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Modelling of microhabitat used by fish in natural and regulated flows in the river Garonne (France)
Ecological Modelling 146 (2001) 131– 142 www.elsevier.com/locate/ecolmodel
Modelling of microhabitat used by fish in natural and regulated flows in the river Garonne (France) Yorick Reyjol a,*, Puy Lim a, Alain Belaud a, Sovan Lek b a
U.R. En6ironnement Aquatique, INP-ENSAT, 1 A6enue de l’Agrobiopoˆle, 31326 Castanet-Tolosan cedex, France b CESAC, UMR 5576, CNRS-Uni6ersite´ Paul Sabatier, 118 Route de Narbonne 31062 Toulouse cedex, France
Abstract The aim of our study was to compare the microhabitat used by three fish species: brown trout (Salmo trutta L.), European minnow (Phoxinus phoxinus L.) and stone loach (Barbatula barbatula L.), in natural and regulated flows of a section of the river Garonne (France). Six Artificial Neural Network (ANN) models were set up, one for each fish species in each flow condition. Models were run and tested with 1107 observations obtained by point abundance sampling performed by electrofishing. Each model had thirteen independent environmental variables (distance from the bank, water depth, water velocity, percentage of different substratum fractions defined as large boulders, small boulders, large pebbles, small pebbles, gravels, sand, mud and bedrock, flooded vegetation cover, and presence or absence of ‘blockage’ which is one or several pieces of wood providing shelter), and one dependent variable (fish density for the considered population). A cross-validation testing procedure (leave-one-out bootstrap) was performed to validate the ANN models. Finally, we used a method based on the first partial derivatives of the network’s output with respect to each input to focus on the sensitivity of some of the variables selected. During the training phase, all models were judged satisfactory with Mean Squared Errors (MSE) ranging from 0.40 to 1.93, and Performance Indexes (PI’s) from 60 to 89%. After the testing procedure, MSE ranged between 1.53 and 8.23, and PI’s between 51 and 80%. With the exception of brown trout in regulated flow, patterns of microhabitat use obtained revealed that fish densities were highly connected to one major influencing variable: water depth for brown trout and stone loach, and water velocity for European minnow, other variables accounting for lower individual contributions. Analysis of the partial derivatives brought into relief some differences when comparing microhabitat use in natural and regulated flows for some of the variables tested, and no differences when comparing others. The results are discussed with regard to the biology and the ecology of each fish species at microhabitat and macrohabitat scales, and according to the relationship between microhabitat utilization and microhabitat availability. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Artificial neural network; Leave-one-out bootstrap; Partial derivatives sensitivity analysis; Flow regulation; Microhabitat; Brown trout (Salmo trutta L.); European minnow (Phoxinus phoxinus L.); Stone loach (Barbatula barbatula L.).
* Corresponding author. Tel.: + 33-5-62193900; fax + 33-5-62193901. E-mail address: yorick – [email protected] (Y. Reyjol). 0304-3800/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 0 4 - 3 8 0 0 ( 0 1 ) 0 0 3 0 1 - 5
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1. Introduction Many statistical tools are available to reproduce the structure and functioning of ecosystems, according to environmental features. Unfortunately, conventional methods, such as correlation analysis (Ricker, 1975), canonical correspondence analysis, or multiple least-square regression (Binns and Eiserman, 1979), suffer from the relationships between variables in environmental sciences often not being linear (James and McCulloch, 1990), while the methods used are based on linear principles. The transformation of non-linear variables by logarithmic, power or exponential functions can appreciably improve the results, but often fail to fit ecological data (Lek et al., 1996a). On the contrary, the principal advantage of Artificial Neural Networks (ANN) with an error back-propagation procedure is that this method is particularly efficient for non-linear data (Rumelhart et al., 1986). The habitat conditions are the principal factor influencing the presence, the abundance and the distribution of organisms in the environment (Southwood, 1977). Each species is associated with its spatial ecological niche, characterized by some particular modalities of the environmental features. Several spatial scales constitute the habitat, each scale being independent but linked to the others within a hierarchical system (Amoros et al., 1987). Concerning the fish communities, this system of top-down nested scales has been adapted (Frissell et al., 1986) and progressively modified and enriched, resulting in the definition of several ones: zoogeographical range, drainage basin, river, section or stretch, sequence, macrohabitat or morphological stream unit and finally microhabitat, from largest to smallest. The last one is the finest scale, and is therefore an integrator of all the phenomena acting on higher scales. Thus, the modelling of microhabitat use by fish is of particular interest to understand the global functioning of aquatic ecosystems. ANN are more and more used in ecological modelling. In aquatic ecology, the capability of ANN to predict the presence or abundance of different fish species at different spatial scales has already been demonstrated (Guegan et al., 1998 at a global scale, Baran et al., 1996 at a macrohabitat
scale, and Mastrorillo et al., 1997 at a microhabitat scale). On the other hand, only few studies assess the effects of flow regulation on microhabitat use by fish (Scheidegger and Bain, 1995; Copp, 1996; Pilcher and Copp, 1997), and none use ANN to analyse this influence. Prediction of the characteristics existing in a community has recently been investigated with multispecific linear models as guidelines for generating river management regulations (Lamouroux, 1997; Lamouroux et al., 1999a,b) although most of the previous studies that used biological models were based on analysis of single species, such as brown trout (Stalnaker, 1979; Bovee, 1982; Belaud et al., 1989; Souchon et al., 1989; Sabaton and Miquel, 1993; Pouilly and Souchon, 1995). So, the aim of our study was to model the abundance of three fish species in natural and regulated flows of a section of the river Garonne in relation with microhabitat characteristics using ANN, in order to facilitate management of fish communities.
