Analyzing Tradeoffs Between the Threat of Invasion by Brook Trout and
Effects of Intentional Isolation for Native Westslope Cutthroat Trout
DOUGLAS P. PETERSON1
US Fish and Wildlife Service
585 Shepard Way
Helena, MT 59601
406.449.5225 x221
doug_peterson@fws.gov
BRUCE E. RIEMAN2
USDA Forest Service
Rocky Mountain Research Station
322 E. Front St, Suite 401
Boise, ID 83702
208.373.4386
brieman@fs.fed.us
JASON B. DUNHAM
US Geological Survey
FRESC Corvallis Research Group
3200 SW Jefferson Way, Corvallis, OR 97331
541.750.7397
jdunham@usgs.gov
KURT D. FAUSCH
Department of Fish, Wildlife, and Conservation Biology
Colorado State University, Fort Collins, CO 80521-1474
970.491.6457
kurtf@warnercnr.colostate.edu
MICHAEL K. YOUNG
USDA Forest Service
Rocky Mountain Research Station
P.O. Box 8089, Missoula, Montana 59807
406.542.3254
mkyoung@fs.fed.us
1
Corresponding Author
Current address: P.O. Box 1541, Seeley Lake, MT 59868; 406-677-3813
2
1
Abstract
Native fishes often face simultaneous threats from habitat fragmentation and invasion by
nonnative trout. Unfortunately, management actions to address one may create or exacerbate the
other. A consistent decision process would include a systematic analysis of when and where
intentional use or removal of barriers is most appropriate. We developed a Bayesian belief
network (BBN) as a tool for such analyses. We focused on native westslope cutthroat trout
(Oncorhynchus clarkii lewisi) and nonnative brook trout (Salvelinus fontinalis). We considered
the environmental factors influencing them, their potential interactions, and the effects of
isolation on the persistence of local cutthroat trout populations. The tradeoffs between isolation
and invasion were strongly influenced by the size and quality of the stream network to be isolated
and existing demographic linkages within and among populations. A strength of our approach
was that it captured interactions where effects would otherwise difficult to visualize. The model
can be used to conduct site-level analysis of barrier management relative to other possible
conservation actions, such as habitat improvement or angling restrictions, and to help prioritize
actions among streams. By eliciting precise definitions of conservation priorities, the model can
also help clarify management objectives and facilitate communication among biologists,
managers, and the public.
Introduction
Conservation of inland cutthroat trout (Oncorhynchus clarkii spp.) can involve either the
placement or removal of migration barriers to address threats from invading species and habitat
fragmentation. There are important tradeoffs, because barriers that may limit invasion can also
isolate a native population making it more vulnerable to local extinction through a variety of
processes. Projects to install or remove barriers may proceed without a formal analysis that
considers potential tradeoffs from addressing these competing threats. Because resources for
conservation management are limited, effective prioritization is important. Tradeoffs may be
relatively clear to biologists and managers with intimate knowledge of a particular system, and
their efforts can be focused effectively. Elsewhere, the tradeoffs may be more ambiguous or the
data and experience more limited, and the result may be a decision that is influenced more by
personal philosophy or public pressure than by knowledge. When the differences in these choices
cannot be clearly supported and articulated, the decision process can appear inconsistent and
arbitrary to the public or the administrators who fund these projects. A consistent decision
process would include an analysis of the relative risks associated with either action.
Fausch et al. (2006) provided a synthesis of the science and theory relevant to this issue and
proposed a framework that could help guide an appropriate analysis. The analysis could be
complex because of the potential interaction of multiple physical and ecological processes. For
example, the probability that brook trout (Salvelinus fontinalis) may invade and displace cutthroat
trout from any stream may depend on the physical characteristics defining the suitability of
stream habitat for either species (e.g. Peterson et al. 2004), the condition of that habitat (e.g.
Shepard 2004), the size, connectivity and complexity of the available habitat network (Rieman
and Dunham 2000), distance of potential source populations, fishing pressure, and their
interactions (Figure 1). Many biologists may inherently understand those processes for systems
they have studied in detail, but consistent evaluation of these processes across multiple
populations and environments could be improved by a formal assessment tool.
We explored the application of a Bayesian belief network (BBN) as a tool to facilitate such
analyses (Cain 2001). We focused on westslope cutthroat trout (Oncorhynchus clarkii lewisi;
hereafter WCT) and nonnative brook trout and current understanding of environmental factors
2
influencing both species, their potential interactions, and the effects of isolation on the persistence
of individual WCT populations.
