**3. Statistical Analysis**

Statistical analysis was performed using SPSS (SPSS IBM version 23.0, Armonk, NY, USA). Data normality was tested with the Kolmogorov–Smirnov test. For correlations, Pearson's correlation or Spearman's rank-order correlation were used accordingly and reported together with the percentage of explained variance (r2). In the case of within-subject measurements, paired sample *t*-tests were used for normally distributed data and two-tailed Wilcoxon signed-rank tests for nonparametric distributions. For nonparametric, independent comparisons, the Kruskal–Wallis test was used. Significance was set at *p* < 0.05.


Gas exchange measurements were made at three additional sites under saturating light conditions with measurements occurring between 11 am and 1500 h local time. These measures were used for the analysis of instantaneous transpiration efficiency (ITE) and intrinsic water use efficiency (IWUE). ITE was calculated as μmol of CO2 assimilated per mol of water transpired (A/T). IWUE was defined as the ratio of light saturated net assimilation rate to stomatal conductance (A/gs) which is thought to have low dependence on environmental parameters and reflects intrinsic plant physiological functioning (Jones, 1992). To determine the light compensation points, the light saturation point and apparent quantum yields light use efficiency, (LUE; Kuppers and Schulze, 1985), non-linear regressions (*A* = *a* × ln(*PAR*) − *b*) were fitted to the PAR and A data for each species. Instantaneous LUE of each species was quantified as μmol of CO2 assimilated per μmol−1 PAR in the light limited region of the light response curves.

# Above-Ground Biomass and Leaf Area Index

In March and May 2003, the above-ground live plant material (leaves and stems) of each species was harvested in three random 2 m × 2 m quadrats at each of the three paired invaded and non-invaded sites used in the gas exchange measurements. Plant material was dried and weighed to give above-ground biomass (AGB) and scaled to leaf area using allometric equations developed for *A. gayanus* (Rossiter, 2001). A generic allometric equation for native grass biomass and leaf area was used as a surrogate for *A. semialata* as this relationship has been shown to hold across a range of northern Australian tropical savanna C4 grasses (Hutley and Williams, unpublished data). Native grass species used to derive the allometric equations were all C4 grasses common in these tropical savanna woodlands (Scott et al., 2012) and included *Aristida hygrometrica*, *Chrysopogon latifolius, Sorghum intrans, Heteropogon triticeus, Themeda triandra, Sehima nervosum, Sorghum plumosum, Chrysopogon fallax, Setaria apiculata, Pseudopogonatherum contortum* variously from four sites (Howard Springs, Claravale, Larrimah, and Katherine). Estimates of leaf area sampled from the 2 m × 2 m area enabled the mean leaf area index (LAI) for each site (invaded or noninvaded) to be estimated.

# Leaf Nitrogen, Carbon, and Isotopes

All leaves used in the gas exchange measurements of both species were collected, dried, pooled, and ground in a Culatti Type grinder (Model MFC CZ13) with a 1 mm screen. Percent elemental carbon (%C) and nitrogen (%N) and stable carbon (δ13C) and nitrogen (δ15N) isotope ratios were determined via Dumas combustion in an IsoChrom which was connected to an EA-1110 Elemental CHN-O Analyser. Analysis was conducted by the Australian National University stable isotope facility. Foliar nitrogen values (g N/g leaf) were divided by Amax values for each leaf to calculate photosynthetic nitrogen use efficiency (PNUE). Foliar δ13C values indicate long term water use efficiency (LTWUE, Dawson et al., 2002). Foliar leaf δ15N values are somewhat indicative of nitrogen source and soil availability, with lower values suggesting preferential uptake of ammonium or higher soil ammonium to nitrate ratios (Handley et al., 1998).

# Statistical Analysis

All statistical analyses were performed using SPSS Version 6.0 (2007, SPSS Inc., Chicago, IL, USA). Clear outliers (>2 SD from the mean) were removed prior to analysis. Outliers were clearly identified from raw data plots and data points that were approximately 2 SD from a measurement run mean were examined. In the wet season, the percentage of outliers was larger than 5% and was 11% for *A. gayanus* and 10% for *A. semialata*. None were identified for the *A. gayanus* dry season data set. Variable PAR conditions and shifts in temperature and VPD with cloud cover of the wet season resulted in a population of leaves that were not at a stable equilibrium when measured and were not included in calculations.

Diurnal leaf temperature, PAR, LAVPD, A, T, and gs, at each time period (10:00, 11:00, 13:00, 14:00, and 16:00 or 17:00) were analyzed using a one-way ANOVA to compare differences between the species/season factor (fixed). Data was normal (skewness, kurtosis values <2) and variances homogeneous (Levene's significance test <0.05). Data for diurnal analyses were based on plants at one paired site only. Differences between species and seasons were assessed using the Student-Neumann– Keuls *post hoc* test. The species/season factor included three variables: *A. gayanus* in the wet season, *A. semialata* in the wet season, and *A. gayanus* in the dry season.

Estimates of daily carbon uptake rates and water use per ground area (canopy scale fluxes) were determined by scaling up integrated diurnal measures of A and T, respectively, to rates per square meter ground area using site based LAI estimates. Extrapolation of leaf gas exchange parameters to the canopy scale using LAI is based on the assumption that for these grasses, canopy self-shading is limited and simple scaling using LAI to obtain canopy level estimates is feasible (Larcher, 2003). Leaf scale estimates and foliar δ13C, δ15N, foliar %N, foliar %C, foliar C:N, and PNUE (Amax/*N* per g leaf, PNUE) for each species/season were analyzed using one-way ANOVA and Student-Neumann–Keuls *post hoc* tests to assess differences between species and season (for *A. gayanus* data). Differences in ITE (A/T) and IWUE (A/gs) for each species/season (all sites data) were analyzed using a one-way ANOVA, Student-Neumann–Keuls *post hoc* tests and pair-wise comparisons with a Bonferroni adjustment.

The LUE of each species was determined as the slope of the linear relationship between PAR and A when light was limiting. Light limitation was assumed to have occurred at PAR <480 μmol m−2 s−1. Differences were analyzed using an ANCOVA with PAR as the covariate and species/season as the fixed factor. Differences in Amax, Tmax, and gs for each species were determined from the light saturated leaves when PAR >750 μmol m−2 s−1 for *A. gayanus* and *A. semialata* in the wet season and PAR >500 μmol m−2 s −1 for *A. gayanus* in the dry season. Differences were compared using ANCOVA, with PAR as the covariate and species/season as the fixed factor.

# Results

### Leaf Microclimate

During the wet season, *A. gayanus* and *A. semialata* leaves experienced a broadly similar microclimate in terms of Tleaf, leaf incident PAR and LAVPD, enabling direct species comparisons of physiological variables (**Figure 2A**, T, gs.). Leaf temperatures

of both species were similar throughout the day, although at approximately 1400 h Tleaf of *A. gayanus* was higher than that of *A. semialata* (**Figure 2A**). Levels of PAR were also similar for both species except at 1000 h, when PAR was significantly higher for *A. gayanus* (**Figure 2B**). The diurnal range of LAVPD of *A. semialata* leaves was similar to that of *A. gayanus* from 1100 to 1600 h, although leaves of *A. gayanus* had significantly higher LAVPD in the morning and afternoon (**Figure 2C**). By the dry season, early morning, and late afternoon Tleaf and PAR were significantly lower for the persistent *A. gayanus* compared to measurements in the wet season (**Figures 2A,B**). The LAVPD of *A gayanus* in the dry season was significantly higher than wet season measurements from 1100 to 1600 h (**Figure 2C**).

# Leaf and Canopy Scale Physiology

Both species showed similar decreasing linear trends in A throughout the day; however, in the wet season *A. gayanus* assimilated carbon at significantly higher rates than *A. semialata* (**Figure 2D**). Mean wet season rates of *A. gayanus* Amax and Tmax were 30% higher than *A. semialata* (**Table 1**), with *A. gayanus* maintaining a longer daily period of assimilation compared to *A. semialata*. *A. semialata* exhibited net respiration by 1600 h while A remained positive for *A. gayanus* leaves until 1700 h (**Figure 2D**). Although lower than wet season rates, leaves of *A. gayanus,* were still assimilating carbon and transpiring in the dry season (**Figures 2D,E**) whereas *A. semialata* was physiologically dormant. In the wet season,

TABLE 1 | Mean eco-physiological traits of *A. gayanus* and *A. semialata* in the wet season, and *A. gayanus* in the dry season.

a*Mean (SE) are given for above-ground biomass (AGB), leaf area index (LAI), maximum net photosynthesis (A*max*), maximum transpiration (T*max*), stomatal conductance (g*s*), carbon uptake (C uptake), evaporation (E), instantaneous transpiration efficiency (ITE), instantaneous light use efficiency (LUE), photosynthetic nitrogen use efficiency (PNUE). Different superscripts in each column indicate significant differences at P* = *0.05.* b*Number of samples used to derive all variables.*

morning levels of T and gs for *A. gayanus* and *A. semialata* leaves were similar, however, by 1300 h, *A. gayanus* had significantly higher rates than *A. semialata. A. gayanus* was still transpiring water at 1700 h by which time *A. semialata* rates of T and gs was close to zero (**Figures 2E,F**. Wet season gas exchange (*A*, *T*) was largely driven by PAR (**Figures 2B,D,E**).

Leaf gas exchange rates were extrapolated to a canopy level using LAI estimates to provide mean daily A and T per unit ground area. In the wet season, *A. gayanus* stands assimilated ∼5 times more C per day and transpired six times more water than *A. semialata* (**Table 1**). Stand scale *A. gayanus* assimilation and water use in the dry season was still double that of the wet season rates of *A. semialata,* although this difference was not significant (**Table 1**).

