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Plant Science

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Morphometric approaches to promote the use of exotic germplasm forimproved food security and resilience to climate change: a kura cloverexample

Brandon Schlautmana,*, Luis Diaz-Garciab, Spencer Barriballa

a The Land Institute, Salina, KS, USAb Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Aguascalientes, Mexico

A R T I C L E I N F O

Keywords:Core collectionDomesticationElliptical Fourier descriptorsMorphometricsPersistent homology

A B S T R A C T

Adaptation of agriculture to climate change and its associated ecological pressures will require new crops, noveltrait combinations, and previously unknown phenotypic attributes to deploy in climate resilient cropping sys-tems. Genebanks, a primary source of exotic germplasm for novel crops and breeding materials, need compre-hensive methods to detect novel and unknown phenotypes without a priori information about the species or traitunder consideration. We demonstrate how persistent homology (PH) and elliptical Fourier descriptors (EFD),two morphometric techniques easily applied to image-based data, can serve this purpose by cataloging leafmorphology in the USDA NPGS kura clover collection and demarcating a leaf morphospace for the species.Additionally, we identify a set of representative accessions spanning the leaf morphospace and propose theyserve as a kura clover core collection. The core collection will be a framework for monitoring the effects ofclimate change on kura clover in situ diversity and determining the role of ex situ accessions in modern agri-culture.

1. Introduction

Crop domestication and the development of agriculture beginningapproximately 10,000 years ago were key steps to achieving a new kindof food security for our human ancestors [1]. We still depend on manyof those early domesticated crops and management practices (i.e. til-lage). However, agricultural productivity and food security have in-creased remarkably during the past century through a combination ofintensive plant breeding, homogenization of agricultural environmentsthrough irrigation and fertilization, and expansion of mechanizationand fossil fuel consumption [2]. These gains in productivity have re-sulted in long-term negative consequences such as expanded agri-cultural land-use, losses of genetic and functional biodiversity, anddisruption of the nitrogen and phosphorus cycles, all which are com-promising essential ecosystem services at a global scale [3]. Further-more, over reliance upon a diminishing number of staple annual graincrop species and a narrowing genetic base of elite germplasm couldrender those crops vulnerable to emerging biotic and abiotic stresses[4,5].

Domestication of our staple grain crops occurred on multiple con-tinents within a relatively short period of time during the climatechange following the last glacial maximum [6,7]. Adapting agricultureto the potential catastrophic effects of modern climate change willlikewise require dramatic changes in our crops and the way we growthem. Future generations of farmers will need not only new crop vari-eties but also new crop species to grow grains in conditions that aremuch more unfavorable than those of the previous century (i.e. variableweather patterns, degraded soils, water restrictions, decreased access tofossil fuels and fertilizers, and etc.) [8]. There is need for modern, ac-celerated plant domestication to design new crops (i.e. perennial graincrops) and diversified cropping systems that provision ecosystem ser-vices rather than dis-services and that have the resilience to providestable yields under climate change and its associated ecological pres-sures [9,10].

Over the past several decades, genebanks around the world haveaccumulated large ex situ collections of exotic germplasm (i.e. crop wildrelatives and wild species), which represent the genetic basis formaintaining current food production levels and confronting future

https://doi.org/10.1016/j.plantsci.2019.110319Received 30 June 2019; Received in revised form 11 October 2019; Accepted 15 October 2019

Abbreviations: PGR, plant genetic resources; PH, persistent homology; EFD, elliptical Fourier descriptor; BLUP, best linear unbiased predictor; REML, restrictedmaximum likelihood

⁎ Corresponding author at: 2440 E. Water Well Rd., Salina, KS, 67401, USA.E-mail address: schlautman@landinstitute.org (B. Schlautman).

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challenges [11]. Genebanks play a crucial role in the conservation ofplant genetic resources (PGRs); but more importantly, they have a keyresponsibility to facilitate the use of these PGRs to build a durable fu-ture for humanity, its crops, and its practice of agriculture [12,13].While a huge number of accessions are accessible worldwide, many –especially germplasm from wild, uncultivated species – have been in-adequately characterized, making it is nearly impossible for breedersand/or domesticators to predict and assess the potential role that anuncharacterized species and/or accession may have in climate changeadaptation [13,14].

Recent advances in high-throughput sequencing technologies haveallowed for genome-wide genetic characterization of many accessionsor even entire ex situ collections in a genebank [15,16]. This type ofgenetic characterization has been used to identify specific allelic var-iants in genes of interest [16,17] and to develop “core collections”,which are expected to reduce the number of accession entries requiredto sample the majority of a collections genetic diversity [18]. However,comprehensive phenotypic characterization, which is expected to pro-mote the use of exotic germplasm and identify relevant species andaccessions as climate resilient crops, is costly and rarely performed[13]. More importantly, such characterization is essential for definingmorphospaces for important plant traits (i.e. leaf shape) and meaningfulcrytotypes when testing “core collections” across a summation of theagronomic and environmental contexts under climate change condi-tions [19].

