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Comparative assessment of einkorn and emmer wheat phenomes: II—phenotypic integration

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Abstract

Phenotypic integration, as the outcome of the number and strength of (co)variation among phenotypic traits in the genetically-related diploid einkorn (Triticum monococcum L. subsp. monococcum; genome AA) and tetraploid emmer wheat [Triticum turgidum subsp. dicoccon (Schrank) Thell.; genome BBAA] was estimated for seven modules (i.e., area, density, dimensions, dry weight, ecophysiology, structure, and yield components) based on 110 traits measured or estimated on plant, tiller, leaf, spike, spikelet and kernel samples at three growth stages during four growing seasons. Classical methods of phenotypic integration assessment using algorithms for dimensionality reduction, matrix correlation, ordination, discrimination, multidimensional scaling and functional relationships, generated insightful but inconsistence estimates of phenotypic integration; thus, making it challenging to compare the strength of integration across species and modules using a single and reliable phenotypic integration index. Divergence between einkorn and emmer due to polyploidy, although evident at the mean phenotypic index at whole plant phenotypic level (0.37 ± 0.08 and 0.59 ± 0.09, respectively), was manifested more at the reproductive than at the vegetative level; while at a multidimensional scale, emmer exhibited larger correct classification (86.0%) than einkorn (69.5%). A standardized z-score, which was based on partial least squares analysis of trait variation, when adjusted for plant size, provided unbiased estimates of phenotypic integration indices comparable across modules and species (minimum z-score of 16.9 ± 1.1 for density module in einkorn to 32.1 ± 2.0, for yield components module in emmer). Despite the polyploidy diversity bottleneck in emmer, both species overlap in the range of their genetic variation for many traits and seemingly share some phenotypic intermediate forms. Phenotypic variances are unequal among einkorn and emmer; their differences could be attributed to emmer’s polyploidy diversity bottleneck and manifested at the different numbers of significant common principal components shared between the species, with a range from three (area module) to nine (dimensions module) shared common components. Future research needs to explore how inter- and intraspecific phenotypic variation affect population dynamics and performance under field conditions. As potential alternative crops in future cropping systems, considerable opportunities and benefits, in influencing the structure and function of agroecosystems, are expected if intraspecific trait databases are developed for these early domesticates.

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Abbreviations

AIC:

Akaike Information Criterion

C:N:

Carbon-to-Nitrogen ratio

CPCA:

Common principal components analysis

CPI:

Compact (horizontal) phenotypic index

Do:

Fractal dimension

FLA:

Flag leaf area

FL SLW:

Flag leaf specific leaf weight

FTP:

Number of fertile tillers per plant

GEM:

Genotype × environment × management

LAI:

Leaf area index

LDWT:

Leaf dry weight

MATR:

Major axis tests and routines

MDS:

Multidimensional scaling

MSL:

Main stem length

PCA:

Principal components analysis

PCoA:

Principal coordinates analysis

PH:

Plant height

PHENIX:

Phenotypic integration index package

PII:

Phenotypic integrated index

PL:

Peduncle length

PLA:

Penultimate leaf area

PL SLW:

Penultimate leaf specific leaf weight

PLSR:

Partial least squares regression

Q2 :

Coefficient of validation

R2 :

Coefficient of determination

RMA:

Reduced major axis

S.D.:

Standard deviation

SEM:

Structural equation modeling

SPAD:

Chlorophyll reading

SpD:

Spike density

SpDo:

Spike fractal dimension

TPP:

Number of tillers per plant

VPI:

Vertical phenotypic index

WUE:

Water use efficiency

References

  1. Abbo S, Lev-Yadun S, Gopher A (2010) Yield stability: an agronomic perspective on the origin of Near Eastern agriculture. Veg Hist Archaeobot 19:143–150. https://doi.org/10.1007/s00334-009-0233-7

  2. Adams D, Collyer ML (2016) On the comparison of the strength of morphological integration across morphometric datasets. Evolution 70–11:2623–2631. https://doi.org/10.1111/evo.13045

  3. Armbruster WS, Pélabon C, Bolstad GH, Hansen TF (2014) Integrated phenotypes: understanding trait covariation in plants and animals. Philos Trans R Soc B 369:20130245. https://doi.org/10.1098/rstb.2013.0245

