Abstract
The ecological niche centrality hypothesis states that population abundance is determined by the position in the ecological niche, expecting higher abundances towards the center of the niche and lower at the periphery. However, the variations in the conditions that favor the persistence of populations between the center and the periphery of the niche can be a surrogate of stress factors that are reflected in the production of metabolites in plants. In this study we tested if metabolomic similarity and diversity in populations of the tree species Eucryphia cordifolia Cav. vary according to their position with respect to the structure of the ecological niche. We hypothesize that populations growing near the centroid should exhibit lower metabolites diversity than plants growing at the periphery of the niche. The ecological niche of the species was modeled using correlative approaches and bioclimatic variables to define central and peripheral localities from which we chose four populations to obtain their metabolomic information using UHPLC-DAD-QTOF-MS. We observed that populations farther away from the centroid tend to have higher metabolome diversity, thus supporting our expectation of the niche centrality hypothesis. Nonetheless, the Shannon index showed a marked variation in metabolome diversity at the seasonal level, with summer and autumn being the periods with higher metabolite diversity compared to winter and spring. We conclude that both the environmental variation throughout the year in combination with the structure of the ecological niche are relevant to understand the variation in expression of metabolites in plants.
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30 August 2023
A Correction to this paper has been published: https://doi.org/10.1007/s10265-023-01489-x
References
Allevato DM, Kiyota E, Mazzafera P, Nixon KC (2019) Ecometabolomic analysis of wild populations of Pilocarpus pennatifolius (Rutaceae) using unimodal analyses. Front Plant Sci 10:258. https://doi.org/10.3389/FPLS.2019.00258/BIBTEX
Alonso A, Marsal S, Julià A, James Carroll A (2015) Analytical methods in untargeted metabolomics: state of the art in 2015. Bioengineer Biotechnol 3:1–20. https://doi.org/10.3389/fbioe.2015.00023
Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol Modell 162:211–232. https://doi.org/10.1016/S0304-3800(02)00349-6
Barve N, Barve V, Jiménez-Valverde A et al (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Modell 222:1810–1819. https://doi.org/10.1016/J.ECOLMODEL.2011.02.011
Boria RA, Olson LE, Goodman SM, Anderson RP (2014) Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Modell 275:73–77. https://doi.org/10.1016/J.ECOLMODEL.2013.12.012
Braun AC, Troeger D, Garcia R et al (2017) Assessing the impact of plantation forestry on plant biodiversity: a comparison of sites in Central Chile and chilean Patagonia. Glob Ecol Conserv 10:159–172. https://doi.org/10.1016/J.GECCO.2017.03.006
Brunetti C, George RM, Tattini M et al (2013) Metabolomics in plant environmental physiology. J Exp Bot 64:4011–4020
Caliñski T, Harabasz J (1974) A Dendrite Method Foe Cluster Analysis. Commun Stat 3:1–27. https://doi.org/10.1080/03610927408827101
Colwell RK, Rangel TF (2009) Hutchinson’s duality: the once and future niche. Proc Natl Acad Sci U S A 106:19651–19658. https://doi.org/10.1073/PNAS.0901650106
de Simón BF, Sanz M, Cervera MT et al (2017) Leaf metabolic response to water deficit in Pinus pinaster Ait. Relies upon ontogeny and genotype. Environ Exp Bot 140:41–55. https://doi.org/10.1016/J.ENVEXPBOT.2017.05.017
