaves at the same positions (350-1800 nm). Thirdly, with the increase of infection degree, red edge slope and yellow edge slope decrease, while green peak reflectance, red valley reflectance, and blue edge slope all increase. Blue shift was detected in the red edge, green peak, and red valley, while red shift appeared at the blue edge and yellow edge. Finally, the best predictive model was that based on green peak reflectance (y=3.6396-0.0693x, R <superscript>2</superscript> =0.5149, RMSE [root-mean-square error] =0.2735), with an R <superscript>2</superscript> =0.92 and RMSE=0.0042 between its predicted vs. observed values. Because of its high inversion accuracy, the model can be used to predict the invasion conditions of M. persiciforma by C. gloeosporioides. Our study demonstrated that when plants are infected by C. gloeosporioides, there was a strong trade-off relationship between leaf functional traits. On the global leaf economics spectrum, the leaves tended toward the "slow investment-return" end when infected by C. gloeosporioides.<br /> (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Details

Language :
English
ISSN :
1614-7499
Volume :
28
Issue :
32
Database :
MEDLINE
Journal :
Environmental science and pollution research international
Publication Type :
Academic Journal
Accession number :
33847889
Full Text :
https://doi.org/10.1007/s11356-021-13697-w