A LANDSCAPE PERSPECTIVE ON THE DISTRIBUTION AND HARVEST OF NORTH
AMERICAN RIVER OTTERS IN MICHIGAN IN RELATION TO PREY AVAILABILITY
By
Kiira J. Siitari

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Fisheries and Wildlife
2012

ABSTRACT
A LANDSCAPE PERSPECTIVE ON THE DISTRIBUTION AND HARVEST OF NORTH
AMERICAN RIVER OTTERS IN MICHIGAN IN RELATION TO PREY AVAILABILITY
By
Kiira J. Siitari

The North American river otter (Lontra canadensis) is considered a top predator in many
freshwater systems in Michigan and throughout the continent of North America. As a semiaquatic piscivore, river otters serve a critical role in linking aquatic and terrestrial ecosystems yet
the current understanding of river otter distribution is limited to relatively small spatial scales. A
landscape analysis was used in this study to assess river otter distribution in Michigan; this
approach allowed for broad spatial assessment at the state scale and furthermore assessed the
importance of both aquatic and terrestrial habitat components. I designed and evaluated an
approach to identify landscape features related to river otter distribution in Michigan at two
spatial resolutions with specific attention given to the relationship between river otter occurrence
via trapping records and prey availability. A multiple linear regression model identified surface
water quantity and wetland configuration, along with percent crop cover, to be effective
2

parameters in predicting river otter harvest at the county scale (adjusted R = 0.67). At the local
catchment scale, human population density, number of road crossings, percent crop cover, and
percent canopy cover explained 62% of a predictive, maximum entropy model of species
occupancy (AUC = 0.733). This analysis helps to identify the river otter’s large scale habitat
needs, highlighting the importance of connectivity between aquatic and terrestrial systems for
effective, landscape-based management of fish and wildlife resources.

ACKNOWLEDGEMENTS
I would like to acknowledge the financial support from Andrea Ostroff and the U.S.
Geological Survey’s Aquatic Gap Analysis Program. This research would not have been possible
without the cooperation of both the Michigan Department of Natural Resources and the National
Fish Habitat Partnership (NFHAP). Specifically, I would like to thank Dwayne Etter and Adam
Bump for providing Michigan river otter harvest information and Drs. Lizhu Wang, Dana
Infante, Peter Esselman, and Arthur Cooper and Dan Wieferich for their development and
sharing of the NFHAP database.
I am grateful for the unyielding academic and personal support from the Department of
Fisheries and Wildlife community at Michigan State University. Specifically, I would like to
thank Drs. Shawn Riley, Dan Hayes, Gary Roloff, Peter Esselman, and Rick Clark, along with
the members of the Center for Systems Integration and Sustainability for their input and
suggestions that were critical in the development of the following analyses. I must also thank my
friends and labmates Abigail Lynch, Chiara Zuccarino-Crowe, Kevin McDonnell, Ralph Tingley
III, Kyle Molton, Eric MacMillon, Jacqui Fenner, Darren Thornbrugh, and Joe Nohner for
helping to overcome obstacles big and small. Last, I am incredibly lucky to have received the
love and guidance from my committee members Drs. Dana Infante, Kelly Millenbah, and
especially my advisor Dr. William Taylor. Thank you for believing in me and pulling me
through. Even when I was hit by a bus. Literally.

iii

TABLE OF CONTENTS
LIST OF TABLES ..................................................................................................................... v
LIST OF FIGURES ................................................................................................................... vi
INTRODUCTION ...................................................................................................................... 1
METHODS ................................................................................................................................. 5
Study area ................................................................................................................................... 5
Landscape features and river otter harvest at two spatial scales ................................................ 5
County scale otter harvest ................................................................................................... 7
Local catchment scale otter harvest .................................................................................... 9
RESULTS ................................................................................................................................. 19
County scale otter harvest ................................................................................................. 19
Local catchment scale otter harvest .................................................................................. 20
DISCUSSION .......................................................................................................................... 25
Limitations and future research ................................................................................................ 29
Management implications ........................................................................................................ 31
APPENDIX .............................................................................................................................. 34
Appendix A: County scale correlation results ......................................................................... 35
LITERATURE CITED............................................................................................................. 37

iv

LIST OF TABLES
TABLE 1. Landscape variables used to model river otter harvest in Michigan, USA at county
scale. Landscape spatial data were derived from the National Land Cover Database (abbreviated
NLCD 2001), National Hydrography Database Plus (abbreviated NHDPlus). ............................ 14
TABLE 2. Landscape variables used in the assessment river otter habitat in Michigan at
catchment scale with associated units, resolution, quartile values, means, and source of data.
NLCD = National Land Cover Dataset; USGS = US Geological Survey; NHDPlus = National
Hydrography Database Plus; USDA = US Department of Agriculture; NOAA = National
Oceanic and Atmospheric Administration; USACE = US Army Corps of Engineers; EPA = US
Environmental Protection Agency. Adapted from Wang et al. 2011. .......................................... 17
TABLE 3. Relative contribution of fish habitat variables (NFHAP 2006) in analysis of 2006 river
otter occupancy in local catchments in Michigan to MaxEnt model. Percent contribution refers to
the regularized gain in the model as each variable is added. ........................................................ 21

v

LIST OF FIGURES
FIGURE 1. Study area for river otters of Michigan, USA (highlighted in grey). Surrounding three
Great Lakes are included for reference, along with the political boundaries for the 83 counties
that comprise the state of Michigan. ............................................................................................. 11
FIGURE 2. Total river otter harvest in Michigan and historical otter pelt price for the upper Great
Lakes region of the U.S. (Michigan, Minnesota, Wisconsin). Pelt price was estimated by the
Wisconsin Department of Natural Resources (WDNR 2009). ..................................................... 12
FIGURE 3. Conceptual diagram for river otter harvest analyzed at two spatial scales, the county
and the local catchment scale. This approach was used to identify how landscape features
affected harvest distribution in relation to fish availability. ......................................................... 13
FIGURE 4. Interspersion and juxtaposition index (IJI) comparison for two landscapes with
identical numbers of classes and area for each class. IJI reaches a maximum when all classes are
equally adjacent to each other. Adapted from Eiden et al. 2012. ................................................. 15
FIGURE 5. Process used to dissolve township (political boundaries) in to hydrological boundaries
of the local stream catchment for MaxEnt analysis of river otter harvest. All local catchments
that intersected a township with river otter harvest were considered an occurrence .................... 16
FIGURE 6. River otter harvest by county for 2006 in Michigan. Each county is classified by
number of otters registered with the Michigan Department of Natural Resources. ..................... 22
FIGURE 7. Response curves for top four fish habitat variables (NFHAP 2006) used in MaxEnt
model of otter occurrence in local catchments in Michigan. The curves show how the logistic
prediction changes as each environmental variable is varied, keeping all other environmental
variables at their average sample value. Red is the mean response after ten replicate runs and
blue is one standard deviation. Variables and their respective percent contribution are human
population density (22.5%), number of road crossings per catchment (19.4%) , percent crop
cover (11.7%), percent tree canopy cover (8.3%). For interpretation of the references to color in
this and all other figures, the reader is referred to the electronic version of this thesis. ............. 23
FIGURE 8. Local stream catchments in Michigan predicted to have >70% likelihood of river otter
occurrence according to a MaxEnt model of harvest-only location (in bold). 24 landscape
variables were used from the National Fish Habitat Action Plan database (Esselman et al. 2011).
County delineations are included for reference. ........................................................................... 24
FIGURE 9. River otter and beaver trapping units in Michigan as defined by the Michigan
Department of Natural Resources (Adapted from the MDNR Furbearer Harvest Regulations
2006).. ........................................................................................................................................... 33
FIGURE 10. Results from Pearson's correlation test on river otter harvest and nine landscape
variables measured at the county scale.. ....................................................................................... 35
vi

