Ecological Indicators 98 (2019) 657–664

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Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind

Original Articles

Environmental indicators to reduce loggerhead turtle bycatch oﬀshore ofSouthern California

T⁎

Heather Welcha,b, , Elliott L. Hazena,b, Dana K. Briscoea,c, Steven J. Bograda,b,Michael G. Jacoxb,d, Tomoharu Eguchie, Scott R. Bensonf,g, Christina C. Fahyh, Toby Garﬁelde,Dale Robinsona,i, Jeﬀrey A. Seminoﬀe, Helen BaileyjaInstitute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, Monterey, CA 93940, USAc
Stanford University, Hopkins Marine Station, 120 Oceanview Blvd, Paciﬁc Grove, CA 93950, USAdNational Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder, CO 80305, USAe
National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, La Jolla, CA 92037, USAf
Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Moss
Landing, CA 95039, USAg
Moss Landing Marine Laboratories, Moss Landing, CA 95039, USAhProtected Resources Division, West Coast Regional Oﬃce, National Marine Fisheries Service, Long Beach, CA 90802, USAi
National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, Santa Cruz, CA 95060, USAj
Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, MD 20688, USAb

A R T I C LE I N FO

A B S T R A C T

Keywords:
BycatchEl Niño
Fisheries
Loggerhead turtle
Protected species
Sea surface temperature

Extreme climatic events are expected to become more frequent under current conditions of increasing global
temperatures and climate variability. A key challenge of ﬁsheries management is understanding and planning forthe eﬀect of anomalous oceanic conditions on the distributions of protected species and their interactions withﬁshing gear. Atypical marine states can cause non-target species to shift outside of their normal distribution
patterns, leading to unwanted bycatch events that threaten ﬁsheries sustainability. Environmental indicators canserve as early warning signals that allow for proactive management responses before signiﬁcant bycatch occurs.
Marine heatwaves in the Paciﬁc have caused shifts in the distributions of endangered loggerhead turtles (Caretta
caretta), increasing overlap with California’s Drift Gillnet ﬁshery and thereby the risk of turtle bycatch events. To
reduce bycatch, a ﬁshery closure oﬀshore of Southern California – The Loggerhead Conservation Area – Isenacted when an El Niño event has been declared and local sea surface temperatures (SSTs) are warmer thannormal. However, this regulation was based on qualitative assessment of bycatch that occurred during past El
Niño events, and no explicit threshold for SST anomalies was deﬁned. Additionally, closures enacted under thecurrent regulation rely on structured expert decision-making. Providing a quantitative indicator could help to
reﬁne future decisions. We developed and evaluated potential new indicators to guide the Loggerhead
Conservation Area closure timing based on thermal indices in three diﬀerent regions: the equatorial Paciﬁc,regional areas oﬀshore of Southern California, and temperate pelagic areas oﬀ the US west coast. Our objectives
were to: 1) quantify thermal indicators and their respective thresholds to guide closure timing, and 2) hindcast
closure scenarios based on these indicator thresholds to evaluate eﬃcacy in terms of opportunity costs to ﬁshers
and ability to avoid turtle interactions. The best indicator in terms of avoiding historical turtle interactions while
minimizing opportunity cost to ﬁshers was a six-month average local SST anomaly indicator with closures enacted above a threshold of 0.77 °C. This result can improve upon the current closure guidelines by providing aquantiﬁed and spatially-explicit indicator and threshold to supplement the structured decision-making process.
Our analysis demonstrates a novel approach to developing ﬁsheries management strategies for species with apaucity of data. Issues with data comprehensiveness are frequently present in ﬁsheries management exercises,and precautionary approaches are needed to allow adherence with legislation while considering the best
available science.

⁎

Corresponding author at: National Oceanic and Atmospheric Administration, Southwest Fisheries Science Center, Monterey, CA 93940, USA.
E-mail address: heather.welch@noaa.gov (H. Welch).

https://doi.org/10.1016/j.ecolind.2018.11.001
Received 24 July 2018; Received in revised form 30 October 2018; Accepted 1 November 2018
Available online 24 November 2018
1470-160X/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Fig. 1. The study area. The map shows the extent of the three loggerhead datasets, management boundaries, and the boxes used to deﬁne the local SST anomaly (A)and pelagic SST (B-D) indicators.

