Delineation and Validation of River Network Spatial Scalesfor Water Resources and Fisheries Management

Lizhu Wang • Travis Brenden • Yong Cao •

Paul Seelbach

Received: 14 February 2012 / Accepted: 8 August 2012

� Springer Science+Business Media, LLC 2012

Abstract Identifying appropriate spatial scales is critically

important for assessing health, attributing data, and guiding

management actions for rivers. We describe a process for

identifying a three-level hierarchy of spatial scales for

Michigan rivers. Additionally, we conduct a variance

decomposition of fish occurrence, abundance, and assem-

blage metric data to evaluate how much observed variability

can be explained by the three spatial scales as a gage of their

utility for water resources and fisheries management. The

process involved the development of geographic informa-

tion system programs, statistical models, modification by

experienced biologists, and simplification to meet the needs

of policy makers. Altogether, 28,889 reaches, 6,198

multiple-reach segments, and 11 segment classes were

identified from Michigan river networks. The segment scale

explained the greatest amount of variation in fish abundance

and occurrence, followed by segment class, and reach.

Segment scale also explained the greatest amount of varia-

tion in 13 of the 19 analyzed fish assemblage metrics, with

segment class explaining the greatest amount of variation in

the other six fish metrics. Segments appear to be a useful

spatial scale/unit for measuring and synthesizing informa-

tion for managing rivers and streams. Additionally, segment

classes provide a useful typology for summarizing the

numerous segments into a few categories. Reaches are the

foundation for the identification of segments and segment

classes and thus are integral elements of the overall spatial

scale hierarchy despite reaches not explaining significant

variation in fish assemblage data.

Keywords River segment � Classification � Validation �Fish assemblage � Spatial scale � River network

Introduction

Identifying appropriate spatial scales for attributing data,

assessing river health, and implementing management

actions is critically important for those who study, manage,

and use river systems. Currently, the most commonly used

spatial scale/unit are individual sampling sites, which typi-

cally range from 0.1 to 2.0 km in length, depending on tax-

onomic groups of interest and stream sizes (e.g., Lyons 1992;

Barbour and others 1999; Cao and others 2001; Pearson and

others 2011). Although individual sampling sites may pro-

vide reliable data at sampled locations, they do not readily

permit extrapolation of information to un-sampled areas

(Fausch and others 2002; Fayram and others 2005; Wang and

L. Wang (&) � P. Seelbach

Institute for Fisheries Research, Michigan Department of Natural

Resources, University of Michigan, 1109N University,

Ann Arbor, MI 48109, USA

e-mail: wangl@windsor.ijc.org

Present Address:L. Wang

Great Lakes Regional Office, International Joint Commission,

100 Ouellette Avenue, 8th Floor, Windsor, ON N9A 6T3,

Canada

T. Brenden

Quantitative Fisheries Center, Department of Fisheries

and Wildlife, Michigan State University, 153 Giltner Hall,

East Lansing, MI 48824-1101, USA

Y. Cao

Illinois Natural History Survey, University of Illinois,

1816 S Oak Street, Champaign, IL 61820, USA

Present Address:P. Seelbach

Great Lakes Science Center, US Geological Survey,

1451 Green Road, Ann Arbor, MI 48105, USA

123

Environmental Management

DOI 10.1007/s00267-012-9938-y

others 2006), which is important because managers are

responsible for entire river networks lying within their

jurisdictions, and limited resources will never permit direct

sampling of most attributes throughout entire river systems.

With the increased demands of addressing land use or cli-

mate change issues, and taking an ecosystem approach to

resource management, identifying and validating appropri-

ate spatial scales/units for extrapolating information from

sampled sites to unsampled areas is of utmost importance to

natural resource management agencies.

Recent advances in fluvial ecology provide both theoret-

ical and operational frameworks for the identification of

appropriate river spatial scales and units. River systems were

historically regarded as heterogeneous networks with every

stream considered to be unique (Hynes 1975), which chal-

lenges both the study and management of rivers (Fausch and

others 2002). In the early 1980s, the river continuum concept

(RCC) was proposed as the first influential model to gener-

alize the heterogeneity of river systems into a holistic

description of the progressive longitudinal changes in

physicochemical, channel morphological, and biological

characteristics that occur along a river’s course (Vannote and

others 1980). The RCC described a generalized gradient of

physicochemical and geomorphic conditions from headwa-

ter streams to river mouths in a continuous fashion. Some-

what paralleling the formulation of the RCC was the view of

many fluvial ecologists that river systems could be divided

into discrete river segments that were distinctive in physi-

cochemical and biological characteristics. This view was

formalized as the river serial discontinuity concept (Ward

and Stanford 1983, 1995; Stanford and others 1988), which

recognized that the downstream physicochemical and bio-

logical continua of rivers are predictably interrupted in

response to channel network and valley morphological

variations, such as those caused by geological boundaries,

alternating canyons and floodplains, bedrock intrusions,

tributary confluences, lakes/reservoirs, and landslides. This

discontinuity perspective stresses that some properties of

fluvial systems (e.g., sediment loads and substrate size) are

hierarchically organized in saw-tooth patterns, while other

properties (e.g., channel width and flow discharge) are

organized in stair-step patterns from headwaters to river

mouths (Rice and others 2001; Ferguson and others 2006).

Such stair-step patterns and the associated physical, chemi-

cal, and biological characteristic changes of river systems

indicate potential natural breaks of river spatial units, and

make it possible to identify relatively homogenous medium-

scale river sections that lie between finer-scale heteroge-

neous and larger-scale continuous river systems.

Advancements in geographic information system (GIS)

technology and increased availability of regional and

national river survey databases have improved the acquisi-

tion of representative data needed to extrapolate data to

entire river networks. The early work of such extrapolation

efforts focused on river classification and assumed that

information from a particular sampling site represented a

particular class such as an ecoregion, a catchment size group,

a river size group, or a channel type that the sampling sites

belong to. Although classification frameworks have been

used by environmental assessment and fisheries manage-

ment programs for decades, the ecological relevance of most

classification frameworks is weak or unknown (e.g., Haw-

kins and others 2000; Pyne and others 2007) because pro-

cesses used in developing the frameworks have often lacked

scientific rigor (Simon and others 2007, 2008) or been crit-

icized for over-simplifying how physical environments

affect biological communities (e.g., Cao and others 2007).

Furthermore, most management and restoration practices are

implemented at relatively small scales, but many classifi-

cations are designed for large scales. For state and regional

water resource and fisheries programs, there is a clear man-

agement need to map appropriate river spatial scales/units,

attribute data that describe key characteristics of the different

scales/units, and identify the ecologically meaningful break-

point values for the key parameters so that the mapped river

scales/units in a specific region can be classified into types

for meeting assessment and management needs.

The pioneering work of mapping, attributing, and classi-

fying mid-scale river units across a region was a manual,

expert-opinion process that was conducted on rivers in

Michigan’s lower peninsula (Seelbach and others 1997). In

this early work, river valley segments believed to be rela-

tively homogenous in hydrologic, limnologic, and geomor-

phic characteristics were delineated as the grain for

classification (Seelbach and others 1997; Seelbach and oth-

ers 2006). River valley segment boundaries were identified

by viewing river network maps in relation to thematic

landscape maps (e.g., elevation, land cover, surficial geol-

ogy, and slope) within a GIS environment and manually

demarcating boundaries based on scientists’ knowledge of

how landscape and network factors influence instream

physicochemical and biological characteristics of a river.

