NRM-174
Oikos 122: 199–208, 2013
doi: 10.1111/j.1600-0706.2012.20592.x
© 2012 The Authors. Oikos © 2012 Nordic Society Oikos
Subject Editor: Enrique Chaneton. Accepted 20 April 2012
Native and non-native grasses generate common types of plant–soil
feedbacks by altering soil nutrients and microbial communities
Lora B. Perkins and Robert S. Nowak
L. B. Perkins ([email protected]), Dept of Natural Resource Management, Box 2140B, South Dakota State Univ., Brookings, SD 57007
USA. – R. S. Nowak, Dept of Natural Resources and Environmental Science, Univ. of Nevada - Reno, Reno, NV 89557, USA.
Soil conditioning occurs when plants alter features of their soil environment. When these alterations affect subsequent
plant growth, it is a plant–soil feedback. Plant–soil feedbacks are an important and understudied aspect of aboveground–
belowground linkages in plant ecology that influence plant coexistence, invasion and restoration. Here, we examine
plant–soil feedback dynamics of seven co-occurring native and non-native grass species to address the questions of
how plants modify their soil environment, do those modifications inhibit or favor their own species relative to other species, and do non-natives exhibit different plant–soil feedback dynamics than natives. We used a two-phase design, wherein
a first generation of plants was grown to induce species-specific changes in the soil and a second generation of plants was
used as a bioassay to determine the effects of those changes. We also used path-analysis to examine the potential chain
of effects of the first generation on soil nutrients and soil microbial composition and on bioassay plant performance.
Our findings show species-specific (rather than consistent within groups of natives and non-natives) soil conditioning
effects on both soil nutrients and the soil microbial community by plants. Additionally, native species produced plant–soil
feedback types that benefit other species more than themselves and non-native invasive species tended to produce plant–
soil feedback types that benefit themselves more than other species. These results, coupled with previous field observations, support hypotheses that plant–soil feedbacks may be a mechanism by which some non-native species increase their
invasive potential and plant–soil feedbacks may influence the vulnerability of a site to invasion.
The interaction between plants and soil is a reciprocal
relationship with plants both being influenced by and influencing their soil environment. During growth, plants influence, or condition, their soil environment through root
exudation and deposition (Wardle et al. 2004, Hinsinger
et al. 2006), differential nutrient uptake (Hinsinger et al.
2006, Johnson et al. 2007, Perkins et al. 2011), mining and
immobilization of nutrients (Dakora and Phillips 2002,
Johnson et al. 2007, Perkins et al. 2011), and accumulation
and selection of microbial communities (Bezemer et al.
2006, Harrison and Bardgett 2010). These changes to the soil
environment can result in altered soil biological properties
(van der Putten et al. 1993, Bever et al. 1997, Klironomos
2002, Harrison and Bardgett 2010, Lankau 2011), soil nutrient dynamics (Ehrenfeld et al. 2001, Johnson et al. 2007,
Blank 2010, Harrison and Bardgett 2010, Meisner et al.
2011, Perkins et al. 2011), and ultimately can affect subsequent plant growth, thus generating a plant–soil feedback
(or PSF, van der Putten 1993, Vinton and Goergen 2006,
Kulmatiski et al. 2008). In this paper, we aim to tease apart
the relative contribution of the soil microbial community
and soil nutrients as mechanisms involved in PSF among
co-occurring native and non-native plant species.
Since plants rarely exist in isolation on the landscape, it
is important to consider how soil conditioning by a plant
affects subsequent performance (e.g. biomass production)
of both its own species (conspecifics) and other species
(heterospecifics, Bever et al. 1997, Mangan et al. 2010).
When a plant conditions the soil in a manner that increases
subsequent plant growth, is it generally considered a
positive feedback (Fig. 1A–B). However, all species may
not respond equally to soil conditioning. Conspecifics may
respond more strongly than other species (i.e. conspecific
positive feedback, Fig. 1A), or heterospecifics may respond
more than the species that conditioned the soil (i.e. heterospecific positive feedbacks, Fig. 1B). Conversely, soil conditioning may decrease subsequent plant growth creating
negative feedbacks (Fig. 1C–D). Again, species may not
respond equally. Conspecifics may incur a larger decrease
in plant performance due to soil conditioning than other
species (i.e. conspecific negative feedback, Fig. 1C) and
alternately, heterospecifics may incur a larger decrease than
the species that conditioned the soil (i.e. heterospecific
negative feedback, Fig. 1D). Possibly, soil conditioning
could increase conspecific performance and decrease heterospecific performance that, from the point of view of the
conditioning plant, would be a conspecific positive feedback. Further, soil conditioning could decrease conspecific
performance and increase heterospecific performance that,
from the point of view of the conditioning plants, would be
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Figure 1. Conceptual diagrams of four possible plant–soil
feedback (PSF) types. Positive feedbacks (A and B) occur if plant
performance (e.g. biomass production) is increased due to soil
conditioning by a given species (e.g. species A) compared to its performance in soils conditioned by other species; negative feedbacks
(C and D) occur if plant biomass production is decreased due to
soil conditioning by species A. Conspecific feedbacks (A and C)
occur when the species that conditioned the soil experience a larger
effect than other species: heterospecific feedbacks (B and D) occur
when other species experience a larger effect than the species that
conditioned the soil.
