Comparing behavioral performance and physiological responses of Sebastes schlegelii with different aggressive strengths

doi:10.21203/rs.3.rs-1345435/v1

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Comparing behavioral performance and physiological responses of Sebastes schlegelii with different aggressive strengths

https://doi.org/10.21203/rs.3.rs-1345435/v1

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Fish aggression has significant individual differences, and different personalities exhibit distinct behavioral performances and physiological stress responses. Under intensive culture conditions, Sebastes schlegelii juveniles display severe aggression and cannibalism, causing fish welfare and economic loss. Herein, we investigated the alterations in behavioral performance and physiological stress indicators of Sebastes schlegelii juveniles with different aggressive strengths. The results revealed that latency to the first movement, distance to center-point, mobile frequency, and immobile frequency were significantly lower in high-aggressive individuals than low-aggressive individuals. In contrast, the immobile time was significantly higher in high-aggressive individuals compared to low-aggressive individuals. PCA was performed to identify the key parameters of fish behavior. From the results of PCA, position, motion state, and physical status could be used as behavioral screening indicators for individuals with different aggressions. The 5-HIAA/5-HT ratio was significantly lower in high-aggressive individuals than in low-aggressive individuals. Moreover, cortisol levels were positively correlated with immobile time, and the ratio of 5-HIAA/5-HT was significantly and positively correlated with the distance to the central point. These results suggest that different aggressive individuals can be identified according to position, motion state, and physical status in a short period of time, while the distance to center-point associated with brain monoaminergic activity may be a more accurate indicator. The results not only provide a non-invasive method for reducing fish aggression but also help to enhance precise management in aquaculture.

aggressive strengths

behavioral performance

cortisol

monoamines

Sebastes schlegelii

Sebastes schlegelii, a carnivorous fish species commonly known as black rockfish, is one of the vital economic fish species in the coastal areas of the Yellow Sea and Bohai Sea of China. Due to overfishing, environmental damage, and habitat degradation, black rockfish resources have decreased sharply in recent years (Lü et al. 2014; Xi et al. 2017a). As a regional fish species with tremendous development potential, black rockfish aquaculture has developed rapidly in recent years. In general, black rockfish were continuously cultured to over 12 cm under controlled conditions before being released in the coastal seas or moved into nets and were used as a food source and to relieve fishing pressure (Guo et al. 2017; Xu et al. 2020). The aim of aquaculture is to improve the survival rate of juveniles; however, under intensive culture conditions, black rockfish juveniles display serious aggression and cannibalism, leading to fish welfare and economic loss (Xi et al. 2017b; Xu et al. 2020).

The fundamental definition of fish welfare includes a state of complete physical and normal behavior (Rault et al. 2020). Under natural conditions, aggression is an adaptive behavior intended to protect one from harm and conserve resources. However, in intensive aquaculture, cultured fishes may have little or no access to escape predation (Metcalfe et al. 2016; Xu et al. 2020). The most direct impact of aggressive behavior is physical impairment, which in turn influences the later health and motility of fish (Brandão et al. 2018; Castanheira et al. 2013; Wolkers et al. 2017). Fish aggression influences the effectiveness and sustainability of aquaculture, such as growth, physical condition, welfare, and food conversion. As a result, various methods should be used to reduce aggression and cannibalism, regardless of the fish being farmed (Hvas et al. 2021; Manuel et al. 2016).

The occurrence and degree of aggressive behavior vary widely among fishes. There are surprising and consistent differences between the fighting parties, even among individuals of the same population, gender, size, and age (Hellinger et al. 2020; Hu et al. 2021; Huntingford and Adams 2005; Zabegalov et al. 2019). In other words, some fish fight more than others. This is not noise but an essential biological variability that must be recognized and rationalized. Although fish personalities are distinct, this does not mean that the potential underlying laws of behavioral expression cannot be elucidated. Fish aggression is complex and includes not only direct biting and nipping but also differing displays and postures (Haraldstad et al. 2019; Qian et al. 2017). With the increased intensity of behavioral expression, the extensive patterns that eventually lead to bite attacks are typical, allowing the possibility of uncovering a pattern in fish behavior.

