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RESEARCH ARTICLE
Cumulative effects of land use on fish metrics in different types
of running waters in Austria
Clemens Trautwein
•
Rafaela Schinegger
•
Stefan Schmutz
Received: 16 December 2010 / Accepted: 7 July 2011
The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract The catchment land-use composition of 249
fish sampling sites in Austrian running waters revealed
effects on the biological integrity. Beyond correlative
analysis, we investigated (1) which land-use category had
the strongest effect on fish, (2) whether metrics of func-
tional fish guilds reacted differently, (3) whether there were
cumulative effects of land-use categories, and (4) whether
effects varied in strength across river types. We fed 5 land-
use categories into regression trees to predict the European
Fish Index or fish metric of intolerant species (mainly
Salmo trutta fario). Agriculture and urbanisation were the
best predictors and indicated significant effects at levels of
[23.3 and [2%, respectively. Model performance was
R
2
= 0.15 with the Fish Index and R
2
= 0.46 with intol-
erant species. The tree structure showed a cumulative
effect from agriculture and urbanisation. For the intolerant
species metric, a combination of high percentages for
agriculture and urbanisation was related to moderate status,
whereas \7.3% agriculture were related to good status,
although urbanisation was higher than 1.8%. Headwater
river types showed stronger responses to land use than river
types of lower gradient and turned out to be more sensitive
to urbanisation than agriculture.
Keywords Land use Fish Cumulative effect
Stream integrity Fish metrics IBI Moran’s I
Landscape composition
Abbreviations
EFT European fish type
CLC CORINE land cover
CRT Classification and regression trees
IBI Index of biotic integrity
MI Moran’s I index
Introduction
Land use and stream response
Rivers in the context of their catchments—also called
riverscapes—are considered to be ecosystems that are
strongly affected by human actions in the landscape (Allan
2004b). The guiding principle of much riverine research at
the landscape-scale is that human actions impact the
composition and function of aquatic organisms, e.g., fish.
Human alterations and impacts that directly affect the
physico-chemical conditions of running waters and
strongly influence the aquatic biota are referred to as
pressures in this study. Many, although not all, impacts on
streams are entirely or partly linked to human activities in
the landscape and thus can be quantified from data on land
use (Allan 2004a). A useful way to measure land use is to
assess landscape composition at the class level (Botequilha
Leitao et al. 2006).
A (higher) proportion of agriculture has been shown to
have detrimental effects on biota (Allan 2004b; Allan et al.
1997; Richards et al. 1996; Roth et al. 1996). Urbanisation,
impervious land cover and roads frequently have signifi-
cant impacts on rivers. The upstream drainage area for fish
sampling locations—referred to as catchment area—is
commonly understood as an important scale of
C. Trautwein (&) R. Schinegger S. Schmutz
Department of Water, Atmosphere and Environment,
Institute of Hydrobiology and Aquatic Ecosystem Management,
BOKU, University of Natural Resources and Life Sciences,
Max Emanuel-Strasse 17, 1180 Vienna, Austria
e-mail: clemens.trautwein@boku.ac.at
URL: http://www.wau.boku.ac.at/ihg.html
Aquat Sci
DOI 10.1007/s00027-011-0224-5
Aquatic Sciences
123
investigation. Even relatively small amounts of urbanised
areas within a stream catchment, e.g., \5%, have adverse
effects on stream integrity. Non-linearity in the relationship
between urbanisation and stream condition has been
reported by Gergel et al. (2002) and Miltner et al. (2004).
Linkages are related to altered hydrology (e.g., increased
peak surface runoff), altered sediment delivery patterns,
intrusion of pollutants and toxins, and habitat degradation
(Beechie et al. 2010).
Fish react to both chemical and physical water quality
and hydro-morphological conditions; therefore, fish are an
ideal indicator for multiple-impacted rivers (EC of Euro-
pean Parliament 2000, WFD). Fish are present in most
surface waters, they occupy a wide variety of riverine
habitats, are relatively easy to identify, and their taxonomy
and ecological requirements are well studied. Because of
their migration patterns and longevity, fish communities
reflect aquatic conditions over relatively large spatial and
long time scales (Pont et al. 2006).
Integrative measures of river condition, such as Indices
of Biotic Integrity (IBIs), are particularly useful for
assessing overall stream health because they integrate
multiple influences. IBIs are multi-metric indices based on
structural (taxonomic) and functional (species guilds and
traits) metrics (Karr 1981). However, Allan (2004a) argues
that multi-metric bioassessment methods may fail in
diagnosing causes of degradation because these indices are
constructed with the intention to reflect multiple stressors.
This calls for testing single metrics of species traits,
feeding and reproductive guilds, taxa of known tolerance to
particular stressors, and other less-aggregated measures for
evaluating pathways and mechanisms between landscapes,
instream habitats and fish IBIs (Poff 1997).
Most studies have identified landscape effects for single
land-use categories only. Agriculture and urbanisation are
well-studied human impacts. Nonetheless, the interaction
effects and cumulative effects from multiple land-use cat-
egories are poorly understood. This can be attributed to the
use of (multiple) linear regression analysis. Because of the
multitude of ecological variables in landscape studies, only
the main effects were considered, whereas interaction
effects were not included. New and innovative statistical
methods are needed to obtain results that better interpret
interactions, cumulative effects and threshold values.
Numerous studies have dealt with few sampling sites
and focused on streams dominated by either agricultural
(Stewart et al. 2001; Roth et al. 1996) or urban land use
(Wang et al. 2001, 2003). Larger datasets covering many
different river types can be used to analyse effects of
multiple land use and differences in response between river
types.
Relationships and processes are considered to have dif-
ferent influences within river typologies. The Ecoregion
concept of Illies and Andra
´
ssy (1978) or Omernik and
Bailey (1997) delineates both geographically and ecologi-
cally homogeneous areas. Huet (1949) conceptualised fish
zones for running waters mainly based on river slope. Fish
zones account for the natural variability of fish communities
along the longitudinal gradient of rivers. They imply that
typical assemblages, e.g., brown trout dominated commu-
nities (S. trutta L.), occur throughout the many ecoregions
all over Europe. Considering these two concepts, Melcher
et al. (2007) identified assemblage types for European fish
fauna and developed a predictive model using abiotic
characteristics. Thus, the model of Melcher covers the
aspects of regionalism and longitudinal river zonation. Steel
et al. (2010) also proposed to examine landscape-fish rela-
tionships across disparate catchments, ecoregions and
ecosystems to test whether there are, in fact, generalisable
effects. However, using a heterogeneous dataset in terms of
spatial distribution and river size requires considering the
underlying abiotic and biotic characteristics, which can lead
to spurious effects in the results.
We hypothesise that different fish assemblages respond
differently to land uses. Upper tributaries that mainly host
assemblages with low species numbers may be less vul-
nerable to land-use impacts compared to assemblages of
medium to large rivers. This is due to the importance of
lateral connectivity and the exchange of nutrients, organic
and inorganic materials, which increases with river size
(Ward 1989).
