Skip to main content
SpringerLink
Log in

A radar image seabird identification method for analyzing the effects of FADs on seabirds

  • Original Article
  • Fisheries
  • Published:
Fisheries Science Aims and scope Submit manuscript

Abstract

In the tuna purse seine fishery, seiners spend a considerable amount of time detecting objects such as seabirds, for which radar is a very efficient method. In this study, we present a radar image seabird identification method that can calculate the number of clusters, the area of seabird clusters, and the activity level of seabird clusters. We used a fishing vessel’s radar to collect information on seabird groups within 29,632 km of the vessel and calculated a spatial clustering of the seabird-echoes. Generalized additive mixed models (GAMM) were used to investigate the relationship between drifting fish aggregating devices (FADs) and seabird dynamics in the Republic of Kiribati. The findings indicate that FAD variables affected seabird behavior. The random effects on cluster number, cluster area, and cluster ability were 3.27, 17.41, and −0.17, respectively. Then, we compared the radar image information that was calculated. The bird cluster around drifting FADs was found to be more concentrated and denser than in areas without FADs, with a lower level of activity observed. The longitude of 165°E had the highest number of bird clusters and the greatest area inhabited by birds, but these decreased to the east. However, model 3 showed that the minimum value of seabird cluster activity level occurred around 165°E and increased to the east.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The data presented in this paper are available on request from corresponding author.

References

  • Alerstam T, Gudmundur GA, Larsson B (1993) Flight tracks and speeds of Antarctic and Atlantic seabirds radar and optical measurements. Philos Trans r Soc London Ser b: Biol Sci. https://doi.org/10.1098/rstb.1993.0048

    Article  Google Scholar 

  • Arcos JM, Massutí E, Abelló P, Oro D (2000) Fish associated with floating drifting objects as a feeding resource for Balearic Shearwaters Puffinus mauretanicus during the breeding season. Centro Oceanogr de Baleares 77(4):177–182

    Google Scholar 

  • Assali C, Bez N, Tremblay Y (2017) Seabird distribution patterns observed with fishing vessel’s radar reveal previously undescribed sub-meso-scale clusters. Sci Rep 7(1):7364

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  • Bertram DF, Cowen L, Burger AE (1999) Use of radar for monitoring colonial burrow-nesting seabirds. J Field Ornithol 70(2):145–282

    Google Scholar 

  • Bromhead D, Foster J, Attard R, Findlay J, Kalish J (2003) A Review of the impact of fish aggregating devices (FADs) on tuna fisheries: final report to fisheries resource research fund. Bureau of Rural Sciences, Commonwealth of Australia

  • Conand F (1995) Etude du comportement alimentaire et du comportement de déplacement suivi par marquage acoustique. Commission de l’Océan Indien, Association Thonière Régionale, Victoria, Seychelles

  • Dagorn L, Holland KN, Restrepo V, Moreno G (2013) Is it good or bad to fish with FADs? What are the real impacts of the use of drifting FADs on pelagic marine ecosystems? Fish Fish 14:391–415. https://doi.org/10.1111/j.1467-2979.2012.00478.x

    Article  Google Scholar 

  • Ding C, Cao X, Dong M, Zhang Y, Yang J (2019) Non-linear relationships between built environment characteristics and electric-bike ownership in Zhongshan, China. Transp Res Part d: Transp Environ. https://doi.org/10.1016/j.trd.2019.09.005

    Article  Google Scholar 

  • Fonteneau A, Chassot E, Bodin N (2013) Global spatio-temporal patterns in tropical tuna purse seine fisheries on drifting fish aggregating devices (DFADs): Taking a historical perspective to inform current challenges. Aquat Living Resour 26:37–48. https://doi.org/10.1051/alr/2013046

    Article  Google Scholar 

  • Fréon P, Dagorn L (2000) Review of fish associative behaviour: toward a generalisation of the meeting point hypothesis. Rev Fish Biol and Fisheries 10(2):183–207. https://doi.org/10.1023/a:1016666108540

    Article  Google Scholar 

  • Hamre J (1963) Some technological aspects of the Norwegian tuna purse seining fishery. Contributions given in Honour of Einar Koefoed’s 60 years service in Norwegian fisheries research 1902–1962

  • Hou Q, Zhou C, Wan R, Zhang J, Xue F (2021) Application of feature point matching technology to identify images of free-swimming tuna schools in a purse seine fishery. J Mar Sci Eng. 9(12):1357. https://doi.org/10.3390/jmse9121357

