OCS Study 

MMS 2000-042 

Coastal Marine Institute 

Potential for Accelerated Bioremediation 

and Restoration of Oil-Impacted Marshes 

through the Selection of Superior 

Oil-Tolerant Vegetation 

U .S. Department of the Interior Cooperative Agreement Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 
OCS Study 

MMS 2000-042 

Coastal Marine Institute 

Potential for Accelerated Bioremediation 

and Restoration of Oil-Impacted Marshes 

through the Selection of Superior 

Oil-Tolerant Vegetation 

Authors 

Mark W. Hester 

Department of Biological Sciences 

Southeastern Louisiana University 

Hammond, Louisiana 70402 

Quixan Lin 

Irving A . Mendellssohn 

Wetland Biogeochemistry Institute 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Dennis J . DesRoches 

Department of Biological Sciences 

Southeastern Louisiana University 

Hammond, Louisiana 70402 

May 2000 

Prepared under MMS Contract 

14-35-0001-30660-19927 

by 

Coastal Marine Institute 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Published by 

U .S. De partment of the Interior Cooperative Agreement 

Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

DISCLAIMER 

This report was prepared under contract between the Minerals Management Service 

(MMS) and Louisiana State University. This report has been technically reviewed by the 

MMS and approved for publication . Approval does not signify that the contents 

necessarily reflect the views and policies of the Service nor does mention of trade names 

or commercial products constitute endorsement or recommendation for use. It is, 

however, exempt from review and compliance with MMS editorial standards 

REPORT AVAILABILTY 

Extra copies of the report may be obtained from the Public Information Office (Mail 

Stop 5034) at the following address: 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 

Public Information Office (MS 5034) 

1201 Elmwood Park Boulevard 

New Orleans, Louisiana 70123-2394 

Telephone Number: (504) 736-2519 or 

1-800-200-GULF 

CITATION 

Suggested citation : 

Hester, M. W., Q. Lin, I . A. Mendelssohn, and D. J. DesRoches . 2000 . Potential for 

accelerated bioremediation and restoration of oil-impacted marshes through the 

selection of superior oil-tolerant vegetation. OCS Study MMS 2000-042 U.S. 

Department of the Interior, Minerals Management Service, Gulf of Mexico OCS 

Region, New Orleans, La. 48 pp. 

iii 

ABSTRACT 

The cleanup of oil spills in coastal marshes remains a problematic issue because 

wetlands can be extremely sensitive to the disturbances associated with cleanup activities . 

As a result, there has been interest in developing alternative, less intrusive, oil spill 

cleanup/bioremediation techniques . The research reported herein assessed the feasibility 

of identifying superior oil-tolerant genotypes of Spartina patens and Spartina alterniflora 

(dominant brackish and salt marsh grasses, respectively) that may be utilized in the 

restoration and bioremediation of oil-impacted marshes, as well as in the creation of 

marshes in areas with a high amount of oil activity and, hence, a high incidence of an oil 

spill occurring. 

Genotypes of S . patens and S . alterniflora were collected from Gulf Coast 

marshes and established in a uniform potting mix of approximately 10% organic matter 

content by weight. The soil was then oiled with South Louisiana crude oil at rates known 

to be stressful from previous research (5 L oil m-2 for S . patens and 8 L oil m-2 for S . 

alterniflora ) . Measurements of plant growth and physiological responses were conducted 

during the growing season, after which an initial harvest of aboveground biomass was 

conducted. Vegetative regrowth through the oiled soil was monitored for an additional 

three months, after which a final harvest was conducted and residual oil in the soil 

determined. 

Highly significant genotype differences in a number of plant responses were 

detected in both species when oiled. Differences between genotypes generally became 

more pronounced with time, with the exception of leaf expansion rate, which proved to 

be a less sensitive short-term indicator of oil stress than photosynthesis (net COZ 

assimilation rate). Three genotypes of S . atens and three genotypes of S . alterniflora 

were identified that when oiled maintained high rates of photosynthesis and also 

generally performed better than the other genotypes in terms of a number of plant growth 

response variables, including regrowth through the oiled soil . However, residual oil 

concentration showed no significant univariate effect of genotype in either species . 

A multivariate analysis (factor analysis) was conducted on a suite of plant growth 

response variables and residual oil. Factor analysis confirmed that three superior, oiltolerant genotypes could be identified in each species that performed significantly better 

than other genotypes. When oiled, these oil-tolerant genotypes characteristically 

displayed less tissue death, maintained higher plant productivity, and demonstrated a 

greater ability to successfully produce new shoots through the oiled soil . Analysis of oildegradation factor scores in S . patens further revealed that two of the three oil-tolerant 

genotypes demonstrated significantly greater oil-degradation potential than other 

genotypes. This study has shown that there is a tremendous amount of natural genetic 

variation in oil tolerance in Gulf Coast populations of S . patens and S . alterniflora that 

may potentially be exploited in the restoration and bioremediation of oiled brackish and 

salt marshes . Although significant genotype differences were not uniformly detected in 

oil degradation potential, it is likely that in the long term (and under field conditions) 

greater rates of oil degradation would be associated with those genotypes that are less 

stressed and have greater rates of plant productivity . 

v 

TABLE OF CONTENTS 

Page 

LIST OF FIGURES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix 

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

MATERIALS AND METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 

Statistical Analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 

RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

Spartina patens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

Spartina alterniflora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 

CONCLUSIUNS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

LITERATURE CITED. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 

vii 

LIST OF FIGURES 

Figure Description Page 

1 Mean net COz assimilation rates of Spartina patens genotypes 

one month and three months after oiling at a rate of 5 L oil m z . . . . . . . 8 

2 Mean water-use efficiencies of S artina patens genotypes 

one month and three months after oiling at a rate of 5 L oil m 2 . . . . . . . 10 

3 Mean leaf expansion rates of Spartina patens genotypes 

one month and three months after oiling at a rate of 5 L oil in .. . . . . . . . 11 

4 Mean number of live stems and live aboveground biomass of Spartina 

patens genotypes three months after oiling at a rate of 5 L oil M-2 

. . . 12 

5 Mean number of dead stems and dead aboveground biomass of S artina 

patens genotypes three months after oiling at a rate of 5 L Oil M-2 . . 13 

6 Mean motal aboveground biomass and proportion of dead aboveground 

biomass of S artina patens genotypes three months after oiling at 

a rate of 5 L oil in .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

7 Mean number of regrowth stems and aboveground biomass regrowth of 

Spartina patens genotypes three months after the initial harvest . . . . . . 16 

8 Mean residual oil remaining in the soil of S artina patens 

genotypes six months after oiling at a rate of 5 L oil m-' . . . . . . . . . . . . . . . . . . 17 

9 Factor loadings plot resulting from a factor analysis 

of residual oil and five plant response variables of 

oiled Spartina patens genotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 

10 Mean factor 1 scores and factor 2 scores of oiled 

Snartina patens genotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 

Mean net COZ assimilation rates of S artina alterniflora genotypes 

one month and three months after oiling at a rate of 8 L oil M-2 

. . . . . . . 21 

12 Mean water-use efficiencies of Spartina alterniflora genotypes 

one month and three months after oiling at a rate of 8 L oil M-2 

. . . . . . . 23 

13 Mean leaf expansion rates of Spartina alterniflora genotypes 

one month and three months after oiling at a rate of 8 L oil m-2 . . . . . . . 24 

ix 
14 Mean number of live stems and live aboveground biomass 

of Spartina alterniflora genotypes three months after 

oiling at a rate of 8 L oil m 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

15 Mean number of dead stems and dead aboveground biomass 

of Spartina alterniflora genotypes three months after oiling 

at a rate of 8 L oil m"z . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

16 Mean total aboveground biomass and proportion of dead aboveground 

biomass of Spartina alterniflora genotypes three months after oiling 

at a rate of 8 L oil M-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

17 Mean number of regrowth stems and aboveground biomass 

regrowth of Spartina alterniflora genotypes three months after 

the initial harvest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

18 Mean residual oil remaining in the soil of Spartina alterniflora 

genotypes six months after oiling at a rate of 8 L oil M-2 . . . . . . . . . . . . . . . . . . 30 

19 Factor loadings plot resulting from a factor analysis 

of residual oil and five plant response variables of 

oiled Spartina alterniflora genotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

20 Mean factor 1 scores and factor 2 scores of oiled 

Spartina alterniflora genotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

x 
LIST OF TABLES 

Table Description Page 

Responses of Spartina patens genotypes to 

oiling expressed as percentages of the controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

2 Responses of Spartina alterniflora genotypes to 

oiling expressed as percentages of the controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

xi 

INTRODUCTION 

Coastal wetlands are among the most productive ecosystems in the world. 

Louisiana's wetlands are the most expansive in the U.S ., comprising 41% of the nation's 

total (Mitsch and Gosselink 1986 ; Turner and Gosselink 1975). Coastal marshes are 

known for their role in improving water quality and enhancing productivity by providing 

protection and valuable nursery grounds for commercially important fisheries (Mitsch 

and Gosselink 1986) . The living marsh grasses are vital elements in these highly 

productive coastal ecosystems. If the plants become stressed and die, either from natural 

or anthropogenic causes, a delicate balance is upset, and without the production and 

binding properties of the living shoots and roots, the marsh surface erodes . 

The coastal marshes of Louisiana have been experiencing rapid rates of marsh 

loss and erosion (Dunbar et al. 1992). Although some marsh loss can occur from direct 

erosion and scouring of vegetated marsh (Nyman et al. 1994), the high rates of marsh loss 

in Louisiana have generally been attributed to plant stress (both natural and 

anthropogenic) followed by plant dieback and subsequent erosion of the marsh substrate. 