# Foliar N, C, and Isotopic Signatures

There was no significant difference between foliar %N, %C, or δ13C for leaves of both species and nor between seasons for *A. gayanus* (**Table 2**). *A. semialata* and *A. gayanus* foliar δ15N values were similar in the wet season but were significantly lower than dry season values for *A. gayanus* (**Table 2**).

# Resource Use Efficiency Traits

*Andropogon gayanus* and *A. semialata* had similar water use efficiency (WUE) according to three different measurements: IWUE (**Figure 3**), LTWUE (δ13C, **Table 2**) and ITE (**Figure 4**; **Table 1**). The PNUE and Amax, Tmax, gs of *A. gayanus* in the wet season were significantly higher than *A. semialata* (**Table 1**), while there were no differences in LUE (**Figure 5**; **Table 1**). From the wet to dry seasons, *A. gayanus* leaves showed significant increases in IWUE and ITE (**Figure 4**; **Table 1**); however, there was no change in LTWUE (δ13C, **Table 2**). By the dry season, Amax of *A. gayanus* leaves had decreased by 82% (**Figure 5**; **Table 1**) relative to wet season rates, while PNUE was significantly higher (**Table 1**). The LUE of *A. gayanus* was significantly lower in the dry season compared to the wet season (**Table 1**; **Figure 5**).

# Discussion

Along with land use change and climate change, alien plant invasion is one of the most threatening processes for the maintenance of biodiversity and ecosystem function. Interdisciplinary research is clearly needed combining ecology, eco-physiology, hydrology, and invasion biology to better understand differences between native and invasive alien species that will assist management and restoration of invaded ecosystems (Gioria and Osborne, 2014). Both instantaneous and time-integrated resource use efficiency (RUE) measures are required to assess performance on short-term (seasonal) as well growth cycles and phases of invasion. In this study, we used comparative measures of both instantaneous (A, Amax, T, Tmax, gs, ITE, LUE) and integrated measures of RUE (PNUE, LTWUE, LAI, biomass) to assess both resource acquisition and resource conservation performance in a highly seasonal environment.

Tropical savanna may represent a strong 'habitat filter' (after Weiher et al., 1998) as species grow and persist in a low N, annual drought affected, high water deficit, high VPD, and fire prone ecosystem. In such an environment, successful invasive species may exhibit similar resource conservation or RUE traits to native species that enable survival, however, this was not the case in this study. Under wet season conditions of high light, moisture, and N availability, rates of stomatal conductance, A and T of the alien species were 30–40% higher than the native species, with assimilation occurring for an additional 2 months of the year supported by a deep (up to 80 cm) and profusely branched, fibrous root mass (Rossiter-Rachor et al., 2009). Growth was maintained into the dry season with *A. gayanus* stand biomass and LAI exceeding that of the wet season (**Table 1**). This finding is consistent with the meta-analysis of Cavaleri and Sack (2010) who found that invaders typically have significantly higher rates of gs, water use and assimilation, although their analysis included few studies comparing invasive and native grasses.

TABLE 2 | Mean (SE) and ANOVA results for foliar percentage and isotopic nitrogen and carbon (%) of *A. gayanus (n* **=** 210) and *A. semialata* (*n* **=** 108) in the wet season and *A. gayanus* in dry season (*n* **=** 120).

*Different superscripts in each column indicate significant differences at P* = *0.05.*

While rates of A, T, and gs were higher for the invader, there were no species differences in instantaneous water and light use efficiencies measures (IWUE, ITE, LTWUE, LUE), also consistent with meta-analysis of WUE of Cavaleri and Sack's (2010), even when this analysis was restricted to arid and semi-arid ecosystems (Funk, 2013). While both species are perennial grasses, in this environment leaf function (and age) is essentially annual, with leaf initiation, development, and gas exchange occurring only after the onset of wet season rainfall. This is followed by senescence after seed set in March–April (native species) or May–June (*A. gayanus*). As a consequence, leaves of both species develop in high water availability and low LAVPD conditions with little difference in leaf-scale WUE. *A. gayanus* showed stomatal down-regulation and increases in IWUE and ITE (**Figures 3** and **4**) during the dry season, suggesting physiological plasticity in response to the higher LAVPD and reduced soil moisture availability. Physiological plasticity has been demonstrated for a number of invasive species compared to native species in low-resource environments where resource availability fluctuates (Funk, 2008; Davidson et al., 2011). This is a favorable attribute for persistence in the seasonal tropics, which are characterized by large seasonal changes in resource availability, in particular available N, P, and moisture (Hutley et al., 2000; Soper et al., 2015).

Differences in leaf scale traits alone were unable to explain the 5–10 times greater stand scale biomass accumulation and fourfold increase in LAI of the invader at these sites (**Table 1**). The exception was PNUE, reflecting one of the most significant limiting resource in these mesic savannas, soil available N (Rossiter-Rachor et al., 2009; Soper et al., 2015). Most studies comparing nutrient-use efficiency in native and alien plants have found higher PNUE in the invasive species (Funk, 2013). For example, the alien African lovegrass (*Eragrostis curvula*) had a higher PNUE compared to native grasses in the low-nutrient soils of eastern Australia (Firn et al., 2012). Plant invasion is thought to mostly occur in resource rich environments, with invasion driven by altered growing conditions and release of resources via disturbance that differentially increases an invader's competitive attributes (Daehler, 2003). Recent evidence suggests that invasion and persistence does occur in low resource environments; however, drivers of this are poorly understood (Gioria and Osborne, 2014).

The invasive traits of *A. gayanus* identified in this study exhibit all three attributes suggested by Funk and Vitousek (2007) that are critical for invasion and perseverance in low-resource environments; (1) high resource acquisition and high RUE, (2) an active increase in resource availability following invasion, and (3) continued disturbance following invasion. Firstly, resource acquisition and RUE were exhibited by *A. gayanus* via higher rates of gs, A, and T, a longer growing season, high biomass and LAI and significantly higher PNUE. Secondly, an invasive species must actively increase resource availability. Comparative values of leaf δ15N (**Table 2**) suggested *A. gayanus* is likely to use more soil ammonium or have increase soil ammonium levels when compared to native grass dominated patches. This is consistent with findings of Rossiter-Rachor et al. (2009) who used labeled 15N experiments that showed *A. gayanus'* preference for ammonium as an N source over nitrate. The presence of *A. gayanus* stimulated soil ammonification and potentially inhibited nitrification (Rossiter-Rachor et al., 2009). This mechanism drives a positive plant– soil feedback that promotes a broader niche width and improved habitat suitability for *A. gayanus*, in this N-limited ecosystem. Thirdly, Funk and Vitousek (2007) suggest an invader must promote continued disturbance that increases resource availability enabling persistence. A feature of *A. gayanus* invasion is high biomass (fuel) production and a shift to a high severity fire regime as described by Rossiter et al. (2003) and Setterfield et al. (2010). Severe invasion reduces woody cover by up to 80% within a decade post invasion (Brooks et al., 2010)

FIGURE 4 | Instantaneous transpiration efficiency (ITE) of *A. gayanus* leaves in the wet (solid squares and solid lines, *n* **=** 210), *A. semialata* in the wet (triangles and broken lines, *n* **=** 120), and *A. gayanus* in the dry (open squares, *n* **=** 120) seasons.

and initiates a grass-fire feedback. The loss in woody cover releases water, nutrient resources and increases radiation to the understory that further accelerates *A. gayanus* growth and invasion.

# Conclusion

This study has shown that collectively, instantaneous, and timeintegrated RUE traits, invasion-derived feedback loops combined with high propagule pressure confers substantial a competitive advantage to *A. gayanus* over both native grassy and woody lifeforms. These attributes largely explain its current invasiveness and persistence in Australia's low-resource savanna ecosystems. This is an ecosystem transformation that is resulting in a rapid loss of biodiversity and significantly increasing fire risk (Setterfield et al., 2013). This study provides evidence from a seasonal tropical savanna ecosystem to support Funk's (2013) assertion that invasive species in low-resource environments possess traits that allow both increased resource acquisition and resource conservation. This superior capacity of *A. gayanus* to compete for resources also supports modeling predictions of continued rapid invasion across the vast savanna region of northern Australia (Adams et al., 2015).

