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Article

Intra- and Interspecific Variability of Non-Structural Carbohydrates and Phenolic Compounds in Flowers of 70 Temperate Trees and Shrubs

by
Sonia Paź-Dyderska
1,*,
Roma Żytkowiak
1 and
Andrzej M. Jagodziński
1,2
1
Institute of Dendrology, Polish Academy of Sciences, Parkowa 5, 62-035 Kórnik, Poland
2
Department of Game Management and Forest Protection, Faculty of Forestry and Wood Technology, Poznań University of Life Sciences, Wojska Polskiego 71c, 60-625 Poznań, Poland
*
Author to whom correspondence should be addressed.
Forests 2022, 13(8), 1256; https://doi.org/10.3390/f13081256
Submission received: 7 June 2022 / Revised: 22 July 2022 / Accepted: 4 August 2022 / Published: 9 August 2022
(This article belongs to the Section Forest Ecophysiology and Biology)
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Abstract

:
(1) The focus on floral functional traits and their variability has been significantly lower than when compared to other plant organs. Here, we focused on the variability of four novel floral chemical traits. We aimed to assess the level of interspecific variability of total non-structural carbohydrates (TNC) and total phenolic compounds (TPh) in the flowers of woody species. (2) We collected data on 70 species of temperate trees and shrubs. We also assessed the intraspecific level of variability by collecting flowers from the high-light and low-light parts of the crown. (3) We found a phylogenetic signal in the variability of starch and TPh. We did not observe statistically significant differences or biologically significant trends between the high-light and low-light parts of the crown. We detected a low impact of light availability on the intraspecimen variability of the TNC and TPh contents. (4) Low intraspecimen variability allows for a more reliable extrapolation of measurements in cases of interspecific comparisons and can be used to better describe the reproductive strategies of different woody species.

1. Introduction

So far, the focus on floral functional traits has been significantly lower compared to other plant organs [1]. Searches for studies on the variability of leaf, seed, and stem functional traits with Google Scholar have provided 293,000, 258,000, and 295,000 results, respectively. Interestingly, there were only one-third as many results for a search regarding flower functional trait variability (i.e., 85,000). The limited temporal availability of flowers in the vegetative season [2], together with their robust morphological diversity [3], may explain why flowers are less frequently studied than other organs. Yet, such a disproportion highlights the need to fill the gaps in our understanding of the traits associated with the flowering strategies of species. Although, in the last decade, more studies on floral traits and how to collect such data have emerged (e.g., flower economics spectrum proposed by Roddy et al. [1]), there is still uncertainty about the intraspecific (including intraspecimen) level of variability of the traits. This is particularly important when taking into consideration that floral traits can be even more functionally diverse than foliar traits [4]. Since trait measurements are often shared via publicly available databases [5,6] and subsequently extrapolated to other specimens representing a given species [7,8,9], a better understanding of the intraspecific variability of those traits has significant implications for further research that compiles data into meta-analyses [10].
One of the most important aspects of flower biology and ecology is flower contents of the substances responsible for attracting pollinators and deterring herbivores [11]. Naturally, there is a whole range of different chemical and mechanical ways that a plant can use to defend itself [12] or to attract pollinators [13]. Here, we wanted to focus on some of the chemical ways to attract and deter. To attract pollinators and predators feeding on florivores, plants can invest in carbohydrates located, e.g., in pollen and nectar [14]. As a chemical defense against florivores, plants can invest in total phenolic compounds (TPh) [15]. The attracting and deterring strategies, to a considerable extent, evolved as responses to the availability of given kinds of pollinators and herbivores [16,17,18]. Therefore, the content of carbohydrates and TPh can be assessed as an evolutionary adaptation. As previous studies already detected a phylogenetic signal in the distribution of traits [19,20], phylogenetic signals in the distribution of carbohydrates and TPh contents can also potentially exist. Therefore, carbohydrates and TPh may be used to compare the plant–animal interactions among species but also (while measured at an individual level) to study how the plant invests the given resources into different parts of the crown. Although their meaning is crucial for reproductive processes, the interspecific and intraspecimen variability of the described compounds is still poorly known.
We aimed to assess the level of interspecific variability of soluble carbohydrates, starch, and TPh in the flowers of woody species. We hypothesized that (H1) the interspecific variability of those parameters is correlated with phylogenesis. We assumed that phylogenetically related species show similar values of the parameters studied and therefore represent similar life strategies [21]. We also aimed to assess the intraspecimen levels of variability of the soluble carbohydrates, starch, and TPh contents. We hypothesized that (H2) flowers collected from the sunny (high-light) and shaded (low-light) parts of the crown vary in terms of the soluble carbohydrates, starch, and TPh contents and that flowers collected from high-light will show higher values of those metabolites [22,23]. We assumed that the exposure of flowers to light and heat correlates with a better supply of defense compounds, as similar patterns have been revealed for leaves [22].

2. Materials and Methods

2.1. Study Site

We conducted our study in the Kórnik Arboretum (Western Poland; 52.2448° N, 17.0969° E, 75 m ASL). The Arboretum was founded in the 19th century and consists of a robust collection of ca. 3500 woody taxa, including numerous mature specimens of rare species from all the Northern Hemisphere continents. Plant age is a crucial constraint for flower studies, as for some taxa they take several decades to start reproductive processes, such as blooming or producing fruits. Therefore, Kórnik Arboretum is a suitable location for comparing a wide group of species occurring in a relatively small area. However, the Arboretum was not originally planned to conduct scientific research, and for this reason, we now face some drawbacks derived from an inhomogeneous design connected with the occurrence of a high number of taxa but usually not replicated. Yet, taking into consideration that, in this study, we try to describe novel traits of a high number of woody species, we consider that the Arboretum is an excellent place to conduct the study. In a previous study conducted in the Kórnik Arboretum, the authors successfully collected the data to assess the intraspecific variability of a specific leaf area of 179 taxa [24], and we think this is a reasonable argument for considering the Arboretum as a valuable study site to conduct research in the field of functional ecology.
The mean growing season length in the study location is 220 days, the mean annual precipitation is 544 mm, and the mean annual temperature is 8.3 °C [25]. The Arboretum is not spatially extensive; thus, the environmental conditions, including climate and soil type, are very similar for all the specimens analyzed in our study. Due to the temporary availability of the flowers and the time-consuming procedure of analyzing the samples, we collected samples from 70 angiosperm species of trees and shrubs representing 27 families. We collected data from one specimen per species, as we wanted to include as many species as possible in our study. On the other hand, the availability of numerous specimens representing the same taxa varies throughout the collection and has its limits. This means that sometimes there was only one flowering specimen of a given taxon of interest. For this reason, we decided not to collect data from more specimens even when possible to maintain a fixed sample size for each species studied.