Potential causes of plant stress include waterlogging stress, due to insufficient elevation 

of the marsh surface, and salinity stress resulting from salt-water intrusion into the more 

interior brackish and fresh marshes (Mendelssohn et al . 1983 ; Sasser et al. 1986) . Oil 

pollution and oil spills are additional sources of anthropogenic stress to wetland 

vegetation that can similarly stress the vegetation and lead to plant dieback and marsh 

erosion (Mendelssohn et al. 1993a; Getter et al. 1984). 

The Louisiana coastal zone and outer continental shelf region are among the most 

intensively developed oil and gas areas in the world (Turner and Cahoon 1987). As a 

region, the Gulf of Mexico, and especially the central Gulf of Mexico, produce the vast 

majority of all U.S . offshore oil . In 1985, U.S. offshore oil wells in the Gulf of Mexico 

accounted for 92% of the total U.S. offshore oil production, with 94.3% of this oil 

coming from the central Gulf of Mexico region (EPA 1985) . However, the tremendous 

volume of oil-related activities in the central Gulf of Mexico and in Louisiana's coastal 

zone also leads to a proportionately higher risk of an oil spill or oil leakage impacting 

Louisiana's fragile and productive coastal marshes. 

Once a marsh is impacted by an oil spill, a decision must be made concerning the 

best method of cleanup and restoration. This issue has caused much controversy. Often 

it appears that the best course of action may be to let the marsh recover on its own to 

avoid secondary impacts to the marsh during the cleanup operation (McCauley and 

Harrel 1981 ; Long and Vandermeulen 1983 ; Getter et al . 1984 ; Baker et al . 1993 ; 

Mendelssohn et al . 1993a). Foot traffic and equipment traffic on the marsh surface 

during cleanup operations are considered secondary impacts that in themselves can have 

significant adverse effects on the recovery of the marsh by trampling vegetation, 

accelerating erosion, and burying oil into anaerobic soils where it may persist for years 

(Letter et al . 1984). Since it is known that wetland environments are sensitive to the 

impacts of cleanup (McCauley and Harrel 1981 ; Long and Vandermeulen 1983; Letter et 

al . 1984 ; Kiesling et al . 1988 ; Baker et al . 1993 ; Mendelssohn et al . 1993a), there is a 

need to develop alternative, less intrusive, oil spill cleanup/bioremediation techniques 

that are compatible with the delicate wetland environment and may be utilized singularly 
(without other cleanup methods) in marshes oiled by minor spills, or utilized in 

conjunction with existing cleanup methodologies in heavily oiled marshes following 

initial cleanup efforts and impacts . The selection and utilization of superior oil-tolerant 

populations of dominant Gulf Coast marsh grasses may provide such a technique for the 

restoration and bioremediation of oil-impacted marshes. 

The natural recovery of an oil-impacted marsh would be expected to be greatest 

when the oiled vegetation is somewhat tolerant to the initial impact of the oil and when 

new shoots originating from belowground rhizomes are able to emerge through the oiled 

soil and survive. If the vegetation is oil sensitive and the oil impact is severe enough to 

both kill the aboveground tissue and inhibit vegetative regrowth, then soil instability and 

erosion of the marsh surface may occur. Once this type of marsh dieback and erosion 

occurs, vegetation rarely again colonizes the open water (Mendelssohn et al. 1990). 

Rather, small dieback ponds often coalesce and form larger, shallow open water bodies. 

If superior oil-tolerant vegetation is available, it may be possible to re-establish 

vegetation in an oil-impacted marsh before substantial erosion of the marsh surface 

occurs and at an earlier date than what may be tolerated by non-oil-tolerant vegetation. 

The early establishment of superior oil-tolerant vegetation at a spill site may also aid in 

accelerating the restoration and bioremediation of the oil-impacted marsh. Furthermore, 

it may be possible to utilize superior oil-tolerant vegetation in certain marsh restoration 

and creation projects, such that oil-tolerant vegetation could be established in areas 

subjected to a high amount of oil-related activity and, hence, subjected to a greater 

probability of an oil spill occurring. 

Snartina patens (Aiton) Muhl. is the single most important coastal grass species 

found in Louisiana's extensive coastal marshes, encompassing intermediate, brackish and 

saline marsh habitats. Spartina patens has been reported to be nearly twice as common as 

the second most important coastal marsh species, Spartina alterniflora Loisel (Chabreck 

1972). Spartina patens is especially dominant in the expansive brackish marshes of 

Louisiana, whereas Spartina alterniflora dominates the higher salinity coastal salt 

marshes (Chabreck 1972). Both of these species are widespread marsh dominants 

throughout the Gulf and Atlantic coasts . 

Although a number of studies have shown that different wetland plant species 

may respond quite differently to oiling (interspecific variation), we are aware of no 

studies to date that have investigated differences within single species (intraspecific 

variation) for differential tolerance to oiling. Furthermore, although there has been 

considerable effort and a fair amount of success in breeding agricultural crops for 

stressful environments (Blum and references therein 1988), there has been little emphasis 

to date on selecting superior stress-tolerant wetland vegetation. The research that has 

been conducted on wetland vegetation has focused on the natural environmental stresses 

of salinity (Pezeshki and DeLaune 1993 ; Hester et al. 1996; Hester et al. 1998) and 

flooding (Lessmann et al . 1997). To the best of our knowledge there has been no 

published research on selecting superior oil-tolerant lines of wetland plant species . The 

research reported herein specifically addresses the question of whether superior oiltolerant populations of Spartina patens and Spartina alterniflora (dominant Gulf Coast 

brackish and salt marsh grasses) can be identified that display 1) superior growth 

response and plant production under oil stress, 2) superior vegetative regrowth through 

oiled soil, and 3) superior oil degradation potential. 

2 
MATERIALS AND METHODS 

METHODS 

Stock populations of Spartina patens and Spartina alterniflora were collected from 

various coastal marsh communities in Louisiana, Texas, and Florida and propagated 

under fresh conditions in a greenhouse for seven vegetative generations prior to use in 

this study. These populations have been shown by our previous research to be 

genetically diverse based on highly significant population differences in morphological 

characteristics and salinity tolerance (Hester et al . 1996; Hester et al . 1998). From these 

stock populations, a subset of 10 populations of each species was selected for the 

experimental screening of oil tolerance. Plants from each population were established in 

a uniform potting mix (approx. 10% organic matter by weight) and potted in 12-liter 

plastic pots equipped with a drain and water-level sight gauge. Ten pots were planted per 

population . For each population, five pots were randomly designated as control pots and 

five randomly designated as treatment (oiled) pots. The S . patens oiling experiment was 

conducted in the Fall of 1995 . The S . alterniflora oiling experiment was conducted in the 

Spring of 1997 . 

Once the plants became established in the experimental vessels, water levels were 

raised slightly above the soil surface and South Louisiana crude oil was applied at a rate 

of 5 L M-2 for Spartina patens and 8 L m-2 for Spartina alterniflora (equivalent to a 5 mm 

thick and an 8 mm thick layer of oil, respectively, on the marsh surface). The oil was 

applied directly to surface flood water of the treatment pots. The drain valve was then 

opened and the applied oil was allowed to slowly drain through and oil the soil column 

over a period of about two hours. The water-level gauge of each pot was monitored 

during this process and drainage was stopped just prior to the drainage of any oil, thereby 

ensuring even oiling of the substrate without loss of oil . For each pot, the drain water 

was collected in a reservoir bucket and subsequently slowly returned to the top of the 

treatment pot soil . The control pots received no oil, but were similarly drained and had 

the solution returned to the surface of the pots. Throughout the study, water levels were 

maintained at the level of the soil surface. 

Measurements of leaf expansion rate and plant photosynthesis (net COz 

assimilation rate) were conducted one month and three months after oiling . Plant 

photosynthesis (net COZ exchange rate) was measured on young, but fully expanded 

leaves (generally the second expanded leaf from the growing tip) during midday 

conditions on a randomly selected mature stem. Measurements were conducted with a 

portable infrared gas analyzer (Analytical Development Company, Herts, England; model 

LCA-2) . The selected leaf was clamped into an ADC Parkinson leaf chamber and the 

difference in COZ concentration between inlet and outlet air was measured. Sampling air 

was taken outdoors at 5 m above the ground surface in order to obtain a relatively stable 

COz concentration, which was led through an ADC air supply unit with silica columns to 

obtain a dry inlet airstream. The flow rate was held constant at 6 .25 ml s-' and 

measurements were conducted under light saturated photosynthetic conditions provided 

by a Kodak projector lamp at a quantum flux density of 1500 - 2000 ~Imol m2 s-' . Gas 

exchange was determined on a per unit leaf area. Molar air flow, transpiration rate, and 

COz uptake were calculated according to von Caemmerer and Farquhar (1981) . Wateruse efficiency was calculated as the ratio of ~tmol COZ fixed per mmol H2O transpired 
(Griffiths 1993). Leaf expansion rate was measured on terminal leaves that were neither 

newly emergent nor fully expanded, but in the range of one-third to two-thirds expanded. 

Leaf length was measured from the tip of the terminal leaf to the ligule of the youngest 

expanded leaf over a time interval of three days. This method has been used successfully 

with these and other grasses and generally produces results which parallel changes in net 

COz assimilation and biomass (Hester et al . 1998 ; Hester et al . 1996 ; Hester and 

Mendelssohn 1990). 

