Introduction
The pikeperch (Sander lucioperca) is a well-known species in Europe, with established protocols for its induced reproduction. However, these methods are highly labor-intensive, requiring repetitive female handling. This procedure causes physiological stress, which may negatively affect progeny development (Rasal et al., 2024) and increase female mortality rates (R��nyai, 2007). Consequently, a critical area of research in modern aquaculture is the identification and application of objective spawning indicators that allow for a more accurate and less invasive recognition of the ovulation. In light of the above, the present study aims to detect and utilize behavioral indicator that enables reliable ovulation detection using camera surveillance, further establishing an animal-friendly and labor-reducing protocol for pikeperch artificial reproduction.
Materials and methods
In January 2025, six breeding pairs were hormonally induced using a salmon gonadotropin-releasing hormone analogue (sGnRHa) under a warming thermal regime (Ljubobratovi�� et al., 2021). The pairs were housed in two 4 m3 tanks, each partitioned into three spawning chambers, resulting in six individual breeding units. Each unit was equipped with a spawning substrate and monitored by an overhead camera. Environmental conditions were controlled via an underwater lighting system set at a 12:12 light:dark (LD) photoperiod. Once the oocyte reached germinal vesicle breakdown (GVBD), the lighting was switched to continuous illumination (24:0 LD) to ensure optimal image quality for the camera systems.
In February 2025, in addition to the nesting pairs (NEST), an additional tank was set up as a control group (TANK), following the common protocol for pikeperch, where a mesh divider separated fish by sex (Ljubobratovi�� et al., 2025). Hormonal, thermal, and photoperiod regimes were identical to those used for the previous spawning batch. Starting from the sixth day post-stimulation, oocyte biopsy was conducted daily in all females. Upon reaching the GVBD stage, the female's genital papilla was sutured (��arski et al., 2017). Further on, ovulation in the TANK group was monitored every 6 h; in contrast, ovulation in the NEST group was monitored only after the detection of a previously defined ethological indicator. NEST females were stripped one hour after the spawning behavior was observed, while the stripping of the TANK group was performed at the moment the ovulation (egg release) was witnessed upon abdominal massage. After fertilization, egg stickiness was removed, and eggs from each female were incubated separately in Zuger jars for evaluation of the egg quality.
Results and Discussion
In the first batch, spawning was observed in all six pairs on the 8th and 9th days after hormonal stimulation. The ethological indicator of spawning was successfully recorded in all fish. In the subsequent trial, the identified indicator led to the precise timing of stripping. In the second experimental cycle, eggs were freshly stripped in all cases based on the deformation observed during the cortical reaction (Nynca et al., 2024). No significant differences were observed between the nesting and control groups regarding latency time or egg quality. However, a statistical trend (P = 0.086) was identified concerning oil globule fragmentation, being higher in the TANK group.
The fact that high egg quality was achieved in all cases using the behavioral indicator suggests that this non-invasive monitoring is as effective as the common ovulation check that requires excessive handling. Recognizing the specific ethological sign before handling the sutured females, the optimal stripping time is ensured, avoiding both early egg stripping and over-ripening of the eggs(Nynca et al., 2024). Furthermore, the success of this monitoring approach opens new perspectives for automated hatchery management. The image processing data collected during the trials will serve as a basis for developing an Artificial Intelligence (AI)- based alarm system, that will automatically detect the real-time onset of spawning. This integration of machine learning algorithms will eliminate the need for manual video surveillance, increasing the efficiency of pikeperch reproduction management.
Acknowledgment
This work was supported by the Flagship Research Groups Programme of the Hungarian University of Agriculture and Life Sciences.
References
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