541.5 541S7 COASTAL ZONE 51984 INFORMATION CENTER :98 SANTEE RIVER ECOSYSTEM BASELINE RESEARCH Property of CSC LibraZt FOR COASTAL ENERGY IMPACT PROGRAM U . S DEPARTMENT OF COMMERCE NOAA COASTAL SERVICES CENTER 2234 SOUTH HOBSON AVENUE CHARLESTON, SC 29405-2413 By H. N. McKellar, Jr. James B. Williams Department of Environmental Health Sciences University of South Carolina 5 ~~Title: Total Phosphorus, Total Nitrogen and Total Organic Carbon Distributions in a Southeastern Coastal Plain River BY Iwe. Paul Osemene I ~~~~~~~~Henry McKellar and James B. Williams S ~~~~Department of Environmental Health Sciences University of South Carolina Columbia, South Carolina 29208 December 1984 INTRODUCTION Coastal plain streams and rivers are often closely linked to forested floodplain swamps (Kuenzler, et. al., 1 ~~1980). Floodplain forests occur in lowlands that border streams and rivers which seasonally inundate them (Wharton, 3 ~~et. al., 1982). These riverine floodplain ecosystems include not only the structural elements such as the stream 3, ~~or river, the swamp forest, the atmosphere, the soil and the i ~~fauna but also the functional and metabolic attributes such as primary productivity and nutrient cycling (Vannote, et. I ~~al., 1980). The one-way flow generated by gravity in streams and rivers has far reaching consequences for every aspect of their ecology. Characteristics of flow which are influential are water velocity, stream discharge and turbulence. Flow velocity is itself influenced by a combination of channel characteristics such as topographic j ~~slope, width and depth. The nature of the mineral substrate, the concentrations of dissolved and suspended I' ~~materials and temperature, are further factors which play important ecological roles (Townsend, C.R. 1980). However, for coastal plain rivers and streams as examplified by the site of this study, tidal influences which have been observed up to 77 km upstream are capable of modifying the nutrients and organic carbon concentrations and dynamics of * ~~delta systems. Lotic ecosystems are not isolated entities. Streams and rivers interact with the atmosphere at their surface and with the land at their edges, and it is across these boundries that movements of materials and energy take place. 5 Two intriging features of such lotic systems are a dependance for the majority of their energy supply on import of organic matter elaborated in the terrestrial system through which they flow (watershed) and the utilization of a great deal of organic input during the fall-winter periods of lowest annual temperature (Cummins, 1974). This lotic communities are generally heterotrophic and temperature compensated by having organisms that can process organic matter at reasonable rates below normal temperature optima. Nutrient cycling in aquatic ecosystems responds to and influences water quality and primary productivity of streams and rives. Water flow, water quality, and patterns of nutrient cycling are all.interacting variables in stream ecosystem functioning. Ripariam ecosystems along small, low-order streams are buffer zones where excessive nutrients and sediments from upland disturbances may be trapped and I assimilated. For larger streams and rivers like the Lower Santee River, overbank flow of water during flood events provides an opportunity for upstream flows to come in 3 contact with the bordering floodplains (Brinson, et. al., 1981). This aequeous transport system provides path-ways j for exchange between river channel and floodplain through lateral imports, sedimentation and export of elements and J organic matter. 2 The vectors transporting energy and materials between a terrestrial landscape and its aquatic subecosystem have been categorized as meterorological, geologic and biologic (Likens, et. al., 1974). The ecosystem-watershed concept, which originated with the Hubbard Brook Watershed Ecosystem Study, provides an important framework for research into contemporary processes in many environments (O'Sullivan, 1979). In many watershed ecosystems, inputs, outputs, and internal processes are affected by cultural factors. Many effects of Man upon environmental systems are thus transmitted and expressed via the material pathways of ecosystem watershed (Meyer and Tate, 1983; Mulholland, et. al., 1979). One of the most dramatic anthropogenic effects on stream ecosystems has been the alteration of water flow patterns for hydropower generation. In 1941, 88 percent of the water flow in the Santee River in S. C. was diverted for hydropower generation. Due to unforseen siltation problems associated with the diversion, plans to redivert 80 percent of the originally diverted water flow back into the Santee river have been authorised. Stream flow changes of this magnitude affect riverine ecosystem functioning and river-floodplain interactions. Study of Santee Swamp located above Lake Marion, which serves as headwaters for the Lower Santee River, docummented significant reduction in 3 nutrient concentrations with little or no oxygen depletionI as the waters coursed through the swamp (Kitchens, et. al.,3 1975). However, at present, very little is known about the distributions of nutrients and organic matter in the lower5 Santee River which is the area directly affected by the rediversion. Therefore the purpose of this study was to document the spatial and temporal distributions of total nitrogen, phosphorus, and organic carbon along the lower Santee River.5 MATERIALS AND METHODS3 DescrivDtion of the Studv area The Santee River originates in the Appalachian5 Mountains of North and South Carolina. Via a number ofg tributaries above Lake Marion, the river traverses the piedmont, meander across the coastal plain and empties intoj the Atlantic Ocean 75 km northeast of Charleston, S. C. (Fig. 1). The Santee river is a tertiary river formed byj the Saluda, Broad, and Congaree rivers in South Carolina and the Wateree River from North Carolina, The 4 3,512 km2 riverI basin is inhabited by 58% of South Carolina's population. Until 1941, the Santee, with an annual discharge mean of 525 m3S-I, was the fourth largest river on the U.S. east coast. The construction of Wilson Dam in 1941, created Lake Marion, South Carolina's largest lake with a 450 km 2 surface5 area (Fig. 2). In order to effectively harness 4 "a aft Am MO mw Om 4m joift "a so Aw mom ORTH CAROLINA FIGURE I SOUTH CAROLINA SANTEE RIVER BASIN MILES 0 -~~~~~~~~~~~~~~~~sa a hydroelectric power from the Santee, Lake Marion was connected through a 12 km diversion canal to Lake Moultrie of Cooper river. Thus 88% of the Santee flow was channeled into Cooper river (Bjorn Kjerfue, 1976). Presently, the Santee River annual mean discharge is 74m3S-1. Among other less documented impacts, the Santee-Cooper project apparently led to severe shoaling problems in Charleston Harbor. To alleviate this shoaling problem and associated dredging costs, the Army Corps of Engineers has been authorized to modify the existing Santee-Cooper system by rediverting approximately 80 percent of the previously diverted discharge back into the Santee river. The rediversion project is expected and predicted to produce environmental impacts on aquatic, riparian and coastal habitats (F.I.S, U. S. Army Corps of Engineers, 1975). The specific site of this study is the Lower Santee River which lies within the tail of the Santee river basin and stretches 140 km from the Wilson Dam to the Atlantic Ocean (Fig. 2). The Lower Santee River has its'headwaters from Lake Marion's hypolimnium and its hydrography during moderate dishcarge conditions showed that 73 and 27% of the discharge reached the ocean via North and South Santee distributaries respectively (Kjerfue, et. al., 1978). The 2 Lower Santee River sub-basin drainage area is 1942 km2. A major feature of the Lower Santee River is the extensive forested floodplain which spans 2 to 8 km wide along the 140 5 km river stretch from Lake Marion to the Coast. The total 2 floodplain area has been measured as 520.2 km producing a floodplain-subbasin area ratio of 0.27 (Lowry and Osemene, 1984). Table 1 shows the distribution of the floodplains area with reference to the Seven Stations studied. We introduce the concept of Wetland Factor (WF). This we define as the Wetland area between two adjoining sampling stations on the river channel divided by the distance between the two stations (river reach). It is the wetland 9~~~~~~~~~ area in km2 per unit length of the riverine reach in km. We propose that it is a rational method for easy projection of the floodplain area bordering a river segment. Riverine floodplain are not evenly distributed along the river stretch. A riverine reach with a WF of 2 has twice the floodplain area of the river segment with a WF of 1. The headwater reach for the Lower Santee River (WD-HW52) has a WF of 4.2 km2/km while the estuarine delta reach (N and S Santee - Atlantic Ocean) has a WF of 7.7 km2/km. The land use of the Lower Santee River subbasin was determined to be 62.5% wooded floodplain forest, 7.1% of cropland, 5.0% of pasture land while the rest, 25.4% is urban and industrial developments (Lowry and Osemene, 1984). The vegetation of the floodplain is-a mix of bottomland hardwoods and cypress-tupelo associations plus various stages of succession in logged area. The bottomland 6 -3 o " r A a A t / m ~ m C m m - M - m Table 1. The distribution of Wetland (Floodplain) area to the Sampled Stations on the Lower Santee River Channel in South Carolina. Distance between Wetland area % of Total Wetland River Reach Stations Station in km in km2 Wetland area Factor* 1 WD - HW52 37 155.5 30 4.2 2 HW52 - AL 30.6 100.6 19 3.3 3 AL - JT 14.5 59.8 12 4.1 4 JT - HH 17.7 17.6 3 1.0 5 HH - N & S Santee 20.9 39.0 8 1.9 6 N. and S. Santee Delta - Ocean 19.3 147.8 28 7.7 Interstation Floodplain Area in km2 Interstation distance in km hardwoods include sweetgum, green ash, American elm, water hickory and other species which require deeper water tables and shorter flood durations than the cypress-tupelo associations (McKellar, et. al., 1981). The seasonal patterns of Lower Santee river flow include winter-spring periods of highflow which ranges from 312 m3S-1 in late December to 496 mn3S-1 in late April and a summer-fall periods of low flow which range from 28m3S -1 in June to 14m3S-1 in November. Presently large winter-spring flood releases from the Marion Lake Dam into the Lower Santee River increase the annual mean flow to about 80m3S1. These flood releases cause periodic inundation of the entire floodplain (U.S.G.S., Columbia 1984). Research Desian In order to study the nutrients and organic carbon dynamics, 7 approximately equidistant stations were set up on the Lower Santee River main channel. These stations from upstream to downstream were Wilson Dam (WD) located 140 km upstream, Highway 52 (HWY 52 or Russeleville) located 103 km,, Alvin (AL) located 72 km, Jamestown (JT) located 58 km, Honey Hill (HH or Pleasant Hill Landing) located 40 km, North Santee at highway 17 (NS) located 21 km and South Santee at highway 17 (SS) located 17.7 km upstream 8 respectively. In this paper, the Wilson Dam station is ref ered to as the source or headwater station, the stations from HW52 to HE are referred to as the riverine stations I ~~while the stations SS and NS on highway 17 are collectively refered to as the delta or estuarine or coastal stations. Replicate water samples were obtained from each of these stations, once a month from March 1983 to February 1984. This period covered the annual hydrologic and ecologic seasonal cycles. The Honey Hill and Alvin Stations were sampled for ten (10) and nine (9) consecutive months respectively. The North and South Santee river stations in R ~~the river delta were consistantly sampled within the last hour of ebb-tide flow to insure maximum riverine effects on nutrient concentrations. Salinity and specific conductivity were also measured at each station. Samples were U ~~immediately embedded in ice and transported to the ft ~~laboratory where they were processed and preserved for later analyses. Samples were generally processed within 4 hours of collection. Except for samples processed for all subspecies of organic carbon which were preserved by R ~~freezing at - 20 0C. all precessed subsamples were chemically preserved from bacterial activities with 0.1 ml of 2% mecuric chloride solution and stored at refrigerator temperature (4 C)until analysed. The respective sub-samples were analysed for total nitrogen, total * ~~phosphorus and organic carbon within 10 days from the sampling data. Approximately 672 processed water samples I ~~were analysed. 9 Analytical Methods Total phosphorus was assayed first by oxidizing all the organic phosphorus to ortho-phosphate ions by adding a strong oxidizing acid-fersulfate solution to samples and 0~~~~~~ holding them for 1 hour at 1320C autoclave high temperature and pressure (Menzel and Corvin, 1965). The orthophosphate ions was determined spectrophotometrically after reaction with acid-molybdate solution (Murphy and Riley, 1962). Total nitrogen was assayed by first oxidizing all the particulate and dissolved organic nitrogen to nitrate ions by adding strong alkaline persulfate solution and heating for 1 hour at 132�C autoclave high temperature and pressur (D'Elia, et. al., 1977). The spectrophotometric method of Alkaline Hydrazine Reduction followed with diazotization was then used to determine the concentrations of total nitrogen in each water sample (E.P.A. - NPDES, 1979). It must be emphasized that in our hands we found this technique to be pH sensitive to color development. See appendix 1 for the complete procedure. The Total organic carbon was determined as the sum of the dissolved organic carbon (DOC) and particulate organic carbon (POC) of each sample. The highly precise 10 carbondioxide-infra red rays absorption standard method was utilized (Menzel and Vaccaro, 1964). For the POC, the cupric oxide dry method of glass ampule preparation was used (Hutchinson, 1983). The Horiba PIR - 2000 (Oceanic Inter- national Co., Texas) infra-red analyser was used for organic carbon analyses. RESULTS The Lower Santee River daily discharge and monthly mean discharge data for the period of study was obtained from the U.S.G.S. in Columbia. Only provisional discharge data was available at the time of this paper. However, history has shown that the confirmed discharge data are usually identical to the provisional data. The discharge measurements were made at Pineville gage station which is 3.9 km downstream from the Wilson Dam. The monthly mean and daily discharge data are plotted in Figure 3. The river discharge on the date of sampling is represented on the graph as "day-discharge". This is compared with the month's mean discharge as determined by U.S.G.S. The graph showed that the time from December to April represents the period of high discharge (354 to 496m3S_1),a while the time from May to November represents period of Low river discharge (8.5 to 57m3 s-)o In general, discharge on the sampling day was similar to the monthly mean discharge. However, there is a significant difference between April day discharge (57m3S-1) and April month mean discharge (496 m3S-1). 