An initial harvest of plant aboveground biomass was conducted three months after 

oil application and a final harvest conducted six months after oil application. During the 

initial harvest the number of live and dead stems per pot were counted and aboveground 

biomass clipped at the soil surface, separated into live and dead components, oven-dried 

at 65° C for three days and weighed. To assess regrowth potential, the clipped pots were 

monitored for emergence of new shoots through the oiled surface for three additional 

months. Newly emergent aboveground biomass was then harvested during the final 

harvest in the same manner as outlined above for the initial harvest. To assess 

differences in oil degradation potential between the different genotypes, residual crude oil 

remaining in the soil was analyzed on a soil core (3 .6 cm diameter x 10 cm depth) from 

each pot using a modification of the gravimetric method (Alexander and Webb 1985) and 

DCM (dichloromethane) as the extractant . 

STATISTICAL ANALYSES 

The experimental design of both the Spartina patens and the Spartina alterniflora 

oiling experiments was a 10 x 2 factorial of ten genotypes and two oiling treatments 

(control and oiled) with five complete replicates, yielding 100 experimental units total 

per experiment. The S . an tens factorial was set up within a randomized block design 

(RBD) with one replicate in each of five blocks positioned along a potential temperature 

gradient in the greenhouse. Upon statistical analysis, the effect of block in the S . patens 

experiment was determined to not be significant, so the subsequent S . alterniflora 

factorial experiment was set up in a completely randomized design (CRD). All data were 

analyzed with SYSTAT 7.0.1 statistical software (SYSTAT 1997) using analysis of 

variance (ANOVA) . All data were inspected for meeting the assumptions of parametric 

statistical analysis. Data were also assessed for outliers . Suspect data points were 

deemed outliers and deleted from the data set only when the studentized residuals were 

greater than 3 .0 or less than -3 .0 (SYSTAT 1997). For all analyses a significance level of 

0.05 was used unless otherwise stated. 

Additional one-way ANOVAs (genotype effects only) were run on a set of 

variables expressed as a percentage of the control response. For these analyses the 

average control response of each genotype was calculated for each variable. Then the 

treatment response (of each treatment replicate within a genotype) was expressed as a 

percentage of the mean genotype control response. This allowed for genotypes to be 

assessed not only in terms of absolute performances (actual treatment responses), but also 

in terms of relative performances (treatment responses relative to control responses) . 

Factor analyses were performed on each species to assist in identifying those 

genotypes with the most desirable responses to oiling . All factor analyses were run on 

the correlation matrix using the principal component solution and varimax rotation of 

4 
SYSTAT 7 .0 .1 statistical software (SYSTAT 1997). Initial factor analyses included all 

of the oiled response variables, which were then subsequently reduced until stable and 

interpretable solutions were obtained . Factor scores were then outputted for analysis of 

genotype differences using a one-way ANOVA. 

5 

RESULTS 

Spartina patens 

Significant genotype and treatment (oil vs . no oil) differences were detected in 

plant photosynthetic response (net COZ assimilation) in Spartina patens within the first 

month after oiling (Figure 1) . These differences became even more pronounced by the 

third month, in which there was also a significant genotype x treatment interaction, 

indicating that genotype responses to oiling relative to their controls were different 

(Figure 1) . For example, it can be seen that genotypes C, G, and J showed no significant 

reduction in photosynthesis relative to their controls by the third month (94%, 104%, and 

93% of their controls, respectively), whereas the other genotypes had photosynthetic rates 

in the range of 31% to 55% of their respective controls (Table 1) . The analysis of 

genotype response expressed as a percentage of the control supports this and yielded a 

highly significant (P<0.01) genotype effect on net COZ assimilation, both one month and 

three months after oiling (Table 1) . Overall, genotypes C, G, and J had the highest 

treatment photosynthetic responses whether expressed as actual responses or as 

percentages of the control (Figure 1 ; Table 1) . Water-use efficiency in S . patens was not 

a very sensitive indicator and failed to yield significant genotype, treatment, or 

interaction effects in either month one or month three (Figure 2). 

Highly significant genotype differences in leaf expansion rate were evident 

throughout the study (Figure 3) . Interestingly, oiling did not significantly affect leaf 

expansion rate until the third month (Figure 3). Also, no significant interaction of 

genotype x treatment was detected in leaf expansion rate. When analyzed as a percentage 

of the control response, there was a highly significant genotype effect, but again this was 

not detected until three months after oiling (Table 1). After three months of oiling many 

of the genotypes (including genotypes C and G) had leaf expansion rates that were 

equivalent to, or greater than, their controls (Figure 3 ; Table 1). 

Live, dead, and total aboveground biomass all displayed a highly significant 

effect of genotype (Figures 4, 5, and 6). The treatment effect of oiling was significant for 

live aboveground biomass and highly significant for dead aboveground biomass (Figures 

4 and 5), but was not significant for total aboveground biomass (Figure 6). The number 

of live and dead stems generally paralleled these biomass responses (Figures 4 and 5). 

These results are interpreted to mean that the relative proportion of live and dead 

aboveground biomass was more affected by oiling than was total aboveground biomass. 

An analysis of the proportion of dead aboveground biomass (dead aboveground total 

aboveground) supports this interpretation and yielded highly significant genotype and 

treatment effects (Figure 6). There were no significant genotype x treatment interactions 

for any of these biomass responses . When analyzed as a percentage of the control 

response, dead biomass showed a significant effect of genotype, as did the proportion of 

dead aboveground biomass, which approached statistical significance (P=0.065 ; Table 1) . 

Genotypes C, G, and J again performed well when oiled and produced some of the 

greatest amounts of live biomass, but not necessarily the lowest amounts of dead biomass 

(Figures 4 and 5). Genotype C consistently produced very little dead biomass whether 

oiled or not, whereas genotypes G and J produced intermediate amounts of dead biomass 

when oiled (Figures 5 and 6). 
25 

1 month 0 oiled 

20 - 

E3 control 

15 a 

m 

CO 5 - 0 10 

~~ 

.r. v v ~ ` _ 

d T`~ 

z 5 

,, . 

0 

A B C D E F G H I J 

Spartina patens genotype 

25 3 months p oiled 

El control 20 - o 

`° 

m _ _ 15 

c* w E 

C4 0 

? 

10 

v ;, 

i 

0 

A B C D E F G H I J 

Saartina patens genotype 

Figure 1 . Mean (t standard error) net COZ assimilation rates (Pmol m 2 S-1 ) of 

Spartina patens genotypes one month (top panel) and three months (bottom 

panel) after oiling at a rate of 5 L oil m 2 (n=5). 

8 
Table l . Responses of SUartina pate ns genotypes to oiling (5 L m"Z) expressed as percentages of the controls. Shown are mean 

responses and standard errors in parentheses (n=5). The probability of a genotype effect for each response is indicated as 

follows:NS=P>0.05,*=P<0.05,**=P<0.01,***=P<0.001 . 

Response Genotype 

A B C D E F G H I J 

Photosynthesis *' 

After 1 month 

Photosynthesis *'* 

After 3 months 

Leaf Expansion "s 

1 month 

Leaf Expansion 

3 months 

Live Stem *"` 

Count 

Live Biomass "s 

Dead Stem "' 

Count 

Dead Biomass 

Total Biomass "s 

Proportion Dead "s 

Biomass/Total 

Stem Count `*" 

Regrowth 

Biomass'"" 

Regrowth 

33.40 68 .00 71 .20 50.60 54.40 36.20 76.8 65.80 34.8 97.25 

(6 .34) (17.0) (10.04) (4 .62) (7 .95) (8 .55) (8 .36) (13.12) (8 .8) (14.55) 

35.60 31 .00 94.20 54 .80 54 .00 41 .20 103.75 41 .20 47.00 92 .80 

(3 .67) (9 .52) (4 .09) (5 .76) (12.33) (11 .65) (7 .31) (7 .83) (6 .44) (4 .10) 

99.80 99 .20 101 .0 101 .20 104.60 99.40 10120 103 .40 100.00 97.00 

(1 .91) (2 .50) (1 .27) (2 .46) (3 .16) (1 .78) (3 .34) (1 .08) (1 .05) (2 .35) 

101 .20 158.40 152.60 237.60 133.4 104.20 159.60 71 .00 279.75 84.40 

(26.03) (37 .71) (35.92) (32.67) (11 .16) (28.71) (27.14) (29.63) (34.28) (12.36) 

48.35 33.33 86.46 47.67 58.0 26.99 73 .67 67.33 40.41 71 .69 

(3 .62) (8 .95) (2 .08) (9 .11) (6 .99) (5 .5) (5 .53) (11 .23) (2 .66) (1 .97) 

74.60 95.75 101 .60 92.60 76.00 61 .80 91 .40 105.40 84.00 95.60 

(9 .90) (16 .13) (8 .81) (13 .48) (10 .99) (15.15) (4 .11) (8 .08) (7 .18) (7 .27) 

81 .39 255.7 522.7 2437.5 433.2 455.6 100 287 .5 333.3 125 

(18.75) (105.6) (276 .2) (62.5) (135 .4) (81 .33) (100) (109.3) (166 .7) (125) 

72.60 154.60 165.2 141 .8 184.33 130.40 99.00 113.80 125.80 132.40 

(14.41) (30.06) (25.04) (7 .55) (18.81) (15.04) (9 .61) (11 .22) (13.64) (25 .71) 

74.14 97.06 115.02 102.98 103.76 77 .44 92.64 107.70 91 .75 102.43 

(10.21) (12.19) (6 .68) (11 .02) (11 .66) (14.78) (4 .51) (7 .66) (5 .97) (8 .55) 

101 .54 132.49 145.17 142.07 139.84 185.15 104.52 105.48 146.65 127.64 

(12.47) (25.05) (20.50) (14.23) (26.44) (26 .22) (7 .76) (7 .35) (15.60) (20.05) 

25.80 27.60 91 .40 67.00 67.60 30.60 71 .20 67.80 30.60 87.00 

(3 .92) (8 .93) (7 .39) (9 .32) (13.00) (11 .14) (6 .18) (9 .30) (4 .73) (5 .18) 

17.40 27.20 82.80 53.00 61 .00 24.60 80.80 62.40 22.00 72.00 

(2 .66) (12.91) (9 .56) (10.12) (13.57) (12 .53) (7 .01) (7 .85) (3 .95) (8 .45) 
s,~ i 

1 month M oiled 

0 

5 E 3 control 

v 

c z 

. m~ . 4 

v - 

G 3 

\. L v . \ 

m 

A 

~ 

.~ 

' 

1 

63 

0 ..<. 

