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Ecology, 80(3), 1999, pp. 882–890
q 1999 by the Ecological Society of America
EFFECTS OF ENVIRONMENTAL CHANGE ON PLANT SPECIES DENSITY:
COMPARING PREDICTIONS WITH EXPERIMENTS
LAURA GOUGH1,3
JAMES B. GRACE2
AND
1
Department of Plant Biology, Louisiana State University, Baton Rouge, Louisiana 70803 USA
2National Wetlands Research Center, U.S. Geological Survey, 700 Cajundome Boulevard,
Lafayette, Louisiana 70506 USA
Abstract. Ideally, general ecological relationships may be used to predict responses
of natural communities to environmental change, but few attempts have been made to
determine the reliability of predictions based on descriptive data. Using a previously published structural equation model (SEM) of descriptive data from a coastal marsh landscape,
we compared these predictions against observed changes in plant species density resulting
from field experiments (manipulations of soil fertility, flooding, salinity, and mammalian
herbivory) in two areas within the same marsh.
In general, observed experimental responses were fairly consistent with predictions.
The largest discrepancy occurred when sods were transplanted from high- to low-salinity
sites and herbivores selectively consumed a particularly palatable plant species in the
transplanted sods. Individual plot responses to some treatments were predicted more accurately than others. Individual fertilized plot responses were not consistent with predictions
(P . 0.05), nor were fenced plots (herbivore exclosures; R2 5 0.15) compared to unfenced
plots (R2 5 0.53). For the remaining treatments, predictions reasonably matched responses
(R2 5 0.63).
We constructed an SEM for the experimental data; it explained 60% of the variance in
species density and showed that fencing and fertilization led to decreases in species density
that were not predicted from treatment effects on community biomass or observed disturbance levels. These treatments may have affected the ratio of live to dead biomass, and
competitive exclusion likely decreased species density in fenced and fertilized plots. We
conclude that experimental validation is required to determine the predictive value of
comparative relationships derived from descriptive data.
Key words: biomass; coastal marsh; disturbance; flooding; herbivory; nutrient enrichment; salinity; species density; structural equation modeling.
INTRODUCTION
Perhaps the greatest challenge facing community
ecologists today is understanding the factors influencing plant community structure well enough to predict
how communities will respond to changing environmental conditions. For example, the ecological literature contains many models of species diversity with
application to a wide variety of communities (see Huston 1994, Palmer 1994, Rosenzweig 1995 for recent
reviews). However, there is little agreement as to what
variables are necessary to parameterize these models
across different communities and ecosystems. Many of
these models are constructed in one habitat with hope
of pertaining to others; rarely are the models tested
outside the initial parameter space (for an exception,
see Shipley et al. [1991]).
One of the few generally agreed upon community
patterns is the hump-shaped relationship between plant
Manuscript received 22 May 1997; revised 7 May 1998;
accepted 7 May 1998.
3 Present address: Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama 35487-0344 USA.
E-mail: [email protected]
species richness (or plant species density [the number
of species per plot]) and community biomass or productivity (Grime 1973, Tilman and Pacala 1993). This
relationship is characterized by peak species density at
a low to intermediate level of biomass and has been
documented in many plant communities, although the
variance explained by the relationship varies and is
likely dependent on scale (reviewed in Mittelbach et
al., unpublished manuscript). Not all studies of species
density and habitat productivity (or, more typically,
biomass) have found a strong relationship (Garcia et
al. 1993, Abrams 1995). In an earlier study in two
Louisiana coastal marshes (Gough et al. 1994), species
density was not well explained by biomass (R2 5 0.02).
Rather, environmental variables (salinity, flooding, and
soil fertility) in addition to biomass explained the observed variation in species density (R2 5 0.82). We
concluded that in environments such as coastal marshes, the relationship between biomass and species density is weak because of environmental regulation of the
species pool. As a result of this interpretation, we proposed a simple conceptual model in which environmental variables regulate the species pool while bio-
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PREDICTING EFFECTS OF ENVIRONMENTAL CHANGE
mass serves as a predictor of competition and exclusion.