510- IF igur e 3 _r CSantee River Discharge 0 = On Sampling Day 400 = Monthly Mean 300 - 2~~~~~~~LOW Flow 100 000 J M M J J A S 0 N D 1984 -1- 983 Tm in Monts Total Phosphorus Temnoral Variabilitv: Variations in the total phosphorus concentrations of the surface water during the period of study are shown in the six-panel graph in Fig. 4. At the headwater station (WD), the graph shows a relatively high TP concentration (6.0 to 5.0 mg-at/L) during the winter months from late December to late March. This was followed by a gradual and steady decline from spring to late August when a minimum was observed as 1.25 mg-at/L. A sudden rise in TP concentration was observed in the Fall season months of September to mid December (Fig. 4a). The riverine stations downstream to H.H Station (Fig. 4b-e) reflect similar pattern as the source water station but with higher nutrient variability. This increase noise would be expected since interstation tributaries and overland flow from agricultural areas contribute some phosphorus to the river channel. Also the Wetland Factor (WF, Table 1) varies between stations and contributes to the variability. The estuarine delta stations (N and S Santee distributaries stations) present quite a different temporal trend (Fig. 4. f.). The overlay plot of TP concentrations observed at the two delta stations show no significant difference between the north and south channel stations even though 73% of the discharge flow through the N. Santee to the Ocean. 13 Figure 4a TP Wilson Dam I ~~~~~~~~~~~~~~~~~~(140 km) 3 5.00L 1.00 -b ~~~Highway 52 (103 kcm) 1 5.00.~~~~~~~~~~~~ ~Low Flow-b 1.0011 10.00 Avn (A(72 km) 3 /100 1* I~c---- Low Flow -.4 5.00 d 1.00 L-~ Low Flow)-- 'C ~ ~~~~~~~~~~~5.00- 1.00- 10.00* a Honey NIM (40 km) 1c- Low ow -Flo 1 ~~~~~~~~~5.00 I 10.00' f Santee Delta I~~~ S u-Low Flow N.Santee (21 kcm) --S. Sante (18 km) 0.00 JIFMl AlMIJIJIAI o INiDi 1084�1083 '4 The major differences observed between these delta stations and the upstream riverine stations are relatively higher TP concentrations (3.75 to 8.5 mg-at/L) especially in the summer and fall. This complex annual pattern perhaps reflects a combination of influences from upstream riverine and floodplain effects and from the intertidal marshes of coastal delta. Eventhough, we sampled these stations during ebb flow, water quality parameters in this area are affected to some extent by previous high tides and upstream dispersion. Spaial Variability: The spatial distribution of TP concentration observed at the 7 stations between Wilson Dam and the delta region is shown on the three-panel graphs (Fig. 5a-c). The delta stations show significantly rising TP concentration (3.25 to 6.00 ug-at/L) as the water flows towards the Ocean. Fig. 5a presents the phosphorus pattern during the winter floodperiods. We observed that TP concentrations at all stations were consistently higher than the-annual means observed for each station. We observed also that the general delta nutrient spike is not present probably because of increased flushing secondary to high river discharge. 15 Figure 5a TP Winter Floods (January) 5.00-t t_ 3.50- b Summer Low Flow (August) 5.00- * p~~/ :� 1.00- Ir E ~ 0.00 (P c Fall High Flow (December) 7.50- 5.00 0 35 70 105 140 Distance Downstream From Wilson Dam (Km) 16 Fig. 5b reflects the pattern of the TP concentrations observed for each station during the month of August (Summer low flow). A consistently low TP concentration is observed all through the headwaters and riverine stations (1.25 to 1.00 ug at/1). This decline may be due to increased use of inorganic P during these summer months of maximum sunlight and optimum temperatures for primary productivity. The tidal effects of coastal estuarine areas are reflected in the delta TP concentration spike (1.50 ug at to 6.25 ug-at P/1). Fig. 5c reflects the upstream to downstream TP concentration trends during the end of December. The double effects of initial winter floods which connects the nutrient dynamics of the channel with that of its floodplains, and the increased liter fall and dry fallout of the preceeding fall season could explain the consistently high TP concentrations (5.75 to 8.75 ug-at P/liter) observed at all stations. During this period the flusing effect of the increased riverine dishcarge may have abrogated the usual riverine delta nutrient spike. Total Nitroaen Temporal Variability: Seasonal variations in total nitrogen (TN) at each station are shown in Figs. 6a-e. The TN seasonal pattern is similar to that observed for TP with high fall and winter concentrations (13 to 26 ug-at N/L) and low spring and summer concentrations (4.0 to 6.5 ug-at N/L). Low TN 17 I I - Figure On TN Wilson Dam (140 km) 20.00 1 I- C~~~~~~~~~~~~~ow Flow 10.00- 1 b Highway 52 1103 km 1 20.00 LowFlow- 40.00 a Alvin (72 km) 30.00- .j 20.00--'-Lo o --r S 2000 IaLow Flow-1 d. 20.00 Jamestown (58 km) L-- ow Flow 10.00- I� 20.00- * Honey HIl (40 Cm) 3 - 1.- Low Flow -.4 10.00.;. S 40.00- t 8~~~~antee Delta 7 Ho - N. Santo* (21km)) 30.00-Z--S. Santee (I8 km) A 20.00- 0.00 - 4L Dlo i I A M J J A 8 0 H D 1984-t 1933 18 Time n MoInths concentrations (3-5 ug-at N/L) were observed in August for the headwaters and all riverine stations. Maxima TN concentrations (32 to 38 ug-at N/L) were observed at the delta stations during the months form August to December. The pattern we observed for the two delta stations are relatively similar (Fig. 6-f). Spatial Variability The graphs in Fig. 7a-c reflect the spatial variations in TN concentrations. During the winter floods (J-Mar) gradual decrease in TN concentration from 17.5 to 11.0 ug at/l was observed from source waters of WD to the delta stations. This suggests that the riverine reach and its floodplains are sinks for TN species (Fig. 7a). The summer low flow (Fig. 7b) presents low TN concentrations of 5.0 to 6 ug at N/L for the riverine stations, however, very high TN concentrations (34.0 ug at/1) was observed during this time (August) for the north Santee delta station. This suggests that miximum tidal effects occur during summer low flow periods. High TN concentrations of 20.5 ug at/l at the headwaters and 17.0 ug at/i at the delta stations were observed during the initial fall-winter flood periods (Fig. 7c), suggesting that the floodplains upstream may be functioning as nitrogen sources while the coastal wetlands may function as nitrogen sinks. 19 -2 Figure TN Wintummer Low Flow ((JanuaAugust)ry) 20.00- 1 10.00- 1"4 Fa S e own Hig Fall High Flow (December) 0 35 70 105 140 Dbtanee Downstream From Wilson Dam (km) 20 Total Oraanic Carbon Temporal Variability: The graphs for the temporal trends of total organic carbon (TOC) (Fig. 8a-f) are a bit sketchy due to analytical problems (POC-June and July missing data). The sources water at WD station show a decline in TOC concentrations from 8.5 mg/l in late summer to 5.0 mg/l in late fall (Fig. 8a). This was probably due to possible autumn decline in organic carbon production in Lake Marion. The riverine stations through Honey Hill (HH) reflect similar decline from late summer but with sharp increase with December flood conditions suggesting a floodplain source of organic carbon with the first month of flooding. The estuarine stations (N and S. Santee) show a linear decline from maximum levels of 19 mg/i in late summer to levels less than 7 mg/l in December. Satial Variability: The Lower Santee River TOC spatial trends are presented in Fig. 9a-d. The annual TOC mean concentrations show a gradual increase from 6.0 mg/liter at Wilson Dame (WD) to near 12 mg/liter in the estuarine stations (Fig. 9a). The winter flood (Jan) period suggests slight linear increase in TOC downstream from Wilson Dam suggesting TOC input from floodplain (Fig. 9b). The summer-fall low flow periods 21 5 ~ Figure sa TOC MiSon DOm (140 km) 10.00t h-- Low Flow -'I b HIghway 52 (103 kIm) 10.00- Low Flow 1 -V5.00 5 C Alvin (72 km) 10.00. 5.o00- i~- Low Flow -. I j 1000' d Jumstown (58 km) I 10.00 p.-- Low Flow -".1 Honey F7ll (40 km) 3~~~~~~~~ ~ 10.00 ~--Low Flow-l 20.00- f Santee Deflta S ~~~~~~~~~15.00-- 1 10.00- 5.00- !me Low Flow -N-Santeo (21 kmn) 5--S. SantOO (18 km) 00.00-'I I I III III " FIY IA MA J A 8 D N D 5� 1984 -11983 TIMe In Months 22. Figure 9a TOC Annual Mean 10.00 - 5.0- 12.0- b Winter Floods (January) 8.00-_ 19.00- c Summer Low Flow (August) .jO~~~~/ , - , 3 / ID I 8.00- , ~.td Fall High Flow (December) 13.00- 5.5-- I 00.0 - 0 .35 70 105 140 Distance Downstream From Wilson Dam (km) 3 23 (August) show small changes in riverine stretches (8.0 to 9.0 mg/liter) but with sharp increase to peak values approaching 20 mg oc/liter in the delta region suggesting source of organics from coastal wetlands (Fig. 9c). The dramatic effects of late fall and initial flood events is reflected in Fig. 9d. There is a gradual increase 5.5 mg/liter from the source water of Lake Marion (WD) to concentrations near 13 mg oc/liter at the HoneyHill station suggesting maximum floodplain source of OC. However a sudden drop in the organic carbon concentration was observed for the same period at the coastal stations suggesting estuarine dilution or consumption of organic rich river water during this period. Discusso Hydrologi effect-- Recent literature suggests that the hydrolic regime of most southeastern floodplain-river ecosystems are characterized by the periodicity and timing of water input, the flushing rate, the quantity and velocity of river discharge downstream and the topographic gradient of the ecosystem (Elder and Mattraw, 1982; Kuenzler, et. al., 1980; Tate and Mayer, 1983). Much of the work done on 3rd to 6th order coastal streams and rivers shows that the highest floods resulting in maximal innadation of the forested floodplains occur in late winter and early spring. In this study we document that the Lower Santee River experienced high discharge from 24 the month of December to April, and low discharge from May to November. During winter and early spring, the discharge (354 to 496 m3S1) is high enough to innadate the entire floodplain forests that boaders the entire river stretch. The Lower Santee River hydrologic characteristic is similar to those reported for most southeastern coastal-lain rivers. Flood events in streams contribute significantly to annual material export from watersheds (Leopold, et. al.,, 1964; Aormann et. al., 1974; Johnson et. al., 1976). Material export is a function of both stream water concentration and dishcarge rate. The hydrologic characteristics of the Lower Santee River floodplain system, as well as the productivity of the floodplain vegetation, are important factors effecting nutrient concentrations and dynamics in the river water. The role of hydrologic factors is complex. The concentrations of some dissolved materials are diluted at high discharge (Leopold et. al., 1964; Johnson. and Needham, 1966) while others, particularly phosphorus and nitrogen are increased (Turner et. al., 1975; Long and Cooke, 1978). A small woodland stream is almost totally enclosed by its ripariam vegetation such that direct litter fall in the stream is an important component of the allochtonous input (Cummins, 1977). For a large river, direct litter fall into the main channel is of monor significance. Flooding is the mechanism by which detritus 25 deposited on the flood plain may be transported. The major role played by the Santee River winter-spring high flow, other than increase nutrient flux, we find to be the transport of organic carbon and nutrients from the bordering flooplain forest bed into the river channel. This aeqneous transport provides pathways for exchange between the river channel and floodplain through lateral import or exports of elements and organic matter. Temxoral effects The temporal variations of N and P along the Lower Santee River were similar. The seasonal patterns (Figs. 4 and 8) document high winter levels, low summer levels and with intermediate spring and fall levels. The source water for this river is Lake Marion and similar seasonal patterns have been observed and reported for lakes (Pearl et. al., 1975; Horne and Goldman 1972). Seasonal nutrient variation has also been reported for riverine systems (Naiman and Gibert 1978; Elder and Mattraw, 1982). The most rationale ecological explanation for these seasonal fluctuations should be the variable rates of nutrients cycling, however the well docummented lateral transport from bordering wetlands complicates the ecological explanation. The natural characteristics of TP concentrations and river flow rates derived from hydrograph 26 and chemograph has been reported to peak almost always before the flow peaks and the TP concentrations declines to low flow values before the flow returns to its approximate steady flow range of values (Verhoff et. al, 1982). The temporal variabilities we observed for TOC (Fig. 8) are high summer concentrations followed by a late summer and fall decline. This can be explained as the ecological effects of high primary productivity in spring and summer and autumn decline of organic carbon production. However the sharp increase in TOC concentrations observed in December may be explained by hydrologic effects of lateral transport of organic carbon from the inuudated floodplain forests. Elder and Mattraw (1982) have documented the importance of flooding on organic carbon flux from wetland systems. Particulate material transport from the floodplain is almost entirely dependent on flood events with adequate flow velocity. Timing has been emphazied as important since major litter-fall production always occurs in late autumn (Elder and Cairns 1982). Spacial effects -- TP, TN and TOC. The TP and TN concentrations show marked difference between the riverine stations (HWY52 to HH) and the delta stations (N and S Santee). Within the riverine stations there is a gradual decrease from the upstreams to 27 downstream. This suggests more nutrient uptake in the riverine stretch and or the nutrient sink effects of the bording floodplain forests. The mechanisms of nutrient removal by wetlands are not well understood and may depend on the hydrology of the ecosystem. Some studies of wetlands have indicated only seasonal or temporary removal of nutrients or even net releases of nutrients (Lee et. al., 1975). Mechanisms suggested for the nutrient sink effects include nitrification and subsequent denitrification across an anaerobic interface (Engler and Patrick, 1974), uptake by growing vegetation (Kitchens et. al., 1975; Klopatek, 1975; Boyd et. al., 1977, Brinson, 1977; Tilton and Kadlec, 1979) and sedimentation (Yarbro, 1979). The delta stations show a spike in the concentrations of TP and TN. However since this spike in nutrient concentration is not observed during periods of high riverine discharge, we interpret the spike as due to maximum estuarine tide effects which peaks during the months of low flow. The spacial pattern observed for TOC is the reverse for TP or TN. There is a gradual increase of TOC concentrations from upstream to downstream delta station. This we interpret as the bordering floodplain forest and delta wetlands functioning as TOC source to the river channel. This is borne out by the 28 dramatic increase in TOC upstream during initial flood events in winter. At the same time the usual delta spike concentration is lost due to increase flushing. The increase in TN, TP, TOC during flood periods has been reported to be due to mobiliztion of particulate subspecies during flood periods (Elder, 1983; Lambon and Hern, 1983). 