2. Study area As the fourth longest river in France, the Garonne is supported by the Maladetta’s glacier, located in the Pyrenean mountains of Spain. The water goes under Tora’s hole and disappears to re-emerge in the Val d’Aran. The water then flows into France and crosses the city of Toulouse to join the river Dordogne at Bordeaux (Fig. 1), forming the Gironde estuary with an area of 625 km2 during high tide. The Garonne is about 525 km in length with a total catchment area of 57 000 km2 and a specific flow of 11.4 l s − 1 km − 2. It exhibits strict and simultaneous nival and pluvio-nival hydrological regimes (Parde´ , 1935), termed Pyrenean, characterized by one period of intensive flow during spring as a result of snowmelt and two periods of low flow during winter and summer (Lambert et al., 1990). The section considered for this study was 51 km in length, with a mean slope of 3.25 m km − 1. It has an approximate distance from the source of 87 km upstream to 138 km downstream and an altitude ranging from 425 to 260 m. Three tributaries are present in this section (Fig. 1): the Neste (mean
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annual discharge= 20 m3 s − 1), the Ger (mean annual discharge=2.8 m3 s − 1) and the Salat (mean annual discharge= 43 m3 s − 1). The mean annual discharge of the Garonne in this area is 60 m3 s − 1. The only large town on the section is Saint-Gaudens (population 13 053) generating a certain amount of organic pollution in the water system. Moreover, a paper mill in this town adds industrial waste. Six hydroelectric dams divide the study area. The last, the Boussens dam, limits the downstream section, just after the confluence with the river Salat. Concerning the fish community, this section constitutes the transitional zone between an upstream area strongly dominated by the Salmoniform taxonomic order, represented by one fish species: brown trout (Salmo trutta L.), and a downstream area dominated by Cypriniforms. The three dominant species in this section are the brown trout, the European minnow (Phoxinus phoxinus L.) and the stone loach (Barbatula barbatula L.). Six sites were selected along the river (Fig. 1), characterized either as sites having natural flow (Qn): Montre´ jeau (site 1), Miramont (site 3), Beauchalot (site 5), or as sites having regulated flow (Qr): Clarac (site 2), La Gentille (site 4) and Lestelle (site 6). The minimum flow downstream of the dams is reduced 40-fold at sites 2 and 4, and
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5-fold at site 6, giving minimum flow values of 1.55 m3 s − 1, 1.63 m3 s − 1 and 12 m3 s − 1, respectively. The maximal usable flow by each power station is 85 m3 s − 1, 60 m3 s − 1 and 50 m3 s − 1, respectively, with a maximal possible power of 14.2, 4.3 and 5 MW. Each site includes one riffle-pool sequence, in order to sample the two dominant morphological stream units of the section.
3. Materials and methods
3.1. Sampling method According to the hydrological regime of the river Garonne, sampling was carried out for three seasons: summer (21–30 July, 1999), automn (11– 14 October, 1999) and winter (18–20 January, 2000). Because of the snowmelt in spring, and consequently the very high water level in the Garonne, sampling could not have been performed in this season. Sampling was performed using point abundance sampling by electrofishing (Nelva et al., 1979), which simultaneously takes into account microhabitat features and density of each fish species in the community. For each sampling point, fishes were identified, counted and returned to the water. Thirteen microhabitat variables were taken into account: distance from the bank (DIS)
Fig. 1. Study area and sampling sites (Qn, natural flow; Qr, regulated flow).
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Fig. 2. Three-layered feed-forward Artificial Neural Network. Input neurons corresponding to environmental variables, hidden layer neurons, and one output neuron to estimate one fish species density (brown trout, European minnow or stone loach). Connections between neurons are shown by solid lines: they are associated to synaptic weights that are adjusted during the training procedure. The bias neurons are also represented; their input value is one.
in metres, water depth (DEP) in metres, water velocity (VEL) in metres per second, percentage of flooded vegetation cover (VEG), represented by only one species, Ranunculus fluitans L., ‘blockage’ (BLO), which is one or several pieces of wood providing shelter, expressed in presence or absence, and percentages of eight substratum fractions: large boulders (LB, smallest section s \ 60 cm), small boulders (SB; s =20 – 60 cm), large pebbles (LP, s = 10 – 20 cm), small pebbles (SP, s = 2– 10 cm), gravels (GRA, s =0.2 – 2 cm), sand (SAN, s= 0.02 cm), mud (MUD), and bedrock (BED).
3.2. Modelling techniques ANN models were set up using a data set of 1107 samples×(13 environmental variables+3 fish species). Six multilayer feed-forward neural network models were used, one for each fish species (i.e. brown trout, European minnow and stone loach) in both of the flow conditions (natural or regulated flow). For each model, the first layer, called the input layer, connects with the input variables. In our case, it had 13 input neurons corresponding to the 13 environmental variables. The last layer, called the output layer, had a single neuron corresponding to the dependent variable to be predicted (fish density for the considered spe-
cies) (Fig. 2). The layer between input and output layers is called the hidden layer. A ‘bias’ neuron was added to each computational layer (i.e. hidden and output layer). These neurons had a constant input value of one and were used to lower biases in the modelling procedure (see Rumelhart et al., 1986 for more detail). We could have used a single neural network with six output neurons (one for each of the three fish populations in natural or regulated flow), but we preferred to use six networks with the same architecture, each predicting the abundance of one fish species, so as to easily extract the influence of the 13 input environmental variables on each fish species, with respect to the flow conditions. The network configuration was approached empirically by testing various possibilities and selecting the solution which provided the best compromise between bias and variance (Geman et al., 1992; Kohavi, 1995). Modelling was carried out in two steps: first, model training was performed using the whole data matrix, according to the back-propagation algorithm (Rumelhart et al., 1986). It consisted of using a training data set to adjust the connection weights in order to minimize error between observed and predicted values, and gave an estimate of the performance of the ANN to learn data and to study the contribution of each variable. The connection weights, initially taken at random in the range [− 0.3, 0.3], are iteratively adjusted by a method of gradient descent based on the difference between the observed and expected outgoing signals. Many iterations are necessary to guarantee the convergence of predicted values toward their expectations, without obtaining overfit (i.e. incapacity of the model to generalize). To test the stability of the network, each modelling procedure was repeated 10 times. Second, we used the ‘leave-one-out’ bootstrap cross-validation test (Efron, 1983; Efron and Tibshirani, 1995), where each sample is left out of the model formulation in turn and predicted once, to validate the models. Computational programs were written in a Matlab® environment.
