Whether on farmland or in a forest, plants do not grow in isolation. Plants compete with their neighbors over limited space and resources, and individual plants respond to this competition in different ways by changing how much they grow and how they use resources. The efficiency with which crop plants use water, for example, is one trait that is dramatically influenced by neighboring plants and is of increasing concern given the warming climate.

Understanding the effects of interactions between individual plants in a population as a whole is complicated, especially in natural plant communities where neighbors are often from different species. For this reason, McGale et al. took a different approach and looked at neighbors that were all from the same species and differed only in the activity of a single gene. The species in question was coyote tobacco, a plant that is native to western North America.

McGale et al. used genetic engineering to silence a gene called MPK4, which was known from previous studies to have the effect of reducing water-use efficiency. Some of these ‘water-inefficient’ plants were then grown in mixed populations with plants that had normal levels of MPK4. In experiments conducted both in a glasshouse and at a field station in the Utah desert, McGale et al. found that populations with a low percentage of the MPK4-silenced plants were actually more productive than ‘monocultures’ that were all one type or the other.

Further analysis showed that the increase in productivity did not depend on the different soil nutrient or water use of the different populations, or even the density of the plants in the populations. Pairs of plants grown in single pots essentially ruled out any interactions between immediate neighbors being responsible for the increased productivity, suggesting that that effect must instead emerge at the level of the population.

Perhaps unexpectedly, McGale et al. also found that the MPK4-silenced plants and control plants did not actually differ in how they used water when grown in the field (previous studies had all been conducted in glasshouses), indicating that this trait also could not explain the observed population-level effect. Finally, experiments that involved grafting the shoots of one plant onto the roots of another suggested that the effect most likely comes from the aboveground parts of the plant.

Ecologists have previously noted that more diverse populations typically have higher productivity. This new finding that a small percentage of slightly different plants in an otherwise uniform population can increase overall productivity will likely to be of special interest to researchers looking to boost the efficiency of agricultural ecosystems. Also, since MPK4 is highly conserved, and thus likely to be found in many plant species, this could be an interesting trait with which to study the interactions of natural plant communities.

Plant trait diversity is known to increase the productivity and stability of plant populations (Cardinale et al., 2012; Isbell et al., 2017; Isbell et al., 2015; Liang et al., 2016; Loreau and de Mazancourt, 2013). These effects may be produced from single gene variation among individuals: previously, wheat populations comprising of two lines varied only in their resistance to powdery mildew through single-gene modifications outperformed monoculture populations, even when the monocultures were comprised of resistant individuals (Zeller et al., 2012). Additionally, forward genetics research recently identified a genetic locus associated with this diversity-productivity relationship (Wuest and Niklaus, 2018). However, establishing the mechanism that confers population productivity from a single genetic basis is difficult due to the complexity of plant population experiments. When individuals varying in traits or in loci of interest are planted in pairs, it is possible to conclude that their responses are due to the neighbor (Gibson et al., 1999; Harper, 1977). When planted in populations, however, it is unclear whether a plant responds only to properties of its direct neighbors or of the entire population (Gibson et al., 1999; Radosevich, 1987). The scale, or the hierarchical level in biological organization (Allen and Starr, 1982), at which diversity-productivity effects are constrained has not yet been determined, despite their importance in the study of complex population interactions (Schneider, 2001). Here, we define and investigate two spatial scales within populations that can be responsible for changes in total yield (Figure 1): the neighbor scale, where responding individuals (RIs) are constrained to change their growth and yield (quantified in biomass and fitness correlates, that is flowers and seed capsules) only in response to direct neighbors that differ in trait expression (divergent plants); and the population scale, where RIs can respond to the total composition of divergent plants in the entire population, creating a change in total population yield in direct proportion to RI abundance (Crawford and Rudgers, 2012; Hughes et al., 2008; Smith and Knapp, 2003).

Figure 1

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Water-use traits are known to result in changes in total population yield (Caldeira et al., 2001; Comas et al., 2013; Forrester, 2015; Kimball et al., 2014; Marguerit et al., 2014; Wang et al., 2016; Wu et al., 2016). However, the scale at which this occurs (neighbor or population) remains unknown. WUE naturally varies among individuals, both within and among species (Anderegg, 2015; Donovan et al., 2007; Heschel et al., 2002; Tortosa et al., 2016; Yoo et al., 2009) and intraspecific variation in WUE traits can be as great as interspecific variation (Messier et al., 2010). Yield effects resulting from intraspecific WUE trait variation are of considerable agricultural interest (Dutra et al., 2018; Sreeman et al., 2018). Interestingly, WUE traits of some trees species alter the photosynthetic parameters and survival of neighboring trees (Bunce et al., 1977), suggesting potential neighbor-scale responses that can dramatically influence the yield of populations. However, studies have not pursued how WUE trait variation cause either population-scale or neighbor-scale responses that are responsible for changes in population growth and yield due to the complications that emerge in studying variations in WUE phenotypes.

To adequately study the scale at which RIs respond to variation in WUE of neighbors, one needs to anticipate several factors that would confound the analysis. WUE is calculated as the ratio of carbon assimilation to transpirational water loss, and WUE phenotypes typically result from altered stomatal function that increases plant transpiration. As the frequency of plants with low WUE (high transpiration) increases in a population, the availability of soil water to the population is known to decrease proportionally (Zea-Cabrera et al., 2006). RIs may change their growth and yield in response to differences in soil water availability, rather than to the abundance of low-WUE plants in a population. Therefore, controlling for soil water availability independently of the frequency of plants with different WUE traits is essential for the analysis. The ecological relevance of variation in WUE traits is best evaluated in field populations, but standardizing water availability across populations in the field is rarely possible and thus combining inferences from field and glasshouse experiments, where soil water availability can be controlled using gravimetrically controlled watering, provides a useful way forward. Plants that vary in WUE as a result of single-gene manipulations greatly facilitate investigations into the scales at which yield responses are realized in populations. Here, we use isogenic plants, silenced in the expression of a single gene that profoundly influences stomatal behavior, to explore the scale at which WUE variation influences population yields.

Mitogen-activated protein kinases (MAPKs) are part of a conserved signaling cascade essential in eukaryotes. The downstream targets of this phosphorylation cascade, such as transcription factors, enable specific plant responses through changes in plant growth and development (Xu and Zhang, 2015). Mitogen-activated protein kinase 4 (MPK4) in Nicotiana attenuata and its homologues, MPK12 in Arabidopsis thaliana and MPK4/MPK4L in N. tabacum, have been implicated in responses to herbivore damage (Gomi et al., 2005; Hettenhausen et al., 2013; Yanagawa et al., 2016), bacterial inoculation (Hettenhausen et al., 2012), changes in exogenous and endogenous abscisic acid (ABA) and hydrogen peroxide levels (Des Marais et al., 2014; Hettenhausen et al., 2012; Jammes et al., 2009), vapor pressure deficits (Des Marais et al., 2014) and ozone levels (Gomi et al., 2005; Yanagawa et al., 2016). Most of these responses involve the regulation of stomatal structure and function: silencing NaMPK4 or NtMPK4/L by RNA interference (Na-irMPK4 and Nt-MPK4/L-IR, respectively) or knocking out AtMPK12 (At-mpk12) results in plants with larger stomata and stomatal apertures, and varying disruptions in stomatal closure (Des Marais et al., 2014; Gomi et al., 2005; Hettenhausen et al., 2012; Marten et al., 2008; Yanagawa et al., 2016).

The alteration of stomatal phenotypes by MPK4/12 expression strongly influences WUE. Na-irMPK4, Nt-MPK4/L-IR and At-mpk12 all have increased transpiration rates which can be attributed to increased stomatal conductance (Des Marais et al., 2014; Gomi et al., 2005; Hettenhausen et al., 2012; Yanagawa et al., 2016). For Na-irMPK4 and At-mpk12, this increase in transpiration rates has been shown to dwarf the associated increases in assimilation rates, resulting in low WUE (Des Marais et al., 2014; Hettenhausen et al., 2012). However, previous glasshouse studies that tested whether the presence of MPK4/12-derived WUE phenotypes results in individual growth and yield effects in paired-plant-in-a-pot interactions did not control for soil water availability (Des Marais et al., 2014; Hettenhausen et al., 2012). To our knowledge, no study has investigated whether variation in the abundance of a low WUE trait, generated from the silencing of a single gene, affects population yield; similarly unstudied is the scale at which this might occur. Here, we conduct such a study using experimental N. attenuata populations in both the glasshouse and field.

