@article{article_1938908, title={Testing the relationship between otolith morphometric and growth patterns in the deep-sea fish Chlorophthalmus agassizi Bonaparte, 1840 (North Aegean Sea): A comparison of classical and ANN approaches}, journal={Ege Journal of Fisheries and Aquatic Sciences}, volume={43}, pages={273–280}, year={2026}, DOI={10.12714/egejfas.43.3.11}, url={https://izlik.org/JA98XZ76KB}, author={Arslan İhsanoğlu, Mukadder and İşmen, Ali and Yığın, Cahide Çiğdem and Öz, Melike İdil}, keywords={Yeşilgöz balığı, derinsu balıkları, morfometrik analiz, otolit yapısı, makine öğrenmesi}, abstract={<p>This study evaluates whether otolith morphometric descriptors can reliably reflect growth patterns in the deep-sea fish <i>Chlorophthalmus agassizi </i>(Bonaparte, 1840) and assesses the additional explanatory value of artificial neural network (ANN) models compared with classical approaches. A total of 95 specimens were collected monthly between September 2006 and September 2008 by trawling at depths of 100–500 m in Saros Bay (North Aegean Sea). Mean total length and weight were 11.43±0.22 cm (range:7.2–17.1 cm) and 11.07±0.66 g (range:1.88–33.42 g), respectively. Otolith weight ranged from 0.0016 to 0.0067 g, while otolith length varied between 2.73 and 4.37 mm. Relationships among total length (TL), total weight (TW), otolith length (OL), and otolith weight (OW) were examined using both classical regression analyses and ANN-based predictions. Strong relationships were observed for TL–TW (R²=0.95), TL–OL (R²=0.90), TL–OW (R²=0.88), and OL–OW (R²=0.86), whereas the TW–OW relationship showed lower explanatory power (R²=0.65). These findings indicate that otolith morphometrics provide reliable proxies for growth patterns in <i>C. agassizi </i>, while ANN models offer complementary insights into potential nonlinear relationships. </p>}, number={3}, organization={TUBİTAK}