FIGURE 3 | Non-skeletal 3-D images of the corn root systems depicted in skeletal form in Figures 2A,B for (A) optimal and (B) salt-stress treatments, respectively; the below-ground part of the stem was false-colored in green, the upper roots in light brown, and the lower roots in dark brown, with a slight glossy effect. See text for the procedures of expansion from the skeletal images and attachment of corn root volumes to the skeletons, using the CT scanning data collected and applying appropriate thresholds to them.

one of them in Week 2. Combining upper roots, when present, with lower roots in the same fractal analysis would not be recommended anyway, because the two types of roots form distinct systems by themselves (**Figures 2** and **3**). Overall, the observed FD values are low to moderately high, that is, above 1.0 (which classically corresponds to a straight line) but well below 2.0 (a plane), and actually below 1.5; this could be expected for young plant material such as root systems of corn seedlings only a few weeks old. On a weekly basis (mean ± SE), FD estimates ranged from 1.084 ± 0.026 (Week 1) to 1.284 ± 0.037 (Week 3) in the control group vs. from 1.132 ± 0.006 (Week 1) to 1.279 ± 0.030 (Week 3) in the salt-stressed group, with 1.231 ± 0.044 vs. 1.188 ± 0.045 in Week 2 for the two groups, respectively. The crossing of curves between the two groups from Week 1–2 in **Figure 4A**, which is the result of a higher FD mean value in Week 1 for the salt-stressed group and a higher FD mean value in Week 2 for the control group, led us to analyze the ratios of FD estimates in Weeks 2 and 3 relative to the corresponding FD estimate in Week 1 (**Figure 4B**), and eventually, perform two ANOVARs instead of one.

The ANOVARs indicate non-significant (*P* ≥ 0.10) betweengroup differences in the FD mean values for the 3 weeks taken separately as well as in the two FDR mean values, considering one ratio at a time. However, on average over the 3 weeks (see Between-subject effects in **Table 1**), the between-group difference in the FD overall mean value is significant (*P <* 0.10). Additionally, the Week main effects (see Within-subject effects in **Table 1**) are highly significant (*P <* 0.01) for the FDs, and are only significant (*P <* 0.10) for the FDR. To be complete, the difference in the FD mean value is significant (*P <* 0.05) between Weeks 1 and 2, and highly significant (*P <* 0.01) between Weeks 1 and 3.

#### ANOVARs on Root Volumes and Relative Growth Rates

As shown in **Figure 4C**, the weekly means of lower-root volume for the control group are systematically above those for the salt-stressed group; furthermore, this is also the case for the weekly means of upper-root volume, due to the delayed onset of upper roots for salt-stressed plants (see "Three-Dimensional Image Analyses" and **Figures 2** and **3**).

FIGURE 4 | Means and SE of corn root system traits measured for optimal (Control) and salt-stress (Salt) treatment groups: (A) fractal dimensions (FD) in Weeks 1–3 (only for lower-root systems; see Table 1); (B) fractal dimension ratios (FDR; i.e., against Week 1); (C) root volumes in Weeks 1–3 (for lower- and upper-root systems); (D) relative growth rates (i.e., relative to the previous week and calculated using volumes of lower and upper roots combined); (E) root lengths in Weeks 1–3 (for lower- and upper-root systems); and (F) length increments (i.e., between successive weeks and calculated using root lengths of lower- and upper-root systems combined).

TABLE 1 | Repeated measures analysis of variance (ANOVAR) results for fractal dimensions (FD) and fractal dimension ratios (FDR) for corn lower-root systems.

†*The Error df is 4 (df: number of degrees of freedom).*

Relative growth rates of lower-root volume between Weeks 1 and 2 are almost equal in the two experimental groups; for all the other relative growth rates that we considered, the mean value for the control group is greater than that for the salt-stressed group, with a difference of 10% or more (**Figure 4D**).

Aside from the obvious difference in means between the two groups for the upper-root volume in Week 1, the ANOVAR performed on root volumes (**Table 2**) provides: (i) significant (*P <* 0.10) between-group differences for the three types of root volumes considered (lower roots alone, upper roots alone, lower and upper roots combined) in Week 3; (ii) significant (*P <* 0.10) Group main effects (Between-subject effects) for

†*Except for N/A, the Error df is 4 (df: number of degrees of freedom).* ‡*Corn plants of the salt-stressed group had no upper root in Week 1. It follows: (i) the non-relevance of performing a statistical test to compare groups for their upper-root volumes in Week 1; (ii) within-subject effects with 1, 1, and 4 df instead of 2, 2, and 8 df, due to the inclusion of 2 weeks (Weeks 2 and 3) instead of the 3 weeks in the ANOVAR for upper-root volume.*

§ *0.3776 if a t-test with an effective number of df is used, to adjust for the sole case of significant (P < 0.05) between-group heterogeneity of the variance in our study.*

the upper-root volume averaged over Weeks 2 and 3; (iii) highly significant (*P <* 0.01) Week main effects (Within-subject effects) for the three types of root volumes; and (iv) a significant (*P <* 0.10) or highly significant (*P <* 0.01) Week-by-Group interaction, indicating an increasing difference in weekly means between groups from the first or second week to the last. As for relative growth rates (**Table 3**), the ANOVAR only found highly significant (*P <* 0.01) Time main effects (Withinsubject effects) for the upper-root volume and the lower and upper-root volumes combined (i.e., no significant Week-by-Group interaction); thus, relatively, space occupancy by the root systems increased at the same pace in the two groups of corn seedlings. To be complete, the ANOVA found a significant (*P <* 0.10) difference between groups in their mean relative growth rates for upper-root volume from Week 2 to Week 3.

#### ANOVARs on Root Lengths and Length Increments

Graphically, the weekly means of lower- and upper-root lengths and the corresponding increments varied over time and differed between the control and salt-stressed groups, in a way similar to the weekly means of lower- and upper-root volumes and the relative growth rates (see **Figures 4E,F** vs. **Figures 4C,D**). Quantitatively and statistically, a smaller number of significant effects were found for lengths than for volumes, with two significant (*P <* 0.10) effects per type of root when analyzed separately, and combined; nevertheless, five of the six Withinsubject effects maintained their statistical significance. On length increments, significant differences between groups are found for lower roots (Weeks 1–2, *P <* 0.10) and upper roots (Weeks 2–3, *P <* 0.01).

TABLE 3 | Repeated measures analysis of variance results for relative growth rates derived from volumes for corn lower- and upper-root systems and the two combined.

†*Except for N/A, the Error df is 4 (df: number of degrees of freedom).* ‡*Corn plants of the salt-stressed group had no upper root in Week 1, so relative growth rates based on upper-root volume could be calculated only from Weeks 2–3 for them, limiting the comparison between groups to a classical type of ANOVA (i.e., without repeated measures).*

TABLE 4 | Repeated measures analysis of variance results for length measures for corn lower- and upper-root systems and the two combined.

†*Except for N/A, the Error df is 4 (df: number of degrees of freedom).* ‡*Corn plants of the salt-stressed group had no upper root in Week 1. It follows: (i) the non-relevance of performing a statistical test to compare groups for their upper-root lengths in Week 1; (ii) within-subject effects with 1, 1, and 4 df instead of 2, 2, and 8 df, due to the inclusion of 2 weeks (Weeks 2 and 3) instead of the 3 weeks in the ANOVAR for upper-root length.*

TABLE 5 | Repeated measures analysis of variance results for length increments for corn lower- and upper-root systems and the two combined.

†*Except for N/A, the Error df is 4 (df: number of degrees of freedom).* ‡*Corn plants of the salt-stressed group had no upper root in Week 1, so length increments based on upper-root length could be calculated only from Weeks 2–3 for them, limiting the comparison between groups to a classical type of ANOVA (i.e., without repeated measures).*

# Discussion

### Experimental Approach

Traditionally, studies of root system architecture under laboratory conditions mostly use platforms such as WinRhizo that can help generate data for plants grown in solid medium. For example, a study conducted on a number of *Arabidopsis* accessions, using EZ-Rhizo to screen for root system architecture related to a quantitative trait locus, suggests natural variations across the accessions (Armengaud et al., 2009). Although such an approach is non-invasive, plants grown in Petri plates for such analyses have a critical time frame of study, since the enclosed environment can eventually lead to stressful conditions for plant growth

Frontiers in Plant Science | www.frontiersin.org April 2015 | Volume 6 | Article 256 |

within the Petri plates. Other techniques to screen root system architecture, such as the use of PVC pipes (Shelden et al., 2013) and gel chambers (Bengough et al., 2004) for fast screening of seedlings, or of a Rhizobox or Rhizotron facility (with PVC boxes of various sizes) to study growth of visible roots along the transparent sides of a box, are also useful for short-term studies (Neumann et al., 2009). However, these studies are restricted to 2-D scanning and cannot predict the 3-D growth of roots.

Our study is one of a few in which root systems and their surrounding soil medium have been repeatedly CT scanned (e.g., Lontoc-Roy et al., 2005; Han et al., 2008, 2009). Repeated CT scanning of plant structures in successive stages has multiple advantages, including those of following the development of the same structure over time and capturing the changes. It also has constraints and limits, starting with the use of low X-ray doses because a root system is living plant material. As pointed out by Dutilleul et al. (2005), the radiation output of a CT scanner increases strongly with tube voltage, but it is the product of tube current (mA) by scan time (s) by number of scans that actually represents the radiation level delivered during exposure time. Helical scanning reduces exposure time substantially, while allowing the reconstruction of several images from CT scanning data acquired in one rotation around the sample installed on the couch. In root system studies involving CT scanning, since the plant structure of interest is generally surrounded by a soil medium (i.e., an exception is provided by hydroponic culture) it is equally important that the X-ray dose be sufficiently high to allow radiation to penetrate the soil column throughout. In fact, CT numbers are function of X-ray attenuation coefficients (Eq. 1), and too-low doses would result in imprecise CT numbers for the part of the CT scanned volume near the center of the column in the case of a soil as dense as sand. All in all, some balance must be found, such as the use of 120 kV as tube voltage and 150 mA as tube current for plastic pots with an 18-cm top diameter in our study. Working with smaller containers may be an option to consider depending on the plant species, but when chosen, this option can be at the expense of the last stage up to which the development of root systems can be followed appropriately, since root tips might reach the edges of small containers more rapidly which would alter root growth.

The soil type × moisture content combination is another very important factor to take into account in the repeated CT scanning for underground plant structures. Lontoc-Roy et al. (2006) have shown that sieved homogeneous sand allows a better isolation of corn roots from the plant–soil CT scanning data when it is relatively dry, than when it is water-saturated at the time of CT scanning; for loamy sand (by mass: 78.0% sand, 14.4% silt, and 7.6% clay), it is the contrary. Furthermore, it is recommended that soil moisture content be as much as possible the same at all the times of CT scanning for all the plants, to keep as much as possible the same plant–soil contrasts in the CT scanning data and images and obtain comparable results; this was achieved by watering the corn seedlings after CT scanning, leaving the sand dry before CT scanning, in our experiment. Otherwise, some data transformation can accommodate the situation (Han et al., 2008, 2009). The constraints above are important, but are readily eclipsed by the tremendous insight

gained by the follow-up made possible week after week, thanks to the repeated CT scanning, on developing root systems and their non-destructive characterization, with no such extra variability as the one that would be introduced if different seedlings were used for analysis at different times. Of course, as discussed in Subsection "Analytical Aspects," the repeated-measures nature of FDs and root volumes, for example, must be incorporated in the statistical tests, for them to be valid, in a study like ours.

#### Analytical Aspects

Three types of analytical aspects are discussed here: (i) graphical, in relation to spatial resolution, and (ii) statistical, concerning (ii.1) the recommended assessment procedure for effects involving time, and (ii.2) the question of power of the tests concerning treatment effects (e.g., optimal vs. salt stress). Our choice of CT scanning configuration parameter values (field of view: SS or 18 cm, zoom factor: 1.0; see Section "Materials and Methods") provided a spatial resolution sufficiently fine to isolate from plant–soil CT scanning data and reconstruct all primary roots and portions of secondary roots for the corn seedlings which were 1, 2, and 3 weeks old in our experiment; the voxel dimensions were 0.35 mm × 0.35 mm × 0.4 mm. This would be coarse for smaller root systems, like wheat and rice (Flavel et al., 2012; Zappala et al., 2013); in these two root CT scanning studies, the voxel dimensions were, in fact, smaller. Thus, everything is relative; for a high-resolution CT scanner such as the Toshiba XVision, the scale of observation is in dm and the scale of resolution is 0.1–1.0 mm, while for a ultra-high-resolution CT scanner, they are in cm and 0.01– 0.1 mm, respectively (see Table 1 in Ketcham and Carlson, 2001).

The repeated-measures nature of the characteristics (e.g., FDs, root volumes) derived from 3-D images of reconstructed plant structures in a repeated CT scanning study like ours has implications. The presence of temporal heterogeneity of variance and temporal autocorrelation, two common properties of temporal repeated measures, contradicts the ANOVA assumptions of homogeneity of variance and independence, and the classical ANOVA *F* tests may be invalid. A multiplicative factor (called "Box's epsilon"; Crowder and Hand, 1990; Dutilleul, 2011) is then applied to the numbers of degrees of freedom of the *F* distribution of the ANOVA test statistic, providing the ANOVAR *F* tests. This generally results in an increase of the *P*-values, thus decreasing the statistical significance of the effects related to the temporal repeated measures (e.g., Week and Week × Group). The modification was slight with three temporal repeated measures in our case, because autocorrelation and heteroscedasticity were then weak, and it is never required with only two repeated measures (i.e., our FDR and relative growth rates).