a conspecific negative feedback. A neutral or null feedback
occurs when soil conditioned by a species does not significantly affect subsequent con- or heterospecific performance. It is important to recognize that conspecifics benefit
relatively more than other species in two of these PSF scenarios: conspecific positive feedback (because its own species performance is enhanced more than other species’
performance) and heterospecific negative feedback (because
other species’ performance is suppressed more than its own
species performance).
Due to their novel presence in an environment and
expanding range, non-native invasive plant species may
condition soils differently and exhibit different plant–soil
feedback types than native species. Generally, it is suggested
that plant species generate PSF types in which they perform
better in soil previously occupied by other species compared
to soil previously occupied by their own species (heterospecific positive feedback, Kulmatiski et al. 2008). However,
invasive species have been observed to induce either less
negative PSF than natives (heterospecific negative, van
Grunsven et al. 2007, Inderjit and van der Putten 2010,
MacDougall et al. 2011) or positive PSF by performing better in soil previously occupied by conspecifics (conspecific
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positive, Klironomos 2002, Jordan et al. 2008). Invasive
species have also been observed to produce heterospecific
feedbacks that reduce natives (Jordan et al. 2008, Grman
and Suding 2010) and promote other invaders (Jordan et al.
2008). Examination of PSF dynamics among non-native
invasive and native species may provide a better understanding of mechanisms underlying plant community dynamics
and plant invasion (Kulmatiski et al. 2008). If PSF is a
strategy by which a species becomes invasive, it would be
expected that the invader would generate PSF types that
benefit conspecifics more than other species (conspecific
positive and heterospecific negative).
We performed a pair-wise, soil conditioning experiment to examine the PSF dynamics of seven co-occurring
native and non-native grass species in two natural soil
types. Our specific objectives were: 1) examination of the
mechanisms (soil nutrients and soil microbial community)
by which the grass species generate plant–soil feedbacks;
2) determination of the types of PSF generated by the seven
grass species, with particular emphasis on whether native
and non-native invasive species differ in PSF dynamics.
To address the first objective, soil nutrients and soil
microbial characteristics were examined after the first generation of plant growth (conditioning generation). Based on
previous work with the study species (Belnap and Phillips
2001, Kuske et al. 2002, Blank 2010), we hypothesized
that the PSF mechanisms would be largely driven by changes
in soil nutrients, rather than soil microbial composition.
To address the second objective, we evaluated the growth
performance of the second (bioassay) generation. Building
on PSF theory (Klironomos 2002, Bever 2003), we hypo­
thesized that native species induce PSF types that benefit
other species more than themselves, whereas non-native
invasive species induce PSF types that benefit themselves
more than other species.
Material and methods
A pair-wise conditioned soil experiment in a glasshouse in
Reno, NV, USA was designed in two phases (a soil conditioning generation and a bioassay generation) to examine the PSF mechanisms and PSF types of seven native
and non-native grass species. The choice of study species
was made to include the most common non-native grass
species (Bromus tectorum, Taeniatherum caput-medusae
and Agropyron cristatum), a newly introduced, non-native,
potential invader (Aegilops triuncialis), and common natives
(Elymus elymoides, Pseudoroegneria spicata and Vulpia
microstachys) found in the Great Basin region of the United
States. Soil texture affects plant growth, plant nutrient
acquisition (Blank 2010), soil microbial communities
(Bezemer et al. 2006), and plant–plant interactions (Blank
2010), thus it is reasonable to anticipate that soil texture
may affect PSF dynamics (Bezemer et al. 2006). Therefore, we collected and used two naturally occurring soils to
represent soil textures that occur within the Great Basin.
To control for differences in ages of invasion among
the study species, we collected soil from interspace (unoccupied and uninvaded) areas and experimentally conditioned
the soil.
The experimental design included eight treatment levels (seven species and an unplanted control) to condition
the soil that was then factorally replanted in the bioassay
generation (again, seven species and an unplanted control)
in two field-collected soil types. These treatment combinations (i.e. eight treatment levels  eight replantings  two
soils for a total of 128 pots per replicate) were replicated
five times for a total experimental design of 640 pots.
The duration of both phases included 80 days of growth
(which was sufficient time for the annual grasses to begin
reproduction). The glasshouse was maintained with ambient light and diurnal temperature fluctuation between
7°C and 24°C that reflected typical spring temperatures of
the Great Basin. Careful and attentive watering maintained
soils near field capacity without allowing any leaching
or water to drain out of the pots. Pots were frequently randomized in the glasshouse to compensate for any environmental variation.