Dunn OJ (1964) Multiple comparisons using rank sums technometrics. Multiple comparisons using rank sums technometrics 6:241–252
Echeverria C, Coomes D, Salas J et al (2006) Rapid deforestation and fragmentation of chilean temperate forests. Biol Conserv 130:481–494. https://doi.org/10.1016/J.BIOCON.2006.01.017
Fernie AR, Aharoni A, Willmitzer L et al (2011) Recommendations for reporting Metabolite Data. Plant Cell 23:2477–2482. https://doi.org/10.1105/TPC.111.086272
Fiehn O (2001) Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp Funct Genomics 2:155–168. https://doi.org/10.1002/CFG.82
Field KJ, Lake JA (2011) Environmental metabolomics links genotype to phenotype and predicts genotype abundance in wild plant populations. Physiol Plant 142:352–360. https://doi.org/10.1111/j.1399-3054.2011.01480.x
Figueroa JA, Cabrera HM, Queirolo C, Hinojosa LF (2010) Variability of water relations and photosynthesis in Eucryphia cordifolia Cav. (Cunoniaceae) over the range of its latitudinal and altitudinal distribution in Chile. Tree Physiol. https://doi.org/10.1093/treephys/tpq016
Gargallo-Garriga A, Sardans J, Granda V et al (2020) Different “metabolomic niches” of the highly diverse tree species of the french Guiana rainforests. Sci Rep 10. https://doi.org/10.1038/s41598-020-63891-y
Garibay-Hernández A, Kessler N, Józefowicz AM et al (2021) Untargeted metabotyping to study phenylpropanoid diversity in crop plants. Physiol Plant 173:680–697. https://doi.org/10.1111/PPL.13458
Gomes AF, Almeida MP, Leite MF et al (2019) Seasonal variation in the chemical composition of two chemotypes of Lippia alba. Food Chem 273:186–193. https://doi.org/10.1016/J.FOODCHEM.2017.11.089
Gorrochategui E, Jaumot J, Lacorte S, Tauler R (2016) Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: overview and workflow. Trends Analyt Chem 82:425–442. https://doi.org/10.1016/J.TRAC.2016.07.004
Gouvea DR, Gobbo-Neto L, Sakamoto HT et al (2012) Seasonal variation of the major secondary metabolites present in the extract of Eremanthus mattogrossensis Less (Asteraceae: Vernonieae) leaves. Quim Nova 35:2139–2145
Hartmann T (2007) From waste products to ecochemicals: fifty years research of plant secondary metabolism. Phytochemistry 68:2831–2846. https://doi.org/10.1016/J.PHYTOCHEM.2007.09.017
Hengl T, De Jesus JM, Heuvelink GBM et al (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12:1–40. https://doi.org/10.1371/JOURNAL.PONE.0169748
Hennig C (2020) Package ‘fpc’: Flexible Procedures for Clustering. 2020
Hervé MR, Nicolè F, Lê Cao KA (2018) Multivariate analysis of multiple datasets: a practical guide for chemical ecology. J Chem Ecol 44:215–234. https://doi.org/10.1007/S10886-018-0932-6/FIGURES/3
Hutchinson GE (1957) Concluding remarks. Cold Spring Harb Symp Quant Biol 22:415–427. https://doi.org/10.1101/SQB.1957.022.01.039
Isah T (2019) Stress and defense responses in plant secondary metabolites production. Biol Res 52:39. https://doi.org/10.1186/S40659-019-0246-3
Iwanycki Ahlstrand N, Havskov Reghev N, Markussen B et al (2018) Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed. Ecol Evol 8:6812–6826. https://doi.org/10.1002/ECE3.4195
Jorge TF, Mata AT, António C (2016) Mass spectrometry as a quantitative tool in plant metabolomics. Philosoph Transact Royal Society A: Math Physical Engineer Sci 374:1–26. https://doi.org/10.1098/RSTA.2015.0370
Karger DN, Conrad O, Böhner J et al (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4:1–20. https://doi.org/10.1038/sdata.2017.122
Kfoury N, Scott ER, Orians CM et al (2019) Plant-climate interaction effects: changes in the relative distribution and concentration of the volatile tea leaf metabolome in 2014–2016. Front Plant Sci 10. https://doi.org/10.3389/fpls.2019.01518