# Acknowledgments

We would like to thank Kath Ryan and Jane Barratt for field data collection. We also thank the Rangers and NT Parks for access to

# References

Mary River National Park (formerly Wildman Reserve). Funding was provided by Charles Darwin University research grant, the Land and Water Australia and the National Environmental Research Programme (NERP).

woody taxa along a savanna–woodland continental rainfall gradient. *Oecologia* 178, 297–308. doi: 10.1007/s00442-014-3176-3

**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 Ens, Hutley, Rossiter-Rachor, Douglas and Setterfield. 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.*

# Herbaceous plant species invading natural areas tend to have stronger adaptive root foraging than other naturalized species

Lidewij H. Keser 1, 2 \*, Eric J. W. Visser 3 , Wayne Dawson1 , Yao-Bin Song4 , Fei-Hai Yu5 , Markus Fischer 2 , Ming Dong4 and Mark van Kleunen1

*1 Ecology, Department of Biology, University of Konstanz, Konstanz, Germany, 2 Department of Biology, Institute of Plant Sciences, University of Bern, Bern, Switzerland, 3 Department of Experimental Plant Ecology, Institute for Water and Wetland Research, Radboud University Nijmegen, Nijmegen, Netherlands, 4 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China, 5 School of Nature Conservation, Beijing Forestry University, Beijing, China*

#### Edited by:

*Judy Simon, University of Konstanz, Germany*

#### Reviewed by:

*Gwen-Aelle Grelet, Landcare Research—Manaaki whenua, New Zealand Vikki L. Rodgers, Babson College, USA*

#### \*Correspondence:

*Lidewij H. Keser, Ecology, Department of Biology, University of Konstanz, Universitaetsstrasse 10, 78457 Konstanz, Germany<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science*

Received: *10 February 2015* Accepted: *05 April 2015* Published: *27 April 2015*

#### Citation:

*Keser LH, Visser EJW, Dawson W, Song Y-B, Yu F-H, Fischer M, Dong M and van Kleunen M (2015) Herbaceous plant species invading natural areas tend to have stronger adaptive root foraging than other naturalized species. Front. Plant Sci. 6:273. doi: 10.3389/fpls.2015.00273* Although plastic root-foraging responses are thought to be adaptive, as they may optimize nutrient capture of plants, this has rarely been tested. We investigated whether nutrient-foraging responses are adaptive, and whether they pre-adapt alien species to become natural-area invaders. We grew 12 pairs of congeneric species (i.e., 24 species) native to Europe in heterogeneous and homogeneous nutrient environments, and compared their foraging responses and performance. One species in each pair is a USA natural-area invader, and the other one is not. Within species, individuals with strong foraging responses, measured as plasticity in root diameter and specific root length, had a higher biomass. Among species, the ones with strong foraging responses, measured as plasticity in root length and root biomass, had a higher biomass. Our results therefore suggest that root foraging is an adaptive trait. Invasive species showed significantly stronger root-foraging responses than non-invasive species when measured as root diameter. Biomass accumulation was decreased in the heterogeneous vs. the homogeneous environment. In aboveground, but not belowground and total biomass, this decrease was smaller in invasive than in non-invasive species. Our results show that strong plastic root-foraging responses are adaptive, and suggest that it might aid in pre-adapting species to becoming natural-area invaders.

Keywords: invasion ecology, multi-species comparison, nutrient heterogeneity, phenotypic plasticity, pre-adaptation, root morphology

# Introduction

Soil nutrients are generally patchily distributed, frequently at scales as small as a few centimeters (Hodge, 2004). As a result, different parts of a single plant may experience different nutrient conditions. They can respond to this heterogeneity by differentiating their root growth and development between nutrient-poor vs. nutrient-rich soil patches (de Kroon et al., 2009). These plastic root-foraging responses are thought to enable plants to optimize nutrient capture, and increase plant performance (Robinson et al., 1999). Therefore, the capacity for a strong root-foraging response when growing in heterogeneous soil may be expected to be positively correlated to species success or invasiveness. However, it is not known whether or to what extent this is the case.

Many plant species have been introduced to new regions. Some of these species have been able to sustain stable populations (have become naturalized), and some of those have formed new populations and spread rapidly (have become invasive) (Williamson and Fitter, 1996). Most invasions start in anthropogenic environments, and most alien species stay there, but a small subset of alien species manages to also invade natural habitats (Richardson et al., 2000). Frequently, this happens after disturbance events, as predicted by the fluctuating-resources hypothesis (Davis et al., 2000). Few, if any, studies have tested what distinguishes these natural-area invaders from other naturalized alien species. In natural habitats, irrespective of whether they are more or less heterogeneous than anthropogenic environments, alien plants are likely to experience stronger competition from resident species. Under such conditions, it may be especially important to be able to rapidly find and exploit high resource patches (Robinson et al., 1999; Parepa et al., 2013). We therefore expect that successful natural-area invaders have stronger root-foraging responses than unsuccessful ones.

Invasiveness of species is partly determined by their traits (Pyšek and Richardson, 2007; van Kleunen et al., 2010b). Phenotypic plasticity—the change in the expressed phenotype of a genotype as a function of the environment (Bradshaw, 1965)—is frequently mentioned as a trait that potentially promotes invasiveness (Baker, 1965; Richards et al., 2006; Hulme, 2008). Plastic species could express optimal phenotypes under different growing conditions, and this increased environmental tolerance could also allow them to grow in novel environments. In other words, a high plasticity of species in their native ranges could pre-adapt them to the novel environments that they may encounter in their non-native ranges. Some studies found support for a relationship between plasticity and invasiveness (Davidson et al., 2011; Dawson et al., 2012a,b; Keser et al., 2014), but others did not (Schlaepfer et al., 2010; Palacio-López and Gianoli, 2011; van Kleunen et al., 2011). These discrepancies could partly reflect that plasticity of a trait in response to a certain environmental variable is not necessarily adaptive (i.e., does not necessarily increase fitness; van Kleunen and Fischer, 2005). Because plastic changes in root morphology may enhance resource acquisition and thereby performance of the plant, root foraging is likely to be an example of adaptive phenotypic plasticity. Surprisingly, however, it has rarely been tested explicitly whether root foraging increases performance (but see Wang et al., 2013).

To test whether root foraging is adaptive, and whether it generally pre-adapts plant species to invade natural areas, we conducted a multi-species greenhouse experiment. In this experiment, we compared the effect of soil heterogeneity on root morphology and on plant performance of native European plant species differing in their invasion success in natural areas in the USA. We used 24 plant species subdivided into 12 congeneric species pairs from eight plant families, all of which have been introduced to and naturalized in North America. Within each pair, one species is listed as a natural-area invader and the other is not. We grew all species in homogeneous and heterogeneous nutrient environments, and assessed (1) the morphological root-foraging response (root length, root diameter, root biomass, and specific root length) of plants growing in a heterogeneous environment with nutrient-rich and nutrient-poor patches, and (2) the effect of nutrient heterogeneity on the production of aboveground, belowground and total biomass (as a proxy for plant performance) of the plants.

We addressed the following three questions: Are plastic rootforaging responses adaptive in heterogeneous soils? Are European herbaceous plant species pre-adapted to invade natural areas in North America through strong root-foraging responses in heterogeneous soils? And do plant species that are invasive in natural areas experience a more positive or a less negative effect of soil heterogeneity on plant performance compared to species that do not invade natural areas?

# Material and Methods

# Species Selection and Pre-cultivation

We selected a total of 12 congeneric pairs of herbaceous species from eight plant families. All 24 species are native to Europe, and have become naturalized in North America (Online Appendix I). In this experiment, we were interested in the distinction between plant species that do or do not manage to invade natural areas in the USA. To decide whether the species are natural-area invaders or not, we used The Invasive Plant Atlas of the United States (www.invasiveplantatlas.org), which is a comprehensive compilation of alien plant species that invade natural areas in the US. These natural areas do not include agricultural land or other heavily anthropogenic sites. Within each species pair, one species is listed in this atlas as a natural-area invader in at least four USA states, and the other one is not listed as such in any USA state (Online Appendix I). From here on, we refer to the first group as invasive species and to the second group as non-invasive species. It could be that some of our non-invasive species are no natural-area invaders because they did not disseminate into such habitats yet (van Kleunen et al., 2014b). However, as also our noninvasive species are already widely naturalized in North America (see Online Appendix I), we think that this explanation is quite unlikely.

Because several studies have found a link between relative growth rate and the strength of the foraging response of plant species (see references in Kembel and Cahill, 2005), and others between relative growth rate and invasiveness (Grotkopp and Rejmanek, 2007; Dawson et al., 2011), we reduced this potential confounding factor by balancing the invasive and non-invasive species in each pair with respect to the average plant size. Average height was calculated by averaging the minimum and the maximum height of the species as listed in Rothmaler et al. (2005) (Online Appendix I; size difference between invasive and noninvasive species in the species pairs tested with a paired t-test: t = 0, p = 1).

We ordered seeds of our species from botanical gardens throughout Europe and from commercial companies selling wild-collected seeds (see Online Appendix II). Pre-cultivation and the experiment took place in the botanical garden of the University of Konstanz, Germany (N: 47◦ 69′ 19.56′′, E: 9◦ 17′ 78.42′′). For each of the 24 species, we mixed the seeds from the different suppliers. On the 14th of June 2011, we sowed the seeds in trays filled with a 1:1 mixture of sand and fine vermiculite. We first put the trays in a cold room (4◦C, 16 h of light per day) for 10 days to break seed dormancy. After that, we put them in a greenhouse compartment and kept them at 70% humidity and temperature between 15 and 25◦C. In a preceding pilot experiment, we found that some species germinated faster than others. Therefore, we sowed the seeds of some of the species pairs (genera) 3 days later (see Online Appendix III) to ensure that seedlings would emerge more or less simultaneously.

# Experimental Set-up

We filled a total of 358 square 1-L pots with a 1:1 mixture of sand and fine vermiculite. Our experiment lasted for 5 weeks. This timespan enabled us to study early foraging responses as they may be particularly critical for the establishment of plants in nature and also allowed us to harvest the plants before the soil volume was limiting to the extent that we could no longer see foraging responses. To reduce the chances that roots would spill over from one quarter into the others due to limited root space and to prevent minimize nutrient leakage between pot quarters, we placed PVC barriers in the pots to divide them into four quarters (barriers stuck out above the soil, and were pushed all the way down). The central 2 × 2 cm of each pot was left barrier free to allow plants to grow roots in any direction (see **Figure 1**).

