Emergence of New Explanatory Variables for 2D Habitat Modelling in ...

Emergence of New Explanatory Variables for 2D Habitat Modelling in Large Rivers: The St. Lawrence Experience Jean Morin1, Marc Mingelbier2, José A. Bechara3, Olivier Champoux1, Yves Secretan4, Martin Jean5 and Jean–Jacques Frenette6

ABSTRACT The St. Lawrence River is one of the most important large rivers in North America. This 600–km long watercourse is characterized by a high degree of physical heterogeneity, including fast moving narrow reaches separated by fluvial lakes reaching 10 km in width. The mean annual discharge from the outflow of Lake Ontario is 7500 m3/s and has been managed for hydropower and transportation since the 1960s. With the management plan currently under review an effort is being made to include criteria that take into account the impacts of regulation on the biotic components of the river ecosystem. High resolution 2D spatial modelling of river habitats and floodplains is a powerful tool to make quantitative impact assessments of the biota. Physical variables commonly used in habitat models include depth, velocity and substrate size. In addition, other abiotic variables such as wind– generated wave stress, light penetration, water temperature, sedimentation of fine particles, specific discharge and bottom slope, that define the local ‘hydroperiod’ have been suggested. Our proposed approach integrates abiotic data obtained from numerical models, field measurements and biological information to overcome problems inherent in temporally and spatially heterogeneous river systems. This approach was tested with a habitat model applied to submerged aquatic vegetation, various categories of wetlands, benthic organisms and various life stages of a number of fish species. Logistic regression is the statistical model currently used to synthesize the relationships between abiotic and biotic factors. The short–term objective of this modelling exercise in the St. Lawrence River is to understand the underlying links Meteorological Services of Canada, Environment Canada, Québec, QC Société de la faune et des parcs du Québec Gouvernement du Québec, QC 3 Consejo Nacional de Investigaciones Científicas y Técnicas–CONICET and Universidad Nacional del Nordeste (UNNE), Corrientes, Argentine 4 Institut national de la Recherche scientifique–Eau, terre et Environnement, Sainte–Foy, QC 5 Centre Saint–laurent, Environment Canada, Montréal, QC 6 Department of Chemistry–Biochemistry, Université du Québec à Trois–Rivières, Trois–Rivieres, QC 1 2

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between fluvial physics and biota. A longer–term objective is to provide a real–time analysis of key variables and to quantify the links between trophic levels.

RÉSUMÉ Le fleuve Saint–Laurent est une des plus importantes rivières d’Amérique du nord. Son parcours de plus 600 km est caractérisé par une forte hétérogénéité spatiale, puisque composé d’une succession de tronçons relativement étroits et de lacs fluviaux de plus de 10 km de largeur. Le débit moyen annuel sortant des Grands Lacs est de 7500 m3/s. Celui–ci est régularisé depuis les années 1960 et le plan de gestion fait actuellement l’objet d’une révision pour ajouter des critères tenant compte des impacts sur le milieu aquatique. La modélisation 2D à haute résolution spatiale des habitats du fleuve et de sa plaine inondable est un outil privilégié pour apporter des réponses quantitatives aux impacts du milieu physique sur les ressources fauniques et floristiques. Les variables traditionnelles de la modélisation des habitats que sont les courants, la profondeur et le type de substrat sont utilisées. Afin d’adapter la modélisation des habitats à des contextes variés, de nouvelles variables abiotiques ont été développées parmi lesquelles on retrouve les contraintes des vagues générées par les vents, la pénétration de la lumière, la température de l’eau, la sédimentation des particules fines, le débit spécifique, la pente du fond ainsi que des variables reliées à l’« hydropériode » locale. Les relations entre habitats et facteurs abiotiques ont été évaluées statistiquement à l’aide de régressions logistiques. La modélisation des habitats a été appliquée à plusieurs compartiments du vivant, tels que les macrophytes submergés, plusieurs classes de milieux humides et diverses espèces de poissons à certains stades de vie critiques. L’approche actuelle intègre les données provenant de la modélisation numérique des facteurs physiques, de la caractérisation du terrain et d’échantillonnages d’organismes vivants, elle permet de simplifier des problèmes complexes reliés à l’hétérogénéité spatiale et aux échelles temporelles inhérentes aux milieux naturels. L’objectif de la modélisation du fleuve est de comprendre les liens entre la physique et les habitats de la faune et la flore et afin de soutenir un suivi fiable de l’écosystème.