To further evaluate the model proposed by Gough et
al. (1994), Grace and Pugesek (1997) developed a
structural equation model (SEM) of the factors believed
to control plant species density in a coastal wetland
landscape. Structural equation modeling is a multivariate technique designed to evaluate complex hypotheses through the analysis of covariances; conventional
path analysis and multivariate analysis are special cases
of the more generalized method of covariance analysis
(Hayduk 1987, Bollen 1989, Hoyle 1995, Schumacher
and Lomax 1996). SEM allows for (1) the evaluation
of alternative models, (2) the partitioning of direct and
indirect effects, and (3) the prediction of dependent
variable responses to changes in independent variables,
singly or in combination (Johnson et al. 1991, Wesser
and Armbruster 1991, Mitchell 1992). Using this method, Grace and Pugesek (1997) proposed both a general
model whereby plant species density is controlled by
abiotic conditions, disturbance, and plant biomass, and
a specific version of the model for a Louisiana coastal
wetland. They concluded that community biomass and
abiotic influences on the species pool equally controlled species density.
In this paper, we parameterize a previously published
structural equation model (Grace and Pugesek 1997)
using descriptive data to generate predictions about
how changes in environmental conditions should affect
plant species density in a coastal marsh landscape.
These predictions are then compared with observed
changes in species density resulting from experimental
field manipulations. The variables manipulated were
based on those suggested to be important in earlier
papers (Gough et al. 1994, Grace and Pugesek 1997),
and included soil fertility, salinity, flooding, and mammalian herbivory. Salinity and flooding are of additional interest as both variables are projected to continue to change in this region due to relative sea level
rise (Turner and Cahoon 1987, Gornitz 1995). Model
results predicted that under both natural and experimental conditions, species density should increase given decreasing salinity, flooding, or disturbance (in this
case, herbivore activity), and should decrease when
increased soil nutrients cause increased biomass. Finally, a structural equation model was developed for
the experimental data to better understand observed
responses. By examining both descriptive landscape
data and experimental data using this relatively new
method, we demonstrate how patterns of species density across natural gradients may be used to predict
responses to environmental change, and how experimental manipulations provide insights into controls on
species density not otherwise detectable from descriptive data.
METHODS
Study area
The research reported here was conducted in the
Pearl River basin, located at the coastal border between
883
Louisiana and Mississippi (White 1983, Gough 1996).
The system consists of three main channels of the Pearl
River with braided distributaries throughout. The wetlands studied here were herbaceous communities ranging from fresh to salt marsh.
Data collection
Descriptive data.—The data used for predicting responses were collected in 1994 according to the methods reported (for 1993 data) in Grace and Pugesek
(1997). The landscape represented by this sampling
extends from coastal salt marshes to the interior boundary of marsh and forested wetland, and includes ;5400
ha. The landscape was divided into 19 community
types, defined a priori using aerial photography and
based on natural transitions along gradients. Ten 1-m2
plots were established in each community type. For
each plot, the following variables were measured in
September 1994: soil salinity, elevation mean and
range for each plot, soil carbon, percentage of the plot
obviously disturbed by waves or animal activities (as
indicated by wrack cover and mudflats), aboveground
dry biomass (live and dead combined), and community
composition. Similar data collected in 1993 were reported and used to parameterize an SEM model in
Grace and Pugesek (1997).
Experimental data.—Experiments to test the important factors believed to control plant species density
were established in early summer of 1993 and harvested in August 1995. They were located at two sites
within the Pearl River basin adjacent to two of the
community types sampled for the descriptive data: a
fresh/oligohaline marsh dominated by Sagittaria lancifolia and a mesohaline/brackish marsh dominated by
Spartina patens and Scirpus americanus (Gough and
Grace 1998). Manipulations of soil fertility, salinity,
and flooding were conducted factorially with mammalian herbivore exclosures. One-meter-square plots
were either fertilized (to produce available nitrogen at
17 g·m22·yr21, using 20-10-5 NPK fertilizer), had sediment added (collected from nearby channels), or were
left undisturbed as controls (Gough and Grace 1998).
