Grantee Research Project Results

The identification of thresholds assumes two steps: identification of primary or explanatory variables controlling the transition between alternative stable states, and the identification of the band of conditions for which there is a high probability of a state change (i.e., thresholds). This project developed a conceptual model of Phragmites invasion and spread (identification of variables), and then constructed a probability surface that relates the set of explanatory variables to the shift in stable states (conditions for which the probability of state change is high). Any set of conditions (i.e., explanatory variables) can then be placed upon the probability surface, illuminating the proximity of threshold conditions and how “close” any given marsh is to a transition to an alternative state. The method could be applied to a wide variety of aquatic ecosystems for which state changes occur over either a spatial and temporal extent, or both. This effort is consistent with Executive Order 13112 "Invasive Species," issued by the White House, that directs all federal agencies to, among other directives, prevent the introduction of invasive species, detect and respond rapidly to and control populations of such species, provide for restoration of native species and habitat conditions in ecosystems that have been invaded, and conduct research on invasive species. Our original objectives for the entire project were as follows:

Our study focuses on three subestuaries in Anne Arundel County, Maryland (Figure 1a). This county was chosen as a focal point for several reasons: (1) prior studies as well as casual observation have noted rapid expansion of Phragmites over a relatively short period of time, (2) the county has experienced rapid urbanization due to its proximity to the Baltimore-Washington corridor, (3) the Anne Arundel County government has amassed a large collection of aerial photography and GIS databases that provided a wealth of data for this investigation, and (4) members of our research team from the Smithsonian Environmental Research Center (SERC) work and reside in the county, and thus have personal familiarity with the area. The three subestuaries chosen for study were Curtis Creek, South River, and Rhode River (Figure 1b). All three support tidal brackish wetlands. The Curtis Creek tidal system, located near Baltimore, and is an area with extensive urban/ suburban development. The South River tidal system, located near Annapolis, has undergone more recent urban/ suburban development. The Rhode River system, which lies to the south of the South River, contains the SERC property; parts of this subestuary have remained undisturbed while others have experienced development pressure.

 

 

Figure 1.  (a) Location of Anne Arundel County, Maryland, and (b) location of the three study subestuaries within the County.

This study was conducted as a series of distinct, though related, lines of investigation, all aimed at eliciting the characteristics and driving forces behind Phragmites invasion and expansion in the study area. We began with refining our conceptual model of Phragmites dispersal and spread. To this end, we conducted a thorough literature review and developed a list of potential factors that drive Phragmites invasion, or serve as surrogates for those factors. We made a subjective assessment of the relative importance of these factors, and investigated the availability of data to represent them.

 

We evaluated and compiled data sets of potential driving variables from a variety of sources. Some of the data sets represented temporal changes, while others focused on spatial differences. We analyzed aerial photographs to document changes in the abundance and spatial extent of Phragmites over time in the study area, and to characterize concurrent changes in development patterns, both for the overall subestuary, and for the area immediately surrounding potential Phragmites habitat (i.e., wetlands). Our initial goal was to build a data set of Phragmites and potential driving factors that would reflect both changes over time as well as variability over space, and which could then be used to construct a multivariate probability surface of Phragmites occurrence. Our initial proposal called for the analysis of aerial photographs from five time periods; we discovered additional periods of aerial photography, and reconstructed land cover and shoreline variables for fifteen time periods, covering the period of 1940 through 2005. However, as the project progressed we discovered limitations in our ability to reconstruct an equivalent, reliable time series of Phragmites invasion. This required a slight mid-course correction, and instead of looking at changes over time we used space as a surrogate for time, and focused on spatial variability within and among the three sub-estuaries. We present the temporal data that were collected and use these to help interpret our spatial results.

 

Spatial and temporal data on potential driving factors were used to develop and test a predictive model for Phragmites invasion and spread. A logistic regression analyses was utilized to construct a preliminary model of both presence/ absence, as well as percent cover. We also utilized this process for the screening of variables before spatial dependencies were added. Probability surfaces for combinations of driving variables were constructed to illustrate potential thresholds. We also outline plans for building a full spatial model

 

As the project progressed, and our model of Phragmites invasion and spread evolved, we added two experimental lines of inquiry to cover population-level variables:  (1) seed viability and (2) the abundance of native vs. non-native haplotypes. More specifically, we asked whether seed viability and seed dormancy differ between patches of Phragmites in subestuarine wetlands in developed vs. forested watersheds. We also performed an extensive microsatellite analysis of all Phragmites patches in Rhode River subestuary, plus randomly selected sites in a number of other subestuaries representing a range of developed vs. forested land covers.

An extensive literature review on the factors driving the invasion of Phragmites was conducted in the spring of 2006. Paper topics included experimental studies, landscape-scale studies, nitrogen and ecosystem functioning studies, genetic studies, and European studies. From this, a conceptual model of Phragmites invasion was developed based on the theory of adaptive cycles of ecosystem change, nested among ecosystem scales (Holling and Gunderson 2002; Holling et al. 2002). Variables that could influence Phragmites invasions at each ecosystem scale (micro, local, regional and global) were identified as well as feedback mechanisms that could help sustain invasion (Figure 2).

 

Figure 2. Conceptual model of significant variables in Phragmites invasion. 

 

The conceptual model was used to determine a list of variables which were potentially important in driving Phragmites invasion (Tables 1 & 2). These variables were classified as either slow or fast processes. Slow processes act either over a large area or a period of years, accumulating potential for an alternative ecosystem. Fast processes act either over a small area or within a short time frame to precipitate a reorganization of the plant community. Variables were prioritized in order of importance to invasion based on the literature review.

 

The most important slow variables chosen were flooding, soil type, and genetic make-up of the populations (see Table 1). Phragmites requires more aerated areas to establish (Burdick and Konisky 2003, Warren et al. 2001 and 2002, Bart and Hartman 2002, Chambers et al. 1998) and draw-downs in water level or mineral soils which are more oxidized (Pyke and Havens 1999) may increase Phragmites. The non-native haplotype has been shown to be more salt tolerant and aggressive compared to the native (Saltonstall 2002, Vasquez et al. 2005) and also require higher nitrogen (Packett and Chambers 2006). However only the invasive haplotype was found in a recent study of brackish marshes of Virginia (Packett and Chambers 2006), and it is likely only this haplotype is found in our three subestuaries as well. Research conducted by SERC in 2007 (described in Section 2.6 of this report) found only the invasive haplotype in these areas. Additionally, SERC found seed viability differed among Phragmites populations and was positively correlated to population genetic diversity.

 

Developed land cover and nutrient status (Bertness et al. 2002, Minchinton and Bertness 2003) were also hypothesized to impact Phragmites invasion. Changes in nutrient status may affect Phragmites invasion in the following year due to lag time (Minchinton and Bertness 2003). Development increases both disturbance and nutrients in the watershed. Phragmites is an efficient colonizer of bare areas (Vasquez et al. 2005, Haslam 1965, 1972, Chambers et al. 1999) and wrack (Minchinton 2002a, Marks et al. 1994), sedimentation (Bart and Hartman 2002, Phillips 1987), or construction (Burdick and Konisky 2003) will create open spaces which may result in Phragmites expansion. Phragmites has also been noted to invade without an obvious disturbance (Chambers et al. 1999, Chambers et al. 2003, Rice et al. 2000, Warren et al. 2001, Hudon et al. 2005, Lathrop et al. 2003, Meyerson et al. 2000b). However, in field experiments conducted by SERC in 2007 (see Section 2.6), Phragmites seed germination increased significantly with above ground disturbance within three plant communities. Additionally, SERC found that higher genetic diversity, which was related to higher seed viability, was positively correlated to the degree of development in the watershed.

