Fundam. Appl. Limnol. Vol. 180/3, 259–270Article
Stuttgart, April 2012
Relative effects of local- and landscape-scale
environmental factors on stream fish assemblages:
evidence from Idaho and Ohio, USA
Adam Kautza* and S. Mažeika P. Sullivan 1
With 4 figures and 1 table
Abstract: Despite increased attention to spatial considerations in catchments, the importance of both environmen-
tal factors and spatial context in understanding stream fish assemblages is not fully resolved. This study explored
the relative influences and spatial relationships of environmental factors at landscape (i.e., catchment) and local
(i.e., reach) scales on characteristics of stream fish assemblages including species richness (S), diversity (H’, evenness, and 1/D), density and biomass, and composition (% top carnivores and % benthic insectivores). We conducted
this research in two geographic regions (northern Idaho and Ohio, USA) characterized by distinct environmental
attributes. Using a partial constrained ordination approach, we found that pure spatial factors explained 26 % of fish
assemblage variation in Ohio (OH) and 18 % in Idaho (ID). Shared (spatially-structured) environmental variables
explained more variation in fish assemblages in ID (28.6 %) than in OH (20.7 %). The influence of pure (nonspatial) environmental characteristics accounted for nearly 24 % of the variation observed in fish assemblages in
OH catchments, whereas pure environmental factors accounted for 31 % of assemblage variation in ID. Within the
pure environmental component, the influence of local, landscape, and joint (i.e., combined effects of environmental
variables from both spatial scales) effects accounted for relatively equal amounts of assemblage variation for both
study regions. However, local-scale variables were slightly more important in ID, whereas joint influences were
more important in OH. Taken as a whole, our results suggest that a complex suite of spatial and environmental
factors influenced fish assemblages but that the relative importance of these components differed in each of our
study areas. The strong influence of spatial patterns in our results reveals the importance of integrating spatial context into studies predicting fish assemblage characteristics. The contrasting findings between geographic regions
encourage additional research efforts that address regional variability in multi-scale environmental influences on
fish assemblages.
Key words: species richness, diversity, density, biomass, composition, shared environmental variables, pure environmental factors, spatial patterns.
Introduction
Understanding, characterizing, and predicting local biological assemblages represent significant challenges
to ecologists. In stream ecosystems fish assemblages
are structured by multiple factors that often operate at
different spatial and temporal scales (Lammert & Al-
lan 1999, Sutherland et al. 2002, Walters et al. 2009).
These factors act independently and in concert to constrain the presence and distribution of stream fishes
through a hierarchy of nested environmental filters
(Poff 1997). Coarse variation in species composition
among assemblages is derived from geographicallyunique species pools (Angermeier & Winston 1998)
Authors’ address:
The Ohio State University, School of Environment and Natural Resources, 2021 Coffey Road, 210 Kottman Hall, Columbus,
OH 43210, USA.
*
Author for correspondence; [email protected]
1 © 2012 E. Schweizerbart’sche Verlagsbuchhandlung, Stuttgart, Germany
DOI: 10.1127/1863-9135/2012/0282
eschweizerbart_XXX
www.schweizerbart.de
1863-9135/12/0282 $ 3.00
260
Adam Kautza and S. Mažeika P. Sullivan
that are thought to differ based largely on historical
patterns of dispersal and speciation (Ricklefs 1987).
For instance, due to duration of time available for
speciation, environmental conditions, and refuge opportunities created by directional orientation of major drainages, streams east of the Rocky Mountains
(USA) typically exhibit greater diversity in their respective species pools than those west of the Rocky
Mountains (Moyle & Herbold 1987, Moyle & Cech
2006).
The spatial hierarchy of physical and biological
stream attributes has been well described at the catchment scale (Frissel et al. 1986, Montgomery 1999).
For example, Vannote et al.’s (1980) River Continuum Concept related longitudinal shifts in carbon and
energy sources (i.e., allochthony vs. autochthony) to
changes in fish assemblage structure. Other investigators have shown that increased species richness and
shifts in trophic structure are concurrent with increases in stream size, catchment area, and habitat volume
(Newall & Magnuson 1999). Further evidence also
suggests that fish assemblages vary by position within
the stream network (Smith & Kraft 2005), especially
relative to distance from major tributary mouths or
from larger, mainstem rivers, which can introduce species typical of larger streams to small stream assemblages (Gorman 1986, Osborne & Wiley 1992).
In spite of this current understanding, the relative
influence of environmental characteristics at different
spatial scales remains largely unresolved (see Allan et
al. 1997, Townsend et al. 2003). In particular, investigations of landscape- versus local-scale environmental
characteristics on aquatic assemblages have yielded
mixed results. Some investigators suggest that broad,
landscape-scale properties, such as catchment land
use and land cover (LULC) better predict in-stream
conditions (Roth et al. 1996, Johnson et al. 1997) and
fish assemblage characteristics (Jones & Grant 1996,
Richards et al. 1996, Allan et al. 1997). For example,
working in distinct biogeographic regions, Pease et
al. (2011) and Esselman & Allan (2010) found strong
effects of both landscape-scale (e.g., catchment land
cover) and local-scale (e.g., in-stream habitat) factors on stream fish assemblage structure. Others have
found finer-scale, reach-level factors including riparian LULC characteristics (Sponseller et al. 2001), instream habitat (Meffe & Sheldon 1988, Talmage et al.
2002, Rowe et al. 2009), and water quality (Meador
& Goldstein 2003) to be more influential on stream
biota. Furthermore, the hierarchical nature of stream
ecosystems suggests that biological communities are
spatially-structured as well and characteristics of local
assemblages are influenced not only by spatial position but also underlying spatial structure of environmental conditions (Dray et al. 2006).
In this study, we explored relationships between
fish assemblages and environmental characteristics at
the local (i.e., reach) and landscape (i.e., catchment)
scales, as well as the effect of spatial influences (i.e.,
relative spatial distribution of study sites and underlying spatial structure of environmental factors) on
stream fish assemblages. We chose two distinct regions
of the continental USA (Idaho – ID and Ohio – OH),
each representing unique climatic and physiographic
regions, for our study. We synthesize results from this
descriptive effort to propose future research directions
that may be fruitful in further understanding the variability of multi-scale environment-fish relationships in
differing biogeographic contexts.