Smaller sods (0.33 m diameter 3 0.15 m deep) were
dug up and placed back in the ground (disturbed control), transplanted to the other site (transplant to manipulate salinity), placed on top of sediment at 10 cm
above the marsh surface (raised), deposited in a hole
at 10 cm below the marsh surface (lowered), or simply
marked and not manipulated (undisturbed control)
(Gough and Grace 1999). One replicate of each treatment was established inside a 7 3 7 m mammalian
exclosure and one set was established outside. Exclosures were ;1.2 m high and buried in the sediment 0.3
m to prevent invasion by nutria (Myocastor coypus
[large rodents native to Argentina]), muskrat (Ondatra
zibethicus), rabbit (Sylvilagus sp.), deer (Odocoileus
virginianus), and wild boar (Sus scrofa). Treatments
were replicated eight times at each site. Two plots were
LAURA GOUGH AND JAMES B. GRACE
884
eliminated from the data set as they were covered by
heavy wooden debris at the conclusion of the study;
254 plots were included in this analysis.
In August 1995, aboveground biomass was harvested
from the center 0.33 m diameter area of the larger plots
and the entire small plots (0.33 m diameter). Elevation
was measured for lowered, raised, and control plots at
the conclusion of the study using a laser survey system
(Spectra-Physics Instruments, Dayton, Ohio). Soil carbon content was determined for a composite sample of
10 cm deep cores obtained throughout each marsh site
using a CHN Elemental Analyzer (Control Equipment
Corporation, Lowell, Massachusetts). Disturbance was
estimated as the percentage of the plot surface that was
bare mud. Species density was recorded as the number
of species found in the harvested sample when the samples were sorted to species before being dried for biomass analysis.
Analysis
Structural equation modeling.—We used Structural
Equation Modeling or SEM (Hayduk 1987, Bollen
1989, Hoyle 1995, Schumacher and Lomax 1996) to
develop prediction equations and to analyze multivariate hypotheses. Structural equation modeling evaluates complex hypotheses of multivariate relationships
through the analysis of covariances, and specifies a
multivariate dependence model that can be statistically
compared to data. In practice, the specification of a
hypothesized model generates an expected covariance
matrix that is compared to the actual covariance matrix.
This technique not only assists in comparing model
alternatives, but also provides an efficient, simultaneous solution to a set of regression relationships (see
Grace and Pugesek 1997).
SEM models are usually presented graphically as
path diagrams, with variables connected to one another
by arrows. As a brief description of SEM terminology,
measured variables are referred to as indicators of the
variables of conceptual interest, the latent variables.
Unstandardized path coefficients between two variables
represent the slope of a relationship, while standardized
coefficients describe the precision of a relationship.
Indirect pathways between variables are those involving intermediary variables while direct pathways represent the residual covariance between two variables
that is not explained by indirect paths. Model fit refers
to measures of how well a hypothesized model corresponds with observed covariances. Many theoretical
and practical issues are involved with the philosophy
and practice of SEM, and the reader is referred to Hair
et al. (1995), Hoyle (1995), and Schumacher and Lomax (1996) for introductory discussions of the methodology.
A number of computer programs are available for
performing structural equation modeling. These programs, however, represent two fundamentally different
approaches: maximum likelihood methods such as LIS-
Ecology, Vol. 80, No. 3
REL (Joreskog and Sorbom 1996), and partial least
square approaches such as LVPLS (Lohmoller 1989).
To briefly describe the difference, LISREL (and other
programs such as EQS, AMOS, and CALIS) provides
a simultaneous evaluation of a specified model through
the analysis of covariances and provides the best unbiased estimate of population parameters by estimating
and partitioning measurement error. LVPLS (also
known as nonlinear, iterative least squares), in contrast,
evaluates a specified model in blocks, using iterative
procedures to arrive at the maximum explanation of
the variance in the data. When sufficient theoretical
knowledge and data are available, maximum likelihood
methods provide for statistically superior estimation of
the relationships among reflective latent variables (Bollen 1989). However, when it is necessary to involve
formative latent variables (which are composites of
concepts), LVPLS provides a simple method for aggregation of specific effects and nonlinear estimations.