 

Changes in climate and sea level may also affect Phragmites invasion. Drier, warmer years add rhizome storage in the current year and expand populations in the following year (Hudon et al. 2005). Metonic cycles, long-term astrologically driven cyclic changes in tidal flooding (18.6 year cycle), may affect establishment (Chambers et al. 2003). El Niño years with increased precipitation, decreased salinity, and increased temperatures may positively impact Phragmites invasion (Minchinton 2002b).

 

The most important fast variables chosen were distance to the nearest Phragmites population, sedimentation, point disturbances, and storms (see Table 2). Short distance to nearby Phragmites populations will increase propagule sources and also change marsh conditions through feedbacks. Phragmites can increase soil oxidation through clonal integration with parent rhizomes (Amsberry et al. 2000, Bart and Hartman 2000), Venturi-enhanced convective throughflow of gases via dead culms (Bart and Hartman 2000, Windham and Lathrop 1999), greater transpiration (Windham 2001), and sediment accumulation which raises elevation and decreases water logging (Hudon et al. 2005, Rice et al. 2000). Phragmites can also decrease salinity through shading, which reduces soil evaporation and decreases salinity, and through the incorporation of salts into belowground tissues (Windham and Lathrop 1999). However, the non-native haplotype of Phragmites has shown to be tolerant of high salinities (Packett and Chambers 2006) and other studies have discounted salinity as an important limiting factor (Chambers et al. 1998, Chambers et al. 1999, Warren et al. 2002). Major storms, such as Hurricane Agnes in 1972, will cause decreases in existing vegetation stands and bring in propagules (Rice et al. 2000). Storms also allow for dispersal of rhizomes and burial (Bart and Hartman 2002) and dispersal with disturbance (Lathrop et al. 2003).

Once variables were chosen, data sources were investigated (Table 3). Ideally, we wanted data that would reflect both changes over time, as well as differences over space, within and among the three subestuaries. Because our initial goal was a temporal reconstruction of Phragmites invasion and changes in related variables, we focused first on collecting historical data.

Since our initial conceptual model included the hypothesis that high nitrogen levels were an important contributing factor to Phragmites invasion and spread, we invested considerable time and effort in determining sources of water quality data. High quality, complete water quality data were available after 1984 from the Chesapeake Bay Program, but with only one sampling location representing each subestuary. Water quality data prior to this time period were very spotty both spatially and temporally and sometimes plagued by inconsistent methods. These early data were collected from the EPA Legacy Data Center (https://www.epa.gov/storpubl/legacy/gateway.htm) and the Chesapeake Bay Program (http://www.chesapeakebay.net/data_cbiwaterquality.aspx). The USGS National Water Information System (http://waterdata.usgs.gov/nwis) had some limited data on water quality upstream of our marshes, which was not used. Data on salinity, dissolved oxygen, temperature and pH had been collected at a monitoring station on the Rhode River at SERC from 1970 – 1978 and 1980 - 2000 (data originators: R. Cory, J.M. Redding, M.M. McCullough, P. Dresler, C. Gallegos, J. Duls, and N. Kobayashi, SERC).

 

Data sources for some variables were either unavailable or incomplete. For example, water level data were available from NOAA at two locations, Annapolis and Baltimore, but converting these data to the detailed spatial scale of the marsh was not feasible. Data collection for some other variables was determined to be too time intensive. Data on nitrogen loads from septic systems were not readily available. The nitrogen-loading rate per person could be estimated (Cole et al. 2006; Elliott and Brush 2006) but a count of the number of homes in each watershed would be needed at each photo time step. Likewise, a count of impervious surfaces within each watershed was deemed too time intensive. The Chesapeake Bay Program had estimated the nitrogen and sediment reduced by best management practices in parts of the Bay, but this variable was determined to be of low importance and not further investigated.

 

Below we present information on historical patterns in potential driving variables. Even though we were unable to incorporate all of this information into a formal statistical analysis, it does provide insights into possible causes of the rapid expansion of Phragmites over time.

 

Monthly averages of water level were downloaded for both Annapolis (COOP ID: 8575512) and Baltimore (8574680) from the NOAA COOPS website (Table 3). Data were downloaded in meters above MSL datum. Yearly averages were calculated for mean sea level (MSL) and mean range (difference between mean high water (MHW) and mean low water (MLW)). The mean range is used to look at metonic cycles (lunar nodal cycles). Data from 1970-2005 was used to investigate seasonal yearly means of MSL. Spring included the months of March, April, and May. Summer included June, July and August. Fall included September, October and November. Means were created using SAS and graphed in Excel. The highest tide of the month in Annapolis was also graphed to investigate these data as a surrogate measure for storm events.

 

Sea level rise is evident at both sites over the years and the lunar nodal cycles are also prominent (Figure 3). Lunar nodal cycles can change tidal amplitudes 5 – 10 cm, and the difference persists throughout the year. The last lunar nodal low was in 1988, and before that around 1969. These lunar nodal lows have been suggested to provide an advantage to Phragmites, especially at sites which are at the high water location (Chambers et al. 2003). Area occupied by Phragmites exploded in the 1970s and 1980s and increased in linear fashion from that point (see Section 2.2c). Only mean sea level rise corresponds to this linear increase, although more complicated relationships with metonic cycles, or other variables, may exist. Hydrologic conditions vary by season, and seasonal variability, particularly drier conditions in the spring, may also have a great influence on Phragmites invasion (Figure 5). Storms are also hypothesized to affect invasion, and the highest tides of the month in Annapolis did corresponded to extreme weather events (Figure 5a). We estimate that the top 5% of highest monthly tides likely correspond to extreme weather events.

 

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. 

 

 

 

Monthly data of the Palmer Drought Severity Index for our region were downloaded from the NCDC Climate Data Online website (see Table 3). This index takes into account moisture supply and demand, and ranges from -6 to +6, with negative numbers indicating dry spells. Values between -3.0 and -4.0 indicate severe drought and values less than -4.0 indicate extreme drought (Figure 6).

 

Figure 6. Maryland monthly Palmer Drought Severity Index scores, 1930-2007. Negative numbers indicate dry spells; positive numbers indicate

wet spells. Values between -3.0 and -4.0 indicate severe drought and values less than -4.0 indicates extremem drought. 

 

Monthly climate data for Annapolis and Baltimore were downloaded from the NCDC Climate Data Online website (Table 3). Monthly parameters of mean temperature, total precipitation, and departures from normal monthly precipitation and temperature were examined. SAS was used to calculate the yearly average of monthly departures from normal precipitation and temperature, which were then plotted with Excel (Figure 7).