Now that the experimental approach is well established (see Subsection "Experimental Approach"), the CT scanning of larger numbers of plants per day can be envisaged. Besides a better representation of the treatment effect in time, larger sample sizes (i.e., numbers of individuals in a group) will enhance the power of the statistical tests, meaning that existing effects will be detected more often. From the results reported in **Tables 1–3** and calculations of degrees of freedom made aside, it can be seen that with a few more units in each group, most of the *P*-values between 0.10 and 0.20 in **Tables 1–3** could become smaller than 0.10 or even 0.05, all other things being the same. This would mean a larger number of significant (*P <* 0.10 or *P <* 0.05) effects, especially those related to the treatment (e.g., salt stress), with greater insight into differences in structural complexity and space occupancy of the developing root systems. That said, the number of significant (*P <* 0.01, 0.05, or 0.10) treatment or time-related effects found with three plants per group is remarkable and very encouraging for larger experimentation.

#### Biological Significance

Corn salinity tolerance studies have most often focused on the physiological, biochemical, phytohormone, transcriptional, and proteomic responses and in comparison with model plants *Arabidopsis*, rice, and tomato. Salinity responses vary between and within plant organs, growth stages, and are genotype-specific. Normally, roots can differentiate between wet surroundings and air pockets in its environment, and the collective response is referred to as hydropatterning. The immediate environment of the root dictates root hair patterning, positioning, and development of aerenchyma and distribution of anthocyanins and auxin, and is independent of abscisic acid signaling. In *Arabidopsis*, genes such as TRYPTOPHAN AMINOTRANSFERASE OF *ARABIDOPSIS* 1 and PIN-FORMED3 are necessary to control auxin to induce lateral root formation under high water availability. This also dictates the position where the lateral root founder cells need to be positioned and activated (Baoa et al., 2014). However, when a 100-mM NaCl stress is imposed in corn, a 10-fold increase in abscisic acid (ABA) accumulation in roots, as compared to a onefold increase in shoots, has been observed (Jia et al., 2002). Salt-resistant hybrid SR03 was found to have increased indole-butyric acid (IBA) and ABA levels in the shoots, while the roots maintained increased indoleacetic acid (IAA) levels upon 100-mM NaCl stress (Zörb et al., 2013). Quantitative differences in responses to salt stress were observed in salt-sensitive corn cultivar Trihybrid 321 and salttolerant cultivar Giza 2. Salt stress decreased fresh weight, dry weight, and relative growth rates of both shoots and roots. An increased accumulation of Na+ and Cl− and a decrease in K+ and Ca2+ were observed in both shoots and roots in cultivar Giza 2 (Mansour et al., 2005). A study on the root growth direction of *Arabidopsis* to salt stress suggests that the roots encountering salt stress have reduced gravity response, and this seems to be modulated by the quantity of salt present in its environment (Sun et al., 2008). The phenomenon of decreased root growth is evident in our study, graphically (**Figures 2** and **3**) as well as quantitatively (**Figure 4**), and statistically (**Tables 1–5**). Salinity stress has an early effect during corn seedling establishment that is more pronounced in the volume aspects than in the structural complexity of the root system.

Given the above understanding of salinity stress on roots, and shoots, of corn, the CT scanning results that we presented add a new dimension to the understanding of root architecture patterns in growing corn seedlings, unstressed, and in salinity stress condition. This root repeated CT scanning experiment was to quantify and illustrate *in situ* the effects of salinity on germinating and growing corn seedlings at optimal growth temperature, as salinity stress is a very simple abiotic stress to simulate under laboratory conditions.

As possible future perspectives or studies, we can see and propose what follows on the basis of the results obtained in our study. Furthering this study to root growth patterns under cold stress will be very insightful because cold stress delays the onset of roots and the growth pattern is slower as compared to optimal conditions, thereby allowing CT scanning for two or three additional weeks using the experimental approach discussed in Subsection "Experimental Approach." Slowing down the root growth can add to the refinement of the study of root branching patterns, volumes and lengths, and derived growth rates and increments. Such a study could be extended to other agriculturally important

## References

crops and their commercial genotypes, especially crops cultivated in temperate regions, with larger sample sizes.

#### Acknowledgments

The authors are grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC) for the Major Equipment Grant that led to the creation of the CT Scanning Laboratory for agricultural and environmental research on Macdonald Campus of McGill University and to the Canadian federal government and the Québec provincial government for a Canada Foundation for Innovation (CFI) Grant that provided us with the computer equipment. The operating aspects of the reported study were supported through the Regroupements stratégiques Grant from the Fonds de recherche du Québec – Nature et technologies (FQRNT) to Centre SÈVE, of which PD and DS are members.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Subramanian, Han, Dutilleul and Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Effects of damping-off caused by *Rhizoctonia solani* anastomosis group 2-1 on roots of wheat and oil seed rape quantified using X-ray Computed Tomography and real-time PCR

*Craig J. Sturrock1, James Woodhall2, Matthew Brown1, Catherine Walker1, Sacha J. Mooney1 and Rumiana V. Ray1\**

*1 School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK, 2 The Food and Environment Research Agency, Sand Hutton, UK*

*Rhizoctonia solani* is a plant pathogenic fungus that causes significant establishment and yield losses to several important food crops globally. This is the first application of high resolution X-ray micro Computed Tomography (X-ray μCT) and real-time PCR to study host–pathogen interactions *in situ* and elucidate the mechanism of *Rhizoctonia* damping-off disease over a 6-day period caused by *R. solani*, anastomosis group (AG) 2-1 in wheat (*Triticum aestivum* cv. Gallant) and oil seed rape (OSR, *Brassica napus* cv. Marinka). Temporal, non-destructive analysis of root system architectures was performed using RooTrak and validated by the destructive method of root washing. Disease was assessed visually and related to pathogen DNA quantification in soil using real-time PCR. *R. solani* AG2-1 at similar initial DNA concentrations in soil was capable of causing significant damage to the developing root systems of both wheat and OSR. Disease caused reductions in primary root number, root volume, root surface area, and convex hull which were affected less in the monocotyledonous host. Wheat was more tolerant to the pathogen, exhibited fewer symptoms and developed more complex root systems. In contrast, *R. solani* caused earlier damage and maceration of the taproot of the dicot, OSR. Disease severity was related to pathogen DNA accumulation in soil only for OSR, however, reductions in root traits were significantly associated with both disease and pathogen DNA. The method offers the first steps in advancing current understanding of soil-borne pathogen behavior *in situ* at the pore scale, which may lead to the development of mitigation measures to combat disease influence in the field.

Keywords: *Rhizoctonia solani*, X-ray Computed Tomography, qPCR, wheat, oil seed rape, fungi, soil

*Edited by: Pierre Dutilleul, McGill University, Canada*

#### *Reviewed by:*

*Pierre Dutilleul, McGill University, Canada Timothy Paulitz, U.S. Department of Agriculture – Agricultural Research Service, USA Norman Gerhard Uhlmann, Fraunhofer Institute for Integrated Circuits IIS, Germany*

#### *\*Correspondence:*

*Rumiana V. Ray, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, LE12 5RD, UK<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Plant Biophysics and Modeling, a section of the journal Frontiers in Plant Science*

*Received: 31 October 2014 Accepted: 10 June 2015 Published: 24 June 2015*

#### *Citation:*

*Sturrock CJ, Woodhall J, Brown M, Walker C, Mooney SJ and Ray RV (2015) Effects of damping-off caused by Rhizoctonia solani anastomosis group 2-1 on roots of wheat and oil seed rape quantified using X-ray Computed Tomography and real-time PCR. Front. Plant Sci. 6:461. doi: 10.3389/fpls.2015.00461*

# Introduction

*Rhizoctonia solani* Kühn (teleomorph = *Thanatephorus cucumeris* Donk) is a ubiquitous soil-borne plant pathogenic fungus which causes significant yield losses in many agriculturally important crops (Verma, 1996; Paulitz et al., 2006). Individual isolates of *R. solani* are classified into anastomosis groups (AGs) based on their hyphal incompatibility and their host specificity (Anderson, 1982). For example, AG2-1 and AG4 are associated with stem and root rot diseases in dicotyledonous crop species belonging to *Brassicaceae* (Gugel et al., 1987; Sneh et al., 1991; Tewoldemedhin et al., 2006) whilst isolates of AG8 cause 'bare patch' or root rot on monocotyledonous crops from *Poaceae* (Paulitz et al., 2002).

The predominant population of *R. solani* causing severe seedling diseases associated with establishment losses of up to 80–100% and final yield loss of up to 30% of oil seed rape (OSR, *Brassica napus*) worldwide belongs to AG2-1 (Tahvonen et al., 1984; Kataria and Verma, 1992; Khangura et al., 1999). Highly virulent isolates of AG2-1 cause pre- and post-emergence damping-off, stem, and root rot with characteristic water soaked lesions on the root and hypocotyl, stunting of plant growth, root necrosis and cortex tissue maceration, and subsequent death in OSR (Yang et al., 1992). Recent soil surveys, carried out in USA (Schroeder et al., 2011) and UK (Brown et al., 2014) on fields growing winter wheat (*Triticum aestivum*) have revealed the most common pathogen present in soils of increased rotational frequency with OSR is *R. solani* AG2-1, shown in *>*69% of fields (*n* = 90) in England.

Whilst the pathogenicity and aggressiveness of AG2-1 to OSR have been previously studied (Yitbarek et al., 1987; Kranz, 1988), less is known of the impact of this group of pathogens on wheat roots. AG2-1 isolates have been shown to be pathogenic to cereals to varying degrees. Tewoldemedhin et al., (2006) reported AG2- 1 isolates were weakly pathogenic to barley and wheat roots. In contrast, Roberts and Sivasithamparam (1986) reported AG2-1 isolates from wheat roots in 'bare patch' in Western Australia were highly pathogenic to wheat causing an 80% disease index which was similar to disease caused by AG8 isolates. Thus, at present, the ability of AG2-1 to cause significant damage to the root system of seedlings of monocotyledonous crops such as wheat remains unclear.

The etiology of soil-borne diseases caused by pathogens such as *R. solani* on plant seeds and roots below ground has until recently been difficult to study. Traditionally, assessment of disease incidence and severity has involved the use of visual observations of symptoms of infection on affected plant organs following the physical extraction of plants from the ground (Kranz, 1988). However, the inherently destructive nature of visual disease inspection means that it is not possible to monitor temporal disease development and effects on root traits and system architecture. Furthermore, destructive sampling in the field often results in an incomplete root system extraction and loss of the most severely infected or severed primary/secondary roots.

Non-destructive methods for imaging plant roots *in situ* in soil, such as X-ray micro Computed Tomography (X-ray μCT), have become an important tool for quantifying plant root system architecture development in three dimensions (see review by Mooney et al., 2012). However, to date the application of X-ray μCT to investigate the impact of root rot pathogens has been relatively limited to Han et al. (2008) who studied the effects of common potato scab caused by *Streptomyces scabies* on tubers in soil. This was the first use of medical X-ray CT in a phytopathological study to successfully segment root structures from CT images and demonstrated diseased plants had significantly less complex root systems, in addition to delayed root growth and branching. A subsequent study by the same researchers using CT showed the effects of common potato scab on the density of seed and peripheral organs of potato plants in soil over a 10 week period (Han et al., 2009). Interestingly, an early application of a medical CT system to soil science by Grose et al. (1996) measured moisture content in bulk soil and in the soil around roots to predict suitable growth conditions for both *R. solani* and *Gaeumannomyces graminis*. Although at relatively coarse resolutions (200 μm) compared to the resolution achievable on modern systems for similar sized pots (6 cm diameter), the study successfully quantified heterogeneous moisture gradient in the vicinity of the plant roots and demonstrated the potential of the technique for investigation of environmental factors on the soil–plant–microbe system. Recent advances in the sensitivity of X-ray detectors within industrial μCT systems have facilitated much faster acquisition times (minutes rather than hours) facilitating easier repeated scanning of the same sample to visualize the temporal dynamics of plant root systems in undisturbed soil a (Tracy et al., 2012; Zappala et al., 2013b)

Microbiological methods for detection and quantification of target AGs of *R. solani* in soil are highly labor intensive and time consuming, involving the use of soil baiting methods that are often inefficient in detecting and isolating *R. solani*, and microscopy (Sneh et al., 1991). Furthermore, low population densities of *R. solani* in the soil and the lack of selective isolation media for the species make quantification difficult and unreliable. In the last decade, several conventional or real-time quantitative polymerase chain reaction (qPCR) assays have become an established tool for rapidly quantifying fungal pathogens including targeted AGs of *R. solani* at low detection limits in both soil and infected plant tissues (Filion et al., 2003a,b; Sayler and Yang, 2007; Okubara et al., 2008; Budge et al., 2009; Woodhall et al., 2013). We propose that the combination of these two powerful techniques, qPCR and X ray μCT, can allow improved new insight into the temporal host–pathogen interactions and provide quantitative data on the impact of soilborne pathogens on root architectural systems of crop plants grown in soil. The main aim of this study was to elucidate the mechanism of disease caused by AG2-1 of *R. solani* on root traits and system architecture of two different crops, the monocot, wheat, and the dicot, OSR.