(Ammodytes marinus) ﬁshery in the northwestern North Sea is closed
when seabird colony productivity falls below a threshold level (Wrightet al., 2002). Temperature is used as a scaling factor in the harvest
control rule for the Paciﬁc sardine (Sardinops sagax) stock, which ismore productive under warmer temperatures and less productive undercooler conditions (Kvamsdal et al., 2016). In an anchovy (Engraulis
encrasicholus) ﬁshery in the Mediterranean Sea, a chlorophyll-a index isused to inform growth rates in the stock model (GFCM, 2012). Similar
approaches can be employed to avoid protected species bycatch events
when driven by environmental conditions. For example, a dynamic
ocean management tool – TurtleWatch (Howell et al., 2008) – Uses atemperature window to help Hawaii-based pelagic longline ﬁshers reduce bycatch likelihood by avoiding waters that are thermally suitablefor vulnerable loggerhead turtles (Caretta caretta).
Loggerhead bycatch in California’s Drift Gillnet Fishery has been
managed through implementing regulations using temperature-based
guidelines since 2003 (50 CFR §660.713 (c)(2)). Loggerheads have been
known to enter waters oﬀshore of Southern California during anomalously warm years, resulting in overlap with the Drift Gillnet Fisheryand increased likelihood of bycatch events. However, the mechanisms
driving loggerheads to utilize this habitat are unknown (Eguchi et al.,2018). In the North Paciﬁc, loggerheads emerge from nesting beaches
in Japan and move to juvenile habitats in oceanic waters of the Central
North Paciﬁc (Polovina et al., 2000). An unknown proportion of theseturtles transition from the Central North Paciﬁc to the eastern Paciﬁc(Tomaszewicz et al., 2015; Briscoe et al., 2016), where a foraging
hotspot is present along the Paciﬁc coast of the Baja California Peninsula, Mexico. Large numbers of loggerheads may also be present
farther north, in the Southern California Bight region, where they residefor unknown periods often coinciding with warm water periods (Allen

1. Introduction
The current rate of increase in oceanic and atmospheric heat content
has increased the frequency and duration of marine heatwaves, or“prolonged discrete anomalously warm water events” (Hobday et al.,2016); a trend that is expected to continue (Oliver et al., 2018) inconjunction with changes in the strength, direction, and variability ofmajor ocean currents (Hoegh-Guldberg and Bruno, 2010). These
anomalous marine states can signiﬁcantly alter the distributions ofmarine species as they shift to remain within their thermal tolerance
limits or to exploit resources in newly suitable habitat (Ling, 2008;
Wernberg et al., 2013). Changes in ecosystem structure can have direct
impacts on marine ﬁsheries, both in terms of target catch composition(Pearcy, 1987; Glynn, 1988; Ñiquen and Bouchon, 2004) and unwanted
interactions with non-target species, or bycatch (MacKenzie et al.,2014).
A key challenge of ecosystem-based ﬁsheries management is providing the tools to understand and plan for the eﬀect of anomalous
oceanic conditions on the distributions of marine species and their interactions with ﬁshing ﬂeets. To implement ecosystem-based ﬁsheries
management, timely information on signiﬁcant ﬂuctuations in ecosystem condition is required. Environmental indicators – Time-series ofphysical or biological variables that communicate information on environmental state – Can serve as early warning signals that allow for
adaptive management to address interactions between marine species
and ﬁshing ﬂeets before signiﬁcant impacts occur (Skern-Mauritzen
et al., 2016). Management actions such as time/area closures or input/
output controls are triggered when indicators cross threshold values,
i.e. predeﬁned reference points over or below which negative ﬁsheries
impacts have been demonstrated to occur. For example, a sandeel
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2. Methods