 

Figure 7. Yearly averages with standard error bars of monthly departures from normal precipitation and 
temperature for Annapolis and Baltimore. 

 

Seasonal variability across years in monthly total precipitation, mean temperature, and departures from normal precipitation and temperature was investigated using a representative month from each season (Figure 8). Means of monthly total precipitation and temperatures by the three months of each season resulted in very large standard errors due to the variability between months; therefore, these values were not examined. March was used to represent spring, July for summer, and October for fall. Seedling and rhizome emergence should occur in March. Warm temperatures could increase shoot elongation and lower precipitation may reduce flooding. Rhizome storage for the following year should occur in October, and less stressful conditions may increase rhizome size.

 

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). 

 

Total monthly precipitation in Annapolis was also used to test the correspondence of high monthly precipitation with extreme weather events (Figure 5b). We estimate that the top 5% of monthly total precipitation likely corresponds to extreme weather events. In addition to hurricane records, extreme weather events recorded in Anne Arundel County, MD were downloaded from the NCDC Storm Events Database (http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwevent~storms) and compared to total monthly precipitation high points. Data on extreme weather events were available from 1970 – current.

 

 

Data were downloaded from the CBP (see Table 3) for the Rhode River (station: WT8.2), South River (WT8.1), and Patapsco (WT5.1). There were no available data specifically for Curtis Creek; therefore data from a point nearby in the Patapsco River were used. These data were collected from cruises conducted 1 – 2 times a month. For all parameters, only the first surface samples per time step, from a depth of either 0.3 or 0.5m, were used. The few samples marked with data quality problems were deleted. Total nitrogen samples from the field were processed according to three different procedures, but all with essentially the same constituents; therefore these methods were treated as interchangeable. Concentration of total nitrogen refer to the addition of the concentrations of total Kjeldahl nitrogen wet (unfiltered) and the concentration of nitrate + nitrite filtered. If values were below the detection limit, they were set to the minimum detection limit by the Chesapeake Bay Program. Total dissolved phosphorus was calculated using the same method over the entire time period, unlike total phosphorus which varied; therefore, total dissolved phosphorus was used in the analysis. Total dissolved phosphorus refers to the measure of total phosphorus without particulate inorganic phosphorus. Measurements of total suspended solids were obtained by filtering the water sample, drying and weighing it. Dissolved oxygen and salinity samples were both taken in the field, dissolved oxygen with an in-situ membrane electrode. SAS was used to compute statistics and Excel to plot data.

 

Yearly averages were calculated for total nitrogen, total dissolved phosphorus, total suspended solids, salinity, (Figure 9) and dissolved oxygen (Figure 10). Total nitrogen appears higher in the Patapsco than in other rivers. Total nitrogen shows no upward or downward trend, but spikes are apparent. Salinity also has high and low peaks across the years. Total dissolved phosphorus decreased over the time step. Total suspended solids peaked in the late 1980’s and early 1990’s. Dissolved oxygen was roughly constant throughout this period. No parameters follow an upward trend similar to the spread of Phragmites. A spike in nutrient levels is not apparent, which suggests the increase in nutrient enrichment in the Bay occurred before this time period, possibly as far back as the late 1904’s. Widespread fertilizer used began in 1945 (http://www.chesapeakebay.net/bayhistory.aspx?menuitem=14591), and pollution has long been a noted problem. In Curtis Bay there were reports of fish kills in the 1930s and 40s, and in the South River complaints of boat wastes in the 1940s (Davidson et al. 1997).

 

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. 

 

Seasonal yearly averages were also calculated for CBP water quality parameters. The spring season included the average of March, April, and May; summer included June, July, August, and fall included September, October, and November. Correlations between seasonal and yearly means in each river were conducted using SAS. Seasonal means of each parameter were almost always correlated with yearly means (P<0.01). Exceptions included spring total nitrogen in the Rhode (r = 0.24, P = 0.30) and Patapsco Rivers (r = 0.49, P = 0.028), and dissolved oxygen in the spring in the Rhode (r = 0.35, P = 0.12) and Patapsco (r = 0.31, P = 0.17) Rivers and in the fall in the South (r = 0.35, P = 0.11) River (Figure 9). Total nitrogen was lower in spring averages than yearly averages in the Rhode River in the late 80’s, but higher in the Patapsco in the 80’s, ranging from roughly 2 – 2.5 mg/L versus 1.5 – 2 mg/L. In 1997 in the Patapsco average spring total nitrogen was over 3 mg/L and less than 1.5mg/L in the yearly average.

 

Daily average and maximum salinity and average and minimum dissolved oxygen data from the monitoring station on the Rhode River at SERC were averaged by year, and season (Figure 11).

 

Figure 11. Salinity and dissolved oxygen data collected by SERC at a monitoring station on the Rhode River. 

 

Maximum salinity and minimum dissolved oxygen data were available from SERC as early as 1970, but average daily data were not available until 1980. Salinity data from 1980-1990 were calculated from conductivity data. Salinity has followed a slight upward trend in the Rhode River since 1970, possibly following mean sea level rise. Seasonal yearly means showed low and high points. Salinity was typically higher in the fall, likely from reduced freshwater flows. Dissolved oxygen has followed a downward trend, the opposite of Phragmites. Dissolved oxygen was typically lower in the summer, due to higher temperatures.

 

 

Two sources of point source data were investigated: the Chesapeake Bay Program and the EPA Envirofacts, Permit Compliance System and Facility Registry System. The EPA data listed all the major and minor NPDES permit holders in our area, but contained no data on historical discharges. The CBP dataset was compiled from Maryland Department of the Environment (MDE) data. These data had much greater detail on the historical discharges of nutrients, but did not include all discharge sources. This dataset only included major facilities if the flow was greater than or equal to 0.5 MGD (million gallons/day) or they discharged an industrial equivalent of 75 lbs TN/day of 25 lbs TP/day. Minor facilities were included if they discharged between 0.001 and 0.5 MGD. Industrial facilities were included if they discharged waters with concentrations of TP of 6 mg/L and TN of 18 mg/L. Drinking water treatment plants and power plants were not included since they were determined not to be a large source of nutrients. The MDE Chesapeake Bay and Watershed Management Administration (now Technical and Regulatory Services Administration, TARSA) checked the database against Discharge Monitoring Reports from companies (monthly summary reports). These reports were checked with facility Monthly Operating Reports (monthly summaries and daily data). If data were still missing, permit applications were used to estimate parameter concentrations not in the DMR. This resulted in a database of monthly summary of several parameters, including TN, TP, and TSS loads discharged from the pipe.

 

From the CBP website (see Table 3), data from point sources connected to our subestuaries were downloaded. For Curtis Bay, point sources located in the Bay or nearby in the Patapsco River, upstream or downstream of Curtis Bay were used. The monthly average loads of total nitrogen (Figure 12), total phosphorus (Figure 13) and total suspended solids (Figure 14) from each facility were graphed by subestuary. Loads were converted from lbs/day into kg/day using conversion lb = 0.45359237 kg. Discharges of TN, TP, and TSS were much higher for Curtis Bay than the Rhode or South Rivers. Curtis Bay had some facilities with very high flows. For example, Sparrows Pt. is putting out between 17-54 MGD. In comparison the Mayo Treatment Plant on the Rhode River is putting out 0.2-0.4 MGD. Regardless, Curtis Bay is receiving huge inputs of nutrients and sediment from industrial inputs.

