- Original Article
Assessing the suitability of a remotely operated vehicle (ROV) to study the fish community associated with offshore gas platforms in the Ionian Sea: a comparative analysis with underwater visual censuses (UVCs)
Helgoland Marine Research volume 67, pages 241–250 (2013)
The effectiveness of a remotely operated vehicle (ROV) to describe the fish communities of three gas platforms located offshore Crotone (Italy, Ionian Sea) was investigated by comparing its observations with underwater visual censuses (UVCs). The study was carried out at two depth layers (0–6 and 12–18 m). Moreover, the ROV was used to survey three deeper depth layers up to 76 m. Overall, the ROV surveys failed to give a truthful representation of the fish communities underestimating the number of species and their abundances as compared to UVCs. The main discrepancies in data regarded crypto-benthic and nekto-benthic species, whereas the ROV proved to be a suitable method to census low-mobile and abundant planktivorous species. The differences between the fish assemblage described by the ROV, with respect to the one depicted by UVC, should be considered in the light of the technical limits of the recording camera, whose resolution and field of vision is clearly lower than the diver’s eye. In addition, video images did not allow for the acquisition of a correct estimate of the distance between the individuals and the platform structures. This led, almost certainly, to an under- or over-estimation of fish abundance as regards to the censused volume. In spite of this, as a result of its capacity to reach depths inaccessible to scuba divers and then to add complementary information, the ROV could be used jointly with UVCs, in studies having as their objective the description of the fish communities associated with offshore platforms.
The extraction of fossil fuels from offshore fields has rapidly expanded in the last decades, becoming the leading activity in the exploitation of marine mineral resources. As a main consequence, thousands of offshore platforms have proliferated over the world’s oceans, and many more will likely be implemented in the future (Pulsipher and Daniel 2000). More recently, various Mediterranean countries have decided to grant new licenses to open oil exploitation in the Mediterranean Sea. For this reason, and in the light of the recent Deepwater Horizon oil spill in the Gulf of Mexico, understanding the role played by extractive platforms in marine ecosystems becomes an important worldwide issue.
Assessment of fish communities associated with artificial structures can be carried out by means of a number of sampling approaches. These include destructive methods, e.g. fishing gears (Fabi et al. 2004; Løkkeborg et al. 2002), and conservative methods which may be direct, e.g. visual census by SCUBA diving (Andaloro et al. 2011; Consoli et al. 2007; Rilov and Benayahu 2000; Wilhelmsson et al. 2006), or indirect with submersibles or remotely operated vehicles (ROVs) (Love et al. 2000; Okamoto 1989).
Given that each methodology offers advantages and disadvantages, comparative analyses should be carried out in order to determine the best approach for studying fish communities of particular habitats. In this direction, Okamoto (1989) has compared ROV visual censuses with other techniques in an aquarium tank while Spanier et al. (1994) stressed the need to conduct comparative studies of the relative utility of scuba versus ROV technologies for observing the behavior of marine animals. Moreover, several studies have also tested census techniques by means of underwater video recordings from a fixed point (Francour et al. 1999), video-operator (Bortone et al. 1991; Harvey et al. 2002; Tessier et al. 2005) or submersibles (Love et al. 2000). ROV, equipped with high definition video cameras, are increasingly used as a tool for underwater investigation, being capable to reach depths inaccessible to scuba divers and to record data even in adverse operative conditions. Besides being employed in several industrial sectors and as a fundamental support to underwater archaeological researches, ROVs have been utilized with success in the field of marine biology and ecology (Bergström et al. 1987; Hamner and Robison 1992). More in detail, visual censuses by means of ROVs have been conducted to describe several benthonic communities (Auster et al. 1991; Kaufmann et al. 1989; Norcross and Mueter 1999), to test their effectiveness in estimating fish species abundance (Adams et al. 1995; Bergström et al. 1987) and to observe the behavior of some species, also in relation to the presence of the ROV itself (Lorance and Trenkel 2006; Spanier et al. 1994).
In offshore platform environments, no studies have been conducted with the aim of evaluating the capability of an ROV to describe and represent the fish community also in relation to other visual census techniques.
