Ecological Applications, 0(0), 2017, pp. 1–15
© 2017 by the Ecological Society of America

Roads to ruin: conservation threats to a sentinel species
across an urban gradient
BLAKE E. FEIST,1,5 ERIC R. BUHLE,2 DAVID H. BALDWIN,3 JULANN A. SPROMBERG,3 STEVEN E. DAMM,4
JAY W. DAVIS,4 AND NATHANIEL L. SCHOLZ3
1

Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA,
2725 Montlake Boulevard East, Seattle, Washington 98112 USA
2
Quantitative Consultants, Inc., Under contract to Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA,
2725 Montlake Boulevard East, Seattle, Washington 98112 USA
3
Environmental and Fisheries Sciences Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA,
2725 Montlake Boulevard East, Seattle, Washington 98112 USA
4
Washington Fish and Wildlife Office, United States Fish and Wildlife Service, 510 Desmond Drive SE,
Lacey, Washington 98392 USA

Abstract. Urbanization poses a global challenge to species conservation. This is primarily
understood in terms of physical habitat loss, as agricultural and forested lands are replaced with
urban infrastructure. However, aquatic habitats are also chemically degraded by urban development, often in the form of toxic stormwater runoff. Here we assess threats of urbanization to
coho salmon throughout developed areas of the Puget Sound Basin in Washington, USA. Puget
Sound coho are a sentinel species for freshwater communities and also a species of concern
under the U.S. Endangered Species Act. Previous studies have demonstrated that stormwater
runoff is unusually lethal to adult coho that return to spawn each year in urban watersheds. To
further explore the relationship between land use and recurrent coho die-offs, we measured mortality rates in field surveys of 51 spawning sites across an urban gradient. We then used spatial
analyses to measure landscape attributes (land use and land cover, human population density,
roadways, traffic intensity, etc.) and climatic variables (annual summer and fall precipitation)
associated with each site. Structural equation modeling revealed a latent urbanization gradient
that was associated with road density and traffic intensity, among other variables, and positively
related to coho mortality. Across years within sites, mortality increased with summer and fall
precipitation, but the effect of rainfall was strongest in the least developed areas and was essentially neutral in the most urbanized streams. We used the best-supported structural equation model to generate a predictive mortality risk map for the entire Puget Sound Basin. This
map indicates an ongoing and widespread loss of spawners across much of the Puget Sound population segment, particularly within the major regional north-south corridor for transportation
and development. Our findings identify current and future urbanization-related threats to wild
coho, and show where green infrastructure and similar clean water strategies could prove most
useful for promoting species conservation and recovery.
Key words: Bayesian; ecotoxicology; Pacific salmon; restoration; Stan; stormwater; structural equation modeling; urbanization.

physical, biological, and chemical forms of habitat
degradation collectively reduce the diversity and abundance of aquatic species (Paul and Meyer 2001, Walsh
et al. 2005, Bernhardt and Palmer 2007, Grimm et al.
2008, Schueler et al. 2009, Pickett et al. 2011, Canessa
and Parris 2013). The relative role of degraded water
quality in the syndrome is poorly understood, in part
because hundreds or even thousands of distinct chemical
contaminants in urban stormwater runoff have never
been toxicologically characterized.
In northwestern North America, Pacific salmon
(Oncorhynchus spp.) are iconic in terms of their cultural,
economic, and ecological significance. They are central
to the identity and traditional practices of indigenous
peoples, vital for recreational and commercial fisheries,
and keystone species (Willson and Halupka 1995,

INTRODUCTION
River networks in landscapes throughout the world
are under increasing pressures from urban and suburban
development. The global human population has more
than doubled over the past 50 yr, intensifying upward
and outward growth in urban areas (Seto et al. 2012,
Frolking et al. 2013, Barrag an and de Andr es 2015).
Increasing imperviousness is a consistent but complex
driver for biological decline in aquatic habitats. This is
classically termed the urban stream syndrome, wherein
Manuscript received 14 March 2017; revised 1 August 2017;
accepted 3 August 2017. Corresponding Editor: David S.
Schimel.
5
E-mail: blake.feist@noaa.gov
1

 2

BLAKE E. FEIST ET AL.

Kaeriyama et al. 2012, LeRoy et al. 2015) for inland
ecosystems as sources of marine-derived nutrients
(Naiman et al. 2002, Helfield and Naiman 2006). Coho
salmon (O. kisutch) in particular are a prominent sentinel species for the urban stream syndrome (Scholz
et al. 2011) and the impetus for emerging green infrastructure methods for filtering pollutants to improve
water quality in salmon spawning and rearing habitats
(McIntyre et al. 2015, Spromberg et al. 2016). Adult
coho return from the ocean to spawn in basins that span
many of the largest metropolitan areas in the Pacific
Northwest and Canada (e.g., Portland, Oregon; Seattle,
Washington; Vancouver, British Columbia). Juveniles
typically spend ~1.5 yr in freshwater before migrating
seaward to the estuary. During freshwater residency the
adults, embryos, and juveniles depend upon the small,
first- through third-order streams that are the most
vulnerable to land use change (Allan 2004).
Adult coho salmon are exceptionally sensitive to the
harmful effects of toxic urban runoff. Field surveys
spanning more than a decade have shown very high rates
of mortality in urban streams from the central Puget
Sound Basin (Scholz et al. 2011). Affected adult males
and gravid females become disoriented and show surface
swimming, gaping, a loss of equilibrium, and finally
death on a timescale of a few hours. Extensive forensic
research has ruled out stream temperature, dissolved
oxygen, spawner condition, tissue pathology, pathogens
or disease, and other factors commonly associated with
fish kills in freshwater habitats (Scholz et al. 2011),
which suggests that toxicants found in stormwater runoff are the most likely culprit. Consistent with this, direct
exposures to untreated urban stormwater reproduce the
mortality syndrome in adult coho, and this toxicity is
prevented by pre-treatment with bioinfiltration to
remove chemical contaminants (Spromberg et al. 2016).
Loss rates to die-offs are typically high, e.g., 60–90% of
an entire fall run within a given urban stream. Initial
modeling has shown that wild Puget Sound coho, presently a species of concern under the U.S. Endangered
Species Act, cannot maintain population abundances at
such high mortality rates (Spromberg and Scholz 2011).
Mortality corresponds with urbanization within a basin,
and many restoration projects in lowland streams have
the potential to become ecological traps (Feist et al.
2011). These threats to the Puget Sound coho evolutionarily significant unit (ESU; defined as a group of populations that (1) are substantially reproductively isolated
from conspecific populations and (2) collectively represent an important component in the evolutionary legacy
of the species [Waples 1991]) can be expected to increase
in the years ahead with expanding regional human population growth and development.
To date, our understanding of the association between
coho die-offs and imperviousness at the landscape scale
was derived from a spatial analysis of a few (n = 6) highly
urbanized streams and one non-urban stream (Feist
et al. 2011). The previous analysis was geographically

