Grantee Research Project Results
Final Report: Constructing Probability Surfaces of Ecological Changes in Coastal Aquatic Systems Through Retrospective Analysis of Phragmites Australis Invasion and Expansion
EPA Grant Number: R832440Title: Constructing Probability Surfaces of Ecological Changes in Coastal Aquatic Systems Through Retrospective Analysis of Phragmites Australis Invasion and Expansion
Investigators: Wardrop, Denice Heller , Whigham, Dennis F. , Patil, G. P. , Taillie, C. , King, Ryan , Easterling, Mary M.
Institution: Pennsylvania State University , Smithsonian Environmental Research Center , Virginia Institute of Marine Science
EPA Project Officer: Packard, Benjamin H
Project Period: July 1, 2005 through June 30, 2007
Project Amount: $299,995
RFA: Exploratory Research: Understanding Ecological Thresholds In Aquatic Systems Through Retrospective Analysis (2004) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Water
Objective:
- Choose an aquatic ecosystem with clearly identifiable alternative states, and define a limited number of variables that are considered to be the driving factors in state changes.
- Establish the database of explanatory and response variables over both a spatial and temporal extent. A retrospective analysis is the most powerful if performed over a truly temporal extent, instead of a space for time experimental design.
- Construct a probability surface of state change over the n-dimensional space of selected explanatory variables.
- Describe thresholds in terms of the probability surface.
- Test the threshold surface with data from a second location.
- Model the potential for state change under three proposed futures scenarios for the system of interest.
Summary/Accomplishments (Outputs/Outcomes):
Conceptual Model Development and Variable Selection
Figure 2. Conceptual model of significant variables in Phragmites invasion.
Documenting Historical Changes in Potential Driving Variables
Availability of Historical Data
Historical Patterns of Potential Driving Variables
Figure 3. Yearly average water level data with standard error bars for Annapolis and Baltimore.
Figure 4. Sesonal water level means by year with standard error bars for Annapolis (top) and Baltimore (bottom).
Figure 5. Annapolis tital and precipitation data used to determine extreme weather events.
Figure 6. Maryland monthly Palmer Drought Severity Index scores, 1930-2007. Negative numbers indicate dry spells; positive numbers indicate
Figure 7. Yearly averages with standard error bars of monthly departures from normal precipitation and
temperature for Annapolis and Baltimore.
Figure 8. Annapolis adn Baltimore departures from normal precipitation in March (a), July (b), and October (c)
and temperature in March (d), July (e), and October (f).
Figure 9. Yearly averages with standard error bars of montly samples of total nitrogen, dissolved phosphorous, total suspended solids and
salinity in the Rhode, South, and Patapsco Rivers.
Figure 10. Comparisons of poorly corre3lated yearly averages and yearly seasosnal averages of total nitrogen and dissolved
oxygen.
Figure 11. Salinity and dissolved oxygen data collected by SERC at a monitoring station on the Rhode River.
Figure 12. Discharge loads of total nitrogen from point sources in each
subestuary. Note differences in y-axis scales.
Figure 13. Discharge loads of total phosphorous from point sources
in each subestuary. Note differences in y-axis scales.
Figure 14. Discharge loads of total suspended solids from
point sources in each subestuary. Note differences in y-scale axis.
Reconstructing Historical Land Cover, Road Networks, and Shoreline Structures
Year | Rhode River | South River | Curtus Bay |
---|---|---|---|
1943 | N/A | Incomplete | Complete |
1952 | Incomplete | Incomplete | Complete |
1957 | Complete | Complete | Complete |
1962/3 | Complete | Complete | Incomplete |
1970 | Complete | Complete | Incomplete |
1977 | Complete | Incomplete | Complete |
1980 | Inomplete | Incomplete | Incomplete |
1984 | Complete | Complete | Complete |
1988 | Incomplete | Incomplete | Incomplete |
1990 | Complete | Complete | Complete |
1995 | Complete | Complete | Complete |
1998 | Complete (digital) | Complete (digital) | Complete (digital) |
2000 | Complete (digital) | Complete (digital) | Complete (digital) |
2002 | Complete (digital) | Complete (digital) | Complete (digital) |
2005 | Complete (digital) | Complete (digital) | Complete (digital) |
Each digital raster image was georeferenced to describe the specific geographic location of the image. Mapping software (ESRI ArcGIS 9.2) was used to complete the georeferencing. A base layer of current aerial imagery was used to compare the location of the scanned aerial image to a layer that already had geographic location information. Control points were added (usually fourone in each general corner region of the scanned image), matching road intersections or buildings. The scanned image was then transformed or warped to match the base image and this warping information was permanently saved with the scanned image. This transformation generally includes some discrepancy in matching to the base layer and so produces a residual error. The root mean square error (RMS error) was calculated to total the error for all of the control points related to the transformation of one image. The RMS error calculated for the images georeferenced for this project ranged from 2.02 to 17.04 with and average RMS error of 7.12.
