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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- 882 April 1999 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 April 1999 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 April 1999 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). LITERATURE CITED Abrams, P. A. 1995. Monotonic or unimodal diversity–productivity gradients: what does competition theory predict? Ecology 76:2019–2027. Baldwin, A. H. 1996. The role of seed banks, disturbance, and sea level rise in determining the plant community structure of oligohaline coastal marshes. Dissertation. 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