Methods
Study areas
We surveyed sixteen stream reaches in northern ID (2006 – 2007)
and sixteen in southern OH (2009) (Fig. 1). Study catchments
were selected to represent dominant landscapes (defined by geology and LULC) of each region and individual study reaches
were selected to be largely representative of overall catchment
LULC. Our northern ID study region is mountainous with a
maritime-influenced climate, dominated by extensive coniferous forests and highly erodible volcanic ash deposits (McGrath
et al. 2002). In OH, rolling glaciated plains consisting of rich,
loamy soils and extensive glacial deposits characterize the western part of our OH study area (Griffith et al. 2008). Forests and
prairie historically covered this section of the state, however,
today, large-scale agriculture and urban development dominate
the landscape (Griffith et al. 2008). In general, landscapes in
the eastern part of out OH study area are unglaciated, hilly, and
largely covered with second-growth mixed oak forests.
Fish surveys
Based on a geomorphic process approach, we established reach
lengths to be 20 X mean bankfull width (Kondolf & Micheli
1995). We surveyed fish assemblages using a Smith-Root® LR24 backpack electrofisher, blocking each reach with fine-mesh
nets to prevent fish emigration or immigration while electrofishing. Given our assemblage-level assessment, we aimed for
a 70 % depletion, which usually required 3 – 4 passes. Because
we would have been unlikely to achieve a depletion of 70 % for
all species in the entire assemblage, we chose common species
to measure our depletion level in the field [ID, salmonid species; OH, creek chub (Semotilus atromaculatus) or bluntnose
minnow (Pimephales notatus), depending on dominance]. Following capture, we weighed (g) and identified each individual
fish to species. All fish were held in aerated live-wells until they
were released back into the stream following processing. We
completed all fish surveys during low flow periods of the summer and early fall.
eschweizerbart_XXX
Landscape-scale environmental factor effects on stream fish assemblages
261
Fig. 1. Location of the study reaches in Ohio (n = 16) and Idaho (n = 16), USA. In the ID map, rectangles enclose the study catchments, and blow-outs depict the spatial distribution of study reaches in each catchment. Blow-outs are not to scale.
Local variables
Our local-scale environmental variables were bounded by the
extent of the reach. We characterized in-stream, bank, and riparian habitat using a combination of quantitative and semi-quantitative methods. Because of the relative importance of wood
in fluvial systems, we counted all pieces of large woody debris
(LWD; > 0.1 m × 1.0 m) within the bankfull channel (Montgomery et al. 1995). We used a hybrid assessment of stream
condition derived from the Idaho Small Stream Ecological
Assessment Framework (Grafe 2002) and the Vermont Rapid
Habitat Assessment (VTDEC 2003) to evaluate habitat quality
in our ID streams. The resulting Idaho Rapid Habitat Assessment (IRHA) incorporated the abiotic ID evaluation descriptors
within the Vermont assessment framework to provide a more
quantitative protocol designed to target habitat homogenization
across the reach (Plafkin et al. 1989, Barbour et al. 1999, Sullivan & Vierling, in press). We used the Ohio Qualitative Habitat
Evaluation Index (QHEI) for OH study reaches (Rankin 2006).
Both assessment protocols were designed to evaluate habitat
heterogeneity within the study reach and subsequently, both focus on similar metrics relative to the quality of in-stream, nearshore, and riparian habitat. Higher scores (IRHA 0–200; QHEI
0–100) indicate better quality habitat overall.
eschweizerbart_XXX
We conducted cross-sectional surveys at equidistant lateral
transects where we measured bankfull width, wetted width,
mean water depth, and mean bankfull depth (e.g., the depth at
which a stream has reached the top of its banks). We quantified median sediment size (D50) using pebble counts following the Wolman method (1954). We used a Rapid Geomorphic
Assessment (RGA) protocol to evaluate geomorphic condition
based on channel adjustment processes (VTDEC 2003). Due to
the differing geological contexts and valley settings, we used
the base RGA protocols developed for unconfined streams in
OH and for confined streams in ID. Both protocols assess four
geomorphic adjustment categories: degradation, aggradation,
widening, and change in planform. Based on field indicators
of sediment accumulation, presence of bars, bank erosion, bed
scour, and other indicators of adjustment, we scored each category from 0 to 20, where 0 represented significant channel adjustment and gross deviation from expected geomorphic conditions; total RGA scores ranged from 0 to 80.
Although our focus was on physical abiotic variables, we
remained aware of the potential influences of water quality on
fish assemblages (Meador & Goldstein 2003, Esselman & Allan 2010). Water quality concerns in our ID catchments primarily relate to sedimentation (Rowe et al. 2003), although legacy
heavy-metal effects have been reported in some locales (Maret
262
Adam Kautza and S. Mažeika P. Sullivan
& MacCoy 2002). Potential influences of water quality on fishes were likely of greater concern in OH given the prevalence of
urban and agricultural land uses. We attempted to limit the potential effects of water-quality through thoughtful experimental design including careful selection of reaches that had intact
vegetated riparian buffers and no contaminant point sources
within or upstream of the reach. We measured pH and conductivity at three points (left bank, mid-channel, right bank) at the
bottom, middle, and top of each reach using a YSI 650 MDS®
with a 600 R® sonde. From these sub-sampling locations, we
generated mean pH and conductivity estimates for each reach.
Landscape variables
The landscape was bounded by the spatial extent of the catchment upstream from the outlet point of each of our study
reaches. We calculated drainage area of the catchment and
landscape-scale characteristics using ArcGIS® 9.3 (ESRI,
Redlands, CA, USA). We selected variables that characterized
predominant LULC and used land-cover data from the National
Land Cover Dataset (USGS 2001) to quantify % development
(i.e., % developed open space + % low-density development
+ % medium-density development), % high-density development, % barren land, % impervious surfaces, % forest (i.e.,
deciduous, evergreen, and mixed forest combined), % shrub/
scrub, % grassland, % pasture/hay, % crops, and % wetlands
in each study catchment. We measured the degree of canopy
closure of forested sections by quantifying the area comprised
of moderate (26–50 %) and high (76–100 %) canopy coverage.
Little, if any, developed land cover was present in our ID study
catchments largely due to the rugged topography, climate, and
remoteness of the area. However, timber harvest operations and
recreational use are prevalent in the region and this has led to an
extensive network of roads across the landscape. We used road
density (km · km2) in the catchment as a measure of development in our ID study region.
