INFLUENCE OF THE ST. LAWRENCE RIVER HYDROLOGIC REGIME ON FISH ASSEMBLAGES IN THE VICINITY OF THE AKWESASNE WETLAND COMPLEX

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1 INFLUENCE OF THE ST. LAWRENCE RIVER HYDROLOGIC REGIME ON FISH ASSEMBLAGES IN THE VICINITY OF THE AKWESASNE WETLAND COMPLEX James E. McKenna, Jr. 1, James H. Johnson 1, and Joyce L. Barkley 2 Tunison Laboratory of Aquatic Science 1 Great Lakes Science Center, United States Geological Survey Cortland, NY St. Regis Mohawk Tribe Environment Division 2 Akwesasne, NY Abstract: The Akwesasne Wetland Complex is a valuable wetland straddling the St. Lawrence River where borders of New York, Ontario and Quebec meet. It is of great importance to the Akwesasne Mohawks, but little is known of its fauna or effects of the Robert Moses-Robert H. Saunders Power Dam, immediately upstream. However, it is clear that the hydrology of this area (particularly water levels and flows) has been drastically altered. As part of the larger water level control project of the International Joint Commission, fish were sampled from shallow aquatic habitats throughout this section of the St. Lawrence River and its tributaries. A suite of abiotic conditions, including measures of water temperature and water level variability, were also recorded. Analysis of fish community structure indicated significantly different assemblages occupying bands encompassing the Main River, the Tributaries, and a Buffer Zone between; each changing with season. Assemblage diversity decreased from the Tributary Zone to the Main River. The influence of water level variability on fish abundance was evident for several species. Key Words: abiotic conditions, Akwesasne, fish assemblages, water level 1

2 Introduction Despite being an essential component of healthy terrestrial and aquatic ecosystems, wetland habitat has suffered disproportionately with regard to direct (e.g., dredge and fill) and indirect (e.g., hydrologic alteration) habitat destruction and decline in biotic health (National Research Council 1992). The ecological health and status of the extensive freshwater wetland fringing the upper St. Lawrence River in the Akwesasne Mohawk Territory is unknown and of high priority for the Akwesasne tribal governments. It is also of special interest to the US Fish & Wildlife Service (FWS) and other environmental managers. The western most extent of the Akwesasne Wetland Complex includes the St. Lawrence River at the Massena Area of Concern (including portions of the Grasse, Raquette and St. Regis Rivers)(NYSDEC 2000) and an eastern component, Lac Saint Francois, designated as a Wetland of International Importance (Ramsar 2005). The Akwesasne Wetland Complex extends from the St. Lawrence River as much as 20 km inland from the Quebec portion of the community and well into upstate New York. Located in the eastern part of the Complex sits a very special marsh, known to the Mohawk people as the Snye Marsh (Fig. 1). This marsh is an important component of the ecological system within and adjacent to Akwesasne, containing flora and fauna of special tribal interest and value. At one time this marsh was a great resource for medicinal plants and animal pelts. A complex interaction between ice damming and river flow probably contributed to its formation. However, since the construction of the Robert Moses-Robert H. Saunders dam ( Moses-Saunders Dam ), the hydrologic regime has not been favorable for ice dam formation in the Snye Marsh. Over the last 50 years water levels have not charged this marsh and it now shows signs of eutrophication, a possible result of these less dynamic conditions. Fish and wetland plant communities are known to respond to altered hydrologic regimes in a number of ways often resulting in reduced species distributions, poor production, and a general decline in species assemblage quality (Bain et al. 1988, Welcome et al Wilcox 1995). Simplification of water level and flow variability and alterations of other aspects of hydrologic conditions have affected the extensive aquatic-wetland interface of the Akwesasne Wetland Complex. Operation of the Moses-Saunders Dam and the associated alteration of hydrologic regime are among the factors that may be degrading the Wetland Complex and it s segments, like the Snye Marsh. This study was designed to provide information critical to effective International Joint Commission (IJC) management of hydrologic regime in this large aquatic-wetland system and, potentially, recommendations for restoration of the natural communities occupying the Akwesasne Wetland Complex. The major objective of this study was to provide a quantitative assessment of the ecological health of the wetland system in relation to hydrologic regime. The investigation examined the longitudinal axis and lateral spread of the wetland complex for changes in wetland characteristics as an indication of temporal change to the wetland since the construction of the Moses-Saunders dam. The performance indicators evaluated help to 2

