advances.sciencemag.org/cgi/content/full/4/5/eaar3001/DC1

Supplementary Materials for

A dynamic ocean management tool to reduce bycatch and support

sustainable fisheries

Elliott L. Hazen, Kylie L. Scales, Sara M. Maxwell, Dana K. Briscoe, Heather Welch, Steven J. Bograd,

Helen Bailey, Scott R. Benson, Tomo Eguchi, Heidi Dewar, Suzy Kohin, Daniel P. Costa,

Larry B. Crowder, Rebecca L. Lewison

Published 30 May 2018, Sci. Adv. 4, eaar3001 (2018)

DOI: 10.1126/sciadv.aar3001

The PDF file includes:

Supplementary Methods

table S1. Species-specific model deviance explained and cross-validation using

area under the curve statistics.

fig. S1. Kernel density plot of fisheries effort and tracking data for leatherback

turtles, California sea lions, and blue sharks.

fig. S2. Sample track with three randomly selected pseudotracks for all three

satellite-tracked species.

fig. S3. Partial response curves from boosted regression trees for sea surface

temperature, bathymetry, chl-a, and SSHa across all species models.

fig. S4. Species-specific predictions with error bounds from boosted regression

tree model fitting process.

fig. S5. Time series of species habitat in a normal (2012) and anomalously warm

(2015) year.

fig. S6. Sensitivity analysis of EcoCast bycatch and integrated risk under varying

species weightings to highlight their influence on the final product.

fig. S7. Operational tool for exploring EcoCast weightings available to managers

to assess how varying scenarios change the integrated risk surface.

Other Supplementary Material for this manuscript includes the following:

(available at advances.sciencemag.org/cgi/content/full/4/5/eaar3001/DC1)

movie S1 (.mp4 format). Animation of daily bycatch predictions for the August to

December 2012 fishing season, with red pixels representing high risk and white

representing low risk.

movie S2 (.mp4 format). Animation of daily bycatch predictions for the August to

December 2015 fishing season, with red pixels representing high bycatch risk and

white representing low risk. movie S3 (.mp4 format). Animation of daily integrated predictions for the August

to December 2015 fishing season, with red pixels representing high bycatch risk

and low target catch and with blue pixels representing high target catch and low

bycatch risk.

movie S4 (.mp4 format). Animation of daily integrated predictions for the August

to December 2015 fishing season, with red pixels representing high bycatch risk

and low target catch and with blue pixels representing high target catch and low

bycatch risk.

Supplementary Methods

We explored three modeling frameworks: generalized linear mixed models (GLMMs),

generalized additive mixed models (GAMMs), and boosted regression trees (BRTs). GLMMs

allow non-linear relationships between predictor and response variables using a link function,

while GAMMs use penalized regression splines instead of a transformed linear relationship,

allowing for more flexibility in model structure. BRTs are an extension to classification and

regression trees that use boosting to optimize the partitioning of variance (34). We found that

BRTs consistently outperformed GLMMs and GAMMs, thus we chose BRTs to generate the

predictive habitat suitability surfaces underlying EcoCast (19). For the models built using

tracking datasets (blue shark, California sea lion, leatherback turtle), we iteratively and randomly

resampled without replacement from presence and absence datasets 1000 times to quantify data

variability, estimate process error, reduce serial autocorrelation and eliminate bias resulting from

arbitrarily selecting individual correlated random walks to match to tracks. For models built

using observer data, we used the presence (set with catch) and absence (set but no catch) of each

of the focal species (swordfish, blue shark) as the response. We included all of the available

environmental predictors in the BRT framework to partition the deviance accordingly rather than

parametric-based information criterion approaches (34). We observed some variability in the

contribution of environmental variables to the overall predictive capabilities of each model

(Table S1). For instance, for blue sharks, SST and bathymetry changed ranking of variable

importance between the models built using fisheries observer data and those based on tracking

data. The difference in variable importance among tag-based and observer-based models are

representing drivers of blue shark habitat use compared to fisheries effort (both species must be

present and gillnet must be set) and ultimately catchability (depth overlap between blue sharks

and gillnet) respectively. Given the model domain, the peak in leatherback habitat suitability at

SST values of 25°C is likely a reflection of migration habitat in the southwest portion of the

study areabut not necessarily overlap with the fishery. A fishery interaction model compared to

the current species distribution model would likely show a much lower peak in SST given where

interactions have been most common.

To validate our model predictions for all five models (swordfish and blue shark observer-based

models and blue shark, leatherback turtle and California sea lion tracking-based models), we

used several formulations of k-fold cross-validation, with area under the Receiver Operating

Characteristic curve (AUC) as a diagnostic (Table S1).Model AUC was calculated using a 75%

training / 25% test approach to estimate the sensitivity and specificity of our predictions (19,34).

In addition, a leave-one-out cross-validation approach was taken to validate the predictive

capabilities of each model over each of the years for which we had data. In each iteration, we left

out a single year from the model fitting process and calculated a mean AUC from each retained

year. AUC is not a perfect evaluation metric, thus we ensured that ecological realism was also

considered in evaluating models.Given the extreme conditions and low effort of 2015, observer

data from 2015 were not including in the model-fitting and instead were treated as an additional

independent dataset for cross-validation.AUC statistics were highest for the leatherback and sea

lion tracking models, and the blue shark and swordfish models performed much better than

random. In addition, the models were more successful in predicting missing data across years

than when a specific year was left out, however the blue shark observer model performed better

on 2015 than when predicting across modeled years and via random sampling. This approach

also ensured that the observer models were able to temporally extrapolate to novel conditions

providing confidence in its use as an operational tool.We created daily predictions and 95%

confidence intervals by speciesto highlight areas where predicted distributions and uncertainty

were highest (Figure S2). Confidence intervals were calculated across 10 boosted regression tree

model fits to account for model stochasticity (19,34).