Ecological Applications
Vol. 0, No. 0

restricted, did not consider possible interactions between
landscape and climate, and did not include a full probabilistic accounting of uncertainty in predictions (Clark
2005). Here we present an expanded analysis based on
field surveys of 51 distinct coho salmon spawning
reaches across a gradient of urbanization in the Puget
Sound basin.
Lack of independence among potential predictor variables (i.e., multicollinearity) is a characteristic of many
ecological data sets and presents challenges for modeling
and causal inference (Dormann et al. 2013). For example, landscape attributes such as impervious surfaces,
road and traffic density, and human population are
expected to covary at relevant scales because they are all
indicators of the underlying process of urban development. Given this challenge, one approach is to use model
selection (Burnham and Anderson 2002) or shrinkage
priors (Carvalho et al. 2012, Hooten and Hobbs 2015)
to identify a “sparse” model that includes only relevant
predictors, with the coefficients of other nuisance variables either fixed at zero or shrunk to small values to
avoid overfitting the data. We do not pursue these
approaches here because the structural, and often severe,
correlations among the landscape variables of interest
make it dubious a priori that such data can reveal a distinct and specific cause for coho die-offs.
Instead, we seek to reduce the high-dimensional,
unwieldy predictor variable space by taking advantage
of the partially redundant signal in the raw landscape
attributes to construct one or more composite indicators
that are calibrated to predict mortality risk. A common
approach to this problem is to use principal component
analysis (PCA) or other ordination procedures to combine the correlated predictor variables into orthogonal
axes, and then to use the dominant PCs as inputs in a
regression model (Dormann et al. 2013). The axes found
by such unsupervised dimension reduction techniques may
be optimal in some sense for uncovering the structure of
the predictor space, but they are not tuned to optimally
predict the response variable of interest (Jolliffe 1982).
By contrast, supervised dimension reduction simultaneously finds the hidden structure among a suite of predictor variables and models the relationship between the
derived trends or gradients and the response (Yu et al.
2006, G€
onen 2013). Structural equation modeling
(SEM; Grace et al. 2010) is a natural framework for
supervised dimension reduction, as it allows the specification of flexible, general causal network models that
can include latent variables representing underlying factors that correspond to multiple observed indicators.
Embedding SEM within a Bayesian inference paradigm
(Lee and Song 2012) permits formal accounting of various sources of stochasticity, including hierarchical structure (e.g., observations grouped by site).
Here we use Bayesian SEM to distill a highdimensional suite of correlated landscape attributes
into a single latent indicator that is designed to predict
the observed rates of coho spawner mortality, in

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SENTINEL SPECIES URBANIZATION THREATS

conjunction with seasonal rainfall. Our goals were twofold: (1) refine our understanding of which types of
human activities are most closely associated with the
recurrent mortality phenomenon and (2) identify areas
where urban stormwater mitigation is most needed to
benefit ongoing coho conservation efforts. Our approach
efficiently identified underlying signals of land-use and
rainfall effects on salmon mortality, given noisy observations of multiple imperfect and partially redundant
proxy variables. The improved, spatially explicit forecasts of coho mortality hotspots incorporate rigorous,
straightforward propagation of uncertainty. Our findings are discussed in the context of motor vehicles as a
primary driver for coho die-offs.
MATERIALS AND METHODS

3

georeferenced onsite using handheld GPS units, exported
to ArcGIS, and overlaid on a fine-grained stream network
(NHDPlus 2012b). The network was then manually
clipped to these upstream and downstream points to create a stream reach for each site. These reach segments
were used to delineate the stream subbasin associated
with each of the 51 study sites by combining and editing
polygons from an existing geospatial data layer
(NHDPlus 2012a), and by modifying these polygons as
necessary using various digital elevation models (DEM)
as a topography guide (PSLC 2010, USGS 2014). We then
intersected the corresponding subbasin boundary for
each of the 51 sites with each of the aforementioned
geospatial data layers. We processed each of the data layers using a variety of approaches, which are described in
detail in Appendix S1: Section S1.

Field surveys for premature coho spawner mortality

Multilevel structural equation model

Overall rates of mortality across a fall spawning season (generally October–December) were enumerated as
the proportion of egg-retaining female carcasses identified during field surveys of freshwater spawning habitats
(Scholz et al. 2011). Males and females are equally
affected by the mortality phenomenon, but the prespawning condition is more reliably diagnosed in the latter. Carcasses with evident signs of predation (e.g., from
river otters) were not included in mortality counts. For
the present study, trained fisheries biologists from
NOAA Fisheries, the U.S. Fish and Wildlife Service, the
Wild Fish Conservancy, and the Suquamish and Stillaguamish Tribes (Appendix S1: Table S1) surveyed 51
distinct spawning reaches using established protocols
from 2000 to 2011 (Scholz et al. 2011). The spawning
locations spanned a gradient of urbanization in Puget
Sound (Fig. 1). Certain streams were surveyed for only a
single spawning season, while others were surveyed
across multiple years (Appendix S1: Table S1).