The two soil types (a sandy loam and a clay soil)
are typically found in the Great Basin and were collected
from unvegetated interspaces in natural undeveloped areas
outside Reno, NV USA (for a detailed soil description see
Perkins et al. 2011). For both soils, only the top 25 cm
of soil was collected and stored for 14 days before it was
homogenized and put into containers (656 ml volume,
6.4  25 cm tubes, commercially available). Each pot was
randomly assigned a grass species or unplanted control;
three seeds were planted in each pot but only the first emergent was allowed to grow. After 80 days, the conditioning
generation concluded at which time aboveground biomass was removed, soils were homogenized (within each
treatment level and replicate combination, separately for
each soil type, in a clean, portable cement mixer), and soils
were sampled (100 g sample) for nutrient and microbial
analyses. Soil homogenization was specifically done to avoid
sampling just the rhizosphere soil. Sampling just the rhizosphere soil might have produced more significant results but
would not be a reflection of how plants affect the bulk soil.
Due to the small size of the grasses, most roots were very fine
and were not removed from the soil.
After soils were sampled, the soil was repotted and the
bioassay generation was planted. Every bioassay species
was sown in: 1) conspecific conditioned soil; 2) the soil of
every other species; and 3) the soil that was unplanted in
the conditioning generation. Planting of the bioassay generation was similar to the conditioning generation with
three seeds planted, of which the first emergent was allowed
to grow. At harvest of the bioassay generation, the above­
ground biomass was removed, dried at 60°C for over 24 h,
and weighed.
Extractable soil nutrients were determined by the same
methods as Blank (2010). Briefly, soil nutrients were extrac­
ted from fresh soil samples using the following standard
procedures: 1.5 M KCl-extraction of NO32 and NH4
from 10 g soil; 1.0 M ammonium acetate (buffered to
pH 7.0) extraction of Ca, Mg, and K from 5 g soil; Fe and
Mn via 0.005 M DTPA extraction from 10 g soil; and
0.5 M (buffered to pH 8.5) bicarbonate extraction for
available P from 5 g soil. Total N was determined for
soil using a LECO TruSpec from 0.5 g soil. Soil microbial community was examined chemotaxonomically with
phospholipid fatty acids analysis (PLFA). Soil microbial
samples were freeze-dried immediately after collection and
sent for extraction using a hybrid PLFA and fatty-acid
methyl ester (FAME) technique (Bligh and Dyer 1959,
Smithwick et al. 2005) and gas chromatography. Individual
PLFA concentrations were determined by comparing
sample peaks to a 13:0 FAME standard and calculated as
mol % PLFA. Chemotaxonomic grouping of PLFAs followed Zelles (1999) and Mitchell et al. (2010): 16:I w9c,
17:0 cy, 17:1 w8c, 17:1 w9c and 18:1 w5c were considered
indicative of gram negative bacteria; 18:2w6,9c  t as fungi;
18:0 10 me, 17:0 10 me and 16:0 10 me as actinomycetes;
and 14:0i, 15:0a, 16:0i, 17:0a, 17:0i, 17:0 and 15:0 plus
gram negative bacteria as total bacteria. Subsequent to our
initial analysis, the use of individual PLFA markers as signatures for specific microorganisms has been questioned
(Frostegård et al. 2011). Thus, the classification of any
PLFA should be considered a potential indicator (not definitive evidence) of a given microbial group, and changes in
our PLFA profiles are a reflection of changes to entire microbial community due to the soil conditioning by different
grass species.
Data analysis
A relative response (RR) index of bioassay species performance was calculated to evaluate the cumulative effects
of the soil conditioning species on bioassay generation
performance, and RR values were then used to determine
PSF type. An index (Rii) with strong mathematical and
statistical properties (i.e. it is symmetrical around zero, is
linear, and has no discontinuities in its range; Armas et al.
2004, Brinkman et al. 2010) that has been used for plant
interactions was adapted to examine PSF. RR x,x was calculated to evaluate how a species (species x) responds to its own
conditioned soil (soil x) compared with soils conditioned
by other species. RRothers,x was calculated to evaluate how
other species respond to soil x compared with other soils.
Equations used were:
RR x,x  ((biomass species x in soil x) 2 (biomass species x in other soils))/
((biomass species x in soil x)  (biomass species x in all other soils)).
RRothers,x  ((biomass other species in soil x)
2 (biomass other species in other soils))/
((biomass other species in soil x)  (biomass other species in other soils)).
A positive value of RR indicates that a species responds
more positively to soil x than other soils, a negative value
indicates that a species responds more negatively to soil x
than other soils, and a value of zero indicates that the species did not respond differently to soil x compared to other
soils. A RR x,x value that is both positive and greater than
RRothers,x indicates that a species had a larger positive relative response to its own conditioned soil than other species
(conspecific positive feedback). A RR x,x value that is both
positive and less than RRothers,x indicates that a species had
a smaller positive relative response to its own conditioned
soil than other species (heterospecific positive feedback).