INTRODUCTION Natural and anthropogenic variations in river water levels have a drastic impact on aquatic habitats (Stalnaker et al., 1989; Petts and Calow, 1996). The complex interactions between riverbed and floodplain, local climate, hydrology, tributary water quality characteristics and substrate type produce a complex pattern of habitat variation across space and time. One example of this is how periodic floodplain inundation is essential for the maintaining the biodiversity and productivity of many large rivers. This periodic flooding has multiple positive effects on vegetation including rejuvenation of riparian vegetation and the structuring species diversity in wetlands (Tockner et al., 2000). Fish use flooded wetlands in a variety of ways at

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Revue canadienne des ressources hydriques

different periods of the year; as spawning grounds, nurseries, refuges and foraging habitats. In terms of management, preserving the integrity of a river ecosystem requires using hydrology to maintain the access to wetlands, having the proper seasonal connections between habitats, and preservation of plant species diversity. In spite of the urgent need for new approaches to fisheries and floodplain management, habitat models, commonly used for evaluating management options in other types of ecosystems, are poorly developed in large rivers. High resolution 2D modelling of the river and its floodplain has been proposed to address several management questions in a quantitative manner. Presently, the environmental impacts due to the Great Lakes outflow regulation and the potential impacts associated with expected global climate change are the most challenging concerns that require detailed analyses. In this paper the impacts of discharge alterations are assessed through simulations of the observed and expected modifications on important physical variables such as hydrodynamics, wave action, light penetration and water temperature.

Environmental Context of the St. Lawrence River The St. Lawrence River (SLR) is one of the largest rivers in North America. It takes its source from the outflow of the Great Lakes, out of Lake Ontario (Figure 1). Numerous tributaries flow into the SLR along 600 km from that lake to the upper estuary near Quebec City. The inflows of these tributaries can have very significant impacts on the total discharge especially during springtime. The mean annual outflow from the Great Lakes is 7500 m3/s and represents, in average, 60% of the outflow at Québec City. The SLR is characterized by significant physical heterogeneity including a succession of relatively narrow reaches several hundred metres wide, alternating with fluvial lakes of more than 10 km in width. There are more than 2500 km 2 of diverse aquatic habitats including extensive emergent wetlands and submerged aquatic vegetation (SAV) beds that host more than 100 fish species (LaViolette et al., 2003). The inflow from the Great Lakes, called the ‘green waters’ with typical Secchi depth varying between 1 and 4 m, contains low concentrations of both dissolved organic carbon and particulate suspended matter, (Cossa et al., 1998; Frenette et al., 2002). Water from the Ottawa River watershed, called ‘brown water’ with typical Secchi depth varying from less than 0.3 to 2.0 m, and water from other tributaries form contrasting water masses generally with high concentration of dissolved organic carbon and suspended sediment loads. Depending on the relative discharge of tributaries, the distribution of current in the mainstem of the St. Lawrence, and lateral mixing limited by low water depth allow these water masses to be laterally differentiated for more than 150 km (Frenette et al., 1989). Major features like light penetration, nutrient concentrations and other water quality parameters within these strongly contrasting water masses need to be characterized spatially in order to model habitat accurately.


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Figure 1. Location and Main Physiographic Features of the St. Lawrence River.

Spatial Variables of the 2D Habitat Model Two–dimensional (2D) habitat modelling combines field measurements and numerical simulations to produce spatially distributed fields of habitat values. Mainly used in small– to medium–size rivers to determine conservation flows, this type of model usually exploits three abiotic variables: simulated currents, simulated depth and measured substrate grain size. The methodology of 2D microhabitat modelling, based on the Instream Flow Incremental Methodology (IFIM) has been applied mainly to fish species, particularly to salmonids and often in relatively small systems (Bovee, 1996; Leclerc et al., 1995; Stalnaker, 1994). In smaller salmonid–dominated streams, substrate composition is often a key independent variable. In contrast, analysis of the SLR shows that current velocity and water depth are consistently better descriptors of habitat than substratum. Because of the combination of the large size of the river, the occurrence of extensive macrophyte beds, and the presence of distinct physical–chemical water masses, several new variables were explored to better address the spatial complexity of the river and its fluvial lakes. The new variables were derived from a detailed Digital Terrain Model (DTM), high density finite element meshes and several mathematical models simulating hydrodynamic, transport–diffusion, wave action and water temperature. The mathematical models allowed the calculation of variables such as the concentration of suspended load and the distribution of water masses, which were then used to

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simulate light intensity on the bottom. Simpler variables are also used. Among them, the local bottom slope and the nodal ‘hydroperiod’ were derived from the DTM while the specific discharge (related to Reynolds number) and the velocity gradient were obtained from the hydrodynamic model. In this paper, we present the state–of–the–art of 2D habitat modelling applied to the SLR. An overview of the available DTM and the sampling methods are presented. Along with the hydrodynamics, new 2D variables developed for the SLR context are documented: bottom slope, light penetration, fine particle accumulation, wind–driven waves and nodal hydroperiod. Some variables still under development are also discussed. Examples of habitat modelling applications with these emerging explanatory variables are presented for both fish and macrophytes species, and also for large classes of fluvial wetlands.

