Range-expanding species that are functionally dissimilar to the native plant community may create strong PSFs, as plant origin alone (native vs. range expanding) does not necessarily predict impacts on soil microbial communities (Manrubia et al., 2019; Ramirez et al., 2019). These PSFs may arise through multiple mechanisms, including changes in the quantity or chemistry of leaf and root litter entering soil organic matter pools, changes to soil hydrology via rooting depth and structure, or association with novel microbial mutualists or pathogens (Klironomos, 2002; Wardle et al., 2004). For example, Mesquite trees expanding into desert grasslands associate with N-fixing bacteria and have deep taproots, thus altering soil nutrient pools, microbial communities and water availability for resident grasses (Wilson et al., 2001). Novel secondary compounds in litter of range-expanding species can also alter interactions of other plants with mycorrhizal fungi and free-living soil microbes (Weaver and Klarich, 1977; Nilsson et al., 1993; Wardle et al., 1998), creating potentially positive or negative PSFs.

Finally, PSFs can alter many components of the plant life cycle, including growth, survival, and reproduction, however the majority of PSF research has only considered effects on plant growth or biomass (Hovatter et al., 2013; Dudenhöffer et al., 2017). For example, seed germination may be limited by species-specific pathogens, particularly in close proximity to conspecific individuals (Mangan et al., 2010) and flower production can be enhanced by spatial heterogeneity of PSFs (Burns et al., 2017). Additionally, PSFs may cause contrasting responses across distinct phases of the plant life cycle, such as increased growth or vegetative biomass but decreased seed germination or flowering (Mehrabi et al., 2015; Dudenhöffer et al., 2017) creating an overall neutral effect on plant fitness. Therefore, all demographic life stages need to be simultaneously considered for a complete picture of how PSFs influence plant population dynamics (Dudenhöffer et al., 2017).

Woody plant range shifts are occurring in mountainous regions globally due to a variety of global change drivers including warming temperatures, increased CO2, altered precipitation, and changes in fire and grazing regimes (Myers-Smith et al., 2011). In the White Mountains of California, climate and land use change has led to an upward range expansion of a dominant subalpine shrub species, Artemisia rothrockii A. Gray (Rothrock sagebrush) into alpine grasslands over the last 60 years (Kopp and Cleland, 2014). This range expansion has coincided with decreased abundance of a native bunchgrass [Koeleria macrantha (Ledeb.) Schult] and cushion plant (Eriogonum ovalifolium Nutt.), however the mechanism(s) of these species' declines are unknown (Kopp and Cleland, 2014). We sought to determine the relative importance of direct competition with sagebrush vs. indirect soil effects, a form of apparent competition, for driving the decline in abundance of K. macrantha and E. ovalifolium in the White Mountains.

Specifically, we asked: Does sagebrush range expansion influence the demography of native alpine plant species in the White Mountains? Are sagebrush influences on demography and population growth rates (lambdas) via direct competition and/or apparent competition via PSFs, and what are the relative strengths of these mechanisms? We hypothesized that sagebrush creates negative PSFs for K. macrantha and E. ovalifolium, which manifest in lower demographic and population growth rates for plants growing in sagebrush soil. Inducing negative PSFs is a common mechanism by which non-native invasive plants gain a competitive advantage over resident species (Suding et al., 2013), and we extend this line of reasoning to a native rangeexpanding species. We predicted that the negative effects of PSFs will be stronger than the effects of direct competition with sagebrush because competitive interactions can be weak or shift to facilitation in stressful abiotic conditions, such as alpine environments (Callaway et al., 2002; Maestre et al., 2009).

#### MATERIALS AND METHODS

#### Location

This study takes place in the subalpine to alpine zones of the White Mountains of California, which lie on the western edge of the Great Basin in the rain shadow of the Sierra Nevada range. The climate is cold and dry, receiving between 327 and 456 mm of precipitation annually and mean annual temperatures span from 0.9 to −1.7◦C (Hall, 1991). These mountains have extremely diverse soil histories (Mooney and Zavaleta, 2016) but this study was confined only to granitic soils (Colluvium derived from granite) and east-/south-east-facing slopes to control for edaphic and topographic variation. Abiotic soil characteristics across the elevation gradient and plant communities of this area are summarized in Collins et al. (2016), but in general, soils have low levels of organic matter (∼1.7–2.6 mg/L TOC, 0.8–0.34 mg/L TON) and low soil moisture (∼1.9–10.3% VWC), which both increase with elevation. Soil pH is slightly acidic (∼6) across the study area.

#### Study Species

We conducted this study across a gradient of A. rothrockii range expansion from subalpine (<3,500 m) to alpine (>3,500 m) zones that has been documented in the White Mountains over the last 50 years (Kopp and Cleland, 2014). In 1961, A. rothrockii was not present at the 3,800 m site, was found in moderate to low densities at the 3,500 m site, and high densities at the 3,200 m site (Mooney et al., 1962; Kopp and Cleland, 2014). Currently, it is found at high densities, but with more spacing between individuals (∼1–2 m) at the low and middle elevation sites, and in isolated circular patches ∼10 m wide but with shrubs more closely clustered at high elevation sites (Kopp and Cleland, 2018, personal observation). Therefore, this gradient spans from the "historic range" of A. rothrockii at low elevations to the leading edge of the "expansion range" at high elevations where A. rothrockii transitions from an almost continuous population to isolated patches.

Artemisia rothrockii is a California endemic shrub, while K. macrantha and E. ovalifolium are more widespread throughout the western United States and span a wide habitat range from pinyon juniper woodlands to subalpine forests, and alpine fellfield (Calflora, 2019). Sagebrush, primarily A. tridentata, is known to strongly compete with herbaceous plant species for water and nutrients, particularly phosphorus, across the intermountain west (Robertson, 1947; Caldwell et al., 1985; Fowler, 1986; Ryel et al., 2004). Sagebrush also directly alters the abiotic soil environment including enhancing water and nutrients below its canopies as compared to herbaceous soils (Collins et al., 2016). Additionally, sagebrush alters the soil microbial community structure and function, including the diversity and community composition of soil bacteria and fungi, substrate induced respiration (CO2 flux) and extracellular enzyme activity (Collins et al., 2016, 2018, and unpublished data).

These changes are likely to have important feedbacks on herbaceous plant species by altering the relative abundances of microbial taxa, such as species-specific soil mutualists and pathogens. In addition, secondary compounds in sagebrush litter may alter how herbaceous plants (grasses, forbs etc.) interact with mycorrhizal fungi and free-living soil microbes (Weaver and Klarich, 1977; Nilsson et al., 1993; Wardle et al., 1998). Aboveground sagebrush removal led to the re-establishment of herbaceous cover (including K. macrantha and E. ovalifolium) after 4 years in the White Mountains suggesting potentially high levels of interspecific competition (Kopp and Cleland, 2018). However, indirect soil effects of sagebrush on alpine plant growth, a form of apparent competition, may be as strong or stronger than the direct effects of competition with sagebrush (Allen et al., 2018).

#### Experimental Design

We monitored populations of K. macrantha and E. ovalifolium at three elevations described previously: 3,200, 3,500, and 3,800 m (3,200 m site: UTM: 396148 E; 4151156 N; 3,500 m site: UTM: 390629 E124157248 N; 3,800 m site: UTM: 390445 E; 4159559 N-UTM Zone 11). In July 2015, we established 30 experimental blocks, each with four 0.5 × 0.5 m plots (treatments) and each block was repeated 5 times for each species at each elevation (site). Each plot has one of the following 4 treatments: shrub competition, shrub removal, herbaceous competition and herbaceous removal (**Figure 1**). Shrub plots (competition and removal) were selected where individuals of K. macrantha and/or E. ovalifolium were growing directly under or very nearby (<0.25 m) a sagebrush canopy. Herbaceous plots (competition and removal) were selected in the interspaces of sagebrush between 1 and 5 m away from the nearest shrub canopy, based on the sagebrush density at each site. For competition plots, the entire plant community was left intact. For shrub removal plots, aboveground sagebrush biomass was removed by cutting down stems at the base. For herb removal plots, aboveground biomass of all non-target herbaceous plant species was removed by manually clipping with scissors. For both removal plots, only aboveground biomass was removed to prevent significant disturbance to soil structure. All treatments were maintained annually, and any regrowth trimmed back.

conditioning by the herbaceous community.

Our experiment was designed to disentangle above- and belowground influences of sagebrush on herbaceous plant demography under natural field conditions. Plants growing in removal plots are experiencing soil legacies (both biotic and abiotic) of either the shrub or the herbaceous community, but without aboveground competition from the removed species. In the non-removal plots (competition), plants are experiencing both the soil conditioning and competition from shrubs and/or herbs. By comparing performance of the focal species in the shrub plots and the shrub removal plots, we can therefore isolate the effect of shrub competition (**Figure 1**). Similarly, by comparing the herbaceous plots to the herb-removal plots, we estimated the effects of herb competition. To estimate the effects of shrub PSFs, in the absence of shrub competition, we compared the shrub removal plots to the herbaceous removal plots, because the primary difference between those plots was the identity of plants conditioning the soil. Sagebrush, soil conditioning overwhelms that of herbaceous plants in its litter chemistry and biomass, and creates a distinct soil environment underneath its crown (Welch, 2005; Collins et al., 2016) and therefore we attribute soil effects to sagebrush directly. However, we do not estimate PSFs of the herbaceous community, as the species composition was more variable across sites, and soil conditioning is much less concentrated than under shrub canopies.

Field-based approaches for measuring plant-soil feedbacks have been used successfully in other studies (Kulmatiski, 2006; Mccarthy-Neumann and Ibáñez, 2012). Nonetheless, there are trade-offs to this approach relative to more traditional controlled experiments in a greenhouse or common garden. A strength of this approach is that soils are conditioned in the field, under natural climate conditions and over longer time periods, creating a more realistic soil environment (Kulmatiski and Kardol, 2008; Pernilla Brinkman et al., 2010). Additionally, this approach does not risk spurious effects that can occur with soil sterilization (Bonanomi et al., 2005). A limitation of a field-based approach, however, is that is not possible to disentangle the influences of soil microbial communities and the physical soil environment in the field (Kulmatiski and Kardol, 2008; Pernilla Brinkman et al., 2010). Thus, our estimated PSFs include all physio-chemical and microbial changes caused by sagebrush soil conditioning to be plant-soil feedbacks potentially influencing plant demography.

#### Demographic Measurements

Within each of the four plots (treatments) in all blocks, we tagged up to five adult individuals, depending on species density at the site, of either K. macrantha or E. ovalifolium and took initial demographic measurements in July 2015. For K. macrantha, plant area was calculated by multiplying height of the tallest leaf (cm) by width of the tussock (cm). For E. ovalifolium, plant area was calculated through digital image analysis. Photos of each individual plant were taken with a ruler for scale in the field and were then analyzed in ImageJ (Version 1.51 J8) based on the methodology of Jarou (2009). For both species, we measured flowering status (Y/N), and number of inflorescences of each flowering individual. Seed production per inflorescence was calculated as a single value for each species on 100 additional inflorescences which were counted in the laboratory using a dissecting microscope to ensure seed maturity/viability. Beginning in 2016, mortality was also recorded as alive (Y/N) for each individual. Plots were re-sampled yearly in mid-July to early August (depending on snow melt) and all measurements taken for three subsequent years, for a total of four years of measurements (2015, 2016, 2017, 2018).

Recruitment probabilities were estimated using seed germination trials for each species. In September 2017, mature seeds from both species were collected from 10 individuals at each elevation. Seeds were placed in 12 × 12 cm mesh bags and then deployed in the field by fixing them to the upper soil surface using metal stakes. Each bag contained 10 seeds and for each species, 12 bags were deployed at each elevation site, six under sagebrush canopies and six in shrub interspace. Bags were collected in mid-July 2018, and total number of germinated seeds in each bag were recorded. Probabilities were calculated as the total of germinated seeds/sum seeds deployed. Due to low overall germination, single probabilities were calculated for each species and were not elevation- or treatment-specific. In addition, due to low germination percentages and slow growth of alpine plants, we were unable to measure recruit sizes in the field. For K. macrantha, we estimated recruit size distribution from the seedling dataset of Chu and Adler (2014). For E. ovalifolium, due to the lack of available information on this species, we simulated seedling size data based on the smallest 2.5% of adults in the dataset, which produced a size distribution of 0.001–2.5 cm2 and a mean of 0.6 cm2 . While this modeling choice could affect the magnitude of estimated population growth rates, it should not bias the analysis of treatment effects.

### Population Modeling

We calculated size-dependent demographic rates (growth, survival probability, flowering probability, and seed production) using 229 and 224 individuals of K. macrantha and E. ovalifolium, respectively. Plant size was logged in all models for normality and seed number was logged to transform from count data to continuous. Germination probability was estimated as a single value for each species based on seed germination trials, and recruit size was estimated using an intercept only linear model using the dataset from Chu and Adler (2014) for K. macrantha and a simulated dataset of realistic recruit sizes for E. ovalifolium as described above.