Fish behavior is a comprehensive representation of extrinsic environmental factors and intrinsic physiological factors (Xu et al. 2020). There have been multiple studies on the physiological research of aggressive fish behavior (Ariyomo et al. 2013; Boscolo et al. 2018), but studies about behavioral performance are scarce. In this paper, we aimed to distinguish the main characteristics of juveniles with contrasting aggressive patterns to identify the features and patterns of aggressive behavior in black rockfish. The results will not only provide a viable and non-invasive method for reducing fish aggression and improving fish welfare but also enhance management in aquaculture, such as optimizing culture systems according to the behavioral performance, purposefully screening specific objects, and domesticating specific behavioral groups.

We have read the policies relating to animal experiments and confirmed this study complied (ARRIVE guidelines; EU Directive 2010/63/EU for animal experiments). All procedures performed in this study involving animals were in accordance with the guidelines and ethical standards of the Dalian Ocean University.

2.1. Source and experimental animal

Experiments were conducted at Key Laboratory of Environment Controlled Aquaculture (AET), Dalian city, Liaoning Province, China, 2020. Black rockfish juveniles with the same genetic background and from the same population were obtained from the laboratory and temporarily reared and acclimated for 14 days. Juveniles were fed commercial feed (crude protein ≥ 44%, crude fat ≥ 5%) at the rate of 2% of their body mass 3 times daily. The water temperature was maintained at 20 ± 1°C, and natural illumination was used throughout the study. Fish were fasted on the day prior to the experiment.

2.2. Experimental design

Dyadic fighting (DF) was used to stimulate aggressive interactions in this experiment (Oliveira et al. 2011). To exclude the influence of body size on aggression, fish specifications were strictly screened according to the following criteria: body length and weight variations were less than 0.1 cm and less than 5%, respectively. A total of 71 juveniles were used with an initial body length of 6.50 ± 0.10 cm and body weight of 7.70 ± 0.30 g. Ten glass tanks of the same size (21×14×17.5 cm) were used herein. The back and lateral sides of the tank were covered with nontransparent white plastic boards to increase contrast and reduce external visual clues during behavioral recording. Cameras (HIK-DS-2CD3T35D-I5) were directly mounted above and in front of the tanks.

Two randomly selected juveniles were placed in the experimental tank and separated by a removable divider. Two days later, the divider was removed, and aggressive interactions were videotaped and registered by recording the time of occurrence. Each video lasted 10 min. For each aggressive interaction, the initiator, the receiver, the winner, the loser as well as the duration of the fight were documented. Winner/loser outcomes could be easily identified and determined by different behavioral performances (Teles and Oliveira 2016). The winner was defined as high aggression (H-agg), and the loser was defined as low aggression (L-agg). The same procedure was performed for the control group (N = 9) but without divider removal.

2.3. Behavioral observations

The activity of the juveniles was tracked and calculated using Noldus EthoVision XT (version 12.0; Noldus Information Technology, Netherlands). A dynamic subtraction method was applied, and the sampling rate was set at 25 frames per second. A smoothing (Lowess) method was used to eliminate small movements such as system background noise. The tracking data were additionally checked by an all-occurrence recording method to ensure the accuracy of the results.

The initiator of the aggressive behavior was defined as the target fish, and the other was defined as the object fish. The frequency and duration of the occurrence of aggressive behavior were recorded, respectively. To analyze behavioral differences in individuals with different aggressive strengths, behavioral parameters related to aggression were further quantified, divided into 3 aspects: position, motion state, and physical status. Ten parameters related to aggressive behavior were determined: distance moved (cm), velocity (cm/s), acceleration (cm/s²), angular velocity (º/s), latency to the first movement (s), distance to center-point (cm), mobile time (s), mobile frequency, immobile time (s) and immobile frequency. Lastly, the principal component analysis (PCA) was used to identify variations in behavioral parameters of the fish aggression process.