2.2. Data Collection

We collected data from April to July 2021. We decided to collect fully developed flowers with no signs of herbivory, pathogenic infections or mechanical damage, as has been suggested for leaves [26]. There is a lack of explicit protocols for measuring many floral traits, but similar sampling designs, including data collection for floral traits on species with limited replications, e.g., in botanical gardens, have been done before [4,27,28]. Thus, we decided to follow some of the previous ideas from the field, e.g., to include the pedicel and peduncle. Additionally, in cases of species such as Corylus spp. or Quercus sp., with small, inconspicuous flowers, we included the whole inflorescences together with the pedicel/peduncle, because we wanted to include all the showy floral structures, as their main convergent function is to attract pollinators [27]. We collected floral organs at the peak flowering time [28]. To assess not only the interspecific but also the intraspecific levels of variability, we collected at least ten flowers (or inflorescences) from the sunniest and ten flowers from the most shaded parts of the crown with a 6-m-long pole pruner. However, there were some exceptions, i.e., we collected fewer flowers of species producing very big flowers (e.g., Magnolia tripetala (L.) L.), as we did not want to harm the rare specimens. We selected the flowers for the study based on qualitative observations with no measurements of the light availability, similar to Paź-Dyderska et al. [24]. As a result, we obtained two samples per species from each variant of the study, i.e., high-light and low-light. We put the harvested flowers in envelopes to minimize the risk of losing fragile parts of the plant material. Then, all flowers were dried in an oven with forced air circulation at 65 °C (ULE 600 and UF450, Memmert GmbH + Co. KG, Regensburg, Germany) to a constant mass using the same methodology as in numerous studies previously carried out in our laboratory [29,30,31].

2.3. Chemical Analysis

We analyzed all chemical compounds as dried flower tissue ground in a Mikro-Feinmühle-Culatti mill (IKA Labortechnik, Staufen im Breisgau, Germany). We assessed the contents of total non-structural carbohydrates (TNC; soluble carbohydrates and starch) as proposed by Hansen and Møller [32] and Haissig and Dickson [33]. We assayed soluble carbohydrates in methanol–chloroform–water extracts (λ = 625 nm), following a color reaction with anthrone. We presented TNC results as a % of the dry mass. The starch analysis consisted of its transformation into glucose, with amyloglucosidase and oxidation using the peroxidase–glucose oxidase complex. We measured the concentrations of starch at λ = 450 nm following the reaction with dianisidine. We measured the TPh content (λ = 660 nm) with Folin and Ciocalteu’s Phenol Reagent (SIGMA F-9252) using the methodology of Johnson and Schaal [34] with the modification of Singleton and Rossi [35]. The results we obtained were expressed in units of μM of a chlorogenic acid g−1 dry mass. We followed the methodology of previous studies on chemical compositions [36,37].

2.4. Data Analysis

We analyzed our data using R software (R Core Team, 2021). We processed the data using the dplyr package [38] and visualized them with the ggplot2 package [39]. We expressed interspecific variability using the coefficient of variation (CV). To assess the impact of the light variant on the content of a given chemical compound, we performed a phylogenetic paired t-test using the phyl.pairedttest function from the phytools package [40]. This way, we could assess the eventual impacts of the canopy position on the contents of the chemical compounds studied in a given sample.
To verify whether the interspecific variability of the soluble carbohydrates, starch, and TPh in the flowers of woody species is correlated with the phylogenesis of the species, we used a phylogenetic tree for the species studied obtained from the V.PhyloMaker package [41]. Then, we assessed the C mean, Blomberg’s K, K.star, and Lambda phylogenetic correlation coefficients using the Phylosignal package [42,43,44].

3. Results

The mean values of the soluble carbohydrates in the flowers (Figure 1 and Table 1) of the species studied ranged from 3.206% (Ailanthus altissima (Mill.) Swingle) to 27.039% (Catalpa bignonioides Walter). The CV for soluble carbohydrates was 44.4%. For starch, the mean values ranged from 0.739% (Viburnum sieboldii Miq.) to 10.323% (Asimina triloba (L.) Dunal), with a CV of 110.4%. The range of the TNC mean values differed from 3.997% (A. altissima) to 27.819% (C. bignonioides), and the CV was 41.4%. Lastly, the TPh values ranged from 46.147 (Laburnum anagyroides Medik.) to 1085.734 (Sorbus torminalis (L.) Crantz) μM of the chlorogenic acid g−1 dry mass, with a CV of 70.5%. Generally, the variability was the highest in starch and lowest in the soluble carbohydrates and TNC.
The variability of starch and TPh showed phylogenetic signals, while we did not observe a correlation between the soluble carbohydrates and TNC variability levels with the phylogenetic signal. We observed a moderate phylogenetic signal in TPh (Figure 2 and Table 2). The starch content showed a stronger phylogenetic signal; however, this was mainly caused by an outlier observation of Paulownia tomentosa (Thunb.) Steud. We did not observe a significant evolutionary signal for soluble carbohydrates or TNC. Although the phylogenetic signal was the strongest for starch, it was not statistically significant due to the strong impact of the outlier. The signal was statistically significant only for TPh.
We did not observe statistically significant differences or biologically significant trends between the high-light and low-light parts of the crown (Table 3 and Figure 3). The phylogenetic mean values of the differences between those two variants of light availability for the soluble carbohydrates, starch, TNC, and TPh were −0.996%, 0.448%, −0.807%, and 51.069 μM of chlorogenic acid g−1 dry mass, respectively (Table 3). The minimum difference for the soluble carbohydrates was 0.23% (Halesia carolina), <0.01% for starch (e.g., Castanea sativa Mill.), 0.18% for TNC (P. tomentosa), and 0.49 μM of chlorogenic acid g−1 dry mass for TPh (Tilia cordata Mill.). The maximum difference value for soluble carbohydrates was 28.80% (Spiraea × nudiflora Zabel), 6.98% for starch (Zelkova serrata (Thunb.) Makino), 29.05% for TNC (S. nudiflora), and 1021.80 μM of chlorogenic acid g−1 dry mass for TPh (Spiraea longigemmis Maxim.).