et al., 2013; Eguchi et al., 2018).
Two hypotheses have been proposed to explain periodic loggerheadpresence oﬀshore of Southern California. First, loggerheads in developmental areas oﬀ of Baja, Mexico may migrate northward. Warm
Paciﬁc Equatorial Water enters the near-shore region south of Southern
California and is advected northward by the California Undercurrent, asubsurface poleward current running along the North American west
coast. During the fall of 2015, an increase of loggerhead sightings oﬀshore of Southern California coincided with anomalously warm waters
associated with the 2014–16 North Paciﬁc marine heat wave (Bondet al., 2015; Zaba and Rudnick, 2016; Rudnick et al., 2017; Jacox et al.,2016; Eguchi et al., 2018). Second, turtles oﬀshore of Southern California may migrate eastward from the Central North Paciﬁc. Under thishypothesis, warming waters in the Eastern North Paciﬁc form an ecological bridge that allows juveniles to move eastward to access the moreproductive waters oﬀshore of Southern California (Briscoe et al., 2016;
Briscoe et al., 2017). A study of body sizes and stable isotope values
indicates that loggerheads oﬀ California most likely have Central Paciﬁc origins (Allen et al., 2013), although the long-term constancy ofthis pattern warrants further study.
The Drift Gillnet Fishery targets swordﬁsh (Xiphias gladius) in waters
oﬀshore of Central and Southern California. Since the 1990s, 16 loggerhead bycatch events have been recorded in the ﬁshery, all of which
occurred in Southern California. To reduce loggerhead bycatch oﬀshoreof Southern California, a federally mandated closure – The Loggerhead
Conservation Area (Fig. 1) – Was implemented in 2003 (50 CFR§660.713). Under this regulation, the National Marine Fisheries Service
is required to enact the ﬁshery closure in months between June andAugust when there are warmer than normal sea surface temperatures(SSTs) present oﬀshore of Southern California for years during which an
El Niño event has been forecast declared (50 CFR §660.713 (c)(2)).
Speciﬁcally, SST data from the second and third months prior to theclosure month are used to determine whether warm conditions associated with El Niño are present oﬀshore of Southern California. For
example, temperatures in March and April are used to determine theclosure status in June. Since the guidelines went into eﬀect, the closure
has been enacted during three full month periods: August 2014, JuneAugust 2015, and June-August 2016 (over seven months closed in
total).
However, these closure guidelines were developed based on relatively few loggerhead bycatch events, all of which occurred in anomalously warm ocean conditions. Furthermore, the closure guidelines are
qualitative in nature (“temperatures warmer than normal”), with no
explicit closure threshold or region for temperature observation. Since
the initial guideline deﬁnition, additional data have been collected onloggerheads in the area via aerial surveys, shipboard surveys, sighting
reports from the public and satellite telemetry. With these additionaldata, we aimed to re-examine the relationships between loggerheadpresence oﬀshore of Southern California and thermal indicators toprovide a quantitative and spatially explicit indicator and threshold toinform Loggerhead Conservation Area closure timing. Our objectives
were to 1) quantify the thermal indicators and their respective
thresholds to guide Loggerhead Conservation Area closure timing, and2) hindcast closure scenarios based on these indicator thresholds toevaluate eﬃcacy in terms of opportunity costs to ﬁshers and ability to
avoid turtle interactions. Despite much evidence demonstrating theinﬂuence of the ﬂuctuating environment on stock status (Hannesson,
2007; Ottersen et al., 2013; Vert-pre et al., 2013), environmental indicators are infrequently included in operational ﬁsheries management(Kvamsdal et al., 2016). Our study seeks to demonstrate the feasibilityof the explicit incorporation of an environmental metric into ﬁsheries
regulation, thereby aiding a movement towards ﬁsheries management
strategies that are responsive to climate variability and change.