2.4. Sampling and analytical methods

At the end of the trial, all juveniles in control group and same number juveniles in treatment group were anesthetized, and the tissues were sampled. The brain tissues of each juvenile were sampled with clean surgical scissors and pointed tweezers to avoid contamination. Afterward, the tissues were flash-frozen in liquid nitrogen and stored at − 80°C. Lastly, the activity of 5-hydroxytryptamine (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), and cortisol were analyzed by double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) kits (Meimian, Jiangsu, China).

2.5. Data analysis

All values were presented as means ± standard error of the mean (SEM). Statistical analysis was performed using Microsoft Office Excel 2017and SPSS 21.0. P ≤ 0.05 was considered to be statistically significant. Nonparametric tests were used when data were not normally distributed. Correlations were examined by the Spearman rank correlation test.

3.1. Behavioral performance

The results obtained from the behavioral analysis of individuals with different aggression are presented in Fig. 1. Both the latency to the first movement and the distance to the center point were significantly higher in the low-aggression group than those in the high-aggression group. The angular velocity was lower in individuals with high aggression than that of low-aggression individuals, but no significant differences were observed. Heat maps of dwell time demonstrated the selection of arena by individuals with different aggression. It appeared that, regardless of movement and locomotion quiescence, individuals with high aggression were mostly situated in the central area of the experimental system. On the contrary, individuals with low aggression were mostly located at the system's edge. Aside from that, the results also determined that high aggression individuals were mostly in a dynamic state, while low aggression individuals were mostly stationary.

The motion state of the high-aggression group did not differ significantly from that of the low-aggression group (P > 0.05; Fig. 3). The high-aggression group had lower movement velocity, lower total distance moved, and higher acceleration, but not significantly different than those in the low-aggression group (P > 0.05; Fig. 3). The acceleration was significantly higher in the high-aggression group than that in the low-aggression group (P ≤ 0.05; Fig. 3). The physical status of the high-aggression group was significantly different from that of the low-aggression group (P ≤ 0.05; Fig. 4). Likewise, there was no difference in mobile time between the high-aggression and low-aggression groups, but there is a noticeable difference in mobile frequency. Indeed, the mobile frequency was significantly higher in the low-aggression group than in the high-aggression group. The immobile time in individuals with high aggression was markedly longer than those with low aggression, but the immobile frequency was significantly lower than that of low-aggression individuals (P ≤ 0.05; Fig. 4).

Figure 5 presents the frequency and duration of behavior of highly aggressive individuals within 10 min to characterize the physical interaction. The results illustrated that the trends of aggression duration and frequency were generally consistent, showing a gradual decrease in duration and frequency over time, with a progressive leveling of aggression frequency and duration after 300 s (Fig. 5).

3.2. Key behavioral parameters extraction

The results of the key behavioral parameters extraction for individuals with different aggressive strengths are outlined in Table 1. For aggressive behavior, the first variable factor (CF1) accounted for 22.80% of the total variation and had a strong positive loading on velocity (0.831). The analysis revealed that the first component of the PCA mainly reflected the movement state of high and low aggression individuals. The second variable factor (VF2) accounted for 20.09% of the total variance, and the immobile time had a strong negative loading (-0.851), while the immobile frequency had a strong positive loading (0.859). VF2 could be interpreted as the physical state of high and low aggressive individuals. The third variable factor (VF3) accounted for 18.92% of the total variance and had a strong positive loading on the distance to center-point (0.805), interpreted as the location distribution of individuals with contrasting aggressions.

