- Email: [email protected]
Relevant variables to predict macrophyte communities in running waters
Ecological Modelling 160 (2003) 205 /217 www.elsevier.com/locate/ecolmodel
Relevant variables to predict macrophyte communities in running waters Aat Barendregt a,*, Ana M.F. Bio b a
b
Department of Environmental Sciences */Utrecht University, P.O. Box 80115, 3508 TC Utrecht, The Netherlands Environmental Group of the Centre for Modelling Petroleum Reservoirs, CMRP/IST, Avenue Rovisco Pais, 1049-001 Lisbon, Portugal
Abstract In both predictive theoretical and empirical models for aquatic plant communities in running waters, the development and competition are many times explained in terms of nutrients. Minerals necessary for growth are generally not assumed to be limiting, although they influence the important pH-value. At the same time it is known that factors such as oxygen-concentration, solar energy, salinity, dimension of the system and soil characteristics (including river sediments) influence the development of the community, and should be considered in modelling. Effects of water quantity and water quality on macrophytes are reviewed. These conditions are caused by processes in the landscape, characterised by a set of nested variables which explain the distribution of macrophyte species and communities. Relevant variables are described and grouped on three scales: regional, local and site conditions. Case studies with direct and indirect gradient analysis are presented. Statistical tests (stepwise regression with forward selection) reveal that each species distribution is explained by a characteristic set of relevant variables, ranging from soil type and dimension of the system, to nutrient and salinity concentration. # 2002 Published by Elsevier Science B.V. Keywords: Aquatic macrophytes; River; Variables; Nutrient; Landscape; Region; Regression model
1. Introduction The modelling of aquatic macrophyte communities can be performed using physiological and chemical relationships between macrophytes and environmental conditions, known from research in laboratory, field experiments and theory. Generally, the major predicting variables, like nutrient
* Corresponding author. E-mail address: [email protected] (A. Barendregt).
and oxygen availability, sediment interactions and light conditions, are combined within one model (e.g. Mitsch and Reeder, 1991; Carr et al., 1997; Ha˚kanson, 1999; Muhammetoglu and Soyupak, 2000) that forms a theoretical description of a community. The modelling of macrophytes in rivers, rivulets and brooks is frequently based on an empirical description of the communities distribution. In these running water systems two complications with major ecological impact are valid that preclude deterministic modelling. Firstly, the stochas-
0304-3800/02/$ - see front matter # 2002 Published by Elsevier Science B.V. PII: S 0 3 0 4 - 3 8 0 0 ( 0 2 ) 0 0 2 5 4 - 5
206
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
tic hydrology of running water has to be considered. Secondly, the river is not an isolated system, but part of the total landscape with all its interrelated characteristics (Mitsch and Gosselink, 2000). In this landscape there is a gradient in morphology from origin to mouth, resulting that the local conditions in a river vary within the catchment area. In many cases the area close to the origin is characterized by higher altitude, resulting in a steeper slope, higher current velocity and erosion. Here, the chemistry of the river water is comparable to precipitation water. Down-stream the dimension of the river is wider, flooding occurs, and accumulation of organic sediments become dominant processes. At these locations the chemistry of the water is altered with enrichment in major ions and, due to the discharge of water (the transport medium in the landscape) from the catchment area, there is an accumulation of nutrients. Moreover, the lower parts of the river are mostly the areas with agriculture and other human interactions, resulting in an extra input of nutrients. The main questions are: which processes have to be incorporated in macrophyte modelling and by which variables can these processes be indicated or actually described? A selection of macrophyte studies in some European streams (in The Netherlands, Belgium, France, Denmark and Poland) illustrates the quantity and diversity of environmental variables that are used to describe ecological conditions (Table 1). Relevant variables are found at several levels or scales. Hydrology, dimension of the river, current velocity, soil and sediment layer constitute characteristics of the whole landscape that mostly cannot be changed. At a smaller scale, unstable conditions in the water, including nutrients, buffer-complex and resulting pH, electric conductivity and turbidity, are frequently mentioned. A major problem for modelling is that there are many interrelations between all variables, next to the presence of a gradient in geomorphology. We will review the complex conditions of hydrology in water quantity and in water quality and, by way of case studies, depict those variables that are important for the description of aquatic macrophyte distribution.
2. Water quantity related effects in running waters The most obvious statement about the free hydrology of brooks and rivers is that nothing is stable. The quantity of discharge during the year is variable and, under natural conditions, the stream can expand or decline. A maximum discharge will cause extreme locations in the valley to be inundated for a short period of time; a low discharge results in maintaining a small stream with the respective area inundated for most of the time (Fig. 1). Therefore, the difference between a riparian and an aquatic system can be formulated only on a gradual scale. Inundation duration constitutes the first hydrological variable which influences the aquatic community directly. Different types of vegetation originate due to the characteristic physiology of species, defining their ability to withstand periods with low water tables (e.g. Auble et al., 1994). Along the river two other direct variables affect the presence of macrophyte communities: width and depth of the river. Both increase from the origin to the mouth. These two strongly interrelated variables define ecological conditions many species react upon (Roberts and Ludwig, 1991; Rea et al., 1998; Barendregt and Gielczewski, 1998; Riis et al., 2000). Altitude itself appears to be a predictive variable too (Kawecka and Szcesny, 1984). At the same time the result of discharge, the current velocity itself, is a determining variable. Growth of macrophytes is mostly stimulated by increased current, associated with high oxygen availability and an intensified exchange of dissolved substances in running waters. Current velocity higher than 1 m/s will reduce macrophyte growth and presence (Nilsson, 1987); at that velocity a negative relation between current velocity and biomass and species number is reported (Chambers et al., 1991). The current velocity as well as associated dynamic processes can be included in modelling (Edwards et al., 1999; Wellnitz et al., 2001). Indirectly, macrophyte communities are affected by two other water quantity related hydrological processes which influence water quality. First, the catchment area of the river receives a part of its input from groundwater discharge. The chemistry
/
/ / / / /
Sapropel / / / /
Soil
/ / /
Current
/ / /
/ / / / / /
pH/Ca/lime
/
/ /
/ / / /
/
Depth
/ / / /
/ / / / / /
Nutrients
/
/
/
Turbidity
/
/ /
/ /
/
EC
EC, electric conductivity. Additional variables in these publications: distance from bank, temperature, region, altitude, slope, oxygen-concentration, area, shading, etc.
Toivonen and Huttunen (1995) */south Finland (lakes) Heegaard et al. (2001) */Northern Ireland (lakes) Srivastava et al. (1995) */Canada (lakes) Khedr and El-Demerdash (1997) */Egypt (irrigation canals)
Mesters (1997) */Netherlands Higler (1993) */Netherlands Bornette and Amoros (1991) */France Riis et al. (2000) */Denmark Barendregt and Gielczewski (1998) */Poland Schneiders et al. (1999) */Belgium
Hydrology
Table 1 Important variables for macrophyte distribution mentioned in publications on river systems (first 6 ones) and others (last 4 ones)
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217 207
208
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
of groundwater is mostly different from that of river water, causing zones with groundwater discharge to be inhabited by different characteristic communities (Grootjans et al., 1998; Wassen et al., 1990). Consequently, reduction of groundwater flow by human impact will result in deterioration of these systems (Barendregt et al., 1992; Stromberg et al., 1996). The second indirect influence by hydrology is probably the most significant one for the differentiation in the ecosystem: the transport of sediments with nutrients. Recent modelling of erosion and sediment transport on river basin level (Van Dijk, 2001) facilitates the prediction of the presence of the sediment layer, where the majority of nutrients is stored (De Wit, 1999). Since the basic physiology of macrophytes is determined by nutrient availability, with strong relationships between plant growth, nutrition and sediment density (Barko and Smart, 1986), the importance of storage of sediments at special locations influenced by stream flow is evident. However, here the complexity of an ecosystem sets in. Macrophyte species can alter the sediment’s chemistry through the oxygen in their roots and mobilise inorganic phosphorus related with active metals such as Fe and Mn by changing the redox-potential (Wigand et al., 1997). Hence, nutrients stored in sediments become available again.
3. Water quality in running waters Next to the dimensions of the river, the chemistry of its water is also changing from the origin to the mouth. It changes from precipitation water, with minerals almost absent, to calcium-enriched water, causing a differentiation in macrophyte communities (e.g. Moss, 1988). In many cases the interest of the society in water quality is driven by problems of eutrophication. This increased availability of nutrients is mostly the final result of a number of processes to be considered for modelling purposes. Concentrations of available nitrogen and phosphorus are frequently used in integrated models, in combination with pH-value, oxygen-concentration, dissolved inorganic carbon availability, sediments, phytoplankton-growth, temperature, and others
(Mitsch and Reeder, 1991; Carr et al., 1997; Ha˚kanson, 1999; Muhammetoglu and Soyupak, 2000). Even in running rivers these models can be applied with a simulation of a spatial pattern (Van der Perk and Bierkens, 1997). Time-dependent relations (fluctuating hydrology, algal growth during the season) might further improve models that relate vegetation to water quality (Jørgensen, 1995; Sanchez-Carrillo and Alvarez-Cobelas, 2001). Next to these complex relations, the presence of macrophyte species is influenced by a number of consequences from other processes associated with eutrophication. Algal bloom resulting from eutrophication has a negative impact on the original communities. First of all the turbidity of the water is increased, so that the solar energy cannot reach the photosynthetic parts of the macrophytes, limiting the growth of the plants (Sand-Jensen and Vindbaek Madsen, 1991; Schwarz and Hawes, 1997; Carr et al., 1997). Furthermore, increased photosynthesis by algae results in rising pHvalues. Since certain plant species can assimilate carbon only as CO2 at lower pH-values, and cannot use the HCO3 or CO2 concentration at 3 higher pH-values, these species might disappear. In opposite direction, the macrophyte growth might also reduce the nutrient levels (Klopatek, 1978). A complication is that due to eutrophication with algal blooms, the original variation in aquatic macrophytes is replaced by other species which react differently to the basic set of conditions. And, although most site conditions remain the same, these species may cause the ecosystem as a whole to head for a different state (Srivastava et al., 1995). Another process to consider, is the increase in major ion concentrations by human impact, in modelling often indicated by an increased Electric Conductivity of the water. Increasing the salinity for only a minor part, can result in reduced growth for some species and in altered competition (Van den Brink and Van der Velde, 1993). Recently, the direct negative effect of salinity on the phosphate binding in organic material has been proved (Beltman et al., 2000). The negative effect of increased SO2 concentration in the surface water 4 on the S2 concentration in the sediment layer
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Fig. 1. Scheme linking conditions affecting aquatic macrophytes with scales in the landscape. Fig. 2. Scales and conditions of aquatic macrophytes.