Figure 15. Population growth in Anne Arundel County.
Figure 16. Land cover in 1943/1952 and 2005 in the three subestuaries of Anne Arundel Co., Md.
Figure 17 a-f. Changes in landcover over time in three subestuaries of Anne Arundel County, MD. Also shown in figures
b, d, and f is the percentages of disturbed vs natural landcover in a 100-m buffer around the wetlands of each.
Figure 18. Change in road density from 1943 to 2005 in three subestuaries of Anne Arundel Co. MD.
Figure 19. Road networks in three subestuaries of Anne Arundel Co. MD. Roads shown in black were presentt in 1943 (1952 for Rhode); roads shown in red
Figure 20. Change in the density of shoreline structures from 1943 to 2005 in three subestuaries of
Anne Arundel Co. MD.
Figure 21. Shoreline structures in three subestuaries of Anne Arunde Co. MD. Structures shown in black were present in 1943 (1952 for Rhode);
Curtis | Rhode | South | ||||
---|---|---|---|---|---|---|
Number in 2005 | Increase since 1952 | Number in 2005 | Increase since 1952 | Number in 2005 | Increase since 1952 | |
Boat house | 13 | 550% | 19 | 217% | 93 | 127% |
Dock | 201 | 319% | 265 | 168% | 1451 | 202% |
Outfall | 0 | 2 | 100% | 21 | 250% | |
Private ramp | 16 | 78% | 12 | 50% | 61 | 144% |
Public ramp | 2 | 0% | 0 | 0 | ||
TOTAL | 232 | 280% | 298 | 161% | 1626 | 195% |
Documenting Temporal Changes in Phragmites
1970s Tidal Marsh Survey
Subestuary Name | Mapped Wetland Area 1975 (m2) | No. Phrag Patches 1975 | Phrag Area 1975 (m2) | Percent Phrag 1975 |
---|---|---|---|---|
Curtis | 108,286 | 2 | 2,633 | 2.4% |
Rhode | 750,664 | 6 | 8,103 | 1.1% |
South | 1,132,174 | 11 | 24,739 | 2.2% |
Total | 1,991,124 | 19 | 35,475 | 1.8% |
SERC Aerial Photo Analysis
Figure 22. Changes in the area of Phragmites from 1970 to 2003 at two sites in the Curtis Creek subestuary
in Anne Arundel Co. MD.
Figure 23. Changes in the area of Phragmites from 1970 to 2003 at seven sites in the Rhode RIver subestuary
in Anne Arundel Co., MD.