INTRODUCTION
A landscape approach to ecology includes the study of how systems function in the
context of the physical structure of the landscape at varying spatial scales (Hobbs et al. 1993).
Examples of physical landscape structure include land cover characteristics such as the amount
of forest cover (composition) or the pattern of forest and nonforest patches (configuration) within
a given area. This spatially-explicit perspective is useful in natural resource management because
it fosters a systems-based approach that spans disciplines, spatial and temporal scales, and
jurisdictional boundaries (Liu and Taylor 2002, Rabeni and Sowa 2002). A landscape
perspective of a region’s ecology accounts for the highly integrated nature of aquatic and
terrestrial landscape elements rather than isolating patches of landscape, such as water from
forest, as self-contained ecosystems. Within the landscape ecology framework, the land-water
interface is commonly represented as a permeable and dynamic boundary with reciprocal flows
of matter and energy (Fausch et al. 2002, Schlosser 1991). Although aquatic-terrestrial
ecological linkages are recognized by managers, traditional management practices have failed to
overcome barriers that inhibit landscape-based management of the fish and wildlife species that
rely on these linked habitats (Dunning 2002, Schneider et al. 2002). For example, Taylor et al.
(2002) point out that fisheries managers are often jurisdictionally bound to localized, in-stream
habitat enhancement (i.e. placement of woody debris, gravel deposition) despite evidence that
large, catchment scale processes (i.e. land use change, groundwater withdrawal) have a greater
effect on fish community dynamics.
Semi-aquatic species are particularly vulnerable to extinction when managers do not use
a landscape perspective that accounts for the entire spectrum of habitat types and connectivity
1

that are required through their life cycles (Bowne et al. 2006, Schneider et al. 2002) as these
animals spend the majority of their lives in aquatic habitats yet require specific upland conditions
in order to reproduce or colonize new areas (Carranza et al. 2012). This research uses a
landscape approach toward understanding one semi-aquatic species’ habitat needs, the North
American river otter (Lontra canadensis, Coues 1877) in the State of Michigan, USA. The river
otter fills a critical ecological role connecting terrestrial and aquatic landscape components by
transporting aquatic-derived nutrients to terrestrial environments (Crait and Ben-David 2007).
Until the time of European settlement, river otters exhibited one of the largest geographic ranges
of any North American mammal (Anderson 1977). Their range encompassed all but the most
arid southwestern deserts of the U.S. or permafrost covered regions of the arctic (Polechla 1988).
By the 1960’s however, river otters had been extirpated from over 70% of their United States
(U.S.) range (Melquist and Dronkert 1987). The reasons for these declines were attributed to
habitat loss (particularly wetland draining), water pollution, and overharvest (Deems and Pursley
1983, Polechla 1990, Toweill and Tabor 1982). Populations that remained intact in the
conterminous U.S. were found in the Pacific Northwest, the coastal marshlands surrounding the
Gulf of Mexico and Atlantic Ocean, and the northern Great Lakes regions of Minnesota,
Wisconsin, and Michigan (Deems and Pursley 1983, Feldhamer et al. 2003).
Broadly speaking, these remnant populations were found in regions of the U.S. that had
relatively low human population densities and/or abundant precipitation and surface water
resources. These trends are purely observational, as no formal assessment of landscape drivers
underlying river otter distribution exists at such a large scale (i.e. state, regional, continental). A
founding principle of landscape ecology is that biological interactions between an organism its
environment are scale-dependant (Levin 1992, Wiens 1989). For example, a tadpole will

2

perceive landscape features on a relatively small scale, such as the few square meters within an
ephemeral pond, compared to an adult frog with the ability to interact with its environment on a
scale of kilometers as it moves cross-country to colonize new wetland patches. Generally
speaking, larger and more mobile organisms view their environment on broader spatial scales.
River otters are highly mobile mammals yet most ecological studies of this organism were
executed at small scales (river reach, single watershed). Large scale studies are useful in
identifying ecological processes operating at such a scale (Wiens 1989) and therefore have the
potential to highlight ecological processes otherwise undetected by smaller scale studies and thus
assist in the management and conservation..
River otters are known to use a wide variety of terrestrial and aquatic landscape structures
for socializing, denning, and foraging. Previous studies have documented otters foraging in lakes
(Reid et al. 1994), wetlands (Newman and Griffin 1994), even man-made farm ponds (Ostroff
2001), but the vast majority of studies emphasize river otter’s use of stream resources (Melquist
et al. 2003) throughout their range. In Europe and Chile, large scale landscape studies (15009900 km2) have found Eurasian river otter (Lutra lutra) and Southern river otter (Lontra
provacax) distribution to be strongly correlated to the amount of prey available in a watershed
(Prenda and Granado-Lorencio 1996, Sepulveda et al. 2009). North American otter prey in the
Upper Great Lakes region of the U.S. (Minnesota, Wisconsin, Michigan) consists primarily of
fish (Knudsen and Hale 1968, Ryder 1955). Advances in remote sensing technology and
landscape analysis have enabled fisheries scientists and managers to predict fish population
dynamics at a continuous, large spatial scale (Zorn et al. 2011). Frissell et al. (1986) proposed a
hierarchical framework to classify stream habitats using nested spatial units ranging in size from
3

-1

the river network (10 m) down to the microhabitat (10 m). Since then, numerous models have

3

used this hierarchical framework for the purpose of predicting overall fish abundance and
assemblage structure in Michigan (Infante et al. 2006, Pace et al. 2004, Steen et al. 2008). These
studies used landscape metrics that summarized climate, geology, and land cover in conjunction
with historical stream fish sampling data to identify and predict patterns in fish community
composition given the landscape features measured. The conceptual framework developed for
these studies was adapted for use in the National River Fish Habitat Condition Assessment
(Wang et al. 2011), which provides a continuous coverage of spatially nested landscape
information on all river catchments in the U.S. My analysis implemented this theoretical
framework in order to explore how well landscape features could predict river otter distribution,
as fish abundance is considered a major influence on river otter habitat selection and production
in smaller scale studies (Blundell et al. 2000, Melquist et al. 2003).
The goal of my study was to explore the distribution and harvest of river otters in
Michigan in relation to landscape features and subsequent fish availability. My first objective
was to develop a predictive model of river otter harvest at the county scale using landscape
metrics relevant to fish resource quantity and quality. This afforded a broad perspective of both
landscape composition and configuration features significant to river otter distribution. My
second objective was to determine if landscape features known to affect fish habitat could
effectively predict river otter distribution at the local catchment scale and if so, identify which of
these local features were most useful in modeling river otter harvest. This large scale analysis
was aimed to enhance resource managers’ ability to understand and more effectively manage
semi-aquatic species and the aquatic-terrestrial processes they represent.

4

METHODS

Study area
This study occurred throughout the state of Michigan, USA (Figure 1). Michigan is
comprised of two peninsulas, divided by Lake Michigan and Lake Huron. The Upper Peninsula
consists primarily of bedrock and coarse surficial geology which in part drives the cold and cool
streams that characterize this portion of the state (Wang et al. 2003). The sand and gravel of the
northern Lower Peninsula similarly facilitate high groundwater recharge in this region while
more clay and fertile soils in the southern and eastern portions of the state result typically in low
gradient, warm water streams. Land use consists primarily of managed forests in the north and
agriculture in the south (Wang et al. 2008). Vegetation varies with latitude and geology, ranging
from pines and spruces in the north to hardwoods such as sugar maple and oak in the south. The
southern Lower Peninsula was originally a large expanse of wetlands that were heavily altered
beginning in the mid-1800’s for agricultural development (Karr et al. 1985). These patterns in
hydrology and land cover have been shown to influence the quality of fish habitat and thus
productivity (Hughes et al. 2006, Wang et al. 2008, Zorn et al. 2002) and are considered here in
terms of river otter prey requirements across the landscape of Michigan.

Landscape features and river otter harvest at two spatial scales
By focusing on landscape characteristics known to affect fish community dynamics, I
developed a state-wide analysis of otter distribution in relation to prey availability using
spatially-explicit information on otter harvest and prey resource patches. For this study, I defined
prey availability to be a function of the density and dispersion of water bodies (potential patches
of prey) and quality of that prey’s habitat. I hypothesized that the relative otter habitat suitability
5

of a location is dependent on the distance between food sources and the potential amount of fish
available in each patch.
A patch is a basic unit of landscape structure; in this case it is an element in the landscape
that provides otters with food. Patch quality and context (spatial configuration) influence how
organisms interact with the landscape (Wiens et al. 2002). Because otters are obligate aquatic
predators (Melquist et al. 2003), they must have access to surface water. Beyond this obligatory
patch type requirement, there is little evidence to assume otters need anything more specific than
water with fish in it. Some studies have suggested that otters select fish species based on their
relative vulnerability, a function of prey size, abundance, and swimming ability (Melquist and
Hornocker 1983, Serfass et al. 1990, Toweill 1974). Early studies of otter diets determined that
these mammals were partial to fish such as minnows (family Cyprindae, Umbridae) and suckers
(Catastomidae) (Field 1970, Knudsen and Hale 1968) and thus otters were not competing
directly with anglers for game fish (Ryder 1955). Unfortunately, relative abundance of these fish
families in the field have not been measured in conjunction with otter diet analyses. Due to this
observed diet plasticity of fish consumption, I broadly defined a resource patch to be any open
water (stream, lake, or wetland).
River otter harvest was used as the dependant variable in my analysis of this
species’ distribution and relative abundance. The Michigan Department of Natural Resources
(MDNR) collects otter harvest data on two spatial scales: the county and the township scale.
2

