Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Yan, Zhengbing; Guo, Zhaoxin; Serbin, Shawn P.; Song, Guangqin; Zhao, Yanxin; Chen, Yang; Wu, Shengbiao; Wang, Jing; Wang, Xin; Li, Jing; Wang, Bin; Wu, Yuntao; Su, Yanjun; Wang, Han; Rogers, Alistair; Liu, Lingli; Wu, Jin

doi:10.1111/nph.17579

Cited by 30 publications

(29 citation statements)

References 93 publications

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“…6; Table S5). This finding is consistent with previous studies transferring models among species (Helsen et al 2021) or sites (Yan et al 2021). In general, trait models performed worse when the trait distributions in the calibration and validation datasets were less similar, but the strength and even the direction of the trend varied among traits (Fig.…”

Section: Discussionsupporting

confidence: 93%

“…S13-15). The general pattern of high VIP along the green hump and at the red edge is shared by many traits across studies (Yang et al 2016;Ely et al 2019;Yan et al 2021;. In general, visible and red edge reflectance are dominated by pigments and leaf structure (Richardson et al 2002).…”

Section: Interpreting Plsr Modelsmentioning

confidence: 99%

“…Moreover, the absorption features of nitrogen-containing bonds often overlap with others, especially after being broadened by multiple scattering (Curran 1989). As a result, leaf nitrogen is not identifiable with unique, distinct absorption features, and is often best predicted using empirical approaches like PLSR (Asner et al 2011; Yan et al 2021). Researchers have likewise built PLSR models to estimate a wide variety of foliar traits from fresh-leaf spectra, including pigments (Yang et al 2016), non-structural carbohydrates (Ely et al 2019), and condensed tannins (Couture et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Predicting leaf traits across functional groups using reflectance spectroscopy

Kothari

Beauchamp‐Rioux

Blanchard

et al. 2022

Preprint

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Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

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Section: Discussionsupporting

confidence: 93%

Section: Interpreting Plsr Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting leaf traits across functional groups using reflectance spectroscopy

Kothari

Beauchamp‐Rioux

Blanchard

et al. 2022

Preprint

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“…4 and S16-18). The pattern of high VIP along the green hump and red edge is common in PLSR models from fresh leaves (Yang et al 2016;Ely et al 2019;Streher et al 2020;Yan et al 2021). The red edge (particularly 700-725 nm) may be so important because of its sensitivity to both chlorophyll content and leaf structure (Richardson et al 2002).…”

Section: Interpreting Plsr Model Performancementioning

confidence: 99%

Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves

et al. 2022

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1. More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections.2. Using 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh-or ground-leaf spectra of the same samples.3. Our pressed-leaf models were best at estimating LMA (R 2 = 0.932; %RMSE = 6.56), C (R 2 = 0.855; %RMSE = 9.03), and cellulose (R 2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R 2 = 0.514-0.790; ). Remaining elements were predicted poorly (R 2 < 0.5, %RMSE > 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for .

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“…Empirical approaches like PLSR are widely used to estimate plant traits from spectroscopic data measured on fresh or ground leaves. These traits include leaf N and leaf mass per area (LMA; Asner et al 2011; Serbin et al 2014; Serbin et al 2019; Streher et al 2020), pigments (Asner et al 2011; Yang et al 2016), defense compounds (Couture et al 2016; Nakaji et al 2019), non-structural carbohydrates (Ely et al 2019), and even photosynthetic capacity (Yan et al 2021). Leaf-level PLSR models have been used to address such varied ecological topics as defense responses to herbivory (Kula et al 2020) and the role of biodiversity in ecosystem function (Schweiger et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

Kothari

Beauchamp‐Rioux

Laliberté

et al. 2021

Preprint

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More than ever, ecologists seek to employ herbarium collections as tools to estimate plant functional traits from the past or from places that are hard to sample. However, many functional trait measurements are destructive, which may preclude their use on valuable herbarium specimens. Reflectance spectroscopy is increasingly used to estimate traits rapidly from fresh or dried, ground leaves, as well as to differentiate and identify taxa. Here, we extend this body of work to pressed, intact leaves such as those in herbarium collections. Using a dataset with 619 plant samples belonging to 70 woody and herbaceous species, we used partial least-squares regression to build validated models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), carbon (Cmass) and nitrogen (Nmass) concentrations, and carbon fractions such as cellulose and lignin. We compared the accuracy of these trait estimates to those from fresh- or ground-leaf spectra of the same samples. Our pressed-leaf models predicted these traits with moderate-to-high accuracy (R2 = 0.586-0.924; %RMSE = 5.7-11.7%). For estimating chemical traits, pressed-leaf models performed better than fresh-leaf models but slightly worse than ground-leaf models. Pressed-leaf models did not perform as well as fresh-leaf models for estimating LMA and LDMC, but outperformed ground-leaf models for LMA. Accuracy was no worse for pressed leaves that underwent discoloration in storage. Finally, on a subset of common species in the dataset, we used partial least-squares discriminant analysis to classify specimens to species with near-perfect accuracy from pressed-(>97%), and ground-leaf (>96%) spectra and slightly lower accuracy from fresh-leaf spectra (>89%). The success of trait estimation and species classification from pressed-leaf spectra may owe to the fact that they combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of internal leaf structure; like ground leaves, they reveal minor absorption features of chemical constituents that would otherwise be masked by water. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits non-destructively. Our study has far-reaching implications for capturing the wide range of functional, phenotypic, and taxonomic information in the world’s preserved plant collections.

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Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Cited by 30 publications

References 93 publications

Predicting leaf traits across functional groups using reflectance spectroscopy

Predicting leaf traits across functional groups using reflectance spectroscopy

Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

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