DIGITAL TERRAIN MODEL: TOPOGRAPHY, SUBSTRATUM AND AQUATIC PLANTS Water levels have been monitored by stations at ~10 km intervals along the shoreline since the beginning of the 20th century. Because actual discharge data are available only in the upper portion of the river, daily discharges since 1932 were reconstituted for key areas in the study zone (Morin and Bouchard, 2000). At Sorel, historical discharges typically vary from 6400 m3/s during dry periods to a maximum of about 20,000 m3/s. Corresponding water levels in the same area fluctuate from 3.50 to 8.00 m (IGLD–85), respectively. Basic soundings were provided by the Canadian Hydrographic Service for the main riverbed either in numerical or paper format. Additional shallow portions of the river and small channels were measured with echosounders and dGPS mainly during springtime. The 350 km 2 of the SLR’s floodplain topography were measured using airborne laser during the fall of 2001. This information was reduced and assembled according to the same datum as the soundings from riverbed depth (Fortin et al., 2002). The complete topographic database has a precision of about 0.15 m in the vertical axis and of less than 2 m in the horizontal plane. The density of topographical information on the riverbed is one point every 30 m, with ~1.1 million points provided by the Canadian Hydrographic Service for the main riverbed, and about 800,000 points obtained for the shallow areas and small channels. For the floodplain, the density is 1 point every 3 m for a total of 320 millions points. The entire database for the Lake Saint–Pierre area is shown in Figure 2. Substrate maps are essential for the calibration of bottom roughness in the hydrodynamic model. They were built using several sources of existing information as well as inhouse field surveys. Submerged plants are another important feature of the SLR; they cover most of the fluvial lakes and large portions of lotic reaches. Their influences on the flow patterns and water levels are considerable (Boudreau et al., 1994; Fortin et al., 1993). SAV distributions were mapped with echosounders, dGPS positioning and submersible video cameras. The relative proportion of plant species, their height and a density index were documented with sampling transects (Coté, 2003; Morin et al., 2002). The effect of SAV distribution on flow was


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Figure 2. Topographic Model of the Lake Saint–Pierre’s Riverbed and Floodplains. Insets Show Simulations of Extreme Spring–High Water and Late Summer Low–Water Hydrodynamic Conditions.

introduced to the hydrodynamic calculations in a similar way as the substrate, using a local ‘Manning’s n’ friction coefficient.

UNDERLYING NUMERICAL TOOLS: GIS, HYDRODYNAMIC MODELLING AND SPATIAL DISCRITISATION Management of distributed data from DTM is performed with the MODELER software (Secretan and Leclerc, 1998). This is typically a Geographical Information System (GIS), specialized for fluvial modelling, which manages several types of meshes and uses a finite element method for discritisation and interpolation. MODELER is used for building triangular irregular networks and/or regular square grids, as well as performing interactions and projections between layers of information. In a fluvial context, hydrodynamics is the most crucial phenomenon to incorporate properly when modelling habitats. In the ongoing project, currents, depths and water levels are calculated with a 2D vertically integrated hydrodynamic model. The HYDROSIM model (Heniche et al., 1999) uses a ‘drying–wetting’ scheme for shallow water equations which are solved by the finite element method. The model applies the flux formulation of momentum and continuity Saint–Venant equations and takes into account the local friction caused by substrate, aquatic plants and ice cover. Hydrodynamic meshes for the SLR have a relatively high density relative to the size of the river. Generally, the characteristic size of the elements varies between 10 m in small channels to 100 m in the flat areas of the floodplain, with a mean size of about 40 m (Figure 3). The mesh includes the entire floodplain in order to cover the spatial extent of the maximum historical discharges. The accuracy of simulated water levels is better than 5 cm for the entire river reach, at both high and low discharge. Validation of velocities has been performed with more than 300 transects of ADCP (Acoustic Doppler Current Profiler) measurements for the main riverbed. Parameterization of submerged plants includes the composition and distribution, and seasonal growth regime of local plants species. The estimation of corresponding Manning’s n friction coefficients is based on species, relative density, plant size and relative concentration of species (Morin et al., 2000).

PRODUCTION OF NEW DISTRIBUTED HABITAT VARIABLES Bottom Slope Bottom slope is not well documented as a controlling habitat factor. We believe it should be perceived as a dynamic representation of the topography. Technically, for most accurate use as a habitat variable, the local bottom slope should be calculated with the most accurate data set available. Hydrodynamic meshes are not the most suitable, because they are generally coarser than the raw topographic data. Connecting the original data points to the most detailed grid available provides more


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Figure 3. Hydrodynamic Mesh of Lake Saint–Pierre Containing 110,468 Nodes and 54,412 Elements. accuracy for this calculated variable, thus resulting in an increase of its significance as a habitat variable in multivariate statistical analysis (see below).