Counties are political boundaries with an average area of 3,051 km . A township is defined as a
one square mile unit in a grid covering the entire state; Michigan is comprised of 1,242
townships. Data were available for river otter harvest from 1989-2009. I selected a single
trapping season for analysis which ran from October 25, 2006 thru April 15, 2007. This year is
6

associated with a relatively high price for otter pelts (Dhuey and Olsen 2010), which increased
trapping effort and harvest for that season (Figure 2), therefore increasing the likelihood of otter
detection (Poole and Mowat 2001). I assumed analyzing a single trapping season would reduce
variation in reported harvest numbers due to temporally dynamic effects such as weather, pelt
price, and bag limits. Otters are harvested for their pelts using jaw and body grip traps that are
partially or fully submerged in shallow water near the banks of water bodies (Frawley 2007).
Michigan residents are legally required to obtain a cost free otter harvest tag in order to trap and
sell otter pelts. These pelts require inspection by the MDNR, at which time the date and location
of harvest is collected. Harvest data were used to infer patterns in relative otter abundance at the
county scale and otter occurrence at the local catchment scale; a conceptual diagram can be
found in Figure 3.
County scale otter harvest
Michigan is comprised of 83 counties (Figure 1): 15 in the Upper Peninsula and 68 in the
Lower Peninsula. In order to identify key landscape features in river otter distribution and
harvest, I analyzed 12 land cover and patch configuration metrics summarized at the county scale
(Table 1) in order to develop a multiple linear regression model of river otter relative abundance.
Relative abundance was defined as the number of otters harvested within a county in the 2006
trapping season. I excluded counties with no reported trapping effort. Effort data was collected
by the MDNR via mail-in survey at the end of the season and is reported as the total number of
nights traps were set for otter or beaver, as otters are often caught incidentally when beaver are
targeted. Poole et al. (2001) found that effort reported by trapper affidavits was not a good
indicator of catch per unit effort due to incidental trapping. Based on preliminary analysis of the
MDNR harvest data, I believe this to be true in Michigan as well and therefore did not use a
7

catch per unit effort as a response variable. Percent land use/land cover class (i.e. forest, open
water) per county was measured using the satellite-derived National Land Cover Database
(Homer et al. 2007). The National Hydrography Dataset Plus (USEPA and USGS 2010) was
used to measure the number, type, and density of water bodies present in each county. Although
the NLCD has an open water land cover class, NHDPlus has a finer resolution (1:100,000) which
was important in differentiating types of open water (e.g. lakes versus streams) and linear,
dendritic pattern of streams and associated floodplains demanded this resolution. These layers
were analyzed using ArcGIS 10.0 (ESRI 2011). The 17 NLCD land cover classifications were
reclassified into eight different categories in order to address the large scale questions at an
equally broad resolution. For example, by default NLCD defines three different types of forest
cover: deciduous, evergreen, and mixed. These three classifications were combined in to one
forest classification. Reclassification also reduced the spatial autocorrelation bias, as evergreen
forests are primarily in northern Michigan and deciduous hardwoods are in southern Michigan.
Landscape pattern analysis was executed using the statistical program FragStats (McGarigal and
Marks 1995) which characterized the relative position of open water and wetland NLCD land
cover classes. Specifically, I used the interspersion/juxtaposition index (IJI) to characterize the
relative connectivity of prey resources. The IJI measured the amount of waterbody patch
adjacencies, yielding a measurement of how well these aquatic land cover classes are intermixed
among other classes in the county (McGarigal and Marks 1995). Regardless of patch size (cell
adjacencies of same class), interspersion will reach a maximum when water and wetland patch
types are equally adjacent to other patch types (Figure 4).
All landscape composition and configuration metrics were transformed for normality and
only significant variables (p < 0.05), as identified by Pearson’s correlation test, were included in

8

the regression analysis (Appendix A). Variable selection and model comparisons were executed
in the R statistical software package (R Core Development Team 2012). The final model was
selected using a stepwise comparison analysis using Akaike’s Information Criterion (Akaike
1973), run in both directions.
Local catchment scale otter harvest
This portion of the analysis used an occupancy modeling approach to estimate the
relationship between otter harvest distribution and fish habitat characteristics. With no effort data
available at the township resolution and an average otter harvest per township of less than 0.2, I
analyzed township scale harvest as presence-only information: all townships were categorized in
a binary fashion as either having otters present or unknown. In order to consider fish habitat
characteristics at this resolution, I dissolved all townships into local catchments (Figure 5), the
basic spatial unit within the framework of the National Fish Habitat Action Plan (NFHAP,
Esselman et al. 2011, Wang et al. 2011). Local catchments were topographically defined as the
land area that drains laterally to an individual, confluence to confluence stream reach (USEPA
and USGS 2010). There are a total of 32,002 local catchments in Michigan. Fish habitat metrics
had been previously assembled by the NFHAP and National River Fish Habitat Condition
Assessment (Wang et al. 2011).
I used a maximum entropy analytical approach, adapted from Elith et al. (2011), to
generate a predictive occupancy model based on the likelihood of otter harvest in the state of
Michigan. This model minimizes the relative entropy (randomness) between two probability
densities, a ratio defined by presence sites versus random (background) sites on the landscape
(Elith et al. 2011). This raw output is transformed in to the following logistic equation:
n(z) = log [f1(z)/f(z)]
9

where f1(z) is the conditional density of local catchment (i.e. NFHAP) covariates at a site with
otter and f(z) is the unconditional (random) density of covariates across Michigan. Thus, the logit
score gives a value in which to compare between catchments. The default value I used to
compare was set to 0.5, i.e. if there is no relationship between otter harvest versus a given
habitat variable, we would expect to see a 50% probability (binary, random probability) that
otters are present in that catchment (Elith et al. 2011).
A total of 20 anthropogenic disturbance variables were used as covariates, defined at the
local catchment scale as part of the national assessment of fish habitats (Wang et al. 2011, Table
2) in the MaxEnt software package (Phillips and Dudik 2008). Otters were considered present in
1,876 local catchments after dissolving townships in to local catchments. Ten thousand randomly
selected local catchments were used as background in the MaxEnt analysis. Background data are
random landscape units (local catchments in this case) chosen without regard to otter presence.
However, catchments within counties with no trapping effort were ineligible to be selected as
background data. No sampling was known to occur in these counties along with catchments in
Pictured Rocks National Lakeshore and the Sleeping Bear Dunes National Park, where all
trapping was prohibited. Default MaxEnt settings were used for model training and a 10-fold
cross-validation was used to measure predictive performance and estimates of uncertainty (Elith
et al. 2011). The approach is nearly identical to that done by Elith et al. (2011) in their
explanation of MaxEnt application toward modeling fish species distribution at a comparable
spatial grain- the river segment.

10

Lake
Superior

N

Lake Huron

Lake
Michigan

100 km

FIGURE 1. Study area for river otters of Michigan, USA (highlighted in grey). Surrounding three Great
Lakes are included for reference, along with the political boundaries for the 83 counties that comprise
the state of Michigan.

11

Study
season

No effort data prior to 2006

FIGURE 2. Total river otter harvest in Michigan and historical otter pelt price for the upper Great Lakes region of the U.S.
(Michigan, Minnesota, Wisconsin). Pelt price was estimated by the Wisconsin Department of Natural Resources (WDNR
2009).

12

FIGURE 3. Conceptual diagram for river otter harvest analyzed at two spatial scales, the
county and the local catchment scale. This approach was used to identify how landscape
features affected harvest distribution in relation to fish availability.