Kindt R, Coe R (2022) Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies
Kotilainen T, Tegelberg R, Julkunen-Tiitto R et al (2010) Seasonal fluctuations in leaf phenolic composition under UV manipulations reflect contrasting strategies of alder and birch trees. Physiol Plant 140:297–309. https://doi.org/10.1111/J.1399-3054.2010.01398.X
Král’ová K, Jampílek J, Ostrovský I (2012) Metabolomics - useful tool for study of plant responses to abiotic stresses. Ecol Chem Engineer S 19:133–161. https://doi.org/10.2478/v10216-011-0012-0
Li Y, Kong D, Fu Y et al (2020) The effect of developmental and environmental factors on secondary metabolites in medicinal plants. Plant Physiol Biochem 148:80–89. https://doi.org/10.1016/J.PLAPHY.2020.01.006
Lieurance D, Chakraborty S, Whitehead SR et al (2015) Comparative herbivory rates and secondary metabolite profiles in the leaves of native and non-native Lonicera species. J Chem Ecol 41:1069–1079. https://doi.org/10.1007/s10886-015-0648-9
Lira-Noriega A, Manthey JD (2014) Relationship of genetic diversity and niche centrality: a survey and analysis. Evolution 68:1082–1093. https://doi.org/10.1111/EVO.12343
Luebert P (2006) Sinopsis bioclimática y vegetacional de Chile. Universitaria, Santiago, Chile
Ma S, Baldocchi DD, Mambelli S, Dawson TE (2011) Are temporal variations of leaf traits responsible for seasonal and inter-annual variability in ecosystem CO2 exchange? Funct Ecol 25:258–270. https://doi.org/10.1111/j.1365-2435.2010.01779.x
Maguire B (1973) Niche response structure and the analytical potentials of its relationship to the Habitat. Am Nat 107:213–246. https://doi.org/10.1086/282827
Manthey JD, Campbell LP, Saupe EE et al (2015) A test of niche centrality as a determinant of population trends and conservation status in threatened and endangered north american birds. Endanger Species Res 26:201–208. https://doi.org/10.3354/ESR00646
Marr S, Hageman JA, Wehrens R et al (2021) LC-MS based plant metabolic profiles of thirteen grassland species grown in diverse neighbourhoods. Sci Data. https://doi.org/10.1038/s41597-021-00836-8. 8:
Martínez-Meyer E, Díaz-Porras D, Peterson AT, Yáñez-Arenas C (2013) Ecological niche structure and rangewide abundance patterns of species. Biol Lett 9:1–5. https://doi.org/10.1098/RSBL.2012.0637
Ochoa-Zavala M, Osorio-Olvera L, Cerón-Souza I et al (2022) Reduction of genetic variation when far from the niche centroid: prediction for mangrove species. Front Conserv Sci 2:1–14. https://doi.org/10.3389/fcosc.2021.795365
Osorio-Olvera L, Lira-Noriega A, Soberón J et al (2020a) ntbox: an r package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods Ecol Evol 11:1199–1206. https://doi.org/10.1111/2041-210X.13452
Osorio-Olvera L, Yañez-Arenas C, Martínez-Meyer E, Peterson AT (2020b) Relationships between population densities and niche-centroid distances in north american birds. Ecol Lett 23:555–564. https://doi.org/10.1111/ELE.13453
Padilla-González GF, Diazgranados M, Da Costa FB (2017) Biogeography shaped the metabolome of the genus Espeletia: a phytochemical perspective on an andean adaptive radiation. Sci Rep 7:1–11. https://doi.org/10.1038/s41598-017-09431-7
Padilla-González GF, Frey M, Gómez-Zeledón J et al (2019) Metabolomic and gene expression approaches reveal the developmental and environmental regulation of the secondary metabolism of yacón (Smallanthus sonchifolius, Asteraceae). Sci Rep 9:1–15. https://doi.org/10.1038/s41598-019-49246-2
Pang Z, Chong J, Zhou G et al (2021) MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 49:W388–W396. https://doi.org/10.1093/NAR/GKAB382
