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Biology 67 (2017) 191–208 brill.com/ab

The effects of Biorock-associated electric fields on the (Carcharhinus perezi) and the (Carcharhinus leucas)

Marcella P. Uchoa1, Craig P. O’Connell1,∗ and Thomas J. Goreau2 1 O’Seas Conservation Foundation, 3636 Waldo Ave, Apt 2B, Bronx, NY 10463, USA 2 Global Reef Alliance, 37 Pleasant St, Cambridge, MA 02139, USA

Submitted: January 7, 2017. Final revision received: June 1, 2017. Accepted: June 10, 2017

Abstract Healthy coral reefs are biologically diverse and provide vital services. However, decreasing water quality and global warming are key contributors to decline, which poses substantial environmental threats. In response to this degradation, an innovative coral reef restoration technology, called Biorock, utilizes weak direct current electric fields to cause limestone deposition on conductive materials, inevitably inducing prolific coral reef growth. Although expediting coral growth, research on how the associated electric fields may impact the behavioural patterns of and/or organisms (i.e. elasmobranchs) possessing capabilities is lacking. Therefore, we studied the be- havioural responses of two shark , the bull shark (Carcharhinus leucas) and the (Carcharhinus perezi) and multiple species towards weak direct current electric fields in Bimini, Bahamas. Generalized linear mixed model analyses based on 90 trials illustrate that both the feeding and avoidance behaviors of C. leucas and C. perezi were significantly associated with treatment type, with the weak experimental electrode treatments resulting in the greatest quantity of avoidances and fewest feedings for both species. However, data analyses illustrate that teleost feeding behavior was not observably impacted by experimental treatments. Although the Biorock technology exhibits promise in coral reef restoration, the findings from this study illustrate a need for future large- scale studies assessing shark behavioral patterns around these devices, since the deterrence of apex predators may impact ecosystem balance.

Keywords Bahamas; Biorock; coral reefs; electroreception; global warming;

∗ ) Corresponding author; e-mail: [email protected]

© Koninklijke Brill NV, Leiden, 2017 DOI 10.1163/15707563-00002531

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Introduction Although coral reefs are among the most threatened in the world, they are among the most diverse, second only to tropical forests (Connell, 1978; Bryant et al., 2000). Coral reefs occupy an area of approximately 225 000 to 284 000 km2, which corresponds to only 0.09% to 0.17% of the area of the (Spalding, 1997, 2000; Spalding & Grenfell, 1997; Burke et al., 2001). This biodiverse ecosys- tem is characterized by exceptionally high (Birkeland, 1997; Bryant et al., 2000; Souter & Linden, 2000) in contrast to the oligotrophic open envi- ronment (Bryant et al., 2000). Despite their economic importance from a global perspective, coral reefs have deteriorated in the last 50 years, largely from an- thropogenic impacts such as: global warming, overfishing, industrial and domestic pollution, and increased sedimentation (Goreau & Hayes, 1994; Downs et al., 2005; Leão & Kikuchi, 2005; Bruno & Valdivia, 2016). One coral reef restoration measure is known as Biorock reef restoration technol- ogy (Goreau, 2012, 2014). This technology utilizes electric fields ranging from 1.2 and 12.0 V/m to facilitate limestone deposition on conductive materials. The miner- als, mainly aragonite (CaCO3) and brucite (Mg(OH)2), are similar in chemical and physical properties to natural coral skeleton limestone, which makes up the bulk of coral reefs. These structures have been demonstrated to have six times higher abun- dance, higher diversity, and greater evenness of fishes than nearby control reefs (Bakti et al., 2012). However, although teleost-based surveys were conducted, the responses of elasmobranchs (i.e. sharks, skates, and rays) to the technology have not been documented. Elasmobranch (sharks, skates, and rays) possess a unique electro-sensory sys- tem known as the (e.g. Kalmijn, 1971, 1982). This sensory system is composed of minute gel-filled pores and has been demonstrated to be used to detect the minute bioelectric fields associated with prey (e.g. Kajiura & Holland, 2002). The mechanism of bioelectric field detection is based around volt- age gradients. Each ampullae surface pore leads to a subcutaneous canal filled with conductive jelly (Kalmijn, 1966, 1971, 1974, 2000; Bastian, 1994), which was re- cently demonstrated to be one of the most effective biological proton conductors (Josberger et al., 2016). Upon encountering a weak electrical stimulus, such as that produced by prey, a voltage gradient is produced between the external pore sur- face and the internal pore. This differential elicits a neurological impulse, which is sent to the brain where the stimulus is perceived (Kalmijn, 1974, 1982, 1984). Weak electrical stimuli (e.g. 5 nV/cm) have been demonstrated to elicit foraging behavior (Kajiura & Fitzgerald, 2009), whereas stronger electrical stimuli ranging (e.g. 3-7 V/m) have been demonstrated to elicit deterrent responses (Smith, 1966, 1973, 1974; Gilbert & Gilbert, 1973; Smit & Peddemors, 2003; Marcotte & Lowe, 2008; Huveneers et al., 2012). Although stronger electrical stimuli have been ob- served to elicit deterrent responses, some studies suggest that deterrent-mediated behaviors may be species-specific (Marcotte & Lowe, 2008) and may also vary on a context-specific basis (Huveneers et al., 2012).

