RIVER RESEARCH AND APPLICATIONS
River Res. Applic. 18: 123–136 (2002)
Published online in Wiley InterScience
(www.interscience.wiley.com). DOI: 10.1002/rra.638
SPATIAL MODELLING OF FRESHWATER FISH IN SEMI-ARID RIVER
SYSTEMS: A TOOL FOR CONSERVATION
A. F. FILIPE,a I. G. COWXb and M. J. COLLARES-PEREIRAa*
a
Centro de Biologia Ambiental/Departamento de Zoologia e Antropologia, Faculdade de Ciências, 1749-016 Lisboa, Portugal
b Hull International Fisheries Institute, University of Hull, HU6 7RX, UK
ABSTRACT
This paper examines the feasibility of using multivariate statistics to model fish species distribution and habitat requirements for intermittent streams in semi-arid regions, many of which are coming under increasing pressure from water
resource development schemes. The assessment was based on the geographical distribution of six endemic fish species in
the Guadiana river, a semi-arid river system in southern Iberia. Their presence was related to 20 environmental variables
linked to climate, geomorphology, riparian vegetation and location in the drainage basin. These variables were collected
in the field or from topographical maps to evaluate habitat suitability and to predict the presence of the species according
to season. Multivariate logistic regression in a geographic information system (GIS) environment was performed to identify regions with high probability of occurrence for each species. The variables that best explained the occurrence of
the species were the sample location in the drainage basin, the geomorphology and the riparian vegetation. The models
presented have a high predictive power and can be used in monitoring and predicting temporal changes caused by human
activities. This modelling approach can be used to predict the areas that need to be conserved to protect or rehabilitate
the endangered species. Armed with this information, managers can formulate conservation measures to prevent further
degradation of the stocks and possibly enhance the populations. Copyright  2002 John Wiley & Sons, Ltd.
KEY WORDS:
modelling distribution; landscape variables; freshwater fish; conservation; intermittent streams
INTRODUCTION
Intermittent river systems are characteristic of many semi-arid regions, and they are coming under increasing
pressure from water resource development schemes. As a result, there is growing concern about conservation
of the ecological integrity of the systems being targeted. Unfortunately, there is a paucity of information
about the ecology of these rivers, and the factors that regulate the distribution and abundance of the flora
and fauna (Davies et al., 1994). This information is crucial if the fish communities are to be managed from
a catchment-wide perspective.
The use of multivariate statistics to model species distribution and habitat requirements has increased in
the past twenty years with a wide variety of techniques. In particular, regression models have been used
widely to predict species distribution, abundance and habitat preferences (e.g. Walker, 1990; Pereira and
Itami, 1991; Bustamante, 1997; Monkkonen et al., 1997; Massolo and Meriggi, 1998; Brito et al., 1999;
Mladenoff et al., 1999). When linked to the geographic information system (GIS) environment it has been
applied for mapping ecological factors (Buckland and Elston, 1993), but studies on freshwater fish are still
very rare (e.g. Evans et al., 1998; Torgersen et al., 1999). As predicted by Margalef (1968), presence/absence
data allow the detection of macro-scale patterns in community ecological studies.
The aims of this study were to assess the feasibility of using multivariate logistic regression in a GIS
environment to quantify the macro-scale factors affecting fish habitat use and habitat suitability in semi-arid
river systems. The assessment was based on a case study in the Guadiana River in Portugal. It is a typical
intermittent system of southern Iberia, with high intra- and inter-annual flow variability, facing considerable
* Correspondence to: M. J. Collares-Pereira, Centro de Biologia Ambiental/Departamento de Zoologia e Antropologia, Faculdade de
Ciências, 1749-016 Lisboa, Portugal. E-mail: [email protected]
Copyright  2002 John Wiley & Sons, Ltd.
Received 20 September 2000
Revised 25 January 2001
Accepted 25 January 2001
124
A. F. FILIPE, I. G. COWX, AND M. J. COLLARES-PEREIRA
changes from water resource schemes and other human activities (Collares-Pereira et al., 1998, 2000). Many
of the native freshwater fish of this river system are endemic, and their conservation status is of great
concern. Consequently, there is an urgent need to assess the distribution of the endemic species and the
factors responsible for regulating their occurrence in different parts of the catchment. Furthermore, if the fish
communities are to be conserved, there is a need for information on macro-scale factors affecting distribution
in such river systems.