4. Discussion

Our study presents novel data on the variability of flowers in terms of the TNC and TPh contents, which are crucial for a better understanding of the reproductive strategies of plants. Similar studies of chemical contents for flowers are sparse. For example, Chen et al. [45] compared floral scent compounds for three Styrax species, but this was focused on a narrow range of taxa. However, analogical research is more developed for other floral traits. Studies regarding floral functional traits often focus on reproductive success and plant–pollinator interactions studied on an interspecific level, considering high floral variability as a form of adaptation to more successful pollination [46]. For example, in the case of well-studied orchids known for their high variability of floral traits, Lussu et al. [47] found that the morphological variability of functional traits related to the fertilization process may constitute a natural barrier preventing the crossing of cooccurring species. Juillet and Scopece [48], based on a review of eight experimental and correlative studies, detected that a high phenotypic variability of orchids probably does not positively affect reproductive success. Generally, we found that previous studies from this field regarded rather narrow groups of species and did not focus on the contents of carbohydrates and TPh in flowers. We also did not find any previous studies focusing on the variability of those traits occurring within individuals. However, it should be highlighted that previous studies have measured numerous pivotal floral traits that we did not include in our study, such as, for example, biomass, size, shape, or color [23]. Therefore, with this study, we contribute to the extension of an already thriving flower-related branch of functional ecology.
In this study, we detected that, among the studied variables, the highest variability occurred in the case of starch, and the lowest in the cases of soluble carbohydrates and TNC. We found no phylogenetic signals for the values of soluble carbohydrates and TNC, but we found that the starch and TPh contents were correlated with phylogenesis. However, only TPh showed a statistically significant correlation with phylogenesis. We assume that not only the presence of outliers could affect our results but also a limited number of species. We analyzed plant materials collected from 70 species, which is a sufficient number for a phylogenetic analysis. The root:shoot ratio [19], specific leaf area [24], variation in seed mass [49], or wood carbon concentration [50] are only some of the examples of the correlation of functional traits with the evolutionary history of species. On the other hand, phylogenetically clustered plants may show higher functional diversity, which can be interpreted as a way to stabilize the coexistence of a set of evolutionarily related species [51]. Thus, the weak phylogenetic signal we detected can be derived from the natural ecological diversity of species within the same family or genus.
We faced some trade-offs at the design stage of our study. Flowering being an ephemeral process that occurs at different times during the growing season prevented us from collecting the data at one point in time (as was possible, for example, with leaves available for months during the vegetative season). Simultaneously, we are aware of the seasonal variability of flowers and their contents in chemical compounds, not only TNC and TPh analyzed in this study. As was detected for other organs (e.g., carbon content increase in aging leaves, as described by Reich and coauthors [52]), there are also seasonal changes in the contents of chemical compounds in flowers [53,54,55]. However, as indicated by the literature review, our study is one of the first attempts at detecting the potential TNC and TPh contents in flowers for use in functional ecology, and therefore, we consciously decided to skip this type of variability as negligible at this stage of the development of this field of knowledge. Moreover, as detected by Siatka and Kašparová [56], seasonal variations of TPh in flowers of Bellis perennis L. showed comparable values throughout the whole growing season, i.e., from spring to autumn. Additionally, we believe that, similar to a previous study by Paź-Dyderska et al. [24], the concept of a common garden design of the study (i.e., numerous species growing in one area with similar climatic conditions) to a certain degree decreases the disadvantages resulting from the abovementioned shortcomings.
We decided to include only one specimen per species to increase the number of species. To our knowledge, the optimum sample size for the study of the intraspecimen variability of flowers has not been assessed yet, but in the case of foliar traits, the most accurate sampling size covers four samples from 10 individuals [57]. Thus, although we are conscious that a higher number of the specimens would better represent the data variability, we collected flowers from singular specimens. This resulted not only from the characteristics of the Arboretum layout, which often includes only one mature specimen of a given species, but also from the time-consuming process of collecting data. Since species flowering periods often overlap and vary from year to year, the harvest of flowers required monitoring the stages of flower development for all species throughout the growing season and waiting for the optimal flowering phase to collect the material for each species. Hence, the collection of flowers takes more time than, e.g., the collection of leaves. Yet, such data can be truly attractive for further use in functional ecology.
The low level of intraspecimen variability and relatively high level of interspecific variability of TNC and TPh can have significant advantages while advocating for the wider use of those parameters as functional traits [58,59,60]. The low intraspecimen variability that we detected is favorable, as the human decision of which flowers to collect has a lesser impact on the results obtained than the leaves, which, in the case of the specific leaf area, may differ ca. 40% just within one individual [24]. Nevertheless, we should keep in mind that, in this study, we focused only on the concentrations of the chemical compounds, with no focus on their absolute amounts, which vary depending on the flower size. As the flower size can potentially vary from high-light to low-light, it can be difficult to assess the importance of the low intraspecific variability detected. Yet, using concentrations is an efficient way of comparing samples from different crown locations, and we believe that this method provides some valuable insight into the flowering biology of the woody species.
Simultaneously, we showed significant interspecific variability, which is useful while searching for potential differences among species and their flowering strategies. Moreover, the data we provide might be used for modeling plant–animal interactions, as the TNC and TPh are related to the species strategies of attracting pollinators (e.g., a high starch concentration in pollen is related to pollen maturity [61]) or defending from herbivores [15]. Previous studies showing the impact of soluble carbohydrates on the longevity of flowers in the floristic industry indicated the potential for the use of TNC in functional ecology. For example, Monteiro and coauthors revealed that a higher TNC content in potted miniature roses correlated positively with the flower longevity and that injecting the plants with sucrose solution increased the flower longevity by 1.5 days [62]. Ranwala and Miller [63] showed that the postharvest longevity of tulips can be increased up to 18–37%, depending on the treatment, by supplying sucrose and trehalose solutions. In the context of functional ecology, however, the potential of using TNC as a tool to study flower longevity is yet to be explored. The same is true for phenolic compounds, where there is a potential for the use of TPh in studies of plant antifungal strategies. In our study, we measured the total phenolic compound contents, but Petersen [64] highlighted that one of the phenolic compounds, i.e., rosmarinic acid, has important antifungal effects in plants with flowers. Thus, delving into more detailed chemical analyses may lead to broadening the possibilities of using the traits we studied in forthcoming ecological studies. That is why we believe that the incorporation of TNC and TPh into studies on flowering biology and ecology may contribute to a better understanding of the reproductive processes that are crucial for the maintenance of diverse ecosystems.