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Due to their sensitivity to weak (e.g. Kajiura & Fitzgerald, 2009) and strong lo- calized (e.g. Marcotte & Lowe, 2008; Huveneers et al., 2012) electric fields, the electric fields (i.e. 1.2-12.0 V/m) associated with Biorock may elicit deterrent re- sponses in various shark species. Therefore, we aimed to assess how bull shark (Carcharhinus leucas) and Caribbean reef shark (Carcharhinus perezi)behaviors may or may not be affected by exposure to strong and localized electrical fields. In addition, this study aimed to determine if teleost behavior was observably impacted. Based on previous research (Marcotte & Lowe, 2008; Huveneers et al., 2012), we hypothesized that the Biorock-associated electric fields may be sufficiently strong to elicit deterrent responses (e.g. cessation of feeding and increased avoidance be- havior) in sharks; however, due to their lack of ampullary pores, teleosts should exhibit no significant behavioral variation in response to electrical fields.

Material and methods Location This study was conducted from February to March 2015 in Bimini, Bahamas. Re- search was conducted at two locations, Triangle Rocks: 25°3848.23N, 79°18 44.68W and Alicetown Channel: 25°4332.58N, 79°1745.90W. Study sites ranged from 2-8 m deep and were characterized as a region containing sandy sub- strate (Alicetown Channel) or a coral reef ecosystem (Triangle Rocks). Study sites were selected based on the reliable presence and feasibility of working with the tar- get species. More specifically, Caribbean reef shark (Carcharhinus perezi)behavior was assessed at Triangle Rocks, whereas bull shark (Carcharhinus leucas)behavior was studied in Alicetown Channel. All research was conducted in accordance with the rules and regulations of the assigned Bahamas Department of Marine Resources permit (MAF/FIS/17).

Materials To assess the effects of Biorock-associated electric fields, three experimental treat- ments were constructed: control, procedural control, and experimental electrode. The control treatment was an experimental baseline trial that lacked any apparatus. The procedural control treatment contained electrodes that were not connected to a power source, and was deployed to assess if the visual stimuli associated with the electrode apparatus were sufficient to elicit attractant or deterrent behavior. Lastly, the experimental electrode treatment contained electrically charged electrodes to assess if Biorock-associated electric fields influenced teleost and elasmobranch be- havior. For the experimental electrode treatment, the electrode apparatus was con- structed out of 160 cm (length) by 5 cm (diameter) polyvinyl chloride (PVC) pipe with two electrodes spaced by 1 m, the cathode and anode. The cathode (nega- tive dipole), a 60 cm long, 9 mm diameter steel reinforcing bar connected to a

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Figure 1. The experimental design employed to assess how the electrical stimuli associated with the Biorock technology may or may not influence elasmobranch behavior. The experimental treatment apparatus is made of one cathode and one anode that are spaced by a distance of 1 m and attached to a 6 V battery. black electrical cable, was securely attached to the PVC pipe. The anode (positive dipole), a 20 cm wire mesh, was connected to a red electrical cable via a small PVC pipe filled with epoxy resin (fig. 1). The long PVC pipe was connected to a piece of wood (65 cm × 10 cm) to maximize structural integrity of the experimen- tal apparatus and make the electrodes float below the water surface. A nylon rope attached to the center of the apparatus allowed it to be lowered from the dock or boat for deployment and retrieval during the trials associated with the procedural control and experimental electrode treatments. The electrodes were powered by a 6 V DC power source that was in-series with a Power Probe CAT IV Multimeter DMM101ES, and a clamp ammeter to measure generated voltage and current.