In our study, we have used LVPLS methods to estimate
the measurement model as formative latent variables
and subsequently used LISREL to estimate and evaluate the structural model. This approach allows us to
examine the relationships among general concepts
(such as ‘‘abiotic conditions’’) allowing for nonlinear
relationships, while still providing a statistically rigorous evaluation of the relationships among latent variables.
Structural equation model of the descriptive data.—
The methods, procedures, and terminology for this
analysis are described in detail in Grace and Pugesek
(1997). For the purposes of this analysis, we used data
collected in 1994 as it was more directly comparable
with the experimental data. In order to assess whether
experimental results could be predicted from the descriptive data from the entire landscape, we formulated
a model in the form of the ‘‘specific’’ model presented
in Grace and Pugesek (1997). In this model, biomass
was hypothesized to be controlled by salinity, flooding,
soil organic matter, and disturbance. Species density
was hypothesized to be controlled by all of the other
variables.
Comparisons between predictions and experimental
results.—Quantitative predictions of the magnitude of
change in biomass and species density were made based
on the measured changes in the independent variables
manipulated in the experiments. To allow for predictions in the original metric, the analysis was based on
the covariance matrix (instead of the correlation matrix) and the total effects estimates. These values are
the net effect of one latent variable on another and give
the per-unit expected change in biomass or species density (e.g., the change in biomass [in grams] per unit
depth [in centimeters] that the sod is lowered). First,
we tested the predictions by examining the grand means
of the treatments and comparing the experimental manipulations to their respective controls. Total effects
could not be used for the fertilization treatment because
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PREDICTING EFFECTS OF ENVIRONMENTAL CHANGE
885
TABLE 1. ANCOVA results of the effects of experimental treatments on values predicted from the structural equation model
(SEM, treated here as the covariate) and observed values of species density.
Source of variation
df
MS
F
P
MARSH†
PREDICTION†
PREDICTION 3 MARSH†
SITE†
FENCE‡
FENCE 3 SITE (error B)
TRT§
TRT 3 SITE (error C)
TRT 3 FENCE\
TRT 3 FENCE 3 SITE (error D)
PREDICTION 3 TRT†
PREDICTION 3 FENCE†
PREDICTION 3 TRT 3 FENCE†
RESIDUAL ERROR (error A)
1
1
1
7
1
7
7
49
7
49
7
1
7
108
86.24
275.54
3.82
13.95
6.61
2.50
13.94
1.95
1.91
1.95
4.85
10.60
1.47
2.63
32.84
104.92
1.45
5.31
2.64
0.001
0.001
0.231
0.001
0.148
7.15
0.001
0.98
0.456
1.85
4.04
0.56
0.086
0.047
0.786
Notes: Sources of variation including the covariate, PREDICTION, are italicized. P values in bold are significant at the
0.10 level.
† Evaluated with error A.
‡ Evaluated with error B.
§ Evaluated with error C.
\ Evaluated with error D.
the fertilization treatment itself was not summarized in
the infertility variable (infertility was measured as soil
carbon content for each marsh). Therefore, predicted
changes for fertilization were made based on the observed changes in biomass that accompanied fertilization.