3 determine the influence of a variety of conditions on biotic communities in this extensive wetland system. Standard indices of wetland health were used to evaluate measures of biodiversity associated with the wetland complex. This information is intended to provide the IJC with data on the response of aquatic and wetland organisms to changes in hydrologic pattern and the Akwesasne Mohawks with a basic assessment of the ecological status and major influences on the marsh system. The analysis objective focused on evaluation of the condition of the observed fish assemblages and their correlations with hydrologic and other abiotic variables. This provides insight into the influence of changes in hydrologic regime on fish assemblages, and will contribute to modeling efforts by the IJC Environmental Technical Working Group (ETWG) Integration Sub-group that may indicate the regime that would maintain optimal biotic conditions in the Akwesasne Wetland Complex and the St. Lawrence River in general. The hypotheses examined were that, (1) there are no significant differences among fish species assemblages occupying habitats within the Akwesasne Wetland Complex or adjacent areas of the St. Lawrence River and its major tributaries; and (2) there is no significant correspondence between fish species assemblages or selected indicator species abundances and water level fluctuation in the sample area. Methods Study area The study area covered an area of ~5,700 ha of water and wetlands and included shallow areas (<1.5 m) of the main St. Lawrence River from below the Moses-Saunders dam to the Christatie Islands Complex. It also included the lower reaches of the major tributaries (Grass, Raquette, St. Regis, and (English) Salmon Rivers) entering this section of the river (Fig. 2). The Akwesasne Mohawk Reservation occupies areas in Ontario, Quebec, and New York; and includes nearly all of the waters and wetlands of this region. Although development is light throughout most of this area, there are three high-priority, Federal and/or State Super Fund toxic waste sites. In addition, the St. Lawrence River is one of the most heavily used commercial shipping lanes in North America, as well as the drainage conduit for all the Great Lakes. Site Selection Initial sample sites were selected from the main river, three large tributaries (Grass, Raquette, and St. Regis Rivers), and numerous sites throughout the wetland complex. Additional sampling areas were carefully evaluated with the help of Mohawk environmental experts and interpretation of water level variability information to ensure representativeness of the whole system and specific areas of interest. Specific sample locations were selected at random and represented conditions in two different areas, those where water level is influenced mainly by changes in the St. Lawrence River and those most affected by changes to upstream conditions in the tributaries. Selected sites represented a range of hydrological variability and environmental quality. Collections occurred in the seinable habitat nearest each pre-selected site. Site 3

4 locations were established and recorded with a Trimble Pro-XR with TXC1 GPS unit (datum: WGS84, Geoid:EGM96). Data Collection The field sampling design was intended to provide baseline data on biological assemblages and the associated habitat conditions, including hydrologic status (e.g., water level), that are necessary to evaluate the hypotheses. Samples were collected monthly (May September 2004) at 27 locations within the study area. Water Measurements A suite of in situ habitat measurements, including, water temperature, conductivity, ph, dissolved oxygen (DO), salinity and turbidity, were measured with an YSI 610 DM datalogger and 6820 sonde. Water current velocity was recorded with a Swoffer Model 3000 meter. Both instruments recorded about 0.3 m below the surface, characterizing the conditions at each sample site. Water samples were collected in 30 ml bottles (after careful rinsing) at each site for chemical analysis and labeled with the month and site code. They were stored on ice until they could be frozen in the laboratory. They will be analyzed for inorganics as time and funding permits. Continuously recording Temperature (Hobo H8 one channel) and Pressure (Hobo-Atm) dataloggers were installed within water proof cases (vented to the atmosphere), attached to cement blocks, and submerged at a fixed depth (0.5 m m) at 12 of the sampling locations that best represent overall system conditions (Fig. 2). Temperature and pressure values were interpolated throughout the region by the kriging facility within ArcGIS 8.2. The variability of water temperature and water level (pressure) were determined by the coefficient of variation (CV) of the continuous measurements collected at each site where an instrument was located. Semi variance analysis followed by kriging interpolation throughout the sampling area was performed on those data using the GS+ software (version. 5.0 for Windows, Robertson 2000). Biological Collection Fish were collected by hauling a 40 long x 6 high, ¼ mesh, center-bag seine for 15 m through each sampling area. The seine haul was repeated twice over the same area and catch information was recorded separately. All fish caught were identified to species level and subsets of 25 individuals of each species were measured for total length. Unidentified individuals were measured, recorded, placed in a labeled cheese-cloth bag, and preserved in 10% buffered formalin for about a week, and then transferred to 80% ethanol for storage. A dissecting microscope was used as necessary to count fin rays and scales or inspect other features to aid in fish identification. Final identification was made in the laboratory using the keys of Scott and Crossman (1973) and Smith (1985). Analysis Abiotic (habitat) data were standardized to a mean of zero and standard deviation of one to remove the differential effects of data range and units. Thus, evaluations of the influences of abiotic data assess the effects 4