3.3. Performance of the models Performance Index (PI) and Mean Squared Errors (MSE) were used to assess performance of
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the model prediction. The PI was based on the percentage of well-predicted values with an error rate lower than 10%, i.e. the proportion of responses plus or minus 10% of the model output value (Brosse et al., 1999).
3.4. Study of the influencing factors The microenvironmental variables that we focused on to explain fish densities were chosen according to their relative contribution in the models, and with respect to the known biological and ecological features of each fish species. In this study we used a method based on the response of the partial derivatives of the network to highlight the influence of each descriptor (Dimopoulos et al., 1995; Dimoupoulos et al., 1999). This algorithm allowed us to visualize the derivative profile for each point, according to each variable. Moreover, we can compare the total contribution of all environmental variables used at the input of ANN models.
4. Results
4.1. Fish community structure Brown trout, European minnow and stone loach were respectively present in 19.8, 25.7 and 37.1% of the 1107 samples. For the 6238 fishes Table 1 Performance Index (PI) and Mean Squared Errors (MSE) in ANN training and testing for the three populations in natural (Qn) and regulated (Qr) parts of the river Garonnea Training
Testing
PI
MSE
PI
MSE
Qn
Brown trout European minnow Stone loach
83 89 68
0.60 0.40 1.68
65 80 61
1.94 1.53 7.25
Qr
Brown trout European minnow Stone loach
88 78 60
0.69 0.83 1.93
70 62 51
6.58 5.00 8.23
a PI is the percentage of well-predicted values with an error rate lower than 10%, see text for details.
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caught, total lengths range from 21 to 430 mm for brown trout, from 15 to 122 mm for European minnow and from 17 to 116 mm for stone loach. Mean total lengths and standard errors of the mean are 142.89 2.7 for brown trout, 45.89 0.3 for european minnow and 62.29 0.3 for stone loach.
4.2. Model fitting, testing and 6alidation The ANN structure used was a three-layered (13 30 1) feed-forward network with bias. The hidden layer had thirty neurons, determined as the optimal configuration which gave the lowest error in the training and testing sets of data with minimal computing time (Geman et al., 1992; Lek et al., 1996b,c). After the training procedure, the PIs obtained were 83 and 88% for brown trout in Qn and Qr, 89 and 78% for European minnow, 68 and 60% for stone loach (Table 1). MSE were 0.60 and 0.69 for brown trout, 0.40 and 0.83 for European minnow, 1.68 and 1.93 for stone loach. After the cross-validation testing procedure, PIs obtained in Qn and Qr (Table 1) were respectively 65 and 70% for brown trout, 80 and 62% for European minnow, 61 and 51% for stone loach. MSE were 1.94 and 6.58 for brown trout, 1.53 and 5.00 for European minnow, 7.25 and 8.23 for stone loach.
4.3. Importance of the en6ironmental 6ariables in population abundance Means and standard errors of the contribution of each variable calculated after ten training procedures are reported in Fig. 3. With the exception of brown trout in Qr, the modelling procedure showed that fish densities were highly connected to one major influencing variable, other variables accounting for lower individual contributions. Moreover, for European minnow and stone loach, patterns of microhabitat use were similar in natural and regulated flows. Water depth was the major variable influencing the distribution of brown trout in Qn (24%), whereas percentage of large boulders and water velocity were the most explanatory factors in Qr (12.7 and 12.3%, respectively). Water velocity was the most influencing
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Fig. 3. Contribution percentage of each of the thirteen environmental variables with regard to the prediction of the density of each fish species, in natural (Qn) and regulated flow (Qr). Bars indicate the mean value of the results of the ten models for each fish population, horizontal lines represent Standard Errors of the Mean (SEM).
variable for European minnow (30.7 in Qn and 23% in Qr), whereas the most explanatory variable for stone loach appeared to be the water depth (20.2 in Qn and 19% in Qr). Water depth and water velocity appear to be the major variables influencing the distribution of the three fish species considered in our study, and have to be taken into account in the partial derivatives sensitivity analysis. More specific variables, known to have a major influence on the ecology of each fish species taken individually, but not having necessary a particularly high contribution on the fish distribution in our
results, deserve our attention. For examples, the need of distinct types of shelters against water velocity for brown trout (Haury and Baglinie`re, 1990; Baran et al., 1995) or stone loach (Mastrorillo et al., 1996), or the role played by the river banks on the microhabitat used by European minnow (Mastrorillo et al., 1996), need to be highlighted. According to this, the microenvironmental variables selected to explain fish density were DEP, VEL, LB and VEG for brown trout, DIS, DEP, VEL and VEG for European minnow, and DEP, VEL, SB, LP and VEG for stone loach.
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4.3.1. Brown trout The positive values of partial derivatives for DEP and VEL in Qn, which became closer to zero for high values of these variables (Fig. 4), indicated that the increase of DEP and VEL firstly induced an increase of the brown trout density, and that these tendencies attenuated when DEP and VEL increased. The opposite patterns were observed in Qr, which meant that the density decreased when DEP and VEL increased, and that these augmentations were less and less pronounced. The positive values of partial derivatives for LB and VEG indicated that these variables
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contributed to the increase of the density in Qn and Qr whatever the flow conditions were.
4.3.2. European minnow The values of partial derivatives for DIS and VEL in Qn and Qr (Fig. 5) showed that these variables caused a decrease of the European minnow density whatever the flow conditions were. The positive values of partial derivatives for DEP in Qn suggested that the corresponding density increased with DEP. On the contrary, the negative values of partial derivatives for the majority of the values of DEP showed that this variable con-
Fig. 4. Partial derivatives of the ANN models according to each descriptor for brown trout, in natural (Qn) and regulated flow (Qr).