# Materials and Methods

#### Soil, Plant, and Inoculum Preparation

The experiment was designed as a factorial block with two main factors, host and inoculation with two levels. The host crops were wheat, (*Triticum aestivum* cv. Gallant) or OSR (*Brassica napus* cv. Marinka) which were either non-inoculated or inoculated with *R. solani* AG2-1 (Isolate 159/8, Goll et al., 2014). The isolate was previously determined to be weakly pathogenic to wheat and pathogenic to OSR. There were nine replicates of the treatment combinations resulting in a total of 36 columns.

Soil columns (30 mm diameter × 70 mm length) were uniformly packed to a bulk density of 1.1 Mg m−3 with a Newport series loamy sand soil (sand 72.6%, silt 13.2%, and clay 14.2%; pH 6.35; organic matter 2.93%) collected from the University of Nottingham farm at Bunny, Nottinghamshire, UK (52.52◦N, 1.07◦W). Prior to packing, the soil was air-dried, sieved to *<*2 mm and sterilized by γ-irradiation at 27 kGy (Isotron, Daventry, UK). The pathogen treated soils were inoculated with five, 5-mm diameter plugs of actively growing *R. solani* mycelium equally distributed in the vertical direction of the soil during packing of the columns. Seeds of cv. Gallant and cv. Marinka were pre-germinated for 48 h on moist filter paper in petri dishes before being planted at 10 and 5 mm below the soil surface, respectively. The columns were then saturated, drained for 2 days (to a notional field capacity which represents the moisture content of the soil after free drainage had ceased) and placed in a growth room under conditions of 14◦C day/night with an 8 h photoperiod and a photosynthetic photon flux density (PPFD) at plant level of 1000 μmol m−2 s−1. A transparent plastic unheated seed propagator was used to maintain high relative humidity levels and avoid surface drying of the soil during seedling establishment in the growth room. Three replicates for each treatment combination were randomly selected and destructively harvested via root washing and scored for disease 2, 4, and 6 days following inoculation (dfi). Root disease severity was assessed at each destructive sampling point on soil-free plants on scales from 0 to 5; 0 = no lesions, clean roots; 1 = small lesion on tap root; 2 = necrosis of upto 30%; 3 = necrosis covering 31–60% of the tap root; 4 = necrosis covering 61–99% of the tap root; 5 = completely severed tap root (Khangura et al., 1999). In addition, the three replicates selected for harvest at 6 dfi were also scanned using X-ray μCT at 2, 4, and 6 dfi to permit non-destructive quantification of root system development. Root architecture of the washed roots was assessed using WinRHIZOR 2002c scanning equipment and software on each harvest day. The images collected were used to compare with the X-ray μCT images. Soil from the columns was further used for DNA extraction and pathogen quantification.

### X-Ray Micro Computed Tomography (**µ**CT)

The replicate subset allocated for destructive sampling at 6 dfi (12 columns), were scanned at 2, 4, and 6 days using a Phoenix NanotomR (GE Measurement & Control Solutions, Wunstorf, Germany) X-ray μCT scanner. The scanner consists of a 180 kV nanofocus X-ray tube fitted with a tungsten transmission target and a 5-megapixel (2304 × 2304 pixels, 50 × 50 μm pixel size) flat panel detector (Hamamatsu Photonics KK, Shizuoka, Japan). A maximum X-ray energy of 110 kV, 140 μA current and a 0.15 mm thick copper filter was used to scan each sample which consisted of 1300 projection images acquired over a 360◦rotation. Each projection image was the average of three images acquired with a detector exposure time of 500 ms in 'Fast CT mode.' The resulting isotropic voxel edge length was 19 μm (i.e., spatial resolution) and total scan time was 35 min. The total X-ray dose for each sample was calculated as 25.2 Gy over the three scans, which is below the 33 Gy threshold reported by Johnson (1936) which no detrimental effects of postgermination plant growth following exposure to X-ray radiation were observed (Zappala et al., 2013a). Reconstruction of the projection images was performed using the software datos| rec (GE Measurement & Control Solutions, Wunstorf, Germany) to produce 3-D volumetric data sets with dimension 30 × 30 mm (diameter × depth).

#### Image Processing and Analysis

Plant root systems were non-destructively segmented using the *Region Growing* selection tool in VG StudioMAXR 2.2 software as described by Tracy et al. (2012). To summarize, the *Region Growing* tool, allows the user to select connected structures within the data that have the same distribution of X-ray attenuation based on their gray values. The user assigns all root material to a region of interest which is then extracted as a separate binary image stack for measurement of root system architecture in RooTrak software. RooTrak software (Mairhofer et al., 2012) permits quantification of descriptive traits on root system architecture, such as total volume, surface area, maximum length and width, convex hull (relates to the space filling in 3D of an object), and centroid Z (relates to the center of mass of a 3D object). Due to small scales differences in seed depth in the reconstructed volumetric data, the measurement field of view was standardized to 30 mm × 25.80 mm (diameter × depth). Therefore, the maximum possible value for root length measurements is limited to 25.80 mm.

Soil porosity (total and incremental with depth) was quantified in FIJI image analysis software (Schindelin et al., 2012) using a modified method of Tracy et al. (2012). To summarize, a resized 16 bit image stack of dimensions 17.1 mm × 17.1 mm × 19 mm (900 × 900 pixels × 1000 images) was first prepared to exclude the area outside of the soil column (i.e., the container and the surrounding air space). Images were binarised to define the air filled pore space with a value of 0 and the 'solid' soil with a value of 1 using the isodata threshold algorithm which performed the best in an evaluation study. Soil porosity for each slice image was calculated based on the percentage of air to the total volume of the resized stack.

### Real Time Quantitative PCR for AG2-1 of *R. solani*

DNA was extracted from soil as described in Woodhall et al. (2012), except sample size was reduced to 45 g and then added to a 250 ml Nalgene bottle with 3 ml antifoam B with six 25.4 mm stainless steel ball bearings and 90 ml grinding buffer (120 mM sodium phosphate buffer pH 8, 2% cetrimonium bromide, 1.5 M sodium chloride). Real-time PCR was undertaken using a 7500 real-time PCR system. Environmental Master Mix 2.0 (Life Technologies, USA) was used for all real-time PCR and consisted of half the total reaction volume of 25 μl, whilst 5 μl consisted of the DNA sample. Primers (MWG Biotech, Germany) and hydrolysis probe specific for AG2-1 (Budge et al., 2009) were used and added to a final concentration in the reaction of 300 and 100 nM, respectively, with the remaining volume made up with molecular grade water. Cycling conditions consisted of 50◦C for 2 min, 95◦C for 10 min, and 40 cycles of 95◦C for 15 s, and 60◦C for 1 min. Each sample was tested in duplicate and an average Ctvalue was determined. Target DNA in soil samples was quantified by including six DNA standards on each PCR run. The standards consisted of a DNA sample of known concentration taken from culture of AG2-1 (Isolate 2023, Food and Environment Research Agency, UK) which was used to produce a dilution series of five 10-fold dilutions. The amount of DNA was then determined by linear regression.

#### Statistical Analysis

Root growth and architecture traits were analyzed using analysis of variance (ANOVA) for repeated measures and corrected for degrees of freedom for all time related effects with Greenhouse– Geisser Epsilon factor. Architecture traits were root volume, surface area, convex hull volume, maximum width, and length. Pathogen DNA data were analyzed by ANOVA containing sampling time, crop, and inoculation as interacting factors in the treatment structure. Regression analysis was used to investigate the relationships between root traits, disease score, and pathogen DNA, using a simple linear model for each crop separately. All analyses were performed in Genstat 15, version <IP_ADDRESS>35.

#### Results

#### Disease Development and Pathogen DNA Accumulation in Soil

No symptoms of root disease were observed in the noninoculated treatments (control) for either crop species (**Figure 1**). OSR plants developed visible lesions on roots as soon as 2 dfi. The symptoms rapidly progressed from moderate (necrosis covering 31–60% of the root, disease score 3) to severe (completely severed taproot, disease score 5) by 4 dfi resulting in complete maceration of root tissue by day 6 (**Figure 1**). Wheat plants exhibited significantly lower disease severity compared to OSR plants (*P* = 0.011), with symptoms classified as slight (small lesions on the primary roots, disease score 1) which were first detected at 6 dfi (**Figure 1**).

DNA of *R. solani*was not detected in the soil of non-inoculated plants at 2 dfi, but was quantifiable at 4 and 6 dfi at low concentrations (0.008 and 0.019 ng g−1) in two soil columns. In contrast, DNA in inoculated soils of both crops at 2 dfi was above 100 ng g−1 (**Figure 2**). The trend of DNA accumulation over the duration of the sampling period was similar for the two crops showing an increase in pathogen DNA by day 4 followed by a plateau by 6 dfi (**Figure 2**). The mean pathogen DNA in the OSR treatment at 4 dfi was approximately 45% higher than in the wheat treatment (*P* = 0.063) although no differences were observed between crops for 2 or 6 dfi.

lesion on tap root; 2 **=** necrosis of up to 30%; 3 **=** necrosis covering 30–60% of the tap root; 4 **=** necrosis covering 61–99% of the tap root; 5 **=** completely severed tap root) assessed 2 days following inoculation (dfi), 4 and 6 dfi on wheat and oil seed rape

No disease symptoms were shown in the control treatment for both crops. Bar shows standard error of difference (SED) for the interaction between sample time (T) at 2, 4, or 6 dfi and crop (C) species (wheat or OSR).

## Impact of *R. solani* AG2-1 on Root System Architecture of Wheat and OSR

Visual assessment of X-ray μCT 3D images and WinRHIZOR images suggested major differences in root system architecture under the experimental factors, inoculation and crop (**Figure 3**; Supplementary Videos 1 and 2). Control OSR plants had a characteristic single tap root that developed lateral roots by 6 dfi. Typically, wheat plants developed between 3 and 5 primary roots with no lateral roots by the end of the experiment. Initial root

FIGURE 4 | Root system volume and surface area over time (T) for crop (C), (A,B) and inoculation (I) with *R. solani* AG2-1 (C,D). Interactions for surface area and volume were detected using repeated measures ANOVA with degrees of freedom (df) corrected by Greenhouse–Geisser epsilon factor. Bars show SED for (1) comparing means for treatment combinations; (2) comparing means with the same level of C; (3) for comparing means for the same level of I and species (wheat or OSR); (4) comparing means with the same level of I.

growth of OSR plants was inhibited in soils inoculated with AG2- 1 of *R. solani* and resulted in complete maceration of root tissue by 6 dfi. Disease effects were less obvious on wheat roots from inoculated soils with *R. solani* (**Figure 3**).

There were significant temporal differences for root volume and surface area measured using X-ray μCT between crops (**Figures 4A,B**; *P <* 0.001) and between inoculated and noninoculated plants (**Figures 4C,D**; *P <* 0.001). The absence of interactions between crop and inoculation suggested root volume and surface area were affected mainly by intrinsic differences in root system characteristics of individual crop species and the presence of the pathogen in the soils. Inoculation significantly reduced root volume and surface area in both crops, however, the effects were greater in OSR, where these traits were affected immediately following inoculation and there were relatively small changes over time in trait parameters (**Figure 4**).

Root system traits for which significant temporal interactions between crop and inoculation were detected are shown in **Table 1**. The root system of wheat increased in length and width in time, despite inoculation, to a maximum of 25.8 and 29.3 mm, respectively (**Table 1**). A similar trend was observed for the control OSR plants with the root system length and width reaching 25.8 and 13.5 mm, respectively, by the end of the experiment. However, for the OSR plants inoculated with *R. solani*, root growth was inhibited from day 2, slight increases in length and width were observed by day 4 but ultimately at 6 dfi roots of inoculated plants were 96% shorter and 78% thinner than the controls (0.97 and 2.90 mm, respectively).

Both inoculation treatments in wheat displayed a significant increase in centroid Z (an indication of root structure with depth) after 4 days incubation with a mean value of 16.04 and 16.47 mm for the control and inoculated plants, which then reduced to 14.86 and 14.5 mm, respectively, after 6 dfi. Control OSR plants displayed a sustained increase in centroid Z from 1.07 mm at 2 dfi to 18.52 mm at 6 dfi. Centroid Z remained consistently low throughout the experiment for the *R. solani* treated OSR plants (1 mm). (**Table 1**; time × crop × inoculation; *P* = 0.010.)

Convex hull (an indication of the volume of soil explored) increased in all treatments except in OSR inoculated plants, where it remained the same after 4 dfi and for wheat was significantly higher compared to OSR (*P* = 0.001). Inoculation with *R. solani* resulted in smaller rates of increase in convex hull in both plants (**Table 1**). The control wheat treatment showed a significantly higher convex hull which was almost twice the volume compared to the *R. solani* inoculated treatment with values of 4123 and 2038 mm3, respectively, after 6 dfi. The control OSR had a lower convex hull compared to wheat with a mean of 413 mm3. *R. solani* treated OSR exhibited the lowest convex hull with a mean of 7 mm3 remaining the same at 4 and 6 dfi (**Table 1**; time × crop × inoculation; *P* = 0.048).