29 LITERATURE CITED Brinson, Mark M., Bryan L. Swift, Renben C. Plantico, and John S. Barclay. 1981. Riparian Ecosystems: Their Ecology and Status, U.S. Department of Commerce, Nation Technical Information Service, FWS/OBS-81/17. Brinson, M.M. 1977. Decomposition and nutrient exchange of litter in an alluvial swamp forest. Ecology 58: 601 - 609. Bormann, F.H., G.E. Likens, T.G. Siccana, R.S. Pierce and J.S. Eaton. 1974. The export of nutrients and recovery of stable conditions follow defrestation at Hubbard Creek. Ecol. Monogr., 44: 255-277. Boyt, F.L., S.E. Bayley and J. Zoltek, Jr. 1977. Removal of nutrients from treated municipal wastewater by wetland vegetation. J. Water Pollut. Control Fed. 49: 789-799. Cummins, Kenneth H. 1974. Structure and Fuction of Stream Ecosystems. BioScience 24 (11): 631-641. Cummins, K.W. 1977. From Headwater Streams to Rivers. The American Biology Teacher 39: 305-312. D' Elia, Christopher F., Paul A. Stendler and Nathaniel Corwin. 1976. Determination of total nitrogen in aequeous samples using persulfate digestion. Limnol. Oceanogr. 22: 760-764. Elder, John F. and Harold C. Mattraw, Jr. 1982. Riverine Transport of Nutrients and Detritus to the Apalachicola Bay Estuary, Florida. Water Resour. Bull. 18 (5): 849- 856. Elder, J.F. and D.J. Cairns. 1982. Production and Decom- position of Forest Litter Fall on the Apalachincola River Flood Plain, Florida. U.S. Geological Survey Open-File Report 82-252:73. Elder, John F. 1983. Nitrogen and Phosphorus flux through forested wetlands in a large Florida river basin. In Press. Engler, R.M., W.H. Patrick. 1974. Nitrate removal from flood waters overlying flooded soil and sediments. J. Environ. Qual. 3: 409-413. 30 EPA. (US) 1979. Methods for Chemical Analyses of Water and Wastes. Mehtod 353.1. Horne, A.J., and C.R. Goldman. 1972. Nitrogen fixation in Clear Laker California. I. Seasonal variation and the role of heterocysts. Limnol. Oceanogr. 17: 678-692. Hutchinson, Steve. 1983. Dry Oxidation method for propara- tion of particulate organic carbon. Unpublished. Belle W. Baruch Institute for Marine Biology and Coastal Research, University of South Carolina, Columbia. Johnson, C.M., and P.R. Needham, 1966. Ionic Composition of Sagehan Creek, California, Following an adjacent fire. Ecology 47: 636-639. Johnson, A.H., D.R. Bouldin, E.A. Goyette, and A.M. Hedges. 1976. Phosphorus Ions by Stream transport from a rural watershed: Quantities, processes, and sources. J. Environ. Qual. 5: 148-156. Kitchens, W.M., Jr., J.M. Dean, L.M. Stevenson, and J.H. Cooper. 1975. The Santee Swamp as a nutrient Link. p. 349-366. In F. G. Howell et. al. (ed) Mineral cycling in Southeastern ecosystems. ERDA Symp. Series (CONF-740513). U.S. Govt. Printing office, Washington D.C. Kjerfve, Bjorn. 1976. The Santee-Cooper: A Study of Estuarine manipulation. In Estuarine processes, Vol. I, Uses, Stresses and Adaptation of the Estuary. Academic press Inc. New York, London. Kjerfve, Bjorn and J.E. Greer. 1978. Hydrography of the Santee River during moderate discharge conditions. Estuaries 1 (2): 111-119. Klopatek, J.M. 1975. The role of emergent macrophyutes in mineral cycling in a freshwater marsh. p. 367-393. In F.G. Howell et. al., (ed) Mineral cycling in South- eastern ecosystems. ERDA Symp. Series (CONF-740513). U.S. Govt. Printing office, Washington D.C. Kuenzler, Edward I., Patrick J. Mulholland, Laura Anne Yarboro, and Leonard A. Smock. 1980. Distributions and budgets of Carbon, phosphorus, iron and manganese in a floodplain swamp ecosystem. Water Resource Research Institute of the Univ. of North Carolina, Report No. 157. 31 Lambon, Victor W., and Stephen C. Hern. 1983. Transport of organic carbon in the Atchafalaya Basin, Louisiana. Hydrobiologia 98: 25-34. Lee, G.F., E. Bently, and R. Amundson. 1975. Effect of marshes on water quality. p. 105-127. In A.D. Baler (ed). Coupling of land and water systems. Springer- Verlag, New York. Leopold, L.B., M.G. Wolman, and J.P. Miller. 1964. Fluvial Processes in Geomorphology, W.H. Freeman San Francisco Calif. Likens, Gene E., and F. Herbert Borman. 1974. Linkages between Terrestrial and Aquatic Ecosystem. BioScience 24 (8): 447-456. Long, E.T., and G.D. Cooke, 1978. Phosphorus variability in three streams during storm events: Chemical analysis vs algal assay. Mitt. lut Ver. Limnol. 21: 441-452. Lowry, Claude and Iwe P. Osemene. 1984. Measurement of the Lower Santee River Floodplain area using the Electronic digital Numonic Planometer. Unpublished. U.S. Soil Conservation Department, Columbia, South Carolina. McKellar, H., M. Homer, L. Pearlstine and W. Kitchens. 1981. Preliminary Analysis of Energy Flow Impacts of a River Rediversion. In M.J. Mitsch et. al. (eds) Energy and Ecological Modelling. Elsevier Publ. Co. New York, N.Y. Menzel, D.W. and N. Corvin. 1965. The measurement of total phosphorus in seawater based on the liberation of organically bound fractions by persulfate oxidation. Limnol. Oceanogr. 10: 280-282. Meyer, Judy L., and Cathy M. Tate. 1983. The effects of watershed disturbance on dissolved organic carbon dynamics of a stream. Ecology 64 (1): 33-44. Mulholland, Patrick J., and Edward J. Kuenzler. 1979. Organic Carbon export from upland and forested wetland watershed. Limnol. Oceanogr. 24 (5): 960-966. I ?'Mulholland, Laura A. Yarbro, Rober P. Sniffen, and Edward J. Kuenzler. 1981. Effects of Floods on Nurtient and Metal concentration in a Coastal Plain Stream. Water Resource Research 17 (3): 758-764. 32 Murphy, J. and J.P. Riley. 1962. A modified single-solution method for the determination of phosphate in natural waters. Anal. Chim. Acta. 27: 31-36. Naiman, R.J., J.R. Gibert. 1978. Transport of nutrients and Carbon from the Namnimo River to its estuary. Limonol. Oceanogr. 23: 1183-1193. O'Sullivan, P.E., 1979. The ecosystem-watershed Concept in the environmental sciences. A review. Intern. J. Environ. Studies 13: 273-281. Pearl, H.W., R.C. Richards, R.L. Leonard, and C.R. Goldman. 1975. Seasonal nitrate cysling as evidence for complete vertical mixing in Lake Tahoe, California - Nevada. Limnol. Oceanogr. 20: 1-8. Redfield, A.C. 1958. The biological control of Chemical factors in the environment. Am. Sci. 46: 205-221. Tilton, D.L., and R.H. Kadlec. 1979. The utilization of a freshwater wetland for nutrient removal from second treated wastewater effluent. J. Environ. Qual. 8: 328- 334. Townsend, Colon R., 1980. The ecology of streams and Rivers, Studies in Biology Series No. 122. The camelot press ltd. SouthHampton Gr. Br. Turner, R.R., R.C. Havviss, T.M. Burton, and E.A. Lanes. 1975. The effect of urban land use on nutrient and suspended-solids export from north Florida watersheds in Mineral Cycling in Southeastern Ecosystems, edited by F.G. Howel et. al., pp. 868-889, U.S. Energy Research and Dev. Adm. Washington D.C. U.S. Army Engineer District. 1975. Final Environmental Impact Statement, Cooper River Rediversion project, Charleston Harbor, South Carolina. Vannote, Robin L., G. Wayne Minshall, Kenneth W. Cummins, James R. Sedel and Colbert E. Cushing. 1980. The river Continuum Concept, perspectives. Cam. J. Fish Aquat. Sci. 37: 130-137. Verhoff, F.H., D.A. Melfi and S.M. Yaksich, 1982. An analysis of total phosphorus in river systems. Hydro- biologia 91: 241-252. 33 Wharton, Charles H., Wiley M. Kitchens, Edward C. Pendleton and Timothy W. Sipe, 1982. The ecology of bottomland hardwood swamps in the sotheast: A community profile, U.S. Department of the Interior, FWS/OBS 81/37. Washington D.C. Yarbor, L.A. 1979. Phosphorus cycling in the Creeping Swamp floodplain ecosystem and exports from the Creeping Swamp Watershed. Ph.D. dissertation. Univ. of N.C. Chapel Hill. 34 ESTIMATION OF BIOMASS AND PRIMARY PRODUCTIVITY OF MATURE AND EARLY SUCCESSIONAL FOREST SITES ON THE SANTEE RIVER FLOODPLAIN Richard D. Bates and James B. Williams Department of Environmental Health Sciences Unviersity of South Carolina ESTIMATION OF BIOMASS AND PRIMARY PRODUCTIVITY OF MATURE AND EARLY SUCCESSIONAL FOREST SITES ON THE SANTEE RIVER FLOODPLAIN ABSTRACT The productivities of two study sites in a coastal South Carolina alluvial river swamp forest were studied from May 1983 to May 1984. The two sites were a mature bottomland hardwood (BLH) forest and an early successional scrub/shrub type forest. Aboveground net primary productivity was determined from measurements on litter-fall, stem growth and harvested samples of the herbaceous understory. Annual stem growth of the BLH site was estimated by regressing diameter breast height and dry weight biomass. The annual stem growth at the scrub/shrub site was determined by a random harvest technique. The annual increase in stem biomass for the BLH was 1232 g drywt/m2/yr. T e scrub/shrub site stem biomass increase was 309 g drywt/m /yr. Litter-fall was measured at 654 g drywt/m /yr and 499 g drywt/m /yr for the BLH and scrub/shrub sites respectivly. Herbaceous layer productivity was measured using a peak biomass by species dethod. Herbaceous layer brduction was 132 g drywt/m /yr for the BLH and 152 g drywt/m /yr for the scrub/shrub site. Estimated total aboveground net primary productivity (NPP) was deter- mined as the sum of the increase in stem biomass, litter-fall and herbaceous2layer biomass. The NPP of the BLH site was 2018 g drywt/m /yr and the scrub/shrub site NPP was 1560 g drywt/m2/yr. The NPP and biomass measurements of these two sites were consistent with productivity data from similar sites in the southeast. INTRODUCTION The Santee River is formed by the confluence of the Congaree and Wateree Rivers about 51 miles downstream from Columbia, South Carolina, and flows 143 miles to the Atlantic Ocean. The Santee originally had an average annual flow of 17,400 cfs and was the fourth largest river on the east coast of the United States. The Santee River basin drains about 16,768 square miles in North and South Carolina. In 1941 the South Carolina Public Service Authority (PSA) significantly altered the flow regime of the Santee River by constructing a dam and a diversion canal to develope the hydro-electric power potential on the coastal plain. At that time almost 90% of the Santee Rivers flow was diverted into the Cooper River. The flow on the Santee is now controlled by releases made from Wilson Dam on Lake Marion. The Cooper River Rediversion Project was designed to alleviate shoaling and dredging problems in Charleston Harbor. Completion of this project will cause the Santee River to be returned to within 80% of its original flow. Table 1. indicates annual average flow regimes for the Santee River. TABLE 1. Average annual flow regimes for the Santee River (from McKellar et al. 1981) Flow (cfs)* Before 1941 17,500 After diversion 2,000 After rediversion 14,000 (planned completion date Jan, 1985) *cfs = cubic feet/sec = 0.0283 m3/sec The flow on the Santee River increases during the winter-spring period, from the normal controlled flow of 500- 600 cfs, increasing the annual mean flow to 2,000 cfs. The Santee River floodplain spans from 2 to 8 km along the 70 km distance from Wilson Dam to the coastal zone. The high productivity of this area is important in that it maintains timber production, fish and wildlife habitats and contributes many "free" values to the region by maintaining natural flood control, water storage and water quality (Wharton 1970, Wharton et al. 1976). The floodplain ecosystem also exports 21 significant amounts of organic detritus, which plays a major role in downstream coastal ecosystems (Brinson et al. 1981, Wharton et al. 1982, and Elder and Mattraw 1982). The higher flow on the Santee River after rediversion will mean that the floodplain will be inundated by water at a greater frequency, depth and duration. Trees put under stress because of this increased flow will not be able to reproduce or maintain themselves. The majority of bottomland species can not survive two years of continuous flooding (Broadfoot and Williston, 1973). SITE DESCRIPTION Mature bottomland hardwood site: near Alvin, S.C. A bottomland hardwood community in the Francis Marion National Forest, representative of the surrounding forestland, was choosen as the site for this particular productivity study. The total area of the site measured 6000 m2 (50 m x 120 m) and was approximately 0.4 km from the Santee River. Species analysis of the stand was obtained by the point- centered quarter technique (Cottom and Curtis, 1956). Table 2 describes a summary of the vegatation analysis. The overstory species that were most important were red maple (Acer rubrum, average dbh = 14.7 cm, importance value = 47.04), sweetgum (Liquidambar styraciflua, dbh = 26.2 cm, IV = 34.65) and green ash (Fraxinus pennslyvanica, dbh = 53.1cm, IV = 47.38). Other overstory species found include American elm (Ulmus americana), water oak (Quercus nigra), overcup oak (Quercus lyrata), laurel oak (Quercus laurelifolia), syc- 3 amore (Platanus occidentalis) and water hickory (Carya aquatica). Overstory species are defined as those species capable of reaching dominant or subdominant positions in the forest canopy. The understory consisted almost entirely of three species. Sugarberry (Celtis laevigata, dbh = 14.8 cm, IV = 43.27), ironwood (Carpinus caroliniana, dbh = 10.1 cm, IV = 39.10) and possumhaw (Ilex decidua, dbh = 7.9 cm, IV = 38.09). American holly (Ilex opaca) and hawthorn (Crategus sp.) were also found. The understory species are defined as those not able to obtain canopy height in a forest setting. The herbaceous layer of the site consisted mostly of green briar (Smilax sp.), sedges (Carex sp.), bignonia (Anisostichus capreolata) and composites (Senecio sp.). Other herbaceous plants found include lizard tail (Suarurus cernuus), poison ivy (Rhus radicans), and blackberry (Rubus sp.). Table 3 summarizes the herbaceous plant species found at the site. On the average water was in the floodplain about 10% of the year (35 - 40 days). The total rainfall in the Francis Marion National Forest during the year of the study was 151.5cm average temperature was 65.40 F. (U.S.G.S. 1982- 1983). Early successional scrub/shrub site: near Germantown, SC The scrub/shrub site was an approximately 19 ha area that was clearcut in 1976 using a high-lead skidding operation (U.S. Forest Service). This type of clearcutting leaves large amounts of stumps, logs, hummucks and other debris which offer sufficient seed bed for a variety of 4 bottomland species. The black willow (Salix nigra, average dbh = 10.0cm, importance value = 68.30) became established shortly after the clearcutting and was by far the largest tree species found (Allen, U.S.Forest Service, 1962). The most important tree species found at the site were the black willow, red maple (dbh = 1.8cm, IV = 37.68), baldcypress (Taxodium distichum, dbh = 0.5cm, IV = 35.32), green ash(dbh = 1.3cm, IV = 25.58), and water tupelo- (Nyssa aquatica, dbh = 1.7cm, IV = 24.45). Shrub species found included virginia tea (Itea virginiana, dbh = 0.6cm, IV = 17.41), American holly (dbh = 1.2cm, IV = 8.25) and possumhaw (dbh = 