In the present study, an in situ experiment was carried out in three different offshore gas platforms located in the Ionian Sea (central Mediterranean Sea), in order to test the appropriateness of the ROV in describing the fish community by comparing its observations with underwater visual censuses (UVCs). The first objective was qualitative, i.e., to evaluate how many species the ROV could determine in comparison with UVCs, while the second objective was quantitative, i.e., to compare the number of individuals by species recorded by the two methods on the same area units.
Moreover, the ROV was tested at depths where UVCs could not be carried out. Finally, the advantages and disadvantages of the ROV are discussed and summarized.
Materials and methods
The study was carried out randomly on three dates (October 2005, February 2006 and May 2006) in three offshore gas platforms (Luna A, Luna B and Hera Lacinia) located in the southern Ionian Sea (central Mediterranean Sea), respectively, 5.3, 6.2 and 2.6 km offshore (Fig. 1). Two of them (Luna A and Luna B) are 8-leg platforms while the third one (H. Lacinia) is a 4-leg platform. All these platforms lie on a sandy seabed in 75, 90 and 30 m of water, respectively, and are fixed to the sea floor by concrete or steel legs, which are connected by an assemblage of cross beams.
The platforms were colonized by several invertebrates providing shelter and food to cryptic and nekto-benthic fish species. The most represented sessile organisms were Mytilus galloprovincialis, Balanidae indet., Ostrea sp., and Arbacia lixula (personal observation).
Fish species and their abundance were recorded by UVC and a ROV (“Hyball” of Hydrovision Ltd) equipped with a high resolution video camera, compass, rate gyro and two fixed 100 W quartz halogen lights aimed forward and two 75 W lights mounted on the camera chassis.
At each sampling month, ROV and UVC surveys were performed randomly on three out of 8 pillars at Luna A and B platforms (8-leg) and out of 4 at Hera Lacinia platform (4-leg). Two depth ranges were chosen: 0–6 and 12–18 m thus dividing pillars into 6 m-high units.
During both UVC and ROV surveys, the underwater “Mobile Point Count” technique was adopted in order to study fish assemblages. This technique, specifically designed for offshore platforms by Rilov and Benayahu (2000) and applied, for the first time, by Consoli et al. (2007) and Andaloro et al. (2011) in the Mediterranean Sea, was chosen because it is particularly suitable for the analysis of species strictly associated with the pillars and also for detecting benthic and cryptic species (Consoli et al. 2007; Andaloro et al. 2011).
The diver (in the case of UVCs) or the ROV, turning around each unit and looking toward the pillar, censused all fishes occurring up to 3 m from it. In particular, they began, firstly, to record the more conspicuous and easily identifiable fishes from a maximum distance of 3 m from the pillar (so that to have an entire view of the census unit) and straight after, approached the pillar and counted the benthic and crypto-benthic species. Since the pillars had a radius of 1 m, the total censused volume corresponded to a cylinder of 7 m of diameter and 6 m high, from which the volume of the pillar (1 m of diameter, 6 m high) was subtracted. The resulting volume was 225.7 m3.
A total of 108 observation units were carried out in study period. Moreover, the ROV was used to survey three deeper depth layers: 22–28 m at H. Lacinia, Luna A and Luna B, 50–56 m at Luna A and Luna B and 70–76 m at Luna B. ROV and UVC trials were carried out in different moments of the same days by exploring the same horizontal units within a maximum time of 5 min.
Fish abundance was estimated by counting single specimens to a maximum of 10 individuals, and using abundance classes (11–30, 31–50, 51–100, 101–200, 201–500, >500) for schools. This recording system leads to a similar degree of error over a wide range of abundances, ensuring homogeneity of variance after log-transformation of the data (Frontier 1986; Guidetti et al. 2003). ROV trials were recorded on video tapes and four different researchers contributed to both underwater and video data recordings thus reducing any systematic error between methods.