The present study was designed to identify empirical
relationships between human land use and the biotic
integrity of rivers and streams. Besides using a general fish
index as a measure of ecological status, we also focus on
fish metrics of certain ecological functional groups (trophic
and reproduction guilds). These metrics of fish species with
special feeding and reproductive behaviour may help to
interpret linkages between landscape-scale human actions
and in-stream biotic responses.
Research questions addressed within this study were (1)
is there a relationship between the composition of land use
and fish assemblages, and which land-use category has the
strongest effect on fish, (2) do metrics of functional fish
guilds respond differently to land use, and which guilds
were most strongly affected by land use, (3) can we iden-
tify a cumulative effect for several land-use categories
showing a stronger impact than single land-use categories
and quantify thresholds, and (4) do land use effects vary in
strength across different Austrian river types?
Methods and data
To characterise landscapes, we calculated the landscape
composition (percentage of six different land-use
C. Trautwein et al.
123
categories) within a catchment area for individual fish
sampling sites. Our scale of investigation was the catch-
ment scale because this has been the most influential scale
in several studies of landscape-river research (Allan 2004b;
Allan and Johnson 1997; Roth et al. 1996). It also showed a
higher relative effect for impacted sites than local or reach
scales (Wang et al. 2006b), which were reported to be
subject to a hierarchy of controls from large to small spatial
extents (Durance et al. 2006).
Land cover data, delineated European watersheds and
river networks were processed with GIS software (ArcGIS
Desktop 9.3, ESRI 1999–2008).
We used the CORINE land cover data 2000 (CLC2000;
European Environmental Agency; www.eea.europa.eu/) for
landscape characterisation. The CCM River and Catchment
database, version 2.0 (CCM2) (Vogt et al. 2007) was used
to determine the catchments associated with each sampling
point. That is, each sampling point was assigned to distinct
hydrologic primary catchments (surface area draining into
confluent to confluent river segment). The tool ‘thematic
raster summary’ (Beyer 2004) performed a spatial overlay
of land cover data to evaluate the absolute area of each
land-cover/land-use (hereafter land-use) category within
primary catchments. The hydrologic coding of CCM
allowed tracking of all upstream primary catchments along
the upstream drainage network. The hydrologically coded
database structure was used to aggregate absolute values of
land-use variables within the whole upstream catchment
area. Finally, land-use composition is the ratio between the
area of each land-use category and catchment size.
Accordingly, 0.40 agriculture means that 40% of the
catchment is occupied by agricultural use.
We evaluated the amounts of six land-use categories in a
slightly modified level three definition of CLC2000 code
(official CLC three-digit code in brackets in Table 1):
agriculture, pasture, urban land, forest, shrubland, and non-
vegetated areas. Table 1 provides details on the organisa-
tion of the land-use variables. Non-vegetated areas were
excluded from further analysis because of their scarce
occurrence (median = 0, standard deviation = 0.0969).
Data from single-pass electric fishing by wading and
boating according to standards of the CEN norm EN 14011
(CEN 2003) provided the basis for the biotic variables in
this study. The fish were identified to species level, their
length and weight recorded, and then released back to the
stream.
Table 1 Variable description in this study with abbreviations, minima and maxima, and levels of measurement
Abbrev. Minimum/
maximum
Measurement
scale
Mean/
median
Description (official CORINE code in italics)
Land-use categories
urban_du 0.00/0.34 Ratio 0.02/0.02 Urban and artificial surfaces (111, 112, 121, 122, 123, 124,
131, 132, 133, 141, 142)
agri_du 0.00/0.82 Ratio 0.17/0.04 Arable land/permanent crops (211, 212, 213, 221, 222, 223,
241, 242, 243, 244)
past_du 0.00/0.60 Ratio 0.10/0.08 Extensively used grass lands/grazing (231)
fores_du 0.13/0.97 Ratio 0.54/0.50 Broad-leaved/coniferous/mixed forest (311, 312, 313)
scrub_du 0.00/0.48 Ratio 0.11/0.01 National grassland/heathland/herbaceous vegetation (321, 322,
323, 324)
noveg_du 0.00/0.51 Ratio 0.05/0.00 Bare rock/sparsely vegetated/glacier (331, 332, 333, 334, 335)
Fish metrics and index Description
p_inse 0.00/0.98 Ratio 0.61/0.66 Density of species feeding on insects [n_inse 9 ha
-1
]
p_omni 0.00/0.98 Ratio 0.51/0.57 Density of omnivorous species [n_omni 9 ha
-1
]
p_phyt 0.00/0.99 Ratio 0.58/0.61 Density of species phytophilic reproduction [n_phyt 9 ha
-1
]
p_bent 0.00/0.99 Ratio 0.53/0.59 # of benthic species [n]
p_rheo 0.00/0.99 Ratio 0.53/0.54 # of rheophilic species for habitat [n]
p_long 0.08/0.99 Ratio 0.43/0.42 # of species migrating over long distances [n]
p_pota 0.00/0.99 Ratio 0.49/0.52 # of potamodromous species [n]
p_lith 0.00/1.0 Ratio 0.67/-0.67 Rel. abundance of lithophilic species [n_lith 9 n_total
-1
]
p_into 0.01/0.99 Ratio 0.55/-0.55 Rel. # of generally intolerant species
[n_sp_into 9 n_sp_total
-1
]
p_tole 0.00/0.94 Ratio 0.56/0.62 Rel. # of generally tolerant species [n_sp_into 9 n_sp_total
-1
]
EFI 0.07/0.80 Ratio 0.55/0.55 European Fish Index scores
EFI_cl High; … Ordinal European Fish Index as ecological status class
EFT A, B, C.. Nominal European Fish Type
Cumulative effects of land use
123
Ecological status was assessed according to Pont et al.
(2007) with the readily available software tool for the
European Fish Index (EFI, http://fame.boku.ac.at/). This
tool derives theoretical fish metric values for individual
sites based on a predictive model of reference conditions.
The larger the difference between predicted and observed
conditions of the fish fauna, the worse the ecological status.
Input variables needed for reference modelling are envi-
ronmental variables describing the sampling site: altitude,
lakes upstream, distance from source, flow regime, wetted
width, geology, air temperature, river slope, and catchment
size (Pont et al. 2007).
Finally, the mean of 10 fish metrics (see Table 1) based
on species richness and densities make up the European
Fish Index. They represent five ecological functional
groups: (1) trophic structure (insectivorous and omnivorous
species), (2) reproduction strategy (phytophilic and litho-
philic sp.), (3) physical habitat preference (benthic and
rheophilic sp.), (4) migratory behaviour (long-distance
migrating and potamodromous sp.), and (5) tolerance to
disturbance (intolerant and tolerant sp.) (Pont et al. 2007;
Noble et al. 2007).
Seven of these 10 fish metrics decrease in response to
human pressures, whereas three tend to increase with such
pressure; the latter are the density of omnivorous and phyt-
ophilic species and relative number of tolerant species. For
consistency Pont et al. (2007) transformed residuals into the
probability of being a reference site. Accordingly, fish met-
rics and the European Fish Index range from 0 to 1. EFI
scores are assigned to five ecological status classes ([0.669
= high; 0.449–0.669 = good; 0.0279–0.449 = moderate;
0.187–0.279 = poor;\0.187 = bad).
