Distribution Patterns of Indigenous Freshwater Fishes in the Tagus

Distribution patterns of indigenous freshwater ﬁshes in the Tagus River basin, Spain

Jose´ A. Carmonaa, Ignacio Doadrioa, Ana L. Marquez´ b, Raimundo Realb, Bernard Huguenyc & Juan M. Vargasb a Museo Nacional de Ciencias Naturales, Jos´e Guti´errez Abascal, 2, 28006, Madrid, Spain (e-mail: [email protected]) b Departamento de Biolog´ıa Animal. Facacultad de Ciencias, Universidad de Malaga,´ 29071, Malaga,´ Spain c CNRS, URA 1974, Laboratoir du Ecologie des Eaux Douces, Baˆt. 403, 43 Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France

Received 19 December 1997 Accepted September 1998

Key words: biogeography, chorotypes, environmental boundaries

Synopsis

Classification and ordination methods used to examine the internal complexity of the Mediterranean Tagus River catchment based on fish distribution revealed that it is not a homogeneous biogeographical unit. The indigenous fishes analyzed in this study are distributed through the basin forming geographical communities (chorotypes), some of which are associated with environmental factors like river morphology, water quality or geographical location. Nevertheless, 40% of the variation in species occurrence remains unexplained by either environmental or geographical variables, suggesting that historical factors may influence the freshwater fish distribution patterns. Three main biogeographical areas, delimited by significant boundaries, were identified. Two of them are identified as the upper and the middle-lower basins of the Tagus River catchment; the third corresponds to the Alagon´ River and seems to be linked to historical factors of the catchment.

Introduction the Iberian Peninsula; 19 species are introduced and 35 are indigenous of which 23 are endemic (Collares- Two main areas have been established in Europe on the Pereira 1980, Coelho 1985, Doadrio et al. 1991, Elvira basis of freshwater fish fauna (Ban˘ arescu˘ 1989): Cen- 1995). tral Europe, with a fish fauna consisting of widespread Historical factors, such us the Miocenic origin species, and Southern Europe, characterized by many of the primitive endorheic basins that developed endemic species. The Iberian Peninsula belongs to the into the current hydrographic basins in the Pliocene southern subdivision and has the highest proportion of (Lopez-Mart´ ´ınez 1989) have exclusively been used endemic fish species in Europe. Nevertheless, it is also to explain freshwater fish distribution in Iberian characterized by low diversity compared with other Peninsula (Almaça 1978, Doadrio 1988, Hernando & areas in the southern European region and with central Soriguer 1992). However, ecological factors may Europe. Most of the Iberian species belong to genera also play a basic role in the fish distribution that also inhabit central Europe such as Barbus Cuvier patterns and communities at smaller time scale and Cloquet, 1816, Chondrostoma Agassiz, 1835, (Grossman et al. 1982, 1985, Pusey et al. 1993, Leuciscus Cuvier, 1817, Rutilus Rafinesque, 1820 Oberdoff et al. 1993) and can contribute valuable and Cobitis Linnaeus, 1758, but few species are information, since the historical explanations do not shared by both biogeographic areas. Currently, 54 imply a homogeneous distribution of freshwater fishes freshwater fish species with stable populations inhabit within a hydrographic basin. 372

Hydrographic basins are geographical units for In this paper we examine biogeographical com- studying freshwater fish distribution since they have plexity variaton in the Spanish Tagus basin and the well-defined boundaries that primary fishes (Myers nature of species replacement patterns. We tested for 1951) rarely cross under natural circumstances. As a the existence of (a) distribution patterns shared by result, various authors have considered river basins several indigenous freshwater fishes, which we call as operational units for the biogeographical analysis ‘chorotypes’ after Baroni-Urbani et al. (1978), (b) bio- of fishes or other freshwater taxa (see Doadrio 1988, geographical boundaries within the basin, which would Matthews & Robison 1988, Real et al. 1992, 1993). explain fish distribution patterns, and (c) environmen- Nevertheless, the distribution of freshwater fishes tal factors that could explain such shared distributions within a hydrographic basin may be heterogeneous. and biogeographical boundaries. Changes in fish composition through the basin can occur either continuously or discontinuously. The River Continuum Concept (Vannote et al. 1980) refers to Material and methods gradual changes in community structure and a gradual increase in species richness from upstream to down- Study area and environmental variables stream with a maximum located in the medium course. However, typical features of fluctuating Mediterranean The Tagus River (Figure 1) is located in the mid- rivers, especially the variety of available microhabi- dle of the Iberian Peninsula, between the Duero tats (Grossman et al. 1987), could produce high het- and Guadiana basins. It runs mostly from east to erogeneity in ecological factors throughout the basin west and flows into the Atlantic Ocean. However it and generate more complexity in distribution patterns presents the typical southern European river typol- of freshwater fish species than expected in a homo- ogy because most of its basin is regulated by the geneous or gradually changing biogeographical unit. Mediterranean climate (Arenillas & Saenz´ 1987). It Southern European rivers show a singular typology contains 24 freshwater fish species, 14 of them native. characterized by short streams with water level fluc- The number of recorded endemic species is espe- tuations and an intermittent flow that depends on alter- cially high and, except for Salmo trutta Linnaeus, nating dry and wet seasons (Arenillas & Saenz´ 1987). 1758 and Atherina boyeri Risso, 1810, all native In most cases the rivers consist of extensive pools con- species are also endemic. They are Barbus bocagei nected by shallower runs. These pools become isolated Steindachner, 1865, Barbus comizo Steindachner, during the dry season, giving way to a high number 1865, Barbus microcephalus Almaça, 1967, Chondros- of fragmented microhabitats isolated by physical and toma polylepis Steindachner, 1865, Leuciscus pyre- ecological gradients (Balon 1981). This phenomena naicus Gunther,¨ 1868, Leuciscus carolitertii Doadrio, may preclude traditional river zonation (Huet 1959) 1987, Rutilus arcasii (Steindachner, 1866), Rutilus into upper, medium and lower streams as a function of lemmingii (Steindachner, 1866), Tropidophoxinellus gradient, current velocity and temperature of the water alburnoides (Steindachner, 1866), Cobitis paludica (see Balon & Stewart 1983). (De Buen, 1930), Cobitis vettonica Doadrio & The Tagus basin is characterized by a rich endemic Perdices, 1997 and Cobitis calderoni Bacescu, 1961. fish fauna due to its ancient and endorheic origin. It was We excluded A. boyeri and B. microcephalus from our formed during the Miocene through the connection of analysis because they are extremely localized. We con- the endorheic basins of Loranca, Madrid and Lisbon sider the samples of S. trutta collected for this work as (De la Pena˜ 1995) as a result of tectonic movements natural populations. Fish reintroducted in the basin are that raised, and later tilted the Iberian Peninsula toward very localized and the genetic introgression detected the Atlantic Ocean (Lopez-Mart´ ´ınez 1989). Such abun- was moderately low in natural populations (Garc´ıa- dance of endemic fishes and the large size of the basin Mar´ın & Pla 1996, Garc´ıa-Mar´ın et al. 1991). (54 769 km2 and 731 km of principal course length) Two main biogeographical areas were defined make the Spanish Tagus River an excellent model for (Doadrio 1988) within the Iberian Peninsula on the studying the influence of microhabitats on the biogeo- basis of freshwater fish distribution: the northern graphical complexity of a basin, and allow examination region containing some central european species and of heterogeneity in fish distribution patterns. the southern Iberian region inhabited by the bulk 373

