ArticlePDF Available

Using a qualitative model to explore the impacts of ecosystem and anthropogenic drivers upon declining marine survival in Pacific salmon


Abstract and Figures

Coho salmon ( Oncorhynchus kisutch ), Chinook salmon ( Oncorhynchus tshawytscha ) and steelhead ( Oncorhynchus mykiss ) in Puget Sound and the Strait of Georgia have exhibited declines in marine survival over the last 40 years. While the cause of these declines is unknown, multiple factors, acting cumulatively or synergistically, have likely contributed. To evaluate the potential contribution of a broad suite of drivers on salmon survival, we used qualitative network modelling (QNM). QNM is a conceptually based tool that uses networks with specified relationships between the variables. In a simulation framework, linkages are weighted and then the models are subjected to user-specified perturbations. Our network had 33 variables, including: environmental and oceanographic drivers (e.g., temperature and precipitation), primary production variables, food web components from zooplankton to predators and anthropogenic impacts (e.g., habitat loss and hatcheries). We included salmon traits (survival, abundance, residence time, fitness and size) as response variables. We invoked perturbations to each node and to suites of drivers and evaluated the responses of these variables. The model showed that anthropogenic impacts resulted in the strongest negative responses in salmon survival and abundance. Additionally, feedbacks through the food web were strong, beginning with primary production, suggesting that several food web variables may be important in mediating effects on salmon survival within the system. With this model, we were able to compare the relative influence of multiple drivers on salmon survival.
Content may be subject to copyright.
Environmental Conservation: page 1 of 13 C
Foundation for Environmental Conservation 2017 doi:10.1017/S0376892917000509
Using a qualitative model to explore the impacts of ecosystem and
anthropogenic drivers upon declining marine survival in Pacific salmon
1Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd.
E, Seattle, WA 98112, USA and 2Long Live the Kings, 1326 5th Ave. #450, Seattle, WA 98101, USA
Date submitted: 12 May 2017; Date accepted: 3 September 2017
Coho salmon (Oncorhynchus kisutch), Chinook salmon
(Oncorhynchus tshawytscha) and steelhead (Onco-
rhynchus mykiss) in Puget Sound and the Strait of
Georgia have exhibited declines in marine survival
over the last 40 years. While the cause of these declines
is unknown, multiple factors, acting cumulatively or
synergistically, have likely contributed. To evaluate
the potential contribution of a broad suite of
drivers on salmon survival, we used qualitative
network modelling (QNM). QNM is a conceptually
based tool that uses networks with specified
relationships between the variables. In a simulation
framework, linkages are weighted and then the models
are subjected to user-specified perturbations. Our
network had 33 variables, including: environmental
and oceanographic drivers (e.g., temperature and
precipitation), primary production variables, food
web components from zooplankton to predators
and anthropogenic impacts (e.g., habitat loss and
hatcheries). We included salmon traits (survival,
abundance, residence time, fitness and size) as response
variables. We invoked perturbations to each node and
to suites of drivers and evaluated the responses of
these variables. The model showed that anthropogenic
impacts resulted in the strongest negative responses
in salmon survival and abundance. Additionally,
feedbacks through the food web were strong, beginning
with primary production, suggesting that several food
web variables may be important in mediating effects on
salmon survival within the system. With this model, we
were able to compare the relative influence of multiple
drivers on salmon survival.
Keywords: network model, indicators, ecosystem, salmon,
Problems of complex interactions are common in many fields,
including medicine, economics and ecology (Levins 1974). In
Correspondence: Dr Kathryn L. Sobocinski email kathryn.
Supplementary material can be found online at
ecology, much attention has been given to describing food
webs and interactions among species (Paine 1966;May1974;
Pimm et al. 1991; Dunne et al. 2002a). But often these food
webs are nested within larger ecological or social–ecological
contexts where exogenous forces influence components of
the food web system. External forcings may include physical
drivers, anthropogenic impacts or ecosystem components that
are not characterized within the focal network. In social–
environmental systems, tools that incorporate ecological
properties, abiotic variables and management actions within
the same analytical framework are needed to accurately
understand the dynamics of complex systems and to evaluate
potential management actions (Liu et al. 2007). However,
rarely are compatible datasets available for this type of analysis.
Here we use a qualitative network model – a conceptually
based modelling approach – and a suite of simulations to
address questions about the relative impacts of human and
natural influences on early marine survival of juvenile salmon.
In recent years, attention has turned to marine life-history
stages of Pacific salmon (Oncorhynchus spp.) in an effort to
understand population declines and the subsequent failure
to rebound given myriad conservation and restoration efforts
in freshwater streams. In Chinook salmon, coho salmon, and
steelhead (Oncorhynchus tshawytscha,Oncorhynchus kisutch and
Oncorhynchus mykiss, respectively), declines in marine survival
have been evidenced within the Salish Sea (Puget Sound,
WA, USA, and the Strait of Georgia, BC, Canada) that
have not been seen in coastal populations (Beamish et al.
2010; Johannessen & McCarter 2010; Zimmerman et al. 2015;
Ruff et al. 2017; Kendall et al. 2017). These inland water
bodies serve as habitats for juvenile salmon as they pass
from natal streams to ocean waters during their outmigration.
Yet because of complex anthropogenic changes brought
about by population increases and the associated human
activity in these waters, it is likely that a number of factors
and their cumulative – synergistic or additive – effects are
contributing to early marine mortality. Other salmon species,
such as chum, pink and sockeye salmon (Oncorhynchus keta,
Oncorhynchus gorbuscha and Oncorhynchus nerka, respectively)
have not experienced similar declines (Debertin et al.
2017), suggesting that life-history characteristics may also
contribute to increased mortality for some species in
this region. Teasing apart which factors have negatively
impacted the survival of juvenile salmon in marine waters
is of concern to local, regional and federal governments
and other stakeholders (e.g. the Salish Sea Marine
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
2Sobocinski K. L. et al.
Survival Project,
management actions are being sought to lessen these impacts
and increase survival during this period.
Correlative studies of salmon abundance and environmental
factors have been ongoing in the greater region for many
years (Pearcy 1988; Beamish et al. 2000;Teoet al. 2009;
Burke et al. 2013). Food web models for the Strait of Georgia
(Preikshot 2008) and Puget Sound (Harvey et al. 2012)show
primary production as an important driver in the biological
system, as well as the effects of top predators in creating
trophic cascades and influencing food web dynamics in the
mid-trophic levels where time-series data are sparse (Harvey
et al. 2012). Even with an understanding of the main variables
in a given system, measuring abundances of each variable
and the flux of energy among them often poses a logistical
challenge (Christensen & Walters 2004). These models do not
easily incorporate non-fisheries anthropogenic impacts, such
as habitat loss or contaminant exposure, yet we understand
that, in many systems, diverse but cumulative impacts can
play a role in species population change. For this reason,
conceptually based models, incorporating a broader array of
variables, are an important tool in providing an integrated
picture of ecological and human drivers of ecosystem
One tool for evaluating the relative influence of ecosystem
components is qualitative network modelling (QNM; also
called qualitative network analysis or loop analysis) (Levins
1974; Puccia & Levins 1985;Raymondet al. 2011; Melbourne-
Thomas et al. 2012;Harveyet al. 2016), which is advantageous
fully specified and when precise measurement is impossible,
but when some mechanistic understanding of the interactions
exists. It allows for the testing of competing hypotheses given
different model structures or the invocation of perturbations
to one or more of the model variables. QNM does not
explicitly include non-linear direct effects, which occur in and
influence social and ecological systems; however, it may help
to determine the relative impacts of competing hypothesized
factors or indicate where empirical work could be focused in
order to improve system understanding (Levins 1974).
Researchers have used QNM for evaluating ecosystem
responses to ocean acidification in shellfish management
(Reum et al. 2015) and the impacts of eutrophication and
species management within a food web (Carey et al.2014), as
well as for discerning the impact of management actions on
species recovery (Harvey et al. 2016) in the Pacific Northwest.
QNM is an important conceptual tool for determining the
relative impacts of ecosystem components from which more
complex, data-driven modelling efforts can stem. Here we
apply this technique in order to evaluate a suite of potential
drivers thought to be contributing to increased early marine
simulation framework, we invoke perturbations to each model
variable and suites of variables based on salmon early marine
survival hypotheses and assess the model responses related to
the salmon species of concern. This work is a foundational step
in understanding the impacts of multiple drivers of marine
survival declines in Salish Sea salmon.
We used QNM to address our primary question regarding
the relative impacts of various factors on salmon early marine
survival. Our analysis had three main steps: (1) construct
an enhanced conceptual model showing positive, negative
and neutral relationships; (2) generate a pool of stable
simulated models with random weights applied to each model
linkage; and (3) invoke one or more perturbations based upon
mechanistic understanding of the system and determine the
model response.
Conceptual model
To construct our conceptual model of the Salish Sea
system, we gathered existing literature and experts on
ecosystem components from within and outside of the
project technical team. The technical team includes scientists
from resource agencies, universities and tribal entities,
with expertise ranging from salmon genetics to disease
ecology to numerical ocean modelling. We began by
developing a list of over 40 possible variables drawn
from hypotheses about the decline of Pacific salmon
survival within the system (Salish Sea Marine Survival
Project hypotheses,
project/key-hypotheses/). The variables included: physical
forcings; biological components from primary production to
top predators and competitors; and anthropogenic variables.
