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© Kay Panten
Institute of

SF Sea Fisheries

Project

Biological Oceanography - How Physics Affects Biology



© Thünen-Institut

Biological Oceanography - How Physics Affects Biology

Factors such as temperature, salinity, oxygen, and oceanic currents shape the environmental conditions of marine life and directly or indirectly impact marine ecosystems. In this project, we aim to understand and quantify these impacts and use climatic trends to predict future changes in marine life.

 

Background and Objective

In this project, we explore the influence of marine physics on marine ecosystems and particularly fish. Data analyses, statistical models as well as computer simulations help us to better understand and predict the complex interactions between physical (e.g. temperature, salinity, current conditions) and biological variables (e.g. fish stock distribution, biomass and productivity).

Target Group

Policy makers; Marine scientists

Approach

We apply a variety of statistical methods to investigate environmental influences on abundance, recruitment, and distribution of commercial fish species. In particular, our habitat models are complex statistical methods (generalized additive and linear (mixed models) and help us to determine relationships between the environment and the spatial distribution of fish. These models allow us to explore the influence of climate change on the fish distribution, as well as to calculate medium- to long-term projections of future distributional changes as a result of climate change. Moreover, we use physiological individual-based models to explore the processes that control variability in fish productivity. Our models simulate individual fish eggs, larvae, and juveniles and estimate their growth and mortality. The models help us investigate the potential influence of environmental and human-induced changes in marine ecosystems (e.g., offshore wind farms, coastal protection measures) on the survival of fish early life stages.

Our Research Questions

What is the influence of environmental parameters and climate change on the distribution and population dynamics of North Atlantic fish stocks?

What are the relevant processes, and on what spatial and temporal scales do they occur?

Publications

  1. 0

    Akimova A, Peck M, Börner G, Damme CJG van, Moyano M (2024) Corrigendum to “Combining modeling with novel field observations yields new insights into wintertime food limitation of larval fish”. Limnol Oceanogr 69(7):1665, DOI:10.1002/lno.12532

    https://literatur.thuenen.de/digbib_extern/dn068576.pdf

  2. 1

    Böer G, Gröger JP, Badri-Höher S, Cisewski B, Renkewitz H, Mittermayer F, Strickmann T, Schramm H (2023) A deep-learning based pipeline for estimating the abundance and size of aquatic organisms in an unconstrained underwater environment from continuously captured stereo video. Sensors 23(6):3311, DOI:10.3390/s23063311

    https://literatur.thuenen.de/digbib_extern/dn066163.pdf

  3. 2

    Moyano M, Illing B, Akimova A, Alter K, Bartolino V, Börner G, Clemmesen C, Finke A, Gröhsler T, Kotterba P, Livdane L, Mittermayer F, Moll D, Nordheim L von, Peck M, Schaber M, Polte P (2023) Caught in the middle: bottom-up and top-down processes impacting recruitment in a small pelagic fish. Rev Fish Biol Fisheries 33(1):55-84, DOI:10.1007/s11160-022-09739-2

    https://literatur.thuenen.de/digbib_extern/dn065680.pdf

  4. 3

    Hodapp D, Roca IT, Fiorentino D, Garilao C, Kaschner K, Kesner-Reyes K, Schneider B, SegschneideJ, Kocsis AT, Kiessling W, Brey T, Froese R (2023) Climate change disrupts core habitats of marine species. Global Change Biol 29(12):3304-3317, DOI:10.1111/gcb.16612

    https://literatur.thuenen.de/digbib_extern/dn066095.pdf

  5. 4

    Akimova A, Peck M, Börner G, Damme CJG van, Moyano M (2023) Combining modeling with novel field observations yields new insights into wintertime food limitation of larval fish. Limnol Oceanogr 68(8):1865-1879, DOI:10.1002/lno.12391

    https://literatur.thuenen.de/digbib_extern/dn066587.pdf

  6. 5

    Núñez-Riboni I, Costas G, Diekmann R, Ulleweit J, Kloppmann MHF (2023) Reviewing and improving spatiotemporal modeling approaches for mackerel's total annual egg production. Rev Fish Biol Fisheries 33(4):1523-1546, DOI:10.1007/s11160-023-09795-2

    https://literatur.thuenen.de/digbib_extern/dn066692.pdf

  7. 6

    Koul V, Brune S, Akimova A, Düsterhus A, Pieper P, Hövel L, Parekh A, Schrum C, Baehr J (2023) Seasonal prediction of Arabian Sea marine heatwaves. Geophys Res Lett 50(18):e2023GL103975, DOI:10.1029/2023GL103975

    https://literatur.thuenen.de/digbib_extern/dn066940.pdf

  8. 7

    Núñez-Riboni I, Chelton DB, Marconi V (2023) The spectral color of natural and anthropogenic time series and its impact on the statistical significance of cross correlation. Sci Total Environ 860:160219, DOI:10.1016/j.scitotenv.2022.160219

    https://literatur.thuenen.de/digbib_extern/dn065708.pdf

  9. 8

    Akimova A, Peck M, Börner G, Damme CJG van, Moyano M (2023) Wintertime growth limitation of herring larvae: combining physiological modelling with novel zooplankton observations. STAGES : Early Life History Section Newsl 44(3):8-9

