Impact of light on catch rate of four demersal fish species during the 2009–2010 U.S. west coast groundfish bottom trawl survey

Fisheries Research 164 (2015) 193–200

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Impact of light on catch rate of four demersal fish species during the 2009–2010 U.S. west coast groundfish bottom trawl survey Mark J. Bradburn, Aimee A. Keller ∗ Fishery Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Boulevard East, Seattle, WA 98112, USA

a r t i c l e

a b s t r a c t

i n f o

Article history: Received 23 January 2014 Received in revised form 13 August 2014 Accepted 26 November 2014 Handling Editor P. He Keywords: Demersal fish Near-bottom light-levels Capture probability Herding Avoidance Bottom trawl survey

To determine the influence of light on catch of demersal fish, we examined the relationship between near-bottom light levels, catch rates, and catch probability for four abundant groundfish species well represented in annual bottom trawl surveys on the U.S. west coast: arrowtooth flounder (Atheresthes stomias), greenstriped rockfish (Sebastes elongatus), longnose skate (Raja rhina), and Pacific hake (Merluccius productus). Relative downward irradiance was measured with MK-9 archival tags during annual trawl surveys along the U.S. west coast in 2009 and 2010. Near-bottom light levels were recorded for 818 hauls at depths less than 400 m. Significant linear relationships were observed between catch per unit effort (CPUE, kg ha−1 ) and near-bottom light (P < 0.05). CPUE of arrowtooth flounder, longnose skate, and Pacific hake was negatively related to near-bottom light. For these species, CPUE decreased 16–22% per unit increase in log10 light (␮E m−2 s−1 ). CPUE of greenstriped rockfish increased 39% per unit increase in log10 light. Light, depth, and latitude explained 15–47% of the variance in CPUE for the four species. Catch probability was significantly related to light, depth, latitude, and relative time of day (P < 0.05). For all species, catch probability varied inversely with light when depth was less than 200 m. At depths of 200–300 m, catch probability increased with light for arrowtooth flounder and greenstriped rockfish. Catch probability for Pacific hake decreased slightly at depths greater than 200 m while longnose skate was relatively unaffected by light at these depths. We used these relationships to explain the variability in catch rates for individual species within bottom trawl surveys. By influencing the density and distribution of these groundfish species, light can alter catch rates. Furthermore, we found possible herding of greenstriped rockfish, and trawl avoidance by arrowtooth flounder, Pacific hake, and longnose skate. Published by Elsevier B.V.

1. Introduction Fisheries assessments rely on fisheries-independent surveys to provide biomass indices for important species. Catch rates, or catch per unit effort (CPUE), for survey trawls have been shown to be correlated with environmental variables such as wind and wave height (Poulard and Trenkel, 2007; Stewart et al., 2010; Wieland et al., 2011), temperature (Hjellvik et al., 2004; Ryer and Barnett, 2006), salinity (Smith et al., 1991), and near-bottom light (Kotwicki et al., 2009). When biomass estimates and spatial distributions are derived from survey catch rates, such environmental variables should not be ignored. Because vision is considered the principal sensory input that controls fish behavior and orientation to trawl gear (Wardle, 1989,

∗ Corresponding author. Tel.: +1 206 860 3460. E-mail address: [email protected] (A.A. Keller). http://dx.doi.org/10.1016/j.fishres.2014.11.010 0165-7836/Published by Elsevier B.V.

1993; Glass and Wardle, 1989; Engås and Ona, 1990), numerous studies have focused on the effect of ambient light availability on behavioral response of fishes to trawl gear in the laboratory (Olla et al., 2000; Ryer and Barnett, 2006; Ryer and Olla, 2000) and the field (Ryer et al., 2010; Williams et al., 2011). These responses are analogous to predator avoidance behavior (Ryer, 2008). Light-mediated behaviors vary between and within morphological groups (e.g., roundfishes and flatfishes) and species, thus species-specific reactions to approaching trawl gear can affect trawl efficiency and catch rates (Olla et al., 2000, 1997; Ryer et al., 2010; Ryer, 2008; Ryer and Barnett, 2006; Ryer and Olla, 2000). Fish maintain comfortable distances from an approaching disturbance through herding behavior, rising off the bottom, and/or burrowing into the sediment (Ryer, 2008; Wardle, 1993). Each reaction alters the probability of capture and the effective area or volume sampled by the trawl. Because the behaviors are light-dependent, avoidance behavior can be inhibited when light is inadequate (Gabr et al., 2007; Olla et al., 1997; Ryer and Olla, 2000). Ryer and Barnett (2006)

