The social zebrafish: Behavioral responses to conspecific, heterospecific, and computer animated fish

Abstract

Zebrafish has been in the forefront of developmental biology and genetics, but only recently has interest in their behavior increased. Zebrafish are small and prolific, which lends this species to high throughput screening applications. A typical feature of zebrafish is its propensity to aggregate in groups, a behavior known as shoaling. Thus, zebrafish has been proposed as a possible model organism appropriate for the analysis of the genetics of vertebrate social behavior. However, shoaling behavior is not well characterized in zebrafish. Here, using a recently developed software application, we first investigate how zebrafish respond to conspecific and heterospecific fish species that differ in coloration and/or shoaling tendencies. We found that zebrafish shoaled with their own species but not with two heterospecific species, one of which was a shoaling the other a non-shoaling species. In addition, we have started the analysis of visual stimuli that zebrafish may utilize to determine whether to shoal with a fish or not. We systematically modified the color, the location, the pattern, and the body shape of computer animated zebrafish images and presented them to experimental zebrafish. The subjects responded differentially to some of these stimuli showing preference for yellow and avoidance of elongated zebrafish images. Our results suggest that computerized stimulus presentation and automated behavioral quantification of zebrafish responses are feasible, which in turn implies that high throughput forward genetic mutation or drug screening will be possible in the analysis of social behavior with this model organism.

Enhanced characterization of the zebrafish brain as revealed by super-resolution track-density imaging

“…Large clutch sizes, transparent larval stages, and the ability to perform forward or reverse genetic analyses have particularly facilitated the use of embryonic and larval zebrafish (Lieschke and Currie 2007). Mature zebrafish, which possess well-developed endocrine, sensory, and motor systems (Bally-Cuif and Vernier 2010), a range of complex behaviours such as social interactions (Saverino and Gerlai 2008), learning and memory (Gomez-Laplaza and Gerlai 2010; Norton and Bally-Cuif 2010;Al-Imari and Gerlai 2008) and a physiology similar to mammals (Bally-Cuif and Vernier 2010), have been recognized as beneficial to investigations of neurological diseases (Alfaro et al 2011;Siebel et al 2011;Wong et al 2010;Keller and Murtha 2004;Mathur and Guo 2010;Ramirez et al 2012;Haud et al 2011;Williams et al 2011). As adult zebrafish continue to be used as an experimental model, new techniques are being developed to better characterize their behaviour , neuronal activity (Pineda et al 2011), neurochemistry (Kabli et al 2009) and neuromorphology (Rao et al 2009;Ullmann et al 2010b;Ullmann et al 2010a; Kabli et al 2006).…”

The Integration of Sociality, Monoamines, and Stress Neuroendocrinology in Fish Models: Applications in the Neurosciences

“…Two of the most frequently employed measures to quantify shoal cohesion have been the inter-individual distance, Delaunay distance, or the nearest neighbour distance (Miller & Gerlai, 2007;Saverino & Gerlai, 2008;Lima et al, 2016). distance) or between all shoal members (Delaunay distance).…”

“…The second way to induce and evaluate shoaling responses in zebrafish is to present social stimuli (live, or computer animated images of, conspecifics) to a single experimental subject (Saverino & Gerlai, 2008;Fernandes & Gerlai, 2009;Qin et al, 2014). A single experimental subject in a novel test tank is highly motivated to join the shoal, thus upon presentation of the social stimuli, it usually approaches the stimuli and stays in close proximity of the stimuli.…”

Toward an Efficient and Integrative Analysis of Limited-Choice Behavioral Experiments: Figure 1.

“…Proper consideration of data structure not only results in a formally correct, hence more reliable, analysis, but also allows a much more efficient use of behavioral experimental data, as it allows experimenters to handle, and probe, their intrinsically multivariate structure, which is inaccessible with the currently preferred styles of analysis. This is particularly useful when it comes to comparing behavioral preferences of animals that differ, say due to defined genetic or pharmacological interventions (Saverino and Gerlai, 2008;Blundell et al, 2010;Satoh et al, 2011;Walton et al, 2012), and substantially adds to the sensitivity of such tests. In the following, (1) we briefly exemplify the specific structure of compositional data as may arise from behavioral experiments; (2) we discuss how such data may be transformed so that they can be effectively interpreted in biological terms and, at the same time, may be handled statistically, including a short discussion of potential limitations of the approach proposed; (3) we sketch efficient ways to visualize such data and propose a powerful procedure for testing hypotheses based on them; and (4) we conclude by pointing out how our approach allows experimenters to put the type of behavioral tests discussed in a broader framework of behavioral analysis.…”

“…Statistical analysis is critical in the interpretation of such experiments. Traditionally, the outcome of such limitedchoice tests is presented as the arithmetic mean and its standard error (SE) and probed using classical parametric tests, such as Student's t test or ANOVA for means (Nadler et al, 2004;Moy et al, 2007;Saverino and Gerlai, 2008;Hines et al, 2008;Mery et al, 2009;Blundell et al, 2010;Peça et al, 2011). …”