0.7 cm,IV = 8.09). Table 4 shows a summary of the woody vegatation analysis at the clearcut site determined by the quadrat sampling technique. The herbaceous layer of the clearcut site consisted mostly of sedges (Carex sp.), blackberry (Rubus sp.), and composites (Scenecio sp.). Other herbaceous plants found included bignonia, Christmas fern (Polystichum acrostichoides), lizard tail, and cat-tail (Typha sp.). Table 5 summarizes the herbaceous plant species found at the site. On the average water was in the floodplain about 10% of the year (35 - 40 days). The total rainfall during the year of the study was 177.3 cm and the average tempature was 64.2�F. (U.S.G.S. 1982-1983). METHODS AND MATERIALS Mature bottomland hardwood site: Stem biomass was determined by using the point-quarter 5 technique (Cottom and Curtis, 1956). Twenty points were selected at random and the closest overstory or understory tree (greater than 5 cm dbh) in each quarter was identified, cored to determine latest increment growth, and measured for point to plant distance and dbh. There were a total of 80 trees sampled. Tree biomass was determined by the regression equation log drywt = 1.9757 + 2.5371 log dbh (Monk et al. 1970). This equation gives whole tree weight for general hardwood species. Annual diameter increase was estimated by subtracting the ring increment for the growing season from the measured dbh. The difference in the starting and ending tree biomass as determined by the regression equation was the annual increase in wood biomass. Litter-fall was collected on a regular basis (averaging every 50 days). The litter was collected in 18 1 x 1 m traps made of burlap and pvc piping. The samples were sorted into leaf, wood, reproductive and other unidentifiable material. All components of the sample were dried at 700 C until a constant weight was obtained. The sum of the average monthly litter-fall collections in g/m2/yr was used to calculate organic matter loss in the study site during the year. Biomass of the understory-herbaceous layer was measured by using the harvest method (Whittaker, 1975). Twenty-one 0.5 x 0.5 m quadrats were collected at five different times during the year (representative of the growth seasons for different herbaceous plant species). The samples were separated into species and dried at 700 C to a constant weight. Productivity of the herbaceous layer was estimated 6I using a peak biomass by species method (Whigham et al. 1978)o Scrub/shrub site: Stem biomass at the clearcut site was measured by using the harvest method (for trees less than 5 cm dbh) and regression equations (for trees greater than 5 cm dbh). Fifteen 10 x 10 m quadrats were harvested and all trees with dbh less than 5 cm-were taken. All the samples were separated into species, measured for basal diameter (db), dbh, height, age, latest ring increment and dried to a constant weight. The biomass of trees with a dbh greater than 5 cm was measured by the regression equation of log drywt = 5.0284 + 2.0903 log dbh (natural log). This equation was generated from measurements made on 196 hardwood trees in the sample. Annual wood production for harvested trees was estimated two ways: 1. Estimated volume increment x average density. 2. Regression on differences in starting and ending dbh's. The estimated volume increment (evi) was determined by the equation of Whittaker and Woodwell (1968): evi = 0.5 (3.1415) (height) (r2-c2) where r = tree radius c r - i i = annual wood radial increment at db. The evi was then multiplied by the average density of each tree (determined by tree weight / tree volume) to determine the wood production for each tree sampled. Annual wood production was also determined by using the regression equations that were generated from sampling at the clearcut site. The difference in starting biomass and ending I ~~~~~~~~~~~~~~~~~~~7 biomass was the annual wood production. Wood production for the large trees was estimated as the total biomass divided by the age of the stand times the area sampled. (wood production'for large trees = total biomass / age(8 years)x 150 m2). This was actually an underestimate of the large tree productivity but increment cores were not taken so there was not a precise measurement. Litter-fall collections were made on a monthly basis (averaging every 50 days) in 18, 1 x 1 m traps constructed as above. The litter was separted into component parts (leaf, wood, reproductive and other), and dried to a constant weight. The sum of the average monthly litter-fall in g/m 2/yr was used to calculate organic matter loss during the year. The productivity of the herbaceous layer was estimated using the harvest technique and peak biomass by species method as described above. TABLE 2. SUMMARY OF THE VEGATION ANALYSIS BY THE POINT-QUARTER TECHNIQUE FOR THE MATURE BOTTOMLAND HARDWOOD SITE. SPECIES no. rel. density rel. dominance rel. frequency importance* green ash 7 8.75 27.52 11.11 47.38 sugarberry 13 16.25 11.15 15.87 43.27 sweetgum 9 11.25 13.88 9.52 34.65 red maple 15 18.75 12.42 15.87 47.04 ironwood 12 15.00 8.23 15.87 39.10 possumhaw 13 16.25 7.56 14.28 38.09 overcup oak 2 2.50 5.68 3.17 11.35 sycamore 2 2.50 4.46 3.17 10.13 water oak 1 1.25 3.89 1.59 6.73 A. elm 2 2.50 0.67 3.17 6.34 laurel oak 1 1.25 0.18 1.59 3.02 baldcypress 1 1.25 3.31 1.59 6.15 A. holly 1 1.25 0.23 1.59 3.07 hawthorn 1 1.25 0.47 1.59 3.31 total 80 100 100 100 300 * Importance value = rel. density + rel. dominance + rel. frequency (Curtis and MCIntosh, 1951) TABLE 3. SUMMARY OF THE HERBACEOUS PLANT SPECIES AT THE MATURE BOTTOMLAND HARDWOOD SITE species* %occurance ** sedges (Carex sp.) 100 bignonia (A. capreolata) 100 greenbriar (Smilax sp.) 100 cane (Arundunaria sp.) 80 Composites (Senecio sp.) 100 lizard tail (S. cernuus) 80 poison ivy (R radicans) 100. clearweed (Pilea pumila) 80 V. creeper (P. aquinquifolia) 60 blackberry (Rubus argutus) 80 ** %occurance = no. of times found in sampling *ref.(Radford, Ashels and Bell, 1983) 9 TABLE 4. SUMMARY OF VEGATION ANALYSIS BY THE QUADRAT SAMPLING TECHNIQUE FOR THE SCRUB/SHRUB SITE SPECIES no. rel. density rel. dominance rel. frequency importance* black willow 14 3.58 56.67 8.05 68.30 red maple 86 21.99 1.99 13.70 37.68 baldcypress 84 21.48 0.14 13.70 35.32 green ash 42 10.74 1.14 13.70 25.58 w. tupelo 48 12.28 1.90 10.27 24.45 w. oak 14 3.58 9.21 8.05 20.84 sweetgum 9 2.30 10.92 4.62 17.84 virginia tea 40 10.23 0.33 6.85 17.41 w. hickory 13 3.32 1.90 3.42 8.64 ironwood 1 0.26 6.91 1.20 8.37 A. holly 10 2.56 1.03 4.62 8.25 possumhaw 12 3.07 0.40 4.62 8.09 A. elm 3 0.77 4.52 0.07 6.49 privet 4 1.02 1.80 1.20 4.02 silverling 7 1.79 0.42 1.20 3.41 cottonwood 1 0.26 0.64 1.20 2.10 sycamore 1 0.51 0.06 1.20 1.77 pine 1 0.26 0.00 1.20 1.46 total 366 100 100 100 300 Importance value = rel. density + rel. dominance + rel. frequency (Curtis and MCIntosh, 1951) TABLE 5. SUMMARY OF THE HERBACEUOS PLANT SPECIES AT THE SCRUB/SHRUB SITE species* %occurance ** sedges (Carex. sp) 100 blackberry (Rubus sp.) 100 composites (Seneci o sp.) 100 greenbriar (Smilax sp.) 20 clearweed (Pilea pumila) 60 poison ivy (R. radicans) 100 bignonia (A. capreolata) 100 fern (P. acrostichoides) 80 lizard tail (S. cernuus) 60 pennywort (Hydrocotlye sp.) 60 rush (Juncus effusus) 40 cat-tail (Typha sp.) 40 ** % occurance = no. of times found in sampling *ref. (Radford, Ashels and Bell, 1983) 10 Net primary production: Aboveground net primary productivity was determined as the sum of the stem biomass increase, litter-fall and herbaceous layer biomass. Underground biomass which may account for as much as 30% of the total biomass in some forest ecosystems (Mitsch and Ewel, 1979) was not included due to the difficulty in sampling. Consumption by herbivores was also not included. Total aboveground net primary productivity can be calculated using Newbould's (1967) equation: Pn = ^B + L + G where Pn = net productivity (aboveground) during time('T) ^B = biomass change during (CT) L = litter-fall during (^T) G = grazing by herbivores during (^T) RESULTS Biomass of the mature bottomland hardwood site: Stem biomass of the mature bottomla hardwood site was calculated by regressing dbh and whole tree dry weight. There where 160 trees sampled (trees greater than 5 cm dbh) with a biomass range of 918 g dry weight to 6,574,000 g dry weight. The average tree biomass was 564,000 (+/- 93,700) g dry weight. Table 6. summarizes the BLH site biomass by species. The biomass of the herbaceous layer was determined by the harvest technique. A peak biomass by species method was used to determine total production. This method more accurately assesses productivity than taking the average biomass of monthly samples. Table 7 summarizes peak biomass estimates for the BLH site. A total of 132 g drywt/m2/yr was determined as the herbaceous layer productivity. 11 TABLE 6. Summary of the BLR site biomass* by species overstory species ave. dbh(cm) biomass(g) ave. biomass(g) green ash 16 53.13 42,835,596 2,677,224 cottonwood 3 48.26 5,134,596 1,711,373 baldcypress 1 51.56 2,072,929 2,072,929 A. elm 3 8.97 118,837 39,612 1. oak 3 5.84 27,510 9,170 o. oak 3 51.71 10,585,369 3,528,456 red maple 24 14.68 3,772,387 157,182 sweetgum 21 26.19 10,912,080 519,622 sycamore 3 29.46 1,950,039 650,013 w. hickory 1 58.89 2,889,648 2,889,648 w. oak 2 40.28 3,229,013 1,614,507 total 80 29.70 83,527,528 1,044,094 understory A. holly 2 4.32 6,260 3,130 sugarberry 29 14.78 5,049,100 174,106 hawthorn 1 7.37 13,708 13,708 possumhaw 25 7.94 599,162 23,966 ironwood 23 10.11 1,006,522 43,761 total 80 10.94 6,674,753 83,434 grand total 160 20.32 90,202,281 563,764 * biomass determined by regression equation of Monk (1970) log drywt = 1.9757 + 2.5371 log dbh TABLE 7. Summary of peak biomass estimates by species for the BLH site species peak month * biomass(g/m2) % Smilax sp. October 36.9 27.9 Carex sp. May 26.5 20.1 Senecio sp. May 18.8 14.2 A. capreolata December 17.1 12.9 Rhus radicans May 8.0 6.1 Arundunaria sp. October 4.9 3.8 Rubus sp. May 3.9 2.9 Pilea pumila May 2.2 1.7 Suarurus cernuus May 1.4 1.1 P. aquinquefolia May 1.3 1.0 others 5.7 4.3 shrub October 5.3 4.0 total 132.0 100 *Samples were taken in May(83),July,October,December,May(84) (n = 21, 0.5 x 0.5 m quadrats / month) 12 Biomass of the scrub/shrub site: Stem biomass of the scrub/shrub site was measured by using the harvest technique for trees less than 5 cm dbh, and by regression for trees greater than 5 cm dbh. There were 366 trees harvested with 15, 10 m2 samples. A total dry weight of 124,787 g (832 g/m2) was recorded. The average tree weighed 340 +/- 33 g. Table 8 lists the biomass of the harvested trees by species. There were 25 trees with a dbh greater than 5 cm. The biomass of these trees was estimated by regression to be 712,973 g dry weight (4753 g/m2). The average tree weighed 28,518 +/- 5,700 g. Table 9 lists the biomass of the large trees (not harvested) by species. Biomass of the harvested trees was also calculated by regression. The total biomass was estimated to be 115,489 g dry weight (n = 279, 87 trees did not attain breast height 2 or 1.37 m) or 770 g/m2. (table 8) Biomass of the herbaceous layer was 167 g/m2. Table 10 summarizes the species and biomass of samples taken in May(83), July, October, December and May(84). Litter-fall Mature BLH site: Litter-fall at the mature BLH site was 654 g/m2/yr. The litter was separated into leaf, wood and reproductive components. Leaf weight accounted for 73.5% (478 g/m2/yr) of the total, wood weight 13.8% (90 g/m2/yr) and reproductive 12.7% (82 g/m2/yr). The peak of litter-fall occured between early October and early December, accounting for 57% of the total weight lost. Reproductive components reached a peak of 13 34% between May and June, with a low of 7% between October and December. Wood weight reached its peak between January and Febuary (40%) and a low of 4% between October and December. (table 11) Scrub/shrub site: Litter-fall at the clearcut site was 499 g/m2/yr. Leaf weight accounted for 78% (389 g/m2/yr) of the total, wood 13% (65 g/m2/yr) and reproductive components 9% (45 g/m2/yr). The peak of litter-fall occured between October and December accounting for 54% of the total weight lost. Reproductive components reached a peak of 15% between July and October and a low of 1% between October and December. Wood weight reached a peak between December and January with 51%, with a low between July and October of 3%. (table 11) TABLE 8. Biomass of harvested trees at the scrub/shrub site species # ave. dbh(cm) biomass(g) ave. biomass(g) green ash 42 1.33 18288 435 silverling 7 0.76 1693 242 ironwood 1 3.50 4037 4037 cottonwood 1 1.14 184 184 baldcypress 84 0.48 7667 91 possumhaw 12 0.72 1662 138 american elm 3 2.67 5172 1724 water hickory 13 1.68 10941 842 american holly 9 0.77 2754 306 virginia tea 40 0.59 3153 78 oak 9 2.05 13133 1459 pine 1 0.00 12 12 privet 4 1.60 2699 675 red maple 81 1.26 28810 356 sweetgum 6 1.55 4507 751 sycamore 2 0.19 62 31 water tupelo 46 1.48 18523 403 black willow 5 0.86 1486 297 total 366 1.05 124787 341 (115489 regression calculation n=279) 14 TABLE 9. Biomass* of large trees ( > 5cm dbh) at the scrub/shrub site. species ave. dbh(cm) biomass(g) ave.biomass(g) american holly 1 5.16 4720 4720 red maple 5 10.11 133657 26731 oak 5 7.74 62159 12432 sweetgum 3 9.80 57130 19043 water tupelo 2 6.53 15452 7726 black willow 9 15.13 439855 48873 total 25 10.92 712973 28519 * determined by regression: log wt = 5.0284 + 2.0903 log dbh TABLE 10. Summary of peak biomass estimates by species for the scrub/shrub site species peak month* biomass(g/m2) % Carex sp. May 52.2 31.2 Senecio sp. May 24.1 14.4 Rubus argutus December 18.6 11.1 Rhus radicans May 7.8 4.7 Juncus effusus October 6.6 3.9 Smilax sp. May 4.7 2.8 P. quinquefolia May 4.6 2.8 Suarurus cernuus May 4.4 2.6 Typha latifolia May 3.9 2.3 A. capreolata May 3.4 2.0 others October 30.5 18.2 shrub October 6.4 3.8 total 167.2 100.0 * samples were taken in May(83), July, October, December and May(84). Annual wood production Bottomland hardwood site: The annual increase in stem biomass was 1232 g/m2/yr (the difference between starting and ending biomass). The productivity and area was estimated for each tree sampled (n = 80). Table 12 summarizes production by species at the BLH site. Approximately 86% of the wood production occured in the overstory species with 14% in the understory. 