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Research Article • DOI: 10.2478/remc-2013-0003 • REMC • 2013 • 11–24
RipaRian Ecology and consERvation
11
* E-mail: [email protected]
Predictability of In-Stream Physical Habitat for Wisconsin and Northern Michigan Wadeable Streams Using GIS-Derived Landscape Data
1Institute for Fisheries Research, Michigan Department of Natural Resources and University of Michigan, 1109 N University, Ann Arbor, MI 48109
Current Address: International Joint Commission, Great Lakes Regional Office, P.O Box 32869, Detroit, MI 48232
2Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, East Lansing, MI 48824
3Wisconsin Department of Natural Resources, 2801 Progress Road, Madison, WI 53716
Lizhu Wang1*, Travis Brenden2, John Lyons3, Dana Infante2
Received 30 July 2012Accepted 12 December 2012
AbstractQuantifying spatial patterns of physical and biological features is essential for managing aquatic systems. To meet broad-scale habitat assessment and monitoring needs, we evaluated the feasibility of predicting 25 in-stream physical habitat measures for wadeable stream reaches in Wisconsin and northern Michigan using geographic information system (GIS) derived stream network and landscape data. Using general additive modeling and boosting variable selection, predictions of reasonable accuracy were obtained for 10 widely used in-stream habitat measures, including bankfull depth and width, conductivity, substrate size, sand substrate, thalweg water depth, wetted width, water depth, and width-to-depth ratio. Biased predictions were obtained for habitat measures such as bank erosion, large woody debris, fish cover, canopy shading, and substrate embeddedness. Model predictions for many commonly-used habitat variables were judged acceptable based on several criteria, including correspondence between prediction errors and observed inter-annual and inter-site variability in habitat measures and agreement in correlation analyses of fish assemblage metric data with both predicted and observed values. Prediction of physical habitat variables from widely available GIS datasets represents a potentially powerful and cost-effective approach for broad-scale (e.g., multi-state, national) assessment and monitoring of in-stream conditions, for which direct measurement is largely impractical because of resource limitations.
KeywordsPhysical habitat • Modeling • Landscape • Stream network • Habitat prediction • Regional database
© Versita Sp. z o.o.
1. Introduction
Hierarchical frameworks that describe rivers and streams as
nested series of habitats ranging from local- (in-stream) to
basin-level (stream network, catchment) scales have become
relatively commonplace due to wide acceptance that stream
biota are concomitantly influenced by in-channel structure
and processes, stream hydrological network descriptors, and
landscape characteristics [1-3]. Stream network and catchment
characteristics can affect aquatic biota directly by influencing
network connectivity and hydrology, sediment, and thermal
regimes, as well as indirectly by influencing water quality, energy
source, substrate composition, and channel morphology [4-6].
As a result, complete understanding of stream-biota habitat
relationships necessitates the assessment of fluvial habitat
across multiple spatial scales.
The increased availability of regional geographic information
system (GIS) databases and technological advancements has
enhanced our ability to remotely capture stream network and
catchment scale information [7]. Among the notable achievements
that have resulted from this increased capacity has been the
development of the Great Lakes regional river database and
classification system (GLRRDACS), which includes all streams
and rivers in Illinois, Michigan, and Wisconsin [8], and the National
Hydrography Dataset plus (NHDPlus), which includes all streams
and rivers in the conterminous United States [9]. These databases
divide stream networks into confluence-to-confluence stream
reaches, with each reach having delineated local and network
catchments. Reaches are the smallest spatial unit for these stream
networks, but they can be easily combined into segments that
possess similar physicochemical and biological characteristics
[10,11]. The local and network catchments provide the basis for
attributing landscape-scale information to the streams, which
can be used to create complex, multi-scale, spatially-explicit
datasets that are essential for comprehensive evaluation of stream
conditions [7].
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L. Wang et al.
12
aforementioned GLRRDACS and NHDPlus frameworks provide
a potential solution to this problem as a result of their using
confluence-to-confluence stream reaches as the basic spatial
unit. Past research has established that a sampling site on a
specific confluence-to-confluence stream reach can reasonably
be considered representative of conditions throughout the reach
[6,10,11,22]. Thus, the GLRRDACS and NHDPlus frameworks
provide a simple basis for forecasting in-stream physical habitat
conditions from models developed using landscape scale data
to entire stream networks by virtue of their underlying structure,
in particular their reliance of stream reaches as the basic spatial
and the delineation of local and network catchments for all
reaches.
The overall goal of this study was to evaluate the feasibility
of modeling major in-stream physical habitat measures for
wadeable streams in Wisconsin and northern Michigan. We
defined wadeable streams as stream reaches with network
catchment areas < 1,600 km2 or stream orders < 5th order [23].
The specific objectives of the project were to (1) develop models
for predicting major in-stream physical habitat measures that
are commonly used by stream managers; (2) evaluate the fit and
prediction performance of the developed models by comparing
difference between modeled and observed data from the model-
development dataset and between modeled and observed data
from a model-validation dataset that had temporal and spatial
variation measures; and (3) evaluate the usefulness of the model
predictions by comparing habitat and fish assemblage metric
relationships between predicted and observed habitats.
2. Materials and Methods
2.1 Great Lakes Regional River Database and Classification System (GLRRDACS)
We conducted our study using the Michigan and Wisconsin
stream network databases, which are part of the GLRRDACS.