209
210
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
and, therefore, on the rooting zone of macrophytes is another example (Smolders and Roelofs, 1995). These processes denote an increased level of nutrient availability, starting an internal eutrophication. In this context, the low retention capacity of rivers compared with lakes and wetlands, is a topic of concern too, because rivers are often subject to high nutrient loading (Saunders and Kalff, 2001). Finally, pollution with heavy metals and herbicides is a critical factor, resulting in growth inhibiting or even lethal conditions (Van Straalen and Verkleij, 1991).
4. Relevant scales for aquatic macrophytes Based on the description of the geomorphology and processes in running waters, we can summarize that the conditions, which the macrophytes experience, are induced on three different scales in the landscape (Fig. 2). If we wish to model running waters at all levels, we will have to link these scales. (1) The regional level . In an undisturbed state, most conditions are fixed, reflecting gradients in the landscape: soil type, hydrology and width of the river and the chemical gradient in river water. (2) The local level . Based on the regional boundary conditions, local conditions are reflected in the quantity in sediments, the concentration in nutrients in the system, the alkalinity/base-buffering, turbidity and the local character of the stream’s hydrology and dimension. Moreover, the human influence from pollution or nutrientinput can be incorporated at this level. (3) The site conditions . On the spot where the macrophytes are growing the actual site condition values affect their distribution. Here, the availability of the nutrients determines the growth; the actual redox and pH-values determine the progress in chemical processes, chemical equilibria in the sediments, algal growth and the resulting turbidity in the water. In reality also a fourth level, the biotic conditions, should be taken into account, since the community itself is a group of living species that experience each other in an equilibrium. There is competition between plant species and fauna
might influence vegetation growth by grazing. Moreover, the biotic component can influence the system itself by processes like succession and accumulation of organic substances or increasing redox-potential in sediments by oxygen from the roots. The integration of the three scales in modelling can be achieved in different ways. One might strive for one general ecological model including submodels with detailed level information (e.g. Fitz et al., 1996) or a dynamic spatial modelling including the main processes (e.g. Sklar et al., 2001). The reverse action is to select the relevant information on a local level and aggregate the components to a higher scale into one model (e.g. Rastetter et al., 1992). Here, information about the ecosystem and species or species groups needs to be included; many times species response curves are applied (e.g. Austin and Gaywood, 1994; Bio et al., 1998). A recent development is the modelling using neural networks with the application of all relevant environmental variables (this issue).
5. Case studies on relevant variables Research into the importance of variables in running water ecosystems may commence from two starting points: the macrophyte community or the conditions in the ecosystem. The first approach is an analysis of the composition of the vegetation with clustering techniques, followed by an analysis of the site condition values at the locations associated to the representatives of the different vegetation clusters. Two cases will be presented: one from the river Narew in Poland (Barendregt and Gielczewski, 1998), another from streams in Denmark (Riis et al., 2000). The latter approach is a direct gradient analysis with, mostly, many variables and the application of models in which the plant species can indicate their preferences and tolerances; seven cases will be presented. 5.1. Indirect gradient analysis The river Narew is located in the north-eastern part of Poland; it discharges into the river Wisla close to Warsaw. The Narew’s length is 484 km;
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
211
Table 2 Clusters and weight of some plant species from cluster analysis in a data set from the river Narew (eastern Poland) and some average abiotic values of the members of the clusters Cluster number Number of releve´es Mean number of species/releve´
1 28 17.9
2 30 17.2
3 13 14.3
4 19 14.5
5 6 14.3
Nuphar lutea Butomus umbellatus Potamogeton pefoliatus Ranunculus aquatilis Hydrocharis morsus-ranae Ranunculus fluitans Potamogeton nodosus Potamogeton trichoides Phragmites australis Potamogeton lucens Typha angustifolia Nymphaea alba Elodea canagensis Lemna trisulca Ranunculus circinatus Callitriche species Carex rostrata Equisetum fluviate Berula eecta Glyceria fluitans Rumex hydrolapathum Scirous sylvaticus Carex riparia Typha latifolia Urtica dioca Potamogeton crispus
1.5 1.9 1.4 0.3 0.5 0.6 0.7 0.3 0.3 0.9 / / 0.5 0.7 0.3 / / / / 0.5 0.2 / / / / 0.5
3.0 1.5 0.7 0.1 0.3 / / / 1.5 1.9 1.1 0.4 0.5 0.5 / / / 0.3 0.4 0.4 0.2 / / 0.2 / 0.5
0.9 / / / / / 0.2 / 0.2 0.3 / / 2.4 1.2 1.1 0.9 0.3 0.8 / 0.7 0.2 / / / / 0.6
0.7 0.6 / / 0.2 / / / / / / / 2.8 1.8 / / / / 1.7 2.2 0.8 0.4 0.3 0.2 / /
/ 0.7 0.3 / / / / / 0.7 / / / 0.3 / / 0.8 / / / 0.2 0.2 / 0.5 0.5 0.7 1.5
19.7 48 2.2 11.6 30.3 0.2 0.4 2.8
6.8 27 1.6 9.4 25.8 0.1 0.3 2.5
1.2 8 0.7 9.4 30.4 0.2 0.4 2.1
1.1 8 0.7 15.1 32.0 0.8 2.6 4.4
1.2 6 1.1 35.4 39.6 1.1 2.3 5.8
Mean Mean Mean Mean Mean Mean Mean Mean
discharge in m3/s width river (m) depth river (m) concentration Cl mg/l concentration SO4 mg/l concentration P-total mg/l concentration N-total g/l concentration K-total mg/l
Data originate from Barendregt and Gielczewski (1998)
the catchment area covers 35 700 km2. The catchment is characterized by low human population; industry is scarce and agricultural management is not intensified, and only a few cities unload waste water into the river. As a result this river is one of the few unaltered rivers in Europe. The 116 samples covering the whole catchment area of the river Narew, indicate that 5 clusters in macrophyte communities are present (Table 2). These
can be arranged in 3 main groups: clusters 1 and 2 with Nuphar lutea , cluster 3 and 4 with Elodea canadensis and cluster 5 with Carex riparia . Computation, per cluster, of the abiotic variable values which describe the locations reveals important information. Clusters 1 and 2 occupy the wider parts of the down-stream river, with higher values for depth, width and discharge, whereas the other clusters are from the up-stream areas. Water
212
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Table 3 Clusters and weight of some plant species from cluster analysis in a data set from most rivers in Denmark and some average abiotic values of the members of the clusters Cluster number Number of relevees Nuphar lutea Butomus umbellatus Iris pseudacoris Potamogeton perfoliatus Potamogeton crispus Potamogeton pectinatus Potamogeton praelongus Phragmites australis Typha latifolia Glyceria maxima Ranunculus aquatilis Elodea canadensis Sparganium emersum Ranunculus baudotii Ranunculus circinatus Ranunculus peltatus Callitriche species Nasturtium species Polygonum amphibium Carex rostrata Potentilla palustre Myriophyllum alterniflorum Potamogeton natans
1 114 0.6 2.2 / 0.6 1.2 1.3 0.6 0.5 / 6.0 / 17.6 29.2 2.0 0.7 1.0 7.4 / /
2 37
3 4
4 31
5 13
6 6 / / / 4.6 0.3 / /
/ / 2.0
/ / / 1.5 2.2 0.4 / / / 5.3 / 3.5 5.5 / / 19.9 20.8 2.6 2.3 / / / /
/ / / / / / / / / / / 6.3 7.3 / / 6.2 3.6 / / 4.2 1.9 22.1 17.8
1.1 4.0 0.7 20.8 18.3 16.4 1.9 0.4 / 3.5 0.4 8.0 4.9 0.5 0.3 / 2.3 / / / / / 0.8
0.8 2.2 2.9 / 0.5 / / 3.0 9.4 25.8 / 2.3 1.4 / / 1.4 1.1 / / 1.2 / / /
/ 8.5 50.4 / 5.3 2.3 / / 6.3 / / / / / /
Mean width (m) Alkalinity (meq/l)
5.8 2.4
3.7 1.2
2.8 0.6
7.0 3.5
6.3 1.4
5.1 2.4
% peat % stones
0 0.5
0 1.6
6.5 0
0 3.1
0 8.1
0 6.7
Data originate from Riis et al. (2000).
chemistry is another source of information. The river as a whole appears to be without major influences from eutrophication, but cluster 5 is characterized by higher concentrations in chloride and sulphate, indications for human waste water. The respective locations of cluster 5 appear to be situated just down-stream from discharge points. The difference between the clusters 3 and 4 is given by the nutrients. The locations of cluster 4 are higher in nutrient concentration, probably originating from agricultural management in a sandy subregion, whereas locations represented by cluster 3 with lower concentrations are mostly located in an area with many forests and nature areas, and moreover with a loamy soil.