  10. 9

    Skogen MD, Ji R, Akimova A, Daewel U, Hansen C, Hjollo SS, Leeuwen SM van, Maar M, Macias D, Askov Mousing E, Almroth-Rosell E, Sailley SF, Spence M, Troost TA, van de Wolfshaar K (2021) Disclosing the truth: Are models better than observations? Mar Ecol Progr Ser 680:7-13, DOI:10.3354/meps13574

  11. 10

    Núñez-Riboni I, Akimova A, Sell AF (2021) Effect of data spatial scale on the performance of fish habitat models. Fish Fisheries 22(5):955-973, DOI:10.1111/faf.12563

    https://literatur.thuenen.de/digbib_extern/dn063616.pdf

  12. 11

    Post SL, Werner K-M, Núñez-Riboni I, Chafik L, Hatun H, Jansen T (2021) Subpolar gyre and temperature drive boreal fish abundance in Greenland waters. Fish Fisheries 22(1):161-174, DOI:10.1111/faf.12512

  13. 12

    Taylor MH, Akimova A, Bracher A, Kempf A, Kühn B, Helaouet P (2021) Using dynamic ocean color provinces to elucidate drivers of North Sea hydrography and ecology. JGR Oceans 126(12):e2021JC017686, DOI:10.1029/2021JC017686

    https://literatur.thuenen.de/digbib_extern/dn064301.pdf

  14. 13

    Cisewski B, Hatun H, Kristiansen I, Hansen B, Larsen KMH, Eliasen SK, Jacobsen JA (2021) Vertical migration of pelagic and mesopelagic scatterers from ADCP backscatter data in the Southern Norwegian Sea. Front Mar Sci 7:542386, DOI:10.3389/fmars.2020.542386

    https://literatur.thuenen.de/digbib_extern/dn063248.pdf

  15. 14

    Emblemsvag M, Núñez-Riboni I, Christensen HT, Nogueira A, Gundersen AC, Primicerio R (2020) Increasing temperatures, diversity loss and reorganization of deep-sea fish communities east of Greenland. Mar Ecol Progr Ser 654:127-141, DOI:10.3354/meps13495

    https://literatur.thuenen.de/digbib_extern/dn062858.pdf

  16. 15

    Püts M, Taylor MH, Núñez-Riboni I, Steenbeek J, Stäbler M, Möllmann C, Kempf A (2020) Insights on integrating habitat preferences in process-oriented ecological models - a case study of the southern North Sea. Ecol Model 431:109189, DOI:10.1016/j.ecolmodel.2020.109189

  17. 16

    Núñez-Riboni I, Taylor MH, Kempf A, Püts M, Mathis M (2019) Spatially resolved past and projected changes of the suitable thermal habitat of North Sea cod (Gadus morhua) under climate change. ICES J Mar Sci 76(7):2389-2403, DOI:10.1093/icesjms/fsz132

    https://literatur.thuenen.de/digbib_extern/dn061168.pdf

  18. 17

    Akimova A, Hufnagl M, Peck M (2019) Spatiotemporal dynamics of predators and survival of marine fish early life stages: Atlantic cod (Gadus morhua) in the North Sea. Progr Oceanogr 176:102121, DOI:10.1016/j.pocean.2019.102121

  19. 18

    Beyraghdar Kashkooli O, Gröger JP, Núñez-Riboni I (2017) Qualitative assessment of climate-driven ecological shifts in the Caspian Sea. PLoS One 12(5):e0176892, DOI:10.1371/journal.pone.0176892

    https://literatur.thuenen.de/digbib_extern/dn058608.pdf

  20. 19

    Núñez-Riboni I, Akimova A (2017) Quantifying the impact of the major driving mechanisms of inter-annual variability of salinity in the North Sea. Progr Oceanogr 154:25-37, DOI:10.1016/j.pocean.2017.04.004

  21. 20

    Cisewski B, Strass VH (2016) Acoustic insights into the zooplankton dynamics of the eastern Weddell Sea. Progr Oceanogr 144:42-92, DOI:10.1016/j.pocean.2016.03.005

  22. 21

    Akimova A, Hufnagl M, Kreus M, Peck M (2016) Modeling the effects of temperature on the survival and growth of North Sea cod (Gadus morhua) through the first year of life. Fisheries Oceanogr 25(3):193-209, DOI:10.1111/fog.12145

  23. 22

    Akimova A, Núñez-Riboni I, Kempf A, Taylor MH (2016) Spatially-resolved influence of temperature and salinity on stock and recruitment variability of commercially important fishes in the North Sea. PLoS One 11(9):e0161917, DOI:10.1371/journal.pone.0161917

    https://literatur.thuenen.de/digbib_extern/dn057166.pdf

  24. 23

    Núñez-Riboni I, Akimova A (2015) Monthly maps of optimally interpolated in situ hydrography in the North Sea from 1948 to 2013. J Mar Syst 151:15-34, DOI:10.1016/j.jmarsys.2015.06.003

  25. 24

    Núñez-Riboni I, Kristinsson K, Bernreuther M, Aken HM van, Stransky C, Cisewski B, Rolskiy A (2013) Impact of interannual changes of large scale circulation and hydrography on the spatial distribution of beaked redfish (Sebastes mentella) in the Irminger Sea. Deep Sea Res Pt 1 Oceanogr Res Paper 82:80-94, DOI:10.1016/j.dsr.2013.08.003

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