13

TABLE 1. Landscape variables used to model river otter harvest in Michigan, USA at county scale. Landscape spatial
data were derived from the National Land Cover Database (abbreviated NLCD 2001), National Hydrography Database
Plus (abbreviated NHDPlus).
Category Abbreviation
Landscape composition
Water
Dev
Forest
Shrub
Grass
Crop

Description (units)

Source

Resolution

Mean

Open water (%)
Developed (%)
Forest (%)
Shrub/Scrub (%)
Grassland/Herbaceous (%)
Cultivated crops & Pasture (%)

NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001

30 m
30 m
30 m
30 m
30 m
30 m

2.8
8.6
39
1.4
6.3
21.8

Wtlnd

Woody/Herbaceous Wetland (%)

NLCD 2001

30 m

stream_st

Stream length/County area (km/km2) NHDPlus

1:100,000

wtrbdA_km2

Area of waterbodies/County area

NHDPlus

1:100,000

NHDPlus

1:100,000

NLCD 2001

30 m

63.99

NLCD 2001

30 m

61.34

numwtrbd_km2 Number waterbodies/County area
Patch configuration
WATER_ IJI
WTL_ IJI

Interspersion / Juxtaposition IndexOpen Water
Interspersion /Juxtaposition IndexWetlands

14

Range

0.24-12.7
2.2-32.2
6.2-82.1
0.05-4.94
0.49-16.6
0.05773.2
19.5
7.0953.116
1.90E-03 9.5E-059.91E-03
7.57E-05 8.6E-61.00E-3
2.94E06 78.7E317.6E6
39.4388.92
29.6-83.3

Low IJI score

High IJI score

FIGURE 4. Interspersion and juxtaposition index (IJI) comparison for two
landscapes with identical numbers of classes and area for each class. IJI reaches a
maximum when all classes are equally adjacent to each other. Adapted from Eiden
et al. 2012.

15

Stream reach

Local
catchment

Township
(with otter)

Local
catchments
with otter

FIGURE 5. Process used to dissolve township (political boundaries) in to hydrological
boundaries of the local stream catchment for MaxEnt analysis of river otter harvest. All local
catchments that intersected a township with river otter harvest were considered an occurrence.

16

TABLE 2. Landscape variables used in the assessment river otter habitat in Michigan at catchment scale with associated
units, resolution, quartile values, means, and source of data. NLCD = National Land Cover Dataset; USGS = US
Geological Survey; NHDPlus = National Hydrography Database Plus; USDA = US Department of Agriculture; NOAA =
National Oceanic and Atmospheric Administration; USACE = US Army Corps of Engineers; EPA = US Environmental
Protection Agency. Adapted from Wang et al. 2011.
Quartile
Units
Resolution 25%
75%
base flow/total flow * 1:250K
59.00
72.00
100%
km
1:100K
0
0

Abbreviation
GWindex

Description
Groundwater index

Canal

Length of streams
designated canal/ditch
Mean catchment slope degrees
Developed, open and low %
intensity
Developed, medium
%
intensity
Developed, high intensity %
Forest, all classes
%
Shrub/Scrub
%
Grassland/Herbaceous %
Pasture/hay
%
Cultivated crops
%
Bare rock/sand/clay
%
Wetlands
%
Open water
%
Tree canopy cover
%
Human population
#/km2
density
Road crossing density
#/km2

Slope
UrbanL
UrbanM
UrbanH
Forest
Shrub
Grass
Pasture
Crops
Barren
Wetlnd
Water
Canopy
Popdens
Roadcr

Mean Source
63.25 USGS
93.32 NHDPlus

1:100K
30 m

0.36
0

18.00
8.18

1.42
7.62

NHDPlus
NLCD 2001

30 m

0

0

0.63

NLCD 2001

30 m
30 m
30 m
30 m
30 m
30 m
30 m
30 m
30 m
1:100K
1 km

0
11.05
0
0
0
0
0
5.19
0
22.76
1.15

0
59.99
0.77
5.97
7.00
24.48
0
32.46
1.22
66.40
14.57

0.23
37.13
0.84
4.45
5.41
15.10
0.76
23.17
4.64
44.82
21.42

NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NLCD 2001
NHDPlus
NOAA

1:100,000

0

2

1.01

US Census 2000

17

TABLE 2 (cont’d)
Abbreviation
Roadlen
Cattle
Imperv
SO
Dams

Description
Road density
Cattle density in farms
Impervious surfaces
Strahler stream order
Dam density

Units
m/km2
#/acre * 10,000
%
NA
#/km2

Quartile
Resolution 25%
75%
1:100,000 386.7
6603
1:100,000 684
1317
30 m
0.05
1.34
1:100,000 1
1
NA
0
0

Mines

Mines or mineral
processing plants
Pollutant Release Sites

#/km2

NA

0

0

Source
US Census 2000
USDA
NLCD 2001
NHDPlus
USACE National
Inventory 2005
0.005 USGS

#/km2

NA

0

0

0.04

EPA

18

Mean
6032
1035
1.78
1.28
0.02

EPA

RESULTS
County Scale otter harvest
A state total of 1,0171 river otters (SE = 39, α = 0.05) were reported captured in
Michigan during the 2006 trapping season. Of this total, 1,017 (95%) were catalogued by the
MDNR, spatially referenced, and used in this analysis. There was a north-south longitudinal
gradient apparent in harvest numbers with northern counties generally reporting more harvested
river otters (Figure 6). Over half (64%) of all otters harvested in the 2006 trapping season were
captured in the 15 counties of the Upper Peninsula.
The best performing linear regression model that predicted total otter harvest based on
the aforementioned landscape metrics selected three parameters as described in the following
equation:
N = 7.99(Water) – 2.03(Crop) – 0.054(Wetland IJI) + 4.8
2

where N is the total number of otter harvested per county (adjusted R = 0.67, p = 1.93 e

-14

).

More open water in the catchment was generally associated with an increase in otters harvested
while greater crop cover was indicative of fewer otters harvested. The wetland IJI was inversely
proportional to otter harvest, meaning that more dispersed wetland patches throughout the county
would increase the likelihood of otter harvest. Conversely, fewer and more clumped wetland
patches were correlated with lower otter harvest numbers. Inclusion of development, forest,
wetland, and stream length variables did not significantly strengthen model performance
according to the AIC analysis.

19

Local catchment scale otter harvest
The maximum entropy analysis generated a predictive model of otter harvest at the local
catchment scale, with an area under the curve of 0.733 (sd = 0.016), indicating moderate
predictive power (Viña et al. 2010). Of the 24 fish habitat variables used to characterize the local
catchment, the top four variables cumulatively explained 62% of the variance in the model
(Table 3). In order of percent contribution, these variables were human population density,
number of road crossings, percent crop cover, and percent tree canopy cover. The response
curves for each of these four variables can be found in Figure 7, which shows that three of these
variables, human population density, percent crop cover, and percent tree canopy cover,
exhibited an exponential decline in likelihood of otter occurrence as the intensity of human
disturbance and canopy cover increased. Conversely, the number of road crossings has an overall
positive relationship, with likelihood of otter harvest increasing with an increase in the number
of roads. The remaining 19 variables contributed to less than 5.5% of the predictive model of
likelihood of otter harvest.
According to the predicted otter habitat suitability map (Figure 8), highly suitable local
catchments (HSI score > 0.7) are abundant and relatively dispersed in the Upper Peninsula and
the northern Lower Peninsula. These are regions of the state with relatively low human
population densities compared to southern regions, and agricultural land use is less prominent. A
small number of suitable catchments were predicted in the southeastern region of the Lower
Peninsula.

20

TABLE 3. Relative contribution of fish habitat variables (NFHAP 2006) in analysis of 2006 river
otter occupancy in local catchments in Michigan to MaxEnt model. Percent contribution refers to
the regularized gain in the model as each variable is added.
Variable

Percent Contribution

PopDens
Roadcr
Crops
Canopy
Pasture
Roadlen
GWindex
Shrub
Wetland
Water
Cattle
Barren
UrbanL
Stream Order
Forest
Slope
UrbanM
Imperv
Dams
Canal
Mines
EPA
Grass
UrbanH

22.5
19.4
11.7
8.3
5.9
5.4
5.1
4.3
4.3
2.9
2.4
1.1
1.0
0.9
0.9
0.7
0.6
0.6
0.5
0.5
0.4
0.3
0.2
0.1

21

LEGEND
Number otter harvested
0 - 14
15 – 40
41-107

N

100 km

FIGURE 6. River otter harvest by county for 2006 in Michigan. Each county is classified by
number of otters registered with the Michigan Department of Natural Resources.

22

Logistic output (probability of presence)

Population density

Number Road Crossings

Percent crop cover

Percent canopy cover

FIGURE 7. Response curves for top four fish habitat variables (NFHAP 2006) used in MaxEnt model of otter
occurrence in local catchments in Michigan. The curves show how the logistic prediction changes as each
environmental variable is varied, keeping all other environmental variables at their average sample value. Red is
the mean response after ten replicate runs and blue is one standard deviation. Variables and their respective percent
contribution are human population density (22.5%), number of road crossings per catchment (19.4%) , percent
crop cover (11.7%), percent tree canopy cover (8.3%). For interpretation of the references to color in this and all
other figures, the reader is referred to the electronic version of this thesis.