The Danish study incorporates 208 locations throughout Denmark (Riis et al., 2000, 2001). Its 6 resulting vegetation clusters are less isolated due to weaker differences in species composition (Table 3). Still some important nodes are given: cluster 4 and 5 with Nuphar lutea and cluster 1 with Elodea canadensis . The analysis of the environmental data shows marked differences. Cluster 2 and 3 represent the narrow rivulets and cluster 4 and 5 the wider ones; cluster 3 is characterized by low alkalinity and peat soils; cluster 5 and 6 by the presence of stones. The locations (regions) themselves appear to be important to describe the difference between clusters too. Nutrient concentrations and salinity are not incorporated in this
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
213
Table 4 Selected variables in 4 multiple logistic regression models (ICHORS, with resp. 163, 76, 135 and 182 species) for aquatic macrophytes in ditches, small lakes and turf-ponds in western Holland
Table 5 Selected variables in multiple logistic regression models (IMRAM, with 467 species at 520 locations; mean 3.1 predictor in each regression) for aquatic macrofauna in ditches, small lakes and turf-ponds in western Holland
Version ICHORS data set n / number of regressions Mean predictor/regression
3.0 770 163 4.8
3.1 193 76 4.0
3.2 745 135 3.8
3.3 675 182 5.4
Tot-P NO3 /N NH4 /N K
19 4 13 13
Cl Mg Na SO4
14 21 8 8
Width of water Depth of water Soil type Sapropel
41 16 82 8
61 17 16 14
23 11 45 5
27 21 26 12
pH Ca HCO3
15 9 6
Fe Oxygen Turbidity
6 6 10
Water table Discharge gr.water
33 18
25 21
13 28
45 10
Width of water Depth of water cm with sapropel
35 16 10
Soil type Vegetation shoreline Aquatic vegetation
33 6 31
Turbidity
12
14
6
22
Cl Mg SO4 Na
20 34 9 10
14 8 8 9
8 26 12 9
14 31 13 12
PO4 K NO3 /N NH4 /N Tot-anorg-N
15 14 11 14 9
5 24 20 12 13
21 15 14 10 6
40 22 12 7 12
pH Ca HCO3
17 19 8
25 12 13
17 10 7
30 17 12
Fe Si
4 16
11 22
13 16
37 26
Average data in 4 versions ICHORS cumulative %, converted %) Width/depth/soil Nutrients Salinity Quantitative hydrology pH/alkalinity Fe/Si Turbidity
(number, variables, mean (4) (5) (4) (2) (3) (2) (1)
106 74 59 48 46 36 13
28 19 15 13 12 9 3
The numbers indicate the percentage of the species models in which the variable is represented. These data are arranged by group and rescaled to 100% (below). Average data in 4 versions ICHORS (number of variables, mean cumulative %, converted %).
data set, so that many explanatory variables are probably added. The conclusion from the above mentioned two studies is that macrophyte communities in rivers
The numbers indicate the percentage of the species models in which the variable is represented.
are well defined, both in floristic terms and in terms of their site conditions. The variables that describe community differences include nutrients and salinity; soil type, regional differentiation and alkalinity are at least equally important. This is in line with the results from lakes in northern Ireland and southern Finland (Heegaard et al., 2001; Toivonen and Huttunen, 1995). Although, contrary to what happens in lakes, for the description of variation in vegetation in rivers, the local dimensions of the river (discharge, width) appears to be dominating.
Table 6 Selected variables in multiple logistic regression models (96 species models, 220 locations and mean 3.7 predictor in each regression) for terrestrial vegetation in the river valley of Dender (southern Flanders, Belgium) Soil chemist pH K P NH4 NO3
Others 65 64 43 33 33
% organic parts in soil
48
Humidity soil
36
Hydrology landscape
50
The numbers indicate the percentage of the species models in which the variable is represented.
214
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Table 7 Selected variables in multiple logistic regression models (83 species models, 2587 locations and mean 4.2 predictor in each regression) for wet terrestrial vegetation in river valleys in Belgium (northern Flanders), and their first selected predictor % in regression % first predictor pH Ca Mg Ionic ratio
28 37 34 22
15 21 15 8
K PO4 NH4 NO3
24 30 15 21
8 4 2 0
Cl Fe SO4
28 28 40
6 2 8
Soil type 8 Soil humidity 48 Treat (/management type) 60
2 0 8
The numbers indicate the percentage of the species models in which the variable is represented.
5.2. Direct gradient analysis Direct gradient analysis enables estimation of parameters of ecological interest for vegetationenvironment modelling (Jongman et al., 1995). It requires a large data set with a number of explaining variables and an observed response, such as presence and absence records of species. Numerous environmental variables of different types (factors or continuous measurements) can be considered simultaneously and sampling should involve as many candidate predictor variables as possible. Statistical selection criteria can be used to select the most significant variables for each species or group of species. Generally, each location will have different abiotic site conditions. The data set should be large enough to incorporate the range of ecological variability we are interested in. The main underlying assumptions are that each species has it own (realised) niche and, therefore, its own preferences en tolerances; and that local conditions will influence the species distribution. Species preferences can be drawn from regression models. We present examples of multiple logistic regression studies, using the framework
of Generalized Linear Modelling (Nelder and Nedderburn, 1972), with forward model selection based on Likelihood Ratio Tests. This approach aims at detecting the most significant variables explaining the distribution of each species and at using these variables and their relation with the species for subsequent species prediction. These regression models are easy to apply in the evaluation of options in scenario analyses (Barendregt et al., 1992; Pearce et al., 2001). We selected seven published studies with a great number of plots (200 /2500), of abiotic explanatory variables (8 /21) and of species, resulting in numerous multiple regression models (76 /467). Within the range of incorporated variables, the predictive parameters are given for each species and the total numbers of selected variables of all species is tabulated for each data set. At issue is again the question: which variables appeared to be important? Four of the data sets were collected in the western part of The Netherlands. They describe conditions in ditches, small lakes and turf-ponds through 21 abiotic variables, including ditch/lake/ pond dimensions and soil type, hydrology, salinity, nutrients and acidity (Barendregt et al., 1993). For each data set, the percentage of the total number of regression models in which each variable is incorporated was calculated (Table 4). The temporary conclusion is that all variables are selected at least some times. Since there exists a distinct relationship between groups of variables (e.g. Cl , Na , Mg , SO4 display a correlation /0.8), some groups are combined, and the mean values for the four data sets are computed and converted to sum up to 100% (Table 4). The result demonstrates that the fixed dimension/soil type of the system is very important in the prediction of the macrophytes; followed by nutrients, salinity, hydrology and pH/alkalinity. With exactly the same technique prediction models for 467 aquatic macrofauna species were obtained (Amesz and Barendregt, 1996). The predictive parameters show a striking resemblance with the former data sets (Table 5), although some incorporated explanatory variables differ. Finally, two data sets from rivulet valleys in Belgium (Tables 6 and 7) are assessed, leading to
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
the same general conclusion: there is not one variable to predict the presence of species, but each species distribution is determined by a characteristic set of variables (De Bekker et al., 2001). Only the Ca-complex with pH is prominent in terms of importance for regression modelling; if the first selected (and hence most significant) predictor in the multiple regression is scored (Table 7), that predictor represents the baseacidity-axis for more than half of the species. Notice, that the first predictors constitute merely 23% of the total number of model predictors.
6. Conclusion From the seven case studies presented we learn that there is not one most-important variable explaining the macrophyte communities. Each individual species shows its own preferences in setting of variables. At least some representatives of sets of interrelated variables have to be considered in macrophyte community modelling. These representatives should comprise two different groups: (1) The physical conditions enforced by the context of the landscape, such as soil type, width and depth of the river, discharge and current velocity, and salinity. These conditions are many times related to geographical differentiation’s, and therefore to regions in the landscape with their characteristic relations, connected with the biogeography of species. Incorporating the simple variable ‘region’ in a model will therefore often explain a major part of species variation (Bootsma and Wassen, 1996; Riis et al., 2000; De Bekker et al., 2001). (2) The physical and the chemical conditions the plants actually experience at the location, such as availability of nutrients and alkalinity, which influence the growth of species directly. This is in line with the theoretical analyse of the relevant scales in the first part of this paper, with the local level and the actual site conditions. Most publications incorporate this group at first (e.g. Mitsch and Reeder, 1991; Fitz et al., 1996; Wigand et al., 1997; Sklar et al., 2001).
215
These two groups reflect processes that manifest themselves on different levels: those in the landscape and those at the site itself. The latter can be influenced by human intervention (Jørgensen, 1995; Ha˚kanson, 1999; Mitsch and Gosselink, 2000). The first is enforced and can generally not be changed; in modelling, it is to be incorporated as a boundary factor or as a fixed condition.
References Amesz, M., Barendregt, A., 1996. IMRAM: een voorspellingsmodel voor aquatische macrofauna in Noord-Holland. Report Universiteit Utrecht, 47. Auble, G.T., Friedman, M., Scott, M.L., 1994. Relating riparian vegetation to present and future streamflows. Ecological Applications 4, 544 /554. Austin, M.P., Gaywood, M.J., 1994. Current problems of environmental gradients and species response curves in relation to continuum theory. Journal of Vegetation Science 5, 473 /482. Barendregt, A., Stam, S.M.E., Wassen, M.J., 1992. Restoration of fen ecosystems in the Vecht River plain: cost-benefit analysis of hydrological alternatives. Hydrobiologia 233, 247 /258. Barendregt, A., Wassen, M.J., De Smidt, J.T., 1993. Hydroecological modelling in a polder landscape: a tool for wetland management. In: Vos, C., Opdam, P. (Eds.), Landscape Ecology of a Stressed Environment. Chapman & Hall, London, pp. 79 /99. Barendregt, A., Gielczewski, M., 1998. Linkage of environmental variation and vegetation in the catchment area of river Narew. In: Piekut, et al. (Eds.), Environmental and Technical Problems of Water Management for Sustainable Development of Rural Areas Zeszyty Problemowe Postepow Nauk Rolniczych, vol. 458. Polish Academy of Sciences, Warsaw, pp. 15 /23. Barko, J.W., Smart, R.M., 1986. Sediment-related mechanisms of growth limitation in submersed macrophytes. Ecology 67, 1328 /1340. Beltman, B., Rouwenhorst, T.G., Van Kerkhoven, M.B., Van der Krift, T., Verhoeven, J.T.A., 2000. Internal eutrophication in peat soils through competition between chloride and sulphate with phosphate for binding sites. Biogeochemistry 50, 183 /194. Bio, A.M.F., Alkemade, R., Barendregt, A., 1998. Determining alternative models for vegetation response analysis: a nonparametric approach. Journal of Vegetation Science 9, 5 / 16. Bootsma, M.C., Wassen, M.J., 1996. Environmental conditions and fen vegetation in three lowland mires. Vegetatio 127, 173 /189.