Spatial variables
We used a Principal Coordinates of Neighbors Matrix (PCNM)
approach to generate a set of spatial variables (Borcard & Legendre 2002). The PCNM is generated from a Euclidean distance
matrix based on the latitudinal and longitudinal coordinates of
each study reach. The PCNM axes provide a method to describe underlying spatial structure of the sample sites across the
entire range of scales present in the study design. The PCNM
results can be used to uncover significant spatial relationships
among sample sites and biological data. This method is often
considered an improvement on trend surface analysis, which
only accounts for coarse spatial variation at the broadest scales
represented in the data set, and can therefore be less effective in
modeling fine-scale relationships (Borcard & Legendre 2002,
Dray et al. 2006).
Numerical analysis
We calculated fish density (number · m– 2) and generated biomass estimates (g · m– 2) based on surface area (mean wetted
width × reach length) of each study reach. We measured fish
assemblage diversity using species richness (S), ShannonWeiner’s Informational Index (H’) (Shannon & Weaver 1949),
evenness (E), and Simpson’s Diversity Index (1/D) (Simpson
1949). H’ increases, based in part, on total number of species
counted, but the relative density of each species is also taken
into account, thereby incorporating a measure of heterogeneity.
Simpson’s index is a measure of dominance and quantifies the
degree to which individuals are concentrated in a few species.
Given the differences in sampling area among our reaches and
the inherent relationship between species richness and habitat
area (Angermeier & Schlosser 1989), we applied a species-area
equation following Preston (1960) to adjust our final diversity
measures. Using Barbour et al. (1999) as a guide, we selected
the following guilds to further characterize fish assemblage
composition: % top carnivores [largemouth bass (Micropterus
salmoides), smallmouth bass (Micropterus dolomieui), and rock
bass (Ambloplites rupestris) – OH] or % ‘catchable’ salmonid
species (ID); and % benthic insectivores [e.g., sculpin species
(ID and OH), darter species (OH), sucker species (OH), madtom species (OH), and benthic-oriented cyprinid species (OH)].
Statistical analysis
We arranged our three sets [landscape, local, and spatial (derived from PCNM)] of explanatory variables into separate matrices for use in a partial constrained ordination analysis. Partial
constrained ordination was a relevant statistical approach in
this case as it allowed us to quantify the relative contributions
of both pure and shared influences of groups of explanatory environmental variables (Anderson & Gribble 1998). Prior to variance partitioning procedures, we performed steps to minimize
the overall degree of collinearity among variables and attain the
appropriate number of predictor variables (i.e., sample size –1;
ter Braak & Verdonschot 1995) for use in a constrained ordination. We created pairwise correlation matrices separately for local and landscape variables for each study region and identified
pairs exhibiting Pearson correlation coefficients (r) > 0.80. In
the event of significant correlations, we retained only one of the
two variables. The retained variable was chosen based on our
judgment of its relative predictive potential.
In partial constrained ordination, either canonical correspondence analysis (CCA) or redundancy analysis (RDA) are
used based on the inherent relationship between the observed
biological parameters and environmental gradients; linear relationships dictate the use of RDA, unimodal responses require
CCA. We ran detrended correspondence analysis (DCA) with
our environmental and fish data to determine which constrained
ordination analyses to use (ter Braak & Verdonschot 1995). Results of gradient lengths along the first DCA axis from both our
OH and ID fish assemblage datasets were < 2 standard deviations, indicating that RDA was the appropriate ordination technique (ter Braak & Verdonschot 1995).
We used forward selection to identify significant predictors (Blanchet et al. 2008). We ran 1000 permutations of Monte
Carlo tests to identify significant predictors. We retained only
significant predictor variables, thereby reducing the total number of variables across the three explanatory matrices (local,
landscape, and spatial) to < 15 for each study region. We retained three (habitat score, LWD, and conductivity) of seven
local-scale environmental variables in our OH dataset and five
(bankfull depth, RGA, LWD, D50, and pH) of seven local-scale
variables in our ID dataset. We retained, four (drainage area,
% development, % wetlands, and % pasture/hay) of fourteen
landscape-scale variables from our OH dataset and four (drainage area, road density, % forest, and % shrub/scrub) of eleven
landscape-scale variables from our ID dataset. Relatively few
of the original landscape-scale variables were retained for
eschweizerbart_XXX
Landscape-scale environmental factor effects on stream fish assemblages
use in our final analyses due in part to the strong associations
among LULC types in our study catchments.
Variance partitioning required twelve runs ([run] constraining variable(covariables)): [1] local; [2] landscape; [3] spatial;
[4] local(landscape); [5] local(spatial); [6] local(landscape
+ spatial); [7] landscape(local); [8] landscape(spatial); [9]
land­scape(local + spatial); [10] spatial(local); [11] spa­
tial(landscape); and [12] spatial(local + landscape). This
method partitions the total variation of the fish assemblage
dataset into components that are explained by local, landscape,
and spatial predictors, as well as the relative amount of variation explained by each explanatory matrix when one or both of
the other matrices are partialled out as covariables (Anderson
& Gribble 1998). Using this approach, we first partitioned total
variation in fish assemblage composition into four components:
pure environmental, pure spatial, shared (i.e., spatially-structured environmental), and unexplained. To quantify the influence of environmental factors at landscape and local scales,
we subsequently partitioned out the three components of pure
environmental variation (i.e., local, landscape, and joint). We
visually displayed select ordination results by creating biplots
for OH and ID environment-fish datasets. We created separate
biplots illustrating the correlations between fish assemblage
263
metrics and pure environmental variables from local- and landscape-scale datasets.
We used JMP 9.0 (SAS Institute Inc., Cary, IN) statistical
software for all summary statistics and correlation analyses,
Canoco 4.5 (Microcomputer Power, Ithaca, NY) for our ordination analyses, and CanoDraw 4.5 (Microcomputer Power,
Ithaca, NY) for the biplots.