15 Table 11. Litter-fall (%) component BLH and scrub/shrub sites sample day component BLH(g/m2) scrub/shrub(g/m2) 0-49 total(%) 27(4) 40(8) (May 83) leaf 14 34 wood 3 4 reprod 10 2 49-83 total(%) 43(6) 27(5) leaf 24 21 wood 7 3 reprod 12 3 83-150 total(%) 102(15) 71(14) leaf 70 58 wood 21 2 reprod 11 11 150-210 total(%) 383(57) 277(54) leaf 339 258 wood 16 18 reprod 28 1 210-244 total(%) 63(9) 36(7) leaf 31 18 wood 21 18. reprod 11 0 244-276 total(%) 25(4) 7(1) leaf 11 5 wood 10 2 reprod 4 0 276-375 total(%) 29(4) 51(10) (May 84) leaf 4 11 wood 15 18 reprod 10 22 total total(%) 671(100) 515(100) (376 days) leaf 493 404 wood 93 65 reprod 85 46 16 16I TABLE 12. Annual wood production by species at the BLH site species # production(g/m /yr) % of total green ash 7 597.4 48.4 sweetgum 9 185.9 15.1 w. oak 1 88.2 7.2 sugarberry 13 81.0 6.6 red maple 15 79.1 6.4 sycamore 2 48.7 3.9 possumhaw 13 46.7 3.8 ironwood 12 41.4 3.4 o. oak 2 40.7 3.3 baldcypress 1 17.0 1.2 1. oak 1 2.2 0.2 hawthorn 1 2.2 0.2 A. elm 2 1.2 0.1 A. holly 1 0.6 <0.1 total 80 1232.3 100 Scrub/shrub site: The annual increase in stem biomass at the clearcut site was 908 g drywt/m2/yr. The productivity was estimated by harvesting 150 m2 (366 trees), the dry weight of which was 314 g/m2/yr. The large tree annual wood production estimated by regression was 594 g/m2/yr. Table 13 summarizes production by species at the scrub/shrub site. Aboveground net primary production BLH site: The NPP of the BLH site was the sum of stem biomass increase (1232 g), litter-fall (654 g) and herbaceous layer biomass (132 g). The total NPP was 2018 g dry weight /m2/yr. Scrub/shrub site: The NPP for the scrub/shrub site was 1560 g dry weight/m2/yr. Stem biomass increase, litter-fall and herbaceous layer biomass were 908 g, 500g and 152 g dry weight /m2/yr respectively. Table 14 summarizes the NPP for both sites. 17 TABLE 13. Annual wood production by species at the scrub/shrub site. species -# production(g/m2/yr) % total b. willow 14 370.4 40.7 r. maple 86 177.5 19.5 oak 14 79.3 8.7 w. tupelo 48 61.1 6.7 sweetgum 9 59.9 6.6 g. ash 42 43.5 4.8 w. hickory 13 28.6 3.1 baldcypress 84 24.4 2.7 elm 3 12.7 1.4 ironwood 1 11.7 1.3 A. holly 10 11.1 1.2 silverling 7 8.4 0.9 privet 4 8.1 0.9 V. tea 40 6.0 0.7 possumhaw 12 5.6 0.6 cottonwood 1 0.4 <0.1 sycamore 2 0.3 <0.1 pine 1 <0.1 <0.1 total 391 909.1 100 Table 14 Net primary productivity values for BLH and scrub/shrub sites BLH Scrub/shrub litter-fall day 1.8 1.4 (g dry wt/m /yr) litter-fall 654 499 (g dry wt/m2/yr) Herbaceous layer biomass 132 152 (g dry wt/m2/yr) Increase in stem biomass 1232 908 (g dry wt/m2/yr) NPP (g dry wt/m2/yr) 2018 1560 DISCUSSION Floodplain swamp forests are among the most productive ecosystems due to several subsidies offered to the floodplain 18 by the watershed and river, including particulate and dissolved organic matter, water, clays and silt and nutrients (Wharton et al. 1982). The estimated productivities of the two sites on the Santee river floodplain were consistent with other studies (Conner and Day, 1976). The flooding regime of wetland areas is important in maintaining this high amount of productivity. Gosselink et al. (1981) stated: "Forest production appears to peak at the once-per-year flood frequency if flooding is during the winter because this regime furnishes the optimum environment for plant growth in terms of nutrient input by flood waters, summer soil moisture and possibly aerobic conditions during the summer leading to inorganic nutrient release from organic debris". This corresponds with Odum's (1978) illustration of the effect of flooding on productivity. Productivity is low with stagnant and abrasive flood regimes, whereas it peaks with seasonal flooding. With an operation schedule of 15 hours at full capacity (24,500 cfs) and 9 hours of zero discharge ( U.S. Army Corps of Engineers, 1976), simulated stage curves (U.S. Army Corps of Engineers, 1976) indicate that the pulsing discharge would cause overbank flooding approximately 50% of the time in the upstream reaches of the river. At lower reaches of the river this pattern would cause overbank flooding essentially 100% of the time. After rediversion the Santee floodplain may be flooded for about 50% of the time over a typical annual cycle (McKellar et al. 1981) instead of the normal 10-15% prior to rediversion. Brink (1954) surveyed areas where flood conditions persisted and found that herbaceous plant species such as sedges (Carex sp.) and 19 rushes (Juncus sp.) were severly affected but returned when the flooding ceased. The lethality of the waters was associated with temperature, depth, duration, movement and physical destruction (deposition of silt). Hosner (1958) studied the effects of inundation upon seedlings of six bottomland hardwood species. He found that only willow (Salix nigra) survived complete inundation during the growing season. The other species according to flood tolerance were green ash, sweetgum, box elder (Acer negundo), cottonwood and silver maple (Acer saccarinum). After 32 days of inundation only the willow seedlings survived (based on death of above- ground parts and roots). Demaree (1932) experimented with Taxodium sp. and found that for baldcypress to become established, the seeds must sprout when they are not submerged, and the seedlings must grow to a sufficient height during the first year to stay above flood level. The flow regime of the Santee after rediversion will prevent any regeneration of new forest growth because there will be a constant stress from flooding on the floodplain ecosystem. The ultimate result of the rediversion on the Santee floodplain may be the loss of productivity due to the mortality of bottomland hardwood areas and dominance by baldcypress-water tupelo areas. The cypress-tupelo swamp was found to have lower net primary productivity (Conner and Day, 1976). Table 15 shows an analysis of the predicted impacts and loss of productivity to the floodplain. 20 TABLE 15. (from Mckellar et al. 1981) Productivity in the Santee floodplain I. before rediversion II. after rediversion Association %cover total area gross productivity (km2) gC/m2/yr gC/yr I. bottomland hardwood 75 82.5 1944 19.7 cypress-tupelo 25 27.5 1399 4.7 total 24.4 II. bottomland hardwood 37.5 41.3 1944 9.9 cypress-tupelo 12.5 68.7 1399 11.8 total 21.7 change - 2.7 CONCLUSION Net primary productivity for 'average forests' is between 0.5 and 5.0 g/m2/day (182.5-1850 g/m2/yr) (Odum, 1959). The estimates of the NPP for the Santee floodplain are within or near this range. High productivity in riverine swamps is extremely important to coastal ecosystems and to fish and wildlife. A comparison of productivities for wetland areas of the southeastern United States (table 16) substantiates the fact that swamp forest production is highly dependent upon the flow regime and is an important part of the regions environmental resources. 21 TABLE 16. Comparison of swamp forest productivies in the southeastern U.S. Area source density wood prod. npp (#/ha) g/m2/yr still water Cypress dome S. Brown 1981 2150-3900 335-501 750-1230 (young) Fla. Cypress dome S. Brown 1981 3951 541 956 (mature) Fla. Cypress dome S. Brown 1981 2573 1060 1794 (sewage) Fla. Cypress forest Schlesinger 1978 1465 330 595 (Okefenokee swamp) flowing water Cypress floodplain S. Brown 1981 1644 1086 1607 Fla. BLH La. Conner & Day 1976 1710 800 1574 Cypress-tupelo " " 1976 1235 500 1140 Tupelo gum Brinson et al 1980 2590 * * (alluvial swamp) NC BLH this study 1984 833 1232 2018 (mature) SC Scrub/shrub this study 1984 26080 908 1560 (successionary) SC 22 REFERENCES Allen,P.H. 1962. Black willow dominates baldcypress- water tupelo swamp eight years after clearcutting. Southeastern Forest Exp. Stn. Res. Note Nb. 177. Brink, V.C. 1954. Survival of plants under flood in the lower Fraser River Valley, British Columbia. Ecology 35:258-262. Brinson, M.M., H.0. Bradshaw, R.N. Holmes and J.B. Elkins, 1980. Litterfall, stemflow and nutrient fluxes in an alluvial swamp forest. Ecology 61:827-835. Broadfoot, W.M. and H.L. Williston. 1973. Flooding effects on southern forests. J. For. 71:584-587. Brown, S. 1981. A comparison of the structure, primary productivity and transpiration of cypress ecosystems in Florida. Ecological Monographs 51(4):403-427. Conner, W.H. and J.W. Day. 1976. Productivity and composition of a baldcypress-water tupelo site and a bottomland hardwood site in a Louisiana swamp. American Journal of Botany 63:1354-1364. Cottom, B. and J. Curtis. 1956. Use of distance measures in phytosociological sampling. Ecology 37:451-460. Curtis, J.T. and R.P. McIntosh. 1951. An upland forest continuum in the prarie-forest border region of Wisconsin. Ecology 32:476-496. Demaree, Delzie. 1932. Submerging experiments with Taxodium. Ecology 13:258-262. Elder, J.F. and H.C. Mattraw,Jr. 1982. Riverine transport of nutrients and detritus to the Apalachicola Bay estuary, Florida. Water Res. Bull. 18:849-856. Gosselink, J.G., S.E. Bayley, W.H. Conner and R.E. Turner. 1981. Ecological factors in the determination of riparian wetland boundaries. In: J.R. Clark and R. Benforado, eds. Wetlands of bottomland hardwood forests. Proceedings of a workshop on bottomland hardwood forests of the southeastern U.S. held at Lake Lanier, Georgia. June 1- 5, 1980. Developement in Agriculture and Managed-forest Ecology, vol. 11. Elsevier Scientific Publ. Co., New York. Hosner, J.F. 1958. The effects of complete inundation upon seedlings of six bottomland tree species. Ecology 39:371-373. McKellar, H.N., M. Homer, L. Pearlstine and W. Kitchens. 1981. Preliminary analysis of energy flow impacts of a river rediversion p. 315-326. In: W.R. Mitsch, Bosserman and J. Klopatek (eds.), Energy and Ecological Modelling. Elsevier Scientific Publ. Co., New York. 23 Mitsch, W.J. and K.C. Ewel. 1979. Comparative biomass and growth of cypress in Florida wetlands. American Midland Naturalist 101(2):417-426. Monk, C.D., G.I. Child and S.A. Nicholson. 1970. Biomass, litter and leaf surface estimates of an oak-hickory forest. Oikos 21:138-141 Newbould, P.J. 1967. Methods for estimating the primary productivity of forests. Wilmer Brothers, Ltd., London. Odum, E.P. 1959. Fundamentals of Ecology. 2nd ed. W.B. Saunders Company. 546p. Odum, E.P. 1978. The value of wetlands: a hierarchial approach pages 16-25. In: P.E. Greeson, J.R. Clark and J.E. Clark, (eds.). Wetland functions and values; the state of our understanding. Am. Water Resour. Assoc., Minneapolis, Minn. Radford, A.E., H.E. Ahles and C.R. Bell. 1983. Manual of the vascular flora of the Carolinas. University of North Carolina Press, Chapel Hill, p. Schlesinger, W.H. 1978. Community structure, dynamics and nutrient cycling in the Okefenokee cypress swamp forest. Ecol. Monogr. 48:43-65. U.S. Army Corps of Engineers. 1976. Design Memorandum No. 9. Intake and Tailrace Canals. The Cooper River rediversion project. Charleston District, Charleston, S.C. U.S. Geological Survey, 1984. Personnel communication. Columbia, S.C. N.O.A.A. Wharton, C.H. 1970. The southern river swamp - a multiple use envirment. Bureau Business Economic Research, School of Business Admin., Georgia St. University. Wharton, C.H., H.T. Odum, E. Ewel, M. Duever, A. Lugo, R. Boyt, J. Bartholomew, E. Debelluvue, S. Brown, M. Brown and L. Duever. 1976. Forested wetlands of Florida- their management and use. Div. of State Plan., State of Fla., Tallahassee. 347p. Wharton, C.H., W. M. Kitchens, E.C. Pendleton and T.W. Sipe. 1982. The ecology of bottomland hardwood swamps of the southeast: a community profile. FWS/OBS-31/37, U.S. Fish and Wildlife Service Biological Services Program, Washington, D.C. 133p. Whigham, D.F., J. McCormick, R.E. Good and R.L. Simpson. 1978. Biomass and primary production in freshwater tidal wetlands of the middle Atlantic Coast. p.3-20. In: R.E. Good, D.F. Whigham and R.L. Simpson (eds.), Freshwater wetlands, ecological processes and management potential. Academic Press Publ. Co., New York. 24 Whittaker, R.H. 1975. Communities and ecosystems. (2nd ed.) MacMillian Publ. Co., Inc. New York. 387p. Whittaker, R.H. and G.M. Woodwell. 1968. Dimension and production relations of trees and shrubs in the Brookhaven Forest, New York Journal of Ecology 56:1-25. 25 Modeling the impacts of a river diversion on bottomland forest communities in the Santee River floodplain, S.C. Leonard Pearlstine1, Henry McKellar2, and Wiley Kitchens1 INational Coastal Ecosystems Team, Research and Development, U.S. Fish and Wildlife Service, 1010 Gause Boulevard, Slidell, LA 70458 2Department of Environmental Health Services, 306 Benson School, University of South Carolina, Columbia, S.C. 29208 Abstract LEONARD PEARLSTINE, HENRY MCKELLAR, and WILEY KITCHENS. U.S. Fish and Wildlife Service, Slidell, Louisiana and University of South Carolina, Columbia, South Carolina. Modeling the impacts of a river diversion on bottomland forest communities in the Santee River floodplain, SC. The U.S. Army Corps of Engineers is completing a river diversion project that will substantially modify water flow in a coastal plain river of South Carolina. Phased discharges from power plant operations on this diversion are expected to cause extensive flooding of the bottomland forest. To study the impact of the altered hydrologic regimes on the growth and succession of the forested floodplain, a bottomland hardwood succession model was developed by modifying an earlier upland forest model by Shugart and West. Interactions of river flow and site elevation in the model determine flooding patterns and depth of the water table. These hydrologic parameters are then used as major controls for seed germination, tree growth, and tree mortality. Individual species responses to these parameters over time control succession and species composition on the simulated site. Coupling the simulation model with a geographical information mapping system has permitted rapid visual inspection of predicted bottomland community changes-in the Santee floodplain. The model predicts a loss of up to 97% of the existing bottomland forest to open water or soil conditions too wet to support tree species; an alternative water release schedule may retain much of this area as cypress-tupelo. 1 ~~~~~~~~~~~~~~~~~~~~~~~~3 Introduction Moisture appears to be one of the most critical factors influencing the distribution of forest species within a river floodplain (Hook and Brown, 1973; Bedinger, 1978; Teskey and Hinckley, 1977). Species are distributed along moisture gradients as a result of their tolerance to flood duration and frequency (Bedinger, 1971; Johnson and Bell, 1976). Flooding is most critical during the growing season. Tree species cannot colonize or survive year-to-year flooding-lasting more than 50-60% of the growing season (Hall and Smith, 1955), although year-round root inundation may be tolerated in isolated years (Gill, 1970). During the dormant season, floods have little effect on the growth of most species (Hall and Smith, 1955; McAlpine, 1961; Bedinger, 1978). Flood stress on woody species is predominantly due to lack of oxygen near the roots (Kramer, 1951). Anaerobiosis has been shown to develop within hours of flooding (Gill, 1970). Those species that best tolerate flooded conditions appear to depend on one or a combination of adaptations. Anatomical adaptations such as aerenchyma formation and adventitious rooting allow oxygen flux from the air or oxygen-rich water to the roots. Metabolic adaptations decrease root oxygen demand (Gill, 1970). The complex interactions among hydrology, geochemistry, and biology in forest ecosystems are not well understood. However, several simulation models have been developed which successfully predict tree growth and succession in a variety of upland and bottomland applications (Botkin, 1972a; Shugart and West, 1977; Phipps, 1979). In this paper we document modifications of earlier models and the further development of a forested floodplain simulation model. 