We used structural equation modeling to relate landscape attributes and seasonal rainfall to observed rates
of coho spawner mortality. A structural equation model
(SEM) is a network model that represents hypothesized
pathways linking observed and unobserved (latent) variables (Grace et al. 2010, 2012, Lee and Song 2012). Variables are linked by regression-like relationships, and the
data (observed variables) are used to estimate the
parameters of these relationships along with the values
of the latent variables. The structure of our SEM can be
interpreted heuristically as a factor analysis for the landscape variables, coupled to a generalized linear mixedeffects model (GLMM; Bolker et al. 2009) for spawner
mortality (Fig. 2). The climatic variables (total summer
and fall precipitation) were data-level predictors of mortality risk, while the latent factors representing the
underlying landscape conditions in each subbasin were
predictors in the group-level (i.e., subbasin-level) model
for the regression coefficients.

Geospatial data layers

Factor analysis for landscape indicators

Geospatial data layers were obtained from the U.S.
Census Bureau, the U.S. Homeland Security Infrastructure Program, NOAA’s Coastal Change Analysis Program, the U.S. Geological Survey’s National Land
Cover Database, and other public sources (Appendix S1:
Table S2). The layers included precipitation, land use
and land cover, imperviousness, roadways, human population density, and the extent of NOAA-documented
physical habitat restoration within a given stream basin.

The factor-analytic component of the model projects
the high-dimensional space of D mutually correlated landscape variables (x1, . . ., xD) onto a lower-dimensional subspace of L latent factors (z1, . . ., zL) that are mutually
independent by construction. In the simplest case, each x
is conditionally normally distributed, such that
xsj ¼ a0j þ

L
X
l¼1

Spatial analyses
We used Esri ArcGIS software suite (v. 10.1 Redlands,
CA, USA), Esri ArcView (v. 3.2a), and QGIS (v. 2.84
Open Source Geospatial Foundation, Beaverton, OR,
USA) for all spatial analyses. The upstream and downstream locations of each coho mortality survey site were

zsl   Nð0; 1Þ
 
 
esj   N 0; rj ;

ajl zsl þ esj
(1)

where xsj, the observed value of variable j in subbasin
s 2 {1,. . ., S}, is represented as a regression on the latent
factors with intercept a0j and slopes (i.e., factor loadings)
ajl for j 2 { 1,. . ., D} and l 2 { 1,. . ., L}, and a residual

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BLAKE E. FEIST ET AL.

FIG. 1. Study region and location of site subbasins within the Salish Sea basin (inset map gray region). (1) Barker, (2) Big Scandia, (3) Blackjack, (4) Bosworth, (5) Canyon, (6) Catherine, (7) Cherry, (8) Chico, (9) Church, (10) Clear WF, (11) Cool, (12) Curley,
(13) Curley Tributary, (14) Des Moines, (15) Dickerson, (16) Dogfish, (17) Dogfish NF, (18) Dry, (19) Dubuque, (20) E.F. Griffin,
(21) Eager Beaver, (22) Fauntleroy, (23) Fish, (24) Fortson, (25) Gorst, (26) Gorst Tributary, (27) Grizzly, (28) Happy Hollow, (29)
Harris, (30) Harris Tributary B, (31) Harris Tributary C, (32) Harris Tributary D, (33) Index, (34) Jarstad, (35) Johnson, (36) Lake,
(37) Lewis, (38) Longfellow, (39) Lost, (40) MF Quilceda, (41) Parish, (42) People’s, (43) Pipers, (44) Pond, (45) Ross, (46) Son of
Deer, (47) Thornton, (48) Valhalla, (49) Weiss, (50) Wildcat, (51) Wildcat Tributary.

error with standard deviation rj. For this model to be
identifiable, L must be less than D/2 (Geweke and Zhou
1996). Identification constraints are discussed further in
Appendix S1: Section S2.
This framework can be extended to non-normally distributed data using a formulation analogous to generalized linear models (GLMs; Yu et al. 2006). Specifically,
a link function g(l) is used to transform the mean l,
which is then described by a linear regression on the link
scale

L
X
   
g lsj ¼ a0j þ
ajl zsl
l¼1

zsl   N ð0; 1Þ
       
xsj   fj g 1 lsj ; /j ;

(2)

where the intercepts and factor loadings are as in Eq. 1,
but the observation xsj has an exponential-family probability distribution with mean g 1(lsj) and a family-specific dispersion parameter /j.

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SENTINEL SPECIES URBANIZATION THREATS

5

seasonal precipitation to the observed frequencies of coho
mortality. This takes the familiar form of a GLMM or
multilevel regression model with a binomial error distribution and logit link function (Bolker et al. 2009). The
“full” data-level model for an observation yis, the number
of prematurely dead female coho out of nis total female
spawners sampled in subbasin s in year i, is
yis   Binðnis ; pis Þ
ðsÞ

ðsÞ

ðsÞ

logitðpis Þ ¼ b0 þ b1 pptsu;is þ b2 pptfa;is þ dis

(3)

where the logit-linear model for mortality risk pis
includes effects of summer and fall precipitation and a
residual error term, dis ~ N(0,rd), that accounts for
overdispersion relative to the binomial distribution. We
found strong support for the overdispersion term (see
Model selection), and therefore included it in subsequent
stages of model development.
The second hierarchical level of the GLMM consists
of linear models for one or more of the subbasin-specific
coefficients in Eq. 3. The most general formulation is a
varying-intercept, varying-slopes model
ðsÞ

b0 ¼ c00 þ
FIG. 2. Structural equation model (SEM) linking land use/
land cover and climate to coho salmon pre-spawn mortality. The
model can be interpreted as a factor analysis for landscape attributes coupled to a generalized linear mixed-effects model
(GLMM) for mortality. Observed variables are in rectangles, latent
variables and random effects are in circles, and variables without
shapes represent stochastic error terms. Arrows pointing from predictor to response variables represent functional relationships
parameterized by coefficients shown beside each arrow. (Note that
c0L, c1L, and c2L are omitted for clarity.) Arrows pointing from
variables to other arrows indicate that the variable is a coefficient
for a functional relationship. See Factor analysis for landscape indicators and Generalized linear mixed-effects model for pre-spawn
mortality for variable and parameter definitions. In the final model,
L = 1 and we refer to z1 = z as a latent “urbanization gradient.”

