Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis
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Background and aims
Plant growth promoting rhizobacteria (PGPR) have been shown to reduce abiotic stress on plants, but these effects have not been quantitatively synthesized. We evaluated the degree to which plant growth promoting rhizobacteria (PGPR) improve plant performance with and without drought stress.
We used meta-analysis to summarize 52 published articles on the effects of PGPR on root mass, shoot mass and yield under well-watered and drought conditions. We also asked whether fertilization treatments, experimental conditions, inoculum taxonomic complexity, plant functional group, or inoculum delivery method introduce variation in the effect size of PGPR.
Across all treatments, plants were highly responsive to PGPR; under well-watered conditions, root mass increased by 35%, shoot mass increased by 28%, and reproductive yield increased by 19%. Under drought conditions, the effect was even higher: root mass increased by 43%, shoot mass increased by 45%, and reproductive yield increased by 40%. The effect of PGPR was significantly larger under drought for shoot mass (p < 0.05) and reproductive yield (p < 0.05), but not for root mass. PGPR responsiveness also varied according to plant functional group, with C3 grass shoot production responding the least strongly to PGPR.
We demonstrate that PGPR are highly effective for improving plant growth, with a greater effect under drought for above ground traits. While previously known for their bio-control abilities, we show that PGPR may also contribute to drought amelioration and water conservation.
KeywordsDrought PGPR Biofertilizer Plant productivity Agriculture Irrigation Meta-analysis
By 2050, the world’s population will exceed 9.1 billion (Carvalho 2006), and food demand is expected to more than double (Green et al. 2005). Although the Green Revolution has vastly increased food production over the past 50 years, drought and salinization threaten more than 50% of earth’s arable lands. In 2003, a continental wide drought reduced Europe’s gross primary productivity by 30% (Ciais et al. 2005), and a severe drought in 2012 reduced US maize yields by 25% (Rosenzweig et al. 2014). In addition to causing plant mortality, drought increases demand for irrigation, which already comprises 70% of global water consumption (Shiklomanov and Rodda 2003), and is expected to rise 10% by 2050 due to climate change alone (Wada et al. 2013). Thus, it will not be possible to combat drought by simply increasing irrigation infrastructure.
To address this growing socioeconomic and ecological crisis, efforts have been devoted to understanding mutualisms in agricultural ecosystems (Wall and Moore 1999; Kiers et al. 2002). This approach was motivated by the stress gradient hypothesis, which posits that inter-specific interactions shift from competitive to facilitative under increasing abiotic stress (Bertness and Callaway 1994). Originally describing interspecific facilitation among salt marsh grasses, the stress gradient hypothesis has also been observed in mutualistic assemblages. For example, positive interactions between mussels and cordgrass increased under drought conditions (Angelini et al. 2016), mutualisms among ants, trees, and scale insects intensified along a precipitation gradient (Pringle et al. 2013), and arbuscular mycorrhizal inoculation increased stomatal conductance more under drought conditions than under well-watered conditions (Augé et al. 2015).
Plant growth promoting rhizobacteria (PGPR) directly associate with plant roots and can exist within root tissue (as endophytes), on the surface of plant roots (the rhizoplane), or within the zone of soil specifically influenced by the root system (the rhizosphere). Characterized by their plant-growth promoting properties, PGPR are a diverse group of bacteria that produce a wide range of enzymes and metabolites, influence nutrient acquisition, modulate hormone levels, and ameliorate the negative impacts of biotic and abiotic stressors (Kloepper 1992; Rudrappa et al. 2008; Hartmann et al. 2009; Lugtenberg and Kamilova 2009; Beneduzi et al. 2012; Ahemad and Kibret 2014; Ngumbi and Kloepper 2016). The term “PGPR” was first introduced in the late 1970’s, when it was shown that fluorescent Pseudomonas strains improved potato yields by up to 500% by producing iron chelating siderophores, depriving native bacterial pathogens of iron (Kloepper et al. 1980a, b). Later, it was shown that PGPR activate several stress response genes, suggesting that biotic and abiotic plant defenses may be co-regulated (Timmusk and Wagner 1999).