The wild tobacco N. attenuata grows in xeric habitats in the western United States, where water limitation and WUE are selective factors throughout the growing season. N. attenuata typically grows in genetically diverse populations, in near-monocultures (Baldwin and Morse, 1994; Baldwin et al., 1994), and is known to respond differently when paired with genetically varied intraspecific neighbors: a N. attenuata accession collected in Utah (UT) sharing a pot with an accession collected in Arizona (AZ) produces significantly smaller stalks than when sharing a pot with another UT plant (Glawe et al., 2003). N. attenuata plants also naturally interact with AMF in the field, establishing networks of connected plants that have the potential to significantly change individuals’ responses to neighbors or populations (reviewed in Oelmüller, 2019). AMF are known to change soil water availability and transport among individuals in populations based on each individual’s ability to interact with AMF (Egerton-Warburton et al., 2007; Reynolds et al., 2003; Yang et al., 2013). Additionally, AMF have been shown to significantly affect plant-plant interactions in populations, as they can transfer nutrients, defense signals and allelopathic chemicals (Ferlian et al., 2018; Gorzelak et al., 2015; Song et al., 2019). Manipulating the ability of a population to interact through an AMF network can provide a means of dramatically altering belowground interactions and narrow the potential causes of population yield changes due to within- (or between-) species plant diversity (Figure 1). Calcium and calmodulin-dependent kinase (CCaMK) is required for successful plant symbiosis with AMF (Lévy et al., 2004), and the abrogation of CCaMK expression provides a valuable tool to disconnect plants from AMF networks in the field (Groten et al., 2015). Field plantations of transgenic N. attenuata crossed with CCaMK-deficient transgenic lines (irCCaMK) in the plant’s native habitat, the Great Basin Desert, allow for the study of population growth and yield effects resulting from trait variation, as well as the scales and tissues in which this variation occurs. Here, we use a single gene manipulation to create variation in WUE in the background of irCCaMK lines to separate the effects of AMF-mediated interactions in our analysis of the spatial scales at which variation in WUE traits influence population yields.

We investigated the spatial scales at which variation in abundance of low WUE N. attenuata plants, generated by the abrogation of MPK4 expression, change total population growth and yield. We used a previously characterized irMPK4 line (Hettenhausen et al., 2012) and varied the percentages of this line in field populations with empty-vector (EV) control plants, both crossed with either irCCaMK and EV lines, to manipulate connectivity to the AMF network. We observed increased yields, referred to as overyielding, in populations with low percentages of MPK4-deficient plants (‘low-irMPK4’), due primarily to increases in EV plant yield. To exclude soil water availability effects, we grew homozygous irMPK4 and EV lines in glasshouse populations under equal water availability and again observed overyielding in low-irMPK4 populations due to increases in EV yield. We further tested responses at the neighbor scale by growing mono- and mixed-genotype pairs of EV and irMPK4 under conditions of equal water availability and found no changes in growth or yield between pair types. We analyzed the yield of individuals with different configurations of immediate neighbor genotypes in our glasshouse populations, but these also did not explain changes in individual yields. From these results, we conclude that neighbor-scale responses are unlikely to be responsible for the overyielding phenomena. In the glasshouse, changes in EV plants’ photosynthetic parameters did not explain the yield increases in low-irMPK4 populations and importantly, EV and irMPK4 plants did not differ in their WUE phenotypes in field populations. Therefore we inferred that irMPK4’s WUE phenotype was not responsible for the observed overyielding at the population scale. In neighbor absence-presence paired tests, we observed that EV plants change their growth and yield when planted with or without a neighbor, while interestingly, irMPK4 plants do not. In similar experiments with EV shoots micro-grafted to irMPK4 roots, MPK4 expression in the shoot could remediate this effect, demonstrating a shoot-localized role for MPK4 in N. attenuata’s ability to alter yield in the presence of a neighbor. Additionally, manipulating field population’s AMF connectivity did not change observed reproductive overyielding, denoting a lack of belowground influence on this effect. From these results, we suggest a novel function of shoot MPK4 in mediating N. attenuata’s yield response to neighbors, unrelated to WUE, which in low-irMPK4 populations may result in reproductive overyielding.

Individual-level variation in resource-use traits, such as water-use efficiency (WUE), can change overall population yields (Campitelli et al., 2016; Kenney et al., 2014; Montazeaud et al., 2017). This occurs when plants respond to neighbors which are divergent in WUE with changes in growth and reproductive yield. However, the scale within a population’s hierarchical organization (Allen and Starr, 1982) at which this phenomenon occurs is not yet known: yield changes in responding individuals (RIs) may be triggered only in immediate neighbors (neighbor scale; Figure 1) or in individuals across a population (population scale). Additionally, interactions may occur at these scales above- or belowground. Our analyses revealed that low abundances of irMPK4 plants intermixed with EV plants result in higher yields for N. attenuata populations, both in the glasshouse and the field (Figure 3). This overyielding effect was not caused by differences in soil water availability, which was controlled for in the glasshouse (Figure 3H–M; Figure 3—figure supplement 5B and Figure 3—figure supplement 8; Figure 4), nor irMPK4’s WUE phenotype (Figure 2A), which was not different from the WUE of EV plants in the field (Figure 5). Interestingly, we find that yield-increasing responses in low-irMPK4 populations likely occurred aboveground, at the population scale (Figures 4, 5 and 7).

Given that manipulating mixing proportions (Weiner, 1980) and total density (He et al., 2005; Stachová et al., 2013) are known to change overyielding results in substitutive experiments, it was important to consider that our glasshouse populations (12 plants, 5 cm apart) differed in plant density from our field populations (four plants, 10 cm apart). However, given that the overyielding results were consistent between the glasshouse and field, we infer that in our case the overyielding observed in mixtures with low proportions of irMPK4 was not due to differences in total plant density of mixtures.

Based on previous neighbor-effect studies using low-WUE phenotypes, we initially hypothesized that the neighbor scale (Figure 1, black arrow) would be critical for producing individual changes in yield. For example, in a paired-plant competition experiment, an A. thaliana CVI mutant (low WUE) produced more seeds than Ler (high WUE) when both were planted with a control neighbor (Campitelli et al., 2016). When extrapolated to a population level, this result could predict a neighbor-scale change on population yield. Specifically, if one plant in each paired interaction were an RI, changes in populations yields would primarily occur when abundant RIs exist in the population in close proximity to few differentiated plants (i.e. populations with low percentages of well-spread differentiated individuals), creating many paired interactions without reducing RI numbers. Alternatively, if both plants were RIs, changed yields would be observed in populations with the either the highest abundance of interaction fronts between the two genotypes if the responses were in the same direction, or the lowest if they were in opposite directions. However, these possibilities rely on the assumption that a yield response is observed in response to trait variation in pairs. We therefore tested this pre-condition with EV and irMPK4 plants planted in monoculture and mixed culture pairs. Despite previously observed changes in neighbor responses to plants altered in their WUE (Campitelli et al., 2016), we did not see differences in any growth or yield parameters in mixed EV and irMPK4 pairs in comparison to monoculture pairs (Figure 4).

This discrepancy may be due to the difference between our methods and those of the earlier study: we controlled for water availability in our glasshouse experiments, to exclude that differences in water availability are driving the observed responses. Plants with low WUE can change the soil water availability in certain microenvironments and thus responses of neighbors due to the decreasing water table (Zea-Cabrera et al., 2006). However, we did not observe differences in soil moisture among field populations with different irMPK4 abundances (Figure 3—figure supplement 4). Even an additional watering treatment on a section of the field plot did not cause differentiated rates of drying-down among the different population types (Figure 3—figure supplement 4). We directly tested the influence of water availability in the glasshouse by controlling for soil water availability among populations (Figure 3H–M). We hypothesized that, with equalized soil water availability, we would not observe the overyielding response in low-irMPK4 populations. Interestingly, we again observed overyielding in low-irMPK4 glasshouse populations and therefore inferred the effect to be independent of soil water availability. Additionally, since controlling for soil water availability seemed to manipulate immediate neighbor plant interactions (i.e. differences between our results and Campitelli et al., 2016), the overyielding in controlled water glasshouse populations suggested that the effect was likely not mediated at the neighbor scale.