We used mixed effects models for each demographic rate, including fixed effects of size, treatment and elevation, and a random effect of year. We fit these as Bayesian models using the brms package (Bürkner, 2018) in R (R Core Team, 2015), and using the default non-informative, improper priors for all models. We used a "nested" model structure with elevation effects nested within treatments. Importantly, we fit this model with elevation nested within treatments in order to allow for "partial pooling" of information across elevations within each treatment. Partial pooling allowed separate estimates of demographic rates at each treatment × elevation combination, but the data from different elevations, within a treatment, informed each other. This approach is therefore a compromise between complete pooling of data across elevations and independent estimates for each elevation × treatment. This was a conservative modeling decision based on the observation that mortality events in particular were sparse in the dataset; the partial pooling prevents biases from sparse data, such as the chance event that an elevation has no mortality (see **Figure S3** for comparison of the "partial pooling" model with a "no pooling" model). We tested for the treatment and elevation effects on each demographic rate by calculating pairwise contrasts using the posterior distributions and computing the probabilities that the difference between each pair was different from zero.

Using the posterior distributions from the demographic rate models, we constructed integral projection models (IPMs) to calculate population growth rates (lambdas) for each species within each treatment × elevation combination. These population models and estimated lambdas were used as a way to integrate the effects of sagebrush across multiple phases of the plant life cycle, rather than accurate projections of population growth rates. Thus, we consider lambda to be an estimate of the relative fitness of each species among the different plot treatments, and do not suggest they will accurately predict changes in population sizes over time. The effects of shrub and herbaceous competition, as well as shrub PSFs, on lambdas were calculated using a-priori contrasts between the lambdas estimated in the different treatments: Shrub Competition = (Shrub competition) – (Shrub removal); Herb Competition = (Herb competition) – (Herb removal); Shrub PSF = (Shrub removal) – (Herb removal) (**Figure 1**). These contrasts were TABLE 1 | Contrasts among vital rates for *Koeleria macrantha* (KOMA) and *Eriogonom ovalifolium* (EROV). Pr(negative) represents the probability that the effect of Treatment 1 < Treatment 2, whereas Pr(positive) is the probability that the effect of Treatment 1 > Treatment 2 [and is equal to 1 – Pr(negative)]. Only contrasts with probabilities >0.75 are displayed.

*(Continued)*

#### TABLE 1 | Continued

calculated from the posterior distributions of estimated lambdas within each treatment × elevation.

Finally, we conducted elasticity analyses to assess the contributions of different demographic rates to lambdas, and the effects of treatments on demographic rate sensitivities. The elasticity analyses on growth, survival, and probability of reproduction determine the sensitivity of lambda to changes in specific demographic rates. For all IPM analyses, we used modified R code from Ellner et al. (2016).

#### RESULTS

#### Demographic Rate Models

All demographic rates varied by treatment and elevation for both species. Pairwise contrasts for all demographic rates among plot treatments within each elevation and the probabilities that each treatment is greater (or less) than another treatment are listed in **Tables S1** and **S2**. Here we report contrasts where the probability was >0.75 (**Table 1**).

We find in general that demographic rates tended to be highest in herbaceous and herbaceous removal plots for both species, and this pattern was strongest at the high and low elevation sites. For K. macrantha, growth was highest in herbaceous plots at the low elevation site and in herbaceous removal plots at the middle and high elevation sites. Growth in herbaceous and herbaceous removal plots was higher in than in shrub and shrub removal plots which suggests that sagebrush has a negative effect on Koeleria growth (**Table 1**, **Figure S1**). Survival varied greatly by treatment and elevation and overall was highest herbaceous removal plots at the middle elevation site and herbaceous plots at the low and high elevation sites (**Table 1**, **Figure 2**, **Figure S1**). Probability of flowering was greatest in herbaceous and herbaceous removal plots particularly at high elevation, however was higher in shrub plots at the middle elevation site (**Table 1**, **Figure S1**). Similarly seed production was higher in

shrub plots at the middle elevation site which suggests that sagebrush may have a positive effect on reproduction at this site which counters the negative effects on growth and survival (**Table 1**, **Figure S1**). At low and high elevations flowering and seed production were greater in herbaceous and herbaceous removal plots than in shrub and shrub removal plots, which follows the general pattern we observe for the other demographic rates (**Table 1**, **Figure S1**).

Many patterns observed in K. macrantha demographic rates were similar for E. ovalifolium. Growth was highest in herbaceous and herbaceous removal plots at the middle and high elevation sites and no treatments were different at the low elevation site. Growth in herbaceous and herbaceous removal plots was higher in than in shrub and shrub removal plots which suggests that sagebrush also has a negative effect on Eriogonum growth (**Table 1**, **Figure S2**). Survival also varied by treatment and elevation and overall was highest shrub and shrub removal plots at the low and middle elevation site, but then dropped significantly at the high elevation site, and fell below both herbaceous and herbaceous removal plots (**Table 1**, **Figure 2**, **Figure S2**). Probability of flowering was greatest in herbaceous and herbaceous removal plots, particularly at high elevation, and was higher than shrub and shrub removal plots except at low elevation where no treatments differed (**Table 1**, **Figure S2**). Seed production was highest in herbaceous and herbaceous removal plots across all elevations but especially at the high elevation site.

This suggests that sagebrush has a more negative influence on Eriogonum than Koeleria reproduction, particularly in the range expansion zone (**Table 1**, **Figure S2**).

#### Population Growth Rates (Lambdas)

Lambda values were higher overall for E. ovalifolium than K. macrantha, while the differences among lambdas across treatments and elevations were distinct for each species. Herb and herb removal plots had higher median lambda values than shrub and shrub removal plots for K. macrantha at low and high elevations and for E. ovalifolium at high elevation (**Figures 3A,B**). This reflects the general pattern observed in the demographic rate models where negative effects of sagebrush were strongest at the two ends of the elevation gradient. At 3,500 m elevation, lambdas did not differ among the plot treatments except for a slight increase in herbaceous removal plots for K. macrantha (**Figure 3A**). Lambdas in shrub and shrub removal plots were higher than in herb and herb removal plots for E. ovalifolium at 3,200 m elevation (**Figure 3B**). This is likely due to the fact that survival was higher in shrub and shrub removal plots at this site, as survival has a very large contribution to lambda in this system (see elasticity analyses).

For the a-priori contrasts, Herb competition (herb competition-herb removal) had a positive effect (probability >0.75) on lambda for K. macrantha at 3,200 and 3,800 m elevations and E. ovalifolium at 3,800 m elevation, and a negative effect on lambda for K. macrantha at 3,500 m elevation (**Figures 3C,D**). Shrub competition (shrub competition – shrub removal) had a positive effect on lambda for E. ovalifolium at 3,800 m elevation, and slightly positive to neutral effect at other elevations (**Figures 3C,D**). Shrub PSFs (shrub removal – herb removal) had a negative effect on lambda for E. ovalifolium at 3,800 m elevation and K. macrantha at 3,500 m elevation and slightly negative to neutral effect on lambda at other elevations except for E. ovalifolium at 3,200 m where the effect was slightly positive (**Figures 3C,D**).

#### Elasticity Analyses

Survival had the highest impact on population lambdas of all demographic rates (max elasticity values ∼0.35–0.45) for both species however E. ovalifolium had slightly higher elasticity than K. macrantha, particularly at the high elevation site in shrub and shrub removal plots (**Figure S4**). Growth had the next highest impact on lambda (max elasticity values ∼0.16–0.25) for both species however K. macrantha, had higher elasticities than E. ovalifolium, which signifies that growth contributed more to population lambdas in the grass species. Additionally, the influence of plant growth on lambdas was more important at larger size class transitions [moving from size class ∼z(log) 4 to ∼z(log) 5] for Koeleria vs. smaller size class transitions [∼z(log) 2 to ∼z(log) 3] for Eriogonum (**Figure S5**). This shows that changes in the growth rate of larger, smaller individuals are more important for population lambdas in the grass, cushion plant, respectively. Growth elasticities did not vary noticeably by elevation or treatment. Probability of reproduction contributed the least to population lambdas, however had a much more significant contribution for Eriogonum (max elasticities ∼0.005– 0.016) than Koeleria (max elasticities ∼0–0.0025) (**Figure S4**).

# DISCUSSION

Understanding the broader implications of species range shifts will be crucial as climate change continues to promote differential species migration and novel species interactions (Midgley et al., 2007; Tomiolo and Ward, 2018). In this study, we sought to tease apart the potential above- and belowground mechanisms by which a range-expanding sagebrush species, Artemisia rothrockii, affected the demography of two herbaceous alpine plant species. We found that sagebrush most often had a net negative effect on the demographic rates and population lambdas of herbaceous species. Our results suggest that these effects were driven by negative PSFs for plants growing in sagebrush conditioned soil, counteracting facilitative effects of sagebrush aboveground. However, we found variation among species and across elevations suggesting that the effects of sagebrush PSFs vary based on abiotic conditions of the site and for herbaceous species identity. Overall these results show for the first time, using a manipulative field experiment and demographic modeling, that shrubs may have both positive and negative impacts on herbaceous plant demography due to distinct aboveground and belowground mechanisms.

### Demographic Rates and Lambdas

Sagebrush affected the demography and population lambdas of both E. ovalifolium and K. macrantha across an elevation gradient in the White Mountains. Specifically, plants growing with sagebrush, or in its conditioned soils, tended to have slower growth, reduced probability of flowering and lower seed production than those growing in herbaceous dominated soils. Thus, population lambdas that were highest in herbaceous and herbaceous removal plots and lowest in shrub and shrub removal plots, respectively (**Figure 3**). This supports the hypothesis that sagebrush would have a negative effect on the demography of native alpine plants. These differences were particularly pronounced at the high elevation site and were consistent for both species of interest, suggesting the strong influence of sagebrush on herbaceous plant population dynamics in the range expansion zone.

Similar to our findings, an herbaceous forb species growing in association with four Mediterranean montane shrubs had reduced reproductive output including lower number of seeds and reproductive stems, and lower infructescence volume compared to individuals growing in open areas (Macek et al., 2016). In our study system, however, previous work showed that A. rothrockii slowed the phenology and reduced the flower production of a Trifolium cushion plant via shading, thus decreasing the benefits of climate warming on reproduction (Kopp and Cleland, 2015). This contrasts somewhat with our finding of reduced reproductive output (flowering and seeds) in both shrub and shrub removal plots, suggesting that the relative importance of shading vs. belowground effects may vary among herbaceous species.

Survival of E. ovalifolium was a notable exception to this overall pattern, being significantly higher in shrub and shrub removal plots at low and middle elevations. However, survival sharply declined in shrub and shrub removal plots at the high elevation site for E. ovalifolium (**Figure 3B**). For K. macrantha, survival was intermediate in shrub and shrub removal plots depending on elevation. These treatment effects on survival were critical because overall mortality was low across the observation period (∼2% Koeleria, ∼2.5% Eriogonum). Low mortality rates are common for slow-growing alpine species that are welladapted to stressful abiotic conditions (Körner, 2003). Therefore, when mortality events do occur, they can strongly impact population growth rates (see Elasticity analyses).

The differences in how E. ovalifolium and K. macrantha responded to the experimental treatments reflect the large differences in their life history characteristics. E. ovalifolium is a slow growing, long lived, cushion plant while, K. macrantha is a/perennial bunchgrass with an average lifespan between 7 and 10 years (Dixon, 2000; Rundel et al., 2005; Anderson, 2006). Koeleria is solely wind pollinated, while Eriogonum is wind pollinated, but also largely insect and bird pollinated, and produces many fewer seeds per inflorescence. Finally, Koeleria senesces most of its aboveground biomass annually, while Eriogonum retains green leaves throughout the winter. Despite these strong differences, overall patterns in lambda were relatively similar, particularly at high elevation sites, where sagebrush establishment is most recent and therefore demographic rates will potentially be most responsive.

#### Elasticity Analyses

We used elasticity analyses to understand which demographic rates contributed most to the observed patterns in population lambdas and how robust lambda values were to changes in demographic rates. Survival had the largest contribution to population lambdas, followed by growth and then probability of reproduction. Elasticities also varied among the two species, treatments and by elevation. Eriogonum had higher elasticities in response to changes in survival, particularly at high elevation in shrub and shrub removal plots. This suggest that in areas of recent sagebrush establishment, population growth may be particularly affected by mortality events for this species, and more so than when growing in the herbaceous plant community.

Although survival was the most important demographic rate for both species, changes in growth were more important for Koeleria than Eriogonum, likely due to faster growth and a shorter lifespan of this grass species. Finally, the probability of reproduction had a very minor influence on lambda but was significantly more important for E. ovalifolium than K. macrantha. Again, this likely reflects differences in the life history characteristics of these species in that Eriogonum produces fewer seeds per inflorescence and has a more complex pollinator strategy, making reproduction a more important component of its overall population growth. Overall, lower lambda values suggest that K. macrantha is more likely than E. ovalifolium to decline in the future (**Figures 3A,B**), however, more years of data are needed to confirm this trend.