Light Intensity on the Bottom and Concentration of Fine Particles Water transparency and therefore the light quality/intensity is one of the most important abiotic factors influencing the structure of fish communities (Robinson and Tonn, 1989; Tonn et al., 1990). In addition, light quality/intensity (i.e. the photic zone) is also fundamental to macrophyte, periphyton, and phytoplankton production. Prediction of the spatial and temporal variation of light penetration is a challenging task. The controlling factors for light distribution in fluvial systems are the concentration and composition of dissolved organic carbon (DOC) and suspended solids. Sedimentation, erosion, decomposition, and transformation of these materials through space and time are complex and non–linear processes (Kirk, 1994). Therefore, simplified approaches have to be used until more complex models are available. Prediction of light conditions was made possible by combining the simulation of suspended solid concentration with the simulation of the spatial distribution of the water masses (Morin, 2001; Bechara et al., 2003; Mingelbier et al., 2003).

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Relationships between the concentration of suspended solids and attenuation coefficients relative to the water masses are then used to calculate light penetration locally. The accumulation of fine particles on the bottom is a by–product of the simulation of fine particle concentrations used in modelling light attenuation. Variables used for habitat simulation are the quantity of light reaching the bottom (ratio of incident light) and the accumulation of fine material (g/m2). Dispersion patterns of suspended matter and of water masses are simulated with the DISPERSIM model. It is a 2D (horizontal) Eulerian transport–diffusion finite element model (Secretan et al., 2000; Bédard, 1997). Input data for DISPERSIM includes water depth, flow diffusivity, velocity distribution and shear stresses simulated with hydrodynamic and wave models. Sedimentation is a function of the ratio of a selected threshold related to the critical shear–stress of the grain size and the local shear–stress (Van Rijn, 1989), near–bottom orbital velocities from waves are used in a similar manner. For a given water mass, the ratio of incidental light reaching the bottom is a function of depth and local concentrations of suspended solids. This is calculated from the following function: Iz =I0 e –KZ , where I0 is the intensity of light at the surface, K is the local extinction coefficient and Z is the depth. The local extinction coefficient is computed for each node using empirical relationships based on measurements of total suspended solids and of light intensity for specific water masses. Similar relationships are available for each water mass in the river. For situations where several water masses are present, the spatial distribution of water masses is simulated with specialized functions of DISPERSIM. The attenuation coefficient K is then computed with the proper relationship.

Wind–Generated Waves Waves play a fundamental role in large water bodies such as the fluvial lakes of the SLR. Their introduction in the habitat model is done using specialized numerical models. The HISWA model has been designed to model the growth and transformation of wind waves in shallow water environments (Holthuijsen et al., 1989; Booij et al., 1993). It simulates wave propagation including refraction and shoaling, growth due to wind action, dissipation and breaking due to bottom friction in horizontal 2D environment. This model calculates various parameters such as wave energy, frequency, height and direction on a regular square grid. For the SLR, the calculation grid for wave simulations uses 50 m cells in several reaches separated with suitable natural boundaries. Generally, such grids comprise approximately 300,000 nodes. Apart from wind intensities and directions, input parameters for simulations include topography, water level and current data supported by the regular grid. The orbital near–bottom velocity generated by waves appears to be the most suitable variable for habitat models. This variable is widely used in sedimentation models to assess grain stability on the bottom.


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Waves are categorized into three intensities: moderate (10–24 km/h), strong (25–44 km/h) and extreme (45–55 km/h). For each of these intensities, 16 compass directions are simulated for several discharge scenarios. The statistical description of wind frequencies and intensities in terms of seasonal mean is essential for a proper use of these variables. The wave simulations for a given intensity of wind are combined together with a weighted average method for each node of the grid as a function of the historical record.

Nodal Hydroperiod Soil saturation is a fundamental constraint for wetland vegetation. Mitsch and Gosselink (1993) have developed a wetland classification based on the hydroperiod, which uses the frequency of flooding and drought during the plant growth period. Following similar ideas, Toner and Keddy (1997) have explored the impact of several aspects of timing, intensity and duration of spring floods on wetland community types. They built time–step hydrological indicators to include the importance of past hydrological events and their effects on wetland composition. Using a 2D approach, such hydroperiod variables were produced for every node of a dense mesh, resulting in a ‘nodal hydroperiod’ (Champoux et al., 2002). Nodal hydroperiod variables have been estimated using long–term discharge series and a wide spectrum of hydrodynamic simulations. For each week of the growing period with a mean air temperature above 5.5°C, the local water depth was computed from the difference between water level and the local topography. Several variables were generated: 1) “number of flooded weeks” (NbSi) is the summation of the number of week per growing season where the area was wet for each time step and each point , 2) the “number of dry weeks” (NbSe) was computed in the opposite way, 3) the “total number of variations” (NbTVA) represents the number of alternations between wet and dry periods within a growing season and for each time step, 4) the “mean water depth” (PFM) is the computation of the mean depth (flooded or not) for the growing season and for each time step, 5) the “mean flooded depth” (MoyIN ) is the mean water depth when the node is flooded and 6) “mean water table” (MoyEx) is the mean depth (underground) when nodes are dry. In order to consider the effect of past hydrological events on wetlands, five temporal steps were chosen: 1, 3, 7, 12, and 18 years prior to sampling. These variables were then developed and applied to fluvial wetland studies as briefly presented in the applications section below.