Procedures At each study site, treatments were deployed independently. Using a random num- ber generator through Microsoft Excel, the temporal sequence of treatments (i.e. control, procedural control, and experimental electrode treatment) were randomly selected. The randomization was essential to eliminate the possibility of ef- fects (Hurlbert, 1984). To commence experimentation, an HD 1080p GoPro camera was placed directly above the treatments to document behavior and determine con- specific density. In addition, the olfactory stimuli (i.e. fine mesh bag filled with minced fish) and experimental apparatus were positioned, and subsequently, bait deployment commenced. Throughout each 6 minute and 15 second trial associ- ated with each individual treatment, one 0.028 kg piece of bait was thrown in the water every 15 seconds approximately one meter from the experimental ap- paratus. A total of 25 pieces of bait were deployed for each trial, or 75 pieces of

Downloaded from Brill.com09/25/2021 10:25:01AM via free access M.P. Uchoa et al. / Animal Biology 67 (2017) 191–208 195 bait per replicate (e.g. all three treatments). In addition, there was a three-minute inter-trial duration, with all experimental materials being removed from the water at the start of this duration and re-deployed for the start of the next trial. Ev- ery trial contained equal quantities of bait from a particular species to eliminate any preference-based behavior. The species used for bait were: great (Sphyraena barracuda), wahoo (Acanthocybium solandri), striped grunt (Haemu- lon striatum), French grunt (Haemulon flavolineatum), snapper (Lutjanus griseus), ( hippos), king (Scomberomorus cavalla), and Atlantic (Clupea pilchardus). During each trial, the maximum number of sharks within view of the apparatus and within a given trial was quantified to assess conspecific density, which was then categorized as low (1-3 sharks; categorized as ‘1’), medium (4-7 sharks; catego- rized as ‘2’), or high (8+ sharks; categorized as ‘3’). However, to determine overall sample sizes, distinctive characteristics (e.g. presence/absence scars, tags, pigmen- tation patterns, and dorsal fin notch characteristics) were used to estimate total shark quantity and whether an individual returned over the experimental timeframe. Since teleost quantity varied throughout each trial and individual identification was dif- ficult, the maximum quantity that could be observed within one video frame was used for each teleost species to create an estimate of sample size. In addition to con- specific density, water visibility was also recorded as either low (i.e. could not see the seafloor; categorized as ‘1’), medium (i.e. could see the seafloor but visibility was reduced due to moderate turbidity; categorized as ‘2’), and high (i.e. could see the seafloor with minimal to no turbidity; categorized as ‘3’). For each trial, the following behaviors were recorded for both sharks and teleosts: feeding and avoidance. A feeding was recorded when an animal was observed to eat the bait. An avoidance was recorded when an abrupt change in swimming pattern, such as an acceleration away or a 45°, 90° or 180° turn away was observed. Each behavior was then aggregated for each species where possible and for each trial.

Data analysis

Data collected throughout the experiment was in the form of frequencies (i.e. counts) for both shark species. However, since the collected data were multi- dimensional, where the main effects of several variables and interaction terms between these variables were of interest, a Poisson generalized linear mixed effect model was used for each behavioural type (i.e. avoidances and feedings). Fur- thermore, since trials were conducted in a similar location between replicates, C. perezi and C. leucas behaviors were not considered independent. This potential non- independence violates the assumption in generalized linear models; thus, replicates were treated as a random effect whereas the other variables (i.e. treatment type and water visibility) were treated as fixed effects.