Fish taxa were pooled into ecological categories on the basis of their spatial organization and behavior in relation to the platform: PLA, planktivorous fishes (e.g., Chromis chromis, Anthias anthias and Boops boops) which form schools and are strictly associated to the artificial structures mostly for shelter and reproduction issues; NEC-BEN, nekto-benthic species (mostly Sparids) feeding on sessile organisms; CRY, crypto-benthic fish (e.g., Blennids and Scorpaenids) which are visually and/or behaviorally cryptic and keep them in close association with the substratum (La Mesa et al. 2006); PEL, pelagic fish (e.g. Scombrids and Carangids) attracted by the artificial structures.
The fish community was described by means of fish abundance (N), species richness (S) and frequency of occurrence (%O) data.
A three-way permutational multivariate analysis of variance (PERMANOVA) (PERMANOVA; Anderson 2001; McArdle and Anderson 2001) was used to test for differences between fish assemblages with regard to the factors method (2 levels, fixed), platform (3 levels, fixed) and period (3 levels, random) at each depth layer. The analysis was based on Gower distances calculated on log(x + 1) transformed data, and each term of the analysis was tested using 4,999 random permutations of appropriate units (Anderson and ter Braa 2003). This permutation method is generally thought to be best suited because it provides the best statistical power and the most accurate Type I error (Anderson and Legendre 1999).
Moreover, to test whether ROV and UVC recorded different average abundances of each of the four ecological categories at each depth layer, a three-way permutational univariate analysis of variance (permutational ANOVA) was performed considering the same factors used in the multivariate analyses. This univariate analysis was also used to detect significant differences between the mean values of species richness recorded by both methods. Unlike multivariate analyses described above, we used a Euclidean distance in this univariate model. Data were transformed at the log(x + 1) in order to reduce the weighting of abundant categories and increase that of the rarer ones.
Both univariate and multivariate analyses were employed using the software package PRIMER 6 with PERMANOVA+add-on (Anderson et al. 2008).
Whereas ANOVA/MANOVA assumes normal distributions, PERMANOVA works with any distance measure that is appropriate to the data and uses permutations to make it distribution free. Thus, the same F-statistics were calculated, but p values were obtained by permutation.
A two-dimensional non-metric multidimensional scaling (nMDS) plot was generated on the basis of Gower similarity matrix of abundance data averaged per platform.
Moreover, the similarity percentage procedure SIMPER (Clarke and Warwick 1994) was used to identify the fish species mostly contributing to the differences between the methods. In this case, crypto-benthic fish were pooled into a single group.
Finally, a rough cost estimate for both methods was provided considering the rent of the boat, ROV and diving equipment, the manpower needed for the surveys, video analysis and data elaboration.
Overall, the ROV censuses allowed for the identification of 17 fish taxa belonging to 12 families (Table 1). However, 4 of these taxa were observed only at the depth layers where the UVCs were not performed. Spicara spp. and Trachurus spp. were not identified to the species level due to the difficulties in determining the above. The families of Sparidae were the most represented with 5 species. The majority of the taxa belonged to the ecological category of nekto-benthic fish followed by planktivorous fish. Crypto-benthic fish were the least represented category with only two species.
The UVC recorded 24 fish taxa in 9 families, with Blennidae and Sparidae being the most diverse with 7 species each. The majority of taxa belonged to the nekto-benthic (8 species) and crypto-benthic (9 species) fish categories.
A first assessment of the methods was done by comparing the average abundance and frequency of occurrence of fish taxa at each depth layer (Table 1). At 0–6 m, the ROV and UVC detected 9 and 16 fish taxa, respectively. Both methods were able to census all the planktivorous species, whereas the differences in crypto-benthic species richness were notable, with 6 fish taxa censused by UVC against 1 recorded by the ROV (Parablennius gattoruggine). The species observed most frequently by both methods was the damselfish Chromis chromis (%O = 81.5 and 74.1, respectively by UVC and ROV), characterized, moreover, by the highest abundance values.
At the following depth layer (12–18 m), the ROV censused 9 taxa while the UVC 19. Also at this depth, the difference in crypto-benthic fish taxa identification was clear (6 taxa by UVC and none by ROV). The damselfish was the most frequent species observed by the ROV (%O = 55.6), whereas, by means of UVC, high values of frequency of occurrence were observed for Thalassoma pavo, Scorpaena maderensis and C. chromis (Table 1).