216
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Bornette, G., Amoros, C., 1991. Aquatic vegetation and hydrology of a braided river floodplain. Journal of Vegetation Science 2, 497 /512. Carr, M.C., Duthie, H.C., Taylor, W.D., 1997. Models of aquatic plant productivity: a review of the factors that influence growth. Aquatic Botany 59, 195 /215. Chambers, P.A., Prepas, E.E., Hamilton, H.R., Bothwell, M.L., 1991. Current velocity and its effect on aquatic macrophytes in flowing waters. Ecological Applications 1, 249 /257. De Becker, P., De Bie, E., Huybrechts, W., Bio, A.M.F., Wassen, M., 2001. Ontwikkeling van een hydro-ecologisch model voor vallei-ecosystemen in Vlaanderen, VLITORS. Report Instituut voor Natuurbehoud, Brussel. De Wit, M.J.M., 1999. Nutrient fluxes in the Rhine and Elbe basins, Ph.D. Thesis Utrecht University. Edwards, P.J., Kollmann, J., Gurnell, A.M., Petts, G.E., Tockner, K., Ward, V.J., 1999. A conceptual model of vegetation dynamics on gravel bars of a large Alpine river. Wetlands Ecology and Management 7, 141 /153. Fitz, H.C., DeBellevue, E.B., Constanza, R., Boumans, R., Maxwell, T., Wainger, L., Sklar, F.H., 1996. Development of a general ecosystem model for a range of scales and ecosystems. Ecological Modelling 88, 263 /295. Grootjans, A.P., Ernst, W.H.O., Stuyfzand, P.J., 1998. European dune slacks: strong interaction of biology, pedogenesis and hydrology. Trends in Ecology and Evolution 13, 96 /100. Ha˚kanson, L., 1999. On the principles and factors determining the predictive success of ecosystem models, with a focus on lakes eutrophication models. Ecological Modelling 121, 139 /160. Heegaard, E., Birks, H.H., Gibson, C.E., Smith, S.J., WolfeMurphy, S., 2001. Species-environmental relationships of aquatic macrophytes in Northern Ireland. Aquatic Botany 70, 175 /223. Higler, L.W.G., 1993. The riparian community of north-west European lowland streams. Freshwater Biology 29, 229 / 241. Jongman, R.H.G., Ter Braak, C.J.F., Van Tongeren, O.F.R., 1995. Data Analysis in Community and Landscape Ecology. Cambridge University Press, Cambridge, p. 299. Jørgensen, S.E., 1995. State of the art of ecological modelling in limnology. Ecological Modelling 78, 101 /115. Kawecka, B., Szcesny, B., 1984. Dunajec. In: Whitton, B.A. (Ed.), Ecology of European Rivers. Blackwell Scientific Publications, Oxford, pp. 499 /525. Khedr, A.H.A., El-Demerdash, M.A., 1997. Distribution of aquatic plants in relation to environmental factors in the Nile Delta. Aquatic Botany 56, 75 /86. Klopatek, J.M., 1978. Nutrient dynamics of freshwater riverine marshes and the role of emergent macrophytes. In: Good, R.E., et al. (Eds.), Freshwater Wetlands. Academic Press, New York, pp. 195 /216. Mesters, C.M.L., 1997. Polluted Dutch transboundary streams: effects on aquatic macrophytes. Ph.D. Thesis Utrecht University. pp. 127.
Mitsch, W.J., Gosselink, J.G., 2000. The value of wetlands: importance of scale and landscape setting. Ecological Economics 35, 25 /33. Mitsch, W.J., Reeder, B.C., 1991. Modelling nutrient retention of a freshwater coastal wetland: estimating the roles of primary productivity, sedimentation, resuspension and hydrology. Ecological Modelling 54, 151 /187. Moss, B., 1988. Ecology of fresh waters */man and medium, second ed.. Blackwell Scientific Publications, Oxford, p. 417. Muhammetoglu, A., Soyupak, S., 2000. A three-dimensional water quality-macrophyte interaction model for shallow lakes. Ecological Modelling 133, 161 /180. Nelder, D.R., Nedderburn, R.W.M., 1972. Generalized linear models. Journal of the Royal Statistical Society, Series A 135, 370 /384. Nilsson, C., 1987. Distribution of stream-edge vegetation along a gradient of current velocity. Journal of Ecology 75, 513 / 522. Pearce, J., Ferrier, S., Scotts, D., 2001. An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales. Journal of Environmental Management 62, 171 /184. Rastetter, E.B., King, A.W., Cosby, B.J., Hornberger, G.M., O’Neill, R.V., Hobbie, J.E., 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Applications 2, 55 /70. Rea, T.E., Karapatakis, D.J., Guy, K.K., Pinder, J.E., III, Mackey, H.E., Jr., 1998. The relative effects of water depth, fetch and other physical factors on the development of macrophytes in a small southeastern US pond. Aquatic Botany 61, 289 /299. Riis, T., Sand-Jensen, K., Vestergaard, O., 2000. Plant communities in lowland Danish streams: species composition and environmental factors. Aquatic Botany 66, 255 /272. Riis, T., Sand-Jensen, K., Larsen, S.E., 2001. Plant distribution in relation to physical conditions and location within Danish streams systems. Hydrobiologia 448, 217 / 228. Roberts, J., Ludwig, J.A., 1991. Riparian vegetation along current-exposure gradients in floodplain wetlands of the river Murray, Australia. Journal of Ecology 79, 117 /127. Sanchez-Carrillo, S., Alvarez-Cobelas, M., 2001. Nutrient dynamics and eutrophication patterns in a semi-arid wetland: the effects of fluctuating hydrology. Water, Air and Soil Pollution 131, 97 /118. Sand-Jensen, K., Vindbaek Madsen, T., 1991. Minimum light requirements of submerged freshwater macrophytes in laboratory growth experiments. Journal of Ecology 79, 749 /764. Saunders, D.L., Kalff, J., 2001. Nitrogen retention in wetlands, lakes and rivers. Hydrobiologia 443, 205 /212. Schneiders, A., Wils, C., Verheyen, R., 1999. The use of ecological information in the selection of quality objectives for river conservation and restoration in Flanders (Belgium). Aquatic Ecosystem Health and Management 2, 137 / 154.
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217 Schwarz, A.-M., Hawes, I., 1997. Effects of changing water clarity on characean biomass and species composition in a large oligotrophic lake. Aquatic Botany 56, 169 /181. Sklar, F.H., Fitz, H.C., Wu, Y., Van Zee, R., McVoy, C., 2001. The design of ecological landscape models for Everglades restoration. Ecological Economics 37, 379 /401. Smolders, A., Roelofs, J.G.M., 1995. Internal eutrophication, iron limitation and sulphide accumulation due to the inlet of river Rhine water in peaty shallow waters in the Netherlands. Archiv fuer Hydrobiologie 133, 349 /365. Srivastava, D.S., Staicer, C.A., Freedman, B., 1995. Aquatic vegetation of Nova Scotian lakes differing in acidity and trophic status. Aquatic Botany 51, 181 /196. Stromberg, J.C., Tiller, R., Richter, B., 1996. Effects of groundwater decline on riparian vegetation of semiarid regions: the San Pedro, Arizona. Ecological Applications 6, 113 /131. Toivonen, H., Huttunen, P., 1995. Aquatic macrophytes and ecological gradients in 57 small lakes in southern Finland. Aquatic Botany 51, 197 /221. Van den Brink, F.W.B., Van der Velde, G., 1993. Growth and morphology of four freshwater macrophytes under the
217
impact of the raised salinity level of the Lower Rhine. Aquatic Botany 45, 285 /297. Van der Perk, M., Bierkens, M.F.P., 1997. The identifiability of parameters in a water quality model on the Biebrza River, Poland. Journal of Hydrology 200, 307 /322. Van Dijk, P.M., 2001. Soil erosion and associated sediment supply to rivers. Ph.D. Thesis University of Amsterdam, pp. 211. Van Straalen, N.M., Verkleij, J.A.C., 1991. Leerboek Oecotoxicologie. VU Uitgeverij, Amsterdam, p. 422. Wassen, M.J., Barendregt, A., Paczynski, A., De Smidt, J.T., De Mars, H., 1990. The relationship between fen vegetation gradients, groundwater flow and flooding in an undrained valley mire at Biebrza, Poland. Journal of Ecology 78, 1106 /1122. Wellnitz, T.A., LeRoy Poff, N., Cosyleon, G., Steury, B., 2001. Current velocity and spatial scale as determinants of the distribution and abundance of two rheophilic herbivorous insects. Landscape Ecology 16, 111 /120. Wigand, C., Stevenson, J.C., Cornwell, J.C., 1997. Effects of different submersed macrophytes on sediment biogeochemistry. Aquatic Botany 56, 233 /244.