Standard morphological ratings and descriptors exist to help cropbreeders and genebank curators characterize their germplasm [20,21].Similar standards have not been developed for wild species, althoughcriteria for selecting promising domestication candidates have beenproposed [10,22]. Even so, standardized morphological descriptors andratings, while useful, are likely too inflexible to observe and cataloguethe extreme phenotypes and novel, adaptive traits like perenniality ortolerance to heat stress that might allow new crops to thrive andflourish in adverse environmental conditions.

Digital images of seeds, leaves, or whole shoots, often alreadymaintained for genebank accessions, are useful and accessible datatypes that can be stored and reanalyzed indefinitely to extract mean-ingful phenotypic variation from public repositories. Image-basedmorphometric approaches, such as persistent homology (PH) and el-liptical Fourier descriptors (EFD), are methods that can be used tocomprehensively measure morphological variation pertaining to leaf,fruit, and seed shapes [23–25], as well as highly branched structureslike roots and shoots [26,27]. EFD and PH have been used to explorethe genetic basis of traits [23,26,28], for phylogenetic reconstructionand studies of trait evolution [29,30], to study seasonal heteroblasty[30,31], and to define leaf morphospaces for whole families of plants[32]. We suggest that the two image-based morphometric approachesbe adopted by genebank curators as simple and standardized ap-proaches to catalog phenotypic variation and define trait morphospacesand morphotypes among the species and accessions in their collections,and we use kura clover as a case study to validate the utility of PH and

EFD for this purpose.Kura clover is a leguminous proto-crop from the Caucasus region in

western Asia undergoing domestication at The Land Institute (Salina,KS) and elsewhere. Reports indicate kura clover is well adapted tomultiple climates, able to grow in low-fertility soils, is drought tolerant,and has general resistance to multiple viruses [33,34]. Efforts havebeen made to introgress these desirable traits into existing clover cropsincluding white clover (T. repens), alsike clover (T. hybridum), and T.occidentale through wide-hybridization with limited success [34,35].However, a potential new use for kura clover is as a perennial livingmulch in annual (i.e. corn-soybean), agroforestry, and perennial grain(i.e. intermediate wheatgrass, Kernza®) cropping systems where it hasdemonstrated effectiveness for suppressing weeds, reducing soil erosionand nitrate leaching, and providing nitrogen credits for companion cashcrops [33,36–41]. The Kura clover living mulch system exemplifies thepotential role for perennial herbaceous crops and the ecosystem-or-iented approach to cropping system design necessary for achievinglong-term sustainability in global agriculture.

The USDA NPGS kura clover (Trifolium ambiguum) collection, com-posed of 141 unique accessions, includes accessions originating frommultiple geographic origins, encompasses multiple ploidy levels, andcontains both wild collected accessions and those that have undergonesome level of genetic improvement or artificial selection. The collectionis too large and phenotypically diverse in its current state for any singlebreeder or biologist to screen and determine whether a specific kuraclover accession or leaf morphotype is particularly well-adapted to acertain geographic region or as a living mulch in a certain croppingsystem. We demonstrate how EFD and PH morphometrics, combinedwith basic shape, size and color descriptors, can be used to catalog andquantify this diversity by performing the first comprehensive analysis ofleaf morphology in the USDA NPGS collection and the species.Furthermore, we use the analysis to define a kura clover leaf morpho-space, and we propose a trait morphospace strategy for defining gen-ebank “core collections” composed of a subset of accessions that spansthe leaf morphospace. Such collections could be used more practicallyto explore and compare the fitness of particular morphotypes across theadaptive landscape of current and future crop plants, and ultimately, toidentify promising candidates or morphotypes for pre-breeding anddomestication. Finally, we show how digital representations of germ-plasm collections - through a combination of imaging, PH, EFD, lineardescriptors, and color estimates - could be a new and effective strategyfor visualizing phenotypic variation among accessions in genebanksand, therefore, increase their utilization. This work represents an im-portant advance in using imaging and computational approaches for thephenotypic characterization of kura clover and PGRs of other under-utilized and under characterized exotic germplasm held in genebanks.

Table 1Traits measured in the USDA NPGS kura clover Collection, using basic descriptors, persistent homology and elliptical Fourier descriptors.

Trait Method Abbreviation Description

Length Basic size descriptors len Major axis lengthWidth Basic size descriptors wid Minor axis lengthLength-to-width ratio Basic shape descriptors lw Aspect ratioArea Basic size descriptors area Leaflet projected areaPerimeter Basic size descriptors perim Leaflet perimeterRGB color Basic color descriptors red/green/blue Mean color per channelCircularity Basic shape descriptors circ Compactness or shape factorConvexity Basic shape descriptors cxty Convexity of the leafletEDF principal components Elliptical Fourier Descriptors EDF PCs 1-5 Principal components of EFDPH principal components Persistent Homology PH PCs 1-5 Principal components of PH matrix

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2. Materials and methods

2.1. Germplasm and sample collection

Seeds of 141 unique kura clover accessions were obtained from theUSDA NPGS system and an additional nine experimental varieties fromthe University of Minnesota (Supplementary Table 1). Included mate-rials are accessions which are wild collected populations from six dif-ferent countries: Turkey, Georgia, Iran, Russia, Ukraine, and Armenia.Collection sites for these accessions have an elevation ranging from200m to 3048m, with most of the accessions coming from the1500–2000m range. Other accessions available from the USDA NPGScollection were genetically improved varieties or experimental popu-lations developed by institutions in the United States, Australia, NewZealand, and the United Kingdom. A 2n= 2x through 2n=6x ploidyseries exist for kura clover [33], and the accessions in this study in-cluded ∼ 50% hexploid, ∼ 10% diploid, and ∼ 10% tetraploid ma-terials; the remaining ∼ 30% were of unknown ploidy.