201
A RR x,x value that is both negative and less than RRothers,x
indicates that a species had a larger negative relative response
to its own conditioned soil than other species (conspecific negative feedback). Finally, a RR x,x value that is both
negative and greater than RRothers,x indicates that a species
had a smaller negative relative response to its own conditioned soil than other species (heterospecific negative
feedback).
Differences in soil nutrients, soil microbial properties, bioassay species biomass, RR x,x, and RRothers,x, among
soil conditioning species, between soil types, and between
conditioning species native status (native versus non-native)
were compared using MANOVA with Wilks’s lambda to
determine F for soil conditioning species and Hotellings
trace to determine F for soil type and native status. Followup univariate tests for between-subject effects were performed with Bonferroni correction. T-tests were used
to determine if RR values were different from zero and if
there were differences between RR x,x and RRothers,x for each
soil conditioning species within each soil type. Prior to
analysis, the following transformations were performed
to ensure multivariate normality: soil N, soil Mg and bio­
assay biomass were log transformed, soil Mn was square
root transformed, and the microbial characteristics were
arcsine transformed. For ease of interpretation, untransformed values are shown.
Path analysis (Sokal and Rohlf 1981) was used to
evaluate PSF mechanisms by which each soil conditioning species affected each bioassay species. This was the first
study to examine plant–soil relationships for most of our
study species, so the hypothesized path model (Fig. 2) is
not based on species-specific knowledge. In our theoretical
model (Fig. 2), conditioning plant biomass has the potential
to directly affect both soil nutrients and soil microbes, and
these in turn may influence bioassay plant biomass. Direct
effects of one variable on another (one-headed arrows)
were calculated as standardized regression coefficients; indirect relationships (two-headed arrows) were calculated as
Pearson’s correlation coefficients (Sokal and Rohlf 1981).
All non-significant paths and any variables with no significant paths leading to or from them were removed from the
diagrams. Because our a priori model was mechanistically
general and not species-specific, our analysis was more of a
model building than model testing approach. Therefore, our
results should be considered as possible PSF mechanisms
ready for further validation.
To synthesize the information included in the path
analyses, a summary table that reports the consistency and
strength of paths was generated. Consistency refers to the
number of bioassay species that responded to a given mechanistic variable in the soil of each conditioning species and
thus the maximum value possible is 7. Strength refers to the
mean of the path coefficients for a given path. Data were
analyzed with PASW Statistics 18 (PASW for Windows,
Rel. 18.0.0. 2009, SPSS Inc.).
Results
Soil conditioning species and soil type had significant
(p  0.01) overall effects (Table 1). Native status did not
produce any significant effects (p  0.99). Follow-up univariate between-subject tests indicate that soil conditioning
species and soil type influenced most soil nutrient and
microbial community characteristics, bioassay generation
biomass, and RR values (Table 1).
PSF mechanisms
After soil conditioning by the first plant generation,
most soil characteristics were significantly different among
species in at least one soil type (Fig. 3) indicating speciesspecific soil conditioning effects. For example, soil N was significantly higher in the B. tectorum conditioned sandy loam
compared with E. elymoides, V. microstachys and A. cristatum
conditioned sandy loam (Fig. 3). Although species-specific
Figure 2. Theoretical path diagram for examination of PSF for individual soil conditioning species in each soil type. Direct effects are
indicated with a one-headed arrow; correlations are indicated by a two-headed arrow. Conditioning species biomass is hypothesized
to directly affect both soil nutrients and the soil microbial community and to only indirectly affect bioassay generation biomass. Soil
nutrients and the soil microbial community are hypothesized to be correlated. F:B  fungi to bacteria ratio. Groupings are for ease of
comprehension, not for statistical reasons.
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Table 1. Overall and between-subject effects of soil conditioning species (identity of the species in the first generation), soil type (sandy loam
vs clay soil), and native status (native vs non-native) from MANOVA. Values in bold are significant (p  0.05). Degrees of freedom are indicated as subscripted numbers in parentheses after the F-value. Univariate tests for native status were not done because the overall MANOVA
was not significant.