Next we examined the ability of the descriptive SEM
model to predict individual plot responses to experimental manipulation. For this analysis, the regression
equation from the LISREL output was used to calculate
a predicted species density value for each of the 254
plots, predicted and observed values were plotted, and
simple linear regressions performed (using PROC REG
[SAS Institute, Cary, North Carolina]). Analysis of covariance (ANCOVA) was then performed using PROC
GLM (SAS Institute, Cary, North Carolina) on the observed species density data with the values predicted
from the SEM model as a covariate. Utilizing the Type
I sums of squares (ss), we determined if the regression
relationship between the covariate and the observed
response variable was affected by the different experimental treatments. Type I ss are sequential and allow
us to evaluate the significance of each variable in order
as it is added to a model containing only the variables
preceding it (Type I ss are also used in stepwise regression). We therefore added the covariate first, with
the other variables included in the appropriate order
reflecting their spatial arrangement in the field (see
Table 1). The class variables were MARSH, SITE,
FENCE, and TRT, and the covariate was PREDICTION. MARSH refers to the two marshes in which the
experiment was conducted, SITE refers to the eight
locations within each marsh where treatments were
blocked (i.e., this was a blocking variable), FENCE
refers to whether the plots were surrounded by a fence
or not, and TRT refers to the eight individual manip-
ulations (treatments) described earlier in Methods:
Data collection: Experimental data. All main effects
were treated as fixed except PREDICTION, which was
considered random. Because of randomization restrictions within each site, the FENCE effect was evaluated
using the FENCE 3 SITE interaction term as the error
term, the TRT term was evaluated using the TRT 3
SITE term as the error term, and the TRT 3 FENCE
interaction was evaluated using the TRT 3 FENCE 3
SITE term as the error term. All terms involving the
covariate, PREDICTION, were evaluated using the RESIDUAL ERROR term (see Table 1).
Structural equation model of the experimental
data.—The experimental data were used to estimate
the model using the same procedures for model estimation and evaluation as described above for the descriptive data. Additional variables were added to the
model as needed (see Results: SEM analysis of experimental data).
RESULTS
Comparison of predictions to experimental results
Predictions.—Predictions of responses to experimental treatments were made based on the specific
model using the descriptive data. The LISREL prediction equations resulting from this model were
SPDN 5 0.84 3 BIOM 2 0.98 3 SALT
2 0.043 3 FLOOD 1 1.32 3 SOIL
and
BIOM 5 20.026 3 FLOOD 1 0.10 3 SOIL
1 0.765 3 DIST
where SPDN 5 species density, BIOM 5 biomass (in
grams per square meter), SALT 5 soil salinity (in
LAURA GOUGH AND JAMES B. GRACE
886
Ecology, Vol. 80, No. 3
TABLE 2. Specific quantitative predictions of species density
(species/0.1 m2) for experimental treatments based on total
effects from the structural equation model.
Manipulation
Fertilization
Decrease elevation 10 cm
Increase elevation 10 cm
Increase salinity 2 g/kg
Decrease salinity 2 g/kg
Exclude herbivores
Mean over all treatments
Predicted Observed Differchange
change
ence
20.2
20.6
10.6
21.9
11.9
21.1
21.2
22.0
10.8
24.2
21.6
20.03
1.0
1.4
0.2
2.3
3.5
1.1
1.6
Notes: Observed change was calculated based on treatment.
grams per kilogram), FLOOD 5 flooding stress (distance in centimeters above or below a standard reference elevation), SOIL 5 percentage soil carbon, and
DIST 5 percentage of plot disturbed (bare mud). Using
these equations, predictions were created for the mean
responses to experimental manipulations as well as for
the responses of individual plots.
Quantitative predictions of mean responses.—The
experimentally observed results for species density
were compared to the predicted values from the landscape model in Table 2. For example, while increasing
flooding stress (decreasing elevation) decreased species
density, the observed decrease was greater than predicted. Increasing elevation caused a gain of approximately one species, very close to the predicted value.
Increasing salinity had a greater effect than predicted,
with sods losing on average four species, while decreasing salinity had the effect opposite to that predicted, with a loss of one species despite alleviation of
salinity stress. Excluding herbivores caused no change
in overall species density. Using biomass means for
predicted response to fertilization, predicted species
density did not change, but in the observed results approximately one species was lost (Table 2).