METHODS
Study area
The lower Guadiana River Basin is in southern Portugal. The area is delimited in the north by Portalegre
and in the south by the mouth of the main river, near Vila Real de Santo António. It has an area of 11 700 km2
(17% of the overall catchment area) and is located at latitude 37° to 39° 30 N and longitude 7° to 8° W. The
Guadiana River originates in the Ruidera Lagoons (Spain) at 1700 m and flows over 810 km to the Atlantic
Ocean, with 550 km in Spanish territory, 110 km along the border and 150 km in Portugal. Some 85 km
upstream from the mouth of the Guadiana there is a natural barrier which impedes migration of fish, apart
from eels (Anguilla anguilla (L.)), except at very high flows. The mean human population density for the
region is 28 inhabitants km−1 , but with a generally higher concentration in the north (INAG/COBA, 1995).
The study area has a north–south orientation, with variation in altitude from about 500 m down to sea
level. The geology is schist-derived, highly impermeable, and with little groundwater resources. The catchment
experiences a typical Mediterranean climate, i.e. long, warm summers with almost no rain, and mild, wet
winters (80% of total annual rainfall); the inter-annual variation in precipitation is large, with series of wet
and dry years. The hydrological regime of the rivers, especially of the smaller tributaries in the south, is
intermittent, with the rivers being reduced to permanent pools or drying up completely (Collares-Pereira
et al., 1998; Bernardo and Alves, 1999; Pires et al., 1999). This regime is mainly dependent on climatic
conditions and riparian vegetation, but an increasing demand for water resources in recent years has modified
flows both in Portugal and Spain.
Fish community
The Guadiana River Basin is considered to have the most diverse fish fauna in Portugal. From the 31
species listed, 19 are freshwater fish species. The lower part of the basin is dominated by two primary species:
Leuciscus alburnoides Steindachner 1866 complex and Barbus steindachneri Almaça, 1967. There are nine
other native species: Anaecypris hispanica (Steindachner, 1866), Chondrostoma willkommii Steindachner,
1866, C. lemmingii (Steindachner, 1866), Leuciscus pyrenaicus Günther, 1868, Barbus microcephalus Almaça,
1967, B. comizo Steindachner, 1865, B. sclateri Günther, 1868, Cobitis paludica (De Buen, 1930) and Salaria
fluviatilis (Asso, 1801)). There are also eight exotic species: Esox lucius L., 1758, Fundulus heteroclitus (L.,
1766), Lepomis gibbosus (L., 1758), Micropterus salmoides (Lacépède, 1802), Gambusia holbrookii Girard,
1859, Cichlasoma facetum (Jenyns, 1842), Cyprinus carpio L., 1758 and Carassius auratus (L., 1758) (Cowx
and Collares-Pereira, 2000).
Data collection
Sampling was performed between November 1997 and July 1998. A total of 306 samples were taken from
149 sites dispersed along the main River Guadiana and its tributaries: 20 sites were sampled on a bimonthly
basis, 41 every four months and the remainder only once. Each site was 60 m long and was UTM (Universal
Transverse Mercator) georeferenced (Figure 1). At each site the fish community structure was evaluated by
electric fishing (DC at 300/600 V and 4–6 A). Fish were identified to species level except for juveniles
(<10 cm) of the genus Barbus because they are difficult to discriminate in the field. All fish were returned
live to the river.
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
125
MODELLING FISH DISTRIBUTION IN SEMI-ARID RIVERS
PORTALEGRE
A
B
1
N
Caia R.
Elyas
Guadiana R.
2
ÉVORA
3
SPAIN
Degebe R.
Ardila R.
PORTUGAL
Moura
BEJA
Chança R.