~. 

A B , C D . . E <f F G I . J 

Spartina patens genotype 

6 

3 months E l oiled 

5 ~ 

- -_ B control 

>. O N 

i 

I 3: - 

d N >>:z 

O 

16. 

4F+ O :;G~ \ \ ;;y: 

I 

XS 

A B C D E F G H I J 

Saartina patens genotype 

Figure 2. Mean (f standard error) water-use efficiencies ([Lmol COZ mmol-l H2O) of 

Snatina pate ns genotypes one month (top panel) and three months (bottom panel) 

after oiling at a rate of 5 L oil M-2 (n=5). 

10 
ns Figure 3 . Mean (f standard error) leaf expansion rates (cm day) of Spart ina pate 

genotypes one month (top panel) and three months (bottom panel) after oiling 

at a rate of 5 L oil M-2 (n=5). 

11 
160 ---- 

oiled 

140 13 control 

120 

100 

m 80 - 

m 60 

40- 4 

20 

r.~ 

0 

A B C D E F G H I J 

Spartina patens genotype 

45 ; - 

40 - 

0 oiled 

- -- -- 

I 

control 

... 35 

30 

25 E 

0 20 @ 

41 

15 

J _ O 

5 

0 ~ - 

A B C D E F G H I J 

Spartina patens genotype 

Figure 4 . Mean (f standard error) number of live stems (# per pot; top panel) and 

live aboveground biomass (g per pot; bottom panel) of SUartina patens 

genotypes three months after oiling at a rate of 5 L oil m 2 (n=5). 

12 
Figure 5 . Mean (± standard error) number of dead stems (# per pot; top panel) and 

dead aboveground biomass (g per pot; bottom panel) of S-parting patens 

genotypes three months after oiling at a rate of 5 L oil M-2 (n=5). 

13 
7 0 

y 

10 oiled 

e a 60 

i E3 control 

o 50 '.. 

-o .. 

~ °a 40 

o ~ 

'M ~ 30 

c -- 20 - 

10 

o F- : 

A B C D E F G H I J 

Spartina patens genotype 

-- 0.6 

oiled 

" 5 0 control 

0 

0.4 i 

ZF, 

.2 0.3 

w 

o m 

0 0 0.2 

ti- ~ 

a 

`y. ~ 

0.1 `' .. 

". .. 

A B C D E F G H I J 

Saartina patens genotype 

Figure 6. Mean (f standard error) total aboveground biomass (g per pot; top panel) and 

proportion of dead aboveground biomass (dead/total aboveground biomass) of 

Snartina patens genotypes three months after oiling at a rate of 5 L oil m-2 (n=5). 

14 
The amount of regrowth through the oiled soil in the three months following the 

initial aboveground harvest displayed highly significant effects of genotype, treatment, 

and genotype x treatment. This was true for both the number of new stems produced and 

the amount of aboveground biomass produced (Figure 7). Although all genotypes of S . 

patens assessed were able to successfully tiller and produce live shoots through the oiled 

soil, genotypes C, G, and J were particularly successful in absolute terms and relative to 

their controls, thereby contributing to the significant genotype x treatment interactions 

(Figure 7) . When analyzed as a percentage of the control, a highly significant genotype 

effect was detected for both of these regrowth responses (Table 1) . Genotypes C, G, and 

J had very high regrowth responses that ranged from 71% - 91 % of their controls, 

whereas other genotypes had responses that ranged from 17% - 67% of their controls 

(Table 1) . 

Results from the analysis of residual oil in the soil six months after oiling were 

highly variable within populations and overall displayed a lack of significant population 

differences in oil degradation (P=0.15 ; Figure 8) . However, the amount of residual oil 

remaining in the soil of genotypes C and J were generally in the lower half of all 

genotype responses and ranged from about 11 - 12 mg oil g-' dry weight of soil (Figure 

8) . In comparison, genotype G had a greater amount of residual oil remaining in the soil, 

which averaged about 15 mg g-' (Figure 8). 

Factor analysis of the S . patens genotype responses in the oiled treatment agreed 

with and helped to clarify the univariate results . The clearest interpretation was obtained 

when utilizing plant growth response variables in addition to the amount of residual oil 

remaining in the soil at the end of the study. The following six variables were utilized: 

photosynthesis (net COz assimilation rate) at one month and three months, total 

aboveground biomass at the three month harvest, the proportion of dead aboveground 

biomass at the three month harvest, the amount of aboveground biomass regrowth that 

occurred through the oiled soil, and residual oil in the soil . This model resulted in a two 

factor solution that accounted for 72% of the variation in these variables (Figure 9). The 

first factor accounted for 54.7% of the variation and is best described as a plant 

growth/regrowth factor. This factor had high positive loadings of photosynthesis at one 

month (0.835) and three months (0 .883), total aboveground biomass (0.884), and 

aboveground biomass regrowth (0 .920), with a negative loading of proportion of dead 

aboveground biomass (-0 .498). Therefore, genotypes with high factor 1 scores would be 

classified as having high rates of photosynthesis and growth, with very little tissue death 

when oiled, and also high rates of new aboveground biomass production through oiled 

soil . The ANOVA that was run on these factor scores detected highly significant 

differences between genotypes, with genotypes C, G, and J having the highest factor 1 

scores (Figure 10) . The second factor accounted for an additional 17.3% of the variation 

and is described solely by a high negative loading of residual oil . Therefore, genotypes 

that have a high factor 2 score would be classified as genotypes whose presence was 

associated with less residual oil remaining at the end of the study (Figure 9) . The 

ANOVA of the factor 2 scores failed to detect an overall significant genotype effect 

(P=0 .224) . However, genotypes C and J tended to be associated with the greatest amount 

of oil degradation and a linear contrast did show that genotypes C and J had significantly 

greater factor 2 scores (and hence less residual oil associated with them) than the other 

genotypes (P=0.014; Figures 8 and 10). 

15 
160 D oiled 

140 13 control 

c 

0 120 

v 

100 

m 

80 t 

60 

< . 

m 40 .TS `- 

oC 20 - . ~.. .,; .., ~ , . P. 

A B C D E F G H I J 

Spartina patens genotype 

16 

oiled 

,.. 14 ~ control 

v 

12 

10 

0 

g - 

s 

3 6 

o 

~ 

.S 

0 

A B C D E F G H I J 

Spartina patens genotype 

Figure 7 . Mean (± standard error) number of regrowth stems (# per pot; top 

panel) and aboveground biomass regrowth (g per pot; bottom panel) of 

Spartina patens genotypes three months after the initial harvest (n=5). 

16 
30 

_- -_ - Ow 25 

20 

'o 

as 

E 15 -

10 

-a 

m 

0 

A B C D E F G H I J 

Spartina patens genotype 

Figure 8. Mean (f standard error) residual oil (mg oil g-1 soil) remaining in the soil of 

S_part a pate ns genotypes six months after oiling at a rate of 5 L oil r n2 (n=5). 

17 
1 .0 

0.5 

TOTBIO 

PRODEAD S3 

0 0.0- 

v Ps1 Q 

-0 .5 -

-1 .0 

-1 .0 -0 .5 0.0 0.5 1 .0 

FACTOR 1 

Figure 9. Factor loadings plot resulting from a factor analysis of residual oil and five 

plant response variables of oiled Spartina patens genotypes . Abbreviations are as 

follows: PS1=photosynthesis (net COZ assimilation rate) one month after oiling, 

PS3=photosynthesis three months after oiling, TOTBIO=total aboveground 

biomass three months after oiling, PRODEAD=proportion of dead aboveground 

biomass three months after oiling, NEWBIO=amount of aboveground biomass 

regrowth between three and six months after oiling, and RESOIL=residual oil in 

the sediment six months after oiling (n=5) . 

18 
2 

1 .5 

1 

0 

0.5 

L 

i~ 0 

v 

ev 

-0.5 

-1 

-1 .5 

A B C D E F G H 1 J 

Spartina patens genotype 

1 .5 

1 

0 .5 

0 

v p 

-0.5 

-1 

-1 .5 

-2 

Factor 2 

T - 

A B C D E F G H I J 

Spartina ap tens genotype 

Figure 10. Mean (± standard error) factor 1 scores (top panel) and factor 2 scores (bottom 

panel) of oiled (5 L oil n f2) SUartina pate ns genotypes. High factor 1 scores are 

indicative of high photosynthesis and biomass with very little tissue death and high 

aboveground biomass regrowth when oiled. High factor 2 scores are indicative of 

low amounts of residual oil remaining after six months (n=5). 