Results
Fish assemblages
Fish assemblages were markedly different between our
two study regions (Table 1), as was expected considering the unique climatic, geological, and biogeographic
settings. We surveyed a total of 42 species from OH
study reaches, whereas we observed only 16 species in
ID. Overall, assemblages were characterized by higher
diversity in OH than in ID. Although mean fish density
Table 1. Summary statistics for multi-scale environmental variables and fish assemblage characteristics from the 32 study catch-
ments in OH and ID, USA.
eschweizerbart_XXX
264
Adam Kautza and S. Mažeika P. Sullivan
was similar, the comparatively large SD of fish density
in our ID study area was largely an artifact of the high
abundances of various sculpin species found at a few
reaches. Biomass was greater in OH assemblages than
in ID assemblages although results may have been inflated by the high biomass measured at one reach that
contained a number of large, heavy-bodied common
carp (Cyprinus carpio). ID assemblages were dominated by a suite of salmonid species [e.g., bull trout
(Salvelinus confluentus), brook trout (Salvelinus fontinalis), rainbow trout (Oncorhynchus mykiss), westslope cutthroat trout (Oncorhynchus clarkii lewisi),
and cutthroat × rainbow hybrid trout (O. clarkii lewisi
× O. mykiss)] and in some cases, northern pikeminnow
(Ptychocheilus oregonensis) as top carnivores. In contrast, we found OH fish assemblages to be comprised
of relatively few top carnivores. Benthic insectivores,
including central stoneroller minnow (Campostoma
anomalum) and various darter species, sucker species,
and bullhead species dominated OH assemblages.
Environmental-fish relationships
We found substantial variability in the environmental
conditions at the local- and landscape-scales in each
study region (Table 1). Our partial constrained ordination results illustrated that the relative effects of environmental factors and spatial influences on fish assemblages were different between the two study regions
as well. Overall, a larger proportion of assemblage
variation was accounted for in our ID dataset (76.7 %)
than in our OH dataset (69.8 %). The proportion of
assemblage variation accounted for by pure spatial
influences (i.e., spatial patterns not shared by environmental data) was larger in OH (25.5 %, p = 0.018)
than in ID (17.5 %, p = 0.002) (Fig. 2). However, the
influence of spatially-structured environmental vari-
Fig. 2. The proportion of variation in fish assemblage characteristics accounted for by pure spatial factors (derived from a principal
coordinates of neighbor matrices approach), shared (spatially-structured) environmental variables, and pure (non-spatial) environmental variables. Proportions of explained variation were obtained from variance partitioning procedures in partial RDA. Also
illustrated (in call-outs) are the relative amounts of the pure, non-spatial environmental variation accounted for by local, landscape,
and joint (i.e., variation shared by both local and landscape-scale variables) influences. The left column shows results for Ohio and
the right column for Idaho.
eschweizerbart_XXX
Landscape-scale environmental factor effects on stream fish assemblages
265
Fig. 3. RDA biplots highlighting relationships between landscape-scale environmental variables and fish assemblage characteris-
tics for (a) OH and (b) ID. Solid arrows indicate the direction of increase for fish assemblage variables and dashed arrows indicate
how environmental variables were ordinated.
ables (i.e., variation shared by both spatial and environmental factors) was more important in ID (28.6 %)
than OH (20.7 %). Pure environmental factors (i.e.,
local and landscape variables with spatial influences
partialled-out) accounted for more variation in ID
(30.6 %, p = 0.002) than OH assemblages (23.6 %,
p = 0.168).
When we explored the influence of pure environmental factors in greater detail (i.e., pure local-scale,
pure landscape-scale, and joint influences of local- and
landscape-scale factors) we found that local-scale factors accounted for a larger proportion of variation in
ID than in OH fish assemblages (Fig. 2). In contrast,
the effect of pure landscape-scale factors was similar
between study regions. Joint influences (i.e., variation
that can be explained by either local or landscape factors) accounted for a larger relative proportion of assemblage variation in OH, suggesting that fish assemblages and local conditions are responding to similar
underlying gradients present at broader, landscape
scales.
We illustrated several key environment-fish relationships in our ordination biplots. At the landscape
eschweizerbart_XXX
scale in OH, fish assemblage richness was strongly
correlated with drainage area (+ ) (Fig. 3a). Fish assemblage H’, % carnivores, and assemblage biomass
were all negatively associated with pasture/hay, and
to some degree, catchment development. The ID assemblage-landscape biplot illustrated a positive relationship between drainage area and species richness,
similar to OH (Fig. 3b). The landscape biplot for ID
also illustrated key positive relationships between (1)
shrub/scrub land cover and assemblage biomass, %
carnivores, 1/D, and (2) forest cover and assemblage
density and % benthic insectivores. Our OH assemblage-local biplot highlighted positive relationships
between assemblage dominance (1/D), assemblage
biomass, and density with habitat score (Fig. 4 a). In
contrast, other measures of assemblage diversity (S,
H’, and E) displayed a negative relationship with habitat scores. In ID, at the local scale, multiple fish assemblage metrics most closely aligned with measures
of geomorphic condition and median sediment size
(Fig. 4 b). However, the proportion of carnivores in the
assemblage was more closely associated with both pH
and bankfull depth (+).
266
Adam Kautza and S. Mažeika P. Sullivan
Fig. 4. RDA biplots highlighting relationships between local-scale environmental variables and fish assemblage characteristics for
(a) OH and (b) ID. Solid arrows indicate the direction of increase for fish assemblage variables and dashed arrows indicate how
environmental variables were ordinated.
Discussion
Understanding the concurrent influences of environmental factors at both landscape and local scales is
becoming increasingly important in the monitoring,
assessment, and conservation of stream fish assemblages. In this study, we explored the relative influence of spatially-explicit environmental factors on fish
assemblages in catchments of ID and OH. Our results
strongly point to the importance of integrating the spatial context of the study sites in predicting fish assemblage characteristics. Our findings also indicate that
the relative influence of both spatial and environmental variables on fish assemblage variation can be markedly different in unique biogeographic regions. Taken
as a whole, our findings contribute to a growing body
of literature addressing multi-scale fish-environment
relationships in differing biogeographic contexts.
Spatial relationships
A more complete understanding of complex multiscale influences of environmental characteristics on
fish assemblages requires that, in addition to linking
spatially-explicit environmental attributes with fish
assemblages, we simultaneously consider the influ-
ence of spatial patterns in fish assemblages relative to
both the distribution of environmental variables and
the distribution of the assemblages themselves. This
is important for two primary reasons. Firstly, although
spatial correlations and ecological patterns and processes are largely thought to be scale-dependent, few
aquatic studies have simultaneously evaluated the associations between different levels of spatial scale and
biotic community patterns and the influence of spatial
structure on these relationships (but see Magalhães et
al. 2002, Esselman & Allan 2010, Sály et al. 2011).