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found increased initiation of herding of flatfish in light versus a diminished response in darkness. Light levels at depth can also alter species-specific availability of fish to the trawl and affect catch rates (Aglen et al., 1999). Vertical position relative to the seafloor can determine the availability of a population to the volume sampled by a bottom trawl. For example, Kotwicki et al. (2009) measured that the availability of walleye pollock (Theragra chalcogramma) to a bottom trawl using acoustic backscatter and noted that when near-bottom light levels were less than 1 × 10−5 ␮E m−2 s−1 , the proportion of pollock available to the trawl was nearly zero. For groundfish species on the U.S. west coast, within-day vertical movements are not well documented. Vertical position changes between night and day are common for Pacific hake (Merluccius productus) (Emmett and Krutzikowsky, 2008; Krutzikowsky and Emmett, 2005) and some rockfishes (Sebastes spp., Hannah et al., 2005). However, movements during daylight-hours and the degree to which they are driven by light levels are not well understood. The potential for vertical movement by Pacific hake, rockfish, and other groundfish species could be important to bottom trawl catch rates. To examine the influence of light availability on bottom trawl catch rates on the U.S. west coast, we measured near-bottom light levels during an annual groundfish trawl survey in 2009 and 2010. Here we report the relationship between near-bottom light levels and catch characteristics of four common groundfishes sampled during the annual groundfish bottom trawl survey on the U.S. west coast: arrowtooth flounder (Atheresthes stomias), greenstriped rockfish (Sebastes elongatus), longnose skate (Raja rhina), and Pacific hake. As noted by Kotwicki et al. (2009) understanding the relationship between near bottom light intensity and demersal fish catch could improve our understanding of fish availability to survey gear, a topic of particular interest to stock assessment scientists. 2. Methods 2.1. Survey design The Northwest Fisheries Science Center (Seattle, WA) has conducted a standardized annual West Coast Groundfish Bottom Trawl Survey (WCGBTS) since 2003 (Bradburn et al., 2011). WCGBTS provides fisheries-independent estimates of biomass for managed species. It is a cooperative research effort which charters commercial trawlers to conduct survey operations. The survey spans 48◦ 10 N to 32◦ 30 N latitude and occurs between May and October each year (Table 1). Sampling is confined to daylight hours (dawn to dusk). Station allocation is based on a stratified random design. The entire U.S. west coast (55–1280 m water depth) has been subdivided into 13,000 adjacent cells of equal area (1.5 nautical miles [nm] longitude by 2.0 nm latitude, Albers Equal Area projection). Cells are stratified by depth and latitude and then randomly selected. Vessels were equipped with a standard four-panel, singlebridle, Aberdeen-type trawl spread by 1.5 m × 2.1 m steel V doors weighing 590 kg. The headrope and footrope measured 25.9 and 31.7 m, respectively. An additional liner (3.81 cm stretched mesh,

Table 1 Characteristics for bottom trawl survey hauls: light (␮E m−2 s−1 ), depth (m), latitude (◦ N), time of day (PDT), and date (2009–2010). Variable

Minimum −2

−1

Light (␮E m s ) Depth (m) Latitude (◦ N) Time of day (PDT) Date

−5

<10 57.3 32.6 5:25:47 24-May

Mean

Maximum −4

1.5 × 10 162.9 41.1 11:29:24 05-August

1.07 398.8 48.5 20:26:00 24-October

center knot to center knot) extended from the middle of the intermediate, through the codend, to retain smaller fishes and invertebrates. Trawl sweeps were composed of one section of mud gear (distal from the trawl), and a bridle consisting of an upper and lower leg, where the lower leg functions as a section of mud gear (proximal to the trawl). Sweeps extended from the trawl door to the footrope of the trawl. The sections of mud gear and lower bridles were 27.4 m bearing point to bearing point and made from 15.88 mm galvanized steel-core wire rope (Independent Wire Rope Core) covered by 8.9 cm rubber discs. Rubber discs were either belt or punched tire material, with 10.3 cm rubber discs centered every 4.57 m sandwiched between two 15.2 cm rubber discs. The target duration of each tow was 15 min with mean towing speed of 2.2 Kts. The Simrad Integrated Trawl Instrumentation (ITI Kongsberg Simrad Mesotech Ltd., Port Coquitlam, BC, Canada) was used to monitor and record net performance and position for each haul. Sensors mounted on the center of the headrope, wings and doors provided information on the vertical opening of the trawl, distance to the seafloor, footrope clearance above the bottom, net width, wing spread, and ambient temperature and depth. The Simrad ITI trawl instruments displayed gear performance in real time and provided geo-referenced trawl positions relative to ship position, supplying a means to track the trawl location along the seafloor throughout each tow. A differential global positioning system (DGPS) navigation unit (Garmin 152, Garmin International Inc., Olathe, KS, USA) was used to monitor towing speed during each haul. Position data, collected at 2-s intervals, were used to monitor ground speed, track the vessel path, and estimate distance fished. A pair of bottom contact sensors (BCSs) was attached four feet from the center-point of the footrope, on either side of the net. BCS data indicated when the net landed on and lifted off bottom and were used to determine duration of tow and distance fished. Standard survey haul positions were estimated from DGPS data – generally the mid-point between the net touchdown and net liftoff positions. Average net speed over ground and distance fished were calculated from the position data for the trawl and actual bottom time (Bradburn et al., 2011). Catch was sorted to species and weighed using an electronic, motion-compensated scale (Marel, Reykjavik, Iceland). Relative density was calculated as catch per unit effort (CPUE) for individual species by dividing total catch weight (kg) per species by area swept (ha) per tow. 2.2. Light In 2009 and 2010, we collected near-bottom light data for 1380 hauls. We limited our analysis to hauls with depth less than 400 m (n = 818) since the four species examined are commonly taken at depths less than 400 m, with peak abundances near 150–200 m (Keller et al., 2012). Light levels at sampling stations beyond 400 m did not vary and were consistently low (less than 1 × 10−5 ␮E m−2 s−1 ). Light data were collected with MK-9 archival tags (Wildlife Computers, Redmond, WA) attached 1 m aft of the center of the head rope on each of four chartered west coast fishing vessels. The MK-9 tags were attached to white plastic sheets (Ultra High Molecular Weight), triangular in shape (height ∼25 cm), with couplings at each point of the triangle. The light sensor was exposed and faced upwards toward the sea surface. Light and depth data were collected at 1-s intervals. Tags measured and recorded light as total relative electromagnetic irradiance. The relative units were converted to ␮E m−2 s−1 according to the calibration function: y = 1 × 10−8 e−0.1322x