    Article  Google Scholar 

  • Hu S, Xiong C, Liu Z, Zhang L (2021a) Examining spatiotemporal changing patterns of bike-sharing usage during covid-19 pandemic. J Transp Geogr 91(11):102997. https://doi.org/10.1016/j.jtrangeo.2021.102997

    Article  PubMed  PubMed Central  Google Scholar 

  • Hu S, Xiong C, Yang M, Younes H, Luo W, Zhang L (2021b) A big-data driven approach to analyzing and modeling human mobility trend under non- pharmaceutical interventions during COVID-19 pandemic. Transp Res Part C Emerg Technol 124:102955. https://doi.org/10.1016/j.trc.2020.102955

    Article  PubMed  PubMed Central  Google Scholar 

  • Itano D (2003) Documentation and classification of fishing gear and technology on board tuna purse seine vessels. 16th sixteenth meeting of the standing committee on tuna and billfish (SCTB16), Queensland, Australia

  • Jaquemet S, Corre ML, Weimerskirch H (2004) Seabird community structure in a coastal tropical environment: importance of natural factors and fish aggregating devices (fads). Mar Ecol-Prog Ser 268:281–292. https://doi.org/10.3354/meps268281

    Article  ADS  Google Scholar 

  • Keys DZ, Orgeret F, Corre ML, Jaeger A, Pistorius PA (2021) At-sea distribution and foraging tactics in a monomorphic tropical seabird. Mar Biol 168(11):1–13. https://doi.org/10.1007/s00227-021-03978-w

    Article  Google Scholar 

  • Majkowski J (2007) Global fishery resources of tuna and tuna-like species. Food and Agriculture Organization of the United Nations, Rome

    Google Scholar 

  • María M, Arroyo GM (2011) Ocean surface winds drive local-scale movements within long-distance migrations of seabirds. Mar Biol 158(2):329–339. https://doi.org/10.1007/s00227-010-1561-y

    Article  Google Scholar 

  • Marsac F, Cayré P (1998) In advances in invertebrates and fish telemetry. Ann Limnol 130:155–171

    Google Scholar 

  • Mills KL (1998) Multispecies seabird feeding flocks in the Galápagos Islands. Condor 100(2):277–285. https://doi.org/10.2307/1370268

    Article  Google Scholar 

  • Park YC, Moon DY, Hwang SJ (1998) Review of changes for the Korean tuna purse seine fleet and fishing methods. Eleventh meeting of the standing committee on tuna and billfish, Hawaii

  • Pitman RL (1993) Seabird associations with marine turtles in the eastern Pacific Ocean. Waterbirds 16:194. https://doi.org/10.2307/1521438

    Article  Google Scholar 

  • Ribic C (1997) The relationships of seabird assemblages to physical habitat features in Pacific equatorial waters during spring 1984–1991. ICES J Mar Sci 54(4):593–599. https://doi.org/10.1006/jmsc.1997.0244

    Article  Google Scholar 

  • Scott GP, Lopez J (2014) The use of FADs in tuna fisheries. EPRS: European Parliamentary Research Service

    Google Scholar 

  • Tidd A, Brouwer S, Pilling G (2017) Shooting fish in a barrel? Assessing fisher-driven changes in catchability within tropical tuna purse seine fleets. Fish Fish 18:808–820. https://doi.org/10.1111/faf.12207

    Article  Google Scholar 

  • Williams PG, Terawasi P (2010) Overview of tuna fisheries in the western and central Pacific Ocean, including economic conditions–2009. WCPFC Sci Comm SC14–2018/GN-WP-01 66pp

  • Wood SN (2017) Generalized additive models: an introduction with R. CRC Press, Florida

    Book  Google Scholar 

Download references

Funding

This research was carried out with financial support from the National Key R&D Program of China (No. 2019YFD0901502 and No. 2020YFD0901202).

Author information

Authors and Affiliations

  1. College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China

    Qinglian Hou, Rong Wan & Cheng Zhou

  2. National Distant-Water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai, 201306, China

    Rong Wan & Cheng Zhou

  3. Key Laboratory of Sustainable Exploitation of Ocean Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306, China

    Cheng Zhou

  4. Key Laboratory of Exploitation of Ocean Fisheries Resources, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China

    Cheng Zhou

Authors
  1. Qinglian Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rong Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rong Wan.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, Q., Wan, R. & Zhou, C. A radar image seabird identification method for analyzing the effects of FADs on seabirds. Fish Sci 90, 161–168 (2024). https://doi.org/10.1007/s12562-024-01749-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12562-024-01749-2

Keywords

Search

Navigation