Environmental response can be detected through leaf spectral profiles
The measured physiological parameter in Figs. 1 and 2 show clearly that the genetically identical plants responded to the applied treatment conditions with environmentally induced phenotypes, demonstrating the plasticity expected of this species (Parker et al., 2003; Bossdorf et al., 2005; Richards et al., 2006, 2008; Van Kleunen et al., 2010; Pichancourt & van Klinken, 2012; Hagenblad et al., 2015; Geng et al., 2016). The acquired ATR-FTIR spectral absorbances in Fig. 3 demonstrate that the environmental conditions under which plants are grown have a marked impact on their spectral profiles suggesting subtle changes in plant composition. Although the overall absorbance pattern of the fingerprint region in Fig. 3a is visually similar that of any other green vegetative plant tissue (Cao et al., 2017; Holden et al., 2021; Macchioni et al., 2022), chemometrics has the power to extract more information, including differences between treatment groups. The unique patterns produced by the ATR-FTIR spectral absorbance profiles of plants grown in different environments could be successfully differentiated through application of the discriminant algorithm, SVM (Fig. 4c-d). This combined method achieved high accuracy, sensitivity, and specificity. This is consistent with observations from previous studies which have identified spectra from plants of different growing environments (Ruoff et al., 2006; Bağcıoğlu et al., 2017; Traoré et al., 2018; Gordon et al., 2019; Zeghoud et al., 2021; Holden et al., 2021) suggesting that the high degree of plasticity exhibited by Japanese knotweed to environmental factors is reflected in key biomolecular changes that may contribute to its success as an invasive species.
Detected biomarkers are associated with plant stress responses
A variety of biological compounds, such as proteins, ketones, terpenes, and carbohydrates, which differ under each condition were identified through linking molecular biomarkers with key wavenumbers affected by environment. The peak at 1709 cm− 1 (Figures S4 and S7), which was assigned to protein absorbance (see Table 1), is a common biomarker across other plant species, and has been associated with plant development in tomato plants (Butler et al., 2015b). This band indicates the relative concentrations of the significant proteins are highest in LD, followed by LC, and lowest in LN. The upregulation of these proteins under conditions of drought stress and increased micronutrient availability suggests that the peak at 1709 cm− 1 may correspond to enzymes that break down polysaccharides in plant cell walls, such as pectin-methylesterase and β-glucosidase. These enzymes allow for leaf expansion during development and stress-induced alterations of cell-wall polymers (Wu et al., 2018). Another response of Japanese knotweed plants to water deficit is highlighted by biomarker 1385 cm− 1 which was assigned to the bioactive plant volatile, pentanone. Spectral comparison suggests that pentanone was higher in control plants (LC) than droughted plants (LD). This is consistent with a similar spectroscopic study on citrus plants in which pentanone was retained in healthy leaves but released under biotic stress (Gandolfo et al., 2016). It seems possible that this finding could be related to the hormone jasmonic acid, which contains a pentanone ring. This hypothesis is supported by hormonal data in which jasmonic acid levels were higher in LC than in plants grown under less ideal conditions (Holden et al., unpublished). The jasmonic acid signalling pathway is a core component in plant response to both biotic and abiotic stress (Yang et al., 2019). The peak at 953 cm− 1, caused by protein phosphorylation, was a key difference in several comparisons; LC vs LD, LLN vs SLN, SC vs SN, and SC vs SLN. This indicates that wavenumber-953 is sensitive to changes induced by drought, and the combination of stresses from a low R:FR and deficiencies in nitrogen and micronutrients. Similarly, the peak at 1732 cm− 1, associated with hemicellulose (Ord et al., 2016), was a key difference between LC vs SC, LD vs SD, and SC vs SN, whilst 1038 cm− 1, associated with the polysaccharide galactan (Ord et al., 2016), allowed discrimination between LLN vs SLN and SN vs SLN. The association of these peaks with abiotic stress is consistent with other spectroscopic studies which have associated them with vehicular pollution in sycamore trees (Ord et al., 2016). Wavenumber 1227 cm− 1, assigned to geranyl acetate, an acyclic monoterpene, was higher in LLN than SLN. This biomarker has previously been associated with response to ozone exposure in sycamore tree leaf tissue (Ord et al., 2016). Amide I peaks at 1628 and 1585 cm− 1, have previously been associated with fungal infection in other studies (Ord et al., 2016). In the present study, these two Amide I peaks were key differences between plants not provided with any nutrients and those provided with nitrogen only. It is common for plant responses to biotic and abiotic stresses to overlap because stress signalling pathways are known to share intermediates such as reactive oxygen species and calcium which allow for crosstalk (Heap et al., 2020). Taken together these results suggest that wavenumbers 953, 1038, 1227, 1709, and 1732 cm− 1 are key indicators of plant-environment interactions, including abiotic stress responses, that are conserved between species.