We drew a draft model based upon our knowledge of the
system and existing literature and then we conducted small
meetings with experts on particular components, such as
disease ecology or oceanography, and iteratively developed a
working conceptual model. We sought out additional feedback
from those working within the Salish Sea but not on the
technical team on both the model components and structure,
and received further feedback during public presentations to
refine the conceptual model.
For the final model, we grouped model variables into several
driver groups: environmental factors, primary production,
food web interactions and anthropogenic impacts (Table 1).
While the conceptual model is not exhaustive, it does include
many of the drivers identified in our working hypotheses
and reflects known interactions within the ecosystem. The
inclusion of model variables that are not biomass pools (e.g.
temperature and habitat loss) highlights the flexibility of
the qualitative modelling approach. Focusing on physical,
bottom-up, top-down and anthropogenic factors fits with
the working hypotheses of the Marine Survival Project and
enabled exploration of combinations of diverse variables.
The emphasis of the modelling effort was on understanding
sources of decreased survival of the focal salmon species
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
Salish sea salmon qualitative network model 3
Table 1 Descriptions of model nodes (variables) in the Salish Sea qualitative network model by driver group. Also shown are connectivity
among nodes, the number of influencing nodes and the distance to the survival node.
Drivers Variables Total Number of Minimum Description
number of influencing nodal
connected nodes distance to
nodes survival
Environmental Sunlight 4 2 3 Solar radiation
Winter storms 5 1 4 Commonly occurring winter storm events
Precipitation 5 2 3 Annual total precipitation
Upwelling 5 2 3 Oceanographic upwelling driven by wind and
currents in the coastal waters
Stratification 8 6 3 Formation of layers in the water column resulting
from ocean conditions
Temperature 11 5 2 Water temperature within the Salish Sea (generalized,
but upper portion of the water column where
salmon occur)
River flow 4 2 2 Annual streamflow
Turbidity 4 3 1 Relative clarity of the water within the Salish Sea
Dissolved oxygen 9 7 2 Amount of oxygen available in Salish Sea waters
Production Nutrients 5 5 4 Total nutrients (generalized to be anthropogenic
sources of nitrogen)
Microplankton 9 6 4 Dinoflagellates (e.g. Noctiluca spp.)
7 6 3 Generalized microbes, including bacteria
Diatoms 11 9 3 Autotrophic phytoplankton
Food web Zooplankton 10 9 2 Energy-rich zooplankton (e.g. copepods, krill,
6 5 3 Zooplankton including ctenophores, medusae and
Forage fish 9 9 2 Herring, smelt and other small-bodied fishes
Ichthyoplankton 7 6 2 Immature stages of fish residing in the water column
Other salmon 10 10 2 Chum, pink and sockeye Salmon
Piscivorous fish 7 6 1 Any fish-eating fish; characterized by gadids and
scorpaenids in the Salish Sea
Piscivorous birds 5 4 1 Any fish-eating bird, such as cormorants and auklets
Marine mammals 7 6 1 Generally harbour seals, sea lions, orcas and dolphins
Anthropogenic Hatcheries 4 1 2 Production, through human intervention, of large
numbers of juvenile fish through breeding
programmes, specifically salmon
Harvest 2 1 3 Catch of fish, specifically steelhead, coho and Chinook
salmon; generalized to include both recreational and
commercial take
Habitat loss 5 1 2 Loss on intertidal and subtidal habitats for spawning
or rearing
Carbon dioxide 5 5 4 Input of carbon dioxide via anthropogenic activities
Global warming 3 1 2 The general warming trend of the earth’s atmosphere
Contaminants 6 1 2 Exposure to common toxins like polychlorinated
biphenyls, polybrominated diphenyl ether, etc., as
well as contaminants of emerging concern (e.g.
Disease 3 2 2 Exposure to diseases such as Nanophyetus and
bacterial kidney disease
Salmon traits Residence time 6 5 1 The amount of time an outmigrating salmon spends
in the Salish Sea
Size 6 5 1 Overall size of salmon
Fitness 7 6 1 Overall health of salmon
Abundance 11 4 2 Number or biomass of salmon
Survival 8 7 Successful completion of the marine life stage by
individuals of a population
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
4Sobocinski K. L. et al.
Figure 1 Salmon population trends
within Puget Sound (left-most
sub-basins), the Strait of Georgia
(centre sub-basins) and the Pacific
Coast (right-most sub-basins) for
species with generally decreasing
trends (Chinook, coho and steelhead,
left) and increasing or stable
populations trends (chum, pink and
cockeye, right). Two abnormally
high pink salmon runs were omitted
from the right panel for ease of
presentation – these runs had trends
of 1.05 and 0.43 and were both from
Puget Sound sub-basins. Data are
from Washington Department of
Fish and Wildlife, Pacific States
Marine Fisheries Commission,
Ogden et al. (2015) and Zimmerman
et al. (2015).
(Chinook, coho and steelhead), which have shown an overall
declining population trend (Fig. 1, left panel; see Appendix
1 for details, available online), in addition to a decline in
marine survival (Zimmerman et al. 2015;Ruffet al. 2017;
Kendall et al. 2017). Central to our approach was specifying
multiple salmon characteristics as modelled network nodes,
namely size, fitness, residence time, abundance and survival.
We used these traits as primary response variables throughout
our analysis. While marine survival (herein ‘survival’) was our
principal variable of interest, we included additional traits to
evaluate the relative impact on metrics of salmon performance.
We included ‘other salmon’ as a model variable, representing
pink, chum and sockeye salmon, because the migration
timing of all Pacific salmon species means that competitive
interactions occur. However, the species represented by
the ‘other salmon’ variable have not experienced the same
negative population trends (Irvine & Ruggerone 2016;see
Fig. 1 and Appendix 1 for details) and are seen as important
to the analysis but different from the focal species. While
the emphasis was on representing the most direct impacts
on the focal salmon traits, we recognize that many of the
model variables (e.g. temperature) could potentially have
direct connections to other model nodes; we have included
these where interactions were important for understanding the
implications for the focal salmon variables or where existing
literature has shown strong connections.
We defined relationships among variables as positive,
negative or null based upon mechanistic understanding of
the Salish Sea system and input from regional experts.
To implement the simulation modelling, we developed a
conceptual digraph using the directed graphing software Dia
(v.0.97.2) to represent the model system and the interactions
between variables. This diagram served as the foundation for
our qualitative modelling.
Simulated networks
We used the QPress package for qualitative network analysis
(Melbourne-Thomas et al. 2012) with custom modifications
inR(RCoreTeam2016) to interpret the conceptual digraph,
construct simulated networks and perform our analyses. The
digraph is interpreted as an interaction matrix, A, where each
directed pairwise interaction is represented as coefficients aij.
Ais treated similarly to a community interaction matrix,
wherein the rate of change of any given node is a continuous
function of all other interacting nodes (Levins 1974; Puccia &
Levins 1985). The interacting components (i.e. model nodes)
are set up as a series of differential equations:
dt =fi(x1,x2,...,xn;c1,c2,...,cm)
where xiis the density of the model component (population)
i,thecvalues are growth parameters and fiis a function
describing the per-capita growth rate of that population
(Raymond et al. 2011). Therefore, the interaction coefficients
aij describe the effect of a change in the level of component jon
the level of component i, as defined by the partial derivative of
fiwithrespecttoNj:aij =fi/∂xjevaluatedattheequilibrium
(Levins 1974;Raymondet al. 2011; Melbourne-Thomas et al.
Given a network model and corresponding interaction
matrix, A, the negative of the inverse community matrix
(A1) yields estimated changes in the equilibrium
abundances of each component xasafunctionofasustained
(press) perturbation of one or more system components
(Puccia & Levins 1985). The QPress analysis package provides
routines for evaluating the impact of a press perturbation
to the system through simulation. For each simulation, a
weight (drawn from a random uniform distribution of 0–
1) was assigned to each linkage (edge). These weights were
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
Salish sea salmon qualitative network model 5
positive or negative depending upon the relationship between
the two endpoints. If the resulting model with all assigned
weights was stable (i.e. converged), the model was accepted.
We simulated the network c. 122 000 times to result in 10 000
stable simulated networks. We then assessed the proportion
of model runs with positive, negative and neutral responses
given changes to particular nodes (see below).
We assessed the sensitivity and robustness of the model.
We experimented with changing both the distribution and
the variance of the weighting scheme, but did not find large
differences in results, so maintained the default weighting
for our analyses. We explored the weights of linkages in
the balanced models to look for anomalies (methods and
results in Appendix 2). Additionally, we calculated distance
to the survival node via pathways from each model variable to
check for the effects of model structure, described network
properties such as connectance and linkage density and
evaluated model behaviour with the sequential addition of
perturbed nodes and a set of ‘cumulative effects’ of both
influential and neutral nodes.
Invoking perturbations
To test hypotheses regarding marine survival, we developed
a priori perturbations to each model node (Table 2). The
direction of the perturbation (increase or decrease) was
based upon our understanding of the system, changes that
have occurred concomitant with declines in salmon marine
survival (since the 1970s) and expected impacts as a result
of anthropogenic change (Appendix 3). We employed several
cumulative effects scenarios and modified existing software
functions to meet our analytical objectives.