Table 1

PCA results of aggressive behavior in high and low aggressive individuals
Variables	VF1	VF2	VF3
Behavioral performance of aggressivity(three significant components)
Latency to first move	-0.682	-0.273	0.394
Distance to center-point	-0.031	0.243	0.805
Angular velocity	0.455	0.345	0.130
Velocity	0.831	-0.287	0.201
Acceleration	-0.077	0.070	-0.523
Mobile time	0.307	-0.077	0.638
Mobile frequency	0.764	-0.014	0.355
Immobile time	0.034	-0.851	-0.123
Immobile frequency	0.043	0.859	-0.144
Rotation sums of squared loadings
Total	2.052	1.808	1.702
% Total variance	22.803	20.094	18.915
Cumulative % variance	22.803	42.898	61.813

3.3. Cortisol levels and brain monoaminergic activity

The results showed that the cortisol levels were significantly higher in both high and low aggressive individuals compared to the control group (F _2,24 = 16.14, P ≤ 0.01), but there was no significant difference in cortisol levels between high-aggressive and low-aggressive individuals (P > 0.05, Fig. 6). The 5-HT of individuals with high and low aggression was significantly higher than those in the control group (F _2,24 = 18.16, P ≤ 0.01). Similarly, there was no significant difference in 5-HT levels between individuals with different aggression (P > 0.05, Fig. 7). The 5-HIAA/5-HT ratio was significantly higher in individuals with low aggression than that in individuals with high aggression (F _2,24 = 7.60, P ≤ 0.01). Again, there was no significant difference in 5-HIAA/5-HT ratio between the control and the other two groups (P > 0.05, Fig. 8).

3.4. Correlation of physiological indicators with behavioral analysis

The relationship between the key behavioral parameters extracted by PCA and cortisol responses is depicted in Fig. 9. Cortisol levels were positively correlated with immobile time (P ≤ 0.05, Fig. 9). The relationship between the key behavioral parameters extracted by PCA and the 5-HIAA/5-HT ratio is delineated in Fig. 10. Furthermore, the ratio of 5-HIAA/5-HT was significantly and positively correlated with the distance to the central point (P ≤ 0.05, Fig. 10).

4.1. Behavioral performance

At the present stage, the study of the aggressiveness of fish mainly adopts mirror image stimulation tests (Falsarella et al. 2017; Gerlai et al. 2000; Liu et al. 2020; Marks et al. 2005; Moretz et al. 2007a; Moretz et al. 2007b) and real opponent stimulation tests (Aguiar and Giaquinto 2018; Boscolo et al. 2018; Brandão et al. 2018; Fontana et al. 2016; Zhang et al. 2020; Zhang et al. 2021). The mirror test is a standardized experimental method that can stimulate the same aggressive behavior in the opponent fish and the target fish. However, some studies have pointed out that hormone levels, behavioral responses, and physiological changes caused by the aggressive response stimulated by the mirror test are different from those caused by real opponent stimulation (Hirschenhauser et al. 2008; Oliveira et al. 2016). Compared with the mirror image test, the real opponent stimulation test induces a more complete expression of aggressive behavior in the opponent fish and the target fish, and the behavior sequence is more complex, which can trigger a behavioral response that is closest to reality and form a typical aggressive phenotypic difference individuals (Hubená et al. 2020). Some studies reported that the real opponent stimulation test could accurately divide aggressiveness after about 5 min (Ariyomo et al. 2013; Teles and Oliveira 2016). Therefore, it is more realistic and accurate to adopt stimulating experiments with real opponents (Teles and Oliveira 2016).