23

N

100 km

FIGURE 8. Local stream catchments in Michigan predicted to have >70% likelihood of
river otter occurrence according to a MaxEnt model of harvest-only location (in bold). 24
landscape variables were used from the National Fish Habitat Action Plan database
(Esselman et al. 2011). County delineations are included for reference.

24

DISCUSSION
The regression model that best predicted the number of river otters harvested per county
included percent open water, crop cover, and the wetlands interspersion/juxtaposition index.
Because river otters are piscivores (Knudsen and Hale 1968, Reid et al. 1994) I expected to see
water and wetland composition and pattern metrics to be important in predicting otter abundance
at this scale given the assumption that any water or wetland patch containing fish could serve as
a potential food resource. Under this assumption, increased open water would increase the
foraging opportunities available to river otters. High interspersion of wetlands equates to less
travel time between wetland patch types, which would benefit an otter’s high metabolism and
their need for readily available prey. Extensive movement between wetland and water patch
types would be costly for a carnivore that requires daily replenishment of 20% of its body weight
(Dekar et al. 2010) in aquatic-derived prey. Therefore, when one patch is reduced or exhausted
of fish, the otter must find a new resource patch in order to satisfy its energetic needs. As such,
landscapes with an abundance of readily accessible water and wetlands may support higher
densities of otters, assuming that these waters maintain environments that promote fish
production.
If total fish abundance was the best, singular predictive variable of river otter distribution
as found for river otters in other regions (Blundell et al. 2000), I would have expected higher
relative harvest in the southern and eastern portions of the state which generally have more warm
water streams and increased nutrient inputs (Wang et al. 2008). Temperature and phosphorus
have been determined to have a positive, effect on fish biomass for both streams (Steen et al.
2008, Zorn and Wiley 2006) and lakes (Hanson and Leggett 1982, Kitchell et al. 1974).This

25

relationship however is nonlinear, as hypereutrophic and anoxic conditions arise when water
bodies are too warm or have an excessive amount of primary production.
The local catchment analysis suggests a positive relationship between otter presence and
biotic integrity. Biotic integrity is defined as the ability of a system to support and maintain a
balanced, integrated, adaptive community of organisms (Karr and Dudley 1981). Indices of
biological integrity are multimetric tools used to rapidly assess the health of aquatic resources
(Karr 1991). The basic approach compares numbers of indicator functional groups present to
those found in the least disturbed reference conditions (Karr and Dudley 1981).The functional
groups vary among approaches, such as measuring the number of native fish species
present(Angermeier and Karr 1986), percent carnivorous fish species (Stewart et al. 2001), or
number lithophilic fish species (Karr and Chu 2000). The NFHAP assessment (Esselman et al.
2011) used indicator fish groups such as these to identify the landscape-based sources of fish
habitat degradation. While temperature and nutrient levels may suggest greater productivity in
southern Michigan, the lack of river otter harvest in this area may suggest that either the fish are
not as abundant or river otters’ ability to access prey resources is limited.
Based on county scale results, I would hypothesize that the low wetland interspersion
reduces functional connectivity of prey patches for river otters in the southern Lower Peninsula.
Alternatively, environmental contaminants may also be limiting river otter population due to the
species’ susceptibility to bioaccumulation (Bowyer et al. 2003). A study of river otter harvest in
the Upper Peninsula and northern lower two-thirds of Michigan concluded that river otter
distribution was primarily limited by poly-chlorinated biphenyl pollutants (Kotanchik 1997). If
correct, this hypothesis would help explain why river otters were not often detected in the

26

warmer waters of the southern Lower Peninsula surrounded by increased agriculture and urban
development.
Human population density would alter the suitability of a local catchment to otters, fish,
and trappers, which may explain why this metric contributed the most to the predictive
occupancy model. From a trapping perspective, areas with high human densities, today, likely do
not receive high if any trapping pressure as a multitude of diverse land ownership (no trespassing
laws) and domestic animals would not be conducive to regular trapping. The NFHAP assessment
found urban development to be a dominant disturbance variable in their analysis of fish habitat
on a national scale (Esselman et al. 2011). In a similar analysis of disturbance gradients specific
to Michigan catchments, Wang et al. (2008) determined that most moderate to severely disturbed
streams (based on fish assemblage structure) in Michigan were degraded by urbanization and
occurred in the southeast Lower Peninsula of Michigan.
I found that not all forms of fish habitat disturbance affected the likelihood of river otter
occurrence, as evidenced by the second-highest ranked local catchment variable, number of road
crossings per stream segment. Road crossings fragment fish habitat and add sediment and
contaminants directly to the stream (Bouska and Paukert 2010, Warren and Pardew 1998). This
form of disturbance has been shown to negatively affect sensitive, stenothermic fish species
(Hudy et al. 2008) and ranked highly as a fish habitat disturbance variable in the national
assessment (Esselman et al. 2011). According to my results, river otters are still able to inhabit
these catchments, otherwise deemed degraded. The trend modeled by the MaxEnt response curve
is almost the opposite of the known effects on fish. In terms of fragmentation, river otters have
been shown to have an affinity to areas with beaver dams (Gallant et al. 2007, Swimley et al.
1998) speculated to provide both shelter and a stream morphology conducive to foraging. A road

27

culvert that aggregates fish, either as a barrier or cover, could increase the favorability of otter
habitat while at the same time increase the ease of access to trappers. Further evidence of the
access bias is found in the parabolic road length response. Probability of otter occurrence
increased with the amount of road coverage. This result suggests that this level of road
development increases trapper accessibility. The response to canopy cover would further support
this hypothesis as existing spatially-explicit river otter habitat research has found forest cover to
be an important, positive factor in predicting otter presence and abundance at both a local and
landscape scale (Jeffress et al. 2011, Shardlow 2009). I found that there was little effect up to
90% canopy cover, when the likelihood of otter presence significantly decreased. Riparian
stream cover typically results in positive effects on fish habitat and productivity by buffering the
water body from runoff (Peterjohn and Correll 1984, Rabeni and Smale 1995, Whitledge et al.
2006). This canopy cover threshold may indicate restricted trapper access in dense, riparian
vegetation rather than a true reduction in otter abundance or fish populations.
Point source pollution sites (abbreviated “EPA”) ranked very highly in predicting fish
habitat quality in the national assessment (Esselman et al. 2011, Wang et al. 2008) yet this metric
minimally (0.3%) contributed to the MaxEnt model for this analysis. This may indicate the
difference between biotic integrity stream health metrics, which include measures of sensitive
fish species (Fausch et al. 1990) versus river otter prey requirements that are less speciesspecific. In consideration of the different responses that fish and otters have to anthropogenic
disturbance, river otter presence may be a unique ecological indicator of landscape integrity.
Landscape integrity measures the relative health of systems in the context of the structure and
function of this landscape mosaic (Liu and Taylor 2002), effectively adding a spatial component,
or landscape perspective, to biotic integrity assessments. Indices of biotic integrity are used

28

because they are believed to integrate the effects of anthropogenic disturbance sources using a
limited number of field measurements and environmentally sensitive biota (Fausch et al. 1990).
Monitoring otter presence could strengthen aquatic biological index assessments by integrating
information currently not represented by measurements in fish communities alone, specifically
the functioning of aquatic-terrestrial linkages. As a semi-aquatic top-predator, river otter
presence and abundance could be used to assess overall fish productivity in conjunction with
connectivity of aquatic and terrestrial landscape components. This would surmount to an
integrated signal of reciprocal landscape processes: nutrients are entering aquatic systems,
fueling fish production and these nutrients are being cycled back to terrestrial systems via a
semi-aquatic piscivore.