Seeds were scarified with sandpaper and planted in Jiffy-7 pellets(www.jiffygroup.com) in the greenhouse in February 2017. The pelletswere surface inoculated with the appropriate rhizobium (PlantProbiotics, Lafayette, IN, USA) after planting. Depending upon germi-nation and greenhouse survival, one to eight seedlings from each ac-cession were planted in a completely randomized design in April 2017at The Land Institute in Salina, KS. Plants were spaced in a square grid0.91m apart to minimize competition between adjacent plants. Weedswere controlled with cultivation and hand weeding. The soil was aCozad silt loam (coarse-silty, mixed, superactive, mesic typic haplus-toll) with pH, weak Bray P1, strong Bray P2, K, and organic matterlevels of 6.8, 13 ppm, 47 ppm, 301 ppm, and 1.8%, respectively.

Ten fully-expanded leaves, one leaf per shoot, were collected fromeach kura clover plant in late September 2017. Three of the ten leaveswere randomly selected, dissected into three leaflets per leaf and placedon a light table. Leaves were arranged by leaf (one leaf per column fromleft to right) and leaflet type (top to bottom rows= left lateral, central,and right lateral leaflet) (Fig. 1, bottom right panel). A piece of clearacrylic was placed over the leaves to flatten them on the light table.Images were taken using a DSLR camera under controlled light condi-tions using a copy stand. On both sides of the image, five 2.54 cmdiameter black circular stickers were included as size references(Fig. 1). All leaf images will be uploaded into the USDA GRIN Globaldatabase.

2.2. Shape analysis

Images were processed with a custom code based on the softwareGiNA implemented in MATLAB [42], which is described below. Rawimages were processed for background extraction and object identifi-cation using regular thresholding. For every object (i.e leaflet and sizereference) in the image, we extracted the mean color for all three RGBchannels as well as the shape contours (to calculate basic descriptorsand elliptical Fourier descriptors, see below). Then, we converted eachobject to a binary image and PH analysis was performed according to[32,43]. Briefly, PH computes shape objects by assigning high and lowvalues to pixels with many or few pixel neighbors, respectively. Then, aGuassian density estimator is multiplied by expanding annuli (or rings),which emphasizes the shape. Given the simplicity of the leaflet shapesexamined here, our PH pipeline computed only seven curves, corre-sponding to seven expanding annuli, each of those composed by 80values. For each leaflet, the numerical components of each annuli(7× 80=560) were added to a n x 560 PH matrix, with n equal to thetotal number of leaflets in the study. Both lateral and central leafletswere flagged for further analysis.

Leaflet contours, RGB color descriptors, and the PH matrix wereloaded into R [44] for further analysis. We performed principal com-ponent analysis on the PH matrix using the base R function prcomp(),

and kept the first five principal components. Additionally, all shapecontours were loaded into an ‘Out’ object using the package Momocs[45]. We calculated elliptical Fourier descriptors (EFD) using thefunction efourier and evaluated the contribution of the EFD harmonics 1to 6; to eliminate asymmetrical variation in leaflets, we removed har-monics B and C as in [23]. Finally, using several functions of the Mo-mocs package, we calculated basic descriptors related to the size and theshape of the leaflet. Basic size descriptors (length, width, perimeter andarea) were normalized using the mean measurements of the 10 refer-ences included in each picture. A summary of the traits evaluated in thisstudy is provided in Table 1. Leaflets that had folds, tears, insect da-mage, or other abnormalities were removed from the dataset, becausethey would not allow for accurate quantification of shape. Scripts forimage processing (MATLAB) and morphometric analysis (R) are foundin Supplementary File 1.

2.3. Statistical analysis

Linear mixed-effects models were fit for each trait via RestrictedMaximum Likelihood (REML) using the function lmer from the lme4 Rpackage [46]. We used models of the form:

∼ + + + +phenotype column row leaflet type date accession(1 | ) (1 | )

The terms column and row were fixed effects corresponding to thefield location of each plant within the experiment; the term leaflet typewas a fixed effect referring to either central or lateral leaflet; accessionand date (i.e. the day the image was taken) were set as random effects.Best Linear Unbiased Predictors (BLUPs) were estimated for each ac-cession for all leaflet basic descriptors and EFD and PH principalcomponents. Repeatability estimates (r2) were obtained using the ac-cession variance-to-accession variance plus the residual variance ratio

+

VV V

accession

accession error

Correlations among leaf phenotypes were calculated using Pearson’scorrelations. All visualization, unless otherwise noted, was performedusing ggplot2 in R [47].