Methods
We started with a conceptual model that represented the key processes and conditions we believe
important to the biology of WCT and their interactions with invading brook trout (Figure 1). The
conceptual model was formalized as a belief network by linking a series of conditional
probability tables that quantified the key relationships implied by the arrows in the conceptual
model (Figure 2). Estimation of the probability distributions for these relationships involved
professional opinion, data based on actual species observations, and simulation of demographic
characteristics with traditional matrix and diffusion-approximation population models. The
resulting BBN predicted the probability of WCT occurrence in a stream segment after 20 years.
The details are available in Peterson et al. (In Press).
Summer
Temperature
Stream
Width
Hydrologic
Regime
Gradient
Habitat
Degradation
Potential WCT
Spawning & rearing
habitat
Potential BKT
Spawning & rearing
habitat
BKT Population
Status
Egg-Age1
WCT survival
Juvenile
WCT survival
Subadult-Adult
WCT Survival
BKT
Connectivity
BKT Invasion
Strength
WCT Effective
Life History
Invasion
Barrier
WCT Potential
Life History
Fishing
Exploitation
WCT Population
Growth Rate
Effective
Network Size
WCT
Persistence
WCT Colonization
& Rescue
WCT
Connectivity
Figure 1. A conceptual model depicting environmental conditions and processes influencing the persistence
of westslope cutthroat trout (WCT) and tradeoffs between intentional isolation and invasion by brook trout
(BKT). Arrows indicate conditional dependencies between variables. Input variables (prior conditions)
believed to affect WCT and BKT populations are those having only arrows from them (shaded ovals, e.g.,
gradient, temperature, stream width, etc.). Dashed lines indicate variables originating outside the local
stream network. This model was implemented as a Bayesian Belief Network (see Figure 2) by developing
conditional probabilities between dependent variables.
3
< 2%
2-8%
> 8%
Gradient
100
0
0
0
Summer Temperature
0
<7C
0
7-10 C
100
10-15 C
0
15-18 C
0
>18 C
10
Potential WCT spawning & rearing ha...
0
Low (Poor)
34.0
Moderate (Suitable)
66.0
High (Optimal)
Habitat Degrad.
Altered & Degraded
0
Minimally Altered
100
Egg to Age-1 WCT survival
< 2.5%
74.2
2.5 - 5%
23.9
> 5%
1.96
1.07 ± 1
Stream Width
<3m
100
3-10 m
0
> 10 m
0
0
BKT Connectivity
100
Strong
0
Moderate
0
None
Potential BKT spawning & rearing hab...
0
Low (Poor)
34.0
Moderate (Suitable)
66.0
High (Optimal)
BKT Pop. Status
88.1
Strong
11.9
Weak
0
Absent
Juvenile WCT survival
25.8
<25%
25-35% 54.3
20.0
>35%
24.4 ± 6.7
BKT Invasion Strength
Strong
100
Moderate
0
None
0
WCT Potential Life History
Resident
0
Migratory
100
Subadult-Adult WCT Survival
< 35%
0
35-45%
0
> 45%
100
45 ± 0
INVASION BARRIER
Yes
0
No
100
WCT Effective Life History
Resident
0
Migratory
100
Fishing Exploitation
>10%
0
0-10%
100
Effective Network Size
0
< 3 km or < 500 age-1+
0
3-5 km or 500-1000 age1+
5-7 km or 1000-2500 age-1+ 100
0
7-10 km or 2500-5000 age...
0
> 10 km or >5000 age-1+
Hydrologic Regime
Snowmelt
100
Mixed
0
WCT Pop. Growth Rate
6.69
< 0.85
0.85 - 0.95 15.8
0.95 - 1.05 22.3
1.05 - 1.15 20.8
34.5
> 1.15
1.16 ± 0.32
WCT Colonization & Rescue
None
0
Moderate
0
Strong
100
WCT Connectivity
0
None
0
Moderate
100
Strong
WCT PERSISTENCE
Extinct
8.70
Present 91.3
0.913 ± 0.28
Figure 2. Bayesian Belief Network (BBN) to analyze tradeoffs between intentional isolation and invasion. The
state or range of values each variable can take are listed below the title of each variable (e.g., for gradient,
the possible states are <2%, 2-8%, and >8%). The size of the bar and the corresponding percentage values
next to each state indicate the probability that the variable is in that state, conditioned on the state of the
variables that influence it. To evaluate the effects of barrier installation or removal in a particular stream, the
user can determine the initial conditions for a stream network by entering probabilities for the input
variables, change the state of invasion barrier (i.e. from Yes to No), and measure the change in probabilities
of persistence, or any intermediate variable of interest. In this example all input variables were set to 100%
for one of the possible states.