Conditioning species
F(DF)
MANOVA
1.40(126,142)
Soil type
p
Overall
0.03
Native
F(DF)
p
F(DF)
p
36.75(18,20)
 0.01
0.01(18,20)
0.99
Univariate tests
Soil nutrients
N
P
K
Ca
Mg
Fe
Mn
Soil microbial community
Actinomycetes
Gram-bacteria
Fungi
F:B
Bioassay generation biomass
E. elymoides
P. spicata
V. microstachys
A. triuncialis
A. cristatum
B. tectorum
T. caput-medusae
Bioassay RR
RRx,x
RRothers,x
16.48(7)
0.95(7)
0.84(7)
2.55(7)
3.24(7)
1.64(7)
1.54(7)
 0.01
0.48
0.56
0.03
0.01
0.16
0.19
33.36(1)
2.44(1)
22.52(1)
0.05(1)
0.39(1)
80.97(1)
111.44(1)
 0.01
0.13
 0.01
0.94
0.54
 0.01
 0.01
1.98(7)
2.28(7)
3.14(7)
1.96(7)
0.08
0.04
0.01
0.09
5.96(1)
0.07(1)
8.61(1)
2.15(1)
0.02
0.78
 0.01
0.15
0.45(7)
0.43(7)
1.14(7)
1.35(7)
2.38(7)
2.40(7)
2.65(7)
0.86
0.88
0.36
0.25
0.04
0.04
0.02
12.54(1)
84.02(1)
24.77(1)
86.72(1)
82.65(1)
20.64(1)
82.29(1)
 0.01
 0.01
 0.01
 0.01
 0.01
 0.01
 0.01
2.66(7)
3.51(7)
0.02
 0.01
0.07(1)
0.01(1)
0.80
0.92
effects were found for most soil chxaracteristics, the most
extreme species effects were found in the soil microbial
community measured by PLFA. The results from PLFA
analysis should be considered a potential indicator (not
definitive evidence) of a given microbial group, and changes
in our PLFA profiles are a reflection of changes to the
entire microbial community due to the soil conditioning
by different grass species (Frostegård et al. 2011). The clay
soil conditioned by V. microstachys had significantly higher
% PLFAs that may be indicative of actinomycetes, and significantly lower % PLFAs that may be indicative of fungi,
than soil conditioned by other species; and A. cristatum
produced soils notably high in PLFAs that may be indicative of gram negative bacteria (Fig. 3). Soil Ca, Mg, Fe and
PLFAs that may be indicative of actinomycetes and fungi
were significantly different among soil conditioning species
in both soil types.
To evaluate PSF mechanisms, two path diagrams
were generated (Fig. 4): one (A) illustrates a PSF mechanism through soil nutrients more than the soil microbial
community, and one (B) illustrates a PSF mechanism
through the soil microbial community more than soil
nutrients. A predominately soil nutrient PSF was generated
by P. spicata in the clay soil because 9 of 12 paths that significantly influence the bioassay generation were soil nutrients (Fig. 4A). Notably, P. spicata in the clay soil induced
changes in soil Fe that significantly affected every bioassay
species. In contrast to the soil nutrient based PSF mechanism, soil conditioning species V. microstachys in the clay
soil generated a predominantly soil microbial community
PSF mechanism (Fig. 4B). V. microstachys conditioned
clay soil significantly affected every bioassay species except
A. triuncialis through PLFAs that might be indicative of
both F:B and gram negative bacteria.
Several species produced discernible patterns in PSF
mechanism consistency and strength (Table 3). Consistency
refers to how many bioassay species responded significantly to a given soil variable, and strength is the mean path
coefficient. For example, E. elymoides conditioned sandy
loam produced significant paths through soil P with a
consistency of 6 and a strength (mean path coefficient)
of 1.17  0.12; soil Ca (consistency  3, strength  20.41  0.03); % gram negative bacteria (consistency  2,
strength  1.34  0.43); and % F:B and % fungi (consistency  1 and strengths  20.86 and 1.42, respectively). In
the clay soil, E. elymoides produced significant paths through
soil P and % gram negative bacteria. Thus, E. elymoides
in both of the soil types exhibited alteration of soil P and
% gram negative bacteria as PSF mechanisms. P. spicata,
A. cristatum and T. caput-medusae (in the sandy loam) generated PSFs though soil nutrients more than the soil microbial
community. Vulpia microstachys and T. caput-medusae (in
the clay soil) exhibited PSF mechanisms via alteration of the
soil microbial community more than soil nutrients (Table 3).
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Figure 3. Soil characteristics after conditioning generation by grass species in sandy loam (light bars) and clay soil (dark bars). Native species
are E. elymoides, P. spicata, and V. microstachys. Non-native species are A. triuncialis, A. cristatum, B. tectorum and T. caput-medusae.
Bars indicated by different letters and numbers are significantly from one another within the particular soil type; differences between sandy
loam and clay soils were not compared.
PSF types
To determine the specific type of PSF generated by each
soil conditioning species in each soil type, we examined
RR x,x and RRothers,x values (Table 2). Every possible PSF type
occurred in this experiment. Only one species generated a
conspecific positive feedback. In both soil types, A. cristatum
conditioned soil in a manner that benefited its subsequent
plant growth (indicated by positive RR values in Table 2).
Additionally, A. cristatum experienced a larger response
(i.e. had a larger relative increase in biomass; RR x,x  0.35  RRothers,x  0.20) compared to other species (Fig. 1A).
One species generated a heterospecific positive feedback.
In the clay soil, P. spicata conditioned soil in a manner that
increased its subsequent plant growth. However, other species tended to respond more strongly (i.e. had a larger relative increase in biomass; RR others,x  0.15  RR x,x  0.11)
compared to P. spicata (Table 2, Fig. 1B).