A simplified European fish assemblage typology (Mel-
cher et al. 2007) served as a grouping variable in the later
analysis to determine special relationships between land
use and fish.
Melcher et al. (2007) identified 15 homogeneous fish
assemblage types in 11 ecoregions and described six main
European Fish Types (EFT). These groups represent river
types of (A) headwaters with low species richness domi-
nated by brown trout (S. trutta fario), (B) sections with a
low gradient dominated by common minnow (Phoxinus
phoxinus), (C) assemblages dominated by Thymallus thy-
mallus, known as the greyling zone, (D) rivers dominated
by anadromous and potamodromous salmonids, i.e., Salmo
salar, S. trutta lacustris, S. trutta trutta, (E) southern fish
assemblages including Mediterranean endemics, and
(F) lowland rivers. We used the EFT calculation tool
included in the EFI software package to predict EFT
based on seven environmental descriptors for each sam-
pling site (http://fame.boku.ac.at). Melcher et al. (2007)
used discriminate functions for altitude, distance from
source, wetted width, river slope, mean annual air tem-
perature, longitude, and latitude to predict EFT. Four
EFTs occurred in the Austrian dataset of the present study
(see Fig. 1).
Statistical methods
In order to overcome spatially nested sampling sites pro-
ducing autocorrelation, the dataset was reduced from 634
to 249 sites in 30 subcatchments in Austria. Reduction was
carried out within subcatchments by selecting dispersed
samples. Distances between samples were measured along
the stream network and Moran’s I correlograms were
computed for each subcatchment with the software SAM
v4.0 (Rangel et al. 2010). Moran’s I correlograms allow
exploratory spatial autocorrelation pattern detection using
D
a
n
u
b
e
AT
DE
IT
CZ
SI HR
HU
CH
SK
Vienna
0 50 100 km
samples EFT type
ABCF
Fig. 1 Overview of Austrian
main rivers ([4,000 km
2
catchment size) and 249 fish
sampling sites with symbols for
4 European Fish Types (EFT):
(A) headwaters dominated by
Salmo trutta fario,(B) sections
with low gradient dominated by
Phoxinus phoxinus,(C) types
dominated by Thymallus
thymallus, i.e., the greyling
zone, (F) lowland rivers
C. Trautwein et al.
123
Moran’s I coefficients, calculated for a set of distance
classes. We set the tool to use default number of classes,
default distance class size (equal number of pairs), sym-
metric distances (upper right distance matrix), and testing
for significance by permutation 999 times. Threshold val-
ues for dispersed samplings that did not show auto-
correlative patterns were drawn from the one distance class
in Moran’s I correlogram where Moran’s I falls below 0.3
for both variables EFI and agri_du.
In the reduced dataset, replicative samples occurred in
25 subcatchments, five were sampled only once. Six sub-
catchments had more than 10 samples and therefore they
were again tested for spatial autocorrelation. Spatial
structure analysis (Moran’s I correlogram) based on stream
network distance between sampling sites within the sub-
catchments after reduction did not show significant
autocorrelation patterns for the dependent variable EFI
(highest in subcatchment Traun: Moran’s I index
(MI) = 0.23, p = 0.32 for distance class centre 29.3 km).
The main explanatory variable agri_du also showed no
autocorrelation patterns in four out of six tested subcatch-
ments. In subcatchment Mur and subcatchment March MI
for each was still 0.57 (p \0.05) in the distance class
34.6 km and 15 km, respectively. Correlograms for
urban_du showed no autocorrelation pattern. MI for
past_du was significantly high in subcatchment Kamp only
(MI = 0.55; p = 0.01) and forest showed no significant MI
values in all but the subcatchment March (MI = 0.48;
p = 0.04).
Further statistics were performed in R: A Language and
Environment for Statistical Computing, version 2.11 (R
Development Core Team 2009). We applied Pearson cor-
relation two-tailed tests within land-use metrics, within fish
metrics and also for relations between both. In order to
reduce the number of dependent variables (biotic vari-
ables), we chose the fish metric that was best correlated to
land use. Other fish metrics with a medium correlation
(|r| [ 0.5) to land use, while at the same time correlating
(|r| C 0.6) to the chosen fish metric, were omitted from
further modelling.
Independent variables (land-use) with a minor occur-
rence (median\1%) were excluded from descriptive plots.
Correlated land-use variables were kept in the modelling
effort because they are not a problem in answer tree
methods, whereas collinear variables are a major problem
in regression analysis. Tree models deal better with non-
linearity and interaction between explanatory variables
than does regression (Zuur et al. 2007). Principal compo-
nent analysis (PCA) was applied for land-use variables. We
used the command ‘prcomp’ from the package ‘stats’ in R.
Wilcoxon rank sum test and Kruskal–Wallis test were used
for testing differences between groupes in descriptive
analysis of land use data. Alpha values in multiple pairwise
tests were adjusted according to Bonferroni.
We used Classification and regression trees (CRT), a
recursive partitioning method, to model the EFI and other
fish metrics (as one dependent variable at a time) as a
function of land-use variables (independent variables).
CRT methods were available in the R-library (R-project
CRAN) rpart. The ‘rpart’ algorithms follow the tree
function of Breiman et al. (1984). In general, predicting the
values of a continuous variable from one or more contin-
uous and/or categorical predictor variables is a regression-
type problem. Common methods for regression-type
problems are multiple regression or some general linear
models (GLM). Nonetheless, classification-type problems
are generally those in which categorical dependent vari-
ables are predicted from one or more continuous and/or
categorical predictor variables. The dependent variables in
this study were of continuous scale and, thus, the ‘‘anova’’
method was used to build the regression models that will be
presented as binary trees.
Tree classification techniques, such as CRT, can pro-
duce predictions based on logical if–then conditions.
Advantages of tree methods are their nonparametric basis,
no implicit assumption of linearity, the simplicity of results
for interpretation, and the ability of predictive classification
for new observations.
One major issue when applying CRT is to avoid over-
fitting the model. In principal there are two mechanisms in
choosing the ‘right-sized’ tree: first, stop generating new
split nodes when an improvement of prediction becomes
low or when certain criteria are met—termed forward
pruning. Second, post pruning means pruning back highly
branched trees to a simpler tree (Dakou et al. 2006).
Reading and interpreting a ‘big’ tree with many nodes is
more difficult. A good tree should be sufficiently complex
to account for the known facts, but at the same time be as
simple as possible. We used forward pruning criteria with
maximum depth of tree = 3 in an iterative process.
The model fitting algorithm ‘rpart’ (Therneau and
Atkinson 1997) uses 10-fold cross-validation. The training
set is split into 10 (roughly) equally sized parts and the tree
is grown on nine parts while using the tenth for testing
(Venables and Ripley 2003, p. 258). This procedure can be
performed in 10 ways (always using another tenth for
testing). The results are averaged and expressed as xerror,
that is the cross-validated error estimation of the model as
mean square error of the predictions at each split in the
tree. We used xerror as an indicator for the model’s per-
formance and to compare different models by the 1-SE rule
(Venables and Ripley 2003). One minus xerror stands for
the explained variance by the regression tree model
(hereafter, R-squared = R
2
).