Mertola
4
Location
Water course
Site
10 km
5
6
V.R. Sto António
Figure 1. Location of Guadiana River Basin in the Iberian Peninsula (A) and location of sample sites in the different rivers of the
Portuguese sector (B). Reservoirs: 1, Caia; 2, Lucefecit; 3, Monte Novo; 4, Chança; 5, Odeleite; 6, Beliche
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
126
A. F. FILIPE, I. G. COWX, AND M. J. COLLARES-PEREIRA
Table I. Environmental variables used in data analysis with indication of description and unit of measurement, classes
that were used, representative value and source
Variable
Description
Class
Value
Source
580–599
600–619
620–639
640–659
660–679
1
2
3
4
5
Mapa
1 : 25 000
Location in the drainage basin
EST
Distance to Greenwich
Meridian UTM (1 km)
DGU
Distance to the main river (km)
0–9
10–19
20–29
30–39
40–49
>50
1
2
3
4
5
6
Mapa
1 : 25 000
TRI
Distance between tributary
mouth and Guadiana mouth
(km)
0–49
50–99
100–149
150–199
>200
1
2
3
4
5
Mapa
1 : 250 000
ORD
Stream order
≤3
4
5
≥6
1
2
3
4
Mapb
1 : 100 000
Climate
INS
Average annual insolation (h)
< 2899
2900–2999
>3000
1
2
3
Mapc
(1931–1960)
PRE
Average annual precipitation
(mm)
400–499
500–599
>600
1
2
3
Mapc
(1931–1960)
TEM
Average annual temperature
( ° C)
15.0–16.4
16.5–17.4
>17.5
1
2
3
Mapc
(1931–1960)
FLO
Average annual run-off (mm)
50–99
100–149
>150
1
2
3
Mapc
(1931–1960)
ELE
Elevation (m)
0–49
50–99
100–149
150–199
200–249
>250
1
2
3
4
5
6
Mapa
1 : 25 000
ROC
Rock type (lithology)
Sedimentary rocks
Schist rocks
Volcanic rocks
1
2
3
Mapc
SLO
Longitudinal slope
0–0.1
0.2–0.3
0.4–0.5
>0.6
1
2
3
4
Mapa
1 : 25 000
Geomorphology
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
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MODELLING FISH DISTRIBUTION IN SEMI-ARID RIVERS
Table I. (Continued )
Variable
Description
Class
Value
Source
VAL
Valley steepness (number of contour
lines in a perpendicular line to the
site in 500 m)
0–2
3–5
6–8
9–11
12–18
1
2
3
4
5
Mapa
1 : 25 000
NFL
Number of afferent streams
<3
3–4
5–6
7–8
9–11
>11
1
2
3
4
5
6
Mapa
1 : 25 000
WID
Median width of river bed (m)
0–9
10–19
20–29
30–39
>40
1
2
3
4
5
Fieldwork
TRE
Arboreal cover (%)
0
1–24
25–49
50–100
1
2
3
4
Fieldwork
BUS
Bush cover (%)
0
1–24
25–49
50–100
1
2
3
4
Fieldwork
PDE
Population density in the municipality
(no. inhabitants km−2 )
0–29
20–49
>50
1
2
3
Mapc
(1931–1960)
DHO
Distance to nearest house (m)
0–199
200–399
400–599
600–800
1
2
3
4
Mapc
1 : 25 000
ROA
Type of nearest road
Unmetalled
Municipal
Highway
1
2
3
Mapd
DAM
Dam capacity >0.9 hm3 within
60 km of the site
Without dam
With upstream dam
With downstream dam
1
2
3
Map a
1 : 250 000
Riparian vegetation
Human impact
= Army Cartographic Institute.
= Portuguese Institute of Cartography and Cadraste.
c CAN = Environmental National Commission: Environment Atlas (1983).
d ACP = Portugal Automobile Club, 90th edition.
a IGE
b IPCC
The topographical and environmental variables were collected at each site or from maps (see Table I for
scale). For each site, twenty macro-scale variables, exhibiting no seasonal variation, and thus likely to have
predictive value in models of fish distribution, were considered (Table I): four relate to location in the drainage
basin, four characterize the climate, six describe the geomorphology, two relate to riparian vegetation and
four relate to human impact. All variables were converted into classes.
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
128
A. F. FILIPE, I. G. COWX, AND M. J. COLLARES-PEREIRA
DATA ANALYSIS
Temporal distribution of fish species
This study concentrated on four highly threatened cyprinid taxa (Anaecypris hispanica, Barbus microcephalus, Chondrostoma willkommii and C. lemmingii ) and two more common Iberian species (Leuciscus
pyrenaicus and Cobitis paludica) which are characteristic of the fish community (SNPRCN, 1991; CollaresPereira et al., 1998, 1999, 2000). Due to the lack of information about migration patterns of these species,
temporal variation in occurrence was included in the analysis. This was based on two seasons, a wet and a dry
season, which were discriminated by average monthly precipitation and temperature at five meteorological
stations (Figure 2). The wet season was considered to be from November 1997 until March 1998 and the dry
season from March until the end of the sampling period. Jaccard’s similarity index was used to discriminate
seasonal variation in occurrence for each species. The similarity between the presence/absence of each species
in different time intervals was compared. This index was chosen because it does not count double-absences
and it smoothes the effect of rare species. The species which exhibited seasonal variation in distribution were
those with a Jaccard similarity index <0.6 (Fausch and Bramblett, 1991; Lohr and Fausch, 1997).
Model construction
Multivariate logistic regression was used to determine the effect of environmental factors on the presence/absence of each species and to calculate probability of occurrence because it is capable of using
categorical and non-normally distributed data and also continuous and/or normally distributed data (Hosmer
and Lemeshow, 1989). The aim of this technique is to find a parsimonious model within sound limits of
statistical and biological validity (Hosmer and Lemeshow, 1989; Trexler and Travis, 1993). The association
between the explanatory variables and interactions with the presence/absence of the species was tested using
the maximum likelihood method (Hosmer and Lemeshow, 1989).