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Fig. 5. Partial derivatives of the ANN models according to each descriptor for European minnow, in natural (Qn) and regulated flow (Qr).
tributed to the reduction of the European minnow density in Qr. For the variable VEG, most of the partial derivative values were positive, implying that the increase of this variable was favourable for the density in Qn and Qr.
4.3.3. Stone loach The negative values of partial derivatives for the majority of the values of DEP and VEL (Fig. 6) showed that these variables contributed to the
reduction of the stone loach density in Qn and Qr. The response of stone loach to the variation of substrate features was complex. Whereas the increase of SB induced a decrease of the density in Qn, and an increase in Qr, the variable LP did not influence the stone loach density whatever the flow regime was. Finally, the values of partial derivatives showed that in Qn the variable VEG did not influence the density, whereas in Qr high values of this variable induced a decrease.
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5. Discussion and conclusion The Artificial Neural Networks (ANN) showed high predictive power to estimate densities of
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brown trout, European minnow and stone loach in natural (Qn) and regulated (Qr) flows of the river Garonne, with regard to 13 input environmental variables. Performance Indexes (PIs) ob-
Fig. 6. Partial derivatives of the ANN models according to each descriptor for stone loach, in natural (Qn) and regulated flow (Qr).
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tained after the training procedure were high, ranging from 60% for stone loach in Qr to 89% for European minnow in natural flow. PIs obtained after the testing procedure were lower than those obtained after training for each of the three fish species, but always higher than 51%, with a maximum of 80% for European minnow in Qn. Mean Squared Errors (MSE) were always lower than 1.93 during training, while the minimum value in testing procedure was 1.94 for brown trout in Qn, and the maximum value was 8.23 for stone loach in Qr. Both PIs and MSE values obtained during the training and the testing procedures are comparable to those obtained by Brosse et al. (1999) in similar modelling conditions, and testify the high modelling power of ANN, compared to classical methods, as already demonstrated by many studies (Baran et al., 1996; Mastrorillo et al., 1997; Guegan et al., 1998; Brosse et al., 1999). With the exception of brown trout in Qr, the analysis of microhabitat use revealed that fish densities were highly connected to one major influencing variable: water depth for brown trout and stone loach, and water velocity for European minnow, other variables accounting for lower individual contributions. The analysis of partial derivatives highlighted distinct or opposite patterns of microhabitat use between natural and regulated flows according to some of the tested variables, and no variation with regard to the others. Brown trout is a rheophilic species mainly found in riffles (Baran et al., 1993, 1995), whereas stone loach both occupies riffles and pools (Maitland, 1965; Zweimu¨ ller, 1995), and European minnow is a limnophilic species which occupies pools, except for breeding, when it searches for areas with high velocities (Frost, 1943; Wootton and Mills, 1979). The role of macrophytes (Haury and Baglinie`re, 1990) and cover, which includes large blocks (Baran et al., 1995), as favourable physical habitats which provide shelter has already been highlighted for brown trout. For European minnow, Mastrorillo et al. (1996) also observed a decrease of the density when the distance from the bank increases, as well as a positive influence of the macrophytic vegetation on
the fish density. For stone loach, the same authors highlighted a preference for a heterogeneous substrate of small blocks, large pebbles and gravel (Mastrorillo et al., 1996), which act as a shelter against sunlight for this nocturnal species. Differences observed between patterns of microhabitat use in Qn and Qr must be linked to microhabitat availability (Baldridge and Amos, 1982; Bovee, 1982; Raleigh et al., 1986; Belaud et al., 1989). Indeed, flow regulation modifies the morphodynamic conditions of the river, particularly water depth and water velocity, which are lower in regulated sites, and fish will choose different microhabitat conditions according to the range of available values in each type of flow. Opposite patterns obtained between natural and regulated flow may not reflect real preferences of these species, but only the use of the available microhabitat conditions. On the contrary, the persistence of some tendencies in natural and regulated flows may reflect the real preferences of the species. Thus, the results obtained will have to be weighted by microhabitat availability to eliminate the effect of the modification of habitat conditions, and to identify real shifts in the microhabitat used by fishes. In conclusion, ANN are an efficient statistical tool to predict fish abundance from the microhabitat features in this section of the river Garonne. Moreover, the use of the partial derivatives of the network output highlights the participation of each of the explanatory variables and focuses on their respective influence, constituting a promising approach to improve the explanatory capacities of ANN models. These results are particularly interesting with regard to population assemblage studies, as applied to regulated river management. Preference curves according to water depth, water velocity and substratum composition already exist for brown trout, in order to manage regulated rivers in upstream areas of lotic ecosystems (Bovee, 1982). This type of curves should now be established for European minnow and stone loach, in order to set up methods to predict fish density from habitat conditions for the management of regulated rivers in areas where these three species dominate.