5 of their deviations from mean conditions. Multivariate statistical techniques were used to explore the data and identify distinct patterns among fish assemblages and suites of abiotic characteristics (McKenna and Saila 1991, McKenna 2003). These were followed by classical hypothesis testing and model building methods. For fish, the general process involved computation of basic descriptive statistics about each fish assemblage, such as diversity (Shannon and Weaver 1949), richness, and relative abundance. Bootstrapping cluster analysis (Nemec and Brinkhurst 1988, McKenna 2003) was then applied to the data to arrange assemblages by similarity and determine which were significantly different. Canonical Correspondence Analysis (ter Braak 1988) was then applied to produce a direct gradient analysis of the biological and habitat data, determine the relative arrangement of distinct assemblages along environmental gradients, and identify the environmental factors that most strongly influenced assemblage structure. These results were then used to generate specific hypotheses and test them by classical means, such as analysis of variance (ANOVA) with Tukey s multiple comparison test and Student s t-test (Zar 1999). Simple and multiple linear regression and locally weighted regression (LOESS) were used to examine relationships between water level variability and fish abundances. Results Habitat and biological data were collected monthly from May through September at 27 sites in the study area; one additional site (G41) was sampled only in July. Dataloggers to continuously record water temperature and pressure (water depth) were installed at 12 sites, at the beginning of June, but many failed after July and only one survived through September. Therefore, only June and July data will be discussed here. Amphibians and reptiles were typically a small component of the vertebrate assemblages and were not the primary target of the sampling gear. Therefore, only fish will be discussed here. Habitat Conditions Both spatial and temporal variations of in situ habitat conditions were observed. Water temperatures throughout the system were coolest in May, peaking in July (Fig. 3a) and declining through August and September. Generally, temperatures were higher and rose earlier in the tributaries than in the main St. Lawrence River (Fig. 4). Conductivity fluctuated considerably from month to month (Fig. 3b), being high in June and September and relatively low in the other months sampled. This pattern corresponded well with rainfall for the same time period (Fig. 3b). Tributaries were consistently less saline than the main St. Lawrence River (Fig. 5). Dissolved oxygen was fairly consistent, but dipped low in August (Fig. 3c). The ph also varied little, except for a low value in July (Fig. 3d). Turbidity varied considerably, but was high in May and August (Fig. 3e). Continuous measurements of both water temperature (Fig. 6) and water level (Fig. 7) showed specific zones of high variability that shifted from June to July. Highest June temperature variability occurred throughout a broad region about mid-way between the Moses-Saunders and Beauharnois Dams (Fig. 6). Water 5

6 temperature variability was much greater in July ( ) than in June ( ) and was focused near the mouth of the St. Regis River (Fig. 6). Water level variability was most intense in the upper half of the main St. Lawrence (Fig. 7). The highest June variability was centered in the lower reaches of the Raquette River and along the south side of the main St. Lawrence River between the mouths of the Grass and Raquette Rivers (Fig. 7a). Like water temperature, the range of water level variability shifted to higher values (though not as large a scale change) and became most intense in July in the main St. Lawrence River just upstream of the mouth of the Raquette River (Fig. 7b). A LOESS regression of water level variability on water temperature explained a large portion (74%) of that relationship and shows a shift from cool, less variable water level conditions in June to warm and more variable conditions in July (Fig. 8). The various regions of this relationship show that the upper portion of the main St. Lawrence was consistently more variable at a given temperature (in June and July) than were other parts of the system (F=151, P<0.001). Biological Collections A total of 8,281 vertebrates (and 48 crayfish) were collected by seining (Table 1). Forty-four species of fish from 14 families were represented in the catch. Five taxa of amphibians (two frogs, two tadpoles, and salamanders) and one species of turtle (eastern painted turtle, Chrysemys picta, EPNT) represented the herptofauna. The mean catch per sample was 65.6 individuals and average species richness (S) and diversity (H ) were 5.7 and 1.05, respectively (Table 2). Total vertebrate abundance varied considerably among locations and exceeded 1,200 at one site (P23) (Fig. 9). The general aquatic vertebrate assemblage was dominated by yellow perch (Perca flavescens, PRCH), pumpkinseed (Lepomis gibbosus, PUNK), Bluntnose minnow (Pimephales notatus, BNOS), white sucker (Catastomus commersoni, WSUK), and banded killifish (Fudulus diaphanous, KILL) (Fig. 10). Cluster analysis revealed significantly different vertebrate assemblages, both by season (Fig. 11) and by spatial region (Fig. 12). May and June assemblages were distinct from each other and from the assemblage present later in the summer (Fig. 11). In general, species richness was low in spring and increased as the seasons advanced (Table 3). The assemblage present in May was strongly dominated by PRCH, with large components as PUNK, KILL, and tessellated darter (Etheostoma olmstedi, TESS) (Fig. 13a); mean abundance (64) was moderate, but mean richness (2.8) and diversity (0.61) were relatively low. Mean diversity and abundance peaked in June (H =1.08, abundance=84.7) when the assemblage shifted to one dominated by BNOS, KILL, PRCH, and PUNK (Fig. 13b). BNOS again became a minor component (6%) of the summer assemblage, which was dominated by PRCH, PUNK, WSUK, and rock bass (Amboplites rupestris, ROCK) (Fig. 13c). The summer assemblage was richest (5.3) and mean diversity was nearly as high as June s (1.05), but abundance was low (59). 6