Paired-plant-in-a-pot experiments have only limited potential to study neighbor-scale interactions in population as individuals can only be observed in 0%, 50% and 100% irMPK4 mixtures (Figure 4). However, the growth and yield responses of plants at the neighbor scale could also depend on the identities of several immediate neighbors, or different percentages of immediate neighbors. Therefore, using a glasshouse population experiment, we compared the growth and yield of EV plants among a range of immediate irMPK4 neighbors (0 irMPK4 in 0% irMPK4 populations, 2 in 17% populations, and 4 in both 50% and 83% populations). We did not observe a correlation between the growth and yield of EV plants and the number of immediate irMPK4 neighbor plants (Figure 3—figure supplement 6). Based on these results and the lack of yield effects from our paired-pot experiments, we reject the hypothesis that the overyielding effects occurred at the neighbor scale, which suggest that the responses resulting in the observed population overyielding likely appear at the population scale.

At the population scale, we initially investigated known mechanisms through which individual plants could affect the yield generation of other individuals in the population, independent of changes in soil water availability. Trees with differing WUE influence photosynthetic parameters of neighboring trees (Bunce et al., 1977) and altered photosynthetic parameters of plants, including WUE, have been shown to cause significant yield changes in plants (Hatfield and Dold, 2019; South et al., 2019). Therefore, we examined the photosynthetic parameters of plants in the field and glasshouse populations. In the field, EV and irMPK4 plants did not differ in their photosynthetic parameters, including WUE (Figure 5C). In the glasshouse populations, irMPK4 showed a decreased WUE phenotype, but the photosynthetic parameters of EV and irMPK4 individuals in low-irMPK4 populations were not different from those in their respective monocultures (Figure 5B). From these results, we infer that the population-scale factor responsible for the low-irMPK4 population overyielding is independent not only of WUE, but also of the other previously observed photosynthetic phenotypes of irMPK4 plants, such as higher stomatal conductance, transpiration, and photosynthesis (Hettenhausen et al., 2012; Hettenhausen et al., 2013, Figure 5B).

Other factors that could cause the observed population-scale overyielding in low-irMPK4 populations include niche complementarity and the exchange of chemical signals. Hydrological niche partitioning could explain the increased population yield through a more diverse, and therefore more efficient, use of local water resources, either spatially or temporally (reviewed in Barry et al., 2019). In these instances, complementary plants may vary in above- or belowground water use through differences in water loss (e.g. transpiration, WUE; reviewed in Silvertown et al., 2015) or water acquisition (e.g. through root spatial distribution, Dimitrakopoulos and Schmid, 2004). However, the lack of differences in the WUE of irMPK4 plants and in the soil moisture among the different populations in the field, where overyielding effects were strongest (Figure 3—figure supplement 4), renders hydrological niche partitioning an unlikely explanation.

Even so, other types of complementarity could have occurred, for example through one neighbor type ameliorating the habitat of its neighbors. This typically occurs through abiotic means (i.e. changes in water or nutrient access; Bertness and Callaway, 1994; Wright et al., 2017) but again, the field results showed no differences in soil moisture or nutrient content among the soils of different population types (Figure 3—figure supplements 3 and 4). However, the habitat could also be improved biotically: it is known that the presence of divergent individuals in a population that are resistant to a particular plant disease can reduce the spread of the disease, therefore allowing control plants in the population to produce more yield than when in monoculture (Schmid, 1994; Zeller et al., 2012). Although irMPK4 plants were shown to be more susceptible to the bacterial pathogen Pseudomonas syringae pv tomato (Hettenhausen et al., 2012), having the presence of susceptible individuals could still confer benefits to a population by serving as disease sinks (see e.g. Keesing et al., 2006). Alternatively, aboveground spatial complementarity could have occurred, where irMPK4 plants’ smaller size (Hettenhausen et al., 2012) may have led to less competition for aerial space with EV plants, leading to their increased growth and population yield benefits (see e.g. Lorentzen et al., 2008; Williams et al., 2017). Although possible biotic factors could not be tested in the glasshouse, the role of spatial complementarity could be evaluated through a better understanding of the neighbor responses of EV and irMPK4 plants.

The response of a plant to a neighbor is commonly evaluated through comparisons of the plant’s growth and yield when planted alone and when planted in a pair (Díaz-Sierra et al., 2017). We therefore included single plants in our paired plant experiments (Figures 4 and 6). Interestingly, EV plants were the only individuals that responded with decreases in growth and yield to the presence of a neighbor (Figure 4). Although the yield in paired-plant experiments cannot be directly compared to the yield of EV plants in various population types, it suggests that EV plants are RIs: they change their growth and yield in response to neighbors, unlike irMPK4 plants (Figure 4). It is conceivable that EV plants could benefit from only certain percentages of irMPK4 plant neighbors to cause an emergent effect at the population scale if irMPK4’s consistent yield with or without a neighbor indicated a non-competitive response, though this cannot be distinguished in this study. However, complementarity effects would generally be expected to be strongest in equal mixtures, decreasing the likelihood of this possibility.

Volatiles can accumulate differently in the headspaces of various plant populations (Schuman et al., 2015) and have been shown to be a mechanism by which plants detect and respond to potentially competitive neighbors (Engelberth and Engelberth, 2019; Ninkovic et al., 2016; Pierik et al., 2013). irMPK4 and EV plants are known to emit distinct volatile profiles in response to herbivory: irMPK4 plants release 5x higher levels of trans-α-bergamotene than EV plants (Hettenhausen et al., 2012). As irMPK4 plants are inhibited in stomatal closure (Hettenhausen et al., 2012), they may continuously release a volatile or volatile blend that causes a population-scale effect only at a certain concentration (e.g. one that is generated in low-irMPK4 populations). This effect may be caused by a direct response of RIs, or might occur through interactions of the plant with various trophic levels, of which the latter has been observed to be volatile-mediated under field conditions (Joo et al., 2018). In N. attenuata, a population yield effect was previously shown to be affected by single gene variation through a tri-trophic interaction: plants silenced in one component of the jasmonic-acid defense pathway (i.e. defense-deficient plants) intermixed with wild-type controls increased the susceptibility of entire populations to a generalist herbivore, which then reduced total canopy damage by a successive specialist herbivore, and increased wild-type yield in mixed versus monoculture populations (Adam et al., 2018). NaMPK4 is known to silence part of a jasmonic acid defense pathway; the uninhibited pathway in irMPK4 plants reduces the mass of a specialist caterpillar feeding on this line (Hettenhausen et al., 2013). This reduction of mass is often associated with higher mortality in young larvae due to disrupted feeding (Cambron et al., 2019; McGale et al., 2018), which could then reduce the presence of this specialist herbivore in the population, therefore decreasing canopy damage, and increasing the yield of control plants. Different volatiles released from irMPK4 plants could enhance this sort of effect (i.e. increase oviposition of this herbivore on irMPK4 plants due to increased attractive volatile emissions; Kessler et al., 2008) or otherwise might facilitate alternate multi-trophic interactions.

Belowground, root exudates can accumulate in populations and provide information for plants about their neighbors’ identity and performance, which may result in plant growth responses (Semchenko et al., 2018). Root exudates can also promote certain microbial community in the rhizosphere that can confer benefits to the entire population (Berg and Smalla, 2009; Huang et al., 2014; Fincheira and Quiroz, 2018). However, irMPK4 root exudates have not yet been characterized. Further research on the chemical profiles and multi-trophic interactions of irMPK4 and EV plants in field populations is needed to identify potential above- or belowground factors which could cause responses at the population scale, but perhaps first narrowing the effect to either the above- or belowground scale would be helpful to study these potential mechanisms.

We sought to distinguish the influence of the above- or belowground scale on the observed overyielding by experimentally altering arbuscular mycorrhizal fungal (AMF) associations in field populations with varied amounts of MPK4-deficient plants. Although AMF associations have a strong impact on plants’ access to soil water and nutrients (Egerton-Warburton et al., 2007; Reynolds et al., 2003; Yang et al., 2013), as well as on belowground plant-plant interactions (Ferlian et al., 2018; Gorzelak et al., 2015; Song et al., 2019), we consistently observed reproductive overyielding in low-irMPK4 populations regardless of the AMF association (Figure 3; Figure 7). The lack of change in the reproductive overyielding pattern supports further attention to aboveground tissues for mechanistic determination, although it does not exclude other belowground interactions as potential causative factors.