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A.
Pristine Habitat
1.0
Probability of persistence
0.8
Degraded Habitat
Connected Pristine
No Barrier
Barrier
0.6
Connected Degraded
Migratory &
Connected
0.4
0.2
0.0
1.0
Isolated - Pristine
Isolated - Degraded
0.8
0.6
Resident &
Isolated
0.4
0.2
0.0
very small medium very large
B.
1.0
very small medium very large
Connected - Degraded
Connected - Pristine
Probability of persistence
0.8
Migratory &
Connected
0.6
0.4
0.2
0.0
1.0
Isolated - Degraded
Isolated - Pristine
0.8
0.6
Resident &
Isolated
0.4
0.2
0.0
very small medium very large
very small medium very large
Effective network size
Figure 3. Predicted response of westslope cutthroat trout (WCT) to installation of an invasion barrier using
the Bayesian Belief Network . Bars denote the predicted probability of persistence relative to habitat size
and quality, life history expression and connection to other WCT populations ), under low (A) and high (B)
fishing exploitation. Results assume that brook trout invasion is imminent in a stream network with a
snowmelt hydrologic regime, characterized by small (<3 m wide) low-gradient (<2%) tributary streams with
o
moderate summer water temperatures (10-15 C). For reference, the middle black bar in the upper left panel
of Figure 3B represents the prediction for the initial conditions represented in Figure 2.
5
Results and Discussion
Analysis with the BBN across a range of environmental conditions indicated the predicted
tradeoff between isolation and invasion was strongly influenced by size of the stream network (or
WCT population) to be isolated and existing demographic linkages within and among cutthroat
trout populations. Intentional isolation was predicted to benefit demographically isolated WCT
populations facing likely invasion by brook trout. Intentional isolation generally reduced the
probability of persistence for migratory populations regardless of invasion threat. The relative
benefits or risks associated with isolation depended strongly on the size and quality of available
habitats that would be isolated (Figure 3).
Our results suggest that the utility of a barrier should be weighed based on some understanding of
these characteristics. Peterson et al. (In Revision) provide several examples of how the BBN
could be used to evaluate management alternatives and prioritize limited resources associated
with the installation or removal of barriers. We believe the model can also facilitate
communication among parties interested in these management issues.
The BBN we developed was based on current understanding of brook trout invasions and the
consequences of incidental or intentional isolation for WCT. Like any model, potential users
should be aware of its limitations. Predictions can only be interpreted in terms of the relative
differences between management options or a set of environmental conditions, not as absolute
probabilities (e.g., Ralls et al. 2002). A BBN provides guidance during the decision process, but
does not supplant or replace a human decision (Marcot 2006), nor does it substitute for the
professional knowledge of an experienced fishery biologist. It does, however, allow biologists
and managers to more clearly think about the relative effects of brook trout and isolation on WCT
populations, and to quickly visualize and evaluate the effects of complex interactions. As a
working hypothesis, the BBN can be directly tested, updated, or modified using examples from
fishery management or challenged and revised based on new empirical or theoretical results.
The use of this BBN does not solve the often opposing problems of brook trout invasion and
habitat fragmentation facing WCT or other native fishes in western North America. Rather, it
provides a process and framework for thinking through the issues, clearly documenting and
defining knowledge and uncertainty, and identifying conservation values and objectives. Sitespecific analysis using our model or similar BBNs may help identify management options and
tradeoffs in a particular stream. The greater utility, however, may be using the model to explore
the relative benefits of isolation or connection across a collection of WCT populations and using
that information to implement more strategic conservation programs and prioritize limited
resources.
A working version of the model and a user’s guide may be obtained from:
Doug_Peterson@FWS.gov.
Acknowledgments
This work was supported in part through funding from Region 1 of the USDA Forest Service.
Important support in the development of the models and examples used in our analysis was
provided by B. Marcot, B. Riggers, and S. Hendrickson.
6
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