Figure 1. Location of the sampling points in the study area: ◦ = sampling points with only physical variables available, •=sampling points with chemical and physical variables available.

of the endemic species. Within the latter region the capture of rare species at each station. We performed Tagus River basin is transitional between the Galician- at least three succesive catches at each sampling point, Portuguese and southern ichthyofaunal sectors. It with a similar sampling effort. Three people conducted forms the southern limit for species such as B. bocagei, the sampling over 150–200 m of river length. Fish were R. arcasii, L. carolitertii and C. calderoni, and the present at only 77 sampling points (Figure 1) because northern limit for others such as B. microcephalus, many stretches were dry and others were too polluted. B. comizo and R. lemmingii. The subspecies of Ch. Physical variables indicative of river morphology polylepis also have their distribution limits in the were measured at each of the 77 sampling points, but Tagus basin (Doadrio et al. 1991). chemical variables indicative of water quality were We performed the sampling between 1991 and 1993, available only from 51 (Hydrographic Confederation always in late summer-early autumn, using electrofish- of River Tagus unpublished data). Both sets of data, ing. We selected 120 sampling stations in the 54 tribu- generating fifteen environmental variables (Table 1), taries of the Spanish Tagus basin, assigning more points were collected in the same season (late summer– to large tributaries where the environmental conditions early autumn). are more variable. We distributed the sampling points as evenly as possible, given constraints of accessibility Species grouping analysis and the suitablity of conditions for electrofishing. We assigned few sampling points to the area near the city We initially established two presence–absence matri- of Madrid because previous collections (unpublished ces for the 12 species captured, one for the 77 sam- data) detected an extreme alteration in fish fauna due pling points, and another for the 51 sampling points to human impacts such us flow regulation by dams, the with complete environmental data available. Using introduction of exotic species, and pollution. We used each matrix, we performed the following procedures the succesive catch technique, derived from maximum for classifying species according to their occurrence likelihood methods applied to population estimates at sites, and for classifying sites by the presence of (see Lobon-Cervi´ a´ 1991, for a review), to ensure the species. 374