# Above and Belowground Effects on Lambdas

We found support for the hypothesis that the effects of sagebrush PSFs on lambda would be more negative than the effects of sagebrush competition. The effects of sagebrush presence on lambda were neutral to slightly positive suggesting weak competition to facilitation, with the strongest positive effect for E. ovalifolium at the high elevation site. This supports the wellknown hypothesis that species interactions will become more positive (facilitative) as abiotic stress increases (Callaway et al., 2002; Maestre et al., 2009).

Nurse plant facilitation of herbs commonly occurs through enhanced resources, such as water and nutrients and by buffering effects of extreme temperatures, wind or snow in the understory (Körner, 2003). Indeed, A. rothrockii has increased soil moisture and higher soil organic matter content below its canopies as compared to shrub interspace areas in the White Mountains (Collins et al., 2016); however, the effect of sagebrush facilitation on herbaceous demography was not consisitent across elevations and herbaceous species. Facilitation intensity can increase with functional dissimilarity among species at the cold and wet end of a stress gradient (Gallien et al., 2018) suggesting that shrubs may most strongly facilitate herbs at high elevations. Despite this, overall lambda values for shrub plots tended to be lower than herbaceous plots regardless of treatment, suggesting that the benefits do not outweigh the costs of growing in association with this shrub species.

As predicted, the effects of shrub PSFs on lambda were generally negative, implying that in the absence of competition, plants growing in shrub-conditioned soils had lower growth, survival and reproduction than those growing in herbaceousconditioned soils. The one exception to this pattern was a slightly positive effect on lambda for E. ovalifolium at the low elevation. PSFs are therefore a potentially strong form of apparent competition by which sagebrush negatively impacts resident plant species. Many factors can determine the strength of PSFs of range expanding species on native communities and whether they are positive or negative. For example a range-expanding forb species had positive PSFs that enhanced the growth of a co-occurring native grass species, but only in the expansion zone (Dostálek et al., 2016). Here we find accordingly that sagebrush PSFs were stronger in the range expansion zone than in the native range (high vs. low elevation), but unlike the previous study, effects on resident plants were negative rather than positive. This may be due to differences in the time of soil conditioning between the historic and range expansion zones, as sagebrush is more recently established at high elevation sites. Range expanding species may also impose different PSFs depending on their relatedness to the resident community. Koorem et al. (2018) found that range expanders that were unrelated to resident plant species reduced the biomass production of the resident plant community, whereas related range expanding species did not. In our study, PSFs may be enhanced because sagebrush is not closely related (congeneric) with either herbaceous species.

Due to our experimental design however, we can only speculate whether the PSFs of sagebrush on resident plant species are due to changes in soil microbial communities, abiotic soil conditions, or both. One potential PSF mechanism is through secondary compounds (e.g., terpenes, jasmonic acid) in aromatic shrubs, such as Artemisia that can enter the soil through leaf litter and root exudates and have strong negative effects on plant growth, metabolism, and seed germination (Weaver and Klarich, 1977; Kelsey et al., 1978; Karban, 2007). These classes of chemicals can also strongly influence soil microbial community structure and function including microbial biomass C and N, respiration, nitrogen fixation, soil faunal substrate choice, and mycorrhizal networks of co-occurring plant species (Weston and Putnam, 1985; Wardle et al., 1998; Asensio et al., 2012; Austin et al., 2014). For example, organic compounds in the dwarf shrub Empetrum hermaphroditum greatly reduced Ectomycorrhizal infection of root tips and mycorrhizal uptake of soil Nitrogen for pine seedlings (Nilsson et al., 1993). Labile C in these compounds may also stimulate free-living (saprotrophic) microbial growth and nutrient immobilization, thus increasing resident plant-microbial competition for limiting soil nutrients. This was proposed as a mechanism by which Betula, Empetrum, and Cassiope shrub species inhibited the growth of nearby graminoid species in arctic soils (Michelsen et al., 1995). Therefore, via secondary chemicals, sagebrush may similarly alter plant-microbe competition in ways that enhance their own growth and nutrient acquisition to the detriment of co-occurring herbaceous plant species.

In previous work, we found that soils under sagebrush had higher bacterial diversity but lower fungal diversity than soils under herbaceous plants, and this corresponded with a decrease in both pathogenic and mutualistic fungi (Collins et al., 2016, 2018, and unpublished data). A change in soil mutualist to pathogen ratios has been shown to facilitate both inter- and intracontinental range expansions, as plants may benefit from decreased species-specific pathogens, while utilizing more generalist soil mutualists (van der Putten et al., 2016). However, the patterns we observe in this study suggest the opposite may be true for herbaceous plants growing in association with the range expander (sagebrush), which may still be experiencing negative effects of their own soil pathogens, but also a decreased abundance of soil mutualists. These effects may strengthen over time in the range expansion zone with more sagebrush soil conditioning, or they may attenuate as herbaceous plants become adapted to the sagebrush soil community (Rout and Callaway, 2012).

We acknowledge that the experimental plot treatments do not completely isolate the effects of sagebrush soil conditioning, and there may be other factors contributing to the changes in lambdas in the shrub PSF contrasts. It is possible, for example, that removal of shrub facilitation could negatively impact lambdas in shrub removal plots. However, we find that shrubs are weaker facilitators than the herbaceous community, and yet the difference between shrub removal and herbaceous removal plots is mostly negative, suggesting that lack of shrub facilitation is not driving the negative PSFs. It is also possible that some belowground competition may still occur between herbaceous and shrub roots after aboveground shrub removal, however we expect these interactions to be minor and short term, while soil legacy effects left by sagebrush can last many years after shrub removal or death (Collins et al., 2016, 2018). Therefore, the demographic patterns observed in shrub removal plots are very likely attributable to sagebrush soil conditioning, although we cannot rule out some remnant belowground competition. Additionally, our experiment lacks distinct "conditioning" and "feedbacks" phases, but instead measures feedbacks over a longer, continuous time frame. While this has the strength of being more relevant to population dynamics, which unfold over longer time periods than most discrete PSF experiments, it also may dampen the ability to disentangle PSFs during the transition period after aboveground biomass removal.

Overall the patterns we observed were variable and sometimes weak, however this is to be expected due to the due to slow growth, low mortality and high stress tolerance of plants in alpine environments, and the relatively short observation period (4 years) given these species' lifespans. Nonetheless, the data show that the effects of sagebrush on lambdas were more negative than the effects of the herbaceous community, particularly in the absence of aboveground competition and at middle and high elevation sites, providing a potential PSF mechanism for the observed declines in cover of K. macrantha and E. ovalifolium in areas of sagebrush expansion. While stronger effect sizes may have been observed in a more controlled greenhouse setting, estimating longer-term population dynamics in a field setting is more indicative of likely consequences for the species in nature.

# CONCLUSIONS

Alpine landscapes are characterized by heterogeneous microclimates, resource availability and species interactions which can have large impacts on plant fitness (Körner, 2003). The movement of woody shrubs upwards in elevation, occurring in alpine ecosystems across the globe due to climate and land use change, may therefore alter these landscapes and affect the persistence of rare and endemic plant species. Here we find that shrubs can have both positive and negative impacts on herbaceous alpine plants simultaneously, and through both above and belowground mechanisms. Shrubs may facilitate herbs growing under or nearby their canopies particularly at high elevations, likely by shielding the effects of extreme temperatures, wind or snow. At the same time, shrubs may cause negative effects on herbaceous plants through PSFs predictably from changes in litter chemistry and their influences on plant growth and soil microbial community structure and function. These changes created by shrubs may impact multiple components of the plant life cycle, including growth, survival, and reproduction, and these components can have divergent responses which together determine the net outcomes for population growth.

The majority of PSF research has been carried out in controlled environments, particularly greenhouse experiments, while little direct connection has been made between plant population dynamics and PSFs in situ (Kulmatiski and Kardol, 2008; Kulmatiski et al., 2008; Heinze et al., 2016). In addition, while conceptually well-developed, attempts to disentangle the effects of direct competition and PSFs on population dynamics and species coexistence have been rare (Bever et al., 1997; Revilla et al., 2013). The experimental field approach used here can help isolate direct competitive interactions vs. influences of soil conditioning on plant demography. By coupling these experiments to demographic modeling, it is possible to scale the aboveground and belowground effects of a range-expanding species to the effects on population dynamics and species coexistence over time. We believe this approach of combining estimates of PSFs in the field with species-specific population modeling is an important next step in understanding plant community dynamics in a changing world.

#### DATA AVAILABILITY STATEMENT

The datasets generated by this study are available on request to the corresponding author.

### AUTHOR CONTRIBUTIONS

CC developed the research questions, designed and set up the experimental plots, collected the field data, and wrote the first draft of the manuscript. JD and TB analyzed the data and ran the statistical models. All authors created the graphics, wrote

#### REFERENCES

the sections of the manuscript, contributed to the manuscript revision, read and approved the submitted version.

### FUNDING

Research funding was provided by the University of California Riverside Department of Botany and Plant Sciences and a White Mountain Research station mini-grant. CC was supported by a University of California President's Dissertation Year Fellowship. Publication of this article was funded by the University of Colorado Boulder Libraries Open Access Fund.

#### ACKNOWLEDGMENTS

We thank Christopher Karunos and Soren Weber for field assistance and help with setup experimental plot treatments. We thank Michele Slaton, Dylan Neubauer, and members of the CNPS Bristlecone chapter for helpful botanical knowledge of White Mountain flora. We thank the Andrew Tran and Amy Hsu for assistance with digital image analyses.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo. 2019.00417/full#supplementary-material

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

Copyright © 2019 Collins, Bohner and Diez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Soil Inoculation Steers Plant-Soil Feedback, Suppressing Ruderal Plant Species

E. R. Jasper Wubs 1,2 \*, Tom van Heusden1 , Pauline D. Melchers 1 and T. Martijn Bezemer 1,3

1 Department of Terrestrial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, Netherlands, 2 Sustainable Agroecosystems Group, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zurich, Switzerland, 3 Section Plant Ecology and Phytochemistry, Institute of Biology, Leiden University, Leiden, Netherlands

Plant-soil feedbacks (PSFs) are important drivers of vegetation composition in nature. Whole-soil inoculations can help to steer plant-community assembly for nature restoration success. However, it is unclear how subsequent colonization by ruderal and late-successional plant species influences PSFs. Here we test the direction and strength of the PSFs for ruderal and target plant species on differently inoculated soils. We hypothesize that inoculation with late-successional field soil promotes positive and negative PSFs for late-successional and ruderal plants, respectively. We conducted a glasshouse experiment with three plant-growth phases. First, we inoculated a common nutrient-rich ex-arable recipient soil with either a heathland, grassland or an arable soil, and grew mixtures of three ruderal, and three late-successional target species in all soils. Subsequently, we divided the experimental units over four new pots and planted half with mixtures of three ruderal and half with mixtures of three late-successional plant species, to simulate local colonization by these species groups. After 9 weeks, we removed the plants and replanted the pots with ruderal and late-successional species mixtures in a full factorial design to quantify the induced PSFs, after a further 9 weeks of growth. We found that ruderal plants developed positive PSF on soils inoculated with arable soil and neutral feedback when soils were inoculated with grassland or heathland inoculum. The positive PSF was most pronounced for Myosotis arvensis, although all ruderal species showed the most positive PSFs on arable-inoculated soils. There was no significant effect of inoculation on the PSF of late-successional species. As a result of changing PSFs, the relative performance of ruderal and late-successional target species became more similar on soils inoculated with grassland or heathland inoculum, leading to higher evenness. We conclude that soil inoculation on nutrient-rich ex-arable soils can affect PSFs. Inoculation with grassland or heathland inoculum prevented ruderal species from developing positive PSF and shifted the competitive balance in favor of late-successional plants. Future studies need to address the longer-term dynamics of soil inoculation-induced shifts in PSFs, particularly because these are expected to develop over longer timescales in the slow-growing late-successional target species, as well as the impact of competitive plant-plant interactions.

Keywords: colonization, nature restoration, plant-community assembly, plant-soil feedback, soil inoculation

#### Edited by:

Mauro Santos, Autonomous University of Barcelona, Spain

#### Reviewed by:

Pilar Andrés, Ecological and Forestry Applications Research Center (CREAF), Spain Conrad Schittko, University of Potsdam, Germany

> \*Correspondence: E. R. Jasper Wubs<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution

> Received: 21 May 2019 Accepted: 11 November 2019 Published: 26 November 2019

#### Citation:

Wubs ERJ, van Heusden T, Melchers PD and Bezemer TM (2019) Soil Inoculation Steers Plant-Soil Feedback, Suppressing Ruderal Plant Species. Front. Ecol. Evol. 7:451. doi: 10.3389/fevo.2019.00451

# INTRODUCTION

Plants and their associated soil biota are continuously interacting and their interplay can lead to net positive and negative effects on plant performance (survival, growth, and reproduction). These phenomena are termed positive and negative plant-soil feedback (PSF), respectively (Bever, 1994; Van der Putten et al., 2013). It is clear that plant-soil feedback effects are important drivers of plant community dynamics, both in the lab (Kulmatiski et al., 2008; Lekberg et al., 2018) and in the field (Bennett et al., 2017; Teste et al., 2017). However, it is becoming increasingly clear that the realized plant-soil feedback is dependent on the local conditions. For instance, plant-soil feedback strengths change with soil type, fertilization and herbivory (Bezemer et al., 2006; Veen et al., 2014; Heinze and Joshi, 2017; Wubs and Bezemer, 2018b). The realized plant-soil feedback is not a property of the plant species per se, but arises through the interplay between plants and their soil biota within the local biotic and abiotic conditions. The latter is illustrated by the observation that PSFs change importantly with the successional stage of the plant as well as the soil (Kardol et al., 2006).