Paolini J, Barboni T, Desjobert JM et al (2010) Chemical composition, intraspecies variation and seasonal variation in essential oils of Calendula arvensis L. Biochem Syst Ecol 38:865–874. https://doi.org/10.1016/j.bse.2010.07.009
Papuga G, Gauthier P, Pons V et al (2018) Ecological niche differentiation in peripheral populations: a comparative analysis of eleven Mediterranean plant species. Ecography 41:1650–1664. https://doi.org/10.1111/ECOG.03331
Peters K, Gorzolka K, Bruelheide H, Neumann S (2018a) Computational workflow to study the seasonal variation of secondary metabolites in nine different bryophytes. Sci Data 5:1–11. https://doi.org/10.1038/sdata.2018.179
Peters K, Worrich A, Weinhold A et al (2018b) Current challenges in plant eco-metabolomics. Int J Mol Sci 19:1385. https://doi.org/10.3390/IJMS19051385
Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Modell 213:63–72. https://doi.org/10.1016/J.ECOLMODEL.2007.11.008
Peterson A, Soberón J, Pearson RG et al (2011) Ecological niches and geographic distributions (MPB-49). Princeton University Press, Princeton and Oxford
Phillips SB, Aneja VP, Kang D, Arya SP (2006) Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. Int J Glob Environ Issues 6:231–252. https://doi.org/10.1016/J.ECOLMODEL.2005.03.026
R Development Core Team (2022) A language for statistical computing. R Foundation for Statistical Computing. Vienna, Austria
Ramawat KG, Goyal S (2020) Co-evolution of secondary metabolites during biological competition for survival and advantage: an overview. Ref Ser Phytochem 3–17. https://doi.org/10.1007/978-3-319-96397-6_45
Reverter M, Tribalat MA, Pérez T, Thomas OP (2018) Metabolome variability for two Mediterranean sponge species of the genus Haliclona: specificity, time, and space. Metabolomics 14:1–12. https://doi.org/10.1007/S11306-018-1401-5
Riquelme S, Campos JV, Pecio Ł et al (2022) Sirex noctilio infestation led to inevitable pine death despite activating pathways involved in tolerance. Phytochemistry 203. https://doi.org/10.1016/J.PHYTOCHEM.2022.113350
Rivas-Ubach A, Pérez-Trujillo M, Sardans J et al (2013) Ecometabolomics: optimized NMR-based method. Methods Ecol Evol 4:464–473. https://doi.org/10.1111/2041-210X.12028
Rodriguez R, Marticorena C, Alarcón D et al (2018) Catálogo de las plantas vasculares de Chile. Gayana Bot 75:1–430. https://doi.org/10.4067/S0717-66432018000100001
Sagarin RD, Gaines SD, Gaylord B (2006) Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol Evol 21:524–530. https://doi.org/10.1016/J.TREE.2006.06.008
Sampaio BL, Edrada-Ebel R, Da Costa FB (2016) Effect of the environment on the secondary metabolic profile of Tithonia diversifolia: a model for environmental metabolomics of plants. Sci Rep 6:1–11. https://doi.org/10.1038/srep29265
Segovia RA, Pérez MF, Hinojosa LF (2012) Genetic evidence for glacial refugia of the temperate tree Eucryphia cordifolia (Cunoniaceae) in southern South America. Am J Bot 99:121–129. https://doi.org/10.3732/AJB.1100013
Smith R, Mathis AD, Ventura D, Prince JT (2014) Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientist’s point of view. BMC Bioinform 15:1–14. https://doi.org/10.1186/1471-2105-15-S7-S9/FIGURES/10
Soberón J, Osorio-Olvera L, Peterson T et al (2017) Diferencias conceptuales entre modelación de nichos y modelación de áreas de distribución. Rev Mex Biodivers 88:437–441. https://doi.org/10.1016/J.RMB.2017.03.011
Soberón J, Peterson, AT (2020) What is the shape of the fundamental Grinnellian niche? Theor Ecol 13:105–115. https://doi.org/10.1007/s12080-019-0432-5