Inoculation with *R. solani* AG2-1 had a major effect on primary root number of both crops and resulted in significant reductions throughout the experiment demonstrated by the absence of significant interactions between experimental factors and time (**Figure 5A**). The number of primary roots was significantly higher in wheat compared to OSR plants which produced just one taproot (**Figure 5B**). Production of primary root numbers in wheat ceased at 4 dfi with no further significant increases being detected (**Figure 5B**). In OSR plants primary root number decreased at each sample time associated with effects of inhibition by the pathogen on root development and digestion of root tissue in time (**Figure 5B**).

Comparison of the WinRHIZOR and RooTrak measurements supported all observations and displayed strong significant relationships for comparable root system traits such as volume (*P <* 0.001, *R*2 = 0.97) and surface area (*P <* 0.001, *R*2 = 0.97). The relationship for root length measured by the two methods was also significant (*P* = 0.024) but weaker than previously mentioned traits accounting for only 39% of the variance.

#### Relationship of Pathogen DNA and Root System Traits

Linear regression analysis with groups for individual crops was carried out to test the fitted data for the measured traits, pathogen DNA and visual disease symptoms for position and parallelism (**Table 2**). There was a significant relationship between disease score and pathogen DNA accounting for 82% of the variance, however, the data fitted separate lines for each crop, with different slope and intercept indicating a positive relationship between pathogen DNA in soil and disease expression on plant roots for OSR only. Data fitted separate lines for each crop for root length measured by μCT on both disease (*P <* 0.001, *R*2 = 0.96)

and pathogen DNA (*P <* 0.001, *R*2 = 0.77) with the same directionality showing negative relationships (**Table 2**). Similarly regressions (*P <* 0.001) of surface area and root length, measured by WinRHIZOR , on disease score accounted for more than 96% of the variance. Fitted separate lines with the same directionality for wheat and OSR suggested that the magnitude of effects on developing traits of the different root systems of individual crops were related to the expression of disease symptoms. All other measured traits by different systems fitted parallel lines for disease expression indicated that the final effects were similar but dependant on intrinsic differences between crops (**Table 2**).

#### Analysis of Soil Porosity

Total mean soil porosity, limited to an extent by the spatial resolution of the scans, was consistent for all soil columns across all treatments (Mean, 15.4%, SEM 1.5). However, measurement of the porosity with depth within a column showed regions of variable porosity indicative of layering created during soil packing which varied between 8 and 50% (**Figure 6C**). Furthermore, there was evidence of higher porosity at the interface of the emerging seedling and the surrounding soil in some of the samples, where the highest porosity values of 50% were recorded. This was particularly evident in one of the OSR replicates treated with *R. solani* AG2-1 showing hypocotyl tissue maceration and decay in the area of high soil porosity (**Figures 6D,E**; Supplementary Video 3). However, there was only

of OSR by *R. solani* AG2-1. (A) 3D X-ray CT image of soil and root (yellow). (B) Image showing only root tissue (white solid arrow indicates maceration of tissue. (C) 2D cross-section (zx plane) image showing high porosity around OSR root (scale bar = 2 mm). (D) Magnified view of image shown in (C), showing necrosis of root cortex (scale bar = 1 mm). (E) 2D cross-section (xy plane) image showing preservation of the stele (solid arrow) but complete necrosis of cortex tissue (scale bar = 0.5 mm).

weak regression between DNA concentration and soil porosity (*R*2 = 0.21).

## Discussion

This work provides the first example of X-ray μCT used for the non-destructive detection of below ground symptoms and impact of *R. solani* on the developing root systems of monocotyledonous and dicotyledonous plants. *R. solani* AG2-1 causes significant pre- and post-emergence damping-off characterized by the inhibition of seed germination, root elongation, and ultimately the digestion of the root and hypocotyl of *Brassica* species (Kataria and Verma, 1992). We found moderate symptoms in OSR as early as 2 dfi and severe disease developed by 4 dfi. In contrast, only mild symptoms developed in wheat plants by 6 dfi for similar initial inoculum in the soil quantified using qPCR as pathogen DNA at 2 dfi. The difference in disease development and severity on the two crops is in agreement with previous reports on the virulence and aggressiveness of AG2-1 to OSR demonstrating that isolates belonging to this group are highly pathogenic to *Brassica* species (Gugel et al., 1987; Verma, 1996).

TABLE 2 | Linear regression models for disease score (y) on pathogen DNA (x) and WinRHIZOR , and X-ray CT based measurements of root system architecture traits (y) on disease score (x), and pathogen DNA (x) for each crop.

*\*Denotes values not applicable.*

The delay in symptom development on wheat suggests that AG2- 1 is unable to cause significant symptoms on wheat confirmed by others in their investigations of pathogenicity of *R. solani* AG2-1 to cereals (Khangura et al., 1999; Oros et al., 2013). The effect of the primary host crop, OSR, on *R. solani* development was evident in the more rapid increase of pathogen DNA, reaching maximum of 300 ng g−1 in soil by 4 dfi in contrast to a twofold less DNA in soils from wheat grown plants (data not shown). This fast DNA accumulation in the soil from OSR, compared to wheat, is most likely related to the differences in the rate of infection and digestion of the emerging radicle and hypocotyl of the primary host species, manifested by the numerous lesions (visualized in this study) inhibited growth and ultimately the complete seedling necrosis by 6 dfi. The plateau of soil pathogen DNA at 6 adfi may be due to an exhaustion of available nutrients from the host plants and return of the pathogen to saprophytic phase of survival. The temporal dynamics of the pathogen during the development of wheat or OSR in field rotations are currently unknown. However, Brown et al. (2014) found no significant differences in pathogen DNA of *R. solani* AG2-1 accumulation in English field soils of wheat following wheat or OSR, suggesting that short wheat/OSR rotations are unlikely to be effective in reducing inoculum concentrations for either crop.

Visualization of the 3-D root system of the two crops grown in soil showed how the contrasting root systems of the monocot and dicot species reacted to the pathogen infection. Differences in the impact of the pathogen appeared to be related to the intrinsic complexity of the architectural root systems of the two crops and their ability to compensate on specific traits. Using time series μCT data importantly revealed that although the infection in the monocot, wheat, appeared asymptomatic, it contrasted the severe symptom expression in the dicot, OSR. *R. solani* AG2-1 was capable of causing significant damage on important developing root architectural traits of both crops including primary root number, root volume and root surface area that were affected less in the monocotyledonous host. Furthermore, the ability of both hosts to explore soil via their developing root system, indicated by the convex hull, was reduced. However, traits such as root length and centroid Z were not affected in the monocot. Both inoculated and control wheat plants developed 3– 4 primary roots that were thicker and longer by 4 dfi compared to OSR plants. In contrast, OSR plants were mostly dependent on the development of strong taproot and subsequent lateral roots for the acquisition of resources, thus early damage to the developing taproot by *R. solani* diminished significantly the ability of the plant to establish or recover from the disease. Wheat was able to compensate by producing more than one primary root (seminal roots) and it is likely that uninfected or less severely infected roots by the pathogen were able to escape the disease and thus compensate for resource use. *R. solani* AG2-1 is most aggressive to young seedlings and host resistance to infection increases with age (Verma, 1996). Therefore faster developing OSR cultivars are more likely to escape the disease and traits related to early germination and establishment, such as seed size will be important for breeding new varieties that are more likely to tolerate *R. solani* infection (Hwang et al., 2014).

Disease score and pathogen DNA were both strongly related to changes in the measured root traits. However, the transiency of these effects in particular in the maturing wheat plant is unknown. The relationship between disease and pathogen DNA was different for the two crops and disease was only predicted successfully for OSR. This has implications in terms of assessment and prediction of disease in the field in relation to individual crop species as clear symptoms were not exhibited in wheat and not related to DNA concentrations. Furthermore, both crops suffered from *R. solani* at the seedling stage thus it is important to elucidate if the disease caused by AG2- 1 is associated with significant yield loss of wheat in the field. Understanding the relationships between initial inoculum concentrations and final yield loss for the two crops can assist in the development of new strategies for prediction of risk and yield loss based on qPCR of soil prior to planting.

From the measured root traits, only root length showed poor correlation between the two imaging approaches which can be attributed to the way the trait was measured. RooTrak root length measurements were limited to a maximum soil depth of 25.80 mm compared to the entire 30 mm column length due to the field of view possible in a single μCT scan. However, as RooTrak can quantify novel root traits such as convex hull, there is potential to measure crop species specific descriptors to define root structure, e.g., differences between the single tap root of OSR versus primary and seminal root system of wheat. A crucial advantage of the μCT imaging is that not only can the developing root systems be quantified non-destructively and temporally but as we have shown changes in the soil microstructure can also be considered. Although, our initial soil conditions were designed as in most repacked column studies to be uniform, verification of the microstructure by imaging showed localized variations in porosity when measured at high resolutions especially at the root surface. This zone, i.e., the rhizosphere, is a crucial interface, where knowledge about the structural arrangement in particular is lacking. Variations in structure as we have revealed here will influence soil moisture availability considering the relationship between matric suction and pore size. Soil bulk density and moisture content are known to significantly influence hyphal growth and disease severity caused by *R. solani* (Glenn et al., 1987; Gill et al., 2001, 2004) but the impact at the pore scale is less well understood. Furthermore, it is generally accepted that the key limiting factor in hyphal proliferation is the availability of air filled pores within the soil (Glenn et al., 1987; Otten et al., 1999; Harris et al., 2003). We found OSR seedlings displaying the highest porosity around the seedling also had the lowest disease severity and longest root and shoot growth (**Figure 6**). This finding is in agreement with Gill et al. (2000) who found that although saprotrophic growth was higher in more porous soils, the disease severity was lower highlighting the potential of X-ray μCT in the study of the physical effects of soil structure on soil-borne pathogenic fungal diseases. This has potential implications for soil management practices, such as conventional and zero tillage as these may

### References

Anderson, N. A. (1982). The genetics and pathology of *Rhizoctonia solani*. *Annu. Rev. Phytopathol.* 20, 329–347. doi: 10.1146/annurev.py.20.090182. 001553

have very different soil structures (Mangalassery et al., 2014). For example, plowing could potentially reduce soil-borne disease severity, by increasing the porous structure of soil, physical disruption to fungal hyphal networks and increasing background microbial activity. Indeed, the most effective cultural control method for soil-borne *Rhizoctonia* root patch in wheat is via tillage practice of soil disturbance by cultivation which destroys established fungal hyphal networks and can increase microbial activity (Paulitz et al., 2002). The effect of tillage on soil-borne pathogens in OSR has received less attention, however, it is likely that reduced or zero tillage maximizes disease and inoculum potential by allowing infected crop residues to remain on the soil surface and preserving hyphal networks in close proximity to the host (Kharbanda and Tewari, 1996). Although soil structure can routinely be imaged at high resolutions (i.e., *<*100 μm), it is still not possible to visualize fungi *per se* using X-ray μCT due to their very low X-ray attenuation (Gleason et al., 2012). However, indirect modeling approaches have been useful to aid understanding of the behavior and functioning of fungi in both real (Pajor et al., 2010; Falconer et al., 2011) and artificial soil microstructures (Otten et al., 2012). These combined approaches may be of value in the future to facilitate further understanding of plant pathogenic fungi in the soil environment.

This study has successfully quantified the impact of *R. solani* on crop root system traits and development through the combined use of X-ray μCT and qPCR. X-ray μCT offers more promise than destructive methods as the development of disease symptoms on the root can be monitored non-destructively in soil. We have shown that disease symptoms developed rapidly in OSR within 2 dfi, whereas wheat displayed a higher tolerance with only mild symptoms present after 6 dfi. Differences in the impact of the pathogen on the two hosts were related to complexity and developmental rates of the different root architectural types of the monocot, wheat, and the dicot, OSR.

### Acknowledgments

The authors would like to acknowledge support from the University of Nottingham and Syngenta for the provision of AG2-1 isolate. JW received support from the Defra Horizon Scanning and Technology Implementation Fund. M. Brown receives a Ph.D. studentship from Syngenta. The authors thank Jim Craigon for his helpful advice on data analysis.