Our analysis included a combination of normal and nonnormal landscape variables. For proportional composition
data types (e.g., land cover classes and impervious surface
cover), we used logistic normal distributions (Aitchison
2003) after bounding the values away from 0 or 1 by a small
increment (10 4). All other landscape attributes are nonnegative continuous (or approximately continuous in the
case of restoration site and human population density) variables with typically highly skewed distributions and many
zero observations. We bounded these variables away from
0, scaled them to have unit variance, and then modeled
them as in Eq. 2 using gamma distributions parameterized
by the mean (via a log link function) and the shape /j.

L
X

c0l zsl þ v0s

l¼1
ðsÞ

b1 ¼ c10 þ

L
X

c1l zsl þ v1s

(4)

l¼1
ðsÞ

b2 ¼ c20 þ

L
X

c2l zsl þ v2s ;

l¼1

where the hyper-parameters for each logistic regression
coefficient bk include an intercept ck0, slopes ckl that
define how the coefficient varies among subbasins as a
function of each of the L latent landscape factors zl, and
a hyper-variance for the subbasin-level random effect
mks   Nð0; rbk Þ. For simplicity, we assume the random
effects v0s, v1s, and v2s are uncorrelated. By including
subbasin-level effects, we account for potential nonindependence among repeated mortality observations at the
same sites, thus avoiding pseudoreplication. This also
allows for the possibility that different spawning habitats
may have different relationships between seasonal rainfall and coho mortality, and that the underlying landscape conditions may explain some of these differences.
From the data-level perspective, the slopes c0l for the
intercept b0 can be interpreted as the “main effects” of
landscape factors on mortality risk, while the slopes c1l
and c2l represent interactions between precipitation and
landscape variables because they modify the precipitation coefficients b1 and b2, respectively.

Generalized linear mixed-effects model for
pre-spawn mortality

Parameter estimation

The second component of the SEM (Fig. 2) is a regression model that relates the latent landscape factors and

The SEM was fit to the landscape and coho mortality
data using a Bayesian framework (Lee and Song 2012).

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We used vague or noninformative priors for most hyperparameters (see Appendix S1: Section S2 for details on the
construction of the joint posterior distribution). Samples
were drawn from the joint posterior distribution using
Hamiltonian Monte Carlo (HMC) tuned by the no-Uturn sampler (NUTS; Hoffman and Gelman 2011) implemented in Stan version 2.12.0 (Carpenter et al. 2016) via
the rstan package in R version 3.3.1 (R Development
Core Team 2016). HMC/NUTS is a Markov chain Monte
Carlo (MCMC) algorithm that efficiently generates
proposals with low autocorrelation and is well suited for
complex, high-dimensional, posterior geometries.
Model selection
We used model selection approaches to compare the
strength of evidence for restricted models or special cases
of the general SEM. In principle, this strategy can be used
to identify the dimension L of the latent factor space
most consistent with the data (Lopes and West 2004). We
found, however, that models with L > 1 had pathologies
such as nontrivial posterior multimodality, strongly suggesting that higher-dimensional factor spaces are inconsistent with the underlying correlation structure of the
landscape variables (Erosheva and Curtis 2011). Our
subsequent analyses were therefore restricted to singlefactor models, focusing on the effects of the latent factor
z (henceforth “urbanization”) and seasonal rainfall on
coho mortality risk in the GLMM-like component of the
model. Specifically, we evaluated 18 candidate models
constructed by setting various combinations of the regression coefficients bk in the data-level model for coho

mortality (Eq. 3) and the coefficients ckl in the subbasinlevel model (Eq. 4) equal to zero (Table 1). Each coeffiðsÞ
cient bk could be either (1) fixed at zero for all subbasins
ðsÞ
s (except for the intercept b0 , which was always estimated); (2) estimated with a hyper-mean ck0 and hyperSD rbk but without any effect of urbanization; or (3) estimated and allowed to vary in response to urbanization
with slope ck1. From the data-level perspective, comparing (1) vs. (2) tests the effect of rainfall on mortality risk,
while comparing (2) vs. (3) tests the effect of urbanization
ðsÞ
(in the case of b0 ) or a rainfall by urbanization interacðsÞ
ðsÞ
tion (in the case of b1 and b2 ).
We used multiple, complementary model-selection
approaches (Hooten and Hobbs 2015). Information criteria were used to estimate out-of-sample predictive performance by penalizing the within-sample fit for optimism
(i.e., overfitting). We calculated the Watanabe-Akaike
information criterion (WAIC) and approximate leaveone-out cross-validation score (PSIS-LOO) for each of
the 18 candidate models (Vehtari and Ojanen 2012, Vehtari et al. 2015). WAIC is a Bayesian generalization of the
familiar Akaike information criterion, while PSIS-LOO
approximates the leave-one-out posterior predictive density (i.e., the probability of an observation, conditioned
on all the other observations in the sample) by Paretosmoothed importance resampling (Vehtari et al. 2015).
Because our focus is on predicting coho mortality risk,
we calculated WAIC and PSIS-LOO from just the posterior predictive distribution of the mortality frequency
data (i.e., the last line in Eq. A5) after marginalizing out
the data-level random effects (the next-to-last line in Eq.
A5) by Monte Carlo integration.