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Mastrorillo, S., Lek, S., Dauba, F., Belaud, A., 1997. The use of artificial neural networks to predict the presence of small-bodied fish in a river. Freshwat. Biol. 38, 237 – 246. Nelva, A., Persat, H., Chessel, D., 1979. Une nouvelle me´ thode d’e´ tude des peuplements ichtyologiques dans les grands cours d’eau par e´ chantillonnage ponctuel d’abondance. CR Acad. Sci. Paris Serie III 289, 1295 – 1298. Parde´ , M., 1935. Fleuves et rivie`res. 5e Edn, A. Collin (ed.), France. Pilcher, M.W., Copp, G.H., 1997. Winter distribution and habitat use by fish in a regulated lowland river system of south-east England. Fish. Mngmt. Ecol. 4, 199 –215. Pouilly, M., Souchon, Y., 1995. Me´ thode des microhabitats, validation et perspectives. Bull. Fr. Peˆ che Pisc. 337/338/ 339, 329 –336. Raleigh, R.F., Zuckerman, L.D., Nelson, P.C., 1986. Habitat suitability index models and stream flow suitability curves: brown trout, revised. U.S. Fish. Wild. Ser. Biol. Rep. 82, 10– 124. Ricker, W.E., 1975. Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Board Can. 191, 1 – 382. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by back-propagating error. Nature 323,
533 – 536. Scheidegger, K.J., Bain, M.B., 1995. Larval fish distribution and microhabitat use in free-flowing and regulated rivers. Copeia 1, 125 – 135. Southwood, T.R.E., 1977. Habitat, the templet for ecological strategies? J. Anim. Ecol. 46, 337 – 365. Sabaton, C., Miquel, J., 1993. La me´ thode des microhabitats: un outil d’aide au choix d’un de´ bit re´ serve´ a` l’aval des ouvrages hydroe´ lectriques. Exp. d’Electr. De France, Hydroe´ col. Appl. tome 5 (1), 127 – 163. Souchon, Y., Trocherie, F., Fragnoud, E., Lacombe, C., 1989. Les mode`les nume´ riques des microhabitats des poissons: applications et nouveaux de´ veloppements. Rev. Sc. Eau 2, 807 – 830. Stalnaker, C.B., 1979. The use of habitat structure preferenda for establishing flow regimes necessary for maintenance of fish habitat. In: Ward, J.V., Stanford, J.A. (Eds.), The Ecology of Regulated Streams. Plenum Press, New York, pp. 321 – 337. Wootton, R.J., Mills, L.A., 1979. Annual cycle in female minnow Phoxinus phoxinus (L.) from an upland Welsh lake. J. Fish. Biol. 14, 607 – 618. Zweimu¨ ller, I., 1995. Microhabitat use by two small benthic stream fish in a 2nd order stream. Hydrobiologia 303, 125 – 137.
Modelling of microhabitat used by fish in natural and regulated flows in the river Garonne (France) Yorick Reyjol a,*, Puy Lim a, Alain Belaud a, Sovan Lek b a
U.R. En6ironnement Aquatique, INP-ENSAT, 1 A6enue de l’Agrobiopoˆle, 31326 Castanet-Tolosan cedex, France b CESAC, UMR 5576, CNRS-Uni6ersite´ Paul Sabatier, 118 Route de Narbonne 31062 Toulouse cedex, France
Abstract The aim of our study was to compare the microhabitat used by three fish species: brown trout (Salmo trutta L.), European minnow (Phoxinus phoxinus L.) and stone loach (Barbatula barbatula L.), in natural and regulated flows of a section of the river Garonne (France). Six Artificial Neural Network (ANN) models were set up, one for each fish species in each flow condition. Models were run and tested with 1107 observations obtained by point abundance sampling performed by electrofishing. Each model had thirteen independent environmental variables (distance from the bank, water depth, water velocity, percentage of different substratum fractions defined as large boulders, small boulders, large pebbles, small pebbles, gravels, sand, mud and bedrock, flooded vegetation cover, and presence or absence of ‘blockage’ which is one or several pieces of wood providing shelter), and one dependent variable (fish density for the considered population). A cross-validation testing procedure (leave-one-out bootstrap) was performed to validate the ANN models. Finally, we used a method based on the first partial derivatives of the network’s output with respect to each input to focus on the sensitivity of some of the variables selected. During the training phase, all models were judged satisfactory with Mean Squared Errors (MSE) ranging from 0.40 to 1.93, and Performance Indexes (PI’s) from 60 to 89%. After the testing procedure, MSE ranged between 1.53 and 8.23, and PI’s between 51 and 80%. With the exception of brown trout in regulated flow, patterns of microhabitat use obtained revealed that fish densities were highly connected to one major influencing variable: water depth for brown trout and stone loach, and water velocity for European minnow, other variables accounting for lower individual contributions. Analysis of the partial derivatives brought into relief some differences when comparing microhabitat use in natural and regulated flows for some of the variables tested, and no differences when comparing others. The results are discussed with regard to the biology and the ecology of each fish species at microhabitat and macrohabitat scales, and according to the relationship between microhabitat utilization and microhabitat availability. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Artificial neural network; Leave-one-out bootstrap; Partial derivatives sensitivity analysis; Flow regulation; Microhabitat; Brown trout (Salmo trutta L.); European minnow (Phoxinus phoxinus L.); Stone loach (Barbatula barbatula L.).
* Corresponding author. Tel.: + 33-5-62193900; fax + 33-5-62193901. E-mail address: yorick – [email protected] (Y. Reyjol). 0304-3800/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 0 4 - 3 8 0 0 ( 0 1 ) 0 0 3 0 1 - 5
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1. Introduction Many statistical tools are available to reproduce the structure and functioning of ecosystems, according to environmental features. Unfortunately, conventional methods, such as correlation analysis (Ricker, 1975), canonical correspondence analysis, or multiple least-square regression (Binns and Eiserman, 1979), suffer from the relationships between variables in environmental sciences often not being linear (James and McCulloch, 1990), while the methods used are based on linear principles. The transformation of non-linear variables by logarithmic, power or exponential functions can appreciably improve the results, but often fail to fit ecological data (Lek et al., 1996a). On the contrary, the principal advantage of Artificial Neural Networks (ANN) with an error back-propagation procedure is that this method is particularly efficient for non-linear data (Rumelhart et al., 1986). The habitat conditions are the principal factor influencing the presence, the abundance and the distribution of organisms in the environment (Southwood, 1977). Each species is associated with its spatial ecological niche, characterized by some particular modalities of the environmental features. Several spatial scales constitute the habitat, each scale being independent but linked to the others within a hierarchical system (Amoros et al., 1987). Concerning the fish communities, this system of top-down nested scales has been adapted (Frissell et al., 1986) and progressively modified and enriched, resulting in the definition of several ones: zoogeographical range, drainage basin, river, section or stretch, sequence, macrohabitat or morphological stream unit and finally microhabitat, from largest to smallest. The last one is the finest scale, and is therefore an integrator of all the phenomena acting on higher scales. Thus, the modelling of microhabitat use by fish is of particular interest to understand the global functioning of aquatic ecosystems. ANN are more and more used in ecological modelling. In aquatic ecology, the capability of ANN to predict the presence or abundance of different fish species at different spatial scales has already been demonstrated (Guegan et al., 1998 at a global scale, Baran et al., 1996 at a macrohabitat
scale, and Mastrorillo et al., 1997 at a microhabitat scale). On the other hand, only few studies assess the effects of flow regulation on microhabitat use by fish (Scheidegger and Bain, 1995; Copp, 1996; Pilcher and Copp, 1997), and none use ANN to analyse this influence. Prediction of the characteristics existing in a community has recently been investigated with multispecific linear models as guidelines for generating river management regulations (Lamouroux, 1997; Lamouroux et al., 1999a,b) although most of the previous studies that used biological models were based on analysis of single species, such as brown trout (Stalnaker, 1979; Bovee, 1982; Belaud et al., 1989; Souchon et al., 1989; Sabaton and Miquel, 1993; Pouilly and Souchon, 1995). So, the aim of our study was to model the abundance of three fish species in natural and regulated flows of a section of the river Garonne in relation with microhabitat characteristics using ANN, in order to facilitate management of fish communities.