The scientists then qualitatively assigned physicochemical

and biological attribute-classes to each river valley segment.

Similar efforts, including hierarchical classification of mul-

tiple spatial scale/units, were conducted for rivers in the

upper Mississippi River basin, the Great Lakes basin, the

Illinois River basin, Michigan’s upper peninsula, Missouri,

Ohio, and Ontario (Baker 2006; Higgins and others 1998;

Higgins and others 2005; Kilgour and Stanfield 2006; Miller

and others 1998; Sowa and others 2007).

More recently, a collaborative effort involving personnel

from the Institute for Fisheries Research, Great Lakes

Aquatic GAP program, and several state agencies and uni-

versities developed improved procedures for mapping river

spatial units, identifying segment boundaries, assigning

Environmental Management

123

attributes, and identifying segment types for rivers in the

states of Illinois, Michigan, New York, and Wisconsin

(McKenna and others 2009; Brenden and others 2006,

2008a, 2008b). Ultimately, this resulted in the development

of the Great Lakes regional river database and classification

system (GLRRDACS). Compared with previous efforts, the

GLRRDACS used contemporary GIS tools and automated

processes for river reach boundary delineation, river network

descriptor calculation and attribution, and reach catchment

delineation. Additionally, the GLRRDACS combined

objective and subjective approaches to group adjacent river

reaches into river segments and classified river segments into

classes. Many of the individual steps that went into devel-

opment of the GLRRDACS have been rigorously docu-

mented and subjected to peer review, making the process

more scientifically duplicable and defensible (Brenden and

others 2006, 2008a, b). The development of the National

Hydrography Dataset plus (NHDPlus), which includes all

streams and rivers in the conterminous United States, is

similar to the GLRRDACS in that it divides stream networks

into confluence-to-confluence river reaches with each reach

having delineated local and network catchment boundaries.

The NHDPlus makes it possible to readily apply the steps of

the GLRRDACS process to other river networks in the

United States (Wang and others 2011).

Although considerable progress has been made in delin-

eating river reaches, attributing essential network and land-

scape data, merging reaches into river segments, and

identifying segment classes, the validation of these different

spatial scales is still in its infancy. Numerous studies have

reported substantial changes in flow volume and channel

width (Richards 1980; Rhoads 1987; Rice and others 2001),

substrate size and channel slope (Rice and others 2001),

macroinvertebrates (Knispel and Castella 2003), and fish

assemblages (Hitt and Angermeier 2008) at the tributary-

main channel junctions or at lotic-lentic transition locations

of impoundments. However, those studies have focused on

providing evidence that the generally continuous river sys-

tem from headwater to mouth can be divided into discrete

parts based on non-arbitrary distinction at certain spatial

scales and tributary junctions that create ‘‘gaps’’ in the

downstream succession of habitat of a river network (Poole

2002). To date, Warner and others (2010) is the only study

that has specifically evaluated if stream physical habitat

within a river valley segment is relatively homogeneous and

if physical habitats are substantially different among abut-

ting river valley segments. Because dividing river networks

into a hierarchy of spatial units and scales, such as reaches,

segments, and segment classes are becoming common

practice for river research and management, thorough eval-

uation of the variability in biotic assemblages among these

different river network spatial scales would be beneficial for

resource managers.

The objectives of this study were to briefly describe how

the river reaches, river segments, and river segment classes

were developed for all the rivers in the state of Michigan; and

to compare the variations in fish assemblages within and

among river confluence-to-confluence reaches, multiple-

reach segments, and segment classes where sampled fish data

are available. Using Michigan as an example to provide a brief

description of the overall process for delineating these three

hierarchical spatial scales and using fish assemblage to vali-

date the spatial scales would be beneficial because previous

publications only have described the steps used to identify

individual units or scales. Because the patterns of changes in

physical and biological characteristics of river systems are

spatial scale dependent (Rice and others 2001), our study

focused only at these three spatial scales considered mean-

ingful for environmental assessment using fish assemblages

and for fisheries management. Fish assemblage and abun-

dance data were judged to be the most appropriate response

variables for conducting this comparison as this was the biotic

community that either explicitly or implicitly formed the basis

for identifying both river segments and segment classes.

Methods

Description of River Reach and Associated Network

and Landscape Data

Data used for this study were from the GLRRDACS. In this

database, streams and rivers identifiable from the

1:100,000 scale National Hydrography Dataset (NHD)

were divided into individual river reaches defined from

headwater to the first confluence, confluence to confluence,

confluence to lake/reservoir, or confluence to the Great

Lakes (all rivers in Michigan flow into the Great Lakes).

The river system in Michigan consisted of 28,889 reaches

with a median length of 2,197 m (range:\100 to[20,000 m).

For each reach, local (i.e., land surface areas where runoff

draining directly into the stream reach) and network (i.e.,

all upstream land areas where runoff draining into the

upstream reaches by either overland or waterway routes)

catchments were delineated using a 30-m resolution digital

elevation model available for the region. Additionally, we

delineated local and network buffers for each reach, where

buffers were defined as 75-m perpendicular distances on

either side of each stream. See Brenden and others (2006)

for a description of how local and network catchments and

buffers were delineated.

A suite of landscape and river network variables known to

influence habitat conditions and fish assemblages were

attributed to each reach. Landscape descriptors, including

catchment area, soil type and permeability, surficial geology

formation and texture, bedrock type and depth, 20-year July

Environmental Management

123

mean air temperature, 20-year mean precipitation, catch-

ment slope, and land use/cover within each of the spatial

scales were attributed to each reach. Stream channel

descriptors, including Shreve linkage number, reach gradi-

ent, reach elevation, sinuosity, Strahler stream order, total

upstream stream length, and distances from upstream most

headwaters and from the Great Lakes were also calculated

using ArcInfo functionalities (ESRI 2002). July mean stream

temperatures, stream flow exceedances, channel bankfull

width and depth, wetted stream width and depth, and sub-

strate size were predicted for each stream reach using sta-

tistical models developed from measurements made at a

subset of the reaches (Wehrly and others 2009; Seelbach and

others 2010; Wang unpublished data). See Brenden and

others (2006) for additional details regarding methods for

source data acquisition and variable attribution to the river

reaches.

River Segment Delineation

River segments (RSEGs) were formed by joining adjacent

river reaches that presumably have relatively similar char-

acteristics of hydrology, limnology, channel morphology,

riparian dynamics, and biological communities. RSEGs

have been advocated as appropriate spatial-scale units for

assessing, monitoring, and managing rivers due in part to;

(1) aligning with scale of landscape and network patch

influences on river character; (2) matching distribution and

life history characteristics of stream fishes, and (3) facili-

tating remote identification (Maxwell and others 1995;

Seelbach and others 1994; Seelbach and others 2006; Wang

and others 2006; Brenden and others 2008a). The RSEGs

were initially identified using an agglomerative, non-hier-

archical clustering method based on the cluster affinity

search technique of Ben-Dor and others (1999). Details of

the clustering methods can be found in Brenden and others

(2008a). Briefly, clusters are formed one at a time based on

the affinity of river reaches to the segment being formed in

relation to a pre-specified affinity threshold. The clustering

algorithm is a heuristic method that attempts to find the best

clustering set by allowing river reaches to move to alter-

native river segments in an effort to group reaches with their

highest affinity RSEGs.