# Supplementary Material

The Supplementary Material for this article can be found online at: http://journal*.*frontiersin*.*org/article/10*.*3389/fpls*.*2015*.*00461

in soil. *Plant Pathol.* 58, 1071–1080. doi: 10.1111/j.1365-3059.2009. 02139.x

in tomato (*Solanum lycopersicum*) by X-ray Micro-Computed Tomography. *Ann. Bot.* 110, 511–519. doi: 10.1093/aob/mcs031

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Sturrock, Woodhall, Brown, Walker, Mooney and Ray. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# X-ray computed tomography uncovers root–root interactions: quantifying spatial relationships between interacting root systems in three dimensions

Alexander M. Paya1 , Jesse L. Silverberg2 , Jennifer Padgett 3 and Taryn L. Bauerle1 \*

*1 School of Integrative Plant Science, Cornell University, Ithaca, NY, USA, 2 Department of Physics, Cornell University, Ithaca, NY, USA, 3 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA*

#### Edited by:

*Pierre Dutilleul, McGill University, Canada*

#### Reviewed by:

*Lars Hendrik Wegner, Karlsruhe Institute of Technology, Germany Pierre Dutilleul, McGill University, Canada Keith Jones, Brookhaven National laboratory, USA*

#### \*Correspondence:

*Taryn L. Bauerle, School of Integrative Plant Science, Cornell University, 134A Plant Science Bldg., Ithaca 14853, NY, USA<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Plant Biophysics and Modeling, a section of the journal Frontiers in Plant Science*

> Received: *24 June 2014* Accepted: *06 April 2015* Published: *29 April 2015*

#### Citation:

*Paya AM, Silverberg JL, Padgett J and Bauerle TL (2015) X-ray computed tomography uncovers root–root interactions: quantifying spatial relationships between interacting root systems in three dimensions. Front. Plant Sci. 6:274. doi: 10.3389/fpls.2015.00274*

Research in the field of plant biology has recently demonstrated that inter- and intra-specific interactions belowground can dramatically alter root growth. Our aim was to answer questions related to the effect of inter- vs. intra-specific interactions on the growth and utilization of undisturbed space by fine roots within three dimensions (3D) using micro X-ray computed tomography. To achieve this, *Populus tremuloides* (quaking aspen) and *Picea mariana* (black spruce) seedlings were planted into containers as either solitary individuals, or inter-/intra-specific pairs, allowed to grow for 2 months, and 3D metrics developed in order to quantify their use of belowground space. In both aspen and spruce, inter-specific root interactions produced a shift in the vertical distribution of the root system volume, and deepened the average position of root tips when compared to intra-specifically growing seedlings. Inter-specific interactions also increased the minimum distance between root tips belonging to the same root system. There was no effect of belowground interactions on the radial distribution of roots, or the directionality of lateral root growth for either species. In conclusion, we found that significant differences were observed more often when comparing controls (solitary individuals) and paired seedlings (inter- or intra-specific), than when comparing inter- and intra-specifically growing seedlings. This would indicate that competition between neighboring seedlings was more responsible for shifting fine root growth in both species than was neighbor identity. However, significant inter- vs. intra-specific differences were observed, which further emphasizes the importance of biological interactions in competition studies.

 The outcomes of these interactions can range from positive (facilitation) to negative (competition), and are therefore highly relevant for the development of agricultural and ecological management practices (Grime, 1979; Tilman, 1987). Traditional parameters that quantify the effect of belowground interactions on root growth dynamics include diameter class, spatial/temporal deployment, growth rate, and fine root abundance (Casper and Jackson, 1997; Eissenstat and Yanai, 1997; Eissenstat et al., 2000; Hodge, 2004, 2009; Kembel et al., 2008). While parameters such as these differ across species, accurate in situ observations are inherently limited by the opaque and heterogeneous nature of soil matrices, and generally require a destructive harvest of roots (Joslin and Henderson, 1984; Steingrobe et al., 2000), or visualization along a two dimensional (2D) surface (Gross et al., 1993; Majdi, 1996; Eissenstat et al., 2000).

However, recent advances in three dimensional (3D) imaging technology such as ground penetrating radar, laser imaging, nuclear magnetic resonance imaging (MRI), neutron radiography (NT), and X-ray computed tomography (CT) have made the observation of undisturbed root systems possible (Macfall et al., 1991; Butnor et al., 2001; Gregory et al., 2003; Kaestner et al., 2006; Perret et al., 2007; Tracy et al., 2010; Moradi et al., 2011; Mairhofer et al., 2012). Innovations in software such as Rootviz, Root track (Tracy et al., 2010; Mairhofer et al., 2012), RootReader3D (Clark et al., 2011), and Avizo (Saoirse et al., 2010), and specific filtering algorithms (Perret et al., 2007) have helped improve 3D image resolution and stream-line the quantification of anatomical parameters such as lateral root length, lateral root number, root-system surface area, and volume of undisturbed root systems. However, accurately isolating roots from root-soil data is complicated by the continuum of water within the root itself, at the root-soil interface, and between soil particles (Lontoc-Roy et al., 2006). As methods for isolating roots improve, steady technological advancements will, and have already increased the scope of viable research questions and objectives. For example, studies have already begun to explore the 3D spatial distribution of fine and coarse roots in forests (Pierret et al., 1999; Butnor et al., 2001), mechanical buckling in plant roots (Silverberg et al., 2012), and water uptake at the root-soil interface (Moradi et al., 2011).

As 3D imaging technologies become more widely available, questions about the occupation and exploration of space by interacting root systems can be better addressed, offering new insights to this important yet problematic component of root growth dynamics. For example, belowground interactions can result in whole root system segregation (reviewed in Schenk et al., 1999), stunted root elongation (Mahall and Callaway, 1996; Falik et al., 2005; Bhatt et al., 2011), and/or over-yielding in response to spatially proximal self-roots (belonging to same plant) and non-self roots (belonging to neighbor) (Gersani et al., 2001; Maina et al., 2002; Falik et al., 2003). An understanding of the mechanisms regulating the growth of roots driven by belowground interactions is still developing, however growing evidence suggests that traditional parameters including root biomass, root surface area, and diameter are insufficient in integrating spatially complex responses.

To our knowledge, the following experiment is the first attempt at observing and quantifying the effect of belowground interactions between two neighboring root systems in 3D. Our research employed micro-CT to capture the spatial distributions of both interacting (inter- vs. intra-specific) and control (solitary) root systems belonging to 2-month-old tree seedlings. 3D models of root system architecture were developed from annotated CT image slices, and traditional belowground parameters such as root length, surface area, volume, and number of root tip were either measured or counted. Moreover, we also developed a series of belowground parameters that take advantage of skeletonized 3D root systems and binary root system data, and evaluate distances between root tips and characterized the distribution of individual root volumes. Broadly, the goal of this work was to investigate the effects of inter- and intra-specific interactions on belowground parameter values, and compare these parameter values with those obtained from solitary individuals.

# Materials and Methods

# Plant Growth

Acrylic tubes (3.5 mm wall thickness, 64 mm inner diameter, 305 mm length) were covered with fine mesh (0.5 mm) along the base, capped, and secured to provide free drainage. Each tube was filled incrementally with polystyrene beads (1–3 mm), gently tamped throughout the filling process in order to reduce pore size and achieve greater bulk density, and then wrapped in aluminum foil to prevent light penetration. Polystyrene was used in place of peat, sandy loam, sand, or vermiculite based on trail experiments which demonstrated very high water retention in soil or soil like mediums. Additionally, contrast agents such as iodine containing compounds, barium sulfate (BaSO4), gold chloride (Au2Cl6), and cow's milk were used, but sufficient contrast was not achieved.

Black spruce (Picea mariana) and quaking aspen (Populus tremuloides) were selected as "interacting" plant species based on differences in phylogeny, morphology, and the fact that they co-occur across northern latitudes of North America (DeByle and Winokur, 1985). Seeds from each species were germinated for 5–7 days between two sheets of damp cellulose, and then transplanted into pre-wet hydroponic growth plugs (Rapid Rooster Grow plug™, General hydroponics, Sebastopol, CA) following radicle emergence (1–3 mm). Each acrylic tube received a single plug containing one individual of either spruce or aspen (control), or two plugs, each containing one individual, to simulate inter- or intra-specific interactions. A total of 25 tubes were prepared. There was five of each of the following tubes: solitary aspen, intra-specific aspen, interspecific aspen/spruce, intra-specific spruce, and solitary spruce. Containers were randomly arranged on a hydroponic flood table modified to re-circulate nutrient solution for top-down irrigation. To prevent competition for light, a sheet of acetate was placed between interacting seedlings.

Plants were grown under greenhouse conditions (17 C◦ night, 20◦C/day; KPL greenhouses, Cornell University, Ithaca NY) with supplemental lighting (12 h/days) for 60 days (April–June, 2012). Irrigation was fed from a 530 liter (140 gallon standard) reservoir to individual micro-spray emitters focused at the base of each plant (900 , 0.5 gph, Hydro Flow™, Redmond, WA). Irrigation provided each tree with a nutrient replete hydroponic solution balanced at 150 ppm N (Peter salts, 21-5-20, 382.8 g/530 L; Epsom salts, MgSO4 7H2O, 130.64 g/530 L). Hydroponic Paya et al. X-ray vision

nutrient solution was maintained at a pH of 5.5–5.8, and electro conductivity of 1.6–2.0 throughout the experiment, and automatically controlled by a programmable timer on a rotating schedule. Half way through the growth phase of the experiment, a pump malfunction resulted in the loss of multiple individuals, which produced an uneven number of replicates for each species.

Irrigation was terminated after 2 months of growth. Plants were allowed to transpire residual water remaining in each container for 2 days prior to imaging in order to reduce imaging artifacts. Plants were then transported to Cornell's imaging facility for CT scanning.

#### Micro-CT Scans

Whole seedling's root systems were imaged at Cornell University's Micro-CT facility. Due to a pump failure during the growth stage of the experiment, only 13 out of 25 tubes were imaged: 3 × solitary aspen, 1 × intra-specific aspen, 3 × inter-specific aspen/spruce, 3 × intra-specific spruce, and 3 × solitary spruce. Each scan was performed using a GE CT120 micro-CT scanner (GE Healthcare, London, ON, Canada). Initially, 10 bright-field images were acquired with no objects in the scanner, providing a correction for detector non-uniformity. Calibration and correction for signal non-uniformity was determined from measurement within a SB3 (GE Healthcare) water/bone phantom, scanned with the samples. Resulting image datasets were calibrated to the conventional scale of Hounsfiel radiodensity units (HU), defined so that water and air have HU values of 0 and -1000, respectively. Each scan digitally acquired 720 projections at 0.5◦ intervals over 360◦ using 80 keV, 32 ma, 32 ms exposure time and 100µm x-y-z resolution. The obtained projections were used to reconstruct a CT dataset using a convolution back-projection algorithm implemented in 3D, giving a 70 × 70 × 50 mm3 volume of image data with 100µm isotropic voxels.

Using a sequential stacking function (MicroView, GE Healthcare), three sequential image stacks (70 × 70 × 50 mm3 ) were taken from each tube and recompiled into a single 70×70 × 150 mm3 data set. Using this function, we successfully increased the visible volume (i.e., visible rooting structure) three-fold: from 5 to 15 cm depth (70×70 × 150 mm3 scan required 1 h of imaging time).

### Destructive Harvesting

Following X-ray scanning, plants were destructively harvested. Leaves/needles and petioles were removed from the main stem and scanned using a photo scanner (Epson Expression 10000XL, 2400 dpi, Epson America Inc., Long Beach, CA). Directly following the removal of aboveground tissues, acrylic containers were inverted and tamped to release the polystyrene medium along with roots, which were gently rinsed under a 0.5 mm sieve. Polystyrene beads still attached to roots were removed using forceps. Individual roots were separated manually to prevent overlapping segments, placed on a photo scanner, and scanned. After scanning, above and belowground tissues were placed in separate paper bags, dried at 55 C for 3 days, and then weighed. Scanned images were analyzed for leaf surface area, root surface area, and total root length using WinRhizo (Winrhizo 2011, Regent Instruments, Canada). The number of root tips were counted manually using the image analysis toolset, ImageJ (National Institute of Health, Bethesda MD). In ImageJ, each 2D root system scan was imported as a TIFF, and using the paintbrush tool, root tips were individually and sequentially numbered to ensure that no root tips were overlooked or counted twice.

#### Image Reconstruction

Projections were exported from the GE CT120 micro-CT scanner as VFF format (Sun TAAC Graphic File) and converted to DICOM format using MicroView's DICOM transfer tool. Image stacks were then imported one at a time into ImageJ using the import, image sequence function. Once the main taproot was found for each seedling, roots originating from a single individual seedling were given an arbitrary color code, and the entire cross sectional area was traced by hand for each root through each CT image slice (70 × 70 × 0.1 mm) (**Figure 1**). This ensured that root diameter, surface area, and volume could be measured in the 3D model/reconstruction, and that root systems belonging to different individuals could be easily differentiated. Once a root came in contact with the container wall, tracing ceased because, (1) the roots were indiscernible from the container, and (2) the roots behaved atypically and tracked the container wall. Color-coded image stacks were then exported as an RGB TIFF stack, and opened in MATLAB R 2012b for three dimensional reconstruction and spatial quantification (The MathWorks Inc., Natick MA). In order to 3D render each root system, color codes were identified and isolated, which allowed for the subtraction of non-colored voxels; annotated circles representing root cross sections within each x-y plane were then stacked across the zplane. This process effectively rendered each root system in 3D with little or no constraints on actual root system dimensions. In MATLAB each root cross section was also converted to a 3D binary matrix in order to measure spatially explicit parameters. Entries of these matrices were either 0 or 1, depending on whether that voxel was occupied by the root.

## Morphological and Anatomical Analyses

Root surface area was determined from the 3D data sets by sequentially analyzing each x-y cross-section with MATLAB's bwtraceboundary function. This identified the coordinates of the root perimeter from which we calculated the circumference of all roots passing through the plane. The circumference was multiplied by the cross-sectional thickness (100µm) to estimate root surface area per image slice. This was performed for all crosssectional images and the results summed to calculate root system surface area. Root system volume was calculated by summing the total number of occupied voxels and multiplying by the volume per voxel, 10−3 mm3 /voxel.