Cumulative effects of land use
123
The graphical output of a regression tree analysis is a
branch-like graph splitting at the nodes by the split con-
dition. Data for which the condition is true follow the left
path. Vertical spacing between the nodes is proportional to
improvement of the fit (Therneau and Atkinson 1997). In
this study, we built models with the CRT method for each
of the variables EFI, p_into, p_omni, and p_lith as
dependent variables (n = 233). Two sub models for
intolerant species on EFT = A and EFT = (B, C, F)
explored land use within these grouped river types.
Study design
We used 249 fish sampling sites from 106 distinct Austrian
rivers nested in 30 subcatchments draining into the Danube
(geographical overview see Fig. 1). The data set comprised
rivers of 1st to 7th stream order. Most of the sites (65%,
n = 162) are 3rd to 5th order, 14 sites 1st, 34 sites 2nd, 33
sites 6th, and 6 sites 7th order. The Austrian sites were
spread over a broad range of environmental characteristics
and over four EFT. Altitude range of sampling sites:
139–1,193 m above sea level; river slope range:
0.001–13.2%; upstream catchment size range: 1.65
to *10,200 km
2
.
Table 2 lists the species abundance in the dataset in total
and for each EFT. Number of species in total and for the
functional guilds of intolerant, lithophilic, and omnivorous
are provided. Brown trout (S. trutta fario), greyling
(T. thymallus), and chub (Leuciscus cephalus) were the
most abundant species in the samples (34, 10.5, 9.4%,
respectively).
Results
We found seven medium-level correlations (|r| [ 0.50)
within 11 biotic variables (10 metrics, 1 Index). Pearson
correlation coefficients of all pairs are shown in Table 3.
Intolerant species, later seen as best correlated with agri-
cultural land use, were correlated with omnivorous and
Table 2 Number of individuals and number of species in total and within river types
European fish type (EFT) Total Cum. (%)
ABCF
Number of intolerant species 10 10 10 9 12
Number of lithophilic species 19 26 18 19 28
Number of omnivorous species 10 15 8 13 16
Number of species total 33 50 34 37 54
Salmo trutta fario
a,b
51.2% 8.3% 38.3% 17.1% 34.0% 34.0
Thymallus thymallus
a,b
2.3% 1.3% 22.9% 19.9% 10.5% 44.5
Leuciscus cephalus
b,c
5.7% 19.4% 5.9% 11.4% 9.4% 53.9
Oncorhynchus mykiss
b
9.5% 1.1% 10.4% 6.3% 7.6% 61.5
Cottus gobio
a,b
11.0% 2.0% 9.2% 1.3% 7.4% 68.9
Gobio gobio 4.4% 12.7% 0.5% 5.6% 5.1% 74.0
Alburnus alburnus
c
0.0% 18.5% 0.0% 4.0% 4.5% 78.6
Rutilus rutilus
c
4.5% 5.6% 0.6% 10.4% 4.1% 82.7
Barbus barbus
b
0.1% 3.7% 7.1% 1.2% 3.2% 85.9
Barbatula barbatula
b
5.1% 2.1% 0.7% 2.1% 2.7% 88.6
Phoxinus phoxinus
b
3.3% 2.7% 1.1% 1.2% 2.3% 90.9
Alburnoides bipunctatus
a,b
0.4% 6.8% 0.5% 3.4% 2.2% 93.1
Leuciscus leuciscus
b,c
0.9% 3.9% 0.3% 5.3% 1.8% 94.9
Chondrostoma nasus
b
0.0% 2.6% 0.7% 2.4% 1.1% 96.0
Others 1.4% 9.2% 1.8% 8.6% 4.0% 100.0
% of total # individuals 100.0% 100.0% 100.0% 100.0% 100.0%
Number of individuals total 19,861 12,370 17,927 5,997 56,155
Species names sorted by % total; bold names are classified as general intolerant species (FAME-Consortium 2004)
EFT: A (headwater streams), B (lower gradient streams), C (greyling zone), F (lowland rivers)
a
Intolerant species
b
Lithophilic species
c
Omnivorous species
C. Trautwein et al.
123
lithophilic species (r values 0.69, 0.58, respectively; both
p \ 0.01).
In general, agriculture and forest were the predominant
land-use categories; these were negatively correlated
(-0.43, p \ 0.01, n = 249; Table 4). The median value
of agriculture was 4.2%, the 3rd quartile at 31.2%. The
median value of forest was 50.1%. The other categories
exhibited lower median values: shrubs = 1.3%, pas-
ture = 8.3%, urban = 1.7%.
In a PCA with urban, agriculture, pasture, forest, and
shrubland, the first component explained 33.9% of the
variation, the second component 27.8% and the third
component 24.3%. Hence, the first three principal com-
ponents (PC) explained 85.9% of the variation. The biplot
(Fig. 2) of the first two PCs of all five land-use variables
showed that forest and pasture are similarly loaded and that
urban and shrubland are clearly inversely related. Agri-
culture was the main antagonist to forest. In the first PC of
the rotated loadings matrix, agriculture loads with 0.67,
shrubland with -0.56 urban with 0.42. In the second PC,
forest loads with 0.59, pasture with 0.54, shrubland with
-0.43.
From correlation analysis, we learned that coefficients
for urbanisation were low (|r| B 0.25, p \ 0.05) with all
other categories. Pasture correlated with all coefficients
below absolute values of 0.27 (Table 4).
We observed differences in land-use composition
between four EFTs. The proportions of forest were quite
high (median values [50%) for fish sampling sites of type
A (headwaters) and type C (greyling zone), whereas agri-
culture was very low (Fig. 3a, b). Agricultural land use was
highest (median = 38.2%) in type B (lower-gradient riv-
ers) (Fig. 3b). Pasture and urban land reached lower levels
and did not differ much between the fish types (Fig. 3c, d).
The plots also showed a high variation in forest and agri-
culture (SD 0.13–0.20, 0.12–0.28, respectively) and that
variation was less for pasture and urban (SD \0.11,
SD \ 0.04, respectively). Tests for differences (Wilcoxon
Rank Sum Test) between the EFT groups showed signifi-
cances (see Fig. 3) for all but one EFT pairing within forest
and agriculture ratios each. Pasture had only two and
urbanisation had three pairs with significant differences.