2. Study area As the fourth longest river in France, the Garonne is supported by the Maladetta’s glacier, located in the Pyrenean mountains of Spain. The water goes under Tora’s hole and disappears to re-emerge in the Val d’Aran. The water then flows into France and crosses the city of Toulouse to join the river Dordogne at Bordeaux (Fig. 1), forming the Gironde estuary with an area of 625 km2 during high tide. The Garonne is about 525 km in length with a total catchment area of 57 000 km2 and a specific flow of 11.4 l s − 1 km − 2. It exhibits strict and simultaneous nival and pluvio-nival hydrological regimes (Parde´ , 1935), termed Pyrenean, characterized by one period of intensive flow during spring as a result of snowmelt and two periods of low flow during winter and summer (Lambert et al., 1990). The section considered for this study was 51 km in length, with a mean slope of 3.25 m km − 1. It has an approximate distance from the source of 87 km upstream to 138 km downstream and an altitude ranging from 425 to 260 m. Three tributaries are present in this section (Fig. 1): the Neste (mean
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annual discharge= 20 m3 s − 1), the Ger (mean annual discharge=2.8 m3 s − 1) and the Salat (mean annual discharge= 43 m3 s − 1). The mean annual discharge of the Garonne in this area is 60 m3 s − 1. The only large town on the section is Saint-Gaudens (population 13 053) generating a certain amount of organic pollution in the water system. Moreover, a paper mill in this town adds industrial waste. Six hydroelectric dams divide the study area. The last, the Boussens dam, limits the downstream section, just after the confluence with the river Salat. Concerning the fish community, this section constitutes the transitional zone between an upstream area strongly dominated by the Salmoniform taxonomic order, represented by one fish species: brown trout (Salmo trutta L.), and a downstream area dominated by Cypriniforms. The three dominant species in this section are the brown trout, the European minnow (Phoxinus phoxinus L.) and the stone loach (Barbatula barbatula L.). Six sites were selected along the river (Fig. 1), characterized either as sites having natural flow (Qn): Montre´ jeau (site 1), Miramont (site 3), Beauchalot (site 5), or as sites having regulated flow (Qr): Clarac (site 2), La Gentille (site 4) and Lestelle (site 6). The minimum flow downstream of the dams is reduced 40-fold at sites 2 and 4, and
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5-fold at site 6, giving minimum flow values of 1.55 m3 s − 1, 1.63 m3 s − 1 and 12 m3 s − 1, respectively. The maximal usable flow by each power station is 85 m3 s − 1, 60 m3 s − 1 and 50 m3 s − 1, respectively, with a maximal possible power of 14.2, 4.3 and 5 MW. Each site includes one riffle-pool sequence, in order to sample the two dominant morphological stream units of the section.
3. Materials and methods
3.1. Sampling method According to the hydrological regime of the river Garonne, sampling was carried out for three seasons: summer (21–30 July, 1999), automn (11– 14 October, 1999) and winter (18–20 January, 2000). Because of the snowmelt in spring, and consequently the very high water level in the Garonne, sampling could not have been performed in this season. Sampling was performed using point abundance sampling by electrofishing (Nelva et al., 1979), which simultaneously takes into account microhabitat features and density of each fish species in the community. For each sampling point, fishes were identified, counted and returned to the water. Thirteen microhabitat variables were taken into account: distance from the bank (DIS)
Fig. 1. Study area and sampling sites (Qn, natural flow; Qr, regulated flow).
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Fig. 2. Three-layered feed-forward Artificial Neural Network. Input neurons corresponding to environmental variables, hidden layer neurons, and one output neuron to estimate one fish species density (brown trout, European minnow or stone loach). Connections between neurons are shown by solid lines: they are associated to synaptic weights that are adjusted during the training procedure. The bias neurons are also represented; their input value is one.
in metres, water depth (DEP) in metres, water velocity (VEL) in metres per second, percentage of flooded vegetation cover (VEG), represented by only one species, Ranunculus fluitans L., ‘blockage’ (BLO), which is one or several pieces of wood providing shelter, expressed in presence or absence, and percentages of eight substratum fractions: large boulders (LB, smallest section s \ 60 cm), small boulders (SB; s =20 – 60 cm), large pebbles (LP, s = 10 – 20 cm), small pebbles (SP, s = 2– 10 cm), gravels (GRA, s =0.2 – 2 cm), sand (SAN, s= 0.02 cm), mud (MUD), and bedrock (BED).