Three related behavioral factors were selected from the 10 parameters related to aggressive behavior, namely, the individual's position, motion state, and physical status. All three factors can represent the significant differences between high aggression and low aggression individuals and can also be used as typical behavioral parameters for rapidly screening individuals with diverse aggression (Colléter and Brown 2011; Oliveira et al. 2011; Quadros et al. 2018). From the perspective of individual motion states, there was no significant difference between the attack speed of high aggression and escape speed of low aggression, which may be due to the small experimental area, leaving limited space for individuals to attack or escape (Ma et al. 2021). The distribution position results determined that aggression was significantly associated with fish personality. Individuals with high aggression usually adopt active coping strategies (approach-attack), while individuals with low aggression usually display passive coping strategies (defense-escape) (Oliveira et al. 2011). The results revealed that the latency to the first movement of high aggression individuals is shorter and closer to the center of the experimental system. In contrast, the latency to the first movement of low aggression individuals is longer, closer to the edge of the experimental system, and with a longer residence time. This finding is consistent with that of Colléter (Colléter and Brown 2011). Assuming that there is no shelter, the moving tendency and position in an open water tank can represent the individual's boldness (Dahlbom et al. 2011). Individuals with a strong tendency to move and who are often located in the central area are typically considered bold ones, while individuals with a weak mobility tendency and often located in the marginal area are considered shy (Oliveira et al. 2011). Therefore, individuals with high aggression are often bolder than individuals with low aggression. In terms of individual physical state, it is interesting to note that the activity of individuals with high aggression is lower than individuals with low aggression. This finding is contrary to most previous studies (Quadros et al. 2018; Yuan et al. 2018). The reasons may be as follows: First, the method used to measure fish activity in this study differs from the general approach, which is measured separately according to the area that fish passed through per unit time. The activity in this experiment was measured in real-time according to the percentage change in the number of pixels when the object moved (Hubená et al. 2020; Whittaker et al. 2021). Obviously, the change in measurement, based on the moving object itself, is more reasonable and accurate. Secondly, during short-term interactions, individuals with low aggression may be more inclined to defend and retreat due to limited space for hiding or escaping (Ma et al. 2021; Paull et al. 2010). This results in more struggling actions, such as swinging the tail in the edge area of the test system, leading to a higher rate in pixel change. Thirdly, studies have established that individuals with high aggression are less flexible to daily changes than individuals with low aggression (Vindas et al. 2017a), suggesting that individuals with high aggression may be less active.

Aggressive behavior can be divided into proactive and reactive aggression based on specific behavioral and physiological characteristics (Wrangham 2018; Zhu et al. 2019). Proactive aggression is triggered by scheduled motivations to achieve personal goals or obtain personal gains (Fanning et al. 2020; Ibabe 2020). Therefore, proactive aggression is often scheduled premeditated, and is associated with lower emotional responsiveness. Conversely, reactive aggression is driven by provocation and/or perceived threat and is usually impulsive and unplanned. Moreover, it is usually linked to higher emotional responsiveness (Dorfman et al. 2013; Fanning et al. 2020; Zhu et al. 2019). The present results showed that the high-frequency aggression in individuals with high aggression was manifested mostly as time concentration, short persistent time, and high repeatability. This result is impressive, as it indicates that the aggressive behavior of individuals with high aggression may actually belong to reactive aggression (Cervantes and Delville 2007).

Herein, differences between different types of aggression were not taken into account. The rationale is that the differences are visible by the naked eye, regardless of aggression frequency and behavioral performance. Individuals with low aggression showed obvious escape and freezing behavior, while high aggression individuals mainly exhibited biting and chasing behavior (Oliveira et al. 2011). In addition, aggression tests that involve three or more interacting individuals were not addressed in the real opponent stimulation test. Earlier studies indicated that different experimental methods lead to differences in behavioral performances between high and low aggression individuals (Oliveira et al. 2005; Way et al. 2015). In fact, fishes form a specific and self-organized structure during group formation. Therefore, the aggressive behavior of fish in a group context is more reflecting of the real aggression situation (Xu et al. 2020). Regrettably, this is challenging to achieve in practice at this stage.

4.2 Physiological indicators

Variations in cortisol levels have been widely used as a stress indicator in fishes (Moltesen et al. 2016). The cortisol responses suggested that the individuals exposed to an aggressive state (high and low) were under significant stress (higher cortisol levels at baseline), which was in agreement with Bessa's finding (Bessa et al. 2021). It is somewhat surprising that no significant difference in cortisol was found between the two aggressive groups. This indicated that the cortical responses evoked by short-duration aggression were similar, regardless of individuals with high or low aggression. This finding does not support the previous research (Sherman and Mehta 2019; Sloman et al. 2001). Studies have shown that the decrease in cortisol levels after stress is more important than the cortisol concentration during stress (Koolhaas et al. 2011), that is, individuals with different aggression may have similar cortisol concentrations at the beginning of the aggression. Another possible reason might be that cortisol was persistently elevated after 10 minutes of aggression (Moltesen et al. 2016).