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The mathematical form of the implemented generalized linear mixed effect model was: Y = Xβ + R + ε (1) Y represents the column vector of the response variable (counts of shark responses), X is the design matrix of explanatory variables, including all possible interaction terms, β is the column vector of coefficients that correspond to explanatory vari- ables, R is the vector of individual replicates, which is a random effect, and ε represents the vector of errors, which are assumed to follow a normal (Gaussian) distribution whose mean is zero and whose variance is constant. The fixed effects (i.e., X) were treatment type (discrete), water visibility (discrete; only implemented in C. leucas as visibility did not vary sufficiently during the C. perezi trials), and conspecific density (discrete). The generalized linear mixed effect model (Eq. (1)) was implemented using the ‘lme4’ package of R (Bates et al., 2012; Hyun et al., 2014; R 3.3.0 Statistical Pro- gram). Starting with the null model, forward selection was used to determine the best fit model for the data. Subsequent models were created by adding one or sev- eral explanatory variables to determine their effect on avoidance frequency (i.e. total avoidances/25) and feeding frequency (i.e. total feedings/25). Typically, visits (i.e. fish swimming within one body length of an experimental apparatus) were used as the denominator and used to create behavioral frequencies within a given trial (e.g. O’Connell et al., 2014). However, for this study, the quantity of baits deployed (i.e. 25 baits) within a trial were used to create the frequencies because the quantity was both standardized within a trial and deemed more accurate, as a standardized region to record a visit within the three-dimensional experimental setting was inherently challenging. We tested the contribution of an explanatory variable, examining the difference in the log-likelihood (Faraway, 2006; Hyun et al., 2014): · ∼ 2 2  χPAR (2)  is the difference in the log-likelihood between nested and non-nested models in the forward selection process, and PAR is the difference in the number of free parameters between two models. Model selection criteria included: Akaike Infor- mation Criteria (AIC), and behavior of model residuals using a quantile-quantile (Q-Q) plot, and associated P -values. Lastly, no C. leucas avoidance behaviors were observed in relation to the control treatment, whereas a substantial quantity of avoidances were observed towards the procedural control and experimental elec- trode treatments. Therefore, C. leucas avoidance data were transformed by adding one behavioral count to each treatment region (i.e. one avoidance towards the con- trol, procedural control, and experimental electrode treatments). Unlike the data associated with C. leucas and C. perezi, the data associated with the teleosts were too insufficient to subject to the aforementioned Poisson gener- alized linear mixed effect model. Therefore, data were separated on a per-species basis and subsequently transformed into behavioral frequencies (e.g. avoidance fre- quency = total trial avoidances per total pieces of bait) for each behavioral type.

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However, due to difficulties associated with identifying the jacks (C. ruber and S. rivoliana) and snappers (L. griseus and L. apodus) to the species level during the Alicetown Channel experiment, data were aggregated within the respective fish group and subjected to analysis. After transformation, data were first subjected to a Shapiro-Wilk’s test for normality. Since the data did not meet the statistical as- sumptions of normality and homogeneity, a non-parametric Kruskal-Wallis test was implemented to assess if any variation in behavioral frequency existed with treat- ment type.

Results Thirty replicates (20 at Alicetown Channel and 10 at Triangle Rocks; 90 total tri- als) were conducted over 16 days during February and March 2015. Throughout experimentation, the mean voltage was 4.7 V for the experimental electrode treat- ment trials. The mean electrical current during the experimental electrode treatment trials was 0.785 A and therefore, maximum power output was 3.73 W.

Triangle rocks: Caribbean reef shark (Carcharhinus perezi) Throughout experimentation, the per trial quantity of C. perezi ranged from 7 to 13. For avoidance frequency, the best fit model (A2) included the main effects of treat- ment type (T) and contained an AIC of 90.80 (table 1). The coefficient and associ- ated P -values with the selected model demonstrate that the experimental electrode (P<0.001) and procedural control (P = 0.02) treatments resulted in a signifi- cantly higher avoidance frequency when compared to the control (table 2; fig. 2). For feeding frequency, the best fit model (B2) included the main effects of treatment type (T) and contained an AIC of 194.01 (table 1). The coefficient and associated P -values with the selected model demonstrate that although the experi- mental electrode and procedural control treatments resulted in fewer feedings, only the experimental electrode treatment (P<0.001) had a significant effect on C. perezi feeding frequency (table 2, fig. 2).

Triangle rocks: teleosts Throughout experimentation, two teleost species, the (Caranx ruber;n= 26) and the chub (Kyphosus sectatrix;n= 31), were observed. Since no avoidances were observed, a Kruskal-Wallis test was only conducted on the associated feeding frequencies of these two species. However, the feeding frequen- cies (fig. 3) between treatments were not significantly different for both C. ruber (P = 0.315) and K. sectatrix (P = 0.165) (table 3).

Alicetown channel: bull sharks (Carcharhinus leucas) Throughout experimentation, the per-trial quantity of C. leucas ranged from 1 to 10. For avoidance frequency, the best fit model (C2) included the main effects of

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Table 1. Results from the mixed effect models pertaining to Caribbean reef shark (Carcharhinus perezi)and bull shark (Carcharchinus leucas)behavior.ForC. leucas avoidance frequency, data were transformed to “total avoidances + 1” for each treatment type to improve the interpretability of the data, as no avoidances occurred towards the control treatment throughout the entire experiment. Trial (R) is treated as a random effect and the others are treated as fixed effects. These fixed variables were T (treatment), Den (conspecific density), and Vis (water visbility). Selected models for avoidance and feeding frequencies were A2, B2, C2, and D2 respectively, based on a combination of Akaike Infor- mation Criteria (AIC), and behavior of the residuals of a model using a quantile-quantile (Q-Q) plot, and associated P -values. Significant models (P  0.05) are in bold.