The river network and landscape attributes used for

merging reaches into RSEGs were selected from over 200

variables in the GLRRDACS database (Brenden and others

2008a). The selected variables were: loge transformed

network catchment area, percent non-forested wetland type

in network catchments, percent lacustrine surficial geology

in reach catchments, percent moraine surficial geology in

reach catchments, mean reach catchment slope, predicted

July mean reach water temperature, and predicted loge

transformed 90th percentile reach baseflow yield. These

variables were selected using statistical procedures as they

are the strongest determinants of fish distributions in the

Great Lakes region (Brenden and others 2008a). Prior to

partitioning the river network database into RSEGs, the

river attribute data were standardized at a statewide scale

using Z-score standardization.

After the initial clustering of river reaches into RSEGs,

maps of the resulting segments were taken to each of the

eight state fisheries management area offices within the state

of Michigan for field biologists to review and correct some

of the RSEG boundaries. Each biologist reviewed only the

river segments they managed. This was considered an

important step in the GLRRDACS process because possible

model error, dated or inaccurate thematic landscape maps,

and the belief that area biologists would be in the best

position to make corrections based on their familiarity with

local areas or due to their having additional physicochemical

and biological measurements describing the streams under

consideration.

River Segment Classification

Every classification is goal-specific and sacrifices some

information power for the benefits of simplification. Our

RSEG classification was developed specifically for envi-

ronmental assessment and fisheries management. The clas-

sification process involved two steps. First, we classified the

RSEGs based on their statistical association with five fish

assemblages, and predicted July mean water temperature

and network catchment area. Fish assemblages representing

different thermal regimes and river sizes were identified

based on literature review and regional expert opinions.

Water temperature and catchment area were calculated for

each RSEG by weighted averaging of the measurements for

reaches that formed the RSEG. The weights used in the

weighted averaging were based on stream reach lengths in

relation to overall RSEG lengths. These are the variables

known integrators of stream network position, catchment

surficial geology and soil characteristics, landscape topog-

raphy, land cover, and local riparian conditions (Wang and

others 2003; Wang and others 2006). Multivariate regres-

sion trees (De’ath 2002) were then used to relate differences

in abundance of species associations to differences in water

temperature and catchment size, and to ultimately group

RSEGs into different classes (Brenden and others 2008b).

The second step of the classification process was to

modify and simplify the classes that resulted from the

aforementioned statistical analysis to aid in understanding,

communication, and management policy development.

Modifications were based on regional expert opinions,

further statistical analysis (Lyons and others 2009), local

Environmental Management

123

biologists’ field verification, and the needs of water with-

drawal policy (Hamilton and Seelbach 2011) and fisheries

management (Hayes and others 2003). As was the case in

the field biologist review of delineated river segments, this

modification of the RSEG classes was considered useful to

overcome map resolution limits, make use of locally

available survey data and expertise, and ultimately make

the resulting classification system as useful as possible to

natural resource managers and policy makers.

Fish Data

Fish data were from the Michigan stream fish survey database

managed by the Institute for Fisheries Research at Ann Arbor,

Michigan. This database contains survey records where fish

were collected using backpack, tow-barge, boom electro-

fishing units, or rotenone sampling from May to October

between 1982 and 2007. Data collected by rotenone were

corrected for comparability with the rest of the electrofishing

data using ratios from sampling sites that had both electro-

fishing and rotenone data (Seelbach and others 1994; T. Zorn

personal communication). The lengths of wadeable streams

sampled were between 80 and 960 m (median = 152 m)

depending on the size of the streams. For all non-wadeable

rivers, the length of river sampled was 1,610 m. From this

fish sampling database, we selected 335 sampling sites where

a river reach had at least two fish sampling sites, a RSEG had

at least two river reaches with fish data, and a RSEG class had

at least two RSEGs with fish data. In the data selection pro-

cess, we excluded sampling sites that had more than 40 %

agricultural or 5 % urban land uses in their network catch-

ments to minimize the influence of human disturbances on

river habitat and biological communities (Wang and others

2008). From the fish sampling data, we calculated 19 fish

metrics (Table 1) known to be influenced by physical habitat

characteristics (Lyons 1992; Wang and others 2010).

Data Analyses

Pairwise similarities in fish assemblage data between all

sampling sites was calculated using the Bray–Curtis index

(Bray and Curtis 1957). The Bray-Curtis Index is a com-

monly used metric in ecological studies for quantifying the

compositional dissimilarity between two different sites or

two groups of sites (e.g., Faith and others 1987; Anderson

and Willis 2003; Cao and Epifanio 2010). Species abun-

dance data were loge transformed prior to the calculation of

the Bray–Curtis dissimilarities. Similarities were calculated

by subtracting dissimilarities from 1.0. As an initial means of

assessing how fish assemblages vary among the three hier-

archical river spatial scales evaluated in this research, we

compared the within- and between-spatial scale frequencies

Table 1 Fish metrics known to be influenced by physical habitat characteristics and were used to evaluate how much observed variability can be

explained by the three hierarchical spatial scales

Fish metrics Description Mean Range

COLDNB Coldwater fish (number/100 m) 74 0–1,328

COOLNB Coolwater fish (number/100 m) 7 0–907

FSNBHM All fishes (number/100 m) 244 2–5,556

%CARNNB Carnivore fish individuals (%) 40 0–100

%COLDNB Coldwater fish individuals (%) 48 0–100

%COOLNB Coolwater fish individuals (%) 2 0–60

%INSENB Insectivore fish individuals (%) 33 0–95

%INTONB Intolerant fish individuals (%) 32 0–100

%LITHNB Lithophilic fish individuals (%) 24 0–97

%MIDRNB Mid-size river fish individuals (%) 4 0–75

%OMNINB Omnivore fish individuals (%) 6 0–83

%TOLENB Tolerant fish individuals (%) 27 0–100

%WMHDNB Warm headwater fish individuals (%) 20 0–100

SPCNT Total number of fish species 8 1–31

WRMDRIVNB Warm mid-size river fish individuals (number/100 m) 21 0–1,123

WRMHAEDNB Warm headwater fish individuals (number/100 m) 48 0–1,240

WRMHAEDSP Number of warm headwater fish species 2 0–7

WRMRIVNB Warmwater large river fish individuals (number/100 m) 1 0–213

WRMRIVSP Number of warm large river fish species \1 0–4

See Lyons (1992) and Wang and others (2010) for detailed description of the metrics

Environmental Management

123

of sampling sites that had substantially similar fish assem-

blages. We used a threshold of 0.5 to distinguish sampling

sites with substantially similar fish assemblages. Our

expectation was that spatial scales to which fish assemblages

most strongly responded to would have a high within-spatial

scale frequency of sampling sites with similarity values[0.5

and a low between-spatial scale frequency of sampling sites

with similarity values [0.5. Thus, appropriateness was

judged based on the relative differences of the within- and

between-spatial scale frequencies.

For the abundance of all fish species and the presence of

the three most abundant fish species datasets, we performed

a permutational multivariate ANOVA (PERMANOVA;

Anderson 2001a, b; McArdle and Anderson 2001) to eval-

uate how much of the total variance in the fish assemblage

data could be explained by each of the three hierarchical

spatial scales (river reach, RSEG, RSEG class) and to test

whether a particular variance component was significantly

different from random variation. PERMANOVA is con-

ceptually similar to regular multivariate ANOVA and can

be used in a wide variety of complex sampling designs (e.g.,

nested and unbalanced designs); however, there are

important differences between the methods. First, PER-

MANOVA can use any similarity index to measure the

distance between two samples. Second, PERMANOVA

constructs a distribution of F-statistics for evaluating a

particular null hypothesis by randomly permuting the group

labels for the experimental or observational units many

times (e.g., 9999 times) and calculating an F statistic for

each of the permutations. The P-values for statistical sig-

nificance of a factor is the proportion of F-values from the

permutations that are equal or greater than the F-value

observed from the real data (Anderson 2001a).