Quantitative predictions for individual plots.—Assessment of the accuracy of predictions for individual
plots based on the descriptive data was made using
analysis of covariance and bivariate regression. ANCOVA was performed using the values predicted from
the SEM model as a covariate. Utilizing the Type I
sums of squares, we examined the consistency of the
regression relationship between the covariate and the
observed values of species density. The covariate,
PREDICTION, had a highly significant effect (P 5
0.001) with F1, 108 5 104.9 (Table 1). PREDICTION did
not interact with the main factor MARSH (P 5 0.231),
indicating that the covariance relationship was not different between marsh types. The SITE effect was significant, but as SITE functions as a blocking variable
in this design, we interpret this as simply representing
plot to plot variation. Based on a somewhat liberal
critical value of a 5 0.10, the PREDICTION 3 TRT
(P 5 0.086) and PREDICTION 3 FENCE (P 5 0.047)
interactions were significant, as was the main effect of
FIG. 1. Plots of observed vs. predicted species density
values for: (A) all treatment combinations (N 5 254 plots),
R2 5 0.35; (B) fenced plots excluded (N 5 126 plots), R2 5
0.53; (C) fenced and fertilized plots excluded (N 5 56 plots),
R2 5 0.63. Regression lines were fitted separately to each
subset.
TRT (P 5 0.001), though the three-way PREDICTION
3 TRT 3 FENCE interaction was not (P 5 0.786).
This result indicated that the fencing treatment, and at
least some of the other treatments, was affecting the
relationship between predicted and observed.
Since ANCOVA results revealed a significant PREDICTION 3 TRT effect, bivariate regression was used
to determine how the experimental manipulations contributed to the variance explained. When all plots were
included, the relationship between predicted species
density and observed values (Fig. 1A) was linear with
R2 5 0.35. The largest increase in variance explained
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PREDICTING EFFECTS OF ENVIRONMENTAL CHANGE
887
FIG. 2. Structural equation model developed for experimental data. Variables in ovals represent latent variables, while
those in rectangles are the measured variables. FENCE and FERT represent experimental treatment variables and are categorical. ABIO is a statistical composite of the effects of the abiotic factors salinity (‘‘salt’’), flooding (‘‘flood’’), and soil
carbon (‘‘soil’’). DIST represents the estimated proportion of the plot recently disturbed, measured as ‘‘dst.’’ BIOM is a
curvilinear transformation of biomass (using ‘‘mass’’ and ‘‘massr’’) that maximizes the variance explained in species density.
SPDN is simply the observed species density in each plot (‘‘sden’’). All arrows represent significant pathways. Coefficients
along arrows are standardized partial regression coefficients (‘‘r’’ values); mathematical signs indicate positive or negative
relationships or a bitonic relationship (6). R2 values are given for dependent variables in this model.
resulted from the elimination of the fertilization treatment, increasing R2 to 0.49. Plots subjected to fertilization had no significant relationship between predicted and observed (P 5 0.1479, R2 5 0.04). All other
experimental manipulations resulted in significant relationships between predicted and observed. However,
fenced plots had a substantially weaker relationship
between predicted and observed species density (R2 5
0.15) compared to unfenced plots (R2 5 0.53) (Fig.
1B). When fertilized plots were also eliminated from
the analysis (Fig. 1C), the relationship explained more
of the variance (R2 5 0.63).
SEM analysis of experimental data
For the experimental data, a structural equation model of the form used to generate predictions explained
only 34% of the observed variance in species density
and 33% of the variance in biomass. This level of precision (as well as more conventional analyses of the
experimental data [Gough 1996]) suggests that some
of the effects of the experimental manipulations were
not included in the general model. For this reason, fencing and fertilization were included in the model as treatment variables (Joreskog and Sorbom 1996), and the
R2 values for SPDN and BIOM increased to 60% and
39% respectively (Fig. 2). In additional, 11% of the
variance in DIST was explained, primarily by the effects of fencing. FERT, by itself, increased the R2 of
SPDN from 34% to 56% while the remaining increase
in R2 to 60% was attributable to FENCE.
