Automated quantification of the schooling behaviour of sticklebacks
- PDF / 469,623 Bytes
- 8 Pages / 595 x 794 pts Page_size
- 86 Downloads / 194 Views
RESEARCH
Open Access
Automated quantification of the schooling behaviour of sticklebacks Reza Ardekani1* , Anna K Greenwood2 , Catherine L Peichel2 and Simon Tavaré1,3 Abstract Sticklebacks have long been used as model organisms in behavioural biology. An important anti-predator behaviour in sticklebacks is schooling. We plan to use quantitative trait locus mapping to identify the genetic basis for differences in schooling behaviour between marine and benthic sticklebacks. To do this, we need to quantify the schooling behaviour of thousands of fish. We have developed a robust high-throughput video analysis method that allows us to screen a few thousand individuals automatically. We propose a non-local background modelling approach that allows us to detect and track sticklebacks and obtain the schooling parameters efficiently. Introduction Threespine sticklebacks (Gasterosteus aculeatus) (Figure 1) have been a model organism in behavioural biology since the pioneering work of Niko Tinbergen over half a century ago [1]. Much is understood about stickleback behaviour in both the field and the laboratory [2,3]. More recently, sticklebacks have become a model system for understanding the genetic basis for divergence in phenotypic traits, including behaviour [4]. Differences in schooling behaviour between two populations of sticklebacks that inhabit dissimilar environments have been characterized [5]. Marine sticklebacks live in open water and school very strongly, whereas freshwater bottomdwelling lake populations (benthics) exhibit reduced schooling [5]. We have developed an assay using an array of artificial stickleback models to elicit and quantify schooling behaviour [5]. Using this assay, we showed that marine sticklebacks spend significantly more time schooling. Our goal is to dissect the genetic basis for the divergent schooling behaviour between marine and benthic sticklebacks. Quantitative trait locus (QTL) mapping has successfully identified the genetic basis for many variant traits in sticklebacks [4]. The plan is to use QTL mapping in benthic-marine hybrids to identify genetic loci that contribute to differences in schooling behaviour. *Correspondence: [email protected] 1 Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA Full list of author information is available at the end of the article
To assay the hundreds of fish necessary for this technique, a robust high-throughput video analysis system is essential. In this paper, we present a custom approach for analysis of videos from our assay. We propose a method for background modelling for videos that are (semi-)periodic; i.e. those in which some or all of the background in each frame is repeated in at least a few other frames in the video. We show the result of this simple yet effective method for processing videos from our experiments.
Target detection for video tracking For any video tracking system, target detection is an essential ingredient. One approach is to detect an object of interest ba
Data Loading...