OTHER VARIABLES UNDER DEVELOPMENT Water Temperature Water temperature is also a crucial environmental factor for aquatic life. Indirectly, it modifies environmental characteristics of the habitat, gas dissolution, density and

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surface tension (Wetzel, 2001). Water temperature is considered as a dominant factor for recruitment variability in fish, especially for those species spawning on the floodplain during early spring (Koonce et al., 1977; Fortin et al., 1982). For the SLR, the size and shape of the floodplains and flats create lateral stratification of water temperature between the main channel and the shallow nearshore zones. In order to predict water temperature and help to manage water level for protecting fish spawning areas, a 2D water temperature model was developed. The temperature model is based on vertically integrated equations of transport–diffusion solved with an Eulerian approach (Secretan et al., 2000). The thermal budget is calculated using climatic data such as solar radiation, air temperature, atmospheric pressure, humidity, precipitation and wind velocity (Heniche et al., 2003). Applications reported here are restricted to the validation of the temperature model (Morin et al., 2002; 2003) but its potential for primary productivity and larval growth is considerable.

Bottom Slope in the Direction of the Currents This variable was produced in order to explore the role of topographical features that act as protective structures mainly for fish habitat. This variable indicates the value of the bottom slope in the direction of the flow, with a positive figure representing topography that increases downstream; conversely, a negative value indicates a rise of depth in the direction of the flow. A variant of this variable, the derivation of the slope in the direction of current, was also tested with some success for predicting walleye (Stizostedion vitreum) habitat quality (Bechara et al., 2003). In general bottom slope in the direction of the current reflects the heterogeneity of the sediment surface and macrohabitat structures sometimes used as refuges or foraging by fish.

Specific Discharge In this study, the specific discharge is defined as the flux of water across a given section per unit width and is produced as output from the hydrodynamic model for every node in the mesh. It is equivalent to the multiplication of the local velocity module with the local depth. This variable allows for an appreciation of the water quantity passing at a given point in the vertical section. Lamouroux and Souchon (2002) found that the average specific discharge of a river reach explained most of the variation of fish Weighted Usable Area (WUA) with total discharge, suggesting that it could be a surrogate of the drifting food carried by the flow. This variable was significant for several fish habitat models of the SLR (Bechara et al., 2003), and in particular for piscivorous species. Thus, it is worth exploring more thoroughly with the potential of the 2D modelling.


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DEVELOPING HABITAT MODELS: THE STATISTICAL APPROACH In classic habitat models, the habitat suitability index (HSI) based on preference curves has been widely used, but with limited accuracy (Scott and Shrivell, 1987; Bourgeois et al., 1996). Recently, Guay et al. (2000) successfully used a probabilistic habitat index (PHI) based on a multivariate logistic regression to account for fish habitat preferences. This more recent approach was chosen for the SLR context, using a categorical, two–stage dependent variable (i.e. presence–absence). Independent variables are continuous and spatially distributed over the entire study area and correspond to the controlling factors. Calibration of this type of model is conducted in two steps. First, univariate logistic regressions are performed to select the most significant controlling factors based on species presence/absence. Second, multivariate logistic regression (forward stepwise selection) is conducted with the selected sub–set of the original controlling factors. Following the guidelines of Tabachnick and Fidel (2001), the fraction of the variance explained is calculated with the max–rescaled R 2 . A jackknife cross–validation (‘ leave one out’ method) and McNemar tests are used to evaluate the predictive capacity of the model.

EXAMPLE OF APPLICATIONS ON THE ST. LAWRENCE RIVER 2D Modelling of Fish Habitats 2D numerical modelling was recently applied to study fish habitats in the SLR. Two research programs were conducted addressing different concerns. The first study evaluated the impacts of changes in water discharge due to climate change and flow regulation on a dozen fish species clustered into reproductive and feeding guilds with distinctive roles in the river ecosystem. A second study focused on a retrospective analysis of the impact of increasing water transparency in Lake Saint–François on the distribution of habitat for several economic fish species. Both studies are intended to provide practical options for fish management. These studies took advantage of 2D modelling and GIS because several environmental variables were obtained from simulations (e.g. currents, light penetration) and then related to the position of fishing gears (e.g. GPS–located gillnets). Habitat models were then adjusted using both field and simulated data.