After the seedlings had been in the greenhouse for 2 weeks, we transplanted, if available, 16 plants per species into the center of the pots. Because some species germinated poorly, we had <16 plants for Arctium tomentosum (8 plants), Centaurea scabiosa (6), Cirsium palustre (15), Linaria repens (12), Trifolium medium (1), and Veronica hederifolia (9) (see Online Appendix III). The relatively low number of replicates for some of our species could have been problematic, if the objective would have been to get accurate values for each species in our experiment. However, we aimed

FIGURE 1 | Pot set-up. We filled 1-L square pots with a 1:1 mixture of fine sand and fine vermiculite, and placed PVC barriers in the pots to create four pot quarters. The middle 2 × 2 cm was left open. We supplied plants with a 40ml nutrient solution three times a week with four syringes in the depicted spots (160 ml in total). Plants in the heterogeneous treatment received a high-strength nutrient solution (40 ml 1/2-strength Hoagland solution) in one quarter and a low-strength nutrient solution (40 ml 1/64-strength Hoagland solution) in the other three pot quarters. Plants in the homogeneous treatment received the same total amount of nutrients as in the other treatment but as equal intermediate-strength nutrient solutions (40 ml of ∼1/8-strength Hoagland solution) in all four pot quarters.

to get representative values for the invasive and non-invasive species as a group and not as individual species. van Kleunen et al. (2014a) recently showed, using simulations, that the statistical power for detecting differences between groups of species increases with an increasing number of species used, even if the number of replicates per species becomes very low. Pots were placed 7 cm apart on a greenhouse bench, and their positions were fully randomized. At the start of the experiment, we counted the number of true leaves (i.e., excluding the cotyledons), and measured the length and width of the largest leaf on each plant.

Half of the pots of each species were assigned to a heterogeneous nutrient treatment; the other half was assigned to a homogeneous nutrient treatment. When we had an odd number of plants for a species, we maximized the number of replicates for the root-foraging measurements by allocating one plant more to the heterogeneous treatment than to the homogeneous treatment. Plants in the heterogeneous treatment received a highstrength nutrient solution (40 ml of a 1/2-strength Hoagland solution in one pot quarter and a low-strength nutrient solution (40 ml of a 1/64-strength Hoagland solution) in the other three pot quarters. Plants in the homogeneous treatment received the same total amount of nutrients as in the other treatment but as equal intermediate-strength nutrient solutions (40 ml of a c. 1/8 strength Hoagland solution) in all four pot quarters. We watered and fertilized all plants three times per week. Nutrients were supplied 1.5 cm from the pot border through four syringes, which simultaneously dripped the solution into the pot quarters at a rate of 25 ml/min. We tested whether we created a real nutrient gradient in the pots by taking soil samples from pots without plants in the first, third, and the last week of the experiment. We then analyzed the N content of these soil samples (see Online Appendix IV for more details on this procedure). The fertilization regime resulted in a 7.4- (first week of the experiment) to 29-fold (last week of the experiment) difference in N concentration between the high-nutrient and low-nutrient pot quarters in the heterogeneous treatment (Online Appendix IV).

# Measurements

Five weeks after the start of the experiment, species were harvested per congeneric species pair (see Online Appendix III for dates). We started with the pairs that had the largest plants. We cut the aboveground biomass at soil level, dried it for at least 72 h at 80◦C, and weighed it. We divided the soil of each pot into four parts according to the pot quarters, and washed the roots from the soil. Thick tap roots and large storage roots (lignified roots, thicker than 2 mm) were separated from the other roots, as they contribute relatively little to nutrient uptake. Moreover, although these thick roots were usually in the middle of the pots, they could strongly bias the foraging results if by chance they ended up in one of the pot quarters. We determined the root length and diameter of the roots of the plants from the heterogeneous treatment. In preparation, we stained and preserved the roots from these plants in a neutral-red solution with 0.01% HgCl2 until further analysis. We then determined the length and diameter of all non-storage roots from each pot quarter using a scanner and WinRhizo software (Regent Instruments Inc., Quebec, Canada). Then all roots, also the ones of the plants in the homogeneous treatment, were dried for at least 72 h at 80◦C, and weighed. For plants in the heterogeneous treatment, we calculated the specific root length for roots in the high-nutrient pot quarter and the opposing low-nutrient pot quarter as the root length divided by the root biomass.

# Statistical Analyses

We analyzed our data with linear mixed models, using the lme function (Pinheiro et al., 2010) in the statistical program R (R development core team, 2010). With these models, it is possible to account for the complex nested design of our multi-species experiment by including it in the models as a random structure. These models are also relatively robust when data are unbalanced. Furthermore, the lme function allowed us to correct for heteroscedasticity, caused by the large differences in variance among the species, by adding species variances as a weighting factor (Zuur et al., 2009).

# Testing the Adaptive Value of Root Foraging

The average performance of species may increase with their average foraging response, and the performance of an individual plant of a species may increase with its foraging response. As we used multiple species, we could test the adaptive value of plastic foraging responses in the heterogeneous nutrient treatment simultaneously at the among-species level and at the within-species level. We teased apart the effects of the strength of within- and among-species foraging responses on biomass production using a random regression model (e.g., Lane et al., 2012) with withinspecies mean centering. To do this, we first calculated a foraging index for each individual plant in the heterogeneous nutrient treatment. For root length, root biomass and specific root length, we calculated the index as (the trait value in the high-nutrient patch - the trait value in the opposite low-nutrient patch)/(the trait value in the high-nutrient patch + the trait value in the opposite low-nutrient patch) (e.g., Wang et al., 2013). This way, a high value would indicate a stronger root-foraging response. Because we expected plants to produce thinner roots in the highnutrient patch, we calculated the foraging index for root diameter as (root diameter in the low-nutrient patch - root diameter in the high-nutrient patch)/(root diameter in the low-nutrient patch + root diameter in the high-nutrient patch). Then we calculated the average of each foraging index per species for each trait, and the deviation of each foraging index of each individual plant from the average foraging index of the species it belongs to. We used linear mixed effects models in which ln(total biomass) of individual plants in the heterogeneous nutrient treatment was used as the response variable, and the average species foraging index (i.e., to test the among-species effect) and the deviations of the individual foraging indices (i.e., to test the withinspecies effect) as explanatory variables. We included the initial size of the plants as a covariable. To account for variation among species and variation in the within-species effect among species, we included the random effects of species identity and initially allowed the slope of the effects of individual foraging index deviations to vary among species. Because random slopes did not significantly improve model fit in the models with foraging indices of root length, root diameter, and specific root length as explanatory variables, we removed the random slopes from those models.

# Testing for Differences between Invasive and Non-invasive Species

For the subset of plants in the heterogeneous nutrient treatment, we tested whether there was a difference in foraging response between invasive and non-invasive plant species. For these analyses, we used as response variables root biomass (biomass of belowground parts excluding large storage structures), root length, root diameter, and specific root length. To quantify the strength of the foraging response, we compared the data from the high-nutrient quarter and the opposite low-nutrient quarter within each pot (i.e., we had two data points per pot). For the whole data set, we tested whether there was a difference in performance between invasive and non-invasive species in response to nutrient heterogeneity. As measures of plant performance, we used total biomass, aboveground biomass, and belowground biomass (all four pot quarters combined). We think that biomass production is a good proxy of performance as size is frequently associated with competitive ability (Dostál, 2011) and seed production (Shipley and Dion, 1992) in herbaceous plants.

For the analyses of foraging responses, the fixed terms of our models included invasiveness of the species (invasive and non-invasive), nutrient patch (high- and low-nutrient patch), their interaction and a measure of initial size of the plants (the length × width of the largest leaf × the number of true leaves). For the analyses of performance traits, the fixed model part included invasiveness of the species, nutrient treatment (homogeneous and heterogeneous treatment), their interaction and initial size of the plants. The hierarchical design of our experiment was included in the models as a nested random term: family/genus/species/pot. In the analyses of performance traits, we had only one value per pot, and accordingly we did not specify "pot" in the random part of these models (i.e., the variance among pots corresponds to the residual variance).

We used likelihood-ratio tests, based on maximum-likelihood estimation, to test which fixed factors were significant (Zuur et al., 2009). We first tested significance of the two-way interaction by removing it from the model and comparing this model to the full model. We then tested significance of the main effects by removing each one in turn and comparing these models to the full additive model (i.e., the model without the two-way interaction). To achieve normality of the residuals, we ln-transformed the data of root length, root biomass, belowground biomass, specific root length, aboveground biomass, and total biomass. For all analyses in which we used belowground biomass, we excluded the data from three plants (two T. pratense and one R. acris) because of accidentally mixed up roots.

# Results

# The Adaptive Value of Root-foraging in Heterogeneous Soils

Within species, some strong individual root-foraging responses had positive effects on biomass production (**Table 1**, **Figure 2**). When the foraging response was measured in terms of root diameter and specific root length, these effects were significant. They were marginally significant when measured in terms of root length and non-significant when measured in terms of root biomass (**Table 1**, **Figure 2**). Among species, species with a stronger average foraging response in terms of root length and root biomass had a significantly higher biomass production than species with a weaker average foraging response (**Table 1**, **Figure 2**).

# Root Foraging of Invasive and Non-invasive Species in Heterogeneous Soils

In the heterogeneous-nutrient treatment, plants produced significantly longer and thinner roots, and had more root biomass and a higher specific root length in the high-nutrient patches than in the low-nutrient patches (**Table 2**, **Figure 3**). Overall,

TABLE 1 | Effects of the strength of the within-species and between-species foraging responses (root length, root diameter, root biomass, specific root length) on the ln-transformed plant biomass.

*Presented are the fixed effects estimates of linear mixed effects models, the associated standard errors and p-values of the among-species and within-species effects. The scaled start size of the plants was used as a model covariable; species identity was used as a random factor. Significant results are in bold. Positive estimates indicate a positive effect of root foraging on performance.*

average root length, root diameter, root biomass, and specific root length (i.e., root length/root biomass) did not differ significantly between invasive and non-invasive species (**Table 2**, **Figure 3**). However, the root-diameter foraging response was stronger in the invasive than in the non-invasive species (**Figure 3B**), as indicated by a significant invasiveness × nutrient patch interaction for root diameter (**Table 2**).