19 
Spartina alterniflora 

Similar to S . patens , oiling resulted in a highly significant (P<0 .01) reduction of 

net COz assimilation rate in S . alterniflora , which was evident within one month after 

oiling and persisted through the third month (Figure 11) . However, unlike S. patens, 

differences between S . alterniflora genotypes in net CO Z assimilation rate only 

approached statistical significance (P=0.059) one month following oiling (Figure 11) . 

Similarly, genotype photosynthetic response to oiling expressed as a percentage of the 

control was also not significant (Table 2). Nonetheless, after three months of oiling, 

differences between genotypes in net CO, assimilation rate were highly significant . Also 

after three months, the interaction of genotype x treatment was significant, thereby 

indicating that the genotypes were not responding uniformly to oiling (Figure 11). 

Genotypes R, U, V, and Y displayed the highest rates of net CO z assimilation after three 

months, both in terms of actual treatment responses and also as a percentage of their 

control responses, which also indicated significant differences between genotypes (Figure 

11 ; Table 2). 

Water-use efficiencies did not display any significant effects one month after 

oiling. Three months after oiling there was a highly significant treatment effect and also 

a highly significant genotype x treatment interaction (Figure 12) . Genotypes V and Y 

possessed some of the highest water use efficiencies, perhaps largely due to their high 

rates of photosynthesis in the third month (Figure 12) . 

Leaf expansion rate proved to be quite variable and not as sensitive to oiling as 

net COZ assimilation rate. One month after oiling, there were no significant effects of 

genotype or treatment on leaf expansion rate (Figure 13). After three months, a highly 

significant reduction in leaf expansion rate was evident with oiling, but genotype 

differences were still not significant unless analyzed as a percentage of the control 

response (Figure 13 ; Table 2). Genotypes S, U, and V all had oiled leaf expansion rates 

that were near or exceeded 100% of their control responses, whereas the other genotypes 

had leaf expansion rates that ranged from 32% - 76% of their control responses (Table 2) . 

Live biornass, total biomass, and the number of live and dead stems harvested 

three months after oiling displayed highly significant effects of both genotype and oiling 

(Figures 14, 15, and 16). The effect of genotype remained either significant or highly 

significant when analyzed as a percentage of the control response for all of these 

variables (Table 2) . The effect of genotype on dead biomass only approached statistical 

significance (P=0 .07), but was significant when expressed as a percentage of the control 

response (Figure 15 ; Table 2). Live biomass and live and dead stems also displayed 

significant interactions of genotype x treatment. This interaction can be explained by the 

outstanding performance of genotype R (and to a lesser extent genotype Z), which did not 

show any reduction of live biomass when oiled, but rather displayed a slight stimulation 

(Figure 14; Table 2). As was observed in S . patens, the number of live and dead stems 

generally paralleled the responses of live and dead aboveground biomass (Figure 14). 

Genotype R, which already had higher biomass per live stem than the other genotypes, 

showed some stimulation upon oiling, which resulted in a further increase in live stem 

weight (Figure 14) . The proportion of dead biomass displayed highly significant 

genotype and treatment effects, as well as a highly significant genotype x treatment 

interaction (Figure 16). Genotypes Q, S, and X did poorest in this regard and had much 

20 
25 

c 20 

0 

~15 

a 

X10 

v v 

m 5 

0 

25 

20 

0 

'~ N 15 

0 X10 

v 

m 5 

0 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 11 . Mean (f standard error) net C02 assimilation rates (gmol m2 S -I ) of Spartina 

alterniflora genotypes one month (top panel) and three months (bottom panel) after 

oiling at a rate of 8 L oil m 2 (n=5). 

21 
Table 2. Responses of Spartina alterniflora genotypes to oiling (8 L -2 ) expressed as percentages of the controls. Shown are mean 

responses and standard errors in parentheses (n=5). The probability of a genotype effect for each response is indicated as 

follows:NS=P>0.05,*=P<0.05,**=P<0.01,***=P<0.001 . 

Response Genotype 

A B C D E F G H I J 

Photosynthesis "S 79.33 95.36 93.48 60.58 94.42 73 .30 52.68 69.66 104.48 73.93 

After 1 month (20.11) (6 .38) (16.61) (23.25) (18.57) (15.59) (4 .48) (21 .89) (14.40) (14.36) 

Photosynthesis * 35.38 69.54 53.16 37 .75 67 .54 80.12 43.78 26.54 97.50 53.03 

After 3months (3 .48) (15.21) (13.58) (21 .68) (16.26) (10.85) (5 .98) (8 .74) (19.0) (9 .31) 

Leaf Expansion "S 48.96 75.16 135.70 69.50 122.68 121 .65 109.56 96.94 56.14 88 .48 

1 month (18.83) (20.35) (10.29) (32.01) (33.82) (49 .17) (26.99) (20.41) (16.34) (7 .37) 

Leaf Expansion `* 44.80 72.80 113.80 32 .25 126.60 99.75 76.00 50.00 74.40 79.80 

3 months (13.82) (17.12) (10.07) (18.87) (27.0) (12 .44) (12.15) (13.98) (7 .39) (22 .34) 

Live Stem **" 27.64 279.90 32 .90 49 .80 49.76 67.56 95.92 38 .54 134.88 146.30 

Count (5 .88) (24.95) (11 .98) (19.49) (9 .81) (13.57) (17.59) (18.65) (29.19) (16.45) 

Live Biomass *** 24.34 246.30 28.94 43.83 40.38 59.46 84.44 33.88 118.70 128.77 

(5 .16) (21 .94) (10.54) (17.15) (10 .25) (11 .94) (15.48) (16.42 (25.70) (14 .48) 

Dead Stem * 41428 100.00 428.58 374.98 400.00 540.00 240.00 218.75 213.32 280.00 

Count (88.64) (31 .62) (59.76) (131 -654) (167.33) (40.0) (24.50) (106 .74) (80.00) (105 .83) 

Dead Biomass * 190.68 52.34 177.54 104.37 100.00 204.68 67.28 221 .50 88.78 161 .2 

(28 .52) (7 .74) (52 .81) (43 .01) (16.03) (56.72) (5 .25) (62 .01) (32 .14) (53.38) 

Total Biomass 46.15 78.01 52.60 52.74 83.35 77.55 72.47 48.40 71 .88 160.38 

(6 .42) (20.13) (13.89) (18.82) (11 .01) (12.03) (12.11) (11 .50) (14.59) (24.97) 

Proportion Dead " 503.54 152.44 509.02 401 .37 749.92 563.38 178.86 565.62 150.38 299.78 

Biomass/Total (71 .49) (71 .24) (120.17) (250 .30) (188 .57) (128 .26) (45.08) (153 .67) (62.69) (170 .02) 

Stem Count "S 3.40 13.0 0 0 6.0 12.25 3.25 0 1 .75 0 

Regrowth (3 .40) (7 .51) (0) (0) (2.95) (7 .22) (3 .25) (0) (1 .75) (0) 

Biomass "S 0.60 17.2 0 0 4.2 11 .4 3.2 0 6.0 0 

Regrowth (0 .6) (12.44) (0) (0) (2 .46) (5 .27) (2 .52) (0) (5 .06) (0) 
1 month 

oiled 

T

control N I i ' ~ O 4 

d 

d E 3 

I 

' 

r{ w ;`. 

r., . 

a 0 

E r `. 

v 1 - 

. . 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

g - - - - - 

3 months moiled I 

0 5 ~ control 

c 4 

m 

o 

d E 3 

V ` 

Z 

`' 

a ~ 

s . . 

0 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 12. Mean (f standard error) water-use efficiencies (Rmol C02 mmol-1 

H2O) of Spartina alterniflora genotypes one month (top panel) and three 

months (bottom panel) after oiling at a rate of 8 L oil m-2 (n=5). 

23 
- ___ 

--- 

1 .6 

1 month a oiled 

1 E3 t l .4 con ro 

'~ 1 .2 

E , 

v 1 

O 0.8 .. "" 

\^I/ 

a 0.6 

"Y 

I 

W X y, ,, E' :F p>'k " ~e~ ~ 1 

0 .4 

~ 

. 

. . \ :y y~% 

d 

J 0.2 - 

r 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

2.5 

,., 3 months 0 oiled 

9 control 

cc 

-a 

E 

v 1 .5 

c 

0 

CL T 

m 

0.5 

as 

J 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 13 . Mean (± standard error) leaf expansion rates (cm day') of S_part ~ 

alterniflora genotypes one month (top panel) and three months (bottom panel) 

after oiling at a rate of 8 L oil m-2 (n=5). 

24 
40 _..-------------- I 

35 - 

13 oiled 

B control 

m 30 .r 

25 

20 

0 

m 15 

0 

.~ 

.r O - '`a 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

60 - 

i - 

0 oiled i o 

50 E3 contro l 

.. 

u~ 4U 

era 

i 

0 30 

m 

20 

.,, 

J 

\ 

Sq 

y:. 

10 ' 

0 ..z, 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 14. Mean (f standard error) number of live stems (# per pot; top panel) and 

live aboveground biomass (g per pot; bottom panel) of Spartina alterniflora 

genotypes three months after oiling at a rate of 8 L oil M-2 (n=5). 

25 
g ----- --- __ --- - - ----- _, 7 _ - -- - - __ - - - - -- - __- --_. ___-_------_._-- . _ -- -- -- - - - --- - - 

D oiled 

control 

6 

s - 

~_ 0 0 4- . 