Second, potential covariation between anthropogenic
activities and natural landscape attributes can lead to
an overestimation of land-use effects (Allan 2004),
suggesting that correlations between human development and natural landscape gradients might also be
expected to vary with the scale of observation. For example, Wang et al. (2001) showed that the influence
of impervious surfaces varied relative to the distance
from the sampling location.
In our study, spatial factors represented the predominant influence on fish assemblages with pure spatial
and shared (spatially-structured) environmental variables cumulatively explaining ~46 % of the variation
in fish assemblage characteristics in both study areas.
eschweizerbart_XXX
Landscape-scale environmental factor effects on stream fish assemblages
However, the relative influences of pure and shared
spatial variation were different for each region. In OH,
the greater influence of pure spatial factors indicates a
strong effect of the relative location and distribution
of study reaches (e.g., among-site distances, geo-coordinates of the reaches) on fish assemblages. Spatial
factors alone can be important predictors of fish assemblages, particularly in highly-modified landscapes
where effects such as historic land-cover conversion
can limit the apparent influence of current environmental factors (Allan et al. 1997). Overall, shared environmental variables were less influential in our OH
catchments, a result also found by Sály et al. (2011)
in agricultural catchments in Hungary. In contrast, the
relative importance of shared environmental variables
in ID suggests that both fish assemblages and environmental conditions were, to some degree, responding to
similar driving factors (e.g., ecoregional factors such
as underlying geology, topography, and climate). Similarly, Esselman & Allan (2010) found that position
within the catchment explained significant variability
in fish presence-absence and community measures in
Mesoamerican catchments. In both study areas, but
particularly in ID, pure environmental (non-spatial)
factors accounted for substantial variation in fish assemblage characteristics indicating that assemblages
were influenced by environmental variables with no
discernible spatial pattern. These non-spatial environmental factors in OH were likely associated with
land-use and land-cover changes that are not explicitly
constrained by ecoregional characteristics.
Multi-scale relationships
The contributions of local- and landscape-scale variables, together with variation explained by the joint
influence of both scales, to the overall pure environmental (non-spatial) component were remarkably
balanced in our OH study region. In ID, the contribution of local-scale variation to the pure environmental
component was predominant. Together, these patterns
suggest that the interactions of fish assemblages and
their environment are different between our ID and
OH study regions. Below, we outline some of these
key pure environment-fish patterns, focusing first on
the landscape- and subsequently on the local-scale.
At the landscape scale, there was a close positive
relationship between species richness and drainage
area in both study regions. This association has been
well documented for low- to mid-order streams (Sheldon 1968, Oberdorff et al. 1993, Matthews & Robison
1998). In fact, drainage area has been found to be one
of the major factors explaining fish species richness in
eschweizerbart_XXX
267
rivers worldwide (Oberdorff et al. 1995). In OH, characterized by high levels of anthropogenic influences
on the landscape, factors related to LULC largely constrained fish assemblages. Wang et al. (2003) found
similar results in that fish assemblages were more
tightly-coupled with broad, catchment-level environmental characteristics in modified landscapes typical
of developed areas. Furthermore, Burcher et al. (2007)
suggest that catchment LULC likely modifies the hierarchical structure of stream ecosystems to the extent
that widespread anthropogenic landscape conversion
can reorganize intrinsic fish assemblage structure.
Urban development and agricultural LULC are widespread across our OH study region and both have led
to considerable modification of hydrological regimes
and geomorphic processes, which can negatively influence stream water quality (Johnson et al. 1997, Paul
& Meyer 2001, Allan 2004). Even small amounts of
impervious surfaces associated with urbanization, for
example, can have a disproportionately large, negative
impact on stream fish richness and diversity (Allan et
al. 1997, Wang et al. 2001).
At the local-scale, we observed that stream habitat and geomorphic condition were important in OH
and ID, respectively. Stream habitat assessments integrate a number of influential habitat variables and
generally have been found to be closely related to a
variety of fish assemblage characteristics (Milner et al.
2006, Sullivan & Watzin 2009). In our study, habitat
scores based on the OH QHEI emerged as an important variable aligning most strongly with assemblage
density and biomass. Furthermore, we observed a
close association between habitat assessment scores
and reach-scale geomorphic condition in both study
regions. Although geomorphic assessment scores were
not significant predictors in the final ordinations for
OH, geomorphic condition may also be contributing
to reach-level fish descriptors as seen in other studies (e.g., Walters et al. 2003, Sullivan et al. 2006). In
general, we also found reaches in better geomorphic
condition (e.g., not actively adjusting) and with larger
mean sediment size supported greater assemblage diversity, density, and benthic insectivores in the mountain drainage basins in ID. Embeddedness and subsequent declines in benthic habitat heterogeneity, fish
assemblage diversity (Berkman & Rabeni 1987), and
individual fish condition (Sullivan & Watzin 2010)
has been found to be tightly linked to influx of fine
sediments to streams. Coarse sediments in mountain
streams have also been shown to be especially critical
as they are utilized as velocity refuge and cover by
many fishes (Fausch 1993).
268
Adam Kautza and S. Mažeika P. Sullivan
The relative importance of joint environmental
factors suggests that both fish assemblages and localscale environmental variables were being influenced
by similar environmental factors present at broader
spatial scales. This pattern points to the interconnectedness between stream fish assemblages and multiple
levels of spatial scale. Together with the influence of
local-scale factors, this also supports the view that
catchments set the upper bounds on environmental
and biological characteristics that are spatially nested
within them (Frissel 1986, Poff 1997). As spatial position of a stream reach within a drainage network is
known to influence fish assemblage properties, this
was not an unexpected result (Gorman 1986, Osborne
& Wiley 1992, Benda et al. 2004).