5. Conclusions

Our results expand the knowledge of the biology and ecology of flowering in woody plants. We provided new, quantitative data on the soluble carbohydrates and TPh contents in flowers of 70 temperate species of trees and shrubs. We showed a weak correlation between their chemical contents and evolutionary history by preparing a phylogenetic tree for the set of species studied. We detected a low impact of the light availability on the intraspecimen variability of the TNC and TPh contents, which highlights the potential of those traits to be used in functional ecology. A low intraspecimen variability is more favorable for intraspecific comparisons and analyses and can be used to better describe the functional differences of flowers and, subsequently, differences in the reproductive strategies of different woody species.

Author Contributions

Conceptualization, S.P.-D. and A.M.J.; methodology, S.P.-D. and A.M.J.; software, S.P.-D.; validation, S.P.-D., A.M.J. and R.Ż.; formal analysis, S.P.-D.; chemical analysis, R.Ż.; investigation, S.P.-D.; data curation, S.P.-D.; writing—original draft preparation, S.P.-D.; writing—review and editing, A.M.J. and R.Ż.; visualization, S.P.-D.; supervision, A.M.J.; project administration, S.P.-D.; and funding acquisition, S.P.-D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Institute of Dendrology, Polish Academy of Sciences, Kórnik, Poland, grant number 2021/01/ZB/FBW/00003.

Data Availability Statement

Not available.