3.2. Modelling techniques ANN models were set up using a data set of 1107 samples×(13 environmental variables+3 fish species). Six multilayer feed-forward neural network models were used, one for each fish species (i.e. brown trout, European minnow and stone loach) in both of the flow conditions (natural or regulated flow). For each model, the first layer, called the input layer, connects with the input variables. In our case, it had 13 input neurons corresponding to the 13 environmental variables. The last layer, called the output layer, had a single neuron corresponding to the dependent variable to be predicted (fish density for the considered spe-
cies) (Fig. 2). The layer between input and output layers is called the hidden layer. A ‘bias’ neuron was added to each computational layer (i.e. hidden and output layer). These neurons had a constant input value of one and were used to lower biases in the modelling procedure (see Rumelhart et al., 1986 for more detail). We could have used a single neural network with six output neurons (one for each of the three fish populations in natural or regulated flow), but we preferred to use six networks with the same architecture, each predicting the abundance of one fish species, so as to easily extract the influence of the 13 input environmental variables on each fish species, with respect to the flow conditions. The network configuration was approached empirically by testing various possibilities and selecting the solution which provided the best compromise between bias and variance (Geman et al., 1992; Kohavi, 1995). Modelling was carried out in two steps: first, model training was performed using the whole data matrix, according to the back-propagation algorithm (Rumelhart et al., 1986). It consisted of using a training data set to adjust the connection weights in order to minimize error between observed and predicted values, and gave an estimate of the performance of the ANN to learn data and to study the contribution of each variable. The connection weights, initially taken at random in the range [− 0.3, 0.3], are iteratively adjusted by a method of gradient descent based on the difference between the observed and expected outgoing signals. Many iterations are necessary to guarantee the convergence of predicted values toward their expectations, without obtaining overfit (i.e. incapacity of the model to generalize). To test the stability of the network, each modelling procedure was repeated 10 times. Second, we used the ‘leave-one-out’ bootstrap cross-validation test (Efron, 1983; Efron and Tibshirani, 1995), where each sample is left out of the model formulation in turn and predicted once, to validate the models. Computational programs were written in a Matlab® environment.
3.3. Performance of the models Performance Index (PI) and Mean Squared Errors (MSE) were used to assess performance of
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the model prediction. The PI was based on the percentage of well-predicted values with an error rate lower than 10%, i.e. the proportion of responses plus or minus 10% of the model output value (Brosse et al., 1999).
3.4. Study of the influencing factors The microenvironmental variables that we focused on to explain fish densities were chosen according to their relative contribution in the models, and with respect to the known biological and ecological features of each fish species. In this study we used a method based on the response of the partial derivatives of the network to highlight the influence of each descriptor (Dimopoulos et al., 1995; Dimoupoulos et al., 1999). This algorithm allowed us to visualize the derivative profile for each point, according to each variable. Moreover, we can compare the total contribution of all environmental variables used at the input of ANN models.
4. Results
4.1. Fish community structure Brown trout, European minnow and stone loach were respectively present in 19.8, 25.7 and 37.1% of the 1107 samples. For the 6238 fishes Table 1 Performance Index (PI) and Mean Squared Errors (MSE) in ANN training and testing for the three populations in natural (Qn) and regulated (Qr) parts of the river Garonnea Training
Testing
PI
MSE
PI
MSE
Qn
Brown trout European minnow Stone loach
83 89 68
0.60 0.40 1.68
65 80 61
1.94 1.53 7.25
Qr
Brown trout European minnow Stone loach
88 78 60
0.69 0.83 1.93
70 62 51
6.58 5.00 8.23
a PI is the percentage of well-predicted values with an error rate lower than 10%, see text for details.
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caught, total lengths range from 21 to 430 mm for brown trout, from 15 to 122 mm for European minnow and from 17 to 116 mm for stone loach. Mean total lengths and standard errors of the mean are 142.89 2.7 for brown trout, 45.89 0.3 for european minnow and 62.29 0.3 for stone loach.
4.2. Model fitting, testing and 6alidation The ANN structure used was a three-layered (13 30 1) feed-forward network with bias. The hidden layer had thirty neurons, determined as the optimal configuration which gave the lowest error in the training and testing sets of data with minimal computing time (Geman et al., 1992; Lek et al., 1996b,c). After the training procedure, the PIs obtained were 83 and 88% for brown trout in Qn and Qr, 89 and 78% for European minnow, 68 and 60% for stone loach (Table 1). MSE were 0.60 and 0.69 for brown trout, 0.40 and 0.83 for European minnow, 1.68 and 1.93 for stone loach. After the cross-validation testing procedure, PIs obtained in Qn and Qr (Table 1) were respectively 65 and 70% for brown trout, 80 and 62% for European minnow, 61 and 51% for stone loach. MSE were 1.94 and 6.58 for brown trout, 1.53 and 5.00 for European minnow, 7.25 and 8.23 for stone loach.
4.3. Importance of the en6ironmental 6ariables in population abundance Means and standard errors of the contribution of each variable calculated after ten training procedures are reported in Fig. 3. With the exception of brown trout in Qr, the modelling procedure showed that fish densities were highly connected to one major influencing variable, other variables accounting for lower individual contributions. Moreover, for European minnow and stone loach, patterns of microhabitat use were similar in natural and regulated flows. Water depth was the major variable influencing the distribution of brown trout in Qn (24%), whereas percentage of large boulders and water velocity were the most explanatory factors in Qr (12.7 and 12.3%, respectively). Water velocity was the most influencing
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Fig. 3. Contribution percentage of each of the thirteen environmental variables with regard to the prediction of the density of each fish species, in natural (Qn) and regulated flow (Qr). Bars indicate the mean value of the results of the ten models for each fish population, horizontal lines represent Standard Errors of the Mean (SEM).
variable for European minnow (30.7 in Qn and 23% in Qr), whereas the most explanatory variable for stone loach appeared to be the water depth (20.2 in Qn and 19% in Qr). Water depth and water velocity appear to be the major variables influencing the distribution of the three fish species considered in our study, and have to be taken into account in the partial derivatives sensitivity analysis. More specific variables, known to have a major influence on the ecology of each fish species taken individually, but not having necessary a particularly high contribution on the fish distribution in our
results, deserve our attention. For examples, the need of distinct types of shelters against water velocity for brown trout (Haury and Baglinie`re, 1990; Baran et al., 1995) or stone loach (Mastrorillo et al., 1996), or the role played by the river banks on the microhabitat used by European minnow (Mastrorillo et al., 1996), need to be highlighted. According to this, the microenvironmental variables selected to explain fish density were DEP, VEL, LB and VEG for brown trout, DIS, DEP, VEL and VEG for European minnow, and DEP, VEL, SB, LP and VEG for stone loach.