Relevant variables to predict macrophyte communities in running waters Aat Barendregt a,*, Ana M.F. Bio b a
b
Department of Environmental Sciences */Utrecht University, P.O. Box 80115, 3508 TC Utrecht, The Netherlands Environmental Group of the Centre for Modelling Petroleum Reservoirs, CMRP/IST, Avenue Rovisco Pais, 1049-001 Lisbon, Portugal
Abstract In both predictive theoretical and empirical models for aquatic plant communities in running waters, the development and competition are many times explained in terms of nutrients. Minerals necessary for growth are generally not assumed to be limiting, although they influence the important pH-value. At the same time it is known that factors such as oxygen-concentration, solar energy, salinity, dimension of the system and soil characteristics (including river sediments) influence the development of the community, and should be considered in modelling. Effects of water quantity and water quality on macrophytes are reviewed. These conditions are caused by processes in the landscape, characterised by a set of nested variables which explain the distribution of macrophyte species and communities. Relevant variables are described and grouped on three scales: regional, local and site conditions. Case studies with direct and indirect gradient analysis are presented. Statistical tests (stepwise regression with forward selection) reveal that each species distribution is explained by a characteristic set of relevant variables, ranging from soil type and dimension of the system, to nutrient and salinity concentration. # 2002 Published by Elsevier Science B.V. Keywords: Aquatic macrophytes; River; Variables; Nutrient; Landscape; Region; Regression model
1. Introduction The modelling of aquatic macrophyte communities can be performed using physiological and chemical relationships between macrophytes and environmental conditions, known from research in laboratory, field experiments and theory. Generally, the major predicting variables, like nutrient
* Corresponding author. E-mail address: [email protected] (A. Barendregt).
and oxygen availability, sediment interactions and light conditions, are combined within one model (e.g. Mitsch and Reeder, 1991; Carr et al., 1997; Ha˚kanson, 1999; Muhammetoglu and Soyupak, 2000) that forms a theoretical description of a community. The modelling of macrophytes in rivers, rivulets and brooks is frequently based on an empirical description of the communities distribution. In these running water systems two complications with major ecological impact are valid that preclude deterministic modelling. Firstly, the stochas-
0304-3800/02/$ - see front matter # 2002 Published by Elsevier Science B.V. PII: S 0 3 0 4 - 3 8 0 0 ( 0 2 ) 0 0 2 5 4 - 5
206
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
tic hydrology of running water has to be considered. Secondly, the river is not an isolated system, but part of the total landscape with all its interrelated characteristics (Mitsch and Gosselink, 2000). In this landscape there is a gradient in morphology from origin to mouth, resulting that the local conditions in a river vary within the catchment area. In many cases the area close to the origin is characterized by higher altitude, resulting in a steeper slope, higher current velocity and erosion. Here, the chemistry of the river water is comparable to precipitation water. Down-stream the dimension of the river is wider, flooding occurs, and accumulation of organic sediments become dominant processes. At these locations the chemistry of the water is altered with enrichment in major ions and, due to the discharge of water (the transport medium in the landscape) from the catchment area, there is an accumulation of nutrients. Moreover, the lower parts of the river are mostly the areas with agriculture and other human interactions, resulting in an extra input of nutrients. The main questions are: which processes have to be incorporated in macrophyte modelling and by which variables can these processes be indicated or actually described? A selection of macrophyte studies in some European streams (in The Netherlands, Belgium, France, Denmark and Poland) illustrates the quantity and diversity of environmental variables that are used to describe ecological conditions (Table 1). Relevant variables are found at several levels or scales. Hydrology, dimension of the river, current velocity, soil and sediment layer constitute characteristics of the whole landscape that mostly cannot be changed. At a smaller scale, unstable conditions in the water, including nutrients, buffer-complex and resulting pH, electric conductivity and turbidity, are frequently mentioned. A major problem for modelling is that there are many interrelations between all variables, next to the presence of a gradient in geomorphology. We will review the complex conditions of hydrology in water quantity and in water quality and, by way of case studies, depict those variables that are important for the description of aquatic macrophyte distribution.
2. Water quantity related effects in running waters The most obvious statement about the free hydrology of brooks and rivers is that nothing is stable. The quantity of discharge during the year is variable and, under natural conditions, the stream can expand or decline. A maximum discharge will cause extreme locations in the valley to be inundated for a short period of time; a low discharge results in maintaining a small stream with the respective area inundated for most of the time (Fig. 1). Therefore, the difference between a riparian and an aquatic system can be formulated only on a gradual scale. Inundation duration constitutes the first hydrological variable which influences the aquatic community directly. Different types of vegetation originate due to the characteristic physiology of species, defining their ability to withstand periods with low water tables (e.g. Auble et al., 1994). Along the river two other direct variables affect the presence of macrophyte communities: width and depth of the river. Both increase from the origin to the mouth. These two strongly interrelated variables define ecological conditions many species react upon (Roberts and Ludwig, 1991; Rea et al., 1998; Barendregt and Gielczewski, 1998; Riis et al., 2000). Altitude itself appears to be a predictive variable too (Kawecka and Szcesny, 1984). At the same time the result of discharge, the current velocity itself, is a determining variable. Growth of macrophytes is mostly stimulated by increased current, associated with high oxygen availability and an intensified exchange of dissolved substances in running waters. Current velocity higher than 1 m/s will reduce macrophyte growth and presence (Nilsson, 1987); at that velocity a negative relation between current velocity and biomass and species number is reported (Chambers et al., 1991). The current velocity as well as associated dynamic processes can be included in modelling (Edwards et al., 1999; Wellnitz et al., 2001). Indirectly, macrophyte communities are affected by two other water quantity related hydrological processes which influence water quality. First, the catchment area of the river receives a part of its input from groundwater discharge. The chemistry
/
/ / / / /
Sapropel / / / /
Soil
/ / /
Current
/ / /
/ / / / / /
pH/Ca/lime
/
/ /
/ / / /
/
Depth
/ / / /
/ / / / / /
Nutrients
/
/
/
Turbidity
/
/ /
/ /
/
EC
EC, electric conductivity. Additional variables in these publications: distance from bank, temperature, region, altitude, slope, oxygen-concentration, area, shading, etc.
Toivonen and Huttunen (1995) */south Finland (lakes) Heegaard et al. (2001) */Northern Ireland (lakes) Srivastava et al. (1995) */Canada (lakes) Khedr and El-Demerdash (1997) */Egypt (irrigation canals)
Mesters (1997) */Netherlands Higler (1993) */Netherlands Bornette and Amoros (1991) */France Riis et al. (2000) */Denmark Barendregt and Gielczewski (1998) */Poland Schneiders et al. (1999) */Belgium
Hydrology
Table 1 Important variables for macrophyte distribution mentioned in publications on river systems (first 6 ones) and others (last 4 ones)
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217 207
208
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
of groundwater is mostly different from that of river water, causing zones with groundwater discharge to be inhabited by different characteristic communities (Grootjans et al., 1998; Wassen et al., 1990). Consequently, reduction of groundwater flow by human impact will result in deterioration of these systems (Barendregt et al., 1992; Stromberg et al., 1996). The second indirect influence by hydrology is probably the most significant one for the differentiation in the ecosystem: the transport of sediments with nutrients. Recent modelling of erosion and sediment transport on river basin level (Van Dijk, 2001) facilitates the prediction of the presence of the sediment layer, where the majority of nutrients is stored (De Wit, 1999). Since the basic physiology of macrophytes is determined by nutrient availability, with strong relationships between plant growth, nutrition and sediment density (Barko and Smart, 1986), the importance of storage of sediments at special locations influenced by stream flow is evident. However, here the complexity of an ecosystem sets in. Macrophyte species can alter the sediment’s chemistry through the oxygen in their roots and mobilise inorganic phosphorus related with active metals such as Fe and Mn by changing the redox-potential (Wigand et al., 1997). Hence, nutrients stored in sediments become available again.
3. Water quality in running waters Next to the dimensions of the river, the chemistry of its water is also changing from the origin to the mouth. It changes from precipitation water, with minerals almost absent, to calcium-enriched water, causing a differentiation in macrophyte communities (e.g. Moss, 1988). In many cases the interest of the society in water quality is driven by problems of eutrophication. This increased availability of nutrients is mostly the final result of a number of processes to be considered for modelling purposes. Concentrations of available nitrogen and phosphorus are frequently used in integrated models, in combination with pH-value, oxygen-concentration, dissolved inorganic carbon availability, sediments, phytoplankton-growth, temperature, and others
(Mitsch and Reeder, 1991; Carr et al., 1997; Ha˚kanson, 1999; Muhammetoglu and Soyupak, 2000). Even in running rivers these models can be applied with a simulation of a spatial pattern (Van der Perk and Bierkens, 1997). Time-dependent relations (fluctuating hydrology, algal growth during the season) might further improve models that relate vegetation to water quality (Jørgensen, 1995; Sanchez-Carrillo and Alvarez-Cobelas, 2001). Next to these complex relations, the presence of macrophyte species is influenced by a number of consequences from other processes associated with eutrophication. Algal bloom resulting from eutrophication has a negative impact on the original communities. First of all the turbidity of the water is increased, so that the solar energy cannot reach the photosynthetic parts of the macrophytes, limiting the growth of the plants (Sand-Jensen and Vindbaek Madsen, 1991; Schwarz and Hawes, 1997; Carr et al., 1997). Furthermore, increased photosynthesis by algae results in rising pHvalues. Since certain plant species can assimilate carbon only as CO2 at lower pH-values, and cannot use the HCO3 or CO2 concentration at 3 higher pH-values, these species might disappear. In opposite direction, the macrophyte growth might also reduce the nutrient levels (Klopatek, 1978). A complication is that due to eutrophication with algal blooms, the original variation in aquatic macrophytes is replaced by other species which react differently to the basic set of conditions. And, although most site conditions remain the same, these species may cause the ecosystem as a whole to head for a different state (Srivastava et al., 1995). Another process to consider, is the increase in major ion concentrations by human impact, in modelling often indicated by an increased Electric Conductivity of the water. Increasing the salinity for only a minor part, can result in reduced growth for some species and in altered competition (Van den Brink and Van der Velde, 1993). Recently, the direct negative effect of salinity on the phosphate binding in organic material has been proved (Beltman et al., 2000). The negative effect of increased SO2 concentration in the surface water 4 on the S2 concentration in the sediment layer
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Fig. 1. Scheme linking conditions affecting aquatic macrophytes with scales in the landscape. Fig. 2. Scales and conditions of aquatic macrophytes.