7 Spatially, cluster analysis identified seven significantly different assemblages (Fig. 12). Most samples were members of only two clusters; those found in the main St. Lawrence River (cluster B) and those samples in cluster A within the buffer zone along the southern margin of the main river (including some of the lowest tributary samples) (Table 4). The Main River assemblage (Cluster B) was dominated by PRCH, BNOS, WSUK, and KILL (Fig. 14b). The primary buffer zone assemblage (Cluster A) was dominated by centrarchids (particularly PUNK and ROCK), though PRCH accounted for nearly ¼ of the assemblage (Fig. 14a). The other assemblages were relatively uncommon and local, occurring at only one or two locations and displaying the widest range of mean richness ( ) and diversity ( ). PRCH was a large component (>10%) of all assemblages (except F), but dominant components varied widely (Fig. 14). Assemblage F was the richest (11) and most diverse (H =1.62) and the only assemblage without PRCH, but was sampled only in July (Fig. 14f). This sample was unique and represented the assemblage found relatively far upstream in the Grass River (Fig. 2). The spatial arrangement (of clusters) indicates three major faunal zones (longitudinal), 1) the main St. Lawrence River (Main River) (Cluster B), 2) tributary samples (clusters C, D, and F), and 3) a buffer zone between the other two bands (clusters A, E, and G) (Figs. 2 and 12). Mean abundance within these zones did not differ (F = 0.42, P = 0.66), but there were significant differences in richness (F = 8.17, P <0.001) and diversity (F = 13.06, P <0.001), with the tributary zone having the highest and the Main River lowest values (Table 5). The uncommon assemblages were found in the tributaries or in the buffer zone but along the main flow of the St. Lawrence River (Fig. 14 c-g). The forward selection process of canonical correspondence analysis (CCA) selected five habitat variables that significantly affected the fish assemblages: water temperature, conductivity, dissolved oxygen, water level variability (pressure CV), and temperature variability (temperature CV) (Fig. 15). The species-environment correlations were generally high for the first three ordination axes ( ) and 81% of that relationship was explained by the habitat variables (Table 6). Overlay of the spatial cluster analysis groups on the space defined by the first two ordination axes showed fairly strong separation between the two largest clusters (A and B), but with an area of overlap (Fig. 15). Much of the space occupied by the other assemblages was within the zone of overlap between clusters A and B. Cluster E (P20) was contained almost exclusively within this zone. Few species had optima in the region occupied only by the Main River assemblage (American eel (Anguilla rostrata, AEEL), brook silverside (Labidesthes sicculus, SILV), smallmouth bass (Micropterus dolomieui, SBAS), round goby (Neogobius melanstomus, GOBY), three-spine stickleback (Gasterosteus aculeatus, 3SPN), black bullhead (Ameiurus melas, BULL), cutlips minnow (Exoglossum maxillingua, LIPS), slimy sculpin (Cottus cognatus, SLIM), and salamander (SALA)) or that of cluster A (emerald shiner (Notropis atherinoides, EMRL), walleye (Sander vitreus, WALL), eastern painted turtle, and silver redhorse (Moxostoma anisurum, SRDH)) and nearly all of 7

8 those species were rare or uncommon. All others were shared by two or more assemblages. The PRCH optimum was located closest to the ordination center (Fig. 15). Significant differences in abundances of dominant species (PRCH, PUNK, BNOS, WSUK, KILL, TESS, ROCK, SBAS, brown bullhead (Ameiurus nebulosus, BBUL), golden shiner (Notemigonus crysoleucas, GOLD), and spottail shiner (Notropis hudsonius, SPOT)) among clusters and zones was tested with one-way ANOVA. Only BBUL, GOLD, PUNK, and ROCK had significantly different abundances among clusters (Table 7). BBUL and GOLD abundances were significantly greater in cluster C than any other and PUNK abundance in cluster A was significantly greater than all other assemblages (Table 8), but the Tukey s multiple comparison test did not identify any significant differences to support the ANOVA results for ROCK. Four species (KILL, PUNK, ROCK, and WSUK) showed significantly different abundances among faunal zones (Table 7). KILL and WSUK were significantly more abundant in the Main River than in the Buffer Zone, PUNK were significantly more abundant in the Buffer Zone than in either the Tributaries or Main River, and ROCK were significantly more abundant in the Tributaries than in the Main River. No significant differences in abundance of the other dominant species were detected (Table 9). Northern Pike (Esox lucius, PIKE) (an ETWG index species) did not occur in the Main River (cluster B) samples, and Tributary and Buffer Zone abundances did not differ significantly. Habitats associated with biotic realms Abiotic conditions associated with the May assemblage were cold and dilute, with elevated D.O., ph, and turbidity levels (Fig. 16a). The June assemblage experienced approximately average temperatures and somewhat low turbidities, but conductivity, D.O., and ph were high (Fig. 16b). Summer assemblage conductivities were average for the dataset and temperatures were high, but D.O. and ph were low (Fig. 16c). Abiotic conditions associated with the main river assemblage (cluster B) were closest to the average for the entire data set (Fig. 17b), though conductivity and D.O. were a little higher than average. As would be expected from the above examination of system-wide conditions (Figs. 4 7), abiotic conditions within the buffer zone tended to be somewhat warmer and less saline with lower D.O. and higher turbidity than average (Fig. 17 a, e, g). Tributary sites had the lowest conductivities (Fig. 17 c, d, f). Cluster D (P11 and P13) conditions were quite similar to those of cluster A, but had much lower conductivity and the high turbidity (Fig. 17d). Cluster C (P25) conditions were close to the average, but had low ph and the lowest D.O. (Fig. 17c). Cluster G habitat had the lowest temperature, but high values of the other variables, including the highest turbidity. The most extreme conditions were associated with the single sample in cluster F (Grass River site G41) where temperature was highest and conductivity, D.O., and ph were lowest (Fig. 17f). No fish were collected on eight sampling events; the abiotic conditions associated with those sites were the coldest and least turbid observed, on average (Fig. 18). Abiotic conditions were also different in each faunal zone. The Main River was the same as described above for Cluster B (Fig. 19a). Tributaries had the highest 8