Table 3 Pearson correlation coefficients for fish metrics
p_inse p_omni p_phyt p_bent p_rheo p_long p_pota p_lith p_into p_tole
p_inse 1.00 0.2** 0.24** -0.10 0.25** -0.01 0.18** 0.36** 0.22** 0.3**
p_omni 0.2** 1.00 0.35** 20.49** 20.47** -0.01 20.35** 0.49** 0.69** 0.39**
p_phyt 0.24** 0.35** 1.00 20.4** -0.04 -0.13 -0.13* 0.47** 0.37** 0.44**
p_bent -0.10 20.49** 20.4** 1.00 0.64** 0.11 0.32** 20.41** 20.43** 20.35**
p_rheo 0.25** 20.47** -0.04 0.64** 1.00 0.06 0.7** -0.15* 20.4** 0.03
p_long -0.01 -0.01 -0.13 0.11 0.06 1.00 0.13* -0.14 -0.08 -0.27**
p_pota 0.18** 20.35** -0.13* 0.32** 0.7** 0.13* 1.00 0.04 20.41** -0.03
p_lith 0.36** 0.49** 0.47** 20.41** -0.15* -0.14 0.04 1.00 0.58** 0.53**
p_into 0.22** 0.69** 0.37** 20.43** 20.4**
-0.08 20.41** 0.58** 1.00 0.44**
p_tole 0.3** 0.39** 0.44** 20.35** 0.03 -0.27** -0.03 0.53** 0.44** 1.00
EFI 0.63** 0.42** 0.48** 0.04 0.4** 0.16* 0.38** 0.65** 0.47** 0.58**
Values |r| [ 0.30 in bold
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Table 4 Pearson correlation coefficients for land-use variables and
fish metrics
urban_du agri_du past_du fores_du scrub_du
urban_du 1.00 0.16* 0.25** -0.15* -0.25**
agri_du 0.16* 1.00 -0.27** 20.43** 20.53**
past_du 0.25** -0.27** 1.00 -0.03 -0.15*
fores_du -0.15* 20.43** -0.03 1.00 20.33**
scrub_du -0.25** 20.53** -0.15* 20.33** 1.00
p_inse -0.07 -0.25** -0.01 0.28** -0.01
p_omni -0.19** 20.48** -0.04 0.18** 0.38**
p_phyt -0.07 -0.23** 0.02 0.14 0.15*
p_bent 0.13* 0.36** 0.08 -0.09 20.33**
p_rheo 0.11 0.13* 0.15 0.09 -0.26**
p_long -0.05 -0.19** 0.18** 0.06 -0.01
p_pota 0.17** 0.03 0.19** 0.09 -0.2**
p_lith -0.13* 20.52** 0.16* 0.14 0.36**
p_into 20.32** 20.56** -0.03 0.21** 0.45**
p_tole -
0.09 -0.27** -0.02 0.21** 0.10
EFI -0.09 20.45** 0.16* 0.3** 0.18**
Values |r| [ 0.30 in bold
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
Cumulative effects of land use
123
Correlation analyses supported a statistical relationship
between agricultural land use and the ecological status of
rivers (Table 4). The results showed that values of the
European Fish Index were negatively correlated with the
amount of agriculture in the catchment (r =-0.45,
p \ 0.01, n = 249). Correlation between urbanised land
and the fish index was very low and not significant (r =
-0.09, p C 0.05).
Agriculture in the catchment was highly correlated with
omnivorous (r =-0.48, p \ 0.01), lithophilic (r =-0.52,
p \ 0.01), and intolerant (r =-0.56, p \ 0.01) species
metrics. The best relating metrics in respect of forest
were insectivorous and intolerant species (r = 0.28 and
r = 0.21, respectively, both p \ 0.01). Finally, urbanisa-
tion—with generally weaker coefficients—was best
correlated with intolerant species (r =-0.32, p \0.01).
Hence, we used intolerant species for further modelling
because of their best correlation with agricultural and urban
land use. Omnivorous and lithophilic species were con-
sidered redundant because they showed a high correlation
(r [ 0.58) with intolerant species.
We explored and tested the relationship between land
use and ecological status by box-whisker plots of the
amounts of agriculture, forest, and urbanisation in
the catchment against all five EFI classes (Fig. 4). The
observed patterns showed a good separation of sites with
high or good status from sites in poor or bad condition. The
moderate class had a large overlap with the other status
classes and indicates a transition class. Pairwise non-
parametric test statistics (Wilcoxon Rank Sum Test) with
Bonferroni-adjusted alpha attest significant differences
between several groups (Fig. 4). Agriculture in the catch-
ment differed significantly (p \ 0.05) between status
classes ‘high’ and ‘moderate’, ‘good’ and ‘moderate’, and
‘good’ and ‘bad’. Forest differed between ‘high’ and
‘moderate’ and ‘high’ and ‘bad’. Differentiation is not
significant for any pairing of urbanisation. When visually
defining separation values, however, we expect thresholds
at a level of *40% for agriculture, *45% for forest, and
*2.5% for urbanised land.
Modelling biotic response variables
with regression trees
When loading all land-use variables and EFT into one
model (Fig. 5) with EFI as the dependent variable, the
model used agriculture, forest, urbanisation, and EFT for
tree construction. Agriculture occurred as a first-split
−0.1 0.0 0.1 0.2
−0.1 0.0 0.1 0.2
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−10 −5 0 5 10 15 20
−10 −5 0 5 10 15 20
urban_du
agri_du
past_du
fores_du
scrub_du
Fig. 2 Biplot of first two principal components (PC) of five land-use
variables; variance explained by first component 33.9%, second
component 27.8%
ABCF
0.0 0.4 0.8
(a) forest
ratio in catchment
ABCF
0.0 0.4 0.8
(b) agriculture
ABCF
0.0 0.4 0.8
(c) pasture
ratio in catchment
ABCF
0.00 0.04 0.08
(d) urban
B
C
F
B
C
F
B
C
F
B
C
F
**
* **
** *
**
**
* **
*
**
**
**
*
Fig. 3 Four boxplots showing ratios of land-use categories within
four European Fish Types (EFT): (A) headwaters dominated by Salmo
trutta fario, n = 102, (B) sections with low gradient dominated by
Phoxinus phoxinus, n = 30, (C) types dominated by Thymallus
thymallus, i.e., greyling zone, n = 75, (F) lowland rivers; **pairwise
Wilcoxon Rank Sum Test was significant at the 0.01 level; *pairwise
Wilcoxon Rank Sum Test was significant at the 0.05 level; alpha
adjustment method: Bonferroni
C. Trautwein et al.
123
variable at 23.3%, and urban land differentiated at the third
level at [2.9% at both paths. Sites of EFT = F combined
with more than 23.3% agriculture had the least mean value
of EFI (0.33/moderate status). Other paths revealed a
cumulative effect by combinations of agriculture and
urbanisation. Sites having both more than 23.3% agriculture
and more than 2.9% urbanisation were in moderate status
(mean EFI = 0.45), whereas those combined with \2.9%
urban are in high status (mean EFI = 0.52). The path
combining agriculture\23.3% and urbanisation[4.4% had
a low impact on fish and the terminal node value was 0.53
(good status); agriculture \23.3% combined with urban
\4.4% had high status (mean EFI = 0.6). Forest also
occurred as split criteria at a percentage of 44.3% but made
little contribution to distinguishing between EFI in this
model. The overall R
2
after five splits in this EFI model with
three variables of land use and one for EFT was 0.35, but
dropped to 0.15 after cross-validation.