2.1. Loggerhead distribution data
This study used ﬁsheries dependent and independent datasets.
Loggerhead bycatch events (n = 16 turtles) in the California Drift
Gillnet Fishery have been recorded since 1990 through the observer
program managed by the National Marine Fisheries Service’s West
Coast Regional Oﬃce. Independent datasets (hereafter: sighted turtles)
included an aerial line-transect survey during September and October
2015 (n = 215 turtles, see details of survey methodology in Eguchi
et al., 2018), a citizen science loggerhead sighting hotline from April
2015 to July (Briscoe et al., 2017), and a satellite telemetry study
conducted by National Marine Fisheries Service’s Southwest Fisheries
Science Center in 2015 and 2016 (n = 3 tagged turtles).
2.2. Indicator selection and quantiﬁcation
Three indicator groups were evaluated to test the two loggerheadpresence hypotheses over a variety of spatial and temporal scales: El
Niño indicators, local SST anomaly indicators, and a pelagic SST indicator. The El Niño indicators were selected to test the eﬀect of broad
scale climate forcing on loggerhead distribution. The local SST anomaly
indicator was selected to test if local conditions drive turtle presence inwaters oﬀshore of Southern California, either by allowing turtles toenter the relatively more productive nearshore environment to exploit
resources during anomalously warm periods (e.g. red crabs,
Pleuronocodes planipes, Eguchi et al., 2018), or by providing a closer-toshore route for turtles transiting between the Central North Paciﬁc andBaja California Sur, Mexico. The ﬁnal indicator, pelagic SST, was selected as a proxy for environmental mechanisms that operate overscales intermediate (hundreds of kilometers) to the broad climate forcing captured by the El Niño indicators and regional mechanisms captured by the local SST anomaly indicators.
The El Niño indicators were quantiﬁed using the monthly Niño-3.4
index, a time-series of temperature anomalies in the Niño-3.4 region(from 170 °W to 120 °W and 5 °S to 5 °N; http://www.cpc.ncep.noaa.
gov/products/analysis_monitoring/ensostuﬀ/detrend.nino34.ascii.txt).
The local SST anomaly indicators were quantiﬁed using a data assimilative implementation of the Regional Ocean Modeling System (ROMS)
conﬁgured for the California Current System (Moore et al., 2013; Neveu
et al., 2016). Monthly ROMS SST anomalies were averaged over box A(Fig. 1 and Table 1). Three diﬀerent smoothing windows were applied
to the El Niño and local SST anomaly data to account for diﬀerent lagsof turtle response to environmental ﬂuctuations, such as the transitingtime to reach waters oﬀshore of Southern California or the time it takes
turtles to aggregate in high enough densities to be detected in the datasets. The smoothing windows were: one-month (i.e. conditions in themonth directly preceding the closure month), two-months (i.e. theaverage condition in the second and third months preceding the closuremonth, as in the existing rule), and six-months (i.e. the average condition in the six months preceding the closure month). For the El Niño
indicators, thresholds were the mean Niño-3.4 index value calculated
across the same smoothing window (one-month, two-months, or sixTable 1
Coordinates of the boxes used to deﬁne the local SST anomaly (A) and pelagic
SST (B–D) indicators. Box letters are consistent with Fig. 1.
Coordinates (decimal degrees)659

Box name

North

East

South

West

Local anomaly box (A)Pelagic SST box (B)Pelagic SST box (C)Pelagic SST box (D)

34.5
25
24.25
27

−116
−123
−122.5−118.5

30.8
23
23
23

−120.3−135−124.5−120

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Fig. 2. The El Niño indicators and thresholds. (A) The raw Niño-3.4 index. (B) One-month average indicator. (C) Two-month average indicator. (D) Six-monthaverage indicator. Thresholds are the averages of Niño-3.4 index values in monthly windows preceding the three historical closure periods (thick black line segments). Closures would be enacted when indicator values exceed thresholds.

Appendix A).

months) preceding the three full month historical closure periods(August 2014, June-August 2015, and June-August 2016). For the local
SST anomaly indicators, thresholds were the minimum monthly
anomaly value within the smoothing window preceding the three historical closure periods.
To quantify the pelagic SST indicator, data from a monthly SST
satellite product generated by NOAA CoastWatch-West Coast andNOAA Southwest Fisheries Science Center from data provide by NASA’s
Jet Propulsion Laboratory (JPL MUR MEaSUREs Project, 2010) at 0.01°
resolution were downloaded from Environmental Research Division's
ERDDAP (Simons, 2017, https://coastwatch.pfeg.noaa.gov/erddap/
griddap/jplMURSST41mday). These data were used to construct a
gridded time-series of mean monthly SST for each calendar month from2003 to 2017. Monthly SST time-series in each grid cell were correlatedwith a time-series of yearly turtle sightings between 2003 and 2017,
which was generated by combining all loggerhead distribution datasets(see Section 2.1, Table A1, Appendix A), to produce monthly spatial
correlation maps. Two methods were tested to handle years with noturtle sightings (n = 7): 1) turtles were assumed to be absent, and 2)
years with no sightings were removed. This analysis identiﬁed parts of
the ocean that were highly correlated with turtle presence oﬀshore ofSouthern California. Areas with high correlation coeﬃcients (> 0.75)
for both methods of handling years with no turtle sightings were further
explored to ﬁnd areas of persistently high correlation across months inorder to deﬁne candidate pelagic SST boxes (e.g. a box that has persistent correlation across January, February, and March). Then for eachmonth, the box most correlated with turtle presence oﬀshore ofSouthern California was identiﬁed, and its monthly threshold was calculated as the mean of SST in that month within the box across years
when turtles were seen oﬀshore of Southern California (Table A1,