TABLE 1. Structural equation model selection based on two Bayesian information criteria (W the Watanabe-Akaike information
criterion [WAIC] and approximate leave-one-out cross-validation score [PSIS-LOO]).
Intercept (b0)
1
z
z
z
1
z
1
1
z
1
z
1
z
1
z
1
z
1

Summer rain (b1)

Fall rain (b2)

 
D

pWAIC

DWAIC

pPSIS-LOO

DPSIS-LOO

z
z
z
1
1
z
z
z
1
1
0
0
0
0
1
1
0
0

0
1
0
0
0
z
1
z
1
1
1
1
z
z
z
z
0
0

437.21
431.33
440.66
443.48
441.46
433.30
432.87
432.77
436.94
437.96
438.11
438.78
436.47
435.93
438.78
436.78
454.72
451.94

30.38
34.10
30.21
29.93
30.77
34.24
34.57
34.90
33.89
33.81
33.44
33.30
34.45
34.47
35.11
35.33
26.87
28.06

1.01 (4.88)
0
4.18 (2.98)
6.41 (4.18)
5.55 (5.08)
2.16 (2.25)
2.23 (4.49)
2.62 (4.53)
5.17 (3.67)
6.12 (5.75)
5.94 (6.73)
6.38 (7.08)
5.85 (6.86)
5.42 (6.41)
8.63 (4.30)
7.19 (5.26)
13.81 (8.42)
12.72 (8.41)

35.35
40.13
34.76
33.96
35.36
40.65
41.12
41.34
39.50
39.87
39.75
39.50
41.36
41.90
41.05
42.40
30.75
32.75

0
1.13 (5.89)
2.32 (4.50)
3.52 (6.12)
3.78 (3.72)
4.04 (6.06)
4.38 (2.64)
4.55 (2.79)
5.44 (6.59)
7.29 (5.01)
7.62 (9.68)
7.83 (7.34)
8.72 (10.07)
9.33 (7.80)
9.57 (7.19)
10.39 (5.41)
10.64 (10.83)
11.15 (9.22)

Notes: Candidate models for coho mortality differ in whether the subbasin-specific intercept and slopes (seasonal rainfall effects)
are fixed at zero (0), estimated as a mean only (1), or modeled as a function of urbanization (z). The latter two cases also include
  complexity penalty (p), and score relative to
random subbasin effects. All models include an intercept. Posterior mean deviance (D),
the best model on each criterion (D, where smaller values indicate stronger support and the SE is in parentheses) are shown.

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SENTINEL SPECIES URBANIZATION THREATS

TABLE 2. Structural equation model selection based on K-fold
cross-validation over years or subbasins.

Intercept
(b0)
z
z
1
1

Leave years
out

Leave
subbasins out

Summer
rain
(b1)

Fall
rain
(b2)

DELPD

SE

DELPD

SE

z
z
z
0

z
1
0
0

6.72
4.48
0.00
16.36

8.75
8.63
0.00
9.58

0.00
8.31
45.61
62.22

0.00
2.68
12.90
13.61

Notes: Candidate models for coho mortality differ in whether
the subbasin-specific intercept and slopes (seasonal rainfall
effects) are fixed at zero (0), estimated as a mean only (1), or
modeled as a function of urbanization (z). The latter two cases
also include random subbasin effects. All models include an
intercept. Expected log predictive deviance (ELPD), relative to
the best model (DELPD, where smaller values indicate stronger
support) and the SE of DELPD are shown for each cross-validation exercise.

As a more realistic test of predictive performance, we
used K-fold cross-validation (Hooten and Hobbs 2015,
Vehtari et al. 2015) structured by years or by sites.
Because cross-validation is computationally intensive,
we restricted the candidate set to four models representing minimal or maximal complexity and two intermediate structures supported by WAIC and PSIS-LOO
(Table 2). Details of the cross-validation procedures are
described in Appendix S1: Section S3.
Out-of-sample prediction and hotspot mapping for coho
mortality
We generated coho mortality predictions for all Puget
Sound subbasins where coho are known to occur, including the subbasins associated with the 51 spawning
reaches that were surveyed for this study and an additional 1,481 unmonitored subbasins, whose mean area
was 13.0 km2 (SE = 0.2). The vector-based subbasin
representation (WADOE 2011) was overlain with the
same geospatial data layers used in the original analysis,
and the covariates for each subbasin were calculated
using the same methodology. Based on the model selection results, the “full” model with all landscape and precipitation effects was used. As in cross-validation over
sites, the posterior predictive distribution was conditioned on all available data; in particular, the landscape
attributes from all subbasins (S = 1,532) were allowed to
inform the factor-analytic component of the SEM. For
these baseline predictions, summer and fall precipitation
were set to their 2000–2011 averages (i.e., precipitation
anomalies were set to zero).
RESULTS
Structural equation modeling identified a single latent
dimension of covariation among the 19 landscape indicators, representing an underlying “urbanization gradient.” Most landscape variables loaded positively on this

7

latent factor in the full model. For example, human population density, traffic volume, density of highways and
major arterial roads, and log ratios corresponding to
percent cover of low, medium, and high development
and impervious surfaces all had strongly positive loadings (Fig. 3). The lone exception was the log ratio for
evergreen forest cover, whose loading had a posterior
distribution that overlapped zero. We caution against
comparing the relative magnitudes of loadings for
gamma-distributed (Fig. 3A) and logistic normal
(Fig. 3B) variables because of the disparate transformations and likelihoods involved. Within each variable
class, however, loadings can be interpreted as a relative
measure of the responsiveness to urbanization. For
example, arterial and interstate road density increase
more strongly than local road density with overall
urbanization, although the differences are modest compared to posterior uncertainty. This suggests that the relative density of vehicle traffic for different road
classifications is more important than the cumulative
landscape-scale area of roads within a given classification. Consequently, despite a lower total area, more
heavily used roads are more strongly associated with
recurrent coho mortality.
The subbasin-level model for the mortality regression
coefficients revealed strong and interacting effects of
landscape and climate on mortality risk. In the full
ðsÞ
model, the intercept of logit mortality probability (b0 )
increased with urbanization, a main effect corresponding
to overall higher rates of coho spawner mortality in
more developed subbasins (Fig. 4A). Urbanization also
modified the influence of seasonal rainfall. In the least
developed (i.e., suburban and exurban) subbasins, summer precipitation was strongly associated with an
ðsÞ
increase in mortality risk (b1 > 0). However, this association weakened along a gradient of urbanization,
becoming indistinguishable from zero in the most developed subbasins (Fig. 4B). A similar pattern emerged for
fall precipitation, although the evidence for an effect on
mortality risk was equivocal; the 95% credible interval
ðsÞ
for b2 overlapped zero even in the least developed subbasins. The influence of the urbanization gradient was
also less evident, with more unexplained random-effect
variation around the regression line (Fig. 4C). As with
summer rainfall, the marginal contribution of fall precipitation to coho mortality risk was negligible in the
most urbanized subbasins, although overall mortality
risk was highest in these subbasins.
The data-level regression in the GLMM-like component of the SEM captured the primary patterns of variation in observed coho mortality across space and time
(Fig. 5). Fitted (i.e., posterior mean) probabilities of
spawner mortality from the full model generally tracked
the observed mortality rates (r = 0.91). The model tended
to slightly underestimate mortality in the most urban
subbasins where annual mortality rates are typically the
highest (50–70%; Fig. 5). However, these discrepancies
were small compared to uncertainty in both the predictions