In the next step, we built a model with four land-use
categories (urban, agriculture, pasture, forest) and EFT as
independent and the relative number of intolerant species as
a dependent variable (Fig. 6). Poor status (mean
EFI = 0.25) occurred in the path with agriculture C7.3%,
forest \56.4% and pasture \18.4%. Compared to the pre-
vious model with the EFI (Fig. 5), this model of intolerant
species also revealed a cumulative effect of agriculture and
urbanisation in the catchment. Sites with both more than
7.3% agriculture and more than 2.0% urban held sites of
moderate status (mean = 0.42). Urban C1.8% but agricul-
ture\7.3% was still in good (nearly high) status (mean value
0.64). The rightmost path with agriculture (\7.3%) in a non-
cumulative association with urban (\1.8%) yielded high
mean metric value of 0.81 (high status). High percentages of
forest (C56.4, C64.6%) led to values of higher integrity.
The result revealed a different reaction in EFT (Fig. 6).
If the amount of agriculture was below 7.3% (right path),
then the data split into EFT A dominated by brown trout in
contrast to the other types. Metric values of intolerant
species dropped to moderate/good status in EFT A com-
pared to high/good in all other types. Finally this model of
a particular fish metric has better explanatory power
(R
2
= 0.46) than for the EFI itself and seems to be more
stable. R
2
before cross-validation was 0.53.
Models on sub datasets of EFT A (headwaters) and
lumped EFTs (B, C, F—river slope lower than in A), both
with intolerant species as dependent variable, had very
different explanatory power (data not shown):
In the EFT = B, C, F model, agriculture and urbanisa-
tion acted in the same manner and at the same levels as in
the general model on intolerants (Fig. 6), where we
observed a cumulative effect of agriculture and urbanisa-
tion. The R
2
value of the EFT = B, C, F model was 0.58
(summary see Table 5).
In the EFT A model, forest was first split variable at
67.6%, urban was second at 2.6% and R
2
of the tree model
was 0.10 (cross-validated) (summary see Table 5).
0.0 0.2 0.4 0.6 0.8 1.0
(a) EFI and agriculture (b) EFI and forest
high good mod. poor bad high good mod. poor bad high good mod. poor bad
(c) EFI and urban
ratio agriculture in catchment
0.0 0.2 0.4 0.6 0.8 1.0
ratio forest in catchment
0.00 0.02 0.04 0.06 0.08 0.10
ratio urban in catchment
good
mod.
poor
bad
** **
*
good
mod.
poor
bad
**
*
good
mod.
poor
bad
Fig. 4 Box plots of five ecological status classes; number of sites in
plots (high = 20, good = 194, moderate = 27, poor = 3, bad = 5);
a amounts of agriculture; b amounts of forest and c urbanised land by
ecological status; Y-axis of urban is scaled to a range from 0 to 0.10
because of low ratio levels in this category; **pairwise Wilcoxon
Rank Sum Test was significant at the 0.01 level; *pairwise Wilcoxon
Rank Sum Test was significant at the 0.05 level; alpha adjustment
method: Bonferroni
Cumulative effects of land use
123
Agriculture was an input variable, but not used by the
EFT = A model. The terminal node with worst ecological
status (mean EFI = 0.13/bad status) contained sites with
urbanisation exceeding 2.6% and pasture below 11.3%.
Pasture occurred in a positive trend to ecological integrity
within this model of headwaters (EFT = A).
Discussion
Agriculture was the primary explanatory variable in all
but one tree model (Table 5). Agriculture as split criteria
occurred at three levels: at 23.3% but also at *7.3 and
36–40%. Very often, urban land served as the secondary
split variable at about 2%, with minimum 1.8% and
maximum even at 4.4%. So, the regression tree method
affirmed our interpretation of thresholds in the descriptive
analysis. There was a clear interaction of agriculture and
urban. Sites with more than 7.3% agriculture and more
than 2.0% urban land were likely to result in poor or
moderate status. Urbanisation acted more strongly in river
types dominated by brown trout (S. trutta fario)
(EFT = A) than in river types of lower gradients
(EFT = B, C, F). The model for the relative number of
EFI
|
agri_du>=0.233
EFT_type=F
urban_du>=0.029
fores_du>= 0.443
urban_du>=0.044
0.33
n=9
0.45
n=25
0.52
n=46
0.54
n=39
0.53
n=11
0.6
n=119
agri_du<0.233
EFT_type=A, B, C
fores_du< 0.443
urban_du<0.044
urban_du<0.029
Fig. 5 Regression tree for EFI
based on land-use categories in
the catchment draining to the
fish sampling sites; n = 233;
R
2
= 0.15; true split criteria at
nodes follows left path
p_into
|
agri_du>=0.073
fores_du< 0.564
past_du< 0.184
urban_du>=0.021
EFT_type=A
fores_du< 0.646
urban_du>=0.018
0.25
n=60
0.43
n=15
0.42
n=14
0.61
n=20
0.4
n=18
0.64
n=37
0.64
n=19
0.81
n=66
agri_du<0.073
EFT_type=B,C,F
fores_du>= 0.564
>= 0.184
<0.021
>= 0.646
<0.018
Fig. 6 Regression tree for
metric of relative number of
intolerant species in the EFI;
independent variables are
agriculture, forest, pasture,
urban and European Fish Types
(EFT); n = 233; R
2
= 0.46;
true split criteria at nodes
follows left path
C. Trautwein et al.
123
intolerant species had better explanatory power than for
EFI.
Our study showed that multiple land-use categories had
an effect on fish. Agricultural land use had the strongest
detrimental effect. Percentages of agriculture in the
catchment were negatively correlated with the European
Fish Index (EFI). Many studies have found comparable
results for selected small to medium-sized catchments of
2nd to 3rd order rivers (Allan 2004b; Richards et al. 1996;
Roth et al. 1996); our findings corroborated this relation-
ship for the Austrian dataset from small to larger rivers (up
to 7th order) and across four different river types. Urban
areas in the catchment showed strong effects on fish met-
rics even at very small percentages. Snyder et al. (2003)
and Wang et al. (2003) also discussed a disproportionately
large effect of urban land use. Yet, significance of the test
results for urban, agriculture, and forest within EFI status
classes are limited due to an uneven distribution of samples
in high to bad status. Snyder et al. (2003) found no cor-
relation between agriculture (lumped with pasture) and
biota and recommended examining more specific land-use
categories such as grazing versus row-crop agriculture. Our
findings identified pasture (grazing and grassland) as less
influential than agriculture (arable land and permanent
crops).