Effect of Water Discharge on Fish Habitat in the Montreal–Sorel Reach We present here the results for walleye, lake sturgeon (Acipenser fluvescens) and cyprinids (Table 1). Multivariate analyses independently revealed contrasting combinations of explanatory variables that are in agreement with the general species habitat preferences from the literature. For example, lake sturgeon is found in deep, fast–flowing waters, while cyprinids prefer quieter, shallower waters. In addition,

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the analysis showed a highly significant negative effect of increasing light intensity on walleye, also consistent with literature on its biology (Ali et al., 1977). The capacity of this statistical method to predict fish presence varied from 63 and 95% and 79 and 89% for fish absence (i.e. sensitivity and specificity, respectively), with rescaled determination coefficient R 2 of 0.47 for lake sturgeon, 0.74 for cyprinids and 0.25 for walleye. Table 1. Multivariate logistic regression results for three key fish species in the SLR. Estimates (Est.) and probability P≤0,001 (***), P ≤ 0,01 (**), P ≤ 0,05 (*) and P > 0,05 are shown with Wald Chi–square for the intercept and the independent variables. Symbols (–) et (+) correspond to the sign of the relationship. Species

Stat.

Lake

Est.

sturgeon

Wald

Cypri–

Est.

nids

Wald

Walleye

Est. Wald

Intercept

Current

Depth

+2,31

+0,45

59,48

3,80

20,16

+5,34

NS

–1,92 ***

–3,48 *

+0,001**

22,05

3,78

6,96

–3,65

Slope

***

*

***

20,33 +1,23

***

13,98

–24,35 8,08

**

Light

Substrate Vegetation

***

–3,79

***

24,55

The probability of occurrence was simulated for the three species and for five water discharge scenarios between 5000 and 12,000 m3s–1. Weighted usable areas (ha) showed on one hand, that decreasing water discharge might diminish suitable habitat availability for the three species. On the other hand, the fluvial section between Montreal and Sorel is a small corridor where the floodplain is relatively reduced. As a consequence, a large surface of habitat in deep and rapid water is available for lake sturgeon and walleye (7300–11,300 ha), while a small nearshore surface habitat is available for cyprinids (3400–4600 ha). Weighted usable areas for cyprinids for the two most contrasting scenarios illustrate this trade–off (Figure 4).

Increased Transparency and Fish Habitat in Lake Saint–François Habitat development for several fish species, with special emphasis on walleye, was evaluated in this fluvial lake in the upper SLR (Bechara et al., 2003). Colonization of the Great Lakes by Zebra mussels (Dreissena polymorpha) since 1990 has decreased the concentration of suspended solids, with a subsequent deepening of light penetration in the water column, and thus has increased the biomass of submerged aquatic plants (Holland et al., 1995; Merriman, 1997; Morin et al., 2000). Both fish biomass and community structure in the upper section of the SLR simultaneously went through major modification during this period (Colby et al.,


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Figure 4. Probability of Occurrence for Cyprinids Between Montréal and Sorel, Based on Samples from Fall 2001, for 5,000 m3/s (left) and 12,000 m3/s (right). 1994; Fournier, 1997; Mathers and Stewart, 2001). It was hypothesized that the changes observed in the fish community were caused chiefly by Zebra mussel–related habitat modifications. For example, the life history of Northern pike (Esox lucius) and smallmouth bass (Micropterus dolomieui) were supposed to be better adapted to the increasing transparency in the water column (e.g. Casselman and Lewis, 1996). Alternatively, the decline in walleye biomass over time should be related to increased transparency because of its well known avoidance of intense light (Ali and Anctil, 1977; Ryder, 1977; Lester et al., 2002). These hypotheses were tested using a retrospective analysis of historical 2D habitat modifications.

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Habitat models, based on multivariate logistic regressions between abiotic factors and fish occurrence, were computed separately for walleye, Northern pike, yellow perch (Perca flavescens) and smallmouth bass. The regressions indicated light penetration was a significant variable for defining habitat in almost all cases, and in particular for walleye. In addition, water depth, sand proportion on the riverbed and specific discharge were also significant for some species. Next, these habitat conditions were simulated for the period between 1985 and 2002 (early summer), based on historical river discharge and suspended solids record. The resulting habitat series correspond to the observed changes in the fish community mentioned earlier; they show a significant decrease from 1993 in the suitable areas for walleye within Lake Saint–François (Figure 5) and a less important decrease was observed for yellow perch and Northern pike, contrasting with an increase habitat suitability for smallmouth bass. Significant positive correlations between temporal changes in habitat surface and fish abundances were found, which was considered as evidence validating the hypothesis concerning walleye abundance reduction.

suitable area (%)

90% 70% 50% 30% 10% 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Years walleye

Northern pike

yellow perch

smallmouth bass

Figure 5. Temporal Development of Suitable Habitat of Four Fish Species in Lake Saint–François. For Each Species, Suitable Area (Expressed in Percentage of Total Lake Surface) Corresponds to The Surface with Probability of Presence of at Least One Individual According to the Logistic Model.