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FIG. 3. Posterior distributions of factor loadings relating land use/land cover variables to the latent variable (“urbanization”) in
a structural equation model to predict coho pre-spawn mortality. Variables were modeled as either (A) gamma-distributed or (B)
logistic normal, and loadings can be compared within but not between these classes. Because urbanization is positively associated
with mortality risk, variables with positive loadings are also positively associated with mortality. Box plots show the posterior mean
(thick line) and the 90% (box) and 95% (whiskers) credible intervals.

(posterior parameter uncertainty) and the field observations (binomial sampling error, which is greatest when
small numbers of female carcasses were sampled).
Information criteria suggested considerable uncertainty in the ranking of the 18 candidate models. Differences among models in both WAIC and PSIS-LOO were
often dwarfed by their estimated standard errors
(Table 1). In addition, Pareto smoothing diagnostics
indicated that many of the PSIS-LOO estimates were
numerically unstable (Vehtari et al. 2015). These
ambiguous results further motivated our use of direct
simulation (i.e., cross-validation) to estimate out-ofsample predictive performance. Although WAIC and
PSIS-LOO yielded different rankings, the top two models for each were the same (Table 1). We therefore
focused on these, along with the full model and the null
model (intercept only, no precipitation or urbanization
effects). K-fold cross-validation over years showed the
strongest support for a model with an effect of summer

precipitation and a summer precipitation-by-urbanization interaction, but no main effect of urbanization and
no effect of fall precipitation. The full model was ranked
third and the null model was ranked last (Table 2).
Again, however, differences in cross-validation scores
among models were often smaller than the associated
standard errors, indicating substantial model selection
uncertainty. K-fold cross-validation over monitored subbasins gave a much more decisive ranking, with the full
model ranked first (Table 2). The second best model differed from the full model only in dropping the fall precipitation-by-urbanization interaction, and performed
significantly worse (D = 8.31, SE = 2.68).
Baseline predictions of coho vulnerability for freshwater habitats across the entire Puget Sound basin, based
on the full SEM, indicated high mortality risk concentrated around major urban population centers
(Fig. 6A). Expected probabilities of mortality were
>10% throughout much of the north-south urban

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SENTINEL SPECIES URBANIZATION THREATS

9

corridor surrounding Interstate 5, with pockets of risk
as high as 54% in watersheds in the Seattle metropolitan
area. More than half (58%) of the habitat currently available to coho in Puget Sound fell within the <10% predicted mortality category, 40% fell within the 10–40%
category, and 2% fell within the >40% category
(Fig. 6A). The precision of these predictions was relatively high in the urban corridor, as reflected in consistently low posterior standard deviations on the logit
scale (Fig. 6B). Uncertainty increased in the least developed, higher-elevation subbasins where few mortality
monitoring data are available and mortality rates are
predicted to be the lowest.
DISCUSSION

FIG. 4. Models for the subbasin-specific regression coefficients (the b(s) in Eq. 4 and Fig. 2) in the GLMM that makes
up one component of an overall SEM relating landscape and
climate to coho pre-spawn mortality risk. Each point is a coefficient in a logistic regression predicting annual mortality within
a given subbasin. Point size corresponds to the number of years
each subbasin was monitored. Each batch of coefficients is
modeled as a function of the latent “urbanization” factor score
in the corresponding subbasins, plus some residual error (subbasin-specific random effects, the scatter of points around the
regression line). Posterior uncertainty (95% credible intervals)
in the coefficients and latent factor scores is represented by vertical and horizontal error bars, respectively, and uncertainty in
the subbasin-level regressions (95% credible intervals) is shown
by gray envelopes. A positive effect of urbanization on the (A)
intercept translates to a positive main effect of urbanization on
mortality, while negative effects on the (B) summer and (C) fall
precipitation slopes correspond to interactions whereby the
per-unit effect of rain on mortality risk declines with increasing
urbanization.

Migratory Pacific salmon return to spawn in the large
river basins of western North America, where the current pace of urbanization is nearly twice that of the rest
of the United States (Minnesota Population Center
2011). The negative ecological effects of development on
salmon habitats have generally been viewed through a
physical habitat lens, and the impacts of dams, water
diversions, dredging, logging, diking, gravel mining, and
many other human activities have been known for decades (NRC 1996, Ruckelshaus et al. 2002, Katz et al.
2007). Accordingly, federal and state investments in salmon habitat restoration have overwhelmingly addressed
physical processes, e.g., restoring stream connectivity,
increasing in-stream flows, adding structure, etc. (Katz
et al. 2007). Water quality improvements are a common
objective but are usually limited to dissolved oxygen,
temperature, and sediment (Barnas et al. 2015). We provide evidence here for a critical loss of spawners across
much of the Puget Sound coho population segment,
which is closely correlated with landscape-scale measures
of human population density and transportation infrastructure. Our findings are consistent with the hypothesis,
supported by direct experimental evidence discussed
below, that contaminants in stormwater runoff from the
regional transportation grid likely cause these mortality
events. Further, it will be difficult, if not impossible, to
reverse historical coho declines without addressing the
toxic pollution dimension of freshwater habitats.
A vexing challenge for many studies of landscape-level
impacts on ecological processes and species of concern is
that landscape attributes covary at relevant spatial scales,
making it difficult to disentangle their individual effects
(Dormann et al. 2013). In a previous study of the urban
coho mortality phenomenon (Feist et al. 2011), spatial
multicollinearity across a small sample of subbasins produced high model selection uncertainty and unstable
parameter estimates, even though candidate predictor
variables were screened to avoid the most severe correlations. Moreover, this approach is an inefficient use of
information, since the discarded variables might have