PGPR are varied in their chemical and physical mechanisms of plant growth promotion under drought. For example, Bacillus licheniformus can produce ACC deaminase, which degrades ethylene, a plant hormone that is responsible for reduced root and shoot growth under drought (Lim and Kim 2013). Additionally, PGPR can physically make soils more drought adapted through the production of an extracellular matrix containing oligo- and polysaccharides that increase water retention capacity (Naseem and Bano 2014; Timmusk et al. 2014). The use of PGPR for drought amelioration is a burgeoning field, and two recent review papers (Ngumbi and Kloepper 2016; Vurukonda et al. 2016) extensively review the drought tolerance and drought avoidance mechanisms discovered thus far, including enhanced root architecture, osmotic adjustment, and antioxidant metabolism.
As a potential management tool to increase plant growth, PGPR have several practical advantages: 1) they are widespread, found on all 7 continents (Sahai 1999; Berríos et al. 2013), 2) they are not phylogenetically constrained, and occur within at least 5 phyla (Egamberdieva et al. 2015) and 24 genera (Barriuso et al. 2008; Babalola 2010), 3) they can be dried and stored, with a shelf life of up to 8 months (Nakkeeran et al. 2005), and 4) most exhibit low host-specificity — many plants will maintain symbioses with introduced PGPR, even when the bacterium has been isolated from a distantly related host plant (Bashan 1988; Antoun et al. 1998; Marasco et al. 2013; Timmusk et al. 2014). Today, 60–75% of cotton, peanut, soybean, corn, vegetables, and small grain crops are treated with Bacillus subtilis as a seed coating, seedling dip or rhizosphere injection to control soil borne pathogens including Fusarium and Rhizoctonia (Nakkeeran et al. 2005).
To what degree does PGPR inoculation improve root mass, shoot mass and yield?
Does drought stress influence the magnitude of this benefit?
Which moderators influence variation in plant response to PGPR inoculation?
Materials and methods
Literature search & data extraction
We conducted an exhaustive search of published literature using ISI Web of Science and Google Scholar to locate PGPR and drought literature. A preliminary screening revealed that shoot mass, root mass, and reproductive yield were the most commonly reported performance metrics. Using the search terms “PGPR and drought” and “rhizobacteria and drought”, we conducted a search on both databases on September 20, 2015, yielding 370 titles from Web of Science and 1375 titles from Google Scholar published between 1985 and 2015. After screening titles for relevancy and filtering duplicates, we reviewed the remaining 314 papers by reading the abstracts, and if necessary, the full paper. These 314 papers were reduced to 52 final papers based on the following criteria: 1) studies must have been written in English; 2) studies must have measured shoot mass, root mass or reproductive yield; 3) studies must have imposed a full factorial drought × PGPR inoculation design; and 4) studies must have imposed drought treatments through irrigation deficit. Studies that manipulated salinity or added polyethylene glycol (PEG) were excluded, due to confounding issues with plant nutrient uptake (Burnett et al. 2005). All PGPR taxa were included in this meta-analysis, including nodule-forming Rhizobium.
For root mass and shoot mass, we extracted data from dry mass when available in order to control for potential differences in plant water content due to PGPR. If fresh mass was reported but dry mass was not reported, we extracted data for fresh mass. For yield, we extracted seed mass when available, but if seed mass was not reported, we extracted values for grain count. The level of drought stress was described differently in each paper, with metrics ranging from volumetric water content, number of days without water, to the degree of evaporation, making it impossible to quantify drought severity across citations. Thus, when citations employed multiple drought levels, we extracted data from the most extreme drought treatment available.
We extracted mean values and variances (standard deviation, standard error) for shoot biomass, root biomass, and reproductive yield from all combinations of drought, well-watered, inoculated and non-inoculated treatments directly from tables, when provided. For figures, we extracted data using Image J 1.5 (Abramoff et al. 2004). We assigned yield measurements to biologically relevant categories. For example, for studies on lettuce and asparagus, “aboveground yield” was classified as shoot mass. For repeated measures studies, we extracted data from the final time-point. Citations and observations that applied amino acids or growth promoting hormones were excluded, as were co-inoculations with mycorrhizal fungi, in the interest of examining the sole impacts of PGPR.