Four species generated conspecific negative feedbacks.
Elymus elymoides (in both soil types), P. spicata (in the sandy
loam), V. microstachys (in both soil types), and T. caputmedusae (in the sandy loam), all conditioned soil in a manner that decreased subsequent plant growth (indicated by
negative RR values in Table 2). Additionally, for all these
species, conspecifics had a larger relative decrease in biomass
compared to other species (Fig. 1C).
204
One species generated a heterospecific negative feedback. In the clay soil, T. caput-medusae conditioned soils
in a manner that decreased all subsequent plant performance (indicated by negative RR values in Table 2).
However, T. caput-medusae experienced a larger relative
decrease in biomass (RR x,x  20.12  RRothers,x  20.01)
compared to other species (Fig. 1D).
Two species generated neutral or null feedbacks.
Unexpectedly, RR x,x and RRothers,x generated by both
A. triuncialis and B. tectorum in both soil types were not different from zero (Table 2), which indicates that both species
in both soil types produced neutral feedback types.
Overall, native species (E. elymoides, P. spicata and
V. microstachys) tended to produce PSF effects that decreased
their own relative performance. In contrast, two nonnative species, A. cristatum and T. caput-medusae (in the
clay soil) produced PSF effects that increased their own
relative performance, and two non-native species, B. tectorum
and A. triuncialis tended to produce neutral PSF types
(Table 2).
Discussion
Plant–soil feedbacks may provide insight into fundamental
questions of plant invasion such as why and how do some
Figure 4. Path diagrams showing PSF mechanisms for (A)
P. spicata in the clay soil, (B) V. microstachys in the clay soil.
(A) shows PSF mechanism predominately induced through soil
nutrients, and (B) shows PSF mechanisms predominately through
the soil microbial community. Single headed arrows indicate
a direct affect and are calculated as standardized path coefficients;
two headed arrows indicate correlation and are calculated as
Pearson’s correlation coefficients. Only significant paths are
included. F:B  fungi:bacteria ratio.
species rise to dominance while others do not? In this study,
we determined that our study species produced speciesspecific effects on soil characteristics (objective 1) that, in
turn, resulted in species-specific PSF mechanisms, which
were not consistent within groups of natives or non-natives
species. However, native and non-native species were
generally consistent in the types of PSF they generated
(objective 2). Native species tended to generate PSF types
that benefited other species (i.e. heterospecific positive and
conspecific negative, Table 2, Fig. 1B–C) and non-native
species (A. cristatum and T. caput-medusae) tended to
generate PSF types that benefited themselves (i.e. conspecific positive and heterospecific negative, Table 2, Fig. 1A,
D). There were exceptions to this pattern, as the non-native
species B. tectorum and A. triuncialis produced neutral PSF
types (Table 2).
Previous studies have observed similar changes in soil
characteristics due to conditioning by B. tectorum and
T. caput-medusae in both glasshouse and natural settings
(Belnap and Phillips 2001, Booth et al. 2003, Blank
and Young 2004, Norton et al. 2004, Blank and Sforza
2007). Our results agree with previous studies that found
an increase in soil N for soils conditioned by B. tectorum
(Belnap and Phillips 2001, Booth et al. 2003, Blank and
Young 2004, Norton et al. 2004) and a decreased N,
Ca and Mg and increased Mn and Fe in soils conditioned
by T. caput-medusae (Blank and Sforza 2007). Our values
for % fungi are higher than those reported in other PSF
studies (Bezemer et al. 2006); however other studies often
occur in more fertile and productive environments. Our
study system, the cold desert Great Basin in western North
America, is relatively infertile, unproductive, and characteristically stressful for plants. Microbial communities in
stressful sites, such as the Great Basin, are thought to have a
much higher fungal component compared to other environs
(Wardle et al. 2004). Bromus tectorum and T. caput-medusae
are the only species included in this study whose plant–soil
dynamics have been examined; consequently no information
is available to validate results for the other study species.
Table 2. Mean (SE) of RRx,x (i.e. how a species responds to its own soil), and RR others,x (i.e. how other species respond to the soil). A negative
RR value indicates that a species performs worse on that conditioned soil than in other soils, a positive RR value indicates that a species
performs better in that soil than in the other soils. PSF type follows Fig. 1. Bold values are significantly different from zero, values marked
with different letters indicate that RRx,x and RRothers,x are significantly different from each other. Native species are E. elymoides, P. spicata,
and V. microstachys.