PERMANOVA tests were conducted on the abundance

of all fish species included in the fish assemblage dataset

and on the presence of only the three most abundant species.

In each case, the Bray–Curtis similarity index was used to

summarize the pairwise differences in the loge transformed

abundances or presence of the species. We chose the Bray–

Curtis similarity index as it has repeatedly been found to be

one of the most robust measures of community similarity in

the literature (e.g., Faith and others 1987; Anderson and

Willis 2003; Cao and Epifanio 2010). Because of the hier-

archical nature of the spatial scales, we treated the scales as

nested effects. The first level consisted of river reaches. The

second level consisted of river RSEGs. The third level was

the RSEG classes. Because our primary interest in this

research was to decompose the variance in the fish assem-

blage data into the different hierarchical spatial scales and

not to test differences between individual factor levels, each

nested effect was considered a random effect. The propor-

tion of variance explained by a given factor was calculated

after accounting for all other factors (i.e., Type-3 analysis),

which is the preferred method when the number of repli-

cates is not equal among groups as in our case (Anderson

and others 2008). PERMANOVA was conducted in PRI-

MER-6 (Clarke and Gorley 2006) using the PERMANO-

VA ? add-on (Anderson and others 2008).

For the 19 fish metrics that were calculated from the fish

assemblage data, generalized linear mixed models (GLMM)

were used to decompose the variance to the three hierar-

chical spatial scales. As with the PERMANOVA analyses,

each nested effect was considered a random effect. For the

abundances of coldwater, abundances of coolwater, abun-

dances of warmwater, abundances of medium river, abun-

dances of warmwater large river, abundances of all fishes,

number of warm headwater species, number of large river

species, and number of all species metrics, GLMMs were fit

assuming a Gaussian distribution and identity link after

loge ? 1 transformation of the metric data. For all other

metrics, GLMMs were fit assuming over-dispersed bino-

mial distributions. The GLMMs were fit by restricted

maximum likelihood estimation, which is the recommended

estimation approach when the focus is on estimating ran-

dom effects because of its accuracy (McCulloch and Searle

2001), in R (R Development Core Team 2011) using the

‘lmer’ function in the ‘lme4’ package (Bates and others

2011). As was done in the PERMANOVA tests of the fish

assemblage data, we used permutations to test whether an

estimated variance component estimated from the GLMM

for a particular fish metric was different from zero. Per-

mutations that were conducted on the GLMMs followed the

methodology described in Fitzmaurice and others (2007),

which is similar to that described for PERMANOVA. To

test whether an estimated variance component was equal to

zero, we calculated a likelihood ratio test statistic using the

full model (all random effects included) and a reduced

model (random effect of interest removed from the model).

We then randomly permuted the labels for the effect of

interest and calculated permuted likelihood ratio test statics.

The P-value for evaluating the statistical significance of the

observed likelihood ratio test statistics was then based on

the proportion of the permutations that yield permutated test

statistics that were as large or larger than the original test-

statistic. According to Fitzmaurice and others (2007), as

few as 200 permutation samples are needed to obtain the

correct Type-1 error rate. To err on the conservative side,

we conducted 499 permutations for each of the fish metrics

and analyzed variance components for testing whether a

variance component was different from zero.

Results

Our delineation framework merged the 28,889 confluence-

to-confluence reaches into 6,198 RSEGs (excluding canal,

Environmental Management

123

coastal, ditch, or undefined stream lines in the NHD) and

grouped these RSEGs into 11 classes (Table 2; Fig. 1).

Although the initial RSEG classification process followed

the established catchment size and thermal criteria, indi-

vidual site RSEG attributes might fall outside the criteria

range due to the final correction process made by the field

biologists. Warm-stream, warm-transitional-stream, and

cold-stream were the most dominant RSEG class types

(number of RSEGs [1,600; RSEG length [13,000 km),

cold-transitional-stream was intermediate dominant class

type (number of RSEG = 584; RSEG length = 5,795 km),

and cold-transitional-large-river, warm-transitional-large-

river, cold-transitional-small-river, and cold-small-river

were least abundance class types (number of RSEG \50;

RSEG length\700 km). In general, the stream class types

had the shortest (\15 km) and the large river class types

had the longest mean RSEG length (C20 km).

Fish Assemblage Dissimilarity Within and Between

Spatial Scales

The Bray–Curtis index comparison analyses indicated

that the abundances of all fish species assemblage were

generally similar for within river reaches, between river

reaches, and within RSEGs, and were different between

RSEGs, within RSEG classes, and between RSEG clas-

ses (Fig. 2). The percentages of fish assemblage com-

parison pairs that had Bray–Curtis values greater than

0.5 were relatively high for both within reach (46 %)

and between reaches (34 %). The percentage of such

comparison pairs was much higher for within RSEG

(43 %) than for between RSEGs (4 %). The percentages

of such comparison pairs were relatively low for both

within RSEG classes (8 %) and between RSEG classes

(3 %).

Variance Decomposition of the Entire Fish

Community and Three Most Abundant Species

PERMANOVA showed that the RSEG spatial scale

explained most of the variation in abundance of all the fish

species data (40 %), followed by RSEG-class scale (12 %),

and river reach scale (5 %), with the remaining 43 % being

unexplainable variation. The effects of RSEG and RSEG-

class scales were significantly different from random

(RSEG: pseudo F = 3.63, P \ 0.0001; RSEG class:

pseudo F = 2.58, P \ 0.0001); however, the effect of

reach scale was not significantly different from random

(pseudo F = 1.15, P [ 0.05, Table 3).

For the presence of the three most abundance fish spe-

cies, PERMANOVA showed that the RSEG scale again

explained most of the variation (32 %), followed by RSEG

class (12 %), and reach scale (5 %), with the remaining

51 % being unexplainable variation. As was the case for

the total fish abundance data, the effects of RSEG and

RSEG-class scales were significantly different from ran-

dom (RSEG: pseudo F = 2.06, P \ 0.0001; RSEG class:

pseudo F = 3.96, P \ 0.0001); however, the effect of

reach scale was not statistically different from random

(pseudo F = 1.11, P [ 0.05, Table 4).

Variance Decomposition of Fish Metric Data

Of the 19 fish assemblage metrics that were analyzed, the

total amount of variation explained by the RSEG spatial

scales ranged from 11 to 70 % (Table 5). The total amount of

variation explained by the RSEG class and river reach scales

ranged from 0 to 71 % and 0 to 23 %, respectively (Table 5).