Logistic regression is sensitive to extremely high correlations between variables that are supposed to be
independent (Trexler and Travis, 1993; Tabachnick and Fidell, 1996). Correlation between variables was
eliminated by retaining only the variable with the highest explanatory power for pairs of variables with
Kendall’s tau-b correlation coefficient (Siegel and Castellan, 1988) r > 0.70.
For each model, a data matrix was built based on a subset of all sites surveyed in the programme. The
selection of sites for the matrix was based on providing between 40 and 60% of sites where the target
species occurred out of the total number of sites selected. The samples eliminated were chosen randomly
(Table II). These subsets were used to confront analytically the environmental factors that influence the
300
Precipitation (mm)
150
Average monthly precipitation
Average monthly temperature
125
250
100
200
75
150
50
100
50
25
0
0
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Temperature (ºC)
350
Jul
Figure 2. Gausen’s ombrometric diagram based on precipitation and air temperature of 1997 and 1998. When precipitation falls below
the temperature line this indicates the dry period
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
129
MODELLING FISH DISTRIBUTION IN SEMI-ARID RIVERS
Table II. Total number of sites (‘Sites’) and number of sites where the species occurred (‘Present’) that
were used in each model
Species
Wet season
Sites
Present
Chondrostoma lemmingii
Chondrostoma willkommii
Anaecypris hispanica
Barbus microcephalus
Leuciscus pyrenaicus
Cobitis paludica
104
75
59
32
51
34
23
13
Dry season
Sites
Present
98
72
48
50
Both seasons combined
Sites
Present
42
31
19
20
110
100
63
75
presence/absence for both rare and common species. The linearity of the presence of a species with each
variable was checked using the Mantel–Haenszel test at a significance level of 0.05. The non-linear variables
were coded as categorical variables (dummy variables) and linear variables were treated as continuous (Hosmer
and Lemeshow, 1989).
In the multivariate analysis a stepwise backward selection procedure was applied to each selected variable
with a probability of entry of 0.15 and removal of 0.20 (Hosmer and Lemeshow, 1989; Tabachnick and Fidell,
1996). In this step-by-step selection, the addition and exclusion of variables was based on Wald’s test and
assessment of correlation was based on differences in the coefficients estimated when a variable is added to
the model and from partial correlation of the estimated coefficients for P < 0.001 (Zar, 1996). The interaction
terms were also modelled and those contributing significantly (G-test) to the model were retained (Hosmer
and Lemeshow, 1989). The type and degree of association of each variable with the presence of the species
was determined using the odds ratio (ψ).
To assess the fit of each model, the G-test and a classification table (Tabachnick and Fidell, 1996) was used.
The G-test examined the deviance of the model with the constant versus the final model and rejection was at
P < 0.05 significance based on a chi-squared distribution (Tabachnick and Fidell, 1996). For constructing the
classification table, the probability interval given by the logistic regression was transformed to a binary variable
(presence/absence), and the cut-off points (ranging between 0.4 and 0.5) that maximized the percentage of sites
correctly classified were chosen. All the statistical analyses were performed using SPSS v7.0 for Windows
package.
The probability of occurrence was calculated from the logistic regression models. Variable maps were built
in Corel PHOTOPAINT v3.0, after being treated in IDRISI v9.0 for Windows Geographic Information System
environment (Eastman, 1995), to obtain the probability of occurrence with a class amplitude of 0.1.
RESULTS
Distribution of fish species
The distribution of the most common species–Leuciscus pyrenaicus and Cobitis paludica –varied little
over the study period (Table III). The greatest differences were found in Anaecypris hispanica and Barbus
microcephalus, followed by Chondrostoma willkommii and C. lemmingii. These differences were associated
with the onset of the dry season in March when the flow declined (Figure 2).
The fish species examined were distributed throughout the study area and were found in all the larger
tributaries. Cobitis paludica, Leuciscus pyrenaicus, Chondrostoma lemmingii and C. willkommii were the
most frequently caught species (Table IV). The latter occurred mainly in the bigger tributaries in the north
of the study area. The most rare species was Anaecypris hispanica, which was not found in the main river
and only occasionally in some of the bigger tributaries, followed by Barbus microcephalus, which was most
abundant in the Ardila River and other larger tributaries. C. lemmingii was caught only once in the main
river during the dry period. All six species were found upstream of the dams on the Caia and Chança
Rivers. However, B. microcephalus and C. willkommii were not captured in the watercourses upstream of
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
130
A. F. FILIPE, I. G. COWX, AND M. J. COLLARES-PEREIRA
Table III. Jaccard measures for the similarity for each species between time intervals and number of sites
used in the calculation
Species
Nov.–Feb./Mar.–Jul.