Streams identified from the 1:100,000 scale National Hydrography
Dataset (NHD) were divided into individual stream reaches
defined from headwater to the first confluence, confluence to
confluence, confluence to lake/reservoir, or confluence to the
Great Lakes or the Mississippi River. The Michigan database
included 28,889 reaches and 77,972 kilometers of streams and
rivers, while the Wisconsin database included 35,799 reaches
and 87,053 kilometers of streams and rivers. For each reach,
local (i.e., all land areas draining directly into a stream reach)
and network (i.e., all upstream areas draining into a stream
reach by either overland or waterway routes) catchments were
delineated using a 1-arc second resolution National Elevation
Dataset available for the Great Lakes region. Additionally, we
delineated local and network buffers for each reach, where
buffers were defined as 75-m horizontal distances on either side
of each stream. See [8] for descriptions of how local and network
catchments and buffers were delineated.
A suite of landscape and stream network variables known
to influence local habitat conditions and fish assemblages were
attributed to each of the stream reaches. Landscape descriptors,
Despite the wide availability of stream network and
catchment datasets, the cost-effective measurement of
local habitat across entire states or multi-state regions
remains a challenge. Traditionally, in-stream physical habitats
have been assessed through on-site sampling of channel
geomorphic measures, hydrological and thermal regimes,
water characteristics (width, depth, and velocity), substrate
composition, channel hydraulics, pool-riffle complexity, in-
stream cover for fish, canopy shading, and bank and riparian
conditions [12-14]. Such direct assessment, however, can be
time consuming and expensive, which prohibits its application
across large areas. As a consequence, many regional stream
and river databases do not include in-stream physical habitat
measurements and this incompleteness can seriously affect their
overall usefulness. In-stream physical habitat measurements
are valuable for examining biota–habitat relationships (e.g.,
identification of limiting habitat for particular organisms),
assessing deviations in habitat conditions from natural states
due to human perturbation, measuring improvements that have
resulted from habitat enhancement and restoration activities, and
predicting distribution and/or abundance of aquatic organisms
[3,5,8]. In-stream physical habitat measures are also needed for
conducting stream classifications for formulating management
policies and enacting regulations [1,2,10,11].
One promising approach for obtaining local habitat
information across large regions is through predictive modeling
based on readily available landscape data [6]. Predictive
modeling for the purpose of quantifying in-stream physical
habitat is supported by the riverscape concept of landscape
factors constraining local habitat conditions [1,15] and by
numerous empirical studies that have demonstrated the
predictability of local habitat variables from stream network
and catchment characteristics [16-20]. Although there are many
examples in the scientific literature of in-stream physical habitat
models being developed, rarely have such models been used to
comprehensively predict habitat conditions across large regions.
In some cases, models have been developed from relatively
few observations [17,18,20], which may not be appropriate
for predicting habitat conditions across large regions because
measures may not be representative of all regional habitat types.
In other cases, the underlying purpose of the modeling efforts
has not been to assess physical habitat predictions; rather, it has
been to develop stream habitat classifications or to assess how
much variability in local habitat conditions can be partitioned to
different spatial scales [16,21].
One explanation as to why in-stream physical habitats have
not been routinely predicted across large regions is that many
studies treat individual sampling sites as the basic spatial unit.
Although the measurement of physical habitat and the landscape
data associated with an individual sampling site (usually
100-1,000 m in length) is likely an accurate representation of
the site’s condition, forecasting conditions beyond the sampled
stream sites is difficult because it is not clear how to scale the
predictions to the remainder of the stream network or how to
attribute landscape data to the remainder of the network. The
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Prediction of stream physical habitat from landscape data
13
a good cross-section of conditions relative to the model
development dataset and thus were appropriate for verification
of model accuracy. These sampling reaches were not part of the
model-development dataset. Among these reaches, 58 sites
from 47 reaches were sampled in multiple years ( x = 4.7 years;
range 2 to 10 years) for assessing temporal variation, and 14
reaches were sampled at multiple sites (2-3 sites per reach) for
assessing within reach spatial variation. Bankfull depth, bankfull
width, and large woody debris measurements were not available
for the model-validation dataset.
2.4 Physical habitat model developmentWe used generalized additive modeling and boosting variable
selection to fit the local habitat prediction models. Generalized
additive modeling is a semi-parametric regression approach for
generating nonlinear response curves between dependent and
independent variables. Because it is not necessary to specify a
particular model equation, this modeling approach is useful in
cases where there is little information available describing how
independent variables are influenced by dependent variables
[27]. Boosting is an algorithmic approach for variable selection
that is rooted in the field of machine learning but which has
recently proven effective for fitting regression models in cases
where there are large sets of candidate predictor variables
[28-30]. Such situations (i.e., large numbers of candidate
predictor variables) frequently arise with geo-referenced
datasets, because of the ease of attributing information at a
variety of spatial scales.
The generalized additive models were fit in R (R Development
Core Team, 2010, R Foundation for Statistical Computing,
Vienna, Austria. http://R-project.org) using the “mboost”
package (Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M.,
Hofner, B., 2010, Mboost: Model-Based Boosting, version
2.0-4, http://CRAN.R-project.org/package=mboost). All of the
153 landscape and stream network variables that were attributed
to the stream sites were used as candidate variables for fitting the
including catchment area, soil type and permeability, surficial
geology formation and texture, bedrock type and depth,
20-year July mean air temperature, 20-year mean precipitation,
catchment slope, and land use and cover within each of
the spatial scales were attributed to the reaches based on
available data for each state [8]. Stream channel descriptors,
including Shreve linkage numbers for each reach and for the
downstream reach that each reach flows into, reach gradient,
reach elevation, sinuosity, stream order, total upstream stream
length, and distances from upstream most headwaters and from
the Great Lakes or Mississippi River, dam density for upstream
network catchment, dam density per up- or down-stream stream
length and distance to the first upstream or downstream dam
were also calculated using ArcInfo functionalities (PC ARC/
GIS Version 8.2. Environmental System Research Institute,
Redlands, California, http://www.esri.com/software/arcgis).