Table 1. Variables and species considered in the analy- We calculated the significance of the similari- sis. Sources: 1 from the Instituto Geografico Nacional de ties using the probability table in Baroni-Urbani & Espana˜ 1 : 50000 scale maps; 2 estimated by J.A. Carmona Buser (1976), because this procedure allows statistical from field notes following the methodology of Matthews & independence between observed and expected values Robison (1988); 3 Confederacion Hidrografica´ del Tajo, following the Appendix of the Directive (78/659/CEE) of (Jackson et al. 1992, Real & Vargas 1996). In this way the European Comunity (18 July 1978). the similarity matrix was transformed into a matrix of significant similarities in which we replaced the val- Physical variables Code ues of Baroni-Urbani & Buser’s similarity index with Distance to the main channel of the Tagus River1 DIS ‘+’, ‘−’ and ‘0’ signs according to whether the index Order1 ORD value was significantly higher than that expected at Width of the river in the sampling point2 WID random (indicating a shared distribution), significantly Depth of the river in the sampling point2 DEE lower (indicating a complementary distribution), or not Velocity of the water in the sampling point2 SPE significantly different (indicating random geographical Transparency of the water in the sampling point2 TRA association) respectively. 2 Aquatic vegetation AV E We used UPGMA (Unweighted Pair-Group Method 2 Riparian vegetation RVE using arithmetic Averages) for classifying species by Chemical variables Code their overall geographical aggregations and segre- 3 gations, as opposed to their pairwise geographical pH PHH relations (Sneath & Sokal 1973). We displayed the Mineral oil present in water3 MIN UPGMA results as a dendrogram. At every fork of the Oxygen dissolved in water3 O22 Hardness of water3 HAR dendrogram we tested for the existence of a significant Nitrites present in water3 NO2 segregation between the species separated by the fork Biochemical oxygen demand3 DBO according to the method described by McCoy et al. Suspensed solids in water3 SUS (1986) and modified by Marquez´ et al. (1999), start- ing with those forks that presented greatest similarity. Species Code For each fork we took a submatrix from the matrix B. bocagei 1 of significant similarities, including only the species B. comizo 2 separated by the fork. We divided each submatrix into R. arcasii 3 three sets: sets A and B, that correspond to each of the R. lemmingii 4 groups of species separated by the fork, and a set AxB, T. alburnoides 5 L. pyrenaicus 6 that corresponds to the intersection of the two groups. Ch. polylepis 7 The species separated by the fork are considered as S. trutta 8 two different chorotypes, with at least weak segrega- C. calderoni 9 tion between them, when the similarities higher than C. vettonica 10 expected (+) tend to be in sets A and B but not in C. paludica 11 AxB. If the similarities lower than expected (−) tend L. carolitertii 12 to be in set AxB but not in A or B, then there is strong segregation between the chorotypes. We call Pp(AxA) the number of pluses between We applied Baroni-Urbani & Buser’s (1976) simi- each pair of species within set A divided by the total larity index to the distribution of each pair of species, number of elements possible for comparisons in set A. a and b: √ So, Pp(AxA) is the proportion of pluses in set A.We (CxD) + C call Psp(AxA) the number of species in set A that S = √ , (1) (CxD) + A + B + C have at least one plus divided by the total number of species for comparisons in set A, which represents the D where is the number of sampling points in which proportion of species in set A with at least one plus. A B neither of the two species is present, and represent We then compute d1(AxA) as follows: If the number the number of sampling points in which only species of pluses in A is zero, then d1(AxA) = 0; otherwise, a and only species b is present, respectively, and C Pp(AxA) × Psp(AxA) represents the number of sampling points in which both d1(AxA) = p . species occur. (Pp(AxA))2 + (P sp(AxA))2 375