First, we perturbed each node individually and observed
outcomes on all other model variables. This allowed for a
simple comparison of impacts on the focal salmon metrics
from each variable and the ability to compare the extent
of the impact to that from any other variable. Second, we
evaluated the relative effects of different groups of drivers
(Table 3). For example, we were interested in food web
effects, so we simultaneously decreased forage fish, increased
marine mammals, decreased piscivorous fish and increased
gelatinous zooplankton – trends that have been observed in
Puget Sound – and observed the impacts on the other model
components. For each driver group, we selected four nodes to
perturb, thereby standardizing the level of change invoked. By
comparing impacts on salmon traits from primary production,
food web, environmental and anthropogenic drivers, we
were able to query the relative impacts of each of these
Finally, we developed scenarios based upon observed
changes within three regions of Puget Sound to see how
well the model reproduced cumulative impacts in terms of
response to the focal salmon metrics, especially survival. The
three regions were: (a) South Sound, with a known decline
in salmon abundance and cumulative impacts including
increased gelatinous zooplankton, nutrients, contaminants
and hatchery production and decreased forage fish abundance;
(b) Hood Canal, which has had relatively stable salmon
abundances, but impacts in terms of oceanography, including
increased stratification and temperature and low dissolved
oxygen; and (c) Central Basin, which has shown a decline
in salmon abundance (relatively less than South Sound), but
with a different suite of cumulative impacts including habitat
loss, contaminant input and decreased primary production
(Table 4). In reality, causes of declining survival are likely
multifaceted, complex and non-linear, and this modelling
exercise allowed us to examine the relative influence of many
factors within one modelling framework.
Our final conceptual model had 33 nodes including salmon
traits and atmospheric, oceanographic, primary production,
food web and anthropogenic drivers (Table 1,Fig. 2,
Appendix 3). There were a total of 150 linkages out of 1089 po-
tential linkages within the model. This gives a network density
or connectance (realized linkages/potential linkages) of 0.138
and a linkage density (average number of linkages/node) of
4.55. Connectance has been linked with network stability
in ecological networks (Dunne et al. 2002b)andinsocial
network theory applied to behavioural ecology (Sih et al.
2009), as well as to resilience in social–ecological systems
(Janssen et al. 2006). The most highly connected nodes were
temperature, diatoms and (salmon) abundance, with a total
of 11 connections each; the other salmon and zooplankton
nodes were both highly connected (ten linkages) and highly
influenced by other variables (ten and nine influencing nodes,
respectively; Table 1). All nodes were a minimum distance
of four nodes or fewer from survival, but the range of
feedback linkages varied greatly, from 1 to 10. Each model
node included a self-regulating feedback in order to better
represent ecological limits and to aid in model convergence;
the exception was survival, which was considered the primary
variable of interest and was not constrained.
To assess whether the proximity of each model node to
survival influenced the outcomes of our analysis (i.e. are nodes
that are more directly connected to survival more likely to
result in stronger outcomes?), we evaluated the proportion of
negative results for survival with the minimum nodal distance
to survival and found no relationship. Both closely connected
nodes (minimum nodal distance of 1) and those more distant
(2 nodes away) resulted in a range of negative responses
(<20% to >95%) with respect to survival. Thus, we do
not believe that the model structure strongly confounded our
The results of the press perturbations to each node showed
that anthropogenic impacts resulted in the most consistent
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
6Sobocinski K. L. et al.
Table 2 Model variables in the
Salish Sea qualitative network
model with the direction of the
press perturbation invoked in the
simulations. The response of
perturbations to each individual
model node on the focal salmon
traits (survival, abundance, fitness,
size and residence) and the other
salmon model group are indicated
by the patterned boxes. The key to
the direction and strength of
responses of the model simulations
is in the lower left portion of the
Drivers Variables
Response variables
Other salmon
Environmental Sunlight ↑
Winter storms
Precipitation ↑
Upwelling ↓
Stratification ↑
River flow
Turbidity ↓
Production Nutrients ↑
Microplankton ↑
Diatoms ↓
Food web Zooplankton ↓
Forage fish
Ichthyoplankton ↓
Other salmon
Piscivorous fish
Piscivorous birds
Marine mammals
Anthropogenic Hatcheries ↑
Harvest ↑
Habitat loss
Carbon dioxide
Global warming
Strong negative effect (>80% of runs negative)
Weak negative effect (60–80% of runs negative)
Neutral (40–60% of runs positive/negative)
Weak positive effect (60–80% of runs positive)
Strong positive effect (>80% of runs positive)
negative responses in salmon traits, specifically survival and
abundance (Table 2, Appendix 4). Here we use consistency of
response to refer to the relative proportion of outcomes that
were positive or negative given a perturbation – a strongly
consistent response was when proportionally more simulated
models (here >80%) had positive or negative responses for
the node of interest, while a neutral response resulted when
the simulated outcomes were equally positive/negative in
Individual perturbations showed that an increase in CO2
resulted in a consistently positive response in survival
and abundance; CO2positively influences diatoms in the
model, with positive effects cascading through the food
web. Conversely, a decrease in diatoms (primary production)
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
Salish sea salmon qualitative network model 7
Table 3 Perturbations and
responses by driver group. Total
nodal distance is the sum of the
nodal distances of each node to the
survival node.
Variable Driver group
Environmental Primary production Food web Anthropogenic
Winter storms
Dissolved oxygen
Nutrients ↑↑
Microbial detritivores
Gelatinous zooplankton
Forage fish
Piscivorous fish
Marine mammals
Habitat loss
Total nodal distance 11 14 8 11
Table 4 Salish Sea sub-basin
analysis with perturbations
invoked and outcomes.
Drivers Perturbations References South
Oceanographic Nutrients Roberts et al.
Stratification Mauger et al.
Dissolved oxygen Roberts et al.
Turbidity PSEMP
Temperature PSEMP
Food web Diatoms PSEMP
Greene et al.
Forage fish Greene et al.
Other salmon Fig. 1,this
Contaminants O’Neill and
West (2009)
Habitat loss Hoekstra et al.
Hatcheries Hoekstra et al.
Response key Responses
ffect (>80%
of runs negative)
(60–80% of runs negative)
Abund ance
Neutral (40
60% of runs
(60–80% of runs positive)
ffect (>80%
of runs positive) Residency
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
8Sobocinski K. L. et al.
Figure 2 Conceptual model of the Salish Sea related to salmon survival. Model variables (shapes) represent food web components (ovals),
physical drivers (rectangles), anthropogenic impacts (diamonds) and salmon traits of interest (triangles). Survival is shown within a hexagon
and was the primary variable of interest.
resulted in a consistently negative response in survival and
abundance, as well as negatives outcomes to zooplankton and
turbidity. A direct perturbation to zooplankton (decrease)
resulted in strongly consistent negative responses in fitness
and size, but less consistently negative results in survival
and abundance. On the other hand, a direct decrease in
turbidity resulted in a consistent positive response in the
individual traits of fitness and size, but slightly negative
responses in survival and abundance, which are population-
level traits. This is despite the fact that turbidity directly
and positively affects survival in the model; this relationship
is a result of the association of turbidity with primary
production and the resulting predation dynamics in the model.
A decrease in the predators (piscivorous fish and birds)
resulted in positive responses in survival and abundance
and more ambiguous impacts on size and fitness. Marine
mammals, which are also known to be predators of salmon,
but have experienced increasing populations (and thus act
as a positive perturbation), had a neutral response on all
salmon response variables. Unexpectedly, increased harvest
had a positive effect on survival; harvest has a direct negative
effect on abundance within the model, but the feedback to
survival is mediated by the food web, specifically forage fish
and zooplankton, which may moderate the harvest impacts
to survival through reduced competition. This same result
indicates that strong feedback mechanisms, like density
dependence, were simulated by the model, highlighting the
importance of complex food web interactions for salmon
The results of the driver group analysis, which evaluated
cumulative impacts from one section of the network, showed
anthropogenic impacts to have predominantly negative effects
on survival, abundance and fitness, with over 85% of the
simulations having negative responses within these model
groups (Fig. 3). For the environmental driver group, most
simulation results were positive for survival and abundance,
but neutral for the other response variables. The primary
production group showed consistently negative results in
survival and abundance, indicating that changes to primary
production can have strong impacts on salmon via the
food web. Interestingly, the food web manipulation yielded
strong negative responses on the salmon individual traits
(size and fitness), but more neutral responses on the
population-level traits (abundance and survival). The other
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
Salish sea salmon qualitative network model 9
Figure 3 Results for the primary
response variables for each of the
four groups of drivers.
Perturbations were according to
Table 3 and, in all four figures,
bars represent the number of runs
resulting in negative (dark bars) or
positive (light bars) responses in
the response variable.
salmon category had a consistently positive response in
both the food web and anthropogenic driver simulations,
indicating that conditions that are less favourable within
the food web for the focal salmon species may not be
detrimental for other species, with diets that tend to be more
planktivorous and generally shorter rearing times within the
Salish Sea.