+3 

3a' 

L 

Q R S T U V W X Y Z 

Saartina altemiflora genotype 

C oiled 

D contro l 

v 

5- 

~ v 

" . 

E 4 - 

,. 

U 

i 

r 

` 

\~ 

` ` 

~ 

4 

Y fl 

S ` 

\ 

0 

} ,~ \`. _ 

Q R S T U V W X Y Z 

Saartina alterniflora genotype 

Figure 15 . Mean (f standard error) number of dead stems (# per pot; top panel) and 

dead aboveground biomass (g per pot; bottom panel) of Svartina alterniflora 

genotypes three months after oiling at a rate of 8 L oil m 2 (n=5). 

26 
so 

13 oiled 

50 B control 

ea 

E 40 - 0 

30 

o 20 ,. . 

` \' .A 

10 J~ ~, 

\ 

F o 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

0.9 - ----- ------- -- -- --, 

O.$ 

oiled 

a control 

7 I I . 

0 . 6 

-o 0 . 5 '` = 

m 0 . 4 

0 0 . 3 

Q 0.2 

0 0 . 1 

a i, .,~ ....>.., 

0 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 16. Mean (f standard error) total aboveground biomass (g per pot; top panel) 

and proportion of dead aboveground biomass (dead/total aboveground 

biomass) of S_nartina alterniflora genotypes three months after oiling at a rate 

of 8 L oil m-2 (n=5). 

27 
larger proportions of dead aboveground tissue than the other genotypes (Figure 16). The 

analysis of the proportion of dead biomass expressed as a percentage of the control also 

showed significant genotype differences (Table 2) . In addition to genotypes Q, S, and X, 

this analysis showed that genotypes U and V had some of the large values, but this was 

because of the relatively low amount of dead aboveground biomass in their controls 

(Figure 16; Table 2) . 

The amount of aboveground biomass regrowth (three months after the initial 

harvest) displayed a highly significant treatment effect of oiling, which drastically limited 

the amount of aboveground regrowth (Figure 17). There was also a significant effect of 

genotype, as well as a significant genotype x treatment interaction on aboveground 

biomass regrowth. The significant interaction resulted from the fact that four of the 

genotypes (S, T, X, and Z) failed to produce any new aboveground tissue in the oiled 

treatments, whereas genotypes R and V had substantial regrowth (Figure 17). In terms of 

regrowth expressed as a percentage of the control, there was also a highly significant 

effect of genotype . Genotypes R and V produced aboveground biomass regrowth in the 

oiled treatments that was 17.2% and 11 .4%, respectively, of their control regrowth values 

(Table 2). Regrowth was also assessed in terms of the number of new stems produced 

after the initial harvest. In general, stem regrowth results were very similar to those of 

aboveground biomass regrowth (both in terms of actual response and percentage of 

control response) with genotypes R and V displaying the greatest regrowth and genotypes 

Q, U, W, and Y displaying measurable, but more limited, regrowth (Figure 17; Table 2). 

The amount of residual oil remaining in the soil at the final harvest (six months 

after oiling) failed to display a significant effect of genotype (Figure 18). Values of 

residual oil ranged from lows of about 42 mg oil g' dry weight of soil in genotypes T and 

U to highs greater than 70 mg g' in genotypes V and X (Figure 18) . 

Results from the factor analysis of S . alterniflora genotype responses in the oiled 

treatment were similar to those obtained for S . atens . The clearest interpretation was 

again obtained when utilizing the same five plant growth response variables 

(photosynthesis (net COZ assimilation rate) at one month and three months, total 

aboveground biomass at the three month harvest, the proportion of dead aboveground 

biomass at the three month harvest, and the amount of aboveground biomass regrowth 

that occurred through the oiled soil) in addition to the amount of residual oil remaining in 

the soil at the end of the study. This six variable model resulted in a two factor solution 

that accounted for 58.8% of the variation in these variables (Figure 19) . The first factor 

accounted for 41 .1 % of the variation and was dominated by a high negative loading of 

proportion of dead aboveground biomass (-0 .812) and large positive loadings of 

photosynthesis at three months (0.752) and total aboveground biomass (0.721) . The 

amount of regrowth biomass and photosynthesis at one month also had fairly large 

positive loadings of 0.654 and 0.554, respectively (Figure 19). As was the case for S . 

patens, this first S . alterniflora factor is also best described as a plant growth/regrowth 

factor, but with slightly more emphasis on the aboveground tissue at the time of oiling 

being resistant to tissue death. Therefore, S. alterniflora genotypes with high factor 1 

scores would be classified as having high rates of photosynthesis and growth, with very 

minimal tissue death when oiled, and also relatively high rates of new aboveground 

biomass production. The ANOVA that was run on these factor scores detected highly 

significant differences between genotypes, with genotypes R, V, and Y having the 

highest factor 1 scores (Figure 20) . The second factor accounted for an additional 17.7% 

28 
50 l 

45 I - - - - -- b oiled 

c ~ control 40 

v 35 ~ 

30 

H 25 

t 

20 

I 1

c~ 5 ', I 

10 

5 .: 

0 `"` 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

20 -------------------------------------- 

D oiled 

0 control 

~ 16 

N 14 - 

cc i 

E 12 

o 

10 

t 

8 o 

~, s i 

4 - 

2 

0 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 17. Mean (± standard error) number of regrowth stems (# per pot; top panel) 

and aboveground biomass regrowth (g per pot; bottom panel) of Spartina 

alterniflora genotypes three months after the initial harvest (n=5). 

29 
120 

- 

100 

e 

. 

O T T O 

o 

.O 

40 

20 

0 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 18. Mean (f standard error) residual oil (mg oil g-' soil) remaining in the soil of 

Spartina alterniflora genotypes six months after oiling at a rate of 8 L oil M-2 (n=5). 

30 
1 .0 

' RESOIL ' 

0.5 

TOTBI 

N 

0 .0 

NEWBIO 

U PRODEAD 

Q PS3 

-0 .5 

-1 .0 

-1 .0 -0.5 0 .0 0.5 1 .0 

FACTOR 1 

Figure 19. Factor loadings plot resulting from a factor analysis of residual oil and five 

plant response variables of oiled S_partina alterniflora genotypes. Abbreviations 

are as follows : PSl=photosynthesis (net COz assimilation rate) one month after 

oiling, PS3=photosynthesis three months after oiling, TOTBIO=total 

aboveground biomass three months after oiling, PRODEAD=proportion of dead 

aboveground biomass three months after oiling, NEWBIO=amount of 

aboveground biomass regrowth between three and six months after oiling, and 

RESOIL=residual oil in the sediment six months after oiling (n=5). 

31 
2 

1 .5 

1 

0 

0.5 

s o v 

ea `L -0 .5 

-1 

-1 .5 

2 

1 .5 

1 

I- 0 

0.5 

0 

v 

-0.5 

-1 

-1 .6 

Factor 1 

T 

1 J- c 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Factor 2 

. . 

Q R S T U V W X Y Z 

Spartina alterniflora genotype 

Figure 20. Mean (± standard error) factor 1 scores (top panel) and factor 2 scores (bottom 

panel) of oiled (8 L oil -2 ) S-nartina alterniflora genotypes. High factor 1 scores 

are indicative of minimal tissue death, high photosynthesis and biomass, with 

relatively high aboveground biomass regrowth when oiled. High factor 2 scores 

are indicative of high amounts of residual oil remaining after six months (n=5) . 

32 
of the variation and is described solely by a high positive loading of residual oil . 

Therefore, genotypes of S . alterniflora that have low (not high) factor 2 scores would be 

classified as genotypes whose presence resulted in less residual oil remaining at the end 

of the study. However, there was a tremendous amount of variation within genotypes in 

this factor 2 response and the ANOVA of genotype effects proved to be insignificant 

(P=0.774; Figure 20). 

33 

DISCUSSION 

The results from this study have provided valuable, first-time information on 

intraspecific variation in oil tolerance in two dominant, wide-spread marsh grasses . In 

both Spartina patens (brackish marsh dominant) and Spartina alterniflora (salt marsh 

dominant) significant intraspecific variation to oiling was observed in a number of plant 

responses . Of the ten genotypes assessed in each species, several genotypes could be 

i 'dentif led that displayed superior oil tolerance in terms of single and multiple plantgrowth responses to oiling . This is a very exciting finding that has important ecological 

and applied value. 

Although there has been considerable research on intraspecific variation in 

agricultural species for identifying those genotypes or varieties best suited for particular 

stressful environments (see Blum 1988 and references therein), researchers have only 

recently begun to seriously investigate the possibility of exploiting natural genetic 

variation to the environmental stresses present in coastal plant communities (Seliskar 

1995) . The ability to identify superior, stress-tolerant vegetation is anticipated to be of 

great value when selecting planting stocks to be used in marsh creation and restoration 

projects where stresses (both natural and anthropogenic) are known to be present, or are 

likely to occur. Much of the research to date on intraspecific variation in coastal plant 

species has focused on the natural environmental stresses of salinity and, to a lesser 

extent, flooding (see Seliskar 1995 ; Hester et al . 1996 ; Lessmann et al . 1997 ; Hester et al. 

1998 and references therein) . Of the coastal grass species, S . patens has been the most 

intensively investigated for intraspecific variation in salinity tolerance, which is indeed 

present across populations, or subpopulations, of this species (Silander 1979 ; Silander 

and Antonovics 1979 ; Pezeshki and DeLaune 1991 ; Seliskar 1995 ; Hester et al. 1996) . 