We define P m(AxA) and P sm(AxA) as the propor- This procedure allows us to test whether the groups tion of minuses in set A and the proportion of species identified by UPGMA have a biogeographical mean- in set A with at least one minus, respectively, and ing or, alternatively, the species are distributed inde- these are computed in the same way as Pp(AxA) and pendently, and the groups are no more than artifacts of Psp(AxA), but by taking into account the minuses. We cluster analysis having to form clusters. However, this then define d2(AxA) in the following way: if the num- biogeographical meaning does not necessarily imply a ber of minuses in set A is zero, then d2(AxA) = 0; historical or ecological cause, which must be tested in otherwise, a further step. In order to analyse the influence of the environmental P m(AxA) × P sm(AxA) variables on the distribution of each chorotype, step- d2(AxA) = p . (P m(AxA))2 + (P sm(AxA))2 wise logistic regression was used in two different analyses: (a) considering the 77 sampling points and their Wedefine Pp(AxB)and Psp(AxB)in a similar way physical variables; (b) using the 51 sampling points as Pp(AxA) and Psp(AxA), but referred to set AxB. with both physical and chemical variables available. Then d4 is zero when the number of pluses in AxB is We used the following logistic regression formula: zero; otherwise, y p = e y (2) Pp(A B) × Psp(A B) 1 + e d (A A) = p x x . 4 x p (Pp(AxB))2 + (P sp(AxB))2 in which represents the likelihood of species of the chorotype being present, e is the basis of the Napierian y The parameters DW(AxA) and DW(BxB) mea- logarithms, and is a regression equation of the fol- sure to what extent the similarities higher than expected lowing type: (+) tend to be in sets A and B, but not in AxB (see y = a + bx1 + cx2 +···+nxn, (3) McCoy et al. 1986), and therefore they are indicative of the internal homogeneity of each of the groups where xn are those variables that improve the statistical analysed. DW(AxA) = d1(AxA) − d2(AxA) − d4. significance of the model, which are successive and Analogously, DW(BxB) = d1(BxB) − d2(BxB) − randomly incorporated into the regression model. The d4, where d1(BxB) and d2(BxB) are calculated as probability of score statistic for variable inclusion was d1(AxA) and d2(AxA), but computing the pluses and 0.05, and the probability of Wald to remove a variable minuses in set B. was 0.1. Wetested the significance of the segregation between We tested the logistic model by a χ 2 test of goodness- groups by a G test of independence that compares the of-fit. The estimation of the parameters a,b,...,nin distribution of the ‘+’ and ‘−’ in the three sets in which the equation (3) was by maximum likelihood and the we divided the submatrix of significant similarities. The test of Wald (1943) was applied. resulting parameters are GW for weak segregation and This procedure facilitates the detection of those vari- GS for strong segregation. We considered a group of ables that best indicate the presence of a chorotype, species to be a chorotype if DW for this group is pos- showing the sampling points the chorotype might itive and either GW or GS are statistically significant. potentially occupy, and the sampling points that do not When the sum of species included in the two groups provide favourable environmental conditions for the in comparison are four or less, the G test is not signif- chorotype. icant due to the low number of ‘+’, ‘−’ or ‘0’ signs implicated. In those cases, if most of the ‘+’ are in Sampling point grouping analysis A or B, and most of the ‘−’ and ‘0’ are in AxB, then the groups compared are considered chorotypes. When In order to group the sampling points by their species ‘−’ predominanted in AxB, strong segregation existed. composition we started from the same two presence– On the contrary, if ‘0’ signs were predominant, we con- absence matrices, with 77 and 51 sampling points, sidered the segregation between the groups to be weak. respectively. We applied the Baroni-Urbani & Buser’s The probability level of the segregation is then that con- similarity index (1) to both matrices for each pair of sidered in the table of Baroni-Urbani & Buser (1976) sampling points, a and b. In this case, D is the num- to obtain the ‘+’, ‘−’ and ‘0’ signs. ber of species present in neither of the two sampling 376 points, A and B represent the number of species that are species variation is the spatial variation that cannot be present only in a or b, respectively, and C represents explained by the environmental variables alone. the number of species found in both sampling points. In practice, this partitioning is realised in four steps: The rest of the classification analysis followed the A CCA is effected (1) between species data and envi- same steps as for the species grouping. The interpre- ronmental data, (2) between species data and spatial tation of the results is in terms of biotic boundaries data, (3) between species data and environmental data, instead of segregation of species. In this case, a biotic after removing the effect of spatial variables and (4) boundary must be refered to a division between groups between species and spatial data after removing the of sampling points that not neccesarily correspond effect of environmental variables. to a given geographical area because of segregation. The percentage of the total variation of the species There is a weak boundary if the similarities higher than matrix accounted for by the joint effect of environmen- expected (+) tend to be in sets A and B but not in AxB. tal and spatial variables is obtained by adding the per- If the similarities lower than expected (−) tend to be in centage of explained variation in steps 1 and 4 (or 2 set AxB but not in A or B, there is a strong boundary and 3). The non-spatial environmental variation is the between the sampling points. variation explained by step 3. The spatially structured environmental variation is the variation explained by step 1 minus the variation explained by step 3 (or step Ordination methods 2 minus step 4). The pure spatial variation is obtained in step 4. Relationships between fish communities and all the In order to integrate spatial structure in the analy- explanatory variables available at 51 sampling points ses, the matrix of two dimensional geographical coor- were also studied using canonical correspondence anal- dinates, x and y, was completed by adding all terms for ysis (CCA) (Ter Braak 1986) as provided by the a cubic trend surface regression of the type: CANOCO software. This ordination method allows 2 2 3 one to measure the amount of variation in the species z = b1x + b2y + b3x + b4xy + b5y + b6x data that can be explained by the set of explanatory 2 2 3 variables. + b7x y + b8xy + b9y . (4) We used a permutation test to assess the statistical significance of the relationship. A major advantage of Cubic trend surface regression ensures that com- this method is that ordination diagrams of species and plex spatial patterns such as patches or gaps are cor- explanatory variables provide a useful basis to check rectly described. As a result nine spatial variables which species and which variables contribute most to were obtained. To reduce the high colinearity observed the relationship. Another advantage of this method is within this set of variables, a principal component anal- that the relationship between species data and an ini- ysis was carried out and the first four axes (accounting tial set of variables can be assessed after removing, by for 98% of the variability displayed by the nine original multiple linear regression, the effect of a second set of variables) were retained in subsequent analyses. Con- variables (covariables). This procedure is called partial sequently, the spatial data set is a four variable × 51 constrained ordination. locality matrix. Borcard et al. (1992) described how partial constrained ordination can be used to partition the variation in the species data into four components: (1) non- Results spatial environmental variation, (2) spatially structured environmental variation, (3) pure spatial species vari- Species grouping analysis ation, and (4) unexplained variation. The non-spatial environmental variation in the species data is the frac- Figure 2a shows the dendrogram that results from tion of the species variation that can be explained by applying the UPGMA on the species data for the 77 the environmental descriptors independently of any sampling points. The matrix of significant similari- spatial structure. The spatially structured environmen- ties can be seen in Table 2a, whereas the significant tal variation is due to the environmental variables segregations between species according to the G test that have a spatial structure and so contribute both to of independence are shown in Table 3a. We distin- environmental and spatial variation. The pure spatial guish six chorotypes. Chorotype I is composed of five 377

Figure 2. Chorotypes of freshwater fishes in the Spanish Tagus River basin obtained at the 77 sampling points (a, in roman numbers) and at the 51 sampling points with complete environmental information available (b, in capital letters). Scale indicates the similarity level. The code numbers of the species are as those in Tables 1, 2 (W = weak segregation; S = strong segregation; ∗∗∗ = p<0.005 according to a G test of independence). species, chorotype II is formed by three species, and Figure 2b shows the dendrogram that results from there are four chorotypes with only one species each. applying the UPGMA on the species data for 51 sam- The species R. arcasii, S. trutta and C. calderoni are pling points. Table 2b shows the significant similarities strongly segregated from the others but do not con- between species, and Table 3b displays the significant stitute a chorotype because the group has no internal segregations between groups of species according to homogeneity (DW(BxB) =−0.298). In this case the G test of independence. Here we also distinguish each individual species is a chorotype. six chorotypes. The first chorotype (A) is composed 378