  4. Basil AO, Ritchie MD (2018) Informatics and machine learning to define the phenotype. Expert Rev Mol Diagn. https://doi.org/10.1080/14737159.2018.1439380

  5. Bolnick DI, Amarasekare P, Araujo MS, Burger R, Levine JM, Novak M, Rudolf VHW, Schreiber SJ, Urban MC, Vasseur DA (2011) Why intraspecific trait variation matters in community ecology. Trends Ecol Evol 26:183–192. https://doi.org/10.1016/j.tree.2011.01.009

  6. Bonhomme V, Foster E, Wallace M, Stillman E, Charles M, Jones G (2017) Identification of inter- and intra-species variation in cereal grains through geometric morphometric analysis, and its resilience under experimental charring. J Archaeol Sci 86:60–67. https://doi.org/10.1016/j.jas.2017.09.010

  7. Chenu K, van Oosterom EJ, McLean G, Deifl KS, Fletcher A, Geetika G, Tirfessa A, Mace ES, Jordan DR, Sulman R, Hammer GL (2018) Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals. J Exp Bot 69:3181–3194. https://doi.org/10.1093/jxb/ery059

  8. Cheverud JM, Marriog G (2007) Comparing covariance matrices: random skewers method compared to the common principal components model. Genet Mol Biol 30:461–469

  9. Conner JK, Cooper IA, La Rosa RJ, Perez SG, Royer AM (2014) Patterns of phenotypic correlations among morphological traits across plants and animals. Philos Trans R Soc B 369:20130246. https://doi.org/10.1098/rstb.2013.0246

  10. Cousins EA, Murren C (2017) Edaphic history over seedling characters predicts integration and plasticity of integration across geologically variable populations of Arabidopsis thaliana. Am J Bot 104:1802–1815. https://doi.org/10.3732/ajb.1700220

  11. Damián X, Fornoni J, Domínguez CA, Boege K (2018) Ontogenetic changes in the phenotypic integration and modularity of leaf functional traits. Funct Ecol 32:24–246. https://doi.org/10.1111/1365-2435.12971

  12. Deans AR, Lewis SE, Huala E, Anzaldo SS, Ashburner M et al (2015) Finding our way through phenotypes. PLoS Biol 13:e1002033. https://doi.org/10.1371/journal.pbio.1002033

  13. Dormann CF, Elith J, Bacher S, Buchmann C et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:027–046. https://doi.org/10.1111/j.1600-0587.2012.07348.x

  14. Ellers J, Liefting M (2015) Extending the integrated phenotype: covariance and correlation in plasticity of behavioral traits. Curr Opin Insect Sci 9:31–35. https://doi.org/10.1016/j.cois.2015.05.013

  15. Esteve-Altava B (2016) In search of morphological modules: a systematic review. Biol Rev. https://doi.org/10.1111/brv.12284.10.1111/brv.12284/

  16. Feldman M, Kislev ME (2007) Domestication of emmer wheat and evolution of free-threshing tetraploid wheat. Isr J Plant Sci 55:207–221

  17. Feldman M, Levy AA (2012) Genome evolution due to allopolyploidization in wheat. Genetics 192:763–774. https://doi.org/10.1534/genetics.112.146316

  18. Fukami T, Bezemer TM, Mortimer SR, van der Putten WH (2005) Species divergence and trait convergence in experimental plant community assembly. Ecol Lett 8:1283–1290. https://doi.org/10.1111/j.1461-0248.2005.00829.x

  19. García O (2018) Reverse causality in size-dependent growth. Math Comput For Nat Res Sci 10:1–5

  20. Gegas VC, Nazari A, Griffiths S, Simmonds J, Fish L, Oxford S, Sayers L, Doonan JH, Snape JW (2010) A genetic framework for grain size and shape variation in wheat. Plant Cell 22:1046–1056

  21. Gianoli E, Palacio-Lopez K (2009) Phenotypic integration may constrain phenotypic plasticity in plants. Oikos 118:1924–1928. https://doi.org/10.1111/j.1600-0706.2009.17884.x

  22. Gioia T, Nagel KA, Beleggia R, Fragasso M, Ficco DBM, Pieruschka R, De Vita P, Fiorani F, Papa R (2015) Impact of domestication on the phenotypic architecture of durum wheat under contrasting nitrogen fertilization. J Exp Bot 66:5519–5530. https://doi.org/10.1093/jxb/erv289