Number Model Log likelihood −2(L) PAR P -value AIC

Caribbean reef shark (Carcharhinus perezi) avoidance frequency A1 1 + R −60.94 – – – 125.88 A2 1 + R + T −41.40 96.41 2 <0.001 90.80 A3 1 + R + Den −60.91 132.03 1 0.06 127.83 A4 1 + R + T + Den −41.37 99.75 1 0.06 92.74 A5 1 + R + T + Den + T × Den −39.12 102.06 2 0.11 92.25 C. perezi feeding frequency B1 1 + R −118.08 – – – 240.15 B2 1 + R + T −93.01 329.16 2 <0.001 194.01 B3 1 + R + Den −118.06 354.22 1 0.88 242.13 B4 1 + R + T + Den −92.99 279.01 1 0.88 195.99 B5 1 + R + T + Den + T × Den −92.46 278.45 2 0.59 198.92 Bull shark (Carcharhinus leucas) avoidance frequency C1 1 + R −60.07 – – – 124.13 C2 1 + R + T −40.35 180.19 2 <0.001 88.69 C3 1 + R + Den −60.07 180.08 1 0.91 126.12 C4 1 + R + Vis −59.95 198.41 1 0.63 125.90 C5 1 + R + T + Den −40.24 121.04 1 0.91 90.69 C6 1 + R + T + Vis −40.24 120.93 1 0.63 90.47 C7 1 + R + T + Den + T × Den −39.98 120.66 2 0.69 93.95 C8 1 + R + T + Vis + T × Vis −39.58 120.05 2 0.52 93.17 C. leucas feeding frequency D1 1 + R −219.53 – – – 443.06 D2 1 + R + T −178.75 617.80 2 <0.001 365.49 D3 1 + R + Den −219.15 658.20 1 0.76 444.29 D4 1 + R + Vis −219.21 658.27 1 0.43 444.43 D5 1 + R + T + Den −178.37 535.86 1 0.38 366.73 D6 1 + R + T + Vis −178.43 535.93 1 0.43 366.87 D7 1 + R + T + Den + T × Den −177.69 533.98 2 0.47 369.37 D8 1 + R + T + Vis + T × Vis −177.69 534.55 2 0.47 369.37

Abbreviations: 1 = y-axis intercept, l = change in log likelihood value between former model and model being considered, PAR = change in degrees of freedom between former model and model being considered, P -value = the level of significance of the explanatory variable added, AIC = Akaike Information Criterion (2 × (log likelihood) + 2 × number of parameters), a model selection criterion.

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Table 2. Coefficients, standard errors, t statistic and P -values of explanatory variables for best models A2, B2, C2, and D2 for avoidance and feeding frequencies associated with the Caribbean reef shark (Car- charhinus perezi) and the bull shark (Carcharhinus leucas), respectively, to the treatment. For C. leucas avoidance frequency, data were transformed to “total avoidances + 1” for each treatment type to improve the interpretability of the data, as no avoidances occurred towards the control treatment throughout the entire experiment. Significant models for main effects (P  0.05) are in bold.

Explanatory variable Coefficient Standard error tP-value

Caribbean reef shark (Carcharhinus perezi) avoidance frequency Intercept −4.86 0.72 −6.83 <0.001 Experimental electrode 2.90 0.72 3.99 <0.001 Procedural control 1.79 0.76 2.35 0.02 C. perezi feeding frequency Intercept −0.32 0.09 −3.198 0.001 Experimental electrode −0.86 0.13 −6.39 <0.001 Procedural control −0.10 0.11 −0.96 0.34 Bull shark (Carcharhinus leucas) avoidance frequency Intercept −6.22 1.00 −6.22 <0.001 Experimental electrode 3.33 1.02 3.27 0.001 Procedural control 1.39 1.12 1.24 0.22 C. leucas feeding frequency Intercept −0.62 0.09 −6.45 <0.001 Experimental electrode −0.95 0.11 −8.49 <0.001 Procedural control −0.25 0.09 −2.76 0.01

Figure 2. Graphical representation of the best fit models (A2 and B2) associated with the Caribbean reef shark (Carcharhinus perezi) behavioral frequencies (per trial behaviors/25) at Triangle Rocks, Bimini, Bahamas.