Several cell wall carbohydrates, both structural and storage, were highlighted as significant changes between environments, detectable via the ATR-FTIR spectral profile. The storage molecule starch, reserves of which are known to be mobilised under conditions of abiotic stress (Thalmann & Santelia, 2017), was identified as a biomarker for five of the comparisons (Lei et al., 2018). As outlined above, the peak for hemicellulose (1732 cm− 1) was a key difference between shaded and unshaded plants both under, and in the absence of, drought stress (Ord et al., 2016). Shading stress is known to have a greater inhibitory effect on the biosynthesis of non-structural than that of structural carbohydrates (Hussain et al., 2019). Some structural carbohydrates, such as xylose and mannose, decrease under shading stress (Hussain et al., 2019). Beta- glucans were highlighted as a key differentiator between plants grown with differing red: far-red ratios, LC and SC again showing hemicellulose to be affected by shading. Also differentiated by this beta- glucan peak were LC and LN, indicating a consequence of environmental micronutrient levels. Wavenumber 1126 cm− 1 identified sucrose, the major transport form of photo-assimilated carbon, as a key discriminator between LC and LN, but not their shaded equivalents SC and SN (Lemoine, 2000; Talari et al., 2017). The peak at 1049 cm− 1 for cellulose (Moskal et al., 2019) was a key discriminator between SC and SLN, showing the impact of nutrient deficiency on plants also experiencing shading. Low levels of soluble sugar, sucrose, lignin and cellulose content can result in weak stem strength (Hussain et al., 2019). However, this was not reflected in the measured stem diameter of plants, variations in which were insignificant across all eight treatments (see Supplementary Figure S3a). Etiolation was observed in shaded plants from SC, SD and SLN which were significantly taller than those of the non-shaded groups LC, LD and LLN (see Fig. 1b). One molecule which can alter elongation capacity of the cell wall is galactan (see peaks 1072 and 1038 cm− 1). It achieves this by controlling porosity and viscoelastic properties of the cell wall (Izquierdo et al., 2018) and levels increase during the cell expansion phase (Waldron & Faulds, 2007). Galactans also play a role as cell wall storage polysaccharide (Waldron & Faulds, 2007). The amplitude of peaks 1072 and 1038 in the rubber band normalised fingerprint spectra indicate that galactan was present in higher concentrations in LN than LLN, SN than SLN, and in LLN than SLN, but this peak was not identified as a key difference for other comparisons. These results suggest that Japanese knotweed plants which are plants experiencing shading and deficiency of both micronutrients and nitrogen have lower levels of galactan, because they have low requirements for carbohydrate storage and lack the excess resources for expansion and growth. ‘Tarping’, an herbicide-free control strategy where soil covered with a plastic tarp is heated by solar radiation and thought to reach a lethal temperature for knotweed growth (Dusz et al., 2021), is more effective when black, light-blocking tarp is used (Miller et al., 2013). The combined stresses of shade and low nutrients observed in this study suggests that the additional shading effect of tarping would be most effective in areas of poor soil quality or those prone to leaching. Artificial shading may also increase the efficacy of herbicides that function through interference with nutrient absorption and metabolism.