(1)

provided by Kotwicki et al. (2009). Light levels below 10−5 ␮E m−2 s−1 were categorized as less than 10−5 ␮E m−2 s−1 to

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avoid potential inaccuracies that may occur in MK-9 tags under these darker light conditions. Near-bottom light levels were estimated for 818 hauls less than 400 m in 2009 and 2010. For each haul, relative irradiance was estimated by finding the mean light level when the net was on bottom, as indicated by bottom contact sensors (NMFS Alaska Fisheries Science Center, Seattle, WA). Relative time of day for each haul was calculated by measuring the absolute difference in minutes between haul touchdown and solar noon by date, longitude, and latitude (www.esrl.noaa.gov). The relative difference in minutes was normalized by day length in minutes. 2.3. Data analysis Since light and catch measurements were not normally distributed, we log10 transformed these predictors to meet the assumptions of linear regression. We used simple linear regression to examine the relationship between log10 light and four covariates: latitude, depth, time of day and day of year. Simple linear regression was also used to model the relationship between log10 catch, log10 light, depth, and latitude. We used a generalized linear regression analysis to test the effect of light on the distribution of each species (Ihaka and Gentleman, 1996; R Core Team, 2012). We examined the probability of catching at least one fish of a particular species in a tow by initially converting catch (CPUE kg ha−1 ) to the presence–absence data and subsequently fitting a logistic regression. The logistic model was used to predict the probability of occurrence for each species from a set of variables that included log10 light (␮E m−2 s−1 ), depth (m), latitude (◦ N), and relative time of day, including interaction terms. The parameters of the logistic equation were estimated using generalized linear regression models with a binomial link via a backward selection process. Models were compared by ANOVA using the ‘stats’ package anova.lm() function (R Core Team, 2012). 3. Results 3.1. Light For stations less than 400 m in depth, near-bottom light ranged from less than 10−5 to 1.07 ␮E m−2 s−1 (Table 1, Fig. 1). We examined the relationship between log10 light and four covariates: latitude, depth, time of day and Julian date by linear regression (Table 2). For all hauls in 2009 and 2010, near-bottom light levels varied inversely with latitude (R2 = 0.01, P < 0.002). There was also a significant relationship between log10 light and depth but not with time of day or Julian date (Table 2, Fig. 2). 3.2. Linear regression model For all species, log10 CPUE was significantly related to log10 light, as well as depth and latitude (P < 0.05) (Table 3). Light, depth, and latitude explained 15–47% of the variance in CPUE for the four species. CPUE was significantly and negatively related to Table 2 The relationship between log10 light (␮E m−2 s−1 ) and the variables: depth (m), latitude (◦ N), day of year (Julian day), and time of day relative to solar noon. Relationships were modeled by linear regression. Variable ()

Model

Adj. R2

P-value

Depth Latitude Day of year Relative time of day

y = 0.11 − 0.028x − 0.000030x2 y = −1.77 − 0.04x y = −3.79 + 0.0017x y = −3.35 + 0.41x

0.59 0.01 0 0.00

<0.001* 0.002* 0.20 0.357

*

Statistical significance (P < 0.01).