To investigate the tissue dependence of the EV and irMPK4 plant neighbor responses we had previously studied in our paired experiment, we again used a single versus paired experimental set-up with grafted plants. The grafting experiment revealed that MPK4 transcript accumulation, or an MPK4-dependent downstream factor, is required in aboveground tissues for N. attenuata plants to be able to change their yield in response to neighbor presence (Figures 4F and 6E). Additionally, the presence of MPK4 transcripts in the shoot alone is sufficient to facilitate the yield-altering neighbor response, specifically in reproductive correlate production (Figure 7E). This tissue-specific role of MPK4 abundance in the shoot could be important in the mediation of reproductive overyielding in populations, but would need to be tested in a population experiment. Because the RNAi silencing signals travel from shoots to roots in N. attenuata (Fragoso et al., 2011), we were unable to investigate the function of shoot-only MPK4 knockdowns. The use of plants with mutant or natural alleles of MPK4 or homologs, such as those identified in A. thaliana (Des Marais et al., 2014), would allow for the analysis of reciprocal mpk12/wt and wt/mpk12 grafts. Together with a metabolic characterization (e.g. volatiles) and evaluations of multi-trophic interactions, these grafting experiments could identify the mechanisms mediating the observed population scale overyielding.

The results of our study are consistent with the well-established phenomenon that divergent individuals in a community can increase community productivity (Chapin III, 1997; Chapin, et al., 1998; Crutsinger, 2016; Hooper et al., 2005; Naeem et al., 1994; Schulze and Mooney, 1994) and advance our understanding by identifying the spatial scale (neighbors vs. population, above vs. belowground) at which this yield response occurs. Additionally, we identify a single gene, MPK4, which when silenced in low abundances in a population, is responsible for population overyielding. By excluding neighbor-scale effects and controlling resource availability, we demonstrate that MPK4 influences population yield through RIs at the population scale independent of water availability, but further experiments are needed to identify the specific mechanisms mediating this effect. This work contributes to our understanding of how populations may become more productive as a result of greater genetic and functional diversity and suggests that experiments exploring the scales at which these effects occur can identify novel means to increase the productivity of agronomic monocultures.

The datasets used in the final fittings of our linear (LM), generalized least-squares (GLS), or linear mixed effects (LME/R) models, from which statistical significances were extracted, are included as Source Data files in our submission. Each Source data file refers to the figure in which the corresponding data/results are displayed.

1. 1. Adam N
   2. Kallenbach M
   3. Meldau S
   4. Veit D
   5. van Dam NM
   6. Baldwin IT
   7. Schuman MC

   (2018) Functional variation in a key defense gene structures herbivore communities and alters plant performance

   PLOS ONE 13:e0197221.

   https://doi.org/10.1371/journal.pone.0197221

   • PubMed
   • Google Scholar
2. Book

   1. Allen TFH
   2. Starr TB

   (1982)

   Hierarchy: Perspectives for Ecological Complexity

   United States: The University of Chicago Press.

   • Google Scholar
3. 1. Anderegg WR

   (2015) Spatial and temporal variation in plant hydraulic traits and their relevance for climate change impacts on vegetation

   New Phytologist 205:1008–1014.

   https://doi.org/10.1111/nph.12907

   • PubMed
   • Google Scholar
4. 1. Baldwin IT
   2. Staszak-Kozinski L
   3. Davidson R

   (1994) Up in smoke: I. Smoke-derived germination cues for postfire annual,Nicotiana attenuata torr. ex. Watson

   Journal of Chemical Ecology 20:2345–2371.

   https://doi.org/10.1007/BF02033207

   • PubMed
   • Google Scholar
5. 1. Baldwin IT
   2. Morse L

   (1994) Up in smoke: ii. germination ofNicotiana attenuata in response to smoke-derived cues and nutrients in burned and unburned soils

   Journal of Chemical Ecology 20:2373–2391.

   https://doi.org/10.1007/BF02033208

   • PubMed
   • Google Scholar
6. 1. Barry KE
   2. Mommer L
   3. van Ruijven J
   4. Wirth C
   5. Wright AJ
   6. Bai Y
   7. Connolly J
   8. De Deyn GB
   9. de Kroon H
   10. Isbell F
   11. Milcu A
   12. Roscher C
   13. Scherer-Lorenzen M
   14. Schmid B
   15. Weigelt A

   (2019) The future of complementarity: disentangling causes from consequences

   Trends in Ecology & Evolution 34:167–180.

   https://doi.org/10.1016/j.tree.2018.10.013

   • PubMed
   • Google Scholar
7. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear Mixed-Effects models using lme4

   Journal of Statistical Software 67:1.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
8. 1. Berg G
   2. Smalla K

   (2009) Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere

   FEMS Microbiology Ecology 68:1–13.

   https://doi.org/10.1111/j.1574-6941.2009.00654.x

   • PubMed
   • Google Scholar
9. 1. Bertness MD
   2. Callaway R

   (1994) Positive interactions in communities

   Trends in Ecology & Evolution 9:191–193.

   https://doi.org/10.1016/0169-5347(94)90088-4

   • PubMed
   • Google Scholar
10. 1. Bubner B
    2. Gase K
    3. Berger B
    4. Link D
    5. Baldwin IT

    (2006) Occurrence of tetraploidy in Nicotiana attenuata plants after Agrobacterium-mediated transformation is genotype specific but independent of polysomaty of explant tissue

    Plant Cell Reports 25:668–675.

    https://doi.org/10.1007/s00299-005-0111-4

    • PubMed
    • Google Scholar
11. 1. Bunce JA
    2. Miller LN
    3. Chabot BF

    (1977) Competitive exploitation of soil water by five eastern north american tree species

    Botanical Gazette 138:168–173.

    https://doi.org/10.1086/336912

    • Google Scholar
12. 1. Caldeira MC
    2. Ryel RJ
    3. Lawton JH
    4. Pereira JS

    (2001) Mechanisms of positive biodiversity-production relationships: insights provided by delta13C analysis in experimental mediterranean grassland plots

    Ecology Letters 4:439–443.

    https://doi.org/10.1046/j.1461-0248.2001.00238.x

    • Google Scholar
13. 1. Cambron LD
    2. Thapa G
    3. Greenlee KJ

    (2019) Effects of high-fat diet on feeding and performance in the tobacco hornworm, Manduca sexta

    Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 236:110526.

    https://doi.org/10.1016/j.cbpa.2019.110526

    • Google Scholar
14. 1. Campitelli BE
    2. Des Marais DL
    3. Juenger TE

    (2016) Ecological interactions and the fitness effect of water-use efficiency: competition and drought alter the impact of natural MPK12 alleles in Arabidopsis

    Ecology Letters 19:424–434.

    https://doi.org/10.1111/ele.12575

    • PubMed
    • Google Scholar
15. 1. Cardinale BJ
    2. Duffy JE
    3. Gonzalez A
    4. Hooper DU
    5. Perrings C
    6. Venail P
    7. Narwani A
    8. Mace GM
    9. Tilman D
    10. Wardle DA
    11. Kinzig AP
    12. Daily GC
    13. Loreau M
    14. Grace JB
    15. Larigauderie A
    16. Srivastava DS
    17. Naeem S

    (2012) Biodiversity loss and its impact on humanity

    Nature 486:59–67.

    https://doi.org/10.1038/nature11148

    • PubMed
    • Google Scholar
16. 1. Chapin III FS

    (1997) Biotic control over the functioning of ecosystems

    Science 277:500–504.

    https://doi.org/10.1126/science.277.5325.500

    • Google Scholar
17. 1. Chapin, FS
    2. Sala OE
    3. Burke IC
    4. Grime JP
    5. Hooper DU
    6. Lauenroth WK
    7. Lombard A
    8. Mooney HA
    9. Mosier AR
    10. Naeem S
    11. Pacala SW
    12. Roy J
    13. Steffen WL
    14. Tilman D

    (1998) Ecosystem consequences of changing biodiversity

    BioScience 48:45–52.

    https://doi.org/10.2307/1313227

    • Google Scholar
18. 1. Comas LH
    2. Becker SR
    3. Cruz VM
    4. Byrne PF
    5. Dierig DA

    (2013) Root traits contributing to plant productivity under drought

    Frontiers in Plant Science 4:442.

    https://doi.org/10.3389/fpls.2013.00442

    • PubMed
    • Google Scholar
19. 1. Crawford KM
    2. Rudgers JA

    (2012) Plant species diversity and genetic diversity within a dominant species interactively affect plant community biomass

    Journal of Ecology 100:1512–1521.

    https://doi.org/10.1111/j.1365-2745.2012.02016.x

    • Google Scholar
20. 1. Crutsinger GM

    (2016) A community genetics perspective: opportunities for the coming decade

    New Phytologist 210:65–70.

    https://doi.org/10.1111/nph.13537

    • PubMed
    • Google Scholar
21. 1. de Wit CT

    (1960)

    On Competition

    Versl. Landbouwk, On Competition, 66.8, Onderzoek, Wageningen.