9 temperatures and elevated turbidity values, but the lowest conductivity, D.O., and ph values (Fig. 19c). The Buffer Zone had moderate values of conductivity and D.O, but ph was elevated, and temperatures and turbidities were the lowest and highest observed, respectively (Fig. 19b). Fish sizes were sampled and a measure of recruitment (young-of-the-year, YOY abundance) is available for each sample, though YOY were rarely collected for the key (ETWG) PI species (PIKE, SBAS, Largemouth Bass (Micropterus salmoides, LBAS), and PRCH except for yellow perch. That analysis has not yet been completed, but the mean abundance of Yellow Perch <80 mm total length in July was 5.6 per sample site and no recruits were collected from 10 sites that month. Models of relationships to water level variability Stepwise multiple regression identified water temperature or temperature variability as a significant predictor of diversity (temp and conductivity), richness (conductivity and temperature CV), or overall abundance (temp and D.O) (Table 10). However, water level variability did not significantly contribute to those predictions. Simple linear regressions identified few significant relationships between common species abundances and water level variability. Of the nine fish species for which 100 or more individuals were collected during the period when most of the continuous recording temperature and pressure instruments were working (June and July), three (BNOS, ROCK, and WSUK) showed significant linear relationships (p < 0.05) with water level variability (coefficient of variation in pressure) and two other species (KILL and PRCH) had nearly significant relationships (0.1 > P > 0.05) (Table 11). Multiple regression models using influential abiotic variables identified by CCA were developed to better characterize these relationships (Table 12). The best significant models explained 36% (ROCK) 47% (WSUK) of variation, though a LOESS regression explained 60% of variation in WSUK abundance (Fig. 20). The WSUK LOESS model showed evidence of juveniles (individuals <50 mm) recruiting to the system in late July (Fig. 20). The absolute values of standardized water level variability and conductivity explained 44% of variation in the linear portion of white sucker abundance (with the effects of recruits removed, and based on only samples where WSUK were present) (Table 12). ROCK abundance also responded in a linear way to water level variability (Table 12); the strongest relationship incorporated the effects of the absolute values of water level variability and conductivity, and ph and turbidity (although the influence of turbidity was not quite significant, P = 0.07). A log-linear model of BNOS abundance explained nearly 40% of variability and included the effects of water level variability and conductivity (Table 12). No significant multiple linear regression model could be constructed for PRCH or KILL. 9

10 A LOESS regression of PRCH abundance on water level variability also showed evidence of juvenile recruitment in July (Fig. 21). However, when the effects of these recruits were removed (by excluding individuals smaller than 80 mm), yellow perch abundance did not respond to water level variability (P = 0.79). Discussion The study area includes a portion of the St. Lawrence valley with a diversity of natural resources. It is a physically dynamic area affected by the influences of major and minor tributaries, and changes in the St. Lawrence River itself. These are in turn affected by operation of numerous local dams, particularly the Moses- Saunders Dam. There was clear variability in habitat conditions within the study area, both in various parts of the system and through the sampling season. Temperature advanced with the seasons and month-to-month changes in the conductivity of the system corresponded with the rainfall pattern (Fig. 3). Tributaries warmed sooner and experienced a wider range of abiotic conditions than did the main river (Fig. 4). Variability of both water temperature and water level were greater in the upper portion of the main river. The areas of most intense water level variation were localized near or above the mouth of the Raquette River (Figs. 6 and 7), suggesting influence of regulation by the Moses-Saunders Dam and the Grass River (though the Grass River s flow is about half that of the Raquette River). The LOESS regression of water level variability on temperature also indicates an affect of the dam. In general for this system, but particularly in mid-summer, warm, dilute tributary waters flowed into cool, dense main St. Lawrence River water. This resulted in a mixing zone along the south side of the main river, particularly in the upper half of the system where the larger tributaries enter. Various scenarios of water flow regulation at the Moses-Saunders Dam is likely to alter the location and spatial extent of that mixing zone. Both spatial and temporal structure was evident in the aquatic community of the system. Distinct aquatic assemblages occupied three different habitat zones defined by the physical conditions, with the Main River and Tributary zones separated by a Buffer Zone. Dominant species were not restricted to a particular faunal zone or species assemblage, but were more abundant in some than others. PRCH, PUNK, and KILL were consistently important components of the fish assemblages, but BNOS, WSUK, and ROCK were important at specific times and places. Particularly diverse species assemblages were found in the Tributary Zone (clusters C, D, and F). Despite having some of the most extreme environmental conditions, cluster F (site G41) had a highly diverse assemblage that was composed of species generally considered intolerant or moderately tolerant (Halliwell et al. 1997). It was dominated by ROCK, SPOT, and fallfish (Semotilus corporalis, FALL (Fig. 14f). The cluster F assemblage may represent a forth faunal zone, but that habitat was poorly sampled. This and the other diverse assemblages, and their associated habitat conditions, deserve special attention to determine if they represent desirable assemblages that might be managed for in other areas using habitat manipulation. 10