195

near-bottom light for arrowtooth flounder (P < 0.001), longnose skate (P < 0.001), and Pacific hake (P < 0.028) (Fig. 3). For these species, CPUE respectively decreased 22, 18, and 16%, when light (␮E m−2 s−1 ) increased one order of magnitude (Fig. 3a, c and d). There was a significant (P < 0.001) positive relationship between greenstriped rockfish CPUE and light (Fig. 3b). The CPUE of greenstriped rockfish increased 39% per order of magnitude increase in log10 light. For all four species, depth and latitude were also significant explanatory variables (Table 3). Within the depth range examined, catch increased monotonically with depth for Pacific hake (Fig. 3d). The three other species had a curvilinear depth distribution with the highest catch rates near 200 m (Fig. 3a–c). Catch rates for all four species were higher in northern latitudes. A summary of the regression coefficients, significance levels, and R2 values is reported in Table 3. 3.3. Logistic regression model The probability of capturing a single fish of each species varied with log10 light, depth, latitude, and relative time of day (Table 4, Fig. 4). There was a significant interaction between light and depth for arrowtooth flounder, greenstriped rockfish, and longnose skate. For all species, there was a negative relationship between log10 light and catch probability for stations shallower than 100 m (Fig. 4). The relationship between the probability of catch for both arrowtooth flounder and greenstriped rockfish and near bottom light switched from negative to positive as depth increased between 100 and 200 m (Fig. 4a and b). The probability of catching Pacific hake and longnose skate in deeper habitats (200–300 m) decreased slightly, relative to 100 m, as near-bottom light levels increased. 4. Discussion Catch patterns for a trawl survey are dependent on the density of fish available to the volume sampled by the trawl and the trawl efficiency for a particular species (Munro and Somerton, 2002). For some species, the interaction between light and fish behavior will influence density, availability, and trawl efficiency. Understanding the mechanisms by which light influences catch rates is not simple. In this study we were able to measure the effect of light on catch rates and availability. A species’ availability can be inferred from catch probability. We used this probability to learn how light levels changed the distribution of species near the seafloor. The chance of encountering an evenly-distributed species would be high compared to that of a species with a patchy distribution. Here we found that light does alter the probability of capturing some species. For example, Pacific hake is a semi-pelagic species known to change vertical position between night and day (Ressler et al., 2007; Stauffer, 1985). The vertical position changes are most likely in response to varying light. Walleye pollock, a similar species, is also known to make light-dependent changes in vertical position during the day (Kotwicki et al., 2009). Based on our results, increasing light decreased the probability of capturing individual Pacific hake (Fig. 3d). The pattern suggests that as near-bottom light levels increase, aggregations of Pacific hake have a tendency to ascend beyond the region sampled by the bottom trawl (mean headrope height for the WCGBTS is about 5 m) (Bradburn et al., 2011). This effect was most pronounced in shallow habitats (∼100 m), where fish are typically smaller and younger (Bailey et al., 1982). The effect of light on catch probability was not as strong beyond 100 m. The effect was minimal at 300 m. However, catch rates still varied with light levels in deeper habitats. Pacific hake CPUE increased as light decreased. While some of the increase could be attributed to changes in vertical position, the trends in catch rates

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Fig. 1. Distribution of near-bottom light (␮E m−2 s−1 ) along the U.S. west coast in 2009 and 2010. The contour lines are 55, 183, 549 and 1280 m water depth.

are also congruent with light-mediated escapement. If the number of individuals escaping over the trawl headrope decreased as light decreased, we would expect higher catch rates in dark tows. Studies have reported that reaction distance to approaching trawl gear varies by light level for other gadiforms, such as Atlantic cod and haddock (Glass and Wardle, 1989; Wardle, 1993). Additionally, the potential for size dependent and species specific fish escapement under the footgear and through meshes of trawls has been reported elsewhere and is a possible partial explanation of results

seen here (Engås and Godo, 1989; Walsh, 1989, 1992). We cannot directly test the influence of visual detection on catch rates, but the size selectivity of trawls does provide clues. Controlling for depth, the mean length of Pacific hake captured significantly increased (P < 0.001) as light levels decreased (Fig. 5), unlike other species examined here (data not shown). Perhaps larger individuals, with larger eye diameters and stronger swimming ability, were less likely to be captured when the light levels were sufficient for gear detection.

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Log10 light (µE m-2 s-1)

y = - 1.77 - 0.040 x R² = 0.012

y = 0.11 - 0.028 x + 0.00003 x2 R² = 0.56

1

197

1

-1

-1

-3

-3

-5

-5

<-5

<-5 50

100

150

200

250

300

350

400

30

Depth (m)

35

40 Latitude (°N)

45

Fig. 2. Significant relationships between: (a) log10 light (␮E m−2 s−1 ) and depth (m) and; (b) log10 light (␮E m−2 s−1 ) and latitude (◦ N). The model and R2 -values are shown at the top of each plot. The predictions for each model are shown by a solid line.