RSEGs explained the greatest amount of variation in 13 of

the 19 analyzed fish metrics, with RSEG class explaining the

greatest amount of variation in the abundance and relative

Table 2 River segments (RSEG), valley segment length, segment mean length and range by thermal and size class types in the state of Michigan

RSEG class Number

of RSEGs

Total RSEG

length (km)

Mean and (range)

of RSEG length (km)

Mean July

Temperature (�C)

Catchment

area km2 (mile2)

Cold stream 1,618 13,293 8 (\1–75) \17.5 \207 (80)

Cold small river 36 669 19 (2–49) \17.5 201–777 (80–300)

Cold transitional stream 584 5,795 10 (\1–63) 17.5–19.5 \207 (80)

Cold transitional small river 42 691 16 (\1–48) 17.5–19.5 207–777 (80–300)

Cold transitional large river 16 337 21 (5–72) 17.5–19.5 [777 (300)

Warm transitional stream 1,704 17,144 10 (\1–104) 19.5–21.0 \207 (80)

Warm transitional small river 130 2,129 16 (\ 1-78) 19.5-21.0 207–777 (80–300)

Warm transitional large river 28 573 20 (\1–74) 19.5–21.0 [777 (300)

Warm stream 1,842 27,660 15 (\1–177) [21.0 \207 (80)

Warm small river 133 2,225 17 (\1–81) [21.0 207–777 (80–300)

Warm large river 69 1,360 20 (\1–177) [21.0 [777 (300)

Environmental Management

123

abundance of coldwater fishes, abundance and number of

species of warm large river fishes, abundance of warm

medium-size river fishes, and number of all fish species.

The amount of variation explained by the RSEG-class

scale was significantly different from zero for all of the fish

metric data except the relative abundances of medium-size

river and coolwater fishes (Table 5). The amount of vari-

ation explained by the RSEG scale was significantly dif-

ferent from zero for all of the fish metric data except the

relative abundance of warmwater large river fish species

(Table 5). Conversely, the amount of variation explained

by the river reach scale was significantly different from

zero for only eight of the 19 fish metrics (Table 5).

Discussion

We described a delineation and classification process for

the identification of a three-level spatial scale hierarchy and

evaluated the capability of the different spatial scales in

explaining variability in fish assemblage data. This process

integrated early conceptual understanding of hierarchical

fluvial systems and landscape ecology (e.g., Frissell and

others 1986; Poff 1997; Montgomery 1999; Rice and others

Fig. 1 Map of the 11 river

segment classes found in the

state of Michigan

Fig. 2 Percentages of Comparison of fish assemblage dissimilarity

within- and between-reaches, within- and between-segments (RSEGs),

and within- and between-RSEG classes using the Bray–Curtis index for

all possible pairs of samples using loge transformed data. The

percentages of all possible pairs of samples with Bray–Curtis index

values[0.5 were for each spatial scale

Environmental Management

123

2001; Poole 2002; Fausch and others 2002; Seelbach and

others 2006) with empirical local knowledge of relation-

ships among catchment geography and in-channel physi-

cochemical and biological characteristics (e.g., Wehrly and

others 2003; Wehrly and others 2006; Seelbach and Wiley

1997; Sowa and others 2007; Zorn and others 2011) using

up-to-date GIS technology, increased available regional

and national river system databases, and sophisticated

statistical procedures.

There is a broad array of potential applications for the

spatial scale delineation and classification process described

herein that would assist natural resource agencies fulfill

Table 3 Nested PERMANOVA of 335 fish samples for variance partitioning by the three hierarchical spatial scales evaluated in this reach—

segment class (Class), segment (RSEG), and river reach (Reach), based on the abundance of all species

Source df MS Estimated variance % of variance explained Pseudo-F P

Class 7 17,979 422.62 11.89 3.63 0.001

RSEG (Class) 115 5,377 1431.40 40.28 2.58 0.001

Reach [RSEG(Class)] 93 1,743 190.12 5.35 1.15 0.065

Residual 120 1,509 1509.20 42.47

Table 4 Nested PERMANOVA of 335 fish samples for variance partitioning by the three hierarchical spatial scales evaluated in this reach—

segment class (Class), segment (RSEG), and river reach (Reach), based on the presence of three most abundant fish species

Source df MS Estimated variance % of variance explained Pseudo-F P

Class 7 19,248 466.85 12.33 3.96 0.001

RSEG (Class) 115 5,209 1196.30 31.59 2.06 0.001

Reach [RSEG(Class)] 93 2,162 170.26 4.50 1.11 0.167

Residual 120 1,954 1953.90 51.59

Table 5 Variance estimates and percentages of total variance explained by segment class (Class), segment (RSEG), and river reach (Reach) for

the 19 fish assemblage metrics using generalized linear mixed models

Fish Variance estimate % of total variance explained

Metrics Class RSEG Reach Residual Class (%) RSEG (%) Reach (%) Residual (%)

COLDNB 1.706 1.322 0.104 0.917 42.1 32.7 2.6 22.6

COOLNB 0.158 0.410 0.110 0.455 13.9 36.2 9.7 40.2

FSNBHM 0.231 0.855 0.159 0.616 12.4 45.9 8.5 33.1

%CARNNB 1.130 2.510 0.271 2.489 17.7 39.2 4.2 38.9

%COLDNB 16.736 4.510 0.000 4.116 66.0 17.8 0.0 16.2

%COOLNB 0.000 3.919 2.098 3.130 0.0 42.8 22.9 34.2

%INSENB 0.862 0.980 0.637 1.280 22.9 26.1 17.0 34.1

%INTONB 0.837 2.431 0.171 3.340 12.3 35.9 2.5 49.3

%LITHNB 1.024 2.650 0.395 1.764 17.6 45.4 6.8 30.2

%MIDRNB 0.000 15.347 0.883 5.638 0.0 70.2 4.0 25.8

%OMNINB 0.685 2.258 0.394 1.151 15.3 50.3 8.8 25.6

%TOLENB 0.592 2.756 0.159 2.160 10.4 48.6 2.8 38.1

%WMHDNB 0.664 2.192 0.127 1.732 14.1 46.5 2.7 36.7

SPCNT 0.145 0.100 0.036 0.104 37.6 26.0 9.4 27.0

WRMDRIVNB 1.125 0.783 0.518 0.584 37.4 26.0 17.2 19.4

WRMHAEDNB 0.967 1.203 0.212 1.225 26.8 33.3 5.9 34.0

WRMHAEDSP 0.049 0.143 0.000 0.152 14.2 41.6 0.0 44.2

WRMRIVNB 0.538 0.085 0.088 0.051 70.7 11.2 11.5 6.6

WRMRIVSP 0.079 0.013 0.014 0.019 63.2 10.2 11.2 15.5

Bold font indicates variance component was significantly different from zero (P \ 0.05). See Table 2 for fish metric abbreviations

Environmental Management

123

research, assessment, and management objectives for river

systems at local to national scales (e.g., Snelder and Hughey

2005; Sowa and others 2007; Brenden and others 2008a;

Wang and others 2008; Higgins and Duigan 2009). For one,

the process assembles in a single database the entire stream

and river network structure of a state along with the asso-

ciated descriptors for channel positions, network connec-

tivity, ecological (e.g., ecoregions) and political boundaries,

and local and network catchment natural variation and

anthropogenic disturbances. It also provides management

agencies with the best available information about the

amounts, types, and locations of the aquatic resources and

their associated natural and anthropogenic landscape vari-

ations. This information is available for any specific section

of a river, for an entire river network, and for rivers located

within a specific local area, planning district, state, or

potentially for multistate regions. Such information can

meet the needs of local stakeholders who are interested only

in river reaches in which they have a vested interest;

watershed groups who are interested in specific rivers; local

governments and planners who are managing counties or

districts; state governments who are responsible for rivers

within their state boundaries in identifying and reporting

river resources and conditions within political or ecological

boundaries, by river types, or by socially, economically, or

ecologically important biological communities.