Examination of the model with FENCE and FERT
included (Fig. 2) reveals a number of effects of these
two variables on the model. FERT had a strong direct
relationship with SPDN (path coefficient 5 20.50)
while its indirect effect was weaker (0.24), with a total
effect of 20.27. FENCE had its greatest effect on DIST
while its effect on BIOM was indirect. Both direct and
indirect paths connect FENCE to SPDN with a direct
coefficient of 20.19 and an indirect of 0.21. These
coefficients represent direct and indirect relationships
that cancel each other out to a large degree, but which
likely represent counteracting processes.
DISCUSSION
SEM model based on descriptive data indicates
multivariate control over species density
As found by Grace and Pugesek (1997) for the 1993
data, the SEM model of the 1994 descriptive data indicates that variation in plant species density in the
888
LAURA GOUGH AND JAMES B. GRACE
Pearl River marshes is controlled by a combination of
direct and indirect environmental effects. Biomass was
primarily affected by disturbance (waves, water, and
herbivory) and influenced to a much lesser degree by
edaphic conditions. Variation in species density, however, was associated with roughly comparable direct
paths from abiotic conditions and biomass. The path
from biomass to species density here represents the
hump-shaped relationship often discussed (e.g., Huston
1994).
Direct effects of environmental variables on species
density were also conspicuous in the descriptive model.
We have argued that these represent environmental gradients in the species pool that can strongly limit potential species density (Gough et al. 1994, Brewer et
al. 1997, Grace and Pugesek 1997). A great deal of
research has been conducted on the abilities of individual marsh plant species to tolerate high salinity regimes and/or flooded conditions (e.g., McKee and Mendelssohn 1989, Naidoo et al. 1992, Broome et al. 1995).
These species, many of which are present at the Pearl
River, have adapted to stressful environmental conditions without necessarily sacrificing biomass production. The species that cannot tolerate these conditions
are filtered out of the species pool before biomass may
become important at the community level. Our findings
indicate that as salinity and flooding stress increase in
the coastal zone because of relative sea level rise, some
species may be eliminated. Unless other more salt or
flood tolerant species can migrate into those areas, the
marsh may erode into open water.
Experimental results revealed unanticipated
effects and interactions
Our ability to predict responses to experimental
treatments was mixed. Predictions were generally consistent with observations (Table 2), validating what is
already known about environmental factors affecting
species density in coastal wetlands (e.g., Howard and
Mendelssohn 1995, Baldwin 1996, Brewer et al. 1997).
However, our analyses revealed interactions and effects
that were not anticipated. Herbivory intensified the effect of both salinity and flooding when sods were subjected to both factors simultaneously, representing a
synergistic effect of multiple stresses (see also McKee
and Mendelssohn 1989, Howard 1995, Gough and
Grace 1999, Grace and Ford 1996). Sods that were
transplanted from the higher salinity marsh into the
lower salinity marsh responded as predicted when
fenced. However, when not fenced, the sods were preferentially consumed by herbivores, presumably because they contained the plant species Scirpus americanus, which did not naturally occur in the lower salinity habitat but is preferred by herbivores (both nutria
and muskrat). The experimental results provide insights
such as this, which are unlikely to be obtained from
purely descriptive analyses.
Ecology, Vol. 80, No. 3
SEM model based on experimental data indicates
additional effects of fertilization and fencing
Results from the comparison of predictions to observations (Fig. 1) suggested that fertilization and fencing had effects on species density that were not included in the initial model; the effects of fertilization
were considerably greater than those of fencing. As
shown in Fig. 2, model results suggest that fertilization
may have affected species density through several different pathways. First, the slight negative relationship
between fertilization and disturbance could subsequently affect species density; however, we hypothesize that this is a spurious relationship rather than a
general process and requires further research. Second,
fertilization increased biomass, which in turn influenced species density. The path from BIOM to SPDN
likely represents an underlying hump-shaped relationship, involving many processes (Gough 1996, Grace
and Pugesek 1997). Thus, the effect of increased BIOM
on SPDN will depend on the value of biomass; at low
values of biomass, the slope will be positive but at high
values of biomass the slope will be negative. Third,
the direct path between FERT and SPDN represents a
direct, negative influence of fertilization on species
density, enhancing competitive exclusion beyond levels expected based on effects on total biomass. This
strong, negative effect of fertilization on diversity has
been found in numerous other cases (e.g., reviewed in
Silvertown 1980, DiTommaso and Aarssen 1989, Tilman 1993, Gough et al. unpublished manuscript) but
the exact nature of the process here is not known. Measures of soil nitrogen content in fertilized plots may
have allowed the original model to better predict responses to fertilization; however, our results imply other processes that may not be captured in the natural
landscape, especially shifts in relative abundance due
to competitive effects. Other analyses (Gough 1996,
Gough and Grace 1998) suggest that fertilization may
have resulted in accumulations of dead plant material
and a shift in ratio of dead to live biomass (by increasing turnover rates), possibly intensifying competitive
exclusion and limiting seedling germination and recruitment (Foster and Gross 1998).