July mean stream temperatures and stream flow exceedances
were predicted for each stream reach using statistical models
developed from measurements made at a subset of the reaches
[24,25]. See [8] for additional details regarding methods for
stream reach identification, spatial boundary delineation, source
data acquisition, and variable attribution to the stream reaches.
2.2 Model-development datasetStream sites representing all wadeable stream conditions
across the entire state of Wisconsin and the northern part of
Michigan were sampled for physical habitat during 1996-2003
(Figure 1). All stream sites were sampled between early June and
late September under low flow conditions. The length of each
site was approximately 35- to 40-times mean stream width or
a minimum distance of 100 m, which is of sufficient length to
characterize fish assemblages and generally encompasses
around three meander sequences [26]. Sites ranged in length
from 100 to 837 m ( x = 273 m). At each site, a suite of habitat
variables were measured or visually estimated following
established sampling protocols [14]. The lengths of riffles, pools,
and runs were measured over the entire length of the site. Stream
width and depth, bottom substrate composition, availability of
fish cover, bank conditions, and riparian vegetation cover were
measured along 10 to 20 transects spaced 2.0- to 3.5-times
mean stream widths apart. Altogether, 29 local habitat variables
were measured (Table 1). Statistical models were not developed
for four of the habitat variables (% algae substrate, % bedrock
substrate, % clay substrate, % macrophyte substrate) because
of a lack of contrast in observed values (i.e., most values were 0).
For stream reaches where multiple sites were sampled, the mean
of each habitat variable was used for each stream reach; this
resulted in a total of 286 stream reaches for which local habitat
measurements were available.
2.3 Model-validation datasetThe model-validation dataset consisted of 54 stream reaches
from southern Wisconsin that were sampled at multiple sites per
stream reach or in multiple years. Although the validation sites
were limited to southern Wisconsin, the streams represented
Figure 1. Maps of Michigan and Wisconsin showing stream sites for model development (filled circles) and sites for model validation (filled triangles).
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L. Wang et al.
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models to percent data, a binomial distribution was specified
as the family distribution. For all other local habitat measures,
a Gaussian distribution was specified as the family distribution.
For those habitat measures that were fit assuming a Gaussian
distribution, Box-Cox transformations of the dependent variables
local habitat prediction models. A random effect corresponding
to measurement year was also included as a candidate variable
for the models. The effects of the candidate variables, with the
exception of measurement year, were modeled using P-splines
with 20 interior knots and 4 degrees of freedom [30]. When fitting
Habitat variables Mean Range Median 1st Quartile 3rd Quartile
Calculated
Catchment area (km2) 134 1-1943 39 17 137
Channel gradient (m/1000m) 2.7 0-31 2 1 4
Channel sinuosity (ratio) 1.3 1.0-4.1 1.2 1.1 1.4
Linkage number (#) 14.3 1-273 4 2 14
Stream order (#) 2.4 1-5 2 2 3
Observed
Bankfull depth (cm) 119 31-316 109 81 136
Bankfull width (m) 12 1-64 9 6 16
Bank erosion (%) 16 0-88 11 3 24
Buffer vegetation (%) 89 0-100 100 91 100
Canopy shading (%) 37 0-99 33 12 58
Conductivity (µS/cm) 461 1-1902 401 169 680
Dissolved oxygen (mg/l) 8.4 1.7-15 8.7 7.6 9.2
Fish cover (%) 13 0-96 9 4 16
Large substrate (%) 40 0-99 35 11 65
Large woody debris (%) 3 0-25 2 0 4
Pool (%) 11 0-100 5 0 15
Riffle (%) 12 0-67 6 0 20
Run (%) 77 0-100 82 65 96
Sediment depth (cm) 10 0-89 6 2 13
Small substrate (%) 55 0-100 55 30 78
Substrate algae (%)* 4 0-8 0 0 3
Substrate bedrock (%)* 1 0-48 0 0 0
Substrate boulder (%) 4 0-33 1 0 5
Substrate clay (%)* 4 0-98 0 0 3
Substrate cobble (%) 14 0-69 8 1 23
Substrate detritus (%) 5 0-53 2 0 5
Substrate embeddedness (%) 59 1-100 64 33 88
Substrate gravel (%) 22 0-72 21 8 33
Substrate macrophyte (%)* 6 0-81 0 0 7
Substrate sand (%) 36 0-100 29 14 52
Substrate silt (%) 14 0-96 7 2 19
Thalweg water depth (cm) 49 10-121 45 31 62
Wetted width (m) 8.7 1-58 6 4 11
Water depth (cm) 37 8-92 35 23 48
Width-to-depth ratio 18 3-86 14 10 21
Table 1. Summary statistics for the streams that were used to develop the local-scale habitat prediction models. Summary statistics are for GIS-calculated variables and the local-scale habitat measures for which prediction models were developed. An * indicates a variable was removed from further analyses because of the preponderances of zero values (Median=0).
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Prediction of stream physical habitat from landscape data
15
observed and predicted habitat values for each habitat variable
using the model-development dataset and the model-validation
dataset separately using the equation
(S(|Observed-Predicted|/Observed)*100)/N, (1)
where N equals the number of equations. We also calculated
the mean absolute relative error between the mean of the
observed habitat values among sampling sites of a stream reach
and the values from each sampling site for the same reach of
the model-validation dataset (i.e., spatial variation) using the
equation
(S(|Mean-Observed|/Mean)*100)/N. (2)
We also calculated the mean absolute relative error between
the mean of the observed habitat values from different years of a
sampling site and the values for each sampling year for the same
site of the model-validation dataset (i.e., temporal variation)
using equation 2.