209
210
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
and, therefore, on the rooting zone of macrophytes is another example (Smolders and Roelofs, 1995). These processes denote an increased level of nutrient availability, starting an internal eutrophication. In this context, the low retention capacity of rivers compared with lakes and wetlands, is a topic of concern too, because rivers are often subject to high nutrient loading (Saunders and Kalff, 2001). Finally, pollution with heavy metals and herbicides is a critical factor, resulting in growth inhibiting or even lethal conditions (Van Straalen and Verkleij, 1991).
4. Relevant scales for aquatic macrophytes Based on the description of the geomorphology and processes in running waters, we can summarize that the conditions, which the macrophytes experience, are induced on three different scales in the landscape (Fig. 2). If we wish to model running waters at all levels, we will have to link these scales. (1) The regional level . In an undisturbed state, most conditions are fixed, reflecting gradients in the landscape: soil type, hydrology and width of the river and the chemical gradient in river water. (2) The local level . Based on the regional boundary conditions, local conditions are reflected in the quantity in sediments, the concentration in nutrients in the system, the alkalinity/base-buffering, turbidity and the local character of the stream’s hydrology and dimension. Moreover, the human influence from pollution or nutrientinput can be incorporated at this level. (3) The site conditions . On the spot where the macrophytes are growing the actual site condition values affect their distribution. Here, the availability of the nutrients determines the growth; the actual redox and pH-values determine the progress in chemical processes, chemical equilibria in the sediments, algal growth and the resulting turbidity in the water. In reality also a fourth level, the biotic conditions, should be taken into account, since the community itself is a group of living species that experience each other in an equilibrium. There is competition between plant species and fauna
might influence vegetation growth by grazing. Moreover, the biotic component can influence the system itself by processes like succession and accumulation of organic substances or increasing redox-potential in sediments by oxygen from the roots. The integration of the three scales in modelling can be achieved in different ways. One might strive for one general ecological model including submodels with detailed level information (e.g. Fitz et al., 1996) or a dynamic spatial modelling including the main processes (e.g. Sklar et al., 2001). The reverse action is to select the relevant information on a local level and aggregate the components to a higher scale into one model (e.g. Rastetter et al., 1992). Here, information about the ecosystem and species or species groups needs to be included; many times species response curves are applied (e.g. Austin and Gaywood, 1994; Bio et al., 1998). A recent development is the modelling using neural networks with the application of all relevant environmental variables (this issue).
5. Case studies on relevant variables Research into the importance of variables in running water ecosystems may commence from two starting points: the macrophyte community or the conditions in the ecosystem. The first approach is an analysis of the composition of the vegetation with clustering techniques, followed by an analysis of the site condition values at the locations associated to the representatives of the different vegetation clusters. Two cases will be presented: one from the river Narew in Poland (Barendregt and Gielczewski, 1998), another from streams in Denmark (Riis et al., 2000). The latter approach is a direct gradient analysis with, mostly, many variables and the application of models in which the plant species can indicate their preferences and tolerances; seven cases will be presented. 5.1. Indirect gradient analysis The river Narew is located in the north-eastern part of Poland; it discharges into the river Wisla close to Warsaw. The Narew’s length is 484 km;
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
211
Table 2 Clusters and weight of some plant species from cluster analysis in a data set from the river Narew (eastern Poland) and some average abiotic values of the members of the clusters Cluster number Number of releve´es Mean number of species/releve´
1 28 17.9
2 30 17.2
3 13 14.3
4 19 14.5
5 6 14.3
Nuphar lutea Butomus umbellatus Potamogeton pefoliatus Ranunculus aquatilis Hydrocharis morsus-ranae Ranunculus fluitans Potamogeton nodosus Potamogeton trichoides Phragmites australis Potamogeton lucens Typha angustifolia Nymphaea alba Elodea canagensis Lemna trisulca Ranunculus circinatus Callitriche species Carex rostrata Equisetum fluviate Berula eecta Glyceria fluitans Rumex hydrolapathum Scirous sylvaticus Carex riparia Typha latifolia Urtica dioca Potamogeton crispus
1.5 1.9 1.4 0.3 0.5 0.6 0.7 0.3 0.3 0.9 / / 0.5 0.7 0.3 / / / / 0.5 0.2 / / / / 0.5
3.0 1.5 0.7 0.1 0.3 / / / 1.5 1.9 1.1 0.4 0.5 0.5 / / / 0.3 0.4 0.4 0.2 / / 0.2 / 0.5
0.9 / / / / / 0.2 / 0.2 0.3 / / 2.4 1.2 1.1 0.9 0.3 0.8 / 0.7 0.2 / / / / 0.6
0.7 0.6 / / 0.2 / / / / / / / 2.8 1.8 / / / / 1.7 2.2 0.8 0.4 0.3 0.2 / /
/ 0.7 0.3 / / / / / 0.7 / / / 0.3 / / 0.8 / / / 0.2 0.2 / 0.5 0.5 0.7 1.5
19.7 48 2.2 11.6 30.3 0.2 0.4 2.8
6.8 27 1.6 9.4 25.8 0.1 0.3 2.5
1.2 8 0.7 9.4 30.4 0.2 0.4 2.1
1.1 8 0.7 15.1 32.0 0.8 2.6 4.4
1.2 6 1.1 35.4 39.6 1.1 2.3 5.8
Mean Mean Mean Mean Mean Mean Mean Mean
discharge in m3/s width river (m) depth river (m) concentration Cl mg/l concentration SO4 mg/l concentration P-total mg/l concentration N-total g/l concentration K-total mg/l
Data originate from Barendregt and Gielczewski (1998)
the catchment area covers 35 700 km2. The catchment is characterized by low human population; industry is scarce and agricultural management is not intensified, and only a few cities unload waste water into the river. As a result this river is one of the few unaltered rivers in Europe. The 116 samples covering the whole catchment area of the river Narew, indicate that 5 clusters in macrophyte communities are present (Table 2). These
can be arranged in 3 main groups: clusters 1 and 2 with Nuphar lutea , cluster 3 and 4 with Elodea canadensis and cluster 5 with Carex riparia . Computation, per cluster, of the abiotic variable values which describe the locations reveals important information. Clusters 1 and 2 occupy the wider parts of the down-stream river, with higher values for depth, width and discharge, whereas the other clusters are from the up-stream areas. Water
212
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Table 3 Clusters and weight of some plant species from cluster analysis in a data set from most rivers in Denmark and some average abiotic values of the members of the clusters Cluster number Number of relevees Nuphar lutea Butomus umbellatus Iris pseudacoris Potamogeton perfoliatus Potamogeton crispus Potamogeton pectinatus Potamogeton praelongus Phragmites australis Typha latifolia Glyceria maxima Ranunculus aquatilis Elodea canadensis Sparganium emersum Ranunculus baudotii Ranunculus circinatus Ranunculus peltatus Callitriche species Nasturtium species Polygonum amphibium Carex rostrata Potentilla palustre Myriophyllum alterniflorum Potamogeton natans
1 114 0.6 2.2 / 0.6 1.2 1.3 0.6 0.5 / 6.0 / 17.6 29.2 2.0 0.7 1.0 7.4 / /
2 37
3 4
4 31
5 13
6 6 / / / 4.6 0.3 / /
/ / 2.0
/ / / 1.5 2.2 0.4 / / / 5.3 / 3.5 5.5 / / 19.9 20.8 2.6 2.3 / / / /
/ / / / / / / / / / / 6.3 7.3 / / 6.2 3.6 / / 4.2 1.9 22.1 17.8
1.1 4.0 0.7 20.8 18.3 16.4 1.9 0.4 / 3.5 0.4 8.0 4.9 0.5 0.3 / 2.3 / / / / / 0.8
0.8 2.2 2.9 / 0.5 / / 3.0 9.4 25.8 / 2.3 1.4 / / 1.4 1.1 / / 1.2 / / /
/ 8.5 50.4 / 5.3 2.3 / / 6.3 / / / / / /
Mean width (m) Alkalinity (meq/l)
5.8 2.4
3.7 1.2
2.8 0.6
7.0 3.5
6.3 1.4
5.1 2.4
% peat % stones
0 0.5
0 1.6
6.5 0
0 3.1
0 8.1
0 6.7
Data originate from Riis et al. (2000).
chemistry is another source of information. The river as a whole appears to be without major influences from eutrophication, but cluster 5 is characterized by higher concentrations in chloride and sulphate, indications for human waste water. The respective locations of cluster 5 appear to be situated just down-stream from discharge points. The difference between the clusters 3 and 4 is given by the nutrients. The locations of cluster 4 are higher in nutrient concentration, probably originating from agricultural management in a sandy subregion, whereas locations represented by cluster 3 with lower concentrations are mostly located in an area with many forests and nature areas, and moreover with a loamy soil.