Acknowledgments

We are grateful to Kinga Nowak for her help in selecting species suitable for our research. We are thankful to Piotr Karolewski for his consultation regarding the chemical analyses of phenolic compounds. We would like to thank Marcin K. Dyderski for his critical comments on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Roddy, A.B.; Martínez-Perez, C.; Teixido, A.L.; Cornelissen, T.G.; Olson, M.E.; Oliveira, R.S.; Silveira, F.A.O. Towards the flower economics spectrum. New Phytol. 2021, 229, 665–672. [Google Scholar] [CrossRef] [PubMed]
  2. Gao, J.; Xiong, Y.-Z.; Huang, S.-Q. Effects of floral sexual investment and dichogamy on floral longevity. J. Plant Ecol. 2015, 8, 116–121. [Google Scholar] [CrossRef] [Green Version]
  3. van der Niet, T.; Johnson, S. Phylogenetic evidence for pollinator-driven diversification of angiosperms. Trends Ecol. Evol. 2012, 27, 353–361. [Google Scholar] [CrossRef]
  4. Roddy, A.B.; Jiang, G.-F.; Cao, K.; Simonin, K.A.; Brodersen, C.R. Hydraulic traits are more diverse in flowers than in leaves. New Phytol. 2019, 223, 193–203. [Google Scholar] [CrossRef] [PubMed]
  5. Kattge, J.; Bönisch, G.; Díaz, S.; Lavorel, S.; Prentice, I.C.; Leadley, P.; Tautenhahn, S.; Werner, G.D.A.; Aakala, T.; Abedi, M.; et al. TRY plant trait database—Enhanced coverage and open access. Glob. Chang. Biol. 2020, 26, 119–188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Kleyer, M.; Bekker, R.M.; Knevel, I.C.; Bakker, J.P.; Thompson, K.; Sonnenschein, M.; Poschlod, P.; van Groenendael, J.M.; Klimeš, L.; Klimešová, J.; et al. The LEDA Traitbase: A database of life-history traits of the Northwest European flora. J. Ecol. 2008, 96, 1266–1274. [Google Scholar] [CrossRef]
  7. Campetella, G.; Chelli, S.; Simonetti, E.; Damiani, C.; Bartha, S.; Wellstein, C.; Giorgini, D.; Puletti, N.; Mucina, L.; Cervellini, M.; et al. Plant functional traits are correlated with species persistence in the herb layer of old-growth beech forests. Sci. Rep. 2020, 10, 19253. [Google Scholar] [CrossRef]
  8. Dyderski, M.K.; Jagodziński, A.M. Impacts of invasive trees on alpha and beta diversity of temperate forest understories. Biol. Invasions 2021, 23, 235–252. [Google Scholar] [CrossRef]
  9. Tecco, P.A.; Díaz, S.; Cabido, M.; Urcelay, C. Functional traits of alien plants across contrasting climatic and land-use regimes: Do aliens join the locals or try harder than them? J. Ecol. 2010, 98, 17–27. [Google Scholar] [CrossRef]
  10. Albert, C.H.; Thuiller, W.; Yoccoz, N.G.; Soudant, A.; Boucher, F.; Saccone, P.; Lavorel, S. Intraspecific functional variability: Extent, structure and sources of variation. J. Ecol. 2010, 98, 604–613. [Google Scholar] [CrossRef]
  11. Farré-Armengol, G.; Filella, I.; Llusia, J.; Peñuelas, J. Floral volatile organic compounds: Between attraction and deterrence of visitors under global change. Perspect. Plant Ecol. Evol. Syst. 2013, 15, 56–67. [Google Scholar] [CrossRef]
  12. Gong, B.; Zhang, G. Interactions between plants and herbivores: A review of plant defense. Acta Ecol. Sin. 2014, 34, 325–336. [Google Scholar] [CrossRef]
  13. Frachon, L.; Stirling, S.A.; Schiestl, F.P.; Dudareva, N. Combining biotechnology and evolution for understanding the mechanisms of pollinator attraction. Curr. Opin. Biotechnol. 2021, 70, 213–219. [Google Scholar] [CrossRef]
  14. Venjakob, C.; Ruedenauer, F.A.; Klein, A.; Leonhardt, S.D. Variation in nectar quality across 34 grassland plant species. Plant Biol. 2021, 24, 134–144. [Google Scholar] [CrossRef] [PubMed]
  15. Nakano, S.; Oguro, M.; Itagaki, T.; Sakai, S. Florivory defence: Are phenolic compounds distributed non-randomly within perianths? Biol. J. Linn. Soc. 2020, 131, 12–25. [Google Scholar] [CrossRef]
  16. Gervasi, D.D.L.; Schiestl, F. Real-time divergent evolution in plants driven by pollinators. Nat. Commun. 2017, 8, 14691. [Google Scholar] [CrossRef] [Green Version]
  17. Knauer, A.C.; Schiestl, F. The effect of pollinators and herbivores on selection for floral signals: A case study in Brassica rapa. Evol. Ecol. 2016, 31, 285–304. [Google Scholar] [CrossRef]
  18. Strauss, S.Y. Floral characters link herbivores, pollinators, and plant fitness. Ecology 1997, 78, 1640–1645. [Google Scholar] [CrossRef]
  19. Burns, J.H.; Strauss, S.Y. Effects of competition on phylogenetic signal and phenotypic plasticity in plant functional traits. Ecology 2012, 93, S126–S137. [Google Scholar] [CrossRef]
  20. Martin, A.R.; Thomas, S.C.; Zhao, Y. Size-dependent changes in wood chemical traits: A comparison of neotropical saplings and large trees. AoB PLANTS 2013, 5, plt039. [Google Scholar] [CrossRef]
  21. Chanderbali, A.S.; Berger, B.A.; Howarth, D.G.; Soltis, P.S.; Soltis, D.E. Evolving ideas on the origin and evolution of flowers: New perspectives in the genomic era. Genetics 2016, 202, 1255–1265. [Google Scholar] [CrossRef] [Green Version]
  22. Karolewski, P.; Giertych, M.J.; Żmuda, M.; Jagodziński, A.M.; Oleksyn, J. Season and light affect constitutive defenses of understory shrub species against folivorous insects. Acta Oecol. 2013, 53, 19–32. [Google Scholar] [CrossRef] [Green Version]
  23. Roddy, A.B. Energy balance implications of floral traits involved in pollinator attraction and water balance. Int. J. Plant Sci. 2019, 180, 944–953. [Google Scholar] [CrossRef]
  24. Paź-Dyderska, S.; Dyderski, M.K.; Nowak, K.; Jagodziński, A.M. On the sunny side of the crown—Quantification of intra-canopy SLA variation among 179 taxa. For. Ecol. Manag. 2020, 472, 118254. [Google Scholar] [CrossRef]
  25. Cedro, A.; Iszkuło, G. Do females differ from males of European yew (Taxus baccata L.) in dendrochronological analysis? Tree-Ring Res. 2011, 67, 3–11. [Google Scholar] [CrossRef]