For the regional differences, where we modelled three
different regions of Puget Sound with respect to salmon
survival, our model replicated the observed trends within
these regions, with strong negative responses in focal salmon
survival, abundance and fitness in both South Sound and
Central Basin. The results for Hood Canal were more weakly
negative for salmon survival and abundance than in the other
regions, and were neutral for fitness and size, suggesting that
some of the oceanographic changes evidenced in Hood Canal
may be less detrimental for salmon. Responses of other salmon
were consistently positive in all three regions, reflecting
observed population trends (Fig. 1). Therefore, although our
model is a generalization of the processes occurring in the
southern portion of the Salish Sea, it does replicate observed
trends in the region.
We recognize that many of the perturbations invoked
within the model are happening concurrently. To evaluate
whether the model would maintain robustness when multiple
interacting factors (e.g. cumulative effects) were invoked, we
sequentially added disturbances to the model and evaluated
the outcomes. When influential individual drivers were
included (from Table 2), the results were strongly negative for
survival and abundance. We compared ten of the strongest-
responding nodes with ten that showed neutral influence
on survival and compared the results (Fig. 4). We observed
that the influential individual nodes resulted in consistently
negative impacts on survival, while the response was neutral
for the weaker suite of disturbances.
Our model showed that a wide variety of drivers had negative
effects on early marine survival of coho salmon, Chinook
salmon and steelhead. The impacts on the other salmon
variable (representing pink, chum and sockeye) were neutral
or positive. With only five drivers (precipitation, river flow,
microbial detritivores, zooplankton and ichthyoplankton)
negatively influencing the other salmon node in our
simulations, the model structure seemed to capture the
reduced impacts on these species within the Salish Sea
(Debertin et al. 2017;Fig. 1). The combination of drivers
having negative effects on attributes of salmon fitness and
survival suggests that a single sector of the network is
insufficient for explaining increased marine mortality and that
feedbacks and complex interactions may both exacerbate and
mediate the effects of individual drivers.
Anthropogenic factors induced negative responses in
salmon traits, especially survival, abundance and fitness. The
factors are both direct (e.g. contaminants and disease) and
mediated by the food web (e.g. hatcheries, with increased
production leading to competitive interactions, and habitat
loss, which has a negative effect on salmon residency and
fitness, but also on forage fish, because nearshore habitat is
critical to forage fish spawning). There are likely indirect
connections that were unaccounted for in our model that may
make these impacts even stronger in the real world. The food
web components individually did not yield strong responses
in salmon survival and abundance, but did impact size and
fitness. Considering that many of these linkages are both
directly and indirectly tied to salmon, the negative outcomes,
even where marginal, should be noted. Additionally, many of
the feedbacks present in the model were through the food web.
In the driver group analysis, the negative impacts of food web
changes on the individual traits of size and fitness are notable.
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
10 Sobocinski K. L. et al.
Figure 4 Comparison of multiple (n=10) strongly influential
(top) and neutral (bottom) variables on salmon survival. The
influential variables (resulting from the analysis presented in
Table 2) were CO2, contaminants, diatoms, disease, gelatinous
zooplankton, habitat loss, hatcheries, other salmon, temperature
and zooplankton. The neutral variables were global warming, forage
fish, ichthyoplankton, marine mammals, microbial detritivores,
nutrients, precipitation, stratification, sunlight and upwelling. The
variables in each set were perturbed simultaneously to simulate
cumulative impacts and to assess model response. Dark bars show
negative impacts on the response variable and light bars show
positive responses.
Our model does not have a temporal component, but negative
impacts on individual traits would likely manifest in survival
and population declines over time.
The conceptual model exhibited particularly strong
sensitivity to changes in key linkages: the effects of fitness upon
size and vice versa, and of survival upon abundance. These
results suggest that processes influencing these factors will
strongly influence marine survival and point to the importance
of monitoring these pathways. Size and fitness (condition)
are relatively easy-to-monitor characteristics measured in
standard salmon sampling programs. The measurement of
size, combined with techniques that can measure growth and
conditions, including the use of chemical-based indicators
of fitness such as fatty acid biomarkers (Hook et al. 2014)
or hormone markers (Beckman 2011), would provide some
indication of how these attributes are changing over time.
However, understanding mechanisms for changes in size and
conditions is more complex. As our model and the existing
literature suggest, several factors contribute to changes in
size over time, with food web alteration being one of the
likely mechanisms. However, these changes are not yet
fully understood in the Salish Sea. Our model allowed for
comparison of multiple factors and showed that a decrease
in primary production had the strongest negative impact
on salmon survival. Additional research on the impacts of
changing primary production in this system and the links
to salmon condition would aid in further teasing apart this
Our response metrics focused on model runs that converged
on an equilibrium (i.e. only balanced models were used
in the perturbation scenarios). The number of model runs
needed (c. 122 000) to get a subset of converged models
(10 000) suggests that most models did not converge to a
stable solution. The ‘real’ Salish Sea is likely represented by
one of the many possible combinations and may in fact be
unstable, not in a ‘converged’ state, as our model assumed.
Such unstable states are predicted outcomes when ecosystems
surpass tipping points (Carpenter & Brock 2006; Samhouri
et al. 2017). Alternatively, the Salish Sea ecosystem may have
reached a new equilibrium: analysis of marine survival trends
indicates a steep decline in the 1970s and 1980s, levelling off at
a low level that has persisted to the present (Zimmerman et al.
2015). Hence, the simulation framework with multiple sets of
initial conditions may have allowed us to detect endpoints that
include a new equilibrium for marine survival of salmon.
The conceptual model underlying the analysis represents a
complex set of feedbacks. Additional interconnections that
we did not represent would tend to stabilize the system
even more (Dunne et al. 2002a; Ives & Carpenter 2007).
And while nonlinear properties are fundamental to ecological
systems, they are nearly always influenced by feedbacks, an
& Waterhouse 1987, Scheffer et al. 2001). The lack of explicit
spatial or temporal components within the model limits our
ability to make predictions beyond a static snapshot. However,
through our comparison of three sub-regions within our
system, we were able to compare different starting conditions
and gauge model responses. While additional complexity in
the temporal component would allow for detecting evidence
of change over time, the lack of comprehensive empirical
data across all ecosystem components currently limits the
tractability of such a modelling approach.
The model results in and of themselves are informative
for comparing among a suite of potential causes of declining
marine survival in salmon and in evaluating the cumulative
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
Salish sea salmon qualitative network model 11
effects of these drivers. Additionally, this conceptual
model forms the foundation for additional research efforts.
Currently, development of multivariable time-series analysis
and an end-to-end ecosystem model (Atlantis) is ongoing via
the Salish Sea Marine Survival Project. The development of
the Atlantis ecosystem model has been aided by the conceptual
underpinnings presented herein. Efforts have been made
to develop data streams where the conceptual model has
shown important linkages (e.g. a sub-project was initiated
using satellite-derived data to better understand variability in
primary production in the system). In this way, the qualitative
network model is foundational for continuing quantitative
modelling work, but is also an important tool for conveying
the complexity of the system and the problem to diverse
Our results suggest that teasing out the causes of declines in
marine survival will be challenging and multifaceted and will
involve both understood and unknown feedbacks. Multiple
singular factors led to declines in most of the simulations,
and suites of ecosystem components had strong effects on
marine survival and other salmon attributes. Nevertheless,
our ability to distinguish causal factors will likely be improved
by tracking multiple ecosystem indicators, especially those
influencing salmon size and fitness. As with any model, ours
is a simplified version of the ecosystem; however, our approach
resulted in a complex representation of declining populations
in an ecosystem context that served as a useful tool for
identifying the relative influences of numerous hypothesized
drivers of marine mortality. Through this examination, we
have identified some sectors of the ecosystem that warrant
further examination, such as the food web and anthropogenic
We thank members of the Salish Sea Marine Survival
Project technical team for input into the conceptual model.
B. Raymond provided assistance with modifying R code.
I. Kaplan, C. Harvey, I. Perry, I. Pearsall, T. Curran
and I. Kemp provided insight into early versions of the
modelling effort and results. A. Dufault and N. Kendall
(Washington Department of Fish and Wildlife) assisted with
data compilation for Fig. 1. The manuscript benefited from
comments provided by I. Kaplan, C. Harvey and R. Zabel
(NOAA – Northwest Fisheries Science Center) and from the
anonymous reviewers.
This work was paid for by the Pacific Salmon Commission’s
Southern Endowment Fund via Long Live the Kings. This is
publication number 12 from the Salish Sea Marine Survival
Project, an international research collaboration designed to
determine the primary factors affecting the survival of juvenile
salmon and steelhead in the Salish Sea.
Supplementary material
To view supplementary material for this article, please visit
Beamish, R.J., Noakes, D.J., McFarlane, G.A., Pinnix, W.,
Sweeting, R. & King, J. (2000) Trends in coho marine survival in
relation to the regime concept. Fisheries Oceanography 9: 114–119.
Beamish, R.J., Sweeting, R.M., Lange, K.L., Noakes, D.J.,
Preikshot, D. & Neville, C.M. (2010) Early marine survival
of coho salmon in the Strait of Georgia declines to very low
levels. Marine and Coastal Fisheries: Dynamics, Management, and
Ecosystem Science 2: 424–439.