41 This model (FORFLO) is designed to predict tree growth, productivity, and changes in species composition in response to river flow modifications.I To examine its effectiveness and practical application, the FORFLO model was used to predict the consequences resulting from a river diversion on the Santee River floodplain forest community in South Carolina. 'The results of that study are reviewed in this report. In addition, FORFLO is currently being implemented along the lower Atchafalaya River, Louisiana, where a combination3 of river building, natural subsidence, and levee construction by the U.S. Army Corps of Engineers complicates predictions of species change. FORFLO will alsoR soon be implemented to assist management decisions in the greentree reservoirp at Felsenthal National Refuge in Arkansas. Area Description and River Flow Modifications The Santee River, South Carolina, was once the fourth largest river on the east coast with average annual flows of 17,500 cubic feet per second (cfs). In 1941, the South Carolina Public Service Authority diverted 88% of the Santee' s f low through two newly created lakes and into the Cooper River in order to develop hydroelectric power potential (Figure 1). After the 1941 diversion, the Cooper River had a discharge of approximately 150 times its former flowg rate, and the Santee River carried only 10% of its former flow. However, the Santee River still floods typically during late winter and early spring due to release of floodwaters from an upstream reservoir. This periodic flooding continues to support a bottomland hardwood forest along most of the length of the Santee's oversized floodplain. High flows on the Cooper River after the diversion carry large quantities of sediments creating shoaling problems in the navigation channels in Charleston Harbor. Maintenance of the main naviga- tion channels in the harbor requires intensive year-round dredging. The U.S. Army Corps of Engineers, the South Carolina Ports Authority, and the United States Navy dredge an average total volume of about 10.2 million cubic yards of sediment from the harbor per year (U.S. Army Corps of Engineers, 1966). Dredging currently costs about $1.00/yd3 (J. Saddler, S.C. Ports Authority, personal communication, 1981), or $10.2 million dollars per year to maintain Charleston Harbor. To alleviate this shoaling problem and the associated dredging cost, the Army Corps of Engineers was authorized by Congress in 1968 to redivert most of the water flow back into the Santee River. The rediversion involves construc- tion of an 18.5-km long canal from Lake Marion back into the Santee River (Figure 1). An additional hydropower plant is being constructed on the rediversion canal near St. Stephen to take advantage of some of the remaining hydrostatic head. For the Cooper River, the Army Corps of Engineers has stipulated (preliminarily) that a weekly average flow of 3,000 cfs must not be exceeded if shoaling in Charleston Harbor is to be effectively reduced (U.S. Army Corps of Engineers, 1975). The magnitude of changes in mean annual discharge patterns between the 1941 diversion project and the planned rediversion are summarized in Table 1. The floodplain is the major ecological feature of the coastal plain which will be impacted by the rediversion. Studies by McKellar et al. (1981 and 1984) suggested that the rediversion as planned would inundate much of the Santee floodplain and result in an extensive decline in the bottomland forest. McKellar et al. suggested that loss of floodplain productivity may be decreased substantially if, during the critical early months of the growing season (April, May, June, July), the power plant on the rediversion canal would be operated with just one of its three turbines. This would allow a maximum of 8,000 cfs to pass through the rediversion canal; the rest of the flow would be shunted to the Cooper River. The seasonal patterns of river flow for present conditions and for the rediversion options discussed above are shown in Figure 2. These long-term average trends indicate a winter-spring period of high flow and a summer-fall period'of low flow. After the rediversion, the Santee River would be returned to within 80% of its original (pre-diversion) flow rate with seasonal patterns including flood stages and periods of low flow. Cooper River flows would be held at a weekly 3,000 cfs average as stipulated by the Corps. The results of the modified rediversion proposed by McKellar et al. would be reduced flow to the Santee during the early growing season with a corresponding increase on the Cooper during these months (Figure 2). Development of the Model A major objective of this research was to develop a floodplain forest model (FORFLO) to quantitatively predict changes in Santee floodplain forest in response to alternative plans of river flow modification. The FORFLO model was developed by modifying the forest simulator FORET (Shugart and West, 1977), a southern deciduous forest succession model which 7 simulates the growth, reproduction, and competition of each tree in a mixed-species 1/12-ha (radius = 16.29 m) forest stand. Tree position is not explicity csml-' ered. -' itseaf atodification of a previous forest model, -BGWtA~ideqoped by 4Ok1in *et4a1. (1972a and 1972b). In this paper, we present those modifications required for modeling the Santee floodplain forest succession in response to river flow conditions. Detailed descriptions of aspects of the model retained from FORET and JABOWA are provided in the references listed above. Figure 3 is a conceptual flow diagram of the FORFLO model. Parameters used to describe tree behavior in the model are presented in Table 2. Computed Flood Conditions In the FLOOD subroutine, flooding duration and height are calculated from the hydrologic regime of the river based on stage-discharge relationships for the study site. This subroutine is specific for 19 kilometers of the Santee River floodplain between Lake Mattassee and Jamestown (Figure 1); however, it can be modified for other regions by substituting an appropriate equation for river stage vs. discharge. The relationships for the study area are shown in Figure 4. The flooding regime of the study plot is determined by considering the elevation of the simulation plot and the relationship between river flow and river stage. The hydrologic regime is entered in the model as the mean and standard deviation river flow twice monthly for a year. River flow for any given year in the simulation is stochastically determined by its mean and standard deviation for each half-month period. TIo water discharge sources are provided for total river filow input to the FLOOD routine. One source represents the seasonal pattern of -watershed discharge based on water release patterns from Lake Marion. The other represents inputs due to the rediversion and can be easily manipulated to examine alternative water release options. Both sources are summed in the model to determine total flow and flood conditions in the study area. A nine year (1970-1978) bimonthly average of river flows was derived from U.S. Geological Survey data for the Santee River. Average bimonthly discharge into the Cooper River for the same time period are taken from South Carolina Public Service Authority data. The U.S. Army Corps of Engineers (1975) has stipulated that discharge into the Cooper River cannot exceed a 3,000 cfs weekly average after the rediversion. Therefore, present monthly average discharge into the Cooper River minus 3,000 cfs were used for the rediversion canal flow. For the Santee floodplain study area, the relationship between river flow and river stage was derived from U.S. Army Corps of Engineers data (1976). These relationships are shown in Figure 4 at Lake Mattassee and at Jamestown. The equation of the curve at Lake Mattassee is . Y = -9.55 + L 595 In(X) (1) (r2 = 0.995) where Y is the river stage in meters above mean sea level (MSL), and X is the river flow in cubic feet per second (cfs). The equation of the curve for both locations was taken to be the same except there is a 3.2 meter elevation drop from Lake Mattassee to Jamestown. Because the drop in river 1 ~~~~~~~~~~~~~~~~~~~~~9 stage elevation from Lake Mattassee to Jamestown was assumed to be linear, the elevation difference per kilometer was computed as a simple proportion equal to 0.1657 m/km. River stage at any location in the study area can be derived from the modified equation Y = -9.55 + 1.595 ln(X) - (0.1657D) (2) where 0 is the distance, in kilometers, of the plot downstream from Lake Mattassee. To predict flooding patterns for a simulation plot, a single value is entered for plot elevation. For the Santee floodplain study area, the University of South Carolina Computer Service Division developed a detailed topograhic map of the study area using data from available 15 minute and 7.5 minute USGS quadrangles. Supplementary elevation data were obtained from the U.S. Coast and Geodetic Survey (horizontal and vertical control stations) and the U.S. Army Corps of Engineers (floodplain cross sections, Santee River bank-to-bank profiles, and several logging road traverses). Linear interpolations were performed for selected streams when surrounding elevation data and contour intervals were sufficient for computation. The value entered into the model for the river stage at which the plot will first be flooded was derived by locating the plot on a one foot interval version of this contour map. In the FLOOD subroutine, river stage is calculated for each half-month period and compared to the elevation of the plot. Duration of flooding on the plot is derived from the length of time the plot was flooded during the year. During flood conditions, flood height is taken to be equal to the difference between the plot elevation and the river stage. The site is defined as flooded when the water level is 15 cm below the plot elevation or higher. Under these conditions, the soil is assumed to be saturated., When river stage drops below 15 cm from the plot elevation, water level on the plot is no longer considered to be influenced by river stage, and is assumed to be equal to the average water table depth in the plot. Seed Germination and Sprouting For most tree species, seeds will not germinate when the ground is floodedR (Fowell, 1965). From Fowell, the period of the year when each species3 germinates is entered in the model. If the simulation plot is continuously * flooded during the germination period for a species, that species is not * allowed to germinate. Two exceptions in the floodplain are black willow and, eastern cottonwood. These trees are allowed to germinate regardless of flood conditions.3 If a species is allowed to germinate, flood duration during the growing season is one of the environmental conditions that determines whether theI seedlings wi'll survive. The calculated flood duration of the plot is compared to the flood duration tolerance of the species. If the flood duration of the plot is within the range of flood tolerance for the species, then a switch is 3 set which indicates that flood conditions are adequate for survival of the seedling in the BIRTH subroutine. This approach is similar to that used by Phipps (1979) except that Phipps used percent duration when flooding was greater than 10%, and flood frequency instead of flood duration where duration was less than 10%. Bedinger (1971) had earlier found this to be the most explicit description of the relationship -between flooding and species 3 ~~distribution at the White River National Wildlife Refuge, Arkansas. Because of the stochastic nature of the FORFLO model, it cannot calculate flooding more I ~~than a year in advance and, therefore, it cannot calculate flood frequency. j ~~~Flooding is always described in the model by percent 'duration. In the future, atten'tion should be directed to other environmental and physiological factors that might account for the relationship described by Bedinger. I ~~~~As in the Shugart and West model (1977), the subroutine BIRTH is essentially a series of switches which test whether conditions are favorable for seedling survival of each tree species. From the list of species which can 3 ~~~survive, the species and numbers of seedlings which are allowed to germinate in the model are randomly selected. FORET uses four switches testing for exposed 3 ~~~mineral soil, leaf litter covering the soil, recruitment as determined by temperature, and animal browsing intensity. I ~~~~Because soils are such an important determinant of species compositio n in ft ~~bottomland forest, we have expanded the soil characteristic qualifiers for 'species survival in two ways related to soil cover and soil texture. Germination of many species is reduced, but not terminated by an inappropriate soil cover. Therefore, FORFLO examines the species preference for either I ~~~exposed mineral soil or a leaf litter cover testing for a strong or moderate preference. This simply adds an intermediate condition to the same FORET model switches mentioned above. If there is no preference and if soil cover'is j ~~~appropriate, then germination is not reduced. If the soil cover is inappropriate because of a moderate preference for the other type, then germination is reduced by half. If the preference is strong for the otherj type, then germination will not occur for that species. Litterfall determines the leaf toyer and organic content of the sail in the inodel and is a -function of leaf area as described by Shugart and West (1977). A test for the appropriate soil texture has also been added to the BIRTH subroutine. Species are described by their preference for clay, loam, orp sandy soils. As above, if a species has no preference or the soil texture on the plot is appropriate, then germination is not restricted. A species is notI allowed to survive on an inappropriate soil texture if that species has a strong preference for some other soil texture; survival is reduced for a species with a moderate soil texture preference on an inappropriate texture. Since loam is intermediate to sand and clay, survival is reduced less if the inappropriate texture is loam than it is if the texture is sand or clay. Trees may also be recruited into the model by stump sprouting. When a tree dies, stump sprouts may be introduced if the tree was in the proper size range for its species. The number of sprouts is randomly selected and is also speci es-dependent. Tree Growth and MortalityN Shugart and West (1.977) described the growth equation for a tree in optimal conditions as: . 