The second approach was to compare relationships
between the predicted and observed local habitat measures with
fish assemblage metrics. We obtained fish data for 213 stream
reaches that were part of our model-development and validation
datasets from the Departments of Natural Resources of Michigan
and Wisconsin. These data were collected using backpack or
tow-barge electro-fishing units from late May to late September
between 1997 and 2002 on stream sites where physical habitat
data were also collected. The lengths of streams sampled
ranged from 100 to 960 m with larger streams having longer
sampling distances. Fish data were collected using single-pass
electrofishing to collect all fish observed, and all captured fish
were identified, enumerated, and weighed in the field. From the
collected fish data, we calculated five metrics that are known to
be affected by physical habitat characteristics: species richness,
total fish abundance, percent of lithophilic spawning individuals,
percent of intolerant individuals, and darter species richness
[5,26]. Prediction performance of the local habitat models was
evaluated by comparing Spearman’s rank correlation coefficients
calculated between the five fish metrics and the predicted and
observed local habitat measures. Because of the number of
correlation analyses that were conducted (5 fish metrics × 25
local habitat measures = 125 correlation analyses), a Bonferroni
correction was used to help ensure that the experiment-wise
error rate for the significance tests was maintained at 0.05.
3. Results
Of the 153 stream network and landscape variables that
were available as candidate predictors for the local habitat
prediction models, 143 were included in at least one of the
fitted models. The number of predictor variables included in
each of the models ranged from 1 to 43 with an overall mean
of 20 variables per model (Table 2). Fit varied considerably
among the prediction models (Table 3, Figure 2). Based on the
were used to help the data meet assumptions of normality. The
number of boosting iterations used in fitting each of the local
habitat prediction models was determined using 10-fold cross
validation [30].
2.5 Evaluation of model fits and prediction performanceFits of the local habitat models were evaluated by using simple
linear regression to regress observed versus predicted habitat
measures [31,32]. The slopes and shifted intercepts from these
regression models were then tested against values of 1.0 and
the mean of the predicted values, respectively, as a check of
the similarity of predicted and observed means and individual
values [33]. Testing of the slopes and shifted intercepts from
the observed versus prediction regressions was accomplished
using a two one-sided test strategy (TOST), which is a form of
equivalence testing [33]. Equivalence tests are commonly used in
the biomedical fields for situations such as vaccination coverage
in different demographic groups [34] and are starting to be used
as a method for validating model predictions [33,35]. Testing of
the slopes and intercepts followed the TOST strategy of using
bootstrapping to construct the two one-sided 95% confidence
intervals around the regression slope and shifted intercept [33].
One of the key aspects of equivalence testing for model validation
is the specification of a region of equivalence or indifference for
the shifted intercepts and slopes, which distinguishes practical
equivalence from scientifically relevant differences [31,33].
It is this region of equivalence that the confidence intervals
for the shifted intercept and slope estimates are compared. If
the confidence intervals for the slope or shifted intercept lie
entirely within their respective regions of equivalence, then the
null hypothesis of dissimilarity is rejected [33]. We chose to set
the equivalence region at a fairly liberal rate ( y ± y ×0.45% for
the shifted intercept and 1.0 ± 0.45 for the slope) given that we
were attempting to model in-stream habitat features that are
oftentimes dynamic and difficult to measure accurately. The
equivalence testing was conducted in R using the “equivalence”
package (Robinson, A., 2010, Equivalence: provides tests and
graphics for assessing tests of equivalence, version 0.5.6,
http://CRAN.R-project.org/package=equivalenc).
We also calculated Theil’s partial inequality coefficients for
each of the modeled local habitat measures, which separate
total error into three components: Ubias, Uslope, and Uerror [31,32].
Ubias represents the proportion of total error associated with
mean differences between observed and predicted values. Uslope
represents the proportion of total error associated with deviance
of the slope from the 1:1 line. Uerror represents the proportion of
total error associated with the unexplained variance [31,32].
The prediction performance of the local habitat models was
evaluated using two approaches. The first approach was to
compare percent differences between observed and predicted
habitat values for the model-development dataset with the
percent differences between observed and predicted values for
the model-validation dataset, and with the percent differences in
temporal and spatial variations for the model-validation dataset.
We first calculated the mean absolute relative error between the
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L. Wang et al.
16
from the observed versus predicted regressions could not be
rejected for any of the in-stream habitat variables, while the
null hypothesis of dissimilarity for the shifted intercepts was
rejected for only 9 of the 15 variables (Table 3). The R2 of the
observed versus predicted regressions for the models that
were considered to have poor fits was generally less than 0.45
with slope estimates greater than 1.4.
Based on the calculated Theil’s partial inequality
coefficients, most (>70%) of the error between observed and
predicted values for each of the local habitat measures was
due to unexplained variance (Uerror) (Table 3). The next largest
source of error differed among the local habitat measures.