    • Google Scholar
22. 1. del Amor FM
    2. Marcelis LFM

    (2005) Regulation of growth and nutrient uptake under different transpiration regimes

    Acta Horticulturae pp. 523–528.

    https://doi.org/10.17660/ActaHortic.2005.697.68

    • Google Scholar
23. 1. Des Marais DL
    2. Auchincloss LC
    3. Sukamtoh E
    4. McKay JK
    5. Logan T
    6. Richards JH
    7. Juenger TE

    (2014) Variation in MPK12 affects water use efficiency in Arabidopsis and reveals a pleiotropic link between guard cell size and ABA response

    PNAS 111:2836–2841.

    https://doi.org/10.1073/pnas.1321429111

    • PubMed
    • Google Scholar
24. 1. Díaz-Sierra R
    2. Verwijmeren M
    3. Rietkerk M
    4. de Dios VR
    5. Baudena M

    (2017) A new family of standardized and symmetric indices for measuring the intensity and importance of plant neighbour effects

    Methods in Ecology and Evolution 8:580–591.

    https://doi.org/10.1111/2041-210X.12706

    • Google Scholar
25. 1. Dimitrakopoulos PG
    2. Schmid B

    (2004) Biodiversity effects increase linearly with biotope space

    Ecology Letters 7:574–583.

    https://doi.org/10.1111/j.1461-0248.2004.00607.x

    • Google Scholar
26. 1. Donovan LA
    2. Dudley SA
    3. Rosenthal DM
    4. Ludwig F

    (2007) Phenotypic selection on leaf water use efficiency and related ecophysiological traits for natural populations of desert sunflowers

    Oecologia 152:13–25.

    https://doi.org/10.1007/s00442-006-0627-5

    • PubMed
    • Google Scholar
27. 1. Dutra WF
    2. Guerra YL
    3. Ramos JPC
    4. Fernandes PD
    5. Silva CRC
    6. Bertioli DJ
    7. Leal-Bertioli SCM
    8. Santos RC

    (2018) Introgression of wild alleles into the tetraploid peanut crop to improve water use efficiency, earliness and yield

    PLOS ONE 13:e0198776.

    https://doi.org/10.1371/journal.pone.0198776

    • PubMed
    • Google Scholar
28. 1. Egerton-Warburton LM
    2. Querejeta JI
    3. Allen MF

    (2007) Common mycorrhizal networks provide a potential pathway for the transfer of hydraulically lifted water between plants

    Journal of Experimental Botany 58:1473–1483.

    https://doi.org/10.1093/jxb/erm009

    • PubMed
    • Google Scholar
29. 1. Engelberth J
    2. Engelberth M

    (2019) The costs of green leaf Volatile-Induced defense priming: temporal diversity in growth responses to mechanical wounding and insect herbivory

    Plants 8:23.

    https://doi.org/10.3390/plants8010023

    • Google Scholar
30. 1. Ferlian O
    2. Biere A
    3. Bonfante P
    4. Buscot F
    5. Eisenhauer N
    6. Fernandez I
    7. Hause B
    8. Herrmann S
    9. Krajinski-Barth F
    10. Meier IC
    11. Pozo MJ
    12. Rasmann S
    13. Rillig MC
    14. Tarkka MT
    15. van Dam NM
    16. Wagg C
    17. Martinez-Medina A

    (2018) Growing research networks on mycorrhizae for mutual benefits

    Trends in Plant Science 23:975–984.

    https://doi.org/10.1016/j.tplants.2018.08.008

    • PubMed
    • Google Scholar
31. 1. Fincheira P
    2. Quiroz A

    (2018) Microbial volatiles as plant growth inducers

    Microbiological Research 208:63–75.

    https://doi.org/10.1016/j.micres.2018.01.002

    • PubMed
    • Google Scholar
32. 1. Forrester DI

    (2015) Transpiration and water-use efficiency in mixed-species forests versus monocultures: effects of tree size, stand density and season

    Tree Physiology 35:289–304.

    https://doi.org/10.1093/treephys/tpv011

    • PubMed
    • Google Scholar
33. 1. Fragoso V
    2. Goddard H
    3. Baldwin IT
    4. Kim SG

    (2011) A simple and efficient micrografting method for stably transformed Nicotiana attenuata plants to examine shoot-root signaling

    Plant Methods 7:34.

    https://doi.org/10.1186/1746-4811-7-34

    • PubMed
    • Google Scholar
34. 1. Gibson DJ
    2. Connolly J
    3. Hartnett DC
    4. Weidenhamer JD

    (1999) Designs for greenhouse studies of interactions between plants

    Journal of Ecology 87:1–16.

    https://doi.org/10.1046/j.1365-2745.1999.00321.x

    • Google Scholar
35. 1. Glawe GA
    2. Zavala JA
    3. Kessler A
    4. Van Dam NM
    5. Baldwin IT

    (2003) Ecological costs and benefits correlated with trypsin protease inhibitor production in Nicotiana attenuata

    Ecology 84:79–90.

    https://doi.org/10.1890/0012-9658(2003)084[0079:ECABCW]2.0.CO;2

    • Google Scholar
36. 1. Gomi K
    2. Ogawa D
    3. Katou S
    4. Kamada H
    5. Nakajima N
    6. Saji H
    7. Soyano T
    8. Sasabe M
    9. Machida Y
    10. Mitsuhara I
    11. Ohashi Y
    12. Seo S

    (2005) A mitogen-activated protein kinase NtMPK4 activated by SIPKK is required for jasmonic acid signaling and involved in ozone tolerance via stomatal movement in tobacco

    Plant and Cell Physiology 46:1902–1914.

    https://doi.org/10.1093/pcp/pci211

    • PubMed
    • Google Scholar
37. 1. Gorzelak MA
    2. Asay AK
    3. Pickles BJ
    4. Simard SW

    (2015) Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities

    AoB Plants 7:plv050.

    https://doi.org/10.1093/aobpla/plv050

    • PubMed
    • Google Scholar
38. 1. Groten K
    2. Nawaz A
    3. Nguyen NHT
    4. Santhanam R
    5. Baldwin IT

    (2015) Silencing a key gene of the common symbiosis pathway in N icotiana attenuata specifically impairs arbuscular mycorrhizal infection without influencing the root-associated microbiome or plant growth

    Plant, Cell & Environment 38:2398–2416.

    https://doi.org/10.1111/pce.12561

    • Google Scholar
39. Book

    1. Harper JL

    (1977)

    Population Biology of Plants

    The Blackburn Press.

    • Google Scholar
40. 1. Hatfield JL
    2. Dold C

    (2019) Water-Use efficiency: advances and challenges in a changing climate

    Frontiers in Plant Science 10:103.

    https://doi.org/10.3389/fpls.2019.00103

    • PubMed
    • Google Scholar
41. 1. He J-S
    2. Wolfe-Bellin KS
    3. Schmid B
    4. Bazzaz FA

    (2005) Density may alter diversity–productivity relationships in experimental plant communities

    Basic and Applied Ecology 6:505–517.

    https://doi.org/10.1016/j.baae.2005.04.002

    • Google Scholar
42. 1. Heschel MS
    2. Donohue K
    3. Hausmann N
    4. Schmitt J

    (2002) Population Differentiation and Natural Selection for Water‐Use Efficiency in Impatiens capensis (Balsaminaceae)

    International Journal of Plant Sciences 163:907–912.

    https://doi.org/10.1086/342519

    • Google Scholar
43. 1. Hettenhausen C
    2. Baldwin IT
    3. Wu J

    (2012) Silencing MPK4 in Nicotiana attenuata enhances photosynthesis and seed production but compromises abscisic acid-induced stomatal closure and guard cell-mediated resistance to Pseudomonas syringae pv tomato DC3000

    Plant Physiology 158:759–776.

    https://doi.org/10.1104/pp.111.190074

    • PubMed
    • Google Scholar
44. 1. Hettenhausen C
    2. Baldwin IT
    3. Wu J

    (2013) Nicotiana attenuata MPK4 suppresses a novel jasmonic acid (JA) signaling-independent defense pathway against the specialist insect Manduca sexta , but is not required for the resistance to the generalist Spodoptera littoralis

    New Phytologist 199:787–799.