2D Modelling of Submerged Plants Submerged plants are very abundant in the SLR, thus the effect of their resistance to flow was important to introduce into the hydrodynamic simulation. In order to improve the spatial description of SAVs, 2D modelling approach was used to predict


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spatial distribution of submerged plants from abiotic parameters (Morin et al. (in prep); Morin, 2000). The abiotic factors controlling the distribution of SAVs were either measured in the field or simulated with numerical models based on spring and fall conditions, i.e. in absence of submerged plants. In Lake Saint–François, results were generated using a dense grid of 114,000 nodes with hydrodynamic simulations of mean annual discharge and water level. A total of 12 abiotic factors were considered: 1) bottom slope, 2) mean grain size of the substrate, 3) velocities, 4) water depth, 5) accumulation of fine particles, 6) light intensity reaching the bottom and 7) to 12) near–bottom velocity caused by wind–generated waves (Nbvw) divided into different intensity and direction categories during two seasons. These six wind variables were: Nbvw of 17, 35 and 45 km/h wind intensity classes, representing conditions typical of spring and fall. Biotic data were extracted from a field characterization of the distribution of plant species. Eight species that dominate the SAV communities due to their height were selected. A database of presence and absence of SAV species with the 806 observations GPS referenced. Of these sample stations, 698 were randomly selected for the calibration of the logistic regression and the 108 samples that were then used for validation steps. Multivariate logistic regressions of eight basic variables explained a significant portion of the variability in species distribution based on their individual life history adaptations. The model correctly predicted the plant species distribution with 81% to 92% accuracy, depending on species, with less than eight basic variables. Substrate composition was not significant for any plant species. Wave stress appears significant for all species during both fall and spring. 35 km/h wind induced near–bottom velocities are most significant during spring and 45 km/h during fall. Water depth and light penetration are statistically important for seven of the species. Current velocity and fine particle accumulation are significant for six species and bottom slope is significant for five species. These abiotic factors were used for prediction and resulted in an accurate spatial distribution of submerged species in this part of the river.

2D Modelling of Large Classes of Wetlands The SLR contains several large littoral wetlands that are influenced by the hydrologic regime (Gauthier, 1982; Jacques, 1986; Jean et al., 1992). Recently, using multi–date aerial photographs from 1964 to 1997, Jean et al. (2002) have documented the migration of swamps and marshes due to long–term water level fluctuations. In order to quantify the links between hydroperiod and wetland composition, Champoux et al. (2002) used a 2D habitat modelling approach. The main objective of their study was to examine the statistical relationships and the predictive potential of the 2D modelling approach for predicting spatial distribution of emergent wetlands, using biotic data and computed 2D abiotic variables (i.e. hydroperiod and slope). Biotic data were extracted from the detailed maps of Jacques (1986). These maps are composed of 23 sheets at a scale of 1:10,000, covering a total of 550 km 2

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of wetlands in the Lake Saint–Pierre area. The 6000 polygons were digitized into a database by Falardeau et al. (2000). A sub–sampling (~5900) was made in a portion of the database. These points and the polygon attributes were used as the biotic sample for calibration/validation of the statistical model via multivariate logistic regression. Sampling points were grouped into the four classes, deep marsh, shallow marsh, shrubby swamp and wooded swamp. For all these points, several abiotic variables were developed; bottom slope of the floodplain and nodal hydroperiod computed for five time steps: 1, 3, 7, 12 and 18 years prior to the sampling year (1985). It is important to note that this work was done with a very rough description of the topography, made with aerial photographs of flooded land. Multivariate regression shows that both the bottom slope and nodal hydroperiod variables are significant descriptors of the spatial distribution of wetlands. Bottom slope is significant for all wetland classes. Within the hydroperiod variables, only the “number of dry weeks” is not significant for any wetland class. All other types of hydroperiod are significant at various levels depending on the time step and on the class considered. Of time step, the 1–year time step is significant for only one class. Longer time steps (3 through 18 years) are significant more often while the 7– and 18–year time steps are the most common. The shallow marsh model was tested successfully in a predictive mode and the results showed the same evolution observed by Jean et al. (2002) in Lake Saint–Pierre.