When provided, measures of variance were extracted directly. For studies that included a variance but did not specify the variance measure, we conservatively assumed it to be standard error. We imputed missing variances using the average coefficient of variation (cv value) for inoculated and control plants (Wiebe et al. 2006; van Groenigen et al. 2011) (Supplementary Data File 1). Imputing the variance allowed us to include 279 out of 448 total observations, and enabled us to construct meta-regression models, which are necessarily weighted on the inverse of the variance (Koricheva et al. 2013).
We also recorded categorical metadata on experimental conditions (greenhouse vs. field), soil fertilization treatments (unfertilized, organic fertilizer, or inorganic fertilizer), inoculation method (seed, seedling, or soil), inoculant taxa richness (single taxon or multiple taxa), and plant functional group (forb, legume, shrub, C3 grass, and C4 grass) to evaluate them as moderators. Unfertilized studies were differentiated into two groups: unfertilized soil that had either been left intact, or steam sterilized to kill indigenous soil microbes. Inoculum delivery method was differentiated by soil applications (either topically applied or injected), seedling dips, or seed coatings.
This meta-analysis was based on 52 papers published in 44 unique journals, with lead authors from 18 countries. These papers yielded a total of 448 total observations, where each observation includes a treatment (inoculated) and control comparison. Some of these observations had independent controls, and some had shared controls (see Sensitivity Analysis, below). There were 140 observations from 27 papers on root mass, 162 observations from 31 papers on shoot mass, and 146 observations from 26 papers on reproductive yield. The primary plant species used in this analysis were Zea mays (maize), (27%), Triticum aestivum (common wheat) (12%), Helianthus annuus (common sunflower) (10%), and Lactuca sativa (lettuce) (8%). The primary bacterial genera used were Pseudomonas (35%), Azospirillum (35%), Azotobacter (13%) and Bacillus (8%). The majority of the observations in this meta-analysis were conducted in the greenhouse: 2/3 of the papers contained at least one greenhouse manipulation, whereas only 1/3 of the papers contained at least one field manipulation. Greenhouse studies typically applied additional manipulations in addition to PGPR and drought (e.g. different plant cultivars or PGPR types); thus, greenhouse studies comprised 3/4 of the total observations in this meta-analysis (Supplementary Data File 1). However, the majority of “yield” papers (70%) were conducted in the field rather than the greenhouse.
To estimate the overall effect of PGPR on root mass, shoot mass, and yield under well-watered and drought conditions, we used the log response ratio as the metric of effect size: LnR = ln (Vi/Vc), where Vi is the treatment (inoculated) mean and Vc is the control (non-inoculated) mean (Hedges et al. 1999). The response ratio is useful when different units are reported across studies (grams/pot vs. kg/ha for example), and the log transformation is needed to maintain symmetry within the analysis (Borenstein et al. 2009). Furthermore, it can easily be transformed to percent change using the formula: %Δ = 100* (exp.(LnR) − 1). We calculated associated pooled variances using the “escalc” function in the metafor package in R (Viechtbauer 2010).
Before building meta-analysis models, we conducted a heterogeneity test to determine whether a fixed effects approach or a random/mixed effects approach was appropriate. Heterogeneity (Q) on the full dataset was highly significant (Q = 8874.6, df = 447, p < 0.0001), indicating that a random/mixed effects approach was warranted. All subsequent meta-analyses were performed as multivariate random or mixed-effects meta-analysis models using the “rma.mv” function in metafor (Viechtbauer 2010) and weighted using the inverse of the variance in effect size (Hedges et al. 1999). This approach enabled us to simultaneously account for observation-level (σ2 obs) and citation-level (σ2 citation) variation by treating them as random effects. To calculate the estimated effect of PGPR under drought and watered conditions, we extracted parameter estimates and 95% confidence intervals from multivariate mixed-effects meta-regression models performed on six subsets of data (root mass, watered; root mass, drought; shoot mass, watered; shoot mass, drought; yield, watered; yield, drought).