3. Results

Leaf shape is a commonly studied morphological feature across theplant sciences that has been useful for delineating taxa and studyingpatterns of evolution [29,30] as well as for determining genetic varia-tion and relationships within wild and crop species [48,49]. The effectsof leaf shape in light interception, the nutrient economy of leaves, andthermoregulation have also been explored [50–52]. As such, leaf mor-phology, including size, shape, and color variation, remains an im-portant theme in studies designed to reveal the impacts of global cli-mate change on plant biodiversity, crop productivity, and the resilienceof modern cropping systems and rotations. Herein we broadly char-acterize leaflet shape diversity in the USDA NPGS Kura Clover Collec-tion using PH and EFD, and we demonstrate how leaflet morphologicalcharacterization in Kura Clover was useful for creating a core collec-tion. This phenotypically diverse kura clover core collection will be animportant resource to monitor the effects of climate change on kuraclover in situ genetic and morphological diversity, and more im-portantly, will assist breeders in making use of the ex situ collection forgenetic improvement of kura clover and the development of modern,climate resilient cropping systems.

3.1. Size and shape variation in leaflets of the USDA NPGS Kura CloverCollection

A computer vision approach was used to measure 20 size and shapemorphometric descriptors for over 7000 leaflets across 875 individualplants and 141 accessions from the USDA NPGS kura clover Collection

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and nine accessions from the former UMN breeding program. Of the 20descriptors computed here, 10 correspond to basic leaflet shape (length-to-width-ratio, circularity, convexity), size (length, width, area, peri-meter) and color (RGB channels) attributes (Table 1). We observedvalues for leaflet area ranging from 0.23 cm2 to 14.47 cm2, with a meanof 3.74 cm2 (Fig. 2A); leaflet lengths ranging from 0.68 cm2 to 6.97 cm,with a mean of 2.90 cm2 (Fig. 2B); and leaflet widths ranging from0.44 cm2 and 3.79 cm2, with a mean of 1.68 cm2 (Fig. 2C). For the basicshape descriptor length-to-width ratio, we observed leaflets with valuesbetween 1 (similar length and width) and 3.86, with a mean of 1.75(Fig. 1D). The large variation observed within accessions (among re-plicated plots) and low observed repeatability estimates (see below)resulted in REML-generated Best Linear Unbiased Predictors (BLUPs)with observed dispersion considerably smaller than what was observedin raw measurements. For example, based on BLUP estimates for leafletlength and area, the accession NSL86671 had the largest leaflet withvalues of 3.94 cm and 5.22cm2, respectively. Repeatability values(Supplementary File 2) ranged from 0.03 (PH PC5) to 0.53 (length-to-width ratio). In general, basic size descriptors showed low-to-mediumrepeatability values (0.39-0.45), whereas shape descriptors (includingEFD PC1 and PH PC1) showed medium values (0.42-0.53). PC3-5 ofboth EFD and PH showed low or very low repeatability estimates(< 0.25).

In addition, both PH and EFD yielded 5 attributes (principal com-ponents 1–5) each, all of them describing object shape variation. PHand EFD captured a significant amount of the shape variation fromleaflets. In particular, the PH first principal components (1–5) captured90.35% of the total phenotypic variance observed; PH PC1 and PC2captured 70.46% and 12.74%, respectively. On the other hand, EFD

PC1-5 explained 98.58% of the variation, with the first two componentsexplaining 88.36% and 4.96%. Given that previous studies reportedthat higher-order PCs captured mostly complex and cryptic variation,we explored only PC1-5 [23]. EFD PC1 seems to explain the elongationvariation of the leaflet, whereas EFD PC2 (Fig. 2E) and EFD PC3(Fig. 2F) are more related to the orientation and flatness of the basalpoint (where the petiole is located). The contribution of EFD harmonics1–6 to the mean leaflet shape is shown in Fig. 2G. Raw measurementsand REML-adjusted values for all traits and all accessions are providedin Supplementary File 2.

3.2. Correlation among shape and size phenotypes

Basic descriptors related to leaflet size (length, width, area andperimeter) were highly correlated (Supplementary File 2, Pearson’scorrelation coefficient R2>0.76, p=0). Similarly, basic shape de-scriptors length-to-width ratio and circularity were highly correlatedwith each other (R2=0.86, p=0) and with both PH PC1 and EFD PC1(R2>0.73 (p=0). Basic size and shape descriptors showed a sig-nificant correlation (p < 0.05) with each other, with R2 coefficientsranging between -0.25 and 0.45. Pearson’s correlation between PC1 ofboth PH and EFD techniques was very high (Fig. 3A, R2=0.93, p=0),implying that these are explaining the same shape characteristics (i.e.leaflet elongation). However, the remaining PH and EFD principalcomponents (PC2-PC5) seem to explain other aspects of shape variation(Fig. 3B and C). An interesting relationship was observed between PHPC1 and PH PC3 (Fig. 3D), where although R2 was non-significant (atp > 0.05), a quadratic regression resulted in an r2=0.66, with PHPC3 values rapidly decreasing when PH PC1 differ from 0 (on both

Fig. 1. An example of the leaf images. References of diameter 2.54 cm are located on each side of the picture. QR code is included for automatic plant identi-fication. Leaves are arranged by columns, leaflets within a column are arranged left lateral (top), central (C), and right lateral (bottom).