Nov.–Mar./Apr.–Jul.
Nov.–Apr./May–Jul.
0.600
0.571
0.407
0.400
0.694
0.686
58
0.556
0.556
0.357
0.250
0.660
0.731
58
0.667
0.593
0.500
0.438
0.738
0.791
50
Chondrostoma lemmingii
Chondrostoma willkommii
Anaecypris hispanica
Barbus microcephalus
Leuciscus pyrenaicus
Cobitis paludica
Number of sites used
Table IV. Percentage of occurrence of the species in the total of sampled sites
Species
Chondrostoma lemmingii
Chondrostoma willkommii
Anaecypris hispanica
Barbus microcephalus
Leuciscus pyrenaicus
Cobitis paludica
Number of sites
a Wet
b Dry
Percentage of occurrence
Nov.–Mar.a
Apr.–Julyb
Nov.–July
46.79
31.19
21.10
11.93
109
42.86
31.63
19.39
20.41
98
69.59
71.6
149
season.
season.
the Odeleite dam, and Anaecypris hispanica and B. microcephalus were absent from watercourses upstream
of Monte Novo dam on the Degebe River. Only L. pyrenaicus occurred in the watercourses upstream of the
Beliche and Lucefecit dams (Figure 1).
Probability of occurrence
In the first step of model construction, mean annual flow (FLO) was excluded because it was based
on records of precipitation and consequently had a high correlation with mean annual precipitation (PRE)
(Kendall’s tau-b correlation coefficient: r = 0.723, P < 0.001).
Fit and classification accuracy of the logistic regression models was high, indicating a strong predictive
power, despite being based on small samples (Table V). The average percentage of correct classification in
all models was high (76.1%) and the average percentage where a species was correctly classified as being
present was also high (76.2%). The lowest percentage of correct classification was for Anaecypris hispanica
in the dry season (70.8%).
The variables describing the distribution of Barbus microcephalus and Chondrostoma willkommii in the
logistic regression models were in contrast from those describing Anaecypris hispanica, C. lemmingii and
Leuciscus pyrenaicus (Table VI). The variables influencing the distribution of Cobitis paludica were again
different from the other species. The models showed that species occurrence was significantly related to
several variables of location in the drainage basin, especially stream order (ORD) and the distance to the
main river (DGU). These variables appeared in the models for both seasons in several species (Table VII).
No interaction terms contributed significantly to the models.
The maps showing probability of occurrence of B. microcephalus for the wet and dry seasons (Plate 1)
illustrate the capacity of these models to predict distributions in the study area. For this species, the main river
is an important area in both seasons (probability of occurrence >0.8). During the dry season, the probability
of occurrence decreased in the small tributaries, and the localities where the fish was not likely to be found
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
PORTALEGRE
PORTALEGRE
N 10 km
ÉVORA
ÉVORA
BEJA
BEJA
[0.0-0.1]
[0.1-0.2]
[0.2-0.3]
[0.3-0.4]
[0.4-0.5]
[0.5-0.6]
[0.6-0.7]
[0.7-0.8]
[0.8-0.9]
[0.9-1.0]
A
B
Plate 1. Probability of occurrence of Barbus microcephalus in the lower Guadiana River Basin (Portugal): (A) wet season;
(B) dry season
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: (2002)
131
MODELLING FISH DISTRIBUTION IN SEMI-ARID RIVERS
Table V. G-test and classification rate for all the logistic regression models
Species
Season
G-test
Cut-off-point
C. lemmingii
C. willkommii
A. hispanica
B microcephalus
65.36∗
24.28∗
16.55∗
27.40∗
18.88∗
13.31∗
16.22∗
27.53∗
42.16∗
31.90∗
Wet
Dry
Wet
Dry
Wet
Dry
Wet
Dry
L. pyrenaicus
C. paludica
Average
Classification rates
%CCT
%CCP
0.4
0.5
0.4
0.5
0.4
0.4
0.4
0.4
0.5
0.5
78.2
71.1
72.0
75.0
75.0
70.8
83.3
82.0
79.0
75.0
76.1
84.8
61.9
63.3
75.9
75.9
73.7
80.0
80.0
84.6
81.7
76.2
%CCA
72.7
78.2
77.8
74.4
74.4
69.0
84.2
83.0
70.0
63.9
74.8
% CCT, percentage of total correctly classified; % CCP, percentage of presences correctly classified; % CCA,
percentage of absences correctly classified.
∗ The models fit the data.