To evaluate whether the beneficial effects of PGPR are greater under drought conditions, we used two different approaches: 1) We used the effect sizes as described above and evaluated “drought” as a fixed effects covariate in a mixed effects model for each dataset for root mass, shoot mass and yield; and 2) Utilizing the full factorial design of each study, we calculated a second effect size: Rdiff = ln (Vdi/Vdc) - ln (Vci/Vcc), where Vdi is the value under drought and PGPR inoculation, Vdc is the value under drought and no PGPR, Vci is the value under well-watered conditions with PGPR inoculation, and Vcc is the value under well-watered conditions and no PGPR (Lajeunesse 2011; Koricheva et al. 2013). This composite effect size shows the average additional gain from PGPR inoculation when the plant is grown under drought conditions, at the study level. We calculated the associated variance as the sum of the pooled variances for the effect sizes of PGPR under drought and control conditions, σ2(Rdiff) = σ2 ln (Vdi/Vdc) + σ2 ln (Vci/Vcc).
We evaluated the following moderators that might introduce variation in plant response to PGPR: fertilization treatments, inoculum complexity, growth conditions, plant functional group, and inoculum delivery method. First, we filtered moderators that had a minimum number of 12 observations, to ensure that moderators contained at least three papers. Next, we used several methods to evaluate moderators that introduce variation in plant response to PGPR. To construct forest plots, we plotted parameter estimates and 95% confidence intervals for individual random effects multivariate meta-regression models (rma.mv function in metafor) conducted on subsets of data for each available moderator that met minimum sample size requirements. Next, to determine which moderators influence significant variation in the effect size, we performed five additional mixed effects multivariate regression models (one for each moderator category), on the root mass, shoot mass, and yield datasets, with each moderator as a fixed-effects covariate and observations and citations as random effects. Forest plots were constructed using the “ggplot2” package (Wickham 2009) in R Statistical Software (Version 3.2.2, R Core Team 2015). Last, we used the glmulti package (Calcagno 2013) to evaluate the most parsimonious set of parameters that can help to explain root mass, shoot mass, and yield responses to PGPR (Supplementary Table 3).
We considered two potential sources of non-independence within this meta-analysis. The first source is when multiple observations come from the same citation, which we accounted for as a random effect in our random effects and mixed effects meta-analysis models. The second source of non-independence comes from shared controls within papers. For example, Bano et al. (2013) compared the effectiveness of seed inoculation vs. soil inoculation with a shared, inoculated control, Barnawal et al. (2013) examined the effects of two different bacterial species (Bacillus subtilis, Ensifer meliloti) against one shared, un-inoculated control, and Tittabutr et al. (2013) evaluated whether inoculum diversity influenced plant performance by inoculating with Bradyrhizobium sp. alone, and with Enterobacter sp. We compared the overall effect sizes under drought and watered conditions due to shared controls (see Supplementary Table 1); none of our conclusions were affected.
Differential assessment of merit at any stage of the process of preparing a manuscript can result in suppression of non-significant findings (the “file-drawer effect”) (Koricheva et al. 2013). We tested for publication bias by visually inspecting a funnel plot. We also tested for asymmetry using a trim-and-fill analysis in metafor and estimated that there were zero studies missing from the left side of the plot. Lastly, we calculated a fail-safe number, which asks how many observations (Nobs) averaging an effect size of zero would need to exist to negate the significance of our observed effects of PGPR (Koricheva et al. 2013). We calculated Rosenberg’s fail-safe N and determined that 113,317 studies with null results for root mass, 137,680 studies with null results for shoot mass, and 11,060 studies with null results for reproductive yield would have to be added to the given set of observed outcomes to reduce the combined significance level of p = 0.05.
Effects of PGPR under drought and watered conditions
Agricultural intensification has been responsible for dramatic increases in crop yields over the past 50 years (Evenson and Gollin 2003). However, continued intensification is unsustainable under climate change and associated drought, which threatens both irrigated and rain-fed agricultural systems (Kijne et al. 2003). This meta-analysis indicates that PGPR can substantially improve plant performance, with a greater effect under drought for shoot mass and reproductive yield. These results have implications for the commercialization of PGPR, as drought conditions are expected to become more frequent and severe (Wang 2005; Dai 2013).