Limitations and future research
It is important to recognize the biases inherent in evaluating populations using harvestonly based data, as was used for this analysis. Furbearer experts agree that harvest data can be a
useful management tool (Hiller et al. 2011) however these data do not directly represent the
actual population dynamics of the species under consideration (Poole and Mowat 2001). Factors
affecting river otter harvest include such things as species abundance, trapping season length and
bag limits, trap type restrictions, fur prices, weather, socio-economic conditions, trapper skill and
motivation, mode of transportation, and access (Erickson 1982). Of these factors, access, season
length and bag limits, and trapper motivation are particularly relevant to this analysis as it
focuses on road length and crossings, canopy cover, agriculture, and human population density.
Bag limits and season length are location dependant in Michigan as the state is divided in to
three otter and beaver management units (Figure 9): Unit A (upper peninsula) allowed three
otters per person and had the longest season from October 6, 2006 to April 15, 2007 while the
29

Unit B (northern lower peninsula- two otters per person) went from November 1 to April 15 and
finally Unit C (southern lower- one otter per person) was shortest in length from November 10 to
March 7. Further confounding these results is the fact that trappers are often targeting beaver and
incidentally catch otter (Bailey and Faust 1981). In accordance with state fur-taker regulations,
incidentally harvested otter carcasses are relinquished to the MNDR. There is no monetary
incentive for trappers to invest time in processing these incidentally harvested otter.
Additionally, a response bias may arise in which a trapper could understate, exaggerate, or
provide inaccurate harvest locations (Poole and Mowat 2001), however Michigan furbearer
biologists believe incidence of this behavior is low in Michigan (Etter and Bump 2011).
Clearly, using harvest-only data to extrapolate otter habitat suitability and relative
abundance inherently includes biases in sampling effort and reporting. This analysis however
does allow for a broad assessment of the potential link between river otter’s prey-based habitat
and their presence in the landscape as evidenced in harvest despite the potential biases discussed
above. This large scale analysis allows for visualization of the known species distribution and
considers river otter’s potential in landscape-based management and potentials for its restoration.
This analysis, by its very nature, cannot identify the causal process behind the species’
occurrence patterns. Nevertheless, I believe there is value in evaluating species from such a large
scale perspective, specifically in identifying avenues for future research and potential areas for
restoration activities. The Michigan otter harvest dataset currently spans from 1987 to 2009.
Inclusion of a temporal component in to this analysis would likely strengthen our ability to
identify causal relationships in otter distribution patterns. Additionally, my study only considered
the bottom-up effects underlying river otter distribution: landscape composition (i.e. percent crop
cover) and configuration (i.e. interspersion and juxtaposition of wetlands) define prey

30

availability which was hypothesized to influence otter presence and abundance. Bioenergetics
research has theorized that otters are capable of altering fish communities based on their
predatory demands (Dekar et al. 2010). Isolating and assessing both top-down and bottom-up
effects is critical to a complete understanding of otter habitat requirements and their role in
influencing the biotic communities of aquatic ecosystems, not to mention a valuable step in
linking landscape and trophic ecology research (Polis et al. 1997). In the U.S., 28 states funded
reintroduction programs for river otters during the 1980’s and 1990’s (Raesly 2001),
demonstrating the species’ importance on the landscape of these states and in the U.S.
Understanding river otter habitat needs and trophic interactions would likely further increase the
success and acceptance of current and future otter reintroduction and management programs.

Management implications
While this research describes the need for a more in-depth study to understand cause-andeffect relationships between landscape changes and otter distribution, my results do demonstrate
that landscape pattern metrics significantly contribute to our ability to predict relative abundance
of river otters at a large spatial unit such as a county. Considering the significance of the wetland
interspersion/juxtaposition index, I would not recommend a linear home range pattern for otters
in Michigan restricted to only streams and riparian zones, as is used in numerous river otter
studies in other regions (Blundell et al. 2000, Mack et al. 1994, Melquist and Hornocker 1983).
As remote sensing tools continue to gain accuracy and utility in ecological analysis, it is
important to consider land pattern metrics, particularly for highly mobile species like river otter.
In the field of fisheries research and management, spatial pattern metrics are not widely used
when defining aquatic condition from landscape variables. Nevertheless, predators such as river
otter and prey such as winged macroinvertebrates are less laterally restricted and therefore the
31

entire aquatic community can be affected by the spatial arrangement of water bodies across the
landscape (Beisner et al. 2006, Chase et al. 2010). If management goals include persistence of
river otter populations and their function in linking aquatic and terrestrial ecosystems, land use
planning models must take landscape configuration as well as composition in to account.
The loss of top predators such as river otter is considered an indication of ecosystem
instability, as cascading effects have resulted in altered biogeochemical cycles, nonnative species
invasion, and disease transmission (Estes et al. 2011). From a conservation standpoint, our
understanding of the river otter’s interaction with the landscape becomes increasingly relevant as
freshwater systems prove extremely vulnerable to anthropogenic disturbance (Carpenter et al.
1992, Jelks et al. 2008, Voeroesmarty et al. 2010). The absence of this species could be a critical
red flag signaling decoupled aquatic and terrestrial systems. If not corrected, this will result in
simplified landscapes incapable of supporting well functioning ecosystems and aquatic
community structure. Linked ecosystems require linked management (Wiens et al. 2002). River
otter monitoring could serve as a common management tool for linking wildlife, fish, water and
land management agencies because their semi-aquatic habitat needs span these organizational
boundaries.

32

Zone 1

Zone 2

N
Zone 3
100 km

FIGURE 9. River otter and beaver trapping units in Michigan as defined by the
Michigan Department of Natural Resources (Adapted from the MDNR Furbearer
Harvest Regulations 2006).

33

APPENDIX

34

APPENDIX A: County scale correlation results

p = 0.021

p < 0.001

p < 0.001

p < 0.001

p < 0.001

p < 0.001

p = 0.021

p < 0.001

p < 0.001

FIGURE 10. Results from Pearson's correlation test on river otter harvest and nine landscape variables measured at the county scale.
35

LITERATURE CITED

36

LITERATURE CITED
Akaike, H. 1973. Maximum likelihood identification of gaussian autoregressive moving average
models. Biometrika 60(2): 255-265.
Anderson, S. 1977. Geographic ranges of North American terrestrial mammals. American
Museum Novitates(2629): 1-15.
Angermeier, P.L., and Karr, J.R. 1986. Applying an index of biotic integrity based on stream-fish
communities considerations in sampling and interpretation. North American Journal of
Fisheries Management 6(3): 418-429.
Bailey, E.P., and Faust, N.H. 1981. Summer distribution and abundance of marine birds and
mammals between Mitrofania and Sutwik islands south of Alaska peninsula USA.
Murrelet 62(2): 34-42.
Beisner, B.E., Peres Neto, P.R., Lindstrom, E.S., Barnett, A., and Longhi, M.L. 2006. The role of
environmental and spatial processes in structuring lake communities from bacteria to fish.
Ecology 87(12): 2985-2991.
Blundell, G.M., Bowyer, R.T., Ben-David, M., Dean, T.A., and Jewett, S.C. 2000. Effects of
food resources on spacing behavior of river otters: does forage abundance control homerange size? Biotelemetry 15: proceedings of the 15th International Symposium on
Biotelemetry, Juneau, Alaska USA, May 9-14, 1999.: 325-333.
Bouska, W.W., and Paukert, C.P. 2010. Road Crossing Designs and Their Impact on Fish
Assemblages of Great Plains Streams. Transactions of the American Fisheries Society
139(1): 214-222.
Bowyer, R.T., Blundell, G.M., Ben-David, M., Jewett, S.C., Dean, T.A., and Duffy, L.K. 2003.
Effects of the Exxon Valdez oil spill on river otters: Injury and recovery of a sentinel
species. Wildlife Monographs(153): 1-53.
Carpenter, S.R., Fisher, S.G., Grimm, N.B., and Kitchell, J.F. 1992. Global change and freshwater ecosystems. Annual Review of Ecology and Systematics 23: 119-139.
Carranza, M.L., D'Alessandro, E., Saura, S., and Loy, A. 2012. Connectivity providers for semiaquatic vertebrates: the case of the endangered otter in Italy. Landscape Ecology 27(2):
281-290.
Chase, J.M., Bergett, A.A., and Biro, E.G. 2010. Habitat isolation moderates the strength of topdown control in experimental pond food webs. Ecology 91(3): 637-643.
Coues, E. 1877. The fur-bearing animals: A monograph of North American Mustelidae.
Government Printing Office, Washington D.C.