  23. Giraldo P, Royo C, González M, Carrillo JM, Ruiz M (2016) Genetic diversity and association mapping for agro-morphological and grain quality traits of a structured collection of durum wheat landraces including subsp. durum, turgidum and diccocon. PLoS ONE 11:e0166577. https://doi.org/10.1371/journal.pone.0166577

  24. Golan G, Oksenberg A, Peleg Z (2015) Genetic evidence for differential selection of grain and embryo weight during wheat evolution under domestication. J Exp Bot 66:5703–5711. https://doi.org/10.1093/jxb/erv249

  25. Gosa SC, Lupo Y, Moshelion M (2018) Quantitative and comparative analysis of whole-plant performance for functional physiological traits phenotyping: new tools to support pre-breeding and plant stress physiology studies. Plant Sci. https://doi.org/10.1016/j.plantsci.2018.05.008

  26. Goswami A, Polly PD (2010) Methods for studying morphological integration and modularity. In: Alroy J, Hunt G (eds) Quantitative methods in paleobiology. Paleontological society short course, October 30th, 2010. The paleontological society papers, vol 1. The Paleontological Society, Baltimore, pp 213–243

  27. Granier C, Vile D (2014) Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 18:96–102. https://doi.org/10.1016/j.pbi.2014.02.009/

  28. Großkinsky DK, Svensgaard J, Christensen S, Roitsch T (2015) Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J Exp Bot 66:5429–5440. https://doi.org/10.1093/jxb/erv345

  29. Hammer K (1984) Das domestikationssyndrom. Kulturpflanze 32:11–34

  30. Hinterthuer A (2017) Can ancient grains find their way in modern agriculture? CSA News. https://doi.org/10.2134/csa2017.62.0412

  31. Iriondo J, Milla R, Volis S, Rubio de Casas R (2017) Reproductive traits and evolutionary divergence between Mediterranean crops and their wild relatives. Plant Biol. https://doi.org/10.1111/plb.12640

  32. Jaradat AA (2016) The integrated phenotype and plasticity of Cuphea PSR23: a semi-domesticated oilseed crop. Commun Biometry Crop Sci 11:10–30

  33. Jaradat AA (2018) Comparative assessment of einkorn and emmer wheat phenomes: I—plant architecture. Genet Resour Crop Evol. https://doi.org/10.1007/s10722-018-0729-z

  34. Kissoudis C, van de Wiel C, Visser RGF, van der Linden G (2016) Future-proof crops: challenges and strategies for climate resilience improvement. Curr Opin Plant Biol 30:47–56

  35. Klingenberg CP (2008) Morphological integration and developmental modularity. Annu Rev Ecol Evol Syst 39:115–132

  36. Klingenberg CP (2014) Studying morphological integration and modularity at multiple levels: concepts and analysis. Philos Trans R Soc B 369:20130249. https://doi.org/10.1098/rstb.2013.0249

  37. Lamb EG, Shirtliffe SJ, May WE (2011) Structural equation modeling in the plant sciences: an example using yield components in oat. Can J Plant Sci 91:603–619

  38. Laughlin DC, Messier J (2015) Fitness of multidimensional phenotypes in dynamic adaptive landscapes. Trends Ecol Evol 30:487–496. https://doi.org/10.1016/j.tree.2015.06.003/

  39. Li P-F, Cheng Z-G, Ma B-L, Palta JA, Kong H-Y, Mo F, Wang J-Y, Zhu Y, Lv G-C, Batool A, Bai X, Li F-M, Xiong Y-C (2014) Dryland wheat domestication changed the development of aboveground architecture for a well-structured canopy. PLoS ONE 9:e95825. https://doi.org/10.1371/journal.pone.0095825

  40. Longin C, Würschum T (2016) Back to the future—tapping into ancient grains for food diversity. Trends Plant Sci 21:731–737. https://doi.org/10.1016/j.tplants.2016.05.005/

  41. Longin C, Ziegler J, Schweiggert R, Koehler P, Carle R, Würschum T (2016) Comparative study of hulled (einkorn, emmer, and spelt) and naked wheats (durum and bread wheat): agronomic performance and quality traits. Crop Sci 56:302–311. https://doi.org/10.2135/cropsci2015.04.0242