Across treatments, the effects of PGPR were of a similar order of magnitude as arbuscular mycorrhizal fungi, which have been shown to improve yields between 30% under drought stress (Jayne and Quigley 2014) and 52% under salt stress (Chandrasekaran et al. 2014). As plant mutualists, PGPR and AM fungi co-evolved with their plant hosts and each other (Revillini et al. 2016). Thus, mutualisms may be enhanced through interactions among PGPR and AM fungi; for example, Bacillus megaterium increased mycorrhizal root colonization from 40% to 70% in drought-stressed clover roots (Ortiz et al. 2015), AM fungi may act as a vehicle to spread PGPR to neighboring rhizospheres (Bianciotto and Bonfante 2002), and phosphorus solubilizing PGPR could enhance P foraging efficiency of extraradical hyphae (Toro et al. 1997; Bianciotto and Bonfante 2002). However, interactions between PGPR and AM fungi may also be antagonistic or commensal under certain conditions (Wu et al. 2005), and there is no general consensus of the expected additional gain in plant growth under AM-PGPR co-inoculations.
Our findings for the effects of PGPR on seed yield (15%) of C3 grasses were slightly higher than the effects of Azospirillum inoculation on wheat yield, also a C3 grass (9%) (Veresoglou and Menexes (2010). However, for shoot mass, our estimate of 5% was slightly lower than their estimate of 18%. Although we did not find a consistent pattern for plant functional group responses to PGPR across the three metrics of root mass, shoot mass, and yield, C4 grasses on average responded more strongly to PGPR. This finding has also been observed in empirical and meta-analytical studies of AM fungi, with greater increases in plant growth for C4 grasses than C3 grasses (Hetrick et al. 1988, 1990; Wilson and Harnett 1997, 1998; Hoeksema et al. 2010; others). These data suggest that PGPR formulations would be a worthwhile pursuit for C4 grasses such as corn, but may exhibit less potential within C3 grasses such as wheat and rice.
Under artificial selection for high yield varieties, plant-soil mutualisms may be compromised (Dasilva et al. 1977; Kiers et al. 2002; Kiers et al. 2007; Pérez-Jaramillo et al. 2016). For example, modern wheat cultivars show reduced dependency on mycorrhizae compared to cultivars released before 1950 (Hetrick et al. 1993), and soybean artificial selection has been shown to impair host plant defense against ineffective rhizobia (Kiers et al. 2007). While modern cultivars may have lost some of the traits necessary to recruit host-specific root microbiota (Pérez-Jaramillo et al. 2016), our study demonstrates that high-yield varieties consistently benefit from inoculation with PGPR. To maximize these benefits, we suggest that plant breeders also select for the ability to maintain symbioses, which appears to be a heritable trait (Kiers et al. 2002).
Furthermore, PGPR that evolved under stressful abiotic conditions could be leveraged in commercial PGPR formulations. For example, several bacterial strains isolated from dry environments have been shown to produce indole acetic acid and increase leaf proline content (Marulanda et al. 2009). Furthermore, bacteria isolated from a south (African) facing, high stress slope in Evolution Canyon, Israel, were more likely to solubilize phosphorus, form biofilms, and produce ACC deaminase than bacteria isolated from the lower stress, north (European) facing slope (Timmusk et al. 2011). Thus, screening bacteria that occur under natural drought conditions may yield optimal candidates for agricultural drought applications.
Management techniques that accompanied plant domestication, including fertilization, pesticide application and tilling can also influence plant-microbe mutualisms (Thomson et al. 1986; Treseder 2004). For example, long term (8 year) N and P addition selected for less mutualistic AM fungi in prairie grasses (Johnson 1993), and 22 years of N addition caused the evolution of less-cooperative rhizobia in several clover varieties (Weese et al. 2015). Furthermore, PGPR propagation in maize roots was inhibited when soil ammonium nitrate concentration exceeded 200 mg kg (Wu et al. 2005). We found that fertilization influenced root mass response to PGPR, with the greatest response under organic fertilization. One possibility is that organic fertilizers typically have a higher C content and a lower nutrient content that is designed to be “slow release”, providing resources and conditions that favor PGPR growth. However, there was no generalizable trend across the three metrics of root mass, shoot mass and reproductive yield. With harvest yield as the primary goal, we suggest that PGPR application may be compatible with moderate fertilizer use. Prior research has shown that select strains of N-fixing and P solubilizing PGPR allow for a 25%–50% reduction in NPK fertilizer application rates (Adesemoye et al. 2009; Kumar et al. 2009) without sacrificing yield. Thus, “bio-fertilizer” PGPR effectiveness may be limited by key fertilization application thresholds (Treseder and Allen 2002; Shukla et al. 2012); future research should address whether these thresholds are affected by drought.