11 Conversely, the areas where no fish were collected are of concern and should also be examined closely to ascertain whether their absence was due to habitat conditions that can be improved or simply a result of random sampling. CCA indicated that variability of both temperature and water level were important influences on the fish assemblages. Temperature variability was not thoroughly investigated as part of this study, but the effects of changes in water level were of primary interest. Aquatic assemblages of the Buffer Zone experienced the least amount of water level variability and Tributary assemblages the most. Several significant relationships between species abundances and water level variability were identified. However, the available time series of water level variability was short and represented conditions for only one year. None of the significant relationships contributes directly to the established ETWG indices, but they provide insights into the condition and function of the Akwesasne Wetland Complex, at this time. Three key species responded to water level variability. WSUK were a dominant component of the summer assemblage (Fig. 13c) and that found in the Main River (Fig. 14b). Their abundance was affected by extremes in water level variability and conductivity (and their interaction) (Table 12). Water level regulation may be providing habitat conditions favorable to WSUK. ROCK were a dominant component of the summer assemblage (Fig. 13c) and found across faunal zones, but were most prevalent in the Tributary and Buffer Zone assemblages (Fig 14). They were significantly more abundant in the Tributaries than in the Main River (Table 9). Their abundance was also affected by extremes in conductivity and water level variability, and where ph was low and turbidity was high (Table 12). ROCK is considered to be an intolerant species (Halliwell et al. 1997) and preliminary examination of life histories (not shown) indicates that it is a key species in distinguishing faunal zones, decreasing in abundance from Tributaries, through the Buffer Zone, to lowest levels in the Main River (Table 9). BNOS is one of the most common fish species in slow lotic and lentic habitats in New York State. BNOS was the most dominant component of the June fish assemblage (Fig. 13b) and second most important in the Main River assemblage (Fig. 14b). BNOS abundance was significantly affected by conductivity and water level variability, preferring low water variability and high conductivity (Table 12, Fig. 15). The characteristics of the species assemblages observed within the three faunal zones tell us much about what the system supports. The zonal boundaries and distinct assemblage locations indicate how they are presently distributed. Habitat conditions associated with those areas inform us of the conditions supporting those assemblages. Together, these provide insights into how assemblage characteristics and their distributions might be altered by changes in hydrologic regimes that would in turn modify habitat conditions and the boundaries of regions able to support a particular type of aquatic assemblage. This study provides good spatial coverage and the best set of data describing aquatic resources in the Akwesasne Wetland Complex. However, 11

12 more extensive temporal sampling is required to accurately characterize the influence of water level variability in this large wetland system. Acknowledgements This research was funded by the IJC, through the ACOE, agreement #: W81EU A. Special thanks go to the field crew, especially S. Peters, S. Perez and H. White. We are indebted to A. Debo, R. Butryn, D. McDonald, and K. Douglass for assistance with GIS issues. 12

13 Literature Cited Bain M.B., Finn J.T.; Booke H.E Stream flow regulation and fish community structure. Ecology 69: Halliwell, D.B., Langdon, R.W., Daniels, R.A., Kurtenbach, J.P., and Jacobson, R.A., Classification of freshwater fishes of the Northeastern United States for use in the development of indices of biological integrity. In Simon, T.P. (ed.). Assessing the sustainability and biological integrity of water resources using fish communities. CRC Press:Boca Raton; McKenna J.E.Jr An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis. Environmental Modeling & Software 18: McKenna J.E.Jr., Saila S.B Application of an objective method for detecting changes in fish communities: Samar Sea, Philippines. Asian Fisheries Science 4: National Research Council Restoration of aquatic ecosystems: science, technology, and public policy. Chapter 6, Wetlands. Committee on Restoration of Aquatic Ecosystems, Water Science and Technology Board, Commission on Geosciences, Environment, and Resources. National Research Council. National Academy Press:Washington, D.C. Nemec, A.F.L., Brinkhurst, R.O., Using the bootstrap to assess statistical significance in the cluster analysis of species abundance data. Canadian Journal of Fisheries and Aquatic Sciences 45: New York State Department of Environmental Conservation St. Lawrence River At Massena, New York Remedial Action Plan Status Report. Prepared by the New York State Department of Environmental Conservation, Albany, NY. 86pp. Ramsar Convention Bureau The Ramsar List of Wetlands of International Importance. Ramsar Convention Bureau. Gland, Switzerland. 35pp. Shannon C.E., Weaver W The mathematical theory of communication. University of Illinois Press, Urbana, IL;117pp. ter Braak, C.J.F., CANOCO a FORTRAN program for canonical community ordination. Microcomputer Power, Ithaca, NY;95pp. Welcomme R.L., Ryder R.A., Sedell J.A Dynamics of fish assemblages in river systems a synthesis. Canadian Special Publication Fisheries and Aquatic Sciences 106: Wilcox D.A Wetland and aquatic macrophytes as indicators of anthropogenic hydrologic disturbance. Natural Areas Journal 15:

14 Table 1. Total abundances for each fish species by cluster (an addition 614 reptiles, amphibians, and crayfish were captured). Numbers of samples/cluster are given in parentheses after each cluster label. Trib. represents the Tributaries zone. Zone: Buffer Buffer Buffer Main Trib Trib Trib River Scientific Name Common Name Species Code Cluster: A (33) E (5) G (2) B (71) C (5) D (10) F (1) Fundulus diaphanus Banded Killifish KILL Notropis heterolepis Blacknose Shiner BLAK Lepomis macrochirus Bluegill BLUE Pimephales notatus Bluntnose Minnow BNOS Labidethes sicculus Brook Silverside SILV Ameiurus nebulosus Brown Bullhead BBUL Cynprinus carpio Common Carp CARP Luxilus cornutus Common Shiner COMM Notropis atherinoides Emerald Shiner EMRL Notemigonus crysoleucas Golden Shiner GOLD Percina caprodes Logperch LOGP Lepomis gibbosus Pumpkinseed PUNK Amboplites rupestris Rock Bass ROCK Neogobius melanstomus Round Goby GOBY Cottus cognatus Slimy Sculpin SLIM Micropterus dolomieu Smallmouth Bass SBAS Cyprinella spiloptera Spotfin Shiner SFSH Notropis hudsonius Spottail Shiner SPOT Etheostoma olmstedi Tessellated Darter TESS Catastomus commersoni White Sucker WSUK Perca flavescens Yellow Perch PRCH Total: 2, ,

15 Table 2. Abundance (N), Richness (S), Diversity (H'), and Evenness (V) for each sample. Site Codes indicate month of sampling (first number) and site location number. Sample N S H' V 5R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R

16 5R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R

17 7R R R R R R R R R R R R R R R R R R R R R R R G

18 Table 3. Mean Abundance (N), Richness (S), and Diversity (H'), for each seasonal cluster of samples. Season N S H' May June Summer

19 Table 4. Mean Abundance (N), Richness (S), and Diversity (H'), within each faunal zone. Results of ANOVA tests for significance of differences among zones are summarized with the associated F statistic (ANOVA F) and probably (P). Zone N S H Main River Buffer Zone Tributaries ANOVA F P 0.66 <0.001 <

20 Table 5. Mean Abundance (N), Richness (S), and Diversity (H'), for each spatial cluster of samples. Cluster # of samples N S H A B C D E F G

21 Table 6. Summary of Canonical Correspondence Analysis results for fish samples and measured habitat variables in the Snye Marsh study area in Axes Total inertia Eigenvalues : Species-environment correlations : Cumulative percentage variance of species data : of species-environment relation: Sum of all eigenvalues Sum of all canonical eigenvalues

22 Table 7. ANOVA results testing differences in abundances of dominant species among clusters or zones. Species Cluster Zone F P F P BBUL 5.58 <0.001* * BNOS GOLD <0.001* 8.48 <0.001* KILL * PRCH PUNK * 22.2 <0.001* ROCK * * SBAS SPOT WSUK * 22

23 Table 8. Results of Tukey s multiple comparison test for significant species abundance differences among clusters. Species Cluster Mean abundance Group membership BBUL C A D B E F G GOLD C A B E D F G PUNK A C D E B F 0 2 G

24 Table 9. Results of Tukey s multiple comparison test for significant species abundance differences among zones. Species Zone Mean abundance Group membership BBUL Tributaries Buffer Zone Main River GOLD Tributaries Buffer Zone Main River KILL Main River Tributaries Buffer Zone PUNK Tributaries Buffer Zone Main River ROCK Tributaries Buffer Zone Main River WSUK Main River Tributaries Buffer Zone

25 Table 10. Best regression models of diversity, richness, and abundance developed with stepwise procedure. Temp = water temperature, Cond = conductivity, TempCV=coefficient of variation of water temperature, and D.O. = dissolved oxygen. Response Variable P R 2 Model Diversity (H ) Temp -0.4 Cond Richness (S) Cond -0.8TempCV Abundance (N) Temp D.O. 25

26 Table 11. Summary of simple linear regression results of abundance on water level variability for species that had an abundance >100 in June and July (combined). Significant model indicated by * and nearly significant models indicated by ~. Both probabilities are italicized. Species KILL ~ BNOS * LOGP PUNK ROCK * SPOT TESS WSUK * PRCH ~ Intercept Coefficient P(F) simple R^

27 Table 12. Best multiple linear regression models describing the relationships between water level variability and WSUK, ROCK, and BNOS. Species P Multiple R 2 Model WSUK WSUK = PressCV conductivity PressCV x conductivity ROCK ROCK = PressCV conductivity ph Turbidity BNOS Log(BNOS)= PressCV conductivity 27