Figure 24. Changes in the area of Phragmites from 1970 - 2003 at six sites in the Suth River subestuary
VIMS Shoreline Survey
2007 Field Mapping
Aerial Photo Interpretation
Composite Presence/ Absence Assessment
Development of Spatial Datasets on Potential Driving Factors
Curtis | Rhode | South | All | |||
---|---|---|---|---|---|---|
Wetland Attributes: | ||||||
Number of Wetlands | 43 | 35 | 72 | 150 | ||
Total Wetland Area (m2) | 136,470 | 328,034 | 954,048 | 1,418,552 | ||
Avg. Perim-Area Ratio | 0.157 | 0.089 | 0.090 | 0.109 | ||
Avg. Elevation (m) | 0.333 | 0.344 | .446 | 0.390 | ||
Avg. Soil Organic Matter (%) | 92 | 28.8 | 33.2 | 25.3 | ||
Avg. Dist to Nearest Wetl. (m) | 171.4 | 158.9 | 209.0 | 186.5 | ||
Avg. Wetland Area (m2) | 3,174 | 9,372 | 13,251 | 9,457 | ||
Phrag-related Attributes: | ||||||
Avg. % Phrag 1976 | 0.92 | .14 | 2.57 | 1.53 | ||
Avg. Dist. Phrag 1976 (m) | 1,803 | 1,212 | 1,702 | 1,617 | ||
N. of Wetl. w/Phrag Present 5005 | 34 | 19 | 46 | 99 | ||
% of Wetlands w/Phrag 2005 | 79.1% | 54.3% | 63.9% | 66.0% | ||
Avg. % PHrag, all wetl. 2007 | 26.3 | 10.3 | ||||
Avg. % Pgrag, wetl. w/Phrag 2007 | 36.5 | 20.1 | ||||
2005 Attributes for 100m Buffer around Wetland: | ||||||
Avg. Density SS | 9.7 | 11.1 | 47.5 | 28.2 | ||
Avg. Density Roads | 2,724 | 475 | 2,280 | 1,986 | ||
Avg. % Developed | 25.5 | 8.6 | 31.5 | 24.4 | ||
Avg. % Ag | 7.1 | 9.7 | 1.6 | 5.1 | ||
Avg. % Forest | 32.3 | 41.0 | 27.4 | 32.0 | ||
Avg. % Wetl | 1.1 | 0.9 | 7.3 | 4.0 | ||
Avg. % Water | 34.3 | 39.8 | 32.2 | 34.6 | ||
Other 2005 Attributes: | ||||||
Avg. Dist to Nearest Shoreline Structure 2005 | 257.7 | 508.2 | 89.6 | 235.5 | ||
2005 Watershed Attributes: | ||||||
% High Intensity Developed | 24.2 | 2.9 | 6.2 | |||
% Low Intensity Developed | 29.2 | 16.6 | 34.9 | |||
% High Intensity Ag | 0.0 | 4.3 | 0.7 | |||
% Low Intensity Ag | 9.5 | 537 | 3.0 | |||
% Forested | 25.5 | 35.1 | 22.0 | |||
% Wetland | 0.1 | 1.5 | 1.0 | |||
% Water | 11.9 | 33.8 | 32.2 |
Table 7. Characteristics of wetlands in the Curtis, Rhode, and South subestuaries. (Includes only wetlands used in the statistical analyses, as described below).
Assumptions and Definitions
Subsestuary Name | No. of Wetlands | Wetland Area (m2) |
---|---|---|
Curtis | 48 | 163,800 |
Rhode | 35 | 736,500 |
South | 85 | 1,098,900 |
Spatial Datasets of Potential Driving Variables
2.5 Statistical Analyses
- It does not provide any idea about our certainty/uncertainty about the estimated probability surface. We would therefore not know about whether we were more confident about p(s) for one combination of predictors versus another.
- It does not reflect the uncertainty about model parameter estimates. We pretend we know these parameters, fixing them at their estimates, but what if our estimates are quite variable (uncertain)? This should be reflected as uncertainty about our corresponding estimates of our probability surface.
- We completely ignore the fact that spatial dependence may have a major role to play (i.e., perhaps Phragmites in one region is more likely to spread to nearby regions.
- In addition, there may be many other factors that influence Phragmites that we have not been able to observe or quantify, and these factors may be spatially similar. Hence, neighboring regions would have roughly the same value for these unobserved variables, influencing the presence of Phragmites.
Variable screening
- Subestuary membership (as reflected by the X and Y centroids) is highly correlated with many variables e.g., the perimeter-area ratio and buffer land cover characteristics, suggesting that subestuaries differ in these respects.
- Dist_phrag76 and pct_phrag76 are slightly significantly negatively correlated
- Road density and shoreline structure density are significantly positively correlated
- There was a high correlation between variables in a time series e.g., the percentage of residential land in 2002 and 2005
Non-Spatial Regression Analyses
Our goal was to develop a predictive model of Phragmites presence-absence using data from Rhode and Curtis subestuaries, and test it using data from the South River subestuary. MiniTab Ver. 15 software was used for statistical analyses. Logistic regression analyses were conducted using Pragmites presence-absence values derived from the percent Phragmites data, and least squares regression analyses were conducted using percent Phragmites data. Based on p values.
Table 9. For each variable in regression equation, summary of P values (p>Chi Square). r2 values for final model are also represented; all p values for final model were <=0.001. [Key to headings: All dta = all wetlands considered; Rhode-Curtis dta = only data from Rhode & Curtis wetlands; log regr = logistic regression analysis with Phragmites presence-absence as response variable.