In teleost fishes, differences in brain monoamine activity have been consistently and reliably utilized to reflect the motivation and outcome of fish aggression (Backström and Winberg 2017; Clotfelter et al. 2007). Considering that monoamines and their metabolites require a certain period of time to accumulate, the 5-HIAA/5-HT ratio may be a more reliable indicator of neural activity for a short duration (Øverli et al. 1999). This can be used to justify the lack of significant difference in the 5-HT content, but the 5-HIAA/5-HT ratio was significantly different in individuals with various aggressive behavior. This finding was also reported by Loveland et al. (Loveland et al. 2014). During aggression, the brain's 5-HT-ergic system was activated, and the activity of 5-HT increased rapidly in low aggression individuals.

In this study, there is a decent correlation between aggressive behavior and physiological indicators. Immobile time and the cortisol level were positively correlated. The longer the immobile time, the higher the cortisol level. A longer immobile time implies that fish have a positive stress coping mechanism (Vindas et al. 2017b). The 5-HIAA/5-HT ratio was also positively correlated with the distance to the central point. This is contrary to studies related to the effects of aggressive fish behavior on brain monoaminergic activity (Xu et al. 2020). This result may be explained by the fact that the aggressive behavior indicators in this experiment were more comprehensive and were not limited to the duration and times involved in the mirror test. The distance to the central point is inversely proportional to the aggressive strength, that is, the closer the distance to the central point, the higher the aggressive strength, and the lower the 5-HIAA/5-HT ratio (Colléter and Brown 2011; Dahlbom et al. 2011). Actually, the knowledge on fish behavior and physiologic parameters is still limited. To improve fish welfare and aquaculture practices, future research needs to combine behavioral and physiological parameters to reduce or avoid significant stress during culture.

Author Contributions Haixia Li: Conceptualization, Methodology, Investigation and Writing-Original Draft. Zhen Ma: Conceptualization, Resources, Writing-Original Draft, Writing-Review & Editing, Supervision and Funding Acquisition. Jie Wang: Methodology and Investigation. Xu Zhang: Methodology and Investigation. Yu Hu: Methodology and Investigation. Ying Liu: Conceptualization, Resources and Funding Acquisition.

Funding This work was supported by the National Natural Science Foundation of China (42030408; 32102753), the Scientific and Technical Innovation Project of Dalian, Liaoning Province, China (2021JJ13SN72), and by contributions from the AET. Special thanks are also given to the anonymous reviewers for their constructive comments regarding the manuscript.

Data availability statement The data required to reproduce these findings are available upon request by contact with the corresponding author.

Ethical approval Animal care and use protocols were approved by the Dalian Ocean University Institutional Animal Care and Use Committee and followed institutional guidelines.

Consent to participate Not applicable.

Consent for publication All authors review and approve the manuscript for publication.

Conflict of interest The authors declare no competing interests.

Code availability Not applicable.

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Editorial decision: Major revisions
17 Jun, 2022
Reviews received at journal
23 Feb, 2022
Reviewers invited by journal
22 Feb, 2022
Editor assigned by journal
14 Feb, 2022
First submitted to journal
09 Feb, 2022

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Comparing behavioral performance and physiological responses of Sebastes schlegelii with different aggressive strengths

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Source and experimental animal

2.2. Experimental design

2.3. Behavioral observations

2.4. Sampling and analytical methods

2.5. Data analysis

3. Results

3.1. Behavioral performance

3.2. Key behavioral parameters extraction

3.3. Cortisol levels and brain monoaminergic activity

3.4. Correlation of physiological indicators with behavioral analysis

4. Discussion

4.1. Behavioral performance

4.2 Physiological indicators

Declarations

References

Status:

Version 1