28 Fig. 1. Map of the study area, includes New York State, parts of Ontario and Quebec, Canada. Fig. 2. Sample site locations, species assemblages, and faunal zones. The Buffer Zone is contained within the dashed lines. The Main River is to the north and the Tributaries Zone is to the south of the dashed lines. Ovals enclose sample sites that were samples considered a priori to be replicate representations of various spatial areas. Spatial cluster assignments of sample locations are designated by capital letters (A G). Fig. 3. Monthly average in situ water conditions within the study area. a) water temperature, b) conductivity and rainfall, c) dissolved oxygen, d) ph, and e) turbidity. Fig. 4. Contour maps of water temperature distributions within the study area for each sampling month. a) May, b) June, c) July, d) August, and e) September. Fig. 5. Contour maps of conductivity distributions within the study area for each sampling month. a) May, b) June, c) July, d) August, and e) September. Fig. 6. Contour maps of coefficient of variation in water temperature within the study area for a) June and b) July. Fig. 7. Contour maps of coefficient of variation in water level within the study area for a) June and b) July. Fig. 8. LOESS regress of water level variability (CV) as a function of water temperature. Faunal zones occupied by each sample site and the month of collection are indicated by each number-letter combination, where the number indicates the month (6 or 7) and the letters indicate zone (T = tributaries, B = buffer zone, LR = lower Main River, and UR = upper Main River). Fig. 9. Median abundance and spread of fish collected at each sample site. Fig. 10. Average fish assemblage found in the Snye Marsh study area. Fig. 11. Dendrogram of cluster analysis with samples arranged as replicated by month. Each * indicates a linkage joining two significantly different assemblages. Fig. 12. Dendrogram of cluster analysis with samples arranged as replicates by location. Each * indicates a linkage joining two significantly different assemblages. Fig. 13. Pie charts depicting the species assemblage comprising each significantly different seasonal cluster; a) May, b) June, and c) Summer (July September). Fig. 14. Pie charts depicting the species assemblage comprising each significantly different spatial cluster; a) Cluster A, b) Cluster B, c) Cluster C, d) Cluster D, e) Cluster E, f) Cluster F, and g) Cluster G. Fig. 15. Triplot of the first two axes of canonical correspondence analysis (CCA) of species abundances and habitat conditions showing species optima (open triangles see Table 1 for key to species codes), sample locations and spatial cluster affiliation (Cluster A = open circle, Cluster B = solid square, Cluster C = open square, Cluster D = open rectangle, Cluster E = open diamond, Cluster G = solid circle), and vectors indicating the direction and magnitude of the most influential habitat gradients. Fig. 16. Bar charts displaying the environmental signature of mean normalized in situ conditions for each distinct seasonal species assemblage. a) May, b) June, and c) Summer. D_O = dissolved oxygen. Fig. 17. Bar charts displaying the environmental signature of mean normalized in situ conditions for each distinct spatial species assemblage. a) Cluster A, b) Cluster B, c) Cluster C, d) Cluster D, e) Cluster E, f) Cluster F, and g) Cluster G. Fig. 18. Bar chart displaying the environmental signature of mean normalized in situ conditions for samples where no fish were caught. 28

29 Fig. 19. Bar charts displaying the environmental signature of mean normalized in situ conditions for samples within each faunal zone. a) Main River, b) Buffer Zone, and c) Tributaries. D_O = dissolved oxygen. Fig. 20. Loess Regression of white sucker abundance as a function of water level variability. Fig. 21. Loess Regression of yellow perch abundance as a function of water level variability. 29

30 Fig. 1. Project Location within Mohawk Territory N Quebec, Canada Ontario, Canada Shipping Channel Snye Marsh Mohawk Reservation New York State 30

31 Fig. 2. N B B B B B E A C A B D B G A A F 31

32 Fig. 3. a. Water Temperature b. Conductivity and Rainfall Celsius us/cm Rainfall (cm) May June July August September May June July August September Sample Month Sample Month c. Dissolved Oxygen d. ph mg/l May June July August Sample Month September a May June July Sample Month August September e. Turbidity NTU May June July August September Sample Month 32

33 Fig. 4. a. 33

34 Fig. 4. b. 34

35 Fig. 4. c. 35

36 Fig. 4. d. 36

37 Fig. 4. e. 37

38 Fig. 5. a. 38

39 Fig. 5. b. 39

40 Fig. 5. c. 40

41 Fig. 5. d. 41

42 Fig. 5. e. 42

43 Fig. 6. a. 43

44 Fig. 6. b. 44

45 Fig. 7. a. 45

46 Fig. 7. b. 46

47 Fig. 8. Water Depth Variability as a Function of Water Temperature 2 Span = 0.4 Gaussian Mult. R 2 = UR 7UR 7UR 7UR 7UR 7T 7T 6T 7B 1 PRESS.CV 0 6UR 7B 7LR 7LR 6T 7LR 7B 7LR 7LR 7LR 7LR 7T 7LR 7B 7B 7LR 7B 7B 7B 6UR 6UR 7LR 7B 6UR 6B 6B 6B -1 6UR 6LR 6LR 6LR 6LR 6LR 6LR 6B 6LR 6B 6LR 6T 6B 6LR 6B 6B 6B 6LR B = Buffer Zone LR = Lower River UR = Upper River T = Tributary WATER.TEMP