Table 2. Matrices of signiﬁcant similarities between the distribution of freshwater species in the 77 sampling points (a) and in the 51 sampling points with complete environmental information available (b). The numbers correspond with species code listed in Table 1 (+=similarity higher than expected at random; −=similarity lower than expected at random; 0 = similarity compatible with that expected at random; the signiﬁcation level is p<0.05).

a 1271561142389 10 + 000−− −−−−− 12 + 0 +−− −−−−− 7 ++0 − −−−−− 1 +00 −−−−− 5 00 0−−−− 6 + 0 −−0 − 11 +−−−− 4 0 −−− 2 −−− 3 00 8 − 9

b 1271561142389 10 ++00−− −−−−− 12 + 0 +−− −−−−− 7 ++0 −−−0−− 1 +00 −−0 −− 5 00 −−−−− 6 + 0 −0 −− 11 0 −−−− 4 0 −−− 2 −−− 3 +0 8 0 9

of the same ﬁve species as chorotype I. Chorotype B are selected. These variables explain 95% of the 41 comprises only L. pyrenaicus and C. paludica, whereas presences, but only 30% of the 10 absences. R. lemmingii is another chorotype (D). Barbus comizo Chorotype II (L. pyrenaicus, C. paludica and R. lem- is a chorotype C, which is the same as chorotype III mingii) is mainly present at sampling points where (Figure 2a). Rutilus arcasii, and S. trutta constitute water speed is slow. This is also the only variable that a new chorotype E, while C. calderoni constitutes characterizes the presence of chorotype B, which is chorotype F identical to chorotype VI. composed of only L. pyrenaicus and C. paludica.Even Table 4 shows the signiﬁcant relationships, anal- when considering all physical and chemical variables, ysed by logistic regression, between the chorotypes the distribution of R. lemmingii (chorotype D) remains at 77 sampling points and the physical variables, and unexplained. The distribution of B. comizo (chorotypes between chorotypes at 51 sampling points and physical III and C) is best explained by river width and the high and chemical variables. level of mineral oil present in the water. Chorotypes I and A are constituted by the same Rutilus arcasii (chorotype IV) occurs mainly at sam- species and are best explained by chemical variables. pling points far from the main channel of the river Their presences are best characterized by water with and with very clear water. Salmo trutta (chorotype V) low pH, but within the area with low pH those sam- occurs mainly at points where the river is narrow and pling points with less mineral oil present in the water water speed is high. Chorotype E, which includes the 379

Table 3. Segregations between groups of species on the dendrogram forks of Figure 2a (a) and Figure 2b (b). Numbers in each group correspond with species code listed in Table 1. GW and GS indicate weak segregation and strong segregation between the groups, respectively (∗ = p<0.05; ∗∗ = p< 0.01; ∗∗∗ = p<0.005). DW(AxA) and DW(BxB) quantify the internal homogeneity of each group analysed.

Groups set up by UPGMA Aggregation Segregation weak strong Group A Group B Coefﬁc. DW(AxA) DW(BxB) GW P GS P a 10–12 7–5 0.561 0.416 0.416 2.415 N.S. 0 N.S. 10–5 6–4 0.276 0.515 0 11.290 *** 10.463 *** 10–2 3–9 0.186 −0.191 −0.298 5.551 * 20.095 *** b 10–7 1–5 0.599 0.383 0.283 1.112 N.S 0 N.S 10–5 6–2 0.252 0.574 −0.147 11.343 *** 12.528 *** 10–2 3–9 0.188 −0.213 0.298 6.535 * 12.963 ***

Table 4. Environmental characterization by logistic regression of the chorotypes shown in Figure 2. Variables code displayed in the equations correspond with those listed in Figure 1.

Chorotype Regression equation Model sig. Percent correct absent present I y = 1.61 − 0.3684TRA + 0.3694WID 0.001 31.25 90.16 II y = 2.1864 − 0.7114SPE 0.005 45.45 81.82 III y =−4.1328 + 0.3296WID − 0.096DIS 0.001 98.6 40.00 IV y =−4.714 + 0.012DIS + 0.8154TRA 0.01 96.72 12.50 V y =−0.9704 − 0.3332WID + 0.6888SPE 0.001 80.77 48 A y = 25.6999 − 2.9828PHH − 0.0154MIN 0.001 30 95.12 B y = 1.8126 − 0.6003SPE 0.05 50 74.07 C y =−15.5496 + 0.5447WID + 0.0625MIN 0.001 97.9 66.7 E y =−17.6388 + 1.8302PHH + 1.1402SPE 0.005 80 57.14

two previous species, is present at points mainly char- Environmental variables explain 42% of the varia- acterized by the high velocity and high pH of the water. tion in species distribution. The ﬁrst axis orders species Finally, the distribution of C. calderoni (chorotypes VI along a gradient of decreasing pH, decreasing hardness, and F) remains unexplained at the 77 and 51 sampling increasing depth and, to a lesser degree, increasing points. width (Figure 3a). The second axis orders species along a gradient of decreasing suspended solids, increas- Ordination method ing current velocity, increasing distance to the main channel and increasing transparency. In light of these The relative lack of agreement between the chorotypes results, S. trutta and R. arcasii are represented mainly obtained using the two data sets, and the relatively poor in narrow streams remote from the main channel, not predictive ability of the logistic regressions recommend very deep, with high pH, high hardness, high current the use of an ordination method, namely CCA, to put velocity, high transparency and few suspended solids all species in a continuous environmental perspective. (Figure 3b). Leuciscus carolitertii and C. vettonica are Barbus comizo and C. calderoni were excluded from located in large streams that are very deep, with low pH the CCA because of their low occurrence. The rela- and low hardness. Cobitis paludica, R. lemmingii and tionships established by the four CCAs performed were L. pyrenaicus occur mainly in streams close to the main all statistically signiﬁcant according to the permutation channel with high suspended solids, high hardness, low test (Table 5). transparency and low current velocity. Other species do 380