Recently, we applied this contextual knowledge on plant-soil interactions to improve nature restoration success through soil inoculation on sandy soils (Wubs et al., 2016). Previous studies show that late-successional plant species, the typical target species for restoration, develop positive PSF, measured as plant biomass (De Deyn et al., 2003; Carbajo et al., 2011) and this is particularly pronounced in late-successional soils (Kardol et al., 2006). In field experiments it has been shown that introduction of latesuccessional soil communities can facilitate the establishment and growth of late-successional plant species (Vécrin and Muller, 2003; Pywell et al., 2011; Buisson et al., 2018) and can determine the composition of the developing plant community for decades (Wubs et al., 2016, 2019). While this approach can thus be a successful intervention method, the areas to be restored are still open to potential colonization by plant species from elsewhere and it is unclear how soil inoculation affects the PSFs experienced by subsequently colonizing plants.

Here we test whether inoculation with soils from early- and late-successional systems affect the direction and strength of the PSFs, measured as plant biomass, of ruderal as well as restoration target plant species. We use the sandy glacial deposits in the central parts of the Netherlands as our model system (Kardol et al., 2006; Carbajo et al., 2011; Wubs et al., 2016). In the area, arable farming has been intensive in many places since World War 2, but many farms are no longer economically viable due to the low inherent soil fertility and limits on spatial expansion. Species-rich grasslands and dry heathlands are important national restoration targets for the region, harboring important flora and fauna typical of dry habitats. Both ecosystems are stages along the secondary succession on sandy soils, but under different management regimes (Kardol, 2007). The grasslands arise from the arable fields within a span of 10–30 years under mowing or grazing regimes. The dry heaths in this system are the result of, historically prolonged, sod cutting and grazing. We refer to the arable system as early-succession, and the grassland and heathland both as late-succession.

Plant-soil feedbacks are typically studied using two-phase experiments (Kulmatiski et al., 2008; Brinkman et al., 2010). In the first phase, a plant species or group of species is allowed to condition the soil by growing in it, which alters the local soil biotic and abiotic conditions (Ehrenfeld et al., 2005; Van der Putten et al., 2013): the conditioning phase. In the second phase, the test phase, the same (or a different) group of plant species is allowed to grow on that soil and metrics of their performance on conditioned and control soil are recorded (e.g., survival, biomass, reproduction). Based on the difference in plant performance on the conditioned and a control soil the net PSF is calculated (Brinkman et al., 2010), with positive PSF indicating better performance in the conditioned soil than in the control and negative PSF the converse. In this study, we couple a soil inoculation experiment (Phase 1) with a classical PSF experiment, with a conditioning phase (Phase 2) and a test phase (Phase 3). In the first phase we inoculated a common exarable soil with either arable, grassland or heathland soil and grew a common plant community of six species to simulate what would happen in a soil inoculation based restoration project (Wubs et al., 2018). After harvest, we then separately grew three ruderal and three target plant species on all the inoculated soils to allow soil conditioning by the ruderal and target species groups, respectively (Phase 2). Finally, after harvest, we again grew the same ruderal and target species groups on each of the conditioned soils in a full factorial design and quantified their performance as shoot biomass production (Phase 3). This design allows us to quantify net PSFs, by comparing performance of plants grown on soils conditioned by the same group of plants to performance on soils conditioned by the other group of plants, across the three different inoculation treatments. We hypothesize that late successional plants experience positive PSF, while ruderal species develop negative PSF (Kardol et al., 2006). Furthermore, when inoculated with late-successional field soil the positive and negative PSFs for late-successional and ruderal plants, respectively, are expected to be exacerbated. This would result in even higher and lower plant biomass for these groups.

#### MATERIALS AND METHODS

We conducted a glasshouse experiment with soils setup in a prior study (Wubs et al., 2018). More details on this part of the methods can be found there. Briefly, in the experiment inocula of three ecosystem types, arable, grassland, and heathland, were sourced from the field (January 2015), with three replicate fields in each type. Within each field an area of 5 × 5 m was selected at least 20 m from the edge of the field. At each corner of the selected area, 5 kg of soil was collected from the upper 10–15 cm. The soil was sieved over a 1 cm mesh to remove stones and large roots. Upon return to the lab, the four samples per field were pooled based on equal amounts of dry weight resulting in homogenized inoculum material of 20 kg per field. These inocula were introduced (1:9 w:w inoculum:soil ratio) into a common exarable field soil (total 4 kg/container). The common ex-arable soil was from a field that had been in intensive agricultural use at least since World War 2 until 2004. Then, it was used for extensive

wheat cultivation for 2 years prior to the implementation of largescale nature restoration measures in 2006 (Wubs et al., 2016). The soil was collected from the central part of the field, where the only management consisted of cattle grazing (25–30 cows throughout the year, roaming freely in the entire 160 ha field) and removal of tree seedlings (particularly Betula spp. and Prunus serotina). We collected soil from the organic layer within 10– 50 cm depth (∼1,300 kg), which was subsequently sieved over a 1 cm mesh to remove major roots and stones and homogenized. The common background soil was sterilized (>25 KGray gamma radiation, Isotron, Ede, the Netherlands) to eliminate the resident soil community. The inocula were not sterilized. The abiotic conditions in the inocula and the common ex-arable soil have been reported in Table S1 of Wubs et al. (2018). Likewise, soil biotic composition data reported previously by Wubs et al. (2016) are available on Figshare (doi: 10.6084/m9.figshare.3435404). The major difference were that bacterial and fungal biomass were lower on the arable soil compared to grassland and heathland (their Table S4) and the microbial and nematode community composition was strongly different (their Figures 1f,g).

#### Phase 1

The containers (17 × 17 × 17 cm) were planted with two seedlings each of three ruderal and three late-successional target species (12 plants per pot). Three species were earlysuccessional ruderals: Crepis capillaris (L.) Wallr. (Asteraceae), Lolium perenne L. (Poaceae) and Myosotis arvensis (L.) Hill (Boraginaceae), and three were late-successional, conservation target species: Arnica montana L. (Asteraceae), Festuca filiformis Pourr. (Poaceae) and Campanula rotundifolia L. (Campanulaceae), with one grass and two forbs in each group. Seeds were obtained from commercial suppliers of wild plant seeds (Cruydthoeck, Assen, the Netherlands and B&T World Seeds, Paguignan, France) and germinated (sterilized 1 min. in 5% NaClO solution) on moistened glass beads in a climate chamber (12 h light/dark cycle, 20◦C by day and 15◦C at night). The mixed plant communities were allowed to grow for 7 weeks (Phase 1) and subsequently shoot biomass was harvested (oven-dried for 48 h at 75◦C). In the prior experiment the three inocula types were mixed in different ratio's and the effects of the different ratio's were assessed (Wubs et al., 2018). Here we only used those experimental containers where 100% pure inoculum (i.e., from one field) was introduced. The present experiment used 36 containers (3 soil inoculum types x 3 replicate fields x 4 replicates per field) in Phase 1.

# Phase 2

After the Phase 1 harvest, the soil from each container was sieved (4 mm mesh) to remove roots. From each original Phase 1 container, four smaller pots were filled with homogenized soil (900 g, 10 x 10 x 11 cm pots). For each Phase 1 container, two Phase 2 pots were randomly selected and planted with one individual of each of the three ruderal plant species. The other two pots per Phase 1 container were planted with one individual each of the three target species (N = 36 Phase 1 containers × 2 Phase 2 treatments in duplicate = 144). Seeds were germinated as before. Pots were placed in the greenhouse in a random spatial design under the same conditions as described before. Any seedlings that died in the first week were replaced. The pots were hand-weeded every week to remove seedlings emerging from the seedbank, and watered three times per week. The plants were allowed to grow for 9 weeks. Subsequently, shoot biomass of each species was cut and dried (48 h, 75◦C), before weighing per species.

# Phase 3

For the final phase (Phase 3), the soils of Phase 2 were again sieved (4 mm) to remove plant roots and put back in same size pots. Next, one of the two pots conditioned by ruderals, per Phase 1 container, was randomly selected and planted with ruderals and the other was planted with target plant species. The same was done for the two pots conditioned by target species, again per Phase 1 container. This led to a full factorial design of soil inoculum type with Phase 2 and Phase 3 plant species groups (ruderal or target; N = 36 Phase 1 containers × 2 Phase 2 × 2 Phase 3 treatments = 144) and the soils of independent replicates were kept separate throughout the three phases of the experiment. The same methods were used as above and the plants were allowed to grow for 9 weeks, after which shoot biomass of each species was determined.

# Data Analysis

Differences in seedling mortality among treatments were analyzed using a generalized linear mixed model with a binomial error distribution. The analysis was conducted at Phase 3 pot level and in case any of the seedlings died this was scored as a case of mortality. Fixed effects included Phase 3 and 2 plant groups, soil inoculation and their interactions, while the random effects consisted of the sampling field of the inoculated soil and the original Phase 1 container.

Plant biomass responses were evaluated at two levels, plant group and individual plant species. The plant group level responses were analyzed using a linear mixed model with the same fixed and random effects as for the mortality model above. In addition, we tested a model where we included Phase 2 shoot biomass as a continuous predictor into the above model and we included all possible interactions. Plant-soil feedbacks were tested directly as planned contrast of biomass production on Phase 2-soils conditioned by ruderal and target species, respectively (Adbi and Williams, 2010). For the individual plant species model, PSF was calculated as the log-ratio of plant biomass in own plant group to the other plant group (Brinkman et al., 2010). The individual plant PSFs were analyzed using a linear mixed effects model, with the same random factors as before. As fixed effects plant species, soil inoculation and their interaction were included.

All analyses were conducted in R v3.5.2 (R Core Team, 2019) and model assumptions were checked graphically. Model heteroscedasticity was modeled explicitly using generalized least squares (Pinheiro and Bates, 2000; Zuur et al., 2009). Linear mixed models were analyzed using the nlme v3.1-137 package (Pinheiro et al., 2017) and the GLMM in package MASS v7.3-51.1 (Venables and Ripley, 2002).

# RESULTS

planned contrasts (\*p < 0.05, ‡

Ruderal plant species developed positive feedback on soil inoculated with arable-soil inoculum, while on grassland and heathland inoculated soil they showed neutral PSFs (**Figure 1a**; **Tables S1, S2**). Plant species differed in their PSF responses (**Figures 1b–g**). The positive PSF in arable-inoculated soil was mostly associated with improved performance in M. arvensis (**Figure 1d**; **Table S3**). On heathland soil, M. arvensis developed a weakly negative PSF, which was unique among the tested species. As a group, the late-successional target species did not develop significant feedback in response to soil inoculation, although most negative values occurred on heathland-inoculated soil. In fact, F. filliformis showed a trend for positive PSF on arableinoculated soil (**Figure 1f**; **Table S3**).

p = 0.0669; Tables S2, S3).

Overall, ruderal biomass was higher than that of the latesuccessional target species (**Figure 2**; **Table S1**). However, the difference in performance between ruderal and target species was smaller in heathland-inoculated soil than in arableinoculated soil (**Figure 2A**; P3 × Inoc interaction; **Table S1**). There was no relationship between the pot biomass in Phase 2 and Phase 3 (**Figure 3**), and Phase 2 biomass did not interact with any experimental treatment (**Table S4**). The interaction between the two plant groups and inoculation (P3 plant × P2 plant x Inoculation) remained significant, indicating that the way the plant groups conditioned the soil (Phase 2) and affected the test plants (PSF, Phase 3) was dependent on the type of soil inoculated, also when accounting for Phase 2 plant biomass. Seedling mortality was overall low and was not affected by the experimental treatments (**Figure S1**; **Table S1**).

# DISCUSSION

Our results show that soil inoculation can alter plantsoil feedbacks. However, contrary to our expectation, positive PSFs for target species were not strengthened by inoculation with late-successional soil. Instead, we found that ruderal species had positive PSFs on arableinoculated soils, in terms of plant biomass. On soil inoculated with late-successional soil, PSFs of ruderal plants became less pronounced and not significantly different from no effect (neutral feedback; Van der Putten et al., 2013). As a group, the late-successional target species developed no significant PSF in this experiment. In the heathlandinoculated soil this led to the most even plant performance between these two groups. Since the positive PSF of ruderal species on arable-inoculated soil was suppressed in pots inoculated with late-successional soil, this suggests that these inoculated soils are robust to colonization by non-target ruderal species.