  26. Pérez-Harguindeguy, N.; Díaz, S.; Garnier, E.; Lavorel, S.; Poorter, H.; Jaureguiberry, P.; Bret-Harte, M.S.; Cornwell, W.K.; Craine, J.M.; Gurvich, D.E.; et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 2013, 61, 167–234. [Google Scholar] [CrossRef]
  27. Roddy, A.B.; Brodersen, C.R.; Dawson, T.E. Hydraulic conductance and the maintenance of water balance in flowers. Plant Cell Environ. 2016, 39, 2123–2132. [Google Scholar] [CrossRef] [PubMed]
  28. Zhang, F.-P.; Yang, Y.-J.; Yang, Q.-Y.; Zhang, W.; Brodribb, T.J.; Hao, G.-Y.; Hu, H.; Zhang, S.-B. Floral mass per area and water maintenance traits are correlated with floral longevity in Paphiopedilum (Orchidaceae). Front. Plant Sci. 2017, 8, 501. [Google Scholar] [CrossRef] [Green Version]
  29. Łukowski, A.; Giertych, M.J.; Mąderek, E.; Adamczyk, D.; Karolewski, P. Effect of Scots pine needle age on preference, performance, and overwintering of pine-tree lappet larvae—The distaste for the oldest foliage. Acta Oecol. 2022, 116, 103836. [Google Scholar] [CrossRef]
  30. Wyka, T.P.; Karolewski, P.; Żytkowiak, R.; Chmielarz, P.; Oleksyn, J. Whole-plant allocation to storage and defense in juveniles of related evergreen and deciduous shrub species. Tree Physiol. 2016, 36, 536–547. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Zadworny, M.; McCormack, M.L.; Żytkowiak, R.; Karolewski, P.; Mucha, J.; Oleksyn, J. Patterns of structural and defense investments in fine roots of Scots pine (Pinus sylvestris L.) across a strong temperature and latitudinal gradient in Europe. Glob. Chang. Biol. 2017, 23, 1218–1231. [Google Scholar] [CrossRef]
  32. Hansen, J.; Møller, I. Percolation of starch and soluble carbohydrates from plant tissue for quantitative determination with anthrone. Anal. Biochem. 1975, 68, 87–94. [Google Scholar] [CrossRef]
  33. Haissig, B.E.; Dickson, R.E. Starch measurement in plant tissue using enzymatic hydrolysis. Physiol. Plant. 1979, 47, 151–157. [Google Scholar] [CrossRef]
  34. Johnson, G.; Schaal, L.A. Accumulation of phenolic substances and ascorbic acid in potato tuber tissue upon injury and their possible role in disease resistance. Am. Potato J. 1957, 34, 200–209. [Google Scholar] [CrossRef]
  35. Singleton, V.L.; Rossi, J.A. Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. Am. J. Enol. Vitic. 1965, 16, 144–158. [Google Scholar]
  36. Karolewski, P.; Zadworny, M.; Mucha, J.; Napierała-Filipiak, A.; Oleksyn, J. Link between defoliation and light treatments on root vitality of five understory shrubs with different resistance to insect herbivory. Tree Physiol. 2010, 30, 969–978. [Google Scholar] [CrossRef] [Green Version]
  37. Łukowski, A.; Giertych, M.J.; Adamczyk, D.; Mąderek, E.; Karolewski, P. Preference and performance of the pine-tree lappet Dendrolimus pini on various pine species. Forests 2021, 12, 1261. [Google Scholar] [CrossRef]
  38. Wickham, H.; Francois, R.; Henry, L.; Müller, K. Dplyr: A Grammar of Data Manipulation. 2020. Available online: https://cran.r-project.org/web/packages/dplyr/index.html (accessed on 7 June 2022).
  39. Wickham, H.; Chang, W.; Henry, L.; Pedersen, T.L.; Takahashi, K.; Wilke, C.; Woo, K.; Yutani, H. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. 2020. Available online: https://cran.r-project.org/web/packages/ggplot2/index.html (accessed on 7 June 2022).
  40. Revell, L.J. Phytools: Phylogenetic Tools for Comparative Biology (and Other Things). 2020. Available online: https://cran.r-project.org/web/packages/phytools/index.html (accessed on 7 June 2022).
  41. Jin, Y.; Qian, H.V. PhyloMaker: An R package that can generate very large phylogenies for vascular plants. Ecography 2019, 42, 1353–1359. [Google Scholar] [CrossRef] [Green Version]
  42. Blomberg, S.P.; Garland, T.; Ives, A.R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 2003, 57, 717–745. [Google Scholar] [CrossRef]
  43. Keck, F. Phylosignal: Exploring the Phylogenetic Signal in Continuous Traits. 2020. Available online: https://cran.r-project.org/web/packages/phylosignal/index.html (accessed on 7 June 2022).
  44. Molina-Venegas, R.; Rodríguez, M.A. Revisiting phylogenetic signal; strong or negligible impacts of polytomies and branch length information? BMC Evol. Biol. 2017, 17, 53. [Google Scholar] [CrossRef] [Green Version]
  45. Chen, C.; Cao, Y.; Chen, H.; Ni, M.; Yu, F. Floral scent compounds and emission patterns of three Styrax species. Dendrobiology 2021, 85, 30–38. [Google Scholar] [CrossRef]
  46. Ortiz, P.L.; Fernández-Díaz, P.; Pareja, D.; Escudero, M.; Arista, M. Do visual traits honestly signal floral rewards at community level? Funct. Ecol. 2021, 35, 369–383. [Google Scholar] [CrossRef]
  47. Lussu, M.; De Agostini, A.; Cogoni, A.; Marignani, M.; Cortis, P. Does size really matter? A comparative study on floral traits in orchids with two different pollination strategies. Plant Biol. 2019, 21, 961–966. [Google Scholar] [CrossRef] [PubMed]
  48. Juillet, N.; Scopece, G. Does floral trait variability enhance reproductive success in deceptive orchids? Perspect. Plant Ecol. Evol. Syst. 2010, 12, 317–322. [Google Scholar] [CrossRef]
  49. Zhang, C.; Li, J.; Willis, C.G.; Ma, Z. Among-population variation in seed mass for 190 Tibetan plant species: Phylogenetic pattern and ecological correlates. Glob. Ecol. Conserv. 2020, 23, e01163. [Google Scholar] [CrossRef]
  50. Martin, A.R.; Doraisami, M.; Thomas, S.C. Global patterns in wood carbon concentration across the world’s trees and forests. Nat. Geosci. 2018, 11, 915–920. [Google Scholar] [CrossRef]