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FIG. 5. Fitted (posterior mean) and observed (sample proportion) probabilities of coho pre-spawn mortality from a structural
equation model incorporating the effects of landscape and climate. Each point is an annual observation of mortality, and the plot
shows data from all subbasins. Point size indicates the number of female spawner carcasses sampled. Error bars show uncertainty in
predictions (95% credible interval) and in sample estimates (95% confidence interval from the binomial distribution). The 1:1 line is
shown for reference.

helped to refine the predictions. In the present study,
rather than pursue the difficult task of pinpointing causal
pathways involving specific features of the built environment, we instead developed an integrative indicator of
mortality risk, which we term the “urbanization gradient.” Bayesian structural equation modeling (SEM) is
well suited to this task, providing a powerful and flexible
framework that can extract the underlying signal (in the
form of latent variables) from noisy multivariate data and
model the directed relationships among these latent variables and observed outcomes (Arhonditsis et al. 2006,
Grace et al. 2010, Lee and Song 2012). SEM, used as a
form of supervised dimension reduction, offers an advantage over the two-stage or unsupervised approach of
regression on ordination axes because the latent factors
derived from SEM are automatically weighted by their
ability to predict the response variable. That is, the ordination is coupled to the prediction task—in our case, the
hierarchical logistic regression model predicting coho
mortality frequencies across space and time.
Predictive skill is an important criterion for models
designed to support decision-making, and the most
appropriate tests of predictive ability depend on the

context in which the model will be used. We found that
different approaches to estimating out-of-sample predictive skill gave different answers about the relative performance of candidate models. These differences highlight
the importance of considering spatial and temporal scale
when building and evaluating complex hierarchical models. For example, information criteria such as WAIC and
PSIS-LOO are approximations to the posterior predictive density for each individual observation, if the model
had been fitted to all the other observations (Vehtari
and Ojanen 2012). These approximations are convenient
because they can be computed directly from MCMC
output without refitting the model, but they are sensitive
to outliers and influential observations. In our case,
information criteria generally favored models with a
main effect of urbanization and a summer rainfallby-urbanization interaction, but uncertainty in the
estimates suggested that model rankings should be interpreted cautiously (Table 1). More to the point, actual
applications of the model are more likely to entail
predicting mortality risk in unmonitored basins or in
multiple basins under future development or climate
scenarios, rather than predicting isolated points in space

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SENTINEL SPECIES URBANIZATION THREATS

A

11

B

FIG. 6. (A) Predicted mean spawner mortality using random draws from joint posterior distribution (incorporates parameter
uncertainty). (B) Uncertainty (expressed as SE on the logit scale) calculated from posterior distribution for each estimate of
mortality.

and time. Cross-validation (Hooten and Hobbs 2015) is
a more direct, albeit computationally intensive way to
simulate these predictive scenarios. Cross-validation over
years suggested that the main effect of urbanization did
not clearly improve predictive skill (Table 2), which
might seem surprising given the evident pattern of much
higher mortality in urban streams than in less developed
watersheds. However, when making predictions for
“new” years within a given set of locations, the model
can account for spatial variation with random effects
informed by the available data from those same locations. By contrast, when the model was asked to predict
mortality at “new” locations (cross-validation over subbasins), its performance clearly suffered unless it explicitly accounted for the effects of urbanization (Table 2).
Our findings have practical applications for restoration practices in terms of avoiding ecological traps (Hale
et al. 2015). The coho mortality phenomenon has been
known since at least the late 1980s (Kendra and Willms
1990). However, it was studied more intensively after a
restoration project (removal of migration barriers, e.g.,
culverts) in the 1990s unintentionally attracted coho to
spawning areas where surface water quality was lethal
(Scholz et al. 2011). Our landscape-scale vulnerability
projections can inform restoration planning operating at
a local scale and avoid the similar creation of nuisance
habitats (Dwernychuk and Boag 1972, Schlaepfer et al.

2002, Battin 2004, Robertson and Hutto 2006, Sih et al.
2011). The significance of ecological traps in salmon
habitat restoration is becoming increasingly clear (Jeffres
and Moyle 2012), and minimizing the potential for this
in urban watersheds is particularly important given proportionally higher project costs.
Our current results are consistent with a growing body
of evidence implicating motor vehicle-derived contaminants as the cause of the coho spawner mortality phenomenon. The symptoms of coho die-offs were recently
reproduced under controlled conditions by exposing
spawners to highway runoff, and this toxicity was
removed when the same runoff was filtered through
experimental soil columns to remove chemical pollutants
(Spromberg et al. 2016). Although the present study was
not designed to identify a specific “smoking gun” among
the many correlated components of urban infrastructure, the factor loadings for different roadway types are
consistent with these experimental results. Roads with
the highest volume of traffic (highways and interstates)
had higher factor loadings compared with less-heavily
traversed local roads, as would be expected if motor
vehicles, rather than asphalt or pavement per se, are the
source of the as-yet-unidentified chemical agent(s)
linked to coho spawner mortality. However, our findings
do not discount the well-known negative impacts of
imperviousness on urban stream health more generally,

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BLAKE E. FEIST ET AL.