There was considerable variation in the responses of the individual plant species within the two species groups. For ruderals, the positive PSF on arable-inoculated soil was only clear in the biomass of M. arvensis. The other two ruderal species also had positive mean PSFs on those soils, but these effects were non-significant. Next, we found a trend for a positive PSF in one target species, F. filliformis, on arable-inoculated soils, while we found no other significant responses for latesuccessional target species. It is well-known that there is large variation in PSF strengths among plant species (Van de Voorde et al., 2011; Cortois et al., 2016) and among plant genotypes (Schweitzer et al., 2008; Evans et al., 2016; Semchenko et al.,

2019). Broadly, plant traits associated with resource acquisition, such as specific root length and root diameter, may explain differences in plant-soil feedbacks among ecological groups, directly or via their interaction with soil biota (Lemmermeyer et al., 2015; Cortois et al., 2016; Semchenko et al., 2018). However, the variation in PSFs within plant functional groups and among genotypes of the same species are more likely to be associated with plant traits that regulate the co-evolutionary dynamics of plants and soil-organisms, e.g., via molecular signaling compounds in the roots (e.g., MAMPs and PAMPs; Jones and Dangl, 2006). The differences in these traits may lead to differential susceptibility of plant species and genotypes to the soil biota that are introduced via soil inoculation (Van der Putten et al., 2013; Bardgett et al., 2014; Wubs et al., 2016).

Earlier studies showed that late-successional species elicit positive PSFs on late-successional soils and ruderal species negative PSFs (De Deyn et al., 2003; Kardol et al., 2006; Kulmatiski et al., 2017). In contrast, our results show that for some species the PSF effects may act differently, i.e., via reduced positive PSFs for ruderal species. Interestingly, the selected target species are largely the same in our experiment and for De Deyn et al. (2003) and Kardol et al. (2006). As we used seeds from ex-situ cultivated populations, it could be that the plants established plant-soil interactions that are weaker than those produced by co-evolved plant and soil populations (Felker-Quinn et al., 2011; Evans et al., 2016) leading to different net PSFs. Alternatively, plant-soil feedbacks are known to vary across environmental gradients (De Deyn et al., 2004; Bezemer et al., 2006; Manning et al., 2008) and our common exarable soil had substantially higher P-Olsen (78.3 ± 6.71 mg P kg−1 soil) and soil organic matter (5.9 ± 0.2%) content than the soil used in these previous studies. At this point we can only speculate about the causes underlying the differences between these studies. Nevertheless, our results do highlight that even though changes in species abundance in response to soil inoculation (Carbajo et al., 2011; Wubs et al., 2016) may follow the expectations derived from greenhouse PSF experiments (De Deyn et al., 2003; Kardol et al., 2006; Kulmatiski et al., 2017), there may be a different combination of feedbacks in operation (Heinze et al., 2016).

The observed plant-soil feedbacks can in principle have been mediated by both abiotic and biotic changes induced in the soil through plant conditioning (Ehrenfeld et al., 2005; Van der Putten et al., 2013). Nevertheless, we suggest that the observed effects were biotically mediated for two reasons. First, we inoculated a common nutrient rich background soil with a limited amount of inoculum, so that differences in abiotic factors were diluted, thus limiting the scope for nutrient limitation. Secondly, Phase 2 plant biomass showed no correlation with Phase 3 biomass, directly nor in interaction with the experimental treatments, suggesting that abiotic factors such as nutrient limitation did not have an overriding role in our study (e.g., Kardol et al., 2006). The three ecosystem types used to inoculate the soil differ in their soil biotic community composition (Kardol et al., 2005; Van der Wal et al., 2006; Wubs et al., 2016): the grassland and heathland have higher microbial biomass than the arable soil, the grassland has more arbuscular mycorrhizae than the heathland, and the species composition of microbes and nematodes is distinct. Furthermore, data from the same study system showed that soil fungi better explain plant responses than soil abiotic factors (Wubs and Bezemer, 2018a). This is in line with results from other systems showing that the soil biota, and particularly fungi, play a prominent role in determining plantsoil feedback strengths (e.g., Kulmatiski et al., 2017; Mommer et al., 2018; Semchenko et al., 2018). Based on these arguments, we suggest that the effects observed in this experiment are due to plant-induced differences in the different inoculated soil communities, although we cannot rule out plant-induced abiotic effects.

Our study was intended as a proof of principle of how soil inoculations may alter plant-soil feedbacks for use in restoration. There are however two important limitations for direct translation of our results to restoration projects in the field. Our experiment was conducted on a sterilized common soil and it is well-known that establishment success of inoculated soil biota depends on the abundance and diversity of the resident soil community (Van Elsas et al., 2012; Mallon et al., 2015). The effects may therefore be smaller when the resident soil community is left intact by not sterilizing the common soil, as would be the case in restoration projects. However, in previous soil inoculation trials on undisturbed field soils, we did observe that novel soil biota could be successfully introduced and alter local soil and plant community composition (Wubs et al., 2016, 2019) and therefore the local resident community may not be a strong barrier to establishment of late-successional soil biota. Secondly, as is the case with most plant-soil feedback studies (Kulmatiski et al., 2008; Van der Putten et al., 2013), we only documented plant biomass responses over a period of 9 weeks. It is unclear how these short-term responses translate into longer-term fitness differences in the field (Trinder et al., 2013; Heinze et al., 2016; Kulmatiski et al., 2016) as plant-soil feedback strengths may change with plant ontogeny and may take longer to develop in later-successional species (Hawkes et al., 2013; Bezemer et al., 2018; Dudenhöffer et al., 2018). Field observations suggest that the reciprocal interactions among plants and soil biota strengthen over time (Meyer et al., 2016; Wubs et al., 2019) and extrapolation may thus be possible. Nevertheless, field based quantifications of plant-soil feedback strength, e.g., using phytometer plants, are needed to test our conclusions under conditions relevant for restoration.

An important aspect of our study is that both in the conditioning and in the response phases we used plant communities rather than individual plants or monocultures of plants. Most PSF studies have been carried out with soil conditioned by individual plants or monocultures (Kulmatiski et al., 2008). Plant competition can alter plantsoil feedbacks (Casper and Castelli, 2007; Kardol et al., 2007; Jing et al., 2015; Xue et al., 2018), for instance via altered root exudation (Bais et al., 2006), the interaction between resource depletion and defense (Lind et al., 2013), and because of altered food web interactions driven by different species mixtures (Bezemer et al., 2010; Bakker et al., 2013; Kulmatiski et al., 2014). In nature, plants typically do not grow in isolation or in monocultures, and we urge that an important step forward in PSF research is understanding these mixed community feedbacks.

#### CONCLUSIONS

Our results show that soil inoculation alters the nature of individual species' plant-soil feedback (PSF) when these species are grown in competition with other species. The effects were species specific and differed from effects observed in other experiments. This suggests that net inoculation effects on natural vegetation depend importantly on the individual PSFs induced by the soil inoculation, which in turn depend on the soil biotic community and abiotic conditions the plants are growing in. Inoculation with late-successional soil led to suppression of the positive PSF of ruderal species found on arable-inoculated soils. Thus, we conclude that inoculation with late successional soil can reduce the colonization by non-target plant species from elsewhere if our results can be extrapolated to non-sterilized soils and long-term differences in plant fitness.

#### REFERENCES

#### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request from the corresponding author.

#### AUTHOR CONTRIBUTIONS

EW and TB designed the experiment. TH and PM collected the data. EW analyzed the data and wrote the first draft. All authors contributed to the final version of the paper.

#### FUNDING

EW was funded by a NWO-Rubicon grant (Netherlands Organization for Scientific Research, contract no. 019.181EN.01). TB was funded by a NWO-Vici grant (865.14.006). This is NIOO-KNAW publication 6848.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo. 2019.00451/full#supplementary-material

by plant–soil feedback. Basic Appl. Ecol. 16, 112–119. doi: 10.1016/j.baae.2015. 01.001

relationship between plant genotype and associated soil microorganisms. Ecology 89, 773–781. doi: 10.1890/07-0337.1

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

Copyright © 2019 Wubs, van Heusden, Melchers and Bezemer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Greenhouse- and Field-Measured Plant-Soil Feedbacks Are Not Correlated

#### Leslie E. Forero1 \*, Josephine Grenzer 1 , Johannes Heinze2,3, Conrad Schittko2,3 and Andrew Kulmatiski 1

*1 Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, United States, 2 Biodiversity Research/Systematic Botany, University of Potsdam, Potsdam, Germany, 3 Berlin-Brandenburg Institute of Advanced Biodiversity Research, Berlin, Germany*

Plant-soil feedbacks (PSFs) have become a commonly invoked mechanism of plant coexistence and abundance. Yet, most PSF experiments have been performed in greenhouse conditions. To test whether or not greenhouse-measured PSF values are of similar magnitude and positively correlated with field-measured PSFs, we compared PSF values from five different studies that measured PSF values in both greenhouse and field conditions. For 36 plant species, greenhouse-measured PSF values were larger than and not positively correlated with field-measured PSF values. Similarly, these 36 species produced 269 soil-specific PSF values, and for each site there was no positive correlation between these greenhouse- and field-measured PSF values. While PSFs were observed in both greenhouse and field conditions, results provided no support at the soil, site or species level that a positive correlation exists between greenhouse- and field-measured PSF. Further, greenhouse-measured PSF appear to overestimate field-measured PSF. Although from five studies, results strongly suggest that field experiments are needed to understand the role of PSFs in plant communities in natural settings.

Keywords: plant-soil feedback, environmental factors, above-belowground interactions, experimental environment, field experiment, greenhouse experiment

#### INTRODUCTION

Plant-soil feedbacks (PSFs) are increasingly used to explain plant community dynamics including succession, invasion, legacy effects, landscape abundance, coexistence, and biodiversity (Klironomos, 2002; Kardol et al., 2006; van der Putten et al., 2013). However, PSF research continues to rely mostly on greenhouse experiments (**Figure 1**). Greenhouse PSF studies are useful for developing conceptual models of plant community dynamics (Bever et al., 1997; Bonanomi et al., 2005; Aguilera, 2011), however, it remains largely untested whether or not PSFs measured in the greenhouse are correlated with PSFs measured in the field (Kulmatiski and Kardol, 2008; Schittko et al., 2016).

Plants can alter soil biota, and these changes in soil biota may subsequently affect their own growth and the growth of neighboring plants (Reynolds et al., 2003; Ehrenfeld et al., 2005). PSFs are typically investigated by testing a plant's growth response to soils cultivated by different plant species (Bever, 1994). Many approaches have been used to test PSF effects (Kulmatiski and Kardol, 2008) including unsterilized vs. sterilized soils,

#### Edited by:

*Hannes Schmidt, University of Vienna, Austria*

#### Reviewed by:

*Jennifer A. Schweitzer, The University of Tennessee, Knoxville, United States Robin Heinen, Netherlands Institute of Ecology (NIOO-KNAW), Netherlands*

> \*Correspondence: *Leslie E. Forero<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Soil Processes, a section of the journal Frontiers in Environmental Science*

Received: *25 July 2019* Accepted: *05 November 2019* Published: *26 November 2019*

#### Citation:

*Forero LE, Grenzer J, Heinze J, Schittko C and Kulmatiski A (2019) Greenhouse- and Field-Measured Plant-Soil Feedbacks Are Not Correlated. Front. Environ. Sci. 7:184. doi: 10.3389/fenvs.2019.00184*

comparisons among different field soil inoculum into sterilized soils, microbial filtrate inoculations, and two-phase experiments in which soil types are cultivated during an experiment. The two-phase approach remains a standard approach (Bever et al., 1997; van der Putten et al., 2013). In a two-phase experiment, during the conditioning phase of the bioassay (Phase 1), plants are used to create a soil with biota specific to that species. In the response phase (Phase 2), phytometers are planted to test the growth response of a species to the altered soil biota. Growth of the Phase 2 species on soil previously conditioned by the same plant ("home") is compared to growth on soil previously conditioned by a different plant ("away"). By using soil from Phase 1 to inoculate sterilized soils, this approach can isolate microbial from soil chemical (Ehrenfeld et al., 2005; Morris et al., 2009; Ke et al., 2015) and physical (Kyle, 2005; Kulmatiski et al., 2017) effects.

PSF studies are typically executed in the greenhouse for several reasons. Greenhouse studies allow for many isolated replicates and can be performed throughout the year in rapid growth conditions. Because it is relatively easy to sterilize greenhouse soils, greenhouse studies more easily control legacy effects and separate soil nutrient effects from soil microbial effects, relative to field studies. However, completely isolating microbial from nutrient PSF may be unrealistic (Kulmatiski and Kardol, 2008; Ke et al., 2015). Greenhouse studies also lack microsite variability which can increase the likelihood of detecting PSFs in the greenhouse (Burns et al., 2015; Rinella and Reinhart, 2017).