37

Crait, J.R., and Ben-David, M. 2007. Effects of River Otter Activity on Terrestrial Plants in
Trophically Altered Yellowstone Lake. Ecology 88(4): 1040-1052.
Deems, E.F., Jr., and Pursley, D. 1983. North American furbearers. A contemporary reference.
North American furbearers. A contemporary reference.: i.
Dekar, M.P., Magoulick, D.D., and Beringer, J. 2010. Bioenergetics assessment of fish and
crayfish consumption by river otter (Lontra canadensis): integrating prey availability,
diet, and field metabolic rate. Canadian Journal of Fisheries and Aquatic Sciences 67(9):
1439-1448.
Dhuey, B., and Olsen, J. 2010. Otter Harvest, 2009-2010. Edited by W.D.o.N. Resources,
Document on file at the Wisconsin Dept. of Natural Resources, Bureau of Research,
Madison, WI. 53716.
Dunning, J.B.J. 2002. Landscape ecology in highly managed regions: The benefits of
collaboration between management and researchers. In Integrating Landscape Ecology
into Natural Resource mangement. Edited by J. Liu and W.W. Taylor. Cambridge
University Press, Cambridge, United Kingdom. pp. 334-348.
Elith, J., Phillips, S.J., Hastie, T., Dudik, M., Chee, Y.E., and Yates, C.J. 2011. A statistical
explanation of MaxEnt for ecologists. Diversity and Distributions 17(1): 43-57.
Erickson, D.W. 1982. Estimating and using furbearer harvest information, Wichita, Kansas.
ESRI. 2011. ArcGIS Desktop. Release 10. Environmental Research Systems Institute, Redlands,
CA.
Esselman, P.C., Infante, D.M., Wang, L.Z., Wu, D.Y., Cooper, A.R., and Taylor, W.W. 2011.
An index of cumulative disturbance to river fish habitats of the conterminous United
States from landscape anthropogenic activities. Ecological Restoration 29(1/2): 133-151.
Estes, J.A., Terborgh, J., Brashares, J.S., Power, M.E., Berger, J., Bond, W.J., Carpenter, S.R.,
Essington, T.E., Holt, R.D., Jackson, J.B.C., Marquis, R.J., Oksanen, L., Oksanen, T.,
Paine, R.T., Pikitch, E.K., Ripple, W.J., Sandin, S.A., Scheffer, M., Schoener, T.W.,
Shurin, J.B., Sinclair, A.R.E., Soule, M.E., Virtanen, R., and Wardle, D.A. 2011. Trophic
Downgrading of Planet Earth. Science 333(6040): 301-306.
Etter, D., and Bump, A. Personal communication. September 12, 2011. I don't think you cite in
literature cited section, but in the text , and also need these people agency affiliations
there.
Fausch, K.D., Lyons, J., Karr, J.R., and Angermeier, P.L. 1990. Fish communities as indicators
of environmental degradation. American Fisheries Society Symposium: 123-144.
Fausch, K.D., Torgersen, C.E., Baxter, C.V., and Li, H.W. 2002. Landscapes to riverscapes:
Bridging the gap between research and conservation of stream fishes. Bioscience 52(6):
483-498.
38

Feldhamer, G.A., Thompson, B.C., and Chapman, J.A. 2003. Wild mammals of North America :
biology, management, and conservation. Johns Hopkins University Press, Baltimore.
Field, R.J. 1970. Winter habits of the river otter lutra-canadensis in Michigan. Michigan
Academician 3(1): 49-58.
Frawley, B.J. 2007. 2006 Otter and Beaver Harvest Survey. Edited by M.D.o.N. Resources,
Lansing, Michigan.
Frissell, C.A., Liss, W.J., Warren, C.E., and Hurley, M.D. 1986. A hierarchical framework for
stream habitat classification - viewing streams in a watershed context. Environmental
Management 10(2): 199-214.
Gallant, D., Vasseur, L., and Bérubé, C.H. 2007. Unveiling the Limitations of Scat Surveys to
Monitor Social Species: A Case Study on River Otters. The Journal of Wildlife
Management 71(1): 258-265.
Hanson, J.M., and Leggett, W.C. 1982. Empirical prediction of fish biomass and yield. Canadian
Journal of Fisheries and Aquatic Sciences 39(2): 257-263.
Hiller, T.L., Etter, D.R., Belant, J.L., and Tyre, A.J. 2011. Factors Affecting Harvests of Fishers
and American Martens in Northern Michigan. Journal of Wildlife Management 75(6):
1399-1405.
Hobbs, R.J., Saunders, D.A., and Arnold, G.W. 1993. Integrated landscape ecology - a westernAustralian perspective. Biological Conservation 64(3): 231-238.
Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N., McKerrow, A.,
VanDriel, J.N., and Wickham, J. 2007. Completion of the 2001 National Land Cover
Database for the conterminous United States. Photogrammetric Engineering and Remote
Sensing 73(4): 337-341.
Hudy, M., Thieling, T.M., Gillespie, N., and Smith, E.P. 2008. Distribution, status, and land use
characteristics of subwatersheds within the native range of brook trout in the Eastern
United States. North American Journal of Fisheries Management 28(4): 1069-1085.
Hughes, R.M., Wang, L., and Seelbach, P.W. 2006. Landscape influences on stream habitats and
biological assemblages : proceedings of the Symposium on Influences of Landscape on
Stream Habitat and Biological Communities held in Madison, Wisconsin, USA, 25-26
August 2004. American Fisheries Society, Bethesda, Md.
Infante, D.M., Wiley, M.J., and Seelbach, P.W. 2006. Relationships among channel shape,
catchment characteristics, and fish in lower Michigan streams. American Fisheries
Society Symposium 48: 339-357.
Jeffress, M.R., Paukert, C.P., Whittier, J.B., Sandercock, B.K., and Gipson, P.S. 2011. Scaledependent Factors Affecting North American River Otter Distribution in the Midwest.
American Midland Naturalist 166(1): 177-193.
39

Jelks, H.L., Walsh, S.J., Burkhead, N.M., Contreras-Balderas, S., Diaz-Pardo, E., Hendrickson,
D.A., Lyons, J., Mandrak, N.E., McCormick, F., Nelson, J.S., Platania, S.P., Porter, B.A.,
Renaud, C.B., Schmitter-Soto, J.J., Taylor, E.B., and Warren, M.L., Jr. 2008.
Conservation Status of Imperiled North American Freshwater and Diadromous Fishes.
Fisheries 33(8): 372-407.
Karr, J.R. 1991. Biological integrity - a long-neglected aspect of water-resource management.
Ecological Applications 1(1): 66-84.
Karr, J.R., and Chu, E.W. 2000. Sustaining living rivers. Hydrobiologia 422: 1-14.
Karr, J.R., and Dudley, D.R. 1981. Ecological perspective on water-quality goals. Environmental
Management 5(1): 55-68.
Karr, J.R., Toth, L.A., and Dudley, D.R. 1985. FISH COMMUNITIES OF MIDWESTERN
RIVERS - A HISTORY OF DEGRADATION. Bioscience 35(2): 90-95.
Kitchell, J.F., Koonce, J.F., Oneill, R.V., Shugart, H.H., Magnuson, J.J., and Booth, R.S. 1974.
Model of fish biomass dynamics. Transactions of the American Fisheries Society 103(4):
786-798.
Knudsen, G.J., and Hale, J.B. 1968. Food Habits of Otters in the Great Lakes Region. The
Journal of Wildlife Management 32(1): 89-93.
Kotanchik, J. 1997. Large-scale distribution of river otters in Michigan's lower peninsula,
Natural Resources and the Environment, University of Michigan, Ann Arbor.
Levin, S.A. 1992. The problem of pattern and scale in ecology. Ecology 73(6): 1943-1967.
Liu, J., and Taylor, W.W. 2002. Coupling landscape ecology with natural resource management:
Paradigm shifts and new approaches. In Integrating landscape ecology into Natural
Resource Management. Edited by J. Liu and W.W. Taylor. Cambridge University Press,
Cambridge, United Kingdom. p. 480.
Mack, C., Kronemann, L., and Eneas, C. 1994. Lower Clearwater aquatic mammal survey
(Project Number 90-51). Bonneville Power Administration, Portland, OR.
McGarigal, K., and Marks, B.J. 1995. FRAGSTATS: Spatial pattern analysis program for
quantifying landscape structure. U S Forest Service General Technical Report PNW
0(351): I.
Melquist, W.E., and Dronkert, A.E. 1987. River otter. Wild furbearer management and
conservation in North America.: 626-641.
Melquist, W.E., and Hornocker, M.G. 1983. Ecology of River Otters in West Central Idaho.
Wildlife Monographs(83): 3-60.