Table 3 Summary of the linear models used to describe the relationships between CPUE (kg ha−1 ), log10 light (␮E m−2 s−1 ), latitude (◦ N), and depth (m) for arrowtooth flounder, greenstriped rockfish, longnose skate, and Pacific hake. Species

Variable

Coefficient

Std. error

P-value

Adj. R2

Arrowtooth flounder

log10 light Latitude Depth Depth2

−0.11 0.12 0.02 −0.000043

0.026 0.011 0.002 0.0000041

<0.001 <0.001 <0.001 <0.001

0.47

Greenstriped rockfish

log10 light Latitude Depth Depth2

0.14 0.083 0.027 −0.000056

0.042 0.01 0.0063 0.000017

<0.001 <0.001 <0.001 0.0014

0.25

Longnose skate

log10 light Latitude Depth Depth2

−0.088 0.047 0.005 −0.000009

0.29 0.0059 0.017 0.0000037

<0.001 <0.001 0.003 0.01

0.20

Pacific hake

log10 light Latitude Depth Depth2

−0.076 0.048 0.002 –

0.034 0.0081 0.00065 –

0.028 <0.001 0.002 –

0.15

Since we saw no increase in catch probability combined with increased CPUE at elevated relative light levels (Figs. 3d and 4d), visual herding of Pacific hake by the mud gear was not supported in this study. However, we cannot dismiss the possibility that Pacific hake were herded toward the path of the footrope. But, if herding did occur, the effect must have been small relative to the effects of

vertical position and escapement over or under the net. The absence of herding behavior has been documented for related species such as Pacific cod (Gadus morhua) and walleye pollock by Engås and Godo (1989) and Somerton (2004). The only species that exhibited signs of herding in this study was greenstriped rockfish, as defined by the observed increase

Table 4 Summary of logistic regressions models used to describe the relationship between catch probability, log10 light (␮E m−2 s−1 ), depth (m), latitude (◦ N), and relative time of day for arrowtooth flounder, greenstriped rockfish, longnose skate, and Pacific hake. Interaction between log10 light and depth indicated by ‘light × depth’. Only significant (P < 0.05) variables and interactions are shown. Species

Variable

Coefficient

Std. error

P-value

Arrowtooth flounder

log10 light Depth Latitude Light × depth

−1.23 0.051 0.59 0.0084

0.19 0.0071 0.04 0.0013

<0.001 <0.001 <0.001 <0.001

Greenstriped rockfish

log10 light Depth Light × depth

−1.9 0.077 0.016

0.16 0.0062 0.0012

<0.001 <0.001 <0.001

Longnose skate

log10 light Depth Latitude Relative time of day Light × depth

−0.72 0.019 0.12 −1.35 0.0027

0.13 0.0047 0.019 0.68 0.00086

<0.001 <0.001 <0.001 0.049 <0.01

Pacific hake

log10 light Depth Latitude Relative time of day

−0.26 0.01 0.066 −1.47

0.78 0.074 0.0018 0.63

<0.001 <0.001 <0.001 0.020

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Fig. 3. Relationships between log10 CPUE (kg ha−1 ), depth (m), and log10 light (␮E m−2 s−1 ) for: (a) arrowtooth flounder, (b) greenstriped rockfish, (c) longnose skate, and; (d) Pacific hake. For these figures latitude was held constant at 45◦ N and time of day at solar noon. Four light levels ranging from less than −5 to 0 log10 light (␮E m−2 s−1 ) are indicated for each species with lower levels represented via darker lines.

in both catch probability and CPUE at higher relative light levels (Figs. 3b and 4b). When light levels were high, the probability of sampling a greenstriped rockfish was nearly 90% at 200 m. As light decreased they were less frequently encountered at that depth. Because greenstriped rockfish became more ubiquitous as light increased, we would expect a concomitant decrease in density. Instead, both the catch probability and CPUE for greenstriped rockfish increased with relative light levels at depths of 200–300 m. The combination of increased catch probability and CPUE is indicative of light-mediated herding. This herding hypothesis depends on greenstriped rockfish having minimal vertical structure and high fidelity to the benthos. King et al. (2004) indicated that greenstriped rockfish are primarily found on the bottom since they did not respond in terms of catch to an experimental net with a cut-back headrope. In contrast, Hannah et al. (2005) found significantly higher catches for greenstriped rockfish in an experimental net with a cut-back headrope. There is no direct evidence that greenstriped rockfish are strictly limited to the seafloor, but adults are benthic and primarily caught in bottom trawls (Love et al., 2002). Longnose skate are presumed to be obligate benthic species, often found buried in sediment (Ebert, 2003). Only 3% of longnose skate caught on the U.S. west coast since 1982 were caught using methods other than bottom trawl (Gertseva, 2009). They also consume primarily benthic prey, like crabs and cephalopods