Moerke and Lamberti (2006) also related four categories
to fish assemblage structure and found higher IBI scores in
forested streams compared to urban and agricultural
streams. Our results go beyond mere correlation (Steel
et al. 2010) because we applied new methods and identified
thresholds at which biota react. Intolerant, lithophilic, and
omnivorous species reacted most strongly to agriculture
and urban development. Metrics for insectivorous and
migratory species were less correlated to land use. Moerke
and Lamberti (2006) used metrics of general tolerance and
showed a positive association between sensitive species
and higher percentages of forest in the catchment, and
between tolerant fish and changes in land use (agriculture,
urban). Pess et al. (2002) reported positive correlated
densities of salmon with forest cover in the watershed. Our
findings revealed that a fish metric of intolerance to general
disturbance (p_into) is a better indicator than the EFI. We
hypothesise that these species react to both direct (e.g.,
toxins, nutrients) and indirect (e.g., sedimentation, hydro-
logic alteration) effects of land use. Wang et al. (2003) also
used percentage of intolerants and found that thresholds for
urban land cover (measured as impervious surface area)
had detrimental effects on fish at 11%. Among four aquatic
organism groups (diatoms, macrophytes, benthic macroin-
vertebrates and fish) fish were found to respond less
strongly to catchment land use than to eutrophication/
organic pollution gradients (Hering et al. 2006). Hering
et al. (2006) found the strongest response for lithophilic
and limnophilic species and related this to direct or indirect
effects of land-use on habitat quality. Hering et al. also
discussed effects of land-use acting through numerous
cause-effect relationships that are difficult to identify.
Few studies have dealt with relative effects of multiple
land-use categories on stream ecosystems (Moerke and
Lamberti 2006; Van Sickle et al. 2004; Snyder et al. 2003).
Moerke and Lamberti (2006) evaluated forest, urban,
agriculture and wetlands. Snyder et al. (2003) additionally
used water and barren land, but in both studies only forest,
agriculture and urban were frequent and associated
strongly enough for further analysis. Using regression
analysis, Snyder et al. (2003) explained 63% of IBI vari-
ation by urban land in the catchment alone. Neither forest
nor agriculture explained a significant amount of the
remaining variation in IBI after accounting for the effects
of urban land use. In our study, using answer tree models,
we determined a cumulative effect of agriculture and urban
land use. Urban or agriculture alone resulted in a lower
detrimental effect than when combined.
River-type-specific reactions
The brown trout assemblage type (EFT A) showed a
stronger decrease in ecological status than river types of
lower sections (greyling zone, lowland rivers). In the latter
fish types (EFT =
BCF), the cumulative effect of agricul-
ture and urban was better pronounced than in headwaters.
In general, agriculture correlated best to fish metrics and
Table 5 Summary of the model results for fish metrics as dependent variable and land-use categories as independent variables
Model title y Independent used 1st level split 2nd level split R
2
N
EFI—AT EFI Agri, fores, urban Agri 23.3% Urban *3% 0.15 249
AT intolerants P_into Agri, fores, urban, EFT Agri 7.3% Fores 44%, urban *2% 0.46 249
AT omnivorous p_omni Agri, urban Agri 7.3% Urban *2% 0.17 249
AT lithophilic p_lith Agri, urban, EFT Agri 40% Urban 3.3% 0.39 249
EFT = A, intolerants p_into Fores, past, urban Fores 67.6% Urban * 2.5% 0.09 102
EFT = B, C, F intolerants p_into Agri, urban Agri 8% Agri 36.7%, urban 1.8% 0.58 131
Abbreviations see Table 1
Cumulative effects of land use
123
EFI, whereas all other land-use categories remained at a
very low correlation level. Headwaters, however, seemed
to be very sensitive although at low levels of agriculture
(\7.3%) and highly forested catchments (\64.6%). We
hypothesise that the underlying mechanisms are effects of
hydrology (e.g., increased peak runoff from impervious
surfaces), morphological alteration (e.g., channelization)
and removal of riparian vegetation (e.g., reduced shading,
increased water temperature) (Allan 2004b), and that they
act more strongly on small headwater streams. Addition-
ally, by nature, trout rivers are low-species-number rivers
with intolerant species and, therefore, intolerant-species-
metric responded more sensitively to disturbance.
Hering et al. (2006) produced weak correlation results
for mountain streams, while in lowland stream types the
explanatory power of the fish metrics was at a higher level
(r
2
= 0.4). These findings are somehow contradictory to
our findings because headwaters respond even more sen-
sitively than other EFTs. However, a model for the
headwater fishtype (EFT = A) was much less powerful
than for streams of lower gradients (EFT = B, C, F). The
explanatory power of our models of land-use seem to be
comparable to Hering et al. (2006) with maximum at
R
2
= 0.46. Nevertheless, we are aware of bias in the model
for headwaters. Full land-use gradients are limited in our
dataset because most headwaters are located in less
developed landscapes.
Conclusions
With this study we provide catchment-level relationships
for land use covering headwater streams (brown trout) to
medium- (greyling) and large-sized rivers in Austria. Mo-
erke and Lamberti (2006) have already concluded that
additional replicated, catchment-level studies in other
geographic areas will enhance our knowledge of how land
use affects stream ecosystems. Such findings help identify
characteristics that make streams more or less sensitive to
land-use change.
The regression tree models we used are very simple and
based on a large sample of sites. They can explain a
moderate amount of variability in biotic integrity based on
a few land-use categories (urban areas, agriculture, pasture,
and forest). These results are promising as building blocks
for designing models of cascades that represent mecha-
nistic relationships.
The present study does not explain the full pathways
from land use via physical habitat, water quality and/or
hydrologic alteration to a resulting biological impact, but
the results do go beyond mere correlative analysis.
Research into underlying mechanisms remains a challenge
(Steel et al. 2010; Wang et al. 2006a). Based on the current
findings, it will be possible to develop first steps of char-
acteristic cause and effect pathways for selected river
types.
Acknowledgments Work on this manuscript was funded by the
Austrian Science Fund (FWF, research project LANPREF, contract
number P 21735-B16). Basic ideas for this work arose within the
EFI ? project supported by the European Commission under FP6
(contract number 044096), and our sincere thanks go to all members
of the EFI ? consortium. We thank Andreas Melcher for valuable
discussions on early stages of the manuscript. Ashley Steel gave
precious input to consistency of the manuscript, discussion of the
results and the English, thank you. Two anonymous reviewers
addressed several substantive issues to improve the manuscript.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
References
Allan JD (2004a) Influence of land use and landscape setting on the
ecological status of rivers. Limnetica 23:187–198
Allan JD (2004b) Landscapes and riverscapes: the influence of land
use on stream ecosystems. Annu Rev Ecol Evol S 35:257–284
Allan JD, Johnson LB (1997) Catchment-scale analysis of aquatic
ecosystems. Freshw Biol 37:107–111
Allan JD, Erickson DL, Fay J (1997) The influence of catchment land
use on stream integrity across multiple spatial scales. Freshw
Biol 37:149–161
Beechie TJ, Sear DA, Olden JD, Pess GR, Buffington JM, Moir H,
Roni P, Pollock MM (2010) Process-based principles for
restoring river ecosystems. Bioscience 60:209–222
Beyer HL (2004) Hawth’s Analysis Tools for ArcGIS. Available at
http://www.spatialecology.com/htools. Accessed 11 Feb 2008
Botequilha Leitao A, Miller J, Ahern J, McGarigal K (2006)
Measuring landscapes. Island, Washington, DC
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification
and regression trees. Chapman and Hall/CRC, Boca Raton
CEN (2003) Water quality—sampling of fish with electricity, EN
14011. European Committee for Standardisation, Brussels
Dakou E, D’heygere T, Dedecker AP, Goethals PLM, Lazaridou-
Dimitriadou M, Pauw N (2006) Decision tree models for
prediction of macroinvertebrate taxa in the river axios (northern
Greece). Aquat Ecol 41:399–411
Durance I, Lepichon C, Ormerod SJ (2006) Recognizing the
importance of scale in the ecology and management of riverine
fish. River Res Appl 22:1143–1152
EC of European Parliament (2000) Water framework directive—
Establishing a framework for community action in the field of
water policy. Off J Eur Comm L327:1–72
ESRI (2008) ArcGIS Desktop, Version 9.3, ArcInfo license. Red-
lands, CA
FAME-Consortium (2004) Manual for Application of the European Fish
Index—EFI. A fish-based method to assess the ecological status of
European rivers in support of the Water Framework Directive
(Version 1.1, January 2005). Available at: http://fame.boku.
