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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
KATHRYN L. SOBOCINSKI∗1,2, CORREIGH M. GREENE1AND MICHAEL W. SCHMIDT2
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
SUMMARY
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,
qualitative
INTRODUCTION
Problems of complex interactions are common in many fields,
including medicine, economics and ecology (Levins 1974). In
∗Correspondence: Dr Kathryn L. Sobocinski email kathryn.
sobocinski@noaa.gov
Supplementary material can be found online at https://doi.org/
10.1017/S0376892917000509
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
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2Sobocinski K. L. et al.
Survival Project, http://marinesurvivalproject.com/)and
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
change.
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
forunderstandingasystemofcomplexinteractionsthatarenot
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
mortalityinagroupofPacificsalmonintheSalishSea.Usinga
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.
METHODS
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, http://marinesurvivalproject.com/the-
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
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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.)
Microbial
detritivores
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,
amphipods)
Gelatinous
zooplankton
6 5 3 Zooplankton including ctenophores, medusae and
salps
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.
pharmaceuticals)
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
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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:
dx
i
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.
2012).
Given a network model and corresponding interaction
matrix, A, the negative of the inverse community matrix
(−A−1) 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
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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
groups.
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.
RESULTS
Model
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
results.
Perturbations
The results of the press perturbations to each node showed
that anthropogenic impacts resulted in the most consistent
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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
table.
Drivers Variables
Invoked
perturbation
Response variables
Survival
Abundance
Fitness
Size
Residence
Other salmon
Environmental Sunlight ↑
Winter storms ↑
Precipitation ↑
Upwelling ↓
Stratification ↑
Temperature ↑
River flow ↑
Turbidity ↓
Dissolved
oxygen ↓
Production Nutrients ↑
Microplankton ↑
Microbial
detritivores ↑
Diatoms ↓
Food web Zooplankton ↓
Gelatinous
zooplankton ↑
Forage fish ↓
Ichthyoplankton ↓
Other salmon ↑
Piscivorous fish ↓
Piscivorous birds ↓
Marine mammals ↑
Anthropogenic Hatcheries ↑
Harvest ↑
Habitat loss ↑
Carbon dioxide ↑
Global warming ↑
Contaminants ↑
Disease ↑
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
outcome.
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)
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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 ↑
Precipitation ↑
Temperature ↑
Dissolved oxygen ↓
Nutrients ↑↑
Microplankton ↑
Microbial detritivores ↑
Diatoms ↓
Gelatinous zooplankton ↑
Forage fish ↓
Piscivorous fish ↓
Marine mammals ↑
Hatcheries ↑
Habitat loss ↑
Contaminants ↑
Total nodal distance 11 14 8 11
Table 4 Salish Sea sub-basin
analysis with perturbations
invoked and outcomes.
Drivers Perturbations References South
Sound
Hood
Canal
Central
Basin
Oceanographic Nutrients Roberts et al.
(2014)
↑
Stratification Mauger et al.
(2015)
↑
Dissolved oxygen Roberts et al.
(2014)
↓
Turbidity PSEMP
(2016)
↓
Temperature PSEMP
(2016)
↑
Food web Diatoms PSEMP
(2016)
↓
Gelatinous
zooplankton
Greene et al.
(2015)
↑↑
Forage fish Greene et al.
(2015)
↓↓
Other salmon Fig. 1,this
paper
↑
Anthropogenic
impacts
Contaminants O’Neill and
West (2009)
↑↑
Habitat loss Hoekstra et al.
(2007)
↑
Hatcheries Hoekstra et al.
(2007)
↑
Response key Responses
South
Sound
Hood
Canal
Central
Basin
Strong
negative
e
ffect (>80%
of runs negative)
Survival
Weak
negative
e
ffect
(60–80% of runs negative)
Abund ance
Neutral (40
–
60% of runs
positive/negative)
Fitness
Weak
positive
e
ffect
(60–80% of runs positive)
Size
Strong
positive
e
ffect (>80%
of runs positive) Residency
Other
salmon
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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
survival.
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
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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.
DISCUSSION
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.
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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
relationship.
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
essentialcomponentbuiltintoourmodelstructure(DeAngelis
& 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
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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
audiences.
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
impacts.
ACKNOWLEDGEMENTS
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.
FINANCIAL SUPPORT
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.
CONFLICT OF INTEREST
None.
ETHICAL STANDARDS
None.
Supplementary material
To view supplementary material for this article, please visit
https://doi.org/10.1017/S0376892917000509
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