Soil conditioning species
E. elymoides
P. spicata
V. microstachys
A. triuncialis
A. cristatum
B. tectorum
T. caput-medusae
Soil type
RRx,x
RRothers,x
PSF type
sandy loam
clay soil
sandy loam
clay soil
sandy loam
clay soil
sandy loam
clay soil
sandy loam
clay soil
sandy loam
clay soil
sandy loam
clay soil
20.12 (0.02)a
20.21 (0.06)a
20.24 (0.06)a
0.11 (0.04)
20.18 (0.09)a
20.44 (0.16)a
20.14 (0.11)
20.23 (0.19)
0.22 (0.07)a
0.35 (0.08)
20.02 (0.12)
0.11 (0.07)
20.12 (0.04)
20.17 (0.10)
20.01 (0.05)b
0.13 (0.03)b
0.08 (0.04)b
0.15 (0.05)
20.03 (0.05)b
20.24 (0.09)b
20.03 (0.05)
20.10 (0.11)
0.04 (0.04)b
0.20 (0.05)
20.08 (0.04)
0.04 (0.10)
20.01 (0.04)
20.30 (0.11)
conspecific negative
conspecific negative
conspecific negative
heterospecific positive
conspecific negative
conspecific negative
neutral
neutral
conspecific positive
conspecific positive
neutral
neutral
conspecific negative
heterospecific negative
205
–
–
–
–
–
–
–
–
–
1
0.75
–
–
–
–
–
–
–
–
cspp
–
–
–
–
4
0.76  0.12
–
–
–
–
–
–
–
–
2
0.79  0.02
–
–
6
0.86  0.06
–
–
–
–
–
7
1.10  0.07
–
–
–
–
–
–
5
1.15  0.11
–
–
–
–
–
–
–
6
0.77  0.15
–
–
–
–
–
–
1
0.48
2
0.70  0.02
3
0.74  0.15
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
fungi
1
1.42
–
–
–
–
–
–
7
1.07  0.07
–
–
–
–
–
–
–
–
2
0.75  0.02
1
20.86
–
–
–
–
–
–
–
–
6
0.89  0.08
1
0.62
–
–
2
0.32  0.02
–
–
F:B
Actino
–
2
1.34  0.43
4
0.72  0.13
2
0.52  0.09
3
0.30  0.02
–
–
5
0.53  0.07
3
0.59  0.24
–
–
3
0.82  0.10
1
0.47
Gram negative
Mn
–
Fe
–
Mg
–
Ca
3
20.41  0.03
–
–
1
0.74
1
0.11
3
0.37  0.14
–
–
1
0.39
–
–
–
–
–
–
–
–
–
–
–
–
1
0.38
–
–
1
0.50
–
–
–
–
4
0.82  0.15
–
–
K
P
6
1.17  0.12
4
0.85  0.04
–
–
–
–
2
20.03  0.02
–
–
5
0.96  0.13
–
–
4
0.74  0.18
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
N
clay
sand
T. caput-medusae
clay
sand
A. cristatum
clay
sand
V. microstachys
clay
sand
P. spicata
clay
consistency
strength
consistency
strength
consistency
strength
consistency
strength
consistency
strength
consistency
strength
consistency
strength
consistency
strength
consistency
strength
consistency
strength
Soil type
sand
E. elymoides
Conditioning species
Table 3. Summary of the path diagrams showing consistency (how often a variable significantly affects the bioassay generation in the path diagram for a given conditioning species in each soil) and
strength (the mean  SE value of the paths). Bold values indicate that the soil conditioning species induced an effect on biomass of the bioassay generation for that mechanism in both the Sandy loam
and the Clay soil. E. elymoides, P. spicata and V. microstachys are native species. A. cristatum and T. caput-medusae are invasive species. Because A. triuncialis and B. tectorum generated neutral
plant–soil feedbacks, no results are shown for these two species.
206
If validated in the field, our results may help explain vegetation patterns observed in the Great Basin.
Comparison of path analyses (Table 3) with soil characteristics (Fig. 3) indicate that PSF mechanisms can only
be moderately anticipated by examination of soil characteristics after the conditioning generation. For example, clay
soil conditioned by P. spicata did have significantly lower
soil Fe content (28% reduction compared to E. elymoides
conditioned clay soil, Fig. 3) to which every bioassay species
responded in the path analysis (Fig. 4A, Table 3). However,
V. microstachys produced a large effect on Ca in the sandy
loam (Fig. 3), but only three of seven bioassay species
responded to that as a PSF mechanism (Fig. 4B, Table 3).
An explanation of this phenomenon can be found in the
fundamental hypothesis for this project that plants actively
alter their soil environment, which of course would occur
in the bioassay generation as well as in the soil conditioning generation. Bioassay plant species may counteract some
of the changes in the soil induced by the conditioning
generation. Thus, all changes induced by the conditioning
generation may not manifest effects on subsequent plant
performance.