Variables | Rhode-Curtis Data: stepwise log regr | Rhode-Curtis data; log regr | Rhode-Curtis data; least sq regr | Rhose-Curtis data stepwise regr | All data; stepwise log regr | All data; nominal log regr | Sign of Regression Coefficient | |
---|---|---|---|---|---|---|---|---|
Area | .084 | .190 | .148 | .492 | .060 | .058 | (-) | * |
P-A ratio | .127 | .073 | .130 | .330 | .151 | .142 | (-) | |
Elevation | .003 | .001 | .001 | .001 | .009 | .019 | (-) | * |
OM | .361 | .295 | .975 | .888 | .107 | .084 | (-) | |
Dist_phrag76 | .065 | .094 | .057 | .042 | .001 | .001 | (+) | * |
Dist_SS | (+) | * | ||||||
BF_SS | .887 | .617 | .428 | .602 | .011 | .009 | (+) | |
BF_roads | .703 | .550 | .093 | .140 | .439 | .443 | (-) | |
BF_wetl | .091 | .096 | .647 | .706 | .072 | .062 | (+) | |
Final model r2 | .377 | .39 | .305 | .266 | .262 | .265 |
*variables used for reduced model
Actual vs Predicted for South River with 50% cutoff, 4-variable model, and Phrag05_P-A as dependent variable | |||
---|---|---|---|
Actual | |||
Predicted | No | Yes | Grand Total |
No | 3 | 1 | 4 |
Yes | 23 | 45 | 68 |
Grand Total | 26 | 46 | 72 |
Actual vs Predicted for South River with 30% cutoff, 4-variable model, and Phrag05_P-A as dependent variable | |||
Actual | |||
Predicted | No | Yes | Grand Total |
No | 9 | 5 | 14 |
Yes | 17 | 41 | 85 |
Grand Total | 26 | 46 | 72 |
Actual vs Predicted for South River with 50% cutoff, 4-varable model, and Phrag07_P-A (percent phrag conveted to presence-absence) as dependent variable | |||
Actual | |||
Predicted | No | Yes | Grand Total |
No | 6 | 2 | 8 |
Yes | 20 | 44 | 64 |
Grand Total | 26 | 46 | 72 |
Actual vs predicted for South River with 50% cutoff and 9 variable model | |||
Actual | |||
Predicted | No | Yes | Grand Total |
No | 11 | 7 | 18 |
Yes | 15 | 39 | 54 |
Grand Total | 26 | 43 | 72 |
Construction of Spatial Model
Depiction of Thresholds
Population Studies in Support of Invasion Model: Seed Viability and Microsatellite Analysis
- Does seed viability vary among Phragmites patches?
- Is patch level seed viability related to the number of genotypes within a patch?
- Does the number of genotypes in a patch differ among forested, mix-developed, and developed watersheds?
- Is patch level seed viability related to the size of a patch?
- Do the number of genotypes within a patch relate to the size of the patch?
- Are spread within patches and establishment of new patches products of seed establishment or rhizome expansion?
- Does the mode of spread differ with degree of development in a watershed?
- Are patches that are close together more closely related than patches farther apart?
- Are patches within subestuaries more closely related to each other than to patches in other subestuaries?