For variables such as bankfull depth, large woody debris,
and detritus substrate, most of the remaining error was due
to differences between the slope of the fitted model and the
1:1 line (Uslope), whereas for variables such as pool habitat, riffle
habitat, run habitat, and gravel substrate, most of the remaining
regression of observed versus predicted values and the tests
of equivalence of the regression slopes and shifted intercepts,
the models for bankfull depth and width, conductivity, large
and small substrates, sand substrate, thalweg water depth,
wetted width, water depth, and width-to-depth ratio were
considered to yield satisfactory fits (Table 3). For these
models, the null hypotheses of dissimilarity for both the slopes
and shifted intercepts from the observed versus predicted
regressions were rejected (Table 3). For most of these models,
the R2 of the observed versus predicted regressions was
greater than 0.50 with slope estimates between 1.0 and 1.3
(Table 3). Conversely, the models for bank erosion, vegetative
buffer, canopy shading, large woody debris, cover for fish,
riffle, pool, run, sediment depth, substrate embededdness,
boulder substrate, cobble substrate, detritus substrate,
gravel substrate, and silt substrate were considered to have
poor fits. The null hypothesis of dissimilarity for the slopes
Table 2. Number of predictor variables by variable type that were selected through the boosting algorithm for each of the final local-scale habitat prediction models.
Landscape variable categories
Habitat variableBedrock
depthBedrockgeology
ClimateLandcover
Soilgeology
Streamnetwork
Surficialgeology
Totalvariables
Bankfull depth (cm) 6 7 5 14 0 3 8 43
Bankfull width (m) 3 6 5 8 0 1 5 28
Bank erosion (%) 4 0 5 3 3 2 8 25
Buffer vegetation (%) 3 3 1 9 0 0 3 19
Canopy shade (%) 0 0 0 2 0 2 0 4
Conductivity (µS/cm) 4 4 4 8 5 2 6 33
Fish cover (%) 6 1 2 3 0 1 5 18
Large substrate (%) 2 4 1 3 3 4 4 21
Large woody debris (%) 7 5 2 7 3 2 531
Pool (%) 1 4 4 3 1 3 11 23
Riffle (%) 0 1 0 2 2 3 1 9
Run (%) 2 5 3 3 2 2 5 22
Sediment depth (cm) 0 0 0 0 0 0 1 1
Small substrate (%) 3 2 3 3 3 2 7 23
Substrate boulder (%) 3 4 2 6 1 4 6 26
Substrate cobble (%) 3 3 0 2 2 2 3 15
Substrate detritus (%) 3 6 3 6 2 4 11 35
Substrate embeddedness (%) 1 2 1 3 3 4 115
Substrate gravel (%) 2 4 2 3 3 2 3 19
Substrate sand (%) 0 3 0 2 3 1 3 12
Substrate silt (%) 4 5 5 11 1 4 9 39
Thalweg water depth (cm) 4 4 2 5 2 3 1030
Wetted width (m) 3 5 3 11 1 1 2 26
Water depth (cm) 3 5 3 5 1 3 9 29
Width-to-depth ratio 3 6 1 7 2 3 5 27
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Prediction of stream physical habitat from landscape data
17
errors between predicted and observed values for both the
model development and model validation datasets among the
variables that were judged to be predicted satisfactorily and those
judged to be predicted unsatisfactorily. For those variables that
were predicted satisfactorily, the mean absolute relative errors
between predicted and observed values ranged from 15 to 38%
for the model development dataset and 15 to 47% for the model
validation dataset (Table 4). Conversely for variables that were
judged to be predicted unsatisfactorily, the mean absolute relative
errors between predicted and observed values ranged from 11 to
90% for the model development dataset and 20 to 101% for the
model validation dataset (Table 4).
For the correlation analyses that evaluated prediction
performance of the local habitat models, there was a good
error was due to mean differences between observed and
predicted values (Ubias).
For the habitat variables that were judged to be predicted
satisfactorily (Table 4, Figure 2), the mean absolute relative
errors between the predicted and observed values for the model
development dataset were greater than the year-to-year and site-
to-site variation for the observed values of the model validation
dataset. However, when the temporal and spatial variability was
assessed together, the mean absolute relative errors for temporal-
spatial test, predicted versus observed for validation dataset, and
predicted versus observed for model-development dataset were
similar (Table 4). Similar results were obtained for those habitat
variables that were predicted unsatisfactorily (Table 4, Figure 3).
There were noticeable differences in the mean absolute relative
Table 3. Regression parameter estimates (Intercept [Int.] and Slope) and coefficients of determination for the regressions of observed versus predicted local habitat measures for the data used to fit the local habitat prediction models. An * next to the intercept and slope estimates indicates that the null hypothesis of dissimilarity for the shifted intercept and slope estimates from the regressions of observed versus predicted values was rejected at an a=0.05. Also shown are Theil’s partial inequality coefficients, which partitions the error between observed and predicted values into the proportion associated with mean differences between observed and predicted values (Ubias), the proportion associated with differences between the slope of the fitted model and the 1:1 line (Uslope), and the proportion associated with unexplained variance (Uerror).