Table VI. Logistic regression models (variables coded as dummy are those where several variables
represent the various classes)
Variable
Chondrostoma lemmingii
Wet season
ELE
BUS
BUSclass2
BUSclass3
BUSclass4
SLO
INS
Constant
Dry season
DGU
TRE
TREclass2
TREclass3
TREclass4
Constant
Chondrostoma willkommii
Wet season
ORD
TRE
DAM
Constant
Dry season
INS
ROC
DGU
DGUclass2
SD (β)
Wald (P)
ψ
CI (ψ) 95%
1.223
0.330
3.397
1.974–5.850
−3.466
−1.878
−6.259
0.982
−1.525
−0.077
1.320
1.292
1.764
0.354
0.555
2.205
< 0.001
0.001
0.009
0.146
< 0.001
0.006
0.006
0.972
0.031
0.153
0.002
2.671
0.218
0.004–0.274
0.018–1.281
0.000–0.035
1.491–4.784
0.087–0.542
0.538
0.144
1.712
1.292–2.269
0.628
2.160
−0.324
−2.721
0.534
0.832
0.761
0.671
< 0.001
0.044
0.239
0.009
0.670
< 0.001
1.874
8.671
0.723
0.658–5.338
1.698–4.290
0.163–3.213
1.151
0.309
0.491
−4.197
0.380
0.252
0.499
1.150
0.002
0.220
0.325
< 0.001
3.160
1.362
1.635
1.500–6.657
0.831–2.234
0.615–4.348
1.3526
−1.5141
0.514
0.861
3.868
0.220
1.412–0.595
0.041–1.191
−2.800
1.376
0.008
0.079
0.143
0.042
0.061
0.004–0.902
β
(continued overleaf )
Copyright  2002 John Wiley & Sons, Ltd.
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132
A. F. FILIPE, I. G. COWX, AND M. J. COLLARES-PEREIRA
Table VI. (Continued )
Variable
DGUclass3
DGUclass4
DGUclass5
DGUclass6
ORD
Constant
Barbus microcephalus
Wet season
ORD
ELE
Constant
Dry season
SLO
TRI
TRIclass2
TRIclass3
TRIclass4
TRIclass5
ORD
Constant
Anaecypris hispanica
Wet season
DGU
SLO
Constant
Dry season
TRI
DGU
NFL
BUS
BUSclass2
BUSclass3
BUSclass4
Constant
Leuciscus pyrenaicus
ORD
ORDclass2
ORDclass3
ORDclass4
ELE
BUS
DAM
DAMclass2
DAMclass3
INS
Constant
Cobitis paludica
NFL
NFLclass2
NFLclass3
NFLclass4
NFLclass5
NFLclass6
SD (β)
Wald (P)
ψ
CI (ψ) 95%
−1.586
−0.973
−1.086
0.683
0.286
−1.040
1.051
1.071
1.127
1.101
0.328
2.565
0.131
0.364
0.335
0.535
0.384
0.685
0.205
0.378
0.337
1.980
1.331
0.026–1.605
0.046–3.085
0.037–3.073
1.980–0.229
0.699–2.534
1.821
−0.466
−2.518
0.728
0.472
2.509
0.012
0.323
0.315
6.176
0.627
1.481–25.748
0.249–1.582
−1.687
0.759
0.026
0.185
0.042–0.820
−1.083
3.091
1.327
−0.113
0.861
0.311
1.498
1.370
1.440
1.245
0.719
3.118
0.470
0.024
0.357
0.928
0.231
0.921
0.338
21.995
3.769
0.893
2.365
0.688
0.239
−3.867
0.187
0.301
1.133
< 0.001
0.425
0.001
1.990
1.271
1.379–2.872
0.705–2.289
−0.804
0.320
−0.391
0.391
0.201
0.259
0.447
1.377
0.677
0.208–0.963
0.929–2.041
0.407–1.124
−3.739
−2.683
−2.599
4.277
1.656
1.665
1.630
2.643
0.040
0.111
0.131
0.137
0.024
0.107
0.111
0.106
0.024
0.068
0.074
0.001–0.610
0.003–1.788
0.003–1.813
2.046
3.852
0.609
0.692
0.563
0.696
0.968
1.295
0.235
0.278
3.545
0.040
−0.322
−4.685
1.446
0.750
0.400
1.827
< 0.001
0.003
< 0.001
0.638
0.003
0.043
0.049
0.014
0.958
0.420
0.010
−0.570
0.943
−0.390
1.351
1.500
0.891
0.872
1.064
0.967
1.372
0.109
0.522
0.279
0.714
0.163
0.274
β
Copyright  2002 John Wiley & Sons, Ltd.