The Danish study incorporates 208 locations throughout Denmark (Riis et al., 2000, 2001). Its 6 resulting vegetation clusters are less isolated due to weaker differences in species composition (Table 3). Still some important nodes are given: cluster 4 and 5 with Nuphar lutea and cluster 1 with Elodea canadensis . The analysis of the environmental data shows marked differences. Cluster 2 and 3 represent the narrow rivulets and cluster 4 and 5 the wider ones; cluster 3 is characterized by low alkalinity and peat soils; cluster 5 and 6 by the presence of stones. The locations (regions) themselves appear to be important to describe the difference between clusters too. Nutrient concentrations and salinity are not incorporated in this
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
213
Table 4 Selected variables in 4 multiple logistic regression models (ICHORS, with resp. 163, 76, 135 and 182 species) for aquatic macrophytes in ditches, small lakes and turf-ponds in western Holland
Table 5 Selected variables in multiple logistic regression models (IMRAM, with 467 species at 520 locations; mean 3.1 predictor in each regression) for aquatic macrofauna in ditches, small lakes and turf-ponds in western Holland
Version ICHORS data set n / number of regressions Mean predictor/regression
3.0 770 163 4.8
3.1 193 76 4.0
3.2 745 135 3.8
3.3 675 182 5.4
Tot-P NO3 /N NH4 /N K
19 4 13 13
Cl Mg Na SO4
14 21 8 8
Width of water Depth of water Soil type Sapropel
41 16 82 8
61 17 16 14
23 11 45 5
27 21 26 12
pH Ca HCO3
15 9 6
Fe Oxygen Turbidity
6 6 10
Water table Discharge gr.water
33 18
25 21
13 28
45 10
Width of water Depth of water cm with sapropel
35 16 10
Soil type Vegetation shoreline Aquatic vegetation
33 6 31
Turbidity
12
14
6
22
Cl Mg SO4 Na
20 34 9 10
14 8 8 9
8 26 12 9
14 31 13 12
PO4 K NO3 /N NH4 /N Tot-anorg-N
15 14 11 14 9
5 24 20 12 13
21 15 14 10 6
40 22 12 7 12
pH Ca HCO3
17 19 8
25 12 13
17 10 7
30 17 12
Fe Si
4 16
11 22
13 16
37 26
Average data in 4 versions ICHORS cumulative %, converted %) Width/depth/soil Nutrients Salinity Quantitative hydrology pH/alkalinity Fe/Si Turbidity
(number, variables, mean (4) (5) (4) (2) (3) (2) (1)
106 74 59 48 46 36 13
28 19 15 13 12 9 3
The numbers indicate the percentage of the species models in which the variable is represented. These data are arranged by group and rescaled to 100% (below). Average data in 4 versions ICHORS (number of variables, mean cumulative %, converted %).
data set, so that many explanatory variables are probably added. The conclusion from the above mentioned two studies is that macrophyte communities in rivers
The numbers indicate the percentage of the species models in which the variable is represented.
are well defined, both in floristic terms and in terms of their site conditions. The variables that describe community differences include nutrients and salinity; soil type, regional differentiation and alkalinity are at least equally important. This is in line with the results from lakes in northern Ireland and southern Finland (Heegaard et al., 2001; Toivonen and Huttunen, 1995). Although, contrary to what happens in lakes, for the description of variation in vegetation in rivers, the local dimensions of the river (discharge, width) appears to be dominating.
Table 6 Selected variables in multiple logistic regression models (96 species models, 220 locations and mean 3.7 predictor in each regression) for terrestrial vegetation in the river valley of Dender (southern Flanders, Belgium) Soil chemist pH K P NH4 NO3
Others 65 64 43 33 33
% organic parts in soil
48
Humidity soil
36
Hydrology landscape
50
The numbers indicate the percentage of the species models in which the variable is represented.
214
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Table 7 Selected variables in multiple logistic regression models (83 species models, 2587 locations and mean 4.2 predictor in each regression) for wet terrestrial vegetation in river valleys in Belgium (northern Flanders), and their first selected predictor % in regression % first predictor pH Ca Mg Ionic ratio
28 37 34 22
15 21 15 8
K PO4 NH4 NO3
24 30 15 21
8 4 2 0
Cl Fe SO4
28 28 40
6 2 8
Soil type 8 Soil humidity 48 Treat (/management type) 60
2 0 8
The numbers indicate the percentage of the species models in which the variable is represented.
5.2. Direct gradient analysis Direct gradient analysis enables estimation of parameters of ecological interest for vegetationenvironment modelling (Jongman et al., 1995). It requires a large data set with a number of explaining variables and an observed response, such as presence and absence records of species. Numerous environmental variables of different types (factors or continuous measurements) can be considered simultaneously and sampling should involve as many candidate predictor variables as possible. Statistical selection criteria can be used to select the most significant variables for each species or group of species. Generally, each location will have different abiotic site conditions. The data set should be large enough to incorporate the range of ecological variability we are interested in. The main underlying assumptions are that each species has it own (realised) niche and, therefore, its own preferences en tolerances; and that local conditions will influence the species distribution. Species preferences can be drawn from regression models. We present examples of multiple logistic regression studies, using the framework
of Generalized Linear Modelling (Nelder and Nedderburn, 1972), with forward model selection based on Likelihood Ratio Tests. This approach aims at detecting the most significant variables explaining the distribution of each species and at using these variables and their relation with the species for subsequent species prediction. These regression models are easy to apply in the evaluation of options in scenario analyses (Barendregt et al., 1992; Pearce et al., 2001). We selected seven published studies with a great number of plots (200 /2500), of abiotic explanatory variables (8 /21) and of species, resulting in numerous multiple regression models (76 /467). Within the range of incorporated variables, the predictive parameters are given for each species and the total numbers of selected variables of all species is tabulated for each data set. At issue is again the question: which variables appeared to be important? Four of the data sets were collected in the western part of The Netherlands. They describe conditions in ditches, small lakes and turf-ponds through 21 abiotic variables, including ditch/lake/ pond dimensions and soil type, hydrology, salinity, nutrients and acidity (Barendregt et al., 1993). For each data set, the percentage of the total number of regression models in which each variable is incorporated was calculated (Table 4). The temporary conclusion is that all variables are selected at least some times. Since there exists a distinct relationship between groups of variables (e.g. Cl , Na , Mg , SO4 display a correlation /0.8), some groups are combined, and the mean values for the four data sets are computed and converted to sum up to 100% (Table 4). The result demonstrates that the fixed dimension/soil type of the system is very important in the prediction of the macrophytes; followed by nutrients, salinity, hydrology and pH/alkalinity. With exactly the same technique prediction models for 467 aquatic macrofauna species were obtained (Amesz and Barendregt, 1996). The predictive parameters show a striking resemblance with the former data sets (Table 5), although some incorporated explanatory variables differ. Finally, two data sets from rivulet valleys in Belgium (Tables 6 and 7) are assessed, leading to
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
the same general conclusion: there is not one variable to predict the presence of species, but each species distribution is determined by a characteristic set of variables (De Bekker et al., 2001). Only the Ca-complex with pH is prominent in terms of importance for regression modelling; if the first selected (and hence most significant) predictor in the multiple regression is scored (Table 7), that predictor represents the baseacidity-axis for more than half of the species. Notice, that the first predictors constitute merely 23% of the total number of model predictors.
6. Conclusion From the seven case studies presented we learn that there is not one most-important variable explaining the macrophyte communities. Each individual species shows its own preferences in setting of variables. At least some representatives of sets of interrelated variables have to be considered in macrophyte community modelling. These representatives should comprise two different groups: (1) The physical conditions enforced by the context of the landscape, such as soil type, width and depth of the river, discharge and current velocity, and salinity. These conditions are many times related to geographical differentiation’s, and therefore to regions in the landscape with their characteristic relations, connected with the biogeography of species. Incorporating the simple variable ‘region’ in a model will therefore often explain a major part of species variation (Bootsma and Wassen, 1996; Riis et al., 2000; De Bekker et al., 2001). (2) The physical and the chemical conditions the plants actually experience at the location, such as availability of nutrients and alkalinity, which influence the growth of species directly. This is in line with the theoretical analyse of the relevant scales in the first part of this paper, with the local level and the actual site conditions. Most publications incorporate this group at first (e.g. Mitsch and Reeder, 1991; Fitz et al., 1996; Wigand et al., 1997; Sklar et al., 2001).
215
These two groups reflect processes that manifest themselves on different levels: those in the landscape and those at the site itself. The latter can be influenced by human intervention (Jørgensen, 1995; Ha˚kanson, 1999; Mitsch and Gosselink, 2000). The first is enforced and can generally not be changed; in modelling, it is to be incorporated as a boundary factor or as a fixed condition.
References Amesz, M., Barendregt, A., 1996. IMRAM: een voorspellingsmodel voor aquatische macrofauna in Noord-Holland. Report Universiteit Utrecht, 47. Auble, G.T., Friedman, M., Scott, M.L., 1994. Relating riparian vegetation to present and future streamflows. Ecological Applications 4, 544 /554. Austin, M.P., Gaywood, M.J., 1994. Current problems of environmental gradients and species response curves in relation to continuum theory. Journal of Vegetation Science 5, 473 /482. Barendregt, A., Stam, S.M.E., Wassen, M.J., 1992. Restoration of fen ecosystems in the Vecht River plain: cost-benefit analysis of hydrological alternatives. Hydrobiologia 233, 247 /258. Barendregt, A., Wassen, M.J., De Smidt, J.T., 1993. Hydroecological modelling in a polder landscape: a tool for wetland management. In: Vos, C., Opdam, P. (Eds.), Landscape Ecology of a Stressed Environment. Chapman & Hall, London, pp. 79 /99. Barendregt, A., Gielczewski, M., 1998. Linkage of environmental variation and vegetation in the catchment area of river Narew. In: Piekut, et al. (Eds.), Environmental and Technical Problems of Water Management for Sustainable Development of Rural Areas Zeszyty Problemowe Postepow Nauk Rolniczych, vol. 458. Polish Academy of Sciences, Warsaw, pp. 15 /23. Barko, J.W., Smart, R.M., 1986. Sediment-related mechanisms of growth limitation in submersed macrophytes. Ecology 67, 1328 /1340. Beltman, B., Rouwenhorst, T.G., Van Kerkhoven, M.B., Van der Krift, T., Verhoeven, J.T.A., 2000. Internal eutrophication in peat soils through competition between chloride and sulphate with phosphate for binding sites. Biogeochemistry 50, 183 /194. Bio, A.M.F., Alkemade, R., Barendregt, A., 1998. Determining alternative models for vegetation response analysis: a nonparametric approach. Journal of Vegetation Science 9, 5 / 16. Bootsma, M.C., Wassen, M.J., 1996. Environmental conditions and fen vegetation in three lowland mires. Vegetatio 127, 173 /189.