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sides). Leaflet area and EFD/PH PC1 showed no correlation (Fig. 3E andF).

To better characterize the clover collection, we recorded the posi-tion/type (central or lateral) of each leaflet, which allowed us tocompute the lateral-to-central leaflet size-ratio. Using only complete

sets of leaves (two lateral and one central leaflet per leaf), we dividedthe area of the central leaflet by the mean area of both lateral leaflets(i.e. values less than one represent leaves with larger lateral than cen-tral leaflets). Central-to-lateral leaflet ratios varied from 0.44 (PI631895) to 1.64 (NSL 448196), and we observed greater among then

Fig. 2. Variation in leaflet phenotypes computed through traditional methods (basic descriptors) and both persistent homology and Elliptical FourierDescriptor approaches. Leaflet variation (n=7050) for the basic descriptors (A) area, (B) length, (C) width, and (D) length-to-width ratio. Black and red verticallines correspond to the mean and median of the data points, respectively. Principal components (E) 1 and 2, and (F) 1 and 3, representing shape variance in EllipticalFourier Descriptor analysis. (G) Harmonic contribution to leaflet shape derived from Elliptical Fourier Descriptor analysis (using amplification factors of 0, 1, 2, 5 and10). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. Persistent Homology and Elliptical Fourier Descriptors explain only basic shape descriptors. Linear regression models and R2 are shown for (A) EDFPC1 and PH PC1, (B) EDF PC2 and PH PC2, (C) EDF PC1 and EDF PC3, (D) PH PC1 and PH PC3, (E) area and EDF PC1, (F) area and PH PC1.

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within accession variation for the trait (Fig. 4A).In general, leaflet size variation does not seem to be shape depen-

dent (Fig. 3E and F); however, extreme shapes (i.e. very elongatedleaflets or leaflets with a length-to-width ratio close to 1) seem to occuronly in smaller leaflets (Fig. 4B). Therefore, we expect that it is possibleto select and/or generate new leaf morphotypes (i.e. combinations ofleaflet shape, size, and color) that are highly adapted to target agri-cultural environments (i.e. specific climates or altitudes, living mulchsystems, etc.) through breeding.

3.3. Genetic improvement status, collection location, and ploidy affectleaflet size and shape characteristics

Metadata available for each accession allowed us to examine if theimprovement status, geography, origin and ploidy shape kura cloverleaflet morphology. First, by comparing the leaflet area and the col-lection site elevation of 86 wild accessions (a total of 4071 leaflets andan elevation range of 250-3250MASL), we observed that samples fromhigher altitudes tend to have smaller leaves than those from lower al-titudes (Fig. 5A; p < 2×10−16, t-test). Moreover, leaflet area andshape (in this case, PH PC1) are, in general, affected by the ploidy level.Both 2x and 4x accessions had smaller leaflets than 6x accessions(Fig. 5B, p < 2.1×10-8, t-test); likewise, all three ploidy levels dif-fered in leaflet shapes ranging from more linear (2x accessions) to or-bicular (4x accessions) (Fig. 5C; p < 2.4×10-10, t-test). Unexpectedly,ploidy levels within a particular countries of origin seem to have adifferentiated impact on leaflet shape and size (Fig. 5D). Both 6x im-proved and 6x collected materials from Ukraine have larger leafletareas than lower ploidy levels. Conversely, the 2x materials fromGeorgia are larger than the 6x material from Georgia as well as the 2xand 4x improved materials. At least for the 6x ploidy level, improvedmaterials are characterized by a larger leaflet areas compared to lowerploidy levels. If the increment of leaflet area is desired from a breedingperspective, incorporating 6x wild accessions from Ukraine could befavorable. Accessions from the United States and Australia, both con-sidered improved material, include all three ploidy levels (2x, 4x and6x), however, accessions from New Zealand, which are also improved,include only 4× . Among the collected/wild materials, Russian Fed-eration is the most represented country of origin in terms of number of

accessions (47 accessions, 1897 leaflets analyzed here), and contributedaccessions from all three ploidy levels: 6x (85%), 4x (13%) and 2x (2%).For certain countries, only one ploidy level is represented in the col-lection. For example, Armenia and Iran contributed only 4x materials,whereas Turkey contributed only 2x accessions. Similarly, countrieslike Ukraine and Georgia are missing certain ploidy level, in this case 2xand 4x, respectively. It is unclear whether these patterns represent truegeographic distributions of the ploidy levels across the Caucasus region,or if the observed variation is due to current gaps in the collection.