Intraspecific variation in salinity tolerance in S . alterniflora has also been documented 

(Pezeshki and DeLaune 1995 ; Seliskar 1995 ; Hester et al . 1998). Similarly, Lessmann et 

al. (1997) have reported significant population variation in flood tolerance in both S . 

patens and S . alterniflora . Together, these studies have demonstrated that intraspecific 

variations in response to the natural stresses of salinity and flooding exist in these two 

wide-spread species, which should prove useful in selecting superior planting stocks for 

specific marsh restoration and creation needs. 

Prior to this study, essentially no research had been conducted that assessed 

intraspecific variation to an anthropogenic stress, such as oiling . The results of this study 

clearly show that both S. patens and S. alterniflora display significant intraspecific 

variation to oiling and that superior genotypes can be identified that are more tolerant to 

oiling and have greater rates of regrowth and overall plant production than other 

genotypes . Not only is this finding valuable from a restoration perspective, but it may 

also help to explain some of the widely-divergent effects that oiling has been reported to 

have in similar wetland plant communities. For example, Mendelssohn et al . (1990) 

reported that a relatively minor spill of South Louisiana crude oil (0.28 L M-2) in a 

Louisiana brackish marsh dominated by a mixture of S. patens and S . alterniflora 

resulted in a 64% reduction of live vegetative cover within three months, whereas 

DeLaune et al . (1979) reported that the addition of up to 8 L m-Z of Louisiana crude oil 

failed to reduce aboveground biomass in a S . alterniflora dominated Louisiana salt marsh 

35 
after four and sixteen months. Some of these observed differences may have been due to 

underlying genetic variation in oil tolerance . This is, however, difficult to assess because 

many factors have been identified that can affect the severity of oil impact on marsh 

vegetation. Factors such as the type and amount of oil spilled, water level (or tidal 

amplitude), degree of oiling to aboveground tissues, extent of penetration of the oil into 

the soil, soil organic matter content, season, species affected, and cleanup activities can 

all drastically influence the severity of an oil impact on marsh vegetation (Webb et al. 

1985 ; Alexander and Webb 1987; Baker et al. 1993 ; Mendelssohn et al. 1990; Lin and 

Mendelssohn 1996) . In the above comparison, Mendelssohn et al. (1990) state that oiling 

of the leaf tissue (due to high water levels) may have contributed to the severity of what 

would normally be considered a relatively low dosage spill. This is because leaf tissue of 

S . alterniflora (and other species) appears more sensitive to oiling than stem tissue (Crow 

1974; Pezeshki et al. 1995). Nonetheless, the important point is that significant natural 

genetic diversity in oil tolerance has now been shown to exist across populations of S. 

patens and S. alterniflora and that this variation represents one more factor that needs to 

be considered, whether attempting to estimate or predict the severity of an oil spill, or 

explain observed differences in the severity of impact following a spill. 

In this study, several oil-tolerant genotypes of S . patens and S . alterniflora 

displayed essentially no reduction in plant photosynthesis when oiled, while other 

genotypes displayed photosynthetic rates that were only about one-third those of the 

controls. In fact, some genotypes actually displayed a tendency toward greater rates of 

photosynthesis when oiled (Figures 1 and 11) . The assessment of suitable short-term 

indicators of plant stress is a research area that has received considerable attention (see 

Mendelssohn and McKee 1992 ; Ewing et al . 1995 ; Larcher 1995 and references therein) . 

Ewing et al. (1995) reported that net COz assimilation rate (photosynthesis) appeared to 

be a more sensitive indicator of salinity stress in S . patens than leaf expansion rate. In 

their study, Ewing et al . (1995) were able to detect salinity stress sooner using net COZ 

assimilation rate than was possible using leaf expansion rate or other indicators. This 

agrees with the results of our study, where we generally found that leaf expansion rate 

was not as sensitive in detecting oil stress as net COz assimilation rate . This was 

especially apparent in S . patens, which did not display a significant effect of oiling until 

the third month, when many of the oiled genotypes actually had higher leaf expansion 

rates than their controls (Figure 3) . Lin and Mendelssohn (1996) also reported significant 

reductions in S . patens photosynthesis within one month of soil oiling, but this occurred 

at dosages greater than that used in this study (16 and 24 L m-Z). 

Spartina alterniflora leaf expansion rates in our study similarly did not display a 

reduction with oiling in the first month, but did by the third month (Figure 13) . However, 

even in the third month after oiling, S. alterniflora genotype differences were not detected 

in leaf expansion rate unless expressed as a percentage of the control, whereas genotype 

differences in net COZ assimilation rate were detectable from both the actual responses 

and these responses expressed as a percentage of the control (Table 2; Figure 11). Also 

using South Louisiana crude oil, Mendelssohn et al . (1993b) reported a reduction in S . 

alterniflora photosynthesis ; but at oiling rates greater than the 8 L M-2 of our study (16 

and 24 L M-2) . Lin and Mendelssohn (1996) did not observed a significant reduction of 

net COz assimilation rate in S . alterniflora relative to controls until oiling at a rate of 24 L 

m_2 and, even then, not until after three months. Photosynthesis in S . alterniflora is 

reported to be reduced much more rapidly when the oil makes contact (either partially or 

36 
fully) with the leaf surface rather than just with the soil and root zone (Pezeshki and 

DeLaune 1993). 

As was observed with leaf expansion rate, water-use efficiency (ratio of COZ 

assimilated per water lost) was also not a sensitive indicator in detecting oil stress or 

genotype differences . The only significant differences detected in water-use efficiency 

were in S . alterniflora in the third month after oiling, but even then these differences were 

largely driven by differences in net COz assimilation rate (Figures 11 and 12). This 

finding is interesting since petroleum hydrocarbons can affect plants in a number of ways 

that may alter their water balance and respiration . Some of the these effects include 

disruption of plant water and ionic relations (McCown and Deneke 1972; Gilfillan et al. 

1989), hindered nutrient uptake (McCown and Deneke 1972), toxicity to cells because of 

membrane disruption from lipophillic hydrocarbons in the oil (Prendeville and Warren 

1977), and reduced gas exchange between the atmosphere and the soil (Ranwell 1968; 

Cowell 1969; Stebbing 1970) or between the atmosphere and the plant itself when leaf 

tissue is oiled (Webb 1994; Pezeshki et al. 1995). 

Long-term plant stress response is most frequently assessed through the 

harvesting and partitioning of biomass into various components because plant production 

and allocation patterns ultimately reflect an integrated response to environmental stress 

(Fitter and Hay 1987 ; Larcher 1995 ; Bazzaz 1996) . The value of partitioning biomass 

into various components was evident in both species' growth responses. In S. patens, 

total aboveground biomass (after three months of oiling) was not significantly affected by 

oiling, although significant oiling effects were evident in the amounts of live and dead 

aboveground biomass, as well as in the proportion of dead aboveground biomass (Figures 

4, 5, and 6) . Furthermore, for both species, differences in genotype performance 

generally became more lucid in the partitioned biomass variables, especially the 

proportion of dead aboveground biomass, which resulted in significant genotype effects 

in S . alterniflora that were not evident from dead aboveground biomass alone (Figures 15 

and 16) . 

The amount of regrowth through the oiled soil proved to be a very critical 

indicator of a genotype's sensitivity and ability to asexually reproduce following an oil 

spill. In our study, all of the S . patens genotypes produced new stems through the oiled 

soil, although the extent of success differed significantly between genotypes (Figure 7). 

This finding generally agrees with the findings of Lin and Mendelssohn (1996), who 

reported significant decreases in the regrowth of S . patens through oiled sediment as 

oiling rates increased from 0 to 4 L m-' and again from 4 to 8 L m-2. The amount of 

regrowth at 8 L in-' was so low that it was reported as not being significantly different 

from the zero regrowth that occurred in their higher oiling rates (Lin and Mendelssohn 

1996) . The oiling rate used in our study of S. patens (5 L in-') severely affected regrowth 

and allowed for good genotype separation without prohibiting at least minimal regrowth 

in the weakest genotypes . Conversely, the oiling rate used in our study of S . alterniflora 

(8 L in-') totally prevented any regrowth in four of the ten genotypes . Nonetheless, the 

six genotypes that did produce new stems through the oiled soil still displayed 

tremendous genotypic variation in this response (Figure 17) . This observed sensitivity of 

emerging shoots of S . alterniflora to oiled sediment agrees with the findings of other 

studies. Mendelssohn et al. (1993b) reported significantly reduced regrowth of S. 

alterniflora through oiled sediment at an oil application rate of 4 L in-', although (as 

stated above) an application rate of greater than 8 L in-' was required to significantly 

37 
reduce net photosynthesis . Furthermore, Lin and Mendelssohn (1996) reported that the 

emergence of new shoots of S . alterniflora through oiled sediment was extremely limited 

(and not different from zero regrowth) at an oiling level of 8 L M-2 . Therefore, as 

evidenced in both species in our study, but particularly in S. alterniflora , plants may 

survive an initial oiling of the soil surface, but experience a tremendous loss of regrowth 

potential as newly emergent shoots and leaf tissue come into contact with the oiled 

sediment. 