0.018–6.377
1.499–322.760
0.224–63.401
0.078–10.245
0.578–9.673
7.739
47.111
1.838
1.999
1.756
1.980–30.253
7.063–314.227
0.1451–23.275
1.260–3.169
1.018–3.030
34.635
1.040
0.724
2.037–588.915
0.239–4.522
0.330–1.587
0.565
2.568
0.667
3.859
4.482
0.099–3.240
0.465–14.176
0.084–5.451
0.579–25.712
0.305–65.943
River Res. Applic. 18: 123–136 (2002)
133
MODELLING FISH DISTRIBUTION IN SEMI-ARID RIVERS
Table VI. (Continued )
Variable
TRE
PRE
TRI
TRIclass2
TRIclass3
TRIclass4
TRIclass5
Constant
SD (β)
Wald (P)
−0.594
−0.889
0.297
0.441
2.660
0.262
0.220
1.668
2.341
1.195
0.671
0.993
0.934
1.197
0.045
0.040
0.078
0.026
0.695
0.825
0.074
0.051
β
ψ
CI (ψ) 95%
0.552
0.441
0.309–0.988
0.173–0.976
14.303
1.300
1.246
5.301
1.375–148.824
0.349–4.841
0.178–8.733
0.849–33.078
β, Estimated coefficients; SD (β), standard deviation of the estimated coefficients; Wald (P), p-value of Wald’s
test: ψ, odds ratio; CI (ψ) 95%, confidence interval of odds ratio 95%. Abbreviations for variables can be
found in Table I.
Table VII. Frequency of occurrence of the
variables in the models by species (number of
species for which an explanatory variable was
selected, max. = 5) and by model (total number
of times explanatory variable selected in two
seasonal models for each species, N = 10)
Variables
ORD
DGU
TRI
INS
ELE
SLO
TRE
BUS
NFL
DAM
PRE
ROC
TEM
WID
EST
VAL
PDE
DHO
ROA
Frequency
By species
By model
3
3
3
3
3
3
3
3
2
2
1
1
0
0
0
0
0
0
0
5
4
3
3
3
3
3
3
2
2
1
1
0
0
0
0
0
0
0
(probability of occurrence <0.5) increased. It is important to note that these probabilities apply to the river
channels only because the reservoirs were not sampled in this study.
The areas of species occurrence can be compared using the logistic regression models. Barbus microcephalus and Chondrostoma willkommii occurred in the rivers of high stream order (ORD). In the wet season
C. willkommii inhabits higher order rivers with high arboreal cover (TRE), and it may occur upstream of the
dams (DAM). In the dry season, it is found in areas with high annual insolation (INS) and low permeability
(SOI). Anaecypris hispanica and C. lemmingii have high probability of occurrence in small tributaries. During
the wet season, both species inhabit rivers with steep gradients (SLO), but the former inhabits areas far away
from the main river (DGU), and the latter inhabits areas with little arboreal cover (TRE), high altitude (ELE)
and low mean annual insolation (INS). In the dry season, both species exist in the tributaries, but the former
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
134
A. F. FILIPE, I. G. COWX, AND M. J. COLLARES-PEREIRA
occurs in areas with low riparian bush cover (BUS) and the latter with median cover of riparian trees (TRE).
Leuciscus pyrenaicus was found mostly in middle-order streams (ORD), at high elevation (ELE), low mean
annual insolation (INS), high bush cover in the margins (BUS) and downstream of dams (DAM). The Cobitis
paludica model was unstable and the variables had less significance than in the other species models. The
species is associated with areas with lower mean annual precipitation (PRE), many inflow channels (NFL)
and a low percentage of tree cover (TRE). For this species, the higher probability of occurrence was in the
rivers in the south of the study area (TRI).
DISCUSSION
The logistic regression modelling allowed the identification of a combination of variables that determined
species’ distributions. The output suggests that the occurrence of species in semi-arid streams can also
be explained to a reasonable degree of precision by landscape patterns, particularly by geomorphological
variables, as was also found by Milner et al. (1993), Paller (1994), Poff and Allan (1995) and Taylor et al.
(1996) in studies on freshwater fish in temperate river systems. This is an important finding because many of
these characteristics are fixed attributes and are relatively simple to obtain.
Chondrostoma lemmingii, C. willkommii, Anaecypris hispanica and Barbus microcephalus all shifted their
distribution in March at the end of the wet season. These movements are probably two-fold: (i) migration of
mature fish upstream to reproduce, followed by (ii) downstream dispersion, especially of larger individuals,
to avoid the harsh drought conditions and being caught in the poor water quality conditions experienced in
the few remaining permanent pools (Collares-Pereira et al., 1999; Pires et al., 1999). Cobitis paludica and
Leuciscus pyrenaicus seem to occupy the same areas throughout the year, suggesting individuals probably
do not move for the reasons given earlier. This finding indicates that in studies of this nature it is important
to select a period that covers all seasons (Doncaster et al., 1996), thus accounting for seasonal changes in
distribution of fish in relation to the intermittent flow regime.