At the deeper layers (22–28, 50–56 and 70–76 m), the ROV recorded 13, 9 and 4 fish taxa, respectively, and new species (such as Epinephelus aeneus, Pomatomus saltator, Sarda sarda, and Phycis phycis) were recorded. Both in terms of abundance and occurrence, A. anthias was the dominant species at these depths showing extremely high values of abundance at 50–56 and 70–76 m (378.6 and 443.3, respectively; Table 1). The dense schools formed by this species screened, in many cases, the pillar’s surface reducing the capability of the ROV to census the crypto-benthic fish.
The permutational multivariate analysis (PERMANOVA) showed highly significant differences between the assemblages censused by the two methods at both depth layers (0–6 m: F = 21.31; p < 0.01—12–18 m: F = 18.97; p < 0.01), and these differences were consistent for each platform and sampling period (interaction terms “Method × Platform” and “Method × Period”, not significant).
The nMDS plots (Fig. 2), confirming PERMANOVA results, showed a clear-cut separation between the fish assemblages censused by the ROV and UVC at both depth layers.
SIMPER identified the Ecological groups contributing the most to the dissimilarities between the methods at each depth layer (Table 2). Crypto-benthic species contributed largely to differentiating the fish communities described by the two methods together with the most abundant planktivorous species (C. chromis, B. boops, O. melanura at 0–6 m and A. anthias, C. chromis, B. boops at 12–18 m).
With regard to the census of the different ecological categories, ROV and UVC showed a different suitability (Fig. 3). At both depth layers, UVC methods censused a higher number of planktivorous fishes than ROV; in spite of this, permutational univariate analysis showed significant differences between the two methods (Table 3) only at 12–18 m (p < 0.05). A significant effect for the period was detected (p < 0.05), which indicates that planktivorous abundances differed according to the factor period.
Abundance of pelagic fish resulted slightly higher when assessed with ROV rather than UVC only at 12–18 m (p < 0.01), but, in any case, univariate analyses were not able to find significant differences according to the factor method.
The number of crypto-benthic and nekto-benthic individuals censused by UVC was significantly higher at both depth layers (p < 0.01), although, for the first group, these differences varied according to the factor platform (p < 0.01) at 12–18 m depth. Finally, The UVC observed a significantly higher number of species at all platforms and at both depth layers (p < 0.01) (Fig. 4; Table 3). On the other hand, the interaction terms “Method × Platform” and “Methods × Period” were significant at 0–6 m, indicating that these differences varied across platforms and sampling periods.
The study by means of ROV and UVCs required four diver-researchers and a ROV driver and was carried out in 18 days. Nevertheless, considering that ROV and UVC trials were performed in different moments of the same days, this 18-day period can be divided in two (9 days for each methods; Table 4). The four researchers needed other 3 days to analyze the ROV videos. In order to save money, both ROV and diving equipments were rent. Overall, the total costs needed to achieve both survey were almost the same (13.080 and 13.500€, respectively). However, the ROV technique required other 3 days for the video analyses thus increasing a little bit the total coast (14,700€).
The present study allowed for the identification of the strengths and weaknesses of the ROV as a tool for studying the fish community associated with offshore gas platforms.
The ROV did not allow for a complete description of the fish assemblage associated with the platforms. This was confirmed by the multivariate analysis which highlighted a higher diversity of the same fish assemblage when investigated by means of UVCs. Similar conclusions were drawn by Francour et al. (1999) in natural habitats by comparing UVC with video recordings from a fixed point.