13 dD GD (1- OH/Dmax Hmax) (3) (274 + 3b2D - 4b3D2) where D is the diameter at breast height in centimeters, H is the height of the tree in centimeters, Dmax and Hmax are maximum recorded diameters and heights, respectively, for each individual species, and G, b2 and b3 are growth rate parameters for each tree species. This equation assumes that a tree under optimum conditions will grow to two-thirds of its maximum height at one-half of its maximum age. The derivation of this equation is treated in detail by Botkin et al. (1972a and 1972b) and Shugart and West (1977). The optimum growth curves for some selected species are shown in Figure 5. This optimum growth is reduced by multiplying the equation by modifying parameters for stand crowding, S(BAR); temperature factors, T(DEGD); and shading tolerance, r(AL). S(BAR) is a crude expression of competition for soil moisture and nutrients. S(BAR) is a function which compares the total biomass of the simulated 1/12 ha circular plot with the maximum recorded biomass in the area. S(BAR) is equal to one for open stands with no crowding and approaches zero as the plot biomass approaches the maximum. Knight and McClure (1981) have estimated maximum biomass in South Carolina lowland hardwood stands to be 336,700 kg green weight/hectare. T(DEGD) is a value derived from a parabolic function ranging from zero to one which assumes that each species has an optimum temperature for photosynthesis.' Growing degree-days were used as an index of these thermal effects. The growing degree days for the simulated plot were calculated as the sum over a year of the deviations of mean daily temperatures above 420 F, which was taken to be the base temperature below which trees are not growing. A 30 14 year monthly average of temperatures for the Central Region, South Carolina is shown in Figure 6. The equation which derives T(DEGD) uses minimum and maximum values of growing degree-days to represent the extremes between which each species can grow. Minimum and maximum values are obtained by comparing species range maps with lines of constant growing degree-days. Botkin et al. (1972a) found that the extremes of a species range closely follow growing degree-day isotherms. For a simulated plot in a region whose growing degree-days approach one of the extremes, the value of T(DEGD) for that species will approach zero. The value of T(DEGD) will be one if the plot is in the middle of the species range. An expression of shade tolerance, r(AL), was modified for the FORFLO model. Shugart and Botkin recognize two classes of shade tolerance - tolerant and intolerant - and describe r(AL) by the equation r(AL) = 1.0 - exp[-4.64(AL - 0.05)], shade tolerant; (4) = 2.24[1.0 - exp(-1.136(AL - 0.08))], shade intolerant (5) where AL is the available light scaled between O(no light) and l(max. light). The light available to any individual tree is a function of the leaf area shading that tree from above. The height of the trees on the simulated plot is calculated as a simple function of the trees' diameter. The FORFLO model introduces a third, intermediate shade classification. Points midway between the shade tolerant and shade intolerant curves were fitted to a modified Michaelis-Menten function r(AL) = [1.87(AL - .064)3/(.5 + AL - .064), intermediate tolerance. (6) The shade tolerance curves are shown in Figure 7. In addition to the functions reducing optimum growth given above, a water table function was added to the FORFLO model to model floodplain conditions. The water table function, H(WTAB), modifies the growth equation to express species tolerance of water levels -on the simulation plot during the growing season. The height of the water level for each half-month period determined in the FLOOD subroutine is taken for each half-month in the early growing season- April through July. These water level heights are compared to the height of each tree of a species. If the water level is more than 3/4 the height of the tree (during flooded conditions), it is assumed that the tree will not grow during that half-month period (H(WTAB)---). if this condition is not met then H(WTAB) is a continuous exponential function which compares water level on the simulation plot for each half-month period during the growing season, to the optimum water table depth for each species. Relationships between water table depth and species growth are not well understood however. Phipps (1979) derived a general equation H(WTAB) = 1. -O.05511(T - W)2 (7) which is used in FORFLO. T is the water level (or water table depth) on the simulation plot and W is the optimum water table depth for each species. H(WTAB) is one when the water table is at an'optimum depth for the species, and it approaches zero as the water table rises or falls away from the optimum. Baldcypress and water tupelo both have a wide range over which they will grow optimally (Eyre, 1980; Harms, 1973). Eyre reports that cypress may be flooded ten feet deep or more and show evidence of oxidation at flood depths up to four feet. Four feet was taken to be the maximum depth of flooding for both cypress and water tupelo before growth would be reduced. The two curves for the 16 hypothesized relationship between floodplain tree growth rate and water table are shown in Figure 8. Optimum water table depths for the species used in the simulations are from Phipps (1979), Teskey and Hinckley (1977), Gosselink et al. (1981) and Wharton et al. (1982). The FORFLO growth equation (3) with modifying functions is dD GD (1 DH/mamaxmax) S(BAR) T(DEGD) r(AL) H(WTAB) (8) dt (274 + 3b2D - 4b3D2) When predicted growth is reduced to less than 10% of optimum conditions by a combination of modifying factors, the probability of the tree's death is increased greatly (subroutine KILL). Other Subroutines The subroutine PLOTIN assigns the initial number, age, and DBH of trees of each species to the plot at the beginning of the simulation. Age and DBH of each tree is determined from a normal distribution with a mean and standard deviation specified for each species. Age and DBH use the same degree of randomness in this routine so that for an individual tree, the magnitude and direction of the deviation from the mean is the same. For example, if a particular tree's age is 0.5 times the standard deviation age plus the mean age, that tree's DBH will be 0.5 times the standard deviation DBH plus the mean DBH. The subroutine OUTPUT calculates biomass and net production of trees on the plot using equations from Johnson and Bell (1976) and understory production values from Conner and Day (1976). A printed output is then provided for biomass, production, the number of stems, flood duration, and the hydrologic regime for each year. An option in OUTPUT is to run the model repeatedly and print the results as importance values. Other support routines in the model are essentially the same as in Shugart and West (1977), and do not affect the ecological interpretation of FORFLO simulations. An Application to the Santee River Floodplain To examine the usefulness of the FORFLO model in predicting successional trends, the model was used to simulate impacts of the rediversion project on floodplain forest along the Santee River. Comparison with Field Conditions The study site used to test model response is located about halfway between Lake Mattassee and Jamestown (Figure 1), and has been documented by the Charleston Office of the U.S. Fish and Wildlife Service (USFWS) with a single transect through it (Karen Harper, USFWS, personal communication, 1983). In addition, the University of South Carolina (USC), Department of Environmental Health Sciences has completed a preliminary tree survey of the site (Parks and Williams, 1983). For a preliminary test of model response, initial conditions were pro- vided to represent species composition in a site representative of wetter conditions before the original diversion. Baldcypress (Taxodium distichum), water tupelo (Nyssa aquatica), and water hickory (Carya aquatica) were the predominant species with considerably fewer numbers of overcup oak (Quercus lyrata), sweetgum (Liquidamber styraciflua), green ash (Fraxinus pennsylvanica), and red maple (Acer rubrium) also present. Present patterns of river flow were then simulated and the model was run for a 50 year period to examine the resulting species composition predicted for present conditions by the model. Because of the stochastic properties of the model and the small (1/12 ha) size of the simulated plots, the model was run 50 times and the results averaged. The results at year 50 are shown in Figure 9. Importance values (the sum of relative density, relative frequency, and relative dominance; Curtis, 1959) are used in the figure to describe observed and predicted forest composition. The species listed along the bottom of the graph in Figure 9 are all the species that were available to the model for recruitment. The model predicts the occurrence of 100% of all of the tree species in the study area, documented either by the U.S. Fish and Wildlife Service or by the University of South Carolina study. In addition, the model predicts the minor occurrence of sassafras (Sassafras album) and loblolly pine (Pinus taeda), two species which were not observed in either field study. However, since loblolly pine occurs in other floodplain-sites in South Carolina with similar flood regimes (Warton et al., 1982) this deviation is not considered significant. For the typical mix of bottomland hardwoods in the area, the model also predicted importance values close to the range of those observed for major 19 species. Although there was some divergence in species importance documented by the two field studies, the model generally predicted importance values for each uajz-nspecies w1ab.nfl- units of ne .er both of the fVe1d studies. -'he model ndekestimated-the-zqportasce of -Vreen ash by 40-47% and overestimated the importance of water hickory by 3-5 fold. It cannot be determined from the conflicting field results whether baldcypress has been overestimated by the model. The importance values for water hickory and possibly baldcypress appear to be high in the FORFLO output because the model was initialized with these species and they are slow to die under the new water regime. No new germination of these species is occurring in the model. If the model is allowed to continue to run up to year 200, baldcypress decreases steadily to a very low importance of 6 units and water hickory is completely removed from the plot while American elm (Ulmus americana) increases in importance to 21 units. The slow replacement of baldcypress and water hickory is apparently due to the conservative equation used by Phipps (1979) and in this model to describe tree growth response to the water table. An additional weakness in this comparision is that the initial species mix was only a best guess. Model runs initialized with different numbers, DBH, or ages of species will result in different importance values in the first fifty years. If the model is allowed to continue running beyond year 50, however, the species selected by the model continue to agree with observed tree species. By year 200 initial conditions have far less impact on importance values. Model Simulations of the Santee-Cooper Rediversion Both the proposed and the modified rediversions were simulated along a -f5i6wife reacb :f-Athe 'Sant'er-1oeested -floodpain from_-the rediversiosn- . . * , .':e,. ; - .-m. site-'downstrea .. :_Ure, 1 . 'In:Jteproposed rediversion, flow to the Santee River from Lake Marion remains the same, flow to the Cooper River is reduced to 3,000 cfs as stipulated by the U.S. Army Corps of Engineers, and the remaining flow is added to the Santee River flow via the rediversion canal (Figure 2). The modified rediversion is the same as the proposed pattern except that during the early growing season (April-July) flow through the rediversion canal is reduced to that which can pass through one turbine of the canal's power plant. This option allows flow to exceed 3,000 cfs on the Cooper River only during these four months of the growing season. Thus, the modified rediversion attempts to preserve the bottomland forest in the Santee floodplain by reducing flooding during the trees' critical growth period. Maps of the areal extent of present habitat types compared to the two options as predicted by the FORFLO model are shown in Figure 10. The later two maps were derived by running the FORFLO model at 1/2 foot intervals and recording the elevation at which one classification became more dominant than another. For example, bottomland hardwood is replaced by cypress-tupelo as elevation decreases and flooding subsequently increases. At each elevation interval, 50 replicate model simulations of 200 years each were run. Two-hundred years allows for most species replacement and stabilization of any new species composition that might occur. The most likely outcome of an introduced flood regime is recorded by averaging the 50 replicate runs. The immature bottomland forest (bottomland shrub/scrub) covering so much area in the present-conditions map is the result of clearcutting by landowners in anticipation of rediversion. The nonforested wetted soils classification only means that the moisture regine is too wt -to" upport forest-:cover. 'Fi - - classification does not-necessarily-mean there-must be ,standing7*ater'on the site all year, although the soil will usually be saturated. Much of this area may support emergent vegetation or very moisture-tolerant shrubs such as buttonbush (Cephalanthus occidentalis). Most striking in these maps is the loss of bottomland forest: a 97% loss of bottomland hardwoods for the proposed rediversion or an only slightly improved 94% loss for the modified rediversion. The difference in the two options is primarily that bottomland hardwoods under the modified rediversion scenario are more likely to succeed to cypress-tupelo rather than open water, thus maintaining a forest cover. The acreage of the habitat types under the three conditions is shown in Table 2. Conclusion From the two rediversion alternatives examined, the FORFLO model indicated that large tracts of bottomland hardwood habitat would be lost in either case study. However, the modified rediversion plan, based on reduced waterflow during the growing season, predicted that a forest cover of primarily cypress-tupelo would be maintained. This project thus demonstrated that mathematical modeling can iSe useful in synthesizing complex ecological rela- tionships for the study of successional trends. The integration of ecological modeling and spatial mapping, as in this project, can provide a valuable tool to the resources manager who needs to forecast the extent of change a flood- plain modification may bring about. 