Conclusions and future directions
Overall, we observed that in both study regions spatial
influences (i.e., pure spatial and spatially-structured
environmental influences) accounted for the largest
proportion of variation in fish assemblages. When we
examined the influence of pure environmental factors
(i.e., non-spatial) we observed that fish assemblages
were driven by a combination of both broad, landscape-scale as well as local-scale factors. However,
fish assemblages were more influenced by local factors in the relatively undisturbed landscapes typical
of northern ID. In the highly-developed landscapes
typical of OH, human development in the catchment
represented the dominant influence on stream fish assemblages, thus providing preliminary evidence related to the potential influence of human activities in
restructuring environment-fish relationships. Future
research will be needed to fully address multi-scale
environmental influences on stream fish assemblages
before applications to conservation and management
can be made.
The spatial and environmental variables that we
used in our ordination analyses accounted for a substantial proportion of assemblage variation. However,
the remaining, unexplained portion of variation was
likely a result of variables and processes that were
beyond the scope of this study. For example, we did
not directly incorporate stochastic events such as major floods or point source inputs that may have had
acute impact on local fish assemblages in the past and
may be reflected in current assemblage composition.
Patterns of species dispersal, which can influence the
structure of local assemblages (Leibold et al. 2004,
Cottenie 2005), were not explicitly modeled in this
study but may have also contributed to unexplained
variation observed. The greater amount of unexplained
variation from our OH catchments may indicate that
high-impact human activities on the landscape may introduce additional ‘noise’ into the relationships among
the catchment landscape, local habitat, and fish assemblages.
Furthermore, we focused on spatial characteristics
that may have links to processes, but we did not explicitly address potential mechanisms. There are myriad
challenges associated with linking pattern and mechanism across scales. For example, many processes can
operate at scales much larger (or smaller) than they
may appear (e.g., pollen dispersal, insect emergence).
However, as McIntire & Fajardo (2009) suggest, we
may be able to establish precise and unique signatures
for spatial processes that we are unable to measure directly. In catchment contexts, this may relate to aquatic-terrestrial exchanges of energy, nutrients, and contaminants (Baxter et al. 2005, Sullivan & Rodewald,
in press); linkages between fire pulses and stream food
webs (Jackson & Sullivan 2009, Malison & Baxter
2010); and transport and deposition of sediment, both
within the channel and across the catchment landscape
(Nelson & Booth 2002, Sullivan & Watzin 2010). We
anticipate that overtly connecting observed patterns to
potential mechanisms will be an important direction of
future research and we expect would contribute to an
improved understanding of complex, multi-scale ecological relationships in catchment contexts.
Acknowledgements
We extend our thanks to Matthew Mason, Ryan Mann, John
Gravelle, Paul Charpentier, John Cassinelli, Jordan Nielson,
Breezy Jackson, Josh Cherubini, Lars Meyer, Dan Giannamore, and Megan Nims for their assistance in the field. We
would also like to express our appreciation for Chi Vuong for
her help with the remote sensing and GIS work. Funding for
this research was provided by USFS Research Joint Venture
Agreement No. 04- D6-11010000-37, The Ohio State University, and MacIntyre-Stennis. We also thank Potlatch Corporation for their cooperation.
References
Allan, J. D., 2004: Landscapes and riverscapes: The influence
of land use on stream ecosystems. – Annu. Rev. Ecol. Evol.
System. 35: 257– 284.
Allan, J. D., Erickson, D. L. & Fay, J., 1997: The influence of
catchment land use on stream integrity across multiple spatial scales. – Freshwat. Biol. 37: 149 –161.
Anderson, M. J. & Gribble, N. A., 1998: Partitioning the variation among spatial, temporal and environmental components
in a multivariate data set. – Austral. J. Ecol. 23: 158 –167.
Angermeier, P. L. & Schlosser, I. J., 1989: Species area relationships for stream fishes. – Ecology 70: 1450 –1462.
eschweizerbart_XXX
Landscape-scale environmental factor effects on stream fish assemblages
Angermeier, P. L. & Winston, M. R., 1998: Local vs. regional
influences on local diversity in stream fish communities of
Virginia. – Ecology 79: 911– 922.
Barbour, M. T., Gerritsen, J., Snyder, B. D. & Stribling, J. B.,
1999: Rapid bioassessment protocols for use in streams and
wadeable rivers: periphyton, benthic macroinvertebrates and
fish. – EPA 841-B-99-002, US Environmental Protection
Agency, Washington, DC, USA.
Baxter, C. V., Fausch, K. D. & Saunders, W. C., 2005: Tangled
webs: reciprocal flows of invertebrate prey link streams and
riparian zones. – Freshwat. Biol. 50: 201– 220.
Benda, L., Poff, N. L., Miller, D., Dunne, T., Reeves, G., Pollock, M. & Pess, G., 2004: Network dynamics hypothesis:
spatial and temporal organization of physical heterogeneity
in rivers. – BioScience 54: 413 – 427.
Berkman, H. E. & Rabeni, C. F., 1987: Effect of siltation
on stream fish communities. – Environ. Biol. Fishes 18:
285 – 294.
Blanchet, F. G., Legendre, P. & Borcard, D., 2008: Forward selection of explanatory variables. – Ecology 89 (9):
2623 – 2632.
Borcard, D. & Legendre, P., 2002: All-scale spatial analysis of
ecological data by means of principal coordinates of neighbor matrices. – Ecol. Modelling 153: 51– 68.
Burcher, C. L., Valett, H. M. & Benfield, E. F., 2007: The landcover cascade: relationships coupling land and water. – Ecology 88: 228 – 242.
Cookson, N. & Schorr, M. S., 2009: Correlations of watershed
housing density with environmental conditions and fish assemblages in a Tennessee ridge and valley stream. – J. Freshwat. Ecol. 24: 553 – 561.
Cottenie, K., 2005: Integrating environmental and spatial processes in ecological community dynamics. – Ecol. Lett. 8
(11): 1175 –1182.
Dray, S., Legendre, P. & Peres-Neto, P. R., 2006: Spatial modelling: a comprehensive framework for principal coordinate
analysis of neighbor matrices (PCNM). – Ecol. Modelling
196: 483 – 493.
Esselman, P. C. & Allan, J. D., 2010: Relative influences of
catchment- and reach-scale abiotic factors on freshwater fish
communities in rivers of northeastern Mesoamerica. – Ecol.
Freshwat. Fish 19: 439 – 454.
Fausch, K. D., 1993: Experimental analysis of microhabitat
selection by juvenile steelhead (Oncorhynchus kisutch) in a
British Columbia stream. – Can. J. Fisher. Aquat. Sci. 50:
1198 –1207.