primer pair | annealing temperature (0C) | flurophor | DNA dilution |
---|---|---|---|
PaGT4 | 50 | FAM | 1:100 |
PaGT9 | 50 | HEX | 1:50 |
PaGT12 | 56 | FAM | 1:100 |
PaGT13 | 50 | HEX | 1:50 |
PaGT14 | 58 | FAM | 1:100 |
PaGT16 | 56 | ned | 1:10 |
PaGT21 | 58 | hex | 1:50 |
PaGT22 | 50 | ned | 1:10 |
Results
Relationships between seed viability, number of genotypes within a patch, patch size, and watershed development
Sub-estuary | Category | %forest | DEV | FIT | FIS | FST | FIT | FIS | FST |
---|---|---|---|---|---|---|---|---|---|
BC | forested | 75.64 | 4.41 | 0.2191 ± 0.0959 | -0.4393 ± 0.1242 | 0.045 ± 0.0428 | |||
PC | forested | 80.13 | 66.53 | 0.3809 ± 0.1388 | 0.0367 ± 0.1242 | 0.3516 ± 0.0902 | 0.403 ± 0.1368 | 0.1348 ± 0.0.14 | 0.3063 ± 0.0967 |
SMR | forested | 66.15 | 56.21 | 0.2983 ± 0.1342 | -0.1219 ± 0.0775 | 0.3704 ± 0.0909 | 0.3088 ± 0.1313 | 0.0689 ± 0.0873 | 0.3496 ± 0.0915 |
MC | mixed | 72.20 | 56.85 | 0.2479 ± 0.1202 | -0.1262 ± 0.0886 | 0.3283 ± 0.0666 | 0.266 ± 0.1234 | -0.0542 ± 0.1025 | 0.2993 ± 0.0627 |
SOR | mixed | 60.48 | 11.32 | 0.3621 ± 0.1605 | 0.1459 ± 0.1593 | 0.2441 ± 0.0751 | 0.3732 ± 0.1596 | 0.2343 ± 0.158 | 0.1732 ± 0.0706 |
SVR | mixed | 53.62 | 6.28 | 0.5301 ± 0.1266 | 0.2154 ± 0.1972 | 0.4028 ± 0.0854 | 0.5206 ± 0.1201 | 0.2931 ± 0.1872 | 0.3278 ± 0.081 |
BR | developed | 18.17 | 6.81 | 0.3352 ± 0.0834 | 0.0382 ± 0.0842 | 0.3068 ± 0.0357 | 0.3305 ± 0.0691 | 0.1171 ± 0.808 | 0.2414 ± 0.0301 |
CB | developed | 21.83 | 14.95 | 0.4496 ± 0.1458 | 0.2067 ± 0.1478 | 0.2959 ± 0.0782 | 0.457 ± 0.146 | 0.2049 ± 0.1485 | 0.3065 ± 0.0802 |
ER | developed | 19.64 | 25.44 | 0.3668 ± 0.0871 | 0.1388 ± 0.0914 | 0.2627 ± 0.0393 | 0.3666 ± 0.0871 | 0.1388 ± 0.0914 | 0.2627 ± 0.0393 |
All 9 | 0.4309 ± 0.0777 | 0.3923 ± 0.08666 | 0.0645 ± 0.131 | 0.4384 ± 0.0766 | 0.4039 ± 0.0838 | 0.0585 ± 0.0111 |
PaGT4 | PaGT9 | PaGT12 | PaGT13 | PaGT14 | PaGT16 | PaGT21 | PaGT22 | mean | SE | mean | SE | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subset | Dev. | A | G | A | G | A | G | A | G | A | G | A | G | A | G | A | G | A | A | G | G |
BC | D | 3 | 3 | 4 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 4 | 5 | 5 | 4 | 6 | 3.75 | 0.25 | 4 | 0.378 |
PC | F | 2 | 3 | 6 | 8 | 6 | 10 | 10 | 21 | 6 | 7 | 5 | 5 | 7 | 14 | 5 | 7 | 0.875 | 0.798 | 9.375 | 2.026 |
SMR | F | 3 | 4 | 5 | 6 | 10 | 14 | 9 | 29 | 5 | 9 | 3 | 4 | 6 | 11 | 4 | 7 | 5.625 | 0.925 | 10.5 | 2.909 |
MC | M | 2 | 3 | 5 | 7 | 9 | 16 | 8 | 18 | 7 | 10 | 3 | 4 | 6 | 10 | 5 | 7 | 5.625 | 0.844 | 9.375 | 1.889 |
SOR | M | 6 | 6 | 6 | 8 | 5 | 5 | 4 | 6 | 6 | 7 | 3 | 7 | 8 | 17 | 4 | 7 | 5.25 | 0.559 | 7.875 | 1.342 |
SVR | M | 5 | 6 | 6 | 9 | 4 | 8 | 4 | 6 | 6 | 14 | 4 | 7 | 8 | 14 | 6 | 7 | 5.5 | 0.463 | 8.875 | 1.172 |
BR | D | 2 | 2 | 4 | 5 | 4 | 6 | 4 | 7 | 7 | 15 | 3 | 5 | 7 | 15 | 4 | 9 | 4.375 | 0.625 | 8 | 1.68 |
CB | D | 2 | 3 | 8 | 9 | 11 | 13 | 12 | 25 | 5 | 12 | 5 | 7 | 6 | 10 | 4 | 9 | 6.625 | 1.224 | 11 | 2.276 |
ER | D | 2 | 3 | 5 | 12 | 8 | 2 | 10 | 21 | 4 | 7 | 41 | 8 | 7 | 16 | 7 | 12 | 5.875 | 0.915 | 12.38 | 2.224 |
ALL | 6 | 7 | 9 | 22 | 12 | 49 | 12 | 76 | 9 | 27 | 7 | 19 | 9 | 42 | 8 | 18 | 9 | 0.756 | 32.5 | 7.835 |
Conclusions:
- Is an upland stand present?