Beckman, B.R. (2011) Perspectives on concordant and discordant
relations between insulin-like growth factor 1 (IGF1) and growth
in fishes. General and Comparative Endocrinology 170: 233–252.
Burke, B.J., Peterson, W.T., Beckman, B.R., Morgan, C., Daly, E.A.
& Litz, M. (2013) Multivariate models of adult Pacific salmon
returns. PLoS ONE 8: e54134.
Carey, M.P., Levin, P.S., Townsend, H., Minello, T.J., Sutton,
G.R., Francis, T.B., Harvey, C.J., Toft, J.E., Arkema, K.K.,
Burke, J.L., Kim, C., Guerry, A.D., Plummer, M., Spiridonov,
G. & Ruckelshaus, M. (2014) Characterizing coastal foodwebs
with qualitative links to bridge the gap between the theory and the
practice of ecosystem-based management. ICES Journal of Marine
Science 71: 713–724.
Carpenter, S.R. & Brock, W.A. (2006) Rising variance: A leading
indicator of ecological transition. Ecology Letters 9: 311–318.
Christensen, V. & Walters, C.J. (2004) Ecopath with ecosim:
Methods, capabilities and limitations. Ecological Modelling 172:
DeAngelis, D.L. & Waterhouse, J.C. (1987) Equilibrium and
nonequilibrium concepts in ecological models. Ecological
Monographs 57: 1–21.
Debertin, A.J., Irvine, J.R., Holt, C.A., Oka, G. & Trudel, M.
(2017) Marine growth patterns of southern British Columbia chum
salmon explained by interactions between density-dependent
competition and changing climate. Canadian Journal of Fisheries
and Aquatic Sciences 74: 1077–1087.
Dunne, J.A., Williams, R.J. & Martinez, N.D. (2002a) Network
structure and biodiversity loss in food webs: robustness increases
with connectance. Ecology Letters 5: 558–567.
Dunne, J.A., Williams, R.J. & Martinez, N.D. (2002b). Food-web
structure and network theory: The role of connectance and size.
Proceedings of the National Academy of Sciences of the United States
of America.99: 12917–12922.
Greene, C.M., Kuehne, L., Rice, C.A., Fresh, K.L. & Penttila, D.
(2015) Forty years of change in forage fish and jellyfish abundance
across greater Puget Sound, Washington (USA): Anthropogenic
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
12 Sobocinski K. L. et al.
and climate associations. Marine Ecology Progress Series 525: 153–
Harvey, C.J., Williams, G.D. & Levin, P.S. (2012) Food web
structure and trophic control in central Puget Sound. Estuaries
and Coasts 35: 821–838.
Harvey, C.J., Reum, J.C.P., Poe, M.R., Williams, G.D. & Kim, S.J.
(2016) Using conceptual models and qualitative network models
to advance integrative assessments of marine ecosystems. Coastal
Management 44: 486–503,
Hoekstra, J.M., Bartz, K.K., Ruckelshaus, M.H., Moslemi,
J.M. & Harms, T.K. (2007) Quantitative threat analysis
for management of an imperiled species: Chinook salmon
(Oncorhynchus tshawytscha). Ecological Applications 17: 2061–2073.
Hook, S., Gallagher, E. & Batley, G. (2014) The role of biomarkers
in the assessment of aquatic ecosystem health. Integrated
Environmental Assessment and Monitoring.10: 327–341.
Holmes, E.E. (2001) Estimating risks in declining populations with
poor data. Proceedings of the National Academy of Sciences of the
United States of America 98: 5072–5077.
Irvine, J.R. & Ruggerone, G.T. (2016) Provisional estimates
of numbers and biomass for natural-origin and hatchery-
origin pink, chum, and sockeye salmon in the North Pacific,
1952–2015. NPAFC Doc. 1660. 45 pp. Fisheries and Oceans
Canada, Pacific Biological Station and Natural Resources
Consultants, Inc. [www document]. URL http://www.npafc.
Ives, A.R. & Carpenter, S.R. (2007) Stability and diversity of
ecosystems. Nature 317: 58–62.
Janssen, M.A., Bodin, Ö., Anderies, J.M., Elmqvist, T., Ernstson,
H., McAllister, R.R.J., Olsson, P. & Ryan, P. (2006) A network
perspective on the resilience of social–ecological systems. Ecology
and Society 11: 15.
Johannessen, S.C. & McCarter, B. (2010) Ecosystem status and
trends report for the Strait of Georgia ecozone. Fisheries
and Oceans Canada, Science Advisory Secretariat, Research
Document 2010/010. vi +45 p. [www document]. URL http://
Kendall, N.W., Marston, G.W. & Klungle, M.M. (2017) Declining
patterns of Pacific Northwest steelhead trout (Oncorhynchus
mykiss) adult abundance and smolt survival in the ocean. Canadian
Journal of Fisheries and Aquatic Sciences 74: 1275–1290.
Levins, R. (1974) The qualitative analysis of partially specified
systems. Annals of the New York Academy of Sciences 231: 123–
Liu, J., Dietz, T., Carpenter, S.R., Folke, C., Alberti, M., Redman,
C.L., Schneider, S.H., Ostrom, E., Pell, A.N., Lubchenco, J.,
Taylor, W.W., Ouyang, Z., Deadman, P., Kratz, T. & Provencher,
W. (2007) Coupled human and natural systems. AMBIO 236: 639–
Mauger, G.S., Casola, J.H., Morgan, H.A., Strauch, R.L., Jones,
B., Curry, B., Busch Isaksen, T.M., Whitely Binder, L., Krosby,
M.B. & Snover, A.K. (2015) State of Knowledge: Climate Change
in Puget Sound. Report Prepared for the Puget Sound Partnership
and the National Oceanic and Atmospheric Administration. Seattle,
WA, USA: Climate Impacts Group, University of Washington.
May, R.M. (1974) Stability and Complexity in Model Ecosystems.
Princeton, NJ, USA: Princeton University Press.
Melbourne-Thomas, J., Wotherspoon, S., Raymond, B. &
Constable, A. (2012) Comprehensive evaluation of model
uncertainty in qualitative network analyses. Ecological Monographs
82: 505–519.
Ogden, A.D., Irvine, J.R., English, K.K., Grant, S., Hyatt,
K.D., Godbout, L. & Holt, C.A. (2015) Productivity (recruits-
per-spawner) data for sockeye, pink, and chum salmon from
British Columbia. Canadian Technical Report of Fisheries and
Aquatic Sciences 3130, vi +57 p. [www document]. URL
O’Neill, S.M. & West, J.E. (2009) Marine distribution, life history
traits, and the accumulation of polychlorinated biphenyls in
Chinook salmon from Puget Sound, Washington. Transactions of
the American Fisheries Society 138: 616–632.
Paine, R.T. (1966) Food web complexity and species diversity.
American Naturalist 100: 65–76.
Pearcy, W.G. (1988) Factors affecting survival of coho salmon off
Oregon and Washington. In: Salmon Production, Management, and
Allocation, ed. W.J. McNeil, pp. 67–73. Corvallis, OR, USA:
Oregon State University Press.
Pimm, S.L., Lawton, J.H. & J.E. Cohen (1991) Food web patterns
and their consequences. Nature 350, 669–674.
Preikshot, D.B. (2008) Public Summary – Computer Modelling of
Marine Ecosystems: Applications to Pacific Salmon Management
and Research. Vancouver, BC, Canada: Pacific Fisheries Resource
Conservation Council.
PSEMP (2016) Puget Sound Marine Waters: 2015 Overview. Eds.
S.K. Moore, R. Wold, K. Stark, J. Bos, P. Williams, K.
Dzinbal, C. Krembs and J. Newton [www document]. URL
Puccia, C.J. & Levins, R. (1985) Qualitative Modeling of Complex
Systems: An Introduction to Loop Analysis and Time Averaging.
Cambridge, MA, USA: Harvard University Press.
R Core Team (2016) R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna,
Austria [www document]. URL
Raymond, B., McInnes, J., Dambacher, J.M., Way, S. & Bergstrom,
D.M. (2011) Qualitative modelling of invasive species eradication
on subantarctic Macquarie Island. Journal of Applied Ecology 48:
Reum, J.C.P., Ferriss, B.E., McDonald, P.S., Farrell, D.M., Harvey,
C.J., Klinger, T. & Levin, P.S. (2015) Evaluating community
impacts of ocean acidification using qualitative network models.
Marine Ecology Progress Series 536: 11–24.
Roberts, M., Mohamedali, T., Sackmann, B., Khangaonkar, T. &
Long, W. (2014) Puget Sound and the Straits Dissolved Oxygen
Assessment: Impacts of Current and Future Nitrogen Sources and
Climate Change through 2070. Olympia, WA, USA: Washington
Department of Ecology.
Ruff, C.P., Anderson, J.H., Kemp, I.M., Kendall, N.W., Mchugh, P.
Velez-Espino, A., Greene, C.M., Trudel, M., Holt, C.A., Ryding,
K.E. & Rawson, K. (2017) Salish Sea Chinook salmon exhibit
weaker coherence in early marine survival trends than coastal
populations. Fisheries Oceanography. Epub ahead of print. DOI:
Samhouri, J.F., Andrews, K.S., Fay, G., Harvey, C.J., Hazen,
E.L., Hennessey, S., Holsman, K.K., Hunsicker, M.E., Large,
S.I., Marshall, K., Stier, A.C., Tam, J. & Zador, S.