4. Discussion

4.1. Utility of EFD and PH in cataloguing phenotypic diversity held ingenebanks

A key purpose of genebanks is to facilitate access to and use of theplant germplasm resources held in their collections by providing reli-able evaluation data that are easily retrievable by systematists, bree-ders, domesticators, and others [53]. Reliable phenotypic data gen-erally must be collected by trained staff using calibrated and a prioristandardized measuring systems and previously characterized checkaccessions [53]. For most species of exotic, wild germplasm, a prioristandardized measuring formats do not exist, and in the case of do-mestication or adaptation to climate change, the actual desired mor-photypes might be completely unknown. Exotic germplasm can becharacterized with sequence-based genotypic data without a priori in-formation; however, sequencing remains too expensive to characterizeexotic germplasm without obvious and immediate utility, and sequencedata does not necessarily provide relevant information about a species’sor accession’s utility or adaptive potential. Therefore, new approachesfor comprehensive germplasm characterization that can capture emer-gent properties of complex phenotypes and that do not require thepresupposed importance of individual traits are necessary [54].

EFD are useful morphometric tools that do not require presupposedlandmarks for measuring simple seed and leaf shapes if shapes areconsistent across every sample and major sources of shape variance (i.e.varying number of lobes or leaflets) are not present in the dataset [32].For the seeds of most potential grain crops, this is probably a validassumption and EFD will work well. However, for species with variable

Fig. 4. Variation in central-to-externalleaflet ratio and relationship between sizeand shape. (A) Distribution of central-to-ex-ternal leaflet ratio across clover accessions;horizontal error bars correspond to the stan-dard error; values greater than 1 indicateleaves with central leaflets larger than externalleaves, whereas values lower than 1 indicatethe opposite. Extreme phenotypes are labeled.(B) Leaflets with extreme PH PC1 values(stratified in intervals of 10) have smaller area.

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leaflet numbers per leaf and for more complex traits like root, in-florescence, and shoot architecture, PH is a much more appropriatetopological method that can be used to analyze disparate leaf shapesand branching architecture of plants [32,55].

Once a core collection is developed, genebanks might expect asubstantial increase in the request and use of those accessions as thecore collection becomes a framework for further studies. It is importantthat this increased use also leads to an increase in the knowledge aboutthe phenotypic diversity that is present in the collection. It is thereforenecessary that, even without a priori knowledge about traits of interest,standard phenotyping methods are adopted by genebanks that areflexible and that can comprehensively detect characteristics or

properties that are interesting to a wide variety of users and that arerelevant to a wide range of potential end purposes. Both PH and EFDprovide standardized strategies for collecting comprehensive pheno-typic data across multiple time periods (i.e. seasonal heteroblasty),environments (i.e. climate gradients), and research studies withoutdepending on well-developed ontologies or coordinated approaches. PHhas been proposed as an adaptable solution for providing a commonstandardized approach in the plant sciences to interpret and describediverse phenotypes (2D, 3D, and 4D plant forms) and to expand knownplant and trait morphospaces through observations across times, en-vironments, and any other number of dimensions [27]. Such an ap-proach is essential to bridge knowledge sharing between curators,

Fig. 5. Leaflet area and shape depends onthe origin, elevation and ploidy of the kuraclover accessions. (A) Leaflet area depends onthe elevation of the collection site. Accessionswith elevation information correspond only towild collected materials (n= 4071 leaflets).(B) Leaflet area and (C) PH PC1 grouped byploidy level; leaflets per ploidy level weren2x= 652, n4x= 589, and n6x= 3268 (D)Distribution of leaflet area and PH PC1 sortedby origin and ploidy level. Within the boxplots,vertical red lines correspond to the meanvalue. Category “Improved” includes acces-sions that are varieties or experimental popu-lations developed in the United States, NewZealand and Australia. (For interpretation ofthe references to colour in this figure legend,the reader is referred to the web version of thisarticle.)

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breeders, taxonomists, and other members of the plant science com-munity as we work together to meet the urgent need for new resilientcrops [56].

Herein, we demonstrate how high-throughput image analysis, usingPH and EFD morphometric methods and combined with conventionalshape, size, and color descriptors, quantitatively cataloged leaf andleaflet morphological variation in the USDA NPGS Kura clover collec-tion for the first time. While PH PC1 and EFD PC1 were highly corre-lated with traditional shape parameters (length-to-width ratio) de-scribing the orbicular (round) to linear (elongated with parallel margis)gradient of kura leaflet shapes (Figs. 2E & 3), other PCs were mostlyuncorrelated with traditional descriptors and represent complex mor-phometric variation that was not measurable using standard proce-dures. For example, EFD PC 2 quantitatively describes leaflet variationranging from ovate (egg shaped, wide at base/petiole attachment) toobovate (egg, shaped, narrow at base/petiole attachment) (Fig. 2E),and EFD PC 3 quantitatively describes variation in leaflet cordateness(degree of heart-shaped lobing) (Fig. 2F). Collectively, EFD PCs 1–3quantitatively describe 96.5% of leaflet shape variation spanningstandard leaf shape terms such as: lanceolate, ovate, cordate, obcor-date, acicular, truncate, elliptic, and etc (Fig. 2E & F).