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4.3.1. Brown trout The positive values of partial derivatives for DEP and VEL in Qn, which became closer to zero for high values of these variables (Fig. 4), indicated that the increase of DEP and VEL firstly induced an increase of the brown trout density, and that these tendencies attenuated when DEP and VEL increased. The opposite patterns were observed in Qr, which meant that the density decreased when DEP and VEL increased, and that these augmentations were less and less pronounced. The positive values of partial derivatives for LB and VEG indicated that these variables
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contributed to the increase of the density in Qn and Qr whatever the flow conditions were.
4.3.2. European minnow The values of partial derivatives for DIS and VEL in Qn and Qr (Fig. 5) showed that these variables caused a decrease of the European minnow density whatever the flow conditions were. The positive values of partial derivatives for DEP in Qn suggested that the corresponding density increased with DEP. On the contrary, the negative values of partial derivatives for the majority of the values of DEP showed that this variable con-
Fig. 4. Partial derivatives of the ANN models according to each descriptor for brown trout, in natural (Qn) and regulated flow (Qr).
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Fig. 5. Partial derivatives of the ANN models according to each descriptor for European minnow, in natural (Qn) and regulated flow (Qr).
tributed to the reduction of the European minnow density in Qr. For the variable VEG, most of the partial derivative values were positive, implying that the increase of this variable was favourable for the density in Qn and Qr.
4.3.3. Stone loach The negative values of partial derivatives for the majority of the values of DEP and VEL (Fig. 6) showed that these variables contributed to the
reduction of the stone loach density in Qn and Qr. The response of stone loach to the variation of substrate features was complex. Whereas the increase of SB induced a decrease of the density in Qn, and an increase in Qr, the variable LP did not influence the stone loach density whatever the flow regime was. Finally, the values of partial derivatives showed that in Qn the variable VEG did not influence the density, whereas in Qr high values of this variable induced a decrease.
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5. Discussion and conclusion The Artificial Neural Networks (ANN) showed high predictive power to estimate densities of
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brown trout, European minnow and stone loach in natural (Qn) and regulated (Qr) flows of the river Garonne, with regard to 13 input environmental variables. Performance Indexes (PIs) ob-
Fig. 6. Partial derivatives of the ANN models according to each descriptor for stone loach, in natural (Qn) and regulated flow (Qr).
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tained after the training procedure were high, ranging from 60% for stone loach in Qr to 89% for European minnow in natural flow. PIs obtained after the testing procedure were lower than those obtained after training for each of the three fish species, but always higher than 51%, with a maximum of 80% for European minnow in Qn. Mean Squared Errors (MSE) were always lower than 1.93 during training, while the minimum value in testing procedure was 1.94 for brown trout in Qn, and the maximum value was 8.23 for stone loach in Qr. Both PIs and MSE values obtained during the training and the testing procedures are comparable to those obtained by Brosse et al. (1999) in similar modelling conditions, and testify the high modelling power of ANN, compared to classical methods, as already demonstrated by many studies (Baran et al., 1996; Mastrorillo et al., 1997; Guegan et al., 1998; Brosse et al., 1999). With the exception of brown trout in Qr, the analysis of microhabitat use revealed that fish densities were highly connected to one major influencing variable: water depth for brown trout and stone loach, and water velocity for European minnow, other variables accounting for lower individual contributions. The analysis of partial derivatives highlighted distinct or opposite patterns of microhabitat use between natural and regulated flows according to some of the tested variables, and no variation with regard to the others. Brown trout is a rheophilic species mainly found in riffles (Baran et al., 1993, 1995), whereas stone loach both occupies riffles and pools (Maitland, 1965; Zweimu¨ ller, 1995), and European minnow is a limnophilic species which occupies pools, except for breeding, when it searches for areas with high velocities (Frost, 1943; Wootton and Mills, 1979). The role of macrophytes (Haury and Baglinie`re, 1990) and cover, which includes large blocks (Baran et al., 1995), as favourable physical habitats which provide shelter has already been highlighted for brown trout. For European minnow, Mastrorillo et al. (1996) also observed a decrease of the density when the distance from the bank increases, as well as a positive influence of the macrophytic vegetation on
the fish density. For stone loach, the same authors highlighted a preference for a heterogeneous substrate of small blocks, large pebbles and gravel (Mastrorillo et al., 1996), which act as a shelter against sunlight for this nocturnal species. Differences observed between patterns of microhabitat use in Qn and Qr must be linked to microhabitat availability (Baldridge and Amos, 1982; Bovee, 1982; Raleigh et al., 1986; Belaud et al., 1989). Indeed, flow regulation modifies the morphodynamic conditions of the river, particularly water depth and water velocity, which are lower in regulated sites, and fish will choose different microhabitat conditions according to the range of available values in each type of flow. Opposite patterns obtained between natural and regulated flow may not reflect real preferences of these species, but only the use of the available microhabitat conditions. On the contrary, the persistence of some tendencies in natural and regulated flows may reflect the real preferences of the species. Thus, the results obtained will have to be weighted by microhabitat availability to eliminate the effect of the modification of habitat conditions, and to identify real shifts in the microhabitat used by fishes. In conclusion, ANN are an efficient statistical tool to predict fish abundance from the microhabitat features in this section of the river Garonne. Moreover, the use of the partial derivatives of the network output highlights the participation of each of the explanatory variables and focuses on their respective influence, constituting a promising approach to improve the explanatory capacities of ANN models. These results are particularly interesting with regard to population assemblage studies, as applied to regulated river management. Preference curves according to water depth, water velocity and substratum composition already exist for brown trout, in order to manage regulated rivers in upstream areas of lotic ecosystems (Bovee, 1982). This type of curves should now be established for European minnow and stone loach, in order to set up methods to predict fish density from habitat conditions for the management of regulated rivers in areas where these three species dominate.
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