Our study addressed the critical question of whether the

identified river network scales reflect biotic resource charac-

teristics. Acknowledging that the appropriateness of spatial

scales is likely application specific, we judged the perfor-

mance of the scales based on the needs of environmental

assessment and water resource management using fish

assemblage data. There are several advantages of using fish to

identify adequate spatial scales. First, fish assemblages are

commonly used in river bioassessment because fish integrate

physicochemical and biological impacts of human activities at

multiple temporal and spatial scales (e.g., Lyons and others

1996; Karr and Chu 1999; Wang and others 2008). Second,

researchers have sufficient knowledge of habitat needs for

different fish species and life stages and managers have

accumulated local knowledge of fish distribution, which is

important for spatial unit boundary identification and valida-

tion. Last, fishes are socially and economical important

resources and the health of fish assemblages generally is a

good indication of overall ecosystem health.

Our evaluations clearly indicated that river segments are

a useful scale for fisheries management and environmental

assessment. It is generally believed that the confluences of

streams result in the major separation of ecological spatial

units in river systems due to changes in river flow and

physicochemical characteristics at stream intersections.

However, not all tributary confluences result in adjustments

to physical habitat variables to such a degree to cause

changes in biological communities (Rice and others 2001),

which perhaps may explain in part why we found segments

to be much more important than river reaches in explaining

differences in fish assemblage data. Our finding that seg-

ment class explained less variance of fish assemblages than

segments is not surprising given that the original basis for

the classification system was an analysis relating catchment

size and mean July water temperature referencing to rela-

tive abundances of fish groups that were believed to be

strongly influence by river size and thermal features

(Brenden and others 2008b). In contrast, the current study

used the abundance of all fishes, presence of three most

abundance fishes, and fish assemblage metrics to evaluate

the capability of the three hierarchy spatial scales in dis-

tinguishing fish distribution. As a result, the fish assem-

blage data included species that were more generalist with

regards to temperature and river discharge, and hence

segment class explained less fish variation than segments.

When using or viewing a river segment as a spatial scale,

it is important to keep in mind that river systems possess

spatially averaged downstream gradual changes in hydro-

logic and geomorphic properties, physical and biological

characteristics, and ecosystem processes as described in the

river continuum concept (Vannote and others 1980). The

downstream interruptions of the continuum resulted from

tributary confluences, impoundments, and groundwater

contributions (Ward and Stanford 1983; Stanford and others

1988; Townsend 1989; Poole 2002) form the basis of river

reach boundaries that we described. However, the magni-

tude of the interruptions is variable in nature. Hence, the

heterogeneity of river physical and biological properties is

spatial scale dependent. The intermediate segment spatial

scale we described is aimed for environmental monitoring,

assessment, and water resource management using fish as

indicators or targets. For other uses, such as nutrient and

sediment measures, further evaluations may be needed.

When using or viewing river segment classes, it is also

important to keep in mind that classes are generated by

placing boundaries on features (e.g., temperature, water-

shed shed size) that lie on a continuum. Although abrupt

ecological changes form the basis of classification, transi-

tional zones certainly occur, which is supported by our

results that segments explained more variance for fish

assemblages than segment class. Our segment classification

presents a means of describing and managing the diverse

segments by supplying a framework for organizing data,

facilitating generalizations among similar spatial units, and

developing and applying similar management policy

and practices for specific class of spatial units (Seelbach and

others 2006). When applying such classification for other

purposes, additional classifiers may need to be included.

This study describes the entire process of river reach

delineation, multi-reach segment identification, and segment

Environmental Management

123

classification and validation using integrated statistical and

empirical approaches. Acknowledging the advancement in

river classification using different approaches in the litera-

ture (e.g., Leathwick and others 2011; Snelder and others

2012), our spatial hierarchical framework and database

classification system has made substantial contribution to the

similar work in North America. First, the processes of

delineating confluence-to-confluence river reaches, merging

multiple reaches into segments, and classifying segments

into ecological classes are done using sophisticated GIS

programs, modeling, and statistical procedures. Such pro-

cedures can be easily repeated in the same region when more

accurate or updated databases become available or in dif-

ferent regions where the development of such database and

classification is needed. Second, our procedures include a

step that incorporated field biologists’ knowledge to improve

the spatial unit delineation and classification, which also

helps their acceptance by users. Last, although our segment

classification is a very simplified version for meeting the

needs of fisheries management and water resource assess-

ment using fish, our database contains all the detailed data

associated with each river reach. New classification can

always be developed using detailed data for meeting

emerging needs. Our spatial hierarchical framework and

database classification system can only be as good as that of

available data, which can be improved by increased resolu-

tion and accuracy of landscape and river network databases

and by inclusion of additional information, such as more

accurate water temperature and flow measurements or

modeling. Although our work is still relatively coarse with a

best data available scenario, it does provide river researchers,

resource managers, and policy makers with essential infor-

mation and tools to effectively address environmental and

management issues.

Acknowledgments The database and spatial framework described

in this study is a product of a team effort of scientists at the Institute

for Fisheries Research (Michigan Department of Natural Resources),

University of Michigan, Michigan State University, and U.S.

Geological Survey during the past 10 years. We acknowledge Jana

Stewart, Arthur Cooper, Stephen Aichele, Edward Bissell, and Ann

Holtrop who collaboratively played major roles in the development

of the stream network database. We additionally acknowledge Mike

Wiley, John Lyons, Kevin Wehrly, Richard Clark, Troy Zorn,

Edward Rutherford, James Breck, and Beth Sparks-Jackson who were

instrumental in the development of the classification framework. We

thank Minako Edgar for preparing the river classification map, and

the Great Lakes Office of International Joint Commission and Illinois

Natural History Survey for providing in-kind support for manuscript

revision. We are grateful to Jonathan Higgins, Ton Snelder, and an

anonymous reviewer who provided comments that substantially

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. This article is contribution 2012-06 of the

Quantitative Fisheries Center at Michigan State University. All views

and opinions presented in this manuscript are solely those of the

authors and do not necessarily reflect those of the agencies that the

authors belong to.

References

Anderson MJ (2001a) A new method for non-parametric multivariate

analysis of variance. Austral Ecology 26:32–46

Anderson MJ (2001b) Permutation tests for univariate or multivariate

analysis of variance and regression. Canadian Journal of

Fisheries and Aquatic Sciences 58:626–639

Anderson MJ, Willis TJ (2003) Canonical analysis of principal

coordinates: a useful method of constrained ordination for

ecology. Ecology 84:511–525

Anderson MJ, Gorley RN, Clarke KR (2008) PERMANOVA? for

PRIMER: guide to software and statistical methods. Primer-E,

Ltd., Plymouth

Baker EA (2006) A landscape-based ecological classification system

for river valley segments in Michigan’s Upper Peninsula.

Michigan Department of Natural Resources, Fisheries Research

Report 2085, Ann Arbor. Available www.michigandnr.com/

PUBLICATIONS/PDFS/ifr/ifrlibra/Research/reports/2085rr.pdf,

January 2008

Barbour MT, Gerritsen J, Snyder BD, Stribling JB (1999) Rapid

bioassessment protocols for use in streams and wadeable rivers:

periphyton, benthic macroinvertebrates, and fish, 2nd edn. EPA

841-B-99-002. U.S. Environmental Protection Agency; Office of

Water; Washington, DC

Bates D, Maechler M, Bolker B (2011) lme4: linear mixed-effects

models using S4 classes. R package version 0.999375-41.