 

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. 

 

According to the CBP, the total nitrogen load delivered to the Bay by point sources has decreased by 14% from 1985 to 1996 and was expected to decrease by 27% from 1985 to 2000. Total phosphorous load has decreased 50% from 1985 to 1996 and was expected to decrease by 55% from 1985 to 2000. Point source loads accounted for 24% of the total N load and 26% of the total P load delivered to the Bay in 1996. Additionally, water quality sampling indicates that total dissolved phosphorus in our estuaries has been decreasing since 1984. Only dissolved oxygen has shown a slightly downward trend during this time period, indicating worsening water quality. These decreases have occurred in the period of time Phragmites has exploded. This suggests that nutrient enrichment may have had little effect on Phragmites invasion. Additionally, research in 2007 by SERC (see Section 2.6) showed no effect of nitrogen addition on above ground or below ground seedling or rhizome sprout growth. Alternatively, earlier increased nutrient levels may have accumulated potential for an ecosystem shift, with another factor precipitating a reorganization of the plant community. Decreasing nutrient levels may not be enough to shift a Phragmites dominated ecosystem back to the original alternative stable state.

 

Development patterns and major storm events may have led to a Phragmites dominated state. Hurricane Agnes in 1972 caused record rainfall in the Bay watershed (5” everywhere, 12” - 18” locally), leading to torrents of freshwater runoff with heavy sediment and nutrient loads. As a result of this storm, the Bay was in the worst condition in recorded history. The Susquehanna usually discharges 0.5 - 1 million metric tons of sediment / year but it carried 31 million tons in 10 days. Nitrogen concentration in upper half of the Bay was two to three times normal; there were large algal blooms and half of the bay grasses died. In another example, the Chesapeake Bay Critical Areas Protection Act was passed by Maryland in 1984, limiting development along the shoreline (Davidson et al. 1997). This legislation triggered a development rush from 1983-1985, with homes built along the shoreline before the law took effect (Davidson et al. 1997, Horton and CBP 2003).

 

Aerial photographs covering the 1-km buffer of each subestuary were obtained from the Anne Arundel County Office of Planning and Zoning Map Room. Photos had been taken every 2-9 years, and were available for 14 time periods for the Rhode River area and 15 time periods for the South River and Curtis Bay areas, with incomplete coverage for only a few dates in the time series (Table 4). Imagery was available in digital format for 1998 and subsequent years; all prior years were in hard copy only. Paper maps were scanned into digital raster format using a Microtek ScanMaker 9800XL large format scanner. Scanning resolution was carefully chosen to find an optimal balance between maximizing image resolution, and minimizing scan speed and file size.

 

Table 4. Inventory of aerial photographs collected from Office of Planning and Zoning, Anne Arundel County, MD. Years marked incomplete indicate photos missing from the collection.

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 four—one 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.

 

 

Vector data for historical landcover, road networks and shoreline structures was created by photo-interpretation of the geo-referenced raster images, beginning with a current GIS vector layer and working backward through time to modify the current layer to reflect the past. The initial land cover/ land use database was for 2004, and was obtained on CD from the Anne Arundel County Office of Environmental and Cultural Resources (OECR). Street centerlines for 2006 were provided by the County’s Office of Planning and Zoning. For shoreline structures, field data collected by VIMS between 2002 and 2005 (Berman et al. 2006) served as the initial data layer.

 

The current shapefile was copied and overlaid on the next closest historical raster images and modified to reflect changes between the two time series. This new coverage was then copied and the process was repeated with the next closest year of historical images. Most of the changes in landcover were related to development and the resulting change of natural landcover categories (agriculture, forest, open space) to developed landcover categories (residential, commercial, industrial). The road network coverage was modified to designate when roads were built throughout the time series. Shoreline structures (boat docks, boat houses, ramps) were tracked to determine when they appeared and disappeared throughout the time series. Shoreline structure type was also modified if it changed throughout the time series.

 

For the purposes of this study we defined the spatial extent of each subestuary as consisting of a one kilometer buffer around its shoreline. This designation served to encompass all associated wetlands, and to define the land area considered in aerial photo interpretation. There was some overlap between the buffers of the South and Rhode rivers. To avoid double-counting, boundaries of the two subestuary buffers were manually edited to assign wetlands, shoreline structures, and other point features to one or the other.

 

 

Population has grown steadily and rather dramatically in Anne Arundel County since 1940 (Figure 15), increasing most rapidly between 1950 and 1970. Related to population increase has been growth in land development. Figure 16 shows the spatial patterns of land use in each subestuary for the years 1943 (1952 for Rhode) and 2005; Figures 17(a),(c) and (e) show this information as a time series. Figures 17(b),(d) and (f) summarize these changes over time into two categories: disturbed (which includes transportation, industrial, commercial, residential, agriculture) versus natural (woods, wetland, open space).

 

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. 

 

The increase in development was most pronounced in the Curtis Creek and South River subestuaries. In Curtis, the most rapid changes occurred early in the time series, between 1950 and 1960, followed by a 30-40 year period of relative stability, and another rapid increase beginning in the early 2000s. In contrast to South River, the Commercial-Industrial-Transportation category of Curtis is only slightly smaller than the Residential category. In the South River subestuary, residential land use is the main driver of increases over time in the disturbed category. The conversion of natural land (mostly wooded) to disturbed occurs more steadily over time than in Curtis, with a period of more accelerated development during the decade of the 1980s.

 

The Rhode River subestuary, in contrast to the other two, shows a relatively constant ratio between the disturbed and natural categories (about 40:60). Residential and, to a lesser extent, agricultural land use are the main components of the disturbed category. Residential development is concentrated in the eastern part of the subestuary. Aforestation has occurred over time, primarily at the expense of land in Open Space.

 

In all three subestuaries, the average land cover in a 100-m buffer around the wetlands is less disturbed than land cover in the overall subestuary [see Figures 17(b),(d) and (f)], suggesting some protection of wetlands from disturbance. The difference tends to be greater in the two latter time periods (1988 and 2005) than in the earlier one (1950s), suggesting more protection now than in early years.

 

Temporal changes in road density are shown in Figures 18 and 19a-c. Road density is highest in the Curtis Creek subestuary; road construction occurred most rapidly in the earlier half of the time series, and leveled off around the late 1970s. Curtis has a higher portion of state highways than the other two subestuaries, where roads are mostly for residential access. Roads have increased at a relatively constant rate over time in the South River, which is consistent with the steady increase in residential land in that subestuary. The Rhode has the lowest road density of the three, and it has changed little over time.

 

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

have been built since then. 