  42. Mądry W, Studnicki M, Rozbicki J, Golba J, Gozdowski D, Pecio A, Oleksy A (2015) Ontogenetic-based sequential path analysis of grain yield and its related traits in several winter wheat cultivars. Acta Agric Scand Sect B Soil Plant Sci 65:605–618. https://doi.org/10.1080/09064710.2015.1039053

  43. Magwene PM (2008) Using correlation proximity graphs to study phenotypic integration. Evol Biol 35:191–198. https://doi.org/10.1007/s11692-008-9030-y

  44. Mankowski DR, Kozdój J, Janaszek-Mankowska M (2016) Structural equation model as a tool to assess the relationship between grain yield per plant and yield components in doubled haploid spring barley lines (Hordeum vulgare L.). Plant Breed Seed Sci 73:63–77

  45. Martin AR, Hale CE, Cerabolini BEL, Cornelissen JHC, Craine J, Gough WA, Kattge J, Tirona CKF (2018) Inter- and intraspecific variation in leaf economics traits in wheat and maize. AoB Plants 10:ply006. https://doi.org/10.1093/aobpla/ply006

  46. Melo D, Marroig G (2015) Directional selection can drive the evolution of modularity in complex traits. Proc Nat Acad Sci. https://doi.org/10.1073/pnas.1322632112

  47. Messier J, Lechowicz MJ, McGill BJ, Violle C, Enquist BJ (2017) Interspecific integration of trait dimensions at local scales: the plant phenotype as an integrated network. J Ecol 105:1775–1790. https://doi.org/10.1111/1365-2745.12755

  48. Mochida K, Saisho D, Hirayama T (2015) Crop improvement using life cycle datasets acquired under field conditions. Front Plant Sci 6:740. https://doi.org/10.3389/fpls.2015.00740

  49. Münzbergová Z, Skuhrovec J (2016) Contrasting effects of ploidy level on seed production in a diploid-tetraploid system. AoB Plants 9:plw077. https://doi.org/10.1093/aobpla/plw077

  50. Murren CJ (2002) Phenotypic integration in plants. Plant Species Biol 17:89–99

  51. Murren CJ (2012) The integrated phenotype. Integr Comp Biol 52:64–76. https://doi.org/10.1093/icb/ics043

  52. Oliveira HR, Jones H, Leigh F, Lister DL, Jones MK, Pena-Chocarro L (2011) Phylogeography of einkorn landraces in the Mediterranean basin and Central Europe: population structure and cultivation history. Archaeol Anthropol Sci 3:327–341. https://doi.org/10.1007/s12520-011-0076-x

  53. Otsuka J (2016) Discovering phenotypic causal structure from nonexperimental data. J Evol Biol. https://doi.org/10.1111/jeb.12869

  54. Pauli D, Chapman SC, Bart R, Topp CN, Lawrence-Dill CJ, Poland J, Gore MA (2016) The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Phys 172:622–634

  55. Pavlicev M, Chevrud JM, Wagner GP (2009) Measuring morphological integration using eignenvalue variance. Evol Biol 36:157–170. https://doi.org/10.1007/s11692-008-9042-7

  56. Payne W (2014) Developments from analysis of variance through to generalized linear models and beyond. Ann Appl 164:11–17

  57. Peleg Z, Fahima T, Korol AB, Abbo S, Saranga Y (2011) Genetic analysis of wheat domestication and evolution under domestication. J Exp Bot 62:5051–5061. https://doi.org/10.1093/jxb/err206

  58. Phillips PC, Arnold SJ (1999) Hierarchical comparison of genetic variance-covariance matrices: I—using the Flury hierarchy. Evolution 53:1506–1515

  59. Pigliucci M (2003) Phenotypic integration: studying the ecology and evolution of complex phenotypes. Ecol Lett 6:265–272

  60. Pigliucci M, Kolodynska A (2006) Phenotypic integration and response to stress in Arabidopsis thaliana: a path analytical approach. Evol Ecol Res 8:415–433

  61. Plaistow SJ, Collin H (2014) Phenotypic integration plasticity in Daphnia magna: an integral facet of G × E interaction. J Evol Biol 27:1913–1920. https://doi.org/10.1111/jeb.12443