(Bizzarro et al., 2007). The association between longnose skate and the seafloor was confirmed by patterns found in this study. Based on the probability of catch, the availability of longnose skate to the volume sampled by survey trawl is nearly constant or uniform at all light levels. Deeper than 150 m, the probability of catch was greater than 80% irrespective of relative light level (Figs. 3c and 4d). The habits and distribution of longnose skate allow us to make inferences about the mechanisms underlying the relationship between near-bottom light levels and CPUE. We expect longnose skate to have no vertical structure and to not flee the bottom when trawl gear approaches. An aversion to rising off the bottom and over the headrope was indicated by King et al. (2004) based on the use of experimental trawls. However, Hannah et al. (2005) showed no significant difference in longnose skate catches between a four-seam Aberdeen high-rise combination trawl and an experimental net with a cut-back headrope, although a smaller sample size was caught in their work as compared to King et al. (2004). For our survey, longnose skate CPUE was highest when light levels were lowest. Since catch probability proved unresponsive to light at depths greater than 150 m, light-dependent changes in trawl efficiency or density could explain the trends in CPUE. It is not obvious how light availability would affect the density of skates, unless longnose skate make within-day horizontal movements along the seafloor, seeking a specific light environment.

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199

Fig. 4. The probability of catch for: (a) arrowtooth flounder, (b) greenstriped rockfish, (c) longnose skate, and; (d) Pacific hake. For these figures latitude was held constant at 45◦ N and time of day at solar noon. Line thickness is proportional to mean CPUE (kg ha−1 ) for each depth ± 50 m.

Pacific hake 2009 - 2010 Mean length (cm tow-1)

As light levels decreased other mechanisms which could led to a change in catch include decreased escapement, increased herding, or other behavioral changes such as decreased coupling to the substrate in darker conditions but still available to the trawl within the headrope height. Other skates (Bathyraja spp.) are known to escape trawls by passing under the footrope (Kotwicki and Weinberg, 2005; Weinberg et al., 2002). In the absence of adequate light, escapement under the footrope or around the mud gear could have been inhibited. Changes in trawl efficiency were not related to changes in sampling gear and protocols since these remained constant throughout the survey. However, there are potential but unknown effects on trawl efficiency possible if the bridle angle and bridle efficiency change with depth. Like longnose skate, light-dependent changes in the catch probability of arrowtooth flounder did not explain the trends in CPUE. At depths greater than 150 m, the effect of light on catch probability was negligible, but catch rates were higher in darker hauls (Figs. 3a and 4a). These trends in catch could be explained by lightdependent variations in density, trawl efficiency, or a combination of these variables. From a trawl efficiency perspective, increased catch rates in the dark are not consistent with herding. The absence of herding is unexpected because most flatfish, including arrowtooth flounder, have demonstrated a herding response when light is available (Ryer et al., 2010; Ryer, 2008; Ryer and Barnett, 2006). It may be related to the fact that survey trawls have shorter sweeps relative to commercial trawls and therefore the effect of herding may be small

y = -2.48 x + 21.6 R² = 0.11

60 40 20 0 -7

< -6 -5

-5 -4 -3 -2 Log10 light (µE m-2 s-1)

-1

0

Fig. 5. Significant relationship (P < 0.0001) between mean length per tow (cm tow−1 ) for Pacific hake in 2009 and 2010 and log10 light (␮E m−2 s−1 ). The fitted regression and R2 -value are shown with the prediction for the model shown by a solid line.

(Ryer and Barnett, 2006). The trends in catch rates were consistent with light-mediated escapement over the headrope. King et al. (2004) found that arrowtooth flounder catches were unaffected by an experimental trawl. However, Hannah et al. (2005) found significantly lower catch rates for arrowtooth flounder in an experimental net with a cut-back headrope. Escapement under the footrope is also a viable explanation. In fact, arrowtooth flounder, especially