(2017) Defining ecosystem thresholds for human activities and
environmental pressures in the California Current. Ecosphere 8:
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
Salish sea salmon qualitative network model 13
Scheffer, M., Carpenter, S., Foley, J.A., Folke, C. & Walker, B.
(2001) Catastrophic shifts in ecosystems. Nature 413: 591–596.
Sih, A., Hanser, S.F. & McHugh, K.A. (2009) Social network theory:
New insights and issues for behavioral ecologists. Behavioral
Ecology and Sociobiology 63: 975.
Teo, S.L.H., Botsford, L.W. & Hastings, A. (2009) Spatio-temporal
covariability in coho salmon (Oncorhynchus kisutch) survival, from
California to southwest Alaska. Deep Sea Research Part II: Topical
Studies in Oceanography 56: 2570–2578.
Zimmerman, M., Irvine, J.R., O’Neill, M., Anderson, J.H., Greene,
C.M., Weinheimer, J., Trudel, M. & Rawson, K. (2015) Spatial
and temporal patterns in smolt survival of wild and hatchery coho
salmon in the Salish Sea. Marine and Coastal Fisheries 7: 116–
Downloaded from IP address:, on 12 Dec 2017 at 21:22:56, subject to the Cambridge Core terms of use, available at
... Model runs are performed by implementing a press perturbation (an increase or decrease to one or more model variables) and model outputs are predictions of the subsequent directional change in each other variable. Researchers have used QNMs to investigate the dynamics of multiple social-ecological systems along the U.S. West Coast, including assessments of fisheries in the California Current marine ecosystem (Harvey et al., 2016), ecosystem and anthropogenetic drivers of early marine survival of Pacific salmon in Puget Sound (Sobocinski et al., 2018), community impacts of ocean acidification in Willapa Bay, Washington (Reum et al., 2015a), and community-wide responses to bivalve aquaculture in South Puget Sound (Reum et al., 2015b). These diverse, regional applications highlight the flexibility of QNMs, which are well suited to leverage existing information and guide coastal management decisions and future research priorities. ...
... negatively, or equivocally to the invoked perturbations. Following the approach of Sobocinski et al. (2018), we characterized sign consistency as follows: strong negative (or positive) effects were nodes that responded negatively (or positively) in greater than 80% of the retained simulations, weak negative (positive) effects were nodes that responded negatively (positively) in 60-80% of simulations, and equivocal effects were nodes with 40-60% of simulations that were negative or positive (e.g., nearly equal numbers of negative and positive responses across simulations). We focused primarily on the response of nodes representing 13 biophysical and human wellbeing recovery objectives and 5 common ecosystem stressors (temperature, nutrients, bacteria, toxics, and algal blooms). ...
Full-text available
Population growth and the associated transformation of landscapes is a major management challenge for coastal ecosystems. Coastal conservation and management should be guided by social, cultural, economic, and ecological objectives, but integrative decision support tools appropriate for complex ecosystems remain underutilized. Evaluating alternative policies for objectives that span the interconnected terrestrial, freshwater, and estuarine habitats along coastlines is limited by the lack of appropriate quantitative tools and available data. We employed qualitative network models (QNMs) to evaluate multi-benefit outcomes of potential management interventions to address population growth and development using a case study of Puget Sound-a large, urbanized fjord-type estuary in Washington, U.S.A. With input from regional scientists and stakeholders, we developed a base conceptual model of the links among human stressors and ecosystem components across the terrestrial-freshwater-estuarine gradient of a generalized Puget Sound watershed. We simulated scenarios representing alternative strategies for accommodating human population growth, namely new development outside of urban centers versus redevelopment (densification) within urban centers, and characterized the responses of multiple recovery objectives and ecosystem stressors for each scenario. Of the urban redevelopment scenarios, reducing stormwater runoff and increasing green infrastructure provided the most favorable outcomes. On rural lands, limiting new development to existing transportation corridors and iincreasing floodplain and riparian habitat extent concomitant with new development produced similar outcomes. Moderate levels of coordinated interventions on both urban and rural lands had favorable outcomes for more ecosystem objectives compared to either moderate intervention applied separately. This study demonstrates the value of qualitative tools for cross-habitat evaluations of possible futures in complex ecosystem-based management systems. Magel CL and Francis TB (2022) Evaluating ecosystem-based management alternatives for the Puget Sound, U.S.A. social-ecological system using qualitative watershed models.
... With QNMs, feedbacks and indirect effects implied by the network are incorporated into predictions (Puccia and Levins, 1985;Melbourne-Thomas et al., 2012). QNMs are flexible enough to be applied in a range of systems, and have been applied to fisheries management (Metcalf et al., 2014;DePiper et al., 2017;Martone et al., 2017;Zador et al., 2017), conservation (Sobocinski et al., 2018;Reum et al., 2019), and climate change . QNMs have also been used to characterize shellfish aquaculture, but only with respect to ecological aspects of the system (Reum et al., 2015a;Reum et al., 2015b). ...
... For example, if 83% of the simulations predict that Economic Vitality will increase as Cultured Geoduck increases, then that is interpreted as support for a predicted increase in that variable. We selected a higher minimum level of sign consistency (70%) relative to some other publications (e.g., 60%: Sobocinski et al., 2018) to focus on results with higher prediction certainty according to sign agreement (level of agreement among model simulations in predicting positive, zero, or negative changes in a given variable) (Reum et al., 2019;. ...
Integrating social and ecological aspects of bivalve aquaculture in research and management processes can improve understanding of the system as a whole, and facilitate management decision-making. We created social-ecological conceptual models of Pacific oyster (Crassostrea gigas), Manila clam (Venerupis philippinarum), and Pacific geoduck (Panopea generosa) aquaculture in a USA estuary, which were the basis of qualitative network analysis to compare: (i) social-ecological models versus truncated ecological- and social- only models, and (ii) two geoduck models representing different stakeholder groups’ perspectives on nature-based recreation and environmental stewardship. The social-ecological models predicted different results compared to individual social or ecological models, including for abundance of invertebrates, eelgrass, and marine water quality. The two alternative geoduck models predicted outcomes that varied across multiple social-ecological variables, including the availability of local harvestable food, sense of place, and abundance of invertebrates in structured habitat. Results demonstrate the interconnectedness of the social and ecological components of the aquaculture system, and how predicted outcomes can vary depending on their inclusion in the model. This study also demonstrates the value in considering a suite of models that represents a range of group perspectives to identify areas of conflict and agreement, and to recognize bias inherent in the models.
... The conceptual model constructed was developed based on parameters observed, and presented by a figure explaining the relationship that exists between the parameters. This model is a qualitative model that can be applied to develop a conceptual model framework [20,21]. A conceptual model is a model composed of a composition of concepts that helps to study, understand, describe, and explain the system represented based on the perspective of the modeler [22]. ...
... In this management plan for Waerole and Nusa Telu area, the conceptual model [18,20] combined with tourism suitability index parameters [14,18] and Marxan approach for the marine protected area is used to produce a management strategy for sustainable marine and coastal tourism. Marxan requires input in the form of ecological spatial data, utilization, and management patterns [31]. ...
Full-text available
In the formulation and designation of the ecotourism area, the use of spatial suitability indexes is usually employed. The social, economic, and cultural parameters have almost been ignored in the analysis. These parameters are essential to be included in the development of marine ecotourism to avoid the loss and the impact of these socio-cultural parameters in the development of marine ecotourism. The objective of this research was to construct a conceptual model and thinking perspective and to identify the connectivity of ecology, socio-economy features spatially in designing marine ecotourism areas. Data were collected through observation, participatory mapping, focus group discussion, and interview. Data obtained were then analyzed descriptively and displayed graphically. The conceptual model constructed shows that the marine ecotourism management plan of Waerole and Nuatelu Cape can be categorized into six spatial marine ecotourism explicitly coastal marine e-tourism, snorkeling e-tourism, diving e-tourism, in-shore angling e-tourism, open sea fishing e-tourism, and habitat rehabilitation. The conceptual model for a sustainable management plan for coastal and marine ecotourism suggested the need for holistic and integrated sustainable management comprise of 27 biophysics and socio-economy feature.
... In the vulnerability assessment, Puget Sound stocks were considered less vulnerable than others along the Pacific Coast due to their life-history diversity, extensive use of multiple habitats types, and shorter freshwater migrations (less time in warming rivers) than other populations (Crozier et al. 2019). But coho and Chinook salmon and steelhead trout all show high sensitivity and exposure to the metrics assessed-and all three species have shown population declines (Sobocinski et al. 2018). Furthermore, there has been an overall decline in marine survival over the last 40 years Kendall et al. 2017;Ruff et al. 2017) to levels so low that additional mortality could be devastating to populations. ...
... For integrative species, like salmon and seabirds, that rely on multiple connected habitats for their life histories, cumulative effects must be documented beyond the Salish Sea in its strict sense (i.e., the estuarine waters). Moreover, it is in these very integrative species where differences in abundance (Ethier et al. 2020) and survival Ruff et al. 2017;Sobocinski et al. 2018) within and outside of the Salish Sea occur. These examples both indicate compromised condition and function within the Salish Sea that is having negative effects on biota. ...
