Results of parallel independent visual evaluation of projective cover of the bottom during macrophyte assesment survey

A simple and cost-effective method for macrophyte stock assessment as visual survey, was tested. It demonstrated good efficiency for counting of Saccharina japonica. The method is based on visual evaluation of SAV projective cover. Such subjective data should be verified. For this purpose, the projective cover along the Tatar Strait coast was estimated independently by two observers. In total, 125 km of the coastline was surveyed with 322 parallel visual estimations at 81 stations. The estimations of both observers agreed well: the concordance coefficient was 0.73 for the total projective cover and 0.78 for the projective cover of S. japonica, at p < 0.0001. About 81 % of the total projective cover and 82 % of S. japonica projective cover were counted with high coherency, whereas poor coherency was noted for < 3 % of both parameters. Average difference between estimations of two observers was 0.083 ± 0.012 for the total projective cover and 0.090 ± 0.012 for S. japonica projective cover. However, comparison of the visual estimations from the sea surface with the SCUBA estimations made near the bottom showed that surface observations resulted in some underestimation of the total projective cover, though the difference was statistically insignificant (p = 0.68). The near-bottom SCUBA survey provides better assessment for small and cortical algae (Ralfsiales and partially Corallinales). Commercial stock of S. japonica was calculated using previously reported relationship between biomass and projective cover (r² = 0.81). The stock estimations for certain areas on the data of two observers had no statistically significant differences (p = 0.46–0.80, depending on the criteria used), the total stock estimations were also very close (34 and 36 thousand tons for total stock and 26 and 24 thousand tons for its commercial part). The visual observations are useful for revealing general features of the vegetation spatial distribution. Linear regressions parameters of the algae abundance on latitude for the data of both observers were similar. They had the angular coefficients –0.13 ± 0.07 and –0.10 ± 0.07 for the total projective cover and –0.25 ± 0.07 and –0.23 ± 0.06 for S. japonica projective cover. In the southern part of survey, the average total projective cover was 0.59 ± 0.04, while it was slightly lower in the northern part: 0.53 ± 0.04; the same estimations for the projective cover of S. japonica differed more considerable: 0.49 ± 0.04 and 0.24 ± 0.03, respectively. There is concluded that the visual surveys of macrophytes are economically effective and allow to reduce labor efforts significantly, comparing with traditional SCUBA surveys (working time in 101 times, costs in 103 times). The video recording ensures the data verification by outside experts, as in the cases when observers cannot identify some species. However, capability of this method is limited by high water turbidity and other cases of worsened visibility. For successful implementation of visual surveys, its algorithm for various environmental conditions is developed and supplemented with necessary instructions.

1. Afanasyev, D.F. and Abdullin, S.R., Experience in analyzing organization of bottom vegetation on the Russian Black Sea shelf using indirect ordination, Russ. J. Ecol., 2014, vol. 45, no. 1, pp. 80–82.

2. Blinova, E.I., Vilkova, O.Yu., Milyutin, D.M., and Pronina, O.A., Metodicheskie rekomendatsii po uchetu zapasov promyslovykh gidrobiontov v pribrezhnoi zone (Methodological Recommendations for the Inventory of Stocks of Commercial Aquatic Species in the Coastal Zone), Moscow: VNIRO, 2003.

3. Bykov, B.A., Geobotanika (Geobotany), Alma-Ata: Nauka, 1978.

4. Gilyarov, A.M., Restructuring ecology: from describing the visible to understanding the invisible, Herald Russ. Acad. Sci., 2005, vol. 75, no. 3. pp. 214–223.

5. Dulenin, A.A., An integrated approach to the organization of coastal fisheries research in the context of reduced funding, in Mater. Vseross. nauchn. konf. mezhdunar. uchastiem, posvyashch. 85-letiyu Kamchatskogo nauchno-issled. inst. rybn. khoz. okeanogr. “Vodnye biologicheskie resursy Rossii: sostoyanie, monitoring, uprav- lenie” (Proc. All-Russ. Sci. Conf. Int. Participation, Dedicated 85th Anniv. Kamchatka Res. Inst. Fish. Oceanogr. “Aquatic Biological Resources of Russia: State, Monitoring, and Management”), Petropavlovsk-Kamchatsky: KamchatNIRO, 2017a, pp. 112–118.

6. Dulenin, A.A., Some methodical problems of SCUBA hydrobiological accounting surveys and the ways of their resolution, Izv. Tikhookean. Nauchno-Issled. Inst. Rybn. Khoz. Okeanogr., 2017b, vol. 190, pp. 231–244. doi 10.26428/1606-9919-2017-190-231-244

7. Dulenin, A.A., On the applicability of visual observations to assess the abundance of macrophytes by the example of saccharin in the Japanese northwestern part of the Tatar Strait, in Mater. 7 Vseros. Nauch.-Prakt. Conf. “Prirodnyye resursy, ikh sovremennoye sostoyaniye, okhrana, promyslovoye i tekhnicheskoye ispol’zovaniye” (Proc. 7th All-Russ. Sci. Pract. Conf. “Natural resources, their current state, protection, commercial and technical use”), Petropavlovsk-Kamchatsky: Kamchatskii Gos. Tekh. Univ., 2016, pp. 80–84.