Our results suggest that considering both soil nutrients and the soil microbial community is essential when
examining species-specific soil conditioning effects and PSF
mechanisms. Soil is a highly dynamic, heterogeneous and
tightly linked system of air, minerals, water, and life (Jenny
1980), all of which are impacted by plants. Many PSF
studies examine only a subset of the soil continuum (i.e. the
soil microbial community), and thus are missing a major
portion of the effects of plants on soil properties. Including measures of both soil nutrients and the soil microbial
community provide a much more complete examination
of the cumulative effects that plants have on soil (Meisner
et al. 2011) and is essential for determination of PSF mechanisms. Species-specific effects on soil nutrients and the soil
microbial community as PSF mechanisms should be further
examined for other systems to identify patterns of similar
soil conditioning effects among groups of plants that are
either related (Brandt et al. 2009, Burns and Strauss 2011,
Hacker et al. 2012), with similar evolutionary histories, or
with similar life histories; and to examine whether the effects
of a single species on soil conditions is consistent in different
environments, with different neighbors, or with increasing
stress.
Soil texture (or particle size distribution) affects many
essential soil properties (Jenny 1980). Soils with more sand
(like our ‘sandy loam’) generally are lower in plant nutrients and nutrient exchangeability, organic matter, and soil
microorganism habitat (pore space) compared to soils with
a higher clay content (like our ‘clay soil’). Soil sand content
also affects the relationship between plants and soil micro­
organisms (Zaller et al. 2011). Therefore, it is not surprising
that we found the effects of soil conditioning by species
tended to be different in each soil type. However, it is
remarkable that most of the types of PSF generated were
consistent between soil types. Only two species (P. spicata
and T. caput-medusae) generated different PSF types in
each soil. Native P. spicata generated a conspecific negative
feedback in the sandy loam and a heterospecific positive
feedback in the clay soil; however both of these feedback
types benefit other species more than itself. Non-native
T. caput-medusae generated a conspecific negative feedback
in the sandy loam and a heterospecific negative in the clay
soil. The feedbacks generated by T. caput-medusae suggest
that T. caput-medusae would be more invasive in clay soils
than sandy soils. Indeed, in the Great Basin T. caput-medusae
has a somewhat limited distribution and is found predominately in clayey soils in the Great Basin (Young 1992), i.e.
field results that are consistent with our PSF greenhouse
experiments.
Our findings and previous work (Inderjit and van der
Putten 2010) can be built upon to erect broad hypotheses
applicable to other systems. First, all of the native plants
(and non-native T. caput-medusae in the sandy loam only) in
our study experienced PSF types that suppressed conspecific
growth more than heterospecific growth, which is in concordance with other studies and meta-analyses (Bever 1994,
Klironomos 2002, Kulmatiski et al. 2008). This result provides support for the hypothesis that most plants, especially
native plants, experience PSF types that decrease conspecific
performance. A broad invasion hypothesis that builds on
this result is plant communities comprised of species that
generate PSF types that preferentially benefit heterospecifics might be especially vulnerable to invasion. That is,
non-natives may preferentially benefit from soil conditions
after introduction to sites occupied by native species (such
as E. elymoides, P. spicata and V. microstachys) that produce
PSF types that reduce conspecific performance more than
heterospecific performance. This hypothesis may help explain
the seemingly idiosyncratic invasion of intact and undisturbed communities. Second, also in agreement with other
studies and meta-analyses ((Klironomos 2002, Kulmatiski
et al. 2008); the non-native species in this study either produced PSF types that preferentially benefited conspecifics,
or did not generate significant PSF effects at all (neutral
PSF). This result provides support for the invasion hypothesis that soil conditioning and plant–soil feedbacks are one
strategy by which some species become invasive. Species that
produce PSF types that preferentially benefit conspecific
performance may have more invasive potential than other
species. Neither a definitive life-history trait (e.g. high
growth rate, van Kleunen et al. 2010) nor interaction strategy
(e.g. competitive ability, Vila and Weiner 2004) that is common among all invasive species has been identified (Alpert
et al. 2000). However, generating beneficial PSFs may be
among the interaction strategies by which some, but not all,
species become invasive.
Conclusion
In this study, natives tended to induce PSF types that benefited other species more than themselves and non-natives
tended to induce PSF types that benefited themselves more
than other species. However, the underlying PSF mechanisms (soil nutrients or soil microbial composition) were
species-specific. Our results indicate that it is essential to
consider the effects on both soil nutrients and the soil microbial community when evaluating soil conditioning or PSF
mechanisms. Furthermore, these results elucidate one factor
that influences species invasion on the landscape.
Acknowledgements – Thanks for help and advice to the Restoration
and Ecology of Arid Lands Laboratory especially Elizabeth
DeLorenze and Nicole DeCrappeo and to the Stable Isotope
Research Lab especially Rockie Yarwood. Ashley Sparrow, Laurie
Greenfield, Zack Aanderud and Scott Mangan provided excellent
feedback on proposals and earlier drafts of this manuscript. Anthony
Regalia, Scot Ferguson, Ryan Urban and Todd Ferguson helped
enormously in the greenhouse and lab. This research was supported
in part by the US Department of Energy through contract
DE-AC07-06ID14680 to SM Stoller Corporation and the Nevada
Agricultural Experiment Station.
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