Root tips were located by scanning through each crosssection and identifying terminal voxels. This generated a list of coordinates that were centered on root tips (**Figure 1**). With this information we were able to determine the depth-wise distribution along the z-axis (complete 3D information). To quantify the radial distribution of root tips (mm2 ), root tip coordinate data was projected into the x-y plane. An ellipse

whose circumference and orientation represents the occupied x-y area of the root system was then projected over the root tip coordinates. Multiplying π by both the major and minor axes of the ellipse provided the radial distribution of each root system. The ratio of the ellipse's major to minor axis is then a metric that defines how radially symmetric the root distribution is. In particular, if the ratio is 1, then the distribution is circularly symmetric. Values higher than 1 indicate the amount of asymmetric root growth in the plane that passes vertically through the ellipse's major axis.

#### Statistics

In order to validate our 3D rendering protocol, we used simple linear regression to compare destructively harvested (2D) and 3D reconstructed root system data. Surface area and the number of root tips were chosen for regression because both were measurable in 2D and 3D, and could therefore be used to validate our manual tracing procedure of fine root cross sections through CT image slices. The intercept (a) in the simple linear regression model (y = a + bx + error) was constrained to be equal to 0, and thus was not estimated (see **Figure 3**). Differences in mean ranks between solitary, intra- and interspecifically growing plants were analyzed using the Kruskal-Wallis test on the following parameters: biomass, 2D/3D root length, 2D/3D surface area, specific root area (SRA), specific root length (SRL), manual and 3D root tip count, 3D root volume, root–root distance, major/minor axes, radial distribution, and rooting depth weighted by volume (P = 0.05, Ho = mean ranks are equal). Where the null was rejected, post-hoc analyses were performed using the Wilcoxon each-pair test (non-parametric multiple comparisons, P = 0.01667). It is important to note that the distribution of minimum Euclidean distances between root tips did not distribute normally, nor did the data behave normally post-transformations (e.g., log x, ex , or x−1 ). Instead, we used a two-parameter model for the probability distribution function that fit both species data where the ratio of the sum of squares of regression (SSreg) to the total sum of squares (SStot) was between 0.75 and 0.99. This model was a good predictor of the fraction of root tips f(x) dx separated by a minimum distance between x and x + dx for control, inter-, and intra-specifically growing seedlings, where the pdf is given by:

$$f(\mathbf{x}) = c\_1 \mathbf{x} e^{-\mathbf{x}/c\_2}.\tag{1}$$

Non-linear regression models were fitted using Sigma Plot 11 (Systat Software, San Jose, CA). All other statistics were done using JMP 10.0 (SAS Institute Inc., Cary, NC).

### Results

#### Destructive Analyses

Analyses of each harvested seedling showed that belowground interactions had no significant effect on the aboveground growth of aspen or spruce (P = 0.1487, **Figures 2A,B**). Belowground interactions had no significant effect on either species' root biomass (P = 0.0606, **Figure 2C**). Belowground interactions had a measurable effect on aspen's root surface area, but not spruce (P = 0.0439, **Figure 2D**). On average, inter-specific aspen root systems were reduced to 31% of the control samples surface

Two additional belowground parameters, SRL (cm mg−1 ) and SRA (cm2 mg−1 ), were also calculated for both species. SRL and SRA depict the cost of root construction, and can be highly informative in establishing whether a treatment had an effect on root morphology. Control, inter-, intra-specifically growing spruce seedlings differed in terms of SRA (P = 0.0389), but similar differences were not observed in aspen (P = 0.0783, **Table 1**). Control, inter-, and intra-specific interactions also had a significant effect on the SRL of spruce seedlings (P = 0.0134), but not aspen (P = 0.2082).

#### Validation of 3D Reconstruction

In order to validate our 3D reconstruction protocol, comparisons were made between destructive (i.e., 2D) and 3D root parameters. Surface area and the number of root tips were two parameters measured in both 2D and 3D, and were therefore chosen to validate the 3D reconstruction by way of simple linear regression. We found that 62% of the total number of root tips, and 76% of the total surface area were successfully captured during 3D image reconstruction (**Figure 3**). Examples of 3D root systems form each species combination are presented in **Figure 4**, as well as **Figure S1**.

#### 3D Utilization of Space

The occupation of 3D space by individual root tips, and whole root systems was quantified via a set of five metrics: radial distribution of root tips, directionality (major/minor axes of radial distribution ellipse), minimum root–root tip distance, root system volume as a function of depth, and the vertical position of root tips. The radial distribution of root tips measured the radial expanse of a root system (mm2 ), i.e., the area of an ellipse that encompassed the x/y distribution of all root tips projected along the z-axis. The radial distribution of aspen roots ranged from 248 to 501, 500 to 522, and 212 to 325 mm2 in the control, intra-specific, and inter-specific seedlings, respectively (**Table 2**). For spruce, the control, intra-specific, and interspecific seedlings ranged from 16 to 43, 9 to 112, and 36 to 314 mm2 , respectively (**Table 2**). We found no significant effect of belowground interactions on the radial distribution of roots.

The directional growth of roots was measured by dividing the major (transverse) by the minor (conjugate) axes of an ellipse that encompassed the radial distribution of each root system. Using this approach, we could determine whether a root system was concentric (major/minor = 1), or skewed/directional (e.g., major/minor >> 1). As was the case with the radial distribution of root tips, belowground interactions had no significant effect on the ratio of major/minor axes for either species. However, notable species trends were observed. The major/minor axes of spruce root systems ranged from 1 to 10, with a mean of 4 across solitary and paired seedlings, indicating that spruce roots systems were relatively planar (**Table 2**). The major/minor axes of aspen root systems ranged from 1.1 to 1.6, with a mean of 1.2 across control, inter- and intra-specific seedlings, indicating that aspen root systems were evenly distributed in all directions relative to each root system's center of mass.

The third metric, minimum root–root tip distance, measured the minimum distance between a root tip (x1, y1, z1) and the

area. Lastly, belowground interactions had a significant effect on fine root lengths in aspen (P = 0.0439), but not spruce (P = 0.2120).

post-destructive harvest. Each point (triangle) represents data from a single individual displayed on a log scale (y-axis); \* denotes significant differences among control, intra-, and inter-specific seedlings (Kruskal-Wallis test,

*P* ≤ 0.05).

TABLE 1 | Belowground parameters of destructively harvested aspen (Populus tremuloides) and spruce (Picea mariana) seedlings grown under three experimental conditions: control, intra-specific, and inter-specific.

*Values listed are the median (maximum, minimum).*

nearest neighboring root tip (x2, y2, z2) for every terminal point in a root system. Belowground interactions had a significant effect on the minimum distance between root tips in both aspen (P = 0.0114) and spruce (P = 0.0002). Post-hoc analyses indicated that aspen roots grown intra-specifically (6.8 ± 0.28 mm) had wider distances between root tips compared to the controls (5.9 ± 0.13 mm) (P = 0.0025, **Figure 5B**). As for spruce, post-hoc analyses indicated that the minimum distances between root tips in controls (6.0 ± 0.78 mm) were significantly less than interspecific seedlings (11 ± 1.0 mm, P < 0.0001, **Figure 5D**). The minimum distances between root tips in intra-specific seedlings (7.8 ± 0.92 mm) were also significantly less than inter-specific seedlings (P = 0.0007, **Figure 5D**).

We also quantified differences between control, inter-, and intra-specifically growing plants in terms of their vertical placement of root system volume (**Figures 6A,B**). For both species, control, inter-, and intra-specific interactions were significant predictors of the depth of roots weighted by volume (Kruskal-Wallis test, P < 0.0001). In aspen, post-hoc analyses indicated that the largest difference was observed between control (deep) and intra-specific (shallow) root systems (P < 0.0001). The second largest difference in rooting depth was observed between inter- and intra-specifically growing seedlings (P < 0.0001). The smallest difference was observed between interspecific and control root systems (P < 0.0001). In spruce, posthoc analyses indicated that the largest difference was observed between inter- and intra-specifically growing root systems (P < 0.0001). Inter-specific and control root systems also occupied significantly different rooting depths (P < 0.0001), while no significant difference was observed between intra-specific and control root systems (P = 0.0864). The mean rooting depth for control, intra-specific, and inter-specifically growing root systems for aspen was 56.2 ± 0.6, 23.6 ± 0.6, and 32.7 ± 0.7 mm, respectively, whereas spruce was 41.0 ± 2.4, 34.4 ± 1.2, and 28.6 ± 2.31 mm, respectively.

The vertical distribution of aspen root tips differed significantly between control, intra-, and inter-specifically growing seedlings (P < 0.0001). Solitary aspen tended to distribute their root tips evenly across vertical space, and occupied a mean depth of 58.6 mm ± 1.43 mm. Intra-specifically growing aspen root tips were predominately located between 300 and 1200 mm, and occupied a mean depth of 72.5 ± 3.01 mm. Inter-specifically growing aspen root tips were concentrated between 400 and 1000 mm, and occupied a mean depth of 85.6 ± 2.41 mm. Post-hoc analyses indicated that inter-specific root tips were located significantly deeper than intra-specific root tips (P < 0.0001), as well as control root tips (P < 0.0001). Intra-specific root tips were also located significantly deeper than control root tips (P = 0.0017). The mean depths of spruce root tips did not differ between control, intra- and inter-specific

FIGURE 4 | 3D reconstruction of solitary and paired root systems (axes in 0.1 mm increments). (A) Control spruce (*Picea mariana*) (*n* = 3 containers, 3 individuals). (B) Intra-specific spruce (*n* = 3 containers, 6 individuals). (C) Inter-specific aspen (*Populus tremuloides*) and spruce root systems (*n* = 3 containers, 3 individuals each species). Aspen is highlighted in orange, spruce is highlighted in blue. (D) Intra-specific aspen (*n* = 1 container, 2 individuals). (E) Control aspen (*n* = 3 containers, 3 individuals). Note the differences in x and y-axes. For a complete list of 3D reconstructions, refer to Figure S1.

TABLE 2 | Measurements made in 3D of aspen (Populus tremuloides) and spruce (Picea mariana) seedlings grown under three experimental conditions: control, intra-specific, and inter-specific.

*Values listed are the median (maximum, minimum).*

seedlings, which were located at 45.2 ± 6.56, 50.4 ± 2.86, and 58.2 ± 6.10 mm, respectively.

### Discussion

#### The Effect of Belowground Interactions on Root Growth

In this experiment, each seedling was grown under full nutrient conditions without competition for light to minimize any variation that could be attributed to aboveground resource competition between solitary and paired individuals. The results of this study suggest that belowground interactions between neighboring root systems had measurable effects on root system architecture and 3D distribution of fine roots in the two species. For example in aspen, inter-specific interactions reduced belowground surface area, root length, and SRL when compared to solitary aspen seedlings, which suggests that inter-specific interactions negatively affect aspen root system growth (**Table 1**). In contrast, inter-specifically growing spruce seedlings, when compared to solitary spruce seedlings, increased root surface area, root length, SRA, and SRL (**Table 1**). This trend was not observed when comparing solitary and intra-specifically growing spruce seedlings, which suggests that spruce may over-yield under inter-specific growing conditions (Brassard et al., 2011).

We found that belowground competition shifted the 3D distribution of aspen roots. One example was root–root tip distances; the minimum distance between neighboring root tips under inter-specific conditions (7.5 mm) was greater than controls (5.9 mm, **Figure 5**). While a 1.6 mm difference in spacing may seem small, phosphorus concentrations increase exponentially over a distance of 1 mm from a root's surface (Hendriks et al., 1981). Therefore, relatively small adjustments in the spacing of root tips could result in distinct levels of competition for nutrients in soil (Hodge, 2009). The observed variation in root–root spacing may also result from a lower average number of root tips per inter-specific root system (102 tips) compared to intra-specific (173 tips) or controls (254 tips). Given a constrained volume, a reduction in the number of root tips could result in greater distances between them.

We also observed that the vertical placement of aspen's roots differed between the control, intra-, and inter-specific

seedlings (**Figures 6A,B**). This observation suggests that aspen can respond within 2 months of germination, and with high plasticity, to the presence of a neighboring root system by shifting the vertical placement of its roots. These changes in fine root vertical distribution may result in distinctly different rooting depths between aspen and inter-specific neighbors, thus reducing belowground competition (Schenk et al., 1999; Schenk, 2006). Alternatively, shifts in aspen's vertical root distribution may result in root system overlap with neighboring plants establishing direct competition. Regardless of whether aspen's strategy is to outcompete, avoid competition, or some combination of both in response to a neighboring plant, we found that the presence of both inter- and intra-specific neighbors is sufficient to alter the vertical growth of aspen roots. Care must be taken in the interpretation of these results, however, as specific changes in the occupancy of space by inter- and intra-specifically growing root

(Wilcoxon multiple comparisons, *P* ≤ 0.01667). Note the differences in axes.

