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ROV vs trawling approaches in the study of benthic communities: the case of Pennatula rubra (Cnidaria: Pennatulacea)

Published online by Cambridge University Press:  08 October 2018

Giovanni Chimienti*
Affiliation:
Department of Biology, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy CoNISMa, Piazzale Flaminio 9, 00197 Roma, Italy
Lorenzo Angeletti
Affiliation:
Institute of Marine Sciences – National Research Council (ISMAR-CNR) UOS Bologna, Via Gobetti 101, 40129 Bologna, Italy
Lucia Rizzo
Affiliation:
CoNISMa, Piazzale Flaminio 9, 00197 Roma, Italy Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
Angelo Tursi
Affiliation:
Department of Biology, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy CoNISMa, Piazzale Flaminio 9, 00197 Roma, Italy
Francesco Mastrototaro
Affiliation:
Department of Biology, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy CoNISMa, Piazzale Flaminio 9, 00197 Roma, Italy
*
Correspondence should be addressed to: Giovanni Chimienti, E-mail: giovanni.chimienti@uniba.it
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Abstract

Megabenthic soft bottom communities of trawlable grounds have been studied since the first few decades of the last century, thanks to trawl fishing technologies. Despite providing an extensive amount of presence data, trawling cannot be considered reliable from a quantitative point of view, frequently giving only weak information about sessile species density, large and small-scale distribution and main habitat features. The recent development of visual technologies on remotely operated vehicles (ROVs) can give a more accurate approach for the study of mega-epibenthic communities. The present study reports the application of both ROV imaging and trawling approaches for the study of a large aggregation (i.e. field) of the red sea pen Pennatula rubra in the Ionian Sea. Density, biomass and population structure were studied in the same population of P. rubra. The density assessed by ROV was significantly higher than that estimated with a three-year series of trawling surveys. Trawling gear efficiency in the removal of P. rubra was low overall. Incidental mortality can be very high due to damage to those specimens that encounter the trawl net but are not directly captured. However, sampling of several colonies by trawling was necessary to establish biometric correlations to estimates of size and biomass from ROV imaging. Trawling catch abundance/biomass data could be useful to identify areas of higher concentration of sea pens, while ROV imaging can be used to monitor these fields in a non-destructive manner that would be consistent with protection measures.

Information

Type
Research Article
Copyright
Copyright © Marine Biological Association of the United Kingdom 2018 
Figure 0

Fig. 1. Study area: (A, B) north-west Punta Alice, southern Italy, Ionian Sea; (C) ROV transects and trawl fishing transects on the field of Pennatula rubra (Ellis, 1761). Measurements: (D) number of polyp leaves and total length.

Figure 1

Table 1. Metadata of the transects carried out using experimental trawl net and ROV on a Pennatula rubra (Ellis, 1761) field

Figure 2

Table 2. Results from PERMANOVA showing differences in density of Pennatula rubra (Ellis, 1761) between approaches tested (ROV and trawling gear)

Figure 3

Table 3. Metadata of ROV imaging for the three transects carried out on the field of Pennatula rubra (Ellis, 1761)

Figure 4

Fig. 2. Pennatula rubra (Ellis, 1761) colonies: (A) haul from trawl fishing net; (B) fracture of axial rod and (C) tissue abrasion of colonies in the net; (D) different sizes of colonies sampled with the net; (E) adult and juvenile (white circle) observed with ROV; (F) facies observed by ROV; (G) detail of a juvenile colony. Scale bars: A–F, 3 cm; G, 1 cm.

Figure 5

Fig. 3. Size–frequency distribution of the same population of Pennatula rubra (Ellis, 1761) assessed with: (A) samples from trawl fishing; (B) ROV imaging.

Figure 6

Fig. 4. Biometric relationships of the sampled colonies of Pennatula rubra (Ellis, 1761), between: (A) dry weight and total length; (B) dry weight and number of polyp leaves; (C) number of polyp leaves and total length.

Figure 7

Table 4. Results from the multivariate permutational analysis (PERMANOVA) testing differences in total length of Pennatula rubra (Ellis, 1761) at several spatial scales

Figure 8

Table 5. PERMANOVA analysis underlining differences in total length of Pennatula rubra (Ellis, 1761) among approaches tested (ROV and trawling gear)

Supplementary material: File

Chimienti et al. supplementary material

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