as extensively documented in Puget Sound (Booth et al.
2004, Alberti et al. 2007) and elsewhere throughout the
world (Arnold and Gibbons 1996, Allan 2004, Schueler
et al. 2009).
Field surveys initially indicated that rainfall may play a
role in the year-to-year variation in spawner deaths
(Scholz et al. 2011). The hypothesis was that prolonged
periods of drought in the weeks or months prior to coho
spawning season, followed by precipitation washing the
concentrated contaminants into spawning streams,
increased the magnitude of mortality. However, our present results surprisingly suggest that cumulative precipitation is only a factor in less urbanized basins and not
related to mortality at all in the most urbanized areas. We
propose that the most urbanized basins are relatively
insensitive to periods of drought and subsequent rainfall
because sufficient toxicants accumulate on roadways over
relatively short periods of time, thereby minimizing the
influence of antecedent dry intervals. In less developed
basins, where contaminants are presumably deposited at
lower rates, periods of drought in the summer followed
by above-average precipitation during the spawning season may influence spawner mortality rates. Consistent
with this, a recent study exposed otherwise healthy adult
coho to runoff from heavily used roadways such as highways and interstates, across multiple fall storm events
(Spromberg et al. 2016). Despite antecedent dry intervals
ranging from a few hours to more than a week, runoff
from every storm caused 100% mortality. Our current
modeling, together with this direct line of evidence, supports the conclusion that coho in more urbanized watersheds are vulnerable to non-point source pollution
irrespective of the timing, intensity, and frequency of
storms. In the Pacific Northwest, global climate change is
expected to reduce annual stream flows due to decreased
winter snow pack and subsequent diminished summer
snow melt (Luce et al. 2013). Lower in-stream flows will
likely result in less dilution of contaminants in receiving
waters, thereby worsening pollution impacts on aquatic
species and communities.
The innovation and implementation of clean water
initiatives will invariably represent an important portfolio of conservation tools for coho salmon in Puget
Sound and elsewhere in western North America (e.g.,
the greater Portland metropolitan area). In the United
States, societal engagement and efforts to reduce urban
non-point source pollution have grown in recent years
(Barbosa et al. 2012, Kaplowitz and Lupi 2012, Kim
and Li 2016). There are myriad clean water strategies for
this, both established and under development. In the
built environment, these are collectively referred to as
green stormwater infrastructure (GSI). The common
goal is to slow, spread, and infiltrate stormwater, to
reduce high flows (i.e., flooding) and filter pollutants
(Sherrard et al. 2004, Elsaesser et al. 2011). In Puget
Sound and elsewhere, initial bioinfiltration research has
shown that simple and inexpensive soil columns can be
very effective at removing chemical contaminants,

thereby protecting the health of fish and aquatic invertebrates (McIntyre et al. 2014, 2015, 2016). As noted
above, bioinfiltrating runoff from a high-traffic urban
arterial prevents lethal impacts on coho spawners
(Spromberg et al. 2016). This suggests that GSI will be
essential for (1) reducing the high rates of coho losses in
subbasins that are currently urban or suburban and (2)
preventing mortality in rural and forested subbasins
where coho runs are presently healthy but vulnerable to
future development pressures. While important, retrofitting the built environment is expensive, and it will be
easier to incorporate clean water strategies into future
growth (Hughes et al. 2014). Natural resource decision
making will be based, in part, on spatially explicit tools
(Shamsi et al. 2014), and this can be guided by the coho
die-off hotspot vulnerability maps that we have generated here.
Our methods are transferrable, since they are based on
geospatial data that are available nationally or globally,
and can be applied to other management contexts where
urban sprawl threatens species conservation (McQueen
et al. 2010, Marmonier et al. 2013). This includes, for
example, assessments of coho vulnerability in other
metropolitan areas of northwestern North America,
now and in the future. Alternative futures analyses, a
cornerstone of urban planning, weigh options related to
the transportation grid as well as residential, industrial,
and commercial development. In the Puget Sound
region, this includes growth management for human
population density and increasing imperviousness in the
coming decades (e.g., Bolte and Vache 2010). Our predictive models could inform alternative growth management scenarios, to identify where non-point source
pollution control measures will be most needed to ensure
long-term coho conservation and, by extension, the
integrity and resiliency of freshwater and nearshore marine communities.
CONCLUSIONS
Extensive fish kills have become rare in the United
States in the decades since the Clean Water Act was
passed to control point-source pollution. The most
important water quality threat to aquatic systems now is
non-point source pollution (Scholz and McIntyre 2015).
The coho mortality phenomenon is one of the few contemporary examples of urban stormwater causing the
overt death of a widely distributed keystone species with
high societal value, both economically and culturally. By
sharpening our ability to predict where coho are dying,
the present study brings us a step closer to understanding
the underlying cause, and highlights the fact that 40% of
the total area of Puget Sound river basins that support
coho are predicted to have adult mortality rates that substantively increase the risk of local population extinction.
Finally, we have shown where green stormwater infrastructure and other clean water strategies are most needed
at the landscape and basin scales. In the coming years,

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SENTINEL SPECIES URBANIZATION THREATS

coho salmon spawners will be a sentinel for the success,
or failure, of these efforts.
ACKNOWLEDGMENTS
The authors wish to thank Jon Oleyar (Suquamish Tribe,
Washington), Jody Brown (Stillaguamish Tribe, Washington),
Jamie Glasgow (Wild Fish Conservancy), Hans Berge (King
County, Washington), Mauro Heine (Kitsap County, Washington), and Kit Paulsen (Bellevue Public Utilities, Washington)
for their assistance in providing data on occurrences of prespawn mortality across Puget Sound. This research was funded,
in part, from a generous grant from the U.S. Environmental
Protection Agency, Region 10, Puget Sound National Estuary
Program with additional support from the National Marine
Fisheries Service and the U.S. Fish and Wildlife Service. Findings and conclusions herein are those of the authors and do not
necessarily represent the views of the sponsoring organizations.
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