# Performance of Invasive and Non-invasive Species in Homogeneous and Heterogeneous Soils

Averages of aboveground, belowground, and total biomass did not differ significantly between invasive and non-invasive species (**Table 3**, **Figure 4C**), most likely because we a priori selected congeneric species of similar size. Overall, plants produced more biomass in the homogeneous than in the heterogeneous treatment (**Table 3**, **Figures 4A–C**). However, while the non-invasive species had reduced aboveground biomass in the heterogeneous treatment, the invasive species had not (significant two-way interaction in **Table 3**, **Figure 4A**).

# Discussion

Our results suggest that root-foraging responses are adaptive in heterogeneous nutrient environments, because we found significant positive effects of root foraging on biomass production of individual plants within species, and significant positive effects of foraging on biomass production of species (**Table 1**, **Figure 2**). We also found stronger root-diameter foraging responses in invasive compared to non-invasive plant species (**Table 2**, **Figure 3B**). In addition, although plants performed worse in

(no line) and 0.1 < *p* > 0.05 (dotted line). The length of the line represents the

within-species and among-species spread in the foraging index.

significant, they are shown by the thin lines through each dot. When the among-species relationship was significant or marginally significant, it is shown by the thick line. Foraging responses were measured as (A) root

the heterogeneous than in the homogeneous nutrient environment overall, invasive species were better able than non-invasive species of maintaining a relatively high aboveground biomass production in the heterogeneous nutrient treatment (**Table 3**, **Figures 4A–C**). Together, these findings suggest that root foraging is adaptive, and might aid species in becoming invasive in natural areas elsewhere.

Although it is frequently implicitly assumed that phenotypic plasticity of a trait in response to an environmental cue is adaptive, there are still surprisingly few plant traits and environmental factors for which this has been empirically tested (van Kleunen and Fischer, 2005). Well-studied examples are shade-avoidance plasticity, such as stem elongation (Dudley and Schmitt, 1996; van Kleunen and Fischer, 2001), and induced resistance against herbivores (Agrawal, 1998; Agrawal et al., 2002; Crispo, 2007). Despite the large number of studies on

TABLE 2 | Effects of high- and low-nutrient patches of the heterogeneous treatment on the biomass and morphology of roots of invasive and non-invasive plant species.

*Presented are the results of likelihood-ratio tests [log-likelihood ratio and corresponding p-value (in parentheses)], which we used to test whether invasiveness of species, high- or low-nutrient patches and their interaction significantly contributed to the fit of linear mixed effects models. Significant results are in bold.*

plastic root foraging (e.g., Robinson et al., 1999; Kembel et al., 2008), we found only one other experimental study in which its adaptive value was explicitly tested. Wang et al. (2013) found that, in a high-contrast nutrient environment, genotypes of Potentilla reptans with a stronger foraging response, measured in terms of root biomass allocation, had a higher total biomass than genotypes with a weaker response. Here, we showed that this holds across a much larger number of herbaceous species.

The strength of the foraging response and its effects on performance can vary with the strength of the nutrient contrast (Wijesinghe and Hutchings, 1999; Wang et al., 2013), and the persistence of nutrient patches in time (e.g., Mommer et al., 2011) and space (e.g., Fransen et al., 1999). If these patch dynamics in nature are faster than the root-foraging responses of the plants, the latter could potentially result in maladaptation (Stuefer, 1996; Dewitt, 1998). However, as long as the foraging response results in at least a temporarily higher nutrient acquisition, it could provide a competitive advantage, and result in a longer term benefit. Another strategy that could allow alien plants to take advantage of resource pulses is to rapidly germinate after a resource pulse. Indeed it has been reported that successful alien species frequently germinate faster than less successful alien species (van Kleunen and Johnson, 2007; Schlaepfer et al., 2010). Recently, Wilsey et al. (2015) showed that invasive species can take advantage of such priority effects. Future studies should address whether invasive species are in general better in taking advantage of resource pulses in dynamic environments, and which strategies they use.

We found that the invasive species showed a more plastic root-diameter foraging response than the non-invasive species (**Table 2**, **Figure 3B**). Similarly, Keser et al. (2014) recently found

*p* < 0.1 (.), 0.01 < *p* < 0.05 (\*), 0.001 < *p* < 0.01 (\*\*), *p* < 0.001 (\*\*\*).

high-nutrient pot quarter and the opposing low-nutrient quarter.

that invasive clonal plants showed stronger root-biomass foraging responses than non-invasive clonal plants. These results are consistent with the idea that invasive species should have stronger phenotypic plasticity than non-invasive species (Baker, 1965; Richards et al., 2006; Davidson et al., 2011; Palacio-López and Gianoli, 2011). Even though invasive species are expected to be more plastic, many empirical tests did not find support for this (Schlaepfer et al., 2010; Palacio-López and Gianoli, 2011; van Kleunen et al., 2011). Several factors may contribute to the lack of support for this hypothesis in other studies. First, and in contrast to our study, most of the studies did not test whether plasticity in the trait studied was adaptive. Second, many studies did not use genetically identical plant material in the different treatments, and thus may have confounded phenotypic plasticity with genetic differences, if there was genotype-by-environment covariation (Schmid, 1992). We avoided this problem by looking at phenotypic plasticity at the within-plant level. Third, not all studies compared invasive alien species to less invasive alien

TABLE 3 | Effects of nutrient heterogeneity on the performance (aboveground biomass, belowground biomass, and total plant biomass) of invasive and non-invasive plant species.

*Presented are the results of likelihood-ratio tests [log-likelihood ratio and corresponding p-value (in parentheses)], which we used to test whether invasiveness of species, soil nutrient heterogeneity and their interaction significantly improved the model fit of linear mixed effects models. Significant results are in bold.*

species, but focused on differences between invasive alien and native species (Davidson et al., 2011; Palacio-López and Gianoli, 2011). Although such comparisons might provide insights into why invasive species can displace certain native species, they do not test why some alien species become invasive and others do not (van Kleunen et al., 2010a; Burns et al., 2013).

One of the assumptions of the benefit of adaptive phenotypic plasticity for invasiveness is that plastic species could express optimal phenotypes under different growing conditions, and are therefore pre-adapted to become invasive (Richards et al., 2006). We therefore expected that the invasive species would be better capable than the non-invasive species of taking advantage of the high nutrient patches, and thus of maintaining a relatively high performance in the heterogeneous nutrient environment. Indeed, we found this for aboveground biomass, and this suggests that plasticity may aid invasive species to grow better under less favorable heterogeneous growing conditions. However, we did not find significant advantages in terms of belowground and total biomass. Possibly, the experiment did not last long enough for such advantages to become apparent. Furthermore, it has been reported that root foraging may increase competitive potential (Robinson et al., 1999). Therefore, future studies should test whether foraging under competitive conditions increases the performance of invasive species over non-invasive species.

Many other plant characteristics that have been reported to be positively correlated with species invasiveness are also related to nutrient responses. Dostál et al. (2013) found that Central European plant species from more productive habitats and species with a wider habitat-productivity niche in their native range have higher success as alien plant species elsewhere in the world. Dawson et al. (2012a) found that invasive species, just like common native species, can capitalize

*p* < 0.01 (\*\*), *p* < 0.001 (\*\*\*).

heterogeneously distributed soil nutrients. Please note the cut

more strongly on extra nutrients than non-invasive alien species, and rare native species, do. Similarly, Dawson et al. (2012b) found that alien plant species with a wider global distribution are better able to capitalize on increased resource availability. Furthermore, Funk and Vitousek (2007) found that in nutrient-poor environments, the nutrient-uptake efficiency of invasive species was higher than that of native species. The ability to effectively exploit soil nutrients may therefore be one of the important factors determining invasiveness of plant species.

Natural areas can be competitive environments, and this may hamper colonization by new species. Strong root foraging could aid colonizing species by allowing them to exploit the available nutrients, and this has been reported to increase the competitive potential of plant species (Robinson et al., 1999). However, Mommer et al. (2012) found that, although a competitively strong species benefited from foraging under competition, a competitively weaker species had a disadvantage because it placed its roots in the empty soil patches instead of in the nutrient-rich patches. Interestingly, we found that on average natural-area invaders had a stronger foraging response than noninvaders. Invasive plant species have been hypothesized to have a stronger competitive ability than non-invasive species (Baker, 1974), although few studies have tested this explicitly (Dawson et al., 2012a). If invasive species are indeed competitively stronger and have a stronger foraging response, this could mean additive benefits for the invasive species in comparison to non-invasive species.

# References

It has been suggested that the importance of certain plant traits for plant invasiveness may change with the stage of the invasion (Pyšek, 1997; Dietz and Edwards, 2006; Theoharides and Dukes, 2007; Dawson et al., 2009; Pyšek et al., 2009). Here, we focused on a transition that has received little research attention: the transition from being naturalized to invading natural areas. We think that this transition deserves special attention, because species that can invade natural areas are likely to become problematic invaders. Our experiment indicates that the potential for nutrient foraging may contribute to species invasiveness in this transition.

# Acknowledgments

We thank Verena Geyer, Otmar Ficht, Christine Giele, Katya Stift-Mamonova, Joana Thiel, Tamara Schumacher, Ingo Breddin, Katarina Varga, Rebecca Fies, Eelke Jongejans, Babette Keser, and Marie-Louise Kocher for their practical help during the experiment and Hans de Kroon for his helpful comments on a previous version of the manuscript. This project was funded by the Sino-Swiss Science and Technology Cooperation (project number: IZLCZ3 123973).

# Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpls.2015. 00273/abstract

**Conflict of Interest Statement:** The Guest Associate Editor Judy Simon declares that, despite being affiliated with the same institute as the authors Lidewij Hester Keser, Wayne Dawson and Mark Van Kleunen, the review process was handled objectively. 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 Keser, Visser, Dawson, Song, Yu, Fischer, Dong and van Kleunen. 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.


*Article* **Biochemical Diversity, Pathogenicity and Phylogenetic Analysis of** *Pseudomonas viridiflava* **from Bean and Weeds in Northern Spain**

**Ana M. Fernández-Sanz 1 , M. Rosario Rodicio 2 and Ana J. González 1,\***

**Abstract:** *Pseudomonas viridiflava* was originally reported as a bean pathogen, and subsequently as a wide-host range pathogen affecting numerous plants species. In addition, several authors have reported the epiphytic presence of this bacterium in "non-host plants", which may act as reservoir of *P. viridiflava* and source of inoculum for crops. A new biotype of this bacterium, showing an atypical LOPAT profile, was found in Asturias, a Northern region of Spain, causing significant damage in beans, kiwifruit, lettuce, and *Hebe*. In order to investigate the involvement of weeds in bean disease, samples were collected from beans and weeds growing in the same fields. A total of 48 isolates of *P. viridiflava* were obtained, 39 from weeds and 9 from beans. 48% and 52% of them showed typical (L− O− P+ A− T+) and atypical (L+ O− P v A− T+) LOPAT profiles, and they displayed high biochemical diversity. Regarding virulence factors, the T-PAI and S-PAI pathogenicity islands were found in 29% and 70.8% of the isolates, 81.2% displayed pectinolytic activity on potato slices, and 59% of the weed isolates produced symptoms after inoculation on bean pods. A phylogenetic tree based on concatenated *rpoD*, *gyrB*, and *gltA* sequences separated the strains carrying S-PAI and T-PAI into different clusters, both containing isolates from beans and weeds, and pathogenic as well as non-pathogenic strains. Closely related strains were found in the two hosts, and more than half of the weed isolates proved to be pathogenic in beans. This is consistent with the role of weeds as a reservoir and source of inoculum for bean infection. Detection of *P. viridiflava* in weeds throughout the year further supports these roles.

**Keywords:** *Pseudomonas viridiflava*; *Phaseolus vulgaris*; LOPAT; pathogenicity islands; pectinolytic activity; phylogenetic analysis

#### **1. Introduction**

*Pseudomonas viridiflava* (Burkholder) Dowson was first described by Burkholder in 1930 as a bean (*Phaseolus vulgaris* L.) pathogen and subsequently reported to cause disease in many other crop plants. These bacteria produce several symptoms, including necrosis, spots, and rots, affecting different parts of the plant (stems, leaves, blossoms, and roots). For instance, it has been shown to cause bacterial blight in kiwifruit, necrosis in tomato, bacterial blight in pea, rots in alfalfa, carrot and *Cucumis sativus*, bacterial shoot blight in sweet crab apple, bacterial canker in stone fruit trees, and bacterial leaf spot in lettuce [1–10]. According to this, *P. viridiflava* is regarded as a generalist pathogen, able to attack multiple host species [11,12]. In Asturias, a Northern region of Spain, a new emerging biotype of *P. viridiflava* (termed BT2) was described [13]. This biotype differs from the typical biotype by the production of yellowish mucoid material in the sucrose medium used for the levan test, and by a variable pectinolytic activity on different potato varieties. First detected in 1999, BT2 has caused significant damage in several crops such as beans, in which symptoms

**Citation:** Fernández-Sanz, A.M.; Rodicio, M.R.; González, A.J. Biochemical Diversity, Pathogenicity and Phylogenetic Analysis of *Pseudomonas viridiflava* from Bean and Weeds in Northern Spain. *Microorganisms* **2022**, *10*, 1542. https://doi.org/10.3390/ microorganisms10081542

Academic Editor: Ana Palacio-Bielsa

Received: 5 July 2022 Accepted: 27 July 2022 Published: 29 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

ranged from red spots mainly in the petioles and pods; to plant death due to systemic infection associated with the destruction of the medulla; kiwifruit, showing dark brown spots in floral buds that developed into extensive rot, leading to the collapse of floral buds or the production of small or distorted fruits; and lettuce, generating soft rot of intermediate leaves, which could also progress and result in plant death [13]. Subsequently, this biotype was also shown to cause defoliation in *Hebe* [14], and has been isolated from rapeseed in South Korea [15].

*P. viridiflava* is a close relative of *P. syringae*. Based on phylogenetic analysis, it is located within the *P. syringae* species complex [16–18]. However, unlike *P. syringae*, the knowledge about the mechanisms of virulence in *P. viridiflava* is still limited. In this species, two alternative pathogenicity islands, T-PAI and S-PAI, have been detected [19]. Some isolates harbor a tripartite PAI, equivalent to that found in *P. syringae* (T-PAI), while in others the PAI has a single component (S-PAI). T-PAI and S-PAI occupy different chromosomal locations, but only one of them is present in each individual isolate [19]. T-PAI consists of a central *hrp*/*hrc* (hypersensitivity reaction and pathogenicity/hypersensitivity reaction and conserved) cluster, flanked by CEL (conserved effector locus) and EEL (exchangeable effector locus) regions. The *hrp*/*hrc* cluster encodes a Type III secretion system (TTSS) responsible for the formation of a syringe-like structure used to translocate effectors into the host cell. These effectors are encoded by the CEL and EEL loci. S-PAI is also composed by a *hrp*/*hrc* cluster, but it is not flanked by the CEL and EEL regions. Instead, the cluster is interrupted by an insertion that harbors genes encoding effector proteins [19]. Other virulence factors detected in *P. viridiflava* are the enzyme pectate lyase and extracellular proteases related to plant tissue maceration [20,21].

Apart from acting as a crop pathogen, *P. viridiflava* also exists as an epiphyte on weeds. Gitaitis et al. [22] found this bacterium on weeds associated with onion crops in the USA and observed that weed control is necessary to avoid plant disease. The weed species from which *P. viridiflava* was isolated included *Oenothera laciniata* Hill, *Taraxacum officinale* Weber, *Fumaria officinalis* L., *Gnaphalium purpureum* L., *Sonchus asper* (L.) Hill, *Lepidium virginicum* L., and *Raphanus raphanistrum* L. Similarly, Basavand & Khodaygan [23] detected *P. viridiflava* in *Alisma plantago-aquatica*, a perennial weed in rice fields in Northern Iran.

In the present study, we performed a comparative analysis of *P. viridiflava* isolated from the common bean type "granja asturiana", and from weeds growing in the bean fields. "Granja asturiana" beans are one of the most popular food products in our region, have great culinary value within the Asturian gastronomy, and are an important economic resource for small farmers. Therefore, the possible role of weeds as a reservoir of *P. viridiflava* and their involvement in transmission of the pathogen to bean crops are worthy of investigation. In this study, strains isolated from the two hosts were characterized, their phylogenetic relationships established, and weed isolates were tested for pathogenicity on bean.

#### **2. Materials and Methods**

#### *2.1. Bacterial Isolates, Culture Conditions and Phenotypic Characterization*

Forty-eight isolates of *P. viridiflava* from weeds and beans were recovered (39 and 9, respectively) in 10 fields located in four councils of Asturias: Navia (Anleo), Valdés (Busto 1 and 2, Constancios, Ronda and Pontigón), Tineo (Carbajal, Bárcena and Yerbo), and Siero (Argüelles).

Bacteria were identified by means of the KOH test, to determine their Gram stain [24], and by standard biochemical tests. These included oxidation-fermentation of glucose [25], the LOPAT determinative tests [26], utilization of mannitol, m-inositol, erythritol, and sorbitol in Hellmers broth [27], utilization of homoserine, D-tartrate, sucrose, L-lactate, trigonelline, quinate, betaine and adonitol in Ayer's solid medium [28], and hydrolysis of esculin, gelatin [29], casein, and Tween 80 [30]. Isolates showing identical features and collected from the same field site and host at the same period of the year, were considered as a single strain. In this way, a total of 39 strains were identified, 30 and 9 from weeds and bean, respectively.

#### *2.2. 16S rDNA Sequencing and ARDRA (Amplified Ribosomal DNA Restriction Analysis*

16S rDNA was amplified by PCR, with primers described by Edwards et al. [31], and ARDRA carried out with the *Sac*I and *Hinf*I restriction enzymes [13].

#### *2.3. Detection of Pathogenicity Islands*

Presence of T-PAI was detected with primers hoppsyAr1 (CYGGCTATGATTGATAAACG-CATCG) and shcAf1 (GGCGCACTTAACCCTCTGKTCAA TGA) [19], while the presence of S-PAI and absence of T-PAI was revealed with primers R1-orf41f1 (GCCTTGCCTCT-GATCTCATTC) and R1-dsorf78r1 (GTAGCAT TCGGCATATCCC) (M. San José, personal communication). The fragments expected are of 1113 bp for T-PAI and 889 bp for S-PAI.

#### *2.4. Pathogenicity Assays*

Pathogenicity was tested by inoculation of isolates grown on King B medium on pods of beans cv. *Helda* with a sterile toothpick [32]. The assays were repeated with three replicates each time.