DISCUSSION New 2D physical variables were developed for three different portions of the Saint– Lawrence River’s ecosystem: emergent wetlands, submerged plants and fish. These represent fundamental characteristics of large rivers. Bottom slope is significant as a controlling variable for all three types of community. As stated earlier, the exact mechanism is not fully understood but we believe that for the wetlands it is probably linked with drainage efficiency for submerged plants with the perennial structure stability and turbulence, while for fish it can be linked with connectivity between deep cover sites and shallow feeding areas or possibly the slow–flowing boundary sub–layer. Water level expressed either as water depth or hydroperiod is also significant for the three community types. Contrary to earlier models developed in smaller rivers, substrate grain size is rarely a significant predictor of fish habitat in the SLR. This may be due to the fact that the riverbed is composed mainly of fine material (finer than sand) whereas in small rivers there is frequently a wider range of grain sizes including boulder and cobble classes. The bottom slope variable in the SLR is likely analogous to the boulder–cobble habitat in smaller streams in that it too provides refuge from current. The ‘accumulation of fine particles’ variable may also reflect nutrient content of the sediment, thus explaining why several plant species that form large standing crops and canopies are positively related to it. Preliminary exploration of habitat models of benthic organisms also shows a significant relationship with this variable.


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Because both sampling location and timing influence habitat models, the design of sampling strategies used for biota sampling is crucial, especially for fish (Frontier, 1983). For example, in the present study, a portion of the fish samples were collected over a 24–hour period with gillnets. This sampling strategy integrates temporal environments (e.g. combinations or light levels and temperatures), that may bias the overall fish distribution. Biases include factors such as the sensitivity of some fish to light, amplitude of movement between nearshore and channel habitat versus territorial fish. Samples collected over a 24–hour period may, however, integrate global habitat conditions and fish community interactions. To obtain more specific information about the importance of certain habitats at different times of the day, samples should be collected within shorter intervals at fixed sites during daylight with methods such as seines, electrofishing, trawling or video recording. This type of discrete point sampling can detect the effects of short–term variables such as wave action and light intensity that integrating over 24–hour periods may mask. For benthic organisms, wetlands or submerged plants that do not move, the challenge is less linked to the sampling strategy than to the long–term influences of variables such as multi–year influence of wave action or the integration duration of hydroperiod for wetlands that can be difficult to pinpoint. Insights from modelling in the SLR can be useful for modelling smaller systems. For example, Guay et al. (2003, in press) found that habitat of Atlantic salmon (Salmo salar) in small rivers is very sensitive to sunlight intensity, which produces different relationships on cloudy versus sunny days. This is related to light penetration in the water column, a variable that is presently being modelled in the SLR. However, in rivers with different transparencies, the responses of fish to incidental light changes may be very distinct, thus limiting the transferability of habitat models among rivers. Incorporating light penetration as a function of suspended or dissolved matter in fish habitat models facilitates the adjustment of applied statistical methods (e.g. multiple linear regression) to establish the link between aquatic organisms and abiotic environmental variables. Unfortunately, we found little information in the scientific literature on fish behaviour relative to light intensity available for most species. More studies on this subject are necessary to develop reliable habitat models, in particular if absolute light intensity for different wavelengths (PAR to UV) is to be employed as a predictive variable. 2D habitat modelling offers promising perspectives for conducting theoretical and applied research in large rivers. Accurate temperature predictions could help to build spatially explicit bioenergetic models that can describe changes in fish growth. Bioenergetic models are the next step in analyzing the effects of habitat changes on fish habitat and behaviour in a more mechanistic–predictive way. This approach has been applied using 2D modelling (Hardy and Addley, 2003, this issue) and offers a promising avenue for future developments. Temperature models with high spatial resolution could also help improve prediction of areas most favourable for fish spawning within a river and its floodplain. Better identification of specific abiotic conditions necessary for reproduction for either the entire year or certain critical shorter periods, is widely sought after in fisheries management.

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In addition, the complex interactions between floods and riparian topography, coupled with temperature and transparency simulations, would produce useful insights into the complex processes that take place in river floodplains. These processes are key elements to understand productivity and biodiversity in rivers (Tockner et al., 2000). When based only on field sampling, such studies are extremely difficult to conduct, requiring a considerable investment of time and money, and have limited extrapolation potential. Like many other simulation approaches, 2D modelling allows optimization of the field sampling effort for calibration and validation, while keeping its superior predictive power.

ACKNOWLEDGEMENTS We thank André Bouchard, Frédéric Lecomte and Paul Boudreau for their constructive comments, Andy Casper for his english review and Gaétan Daigle for statistical advice. This work was supported by grants from Environment Canada, Société de la faune et des parcs du Québec, Saint–Laurent Vision 2000 program, the International Joint Commission, NSERC and FCAR. Also, we wish to thank all those involved in the St. Lawrence River Fish Monitoring Network of the Société de la faune et des parcs du Québec for providing the fish data.

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