2.3. Hindcasting indicator eﬃcacy
To test relative indicator eﬃcacy, three hindcast metrics were used:
the opportunity cost to ﬁshers (i.e. the number of months the closure
was enacted between 2003 and 2017, the same time period over which
the current guidelines have been employed), the overlap with sighted
turtles, and the proportion of historical bycatch avoided. To evaluateeach metric, the seven indicators (the El Niño indicators and local SST
anomaly indicators, each calculated over three smoothing windows,
and the pelagic SST indicator) were hindcast, and thresholds were used
to determine closure status. Because the monthly SST dataset used toquantify the pelagic SST indicator only spans 2003-present, an SST
product generated by NOAA CoastWatch-West Coast and NOAASouthwest Fisheries Science Center (https://coastwatch.pfeg.noaa.gov/
erddap/griddap/erdPH2sstamday) at ∼0.04° resolution from data
provided by the Pathﬁnder program (Casey et al., 2010) was utilized tohindcast earlier dates. The current guidelines consider local temperatures and El Niño declarations in tandem; therefore an eighth indicatorthat combined El Niño and local SST anomalies was also considered. In
the combined indicator scenario, thresholds for both the six month El
Niño and local SST anomaly indicators must be exceeded in order forthe closure to be enacted. Two closure window scenarios were hindcast
and compared, 1) a partial closure window scenario in which the closure may be enacted in June, July, and/or August (following the current guidelines), and 2) a full closure window scenario in which theclosure may be enacted in any month of the year.

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3. Results

weaker impacts on SST anomalies oﬀshore of Southern California andwould not have triggered closures, with the exception of the 2006–07
event under the one-month indicator and the full closure window scenario (Fig. 3B).
Spatial correlation analyses between loggerhead presence oﬀshoreof Southern California and pelagic SST (Fig. A1, Appendix A) revealed
three areas with high correlations (> 0.75) that persisted across several
months: pelagic box B for July – September, pelagic box C for May –July, and pelagic box D for January – May (Fig. 1, Table 1, Fig. A2.
Appendix A, Table 1). Of the three pelagic boxes, box B had SST moststrongly correlated with turtle presence oﬀshore of Southern California
in July, August, and October-December (0.45 ≤ R ≤ 0.93), box C wasmost strongly correlated in February, June, and September(0.86 ≤ R ≤ 0.91), and box D was most strongly correlated in January
and March through May (0.80 ≤ R ≤ 0.84) (Table A2, Appendix A).
Thresholds (calculated as the mean temperature across years turtles
were seen oﬀshore of Southern California) for the most correlated box
in each month ranged from 18.88 °C in March in box D to 23.12 °C in
September in box C (Table A2, Appendix A). Based on these thresholds,
closures would have been enacted in 2003–04, 2009, and 2014–16
under the full window closure scenario (Fig. 4).

3.1. Indicator quantiﬁcation and evaluation
During the time period the current guidelines have been in eﬀect(2003–2017), there have been six El Niño events (2002–03, 2004–05,
2006–07, 2009–10, 2014–15, and 2015–16), deﬁned by at least ﬁvestraight months of Oceanic Niño Index values above 0.5. Niño-3.4
anomaly thresholds for the El Niño indicators (Fig. 2) increased withsmoothing window width: 0.45 (one-month threshold), 0.64 (twomonths threshold), and 0.69 (six-month threshold). This trend derives
from the fact that closures are enacted during summer months when
Niño-3.4 anomalies are generally small, while longer averaging times
capture winter/spring periods closer to the peak strength of El Niño
events. Under the full closure window scenario, the declared El Niño
events of 2002–03, 2006–07, 2009–10, and 2015–16 each would haveresulted in enacted closures for the three El Niño indicators (Fig. 2).
Also under the full closure window scenario, weak El Niños in 2004–05
and 2014–15 would have resulted in closures under the one-monthindicator (Fig. 2B), and the 2004–05 event would have resulted in an
additional closure under the two-month indicator (Fig. 2C).
Thresholds for the local SST anomaly indicators (Fig. 3) were 0.92
(one-month threshold), 1.40 (two-month threshold), and 0.77 (sixmonth threshold). These thresholds are sensitive to the monthly SST
ﬂuctuations of 2014–2016, as each threshold is derived from a diﬀerent
set of months. The two-month threshold uses an SST anomaly from May
2014, which was relatively warm compared to the SST anomalies usedby the one-month and six-month thresholds in May 2016 and February
2014, respectively. In all three cases, closures would have been enacted
each year from 2014 to 2016 (Fig. 3). El Niño events prior to 2014 had