Our finding that reproductive yield was significantly higher in the greenhouse is consistent with the notion that greenhouse conditions can magnify treatment effects (Hester and Harrison 2005), and suggests that the effect sizes we observed may be smaller under conditions typical of agricultural operations. We urge caution in evaluating these results because the majority of the papers included in this study were greenhouse studies, and most agricultural operations occur in the field. Within our meta-analysis, the subset of studies that measured reproductive yield in the field are the most similar to genuine agricultural systems, because plants are harvested at reproductive maturity and because field soil contains indigenous bacterial flora. It is also important to note that the way that drought is applied in the greenhouse (continuous drought), can differ from the way drought is typically experienced in the field (cyclic drought). In general, PGPR effects are likely context dependent, and the large confidence intervals for both greenhouse and field studies may reflect differences in site conditions and the ways that drought and PGPR treatments were applied.
Prior research has shown that the root microbiome has the greatest effect on plants during the early stages of development (Bell et al. 2015). Our finding that seed coatings resulted in the largest increase in shoot mass is likely due to an increased concentration of PGPR around the germinating plant, facilitating recruitment and assimilation of PGPR into the root microbiome. We recommend the use of seed coatings to maximize PGPR effectiveness; however, further research on the effects of different carriers (e.g. talc or guar gum) on inoculum viability and performance (Nakkeeran et al. 2005; Malusà et al. 2012) is still needed.
Agricultural soils often exhibit reduced microbial community complexity compared to natural systems (Levine et al. 2011; Hartmann et al. 2015). In severe cases of soil degradation, growers may choose to inoculate using multi-taxon blends, as a “consortium” (Naseem and Bano 2014). We did not find any differences in effectiveness between single taxon and multiple taxa inoculations; however, studies applied three taxa at most, so we were unable to fully explore the effects of microbial diversity on agricultural productivity. A significant hurdle remains in addressing whether commercial PGPR, indigenous PGPR, dark septate endophytes and AM fungi can coexist in the rhizosphere or whether they undergo competitive exclusion (Requena et al. 1997; Barea et al. 2002; Kiers et al. 2002; Waag et al. 2011). Further research should address the longevity of inoculants applied in the field, as well as interactions with indigenous flora.
Overall, this study demonstrates that PGPR can substantially enhance plant growth, with shoot mass increasing the most from PGPR. PGPR also increased shoot mass and yield by a greater magnitude under drought than under well-watered conditions. Our findings suggest that PGPR can be used in situations other than biological control, the feature that PGPR are best known for. Furthermore, arid regions of the developing world may benefit the most from PGPR because these regions are characterized by rapid population growth and are also the most vulnerable to climate change (Schmidhuber and Tubiello 2007). Lastly, improved understanding of “mycorrhizosphere” interactions (Barea et al. 2002) is imperative in order to preserve crop yield stability under an increasingly unstable climate.
We would like to acknowledge Dr. George Koch, Molly Shuman-Goodier, Brianna Finley and Elaine Pegoraro for assistance in reviewing this manuscript. Dr. Liza Holeski and Dr. Nathan Nieto provided conceptual feedback during the formulation of this study. We would also like to acknowledge three anonymous reviewers for their helpful critiques and suggestions. Funding to support Rachel Rubin was provided by NSF Dimensions of Biodiversity award no. 1241094, the ARCS Phoenix AZ Chapter, and the Soil Science Society of America (SSSA) Francis and Evelyn Clark Soil Biology Scholarship.
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