The models proved relatively strong and explained a reasonable number of the factors responsible for the
presence of the six species studied. However, their interpretation in ecological terms can be problematic. In
Cobitis paludica, for example, the inclusion of a large number of variables suggests that none had an overriding
effect to explain the distribution of the species. Barbus microcephalus and Chondrostoma willkommii are
characteristic of higher stream orders (ORD), and their presence is more likely in the wet season. These
higher order streams have greater probability of experiencing continuous flow, even during drought periods,
compared to lower order streams which frequently dry up to isolated pools, and this might explain the presence
of these larger species. In contrast, Anaecypris hispanica, C. lemmingii and Leuciscus pyrenaicus are smaller
species that have positive associations with distance to the main river (DGU).
Variables relating to location in the drainage basin, e.g. stream order (ORD) and distance to the main river
(DGU), play a major role in discriminating the presence of fish species. Also geomorphological variables, e.g.
elevation (ELE), longitudinal slope (SLO) and vegetation cover of the banks (TRE and BUS), are important,
as found in other studies on fish distribution in the Guadiana (Godinho et al., 1997; Godinho and Ferreira,
1998). Their importance is probably linked to the role these variables play in describing the hydrological
characteristics of the river, thus providing favourable or non-favourable habitats. In this respect, the presence
of dams appears to restrict the distribution of all species, except Leuciscus pyrenaicus. This is probably
because the rivers upstream of reservoirs often dry up during the dry period and recolonization is dependent
on the species surviving the lentic conditions in the reservoirs.
The main problem that exists in the models in their current form involves the reliability of absence
observations. In the case of Barbus microcephalus, according to the logistic regression model there was a
high probability of occurrence in the main river throughout the year. The relative absence of B. microcephalus
in the electric fishing samples from the main river was probably a result of the poor sampling efficiency in
this kind of habitat (Zalewski and Cowx, 1990), and not a reflection of their true absence. This is supported by
local fishermen, who confirmed the presence of this barbel in many sites along the main river. Consequently,
assessment of the occurrence of fish species should not rely on sampling methods that may not provide a true
Copyright  2002 John Wiley & Sons, Ltd.
River Res. Applic. 18: 123–136 (2002)
MODELLING FISH DISTRIBUTION IN SEMI-ARID RIVERS
135
reflection of the distribution range. Another factor that needs to be taken into account in these intermittent
streams is that the fish may be forced to occupy unfavourable habitats when the river recedes and they are
stranded in permanent pools. This could help explain some of the variability unaccounted for in the models.
Perhaps the greatest value of logistic regression modelling is that it is possible to identify where the fish
species would be expected to be found but are absent. This will allow those responsible for the conservation of
the species to assess the reasons for the absence of the species, e.g. whether it is due to a shift in hydrological
regime through river regulation, obstruction to free movement of fish, pollution, or change in landscape use.
Armed with this information, managers can formulate conservation measures to prevent further degradation
of the stocks and possibly enhance the populations.
It is likely that interpretation of these models is precluded by the absence of basic ecological data on the
target species, so parallel studies to address this problem are necessary. Unfortunately, this may be difficult
for many endangered fish species, especially in semi-arid streams where few biological data are available and
their status prevents any further detailed studies into aspects such as the diet and reproduction ecology being
carried out. Notwithstanding, every effort should be made in studies of this nature to provide as wide a range
of data on distribution and abundance as is feasible. This will improve the predictive ability of the model and
improve decisions made regarding conservation management.
In conclusion, the models presented have a high predictive power in semi-arid river systems, and they
can be used in monitoring and predicting temporal changes caused by human activities and shifts in climate.
Furthermore, this modelling approach can be used to predict the areas that need to be conserved to protect
or rehabilitate the endangered species of the catchment. However, further research is needed, on wider
temporal and also spatial scales, to improve the predictability of the models. This should also include temporal
information about land use and land cover changes.
ACKNOWLEDGEMENTS
The authors thank Patrı́cia Tiago, Filipe Ribeiro, Tiago Marques, Luı́s Moreira da Costa, José Rodrigues and
Leonor Rogado for their constant help in the field work. This research was undertaken in the European Union
Project LIFE-Nature ‘A conservation strategy for Anaecypris hispanica’ (contract B4-3200/97/280).
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