Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

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, R2=0.5149, RMSE [root-mean-square error] =0.2735), with an R2=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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

References

  • Adler PB, Milchunas DG, Lauenroth WK, Burke SIC (2004) Functional traits of graminoids in semi-arid steppes: a test of grazing histories. J Appl Ecol 41:653–663

    Article  Google Scholar 

  • Allen GJ, Chu SP, Harrington CL, Schumacher K, Schroeder JI (2001) A defined range of guard cell calcium oscillation parameters encodes stomatal movements. Nature 411:1053–1057

    Article  CAS  Google Scholar 

  • Anderson JT, Gezon ZJ (2015) Plasticity in functional traits in the context of climate change: a case study of the subalpine forb boechera stricta (Brassicaceae). Glob Chang Biol 21:1689–1703

    Article  Google Scholar 

  • Atkinson LJ, Campbell CD, Zaragoza-Castells J, Hurry V, Atkin OK (2010) Impact of growth temperature on scaling relationships linking photosynthetic metabolism to leaf functional traits. Funct Ecol 24:1181–1191

    Article  Google Scholar 

  • Ayala-Silva T, Beyl CA (2005) Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency. Adv Space Res 35:305–317

    Article  CAS  Google Scholar 

  • Bowyer P, Danson FM (2004) Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level. Remote Sens Environ 92:297–308

    Article  Google Scholar 

  • Brackx M, Van Wittenberghe S, Verhelst J, Scheunders P, Samson R (2017) Hyperspectral leaf reflectance of Carpinus betulus l. saplings for urban air quality estimation. Environ Pollut 220:159–167

    Article  CAS  Google Scholar 

  • Champagne CM, Staenz K, Bannari A, Mcnairn H, Deguise JC (2003) Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies. Remote Sens Environ 87:148–160

    Article  Google Scholar 

  • Davoud A, Mohammad M, Alfredo H (2014) Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sens 6:5107–5123

    Article  Google Scholar 

  • Edwards KF, Litchman E, Klausmeier CA, Elser J (2013) Functional traits explain phytoplankton community structure and seasonal dynamics in a marine ecosystem. Ecol Lett 16:56–63

    Article  Google Scholar 

  • Fuentes DA, Gamon JA, Cheng YF, Claudio HC, Qiu HL, Mao ZY, Sims DA, Rahman AF, Oechel W, Luo HY (2006) Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote Sens Environ 103:312–323

    Article  Google Scholar 

  • Geber MA, Griffen LR (2003) Evolution of functional traits in plants inheritance and natural selection on functional traits. Int J Plant Sci 164:S21–S42

    Article  Google Scholar 

  • Gorter GJMA (1956) Anthracnose Fungi of Olives. Nature 178:1129–1130

    Article  Google Scholar 

  • Heijden MGAVD, Scheublin TR (2007) Functional traits in mycorrhizal ecology: their use for predicting the impact of arbuscular mycorrhizal fungal communities on plant growth and ecosystem functioning. New Phytol 174:244–250

    Article  Google Scholar 

  • Hui YW (1987) Observation and prevention on early defoliate disease of Amygdalus communis L. Fruit Trees:46–47 (in Chinese)

  • Itoh H, Kanda S, Shiraishi N, Sakai K, Sasao A (2010) Measurement of nitrate concentration distribution in vegetables by near-infrared hyperspectral imaging. J Cell Biol 16:695–702

    Google Scholar 

  • Kalacska M, Lalonde M, Moore TR (2015) Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: scaling from leaf to image. Remote Sens Environ 169:270–279

    Article  Google Scholar 

  • Lei TT, Tabuchi R, Kitao M, Koike T (2010) Functional relationship between chlorophyll content and leaf reflectance, and light-capturing efficiency of Japanese forest species. Physiol Plant 96:411–418

    Article  Google Scholar 

  • Li XZ, Huang YM, Huang JB, Yang Q (2011) Ecological effects of common roadside trees Roystonea regia and Amygdalus communis in Nanning City. J Southern Agricul 42:531–534 (in Chinese)

    Google Scholar 

  • Liu W, Li M, Zhang M, Long S, Yang S (2020) Hyperspectral inversion of mercury in reed leaves under different levels of soil mercury contamination. Environ Sci Polluti R

  • Luz BRD (2006) Attenuated total reflectance spectroscopy of plant leaves: a tool for ecological and botanical studies. New Phytol 172:305–318

    Article  CAS  Google Scholar 

  • Mackenzie SJ, Legard DE, Timmer LW, Chandler CK, Peres NA (2007) Resistance of strawberry cultivars to crown rot caused by Colletotrichum gloeosporioides isolates from Florida is nonspecific. Plant Dis 90:1091–1097

    Article  Google Scholar 

  • Manzo C, Salvini R, Guastaldi E, Nicolardi V, Protano G (2013) Reflectance spectral analyses for the assessment of environmental pollution in the geothermal site of Mt. Amiata (Italy). Atmos Environ 79:650–665

    Article  CAS  Google Scholar 

  • Kübarsepp L, Laanisto L, Niinemets L, Talts E, Tosens T (2020) Are stomata in ferns and allies sluggish? stomatal responses to CO2, humidity and light and their scaling with size and density. New Phytol 225:183–195

    Article  CAS  Google Scholar 

  • Mcgill BJ, Enquist BJ, Weiher E, Westoby M (2006) Rebuilding community ecology from functional traits. Trends Ecol Evol 21:185

    Google Scholar 

  • Merzlyak MN, Gitelson AA, Pogosyan SI, Lekhimena L, Chivkunova OB (2010) Light-induced pigment degradation in leaves and ripening fruits studied in situ with reflectance spectroscopy. Physiol. Plantarum,104:661-667.