The model in Fig. 2 indicates that fencing also affected species density through more than one path. As
expected, fencing had a negative effect on disturbance
through the elimination of mammalian herbivory. Unlike in the descriptive model, DIST had a direct effect
on SPDN in the experimental model, perhaps representing the continuous disturbance caused by herbivores that cannot be fully assessed by a single measure
of biomass. Alternatively, it may represent the select
removal of rare species, lowering species density disproportionately compared to nonselective grazing, as
found in a study of burning and fencing conducted at
the Pearl River during the same time period (Ford and
Grace 1998). Disturbance clearly reduces biomass that,
April 1999
PREDICTING EFFECTS OF ENVIRONMENTAL CHANGE
in turn, influences species density and represents another pathway through which fencing affected species
density. Finally, there was a negative path from FENCE
to SPDN. This represents an enhancement of competitive exclusion by fencing that has been observed in
other studies (e.g., Ford 1996). Other analyses of the
experimental data indicate that plots that were fertilized
and fenced lost a significant number of species over
time, presumably due to the combined effects of enhanced dead biomass accumulation and reduced disturbance (Gough and Grace 1998).
CONCLUSIONS
Overall, the results from these studies and analyses
are generally consistent with the earlier conceptual
model proposed by Gough et al. (1994). The descriptive
data are consistent with the hypothesis that variation
in species density in this system is primarily associated
with variation in abiotic conditions and biomass, with
a lesser and largely indirect influence from disturbance.
However, the experimental data revealed a number of
important specific effects that were not apparent from
the descriptive data. Perhaps of greatest significance
are the direct effects of fertilization and fencing, which,
especially in combination, led to more dramatic reductions in species density than could be explained by
changes in total biomass. These treatments may be affecting factors such as the ratio of living to dead biomass and relative dominance of superior competitors
that could influence shading, the morphology of the
plant canopy, and seedling germination. Our ability to
predict the responses of experiments based on descriptive data was somewhat limited, being reasonably successful for certain treatments and less successful for
others, particularly fertilization and fencing. Our understanding of naturally occurring gradients in coastal
marshes may be enhanced and affirmed by experimental manipulations, increasing our ability to predict responses of plant communities to environmental change,
in particular in this case in response to relative sea
level rise. Experimental manipulations revealed additional factors important in controlling species density
that were undetected by the model of descriptive data.
We conclude that the combination of modeling and
testing experimental and descriptive data will provide
the greatest capability for predicting the effects of environmental change on plant species density.
ACKNOWLEDGMENTS
We thank the Louisiana Department of Wildlife and Fisheries for permission to work at the Pearl River. H. Haecker’s
assistance in the field and the laboratory was invaluable and
greatly appreciated during all aspects of this project; we also
wish to thank M. Ford, A. Baldwin, and many others for field
assistance. C. Canham, J. S. Brewer, J. Geaghan, G. Guntenspergen, Z. Malaeb, I. Mendelssohn, B. Shipley, G.B. Williamson, and two anonymous reviewers provided comments
that significantly improved the manuscript. Assistance with
the analysis of covariance was provided by D. Johnson, statistical consultant, National Wetlands Research Center. Partial
889
support was provided by an NSF Dissertation Improvement
Grant to L. Gough (DEB-9310890).
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