8. Dulenin, A.A., The depth distribution of dominant species of macrophytes in the northwestern part of the Tatar Strait, Russ. J. Mar. Biol., 2019, vol. 45, no. 2, pp. 96–105. doi 10.1134/S1063074019020032

9. Dulenin, A.A. and Gusarova, I.S., Latitudinal variations in the composition and structure of vegetation in the northwestern Tatar Strait, Russ. J. Mar. Biol., 2016, vol. 42, no. 4, pp. 299–307. doi 10.1134/S1063074016040040

10. Dulenin, A.A. and Kudrevskiy, O.A., The use of lightweight remote operated vehicle for marine coastal hydrobiological investigations, Vestn. Kamchatsk. Gos. Tekh. Univ., 2019, no. 48, pp. 6–17. doi: 10.17217/2079-0333-2019-48-6-17

11. Klochkova, N.G., Flora vodorosley-makrofitov Tatarskogo proliva i osobennosti yeye formirovaniya (Flora of algae-macrophytes of the Tatar Strait and features of its formation), Vladivostok: Dal’nauka, 1996.

12. Kulepanov, V.N. and Zhiltsova, L.V., Dynamics of resources of Phyllospadix iwatensis Makino at the coast of the Sea of Japan (Primorje), Rastitel’nyye resursy, 2004, vol. 40, no. 3, pp. 29–35.

13. Lukin, V.I. and Fadeev, V.I., Specifics of planning hydrobiological works in waterbodies of large extent, Podvodnye gidrobiologicheskiye issledovaniya (Underwater Hydrobiological Research), Vladivostok: Dal’nevost. Nauchn. Tsentr, Akad. Nauk SSSR, 1982, pp. 13–20.

14. Mirkin B.M. and Naumova, L.G., Braun-Blanquet method of vegetation classification in Russia, Zh. Obshch. Biol., 2009, vol. 70, no. 1, pp. 66–77.

15. Polyakov, A.V., KartMaster 4.1. Postroyeniye i analiz kart raspredeleniya zapasa (CartMaster 4.1. Construction and analysis of stock distribution maps), Moscow: VNIRO, 2008.

16. Reznik, A.D., Kniga dlya tekh, kto ne lyubit statistiku, no vynuzhden yeyu pol’zovat’sya (A book for those who do not like statistics, but are forced to use it), St. Petersburg: Rech’, 2008.

17. Ruposov, V., Methods to determine a number of experts, Vestn. Irkutsk. Gos. Tekh. Univ., 2015, no. 3(98), pp. 286–292.

18. Sidorenko, E.V., Metody matematicheskoy obrabotki v psikhologii (Methods of mathematical processing in psychology), St. Petersburg: Rech’, 2003.

19. Shmakov, V.M. and Shulipenko, T.F., Determination of the size of the projective cover in thickets of air-aquatic plants, Gidrobiol. Zh., 1981, vol. 17, no. 2, pp. 103–105.

20. Carpenter, D.E., Luczkovich, J.J., Kenworthy, W.J., Eggleston, D.B., and Plaia, G.R., Development of a performance-based submerged aquatic vegetation monitoring and outreach program for North Carolina, Project Dates: May 1, 2009 to September 21, 2012, 2012. https://files.nc.gov/apnep/documents/files/committees/SAVMonitoringAPNEPtoCRFLFinal21Sep12.pdf

21. Madsen, J.D. and Bloomfield, J.A., Aquatic vegetation quantification symposium: An overview, Lake Reserv. Manage., 1993, vol. 7, pp. 137–140.

22. Madsen, J.D. and Wersal, R.M., A review of aquatic plant monitoring and assessment methods, J. Aquat. Plant Manage., 2017, vol. 55, pp. 1–12.

23. Popper, K., The logic of scientific discovery, L. and N.Y.: Routledge, 2002.

24. Sales, M.V., Cystoseira-dominated assemblages from sheltered areas in the Mediterranean sea: diversity, distribution and effect of pollution, Ph.D. Thesis, Blanes, 2010.

25. Sameoto, J.A., Lawton, P., and Strong, M.B., An approach to the development of a relational database and GIS applicable scheme for the analysis of video-based surveys of benthic habitats, St. Andrews: Fisheries and Oceans Canada Biological Station, 2008.

26. Sheehan, E., Rodriguez-Rodriguez, D., Foster, N., Nancollas, S., Cousens, S., Holmes L., Attrill, M., Pettifer, E., Jones, I., Vaz, S., Facq, J.-V., and Germain, G., A comparative study of towed underwater video methodology to monitor benthic habitats in Marine Protected Areas, Ifremer, Sussex IFCA and Marine Institute for the Protected Area Network Across the Channel Ecosystem (PANACHE) project. INTERREG programme, 2014. https://www.panache.eu.com/upload/iedit/12/pj/2145_5698_WP2ENcomparative_study_of_towed_underwater_video.pdf

27. Hammer, Ø., PAST: Paleontological statistics. Version 3.25. Reference manual, Oslo: Natural History Museum. Univ. of Oslo, 1999–2019.