Operationalization

To allow for applied use by managers and fishers, we operationalized our dynamic ocean

management approach into a real-time tool that produces daily predictions. As management

priorities can shift based on new stock data or high levels of mortality in a species of concern,

the EcoCast tool allows adjustment of weightings in reflection of new information on bycatch

risk in the middle of a fishing season (Figure S7). The tool also allows for exploration of

historical predictions, has the ability to zoom, and can show additional datasets such as nautical

charts (https://heatherwelch.shinyapps.io/ecocastapp/). The EcoCast product is hosted online and

updated daily (http://oceanview.pfeg.noaa.gov/ecocast/) and thus can be delivered directly to

fishers when minimal internet is available. In addition, when risk is highest, managers can close

the areas of greatest concern to reduce risk yet still considering areas of high swordfish catch.

table S1. Species-specific model deviance explained and cross-validation using area under

the curve statistics. We calculated AUC using 75% of the data as training with 25% as testing in

column 2, CV by holding out each year as test and using the remaining years as train in column

3, and CV 2015 representing the AUC calculated on an entirely novel year.

Table S1.

Dev. AUC CV CV 2015

swordfish 12.7 0.72 0.66 0.69

blue shark 12.2 0.73 0.67 0.82

blue shark tracking 16.3 0.76 0.73 NA

leatherback 49.7 0.93 0.85 NA

sea lion 69.7 0.86 0.81 NA

fig. S1. Kernel density plot of fisheries effort and tracking data for leatherback turtles,

California sea lions, and blue sharks. Map of tracking and DGN fisheries observer data as

kernel densities. The Pacific Leatherback Conservation Area (PLCA) is shown in grey dashed

lines. Fisheries observer data are shown as 50 and 95% kernel densities from low effort (yellow)

to high (red). Home ranges for the three tagged species are shown as dashed lines with

leatherback turtles (green - 50% kernel density), blue sharks (blue - 50% kernel density), and

California sea lions (brown – 50 and 95% kernel densities).

fig. S2. Sample track with three randomly selected pseudotracks for all three satellite-

tracked species. Randomly selected example track line for a single sea lion (brown), leatherback

turtle (green), and blue shark (blue) with 3 example pseudotracks shown as solid points.

−150 −140 −130 −120 −110

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45

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fig. S3. Partial response curves from boosted regression trees for sea surface temperature,

bathymetry, chl-a, and SSHa across all species models. Response curves from Boosted

Regression Trees for A) Sea Surface Temperature (SST) in °C, B) Bathymetry in m, C)

Chlorophyll-a in mg/m3, and D) Sea Surface Height anomaly (SSHa) in cm. The five curves

represent swordfish (black), sea lions (brown), leatherbacks (green), blue shark tracking (dark

blue) and blue shark observer data (light blue).

fig. S4. Species-specific predictions with error bounds from boosted regression tree model

fitting process. Boosted regression tree model predictions including 95% confidence intervals as

upper and lower bounds around the mean for A-C) swordfish observer data, D-F) leatherback

turtle tracking data, G-I) California sea lion tracking data, J-L) blue shark tracking data, and M-

O) blue shark observer data. Dark blue represents higher predicted species presence while light

blue represents lower predicted presence.

fig. S5. Time series of species habitat in a normal (2012) and anomalously warm (2015)

year. Species habitat time series (4 panel) - Time series of top quartile of predicted habitat as

percentage of the total study area (-115° to -130° E longitude and 30° to 50° N latitude) were

calculated to examine whether finer-scale temporal closures could be more successful than

current PLCA closure dates. (A) Sea lions (brown) and (B) blue sharks (light and dark blue)

were predicted to have the smallest habitat range, followed by (C) leatherback sea turtles (green),

with (D) swordfish (black) the most broadly distributed. All individual bycatch time series

showed a stable or decreasing trend in the top quartile of habitat throughout the fishing season

except for sea lions in 2015. This is in contrast to swordfish that showed a stable trend in 2012

and an increasing trend for 2015 throughout the season.

fig. S6. Sensitivity analysis of EcoCast bycatch and integrated risk under varying species

weightings to highlight their influence on the final product. EcoCast sensitivity analysis

showing how integrated results vary based on species weightings for bycatch only models (A,D)

and integrated EcoCast models (B,C,E,F). Areas of high bycatch risk are shown in red with low

risk in white, while integrated EcoCast risk varies from low target catch / high bycatch in red to

high target catch / low bycatch in blue. Transparency of species’ silhouettes indicate the relative

influence in the surface with less transparent indicating less overall influence in the integrated

surface.

fig. S7. Operational tool for exploring EcoCast weightings available to managers to assess

how varying scenarios change the integrated risk surface. Tool for serving updated EcoCast

product with manager-adjustable risk weightings. This tool also allows viewing of previous

predictions, adding addition data layers, and downloading of raw data.