#### *2.5. Phylogenetic Analysis*

The phylogenetic analysis was performed for the 39 detected strains (see above), using the *rpoD* (RNA polymerase sigma D factor), *gltA* (citrate synthase), and *gyrB* (DNA gyrase subunit B) genes [33]. Amplification was conducted as in Hwang et al. [34]. The obtained fragments were sequenced by Secugen S.L. (Spain) or Eurofins (Germany). Sequences were submitted to GenBank (accession numbers MT683625-MT683672, MT709110- MT709148, and ON838894-ON838932, for *gyrB*, *rpoD* and *gltA*, respectively). Concatenated sequences of the three genes were aligned using Clustal W [35], and phylogenetic trees were constructed using Maximum-Likelihood with the Tamura–Nei model [36]. Their topological robustness was evaluated by bootstrap analysis based on 1000 replicates using Mega 6 software [37]. Sequences from *P. viridiflava* DSM 6694T , *P. asturiensis* LPPA 221T and *P. protegens* ChaoT were included as references.

In addition, the number of segregating sites (S) and the mean of the nucleotide diversity (π), defined as the average number of nucleotide differences by site between sequences of the whole population [38], were calculated both for the individual genes and the concatenated sequences, also using the Kimura two parameters model [37].

#### **3. Results and Discussion**

#### *3.1. Identification and Biochemical Characterization of the Isolates*

Forty eight isolates identified as *P. viridiflava* by ARDRA, and recovered during the period 2007–2009, were included in the present study. Thirty nine of them were isolated from weeds and nine from beans (Table 1). All isolates shared the ARDRA profile characteristically associated with *P. viridiflava* (not shown) and were Gram-negative bacilli.

**Table 1.** Origin and general features of *Pseudomonas viridiflava* isolates used in this study.

LPPA, Laboratory of Phytopathology of the Principality of Asturias; a–g, isolated from the same sample; ni, not identified, but all were different; BT, biotype; 2, atypical profile; 1, typical profile; BP, biochemical profile according to Table S1; PAI, pathogenicity island; T, T-PAI; S, S-PAI; PP, pectinolysis on potato; P, pathogenicity; +, positive; −, negative; v, variable.

Regarding the LOPAT scheme, presence of the two previously described biotypes was revealed, with 48% of the isolates (20 from weeds and 3 from beans) showing the typical (L−, O−, P+, A−, T+), and 52% (19 from weeds and 6 from beans) showing the atypical (L+, O−, Pv, A−, T+) biotype. The latter profile has persisted in beans in Asturias, at least since 2003 when it was first reported. In addition, it has also been described in South Korea in rapeseed [15]. Other biochemical features of the isolates are compiled in Table 2.

**Table 2.** Biochemical features of the isolates under study.

The numbers correspond to the percentage of isolates positive for a given test.

Results of the biochemical tests were highly variable, distributing the 48 isolates into 33 biochemical profiles (BP1 to BP33; Table 1 and Table S1). A correlation between profile and host plant or sample site was not found. Thus, several profiles were associated with the same host or field, and the same profile was shared by isolates from different hosts and sites. This wide variability makes phenotypic identification rather difficult. Consistent results were only obtained for the sucrose and lactose tests, both negative, and for the tobacco, esculin, quinate, and xylose tests, all positive, in 100% of the isolates.

Following the proposal of Billing [39], Wilkie et al. [40] found that the use of sucrose and tartrate could help in the initial identification of members of the species. Our results coincided in the case of sucrose utilization but not of D-tartrate, a test in which 29% of the isolates were positive. Nor do they agree with the results of Sarris et al. [41] who studied 18 isolates of *P. viridiflava* obtained from different hosts in Crete (Greece) and found no variability in the biochemical tests performed, except for the L-tartrate test. Regarding the latter, isolates obtained from tomato were positive, while the type strain was negative, as well as isolates from other hosts.

#### *3.2. Occurrence of the Bacterium in Weeds and Bean Samples*

In this study, *P. viridiflava* was isolated from twelve genera/species of weeds: *Capsella bursa-pastoris*, *Chenopodium album*, *Cyperus rotundus*, *Fumaria* sp, *Galinsoga parviflora*, *Hypochaeris radicata*, *Malva sylvestris*, *Senecio vulgaris*, *Solanum nigrum*, *Sonchus oleraceus*, *Stellaria media*, and *Trifolium* sp., and from three unidentified weeds which were different to each other (Table 1). The bacterium was most frequently found in *Fumaria* sp. (nine isolates from eight samples), followed by *S. olereaceus* and *S. nigrum* (four isolates from four samples), and *C. rotundus* (four isolates from three samples). This further expands the already wide host range of *P. viridiflava* which, as far as we know, has not been previously reported

in 10 out of the 12 weed species/genera mentioned before. However, the bacterium has already been described by Gitaitis et al. [22] in *Sonchus* sp and *Fumaria* sp. associated with an onion crop. It is important to note that none of the weeds showed disease symptoms, consistent with an epiphytic existence of *P. viridiflava* in weeds.

Unlike Gitaitis et al. [22], who only isolated the bacteria from weeds during the onion growing season, we verified their presence throughout the year, i.e., before, during, and after the crop season. The survival of *P. viridiflava* in five species of weeds had already been described by Aysan and Uygur [42] before and after the tomato crop, and by Mariano and McCarter [43] also on tomato. In the latter study, persistence of the bacterium on the surface of the leaves of two weed species was observed by electron microscopy, for at least 16 weeks.

*Pseudomonas viridiflava* was less frequently detected in bean samples than in weeds. This species was isolated as a sole pathogen in 8% of the bean samples tested and together with *P. syringae* pv. *phaseolicola* in 1.1%. In contrast, a previous study in our region revealed *P. viridiflava* as the only pathogen in 28% of the samples, and together with *P. syringae* pv. *phaseolicola* and pv. *syringae* in a small percentage [13]. These differences could be due to the fact that the bean fields sampled in the present study had a significant presence of halo blight caused by *P. syringae* pv. *phaseolicola*, which could have displaced the less aggressive *P. viridiflava*. In any case, the simultaneous presence of *P. viridiflava* with other pathogens like *P. syringae* pv. *syringae* or pv. *phaseolicola* in the same sample highlights the epiphytic and opportunistic nature of the former species. Moreover, the relatively frequent detection of *P. viridiflava* in weeds, suggests that they could be an important reservoir and source of inoculum for crops. This is particularly true in Asturias, where the climatic conditions: mild temperatures, frequent rainfall, and high relative humidity values, are favorable both for growing of the weeds and for the development of the disease.

#### *3.3. PAI Distribution, Pectinolysis Activity, and Pathogenicity Tests*

Each isolate carried one of the pathogenicity islands (T-PAI or S-PAI) previously reported in *P. viridiflava*, thus confirming the polymorphism in terms of the presence/absence of these islands [19]. S-PAI was the most frequent, found in 26 and 8 of the isolates from weeds and beans, respectively (Table 1).

An important virulence factor in *P. viridiflava* is the enzyme pectate lyase, which causes maceration of plant tissues [20; 44]. In our study, 81.2% of the strains produced pectinolysis on potato slices, and 58.3% on bean pods (Table 1). Pectinolysis on potato slices was observed for isolates carrying both T-PAI (92.8%) and S-PAI (79.4%), although Jakob et al. [44] have shown that isolates with S-PAI had higher enzyme activity than those carrying T-PAI.

When pathogenicity tests were performed, different kinds of symptoms were observed (Figure 1). Some isolates caused only a small brown spot in the pods, around the inoculation point. Others produced a reddish or ferrous halo 24–48 h post-inoculation, which could be followed or not by maceration of the tissues, observed after 48–72 h. This coincides with results reported by Wilkie et al. [40] who, using the same method of inoculation, found different responses depending on the inoculated strain.

Twenty-seven strains of the 48 studied were pathogenic on bean pods, and one gave a variable response. Five of the pathogenic isolates came from bean samples, while the remaining 23 were from weeds belonging to the species *C. bursa-pastoris*, *C. rotundus*, *C. album*, *Fumaria* sp., *G. parviflora*, *M. sylvestris*, *S. vulgaris*, *S. nigrum*, *S. media*, and *Trifolium* sp. In total, 59% of the weed isolates were pathogenic to bean, which is highly relevant with respect to the epidemiology of the disease. This coincides with previous studies performed on tomato and onion, where *P. viridiflava* cause serious diseases [22,42,43] and a high percentage of weed isolates were pathogenic.

**Figure 1.** Inoculation of bean pods cv. *Helda* with representative isolates of *Pseudomonas viridiflava* (**A**) and enlarged details without (**B**) and with symptoms (**C**). **Figure 1.** Inoculation of bean pods cv. *Helda* with representative isolates of *Pseudomonas viridiflava* (**A**) and enlarged details without (**B**) and with symptoms (**C**).

sp. In total, 59% of the weed isolates were pathogenic to bean, which is highly relevant with respect to the epidemiology of the disease. This coincides with previous studies performed on tomato and onion, where *P. viridiflava* cause serious diseases [22,42,43] and

It is finally of note that the ability to produce maceration in bean pods did not correlate with biotype (since it was observed for 47.8% and 68% of the isolates with BT1 and BT2, respectively), nor with the type of PAI (soft rot was produce by 57% and 58.8% of the isolates with T-PAI and S-PAI, respectively). The latter observation is in line with results obtained by Bartoli et al. [45], who also found that the presence of S-PAI and T-PAI was not correlated with the ability to produce soft rot and with pathogenicity.

a high percentage of weed isolates were pathogenic.