3.2. Hindcasting indicator eﬃcacy
The El Niño/local anomaly combined indicator had the lowest opportunity cost to ﬁshers, resulting in closures 3.6% and 10.7% of thetime across the 2003–2017 time-series under the partial and full closure
windows, respectively (Table 2). This was closely followed by the twomonth and six-month local SST anomaly indicators, resulting in closures 2.4% & 11.9% and 5.4% & 18.5% of the time under the partial

Fig. 3. The local SST indicators and thresholds. (A) The raw ROMS SST anomalies. (B) One-month indicator. (C) Two-month average indicator. (D) Six-monthaverage indicator. Thresholds are the minimum monthly ROMS SST anomaly values in monthly windows preceding the three historical closure periods (thick blackline segments). Closures would be enacted when indicator values exceed thresholds.
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and 5) to have the highest overlap with loggerhead presence oﬀshore ofSouthern California with a low opportunity cost to ﬁshers. Notably,
while an expected strong El Niño in 2014–15 largely failed to materialize, waters oﬀshore of Southern California were extremely warm
owing to a separate event – The northeast Paciﬁc warm anomalycommonly referred to as ‘the Blob’ (Bond et al., 2015; Zaba andRudnick, 2016), which coincided with elevated loggerhead sightings. In
cases like this and others, temperatures along the US west coast are notnecessarily closely tied to ENSO variability (Fiedler and Mantua, 2017).
The local SST anomaly indicator enables tracking of turtle responses towarm events independent of El Niño. While El Niño events were reﬂected in all indicators to some degree, the El Niño based indicators had
decreased ability to avoid turtle interactions despite increased opportunity cost. Additionally, the pelagic SST indicator also had a decreased
ability to avoid turtle interactions. These results postulate that turtles
are most directly responding to local temperature conditions as opposed
to broad-scale climate forcing (tested via the El Niño indicators) orintermediate mechanisms proxied by the pelagic SST indicators; however, given paucity of the loggerhead datasets, more distribution data
are needed to fully parse out the environmental mechanisms driving
loggerheads to waters oﬀshore of Southern California.
The strength of the six-month local SST anomaly indicator over the
two-and one-month local SST anomaly indicators suggests that loggerhead presence oﬀshore of Southern California is tied not just to
warm temperatures, but to persistently warm temperatures. However,additional information on loggerhead residency time oﬀshore ofSouthern California and movement data on eastward migrating juveniles is needed to understand the ecological processes captured by thesix-month lag. One possible mechanism for the delay in response is thetransiting time it takes turtles in the Central Paciﬁc or oﬀ the Baja
California Peninsula to reach warming waters oﬀshore of Southern
California. Another possible mechanism is that it takes several monthsof locally suitable thermal conditions for turtles to aggregate oﬀshore ofSouthern California in high enough densities to be detected in thesightings and/or ﬁsheries data sets. Additional tagging studies andstable isotope analyses (e.g. Allen et al., 2013) could help elucidate theprocesses underpinning loggerhead habitat usage oﬀshore of Southern
California.

Fig. 4. The pelagic SST indicator and threshold. Time-series shows the monthly
SST value in the box most correlated with turtle presence in waters oﬀshore ofSouthern California (i.e. the indicator), minus the threshold. For each month,thresholds are the average monthly temperature calculated across years turtles
were seen oﬀshore of Southern California. Closures would be enacted when thetime-series is greater than zero (i.e. when indicator values exceed thresholds).