216
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217
Bornette, G., Amoros, C., 1991. Aquatic vegetation and hydrology of a braided river floodplain. Journal of Vegetation Science 2, 497 /512. Carr, M.C., Duthie, H.C., Taylor, W.D., 1997. Models of aquatic plant productivity: a review of the factors that influence growth. Aquatic Botany 59, 195 /215. Chambers, P.A., Prepas, E.E., Hamilton, H.R., Bothwell, M.L., 1991. Current velocity and its effect on aquatic macrophytes in flowing waters. Ecological Applications 1, 249 /257. De Becker, P., De Bie, E., Huybrechts, W., Bio, A.M.F., Wassen, M., 2001. Ontwikkeling van een hydro-ecologisch model voor vallei-ecosystemen in Vlaanderen, VLITORS. Report Instituut voor Natuurbehoud, Brussel. De Wit, M.J.M., 1999. Nutrient fluxes in the Rhine and Elbe basins, Ph.D. Thesis Utrecht University. Edwards, P.J., Kollmann, J., Gurnell, A.M., Petts, G.E., Tockner, K., Ward, V.J., 1999. A conceptual model of vegetation dynamics on gravel bars of a large Alpine river. Wetlands Ecology and Management 7, 141 /153. Fitz, H.C., DeBellevue, E.B., Constanza, R., Boumans, R., Maxwell, T., Wainger, L., Sklar, F.H., 1996. Development of a general ecosystem model for a range of scales and ecosystems. Ecological Modelling 88, 263 /295. Grootjans, A.P., Ernst, W.H.O., Stuyfzand, P.J., 1998. European dune slacks: strong interaction of biology, pedogenesis and hydrology. Trends in Ecology and Evolution 13, 96 /100. Ha˚kanson, L., 1999. On the principles and factors determining the predictive success of ecosystem models, with a focus on lakes eutrophication models. Ecological Modelling 121, 139 /160. Heegaard, E., Birks, H.H., Gibson, C.E., Smith, S.J., WolfeMurphy, S., 2001. Species-environmental relationships of aquatic macrophytes in Northern Ireland. Aquatic Botany 70, 175 /223. Higler, L.W.G., 1993. The riparian community of north-west European lowland streams. Freshwater Biology 29, 229 / 241. Jongman, R.H.G., Ter Braak, C.J.F., Van Tongeren, O.F.R., 1995. Data Analysis in Community and Landscape Ecology. Cambridge University Press, Cambridge, p. 299. Jørgensen, S.E., 1995. State of the art of ecological modelling in limnology. Ecological Modelling 78, 101 /115. Kawecka, B., Szcesny, B., 1984. Dunajec. In: Whitton, B.A. (Ed.), Ecology of European Rivers. Blackwell Scientific Publications, Oxford, pp. 499 /525. Khedr, A.H.A., El-Demerdash, M.A., 1997. Distribution of aquatic plants in relation to environmental factors in the Nile Delta. Aquatic Botany 56, 75 /86. Klopatek, J.M., 1978. Nutrient dynamics of freshwater riverine marshes and the role of emergent macrophytes. In: Good, R.E., et al. (Eds.), Freshwater Wetlands. Academic Press, New York, pp. 195 /216. Mesters, C.M.L., 1997. Polluted Dutch transboundary streams: effects on aquatic macrophytes. Ph.D. Thesis Utrecht University. pp. 127.
Mitsch, W.J., Gosselink, J.G., 2000. The value of wetlands: importance of scale and landscape setting. Ecological Economics 35, 25 /33. Mitsch, W.J., Reeder, B.C., 1991. Modelling nutrient retention of a freshwater coastal wetland: estimating the roles of primary productivity, sedimentation, resuspension and hydrology. Ecological Modelling 54, 151 /187. Moss, B., 1988. Ecology of fresh waters */man and medium, second ed.. Blackwell Scientific Publications, Oxford, p. 417. Muhammetoglu, A., Soyupak, S., 2000. A three-dimensional water quality-macrophyte interaction model for shallow lakes. Ecological Modelling 133, 161 /180. Nelder, D.R., Nedderburn, R.W.M., 1972. Generalized linear models. Journal of the Royal Statistical Society, Series A 135, 370 /384. Nilsson, C., 1987. Distribution of stream-edge vegetation along a gradient of current velocity. Journal of Ecology 75, 513 / 522. Pearce, J., Ferrier, S., Scotts, D., 2001. An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales. Journal of Environmental Management 62, 171 /184. Rastetter, E.B., King, A.W., Cosby, B.J., Hornberger, G.M., O’Neill, R.V., Hobbie, J.E., 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Applications 2, 55 /70. Rea, T.E., Karapatakis, D.J., Guy, K.K., Pinder, J.E., III, Mackey, H.E., Jr., 1998. The relative effects of water depth, fetch and other physical factors on the development of macrophytes in a small southeastern US pond. Aquatic Botany 61, 289 /299. Riis, T., Sand-Jensen, K., Vestergaard, O., 2000. Plant communities in lowland Danish streams: species composition and environmental factors. Aquatic Botany 66, 255 /272. Riis, T., Sand-Jensen, K., Larsen, S.E., 2001. Plant distribution in relation to physical conditions and location within Danish streams systems. Hydrobiologia 448, 217 / 228. Roberts, J., Ludwig, J.A., 1991. Riparian vegetation along current-exposure gradients in floodplain wetlands of the river Murray, Australia. Journal of Ecology 79, 117 /127. Sanchez-Carrillo, S., Alvarez-Cobelas, M., 2001. Nutrient dynamics and eutrophication patterns in a semi-arid wetland: the effects of fluctuating hydrology. Water, Air and Soil Pollution 131, 97 /118. Sand-Jensen, K., Vindbaek Madsen, T., 1991. Minimum light requirements of submerged freshwater macrophytes in laboratory growth experiments. Journal of Ecology 79, 749 /764. Saunders, D.L., Kalff, J., 2001. Nitrogen retention in wetlands, lakes and rivers. Hydrobiologia 443, 205 /212. Schneiders, A., Wils, C., Verheyen, R., 1999. The use of ecological information in the selection of quality objectives for river conservation and restoration in Flanders (Belgium). Aquatic Ecosystem Health and Management 2, 137 / 154.
A. Barendregt, A.M.F. Bio / Ecological Modelling 160 (2003) 205 /217 Schwarz, A.-M., Hawes, I., 1997. Effects of changing water clarity on characean biomass and species composition in a large oligotrophic lake. Aquatic Botany 56, 169 /181. Sklar, F.H., Fitz, H.C., Wu, Y., Van Zee, R., McVoy, C., 2001. The design of ecological landscape models for Everglades restoration. Ecological Economics 37, 379 /401. Smolders, A., Roelofs, J.G.M., 1995. Internal eutrophication, iron limitation and sulphide accumulation due to the inlet of river Rhine water in peaty shallow waters in the Netherlands. Archiv fuer Hydrobiologie 133, 349 /365. Srivastava, D.S., Staicer, C.A., Freedman, B., 1995. Aquatic vegetation of Nova Scotian lakes differing in acidity and trophic status. Aquatic Botany 51, 181 /196. Stromberg, J.C., Tiller, R., Richter, B., 1996. Effects of groundwater decline on riparian vegetation of semiarid regions: the San Pedro, Arizona. Ecological Applications 6, 113 /131. Toivonen, H., Huttunen, P., 1995. Aquatic macrophytes and ecological gradients in 57 small lakes in southern Finland. Aquatic Botany 51, 197 /221. Van den Brink, F.W.B., Van der Velde, G., 1993. Growth and morphology of four freshwater macrophytes under the
217
impact of the raised salinity level of the Lower Rhine. Aquatic Botany 45, 285 /297. Van der Perk, M., Bierkens, M.F.P., 1997. The identifiability of parameters in a water quality model on the Biebrza River, Poland. Journal of Hydrology 200, 307 /322. Van Dijk, P.M., 2001. Soil erosion and associated sediment supply to rivers. Ph.D. Thesis University of Amsterdam, pp. 211. Van Straalen, N.M., Verkleij, J.A.C., 1991. Leerboek Oecotoxicologie. VU Uitgeverij, Amsterdam, p. 422. Wassen, M.J., Barendregt, A., Paczynski, A., De Smidt, J.T., De Mars, H., 1990. The relationship between fen vegetation gradients, groundwater flow and flooding in an undrained valley mire at Biebrza, Poland. Journal of Ecology 78, 1106 /1122. Wellnitz, T.A., LeRoy Poff, N., Cosyleon, G., Steury, B., 2001. Current velocity and spatial scale as determinants of the distribution and abundance of two rheophilic herbivorous insects. Landscape Ecology 16, 111 /120. Wigand, C., Stevenson, J.C., Cornwell, J.C., 1997. Effects of different submersed macrophytes on sediment biogeochemistry. Aquatic Botany 56, 233 /244.