Figure 3. First two axes of a CCA ordination of environmental variables (a) and of species (b). No covariables are included.

Figure 4. First two axes of a CCA ordination of environmental variables (a) and of species (b). Environmental variables are explanatory variables and spatial variables are covariates. not display a marked relationship with environmental tion. From Figure 4a, it appears that distance to main variables correlated with the first and second axes. channel, current velocity, depth and width contribute A CCA between species data and environmen- little to the first and second axes, suggesting that these tal variables, using spatial variables as covariates, variables are spatially structured. Along the first axis, accounts for 19% of the variation in species distribu- species are ordered by decreasing aquatic vegetation, 381

Figure 5. First two axes of a CCA ordination of species with spatial variables as explanatory variables with no covariables (a) and when environmental variables are included as covariables (b).

Table 5. Main results of the different CCAs performed.

Explanatory variables Covariables % explained p Environmental None 42.1 < 0.001 Environmental Spatial 18.7 < 0.001 Spatial None 41.0 < 0.001 Spatial Environmental 18.5 < 0.001

decreasing mineral oil, increasing suspended solids and and R. arcasii, which occur mainly in the upper right increasing hardness. Along the second axis, species are part of the basin, (2) L. carolitertii and C. vettonica ordered by increasing mineral oil, increasing hardness which also have a restricted distribution, (3) C. palu- and decreasing pH. Ordination of species is mainly dica, R. lemmingii and L. pyrenaicus with a more cen- explained by physico-chemical variables and vege- tral distribution not overlapping with distribution of the tation. Variables linked to river morphology (width, second group, and (4) B. bocagei, T. alburnoides and depth, current velocity) are correlated with spatial Ch. polylepis, which have a broad distribution with no structure. Some species are associated in different ways geographical trend. to environmental variables, after removing the effect of When environmental variables are integrated in the spatial structure (Figure 4b). For instance in the ﬁrst CCA as covariates, spatial variables account for 18% of analysis S. trutta and R. arcasii were close to each the variation in species distribution (Table 5). Species other in the diagram (Figure 3b), but they are distant ordination along axes 1 and 2 (Figure 5b) leads to the in Figure 4b. recognition of the same groups as above, suggesting A CCA between species data and spatial vari- that species distribution is not completely explained ables accounts for 41% of the variation in species by spatial variation of environmental data. This agrees distribution. From the ordination (Figure 5a) four with the existence of signiﬁcant associations of species groups of species can be distinguished: (1) S. trutta in Figure 2, due to similar spatial distribution but which 382 are poorly explained by environmental variables in Table 4, specially when only physical variables are considered. The percentage of the total variation in the species matrix accounted for by each component was 18.7% by non-spatial environmental variation, 23.4% by spatially structured environmental variation and 18.5% by pure spatial variation. The remaining 39.4% was unexplained.

Sampling point grouping analysis

Figures 6 and 7 show the UPGMA dendrograms for the 51 and 77 sampling points, respectively. We distinguished in both cases the same four strong boundaries along the Spanish Tagus basin (Table 6), which define five biotic groupings of sampling points with similar biota. Logistic regression, performed to characterize the biotic regions, yielded the results shown in Table 7. When considering only physical variables, the first strong biotic boundary (A and A0) separates a group of sampling points where the water was more transpar- ent and the river narrower. When chemical variables are also taken into account, this biotic boundary segregates the points with a higher pH and a higher water transparency. The second biotic boundary (B and B0) cannot be characterized environmentally by the variables used here. The third biotic boundary (C and C0) is only characterized when chemical variables are also considered, and separates the points with lower pH that, within the points with lower pH, are located further from the main channel. The last biotic boundary (D and D0), which segregates sampling points 19 and 24, is characterized by low water speed and high hardness.

Figure 6. Significant biotic boundaries between the 51 sampling Discussion points with chemical and physical variables available. Scale indicate similarity level (S = strong boundary; ∗∗∗ = p<0.005). The chorotypes of freshwater fishes The strong segregation between R. arcasii, S. trutta The results from the two data sets are consistent for and C. calderoni and the rest of species (Figure 2b) 9 species, but differ for R. lemmingii, R. arcasii and reflects two groups of species that are distributed S. trutta. This relative lack of agreement indicates through two different regions of the Tagus basin. that chorotypes are fuzzy sets (Zimmerman 1985), According to the results of the logistic regression and which are sensitive to sample size, number of elements CCA these two regions can be related to the upper and classified and the geographical scale analysed, but lower courses of the basin (streams and tumbling waters notwithstanding reflect natural phenomena. In this way, of big rivers and highland small tributaries versus low- fuzzy set theory provides a theoretical basis for the land rivers) since these chorotypes are explained by interpretation of our results. variables with gradients along the basin. 383