 

We also examined historical changes in the presence of shoreline structures in the three subestuaries, as a measure of shoreline disturbance (Figures 20 and 21a-c). Values were normalized to density figures (number of structures/ km of shoreline), to facilitate comparisons among the three subestuaries (Table 5). Density is highest overall in the South River, and the rate of increase has been the most rapid there. The second highest density and rate of increase occurs in the Rhode River. Shoreline structure density increases more rapidly in the Rhode than does the amount of residential land. This suggests either increasing affluence in this area (i.e., more people are able to afford docks and boat houses) and/ or a higher housing density over time (i.e., larger lots may be sub-divided). The density of shoreline structures also increases more steadily in Curtis than does residential land use, which may be due to similar causes. However if one looks at the percentage increase in the number of shoreline structures in the three subestuaries, it is Curtis Creek that has experienced the greatest changes, with the total number of shoreline structures rising from 61 in 1952 to 232 in 2005, or a 280% increase. The comparable values for Rhode and South are 161% and 195%, respectively. So, by either measure the Rhode River subestuary has the least shoreline disturbance of the three. The most disturbed is either Curtis or South, depending on the metric examined.

 

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);

structures shown in red have been built since then. 

 

Table 5. Number of shoreline structures present in 2005, by type, and the percentage increase since 1952, for three subestuaries in Anne Arundel County, MD.

	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%

 

 

Several sources of existing and newly collected data lend support to the casual observation that Phragmites has increase dramatically and non-linearly over time in this area. These are described below.

 

The first of these - a survey of tidal marsh vegetation conducted by the Maryland Department of Natural Resources (McCormick and Somes, 1982) - documents the extent of Phragmites presence in Maryland tidal marshes in the 1970s. In this study, wetland boundaries were delineated from aerial photos. Next vegetation mapping was conducted through interpretation of natural color stereoscopic aerial photos taken in 1976, with some earlier photography. Wetlands of at least 0.25 acres in size were mapped. Vegetation boundaries were hand-drawn and coded on 1:24,000-scale aerial photography. As of June 2006, the Maryland Department of the Environment (MDE) was in the process of scanning these paper maps and storing them in digital format. Scans of Anne Arundel County were complete at this time, and were made available to this project (Denise Clearwater, personal communication). Due to the large number of images for the county (156 of them), only those falling within the 1-km buffer of the three study subestuaries were processed for this project.

 

For each of the three subestuaries, images were rectified and wetland vegetation boundaries were digitized and coded. ArcGIS 9.2 software was used to derive information from the resulting data layer. This survey indicates a total of 1,991,124 m² of wetlands in the three subestuaries, of which approximately 35,500 m², or 1.8 percent, was occupied by Phragmites (Table 6). Percentages ranged from a low of 1.1% in the Rhode River subestuary, to 2.2% and 2.4%, respectively, in the South and Curtis subestuaries. Phragmites occurred in 19 distinct patches. This mid-1970s study establishes a baseline of Phragmites presence in the three subestuaries.

 

Table 6. Wetland area, number of patches of Phragmites, areas of Phragmites, and Phragmites as a percentage of mapped wetland area, for three subestuaries in Anne arundel County, MD. Data are derived from digitized wetland vegetation boundaries from McCormic and Somes, 1982.

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%

 

A second source of evidence is an unpublished study conducted by researchers at SERC in 2005 (Whigham, unpublished). Four subestuaries in Anne Arundel County were chosen for analysis, including the three examined in this study (Curtis, South, and Rhode). First, the MD-DNR survey described above was used to identify wetlands that contained Phragmites in the 1970s. Some additional wetlands were selected for analysis by conducting a windshield survey to locate areas where Phragmites occurs at present but was not present on the 1970s maps. This survey was not comprehensive, and tended to focus on larger wetlands at the head of inlets.

 

For each wetland chosen for evaluation, aerial photography was obtained from Anne Arundel County for six time periods (the years 1970, 1977, 1984, 1990, 2000, and 2003). Photo-interpretation was used to manually trace the boundaries of Phragmites in each photograph and estimate changes in area over time. Figures 22-24 show the estimated change in Phragmites area at sites in each subestuary over the period 1970–2003. The graphs show that the period of most rapid change in Phragmites area began between 1975 and 1985 for most sites. For two of the larger sites - one in the South and one in Rhode - the most rapid change occurred after 1990.

 

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

in Anne Arundel Co., MD. 

 

 

The boundaries of Phragmites patches for 2003 were digitized using ArcGIS, to facilitate their use in subsequent analyses of the current study.

 

A Comprehensive Shoreline Inventory for Maryland (Berman et al. 2006) was conducted in 2002-2005 by the Comprehensive Coastal Inventory Program (CCI) at the Virginia Institute of Marine Science (VIMS), following protocols developed by CCI for use in Virginia. Surveys were conducted from a shoal draft boat moving along the shoreline using handheld Global Positioning Systems (GPS) units to log conditions observed. Riparian land use, bank characteristics, shoreline modifications, shoreline habitat, and bank and shoreline stability were classified in this manner. Phragmites presence-absence was one of the factors recorded. Data are available for each county in Maryland as a GIS file containing shoreline line segments with associated attributes. We obtained data for Anne Arundel County from the CCI project website (http://ccrm.vims.edu/gis_data_maps/data/).

 

Variables available from the shoreline survey include:

a)     Location of Phragmites (present, absent or unknown)

b)     Location of marshes (present (erosion yes-no), absent, or unknown)

c)     Location and type of shoreline point structures (docks, etc.)

d)     Location and type of shoreline linear structures (riprap, etc.)

e)     Land use adjacent to shoreline (9 categories)

f)      Shoreline erosion (high, undercut, low)

g)     Shoreline cover (bare, partial, total)

h)     Shoreline height (4 height categories – ranging from 0-5 to >30)

 

Some results of exploratory analyses of these data are presented in Appendix A.

The two spatial data sets described above, when overlain using a GIS, tended to be complementary rather than redundant. The SERC study focused on larger wetlands at the head of inlets, which tended to be too shallow for VIMS to access by boat. Where overlap occurred, there were occasional discrepancies in their estimates of Phragmites occurrence. Furthermore, the VIMS data yielded a linear rather than areal estimate. To resolve these discrepancies and provide more comprehensive area estimates, personnel from SERC and the Penn State Cooperative Wetlands Center (CWC) conducted field surveys by boat during the summer of 2007 using a GPS (Global Positioning System) to record latitude-longitude at points along the edge of Phragmites patches. The GPS points were used in conjunction with aerial photography and a GIS to map the boundaries of Phragmites patches. This produced comprehensive digital maps of 2007 Phragmites boundaries for two of the three subestuaries – Rhode and Curtis. Time did not permit mapping of the South River subestuary, due to its larger size. For the purpose of our analyses, ArcGIS software was used to clip the mapped Phragmites patches to mapped wetland boundaries. This step was consistent with our decision to consider only Phragmites that occurred within mapped wetlands. We then computed the area of each Phragmites patch, and the area of Phragmites as a percent of the mapped wetland area.

 

Our original intent was to supplement the SERC Aerial Photo Survey by either:  1) examining additional aerial photos to “fill-in” Phragmites cover on a temporal basis, and 2) establishing historical Phragmites cover at sites that were not included in the original survey (i.e., at sites where Phragmites was not present in the 1970 survey, has Phragmites currently, and was not detected in the windshield survey; see previous SERC survey description). The quality of the aerial photography precluded the addition of sites. However, all current Phragmites cover was ground-truthed for accuracy.