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Table 3. Kruskal-Wallis tests assessing if any associations with treatment type exist pertaining to the feeding frequencies of two teleost species, bar jack (Caranx ruber) and Bermuda chub (Kyphosus sectatrix), at Triangle Rocks and numerous teleost species, (Remora remora), bar jack (C. ruber)and almaco jack (Seriola rivoliana), mangrove snapper (Lutjanus griseus) and schoolmaster snapper (L. apodus), and Bermuda chub (K. sectatrix), at the Alicetown Channel.

Behaviors Kruskal-Wallis statistic d.f. P -value

Triangle Rocks Bar Jack Feeding 3.549 3 0.315 Bermuda Chub Feeding 5.097 3 0.165 Alicetown Channel Remora Feeding 1.4538 3 0.693 Bar Jack; Almaco Jack Feeding 1.1375 3 0.768 Mangrove Snapper; Schoolmaster Snapper Feeding 3.8304 3 0.280 Bermuda Chub Feeding 0.59613 3 0.897

Figure 3. Represents the mean behavioral frequencies (per trial behaviors/25) associated with the teleost species at Triangle Rocks and Alicetown Channel, Bimini, Bahamas. The species encountered were bar jacks (Caranx ruber), Bermuda chubs (Kyphosus sectatrix), remoras (Remora remora), al- maco jacks (Seriola rivoliana), mangrove snappers (Lutjanus griseus), and schoolmaster snappers (L. apodus).

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Figure 4. Graphical representation of the best fit models (C2 and D2) associated with the bull shark (Carcharhinus leucas) behavioral frequencies (per trial behaviors/25) at Alicetown Channel, Bimini, Bahamas. treatment type (T) and contained an AIC of 88.69 (table 1). The coefficient and as- sociated P -values with the selected model demonstrate that only the experimental electrode treatment resulted in a significant increase in C. leucas avoidance fre- quency (P<0.001; table 2; fig. 4). For feeding frequency, the best fit model (D2) included the main effects of treatment type (T) and contained an AIC of 365.49 (table 1). The coefficient and associated P -values with the selected model demonstrate that both the experimen- tal electrode (P<0.001) and the procedural control (P = 0.01) treatments had a significant effect on C. leucas feeding frequency (table 2); however, the experimen- tal electrode treatment resulted in fewer feedings than did the procedural control treatment (table 2; fig. 4).

Alicetown channel: teleosts Throughout experimentation, six teleost species were observed: bar and almaco jack (Caranx ruber and Seriola rivoliana, respectively; n = 18) Bermuda chub (Kyphosus sectatrix;n= 12), remora (Remora remora;n= 8), and mangrove and schoolmaster snapper (Lutjanus griseus and Lutjanus apodus, respectively; n = 66). No avoidances were observed, and the feeding frequencies (fig. 3) between treatments were not significantly different for all teleost species: C. ruber and S. rivoliana (P = 0.768), K. sectatrix (P = 0.897), R. remora (P = 0.693), and Lut- janus spp. (P = 0.280) (table 3).

Discussion This study demonstrated that the Biorock-associated electric fields significantly al- ter shark behavior, reducing feedings and increasing avoidances (table 2; figs. 2, 4); whereas the behavior of interacting teleosts was not observably influenced (table 3;

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fig. 3). Interestingly, sharks approached the bait yet completely missed and ex- hibited signs of disorientation during the experimental electrode treatment. These findings are similar to those studying the effects of various deterrent materials (e.g. electropositive metals and permanent magnets) on spiny dogfish (Squalus acan- thias), where observations revealed bait localization difficulties when subjected to deterrents (Stoner & Kaimmer, 2008). Since electroreception has been demon- strated to be important in the final phases of prey capture (e.g. Kajiura & Holland, 2002; Kajiura & Fitzgerald, 2009; Jordan et al., 2013), the electric fields associated with the Biorock technology may mask the unique cues associated with bait thus negatively influencing feeding capabilities.

