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Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter

Cilt: 43 Sayı: 4 23 Ekim 2026
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Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter

Öz

The Mediterranean mussel Mytilus galloprovincialis is the principal cultured bivalve in Turkish coastal waters, and Dynamic Energy Budget (DEB) models forced with satellite ocean colour offer a low-cost route to growth prediction. In practice, published parameter sets underestimate shell growth in coastal settings, and the reason is unresolved. We tested a published covariation-method DEB parameterisation against ten grow-out experiments in Türkiye (43 cohort groups in the Aegean, the Dardanelles, the Marmara and the Black Sea), forced with reanalysis sea-surface temperature at culture depth and satellite Level-4 chlorophyll-a. Applied as published (XK=2,1 µg L-1) the model under-predicted at every experiment (RMSE 19.8 mm, bias  mm). Recalibrating the half-saturation constant  alone, with no biological parameter changed, reduced error to 12.7 mm (95% CI 8.1–17.9) under leave-one-experiment-out cross-validation with every data-derived choice refitted inside each fold, and out-performed a satellite-to-in-situ ratio correction by 4.5 mm. We then tested the measurement-scale explanation for that improvement and did not find support for it: at the one site where in-situ chlorophyll is resolved in time and the product observes the site itself the ratio varied three-fold between seasons, rescaling  XK is algebraically identical to rescaling the forcing, and at two sites the product returns no valid retrieval at the deployment at all. The corrected model is therefore an empirical forcing-context calibration, whose residual absorbs unmodelled food quality and culture-system effects, rather than a measurement-scale conversion. Its limits matter as much: a near-zero overall bias masks a mean absolute per-experiment bias of 9.6 mm, and no site-ranking skill was demonstrable against baselines using no chlorophyll or no model. Satellite-forced DEB can reproduce the central tendency of Turkish mussel growth after calibration, but does not yet resolve the between-site differences on which farm siting depends.

Anahtar Kelimeler

Destekleyen Kurum

No specific funding was received.

Etik Beyan

This article does not contain any human or animal studies performed by the authors. All growth and environmental data were obtained from previously published sources and publicly available remote-sensing products, so no specific ethical approval was necessary.

Teşekkür

We thank the Copernicus Marine Service for freely available ocean products and the Add-my-Pet community for curating the DEB parameters our gap-filling relied on. We are grateful to the authors of the validation studies, whose thorough reporting enabled an independent quantitative test.

Kaynakça

  1. Acarlı, S., Vural, P., & Yıldız, H. (2024). Temporal variability in biochemical composition and morphometric parameters of cultivated Mediterranean mussels (Mytilus galloprovincialis) in Çanakkale, Türkiye. Regional Studies in Marine Science, 78, 103759. https://doi.org/10.1016/j.rsma.2024.103759
  2. Alunno-Bruscia, M., Bourlès, Y., Maurer, D., Robert, S., Mazurié, J., Gangnery, A., Goulletquer, P., & Pouvreau, S. (2011). A single bio-energetics growth and reproduction model for the oyster crassostrea gigas in six Atlantic ecosystems. Journal of Sea Research, 66(4), 340–348. https://doi.org/10.1016/j.seares.2011.07.008
  3. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.
  4. Cherif, E. K., Mozetič, P., Francé, J., Flander-Putrle, V., Faganeli-Pucer, J., & Vodopivec, M. (2021). Comparison of in-situ chlorophyll-a time series and Sentinel-3 ocean and land color instrument data in Slovenian national waters (Gulf of Trieste, Adriatic Sea). Water, 13(14), 1903. https://doi.org/10.3390/w13141903
  5. Ciavatta, S., Kay, S., Brewin, R.J.W., Cox, R., Di Cicco, A., Nencioli, F., Polimene, L., Sammartino, M., Santoleri, R., Skákala, J., & Tsapakis, M. (2019). Ecoregions in the Mediterranean Sea through the reanalysis of phytoplankton functional types and carbon fluxes. Journal of Geophysical Research: Oceans, 124(10), 6737 6759. https://doi.org/10.1029/2019JC015128
  6. Çelik, M. Y., Karayücel, S., Karayücel, İ., Eyüboğlu, B., & Öztürk, R. (2015). Settlement and growth of the mussels (Mytilus galloprovincialis, Lamarck, 1819) on different collectors suspended from an offshore submerged longline system in the Black Sea. Aquaculture Research, 47(12), 3765–3776. https://doi.org/10.1111/are.12827
  7. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. https://doi.org/10.1080/07350015.1995.10524599
  8. E.U. Copernicus Marine Service Information (CMEMS). (2022a). Black Sea Physics Reanalysis (BLKSEA_MULTIYEAR_PHY_007_004). Marine Data Store (MDS). https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004

Ayrıntılar

Birincil Dil

İngilizce

Konular

Kabuklu Su Ürünleri Yetiştiriciliği

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

23 Ekim 2026

Gönderilme Tarihi

8 Haziran 2026

Kabul Tarihi

21 Eylül 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 43 Sayı: 4

Kaynak Göster

APA
Hasşerbetçi, C., Tunçelli, G., & Can Tunçelli, İ. (2026). Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter. Ege Journal of Fisheries and Aquatic Sciences, 43(4), 372-382. https://doi.org/10.12714/egejfas.43.4.06
AMA
1.Hasşerbetçi C, Tunçelli G, Can Tunçelli İ. Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter. EgeJFAS. 2026;43(4):372-382. doi:10.12714/egejfas.43.4.06
Chicago
Hasşerbetçi, Cem, Gökhan Tunçelli, ve İdil Can Tunçelli. 2026. “Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter”. Ege Journal of Fisheries and Aquatic Sciences 43 (4): 372-82. https://doi.org/10.12714/egejfas.43.4.06.
EndNote
Hasşerbetçi C, Tunçelli G, Can Tunçelli İ (01 Ekim 2026) Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter. Ege Journal of Fisheries and Aquatic Sciences 43 4 372–382.
IEEE
[1]C. Hasşerbetçi, G. Tunçelli, ve İ. Can Tunçelli, “Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter”, EgeJFAS, c. 43, sy 4, ss. 372–382, Eki. 2026, doi: 10.12714/egejfas.43.4.06.
ISNAD
Hasşerbetçi, Cem - Tunçelli, Gökhan - Can Tunçelli, İdil. “Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter”. Ege Journal of Fisheries and Aquatic Sciences 43/4 (01 Ekim 2026): 372-382. https://doi.org/10.12714/egejfas.43.4.06.
JAMA
1.Hasşerbetçi C, Tunçelli G, Can Tunçelli İ. Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter. EgeJFAS. 2026;43:372–382.
MLA
Hasşerbetçi, Cem, vd. “Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter”. Ege Journal of Fisheries and Aquatic Sciences, c. 43, sy 4, Ekim 2026, ss. 372-8, doi:10.12714/egejfas.43.4.06.
Vancouver
1.Cem Hasşerbetçi, Gökhan Tunçelli, İdil Can Tunçelli. Toward satellite-based growth prediction for mussel aquaculture in Turkish coastal waters: A Dynamic Energy Budget model with an empirically calibrated food parameter. EgeJFAS. 01 Ekim 2026;43(4):372-8. doi:10.12714/egejfas.43.4.06