Since our field mapping of Phragmites did not include the South River, we combined all sources of existing data to produce a composite presence/ absence estimate of current Phragmites for all three subestuaries. We overlaid each data set using GIS, and coded four variables:

(1)  VIMS05_Phrag:  presence/ absence or unassessed (yes-Y, no-N, or unassessed-U), as estimated by overlapping VIMS shoreline survey data and DNR wetland boundaries,

(2)  SERC07_Phrag:  Y, N, or U, as estimated by 2007 SERC field mapping,

(3)  CWC07_Phrag:  Y, N, or U, as estimated by 2007 CWC field mapping, and

(4)  SERC05_Phrag:  Y, N, or U, as estimated by 2005 SERC photo-interpretation.

We looked for consensus among these four data sets; if there was only one estimate, or agreement among multiple assessments, that estimate was used; if there was conflict, GIS maps were examined visually and a "best guess" estimate was made, where reasonable. Priority was generally given to SERC07 and CWC07 field data over VIMS05; if unassessed by all four studies, or if discrepancies could not be resolved, a “U” was assigned and the wetland was dropped from further analyses.

 

As noted previously, our investigation of historical data on potential driving variables and our difficulties in reconstructing Phragmites from aerial photography, led us to use a space for time approach in our statistical analysis. Based on our review of the literature and our assessment of data availability, we selected the following list of spatially-explicit variables to consider for use in our statistical analysis (Table 7). Below we provide some details on data sources and methods of computation.

 

Conclusions:

1. Is an upland stand present?
2. Will activity disperse rhizomes to the site?
3. Will activity bury rhizomes?
4. Are burial sites well-drained at time of burial?
5. Is early salinity at burial sites consistently below 18%?
6. Will activity lower burial site salinity?
7. Are clones spreading into low salinity/sulfide areas?
8. 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.

References:

AA Co. OECR.  2005.  LandCover2004.  Digital data files.  Anne Arundel County Office of Environmental and Cultural Resources, Annapolis, MD.  On CD.

Ailstock, M.S., C. M. Norman, P. J. Bushmann.  2001. Common Reed Phragmites australis: Control and Effects Upon Biodiversity in Freshwater Nontidal Wetlands.  Restoration Ecology 9(1):  49-59.

Alvarez, M.G., F. Tron, A. Mauchamp. 2005. Sexual versus asexual colonization by Phragmites australis: 25-year reed dynamics in a Mediterranean marsh, southern France. Wetlands 25: 639-647.

Amsberry, L. Baker, M.A., Ewanchuk, P.J., and Bertness, M.A. 2000. Clonal integration and the expansion of Phragmites australis. Ecological Applications. 10: 1110-1118.

Balloux, F., and N. Lugon-Moulin. 2002. The estimation of population differentiation with microsatellite markers. Molecular Ecology 11: 155-165.

Balloux, F., L. Lehmann, and T. de Meeus. 2003. The population genetics of clonal and partially clonal diploids. Genetics 164: 1635-1644.

Baron, H.M., K.M. Kettenring, M.K. McCormick, D.F. Whigham. In prep. Variation in seed viability, genetic diversity, and foliar nutrients of non-native Phragmites australis in the Rhode River, a subestuary of the Chesapeake Bay.

Bart, D. and Hartman, J.M. 2000. Environmental determinants of Phragmites australis expansion in a New Jersey salt marsh: an experimental approach. 2000. Oikos. 89: 59-69.

Bart, D. and Hartman, J.M. 2002. Environmental constraints on early establishment of Phragmites australis in salt marshes. Wetlands. 22: 201-213.

Bart, D. and Hartman, J.M. 2003. The role of large rhizome dispersal and low salinity windows in the establishment of common reed, Phragmites australis, in salt marshes: New links to human activities. Estuaries. 26: 436-443.

Berman et al.  2006.  Development of the Maryland Shoreline Inventory: Methods and Guidelines for Anne Arundel County.  Report prepared by the Comprehensive Coastal Inventory Program, Center for Coastal Resources Management, Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia. August 2006.

Bertness, M. D., P. Ewanchuk, and B. R. Silliman. 2002. Anthropogenic modification of New England salt marsh landscapes. Proceedings of the National Academy of Sciences of the United States of America 99(3):1395–1398.

Beyer, H. L. 2004. Hawth's Analysis Tools for ArcGIS. Available at http://www.spatialecology.com/htools.

Burdick, D.M, Buchsbaum, R. and Holt, E. 2001. Variation in soil salinity associated with expansion of Phragmites australis in salt marshes. Environmental and Experimental Botany. 46: 247-261.

Burdick, D.M. and Konisky, R.A. 2003. Determinants of Expansion for Phragmites australis, Common Reed, in Natural and Impacted Coastal Marshes. Estuaries. 26: 407-416.

Chambers et al., draft MS. 

Chambers, R.M., Meyerson, L.A., and Saltonstall, K. 1999. Expansion of Phragmites australis into tidal wetlands of North America. Aquatic Botany. 64: 261-273.

Chambers, R.M., Mozdzer, T.J., and Ambrose, J.C. 1998. Effects of salinity and sulfide on the distribution of Phragmites australis and Spartina alterniflora in a tidal saltmarsh. Aquatic Botany. 62: 161-169.

Chambers, R.M., Osgood, D.T., Bart, D.J., and Montalto, F. 2003. Phragmites australis invasion and expansion in tidal wetlands: interactions among salinity, sulfide, and hydrology. Estuaries. 26(2B): 398-406.

Clevering, O.A., and J. Lissner. 1999. Taxonomy, chromosome numbers, clonal diversity and population dynamics of Phragmites australis. Aquatic Botany 64: 185-208.

Cole, M.L., Kroeger, K.D., McClelland, J.W., Valiela, I. 2006. Effects of watershed land use on nitrogen concentrations and δ¹⁵ nitrogen in groundwater. Biogeochemistry. 77: 199-215.

County, Maryland.  Digital data files.  US Department of Agriculture, Natural Resources Conservation Service.  http://SoilDataMart.nrcs.usda.gov/

Davidson, S.G., Merwin, Jr., J.G., Capper, J., Power, G., and Shivers, Jr., F.R. 1997. Chesapeake Waters: Four Centuries of Controversy, Concern, and Legislation.2^nd ed. Tidewater Publishers Centreville, MD.

Elliott, E.M. and Brush, G.S. 2006. Sedimented organic nitrogen isotopes in freshwater wetlands record long-term changes in watershed nitrogen source and land us. Environmental Science and Technology. 40: 2910-2916.

Ellstrand, N.C. and M.L. Roose. 1987. Patterns of genotypic diversity in clonal plant species. American Journal of Botany 74: 123-131.

ESRI.  1999.  ArcView GIS 3.2.  Environmental Systems Research Institute, Inc. 

ESRI.  2006.  ArcMap 9.2.  Environmental Systems Research Institute, Inc.