  62. Preece C, Livarda A, Christin P-A, Wallace M, Martin G, Charles M, Jones G, Rees M, Osborne CP (2017) How did the domestication of Fertile Crescent grain crops increase their yields? Funct Ecol 31:387–397. https://doi.org/10.1111/1365-2435.12760

  63. Prieto I, Litrico I, Violle C, Barre P (2017) Five species, many genotypes, broad phenotypic diversity: when agronomy meets functional ecology. Am J Bot 104:62–71

  64. Qin X-L, Weiner J, Qi L, Xiong Y-c, Li F-m (2013) Allometric analysis of the effects of density on reproductive allocation and harvest index in 6 varieties of wheat (Triticum). Field Crops Res 144:162–166. https://doi.org/10.1016/j.fcr.2012.12.011

  65. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org/

  66. Rebetzke GJ, Jimenez-Berni J, Fischer RA, Deery DM, Smith DJ (2018) Review: high-throughput phenotyping to enhance the use of crop genetic resources. Plant Sci. https://doi.org/10.1016/j.plantsci.2018.06.017

  67. Reiss ER, Drinkwater LE (2018) Cultivar mixtures: a meta-analysis of the effect of intra-specific diversity on crop yield. Ecol Appl 28:62–77

  68. Rohlf FJ (2011) NTSYS-pc: numerical taxonomy and multivariate analysis system. Applied Biostatistics Inc., New York. ISBN 0-925031-31-3

  69. Roucou A, Violle C, Fort F, Roumet P, Ecarnot M, Vile D (2018) Shifts in plant functional strategies over the course of wheat domestication. J Appl Ecol 55:25–37. https://doi.org/10.1111/1365-2664.13029

  70. Sarstedt M, Ringle CM, Smith D, Reams R, Hair JF Jr (2014) Partial least squares structural equation modeling (PLS-SEM): a useful tool for family business researchers. J Fam Bus Strategy 5:105–115. https://doi.org/10.1016/j.jfbs.2014.01.002/

  71. SAS Institute Inc. JMP® Pro. (2016) Version 13.2.0. SAS Institute Inc., Cary, 1989–2016

  72. Teichmann T, Muhr M (2015) Shaping plant architecture. Front. Plant Sci 6:233. https://doi.org/10.3389/fpls.2015.00233

  73. Torices R, Munoz-Pajares J (2015) PHENIX: an R package to estimate a size-controlled phenotypic integration index. Appl Plant Sci 3:1400104

  74. Violle C, Enquist BJ, McGill BJ, Jiang L, Albert CH, Hulshof C, Jung V, Messier J (2012) The return of the variance: intraspecific variability in community ecology. Trends Ecol Evol 27:244–252. https://doi.org/10.1016/j.tree.2011.11.014

  75. Volis S, Ormanbekova D, Yermekbayev K (2015) Role of phenotypic plasticity and population differentiation in adaptation to novel environmental conditions. Ecol Evol. https://doi.org/10.1002/ece3.1607

  76. Walter GM, Aguirre JD, Blows MW, Ortiz-Barrientos D (2017) Evolution of genetic variance during adaptive radiation. bioRxiv. http://dx.doi.org/10.1101/097642

  77. Warton D, Duursma R, Falseter D, Taskinen S (2012) SMATR 3: an R package for estimation and inference about allometric lines. Methods Ecol Evol 3:257–259. https://doi.org/10.1111/j.2041-210X.2011.00153.x

  78. Watanabe N (2017) Breeding opportunities for early, free-threshing and semi-dwarf Triticum monococcum L. Euphytica 213:201. https://doi.org/10.1007/s10681-017-1987-0

  79. Xu Y, Li Y, Nettleton D (2018) Nested hierarchical functional data modeling and inference for the analysis of functional plant phenotypes. J Am Stat Assoc 113(522):593–606. https://doi.org/10.1080/01621459.2017.1366907

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Jaradat, A.A. Comparative assessment of einkorn and emmer wheat phenomes: II—phenotypic integration. Genet Resour Crop Evol 67, 655–684 (2020). https://doi.org/10.1007/s10722-019-00840-3

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Keywords

  • Covariation
  • Hulled wheat
  • Integration
  • Phenotype
  • Plant modules