Instead of assigning each kura clover leaflet shape to one or moretraditional leaf shape categories, we use PH and EFD PCs to demarcate akura leaflet shape morphospace spanning all standard categories, andwe project the position of each accession in the USDA NPGS collectiononto that morphospace. Using the PCs shifts the treatment of leaf shapeas a traditional qualitative trait description to one that is insteadquantitative and usable in downstream statistical analyses. Ultimately,this strategy should allow clover breeders to manipulate leaf shapeusing quantitative genetics and recurrent selection; allow evolutionaryand ecological biologists to measure or monitor changes in clover leafshape variation across geographic ranges, over evolutionary timeframes, and during climate change; and allow plant physiologists toestablish links between quantitative variation in clover leaf shape andlight interception. Our analysis of the available metadata suggested thatgeography, genetic improvement, and ploidy number affect kura cloverleaf morphology and demonstrated the effectiveness of PH and EFD toquantify this variation and enable such studies (Fig. 5). However, wehesitate to provide inferences about our observations because metadatawas only available for a subset of accessions; furthermore, while thewild collection was large and diverse, it was made over many decadeswithout attempting to thoroughly or equally sample all regions andploidy levels.

4.2. Defining a kura clover leaf morphospace and building a kura clover“core collection” of distinct leaf morphotypes

To define a phenotypically-diverse kura clover leaf morphospace,we identified accessions with mean and extreme values in both the wildcollected and genetically improved groups for the 20 leaf phenotypesevaluated here (based on accession BLUP values) (Fig. 6A). We chose toseparate and visualize the two groups by projecting the mean leaf size,shape, and color for each accession representing a key morphotypeseparately onto the overall morphospace (Fig. 6A). Following thisstrategy, we ended up with 56 (37%) unique morphotypes (i.e. acces-sions) combined for the wild collected and genetically improvedgroups. This current kura clover leaf morphospace is diverse in that itwas defined by wild collected accessions from across the native rangefor T. ambiguum, genetically improved accessions from multiple pro-grams in multiple countries, and accessions representing each of thethree ploidy levels. However, the morphospace is specific to measure-ments collected from a single location-year and is expected to expand toinclude new or missing morphotypes when the accessions are tested innew environments, when additional collection trips are completed, andas plant breeders develop new improved populations by selecting andcombining extreme morphotypes.

Core collections, while they do not replace whole collections, reducethe size of germplasm collections to optimum sets of dissimilar groupsthat can be assessed across multiple experiments [57]. Core collectionsof this type are critical for pre-breeding studies or studies of new can-didate species for domestication that aim to evaluate the full range ofgenetic and phenotypic characteristics for a species, to compare theresponses of particular morphotypes or an entire morphospace to arange of unique environments or management practices (i.e. morpho-type x environment interactions), or to identify optimal or missingmorphotypes that can serve as selection criteria in their breeding pro-grams. Perennial, herbaceous grain crops have been identified as onesuch morphotype which is missing from global agriculture, but thatcould improve carbon sequestration, improve nutrient and water useefficiency, prevent soil erosion and degradation, and provide variousother ecosystem services to agricultural lands [8,58,59]. A recent in-ventory of the Fabaceae found that 6644 perennial, herbaceous specieswere present in the family [60]; however, no strategies were presentedfor identifying the most promising species for domestication as per-ennial grain legumes or living mulches. We expect if such core collec-tions existed for a range of wild, undomesticated species, coordinatedand effective strategies could be developed to screen and detect can-didate species with novel or agronomically useful traits that merit theirinclusion in domestication pipelines to develop climate resilient crops.

To facilitate future breeding and domestication efforts in kuraclover, we propose a core collection for this species based on the leafmorphospace developed here (Fig. 2B). The original leaf morphospacespanned 56 (37%) of the total accessions. We further reduced thenumber of accessions for the core collection by grouping highly cor-related traits and removing the leaflet traits with low repeatability es-timates. In particular, the green color channel was used as an estimateof leaf color; length, width, area, and perimeter, were used as estimatesof leaflet size; length-to-width ratio, circularity, PH PC1 and EFD PC1were used as estimates of leaflet aspect ratio; and PH PC2-3 and EFDPC2-3 were considered individually as unique leaflet shape measures.The blue and red color channels, leaflet convexity, PH PC4-5, and EFDPC4-5, were not taken into consideration. Based on this approach, weidentified 30 unique accessions (20% of the total accessions) that re-present most of the leaflet color, shape and size variation, of which halfcorresponded to improved and half to wild collected materials (Fig. 5B;Supplementary File 2).