Habitat variable Int. Slope R2 Ubias Uslope Uerror
Habitat variables predicted satisfactorily
Bankfull depth (cm) -0.3* 1.3* 0.72 0.036 0.129 0.835
Bankfull width (m) 0.4* 1.0* 0.86 0.047 0.007 0.946
Conductivity (µS/cm) -38.4* 1.2* 0.81 0.038 0.073 0.888
Large substrate (%) -0.1* 1.3* 0.48 0.064 0.044 0.891
Small substrate (%) -0.1* 1.2* 0.50 0.010 0.024 0.966
Substrate sand (%) -0.0* 1.1* 0.48 0.028 0.012 0.960
Thalweg water depth (cm) -0.1* 1.3* 0.66 0.029 0.074 0.897
Wetted width (m) 0.5* 1.0* 0.84 0.050 0.005 0.945
Water depth (cm) -0.1* 1.3* 0.61 0.026 0.070 0.905
Width-to-depth ratio -0.3* 1.2* 0.58 0.068 0.025 0.907
Habitat variables predicted unsatisfactorily
Bank erosion (%) -0.0 1.7 0.35 0.084 0.075 0.842
Buffer vegetation (%) -0.9* 1.9 0.35 0.071 0.095 0.834
Canopy shade (%) -0.0* 2.5 0.42 0.070 0.109 0.820
Large woody debris (%) -0.0* 2.4 0.47 0.004 0.224 0.772
Fish cover (%) -0.0* 1.6 0.39 0.049 0.077 0.874
Pool (%) 0.0 1.5 0.38 0.107 0.054 0.839
Riffle (%) -0.0 2.0 0.20 0.143 0.053 0.804
Run (%) -0.5* 1.5 0.31 0.128 0.038 0.833
Sediment depth (cm) -1.2* 13.0 0.09 0.000 0.081 0.919
Substrate boulder (%) -0.0* 2.5 0.48 0.020 0.244 0.735
Substrate cobble (%) -0.0 1.7 0.41 0.117 0.096 0.788
Substrate detritus (%) -0.1 2.9 0.44 0.026 0.241 0.733
Substrate embeddedness (%) -0.3* 1.4 0.45 0.075 0.058 0.867
Substrate gravel (%) -0.1* 1.8 0.25 0.125 0.051 0.824
Substrate silt (%) -0.0 1.8 0.57 0.072 0.184 0.744
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L. Wang et al.
18
4. Discussion
Our results demonstrate that important and commonly-used
local physical habitat variables, including bankfull depth and
width, water depth, thalweg depth, wetted width, large and
small substrates, conductivity, sand substrate, and width-to-
depth ratio, can be predicted satisfactorily from stream network
and landscape-scale catchment measures. It is expected
that these variables can be predicted from landscape-scale
datasets because they are strongly linked with factors such as
geology type, soil structure, topography, land cover, and climate
conditions [5,6,19]. Many of the variables for which satisfactory
fits were obtained are among those that are considered to be
the most influential physical habitat measures governing the
distribution and abundance of biological assemblages [5,36],
which underscores the potential utility of these habitat models
for broad-scale assessment and monitoring. Models for other
important and commonly used in-stream physical habitat
agreement in the relationships between fish metrics vs. observed
habitat values and the fish metrics vs. predicted habitat values
for habitat variables predicted satisfactorily (Table 5). As
expected, not all physical habitat measures correlated with all
selected fish metrics. Of the 125 pairs of correlations that were
considered, there were 57 significant correlations between
fish metrics and observed and/or predicted habitat measures.
Among those significant correlations, 75% of the correlation
pairs were in agreement between observed and predicted
habitat measures in their relationship with fish metrics indicated
by correlation significance and correlation directions. About
25% of the correlation pairs were in disagreement in that only
observed habitat measures were significantly correlated with fish
metrics. Among those correlations that were not in agreement
between fish metrics-observed and fish metric-predicted habitat
relationships, 12% were for habitat measures judged to be
predicted satisfactorily and 35% were for habitat measures
judged to be predicted unsatisfactorily.
Figure 2. Plots of observed versus predicted habitat values for models with satisfactory predictions for the model-development dataset.
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Prediction of stream physical habitat from landscape data
19
has previously been demonstrated in several smaller-scale
studies, which together with our results suggest that statistical
modeling is a viable approach for assessment and monitoring of
habitat condition for a variety of system types. In a study of 53
southeastern Queensland streams, 5 of 21 local habitat variables
(width, sand, cobble, rocks, and large woody debris) were
predicted with R2s ranging from 0.22 and 0.65 using elevation,
stream order, distance from source, and longitude as predictor
variables [18]. The authors concluded that the predictive
performances of fish distribution models were similar when
using observed or predicted local habitat variables, although
fish distribution predictions were clearly biased at the extremes
of the habitat variable ranges. In a study of 76 New Hampshire
streams, 11 catchment and regional variables were used to
variables, such as bank erosion, large woody debris, fish cover,
canopy shading, substrate embeddedness, and riffle-pool
measures, were found to have poor predictive performances,
suggesting that they would be of limited utility for assessment
and monitoring. It is perhaps not surprising that variable such
as bank erosion, large woody debris, and fish cover could not
be adequately predicted solely from catchment-level and stream
network characteristics since these variables are strongly
influenced by riparian conditions [5,37]. We anticipate that the
performance of prediction models for these variables likely could
be improved by including measures from riparian-scale GIS
datasets among the candidate predictor variables.
The ability to accurately predict in-stream physical habitat
measures from stream network and catchment variables
Figure 3. Plots of observed versus predicted habitat values for models with unsatisfactory predictions for the model-development dataset.
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L. Wang et al.
20
to acknowledge that there can be considerable measurement
error when direct sampling of local-scale habitats is conducted.
Accurate sampling of in-stream physical habitats is difficult
because habitat characteristics vary both laterally and
longitudinally. As well, in-stream physical habitat measures are
affected by a wealth of landscape-level factors, including climate
which fluctuates daily, seasonally, and annually. With standard
sampling protocols [12-14], individual measurements can deviate
from sample means by as much as 50% [38], as can inter-
annual variability in measurement as demonstrated in this study.