In terms of species richness, the major differences concerned the group of crypto-benthic species, with the ROV capable of detecting only one large-sized species (Parablennius gattoruggine), in comparison with 9 species recorded by the UVCs. Most of the difficulties encountered by the ROV in identifying crypto-benthic species (the majority of which belonged to the family Blennidae) is related to their small size and to their tendency to hide in holes or crevices. This cryptic behavior, which represents a well-known constraint also for the UVC methodology (Smith 1988; Willis 2001), make these fish not visible to the camera. Similarly, the ROV was less accurate than UVCs in detecting the species belonging to the nekto-benthic group, in accordance with other studies (Bortone et al. 2000; Tessier et al. 2005). The latter authors explain these results by the fact that the video recording device have a much more limited field of view than the diver’s eye, and this reduces the probability of detecting species present in low abundances. In this study, this result seemed more related to the better capacity exhibited by the divers to move around the pillars and thus to census the individuals hidden by the platform structures.
In general, future researches having as their objective the study of the crypto-benthic and nekto-benthic species should avoid using ROV devices, if UVC methodology can be applied. The ROV seemed to be successful in censusing some pelagic species as confirmed by their relatively high occurrences and abundances. Anyhow, the repetitive passage of the same individuals in front of the camera, which demonstrated an attractive behavior toward it, could have biased the results obtained.
In the case of high mobile species such as nekto-benthic and pelagic fish, an increase in the observation time could strongly enhance the probability of the ROV to detect their individuals, as also suggested by many authors for UVC (Bortone et al. 2000; Harmelin-Vivien et al. 1985; Stoner et al. 2008).
However, the chance of counting the same individuals several times should be considered, possibly producing an overestimation of fish abundance. Finally, the ROV device was found to be an appropriate method with which to census planktivorous fish, both from a qualitative and quantitative point of view, mostly in relation to their high abundance and low mobility. The same result was obtained by Tessier et al. (2005) who consider it functional to employ video recording for the estimation of permanent species present in high densities, especially those which tend to school. It should be observed that, although the abundances of the latter group were comparable in value, for the most permanent species (C. chromis at 0–6 m and A. anthias at 12–18 m), the UVCs revealed significantly higher abundances than the ROV did, in accordance with other studies (Francour et al. 1999; Tessier et al. 2005).
Unlike UVC performed by divers, the ROV can be implemented without expertise in the identification of marine life (Hill and Wilkinson 2004). The video method also requires much less time in the field than UVC methodology and is therefore useful for sampling a large area or a number of sites. It requires, however, much more laboratory time spent in image analysis (Lam et al. 2006). The most relevant potentiality of the ROV, in agreement with Bortone (1992) and Lam et al. (2006), reside in its capability to reach depths not accessible to scuba divers and to operate in unfavorable diving conditions such as limited visibility, adverse weather, and unpredictable, rapid water currents dangerous for divers. In this study, these features improved the dataset adding seven new species to the platforms’ fish community. What is more, there is no time limit for how long time the vehicle can stay submerged and video recordings represent a permanent record which can be re-examined as many times as needed. Francour et al. (1999) highlight the minor invasiveness of the ROV in comparison with the presence of scuba divers, the possibility of recording at dawn and dusk by means of highly sensitive cameras and, at last, the ability to gather data for a longer time than a single dive.
The differences between the fish assemblage described by the ROV, with respect to the one depicted by UVC, should be considered in the light of the technical limits of the recording camera, whose resolution and field of vision is clearly lower than the diver’s eye. In addition, video images did not allow for the acquisition of a correct estimate of the distance between the individuals and the platform structures. This led, almost certainly, to an under- or over-estimation of fish abundance as regards to the censused volume. With regard to the ROV, the device employed, mostly in relation to the presence of the tether, showed some difficulties of movement within the platforms’ environment, limiting the observation of their inner sectors. Some of these difficulties are probably issues which even an excellent technology cannot overcome (Norcross and Mueter 1999); however, up to a certain extent, some technical features of the ROV might improve its suitability. On the basis of our experience, we learned that the power of the thrusters, the lighting apparatus and the video camera resolution are chiefly important for the use of ROV in the fish census around vertical pillars.