and full closure window scenarios, respectively. All eight indictors (theEl Niño indicators and local SST anomaly indicators, each calculated
over three smoothing windows, the pelagic SST indicator, and the El
Niño/local anomaly combined indicator) overlapped with the majorityof sighted turtles under the full closure window scenario (minimum of
95.1% avoided in the two-month local SST anomaly indicator), largely
due to the coincidence of elevated turtle sightings with anomalous
warming in 2015 that resulted in closures for all indicators. Overlap
with sighted turtles decreased to 6.7–10.8% under the partial closure
window scenario. Under the full closure window scenario, the sixmonth local SST anomaly indicator outperformed all other indicators by
30–55% in terms of avoided bycatch, avoiding all but two historical
bycatch events, in 2001 and 2006 (Table 2, Fig. 5). Under the partial
closure window scenario, seven historical bycatch events were not
avoided. Due to superior performance in terms of low opportunity costand avoidance of both sighted and bycaught turtles (Fig. 5), the sixmonth local SST anomaly indicator (Fig. 3D) was selected as the bestindicator to inform closure timing.

4.1. Operationalizing the indicator
Environmental indicators must be sophisticated enough to accurately capture information on ecosystem state, yet simple and cost-effective enough to be integrated into management schemes. These dual
objectives can be in direct competition – Indicator quality can come at a
cost of increased complexity (Hilborn, 2012), which can lead to implementation barriers (e.g. increased monitoring costs, challenges
communicating information to managers and stakeholders). We tested

4. Discussion
We developed and explored a suite of environmental indicators
designed to inform the timing of the Loggerhead Conservation Area
closure to reduce loggerhead bycatch in California’s Drift Gillnet
Fishery. We found the six-month local SST anomaly indicator (Figs. 3D

Table 2
Evaluating the eﬃcacy of thermal indicator groups. Hindcast closure scenarios based on indicator thresholds were used to quantify opportunity costs to ﬁshers(number of months between 2003 and 2017 in which the closure was enacted), overlap with sighted turtles, and ability to avoid turtle bycatch. Bolded values
indicate the best performing indicator for each evaluation metric. Values for each metric under the partial and closure window scenarios are shown separated by a“/”, respectively. Percent overlap with the seven months the closure was historically enacted is shown in the far-right column.
Indicator

Opportunity cost

Overlap with sighted turtles% Bycatch avoided

Overlap

El Niño

One-month average
Two-months averageSix-months average

3.6/27.4%
4.2/28.0%
6.5/28.0%

10.8/100.0%
10.8/100.0%
10.8/98.9%

25.0/43.8%
37.5%/50.0%
43.8%/56.3%

57.1%
71.4%
85.7%

Local

One-month average
Two-months averageSix-months average

6.5/21.4%
2.4/11.9%
5.4/18.5%

10.8/100.0%
6.7/95.1%
10.8/100.0%

37.5%/50.0%
37.5%/50.0%
56.3%/87.5%

100.0%
57.1%
100.0%

Pelagic SST
El Niño/local anomaly combo

5.4/22.0%
3.6/10.7%

10.4/99.6%
10.8/98.9%

25.0%/31.3%
43.8%/56.3%

85.7%
85.7%

Possible totals

168 months

268 sightings

16 bycatch events

7 closures662

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Fig. 5. The local SST anomaly indicator
based on temperature conditions in the six
months preceding closures, 1992–2017.
Plot demonstrates the indicator’s hindcast
ability to avoid turtle interactions. Closureswould be enacted when indicator values
exceed the threshold. Turtle data include
ﬁsheries dependent (1992–2006) and independent (2015–2017) data within the
Loggerhead Conservation Area.

reﬁne our representations of them. Indeed, as more data on loggerhead
distribution along southern California become available, loggerheadthermal responses can be better constrained, enabling development ofbetter indicators. To that end, we suggest that surveys of loggerhead
and prey species (e.g. red crabs, Cavole et al., 2016) be conductedperiodically. We also recommend that additional loggerhead tagging is
conducted to better understand ﬁne-scale movements of this endangered species within waters oﬀshore of Southern California in relation to oceanographic conditions and prey abundance for further reﬁnement of the management approaches described here.