Technical Report
Full-text available
This report synthesizes information on past, current, and emerging stressors within the Salish Sea estuarine ecosystem. The Salish Sea is a complex waterbody shared by Coast Salish Tribes and First Nations, Canada, and the United States. It is defined by multiple freshwater inputs and marine water from the Pacific Ocean that mix in two primary basins, Puget Sound and the Strait of Georgia. Human impacts are multifaceted and extensive within the Salish Sea, with a regional population of almost 9 million people. Population growth has driven urbanization and development, which in turn has triggered structural changes to the landscape and seascape. Meanwhile, the growing effects of climate change are fundamentally altering physical and biological processes. The report describes the most pervasive and damaging impacts affecting the transboundary ecosystem, recognizing that some are generated locally while others are the locally realized impacts from global-scale changes in climate, oceans, land use, and biodiversity. The Salish Sea is under relentless pressure from an accelerating convergence of global and local environmental stressors and the cumulative impacts of 150 years of development and alteration of our watersheds and seascape. Some of these impacts are well understood but many remain unknown or are difficult to predict. While strong science is critical to understanding the ecosystem, the report provides a spectrum of ideas and opportunities for how governments, organizations, and individuals can work together to meet the needs of science and science-driven management that will sustain the Salish Sea estuarine ecosystem.
... Population diversity has also been declining (Price et al., 2021;Slaney et al., 1996). Conditions leading to the decline and repressed recovery of Pacific salmon are complex and interacting (Cohen, 2012a;Sobocinski et al., 2018; Figure 1), and in Canada management bodies charged with salmon governance, including recovery initiatives are also responsible for supporting harvest interests. This conflict has contributed to the slow reaction of management bodies to address these pressures (Cohen, 2012b). ...
Full-text available
Pacific salmon (Oncorhynchus spp.) support coastal and freshwater ecosystems, economies and cultures, but many populations have declined. We used priority threat management (PTM), a decision‐support framework for prioritizing conservation investments, to identify management strategies that could support thriving populations of wild salmon over 25 years. We evaluated the potential benefits of 14 strategies spanning fisheries, habitat, pollution, pathogens, hatcheries and predation management dimensions on 19 conservation units (CUs)—genetically and ecologically distinct populations—of the five Pacific salmon species in the lower Fraser River, British Columbia, Canada. The PTM assessment indicated that under the current trajectory of ‘business as usual’, zero CUs were predicted to have >50% chance of thriving in 25 years. Implementation of all management strategies at an annual investment between 45 and 110 million CAD was, however, predicted to achieve >50% chance of thriving for most CUs (n = 16), with nearly half (seven CUs) having a > 60% chance, indicating there is a pathway towards recovery for most populations if we invest now. In fact, substantial gains could be made by investing in five combined habitat strategies, costing 20M CAD annually. These habitat strategies were estimated to bring 14 of 19 salmon CUs above this 50% threshold. Co‐governance between First Nation and provincial and federal Canadian governments to manage salmon populations and harvest, and improved CU‐level monitoring emerged from the expert elicitation as critical ‘enabling’ strategies. By improving the feasibility of different management options, co‐governance brought an additional five CUs above the 60% threshold. Synthesis and applications. Supporting wild salmon in the face of cumulative threats will require strategic investment in effective management strategies, as identified by this priority threat management (PTM) assessment. PTM uses the best available data to objectively assess the potential outcomes of management alternatives. With renewed commitments from provincial and federal Canadian governments to protect and restore salmon populations and their habitats, positive conservation outcomes following implementation of targeted management strategies may be within reach. Supporting wild salmon in the face of cumulative threats will require strategic investment in effective management strategies, as identified by this priority threat management (PTM) assessment. PTM uses the best available data to objectively assess the potential outcomes of management alternatives. With renewed commitments from provincial and federal Canadian governments to protect and restore salmon populations and their habitats, positive conservation outcomes following implementation of targeted management strategies may be within reach.
... Using this approach, which sacrificed precision for generality and realism [35,36], we circumvented many of the uncertainties (e.g., observation error and variable functional responses) that impede quantitative models [37]. Recent applications of qualitative modelling to ecological and environmental systems are found in [38][39][40][41][42]. ...
Full-text available
The conservation of threatened species is prevalently oriented towards two management strategies, i.e. the habitat-level and species-level approaches. The former is focused on the improvement of the conditions of the habitat of a certain species, the latter is aimed to directly strengthen the species of interest. In this work we adopted a different solution based on a community-level approach. Firstly, we individuated the species (predators, competitors, preys) that interact with the species of interest (the lesser kestrel Falco naumanni) in Southern Italy, and mapped all the ecological interactions among these species. Secondly, we built a simulation framework of the entire ecological network of the lesser kestrel. Thirdly, we simulated different management strategies that could lead to increase the lesser kestrel population stock through actions upon the species that interact with it. We found that the lesser kestrel in Southern Italy can be effectively protected by acting upon the interacting species, and that natural changes in the abundance of the interacting species could be used to pro-actively predict the dynamics of the lesser kestrel population. Our study demonstrates that a community-level approach to species conservation is highly appropriate at local scale. Our methodological framework, based on qualitative modeling and what-if scenarios, can be applied in absence of quantitative estimations of population stocks and interaction strengths
... Consequently, the understanding of social and economic aspects is a crucial consideration apart from the bio-ecology of the resources. Inclusion of the socio-economic factor into the ecological factors in fisheries management is crucial to understand the complexity of the fisheries system (Barclay et al., 2017;Sobocinski et al., 2017). A decrease in fish production, including mud crab, can be resulted from several causes like high fishing intensity, unsustainable management, diseases, habitat destruction, etc. ...
Full-text available
The coastal area of Pelita Jaya Bay and Kotania Bay is a semi enclose estuary area having three typical most productive ecosystems i.e. mangrove, seagrasses, and coral reefs with the mangrove ecosystem being the dominant one making this area a productive in fish resources. Local community neighboring this area used mangrove ecosystem for many different purposes, some of it threatening the sustainability oh the ecosystem. The objective of this study was to analyze mangrove forest sustainability and to propose sustainable mangrove forest management. Rapfish analysis was used to analyze mangrove sustainability status. A sustainable management strategy was developed using a conceptual model framework combined with the DPSIR approach. The two most sensitive attributes affecting mangrove sustainability from Leverage analysis were used as the State component from DPSIR. The result shows that overall mangrove forest sustainability was 60% and was considered fair sustain with the ecological dimension having the highest sustainable scale (85.35%) and considered sustain, whilst institutional dimension having the lowest sustainable scale (29.10%) and considered unsustain. The sustainable mangrove management strategy proposed consists of workshops, training, vocational education concerning EAM, as well as replanting degraded mangrove forests, monitoring, surveying, and controlling. The management strategy should be conducted based on a co-management approach.
Full-text available
Atlantic salmon Salmo salar is a socio-economically important anadromous fish species that has suffered synchronous population declines around the North Atlantic over the last five decades. Reduced marine survival has been implicated as a key driver of the declines, yet the relative importance of different stressors causing mortality at sea is not well understood. This review presents a synopsis of the principal stressors impacting Atlantic salmon in estuarine and marine environments. It also applies a semi-quantitative 2-D classification system to assess the relative effects of these stressors on English salmon stocks and their likely development over the next decade. Climate change and predation were identified as the biggest threats at present and over the next decade. Poor water quality and bycatch were classified as relatively high impact stressors, but with a lower likelihood of becoming more prevalent in the future due to available mitigation measures. Other, less influential, stressors included tidal barrages, artificial light at night, impingement in power-station cooling waters and thermal discharges, pile-driving noise pollution, invasive non-native species, electromagnetic fields, salmon mariculture, and tidal lagoons. Salmon fisheries exploitation was not regarded as an important stressor currently because effective exploitation rate controls have been implemented to substantially reduce fishing pressure. Future research priorities include addressing knowledge gaps on expanding stressor impacts from climate change, predation, renewable energy developments, and artificial light at night. Local management actions directed towards improving freshwater and estuarine habitats to maximise ecosystem resilience to stressors and minimise their cumulative impacts are recommended.
Full-text available
Protected Steller sea lions (Eumetopias jubatus) aggregate at Bonneville Dam on the Columbia River and prey upon multiple species of endangered salmon ascending the river. Hazing is a non-lethal activity designed to repel sea lions that includes aversive auditory and physical stimuli to deter animals from an area and has been employed with sea lion-fisheries interactions for more than 40 years but sea lion responses to hazing through time is not well-documented. We observed the behavior of Steller sea lions in periods with and without hazing during two spring Chinook salmon passage seasons to evaluate: (1) what effect hazing had on the number of animals present and their foraging behavior, and (2) whether they habituated to hazing. We found that hazing temporarily reduced the number of Steller sea lions, but only when actively hazed. During hazing, Steller sea lions were more likely to move away from hazers on the dam, decreased their foraging, and increased their time investigating the environment. However, these effects were temporary; their behavior returned to initial observation levels once hazing ceased. Furthermore, their responsiveness to hazing declined throughout the season, indicating habituation and raising concern for the application and long-term efficacy of hazing in managing predation on endangered salmon.