*3.4. Phylogenetic Analysis* To establish the phylogenetic relationships of the isolates under study, the *gyrB*, *rpoD* and *gltA* genes from the 39 identified strains were sequenced. The 16S rDNA was not included because, being a highly conserved gene, it does not provide intraspecies variability [12]. The *gyrB* and *rpoD* genes were used for the investigation of populations of *P. viridiflava* in two previous studies [12,41]. It is finally of note that the ability to produce maceration in bean pods did not correlate with biotype (since it was observed for 47.8% and 68% of the isolates with BT1 and BT2, respectively), nor with the type of PAI (soft rot was produce by 57% and 58.8% of the isolates with T-PAI and S-PAI, respectively). The latter observation is in line with results obtained by Bartoli et al. [45], who also found that the presence of S-PAI and T-PAI was not correlated with the ability to produce soft rot and with pathogenicity.

#### The concatenated sequences of the three loci had a total length of 2450 bp (610 bp *3.4. Phylogenetic Analysis*

*gyrB*, 882 bp *rpoD,* and 958 bp *gltA*). By means of the Tajima's test of neutrality, we have been able to verify that the concatenated sequence had 168 segregating sites and a nucleotide diversity (π) of 0.019. Tajima's D-statistic test distinguishes between DNA sequences that evolve randomly ("neutrally") from those that evolve under a non-random process. In our case, the D value was >0 so there are more haplotypes than number of To establish the phylogenetic relationships of the isolates under study, the *gyrB*, *rpoD* and *gltA* genes from the 39 identified strains were sequenced. The 16S rDNA was not included because, being a highly conserved gene, it does not provide intraspecies variability [12]. The *gyrB* and *rpoD* genes were used for the investigation of populations of *P. viridiflava* in two previous studies [12,41].

segregating sites (Table 3). The gene that most contributed to nucleotide diversity was *gyrB*, a result already obtained by Yin et al. [46]. However, *rpoD* was the gene that provided nucleotide diversity (0.019617) closer to that obtained with the three concatenated genes (0.019432). **Table 3.** Results from Tajima's neutrality test. **Gene m n S π D** *gyrB* 40 610 54 0.025542 0.812835 *rpoD* 40 882 63 0.019617 0.605882 The concatenated sequences of the three loci had a total length of 2450 bp (610 bp *gyrB*, 882 bp *rpoD*, and 958 bp *gltA*). By means of the Tajima's test of neutrality, we have been able to verify that the concatenated sequence had 168 segregating sites and a nucleotide diversity (π) of 0.019. Tajima's D-statistic test distinguishes between DNA sequences that evolve randomly ("neutrally") from those that evolve under a non-random process. In our case, the D value was >0 so there are more haplotypes than number of segregating sites (Table 3). The gene that most contributed to nucleotide diversity was *gyrB*, a result already obtained by Yin et al. [46]. However, *rpoD* was the gene that provided nucleotide diversity (0.019617) closer to that obtained with the three concatenated genes (0.019432).

*gltA* 40 958 51 0.015371 0.813580 *gyrB* + *rpoD* + *gltA* 40 2450 168 0.019432 0.760686 **Table 3.** Results from Tajima's neutrality test.

m = number of sequences, n = number of positions, S = number of segregating sites, π = nucleotide diversity, D = Tajima Test statistic.

Figure 2 shows the phylogenetic tree based on the three concatenated *gyrB, rpoD*, and *gltA* genes of the 39 strains. They were distributed into two clades, A and B, which contain the S-PAI- and T-PAI-positive strains, respectively. In contrast, the strains were not separated according to biotype, soft rot activity, and pathogenicity, since isolates with these properties appear in the two clusters. Strains from weeds and beans were represented in both clusters, and closely related strains were obtained from the two hosts.

Figure 2 shows the phylogenetic tree based on the three concatenated *gyrB, rpoD, and gltA* genes of the 39 strains. They were distributed into two clades, A and B, which contain the S-PAI- and T-PAI-positive strains, respectively. In contrast, the strains were not separated according to biotype, soft rot activity, and pathogenicity, since isolates with these properties appear in the two clusters. Strains from weeds and beans were represented in both clusters, and closely related strains were obtained from the two hosts.

**Figure 2.** Phylogenetic tree based on concatenated partial sequences of the *gyrB*, *rpoD,* and *gltA* genes, inferred with the Maximum Likelihood method. The evolutionary distances were computed by the Tamura–Nei model. Bootstrap values ≥50% (based on 1000 replicates) are indicated at **Figure 2.** Phylogenetic tree based on concatenated partial sequences of the *gyrB*, *rpoD*, and *gltA* genes, inferred with the Maximum Likelihood method. The evolutionary distances were computed by the Tamura–Nei model. Bootstrap values ≥50% (based on 1000 replicates) are indicated at branch points. *P. viridiflava* DSM 6694T was used as control, *P. asturiensis* LPPA 221T as a member of the closest-related species, and *P. protegens* strain ChaoT as outgroup. Bar scale, substitutions per site. Relevant features related to the strains are shown at the right of the figure. BT, Biotype; PAI, pathogenicity island; PP, pectinolysis on potato; P, pathogenicity on bean pods. Accession numbers of the sequences and the pairwise distance matrix used to construct the phylogenetic tree are shown in Tables S2 and S3, respectively.

#### **4. Conclusions**

*P. viridiflava* was isolated not only from beans but also from fifteen different weeds growing in the same fields. The bacterium is reported for the first time in ten of the twelve identified weed genera/species (with the remaining three weeds, which could not be identified, but were all different than each other). Regardless of their origin, the isolates displayed wide biochemical diversity, hindering identification by traditional methods. Consistent results were only obtained for the sucrose and lactose tests (negative), and for the tobacco, esculin, quinate, and xylose tests (positive). Phylogenetic analysis with concatenated *gyrB*, *rpoD*, and *gltA* sequences separated the strains carrying S-PAI and T-PAI into two different clusters, with no correlation observed for other characteristics, such as plant host, LOPAT profile, pectinolytic activity, or pathogenicity. Detection of *P. viridiflava* before, during, and after the crop season shows survival of the bacteria in weeds throughout the year, hence supporting the role of weeds as reservoir of *P. viridiflava*, and as a source of inoculum for bean infection. The fact that 59% of the weed isolates behave as bean pathogens, and that some strains recovered from beans and weeds were closely related, further highlights the role of weeds on the epidemiology of the disease.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/microorganisms10081542/s1, Table S1: Biochemical profiles of the isolates under study; Table S2: Accession numbers of the *gyrB*, *rpoD* and *gltA* sequences used for phylogenetic analysis; Table S3: Pairwise distance matrix used to construct the phylogenetic tree.

**Author Contributions:** Conceptualization, A.J.G. and M.R.R.; methodology, A.M.F.-S. and A.J.G.; formal analysis, A.M.F.-S., M.R.R., and A.J.G.; investigation, A.M.F.-S. and A.J.G.; resources, A.J.G. and M.R.R.; data curation, A.M.F.-S.; writing—original draft preparation, A.J.G. and M.R.R.; writing review and editing, A.M.F.-S., M.R.R., and A.J.G.; supervision, M.R.R. and A.J.G.; project administration, A.J.G.; funding acquisition, A.J.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, grant number RTA2005-00076 and RTA2008-00019, and Ana M Fernández-Sanz is recipient of a grant from the "Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria", both co-funded by the European Social Fund. The APC was funded by Consejería de Medio Rural y Cohesión Territorial, Government of the Principality of Asturias.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The *gyrB*, *rpoD* and *gltA* sequences have been deposited in GenBank under accession numbers shown in Table S2.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Mediterranean Diet and Physical Activity as Healthy Lifestyles for Human Health**

Editor

**Daniela Bonofiglio**

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This is a reprint of articles from the Special Issue published online in the open access journal *Nutrients* (ISSN 2072-6643) (available at: https://www.mdpi.com/journal/nutrients/special issues/ Mediterranean Diet Human Health).

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*Article* **Fungal Biomarkers Stability in Mars Regolith Analogues after Simulated Space and Mars-like Conditions**

**Alessia Cassaro 1 , Claudia Pacelli 1,2,\* , Mickael Baqué 3 , Jean-Pierre Paul de Vera 4 , Ute Böttger 5, Lorenzo Botta 1 , Raffaele Saladino 1 , Elke Rabbow 6 and Silvano Onofri 1**

**Abstract:** The discovery of life on other planets and moons in our solar system is one of the most important challenges of this era. The second ExoMars mission will look for traces of extant or extinct life on Mars. The instruments on board the rover will be able to reach samples with eventual biomarkers until 2 m of depth under the planet's surface. This exploration capacity offers the best chance to detect biomarkers which would be mainly preserved compared to samples on the surface which are directly exposed to harmful environmental conditions. Starting with the studies of the endolithic meristematic black fungus *Cryomyces antarcticus*, which has proved its high resistance under extreme conditions, we analyzed the stability and the resistance of fungal biomarkers after exposure to simulated space and Mars-like conditions, with Raman and Gas Chromatography–Mass Spectrometry, two of the scientific payload instruments on board the rover.

**Keywords:** spectroscopy; Mars exploration; life-detection; pigments; nucleic acids


*1.9.1 AMPL*

AMPL, an acronym for "A Mathematical Programming Language" is a comprehensive and powerful algebraic modeling language for linear and nonlinear optimization problems, with discrete or continuous variables. It is a language for solving high complexity problems for large scale mathematical computation. It was developed by Robert Fourier, David Gay and Brian Kernighan at Bell Laboratories [1]. By using AMPL, we can get the solution of a problem in which the model of the formulation with sets, variables, parameters, constraints, etc. are written in a mod. file and the data of the formulation are written in a dat. file. Then the solution is found after running the program in the console window.