  51. Castro, S.A.; Silveira, F.A.; Marcato, M.S.; Lemos-Filho, J.P. So close, yet so different: Divergences in resource use may help stabilize coexistence of phylogenetically-related species in a megadiverse grassland. Flora 2018, 238, 72–78. [Google Scholar] [CrossRef]
  52. Reich, P.B.; Walters, M.B.; Ellsworth, D.S. Leaf age and season influence the relationships between leaf nitrogen, leaf mass per area and photosynthesis in maple and oak trees. Plant Cell Environ. 1991, 14, 251–259. [Google Scholar] [CrossRef]
  53. Ernst, W.H.O.; Kuiters, A.T.; Nelissen, H.J.M.; Tolsma, D.J. Seasonal variation in phenolics in several savanna tree species in Botswana. Acta Bot. Neerl. 1991, 40, 63–74. [Google Scholar] [CrossRef]
  54. Kenis, J.D.; Silvente, S.T.; Trippi, V.S. Nitrogen metabolism and senescence-associated changes during growth of carnation flowers. Physiol. Plant. 1985, 65, 455–459. [Google Scholar] [CrossRef]
  55. McMann, N.; Peichel, A.; Savage, J.A. Early spring flowers rely on xylem hydration but are not limited by stem xylem conductivity. New Phytol. 2022, 233, 838–850. [Google Scholar] [CrossRef]
  56. Siatka, T.; Kašparová, M. Seasonal variation in total phenolic and flavonoid contents and DPPH scavenging activity of Bellis perennis L. flowers. Molecules 2010, 15, 9450–9461. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Petruzzellis, F.; Palandrani, C.; Savi, T.; Alberti, R.; Nardini, A.; Bacaro, G. Sampling intraspecific variability in leaf functional traits: Practical suggestions to maximize collected information. Ecol. Evol. 2017, 7, 11236–11245. [Google Scholar] [CrossRef] [Green Version]
  58. Albert, C.H.; Grassein, F.; Schurr, F.M.; Vieilledent, G.; Violle, C. When and how should intraspecific variability be considered in trait-based plant ecology? Perspect. Plant Ecol. Evol. Syst. 2011, 13, 217–225. [Google Scholar] [CrossRef]
  59. Violle, C.; Enquist, B.J.; McGill, B.J.; Jiang, L.; Albert, C.H.; Hulshof, C.; Jung, V.; Messier, J. The return of the variance: Intraspecific variability in community ecology. Trends Ecol. Evol. 2012, 27, 244–252. [Google Scholar] [CrossRef] [PubMed]
  60. Violle, C.; Navas, M.-L.; Vile, D.; Kazakou, E.; Fortunel, C.; Hummel, I.; Garnier, E. Let the concept of trait be functional! Oikos 2007, 116, 882–892. [Google Scholar] [CrossRef]
  61. Datta, R.; Chamusco, K.C.; Chourey, P.S. Starch biosynthesis during pollen maturation is associated with altered patterns of gene expression in maize. Plant Physiol. 2002, 130, 1645–1656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Monteiro, J.A.; Nell, T.A.; Barrett, J.E. Effects of exogenous sucrose on carbohydrate levels, flower respiration and longevity of potted miniature rose (Rosa hybrida) flowers during postproduction. Postharvest Biol. Technol. 2002, 26, 221–229. [Google Scholar] [CrossRef] [Green Version]
  63. Ranwala, A.P.; Miller, W.B. Comparison of the dynamics of non-structural carbohydrate pools in cut tulip stems supplied with sucrose or trehalose. Postharvest Biol. Technol. 2009, 52, 91–96. [Google Scholar] [CrossRef]
  64. Petersen, M. Rosmarinic acid: New aspects. Phytochem. Rev. 2013, 12, 207–227. [Google Scholar] [CrossRef]
Forests 13 01256 g001 550
Figure 1. Distribution of the soluble carbohydrates, starch, TNC, and TPh contents for the species studied.
Figure 1. Distribution of the soluble carbohydrates, starch, TNC, and TPh contents for the species studied.
Forests 13 01256 g001
Forests 13 01256 g002 550
Figure 2. Mean standardized values of soluble carbohydrates, starch, TNC, and TPh for each species. The number zero on the X-axes defines the mean value of each component calculated for all the species. The longer the bar, the greater the difference between the mean value of the trait and the trait value obtained for a particular species.
Figure 2. Mean standardized values of soluble carbohydrates, starch, TNC, and TPh for each species. The number zero on the X-axes defines the mean value of each component calculated for all the species. The longer the bar, the greater the difference between the mean value of the trait and the trait value obtained for a particular species.
Forests 13 01256 g002
Forests 13 01256 g003 550
Figure 3. Observed soluble carbohydrates, starch, TNC, and TPh species-wise differences between high-light (H) and low-light (L) parts of the crown. Each line represents the slope of a trait difference for a particular species.
Figure 3. Observed soluble carbohydrates, starch, TNC, and TPh species-wise differences between high-light (H) and low-light (L) parts of the crown. Each line represents the slope of a trait difference for a particular species.
Forests 13 01256 g003
Table
Table 1. Overview of the species studied and their mean soluble carbohydrates, starch, TNC, and TPh values.
Table 1. Overview of the species studied and their mean soluble carbohydrates, starch, TNC, and TPh values.
SpeciesFamilySoluble Carbohydrates (%)Starch (%)TNC (%)TPh (μM of Chlorogenic Acid g−1 Dry Mass)
Acer pensylvanicum L.Sapindaceae11.3700.90812.278151.140
Acer pseudoplatanus L.Sapindaceae9.9370.78410.721462.983
Aesculus glabra Willd.Sapindaceae16.5410.94717.487148.197
Aesculus hippocastanum L.Sapindaceae24.0970.88924.985118.706
Aesculus parviflora WalterSapindaceae14.2900.83815.128180.177
Aesculus turbinata BlumeSapindaceae17.2390.98318.222106.805
Ailanthus altissima (Mill.) SwingleSimaroubaceae3.2060.7913.997163.528
Asimina triloba (L.) DunalAnnonaceae10.41510.32320.738306.558
Berberis amurensis Rupr.Berberidaceae14.1470.84914.996199.478
Berberis aquifolium PurshBerberidaceae9.4770.81310.290308.063
Berberis julianae C.K.Schneid.Berberidaceae18.2140.82919.043268.745
Calycanthus fertilis WalterCalycanthaceae10.6831.87512.558411.063