ac.at/downloads/manual_Version_Februar2005.pdf. Accessed 2
Apr 2008
Gergel SE, Turner MG, Miller JR, Melack JM, Stanley EH (2002)
Landscape indicators of human impacts to riverine systems.
Aquat Sci 64:118–128
C. Trautwein et al.
123
Hering D, Johnson RK, Kramm S et al (2006) Assessment of
European streams with diatoms, macrophytes, macroinverte-
brates and fish: a comparative metric-based analysis of organism
response to stress. Freshw Biol 51(9):1757–1785
Huet M (1949) Aperc¸u des relations entre la pente et les populations
piscicoles des eaux courantes. Schweiz Z Hydrol 11:332–351
Illies J, Andra
´
ssy I (1978) Limnofauna europaea—a checklist of the
animals inhabiting European inland waters, with accounts of
their distribution and ecology (except protozoa), vol 2, Second
revised edn. Gustav Fischer Verlag, Stuttgart, NY
Karr JR (1981) Assessment of biotic integrity using fish communities.
Fisheries 6:21–27
Melcher A, Schmutz S, Haidvogl G, Moder K (2007) Spatially based
methods to assess the ecological status of European fish
assemblage types. Fish Manag Ecol 14:453–463
Miltner R, White D, Yoder C (2004) The biotic integrity of streams in
urban and suburbanizing landscapes. Landsc Urban Plan
69:87–100
Moerke AH, Lamberti GA (2006) Relationships between land use and
stream ecosystems: a multistream assessment in southwestern
Michigan. Am Fish Soc Symp 2006:323–338
Noble RAA, Cowx IG, Goffaux D, Kestemont P (2007) Assessing the
health of European rivers using functional ecological guilds of
fish communities: standardising species classification and
approaches to metric selection. Fish Manag Ecol 14(6):381–392
Omernik JM, Bailey RG (1997) Distinguishing between watersheds
and ecoregions. J Am Water Resour Assoc 33:935–949
Pess GR, Montgomery DR, Steel EA, Bilby RE, Feist BE, Greenberg
HM (2002) Landscape characteristics, land use, and coho salmon
(Oncorhynchus kisutch) abundance, Snohomish River, Wash-
ington, USA. Can J Fish Aquat Sci 59:613–623
Poff NL (1997) Landscape filters and species traits: Towards
mechanistic understanding and prediction in stream ecology.
J N Am Benthol Soc 16:391–409
Pont D, Hugueny B, Beier U, Goffaux D, Melcher A, Noble R,
Rogers C, Roset N, Schmutz S (2006) Assessing river biotic
condition at a continental scale: a European approach using
functional metrics and fish assemblages. J Appl Ecol 43:70–80
Pont D, Hugueny B, ROGERS C (2007) Development of a fish-based
index for the assessment of river health in Europe: The European
fish index. Fish Manag Ecol 14:427–439
R Development Core Team (2009) R: A language and environment
for statistical computing. Vienna, Austria
Rangel TF, Diniz-Filho JAF, Bini LM (2010) SAM: a comprehensive
application for spatial analysis in macroecology. Ecography
33:1–5
Richards C, Johnson LB, Host GE (1996) Landscape-scale influences
on stream habitats and biota. Can J Fish Aquat Sci 53:295–311
Roth NE, Allan JD, Erickson DL (1996) Landscape influences on
stream biotic integrity assessed at multiple spatial scales.
Landscape Ecol 11:141–156
Snyder CD, Young JA, Villella R, Lemarie DP (2003) Influences of
upland and riparian land use patterns on stream biotic integrity.
Landscape Ecol 18:647–664
Steel EA, Hughes RM, Fullerton AH, Schmutz S, Young J,
Fukushima M, Muhar S, Poppe M, Feist BE, Trautwein C
(2010) Are we meeting the challenges of landscape-scale
riverine research? A review. Living Rev Lands Res 4:1
Stewart JS, Wang L, Lyons J, Horwatich JA, Bannerman R (2001)
Influences of watershed, riparian-corridor, and reach-scale
characteristics on aquatic biota in agricultural watersheds.
J Am Water Resour Assoc 37:1475–1487
Therneau TM, Atkinson EJ (1997) An introduction to recursive
partitioning using the rpart routines. http://www.mayo.edu/hsr/
techrpt/61.pdf (cited on 12/03/2010)
Van Sickle J, Baker J, Herlihy A, Bayley P, Gregory S, Haggerty P,
Ashkenas L, Li J (2004) Projecting the biological condition of
streams under alternative scenarios of human land use. Ecol
Appl 14:368–380
Venables WN, Ripley BD (2003) Modern applied statistics with S,
4th edn. Springer, New York
Vogt JV, Soille P, Jager AD, Rimaviciute E, Mehl W, Foisneau S,
Bodis K, Dusart J, Paracchini ML, Haastrup P, Bamps C (2007)
A pan-European river and catchment database. European
Commission, Joint Research Center, Institute for Environment
and Sustainability, Luxembourg
Wang L, Lyons J, Kanehl P, Bannerman R (2001) Impacts of
urbanization on stream habitat and fish across multiple spatial
scales. Environ Manage 28:255–266
Wang L, Lyons J, Kanehl P (2003) Impacts of urban land cover on
trout streams in Wisconsin and Minnesota. Trans Am Fish Soc
132:825–839
Wang L, Seelbach PW, Hughes RM (2006a) Introduction to
landscape influences on stream habitats and biological assem-
blages. Am Fish Soc Symp 2006:1–23
Wang L, Seelbach PW, Lyons J (2006b) Effects of levels of human
disturbance on the influence of catchment, riparian, and reach-scale
factors on fish assemblages. Am Fish Soc Symp 2006:199–219
Ward JV (1989) The 4-dimensional nature of lotic ecosystems. J N
Am Benthol Soc 8:2–8
Zuur AF, Ieno EN, Smith GM (2007) Analysing ecological data.
Springer, New York, NY
Cumulative effects of land use
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