Abiotic and biotic conditions can be very different between the greenhouse and the field (Heinze et al., 2016; Schittko et al., 2016). Greenhouse soils are typically sterilized and inoculated with small amounts of live soil; this likely creates soil conditions favoring fast-growing microbes and fast-growing plant species (Eno and Popenoe, 1964; De Deyn et al., 2004; Howard et al., 2017). Frequent fertilization and watering can cause arbuscular mycorrhizal fungi to become parasitic as conditions change from low to high fertilization regimes, and dry to wet water regimes (Johnson et al., 2003; Schmidt et al., 2011). This could cause PSF to appear neutral or positive in dry field conditions, and negative or neutral in a greenhouse with a consistent water regime (Mohan et al., 2014). Large soil organisms are typically absent in greenhouses which would affect plant-soil interactions (Kutáková et al., 2018), such as below-ground herbivory (Hol et al., 2010; Bezemer et al., 2013). More broadly, stressful conditions found in field studies may induce greater facilitation and a more positive PSF in the field (Maestre et al., 2009). These differences have led several authors to recommend greater field experimentation (Kulmatiski and Kardol, 2008; Heinze et al., 2016; Schittko et al., 2016).

Here, our goal was to test whether or not greenhousemeasured PSFs are of a similar magnitude and positively correlated with field-measured PSFs. We predicted that greenhouse- and field-measured PSF would be positively correlated because we expected that plants have a dominant effect on soil microbial community composition and subsequent PSF; these effects should be similar in both settings due to similar plant species and soil microbial communities. A negative correlation or a lack of correlation between greenhouse- and field-measured PSF suggests that greenhouse conditions change plant-soil interactions in ways that reverse or change PSF values. To test this prediction, we compared greenhouse- and field-measured PSF values from published studies and publicly available datasets. To assess whether PSF is overestimated in greenhouse or field conditions, we compared the magnitude of PSF values (regardless of sign) by taking the absolute values of greenhouse- and field-measured PSF.

#### METHODS

A Scopus search for PSF studies with the term "plant-soil feedback" or "plant-soil feedbacks" in the title, abstract, or keywords was performed on March 19, 2019. Of the resulting 515 studies, meta-analyses, modeling papers, reviews, and non-English studies were removed. The remaining studies were reviewed to identify studies containing (1) a home/away PSF method (Brinkman et al., 2010), (2) aboveground biomass or cover as the response variable, and (3) grasslands as the study ecosystem (Burns et al., 2017; Teste et al., 2017), which left 297 studies. Species from grassland ecosystems were selected as the focal organisms because most PSF research has been conducted in grassland ecosystems, so sufficient sample sizes from nongrassland ecosystems were unlikely (Kulmatiski et al., 2008; van der Putten et al., 2013). Of these 297 studies, 237 occurred in the greenhouse, 50 in the field, seven in mesocosms, and three included both greenhouse and field approaches. Of these three studies, data was collected from two, but one possible study did not respond to requests for data. An additional three datasets produced by the authors, which are publicly available at the USU Digital Commons, were also included.

The resulting dataset contained paired greenhouse-measured and field-measured PSF values for 36 species derived from 2,975 field observations and 2,907 greenhouse observations at five different study sites. We used the paired dataset to (1) calculate PSF values using a single method for all data, (2) test for correlations between greenhouse- and field-measured PSF, and (3) compare PSF values and PSF magnitudes (absolute values) between greenhouse- and field-measured PSF.

#### Study Sites

Of the five study sites included, three were from Europe (Berlin, Potsdam, and Jena in Germany) and two were from North America (Winthrop, Washington and Cedar Creek, Minnesota in the United States). At all sites, the focal species selected were abundant in local plant communities. Four species were common among at least two study sites (**Supplementary Material**).

All five studies compared phytometer growth responses to "home" and "away" conditioned soil (Bever, 1994). When more than two species are used in a PSF experiment, this comparison can be undertaken by mixing all conditioned "away" soils together to create a single "away" treatment. This approach was used in the Berlin study; it eliminates site-by-site variation in soil microbes (Reinhart and Rinella, 2016; Rinella and Reinhart, 2017), and can be useful when the research question is not focused on spatial variability (Cahill et al., 2017; Gundale et al., 2017). Alternately, phytometer responses can be measured on each "away" soil creating a species∗ soil-level design. This approach was used in the studies at Cedar Creek, Jena, Potsdam, and Winthrop. Data from species∗ soil-level PSF experiments were converted to species-level PSF values by averaging a species' growth across "away" soil types.

#### Greenhouse Experiments

The experiments at Cedar Creek, Jena, and Winthrop implemented a cultivated two-phase approach (Rinella and Reinhart, 2018). The experiments at Berlin and Potsdam collected conditioning soils from underneath monotypic stands in the field (Kulmatiski and Kardol, 2008) (**Table 1**).

For Phase 1, the Cedar Creek greenhouse experiment steamsterilized a six-to-one mixture of sand and sphagnum peat inoculated with ten percent field soil. The prepared 1-L pots were planted and grown for a 6-months Phase 1. The Jena greenhouse experiment inoculated a three-to-one mixture of compost and sand with ten percent field soil. The prepared 1-L pots were planted and grown for an 8-months Phase 1. The Winthrop greenhouse experiment steam-sterilized a six-to-one mixture of coarse sand and sphagnum peat and inoculated with five percent field soil. The prepared 1-L pots were planted and grown for a 3-months Phase 1. At the end of Phase 1, plants were removed by hand-clipping; 2,282 pots at Cedar Creek, 239 pots at Jena, and 216 pots at Winthrop had growth.

For Phase 1, the greenhouse experiment at Potsdam collected field soil from underneath three different species' monotypic stands and filled 90 0.41-L pots with 100% field soil (Heinze et al., 2016). In Berlin, Schittko et al. (2016) collected field soil from underneath eight different species' monotypic stands. The soil for the "away" treatment was mixed, where the soil for the "home" treatment was not mixed. A steam-sterilized sandy loam soil was inoculated with 23% "home" or "away" soils collected in the field and used to fill 240 pots, 80 of which were retained for the greenhouse experiment.

For the greenhouse experiment at Cedar Creek, the Phase 2 length was 6 months; at Jena 3 months; at Winthrop, 3 months; at Potsdam two and one-third months; and at Berlin 4 months. Pots were clipped and aboveground biomass weighed for all species at the end of Phase 2 (**Table 1**).

#### Field Experiments

At Cedar Creek and Jena, the field site area was sprayed with glyphosate and disked. Experimental plots (0.35/0.75 m) were established with 0.75 mm thick HDPE root barrier inserted to 35 cm deep between each plot. For Phase 1 at Cedar Creek, ten grams of pure live seed per m2 was applied to each of the plots. At Jena, 2,000 total pure live seeds per m2 were applied to each of the plots. After a 2-years Phase 1, the area was sprayed with glyphosate and hand-tilled using a garden claw. Non-target species were removed by hand-weeding. At Cedar Creek, plots containing C3 grasses and forbs were hand-tilled using a garden claw, but vigorous root growth in the C4 grasses necessitated tilling using a miniature tiller on plots containing that functional group. Seed was re-applied at the same respective rates. After a 2-years Phase 2, aboveground biomass was clipped, dried and weighed; 2,066 Cedar Creek plots and 345 Jena plots had growth.

At Winthrop, the top 10 cm of vegetation and soil was removed (Kulmatiski, 2019). A one-to-one mix of native soil inoculum and sand was applied to the prepared site, and disked to 15 cm to homogenize. A grid of 1.2 m wide geotextile cloth was laid down to create 315 1.5/1.5 m PSF plots in the area. Ten grams of pure live seed per m2 was applied to each plot, and allowed to grow for a 4-years Phase 1. After 4 years, Phase 1 plants were sprayed with glyphosate. Seed was re-applied for Phase 2 and plots were allowed to grow for 3 years. Growth was estimated using percent cover in June 2013.

At Potsdam, 30 (0.4/0.4 m) plots were prepared by cutting the first 25 cm of roots under three different monotypic stands to create three Phase 1 treatments (Heinze et al., 2016). Three individuals of each species were planted in each plot. Individuals were spaced 10 cm apart and allowed to grow for 10 weeks. After the 10 weeks, aboveground biomass was harvested, and 89 individuals had growth.

At Berlin, at week 14 of the greenhouse experiment, 160 pots were transferred to the field and left to sit on top of the soil for a period of 2 weeks (Schittko et al., 2016). After 2 weeks, the aboveground biomass was harvested. Extended methods for Cedar Creek and Jena are in **Supplementary Material**; for Potsdam, Winthrop, and Berlin extended methods are in Heinze et al. (2016), Kulmatiski et al. (2011, 2017), and Schittko et al. (2016).

#### Statistical Analyses

To avoid bias from different calculation methods, original plant growth data on "home" and "away" soils was used to calculate PSF values using a single method for all data (Brinkman et al., 2010). PSFs were calculated as (H-A)/maximum (H,A), where H is the aboveground growth (ground cover or biomass) produced by a species in Phase 2 on "home" soils, and A is the aboveground growth produced by a species in Phase 2 on "away" soils. The denominator refers to the maximum aboveground growth produced by a species regardless of soil type. This calculation has similar mathematical properties to the commonly used ln(H/A) metric (i.e., values that are symmetric around zero and bounded between +1 and −1). In addition, it has the advantage of being easily interpretable as the proportion increase or decrease in growth due to soil type (Brinkman et al., 2010). Plots or pots where the Phase 1 or the Phase 2 realized no growth were removed from the dataset. To prepare the data from species∗ soillevel PSF studies for a species-level analysis, one PSF value was calculated for each "away" species by taking the mean PSF value for each species across soil types.

To determine if the mean PSF value for each experiment was different from zero, we took the standard error of the mean. For data from species-level PSF studies, one home vs. away PSF was calculated for each species. For species-level PSF values, we used linear models to test for a correlation between greenhouseand field-measured PSF within each study site and overall. For species∗ soil-level PSF values, we used linear models to test for a correlation between greenhouse- and field-measured PSF within each study site only, to control for the outsized effect of Cedar Creek's data on the overall dataset. Linear models were performed using the polyfit and fitlm scripts in MATLAB (The MathWorks Inc., 2015). Residuals for the species-level data were checked for normality using the Shapiro-Wilk test.

#### Comparisons

To compare PSF values and PSF magnitudes (absolute values) among study sites and regions, we performed a one-way analysis of variance (ANOVA) using the script anova1 in MATLAB. Significance was evaluated at α = 0.05. When significant, differences were explored with a Tukey's Honest Significant Difference test in MATLAB using the script multcompare.

# RESULTS

From species-level data, 36 paired PSF values were compared. Of these 36 values, eight came from the mixed-soil PSF experiment at Berlin, and the remainder from species∗ soil-level studies where the mean PSF value across all soil types was calculated to create a single PSF value per plant species: 16 PSF values came from Cedar Creek, five from Jena, three from Potsdam, and four from Winthrop. Greenhouse PSF values were positive in Berlin and Potsdam, and neutral in Jena, Winthrop, and Cedar Creek (**Figure 2A**). Field PSFs were positive in Berlin and Winthrop, neutral in Jena and Potsdam, and negative in Cedar Creek (**Figure 2A**). For the species-level greenhouse-measured data the average PSF was 0.046 and the coefficient of variance was 5.14; for the field-measured data the average PSF was −0.008 and the coefficient of variance was 24.01.

A total of 269 PSF values from species∗ soil-level field/greenhouse paired experiments were compared. Of these values, 239 came from the Cedar Creek study, 20 from the Jena study, six from the Potsdam study, and four from the Winthrop study. PSF values for Berlin were excluded from the species∗ soil-level dataset because the study was not species∗ soillevel in design. Greenhouse PSF values were positive in Potsdam and Jena, and neutral in Winthrop and Cedar Creek (**Figure 2B**). Field PSF values were positive in Winthrop, neutral in Jena and Potsdam, and negative in Cedar Creek (**Figure 2B**). For the species∗ soil-level greenhouse-measured data the average PSF was −0.007 and the coefficient of variance was 50.59; for the field-measured data the average PSF was −0.064 and the coefficient of variance was 4.81.

We tested for correlations in species-level data both within and among sites. For species∗ soil-level data we tested within but not among sites because 86% of species-level data was from one site. For species-level data, there was no correlation between greenhouse- and field-measured PSF values across all study sites (F1,34 = 0.179, P = 0.675, **Figure 3A**). Similarly, there was no correlation between greenhouse- and field-measured PSF values within study sites (P > 0.05, **Figure 3A**). For the species∗ soil-level PSFs, there was no correlation between greenhouse- and fieldmeasured PSF values at the Cedar Creek, Jena, and Winthrop sites (F1,237 = 0.001, P = 0.972; F1,18 = 0.003, P = 0.959; and F1,2 = 0.039, P = 0.801; respectively; **Figure 3B**). There was a negative correlation between greenhouse- and fieldmeasured data from the Potsdam site (F1,4 = 10.129, P = 0.034, R 2 = 0.717; **Figure 3B**).

We tested for differences in magnitude (absolute value) for species-level data only because of the strong effects Cedar Creek had on species∗ soil-level data. While there were few correlations between greenhouse- and field-measured PSF values, there were differences between the magnitude of greenhouse- and field-measured PSF values, indicating that PSF (either positive or negative) were larger in greenhouse than field conditions (F1,70 = 5.056, P = 0.028).