    https://doi.org/10.1111/nph.12312

    • Google Scholar
45. 1. Hooper DU
    2. Chapin FS
    3. Ewel JJ
    4. Hector A
    5. Inchausti P
    6. Lavorel S
    7. Lawton JH
    8. Lodge DM
    9. Loreau M
    10. Naeem S
    11. Schmid B
    12. Setälä H
    13. Symstad AJ
    14. Vandermeer J
    15. Wardle DA

    (2005) Effects of biodiversity on ecosystem functioning: a consensus of current knowledge

    Ecological Monographs 75:3–35.

    https://doi.org/10.1890/04-0922

    • Google Scholar
46. 1. Huang X-F
    2. Chaparro JM
    3. Reardon KF
    4. Zhang R
    5. Shen Q
    6. Vivanco JM

    (2014) Rhizosphere interactions: root exudates, microbes, and microbial communities

    Botany 92:267–275.

    https://doi.org/10.1139/cjb-2013-0225

    • Google Scholar
47. 1. Hughes AR
    2. Inouye BD
    3. Johnson MT
    4. Underwood N
    5. Vellend M

    (2008) Ecological consequences of genetic diversity

    Ecology Letters 11:609–623.

    https://doi.org/10.1111/j.1461-0248.2008.01179.x

    • PubMed
    • Google Scholar
48. 1. Isbell F
    2. Craven D
    3. Connolly J
    4. Loreau M
    5. Schmid B
    6. Beierkuhnlein C
    7. Bezemer TM
    8. Bonin C
    9. Bruelheide H
    10. de Luca E
    11. Ebeling A
    12. Griffin JN
    13. Guo Q
    14. Hautier Y
    15. Hector A
    16. Jentsch A
    17. Kreyling J
    18. Lanta V
    19. Manning P
    20. Meyer ST
    21. Mori AS
    22. Naeem S
    23. Niklaus PA
    24. Polley HW
    25. Reich PB
    26. Roscher C
    27. Seabloom EW
    28. Smith MD
    29. Thakur MP
    30. Tilman D
    31. Tracy BF
    32. van der Putten WH
    33. van Ruijven J
    34. Weigelt A
    35. Weisser WW
    36. Wilsey B
    37. Eisenhauer N

    (2015) Biodiversity increases the resistance of ecosystem productivity to climate extremes

    Nature 526:574–577.

    https://doi.org/10.1038/nature15374

    • PubMed
    • Google Scholar
49. 1. Isbell F
    2. Adler PR
    3. Eisenhauer N
    4. Fornara D
    5. Kimmel K
    6. Kremen C
    7. Letourneau DK
    8. Liebman M
    9. Polley HW
    10. Quijas S
    11. Scherer-Lorenzen M

    (2017) Benefits of increasing plant diversity in sustainable agroecosystems

    Journal of Ecology 105:871–879.

    https://doi.org/10.1111/1365-2745.12789

    • Google Scholar
50. 1. Jammes F
    2. Song C
    3. Shin D
    4. Munemasa S
    5. Takeda K
    6. Gu D
    7. Cho D
    8. Lee S
    9. Giordo R
    10. Sritubtim S
    11. Leonhardt N
    12. Ellis BE
    13. Murata Y
    14. Kwak JM

    (2009) MAP kinases MPK9 and MPK12 are preferentially expressed in guard cells and positively regulate ROS-mediated ABA signaling

    PNAS 106:20520–20525.

    https://doi.org/10.1073/pnas.0907205106

    • PubMed
    • Google Scholar
51. 1. Joo Y
    2. Schuman MC
    3. Goldberg JK
    4. Kim S-G
    5. Yon F
    6. Brütting C
    7. Baldwin IT

    (2018) Herbivore-induced volatile blends with both “fast” and “slow” components provide robust indirect defence in nature

    Functional Ecology 32:136–149.

    https://doi.org/10.1111/1365-2435.12947

    • Google Scholar
52. 1. Karpiuk UV
    2. Al Azzam KM
    3. Abudayeh ZH
    4. Kislichenko V
    5. Naddaf A
    6. Cholak I
    7. Yemelianova O

    (2016) Qualitative and quantitative content determination of Macro-Minor elements in Bryonia alba L. roots using flame atomic absorption spectroscopy technique

    Advanced Pharmaceutical Bulletin 6:285–291.

    https://doi.org/10.15171/apb.2016.040

    • PubMed
    • Google Scholar
53. 1. Keesing F
    2. Holt RD
    3. Ostfeld RS

    (2006) Effects of species diversity on disease risk

    Ecology Letters 9:485–498.

    https://doi.org/10.1111/j.1461-0248.2006.00885.x

    • PubMed
    • Google Scholar
54. 1. Kenney AM
    2. McKay JK
    3. Richards JH
    4. Juenger TE

    (2014) Direct and indirect selection on flowering time, water-use efficiency (WUE, δ (13)C), and WUE plasticity to drought in Arabidopsis thaliana

    Ecology and Evolution 4:4505–4521.

    https://doi.org/10.1002/ece3.1270

    • PubMed
    • Google Scholar
55. 1. Kessler D
    2. Gase K
    3. Baldwin IT

    (2008) Field experiments with transformed plants reveal the sense of floral scents

    Science 321:1200–1202.

    https://doi.org/10.1126/science.1160072

    • PubMed
    • Google Scholar
56. 1. Kimball S
    2. Gremer JR
    3. Barron-Gafford GA
    4. Angert AL
    5. Huxman TE
    6. Venable DL

    (2014) High water-use efficiency and growth contribute to success of non-native Erodium cicutarium in a sonoran desert winter annual community

    Conservation Physiology 2:cou006.

    https://doi.org/10.1093/conphys/cou006

    • PubMed
    • Google Scholar
57. Software

    1. Lenth R
    2. Singmann H
    3. Love J
    4. Buerkner P
    5. Herve M

    (2019) R Package emmeans

    CRAN.

    https://CRAN.R-project.org/package=emmeans
58. 1. Lévy J
    2. Bres C
    3. Geurts R
    4. Chalhoub B
    5. Kulikova O
    6. Duc G
    7. Journet EP
    8. Ané JM
    9. Lauber E
    10. Bisseling T
    11. Dénarié J
    12. Rosenberg C
    13. Debellé F

    (2004) A putative Ca2+ and calmodulin-dependent protein kinase required for bacterial and fungal symbioses

    Science 303:1361–1364.

    https://doi.org/10.1126/science.1093038

    • PubMed
    • Google Scholar
59. 1. Liang J
    2. Crowther TW
    3. Picard N
    4. Wiser S
    5. Zhou M
    6. Alberti G
    7. Schulze E-D
    8. McGuire AD
    9. Bozzato F
    10. Pretzsch H
    11. de-Miguel S
    12. Paquette A
    13. Herault B
    14. Scherer-Lorenzen M
    15. Barrett CB
    16. Glick HB
    17. Hengeveld GM
    18. Nabuurs G-J
    19. Pfautsch S
    20. Viana H
    21. Vibrans AC
    22. Ammer C
    23. Schall P
    24. Verbyla D
    25. Tchebakova N
    26. Fischer M
    27. Watson JV
    28. Chen HYH
    29. Lei X
    30. Schelhaas M-J
    31. Lu H
    32. Gianelle D
    33. Parfenova EI
    34. Salas C
    35. Lee E
    36. Lee B
    37. Kim HS
    38. Bruelheide H
    39. Coomes DA
    40. Piotto D
    41. Sunderland T
    42. Schmid B
    43. Gourlet-Fleury S
    44. Sonke B
    45. Tavani R
    46. Zhu J
    47. Brandl S
    48. Vayreda J
    49. Kitahara F
    50. Searle EB
    51. Neldner VJ
    52. Ngugi MR
    53. Baraloto C
    54. Frizzera L
    55. Ba azy R
    56. Oleksyn J
    57. Zawi a-Nied wiecki T
    58. Bouriaud O
    59. Bussotti F
    60. Finer L
    61. Jaroszewicz B
    62. Jucker T
    63. Valladares F
    64. Jagodzinski AM
    65. Peri PL
    66. Gonmadje C
    67. Marthy W
    68. OBrien T
    69. Martin EH
    70. Marshall AR
    71. Rovero F
    72. Bitariho R
    73. Niklaus PA
    74. Alvarez-Loayza P
    75. Chamuya N
    76. Valencia R
    77. Mortier F
    78. Wortel V
    79. Engone-Obiang NL
    80. Ferreira LV
    81. Odeke DE
    82. Vasquez RM
    83. Lewis SL
    84. Reich PB

    (2016) Positive biodiversity-productivity relationship predominant in global forests

    Science 354:aaf8957.