  • Mõttus M, Hovi A, Rautiainen M (2017) Theoretical algorithm and application of a double-integrating sphere system for measuring leaf transmittance and reflectance spectra. Appl Opt 56:563–571

    Article  Google Scholar 

  • Nadja R, Wirth C, Wright SJ, Condit R (2012) Functional traits explain light and size response of growth rates in tropical tree species. Ecology 93

  • Noda HM, Motohka T, Murakami K, Muraoka H, Nasahara KN (2014) Reflectance and transmittance spectra of leaves and shoots of 22 vascular plant species and reflectance spectra of trunks and branches of 12 tree species in Japan. Ecol Res 29:111

    Article  Google Scholar 

  • Nong C, He W, Fleming D, Pan L, Huang H (2005) Capillary electrophoresis analysis of Mangiferin extracted from Mangifera indica L. bark and Mangifera persiciformis C.Y. Wu et T.L. Ming leaves. J Chromatogr B 826:226–231

    Article  CAS  Google Scholar 

  • Pal M, Foody GM (2010) Feature Selection for Classification of Hyperspectral Data by SVM. IEEE T. Geosci Remote 48:2297–2307

    Article  Google Scholar 

  • Pietsch KA, Ogle K, Cornelissen JHC, Cornwell WK, Wirth C (2014) Global relationship of wood and leaf litter decomposability: the role of functional traits within and across plant organs. Global Ecol Bio

  • Qin J, Deng J, Feng X, Wang Q, Wang S (2008) Quantitative RP–LC analysis of Mangiferin and Homomangiferin in Mangifera indica l. leaves and in Mangifera persiciforma C.Y. Wu et T.L. Ming leaves. Chromatographia 68:955–960

    Article  CAS  Google Scholar 

  • Richardson AD, Berlyn GP, Gregoire TG (2011) Spectral reflectance of Picea rubens (Pinaceae) and Abies balsamea (Pinaceae) needles along an elevational gradient, Mt. Moosilauke, New Hampshire. USA Am J Bot 88:667–676

    Article  Google Scholar 

  • Richardson AD, Berlyn GP (2002) Spectral reflectance and photosynthetic properties of Betula papyrifera (Betulaceae) leaves along an elevational gradient on Mt. Mansfield, Vermont, USA. Am J Bot 89:88–94

    Article  Google Scholar 

  • Russell PE, Brown L (1977) Alternaria alternata on Phaseolus vulgaris. Plant Pathol 26:47–48

    Article  Google Scholar 

  • Sampson PH, Treitz PM, Mohammed GH (2001) Remote Sensing of Forest Condition in Tolerant Hardwoods: An Examination of Spatial Scale, Structure and Function. Can J Remote Sens 27:232–246

    Article  Google Scholar 

  • Schleicher A, Peppler-Lisbach C, Kleyer M (2011) Functional traits during succession: is plant community assembly trait-driven? Preslia 83:347–370

    Google Scholar 

  • Liu H, Liu G, Xing W (2021) Functional traits of submerged macrophytes in eutrophic shallow lakes affect their ecological functions. Sci Total Environ

  • Shan J, Zhao J, Liu L, Zhang Y, Wang X, Wu F (2018) A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics. Environ Pollut 238:121–129

    Article  CAS  Google Scholar 

  • Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337–354

    Article  Google Scholar 

  • Smith KL, Steven MD, Colls JJ (2004) Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sens Environ 92:207–217

    Article  Google Scholar 

  • Smith ML, Ollinger SV, Martin ME, Aber JD, Goodale HCL (2002) Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol Appl 12:1286–1302

    Article  Google Scholar 

  • Sonobe R, Wang Q (2018) Assessing hyperspectral indices for tracing chlorophyll fluorescence parameters in deciduous forests. J Environ Manag 227:172–180

    Article  CAS  Google Scholar 

  • Sridhar BBM, Han FX, Diehl SV, Monts DL, Su Y (2007) Spectral reflectance and leaf internal structure changes of barley plants due to phytoextraction of zinc and cadmium. Int J Remote Sens 28:1041–1054

    Article  Google Scholar 

  • Suding KN, Goldstein LJ (2008) Testing the holy grail framework: using functional traits to predict ecosystem change. New Phytol 180:559–562

    Article  Google Scholar 

  • Tang YL, Wang RC, Huang JF, Kong WS, Cheng Q (2004) Hyperspectral data and their relationships correlative to the pigment contents for rice under different nitrogen support level. J Remote Sens 8:185–192