#### DISCUSSION

Although PSFs are commonly invoked as a mechanism to explain complex plant community dynamics in the field, the majority of PSF experiments take place in controlled greenhouse conditions. We had predicted that greenhouse- and field-measured PSF would be positively correlated due to the dominant effects of plants on their soil microbial communities, but found no evidence to suggest that greenhouse-measured PSF data are positively correlated with field-measured PSF. We also found greenhouse-measured PSF values were exaggerated relative to field-measured PSF values. Together, results suggest that the greenhouse-measured PSFs that predominate in the literature both overestimate and provide little direct inference into PSF effects in the field. Although our dataset is derived from only five sites, our results strongly suggest that PSFs are sensitive to growth conditions (Casper et al., 2008). Consequently, field experiments are likely to be needed to fully understand the role of PSFs in natural systems.

There are several potential reasons that could explain why PSF values were smaller in the field than in the greenhouse. More stressful growing conditions (for example, competition, drought, or herbivory) may minimize PSF effects (van der Putten et al., 2016; Crawford and Knight, 2017; Fry et al., 2018). Although researchers in all five field experiments attempted to decrease competitive effects by hand-weeding, it is likely that competitive pressure was still greater in the field than greenhouse experiments due to the larger seed bank in unsterilized field soils (Lekberg et al., 2018). Similarly, greater aboveground herbivory in the field was likely to decrease PSF values directly by removing aboveground biomass and potentially indirectly by inducing increased belowground growth (Heinze and Joshi, 2018). Drought in the field may also decrease PSF values by decreasing plant growth, microbial growth, and nutrient cycling rates (van der Putten et al., 2016). With only five studies and many potential factors affecting differences between greenhouse and field results, it was not possible to test these hypotheses, but they are consistent with our observation of larger PSF values in the greenhouse.

Methodological differences were likely to explain why there was no positive correlation between field and greenhouse PSF values, though we were unable to isolate any specific methodological difference that would explain our results. Compared to the field, growing space is restricted, experiment length is shorter, and dominant soil microbes differ in the greenhouse. Excepting Berlin, greenhouse pots were smaller than field plots. Yet, we did not observe a qualitatively different

FIGURE 3 | Greenhouse- vs. field-measured species-level (A) and species\*soil-level (B) PSF values from five study sites.  Though only significant for the Potsdam species\*soil-level data, best-fit regression lines are shown for each site to demonstrate that slopes were close to zero.

relationship between greenhouse and field PSF values at Berlin. The Winthrop site had the largest difference between field plot and greenhouse pot size, yet PSF values were not notably different from other sites.

Differences in temporal scales among sites similarly did not appear to drive our results. PSFs have been suggested to accumulate over time (Kardol et al., 2006; Kulmatiski et al., 2008; Diez et al., 2010; Lepinay et al., 2018), but Potsdam, which had similar greenhouse and field experiment lengths, did not have a positive correlation between greenhouse- and field-measured PSF. Sterilized soils, which were used at three of the five reviewed experiments, often have higher nutrient availability and promote faster plant growth, changing PSF values and soil microbial communities that drive PSF (De Deyn et al., 2004). However, sites using sterilized soils and sites using unsterilized soils both had uncorrelated PSF values. Little can be inferred from the five studies reviewed, but results did not provide strong evidence to suggest that pot size, experiment length, or sterilization technique provided a strong explanation for the difference between greenhouse and field results.

The only correlation observed between greenhouse- and fieldmeasured PSF, was a negative correlation at the Potsdam site. This site was the only site to use 100% field soil in the greenhouse experiment. It is possible that a negative correlation occurred because under decreasing light conditions PSF can be reversed (Smith and Reynolds, 2015), but it is not clear why this effect would only appear when 100% field soils were used. To the contrary, we would have expected that the use of 100% field soil would produce more similar results to the field.

Although our results and results from previous studies suggest that PSF values are very context-dependent (Casper and Castelli, 2007), the PSF concept remains relevant to plant community ecology. Greenhouse-measured PSFs have been found to improve predictions of plant growth in communities in the greenhouse (Kulmatiski et al., 2011, 2017) and field-measured PSFs have been found to improve predictions of plant growth in communities in the field (Klironomos, 2002; Kardol et al., 2006; Mangan et al., 2010; Mariotte et al., 2018; Kulmatiski, 2019). Thus, our results suggest that while greenhouse studies are useful for conceptual model development and predicting plant growth in greenhouse conditions, ecologists who wish to understand the role of PSFs for specific plant species in the field should rely on field studies.

While from five studies, our results suggest that the PSF literature, which is predominantly derived from greenhouse experiments, overestimates PSF effects and while it may provide insight into general patterns of interactions that occur in plant communities in the field, it provides little insight into the specific PSFs that determine the growth and abundance of specific plants in natural communities. Our findings are consistent with results from previous studies (Heinze et al., 2016; Schittko et al., 2016), and suggest that although greenhouse-measured PSFs are important for conceptual models, field experiments will likely be needed to understand the role of PSFs in complex plant community dynamics in the field.

#### DATA AVAILABILITY STATEMENT

The Potsdam dataset analyzed for this study is available on request from JH. The Cedar Creek, Jena, and Winthrop datasets analyzed for this study can be found at USU Digital Commons (https://doi.org/10.26078/52k0-jr94, https://doi.org/ 10.15142/T3XM19). The Berlin dataset analyzed for this study can be found in the Dryad Digital Repository (https://doi.org/10. 5061/dryad.r7c23).

# AUTHOR CONTRIBUTIONS

LF and AK coordinated the writing. LF coded the papers included in the literature search, and conducted the meta-analyses with input from AK, JG, JH, and CS. LF, JG, and AK performed the studies at Cedar Creek and Jena. AK performed the study at Winthrop. JH performed the study at Potsdam. CS performed the study at Berlin. All co-authors participated in generating the ideas discussed here.

#### FUNDING

CS was funded by the German Federal Ministry of Education and Research BMBF within the Collaborative Project Bridging in Biodiversity Science–BIBS (funding number 01LC1501A-H). LF, JG, and AK were supported by the National Science Foundation (funding number 1354129) and the Utah State University Ecology Center.

#### REFERENCES

# ACKNOWLEDGMENTS

Susan Durham provided the statistical assistance. Karen Beard and Peter Adler assisted with the revisions, and Scott Kuehl-Shelby assisted with the proofreading. This research was supported by the Utah Agricultural Experiment Station, Utah State University, and approved as journal paper number 9252.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs. 2019.00184/full#supplementary-material

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

Copyright © 2019 Forero, Grenzer, Heinze, Schittko and Kulmatiski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Aboveground Competition and Herbivory Overpower Plant-Soil Feedback Contributions to Succession in a Remediated Grassland

#### Lee H. Dietterich1,2 \*, Amy Li 1,3, Sarah M. Garvey 1,4 and Brenda B. Casper 1

*1 Department of Biology, University of Pennsylvania, Philadelphia, PA, United States, 2 Department of Ecosystem Science and Sustainability, Warner College of Natural Resources, Colorado State University, Fort Collins, CO, United States, 3 Department of Environmental Science and Public Policy, Harvard University, Cambridge, MA, United States, 4 Department of Earth & Environment, Boston University, Boston, MA, United States*

#### Edited by:

*Martijn Bezemer, Netherlands Institute of Ecology (NIOO-KNAW), Netherlands*

#### Reviewed by:

*Jonathan Richard De Long, Wageningen University and Research, Netherlands Robin Heinen, Netherlands Institute of Ecology (NIOO-KNAW), Netherlands Jingying Jing, China Agricultural University (CAU), China*

> \*Correspondence: *Lee H. Dietterich<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution*

> Received: *05 August 2019* Accepted: *13 November 2019* Published: *29 November 2019*

#### Citation:

*Dietterich LH, Li A, Garvey SM and Casper BB (2019) Aboveground Competition and Herbivory Overpower Plant-Soil Feedback Contributions to Succession in a Remediated Grassland. Front. Ecol. Evol. 7:459. doi: 10.3389/fevo.2019.00459* Plant-soil feedback (PSF) can provide a driving force during ecological succession by altering soil properties in ways that benefit or disadvantage other species in the successional sequence. Succession may be inevitable in disturbed sites remediated by planting early successional species, but information on PSF in such settings is lacking. We investigated whether gray birch (*Betula populifolia*), a native species but strong invader, alters succession from grassland to deciduous forest at a site contaminated with zinc, lead, and cadmium. We investigated PSF within the context of competition, herbivory, and soil contaminants, and evaluated whether gray birch, as a high metal accumulator, engages in elemental allelopathy, poisoning other species through its metal-contaminated leaf litter. We assessed the effects of gray birch on neighboring plant community structure, soil chemistry, fungal root symbionts, and the germination, growth, and herbivory of seedlings of black oak (*Quercus velutina*) and sugar maple (*Acer saccharum*), two tree species expected to follow gray birch in succession. Gray birch was associated with increased diversity in its neighborhood grassland community, increased herbivory on black oak seedlings, and influenced colonization by fungal root symbionts in both species. Seedling biomass was correlated with colonization by ectomycorrhizal fungi in black oak, but not with arbuscular mycorrhizal or dark septate fungal colonization in sugar maple. Gray birch had no effect on maple seedling performance or soil chemistry, and a small effect on black oak performance in the absence of aboveground competition. We found little evidence consistent with elemental allelopathy. Black oak and sugar maple seedlings responded more strongly to variation in soil nutrients than soil heavy metals, and they maintained leaf metal concentration profiles markedly different from those in their soils. We conclude that gray birch alters its environment in ways that could promote establishment and succession of woody species, potentially favoring those that are ectomycorrhizal, but most effects of PSF are overpowered by aboveground competition, herbivory, and/or existing abiotic soil factors. This study well illustrates why the potential for PSF to affect plant performance and community structure must be examined within the context of other ecological processes. Such a broad understanding can inform decisions made in the remediation and management of disturbed sites, and our understanding of plant succession and coexistence in general.

Keywords: competition, heavy metals, herbivory, plant-soil feedback, succession, remediation, restoration

#### INTRODUCTION

Succession is the process by which the species composition of ecological communities changes over time, in ways that are often orderly and directional (Connell and Slatyer, 1977). In terrestrial systems, plant-soil feedback (PSF) is a driver of plant species replacements (Connell and Slatyer, 1977; Bauer et al., 2015; Wubs and Bezemer, 2017), and can either speed or slow succession, depending on whether plant-induced soil alterations improve or worsen conditions for contemporary species relative to later arriving species (Bever et al., 1997; Reynolds et al., 2003; Kardol et al., 2006; Castle et al., 2016; Lozano et al., 2018; Crawford et al., 2019). PSFs can act via soil organisms, including pathogens and symbionts such as mycorrhizal fungi, as well as any number of chemical or physical soil properties (Ehrenfeld et al., 2005; Png et al., 2018; Bennett and Klironomos, 2019). While PSFs have been shown broadly to be involved in ecological succession (Kardol et al., 2006; Kulmatiski et al., 2008; Bauer et al., 2015), connecting specific feedback mechanisms to succession remains rare in the literature, and calls are made for more such studies and for more field studies of PSFs in general (Van Der Putten et al., 2013, 2016; De Long et al., 2018).

Field studies are essential for evaluating the importance of PSF in the presence of other prevalent ecological processes affecting a species' performance, such as plant-plant competition and herbivory (Casper and Castelli, 2007; Heinze and Joshi, 2017). Both competition and herbivory have the potential to augment or reduce effects of PSF, based on the strength of these other processes and whether their effect on plant performance is in the same or opposite direction as that of PSF (Casper and Castelli, 2007; Lekberg et al., 2018; Heinze et al., 2019). Conversely, PSF effects on plant performance could indirectly alter a plant's susceptibility to herbivory based on information that herbivory levels can vary with mycorrhizal status (Gehring and Bennett, 2009; Koricheva et al., 2009), leaf nutritional quality (Ayres et al., 1997), or plant vigor (Cornelissen et al., 2008), and evidence that soil biota can influence plant nutritional quality (Kos et al., 2015) and herbivory defenses (Kostenko et al., 2012; Zhu et al., 2018). PSF may contribute to plant-plant competition directly through altered soil characteristics or indirectly (Hortal et al., 2017; Bezemer et al., 2018) since competitive ability is usually related to plant size (Weiner and Thomas, 1986).

Understanding how plant communities change over time and which mechanisms underlie these changes is deeply important for managing ecosystems and particularly pertinent for previously disturbed sites undergoing restoration, where installed vegetation may represent an early successional stage. Grasses and other early successional plants are often planted on abandoned mines or metal-polluted sites in order to curtail erosion and leaching, achieve quick phytostabilization through ground cover, and create a surface soil enriched in organic matter (Turnau et al., 2008; Bolan et al., 2011), even in regions where woody plants are the expected successional climax (Turnau et al., 2010; Zhang et al., 2012). We need to know more about succession in these systems and the possible role of PSFs, particularly in heavy metal contaminated soils (Krumins et al., 2015).