Elasmobranchs: treatment type This experiment demonstrated that the behavioral frequencies (i.e. avoidance, feed- ing) of both species varied significantly between treatments. Mixed effect model analyses illustrate that the experimental electrode treatment contributed substan- tially to these significant differences (i.e. table 2), which is suggestive that the Biorock-associated electric fields (i.e. 4.7 V) were sufficient to elicit deterrent be- havior. The present findings are consistent with previous studies that demonstrate the elasmobranch deterrent capabilities of electro-sensory stimuli (Huveneers et al., 2012; SANSA, 2012), and thus suggest that strong electro-sensory stimuli exhibit the capability to override the desire to feed in certain contexts. In addition to the experimental electrode treatment, the procedural control treatment had a similar effect for C. perezi avoidance frequency and C. leucas feeding frequency. More specifically, avoidance frequencies significantly increased (P = 0.02) and feeding frequencies significantly decreased (P = 0.01) with respect to the procedural con- trol treatment. With binocular vision and depth perception as a direct result of eye positioning, eye rotation, and head yaw (e.g. McComb et al., 2009), it is possible that the associated visual stimuli of both the procedural control and experimental electrode treatments were visually detected and sufficient to elicit these behavioral responses. However, although the procedural control treatment did impact C. perezi avoidance frequency and C. leucas feeding frequency, the results pertaining to the avoidance and feeding frequencies (figs. 2, 4) illustrate the heightened effect of the experimental electrode treatment, and thus the sole effect of visual stimuli is not sufficient to explain the findings. Therefore, the potential utilization of Biorock- associated electric fields for both coral restoration and shark deterrence warrants further investigation. For feeding frequency, it was determined that C. perezi and C. leucas were more likely to successfully feed from the procedural control and control treatments than the experimental electrode treatment (4.7 V; table 2; figs. 2, 4). This find- ing therefore suggests that an electro-sensory threshold exists near or at 4.7 V and this threshold may contribute to temporary feeding inhibition. Although this study aimed to assess the influence of Biorock-associated electric fields on fish behavior, the feeding inhibition threshold has increasing relevance. Due to overexploitation

Downloaded from Brill.com09/25/2021 10:25:01AM via free access M.P. Uchoa et al. / Animal Biology 67 (2017) 191–208 203 and lack of proper management, sharks now represent the largest group of threat- ened marine species from a global perspective according to the International Union for the Conservation of Nature (IUCN) Red List (e.g. Manire & Gruber, 1990; Mu- sick et al., 2000; Stevens et al., 2000; Baum & Myers, 2004; Lucifora et al., 2011). Furthermore, sharks are K-selected (i.e. slow growth rate, late maturity, and low fecundity); therefore the likelihood of population rebound in response to their un- sustainable harvest is minimal if the present mortality rates continue (Stevens et al., 2000; Dulvy et al., 2014). Since many shark species play a top-down predatory role within their respective ecosystems, population declines at such high levels may have substantial ecological consequences (Myers et al., 2007; Heithaus et al., 2008). Therefore, with both commercial and recreational fisheries continuously contribut- ing to substantial shark population decline (Stevens et al., 2000; Dulvy et al., 2014), these C. perezi findings suggest that deterrents containing higher associated volt- ages may have an increased elasmobranch-specific deterrent capability.

Elasmobranchs: conspecific density Organisms occupying a similar ecological niche often compete between (i.e. in- terspecific competition) and within (i.e. intraspecific competition) species (Nelson & Johnson, 1980; Schoener, 1983), which has been demonstrated to alter shark behavior (Crombie, 1947; Polis, 1981; Stiling et al., 1984; Munday et al., 2001). In previous shark-related studies, conspecific density was determined to negatively influence the effectiveness of deterrent-associated baits/hooks (Brill et al., 2009; Jordan et al., 2011; Robbins et al., 2011). However, in contrast to these findings, conspecific density was not observed to significantly influence the deterrent ca- pability of the experimental electrode treatment. Therefore, this finding may be explained by the fact that density-induced behavioral responses to electro-sensory stimuli are species-specific and the electrodes were so effective that even though a competitive mentality may have been induced, behavioral manipulation was not compromised.

Elasmobranchs: water visibility Water visibility has been previously observed to alter fish behaviour (Ranåker et al., 2012; O’Connell et al., 2013). With declining water visibility parameters, a height- ened reliance on another sense has been observed, known as context-dependent switching (Leahy et al., 2011; Ranåker et al., 2012). For example, in a previous study and when compared to sighted sharks, visually deprived lemon sharks (Ne- gaprion brevirostris) exhibited significant behavioral changes towards permanent magnetic stimuli (O’Connell et al., 2013). All trials in the present study were con- ducted during daylight hours and water visibility conditions were greater than or equal to 3 m. Thus, the visual range may not have been sufficiently restricted to elicit these previously observed behavioral changes (e.g. O’Connell et al., 2013),

Downloaded from Brill.com09/25/2021 10:25:01AM via free access 204 M.P. Uchoa et al. / Animal Biology 67 (2017) 191–208 and therefore, may explain why water visibility did not significantly impact shark behavior.