The red: far-red ratio had the greatest effect on leaf spectral profiles
Differences in spectral profiles indicate key biomolecular alterations occurring within the leaves under different growth environments, reflective of the high degree of environmental plasticity exhibited by Japanese knotweed that may contribute to its success as an invasive species (Liao et al., 2016; Mounger et al., 2021). Although IAS generally display greater plasticity, this is not always correlated with a fitness benefit (Davidson et al., 2011). Physiological variation of Japanese knotweed grown in different habitats has been recorded in previous studies, including differences in height, number of leaves, leaf surface area and biomass allocation (Walls, 2010; Yuan et al., 2022). All the rhizomes used for these controlled growth experiments were extracted from the same source, but recent research indicates that the environmental adaptations observed here may be influenced by the original source of the rhizomes and could have differed if these were collected from another habitat type (Yuan et al., 2022). The growth environment plays a significant role in phenotypic presentation, and this study has explored the influence of specific environmental variables.
Chlorophyll content under drought stress can both increase (da Costa & Tom Cothren, 2011) and decrease (Mafakheri et al., 2010). Here, LD had significantly higher chlorophyll levels compared with LC (Fig. 1g). This may be because LD had fewer (Fig. 1c), smaller (Fig. 1a), leaves compared with LC, leading to a necessity for increased chlorophyll levels per unit leaf area. Figure 1 shows that plants of the nitrogen supplemented category, SN, had a significantly greater number of leaves than SC or SLN, but this was not statistically different to SD. Although drought is usually associated with a reduced leaf number (Álvarez et al., 2013; Kebbas et al., 2015), this observation is consistent with water having only been withheld completely for seven days out of a total growth period of fifty days in the present study, representing a short-term drought rather than a long term water deficit. However, nitrogen availability, sugar demand, R:FR and auxin concentrations have all been linked to the control of apical branching (Mason et al., 2014; Hou et al., 2021; Holalu et al., 2021). These pathways interlink via common intermediates, for example a low R:FR promotes auxin signalling (Holalu et al., 2021) and nitrogen fluctuations have a significant impact on auxin distribution (Hou M et al., 2021). These complex interacting signals could explain the lack of significant differences in the number of leaves between categories. Growth was markedly affected in shaded plants (LC, LD, LN and LLN) which were generally taller in height (Fig. 1). This is consistent with ethylene-mediated stem elongation which is a stress response known to be induced by low R:FR (Pierik et al., 2004). Additionally, transpiration rate is elevated under high R:FR (Hoad & Leakey, 1994) thereby increasing the likelihood of plants experiencing drought under conditions of reduced water availability, possibly leading to the larger root water potentials measured in category LD compared to category SD. This increased transpiration rate may also have altered the effect of drought on leaf quantity under different lightings; under a high R:FR LC plants had more leaves than LD, however under a low R:FR then SD had more leaves than SC, see Fig. 1.
Of the altered environmental parameters, the R:FR of the growth environment had the greatest effect on the spectral profiles of intact dried leaf material. This was indicated by the highest PLS regression R2 in Table 2. A possible explanation for this is that these spectra were taken from leaf vegetative tissue, which may be more prone to changes in light as leaves are photosynthetic organs. The primary function of leaves is to absorb sunlight for photosynthesis, and spectra of other plant organs may be affected differently by different environmental factors. The clustering pattern of the 2D PCA-LDA scatter graph (Fig. 4b) provides an indication of why the R:FR has the greatest impact on the spectra. This displays a general separation along the axis LD1 of shaded samples on the left and non-shaded samples on the right. This LD1 axis is significant for identifying the wavenumbers most affected by the different environments, used for the classification of groups. Molecular biomarkers were found from the LD1 loadings and indicate which molecules differ most between leaves of plants grown in different environments: lipid (1732 cm− 1), CH2 bending of the methylene chains in lipids (1470 cm− 1), ring breathing (995 cm− 1), C-H and O-H bending in hemicellulose (1423 cm− 1), C-O vibration in sucrose (1126 cm− 1), overlapping of the protein amide III and the nucleic acid asymmetric phosphate vibration (1231 cm− 1), cellulose (1319 cm− 1), peak of nucleic acids due to the base carbonyl stretching and ring breathing mode (1620 cm− 1), starch (1030 cm− 1), and CH3 rocking (957 cm− 1) (Zhou et al., 2015; Talari et al., 2017; Rana et al., 2018; Jin et al., 2018; Lei et al., 2018; Kharrat et al., 2020).