Ch. polylepis, B. bocagei and T. alburnoides, from the remaining species. Cobitis vettonica and L. carolitertii present a distribution range limited to the basin of the River Alagon´ and its tributaries (Doadrio & Perdices 1997) in the western zone of the Spanish Tagus, where they are rather common. The other three species present a larger distribution range through the lower Tagus basin (Doadrio et al. 1991) and are less influenced by environmental and spatial variables than the other species (Figure 3), therefore they can be considered generalist species. The environmental variables that best characterize the chorotypes formed by these five species (chorotypes I and A) are typical of lower courses of the rivers (See Table 4). Cobitis paludica, L. pyrenaicus and R. lemmingii (chorotype II) are more restricted in distribution and usually prefer typical lower-course stretches where the current is slow or forms pools. There they may be locally abundant, probably due to better adaptation to dry periods, which allows them to compete favourably for food (Kolasa 1989, Magalhaesˆ 1993). Their coex- istence is facilitated by their lack of competition, since they are small sized, exploit different preys and utilize different depths within the water column (Rodriguez- Jimenez 1987, Doadrio et al. 1991). This chorotype is absent from the Alagon´ River basin, where C. paludica and L. pyrenaicus are replaced by their sister species C. vettonica and L. carolitertii (Coelho et al. 1996, Doadrio & Perdices 1997). Barbus comizo segregates from the rest of the species in the lower course (chorotype III/C) likely due to its scarce presence within the basin. This species prefers wider stretches probably because of its large size and piscivorous habits, since wider stretches have a higher abundance of preys (Hugueny 1990). Regarding species inhabiting the upper courses, C. calderoni is segregated from the rest of the species. No variable studied here explains its distribution, which is restricted to some points in the north-eastern area of the basin (Doadrio 1981, Doadrio et al. 1991). Histori- cal events, such as fluvial captures, might be the causal factor of this distribution pattern. Salmo trutta and R. arcasii are found together in the headwaters, and usually they are the only species Figure 7. Significant biotic boundaries between the 77 sampling present. The variables that characterize their distribu- = points. Scale indicate similarity level (S strong boundary; tions are related to the upper course of the river for both ∗∗∗ = p<0.005). data sets. Regarding the species distributed along the lower In most cases, when physical and chemical vari- course of the river, a strong segregation separates ables are considered together, logistic regression firstly the chorotype comprising L. carolitertii, C. vettonica, displayed the chemical ones, thus chemical variables 384

Table 6. Signiﬁcant biotic boundaries between groups of sampling points on the dendrogram forks of Figure 3 (a) and Figure 4 (b). Numbers in each group correspond with species code listed in Table 1. GW and GS indicate a weak boundary and strong boundary between the groups, respectively (∗ = p<0.05; ∗∗ = p<0.01; ∗∗∗ =: p<0.005). DW(AxA) and DW(BxB) quantify the internal homogeneity of each group analysed.

Groups set up by UPGMA Aggregation Segregation weak strong Group A Group B Coefﬁc. DW(AxA) DW(BxB) GW P GS P a 19–24 13–20 0.496 0.659 0.258 28.836 *** 18.389 *** 6–12 16–37 0.485 0.202 0.185 11.413 *** 38.121 *** 55–60 19–20 0.443 0.270 0.645 480.679 *** 109.088 *** 6–37 55–20 0.226 0.294 0.144 336.850 *** 733.650 *** b 19–24 32–50 0.490 0.664 0.285 18.916 *** 8.647 *** 59–56 19–50 0.458 0.704 0.270 232.138 *** 16.945 *** 6–12 15–37 0.335 0.348 0.155 13.038 *** 31.714 *** 6–37 59–50 0.247 0.189 0.183 182.018 *** 300.253 ***

Table 7. Environmental characterization by logistic regression of the biotic boundaries obtained between the 77 sampling points (a) and between the 51 sampling points (b) with complete environmental information available. Variables code displayed in the ecuations correspond with those listed in Figure 1.

Boundary Regression equation Model sig. Percent correct Absences Presences a A y =−2.599 + 0.9642TRA − 0.2733WID 0.001 91.38 57.89 D y = 2.8247 − 0.7868SPE 0.005 52.63 82.05 b A0 y =−20.9758 + 2.22PHH + 0.896TRA 0.001 94.59 57.14 C0 y = 16.5235 − 2.699PHH + 0.0266DIS 0.001 87.50 69.08 D0 y =−4.1269 + 0.0275HAR 0.05 95.45 50.00