Teleosts Experimental treatments did not observably influence the feeding behavior of teleosts at either study site (table 3; fig. 3). One explanation for these findings may be the apparent lack of a well-developed electro-sensory system in the teleosts targeted (Montgomery et al., 1995; Bell, 2001). However, continued research is needed as strong electric fields (300-400 V) have been demonstrated to have neg- ative consequences on a variety of marine teleosts (e.g. Snyder, 2003) and thus, although not observed in the present study, interspecific variation in teleost re- sponses to Biorock-associated electric fields may occur. Although not the main focus of the present study, the results illustrate that the electric fields generated by the experimental electrode treatment elicit deterrent re- sponses in the elasmobranchs in this study, but not in the teleosts. Therefore, with elasmobranch being a substantial concern to the overall health of current elasmobranch populations (Molina & Cooke, 2012), the application of the electric fields used in the present study may also be a great approach to future conserva- tion engineering techniques to prevent shark bycatch by long lines and drift nets. Whether individual electric field devices (e.g. battery operated) are applied directly to hooks or electrodes are continuously spaced along a commercial longline, both warrant further investigation.

Conclusions Coral reef restoration has become a rapidly expanding discipline that encompasses a variety of approaches and scientific fields, including ecology, geology, physics, eco- nomics, and engineering. Within this discipline, a variety of restoration techniques exist, including (1) structural restoration (e.g. creating artificial reefs through activ- ities such as the relocation of rocks/dead coral heads or the sinking of vessels; Carr & Hixon, 1997), (2) biological restoration (e.g. collecting, rehabilitating, and/or transplanting naturally broken coral fragments, culturing coral larvae, or propagat- ing coral colonies; Ellis & Ellis, 2001), and (3) physical restoration (e.g. assessing the environmental parameters where coral growth is occurring to improve health, growth rates, or fecundity, which includes mid-water coral nurseries or the Biorock technology; Shafir & Rinkevich, 2010). In the present study, the effects of the weak direct current electric fields associated with the Biorock reef restoration technol- ogy were assessed. Although the electricity makes Biorock reefs grow faster and healthier than other , this study found that Biorock-associated electric fields significantly alter C. leucas and C. perezi behavior, but have little observable impact on teleosts. Despite the Biorock technology exhibiting promise in coral reef restora- tion, the possibility of the technology negatively influencing the “natural behavior”

Downloaded from Brill.com09/25/2021 10:25:01AM via free access M.P. Uchoa et al. / Animal Biology 67 (2017) 191–208 205 of interacting elasmobranchs is concerning. If long-range inhibition of feeding oc- curs around these Biorock structures, general and localized top-town ecosystem dynamics could be threatened and therefore, future investigations assessing the dis- tance of influence of Biorock structures on shark behavioral patterns are warranted. In contrast, if Biorock structures elicit close-range deterrent responses, it is possible that these structures may provide localized ecosystem benefits while concurrently serving as a short-range shark deterrent system in relevant areas. Due to the infancy of fish behavioral studies surrounding Biorock structures and the potential positive and/or negative implications of their presence, future research on more species is needed.

Author Contributions TJG and MPU built the experimental apparatus to ensure it appropriately mimicked the functionality of the Biorock Technology. MPU and CPO designed, conducted, and statistically analyzed all fieldwork. It is important to note that the fieldwork and statistical analysis was conducted independently of TJG as a means to eliminate any potential experimental bias due to TJG’s vested interest in the technology. MPU, CPO, and TJG collectively wrote the paper.

Acknowledgements We thank the Global , the Wave Foundation, and O’Seas Con- servation Foundation for providing partial funding, equipment, and training to make this project possible. We thank the Bahamian Government for granting us permits for this scientific research. We thank the Bimini Big Game Resort and Marina for permitting us to use their facilities. With are thankful for the comments pro- vided to us by several anonymous reviewers, as the comments largely benefitted this manuscript. We thank Guido Leurs, Lauren Smith Diack, Ryan Reed, Sarah Reed, Rachel Ariel Jacobson, Caroline Collatos, Nicole O’Connell, Debra Fried- man, Mary Fraga and many other volunteers who assisted us. Lastly, we thank Dr. Saang-Yoon Hyun who provided the initial framework and guidance needed to un- derstand and carry out the statistical approaches used in this study.

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