The sensitivity of ATR-FTIR spectral profiles to the R:FR ratio of the growth environment could account for some of the spectral differences in plants grown in different regions of the UK, observed previously by Holden et al. (2021). At higher latitudes, plants experience longer durations of sunlight from low solar angles (Chiang et al., 2019). The R:FR ratio at low solar angles is lower (Kotilainen et al., 2020) and more variable (Chiang et al., 2019). Water vapour increases the R:FR photon ratio by preferentially absorbing the FR light, due to the water absorption band at 728 nm, meaning that plants in growing regions with overcast skies tend to experience a lower R:FR ratio than those under clear skies (Durand et al., 2021). Additionally, modelled climate scenarios predict that increasing global temperatures will result in increased atmospheric water vapour, which will reduce the proportion of far-red photons in sunlight (Kotilainen et al., 2020). Plants sense the R:FR ratio with phytochromes which allows them to trigger their shade-avoidance response and detect above-ground neighbours (Ballaré & Pierik, 2017). As a pioneer species, one could predict Japanese knotweed to be a competitive shade-avoider, likely to have a strong avoidance response compared with a shade-tolerant woodland floor species, however, leaves within the dense knotweed canopy are known to experience reduced light-penetration. Martin FM et al., (2019) noted the lack of information on the significance of shading for Japanese knotweed, particularly in interaction with mechanical control, whilst observing its importance for ramet density (the space between independent members of a clone). Plants grown in shaded conditions (SC, SD, SN, SLN) tend to have lower aboveground fresh and dry weights compared with light (LC, LD, LN, LLN), see Fig. 1 and Supplementary Figure S2, supporting the importance of light quality for Japanese knotweed. These results have been echoed in field studies which found that a reduction in soil fertility had no significant effect on knotweed biomass production, and concluded that light quality was the most important of the tested parameters (Dommanget et al., 2013).
ATR-FTIR spectroscopy provides a novel tool for predicting physiological responses
The model in Fig. 5 was created using training data from plants of LC and LD, which differed only in the amount of water supplied to them with the other controlled environmental variables remaining the same. The use of larger training sets would allow the generation of more robust models which take account of the breadth of the variables in the growth environment enabling this approach to be widely applied. We have previously shown the power of ATR-FTIR spectroscopy for predicting plant physiological responses such as hormone concentrations (Holden et al., unpublished). Applying this approach to the analysis of plant water status in Fig. 5 further highlights the importance of such predictive models for non-destructively studying the responses of plants to their environment in situ. Near-infrared (NIR) spectroscopy (Vohland et al., 2022) using portable handheld NIR spectrometers, whilst less rich in the spectral information provided compared with ATR-FTIR spectroscopy, has been used for monitoring plant water (Diago et al., 2017, 2018; Fernández-Novales et al., 2018) and nutrient (Pandey et al., 2017) status. Advances in technology mean that portable MIR spectrometers are now available (Dhawale et al., 2015; Ji et al., 2016; Soriano-Disla et al., 2018; Bureau et al., 2019; Hutengs et al., 2019) highlighting the potential of this method for future applications of MIR spectroscopy to the prediction of physiological responses in the field, providing a more sensitive alternative to NIR spectroscopy. The use of MIR as an indicative tool to determine the efficacy of treatment approaches for invasive knotweeds could accelerate studies which normally span several years (Jones D et al., 2018). ATR-FTIR spectral ground-readings could complement spatial dynamic data collected by remote sensing (Martin et al., 2018), to create detailed predictive maps that enhance our ability to monitor invasive alien species, providing further information on the ‘what’ in addition to the ‘where’.