FIGURE 6 | Heat map representing root system volume as a function of depth for aspen (A, Populus tremuloides) and spruce seedlings (B, Picea mariana). Control, intra-, and inter-specific interactions were significant predictors of rooting depth weighted by volume for both aspen and spruce (Kruskal-Wallis test, *P* < 0.0001). *Post-hoc* analyses indicated that in aspen, control, inter-, and intra-specifically growing seedlings were all significantly different in terms of their rooting depth weighted by volume (*P* < 0.0001). In spruce, *post-hoc* analyses indicated that inter-specific vs. control plants, as well as control vs. inter-specific plants were significantly different in terms of rooting depth weighted by volume (*P* < 0.0001). Units are in mm3. Each striated column represents the full root volume of a single seedling. Red arrows indicate the mean depth of root tips, while striped black lines indicate the mean depth of root system volume.

systems may result from their being planted side-by-side and not in the center of each container.

Belowground interactions also shifted the vertical positioning of aspen root tips, and to a lesser extent in spruce. In both solitary and paired aspen seedlings, the whole root system's mean rooting depth was shallower than the mean depth of root tips alone. Specifically, inter-specific aspen exhibited the greatest difference between mean root system depth and mean root tip depth (**Figure 6**). Under inter-specific conditions, the mean depth of root tips (85.6 mm) was markedly deeper than the mean depth of the whole root system (32.7 mm). This observable difference between the mean depth of an entire root system, and the mean depth of it's root tips is noteworthy, mainly because not all roots within a root system are functionally equivalent (Pregitzer et al., 1997). Especially in woody plants, a large proportion of a root system is in the form of long-lived, woody roots that anchor the plant and support essential transport functions, as opposed to the most distal part of the root system, the root tips, which are highly metabolically active and demonstrate the highest rates of nutrient and water uptake among all root classes (Pregitzer et al., 1997; Volder et al., 2005). By reducing overlap among "transport" roots and root tips, a root system can occupy an exclusive volume of soil space while simultaneously foraging for resources, and minimizing competition with itself. Therefore, when quantifying root growth dynamics in 3D volumes, either in response to itself or non-self interactions belowground, special attention should be paid to the dynamic growth and placement of root tips independently of whole root systems.

The root system architecture of spruce was dominated by a main taproot with relatively few lateral roots (**Figure 4**), which resulted in very few significant differences between treatments. However, there were some notable trends worth discussing. SRA and SRL tended to be higher under interspecific conditions compared to intra-specific conditions, as well as solitary individuals, indicating a lower cost of construction for spruce roots under inter-specific conditions. Also, inter-specific spruce roots tended to grow deeper, place root tips deeper, and root tips were spaced farther apart when compared to solitary plants—a response that was similar to aspen.

#### Modeling Root–Root Interactions

The use of mathematical models to describe biological phenomena is inherently complicated by the nature of organismal responses to heterogeneously distributed biotic and abiotic cues (Hodge, 2009), though in the context of root systems, both mechanical (Moulia, 2013), and fractal analysis (Tatsumi et al., 1989; Fitter and Stickland, 1992; Ozier-Lafontaine et al., 1999) have been applied with some success. Belowground, root system responses can manifest as a proliferation of roots into a nutrient rich patch (Robinson et al., 1999), altered root morphology (Bolte and Villanueva, 2006), or shifts in the direction of growth (Falik et al., 2005). Accurately modeling this type of non-random growth response is possible (e.g., Godin, 2000), but requires data that is highly resolved, both spatially and temporally. In this study, we demonstrate high spatial resolution for a single point in time, which limits our ability to quantify dynamic growth processes. Nevertheless, 3D structural information, such as that captured by the micro-CT techniques used here, provides insights otherwise inaccessible with 2D destructive imaging. We support that these findings can be used to verify or refute predictions of derived equations that incorporate the interactions between plants.

When modeling root–root distances, we discovered that a phenomenological exponential "growth and decay" model fit well for both species (SSreg/SStot = 0.75–0.99). This model was chosen from a number of mathematical models that were developed to accurately describe the distribution of root–root distances. Alternative models that were generated but not included in this study tended to fit the data somewhat better, but were species specific. This 2-parameter model was adopted because it accurately described the distribution of data for both species across all belowground conditions, whereas other similarly simple models could not. When developing and applying such models, it is important to keep in mind not only the model's fit, but the scope of the experimental question, the complexity of the model, its predictive value, and whether it can be applied broadly across species.

Also, based on equal amounts of nutrient, water, and light supplied to each container housing either one or two individual seedlings, and the equidistant orientation of paired individuals (intra- and inter-), we assumed that belowground interactions (intra- vs. inter-) would have the same effect on each paired seedling. There is no way for us to know with certainty that individuals were experiencing a treatment effect, or simply resource competition, which would result in similar belowground outcomes. However, as previously mentioned, nutrient levels were maintained at an EC of 1.6–2.0 throughout the experiment, reducing the likelihood of resource competition throughout the experiment. Future experiments should aim to parse out the different effects of nutrient competition and non-resource interactions.

#### Comparing Methods and Constraints

Previous attempts to image undisturbed root systems have been met with mixed success. The current benchmark for successfully rendering roots in a 3D data set is set at roughly 90%. For example, Gregory et al. (2003) captured 90% of 7-day-old wheat roots that did not exceed 10 cm in total length. Kaestner et al. (2006) reported that they could successfully capture 90% of Alnus incana (alder) roots larger than 0.18 mm. However, alder roots had to first be removed from their growing medium and packed into quartz sand prior to imaging. In another experiment, Perret et al. (2007) captured around 87% of the total root segments, and 78% of the total root length in 21-day-old chickpea.

In our experiment using P. mariana (spruce) and P. tremuloides (aspen), we successfully rendered between 62 and 76% of the actual root system architecture (**Figure 3**). We believe that roughly 30% of the root systems were lost in the manual root tracing/annotation phase of the methodology because of the criteria we followed for each annotation. Specifically, roots that contacted the container wall were excluded on the basis that these roots behave uncharacteristically, i.e., container tracking and circling. We predicted that based on aspen's larger root system size, a larger proportion of aspen roots would have been lost in the reconstruction phase when compared to spruce. However, data from both species were included in our general linear model (**Figure 3**). We found that both species realized a similar percentage loss of roots, which suggests that any bias introduced as a result of the annotation criteria was similar for both species.

Limitations of our methodology include (1) the use of synthetic growth medium, (2) manual identification of roots in the annotation process, and (3) the relatively small instrument aperture. Regretfully, we could not heed the call of Gregory and Hinsinger (1999), who argued that future advancements in research involving micro-CT and plant roots must focus on using natural soils in place of sand or hydroponics. Distinguishing between water within roots, and water in the medium is an oftenreported limitation—one we experienced early on during method development. We attempted to circumvent this issue by growing plants in hydrophobic, synthetic "sand." This way, the amount of water remaining in the container during imaging would be minimized, facilitating root identification. While this worked well for us, we cannot conclude that either species' growth was unaffected by this growth method. Though residual water was minimized, there were still trace amounts that disrupted several automated root-tracking algorithms that were developed during this experiment. Thus, the data required manually tracing each root through +1400 CT image slices, which required 6–8 h per dataset. Future plant research employing micro-CT should strongly consider developing a robust root-tracking approach that is insensitive to artifacts imposed by residual water in the growth medium.

Lastly, the instrument's aperture for accepting samples greatly limited our container size. Future work that employs micro-CT for phenotyping or quantifying belowground phenomena in an undisturbed space must consider the physical size constraints, and perhaps modify their experimental design to ensure that roots remain unimpeded by the boundaries of the container. The containers used for this experiment were sufficiently long (∼300 mm), but insufficiently wide (max ∼70 mm). Had the container width not been limiting, it is likely that a fewer roots would have been lost during 3D reconstruction.

# Conclusion

In this experiment, we could not conclude with any certainty that intra- and inter-specifically growing seedlings differed in terms of root system architecture and use of 3D space. We showed that, when compared to solitary individuals, interspecific interactions could have the effect of reducing root production, shifting the depth of root tips, increasing spacing between root tips, and altering the distribution of root system volume over vertical space. Because predictable shifts in rooting depths, lateral root placement, and/or root abundance based on neighbor identity may have far reaching implications in terms of ecosystem function (Hooper et al., 2005), species coexistence (Grime, 1997; Stoll and Prati, 2001; Bruno et al., 2003; Kembel et al., 2008), and plant evolution (Myers et al., 2000), interactions at the community level down to the individual and tissue level must be better understood. The future of this technique is in quantifying both very fine and coarse scale morphological and architectural shifts in root system growth. We demonstrate the ability to quantify 3D parameters and track multiple 3D root systems within a shared volume, which is an important advancement in the field of plant imaging. By coupling CT imaging with algorithms tailored to specific experimental conditions, a wide range of relevant architectural, morphological, and 3D parameters can be analyzed, and the effects of belowground interactions better understood. It is our aim that the marriage of CT with novel algorithms will continue to pave the way toward understanding how plants sense, react, and respond belowground to neighboring plants, and shed light on this highly plastic, ecologically significant, and dynamic process that remains almost entirely unnoticed.

# Author Contributions

This work was made possible through intensive, cross-discipline collaboration. Individual contributions are as follows: AP for the experimental design, plant production, data analyses, and lead authorship. JS for 3D metrics, image construction, and major intellectual contributions. JP for intellectual contributions, data processing, and developing novel root-tracking algorithms. TB for intellectual contributions, including experimental design and critical revisions.

# Acknowledgments

We acknowledge the invaluable assistance provided by Cornell's nano- and micro-CT facility run by Mark Riccio. We thank Professor Neil Matson for providing us with reservoirs and tools needed to grow our plants, professor Anthony Reeves for his assistance in developing preliminary root tracking algorithms, and Professor Joseph Fetcho for providing key software. We would also like to acknowledge Annika Kreye and Lindsey Saum for their help in designing and building hydroponic infrastructures, as well as Kim Goodwin and Kendra Hutchins for their help in the greenhouse. This work was partially supported by the Mario Einaudi Center for International Studies, and JS was funded through an NSF GRFP fellowship.

# Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2015. 00274/abstract

Figure S1 | 3D reconstruction of all solitary and paired root systems (axes in 0.1 mm increments). (A) Control spruce (*Picea mariana*) (*n* = 3 containers, 3 individuals). (B) Intra-specific spruce (*n* = 3 containers, 6 individuals). (C) Inter-specific aspen (*Populus tremuloides*) and spruce root systems (*n* = 3 containers, 3 individuals each species). Aspen is highlighted in orange, spruce is highlighted in blue. (D) Intra-specific aspen (*n* = 1 container, 2 individuals). (E) Control aspen (*n* = 3 containers, 3 individuals). Note the differences in x- and y-axes.

# References

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using X-ray computed tomography. J. Exp. Bot. 61, 311–313. doi: 10.1093/jxb/ erp386

Volder, A., Smart, D. R., Bloom, A. J., and Eissenstat, D. M. (2005). Rapid decline in nitrate uptake and respiration with age in fine lateral roots of grape: implications for root efficiency and competitive effectiveness. New Phytol. 165, 493–502. doi: 10.1111/j.1469-8137.2004.01222.x

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Paya, Silverberg, Padgett and Bauerle. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Unraveling the hydrodynamics of split root water uptake experiments using CT scanned root architectures and three dimensional flow simulations

Nicolai Koebernick 1 † , Katrin Huber 2 \* † , Elien Kerkhofs 3 , Jan Vanderborght 2, 3 , Mathieu Javaux 2, 4, Harry Vereecken2 and Doris Vetterlein1

1 Department of Soil Physics, Helmholtz Centre for Environmental Research (UFZ), Halle, Germany, 2 Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany, 3 Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium, 4 Earth and Life Institute/Environmental Sciences, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Split root experiments have the potential to disentangle water transport in roots and soil, enabling the investigation of the water uptake pattern of a root system. Interpretation of the experimental data assumes that water flow between the split soil compartments does not occur. Another approach to investigate root water uptake is by numerical simulations combining soil and root water flow depending on the parameterization and description of the root system. Our aim is to demonstrate the synergisms that emerge from combining split root experiments with simulations. We show how growing root architectures derived from temporally repeated X-ray CT scanning can be implemented in numerical soil-plant models. Faba beans were grown with and without split layers and exposed to a single drought period during which plant and soil water status were measured. Root architectures were reconstructed from CT scans and used in the model R-SWMS (root-soil water movement and solute transport) to simulate water potentials in soil and roots in 3D as well as water uptake by growing roots in different depths. CT scans revealed that root development was considerably lower with split layers compared to without. This coincided with a reduction of transpiration, stomatal conductance and shoot growth. Simulated predawn water potentials were lower in the presence of split layers. Simulations showed that this was related to an increased resistance to vertical water flow in the soil by the split layers. Comparison between measured and simulated soil water potentials proved that the split layers were not perfectly isolating and that redistribution of water from the lower, wetter compartments to the drier upper compartments took place, thus water losses were not equal to the root water uptake from those compartments. Still, the layers increased the resistance to vertical flow which resulted in lower simulated collar water potentials that led to reduced stomatal conductance and growth.

Keywords: split-root, R-SWMS, root water uptake, plant root growth, Vicia faba

#### Edited by:

Pierre Dutilleul, McGill University, Canada

#### Reviewed by:

Lars Hendrik Wegner, Karlsruhe Institute of Technology, Germany Stanislaus Josef Schymanski, Swiss Federal Institute of Technology Zurich, Switzerland Pierre Dutilleul, McGill University, Canada Jonathan A. Lafond, Université Laval, Canada

#### \*Correspondence:

Katrin Huber, Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, D - 52425 Juelich, Germany<EMAIL_ADDRESS>
† These authors have contributed equally to this work.