According to [38], the overall accuracy and seasonal variability of
in-stream physical habitat measures is strongly influenced by the
heterogeneity of habitat composition, with the greatest variability
observed for the most heterogenous environments. Thus, the
predict 64 local chemical and physical habitat variables using
several modeling approaches; the best performing models had
an error of 27% of the mean index value [20]. It was suggested
that other catchment variables that at the time were unavailable
to the authors could improve prediction performance. In a
study of 51 streams in the Upper Murrumbidgee catchment in
southeastern Australia, catchment area, stream length, relief
ratio, alkalinity, percentage of volcanic rocks, percentage of
metasediments, dominant geology, and dominant soil type were
found to provide sufficient information to classify 69% of sites
into appropriate site groups developed by direct sampling of
local stream habitat measures [17].
Although the use of prediction models impart some
uncertainty on the physical habitat measures, it is important
Table 4. Mean absolute relative errors in habitat values among years for the model-validation dataset (Temporal-test), among sampling sites within a reach for the validation dataset (Spatial-test), the sum of Temporal-test and Spatial-test (Temporal-spatial together) for the validation dataset, between predicted and observed for the validation dataset (Predicted-test), and between predicted and observed for the model development dataset (Predicted-observed). Bankfull depth and width and large woody debris were not measured for the validation data set.
Habitat variableTemporal-test
(n=58)Spatial-test (n=14)
Temporal-spatial together
Predicted-test (n=54)
Predicted-observed (n=286)
Habitat variables predicted satisfactorily
Bankfull depth (cm) -- -- -- -- 15
Bankfull width (m) -- -- -- -- 24
Conductivity (µS/cm) 6 6 12 15 28
Large substrate (%) 13 18 31 37 38
Small substrate (%) 12 15 27 30 26
Substrate sand (%) 20 24 44 47 38
Thalweg water depth (cm) 9 11 20 26 26
Water depth (cm) 10 12 22 24 27
Wetted width (m) 6 9 15 29 29
Width-to-depth ratio 9 13 22 24 31
Habitat variables predicted unsatisfactorily
Bank erosion (%) 30 33 66 80 53
Buffer vegetation (%) 12 13 25 36 11
Canopy shading (%) 12 24 36 78 57
Fish cover (%) 22 23 45 20 21
Large woody debris (%) -- -- -- -- 85
Pool (%) 45 51 95 71 90
Riffle (%) 23 31 54 87 74
Run (%) 9 11 20 21 19
Sediment depth (cm) 26 32 58 58 55
Substrate boulder (%) 35 47 82 101 83
Substrate cobble (%) 22 27 49 75 62
Substrate detritus (%) 48 56 104 89 85
Substrate embeddedness (%) 23 25 48 35 32
Substrate gravel (%) 21 25 46 43 46
Substrate silt (%) 30 33 63 62 72
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Prediction of stream physical habitat from landscape data
21
from landscape datasets may have significant biases at that
scale. From a regional assessment and monitoring perspective,
we are of the opinion that the reach scale is the best spatial scale
for developing and applying prediction models. The reach scale
represents a section of a stream that is relatively homogenous
in chemical and biological characteristics and is closer to the
scale at which local management decisions are often made.
Accounting for natural spatial and temporal variation at reach
scale is also a component of established sampling protocols
[12-14,39]. Admittedly, assessing and monitoring physical habitat
at reach scales will result in some information being sacrificed,
particular at micro-scale levels which are important determinants
uncertainty that results by predicting in-stream physical habitat
measures does not necessarily lead to additional complexity
from a management perspective, as regardless there is a need
to consider how prediction or measurement biases may affect
management outcomes.
In-stream physical habitat variables can be measured or
predicted at a variety of spatial scales, from individual rocks
to entire stream networks. The accuracy of stream habitat
measurements and predictions is anticipated to be strongly
influenced by the spatial scale of sampling or modeling. At small
spatial scales, direct sampling of habitats will likely yield very
accurate measurements, while prediction models developed
Table 5. Spearman correlation coefficient estimates between selected fish metrics and predicted (Pred.) and observed (Obs.) local habitat measures. Coefficient estimates presented only for those that were significantly different from zero using a Bonferroni-corrected a of 0.05/125 = 0.0004. Where correlations between fish metrics and observed habitat measures were not significant, coefficient estimates for correlation between fish metrics and predicted habitat measures were not presented. Numbers in bold indicate that fish metrics were correlated with only observed, but not predicted habitat variables.
Species richness Darter speciesIntolerant
individuals (%)Lithophilous
individuals (%)Abundance
(#/100m)
Habitat variable Obs. Pred. Obs. Pred. Obs. Pred. Obs. Pred. Obs. Pred.