    Google Scholar 

  • Thorhaug A, Berlyn RGP (2006) Spectral reflectance of thalassia testudinum (hydrocharitaceae) seagrass: low salinity effects. Am J Bot 93:110–117

    Article  CAS  Google Scholar 

  • Weber VS, Araus JL, Cairns JE, Sanchez C, Melchinger AE, Orsini E (2012) Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crop Res 128:82–90

    Article  Google Scholar 

  • Wilfahrt PA, Collins B, White PS (2014) Shifts in functional traits among tree communities across succession in eastern deciduous forests. Forest Ecol Manag 324:179–185

    Article  Google Scholar 

  • Wragg PD, Johnson SD (2011) Transition from wind pollination to insect pollination in sedges: experimental evidence and functional traits. New Phytol 191:1128–1140

    Article  Google Scholar 

  • Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender Bares J, Chapin TJHCC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, Lamont BB, Lee T, Lee W, Lusk C, Midgley JJ, Navas ML, Niinemets U, Oleksyn J, Osada N, Poorter H, Poot P, Prior L, Pyankov VI, Roumet C, Thomas SC, Tjoelker MG, Veneklaas EJ, Villar R (2004) The worldwide leaf economics spectrum. Nature. 428:821–827

    Article  CAS  Google Scholar 

  • Wu CY, Wang XF (2016) Research of foliar dust content estimation by reflectance spectroscopy of Euonymus japonicus Thunb. Environ Nanotech Monitor Mana 5:54–61

    Google Scholar 

  • Xu HC, Luo YQ, Zhang TT, Shi YJ (2011) Changes of reflectance spectra of pine needles in different stage after being infected by pine wood nematode. Spectrosc Spectr Anal 31:1352–1356

    Google Scholar 

  • Yang CM, Chen RK (2004) Modeling rice growth with hyperspectral reflectance data. Crop Ence 44:1283–1290

    Google Scholar 

  • Yang J, Tian C, Yao X, Cao W, Zhang Y, Zhu Y (2009) Hyperspectral estimation model for chlorophyll concentrations in top leaves of rice. Acta Ecol Sin 29:6561–6571

  • Zhao Y, Damicone JP, Demezas DH, Rangaswamy V, Bender CL (2000) Bacterial leaf spot of leafy crucifers in Oklahoma caused by pseudomonas syringae pv. maculicola. Plant Dis 84:1015–1020

    Article  Google Scholar 

  • Zhou Y, Wei Y (2017) Learning hierarchical spectral-spatial features for hyperspectral image classification. IEEE T Cybernetics 46:1667–1678

    Article  Google Scholar 

  • Zhu J, Yu Q, Liu X, Yu Y, Yao J, Su K, Niu T, Zhu H, Zhu Q (2020) Effect of leaf dust retention on spectral characteristics of Euonymus japonicus and its dust retention prediction. Spectrosc Spectr Anal 40:517–528

    Google Scholar 

Download references

Acknowledgment

The English in this document has been checked by at least two professional editors; both were native speakers of English.

Funding

This study was funded by “National Natural Science Foundation of China (NSFC project NO.31901277)” and “Integration and Demonstration of Key Technologies for Oriented Tending of Plain Ecological Forest in Chaoyang District (CYSF-1904)”.

Author information

Authors and Affiliations

  1. Research Center for Urban Forestry of Beijing Forestry University, Key Laboratory for Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem of State Forestry Administration, Beijing Forestry University, Beijing, 100083, China

    Jiyou Zhu, Yujuan Cao, Xuan Guo, Jiajia Zhao, Qing Xu, Xinna Zhang & Chengyang Xu

  2. College of Forestry, Guangxi University, Nanning, 530005, Guangxi, China

    Jiangming Yao

  3. Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, 510520, Guangdong, China

    Weijun He

Authors
  1. Jiyou Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yujuan Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiangming Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weijun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiajia Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qing Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xinna Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chengyang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J. Zhu conceived and designed the study. J. Zhu, C. Xu, X. Zhang, Y. Cao, and X. Guo contribute materials and tools. J. Zhu, W. He and J. Yao performed the experiments. J. Zhu, Y. Cao, C. Xu, J. Zhao, and Q. Xu contributed to literature collection. J. Zhu, J. Yao, and W. He contributed to data analysis. J. Zhu contributed to paper preparation, writing, and revision.

Corresponding author

Correspondence to Chengyang Xu.

Ethics declarations

Consent to publish

All the authors read and approved it for publication.

Ethics approval and consent to participate

This experiment does not involve human experiments and animal experiments. The field trial experiments in the current study were permitted by Guangxi University, including the collection of leaf samples.

Conflict of interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, J., Cao, Y., Yao, J. et al. Estimation model and its trade-off strategy of Mangifera persiciforma Colletotrichum gloeosporioides degree based on leaf reflection spectrum. Environ Sci Pollut Res 28, 44288–44300 (2021). https://doi.org/10.1007/s11356-021-13697-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-021-13697-w

Keywords

Search

Navigation