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individuals smaller than 25 cm, have been captured by auxiliary nets attached beneath a bottom trawl (Weinberg et al., 2002). Finally the higher catch rates of arrowtooth flounder in lower light may have been associated with variable density. However, the relationship between light and density is unclear. When light levels were relatively low (less than 1 × 10−5 ␮E m−2 s−1 ), it appeared that the survey captured dense aggregations near the northern limit of the survey. As light availability increased, these ‘hot spots’ faded, and the distribution of smaller catches expanded. Conversely, increased trawl efficiency related to mechanisms such as increased herding, decreased escapement or light-mediated behavioral changes could also be responsible for the large catches. 5. Conclusion Survey trawl catch rates were dependent on light availability, however the mechanisms varied. Understanding the relationship between light and catch rates requires knowledge about the density and biology of fish within the volume sampled. Without the proper context, it is difficult to arrive at conclusions about visual avoidance behavior and trawl efficiency. However, inferences about the mechanisms can be made by comparing species with different body plans, behaviors, and ecologies. Regardless of the mechanism, light availability influenced trawl survey catch rates. Given the importance of light to catch rates, light measurements are required to properly interpret the results of fisheries-independent bottom trawl surveys and the subsequent use of trawl data in population assessments. Acknowledgements The authors are indebted to the captains and the crew of the chartered fishing vessels Excalibur, Ms. Julie, Noah’s Ark, and Raven for providing at-sea support. We especially thank the 2009 and 2010 West Coast Groundfish Bottom Trawl Survey team (V. Simon, K. Bosley, D. Bryan, D. Kamikawa, V. Tuttle, J. Buchanan, E. Fruh and J. Harms) for their skill and dedication in collecting high quality data for the groundfish survey, and B. Horness for providing data. We are also grateful to C. Harvey and J. Cope for their assistance with data analysis and thoughtful reviews and especially grateful to Kayleigh Somers for her help with GIS charts. References Aglen, A., Engås, A., Huse, I., Michalsen, K., Stensholt, B.K., 1999. How vertical fish distribution may affect survey results. ICES J. Mar. Sci. 56, 345–360. Bailey, K.M., Francis, R.C., Stevens, P.R., 1982. The life history and fishery of Pacific whiting, Merluccius productus. Calif. Coop. Ocean. Fish. Invest. Rep. 28, 81–98. Bizzarro, J.J., Robinson, H.J., Ebert, D.A., 2007. Comparative feeding ecology of four sympatric skate species off central California, USA. Environ. Biol. Fishes 80, 197–220. Bradburn, M.B., Keller, A.A., Horness, B.H., 2011. The 2003 to 2008 U.S. West Coast Bottom Trawl Surveys of Groundfish Resources Off Washington, Oregon, and California: Estimates of Distribution, Abundance, Length, and Age Composition. U.S. Dept. Commerce, NOAA Tech. Memo. NMFS-NWFSC–114. Ebert, D.A., 2003. Sharks, Rays and Chimaeras of California. University of California Press, Berkeley, CA, pp. 284. Emmett, R.L., Krutzikowsky, G.K., 2008. Nocturnal feeding of Pacific hake and jack mackerel off the mouth of the Columbia River, 1998–2004: implications for juvenile salmon predation. Trans. Am. Fish. Soc. 137, 657–676. Engås, A., Godo, O., 1989. The effect of different sweep lengths on the length composition of bottom-sampling trawl catches. ICES J. Mar. Sci. 45, 263–268. Engås, A., Ona, E., 1990. Day and night fish distribution pattern in the net mouth area of the Norwegian bottom-sampling trawl. In: Rapports et Proce‘s-Verbaux ´ des Reunions du Conseil International pour l’Exploration de la Mer, vol. 189, pp. 123–127. Gabr, M., Fujimori, Y., Shimizu, S., Muira, T., 2007. Trawling experiment in a circular water tank to assess the effects of towing speed, light intensity, and mesh shape on active escape of undersized fish. Fish. Sci. 73, 557–564.