the trade-oﬀ between comprehensiveness and complexity by evaluating
two simple indicator groups (El Niño and local SST anomaly indicators)
with single observation boxes and thresholds, and one complex indicator (the pelagic SST indicator), designed to capture loggerheadpresence with highest possible precision (three observation boxes and12 thresholds). The outperformance of the simple local SST anomaly
indicator over the complex pelagic SST indicator demonstrates thatcomprehensiveness is not being sacriﬁced for simplicity in this case.
Under the current closure guidelines, environmental information is
integrated with expert opinion and information on recent turtle sightings in a structured decision-making process to guide closure timing.
We propose the six-month local SST anomaly indicator to inform, as
opposed to replace, the current process. When the local SST anomaly
threshold is exceeded, managers could decide to directly enact theclosure, or to launch additional monitoring eﬀorts (e.g. aerial or shipboard) to survey turtles oﬀshore of Southern California to further inform managers about bycatch risk. Expert opinion provides an invaluable source of information regarding turtle behavior particularly indata-poor scenarios, and we stress the importance of maintaining thiscomponent of the structured decision-making process. Additionally, we
have provided a comparison of the indicator’s hindcast eﬃcacy underpartial closure window (June, July, and August, following the current
guidelines) and full closure window (year-round) scenarios. It will fallto the managers to determine if the current closure window should bemaintained, or if the increases in overlap with sighted turtles and historical bycatch avoidance warrant moving to a longer closure window.
To help with these determinations, we have developed a websitethat assembles information that may be useful for managers during thestructured decision-making process and for ﬁshers to determine closure
status and the rationale behind it (https://coastwatch.pfeg.noaa.gov/
loggerheads). The website displays the current environmental conditions within the Loggerhead Conservation Area, including SST and the
SST anomaly, and conditions present for the last six months. The ENSO
status, as reported by NOAA's Climate Prediction Center, and closure
status of the Loggerhead Conservation Area can also be viewed. In
addition, a data dashboard tool allows exploration of historical environmental data, conservation area closure dates, and ENSO status.
The diﬀerences between the anomaly product used on the website,which is based on the GHRSST MUR satellite product (JPL MUR
MEaSUREs, 2010), and the ROMS SST anomalies used to evaluate indicators in the present study are negligible (Fig. B1, Appendix B), thus
the products are comparable for use in informing closure timing.
Ecosystem-based ﬁsheries management requires continuous eﬀorts
to understand underlying species-environmental relationships and4.2. Conclusions
We have developed an operationally feasible environmental indicator designed to help minimize loggerhead bycatch in an appliedﬁsheries management scenario. Despite signiﬁcant evidence thatmarine environment impacts stock productivity and distribution, a recent meta-analysis of applied ﬁsheries management frameworks foundthat environmental drivers were only integrated for 2% of managed
stocks (24/1250 reviewed stocks, Skern-Mauritzen et al., 2016). Although many proof-of-concept environmental indicators have beendeveloped for ﬁsheries management (e.g. Roth et al., 2007; Burke et al.,2013), they are infrequently implemented due in part to concerns regarding the non-stationarity of species-environmental relationships (e.g
Paciﬁc sardines, McClatchie et al., 2010; Deyle et al., 2013) confounded
by a paucity of species distribution time-series data. However, issues
with data comprehensiveness will likely always be present in ﬁsheries
management exercises, and precautionary approaches such as the present methodology are needed to allow adherence with legislation while
incorporating the best available data. Additionally, climate change andvariability are expected to increase the frequency of anomalous oceanstates and potentially new ﬁsheries – Protected species interactions,further necessitating the integration of environmental indicators intoﬁsheries management plans.
Acknowledgements
This work was supported by NOAA Fisheries’ Bycatch Reduction
Engineering Program (grant number NA16NMF4720278). We thank
NOAA CoastWatch West Coast Regional Node (https://coastwatch.pfeg.
noaa.gov) for assistance in accessing satellite data and for providing a
web presence for the project. NOAA CoastWatch-West Coast and NOAASouthwest Fisheries Science Center generated monthly SST composites
with data obtained from the following sources: 1) AVHRR Pathﬁnder
663

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H. Welch et al.

Version 5.2 (PFV5.2) data, obtained from the US National
Oceanographic Data Center and GHRSST (http://pathﬁnder.nodc.noaa.
gov) and 2) the Group for High Resolution Sea Surface Temperature(GHRSST) Multi-scale Ultra-high Resolution (MUR) data obtained fromthe THREDDS server at the NASA EOSDIS Physical Oceanography
Distributed Active Archive Center (PO.DAAC), Jet Propulsion
Laboratory, Pasadena, CA (https://thredds.jpl.nasa.gov/thredds)

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Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2018.11.001.
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