Available http://CRAN.R-project.org/package=lme4, October

2011

Ben-Dor A, Shamir R, Yahkini Z (1999) Clustering gene expression

patterns. Journal of Computational Biology 6:281–297

Bray RJ, Curtis IT (1957) An ordination of the upland forest

communities of south Wisconsin. Ecological Monographs

27:325–349

Brenden TO, Clark RD, Cooper AR, Seelbach PW, Wang L, Aichele

SS, Bissell EG, Stewart JS (2006) A GIS framework for

collecting, managing, and analyzing multi-scale landscape

variables across large regions for river conservation and

management. In: Hughes RM, Wang L, Seelbach PW (eds)

Landscape influences on stream habitats and biological assem-

blages. American Fisheries Society, Special Publication 48,

Bethesda

Brenden TO, Wang L, Seelbach PW, Clark RD, Wiley MJ, Sparks-

Jackson BL (2008a) A spatially-constrained clustering program

for river valley segment delineation from GIS digital river

networks. Environmental Modeling and Software 23:638–649

Brenden TO, Wang L, Seelbach PW (2008b) A river valley segment

classification of Michigan streams based on fish and physical

attributes. Transactions of American Fisheries Society 137:

1621–1636

Cao Y, Epifanio J (2010) Quantifying the responses of macroinver-

tebrate assemblages to simulated stress: are more accurate

similarity indices less useful? Methods in Ecological Evolution

1:380–388

Cao Y, Larsen DP, Hughes R (2001) Evaluating sampling sufficiency

in fish surveys: a similarity-based approach. Canadian Journal of

Fisheries and Aquatic Sciences 58:1782–1793

Cao Y, Hawkins CP, Olsen J, Nelson M (2007) Modelling natural

environmental gradients improves the accuracy and precision of

diatom-based indicators for Idaho streams. Journal of the North

American Benthological Society 26:566–585

Environmental Management

123

Clarke KR, Gorley RN (2006) PRIMER v6: user manual/tutorial.

PRIMER-E, Plymouth

De’ath G (2002) Multivariate regression trees: a new technique for

modeling species-environment relationships. Ecology 83:1105–

1117

ESRI (2002) PC ARC/GIS version 8.2. Environmental System

Research Institute, Redlands

Faith DO, Minchin PR, Belbin L (1987) Compositional dissimilar-

ity as a robust measure of ecological distance. Vegetatio 69:

53–68

Fausch KD, Torgersen CE, Baxter CV, Li HW (2002) Landscapes to

riverscapes: bridging the gap between research and conservation

of stream fishes. BioScience 52:483–498

Fayram AH, Miller MA, Colby AC (2005) Effects of stream order and

ecoregion on variability in coldwater fish index of biotic

integrity scores within streams in Wisconsin. Journal of Fresh-

water Ecology 20:17–25

Ferguson RI, Cudden JR, Hoey TB, Rice SP (2006) River system

discontinuities due to lateral inputs: generic styles and controls.

Erath Surface Processes and Landforms 31:1149–1166

Fitzmaurice GM, Lipsitz SR, Ibrahim JG (2007) A note on

permutation tests for variance components in generalized linear

mixed models. Biometrics 63:942–946

Frissell CA, Liss WJ, Warren CE, Hurley MD (1986) A

hierarchical framework for stream habitat classification: view-

ing streams in a watershed context. Environmental Manage-

ment 10:199–214

Hamilton DA, Seelbach PW (2011) Michigan’s water withdrawal

assessment process and internet screening tool. Michigan

Department of Natural Resources, Fisheries Special Report 55,

Ann Arbor

Hawkins CP, Norris RH, Gerritsen J, Hughes RM, Jackson SK,

Johnson RK, Stevenson RJ (2000) Evaluation of the use of

landscape classifications for the prediction of freshwater biota:

synthesis and recommendations. Journal of the North American

Benthological Society 19:541–556

Hayes D, Baker DE, Bednarz R, Borgeson D, Braunscheidel J, Breck

J, Bremigan M, Harrington A, Hay R, Lockwood R, Nuhfer A,

Schneider J, Seelbach P, Waybrant J, Zorn T (2003) Developing

a standardized sampling program: the Michigan experience.

Fisheries 28(7):18–24

Higgins JV, Duigan C (2009) So much to do, so little time:

identifying properties for freshwater biodiversity conservation in

the United States and Britain, Chap 4. In: Boon PJ, Pringle CM

(eds) Assessing the conservation value of freshwaters. Cam-

bridge University Press, New York, pp 61–90

Higgins J, Lammert M, Bryer M, DePhilip M, Grossman D (1998)

Freshwater conservation in the Great Lakes Basin: development

and application of an aquatic community classification frame-

work. The Nature Conservancy, Chicago

Higgins JV, Bryer MT, Khoury ML, Fitzhugh TW (2005) A

freshwater classification approach for biodiversity conservation

planning. Conservation Biology 19:432–445

Hitt NP, Angermeier PL (2008) Evidence for fish dispersal from

spatial analysis of stream network topology. Journal of North

American Benthological Society 27:304–320

Hynes HBN (1975) The stream and its valley. Verhandlungen der

International Vereiniggung Fur Theoretische und Angewandte

Limnologie 19:1–15

Karr JR, Chu EW (1999) Restoring life in running waters, better

biological monitoring. Island Press, Covelo

Kilgour BW, Stanfield LW (2006) Hindcasting reference conditions

in streams. In: Hughes RM, Wang L, Seelbach PW (eds)

Landscape influences on stream habitats and biological assem-

blages. American Fisheries Society, Special Publication 48,

Bethesda

Knispel S, Castella E (2003) Disruption of a longitudinal pattern in

environmental factors and benthic fauna by a glacial tributary.

Freshwater Biology 48:604–618

Leathwick J, Snelder T, Chadderton W, Elith J, Julian K, Ferrier S

(2011) Use of generalized dissimilarity modeling to improve the

biological discrimination of river and stream classifications.

Freshwater Biology 56:21–38

Lyons J (1992) The length of stream to sample with a towed

electrofishing unit when fish species richness is estimated. North

American Journal of Fisheries Management 12:198–203

Lyons J, Wang L, Simonson TD (1996) Development and validation

of an index of biotic integrity for cold-water streams in

Wisconsin. North American Journal of Fisheries Management

16:241–256

Lyons J, Zorn T, Stewart J, Seelbach PW, Wehrly K, Wang L (2009)

Defining and characterizing coolwater streams and their fish

assemblages in Michigan and Wisconsin, USA. North American

Journal of Fisheries Management 29:1130–1151

Maxwell JR, Edwards CJ, Jensen ME, Paustian SJ, Parrott H, and Hill

DM (1995) A hierarchical framework of aquatic ecological units

in North America (Nearctic Zone). General Technical Report

NC-176. U.S. Forest Service, North Central Forest Experiment

Station, Saint Paul, Minnesota, USA

McArdle BH, Anderson MJ (2001) Fitting multivariate models to

community data: a comment on distance-based redundancy

analysis. Ecology 82:290–297

McCulloch CE, Searle SR (2001) Generalized, linear, and mixed

models. Wiley, New York

McKenna JE Jr, Steen PJ, Lyons J, Stewart J (2009) Applications of a

broad-spectrum tool for conservation and fisheries analysis:

aquatic gap analysis. GAP Analysis Bulletin 16:44–51

Miller S, Higgins J, Perot J (1998) The classification of aquatic

communities in the Illinois River watershed and their use in

conservation planning. The Nature Conservancy of Illinois,

Champaign

Montgomery DR (1999) Process domains and the river continuum.