#### Specialty section:

This article was submitted to Plant Biophysics and Modeling, a section of the journal Frontiers in Plant Science

> Received: 30 October 2014 Accepted: 09 May 2015 Published: 29 May 2015

#### Citation:

Koebernick N, Huber K, Kerkhofs E, Vanderborght J, Javaux M, Vereecken H and Vetterlein D (2015) Unraveling the hydrodynamics of split root water uptake experiments using CT scanned root architectures and three dimensional flow simulations. Front. Plant Sci. 6:370. doi: 10.3389/fpls.2015.00370

# Introduction

Water scarcity is an important abiotic limitation to plant growth and agricultural productivity. Under water limited conditions, changes in root system architecture (RSA) play a major role to reach locations where water is still present, which is often the subsoil. There is no simple relationship between the amount of roots present in certain locations and the actual root water uptake (RWU) from these sites (Pohlmeier et al., 2008). RWU is repeatedly described as a sink moving down the profile with time, only weakly related to root length density in a certain depth (Hainsworth and Aylmore, 1986; Pierret et al., 2003; Garrigues et al., 2006). In many of these studies change in soil water content in a certain depth is assumed to be synonymous with root water uptake. The illustrative Martini glass analogy first used by Zwieniecki et al. (2002) demonstrates that this assumption is too simple. When drinking a sip of Martini with a straw, the Martini is taken up from the bottom of the glass, but a change in "Martini content" is only observed in the upper layer of the glass due to the very high hydraulic conductivity within the glass. Roots and soil matrix are much more complex than the Martini-glass system; however, in soil-plant system the soil hydraulic conductivity and resulting soil hydraulic redistribution also obstruct the view on the site of root water uptake and its temporal dynamics. This has been known for a long time and a number of strategies have been developed to overcome this problem.

An experimental strategy to prevent soil hydraulic redistribution is to divide the root zone into different compartments, which prevent water flow between compartments to permit controlled heterogeneous distribution of soil moisture (Drew, 1975; Herkelrath et al., 1977). In case of horizontal splits, the split layers should additionally be penetrable by roots, which can be, for example, achieved by applying wax or paraffin. When roots take up water in a given compartment the change in total water content can be directly related to root water uptake from this compartment. This assumption can, however, only be drawn if the split layers are completely hydraulically isolated. In the case of water redistribution through the layers, the leakage rate has to be known. Another problem to determine RWU from a soil compartment arises due to the non-linearity of the soil water retention curve. Water content or soil water potential is usually measured at discrete points in the soil. When roots take up water from the soil, strong gradients in soil water potential can develop around the roots. Thus, an extrapolation between point measurements to the complete soil compartment becomes erroneous. A second experimental strategy is to directly observe water flux in soil as it has been successfully demonstrated by Zarebanadkouki et al. (2012). They imaged water flow into roots using neutron imaging of deuterated water. However, this method is hitherto either constrained to quasi two-dimensions (rhizotrons) or very small root systems and to short time scales.

An alternative approach is to quantify the amount of water being translocated by root or soil hydraulic redistribution. Mechanistic root water uptake models that describe water flow in soil, into, and within roots allow quantifying and locating root water uptake and redistribution of water within the soil and root system. The use of mechanistic models, like R-SWMS (root-soil water movement and solute transport, Javaux et al., 2008), has two prerequisites: (i) that the dominant processes are known and (ii) that the required input parameters are available. To fulfill the latter, dynamic information about RSA as well as hydraulic properties of individual root segments have to be available.

RSA has been obtained in the past using root growth models, i.e., RSA is artificially created based on a set of crop specific parameters and rules (e.g., branching rules, growth rates, etc.) derived from experiments (Clausnitzer and Hopmans, 1994; Lynch et al., 1997; Pagès et al., 2004; Leitner et al., 2010). Mostly, one or several typical realizations of RSA obtained from such models for a plant of a certain age have been used to calculate different scenarios, like root water uptake from saline soils (Schröder et al., 2013), performance of varying root architectural traits under different soil moisture regimes (Leitner et al., 2014), or the impact of stomatal regulation type on root water uptake (Huber et al., 2014).

Root growth models have been used as an alternative to 3Ddata of root systems as these were not available in the past. However, such data are now becoming increasingly accessible with non-invasive methods reaching a level of resolution which is sufficient to visualize most or all of the root system. The most advanced techniques for imaging soil-grown roots include X-ray computed tomography (Mooney et al., 2012), neutron radiography (Oswald et al., 2008), magnetic resonance imaging (Pohlmeier et al., 2008), or transparent soils (Downie et al., 2012). These techniques are of particular interest because they allow for repeated measurements. When ionizing radiation is used, it is however important to choose appropriate scan parameters to minimize potential damage to living tissues (Dutilleul et al., 2005; Zappala et al., 2013). Previous studies clearly demonstrated the potential of X-ray CT to analyze the temporal dynamics of growing roots (Jenneson et al., 1999; Gregory et al., 2003; Lontoc-Roy et al., 2005). While these early studies were limited to young seedlings, more recent work shows that the same is possible for considerably older root systems (Han et al., 2008; Tracy et al., 2012; Koebernick et al., 2014). First modeling approaches based on the use of RSA from non-invasive imaging are available (Stingaciu et al., 2013). The second challenge remains, i.e., the scarcity of data on root hydraulic properties. Measured data are primarily from hydroponically grown very young root systems. Certain assumptions have to be made to separate radial and axial conductivity during the measurements. Nevertheless, there is a wealth of information on how conductivity changes during root development and these have been used to scale the conductivity of individual root segments (Doussan et al., 1998, 2006). As roots age the resistance in the axial pathway typically decreases due to the maturation of xylem vessels, while in the radial pathway resistance increases with the development of apoplastic barriers (Frensch and Steudle, 1989; Bramley et al., 2009).

In order to avoid confounding root water uptake and hydraulic redistribution by the interpretation of local changes in soil water content we have chosen two of the above strategies: (i) an experimental approach of introducing barriers to avoid soil hydraulic redistribution; (ii) a modeling approach which takes soil and root hydraulic redistribution into account.

The objective of the current study is to compare experimental (introducing barriers to avoid soil hydraulic redistribution) and modeling approaches (calculation of soil and root water flow) with respect to their capacity to localize root water uptake in the presence of strong gradients in soil water potential. Local changes in soil water content will be compared to measured and modeled root water uptake.

For the experimental approach we combined a classical set up using wax barriers (Drew, 1975) with quantitative measurement of RSA over time via X-ray CT. This setup allowed the observation of the relation between RSA and water uptake and how it is affected by soil drying. The addition of paraffin layers allowed for the development of strong spatial heterogeneities in soil water potential, as is generally the case under field conditions.

For the modeling approach we used the mechanistic 3D model R-SWMS (Javaux et al., 2008), which enables a detailed description of soil and root water flow. While R-SWMS so far has only been applied for static (non-growing) root systems, mostly created by root architectural models, we now extended the existing model by an additional root development module, which uses the measured CT-data of RSA over time. Doussan's concept of changing axial and radial conductivity with age (Doussan et al., 2006) was included by using his root hydraulic parameterization by assigning these parameters to root age classes derived from the time lapse 3D RSA CT-Data.

Apart from modeling the actual experimental setup, root distributions obtained from split experiments were also used in simulations without splits and vice versa. This approach allowed us to (i) reinterpret measurement results, (ii) show the influence of split layers on plant water potentials that could be linked to differences in plant/root growth and eventually on root water uptake and (iii) show where soil water is taken up during root growth.

# Materials and Methods

#### Experiments

Two subsequent experiments under the same environmental conditions (growth chamber, 23◦C day/18◦C night, 65% relative humidity, photoperiod of 14 h, photon-flux density of 350µmol m−2 s −1 ) were conducted with Vicia faba L. cv. Fuego.

The first experiment (3 replications), which will be referred to as "NoSplit" in the following, was conducted with homogeneously filled soil columns of 21.5 cm height with unrestricted soil water flow. The second (4 replications), referred to as "Split" was similar to the first one, but paraffin layers at 5, 10, and 15 cm height were established to interrupt soil water redistribution. This method was adopted from Drew (1975), who showed that root growth was unaffected by such layers. Both experiments were conducted consecutively, which explains the differences in the two setups.

#### Experimental Setup **"NoSplit" (without paraffin layers)**

The porous substrate was prepared by mixing quartz particles of different size classes, consisting of 85% sand, 10% silt, and 5% clay (Vetterlein et al., 2007). Additionally 50 g kg−1 of gravel (2– 3 mm Ø) and 20 g kg−1 of plastic beads (polypropylene, 2–3 mm Ø) were added to the substrate as internal reference for digital image analysis.

PVC cylinders (inner Ø = 12.5 cm, h = 21.5 cm) were filled up with the substrate by passing it through two sieves of 4 mm mesh size separated by a distance of 10 cm. This procedure was chosen to avoid particle size separation during filling. Resulting bulk density of the substrate was 1.52 ± 0.01 g cm−3 . The cylinders had porous plates at the lower end (**Figure 1A**), which were connected with plastic tubing to a water source. The soil was gently watered with a nutrient solution (modified from Römheld and Marschner, 1990) by capillary rise from the bottom of the sample (soil water potential ψ = 0 hPa at z = −21.5 cm). Average volumetric soil water content (θ) at the start of the experiment was 31.1 ± 1%. Vicia faba seeds were surface sterilized in 10% H2O2 solution for 10 min, thoroughly rinsed in deionised water and subsequently imbibed for 1 h in a saturated CaSO4solution. Seeds were placed on wet blotting paper and placed in a dark cabinet at room temperature for 2 days. For each cylinder, one pre-germinated seed was carefully placed in a prepared cavity in the soil at a depth of 1 cm. The soil surface was covered by a 2 cm layer of fine quartz gravel. Until shoot emergence columns were covered with aluminum foil to further minimize evaporation. With the removal of aluminum foil the drying period was initiated (Day 6).

### **"Split" (with paraffin layers)**

The substrate was the same as in the "NoSplit" experiment, however, without the addition of plastic beads as these caused problems in the segmentation procedure (see below). Soil bulk density was slightly higher (1 = 0.12 g/cm3 ).

For the split layers, molten paraffin was casted and flattened to a thickness of approximately 0.5 mm and cut into a circular shape. At -5, -10, and -15 cm depth a layer of paraffin was placed on top of the soil and sealed to the cylinder walls using molten paraffin (**Figure 1B**). For initial irrigation, we placed rhizon soil moisture samplers (Eikelkamp, Giesbeek, NL) in each soil compartment. Those were connected over night to bottles filled with 150 ml nutrient solution each. Volumetric water content at the start of the experiment was 23.8 ± 0.5% in each compartment. Seed preparation was the same as in the "NoSplit" experiment. To avoid the formation of cracks in the soil due to the placement of large Vicia faba seeds, these were planted in a separate seed compartment: a cylinder (Ø = 6 cm, h = 3 cm) filled with the soil mixture and 20 ml of water. When the roots emerged through the paraffin layer at the bottom of the seed compartment, the small cylinder was placed on the topsoil (Day 0). The remaining bare topsoil was covered with gravel to reduce evaporation. The split samples were initially also covered with aluminum foil, which was removed on Day 4 to start the drying period.

#### Transpiration and Soil Matric Potential

The PVC cylinders were placed on weighing cells (KERN 572, Kern and Sohn GmbH, Balingen, Germany), and grown for 30- 36 days with no additional watering. Weight data were recorded

are given in cm.

every 10 min throughout the experimental period. Four microtensiometers (Vetterlein et al., 1993) were inserted horizontally through sealed boreholes ("NoSplit": -1.5, -6.5, -11.5, and - 16.5 cm soil depth; "Split": -2.5, -7.5, -12.5, -17.5 cm, **Figure 1**) to monitor the soil matric potential (ψm), during drying.

The daily transpiration rate was calculated from weight differences between two subsequent days. Evaporation was assumed to be negligible due to the layer of coarse gravel on the surface and as surface was never rewetted during the experiment. Relative humidity was constant day and night hence dew formation could also be excluded. Only on the seed compartment used in "Split" experiment, there was no gravel layer and hence water applied initially (20 ml) was assumed to be lost by evaporation uniformly within the first 7 days.

Leaf area development was estimated by daily measuring the length and width of the lamina of each leaflet and using the linear model of Peksen (2007):

$$LA = 0.919 + 0.682 \, L \, \* \, W \tag{1}$$

where LA [cm2 ] is the one-sided leaf area, L [cm] is the length of the lamina, and W [cm] is the width of the lamina. After harvest, we used a flatbed scanner to measure leaf area. The results agreed well with the estimation using Peksen's model. Stomatal conductance was measured at the end of each day using a steadystate porometer (SC-1 Leaf Porometer, Decagon Devices, Inc., Pullman, WA, USA). Two measurements per plant were taken on the abaxial side of the youngest unfolded leaf pair and the mean value of the two measurements was stored.

#### CT Scanning and Image Analysis