- Will activity disperse rhizomes to the site?
- Will activity bury rhizomes?
- Are burial sites well-drained at time of burial?
- Is early salinity at burial sites consistently below 18%?
- Will activity lower burial site salinity?
- Are clones spreading into low salinity/sulfide areas?
- Will activities lower salinity/sulfides?
- The almost exponential increase in the number of shoreline structures. Shoreline structures increase the number of dispersal opportunities, through earth moving and distribution of seed and rhizome fragments. Structures also increase the number of both small and large disturbed areas available for colonization. In this manner, a critical gravity measure may be the true threshold. Gravity was expressed as Forman and Godron (1986) as a function of the amount of material to be communicated (in this case, the genetic information embodied in different genotypes of seed or rhizome) and the distance between the patches. This model of gravity assumes that the rate of movements, or flows, between elements depends primarily on linkage distance, and secondarily on the node size. Development in a watershed can increase the number of patches, the distance between them, and their size. There would, therefore, be multiple ways to reach the critical gravity measure, by altering any or all of these. Wetlands surrounded by more development may experiences more within-wetland disturbance which, in turn, would provide a greater number of safe sites for seedling establishment. If seedling establishment is higher in wetlands within developed watersheds, the higher genetic diversity could result in a high incidence of cross pollination among flowering plants which result in the production of more viable seeds and ultimately increased establishment of seedlings in habitats that experience a higher rate of disturbance.
- Increase in road density. A higher density of road networks can provide enhanced dispersal of seed, creation of habitat via either direct disturbance and/or sedimentation impacts, or habitat suitability through increased nutrients and changes in drainage patterns.
- Seed dispersal is the dominant form of spread of Phragmites in the subestuaries incuded in the study.
- Thresholds occur temporally at the subestuary scale and appear to be linked to the accumulation of genetic variation and the ability of patches to communicate. The system then flips to a nested alternative invasion cycle, whereby colonization success is greatly increased by the production of viable seed and the availability of new habitat.
- Development in the watershed, at the subestuary scale, serves to accelerate the accumulation of genetic variation and the availability of habitat. Through a variety of finer scale processes, it moves the invasion process toward the nested alternative cycle.
- Shoreline structure development appears to initiate movement toward this nested alternative invasion cycle, and should therefore be assessed further. Potential alterations to the number of shoreline structures allowed, their density, size of disturbed area, and vegetative controls after structure establishment may all be potential management controls.
- The level of development in the subestuaries studied appears to be already sufficient for establishment of Phragmites, and is not a continuing factor in controlling invasion.
- The Battle Creek subestuary provides an important opportunity for control strategies in watersheds that are currently primarily forested. The few patches of Phragmites that were growing in Battle Creek had very few genotypes per patch. This is likely an example of a watershed where management now, while most patches are monoclonal and producing few viable seeds, would likely be very effective at limiting establishment of new patches.
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Supplemental Keywords:
Estuary, Mid-Atlantic, coastal marshes, CART, ecosystem condition, Ecosystem Protection/Environmental Exposure & Risk, Scientific Discipline, Aquatic Ecosystems & Estuarine Research, Ecological Risk Assessment, Aquatic Ecosystem, Ecology and Ecosystems, Environmental Monitoring, index of environmental stress, community structure, ecosystem indicators, computer models, probabilty surface, diagnostic indicators, aquatic indicators, aquaculture, coastal ecosystem, ecosystem response, modeling ecosystems, RFA, Scientific Discipline, ECOSYSTEMS, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, Aquatic Ecosystem, Aquatic Ecosystems, Environmental Monitoring, Ecological Risk Assessment, Ecology and Ecosystems, coastal ecosystem, aquaculture, probabilty surface, computer models, diagnostic indicators, ecosystem indicators, index of environmental stress, modeling ecosystem change, aquatic indicators, community structure, ecosystem responseProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.