Given that the specific methods employed often define the limitations of the data (Bortone et al. 2000), the results of this study suggest the use of ROVs specifically to estimate abundance of permanent and low-mobile species and to explore depths where UVCs cannot be performed. Therefore, limits and capabilities of ROVs should be taken into account in order to formulate appropriate experimental designs and to maximize the collectable information. The methods of studying biodiversity presented in this study appear to be highly reproducible and suitable for monitoring fish diversity in such complex and artificial habitats like gas platforms. As assessing biodiversity in these highly complex contexts is a challenge for the near future, a reliable method would be able to influence the international research in this field. Indeed, these techniques are highly specific, cost-effective, and sufficiently affordable for all research teams to get both accurate and highly repeatable data. In conclusion, the above-mentioned considerations could allow for the lowering of the costs of future surveys aimed at studying fish assemblages associated with extractive platforms.
Adams PB, Butler JL, Baxter CH, Laidig TE, Dahlin KA, Wakefield WW (1995) Population estimates of Pacific coast groundfish from video transects and swept-area trawls. Fish B-NOAA 93:446–455
Andaloro F, Castriota L, Ferraro M, Romeo T, Sara G, Consoli P (2011) Evaluating fish assemblages associated with gas platforms: evidence from a visual census technique and experimental fishing surveys. Cienc Mar 37:1–9
Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46
Anderson MJ, Legendre P (1999) An empirical comparison of permutation methods for tests of partial regression coefficients in a linear model. J Stat Comput Sim 62:271–303
Anderson MJ, ter Braa CJF (2003) Permutation tests for multi-factorial analysis of variance. J Stat Comput Sim 73:85–113
Anderson MJ, Gorley RN, Clarke KR (2008) PERMANOVA+ for PRIMER: guide to software and statistical methods. PRIMER-E, Plymouth
Auster PJ, Malatesta RJ, LaRosa SC, Cooper RA, Steward LL (1991) Microhabitat utilization by the megafaunal assemblage at a low relief outer continental shelf site—Middle Atlantic Bight USA. J Northwest Atl Fish Sci 11:59–69
Bergström BI, Larsson J, Pettersson JO (1987) Use of a remotely operated vehicle (ROV) to study marine phenomena: I. Pandalid shrimp densities. Mar Ecol Prog Ser 37:97–101
Bortone SA (1992) Field research: monitoring and evaluating visual census to monitor and assess fish life history and assemblages. In: Schaeffer DO, Kleinow KM, Krulisch L (eds) The care and use of amphibians, reptiles and fish in research. Scientists Center for Animal Welfare, Bethesda
Bortone SA, Martin T, Bundrick CM (1991) Visual census of reef assemblages: a comparison of slate, audio and video recording devices. Northeast Gulf Sci 12:17–23
Bortone SA, Samoilys MA, Francour P (2000) Fish and macroinvertebrate evaluation. In: Seaman W Jr (ed) Artificial reef evaluation with application to natural marine habitats. CRC Press, Boca Raton, pp 127–164
Clarke KR, Warwick RM (1994) Change in marine communities: an approach to statistical analysis and interpretation, 1st edn. Plymouth Marine Laboratory & PRIMER-E, Plymouth
Consoli P, Azzurro E, Sarà G, Ferraro M, Andaloro F (2007) Fish diversity associated with gas platforms: evaluation of two underwater visual census techniques. Cienc Mar 33:121–132
Fabi G, Grati F, Puletti M, Scarcella G (2004) Effects on fish community induced by installation of to gas platforms in the Adriatic Sea. Mar Ecol-Prog Ser 273:187–197
Francour P, Liret C, Harvey E (1999) Comparison of fish abundance estimates made by remote underwater video and visual census. Nat Sicil 23:155–168
Frontier S (1986) La méthode des cotations d’abondance appliquée aux pêcheries artisanales. In: Frontier S (ed) Evaluation et optimisation des plans d’échantillonnage en écologie littorale. Piren, ATP 9-82-65. Centre National de la Recherche Scientifique, Paris, pp 262–285