Gertseva, V.V., 2009. The population dynamics of the longnose skate, Raja rhina, in the northeast Pacific Ocean. Fish. Res. 95, 146–153. Glass, C.W., Wardle, C.S., 1989. Comparison of the reactions of fish to a trawl gear, at high and low light intensities. Fish. Res. 7, 249–266. Hannah, R.W., Parker, S.J., Buell, T.V., 2005. Evaluation of a selective flatfish trawl and diel variation in rockfish catchability as bycatch reduction tools in the deepwater complex fishery off the U.S. West Coast. N. Am. J. Fish. Manag. 25, 581–593. Hjellvik, V., Godø, O.R., Tjøstheim, D., 2004. Decomposing and explaining the variability of bottom trawl survey data from the Barents Sea. Sarsia 89, 196–210. Ihaka, R., Gentleman, R., 1996. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314. Keller, A.A., Wallace, J., Horness, B.H., Hamel, O., Stewart, I., 2012. Variations in Eastern North Pacific demersal fish biomass based on the U.S. West Coast Groundfish Bottom Trawl Survey (2003–2010). Fish. Bull. 110, 63–80. King, S.E., Hannah, R.W., Parker, S.J., Matteson, K.M., Berkeley, S.A., 2004. Protecting rockfish through gear design: development of a selective flatfish trawl for the U.S. west coast bottom trawl fishery. Can. J. Fish. Aquat. Sci. 61, 487–496. Kotwicki, S., Weinberg, K.L., 2005. Estimating capture probability of a survey bottom trawl for Bering Sea skates (Bathyraja spp.) and other fish. Alsk. Fish. Bull. 11, 135–145. Kotwicki, S., De Robertis, A., von Szalay, P., Towler, R., 2009. The effect of light intensity on the availability of walleye pollock (Theragra chalcogramma) to bottom trawl and acoustic surveys. Can. J. Fish. Aquat. Sci. 66, 983–994. Krutzikowsky, G., Emmett, R., 2005. Diel differences in surface trawl fish catches off Oregon and Washington. Fish. Res. 71, 365–371. Love, M.S., Yoklavich, M.M., Thorsteinson, L.K., 2002. The Rockfishes of the Northeast Pacific. University of California Press, Berkeley, CA. Munro, R.T., Somerton, D.A., 2002. Estimating net efficiency of a survey trawl for flatfishes. Fish. Res. 55, 267–279. Olla, B.L., Davis, M.W., Schreck, C.B., 1997. Effects of simulated trawling on sablefish and walleye pollock: the role of light intensity, net velocity and towing duration. J. Fish Biol. 50, 1181–1194. Olla, B.L., Davis, M.W., Rose, C., 2000. Differences in orientation and swimming of walleye pollock Theragra chalcogramma in a trawl net under light and dark conditions: concordance between field and laboratory observations. Fish. Res. 44, 261–266. Poulard, J., Trenkel, V., 2007. Do survey design and wind conditions influence survey indices? Can. J. Fish. Aquat. Sci. 64, 1551–1562. R Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0, http://www.R-project.org/ Ressler, P.H., Holmes, J.A., Fleischer, G.W., Thomas, R.E., Cooke, K.C., 2007. Pacific hake, Merluccius productus, autecology: a timely review. Mar. Fish. Rev. 69, 1–24. Ryer, C.H., 2008. A review of flatfish behavior relative to trawls. Fish. Res. 90, 138–146. Ryer, C.H., Barnett, L.A., 2006. Influence of illumination and temperature upon flatfish reactivity and herding behavior: potential implications for trawl capture efficiency. Fish. Res. 81, 242–250. Ryer, C.H., Olla, B.L., 2000. Avoidance of an approaching net by juvenile walleye pollock Theragra chalcogramma in the laboratory: the influence of light intensity. Fish. Res. 45, 195–199. Ryer, C., Rose, C.S., Iseri, P., 2010. Flatfish herding behavior in response to trawl sweeps: a comparison of diel responses to conventional sweeps and elevated sweeps. Fish. Bull. 108, 145–154. Smith, S.J., Perry, R.I., Fanning, L.P., 1991. Relationships between water mass characteristics and estimates of fish population abundance from trawl surveys. Environ. Monit. Assess. 17, 227–245. Somerton, D., 2004. Do Pacific cod (Gadus macrocephalus) and walleye pollock (Theregra chalcogramma) lack a herding response to the doors, bridles, and mudclouds of survey trawls? ICES J. Mar. Sci. 61, 1186–1189. Stauffer, G.D., 1985. Biology and life history of the coastal stock of Pacific whiting, Merluccius productus. Mar. Fish. Rev. 47 (2), 2–7. Stewart, I.J., Keller, A.A., Fruh, E.L., Simon, V.H., Horness, B.H., 2010. Throwing in the towel: when do adverse conditions dictate a weather day during a bottom trawl survey? Fish. Res. 102, 130–140. Walsh, S.J., 1989. Escapement of fish underneath the footgear of a groundfish survey trawl. Int. Counc. Explor. Sea 21, 22. Walsh, S.J., 1992. Size-dependent selection at the footgear of a groundfish survey trawl. N. Am. J. Fish. Manag. 12, 625–633. Wardle, C.S., 1989. Understanding fish behavior can lead to more selective fishing gears. In: Fox, S.G., Huntington, J. (Eds.), Proceedings of the World Symposium on Fishing Gear and Fishing Vessel Design. Newfoundland and Labrador Institute of Fisheries and Marine Technology, St. John’s, NL, Canada, pp. 12–18. Wardle, C.S., 1993. Fish behaviour and fishing gear. In: Behaviour of Teleost Fishes. Chapman and Hall, London. Weinberg, K.L., Somerton, D.A., Munro, P.T., 2002. The effect of trawl speed on the footrope capture efficiency of a survey trawl. Fish. Res. 58, 303–313. Wieland, K., Olesen, H.J., Pedersen, E.M., Beyer, J., 2011. Potential bias in estimates of abundance and distribution of North Sea cod (Gadus morhua) due to strong winds prevailing prior or during a survey. Fish. Res. 110, 325–330. Williams, K., Punt, A.E., Wilson, C., Horne, J.K., 2011. Length-selective retention of walleye pollock, Theragra chalcogramma, by midwater trawls. ICES J. Mar. Sci. 68, 119–129.