    https://doi.org/10.1126/science.aaf8957

    • Google Scholar
60. 1. Loreau M
    2. de Mazancourt C

    (2013) Biodiversity and ecosystem stability: a synthesis of underlying mechanisms

    Ecology Letters 16 Suppl 1:106–115.

    https://doi.org/10.1111/ele.12073

    • PubMed
    • Google Scholar
61. 1. Lorentzen S
    2. Roscher C
    3. Schumacher J
    4. Schulze E-D
    5. Schmid B

    (2008) Species richness and identity affect the use of aboveground space in experimental grasslands

    Perspectives in Plant Ecology, Evolution and Systematics 10:73–87.

    https://doi.org/10.1016/j.ppees.2007.12.001

    • Google Scholar
62. 1. Marguerit E
    2. Bouffier L
    3. Chancerel E
    4. Costa P
    5. Lagane F
    6. Guehl JM
    7. Plomion C
    8. Brendel O

    (2014) The genetics of water-use efficiency and its relation to growth in maritime pine

    Journal of Experimental Botany 65:4757–4768.

    https://doi.org/10.1093/jxb/eru226

    • PubMed
    • Google Scholar
63. 1. Marten H
    2. Hyun T
    3. Gomi K
    4. Seo S
    5. Hedrich R
    6. Roelfsema MR

    (2008) Silencing of NtMPK4 impairs CO-induced stomatal closure, activation of anion channels and cytosolic casignals in Nicotiana tabacum guard cells

    The Plant Journal : For Cell and Molecular Biology 55:698–708.

    https://doi.org/10.1111/j.1365-313X.2008.03542.x

    • PubMed
    • Google Scholar
64. 1. Martínez-Gimeno MA
    2. Castiella M
    3. Rüger S
    4. Intrigliolo DS
    5. Ballester C

    (2017) Evaluating the usefulness of continuous leaf turgor pressure measurements for the assessment of persimmon tree water status

    Irrigation Science 35:159–167.

    https://doi.org/10.1007/s00271-016-0527-3

    • Google Scholar
65. 1. McGale E
    2. Diezel C
    3. Schuman MC
    4. Baldwin IT

    (2018) Cry1Ac production is costly for native plants attacked by non-Cry1Ac-targeted herbivores in the field

    New Phytologist 219:714–727.

    https://doi.org/10.1111/nph.15207

    • Google Scholar
66. 1. Messier J
    2. McGill BJ
    3. Lechowicz MJ

    (2010) How do traits vary across ecological scales? A case for trait-based ecology

    Ecology Letters 13:838–848.

    https://doi.org/10.1111/j.1461-0248.2010.01476.x

    • PubMed
    • Google Scholar
67. 1. Montazeaud G
    2. Violle C
    3. Fréville H
    4. Luquet D
    5. Ahmadi N
    6. Courtois B
    7. Bouhaba I
    8. Fort F

    (2017) Crop mixtures: does niche complementarity hold for belowground resources? an experimental test using rice genotypic pairs

    Plant and Soil 424:1–16.

    https://doi.org/10.1007/s11104-017-3496-2

    • Google Scholar
68. 1. Naeem S
    2. Thompson LJ
    3. Lawler SP
    4. Lawton JH
    5. Woodfin RM

    (1994) Declining biodiversity can alter the performance of ecosystems

    Nature 368:734–737.

    https://doi.org/10.1038/368734a0

    • Google Scholar
69. 1. Ninkovic V
    2. Markovic D
    3. Dahlin I

    (2016) Decoding neighbour volatiles in preparation for future competition and implications for tritrophic interactions

    Perspectives in Plant Ecology, Evolution and Systematics 23:11–17.

    https://doi.org/10.1016/j.ppees.2016.09.005

    • Google Scholar
70. 1. Oelmüller R

    (2019) Interplant communication via hyphal networks

    Plant Physiology Reports 24:463–473.

    https://doi.org/10.1007/s40502-019-00491-7

    • Google Scholar
71. 1. Pierik R
    2. Mommer L
    3. Voesenek LACJ

    (2013) Molecular mechanisms of plant competition: neighbour detection and response strategies

    Functional Ecology 27:841–853.

    https://doi.org/10.1111/1365-2435.12010

    • Google Scholar
72. Software

    1. Pinheiro J
    2. Bates D

    (2019) nlme: Linear and Nonlinear Mixed Effects Models

    CRAN, R Core.

    https://cran.r-project.org/package=nlme
73. Software

    1. R Development Core Team

    (2017) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.R-project.org/
74. 1. R-core Team

    (2012) The R Graphics Package

    The R Graphics Package, 3.6.1, https://www.rdocumentation.org/packages/graphics/versions/3.6.1.

    https://www.rdocumentation.org/packages/graphics/versions/3.6.1

    • Google Scholar
75. 1. R-core Team

    (2015) The R Stats Package

    The R Stats Package, 3.6.1, https://www.rdocumentation.org/packages/stats/versions/3.6.1.

    https://www.rdocumentation.org/packages/stats/versions/3.6.1

    • Google Scholar
76. 1. Radosevich SR

    (1987) Methods to study interactions among crops and weeds

    Weed Technology 1:190–198.

    https://doi.org/10.1017/S0890037X00029523

    • Google Scholar
77. 1. Reynolds HL
    2. Packer A
    3. Bever JD
    4. Clay K

    (2003) Grassroots ecology: Plant–microbe–soil interactions as drivers of plant community structure and dynamics

    Ecology 84:2281–2291.

    https://doi.org/10.1890/02-0298

    • Google Scholar
78. 1. Rstudio Team

    (2016)

    RStudio: Integrated development for R. RStudio, Inc

    RStudio: Integrated development for R. RStudio, Inc, Boston.

    • Google Scholar
79. 1. Schmid B

    (1994) Effects of genetic diversity in experimental stands of Solidago altissima -- evidence for the potential role of pathogens as selective agents in plant populations

    The Journal of Ecology 82:165.

    https://doi.org/10.2307/2261395

    • Google Scholar
80. 1. Schneider DC

    (2001) The rise of the concept of scale in ecology

    BioScience 51:545.

    https://doi.org/10.1641/0006-3568(2001)051[0545:TROTCO]2.0.CO;2

    • Google Scholar
81. Book

    1. Schulze E-D
    2. Mooney HA

    (1994)

    Biodiversity and Ecosystem Function (First Edition)

    Berlin Heidelberg: Springer-Verlag .

    • Google Scholar
82. 1. Schuman MC
    2. Allmann S
    3. Baldwin IT

    (2015) Plant defense phenotypes determine the consequences of volatile emission for individuals and neighbors

    eLife 4:04490.

    https://doi.org/10.7554/eLife.04490

    • Google Scholar
83. 1. Semchenko M
    2. Lepik A
    3. Abakumova M
    4. Zobel K

    (2018) Different sets of belowground traits predict the ability of plant species to suppress and tolerate their competitors

    Plant and Soil 424:157–169.

    https://doi.org/10.1007/s11104-017-3282-1

    • Google Scholar
84. 1. Signarbieux C
    2. Feller U

    (2011) Non-stomatal limitations of photosynthesis in grassland species under artificial drought in the field

    Environmental and Experimental Botany 71:192–197.

    https://doi.org/10.1016/j.envexpbot.2010.12.003

    • Google Scholar
85. 1. Silvertown J
    2. Araya Y
    3. Gowing D

    (2015) Hydrological niches in terrestrial plant communities: a review

    Journal of Ecology 103:93–108.

    https://doi.org/10.1111/1365-2745.12332

    • Google Scholar
86. 1. Smith MD
    2. Knapp AK

    (2003) Dominant species maintain ecosystem function with non-random species loss

    Ecology Letters 6:509–517.

    https://doi.org/10.1046/j.1461-0248.2003.00454.x

    • Google Scholar
87. 1. Song Y
    2. Wang M
    3. Zeng R
    4. Groten K
    5. Baldwin IT

    (2019) Priming and filtering of antiherbivore defences among Nicotiana attenuata plants connected by mycorrhizal networks

    Plant, Cell & Environment 42:2945–2961.

    https://doi.org/10.1111/pce.13626

    • PubMed
    • Google Scholar
88. 1. South PF
    2. Cavanagh AP
    3. Liu HW
    4. Ort DR

    (2019) Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field

    Science 363:eaat9077.

    https://doi.org/10.1126/science.aat9077

    • PubMed
    • Google Scholar
89. 1. Sreeman SM
    2. Vijayaraghavareddy P
    3. Sreevathsa R
    4. Rajendrareddy S
    5. Arakesh S
    6. Bharti P
    7. Dharmappa P
    8. Soolanayakanahally R