22 Options to improve FORFLO are primarily focused on routines describing water relations in the model. The equation used in FORFLO to relate tree growth to depth of the water table is purip!ely conservative because this relationship is not-well studied. An increased understanding of water relations would benefit modeling e"forts to successfully predict floodplain forest succession. The suite of environmental conditions (water table, flooding, temperature) an individual tree has adapted to are often more important than a general optimum for that species. A substantial change inma site's environment can cause rapid loss of the existing trees even when that change is within the limits of the species tolerance (Robert Johnson, U.S. Forest Service, personal communication, 1984). Work is presently underway to incorporate species acclimation into future modeling attempts. A principle result would likely be the speeding up of the successional process after a major change in environmental conditions such as a river diversion. This may be one reason the model appears to be slow in replacing species such as water hickory. Projects are underway to adapt FORFLO to model the Atchafalaya River Basin in Louisiana and the greentree reservoir in the Felsenthal National Refuge, Arkansas. These studies will increase the species list the model can handle, introduce elements to handle sediment subsidence and accretion, and provide opportunities to validate the model. Bottomland forest wetlands are ecologTcally and economically important for wildlife habitat, timber resources, detrital output to downstream marshes, downstream flood mitigation, water quality improvement, and scenic quality. Predictive models such as FORFLO can assist our understanding of bottomland I 1 23 I forest succession interrupted by changing environmental conditions. Such 5 models are effective quantitative prediction tools that can assist resource managers in planning river basin management alternatives. I I I I I I I I I I. I I I I 24 Acknowledgments 5 ~~~~The authors of this report would like to thank Dr. David Cowen and his graduate students, especially Jeff Booth, at the University of South Carolina, 1 ~~Social and Behavioral Sciences Laboratory, for their work in gathering base-line elevation data and producing the map products needed to complete this U ~~study. In addition, we owe much gratitude to Dr. Hank Shugart and Lynn Tharp p ~~at the Oak Ridge National Laboratories for their time and open sh aring of their work. This work was supported in part by USFWS Cooperative Agreement Number 25 References Bedinger, M.S., 1971. Forest species as indicators of flooding in the lower White River Valley, Arkansas. U.S. Geological Survey Professional Paper, 750-C:C248-253. Bedinger, M.S., 1978. Relation between forest species and flooding. In: P.E. Greeson, J.R. Clark and J. E. Clark (editors), Wetland Functions and Values: the State of Our Understanding. American Water Resources Association, Minneapolis, Minn., 674 pp. Botkin, D.B., J.F. Janak, and J.R. Wallis, 1972a. Rationale, limitations, and assumptions of a northeastern forest growth simulator. IBM Journal of Research and Development, 16:101-116. Botkin, D.B., J.F. Janak, and J.R. Wallis, 1972b. Some ecological consequences of a computer model of forest growth. Journal of Ecology, 60:849-873. Conner, W. H., and J. W. Dry, 1976. Productivity and Composition of a baldcypress-water tupelo site and a bottomland hardwood site in a Louisiana swamp. American Journal of Botany, 63(10): 1354-1364. Curtis, J.T., 1959. The Vegetation of Wisconsin and Ordination of Plant Communities. University of Wisconsin Press, Madison, 657 pp. Fowell, H.A., 1965. Silvics of Forest Trees of the-United States. USDj Forest Service Handbook. No. 271. Gill, C.J., 1970. The flooding tolerance of woody species - a review. Forestry Abstracts, 31:671-688. Gosselink, J. G., S. E. Baylery, W. H. Conner, and R. E. Turner, 1981. Ecological factors in the determination of riparian wetland boundaries. In: J. R. Clark and J. Benforado (editors), Wetlands of Bottomland Hardwood Forests. Elsevier Scientific Publishing Co., N. Y., 401 pp. Hall, T.F., and G.E. Smith, 1955. Effects of flooding on woody plants, West Sandy Dewatering project, Kentucky Reservoir. Journal of Forestry, 53:281-285. Hook, D.D., and C.L. Brown, 1973. Root adaptations and relative flood tolerance of five hardwood species. Forest Science, 19:225-229. Johnson, F.L., and D.T. Bell, 1976. Plant biomass and net primary production along a flood-frequency gradient in the streamside forest. Casternea, 41(2):156-165. Knight, H.A., and J.P. McClure, 1981. Multiresource inventories-forest biomass in South Carolina. U.S. Dept. of Agriculture Research Paper SE-230. Kramer, P. J., 1951l. Causes of injury to plants resulting from flooding of the soil. Plant Physiology, 26:722-736. McAlpine, R.G., 1961. Yellow-poplar seedlings intolerance to flooding. Journal of Forestry, 59:566-568. McKellar, H.N., M. Homer, L. Pearlstine, and W. Kitchens, 1981. Preliminary analysis of energy flow impacts of a river rediversion. In: M.J. Mitsch, R.N. Bosserman and J.M. Klopatek (editors), Energy and Ecological Modelling. Elsevier Publishing Co., N.Y. 839 pp. McKl]lar, H.N., J. Booth, L. Pearlstine, and D. Cowen, 1984. Conceptual dt. ~models and energy analysis of river flow modifications - A case study of water control options for the Santee-Cooper River Rediversion, S.C. Final 27 report to the U.S. Fish and Wildlife Service, National Coastal Ecosystems Team, Slidell, La. N.O.A.A., 1973. Climatography of the United States No. 85 (by state): Monthly averages of temperature and precipitation for state climatic divisions, 1941-1970. National Climatic Center, Ashville, N.C. Odum, H.T., 1983. Systems Ecology: An Introduction. John Wiley and Sons, N.Y. pg. 8. Parks, W., and J. Williams, 1983. Preliminary results of forest stand analysis and litter production estimates: Santee River floodplain forest, 1983. University of South Carolina, Department of Environmental Health Sciences, Columbia, (unpubl.), 6 pp. Phipps, R.L., 1979. Simulation of wetlands forest vegetation dynamics. Ecological Modelling, 7:257-288. Shugart, H.H., and D.C. West, 1977. Development of an Appalachian deciduous forest model and its application to assessment of the impact of the chestnut blight. Journal of Environmental Management, 5:161-179. Teskey, R.O., and T.M. Hinckley, 1977. Impact of water level changes on woody riparian and wetland communities, Vol. II: Southern forest region. U.S. Fish and Wildlife Services FWS/OBS-77/59. U.S. Army Corps of Engineers, 1966. Survey report on Cooper River, S.C. (Shoaling in Charleston Harbor). Charleston District, Charleston, S.C. U.S. Army Corps of Engineers, 1975. The final environmental statement, Cooper River rediversion project. Charleston District, Charleston, S.C. U.S. Army Corps of Engineers, 1976. Design memorandum no. 9. Charleston District, Charleston, S.C. 28 Wharton, C. H., W. M. Kitchens, E. C. Pendleton, and T. W. Sipe, 1982. The ecology of bottomland hardwood swamps of the Southeast: a community profile. U.S. Fish and Wildlife Service, Biological Services Program, Washington, D.C. FWS/085-81/37. 29 Table 1. Annual average flow regimes for the Santee and Cooper Rivers. Flow (cfs)* TIME Cooper River Santee River Before Diversion in 1941 100 17500 After Diversion 15500 2000 After Planned Rediversion 3000 14500 Icfs = cubic feet/second = 0.0283 m3/sec. 30 Table 2. Parameters used in the FORFLO model.* Species Name DMAX OMIN G 83 82 ITOL AGEMX SND SuN SMX 12345 W FRI FR2 GWi GW12 KSL KPF Acer rubrum 13395. 1810. 222.2 .1479 46.62 1 150. 3. 12.0 100. FTTTF .60 .00 30.0 5 14 1 0 Carpinus caroliniana 10820. 2420. 142.1 .2592 48.20 1 150. 2. 6.0 70. .TT7FF .60 .00 16.0 5 14 0 0 Carys aquatica 11100. 5800. 159.0 .0959 41.24 2 250. 3. 6.0 70. TFFTF .30 10.00 40.0 8 11 0 0 Carya tomentosa 12652. 4105. 98.1 .1900 49.41 3 300. 1. 12.0 200. TTTFF 2.10 .00 5.0 8 12 1 0 Celtis laevigata 12560. 4820. 100.0 .3846 70.00 2 200. 2. 6.0 30. FTFTF .60 5.00 25.0 4 7 0 0 Cornus florida 10947. 3686. 88.7 .5360 40.81 1 100. 3. 12.0 200. TTTFF 1.80 .00 '2.0 4 8 0 0 Fraxinus pennsylvania 9900. 1600. 188.6 .1518 43.14 2 150. 1. 6.0 50. FF7FF .90 8.00 30.0 4 7 0 a Lirlodendron tullpifera 10947. 3686. 174.8 .0440 32.35 3 300. 2. 12.0 200. TITFT .60 .00 4.0 5 8 4 1 Liquldambar styraciflua 10947. 5526. 140.0 .1009 38.50 3 250. 2. 12.0 100. FTFTF 1.00 6.00 20.0 4 7 3 0 Nyssa aquatica 9300. 6000. 95.6 .0444 23.32 2 300. 1. 6.0 50. FTFFF .00 30.00 60.0 3 14 1 0 Pinus echinata 9461. 5526. 96.7 .3266 64.51 3 300. 2. 6.0 20. TFFFF 2.00 .00 4.0 4 6 3 0 Pinus taeda 10820. 5730. 101.5 .1790 53.00 3 350. 0. .0 0. FTFFF 1.80 .00 4.0 14 6 1 0 Platanus occiteqtalis 9900. 3500. 93.0 .0388 28.42 2 500. 2. 6.0 50. TTFFF .60 .00 20.0 7 10 0 0 31 Table 2. contfnued. $pectes Nan~ DHAX DH[N G 83 B2 ZTOL AGEHX SNO SHN SHX 12345 W FR1 FR2 G~L GT/2 KSL KPF Populus dettotdes 10000. 1600. 309.0 .0388 20.42 2 150. 1. 6.0 30. TTFFT 1.02 .00 20.0 7 16 3 0 Prunus serotins 10947. 3899. 138.6 .0830 35.57 3 250. 3. 12.0 200. -FFFTF 1.10 .00 1.3 5 10 0 0 quercus alba 10204. 2966. 100.3 .0740 36.37 2 400. 2. 12.0 40..TFFFF 2.40 .00 2.0 17 20 I 0 quercus faicata 10947. 5526. 62.6 .0780 33.57 2 400. 2. 12.0 30. FFFFF 1.00 .00 5.0 5 10 I 0 quercus !aurtfolfa 11800. 6000. 104.8 .0860 31.97 2 150. 3. 6.0 ]2. TFFTF 1.00 .00 20.0 17 20 3 0 Queecus lyrmts 9600. 5300. 128.5 .1221 41.47 2 250. 1. 6.0 12. FTFTF .60 5.00 30,0 17 20 3 0 Quercus Ilchaux!! 10200. 5000. 117.0 .0508 27.09 2 300. 1. 6.0 12. TTFTF 1.00 .00 30.0 17 20 I 1 quercus nt�rm 10700. 5500. 114.9 .0866 32.74 2 250. 2. 6.0 12. TTFTF 1.$0 10.00 15.0 5 9 I 0 Sallx nfgra 11200. 2900. 169.8 .1348 24.15 3 70. 3. 6.0 30. TFFFT .50 .00 20.0 7 11 2 1 Sassafras albJdum 10947. 3686. 135.8 .1039 34.79 3 200. 3. 12.0 200. TTFFF 2.00 .00 4.0 S 9 I 0 32 Table 2. continued. Species Name DHAX DMIN G B3 82 ITOL AGEMX SND SMN SMX 12345 W FR1 FR2 G GW2 KSL KIPF Taxodium distichum 11800. 5300. 82.9 .0242 18.61 2 400. O. .0 O. TTFFF .15 30.00 50.0 4 12 1 0 Ulmus americana 12560. 2200. 85.5 .0465 22.40 2 300. 2. 6.0 240. TTFTF 1.50 .00 20.0 5 10 I 0 *DMAX and DMIN are maximum and minimum degree-days respectively. G. B3 and B2 are the growth equation (3) (text) parameters for each species. ITOL is the shade tolerance class. Class I is shade tolerant and equation (4) is used to calculate the growth modifier. Class 2 is intermediate and uses equation (6) while class 3 is shade intolerant and uses equation (5). AGEMX is the maximum recorded age of each species. SND is the tendency to reproduce vegetatively end SW and SW are minimum and maximum size trees (cm, DOBH) that will stump-sprout. Reproduction switches (12345) are used in the BIRTH subroutine and take values of T (true) or F (false). Switch I Is T If the species prefers sandy soil. Switch 2 is T if the species prefers clay so11. Switches I and 2 are both T if the species prefers loam. If switch 1 and 2 are both F the species has no soil texture preference. Switch 3 is T if species recruitment is reduced by a hot year. Switch 4 is T If the species is a preferred food of deer or small mammals. Switch 5 is T it the species can't germinate when the plot biomass Is high. W is the optimo depth to the water table (m) and is used to calculate the growth modifier in equation (7). FRI and FR2 are the range of annual flood durations (percent) tolerated by the species during germination. GW1 and GW2 are the range of half-month periods during which the species will normally germinate (e.g., I - first 33 Table 2. Continued. half of January; 24 last halt of December). KSL measures the degree of preference for soil cover. 0 is no preference. I and 2 are moderate and strong preferences, respectively, for exposed mineral soil. 3 and 4 are moderate and strong preferences, respectively, for littercover. KPF measures the degree of preference for soil texture. 0 is a moderate or no preference; 1 is a strong preference. 34 Table 3. Habitat types in the Santee River floodplain study area. Hectares* Proposed Modified Present Rediversion Rediversion --- Nonforested, wetted soils 743 6655 3060 Cypress-Tupelo - 1962 1918 5512 Bottomland hardwoods 7362 257 417 *Hectares = 2.47 Acres 35 Figure Captions Figure 1. Location map. Figure 2. Seasonal discharge patterns for the Santee-Cooper Rivers. Figure 3. Overview diagram of the FORFLO forest floodplain succession model. Symbols are from Odum, 1983. Figure 4. Stage discharge relationships for stations on the Santee River. Figure 5. Optimum growth curves for some selected species as described by the FORFLO model. Figure 6. Sine curve fitted to 30 year monthly average temperatures for the central region, South Carolina. Y = 63.6 - 16.95 In [(2n/365) (time-105)] Reference: N.O.A.A, 1973. Figure 7. Available light growth modifier. Figure 8. Water table growth modifier. Figure 9. Comparison of model results with field observations. Figure 10. Areal extent of habitat types currently and as predicted by FORFLO after the rediversion. - ~Rediversion Site S.c"C Lake Mat tassee St. * ~~SITE Georgetown, N4~~~~ e Moulti ChamlestonHror .30 30 30 PRESENT PROPOSED MOD0IIED -4 REDI VERSION REDIVERSION U- 20- 20- 20- COOPER RIVER SANTEE RIVER SANTEE RIVER Z X- z 10- 10- to- iE COOPER RIVER - - COOPER RIVEC SANTEE RIVER J'F'M'A'M'J'J'A's'o'ND J'F'M'A'M'J'J'A'S'O'N'D J'F' M' A 'M'J'J'JA' S 'O' N'ID m -~ - 1/12 .HECTARE (RIVERFLOOD DURATION LOCAL HEIGHT CPECIEN (9 3- _OF WATER CROWDING PRODUCTION NUMBER HEIGHT TEMP- x s X X BIOMASS DBH f -, ,=M.-\ I\ .) .� � 1 B'AGE / \LITTER FALL/AA LITTER - ORGANIC EXPORT I SPROUTS ORGANIC/MINERAL SouTs LI CONTENT OF SOIL ! SEEDLING P SURFACE GERMINATION STUMPS ABILITY IN - SEXUR I I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ao 8- ~~~LAKE MATTASSEE ()**3.2 METER ELEVATION DIFFERENCE (0 0~~~~~~~~~~~~~~~~~ 00" o E5~~~~~~~~~~~~~~~c 000 0 0 4 ~ ~~~~~~~~~~~~~~~~ 0 0~~~~~~~~~~~~ 0 w~~~~~~~~~~~~ 00 00~~~~~' 00 ~~~~~I1~~~~~ 0 000OW (0 0~~~~~ 0 *acooxx) 0 0 0 0 0 00~~~~ 0 ~~~~20 40 60 80 * I ~~~~~~~~~~~~~~~RIVER FLOW (461 X 103) .~~~~~~~~~~~~... .. ..i�~*1...- 400 2 300 EATRN o ~~~~COTTONWOOD m BLACK ~~~~OVERCUP OAK 00 100 200 ~~~~~300 400 TIME, YEARS .07 80- u. O470 w A w 50 - 40 JIFIMIaIMIJIJIAIsIoINID -~11 -- -1 _ C, E ~~SHADE. TOLERANT INTERMEDIATE X TOLERANCE (5 w -j co SHADE INTOLERANT > 0.0 0. 0.0 0.5 1.0 AVAILABLE LIGHT BALDCYPRESS AND WATER TUPELO 1.0 - ,: - / 1 : 0.5 0 : - /I 0.0 / I I i I 2 1 1 OPTIMUM DEPTH ALL OTHER SPECIES 1.0 O~ 0.5/| O - /. -"I cc~~~~~~~~~~� I.0 ;-- 2 I v 1 ~~~~~~~~~~~~~~In ~~~~-- ~~OPTIMUM DEPTH RELATIVE DISTANCE FROM OPTIMUM DEPTH TO WATER TABLE (METERS) INCREASING DEPTH TO WATER TABLE- - INCREASING HEIGHT OF FLOODING 300)I ,t300 1002 - U.S. FISH AND WILDLIFE SERVICE, CHARLESTON, SC -100 ffl 90- '-- UNIVERSITY OF SOUTH CAROLINA -90 - *"'~v** FORFLO SIMULATION, 50 RUN MEAN 80- ~~~~~~~~~~~~~~~~~~~~~~~-80 70- -70 w 0 60- -60 z 50- -50 40- -40 30- 30 Ii I 20- I. -20 I I I~~~ IS i: II~~~~~~~~~~~ ; I *I! 2 10-~I iI IP PSI : 1. 1 A 0 9: K o KZ PZo IPs 14~ ~ ~~~~~ 111 881tz g r 14%3 PRESENT REDI VERSION F AS PROPOSED REDI VERSION MODIFIED CYPRESS/ BOT~TOMLAND BOTTOMLAND SHRUB/SCRUB CAN4AL i *NONFORESTED-WETTED SOILS ........ .. ........ ....... ...... . . ... .- .....�
Which of the following best predicts the effect of the enclosure on the O'Neill population?
Which of the following best predicts the effect of the enclosure on the O'Neill population? The frequency of the MC1RDMC1RD allele will decline due to natural selection.
Which of the following could best explain the increase in the frequency of the allele in the population after five years?
Which of the following could best explain the increase in the frequency of the B allele in the population after five years? The frequency of the B allele increased due to the selective pressures of the environment.
Which statement explains how geographic isolation causes speciation?
How Does Geographic Isolation Cause Speciation? When a population is separated because of a geographic feature, like distance, a canyon, a river, or a mountain range, those two subgroups of the population are no longer able to reproduce together.
Which of the following best describes the process responsible for the change in the percent of Tuskless female elephants in the Gorongosa?
Which of the following best describes the process responsible for the change in the percent of tuskless female elephants in the Gorongosa National Park population shown in Figure 1 ? Human hunting of elephants with tusks between 1977 and 1992.