Frissel, C. A., Liss, W. J., Warren, C. E. & Hurley, M. D., 1986:
A heirarchical framework for stream habitat classification:
Viewing streams in a watershed context. – Environ. Manage.
10: 199 – 214.
Gorman, O. T., 1986: Assemblage organization of stream fishes: the effect of rivers on adventitious streams. –Am. Natur.
128: 611– 616.
Grafe, C. S., 2002: Idaho small stream ecological assessment
framework: an integrated approach. – Idaho Department of
Environmental Quality. – Boise, Idaho, USA.
Griffith, G. E., Omernik, J. M. & McGinley, M., 2008: Ecoregions of Indiana and Ohio. – In: Cleveland, C. J. (ed.):, Environmental Information Coalition National Council for Science and the Environment. – Washington, DC.
Jackson, B. J. & Sullivan, S. M. P., 2009: Influence of wildfire severity on riparian plant community heterogeneity in an
Idaho, USA wilderness. – Forest Ecol. Manage. 1: 24 – 32.
eschweizerbart_XXX
269
Johnson, L. B., Richards, C., Host, G. E. & Arthur, J. W., 1997:
Landscape influences on water chemistry in Midwestern
stream ecosystems. – Freshwat. Biol. 37: 193 – 208.
Jones, J. A. & Grant, E., 1996: Peak flow responses to clear-cutting and roads in small and large basins, western Cascades,
Oregon. – Wat. Resour. Res. 32: 959 – 974.
Kondolf, G. M. & Micheli, E. M., 1995: Evaluating stream restoration projects. – Environ. Manage. 19: 1–15.
Lammert, M. & Allan, J. D., 1999: Assessing biotic integrity of
streams: effects of scale in measuring the influence of land
use/cover and habitat structure on fish and macroinvertebrates. – Environ. Manage. 23: 257– 270.
Leibold, M. A., Holyoak, M., Mouquet, N., Amarasekare, P.,
Chase, J. M., Hoopes, M. F., Holt, R. D., Shurin, J. B., Law,
R., Tilman, D., Loreau, M. & Gonzalez, A., 2004: The metacommunity concept: a framework for multi-scale community
ecology. – Ecol. Lett. 7: 601– 613.
Magalhães, M. F., Batalha, D. C. & Collares-Pereira, M. J.,
2002: Gradients in stream fish assemblages across a Mediterranean landscape: contributions of environmental factors and
spatial structure. – Freshwat. Biol. 47: 1015 –1031.
Malison, R. L. & Baxter, C. V., 2010: The “fire pulse”: wildfire
stimulates flux of aquatic prey to terrestrial habitats driving
increases in riparian consumers. – Can. J. Fisher. Aquat. Sci.
67: 570 – 579.
Maret, T. R. & MacCoy, D. E., 2002: Fish assemblages and
environmental variables associated with hard-rock mining
in the Coeur d’Alene River Basin, Idaho. – Transact. Am.
Fisher. Soc. 131: 865 – 884.
Matthews, W. J. & Robison, H. W., 1998: Influence of drainage connectivity, drainage area and regional species richness
on fishes of the interior highlands in Arkansas. – Am. Midl.
Natur. 139: 1–19.
McGrath, C. L., Woods, A. J., Omernik, J. M., Bryce, S. A.,
Edmondson, M., Nesser, J. A., Shelden, J., Crawford, R. C.,
Comstock, J. A. & Plocher, M. D., 2002: Ecoregions of Idaho
(color poster with map, descriptive text, summary tables, and
photographs). – U.S. Geological Survey, Reston, VA.
McIntire, E. J. B. & Fajardo, A., 2009: Beyond description: the
active and effective way to infer processes from spatial patterns. – Ecology 90: 46 – 56.
Meador, M. R. & Goldstein, R. M., 2003: Assessing water quality at large geographic scales: Relations among land use, water physicochemistry, riparian condition, and fish community
structure. – Environ. Manage. 31: 504 – 517.
Meffe, G. K. & Sheldon, A. L., 1988: The influence of habitat structure on fish assemblage composition in southeastern
blackwater streams. – Am. Midl. Natur. 120: 225 – 240.
Milner, N. J., Hemsworth, R. J. & Jones, B. E., 2006: Habitat
evaluation as a fisheries management tool. – J. Fish Biol. 27:
85 –108.
Montgomery, D. R., 1999: Process domains and the river continuum. – J. Am. Wat. Resour. Assoc. 35: 397– 410.
Montgomery, D. R., Buffington, J. M., Smith, R. D., Schmidt,
K. M. & Pess, G., 1995: Pool spacing in forest channels. –
Wat. Resour. Res. 31: 1097–1105.
Moyle, P. B. & Cech, J. J., 2006: Fishes: An Introduction to
Ichthyology. – Prentice Hall, Upper Saddle River, NJ.
Moyle, P. B. & Herbold, B., 1987: Life history patterns and
community structure in stream fishes of western North America: comparisons with eastern North America and Europe. –
In: Matthews, W. J. & Heins, D. C. (eds): Community and
Evolutionary Ecology of North American Stream Fishes. –
University of Oklahoma Press, Norman, OK.
270
Adam Kautza and S. Mažeika P. Sullivan
Nelson, E. J. & Booth, D. B., 2002: Sediment sources in an
urbanizing, mixed land-use watershed. – J. Hydrol. 264:
51– 68.
Newall, P. R. & Magnuson, J. J., 1999: The importance of
ecoregion versus drainage area on fish distributions in the St.
Croix River and its Wisconsin tributaries. – Environ. Biol.
Fish. 55: 245 – 254.
Oberdorff, T., Guegan, J. F. & Hugueny, B., 1995: Global scale
patterns of fish species richness in rivers. – Ecography 18:
345 – 352.
Oberdorff, T., Guilbert, E. & Lucchetta, J. C., 1993: Patterns of
fish species richness in the Seine River Basin, France. – Hydrobiologia 259: 157–167.
Osborne, L. L. & Wiley, M. J., 1992: Influence of tributary spatial position on the structure of warm-water fish communities. – Can. J. Fisher. Aquat. Sci. 49: 671– 681.
Paul, M. J. & Meyer, J. L., 2001: Streams in the urban landscape. – Annu. Rev. Ecol. System. 32: 333 – 365.