Journal of the American Water Resources Association 35:397–410

Pearson MS, Angradia TR, Bolgriena DW, Jichaa TM, Taylora DL,

Moffetta MF, Hill BH (2011) Multimetric fish indices for

midcontinent (USA) great rivers. Transactions of the American

Fisheries Society 140:1547–1564

Poff NL (1997) Landscape filters and species traits: towards mech-

anistic understanding and prediction in stream ecology. Journal of

the North American Benthological Society 16:391–409

Poole GC (2002) Fluvial landscape ecology: addressing uniqueness

within the river discontinuum. Freshwater Biology 47:641–660

Pyne MI, Rader RB, Christensen WF (2007) Predicting local

biological characteristics in streams: a comparison of landscape

classifications. Freshwater Biology 52:1302–1321

Rhoads BL (1987) Longitudinal variations in the size and sorting of

bed material along six arid-region mountain streams. Catena

Supplement 14:87–105

R Development Core Team (2011) R: a language and environment forstatistical computing. R Foundation for Statistical Computing.

www.R-project.org

Rice SP, Greenwood MT, Joyce CB (2001) Tributaries, sediment

sources, and the longitudinal organization of macroinvertebrate

fauna along river system. Canadian Journal of Fisheries and

Aquatic Science 58:824–840

Richards KS (1980) A note on changes in channel geometry at

tributary junctions. Water Resources Research 16:241–244

Seelbach PW, Wiley JM (1997) Overview of the Michigan rivers

inventory project. Michigan Department of Natural Resources,

Fish Division Technical Report 97-3, Ann Arbor, Michigan.

Available: www.michigandnr.com/PUBLICATIONS/PDFS/ifr/

ifrlibra/technical/reports/97-3tr.pdf

Environmental Management

123

Seelbach PW, Wiley MJ, Kotanchik JC, Baker ME (1997) A

landscape-based ecological classification system for river valley

segments in lower Michigan (MI-VSEC version 1.0). Michigan

Department of Natural Resources, Fisheries Research Report

2036, Ann Arbor, Michigan

Seelbach PW, Lockwood RN, Ryckman JR (1994) Efficiency of

sampling river fishes with rotenone. Michigan Department of

Natural Resources Fisheries Research Report 2009, Ann Arbor,

Michigan

Seelbach PW, Wiley MJ, Baker ME, Wehrly KE (2006) Initial

classification of river valley segments across Michigan’s lower

peninsula. In: Hughes RM, Wang L, Seelbach PW (eds)

Landscape influences on stream habitats and biological assem-

blages. American Fisheries Society, Special Publication 48,

Bethesda

Seelbach PW, Hinz LC, Wiley MJ, Cooper AR (2010) Using multiple

linear regression to estimate flow regimes for all rivers across,

Illinois, Michigan, and Wisconsin. Michigan Department of

Natural Resources, Fisheries Technical Report 2095, Ann Arbor,

Michigan

Simon A, Doyle M, Kondolf M, Shields FD, Rhoads B, McPhillips M

(2007) Critical evaluation of how the Rosgen classification and

associated ‘‘natural channel design’’ methods fail to integrate

and quantify fluvial processes and channel response. Journal of

the American Water Resources Association 43:1117–1131

Simon A, Doyle M, Kondolf M, Shields FD, Rhoads B, McPhillips M

(2008) Critical evaluation of how the Rosgen classification and

associated ‘natural channel design’ methods fail to integrate and

quantify fluvial processes and channel responses: reply to

discussion. Journal of the American Water Resources Associa-

tion 44:793–802

Snelder TH, Hughey KFD (2005) The use of an ecologic classifica-

tion to improve water resource planning in New Zealand.

Environmental Management 36:741–776

Snelder T, Ortiz JB, Booker D, Lamouroux N, Pella H, Shankar U

(2012) Can bottom-up procedures improve the performance of

stream classifications? Aquatic Sciences 74:45–59

Sowa SP, Annis G, Morey ME, Diamond DD (2007) A gap analysis

and comprehensive conservation strategy for riverine ecosystems

of Missouri. Ecological Monographs 77:301–334

Stanford JA, Hauer FR, Ward JV (1988) Serial discontinuity in a large

river system. Verhandlungen der International Vereiniggung Fur

Theoretische und Angewandte Limnologie 23:1114–1118

Townsend CR (1989) The patch dynamics concept of stream

community ecology. Journal of North America Benthological

Society 8:36–50

Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE

(1980) The river continuum concept. Canadian Journal of

Fisheries and Aquatic Science 37:130–137

Wang L, Lyons J, Rasmussen P, Kanehl P, Seelbach PW, Simon T,

Wiley M, Baker E, Niemela S, Stewart M (2003) Influences of

landscape- and reach-scale habitat on stream fish communities in

the Northern Lakes and Forest ecoregion. Canadian Journal of

Fisheries and Aquatic Science 60:491–505

Wang L, Seelbach PW, Hughes RM (2006) Introduction to landscape

influences on stream habitats and biological assemblages. In:

Hughes RM, Wang L, Seelbach PW (eds) Landscape influences

on stream habitats and biological assemblages. American

Fisheries Society, Special Publication 48, Bethesda

Wang L, Brenden TO, Seelbach PW, Cooper A, Allan D, Clark R,

Wiley M (2008) Landscape based identification of human

disturbance gradients and references for streams in Michigan.

Environmental Monitoring and Assessment 141:1–17

Wang L, Wehrly K, Breck J, Kraft LS (2010) Landscape based

assessment of human disturbance for Michigan lakes. Environ-

mental Management 46:471–483

Wang L, Infante D, Esselman P, Cooper A, Wu D, Taylor W, Beard

D, Whelan G, Ostroff A (2011) A hierarchical spatial framework

and database for the national river fish habitat condition

assessment. Fisheries 36:436–449

Ward JV, Stanford JA (1983) The serial discontinuity concept of lotic

ecosystems. In: Fontaine TD, Bartell SM (eds) Dynamics of lotic

ecosystems. Ann Arbor Science Publishers Inc, Ann Arbor

Ward JV, Stanford JA (1995) The serial discontinuity concept:

extending the model to floodplain rivers. Regulated Rivers

Research and Management 10:159–168

Warner SS, Fischer RU, Holtrop AM, Hinz LC, Novak JM (2010)

Evaluating the Illinois stream valley segment model as an

effective management tool. Environmental Management 46:

761–770

Wehrly KE, Wiley MJ, Seelbach PW (2003) Classifying regional

variation in thermal regime based on stream fish community

patterns. Transactions of the American Fisheries Society 132:

18–38

Wehrly KE, Wiley MJ, Seelbach PW (2006) Influence of landscape

features on summer water temperatures in lower Michigan

streams. Pages 113–128 in Hughes RM, Wang L, Seelbach PW

(eds) Influences of landscapes on stream habitats and biological

assemblages. American Fisheries Society 131, pp 70–85

Wehrly KE, Brenden TO, Wang L (2009) A comparison of statistical

approaches for predicting stream temperatures across heteroge-

neous landscapes. Journal of the American Water Resources

Association 45:986–997

Zorn TG, Seelbach PW, Wiley MJ (2011) Developing user-friendly

habitat suitability tools from regional stream fish survey data.

North American Journal of Fisheries Management 31:1–41

Environmental Management

123