Traquete F, Luz J, Cordeiro C et al (2021) Binary simplification as an effective tool in metabolomics data analysis. Metabolites 11:788. https://doi.org/10.3390/METABO11110788
Valledor L, Escandón M, Meijón M et al (2014) A universal protocol for the combined isolation of metabolites, DNA, long RNAs, small RNAs, and proteins from plants and microorganisms. Plant J 79:173–180. https://doi.org/10.1111/TPJ.12546
VanDerWal J, Shoo LP, Johnson CN, Williams SE (2009) Abundance and the environmental niche: environmental suitability estimated from niche models predicts the upper limit of local abundance. Am Nat 174:282–291. https://doi.org/10.1086/600087
Viteri R, Giordano A, Montenegro G, Zacconi FC (2022) Flavonoids and triterpenes isolated from Eucryphia cordifolia (Cunoniaceae). Biochem Syst Ecol 104:104476. https://doi.org/10.1016/j.bse.2022.104476
Walker TWN, Alexander JM, Allard PM et al (2022) Functional traits 2.0: the power of the metabolome for ecology. J Ecol 110:4–20. https://doi.org/10.1111/1365-2745.13826
Wink M (2003) Evolution of secondary metabolites from an ecological and molecular phylogenetic perspective. Phytochemistry 64:3–19
Xu Y, Gong F, Dixon SJ et al (2007) Application of dissimilarity indices, principal coordinates analysis, and rank tests to peak tables in metabolomics of the gas chromatography/mass spectrometry of human sweat. Anal Chem 79:5633–5641. https://doi.org/10.1021/AC070134W
Yañez-Arenas C, Martínez-Meyer E, Mandujano S, Rojas-Soto O (2012) Modelling geographic patterns of population density of the white-tailed deer in central Mexico by implementing ecological niche theory. Oikos 121:2081–2089. https://doi.org/10.1111/J.1600-0706.2012.20350.X
Zhou P, Hu O, Fu H et al (2019) UPLC–Q-TOF/MS-based untargeted metabolomics coupled with chemometrics approach for Tieguanyin tea with seasonal and year variations. Food Chem 283:73–82. https://doi.org/10.1016/j.foodchem.2019.01.050
Acknowledgements
C.F.C gratefully acknowledge financial support was provided by National Research and Development Agency (ANID), Doctoral Scholarship Nº 21170525. O.T.N. was funded by CONICYT PAI Subvención a la Instalación en la Academia Convocatoria 2019 N°77190055. A.J.P was supported by ANID/CONICYT FONDECYT Regular 1181915 and FONDEQUIP EQM170023. We thank a National Forestry Corporation (CONAF) and Forestal Mininco S.A. (CMPC) or allowing us access to obtain plant material at their facilities. To the Laboratory of Natural Products Chemistry and Plant Metabolomics Laboratory, Universidad de Concepción for providing us with the facilities for processing the samples.
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Camila Fuica Carrasco contributed to the research design, fieldwork, laboratory work and drafting of the manuscript.
Óscar Toro-Núñez contributed to research design, elaboration of the ecological niche modeling, data processing in R, revision and editing manuscript.
Andrés Lira-Noriega contributed to research design, elaboration of the ecological niche modeling, revision and editing of the manuscript.
Andy J. Pérez contributed to interpretation of metabolomic data and revision of the manuscript.
Víctor Hernández contributed to the management of access to Chile protected and private areas, financing, and manuscript review.
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Fuica-Carrasco, C., Toro-Núñez, Ó., Lira-Noriega, A. et al. Metabolome expression in Eucryphia cordifolia populations: Role of seasonality and ecological niche centrality hypothesis. J Plant Res 136, 827–839 (2023). https://doi.org/10.1007/s10265-023-01483-3
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DOI: https://doi.org/10.1007/s10265-023-01483-3