Pease, A. A., Taylor, J. M., Winemiller, K. O. & King, R. S.,
2011: Multiscale influences on fish assemblage structure in
central Texas streams. – Transact. Am. Fisher. Soc. 140 (5):
1409 –1427.
Plafkin, J. L., Barbour, M. T., Porter, K. D., Gross, S. K. &
Hughes, R. M., 1989: Rapid bioassessment protocols for use
in streams and rivers: benthic macroinvertebrates and fish. –
US Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC.
Poff, N. L., 1997: Landscape filters and species traits: Towards
mechanistic understanding and prediction in stream ecology.
– J. N. Am. Benthol. Soc. 16: 391– 409.
Preston, F. W., 1960: Time and space and the variation of species. – Ecology 41: 611– 627.
Rankin, E. T., 2006: Methods for assessing habitat in flowing
waters: using the qualitative habitat evaluation index. – Ohio
Environmental Protection Agency, Columbus, OH.
Richards, C., Johnson, L. B. & Host, G. E., 1996: Landscape
scale influences on stream habitats and biota. – Can. J. Fisher. Aquat. Sci. 53: 295 – 311.
Ricklefs, R., 1987: Community diversity – relative roles of local and regional processes. – Science 235: 167–171.
Roth, N. E., Allan, J. D. & Erickson, D. L., 1996: Landscape
influences on stream biotic integrity assessed at multiple spatial scales. – Landscape Ecol. 11: 141–156.
Rowe, D. C., Pierce, C. L. & Wilton, T. F., 2009: Fish assemblage relationships with physical habitat in wadeable Iowa
streams. – N. Am. J. Fisher. Manage. 29: 1314 –1332.
Rowe, M., Essig, D. & Jessup, B., 2003: Guide to selection of
sediment targets for use in Idaho TMDLs. – Idaho Department of Environmental Quality, Boise, ID.
Sály, P., Takács, P., Kiss, I., Bíro, P. & Erös, T., 2011: The relative influence of spatial context and catchment- and sitescale environmental factors on stream fish assemblages in
a human-modified landscape. – Ecol. Freshwat. Fish 20:
251– 262.
Shannon, C. E. & Weaver, W., 1949: A mathematical model of
communication. – University of Illinois Press, Urbana, IL.
Sheldon, A. L., 1968: Species diversity and longitudinal succession in stream fishes. – Ecology 29: 194 –198.
Simpson, E. H., 1949: Measurement of Diversity. – Nature 163:
1.
Submitted: 27 November 2011; accepted: 26 April 2012.
Smith, T. A. & Kraft, C. E., 2005: Stream fish assemblages in
relation to landscape position and local habitat variables. –
Transact. Am. Fisher. Soc. 134: 430 – 440.
Sponseller, R. A., Benfield, E. F. & Valett, H. M., 2001: Relationships between land use, spatial scale and stream macroinvertebrate communities. – Freshwat. Biol. 46: 1409 –1424.
Sullivan, S. M. P. & Rodewald, A. D., in press: In a state of flux:
the energetic pathways that move contaminants from aquatic
to terrestrial environments. – Environ. Toxicol. Chem.
Sullivan, S. M. P. & Vierling, K., in press: Exploring the influences of multiscale environmental factors on the American
dipper (Cinclus mexicanus). – Ecography.
Sullivan, S. M. P. & Watzin, M. C., 2009: Stream-floodplain
connectivity and fish assemblage diversity in the Champlain
Valley, Vermont, USA. – J. Fish Biol. 74: 1394 –1418.
Sullivan, S. M. P. & Watzin, M. C., 2010: Towards a functional understanding of the effects of sediment aggradation on
stream fish condition. – Riv. Res. Appl. 26: 1298 –1314.
Sullivan, S. M. P., Watzin, M. C. & Hession, W. C., 2006: Influence of geomorphic condition on stream fish communities in
Vermont, USA. – Freshwat. Biol. 10: 1811–1826.
Sutherland, A. B., Meyer, J. L. & Gardiner, E. P., 2002: Effects
of land cover on sediment regime and fish assemblage structure in four southern Appalachian streams. – Freshwat. Biol.
47: 1791–1805.
Talmage, P. J., Perry, J. A. & Goldstein, R. M., 2002: Relation
of instream habitat and physical conditions to fish communities of agricultural streams in the northern Midwest. – N. Am.
J. Fisher. Manage. 22: 825 – 833.
ter Braak, C. J. F. & Verdonschot, P. F. M., 1995: Canonical
correspondence analysis and related multivariate methods in
aquatic ecology. – Aquat. Sci. 57: 255 – 287.
Townsend, C. R., Doledec, S., Norris, R., Peacock, K. & Arbuckle, C., 2003: The influence of scale and geography on
relationships between stream community composition and
landscape variables: description and prediction. – Freshwat.
Biol. 48: 768 –785.
USGS, 2001: United States Geological Survey Multi-Resolution Land Characteristics Consortium (MRLC) National
Land Cover Database. – www.mrlc.gov/.
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R.
& Cushing, C. E., 1980: The river continuum concept. – Can.
J. Fisher. Aquat. Sci. 37: 130 –137.
VTDEC, 2003: Stream geomorphic assessment handbook:
rapid stream assessment – phase 2 field protocols. – Vermont
Agency of Natural Resources, Department of Environmental
Conservation, Water Quality Division, Waterbury, VT, USA.
Walters, D. M., Leigh, D. S., Freeman, M. C. & Freeman, B. J.,
2003: Geomorphology and fish assemblages in a Piedmont
river basin, USA. – Freshwat. Biol. 48: 1950 –1970.
Walters, D. M., Roy, A. H. & Leigh, D. S., 2009: Environmental
indicators of macroinvertebrate and fish assemblage integrity
in urbanizing watersheds. – Ecol. Indicators 9: 1222 –1233.
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. Z., Lyons, J., Rasmussen, P., Seelbach, P., Simon,
T., Wiley, M., Kanehl, P., Baker, E., Niemela, S. & Stewart, P. M., 2003: Watershed, reach, and riparian influences
on stream fish assemblages in the Northern Lakes and Forest
Ecoregion, USA. – Can. J. Fisher. Aquat. Sci. 60: 491– 505.
Wolman, M. G., 1954: A method of sampling coarse river-bed
material. – Transact. Am. Geophys. Union 35: 951– 956.
eschweizerbart_XXX