Heavy metal soils hold the potential for a particular PSF mechanism called elemental allelopathy. The idea is that plants with high leaf metal uptake create allelopathy (Inderjit et al., 2011) when their contaminated leaf litter locally increases soil metal concentrations to the detriment of less metal tolerant plants and/or soil microbes growing nearby (Baker and Brooks, 1989; Wilson and Agnew, 1992; Boyd and Martens, 1998). Some field studies have found elevated levels of metal contaminants around hyperaccumulating plants, sometimes with negative effects on other species, consistent with elemental allelopathy (Boyd and Jaffré, 2001; Mehdawi et al., 2011; Jaffe et al., 2017), but most cannot eliminate alternative explanations (Morris et al., 2009; Mehdawi et al., 2011).

We looked for evidence of PSF and elemental allelopathy in particular during succession from grassland to deciduous forest at a revegetated site. The site was historically mostly forested but was then severely contaminated and devegetated by Zn, Pb, Cd, and SOx emissions from two zinc smelters operating for approximately 80 years, with the combination of metal pollution and acidity from SOx likely responsible for most plant deaths (Buchauer, 1973; Jordan, 1975; EPA, 2007a,b). Land managers chose a phytostabilization approach and planted primarily C4 grasses with low metal uptake rates (EPA, 2007a,b). Among the most successful species that have since encroached from forest remnants or colonized de novo (Dietterich and Casper, 2017) is gray birch (Betula populifolia), a native pioneer tree that accumulates high foliar concentrations of the metal pollutants (Gallagher et al., 2011).

Our main goal was to determine whether gray birch affects succession through PSF at this revegetated, heavy metal polluted site. We examined how gray birch affects local plant community structure; soil organic matter, metal, and nutrient concentrations; and soil temperature and moisture. To address PSF, we crossed soils conditioned by either gray birch or grasses with the presence or absence of aboveground competition in the field to identify any interactions between these two factors in affecting succession. In particular, we examined seed germination and seedling size, root colonization by fungal symbionts, herbivory levels, and plant metal uptake of two later successional tree species, black oak (Quercus velutina, Fagaceae) and sugar maple (Acer saccharum, Sapindaceae).

Because of gray birch's high metal uptake, we predicted (1) lower seedling growth under birches than grasses, but higher growth in plots where aboveground competition was removed. Because gray birch and black oak both associate with ectomycorrhizal fungi (ECM) while the grasses and sugar maple are only colonized by arbuscular mycorrhizal fungi (AMF) (Harley and Harley, 1987; Dickie et al., 2001; Wang and Qiu, 2006), we predicted (2) black oak would have greater ECM colonization in soils conditioned by gray birch, sugar maple would have more AMF colonization in soils influenced primarily by grasses, and increased root colonization would translate to increased plant growth. To examine the effects of PSF and competition in the context of standing variation in soil metal contamination, we further explored whether spatial variation in soil contaminant and nutrient concentrations explained variation in seedling performance or leaf chemistry, independent of our experimental treatments. We predicted (3) strong relationships between soil contaminant concentrations, plant growth, and leaf contaminant concentrations, on the premise that the presence and availability of metals in the soil would shape plant uptake of those metals. Finally, through the field experiment and a companion greenhouse experiment, we evaluated (4) whether gray birch engages in elemental allelopathy.

### MATERIALS AND METHODS

Our study was based in the portion of the Palmerton Zinc Superfund Site (Palmerton, PA, USA) owned and managed by the Lehigh Gap Nature Center. The large area (>800 ha) and steep, rocky terrain made removing the metal contaminants unfeasible (EPA, 2007a,b). To remediate the devegetation and high levels of soil metal contaminants, land managers applied fertilizer and compost to inoculate the soil with microbes, including decomposers and mycorrhizal fungi. They planted C4 grasses, known to have low metal uptake rates, with the goal of creating an organic enriched surface layer, sequestering the metals away from humans and the rest of the food web (EPA, 2007a,b). The planted grasses remain abundant but now represent a minority of the site's species, as many others have encroached from forest remnants or colonized since the revegetation efforts (Dietterich and Casper, 2017). Gray birch, the most striking colonizer, is spreading rapidly and forming dense stands.

Our field experiment to determine how seed germination and seedling performance of black oak and sugar maple respond to grass vs. birch, as two dominant vegetation types, was set up from May 2014 to September 2015. At each of 10 locations where grassland communities grew in close proximity to dense gray birch stands, we established four 1.5 × 3.0 m experimental plots; two dominated by gray birch and two dominated by the grasses. At each location, to examine possible interactive effects of aboveground competition and vegetation type, every ∼2 weeks during the growing season, we clipped and removed all aboveground vegetation from one plot of each vegetation type while leaving the other intact. We chose not to remove belowground vegetation in order to preserve soil structure and possible variation in soil chemistry with depth.

# Gray Birch Effects on Community Structure and Soil Characteristics

Before setting up the experiment, we evaluated plant community structure and soil nutrients, metal contaminants, and organic matter. In a May 2014 census, we identified all plants, most to species, and visually estimated their percent cover within each plot. Because the plots' dominant grasses could not consistently be identified to species at that time of year, we grouped them into a single taxon for analysis. We examined total cover of the birch and grasses as well as cover, species richness, and Shannon diversity of newly colonizing species apart from birch and grasses.

To investigate soil chemistry, we collected ∼100 g of surface soil (primarily organic horizon material from the top ∼10 cm of soil) at the center of each plot using a clean trowel. Soils were air-dried in the lab and sieved to 2 mm. We used inductively coupled plasma optical emissions spectroscopy (ICP-OES; details below) to measure concentrations of the nutrients Ca, Mg, and K, the known contaminants Zn, Cd, and Pb, and the heavy metals Cu, Mn, and Ni, which were not expected to be enriched in the site. The procedure yields total extractible concentrations of these metals and nutrients, which we interpret as the amount of the metals and nutrients a long-lived plant may have access to in its lifetime, but more than would be bioavailable to a plant at any given time. We also measured soil organic matter content as percent loss on ignition (LOI).

To further characterize growing conditions, in the second growing season, we periodically measured soil moisture and temperature at three locations in each plot with a WET-2 sensor connected to an HH2 moisture meter (Delta-T Devices Ltd, Cambridge, UK). We measured leaf area index as a proxy for shading intensity with a CI-110 Plant Canopy Imager (CID Bio-Science, Inc., Camas, WA, USA).

#### Gray Birch and Abovegound Competition Impacts on Tree Species Establishment, Mycorrhizal Colonization, and Leaf Metal Content

We planted six bare-root 1–2 year old seedlings each of black oak and sugar maple (Musser Forests, Indiana, PA, USA) into each plot. For each seedling, we dug a hole with shovels and trowels, put the seedling into the hole, and backfilled the excavated soil up to the top of the root collar. Each seedling received 10–20 mL of water at planting and one or two days later, but otherwise were irrigated by rainfall. To protect seedlings from deer, we used plastic mesh tubes (Forestry Suppliers, Jackson, MS, USA, Stock No. 17048), supported by wooden garden stakes and cable ties. Every 2–3 weeks during both growing seasons, we re-cleared all aboveground vegetation from cleared plots and examined all planted tree seedlings, adjusting displaced protective tubes as necessary. In spring 2014, we also assessed germination by planting 20 cold-stratified seeds of each species (Sheffield's Seed Co., Inc., Locke, NY, USA) near the center of each plot and counting seedling emergence after nine weeks. Seeds were planted under a ∼40 × 40 cm plastic ∼0.5 cm mesh to reduce seed herbivory and aid in seedling recovery.

We measured seedling height at planting and again at harvest, where we conducted additional size and biomass measurements at the end of the second growing season. We separated seedlings into aboveground and belowground parts by clipping at the top of the root collar. We recorded aboveground plant height, fresh and dry leaf biomass, dry stem biomass, leaf area, number of leaves, and the length of apical and lateral branches grown during the current and previous growing seasons. We estimated whether leaf herbivory was absent, mild (5–20% of leaf area), moderate (20– 50% of leaf area), or severe (>50% of leaf area) after Johnson et al. (2016). We restricted our analysis to herbivory that appeared to be due to insects based on the pattern of leaf damage. We saw evidence of deer herbivory, including stem and leaf damage in the shape of a single large bite mark, as well as live deer and deer scat in the area. We did not analyze deer herbivory because it was impossible to know how many leaves were missing.

We also measured root biomass and fungal colonization. We washed roots thoroughly with tap water and recorded the fresh weight of the whole root system. For all 228 black oak seedlings harvested, we recorded percent colonization by ECM as the proportion of colonized root tips out of a representative sample of at least 50 root tips, unless fewer were available. For sugar maples, we removed and weighed sub-samples of fine roots to clear and stain for colonization by AMF and dark septate endophytes (DSE). We estimated dry biomass of roots used for endophyte colonization by obtaining wet and dry weights of additional fine root sub-samples. We dried root systems at 60◦C for at least 48 h, then manually separated and recorded the dry biomass of fine (≤1 mm diameter) and coarse (>1 mm diameter) roots.

To quantify AMF and DSE colonization in sugar maple, subsamples of fine roots were cleared in 10% KOH for 12–24 h or as needed to remove abundant dark pigments, bleached in 9:1 household H2O2: household NH3, acidified in 5% HCl for 15–30 min, and stained in hot 0.01% Trypan blue in 1:1:1 lactic acid: glycerol: water. For each of 44 seedlings sampled at random, at least 10 root segments at least 1 cm long were mounted in parallel on a microscope slide and fixed with polyvinyl lactic acid glycerol (INVAM, 2014). We recorded percent colonization of AMF and DSE by assessing the presence or absence of their structures on each 1-mm section of each root segment (modified from McGonigle et al., 1990).

We measured metal concentrations in leaves collected at the end of the first growing season, in September 2014. We collected one leaf from each planted seedling with three leaves and two leaves from each seedling with four leaves or more, and combined them by species and plot. We washed leaves thoroughly with tap water, dried them at 60◦C for at least 48 h, and ground them with mortar and pestle using liquid nitrogen as necessary. We then weighed 0.25–0.50 g of the samples into ceramic crucibles, ashed them in a muffle furnace at 475◦C for at least 4 h, and weighed them again to estimate organic matter content by LOI. We digested the residue in 2 mL concentrated HCl for 10 min at 90–100◦C. Digest solutions were diluted to 25 mL with ultrapure (18 Ω) water and stored at 4◦C.

Concentrations of Ca, Mg, K, Zn, Pb, Cd, Cu, Ni, and Mn in digest solutions were measured by ICP-OES using standard methods modified from Zarcinas et al. (1987). We included as standard reference materials peach leaves (NIST 1547) and either olive leaves (BCR 062) or citrus leaves (NIST 1572), as well as a reagent blank, to confirm the quality of each digest. We further verified ICP-OES measurements by including standard solutions of known concentrations in each run. We used a similar method to measure metal concentrations in soils collected at the beginning of the experiment. Soils were sieved to 2 mm, airdried, then ashed, digested, and analyzed by ICP-OES as above, a protocol modified from EPA method 3050B.

# Testing Elemental Allelopathy

We performed a series of experiments to test three fundamental components of the mechanism of elemental allelopathy. To determine the extent that gray birch trees accumulate heavy metals in their leaves relative to their neighbors, a criterion for elemental allelopathy, we compared foliar metal concentrations in birch and the planted grasses. To test whether birch trees were associated with local increases in soil metal concentrations, we measured topsoil metal concentrations as a function of distance from gray birch trees in the field. Finally, we conducted a greenhouse experiment to evaluate phytotoxicity of gray birch leaf litter.

We collected leaves from 12 gray birch trees and 4–8 individuals each of nine planted grass species in July 2014 and measured metal and nutrient concentrations for each individual plant by ICP-OES as described above. To determine potential effects of birch on soil chemistry, in August 2013, we sampled soil from the top ∼10 cm at points located 0, 50, 100, and 200 cm from the stems of 18 gray birch trees located throughout the Palmerton site. We selected from among larger trees (∼5–10 years old, 2– 4 m tall, canopy radius <2 m), and established our soil collection points along a cardinal direction chosen randomly for each tree such that the focal tree was the closest gray birch tree to each collection point. We then measured soil metal concentrations and organic matter content as described above.

We tested the phytotoxicity of metal-contaminated gray birch leaves from the site using a greenhouse experiment. We grew seedlings of autumn bent grass (Agrostis perennans; Poaceae), the forb white snakeroot (Ageratina altissima; Asteraceae), black oak, and sugar maple individually in pots of contaminated soil collected from the site, into which we manually mixed dried, crushed gray birch leaves, with three levels of leaf treatments: collected from inside the contaminated site, collected at nearby uncontaminated sites, or no leaves. To confirm differences in elemental concentration between contaminated and uncontaminated leaves, we measured metal and nutrient concentrations in 40 samples of each using ICP-OES. We collected seeds of autumn bent grass and white snakeroot from the Palmerton site in Fall 2013, and germinated them in autoclave-sterilized sand in Spring 2014 before transplanting into experimental pots. These two understory species colonized the site naturally and are abundant there, so they evidently have some degree of heavy metal tolerance. Seedlings of black oak and sugar maple came from the same batch as those used for the field experiment, and were transplanted into experimental pots upon arrival. We replaced any seedlings that died within the first week of the experiment.