40

Melquist, W.E., Polechla Jr., P.J., and Toweill, D. 2003. River Otter, Lontra canadensis. In Wild
mammals of North America: biology, management, and conservation. Second edition.
Edited by G.A. Feldhamer, B.C. Thompson and J.A. Chapman. Johns Hopkins University
Press, London and Baltimore. pp. 708-734.
Newman, D.G., and Griffin, C.R. 1994. Wetland Use by River Otters in Massachusetts. The
Journal of Wildlife Management 58(1): 18-23.
Ostroff, A. 2001. Distribution and mesohabtiat characteristics of river otter in eastern Kansas,
Emporia State University, Emporia, Kansas.
Pace, M.L., Cole, J.J., Carpenter, S.R., Kitchell, J.F., Hodgson, J.R., Van de Bogert, M.C., Bade,
D.L., Kritzberg, E.S., and Bastviken, D. 2004. Whole-lake carbon-13 additions reveal
terrestrial support of aquatic food webs. Nature 427(6971): 240-243.
Peterjohn, W.T., and Correll, D.L. 1984. Nutrient dynamics in an agricultural watershed observations on the role of a riparian forest. Ecology 65(5): 1466-1475.
Phillips, S.J., and Dudik, M. 2008. Modeling of species distributions with MaxEnt: new
extensions and a comprehensive evaluation. Ecography 31(2): 161-175.
Polechla, P. 1990. Action plan for North American otters.
Polechla, P.J. 1988. Neararctic river otter (<i style=&quot;">Lutra canadensis). In Audubon
Wildlife Report. Edited by C. W.J. and L. Labate. National Audubon Society, Academic
Press, New York. pp. 668-682.
Polis, G.A., Anderson, W.B., and Holt, R.D. 1997. Toward an integration of landscape and food
web ecology: The dynamics of spatially subsidized food webs. Annual Review of
Ecology and Systematics 28: 289-316.
Poole, K.G., and Mowat, G. 2001. Alberta furbearer harvest data analysis. Alberta Species at
Risk Report 31: i-viii, 1-52.
Prenda, J., and Granado-Lorencio, C. 1996. The relative influence of riparian habitat structure
and fish availability on otter Lutra lutra L. sprainting activity in a small Mediterranean
catchment. Biological Conservation 76(1): 9-15.
R Core Development Team. 2012. R: A Language and Environment for Statistical Computing.
Vienna, Austria.
Rabeni, C.F., and Smale, M.A. 1995. Effects of siltation on stream fishes and the potential
mitigating role of the buffering riparian zone. Hydrobiologia 303(1-3): 211-219.
Raesly, E.J. 2001. Progress and Status of River Otter Reintroduction Projects in the United
States. Wildlife Society Bulletin 29(3): 856-862.

41

Reid, D.G., Code, T.E., Reid, A.C.H., and Herrero, S.M. 1994. Food-habits of the river otter in a
boreal ecosystem. Canadian Journal of Zoology-Revue Canadienne De Zoologie 72(7):
1306-1313.
Roth, N.E., Allan, J.D., and Erickson, D.L. 1996. Landscape influences on stream biotic integrity
assessed at multiple spatial scales. Landscape Ecology 11(3): 141-156.
Ryder, R.A. 1955. Fish Predation by the Otter in Michigan. The Journal of Wildlife Management
19(4): 497-498.
Schlosser, I.J. 1991. Stream fish ecology - a landscape perspective. Bioscience 41(10): 704-712.
Schneider, R.L., Mills, E.L., and Josephson, D.C. 2002. Aquatic-terrestrial linkages and
implications for landscape management. In Integrating Landscape Ecology into Natural
Resource Management. Edited by J. Liu and W.W. Taylor. Cambridge University Press,
Cambridge, United Kingdom. pp. 241-262.
Sepulveda, M.A., Bartheld, J.L., Meynard, C., Benavides, M., Astorga, C., Parra, D., and
Medina-Vogel, G. 2009. Landscape features and crustacean prey as predictors of the
Southern river otter distribution in Chile. Animal Conservation 12(6): 522-530.
Serfass, T.L., Rymon, L.M., and Brooks, R.P. 1990. Feeding relationships of river otters in
northeastern Pennsylvania. Transactions of the Northeast Section of the Wildlife Society
47: 43-53.
Shardlow, M.R. 2009. Factors affecting the detectability and distribution of the North American
River Otter, Biology, Kansas State University, Manhattan, Kansas.
Steen, P.J., Zorn, T.G., Seelbach, P.W., and Schaeffer, J.S. 2008. Classification Tree Models for
Predicting Distributions of Michigan Stream Fish from Landscape Variables.
Transactions of the American Fisheries Society 137(4): 976-996.
Stewart, J.S., Wang, L.Z., Lyons, J., Horwatich, J.A., and Bannerman, R. 2001. Influences of
watershed, riparian-corridor, and reach-scale characteristics on aquatic biota in
agricultural watersheds. Journal of the American Water Resources Association 37(6):
1475-1487.
Swimley, T.J., Serfass, T.L., Brooks, R.P., and Tzilkowski, W.M. 1998. Predicting river otter
latrine sites in Pennsylvania. Wildlife Society Bulletin 26(4): 836-845.
Toweill, D.E. 1974. Winter Food Habits of River Otters in Western Oregon. The Journal of
Wildlife Management 38(1): 107-111.
Toweill, D.E., and Tabor, J.E. 1982. River otter - Lutra canadensis. Wild mammals of North
America.: 688-703.

42

Townsend, C.R., Doledec, S., Norris, R., Peacock, K., and Arbuckle, C. 2003. The influence of
scale and geography on relationships between stream community composition and
landscape variables: description and prediction. Freshwater Biology 48(5): 768-785.
USEPA and USGS. 2010. National Hydrography Dataset Plus. Horizon Systems.
Vina, A., Tuanmu, M.N., Xu, W.H., Li, Y., Ouyang, Z.Y., DeFries, R., and Liu, J.G. 2010.
Range-wide analysis of wildlife habitat: Implications for conservation. Biological
Conservation 143(9): 1960-1969.
Voeroesmarty, C.J., McIntyre, P.B., Gessner, M.O., Dudgeon, D., Prusevich, A., Green, P.,
Glidden, S., Bunn, S.E., Sullivan, C.A., Liermann, C.R., and Davies, P.M. 2010. Global
threats to human water security and river biodiversity. Nature 467(7315): 555-561.
Wang, L., Brenden, T., Seelbach, P., Cooper, A., Allan, D., Clark, R., Jr., and Wiley, M. 2008.
Landscape based identification of human disturbance gradients and reference conditions
for Michigan streams. Environmental Monitoring and Assessment 141(1-3): 1-17.
Wang, L., Infante, D., Esselman, P., Cooper, A., Wu, D., Taylor, W., Beard, D., Whelan, G., and
Ostroff, A. 2011. A Hierarchical Spatial Framework and Database for the National River
Fish Habitat Condition Assessment. Fisheries 36(9): 436-449.
Wang, L.Z., Lyons, J., Rasmussen, P., Seelbach, P., Simon, T., Wiley, M., Kanehl, P., Baker, E.,
Niemela, S., and Stewart, P.M. 2003. Watershed, reach, and riparian influences on stream
fish assemblages in the Northern Lakes and Forest Ecoregion, USA. Canadian Journal of
Fisheries and Aquatic Sciences 60(5): 491-505.
Warren, M.L., and Pardew, M.G. 1998. Road crossings as barriers to small-stream fish
movement. Transactions of the American Fisheries Society 127(4): 637-644.
Whitledge, G.W., Rabeni, C.F., Annis, G., and Sowa, S.P. 2006. Riparian Shading and
Groundwater Enhance Growth Potential for Smallmouth Bass in Ozark Streams.
Ecological Applications 16(4): 1461-1473.
Wiens, J.A. 1989. Spatial Scaling in Ecology. Functional Ecology 3(4): 385-397.
Wiens, J.A., Van Horne, B., and Noon, B.R. 2002. Integrating landscape structure and scale into
natural resource management. Integrating landscape ecology into natural resource
management.: 23-67.
Zorn, T.G., Seelbach, P.W., and Wiley, M.J. 2002. Distributions of Stream Fishes and their
Relationship to Stream Size and Hydrology in Michigan's Lower Peninsula. Transactions
of the American Fisheries Society 131(1): 70-85.
Zorn, T.G., Seelbach, P.W., and Wiley, M.J. 2011. Developing User-Friendly Habitat Suitability
Tools from Regional Stream Fish Survey Data. North American Journal of Fisheries
Management 31(1): 41-55.

43

Zorn, T.G., and Wiley, M.J. 2006. Influence of landscape characteristics on local habitat and fish
biomass in streams of Michigan's Lower Peninsula. American Fisheries Society
Symposium 48: 375-393.

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