Full-text available
Mud crab Scylla serrata of Kotania Bay and Pelita Jaya Bay of Western Seram District, has been harvested by local fishermen for more than 25 years. The mud crab has high economic value, and there is always a market for this fishery. The economic dependence of the fishermen forces them to harvest this resource extensively. No existing management strategy and extensive exploitation leads to unsustainable conditions of this fishery. With inadequate data condition, the Driver-Pressure-State-Impact-Response (DPSIR) model constructs an ecological, social-economy, and institutional conceptual model framework for sustainable management of this fishery. The driving force (D) in this fishery comes from the local fishers harvesting the mud crab. The two most sensitive attributes that affected mud crab sustainability from Rapfish analysis were used as state-level of DPSIR methodology. The result shows that the most sensitive variables from ecological, socio-economy, and institution were: caught before maturity, mud crab size, consumer attitude towards sustainability, just management, government quality, and monitoring and reporting, respectively. It was concluded that this conceptual model allows a better understanding of how the mud crab S. serrata system works and management actions taken at different system components. This conceptual model framework can be a useful tool to incorporate the participation of stakeholders, managers, and scientists in the process of a sustainable management plan.
Full-text available
Examination of population abundance and survival trends over space and time can guide management and conservation actions with information about the spatial and temporal scale of factors affecting them. Here, we analyzed steelhead trout (anadromous Oncorhynchus mykiss) adult abundance time series from 35 coastal British Columbia and Washington populations along with smolt-to-adult return (smolt survival) time series from 48 populations from Washington, Oregon, and the Keogh River in British Columbia. Over 80% of the populations have declined in abundance since 1980. A multivariate autoregressive statespace model revealed smolt survival four groupings: Washington and Oregon coast, lower Columbia River, Strait of Juan de Fuca, and Puget Sound – Keogh River populations. Declines in smolt survival rates were seen for three of the four groupings. Puget Sound and Keogh River populations have experienced low rates since the early 1990s. Correlations between population pairs’ time series and distance apart illustrated that smolt survival rates were more positively correlated for proximate populations, suggesting that important processes, including those related to ocean survival, occur early in the marine life of steelhead.
Full-text available
The oceans are changing more rapidly than ever before. Unprecedented climatic variability is interacting with unmistakable long-term trends, all against a backdrop of intensifying human activities. What remains unclear, however, is how to evaluate whether conditions have changed sufficiently to pro- voke major responses of species, habitats, and communities. We developed a framework based on multi- model inference to define ecosystem-based thresholds for human and environmental pressures in the California Current marine ecosystem. To demonstrate how to apply the framework, we explored two dec- ades of data using gradient forest and generalized additive model analyses, screening for nonlinearities and potential threshold responses of ecosystem states (n = 9) across environmental (n = 6) and human (n = 10) pressures. These analyses identified the existence of threshold responses of five ecosystem states to four environmental and two human pressures. Both methods agreed on threshold relationships in two cases: (1) the winter copepod anomaly and habitat modification, and (2) sea lion pup production and the summer mode of the Pacific Decadal Oscillation (PDO). Considered collectively, however, these alternative analytical approaches imply that as many as five of the nine ecosystem states may exhibit threshold changes in response to negative PDO values in the summer (copepods, scavengers, groundfish, and marine mammals). This result is consistent with the idea that the influence of the PDO extends across multiple trophic levels, but extends current knowledge by defining the nonlinear nature of these responses. This research provides a new way to interpret changes in the intensities of human and environmental pressures as they relate to the ecological integrity of the California Current ecosystem. These insights can be used to make more informed assessments of when and under what conditions intervention, preparation, and miti- gation may enhance progress toward ecosystem-based management goals.
Full-text available
Identifying factors that influence anadromous Pacific salmon (Oncorhynchus spp.) population dynamics is complicated by their diverse life histories and large geographic range. Over the last several decades, Chinook salmon (O. tshawytscha) populations from coastal areas and the Salish Sea have exhibited substantial variability in abundance. In some cases, populations within the Salish Sea have experienced persistent declines that have not rebounded. We analyzed a time series of early marine survival from 36 hatchery Chinook salmon populations spanning ocean entry years 1980–2008 to quantify spatial and temporal coherence in survival. Overall, we observed higher inter-population variability in survival for Salish Sea populations than non-Salish Sea populations. Annual survival patterns of Salish Sea populations covaried over smaller spatial scales and exhibited less synchrony among proximate populations relative to non-Salish Sea populations. These results were supported by multivariate autoregressive state space (MARSS) models which predominantly identified region-scale differences in survival trends between northern coastal, southern coastal, Strait of Georgia, and Puget Sound population groupings. Furthermore, Dynamic Factor Analysis (DFA) of regional survival trends showed that survival of southern coastal populations was associated with the North Pacific Gyre Oscillation, a large-scale ocean circulation pattern, whereas survival of Salish Sea populations was not. In summary, this study demonstrates that survival patterns in Chinook salmon are likely determined by a complex hierarchy of processes operating across a broad range in spatial and temporal scales, presenting challenges to the management of mixed-stock fisheries.
Full-text available
Thirty-nine years of scale growth measurements from Big Qualicum River chum salmon (Oncorhynchus keta) in southern British Columbia demonstrated that competition and climate variation affect marine growth and age-at-maturity. A longitudinal study design that accounted for correlation among individuals revealed growth at all ages was reduced when the biomass of North American chum, sockeye (Oncorhynchus nerka), and pink salmon (Oncorhynchus gorbuscha) was high. When North Pacific Gyre Oscillation (NPGO) was positive, indicating increased primary productivity, predicted growth increased. Climate variation influenced competition effects. For instance, density-dependent competition effects increased when NPGO became more positive and Pacific Decadal Oscillation became more negative (indicating cool conditions), causing the greatest range in predicted scale size. Chum salmon are likely to exhibit continued reduction in growth at age due to increased ocean temperatures driven by climate change and high aggregat...
Full-text available
The complexity of ecosystem-based management (EBM) of natural resources has given rise to research frameworks such as integrated ecosystem assessments (IEA) that pull together large amounts of diverse information from physical, ecological, and social domains. Conceptual models are valuable tools for assimilating and simplifying this information to convey our understanding of ecosystem structure and functioning. Qualitative network models (QNMs) may allow us to conduct dynamic simulations of conceptual models to explore natural–social relationships, compare management strategies, and identify tradeoffs. We used previously developed QNM methods to perform simulations based on conceptual models of the California Current ecosystem's pelagic communities and related human activities and values. Assumptions about community structure and trophic interactions influenced the outcomes of the QNMs. In simulations where we applied unfavorable environmental conditions for production of salmon (Oncorhynchus spp.), intensive management actions only modestly mitigated declines experienced by salmon, but strongly constrained human activities. Moreover, the management actions had little effect on a human wellbeing attribute, sense of place. Sense of place was most strongly affected by a relatively small subset of all possible pair-wise interactions, although the relative influence of individual pair-wise interactions on sense of place grew more uniform as management actions were added, making it more difficult to trace effective management actions via specific mechanistic pathways. Future work will explore the importance of changing conceptual models and QNMs to represent management questions at finer spatial and temporal scales, and also examine finer representation of key ecological and social components.
Full-text available
There was a synchronous and significant decrease in marine survival of coho salmon in the Strait of Georgia, Puget Sound, and off the coast from California to Washington after 1989. This large-scale, synchronous change indicates that trends in coho marine survivals were linked over the southern area of their distribution in the north-east Pacific, and that these linkages were associated with a common event. Indicators of large-scale climate change (the Aleutian Low Pressure Index) and of recent regional climate change (the April flows from the Fraser River) also changed abruptly about the same time. The synchrony of trends in marine survival of aggregates of coho stocks from three distinct marine areas and trends in climate indices implies that climate/ocean changes can have profound impacts on the population dynamics of coho salmon. The trend towards low marine survival may persist as long as the trends in the climate indicators do not change.
Full-text available
This report provides provisional abundance estimates for pink, chum, and sockeye salmon in major regions of the North Pacific from 1952 through 2015 in terms of: numbers of natural origin and hatchery-origin salmon returns (i.e., catch plus escapement), numbers and biomass (metric tonnes) of total returns (natural-origin and hatchery-origin), as well as biomass expanded to include immature salmon remaining at sea. Estimates in this report update and replace those published previously. Data quality and methodology, which vary among regions and years, are briefly discussed. Temporal abundance patterns generally follow commercial catch patterns documented elsewhere. Results suggest that the proportion of hatchery-origin chum salmon abundance peaked in the late 1990’s at ~70%, and is currently ~45%. Hatchery-origin pink and sockeye salmon currently constitute ~19% and ~4% of the total returns for these species, respectively. Total adult abundance and biomass peaked in 2009 (910 million pink, chum, and sockeye salmon; 1.7 million metric tonnes); when immature salmon were included, total biomass exceeded 5 million metric tonnes in 2009 and again in 2013. We encourage experts within NPAFC member nations to examine these data and the methods used to generate them with a view to generating revised estimates. In the future, it would be useful to report on these data types annually and provide data in a publicly accessible website.