Fell, P.E., S.P. Weisbach, and D.A. Jones.  1998.  Does invasion of oligohaline tidal marshes by reed grass Phragmites australis (Cav.) Trin. Ex Steud., affect the availability of prey resources for the mummichog, Fundulus heteroclitus L.?  Jour. Exp. Mar. Bio. Ecol. 222:  59-77.

Forman, R.T.T. and M. Godron.  1986.  Landscape Ecology.  John Wiley, New York, NY. 

Forsell, D. and L. Gerlich. 2000. Distribution and abundance of Phragmites in estuarine wetlands in Virginia’s portion of the Chesapeake Bay (speaker abstract).  In Phragmites in Virginia:  A Management Symposium.  December 14, 2000.  Virginia Department of Conservation and Recreation.

Gervais, C., R. Trahan, D. Moreno, A.-M. Drolet. 1993. Le Phragmites australis au Québec: distribution, géographique, nombres chromosomiques et reproduction. Canadian Journal of Botany 71:1386-1393.

Gregorius, H-R. 2005. Testing for clonal propagation. Heredity 94: 173-179.

Guo, W., R. Wang, S. Zhou, S. Zhang, Z. Zhang. 2003. Genetic diversity and clonal structure of Phragmites australis in the Yellow River delta of China. Biochemical Systematics and Ecology 31: 1093-1109.

Halkett, F., J-C. Simon, and F. Balloux. 2005. Tackling the population genetics of clonal and partially clonal organisms. Trends in Ecology and Evolution 20:194-201.

Hardy, O.J., and X. Vekemans. 1999. Isolation by distance in a continuuous population: reconciliation between spatial autocorrelation analysis and population genetics models. Heredity 83: 145-154.

Hardy, O.J., and X. Vekemans. 2002. SPAGeDi: a versitile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes 2:618-620.

Haslam, S.M. 1965. Ecological Studies in the Breck Fens. I. Vegetation in relation to habitat. Journal of Ecology. 53: 599-619.

Haslam, S.M. 1972. Biological Flora of the British Isles: Phragmites communis Trin. Journal of Ecology. 60: 585-610.

Holling, C.S. and Gunderson, L.H. 2002. Resilience and Adaptive Cycles. In: Panarchy. Understanding Transformations in Human and Natural Systems. Eds. L.H. Gunderson & C.S. Holling. Island Press. Washington.

Holling, C.S., Gunderson, L.H., Peterson, G.D. 2002. Sustainability and Panarchies. In: Panarchy. Understanding Transformations in Human and Natural Systems. Eds. L.H. Gunderson & C.S. Holling. Island Press. Washington.

Horton, T. and Chesapeake Bay Foundataion. 2003. Turning the Tide: Saving the Chesapeake Bay. Island Press. Washington D.C.

Hudon, C. P. Gagnon, and M. Jean. 2005. Hydrological factors controlling the spread of common reed (Phragmites australis) in the St. Lawrence River (Québec, Canada). Ecoscience 12: 347-357.

Ishii, J., and Y. Kadono. 2002. Factors influencing seed production of Phragmites australis. Aquatic Botany 72: 129-141.

Keller, B.E.M. 2000. Genetic variation among and within populations of Phragmites australis in the Charles River watershed. Aquatic Botany 66:195-208.

Kettenring and Whigham. In prep. The seed ecology of non-native Phragmites australis in developed and forested watersheds of the Chesapeake Bay, USA.

King, R.S. W.V. Deluca, D.F. Whigham, and P.P. Marra. 2007. Threshold effects of coastal urbanization on Phragmites australis (Common Reed) abundance and foliar nitrogen in Chesapeake Bay. Estuaries and Coasts 30:1-13.

Koppitz, H. 1997. Some aspects of the importance of genetic diversity in Phragmites australis (Cav.) Trin. Ex Steudel for the development of reed stands. Botanica Acta 110: 217-223.

Koppitz, H. 1999. Analysis of genetic diversity among selected populations of Phragmites austalis world-wide. Aquatic Botany 64: 209-221.

Lambert, A.M., R.A. Casagrande. 2007. Characteristics of a successful estuarine invader: evidence of self-compatibility in native and non-native lineages of Phragmites australis. Marine Ecology Progress Series 337: 299-301.

Lathrop, R.G., L. Windham, P. Montesano. 2003. Does Phragmites expansion alter the structure and function of marsh landscapes? Patterns and processes revisited. Estuaries 26:423-435.

Lelong, B., C. Lavoie, Y. Jodoin, and F. Belzile. 2007. Expansion pathways of the exotic common reed (Phragmites australis): a historical and genetic analysis. Diversity and Distributions 13: 430-437.

Marks, M., V. Lapin, J. Randall. 1994. Phragmites australis (P. communis): Threats, management, and monitoring. Natural Areas Journal 14: 285-294.

McCormick, J. and H.A. Somes, Jr.  1982.  The Coastal Wetlands of Maryland.  Maryland Department of Natural Resources Coastal Zone Management Program.  J. McCormick and Associates, Chevy Chase, MD.  261 pp.

MD-DNR.  1993.  dnrwet - DNR_Wetlands.  Digital data files.  Maryland Department of Natural Resources, Geographic Information Services Division, Annapolis, MD.  http://dnrweb.dnr.state.md.us/gis/data/data.asp

Meyerson, K.A., Vogt, K.A., and Chambers, R.M. 2000a. Linking the success of Phragmites to the alteration of ecosystem nutrient cycles. pp. 827-844 in Concepts and Controversies in Tidal Marsh Ecology. Ed. Weinstein, M.P. and Kreeger, D.A. Kluwer Academic Publishers, Dordrecht, Netherlands. 875 pp.

Meyerson, L.A., K. Saltonstall, L. Windham, E. Kiviat, S. Findlay. 2000. A comparison of Phragmites australis in freshwater and brackish environments in North America. Wetland Ecology and Management 8:89-103.

Minchinton, T. E., and M.D. Bertness. 2003. Disturbance–mediated competition and the spread of Phragmites australis in a coastal marsh. Ecological Applications 13:1400–1416.

Minchinton, T.E. 2002a. Disturbance by wrack facilitates spread of Phragmites australis in a coastal marsh. J. of Experimental Marine Biology and Ecology. 281: 89-107.

Minchinton, T.E. 2002b. Precipitation during El Niño correlates with increasing spread of Phragmites australis in New England, USA, coastal marshes. 242: 305-309.

Minchinton, T.E., J.C. Simpson, M.D. Bertness. 2006. Mechanisms of exclusion of native coastal marsh plants by an invasive grass. Journal of Ecology 94: 342-354.

Minitab.  2006.  Minitab Ver. 15.  Statistical software package.  Minitab, Inc.

Neuhaus, D. H. Kühl, J.-G.Kohl, P. Dörfel, and T. Börner. 1993. Investigation on the genetic diversity of Phragmites stands using genomic fingerprinting. Aquatic Botany 45: 357-364.

Osgood, D.T., D.J. Yozzo, R.M. Chambers, S. Pianka, J. Lewis, and D. Jacobson.  2002.  Factors controlling nekton habitat utilization patterns within Phragmites and non-Phragmites marshes (abstract).  In Phragmites australis:  A Sheep in Wolf’s Clothing?  A Special Technical Forum and Workshop, p. 14, New Jersey Marine Sciences Consortium Workshop, January 6-9, 2002.  Cumberland County College, Vineland, NJ.