    (2018) Introgression of physiological traits for a comprehensive improvement of drought adaptation in crop plants

    Frontiers in Chemistry 6:92.

    https://doi.org/10.3389/fchem.2018.00092

    • PubMed
    • Google Scholar
90. 1. Stachová T
    2. Fibich P
    3. Lepš J

    (2013) Plant density affects measures of biodiversity effects

    Journal of Plant Ecology 6:1–11.

    https://doi.org/10.1093/jpe/rts015

    • Google Scholar
91. 1. Tortosa I
    2. Escalona JM
    3. Bota J
    4. Tomás M
    5. Hernández E
    6. Escudero EG
    7. Medrano H

    (2016) Exploring the genetic variability in water use efficiency: evaluation of inter and intra cultivar genetic diversity in grapevines

    Plant Science 251:35–43.

    https://doi.org/10.1016/j.plantsci.2016.05.008

    • PubMed
    • Google Scholar
92. 1. Valim HF
    2. McGale E
    3. Yon F
    4. Halitschke R
    5. Fragoso V
    6. Schuman MC
    7. Baldwin IT

    (2019) The clock gene TOC1 in shoots, not roots, determines fitness of Nicotiana attenuata under Drought

    Plant Physiology 181:305–318.

    https://doi.org/10.1104/pp.19.00286

    • PubMed
    • Google Scholar
93. 1. Wang Y
    2. Zhang Y
    3. Ji W
    4. Yu P
    5. Wang B
    6. Li J
    7. Han M
    8. Xu X
    9. Wang Z

    (2016) Cultivar mixture cropping increased water use efficiency in winter wheat under limited irrigation conditions

    PLOS ONE 11:e0158439.

    https://doi.org/10.1371/journal.pone.0158439

    • PubMed
    • Google Scholar
94. 1. Weiner J

    (1980) The effects of plant density, species proportion and Potassium-Phosphorus fertilization on interference between Trifolium incarnatum and Lolium multiflorum with limited nitrogen supply

    The Journal of Ecology 68:969.

    https://doi.org/10.2307/2259469

    • Google Scholar
95. 1. Weiner J

    (2004) Allocation, plasticity and allometry in plants

    Perspectives in Plant Ecology, Evolution and Systematics 6:207–215.

    https://doi.org/10.1078/1433-8319-00083

    • Google Scholar
96. 1. Williams LJ
    2. Paquette A
    3. Cavender-Bares J
    4. Messier C
    5. Reich PB

    (2017) Spatial complementarity in tree crowns explains overyielding in species mixtures

    Nature Ecology & Evolution 1:63.

    https://doi.org/10.1038/s41559-016-0063

    • PubMed
    • Google Scholar
97. 1. Wright AJ
    2. Wardle DA
    3. Callaway R
    4. Gaxiola A

    (2017) The overlooked role of facilitation in biodiversity experiments

    Trends in Ecology & Evolution 32:383–390.

    https://doi.org/10.1016/j.tree.2017.02.011

    • Google Scholar
98. 1. Wu R
    2. Ma C-X
    3. Lou X-Y
    4. Casella G

    (2003) Molecular dissection of allometry, ontogeny, and plasticity: a genomic view of developmental biology

    BioScience 53:1041–1047.

    https://doi.org/10.1641/0006-3568(2003)053[1041:MDOAOA]2.0.CO;2

    • Google Scholar
99. 1. Wu J
    2. Hettenhausen C
    3. Meldau S
    4. Baldwin IT

    (2007) Herbivory rapidly activates MAPK signaling in attacked and unattacked leaf regions but not between leaves of Nicotiana attenuata

    The Plant Cell 19:1096–1122.

    https://doi.org/10.1105/tpc.106.049353

    • PubMed
    • Google Scholar
100. 1. Wu J
     2. Wurst S
     3. Zhang X

     (2016) Plant functional trait diversity regulates the nonlinear response of productivity to regional climate change in tibetan alpine grasslands

     Scientific Reports 6:35649.

     https://doi.org/10.1038/srep35649

     • PubMed
     • Google Scholar
101. 1. Wuest SE
     2. Niklaus PA

     (2018) A plant biodiversity effect resolved to a single chromosomal region

     Nature Ecology & Evolution 2:1933–1939.

     https://doi.org/10.1038/s41559-018-0708-y

     • Google Scholar
102. 1. Xu J
     2. Zhang S

     (2015) Mitogen-activated protein kinase cascades in signaling plant growth and development

     Trends in Plant Science 20:56–64.

     https://doi.org/10.1016/j.tplants.2014.10.001

     • PubMed
     • Google Scholar
103. 1. Yanagawa Y
     2. Yoda H
     3. Osaki K
     4. Amano Y
     5. Aono M
     6. Seo S
     7. Kuchitsu K
     8. Mitsuhara I

     (2016) Mitogen-activated protein kinase 4-like carrying an MEY motif instead of a TXY motif is involved in ozone tolerance and regulation of stomatal closure in tobacco

     Journal of Experimental Botany 67:3471–3479.

     https://doi.org/10.1093/jxb/erw173

     • PubMed
     • Google Scholar
104. 1. Yang H
     2. Yu Z
     3. Zhang Q
     4. Tang J
     5. Chen X

     (2013) Plant neighbor effects mediated by rhizosphere factors along a simulated aridity gradient

     Plant and Soil 369:165–176.

     https://doi.org/10.1007/s11104-012-1565-0

     • Google Scholar
105. 1. Yoo CY
     2. Pence HE
     3. Hasegawa PM
     4. Mickelbart MV

     (2009) Regulation of transpiration to improve crop water use

     Critical Reviews in Plant Sciences 28:410–431.

     https://doi.org/10.1080/07352680903173175

     • Google Scholar
106. 1. Zavala JA
     2. Baldwin IT

     (2004) Fitness benefits of trypsin proteinase inhibitor expression in Nicotiana attenuata are greater than their costs when plants are attacked

     BMC Ecology 4:11.

     https://doi.org/10.1186/1472-6785-4-11

     • PubMed
     • Google Scholar
107. 1. Zea-Cabrera E
     2. Iwasa Y
     3. Levin S
     4. Rodríguez-Iturbe I

     (2006) Tragedy of the commons in plant water use

     Water Resources Research 42:W06D02.

     https://doi.org/10.1029/2005WR004514

     • Google Scholar
108. 1. Zeller SL
     2. Kalinina O
     3. Flynn DFB
     4. Schmid B

     (2012) Mixtures of genetically modified wheat lines outperform monocultures

     Ecological Applications 22:1817–1826.

     https://doi.org/10.1890/11-0876.1

     • Google Scholar
109. 1. Zimmermann D
     2. Reuss R
     3. Westhoff M
     4. Gessner P
     5. Bauer W
     6. Bamberg E
     7. Bentrup FW
     8. Zimmermann U

     (2008) A novel, non-invasive, online-monitoring, versatile and easy plant-based probe for measuring leaf water status

     Journal of Experimental Botany 59:3157–3167.

     https://doi.org/10.1093/jxb/ern171

     • PubMed
     • Google Scholar
110. Book

     1. Zuur A
     2. Ieno EN
     3. Walker N
     4. Saveliev AA
     5. Smith GM

     (2009) Mixed Effects Models and Extensions in Ecology with R

     New York: Springer-Verlag.

     https://doi.org/10.1007/978-0-387-87458-6

     • Google Scholar

Author details

1. Erica McGale

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Project administration, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5996-4213

2. Henrique Valim

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Deepika Mittal

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Present address

   Plant Pathogen Interaction, National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, Delhi

   Contribution

   Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Jesús Morales Jimenez

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Present address

   CONACYT-Consorcio de Investigación Innovación y Desarrollo para las Zonas Áridas (CIIDZA), Instituto Potosino de Investigación Científica y Tecnológica A. C., San Luis Potosí, México

   Contribution

   Conceptualization, Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Rayko Halitschke

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Contribution

   Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1109-8782

6. Meredith C Schuman

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Present address

   Geography, University of Zurich, Zürich, Switzerland

   Contribution

   Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3159-3534

7. Ian T Baldwin

   Department of Molecular Ecology, Max Planck for Chemical Ecology, Jena, Germany

   Contribution

   Validation, Data curation, Investigation, Methodology, Project Administration, Writing - reviewing and editing

   For correspondence

   baldwin@ice.mpg.de

   Competing interests

   Senior editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0001-5371-2974

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