Guidetti P, Terlizzi A, Fraschetti S, Boero F (2003) Changes in Mediterranean rocky-reef fish assemblages exposed to sewage pollution. Mar Ecol-Prog Ser 253:269–278
Hamner WM, Robison BH (1992) In situ observations of giant appendicularians in Monterey Bay. Deep Sea Res 39:1299–1313
Harmelin-Vivien ML, Harmelin JG, Chauvet C, Duval C, Galzin R, Lejeune P, Barnabé G, Blanc F, Chevalier R, Duclerc J, Lasserre G (1985) Evaluation visuelle des peuplements et populations de poissons: méthodes et problèmes. Rev Ecol (Terre Vie) 40:467–539
Harvey E, Fletcher D, Shortis M (2002) Estimation of reef fish length by divers and by stereo-video. A first comparison of the accuracy and precision in the field of living fish under operational conditions. Fish Res 57:255–265
Hill J, Wilkinson C (2004) Methods for ecological monitoring of coral reefs. Australian Institute of Marine Science, Townsville
Kaufmann RS, Wakefield WW, Genin A (1989) Distribution of epibenthic megafauna and lebensspuren on two central North Pacific seamounts. Deep Sea Res 36:1863–1896
La Mesa G, Di Muccio S, Vacchi M (2006) Structure of a Mediterranean cryptobenthic fish community and its relationships with habitat characteristics. Mar Biol 149:149–167
Lam K, Shin P, Bradbeer R, Randall D, Ku K, Hodgson P, Cheung S (2006) A comparison of video and point intercept transect methods for monitoring subtropical coral communities. J Exp Mar Biol Ecol 333:115–128
Løkkeborg S, Fernö A, Jørgensen T (2002) Effect of position-fixing interval on estimated swimming speed and movement pattern of fish tracked with a stationary positioning system. Hydrobiologia 483:259–264
Lorance P, Trenkel VM (2006) Variability in natural behaviour, and observed reactions to an ROV, by mid-slope fish species. J Exp Mar Biol Ecol 332:106–119
Love MS, Caselle JE, Snook L (2000) Fish assemblages around seven oil platforms in the Santa Barbara Channel area. Fish B-NOAA 98:96–117
McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82:290–297
Norcross BL, Mueter FJ (1999) The use of an ROV in the study of juvenile flatfish. Fish Res 39:241–251
Okamoto M (1989) Ability of a small observation ROV to observe fish fauna around artificial fish reefs in comparison with diving observation (in Japanese). Nippon Suisan Gakk 55:1539–1546
Pulsipher AG, Daniel WB (2000) Onshore disposition of offshore oil and gas platforms: Western politics and international standards. Ocean Coast Manage 43:973–995
Rilov G, Benayahu Y (2000) Fish assemblage on natural versus artificial reefs: the rehabilitation perspective. Mar Biol 136:931–942
Smith MPL (1988) Effects of observer swimming speed on sample counts of temperate rocky reef fish assemblages. Mar Ecol Prog Ser 43:223–231
Spanier E, Cobb JS, Clancy M (1994) Impacts of remotely operated vehicles (ROVs) on the behaviour of marine animals: an example using American lobsters. Mar Ecol Prog Ser 104:257–266
Stoner AW, Ryer CH, Parker SJ, Auster PJ, Wakefieldd WW (2008) Evaluating the role of fish behavior in surveys conducted with underwater vehicles. Can J Fish Aquat Sci 65:1230–1243
Tessier E, Chabanet P, Pothin K, Soria M, Lasserre G (2005) Visual census of tropical fish aggregations on artificial reefs: slate versus video recording techniques. J Exp Mar Biol Ecol 315:17–30
Wilhelmsson D, Malm T, Öhman MC (2006) The influence of offshore windpower on demersal fish. ICES J Mar Sci 63:775–784
Willis TJ (2001) Visual census methods underestimate density and diversity of cryptic reef fishes. J Fish Biol 59:1401–1408
Communicated by H.-D. Franke.
About this article
Cite this article
Andaloro, F., Ferraro, M., Mostarda, E. et al. Assessing the suitability of a remotely operated vehicle (ROV) to study the fish community associated with offshore gas platforms in the Ionian Sea: a comparative analysis with underwater visual censuses (UVCs). Helgol Mar Res 67, 241–250 (2013). https://doi.org/10.1007/s10152-012-0319-y
- Gas platforms
- Fish community
- Mediterranean Sea
- Census techniques