better account for the distribution of the chorotypes geographical coordinates of the samples. This can be than physical variables. This is opposite to results the result of two processes: some environmental vari- obtained by other authors (Freeman & Grossman 1993, ables have a spatial structure and as a result their effect Hutchison 1993, Pusey et al. 1993). The results from is confounded by the effect of geography; and/or one CCA show that chemical variables are less affected by (or more) variable, not included in the analyses, is cor- a spatial component than physical variables. However, related both with geography and some spatially struc- there is a relationship between high pH values and the tured environmental variables. The results suggest that sampling points located at the eastern end of the basin variables linked to river morphology (width, depth, cur- because the eastern Tagus River and its main tributaries rent velocity and transparency) have a latitudinal or lon- flow through limestone areas that increase the hard- gitudinal component, which is to be expected as rivers ness and the pH of the water. The western course of in the study area flow mostly in the same westward the Tagus River flows through the Hesperic landmass, direction. Obviously, in lower parts of rivers, where the oldest region of the Iberian Peninsula with a meta- the slope is smaller, width and depth increase while morphic geology in which the pH swiftly decreases, current velocity and transparency decrease. producing a relatively abrupt change in pH along the Nevertheless, some chorotypes are not explained river (Arenillas & Saenz´ 1987). with the variables used here, and nearly 40% of varia- We found that more than half of the variation in the tion in the presence of species remains unexplained by species matrix accounted for by environmental data both environmental and geographical variables. Some can also be predicted by the supplied function of the other variables, unfortunately not available here like 385 maximum summer water temperature, could be impor- the RCC to the Tagus River, although oviously, a lon- tant to explain stream fish distribution. Nevertheless, gitudinal analysis of the river is needed to test properly the absence of data on variables like water temperature its applicability. is minimized by data of some other directly related, for The cluster analysis shows that the hydrographic instance, disolved oxygen, pH and hardness. basin of the Tagus River is divided into three Moreover, the unexplained 40% of variation in the main regions according to its freshwater fish fauna presence of species suggests that historical factors and (Figures 6 and 7). The first biotic boundary (A) sep- biotic relationships may play a major role in the dis- arates a set of sampling points characterized by the tributions of freshwater fishes in the Tagus River, as presence of C. calderoni, S. trutta and R. arcasii, which it has been shown in other rivers (Gilliam et al. 1993, may be considered as the upper course of the basin. The Hutchison 1993, Strange et al. 1992). These factors rest of sampling points may be considered as the lower may be relevant to the distribution of the environmen- course of the basin. This is a strong boundary and it is tally unexplained chorotypes VI, D and F. explained by variables that vary from upper to lower Because 18% of the variation in the species matrix course such as river width, water transparency and pH can be predicted from pure spatial structure, we con- (Table 7). sider that historical factors (such as those leading to The nine species inhabiting the lower Tagus are endemicity) are important in explaining species distri- endemic to the Iberian Peninsula and are threatened bution and/or that there are contemporary factors with a by human uses of water for irrigation, construction geographical variation (such as climate or topography) of dams, water pollution, and introduction of exotic shaping fish communities in the study area. fishes. Fish species composition in the upper basin is more affected by direct management of fish fauna, for The biotic regions of the Spanish Tagus basin example Rutilus arcasii is actively suppressed due to its supposed predatory effect on juvenile trouts, whereas Our broad conclusion is that the Spanish Tagus River Salmo trutta is fished and bred with foreign stocks for basin does not constitute an homogeneous biogeo- intensive fisheries. graphical unit. Fish communities along an upstream– Biotic boundary C separates the majority of the sam- dowstream gradient and within the lower course of the pling points established along the Tagus basin, mainly river are more discrete than those reported for other inhabited by the species of chorotypes I and II, from European rivers (Balon et al. 1986, Lelek 1989, Boet¨ the basin of the Alagon´ River (Figure 1), which is et al. 1991, Penczak et al. 1991, Oberdorf & Hughes characterized by the presence of a local endemism 1992, Oberdoff et al. 1993), and therefore, different (C. vettonica) and a species typical of the Duero basin biotic regions can be distinguished according to indige- (L. carolitertii). This biotic boundary is explained by nous fish composition. the pH and the distance to the main course on the 51 The headwater stretches of the Tagus River basin are sampling points analysis, but remains unexplained on less rich in fish species than the lower course stretches, the 77 sampling points analysis (Table 7). It can be a feature that is well known in temperate rivers of higher assumed that historical factors are also important in latitudes (Mahon 1984, Balon et al. 1986, Penczak & explaining this region because there is a substitution of Mann 1990, Oberdorff et al. 1993) for which the River species of the same genera. Cobitis vettonica is prob- Continuum Concept (RCC) was developed (Vannote ably the most threatened species of this area due to its et al. 1980, Minshall et al. 1985). The RCC suggests restricted distribution. a gradual replacement in fish communities and a pro- Two other strong biotic boundaries were detected gressive increase of species richness with a maximum (Figures 6 and 7). Boundary B separates some points located in the medium course, where the higher num- of the high course where also L. pyrenaicus exists. ber of available microenvironments allows for greater These points constitute a minimum zone of transition fauna diversity. Contrary to the predictions, we found between the upper and the lower courses and cannot a complete species replacement as regards the upper be considered as a true biotic region within the basin. course which is clearly defined by boundary A. More- Biotic boundary D separates two points where only B. over, differences in fish communities between close bocagei was recorded, and we do not consider them to points within the lower course were noteworthy, and constitute a region in a biogeographic sense. two biotic regions are defined there by boundary C. In short, the Tagus River basin cannot be consid- The lack of graduality detected in a large framework ered a homogeneous biogeographical unit on the basis could indicate some questions to the applicability of of fish distribution. Three main biogeographical areas, 386 delimited by significant boundaries, can be identified. Boet,¨ P., P.Allardi & J. Leroy. 1991. Le peuplement ichtyologique Two of them are related to the upper and the middle- du bassin de l’Yonne. Bulletin Francaiçe du Pisciculture 320: lower basins of the Tagus River catchment and the third 7–28. corresponds to the Alagon´ River and seems to be linked Borcard, D., P. Legendre & P. Drapeau. 1992. 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