Assessing regrowth potential through oiled sediment is an important consideration 

when evaluating oil tolerance in wetland vegetation since the continued (or renewed) 

production of new shoots is essential in maintaining marsh surface integrity. If no new 

growth occurs for an extended period of time following oiling, then sediment instability 

and erosion of the marsh surface may occur, resulting in a loss of marsh surface elevation 

and pond formation. Mendelssohn et al . (1993a) reported that a decrease in marsh 

surface elevation was believed to be a contributing factor in the failure of certain areas of 

Louisiana brackish and salt marsh to revegetate following aboveground tissue death after 

an oil spill. Several studies have found marsh elevational differences to have a 

significant impact on plant establishment and productivity (Mendelssohn and McKee 

1988 ; Burdick and Mendelssohn 1990; Wilsey et al . 1992) . Therefore, it may be highly 

desirable to have superior, oil-tolerant vegetation available that can be utilized to 

revegetate oiled marshes before substantial erosion of the marsh substrate occurs . It is 

possible that oil-tolerant genotypes may be directly planted in marshes impacted by 

minor spills, or planted following initial cleanup efforts in marshes impacted by major 

spills . Similarly, if oil tolerant vegetation has already been established in a restored 

marsh, then the probability of marsh loss resulting from future oil spills may be greatly 

reduced. 

In addition to preventing erosion of the marsh substrate, the establishment of oiltolerant vegetation at a spill site may aid in bioremediation by accelerating the oxidative 

degradation of residual oil via oxygen transport from the atmosphere through the leaves 

and stems and into the roots and rhizosphere, where the oxygen may diffuse into the oiled 

soil and potentially accelerate the degradation process (Armstrong 1978; Smirnoff and 

Crawford 1983) . It was disappointing that significant differences between genotypes 

were not detected in their ability to degrade oil when analysed with univariate statistics . 

However, it must be kept in mind that the time frame of our study was relatively short, 

with the initial harvest of aboveground tissue taking place three months after oiling, and 

the final harvest of regrowth shoots occurring at six months after oiling . Also, there was 

considerable variation between replicates that was likely due to spatial variation in the 

distribution of the residual oil in the soil (Figures 8 and 18). In S . patens two of the three 

most oil-tolerant genotypes (C and J) were, in fact, associated with less residual oil, and 

had significantly greater oil degradation factor scores than the other genotypes (Figures 8 

and 10) . In S . alterniflora, there was less evidence of a trend associating the more oiltolerant genotypes with less residual oil . However, it is likely that in the long term (and 

under field conditions) greater rates of oil degradation would be associated with those 

genotypes that are less stressed and have greater rates of plant productivity since air 

transport through the stems, leaves and roots of marsh vegetation is well-documented 

(Armstrong 1978; Smirnoff and Crawford 1983; Seliskar 1985), which should facilitate 

the degradation of oil. This may be especially important in marshes subject to a high 

incidence of oil pollution since chronic oil pollution has been implicated in causing the 

38 
development of anaerobic conditions that may also inhibit vegetative regrowth (Ranwell 

1968 ; Cowell 1969) . 

Factor analysis proved indispensable in identifying genotypes in both species that 

displayed a suite of desirable plant responses when oiled. Univariate analyses showed 

that there were genotypes within each species that performed significantly better when 

oiled than others in terms of a number of single plant responses. However, to truly assess 

and test for significant genotype differences in performance across multiple variables, a 

multivariate approach, such as factor analysis, must be utilized (Johnson and Wichern 

1988) . The goal of factor analysis is to reduce the data from numerous variables into a 

fewer number of factors that still account for much of the information (variation) 

contained in the original variables. Initially, all the plant response variables plus the 

residual oil variable were included in the factor analysis of each species. Subsequent 

factor analyses were then conducted which systematically removed variables. Ideally, we 

wanted our final factor solution to consist of only a few factors, and for each factor to 

have variable loadings that would be consistent with either highly desirable or highly 

undesirable plant traits in terms of oil tolerance and oil degradation . The most suitable 

factor solutions were obtained for both species when utilizing the following variables: 

net COz assimilation rate at one and three months, total aboveground biomass, the 

proportion of dead aboveground biomass, biomass regrowth through the oiled soil, and 

residual oil in the soil . Both species had a two factor solution that accounted for 72% of 

the variation in the S . ap tens data and 58 .8% of the variation in the S . alterniflora data 

(Figures 9 and 19). 

For both species, genotypes with significantly greater factor 1 scores were 

identified and are considered to be the most oil-tolerant genotypes . When oiled, these 

genotypes display higher photosynthesis, higher biomass accumulation with little tissue 

death, and higher regrowth of new stems than the other genotypes. For S . patens , these 

were genotypes C, G, and J (Figure 10), and for S . alterniflora , genotypes R, V, and Y 

(Figure 20) . This finding is extremely encouraging because it identifies several 

genotypes within each species that truly display a suite of desirable, oil-tolerant 

characteristics. Factor 2 scores were associated with residual oil in both species. In S. 

patens , factor 2 had a large negative loading of residual oil, whereas in S. alterniflora, 

factor 2 had a large positive loading of residual oil. As a result, for S . patens , large factor 

2 scores would be associated with low amounts of residual oil, whereas for S. alterniflora , 

small (not large) factor 2 scores would be associated with low amounts of residual oil. In 

neither species was an overall effect of genotype on factor 2 scores detected . However, 

in S . patens , a linear contrast did show that genotypes C and J had significantly larger 

factor 2 scores than the other genotypes, implying that genotypes C and J may, in fact, 

possess a greater ability to degrade oil than the other genotypes (Figure 10) . Future 

research is needed to assess the long-term performance of selected genotypes to oiling 

with regard to sustained productivity and potential genotype differences in oil 

degradation . 

39 

CONCLUSIONS 

This study has provided several findings of interest to state and federal agencies 

involved in oil spill response and the restoration and creation of wetlands . This project 

has provided first-time information on the amount of intraspecific variation in oil 

tolerance in two wide-spread marsh plant species that are found throughout the Gulf and 

Atlantic Coasts of the United States . In both Spartina patens (brackish marsh dominant) 

and Spartina alterniflora (salt marsh dominant) significant intraspecific variation was 

documented in a number of plant responses to oiling . Plant photosynthetic response (net 

COZ assimilation) was found to be an excellent short-term indicator of oil stress that 

displayed significant variation between genotypes, thereby facilitating the identification 

of superior, oil-tolerant genotypes to a much greater extent than was possible using leaf 

expansion rate . Partitioning plant aboveground biomass into live and dead components 

and, especially, assessing the proportion of dead to total aboveground biomass proved 

very useful in discerning significant genotype differences in longer-term response 

variables to oiling . 

Factor analysis confirmed that several oil-tolerant genotypes (three in each 

species) could be identified that displayed suites of desirable plant responses when oiled 

that were superior to the other genotypes . These superior responses when oiled included 

the ability to maintain high photosynthetic rates over time, high plant production with 

limited tissue death, and high regrowth potential of new shoots through the oiled soil. 

Genotype differences in oil degradation potential, based on residual oil remaining in the 

soil six months after oiling, were not detected with univariate statistics in either species. 

However, in S. patens, a linear contrast did reveal that two of the three oil-tolerant 

genotypes were associated with significantly greater oil-degradation factor scores, 

indicating that these genotypes may possess a greater potential to degrade oil than other 

genotypes . Longer term studies, and more intensive sampling to reduce spatial 

heterogeneity of residual oil in the soil, may be necessary to elucidate genotype 

differences with regard to oil degradation potential . 

The significant amount of intraspecific variation to oiling observed in S. patens 

and S . alterniflora in this study has several implications. Some of the reported variation 

in the severity of oiling in brackish and salt marshes may be at least partially explained 

by natural genetic variation in these dominant species . Intraspecific variation to oiling is, 

therefore, one more factor that should be considered when oil spill impacts are predicted 

or assessed in coastal plant communities . Also, the results of this study have shown that 

natural genetic variation in response to oiling can be exploited in the identification of 

superior, oil-tolerant genotypes . Oil-tolerant genotypes of these dominant grasses may be 

transplanted into severely oiled marshes, thereby re-establishing vegetation more rapidly, 

lessening the potential of wetland loss, and possibly accelerating the bioremediation of 

the site. A potential future application of this research may be planting oil-tolerant 

genotypes of S . patens and S. alterniflora in areas that historically have a high incidence 

of oil spills or oil leakage, thereby lessening the severity of impact that may occur in 

other genotypes . 

41 

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47 

QPQS~,EN7 OF Ty~/ The Department of the Interior Mission 

o`' , ,kr yp As the Nation's principal conservation agency, the Department of the Interior has responsibility for 

most of our nationally owned public lands and natural resources. This includes fostering sound use of 

our land and water resources; protecting our fish, wildlife, and biological diversity; preserving the 

_ a9 environmental and cultural values of our national parks and historical places; and providing for the 

4ACH 9 ~9 enjoyment of life through outdoor recreation . The Department assesses our energy and mineral 

resources and works to ensure that their development is in the best interests of all our people by 

encouraging stewardship and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who live in island territories 

under U.S . administration . 

4 ~or The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) primary 

responsibilities are to manage the mineral resources located on the Nation's Outer Continental Shelf 

(OCS), collect revenue from the Federal OCS and onshore Federal and Indian lands, and distribute 

those revenues. 

4nwce~~ Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally sound 

exploration and production of our Nation's offshore natural gas, oil and other mineral resources . The 

MMS Royalty Management Program meets its responsibilities by ensuring the efficient, timely and 

accurate collection and disbursement of revenue from mineral leasing and production due to Indian 

tribes and allottees, States and the U.S . Treasury . 

The MMS strives to fulfill its responsibilities through the general guiding principles of : (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially affected 

parties and (2) carrying out its programs with an emphasis on working to enhance the quality of life for 

all Americans by lending MMS assistance and expertise to economic development and environmental 

protection .