Packett, C.R. and Chambers, R.M. 2006. Distribution and nutrient status of haplotypes of the marsh grass Phragmites australis along the Rappahannock River in Virginia. Estuaries and Coasts. 29: 1222-1225. 

Parsons, K.C.  2002.  Reproductive success of wading birds utilizing Phragmites marsh and upland nesting habitats (abstract). In Phragmites australis:  A Sheep in Wolf’s Clothing?  A Special Technical Forum and Workshop, p. 14, New Jersey Marine Sciences Consortium Workshop, January 6-9, 2002.  Cumberland County College, Vineland, NJ.

Pellegrin, D. and D.P. Hauber. 1999. Isozyme variation among populations of the clonal spcies, Phragmites australis (Cav.) Trin. Ex Steudel. Aquatic Botany 63: 241-259.

Phillips, J.D. 1987. Shoreline processes and establishment of Phragmites australis in a coastal plain estuary. Vegetatio. 71: 139-144.

Pollux, B.J.A., M.D.E. Jong, A. Steegh, E. Verbruggen, J.M. van Groenendael, and N.J. Ouborg. 2007. Reproductive strategy, clonal structure and genetic diversity in populations of the aquatic macrophyte Sparganium emersum in river systems. Molecular Ecology 16: 313-325.

Pons, O. and R.J. Petit. 1996. Measuring and testing genetic differentiation with ordered versus unordered alleles. Genetics 144:1237-1245.

Pyke, C.R., and Havens, K.J. 1999. Distribution of the invasive reed Phragmites australis relative to sediment depth in a created wetland. Wetlands. 19: 283-287.

Rice, D., Rooth, J., Stevenson, J.C. 2000. Colonization and expansion of Phragmites australis in upper Chesapeake Bay tidal marshes. Wetlands. 20: 280-299.

Richburg, J.A., Patterson, III, W.A., Lowenstein, F. 2001. Effects of road salt and Phragmites australis invasion on the vegetation of a western Massachusetts calcareous lake-basin fen. Wetlands. 21: 247-255.

Rickey, M.A. and Anderson, R.C. 2004. Effects of nitrogen addition on the invasive grass Phragmites australis and a native competitor Spartina pectinata. J. of Applied Ecol. 41: 888-896.

Saltonstall, K. 2002. Cryptic invasion by a non-native genotype of the common reed, Phragmites australis, into North America. Proceedings of the National Academy of Sciences 99: 2445-2449.

Saltonstall, K. 2003a. Microsatellite variation within and among North American lineages of Phragmites australis. Molecular Ecology 12: 1689-1702.

Saltonstall, K. 2003b. Genetic variation among North American populations of Phragmites australis: implications for management. Estuaries 26:444-451.

Saltonstall, K. and J.C. Stevens. 2007. The effect of nutrients on seedling growth of native and introduced Phragmites australis. Aquatic Botany 86:331-336.

SAS Institute Inc. 2007. JMP 7.0.2.

Silliman B.R, and M.D. Bertness. 2004. Shoreline development drives invasion of Phragmites australis and the loss of plant diversity on New England salt marshes. Conservation Biology 18: 1424-1434.

Slatkin, M., 1995. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139:1463-1463.

Stevenson, J.C., and J. Roothe.  2002. Historical and ecological perspectives of Phragmites australis in the Mid-Atlantic landscape (abstract).  In Phragmites australis:  A Sheep in Wolf’s Clothing?  A Special Technical Forum and Workshop, p. 14, New Jersey Marine Sciences Consortium Workshop, January 6-9, 2002.  Cumberland County College, Vineland, NJ.

Templer, P., Findlay, S., and Wigand, C. 1998. Sediment chemistry associated with native and non-native emergent macrophytes of a Hudson River marsh ecosystem. Wetlands. 18: 70-78.

USDA-NRCS.  2006.  Soil Survey Geographic (SSURGO) database for Anne Arundel

USFWS.  1981-2002.  nwi – NationalWetlandsInventory.  Digital data files.  US Fish and Wildlife Service, National Wetlands Inventory, St. Petersburg, Florida.  http://dnrweb.dnr.state.md.us/gis/data/data.asp

van der Putten, W.H. 1997. Die-back of Phragmites australis in European wetlands: an overview of the European Research Programme on reed die-back and progression. Aquatic Botany. 59:263-275.

Vasquez, E.A., Glenn, E.P., Brown, J.J., Guntenspergen, G.R., Nelson, S.G. 205. Salt tolerance underlies the cryptic invasion of North American salt marshes by an introduced haplotype of the common reed Phragmites australis (Poaceae). Marine Ecology Progress Series. 298: 1-8.

Warren et al. 2001

Warren, R.S., Fell, P.E., Grimsby, J.L., Buck, E.L., Rilling, C., and Fertick, R.A. 2001. Rates, patterns, and impacts of Phragmites australis expansion and effects of experimental Phragmites control on Vegetation, macroinvertebrates and fish within Tidelands of the Lower Connecticut River. Estuaries. 24: 90-107.

Warren, R.S., Fell, P.E., Rozsa, R., Brawley, A.H., Orsted, A.C., Olson, E.T., Swamy, V., Niering, W.A. 2002. Salt marsh restoration in Connecticut: 20 years of science and management. Restoration Ecology. 10: 497-513.

Weigel, J.  2004.  Nearest Neighbor 3.5, extension for ArcView, available from http://arcscripts.esri.com.

Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38: 1358-1370.

Wilcox, K.L., Petrie, S.A., Maynard, L.A., and Meyer, S.W. 2003. Historical distribution and abundance of Phragmites australis at Long Point, Lake Erie, Ontario. Journal of Great Lakes Research. 29: 664-680.

Windham, L. 2001. Comparison of biomass production and decomposition between Phragmites australis (Common Reed) and Spartina patens (Salt hay grass) in brackish tidal marshes of New Jersey, USA. Wetlands. 21(2): 179-188.

Windham, L. and Lathrop, Jr., R.G.1999. Effects of Phragmites australis (common reed) invasion on aboveground biomass and soil properties in brackish tidal marsh of the Mullica River, New Jersey. Estuaries. 22: 927-935.

Zeidler, A., S. Schneider, C. Jung, A.E. Melchinger, and P. Dittrich. 1994. The use of DNA fingerprinting in ecological studies of Phragmites australis (Cav.) Trin. ex Steudel. Botanica Acta 107: 237-242.

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, Ecosystem Protection/Environmental Exposure & Risk, ECOSYSTEMS, Aquatic Ecosystem, Aquatic Ecosystems & Estuarine Research, Ecological Risk Assessment, Aquatic Ecosystems, Ecology and Ecosystems, Environmental Monitoring, computer models, aquaculture, probabilty surface, coastal ecosystem, diagnostic indicators, community structure, ecosystem indicators, modeling ecosystem change, aquatic indicators, ecosystem response, modeling ecosystems

Progress and Final Reports:

Original Abstract

2006

		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