4.3. Imaging to create digital illustrations of entire germplasm collections

Establishing a core collection and characterizing it with quantitativedata is an important way to promote its utilization. Visualizing an ac-cession prior to dedicating precious field space and resources to it, is aninvaluable tool for breeders and many other potential users. Images areone of the easiest ways to make information about genebank accessionsaccessible [12,61]. In fact, at the 2018 USDA Alfalfa Crop GermplasmCommittee meeting, breeders asked curators to collect, if nothing else,at least an image of the accessions during seed multiplication (personalcommunication, Heathcliffe Riday). These images are useful, but wehave found it to be difficult to simultaneously visualize multiple ac-cessions for comparison purposes, especially if the images are taken atdifferent times or in different ways.

PH and EFD both begin with a step that compresses the complexdimensionality of an object shape to provide a scalable approach fordescribing and comparing shapes within a scaled and standardizedmorphospace. At least for EFD, a few descriptors can be used to recreateand visualize, at any given time, digital illustrations of leaves or otherplant organ types using existing functions from various softwarepackages. We developed an illustration of leaf morphological variationthat allows users to simultaneously visualize the leaflet diversity con-tained in the entire kura clover collection (Fig. S1) and could aid po-tential users in making side by side visual comparisons to select ac-cessions that are most likely to fit their particular goals.

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In further studies, such digital illustrations could also help breedersand researchers visualize variations in leaf shape that exist across“cryptotypes” (plant ontologies, unique environments, agriculturalpractices, etc.). While the current example is limited to leaf mor-phology, we can imagine expanding these results in future to createdigital illustrations of more complex structures using 3D imaging orimage reconstructions. PH in particular is useful for quantifying andanalyzing highly topological 3D structures, and was recently used tocharacterize wild grapevine inflorescence architecture [55]. Such stu-dies suggest that virtual reality could soon be a tool that plant breeders,curators, and even plant taxonomists can use to explore and comparematerials in their programs, collections, or herbaria using 3D plantresource databases [62].

4.4. Conclusions and future directions

Breeding kura clover to be a better companion in living mulches is achallenge because no a priori information exists about which traits di-rectly affect the compatibility of kura clover with its companion graincrops. Similar situations exist for all crops in the early stages of do-mestication, because yield component studies have not been thoroughlyconducted to identify criteria for selection. We observed variation inleaf area in this study corresponding to collection site elevation thatsuggests that divergent adaptation of certain leaf morphotypes (Fig. 5).Similarly, we have observed that plants with smaller, bluer leaves ap-pear to be more drought tolerant in Kansas than other morphotypes(Schlautman, unpublished). Such environmentally dependent variationsuggests that certain kura clover morphotypes will be more adaptedthan others to new agricultural environments, including kura clover-grain living mulch systems. The kura clover core collection developedherein, which spans the species leaf morphospace, will provide us andother researchers the opportunity to test this hypothesis, to select theoptimal leaf morphotypes for the living mulch system, and to exploreadaptation of leaf morphotypes to other diverse climates and environ-ments.

We have demonstrated how EFD and PH, two morphometric tech-niques easily applied to image-based data, are useful for characterizingand cataloguing phenotypic variation in germplasm collections, de-marcating trait morphospaces, producing digital illustrations forgermplasm visualization, and creating core collections. We have fo-cused here on leaf form, but the techniques could and should similarlybe used to develop morphospaces for other plant organs (flowers, fruits,tendrils) or whole plant phenotypes (shoots, roots). We believe thesetools offer the ease and flexibility to be adopted by genebank curatorsand to be used during accession multiplication. Furthermore, devel-oping morphospaces and core collections will allow for the design offuture plant science experiments exploring the ecophysiology of adap-tation to adverse conditions likely under climate change, promote theuse of exotic germplasm in modern crop domestication efforts, andultimately play an important role in enabling the research necessary todevelop new cropping systems using an ecosystem first approach thatwill ensure long term global food security.

Author contributions

BS conceptualized the project. BS and SB collected and curated data.LDG and BS performed investigations, developed methodology, andperformed data analysis, validation, and visualization. BS and LDGwrote the original draft. BS, LDG, and SB reviewed, edited, and ap-proved the final manuscript.

Declaration of Competing Interest

The authors have no conflicts of interest to declare.

Acknowledgments

This work was made possible through the charitable donations ofmultiple persons (“Friends of the Land”) and organizations given to TheLand Institute, Salina, KS. The funders had no role in the study design.

Fig. 6. Leaflet size and shape variation asguidelines for building a kura clover corecollection. (A) Digital representation of ex-treme and mean phenotypes from improvedand wild collected accessions. For every traitand group (improved or collected), the threedigital leaflets correspond with the accessionshaving the minimum, mean, and maximumBLUP value. Each shape was computed byaveraging the replicates available for each ac-cession. (B) Proposed kura clover core collec-tion based on highly informative leaf traits.Accessions marked with (i) and (c) correspondto improved and wild collected materials, re-spectively.

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We also thank the USDA National Plant Germplasm System (and kuraclover curator Dr. Brian Irish) for providing the germplasm used in thisstudy. We also thank Omar Tesdell, David Van Tassel, and Aubrey StreitKrug for their insightful comments on earlier versions of the manu-script.

Appendix A. Supplementary data

Supplementary material related to this article can be found, in theonline version, at doi:https://doi.org/10.1016/j.plantsci.2019.110319.

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