Habitat variables predicted satisfactorily
Bankfull depth (cm) -- -- -- -- -- -- -- -- -0.40 -0.28
Bankfull width (m) 0.49 0.37 0.38 0.20 -- -- 0.46 0.31 -- --
Conductivity (µS/cm) -- -- -- -- -0.48 -0.46 -- -- -- --
Large substrate (%) 0.44 0.32 0.30 0.21 -- -- 0.36 0.25 0.22 0.26
Small substrate (%) -0.32 -- -0.25 -- -- -- -0.34 -- -- --
Substrate sand (%) -- -- -- -- 0.28 0.51 -- -- -0.26 -0.21
Thalweg water depth (cm) -- -- -- -- -- -- -- -- -0.24 -0.35
Wetted width (m) 0.50 0.38 0.28 0.20 -- -- 0.38 0.30 -- --
Water depth (cm) -- -- -- -- -- -- -- -- -0.23 -0.34
Width-to-depth ratio 0.52 0.51 0.40 0.34 -- -- 0.36 0.33 -- --
Habitat variables predicted unsatisfactorily
Bank erosion (%) -- -- -- -- -- -- -- -- -- --
Buffer vegetation (%) -- -- -- -- -- -- -- -- -- --
Canopy shade (%) -- -- -0.22 -0.21 -- -- -0.27 -0.31 -- --
Large woody debris (%) -- -- -- -- 0.27 0.28 -- -- -0.23 -0.39
Fish cover (%) -- -- -- -- -- -- -- -- 0.21 --
Pool (%) -- -- -- -- -- -- -- -- 0.21 --
Riffle (%) 0.29 -- 0.21 -- -- -- 0.31 -- 0.21 0.29
Run (%) -0.23 -0.19 -- -- -- -- -0.21 -0.24 -0.28 -0.34
Sediment depth (cm) -0.35 -- -0.28 -- -- -- -0.29 -- -0.23 --
Substrate boulder (%) 0.24 0.23 -- -- -- -- -- -- -- --
Substrate cobble (%) 0.38 0.20 0.23 -- -- -- 0.31 0.24 0.27 0.22
Substrate detritus (%) -0.26 -- -0.27 -0.23 -- -- -0.36 -0.33 -- --
Substrate embeddedness (%) -0.47 -0.25 -0.35 -0.21 -- -- -0.38 -0.23 -0.22 -0.24
Substrate gravel (%) 0.43 0.30 0.35 0.23 -- -- 0.28 0.21 0.25 0.25
Substrate silt (%) -- -- -- -- -0.22 -0.37 -- -- 0.30 0.30
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L. Wang et al.
22
influenced by anthropogenic disturbances (e.g., urban land
cover, agriculture land cover) to zero. Although such estimated
potential conditions are relatively coarse due to the predictive
models not excluding all disturbance influences, this approach
has been considered useful and used for assessing regional
scale environmental impairment in several studies [52-54]. The
models that we constructed for this research were an attempt
to meet the management needs for the states of Michigan and
Wisconsin by filling in in-stream physical habitat data gaps for the
GLRRDACS. As previously mentioned, the spatially referenced
database associated with GLRRDACS includes entries for every
stream reach in a three-state regions, and consists primarily of
data attributed using GIS processes. The database also includes
water temperature [24] and flow discharge [25] predictions and
the results from an integrated biotic-abiotic stream classification
analysis [55,56]. Our in-stream physical habitat predictions are
an effort to complete the hierarchical spatial data coverage from
stream reach to network and to catchment, which is urgently
needed by aquatic resource managers and policy makers.
Before the model predictions are integrated with the GLRRDACS
database, additional evaluations of prediction accuracy,
particularly for streams in southern Michigan, will need to be
conducted. As well, additional attempts at improving models for
those in-stream physical habitat variables for which predictions
were found in this research to be unsatisfactory will need to be
conducted, possibly by incorporating riparian-scale measures
among the set of candidate predictor variables.
Acknowledgement
We thank Paul Seelbach, Jana Stewart, Arthur Cooper, Stephen
Aichele, and Edward Bissell for their effort in the development of
the GLRRDACS, which ultimately made this research possible.
We also thank Paul Kanehl, Edward Baker, and many seasonal
technicians for collecting the on-site physical habitat data that
were used for our analyses and Minako K. Edgar for preparing
the sampling site map. We are grateful to Dr. Yong Cao and an
anonymous reviewer who provided very helpful suggestions
that improved the manuscript. This project was partially
supported by Federal Aid in Sport Fishery Restoration Program,
Project F-80-R, through the Fisheries Division of the Michigan
Department of Natural Resources and Project F-95-P, through
the Wisconsin Department of Natural Resources.
of localized site selection of many aquatic organisms [40,41].
From a regional management perspective, however, we believe
that the benefits of reach-scale assessments for formulating
policy and enacting regulations outweigh the loss of information
at micro-scale levels.
The need for in-stream physical habitat prediction models
largely stems from aquatic managers having to understand
biota-habitat relationships and to assess and monitor natural
and human-induced variations in habitat conditions at broad
scales. One of the important applications of understanding
biota–habitat relationships is the identification of factors that
limit distribution and abundance of important fish species
[42,43]. Presently, regional scale fish distribution and abundance
models rely generally on stream network and catchment factors
[44,45] because in-stream physical habitat data are not available
at regional scales. However, habitat conditions at local scale
(e.g., channel morphology, substrate, and water conditions)
can have strong effects on localized fish distribution patterns in
streams [5,46,47]. Although landscape factors constrain local-
scale conditions, without direct measures of in-stream physical
habitat measures the ability to explain variation in aquatic
communities and assemblages may be seriously compromised.
Also, using landscape factors to approximate local characteristic
in identifying limiting factors, one will not be able to pinpoint
local factors that limit fish distribution for targeting management
activities. Our previous study has shown that local habitat alone
explained 36-46% and local habitat interaction with other factors
explained additional 18-41% of variances for fish presence,
abundance, and integrity metrics [19]. Not taking into account
local habitat in fish distribution prediction or identifying limiting
habitat factors could result in an incomplete understanding of
biota-habitat relationships at a regional scale.
Rivers and streams are considered among the world’s most
imperiled ecosystems and biodiversity loss in these systems
has been high due to a myriad of factors, including intensive
land use practices, damming, pollution, and exotic species
[48-51]. Thus, the importance of assessing and monitoring
natural and human-induced variations and perturbations in in-
stream physical habitats cannot be over-stressed. Using the
modeling approach described herein, physical habitat conditions
can be predicted across very legions. As well, constructed
models can be used to predict potential physical habitat
conditions by setting land cover variables that are strongly
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