Carpinus orientalis Mill.Betulaceae17.8300.94518.774264.294
Castanea sativa Mill.Fagaceae12.0400.84612.885294.083
Catalpa bignonioides WalterBignoniaceae27.0390.78027.819316.005
Cercidiphyllum japonicum Siebold & Zucc.Cercidiphyllaceae3.8220.8954.717897.379
Cercis chinensis BungeFabaceae15.1340.86916.003140.805
Cornus florida L.Cornaceae10.6610.89411.555132.362
Cornus mas L.Cornaceae16.4600.82717.287391.879
Cornus officinalis Siebold & Zucc.Cornaceae12.8400.87313.714320.996
Corylopsis platypetala Rehder & E.H.WilsonHamamelidaceae9.5970.82910.426300.624
Corylopsis sinensis Hemsl.Hamamelidaceae11.0290.91611.944278.399
Corylus avellana L.Betulaceae5.4920.7876.279177.459
Corylus colurnoides C.K.Schneid.Betulaceae5.6040.8766.481207.546
Crataegus holmesiana AsheRosaceae14.7590.78515.544638.765
Crataegus submollis Sarg.Rosaceae13.3850.86014.244253.148
Cydonia oblonga Mill.Rosaceae16.1090.89417.003410.986
Davidia involucrata Baill.Nyssaceae13.7650.90214.667405.669
Euonymus atropurpureus Jacq.Celastraceae23.8510.81624.667289.730
Exochorda korolkowii LavalléeRosaceae8.3340.8859.219102.092
Exochorda racemosa (Lindl.) RehderRosaceae5.7390.9176.656180.500
Forsythia giraldiana Lingelsh.Oleaceae15.7860.80416.590170.925
Fothergilla major (Sims) Lodd.Hamamelidaceae3.3380.8904.228353.830
Halesia carolina L.Styracaceae7.7100.9798.689168.546
Hamamelis mollis Oliv.Hamamelidaceae5.1270.9616.087288.367
Jasminum fruticans L.Oleaceae20.7171.82322.540224.298
Kolkwitzia amabilis Graebn.Caprifoliaceae15.2400.81516.055192.775
Laburnum anagyroides Medik.Fabaceae14.1790.89915.07846.147
Lonicera standishii JacquesCaprifoliaceae13.2080.89914.106149.106
Magnolia kobus DC.Magnoliaceae10.6480.88011.529106.566
Magnolia stellata (Siebold & Zucc.) Maxim.Magnoliaceae6.6340.8557.489130.447
Magnolia tripetala (L.) L.Magnoliaceae8.1621.2399.400134.390
Malus baccata (L.) MoenchRosaceae11.7950.87912.674207.765
Malus ×hartwigii KoehneRosaceae8.7460.9049.650187.132
Parrotia persica (DC.) C.A.Mey.Hamamelidaceae5.0890.8525.940394.519
Paulownia tomentosa (Thunb.) Steud.Paulowniaceae13.1540.75613.910849.438
Prunus incisa Thunb.Rosaceae9.1140.8239.937128.512
Prunus laurocerasus L.Rosaceae18.4760.88219.358125.202
Prunus padus L.Rosaceae22.3161.14623.462576.903
Prunus serrulata Lindl.Rosaceae15.7470.85916.606160.956
Quercus rubra L.Fagaceae3.8320.7834.615471.303
Rhododendron luteum SweetEricaceae8.8090.9789.787621.978
Rhus aromatica AitonAnacardiaceae11.3720.93512.307430.295
Salix gracilistyla Miq.Salicaceae6.8050.9217.726187.302
Sambucus siberica NakaiAdoxaceae5.1890.7725.961161.683
Sorbus aucuparia L.Rosaceae9.0040.7569.760313.785
Sorbus torminalis (L.) CrantzRosaceae8.4780.7439.2221085.734
Spiraea longigemmis Maxim.Rosaceae19.2261.33520.561677.810
Spiraea media F.SchmidtRosaceae14.6170.84015.457488.532
Spiraea ×nudiflora ZabelRosaceae25.6080.89626.504526.887
Staphylea pinnata L.Staphyleaceae11.9840.95012.93597.215
Syringa josikaea J.Jacq. ex Rchb.Oleaceae19.1310.85019.981802.407
Syringa meyeri C.K.Schneid.Oleaceae22.1740.74422.918378.130
Syringa vulgaris L.Oleaceae11.0670.89611.963149.656
Tilia cordata Mill.Malvaceae13.5100.77314.283414.117
Viburnum carlesii Hemsl. ex Forbes & Hemsl.Adoxaceae20.0330.79720.830274.190
Viburnum lantana L.Adoxaceae15.0600.86715.928139.919
Viburnum sieboldii Miq.Adoxaceae10.4500.73911.189133.098
Weigela florida (Bunge) A.DC.Caprifoliaceae16.5140.88317.39776.666
Zelkova serrata (Thunb.) MakinoUlmaceae5.1804.4039.583875.596
Table
Table 2. Phylogenetic signals of the soluble carbohydrates, starch, TNC, and TPh represented by the C mean, I, Blomberg’s K, K.star, and Lambda phylogenetic correlation coefficients. Statistically significant coefficient values are italicized.
Table 2. Phylogenetic signals of the soluble carbohydrates, starch, TNC, and TPh represented by the C mean, I, Blomberg’s K, K.star, and Lambda phylogenetic correlation coefficients. Statistically significant coefficient values are italicized.
Parameter StudiedCmeanIKK.starLambda
Soluble carbohydrates−0.067−0.0070.1350.1436.70 × 10−5
Starch−0.033−0.0110.5500.5671.011
TNC−0.051−0.0070.1380.1466.70 × 10−5
TPh0.2070.0000.2750.2686.78 × 10−1
Table
Table 3. Parameters of phylogenetic paired t-tests comparing soluble carbohydrates, starch, TNC, and TPh variability between flowers from high-light and low-light parts of the crown.
Table 3. Parameters of phylogenetic paired t-tests comparing soluble carbohydrates, starch, TNC, and TPh variability between flowers from high-light and low-light parts of the crown.
Phylogenetic Mean Difference95% CItp-Value
Soluble carbohydrates−0.996−2.410, 0.419−1.3790.172
Starch0.448−0.295, 1.1921.1820.241
TNC−0.807−2.239, 0.624−1.1060.273
TPh51.069−172.195, 274.3330.4480.655
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Paź-Dyderska, S.; Żytkowiak, R.; Jagodziński, A.M. Intra- and Interspecific Variability of Non-Structural Carbohydrates and Phenolic Compounds in Flowers of 70 Temperate Trees and Shrubs. Forests 2022, 13, 1256. https://doi.org/10.3390/f13081256

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Paź-Dyderska S, Żytkowiak R, Jagodziński AM. Intra- and Interspecific Variability of Non-Structural Carbohydrates and Phenolic Compounds in Flowers of 70 Temperate Trees and Shrubs. Forests. 2022; 13(8):1256. https://doi.org/10.3390/f13081256

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Paź-Dyderska, Sonia, Roma Żytkowiak, and Andrzej M. Jagodziński. 2022. "Intra- and Interspecific Variability of Non-Structural Carbohydrates and Phenolic Compounds in Flowers of 70 Temperate Trees and Shrubs" Forests 13, no. 8: 1256. https://doi.org/10.3390/f13081256

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