The performance of cleaner wrasse, Labroides dimidiatus, in a reversal learning task varies across experimental paradigms
2018, Gingins, Simon, Marcadier, Fanny, Wismer, Sharon, Krattinger, Océane, Quattrini, Fausto, Bshary, Redouan, Binning, Sandra A.
Testing performance in controlled laboratory experiments is a powerful tool for understanding the extent and evolution of cognitive abilities in non-human animals. However, cognitive testing is prone to a number of potential biases, which, if unnoticed or unaccounted for, may affect the conclusions drawn. We examined whether slight modifications to the experimental procedure and apparatus used in a spatial task and reversal learning task affected performance outcomes in the bluestreak cleaner wrasse, Labroides dimidiatus (hereafter “cleaners”). Using two-alternative forced-choice tests, fish had to learn to associate a food reward with a side (left or right) in their holding aquarium. Individuals were tested in one of four experimental treatments that differed slightly in procedure and/or physical set-up. Cleaners from all four treatment groups were equally able to solve the initial spatial task. However, groups differed in their ability to solve the reversal learning task: no individuals solved the reversal task when tested in small tanks with a transparent partition separating the two options, whereas over 50% of individuals solved the task when performed in a larger tank, or with an opaque partition. These results clearly show that seemingly insignificant details to the experimental set-up matter when testing performance in a spatial task and might significantly influence the outcome of experiments. These results echo previous calls for researchers to exercise caution when designing methodologies for cognition tasks to avoid misinterpretations.
Tracktor : Image‐based automated tracking of animal movement and behaviour
2019-06, Sridhar, Vivek H., Roche, Dominique G., Gingins, Simon
1. Automated movement tracking is essential for high‐throughput quantitative analyses of the behaviour and kinematics of organisms. Automated tracking also improves replicability by avoiding observer bias and allowing reproducible workflows. However, few automated tracking programs exist that are open access, open source and capable of tracking unmarked organisms in noisy environments.
2. Tracktor is an image‐based tracking freeware designed to perform single‐object tracking in noisy environments, or multi‐object tracking in uniform environments while maintaining individual identities. Tracktor is code‐based but requires no coding skills other than the user being able to specify tracking parameters in a designated location, much like in a graphical user interface. The installation and use of the software is fully detailed in a user manual.
3. Through four examples of common tracking problems, we show that Tracktor is able to track a variety of animals in diverse conditions. The main strengths of Tracktor lie in its ability to track single individuals under noisy conditions (e.g. when the object shape is distorted), its robustness to perturbations (e.g. changes in lighting conditions during the experiment), and its capacity to track multiple unmarked individuals while maintaining their identities. Additionally, summary statistics and plots allow measuring and visualising common metrics used in the analysis of animal movement (e.g. cumulative distance, speed, acceleration, activity, time spent in specific areas and distance to conspecific, etc.).
4. Tracktor is a versatile, reliable and easy‐to‐use automated tracking software that is compatible with all operating systems and provides many features not available in other existing freeware. Access Tracktor and the complete user manual here: https://github.com/vivekhsridhar/tracktor
The global structure of marine cleaning mutualistic networks
2018-10, Quimbayo, Juan Pablo, Cantor, Mauricio, Dias, Murilo S., Grutter, Alexandra S., Gingins, Simon, Becker, Justine H. A., Floeter, Sergio R.
We studied the underlying biotic and abiotic drivers of network patterns in marine cleaning mutualisms (species feeding upon ectoparasites and injured tissues of others) at large spatial scales.
Eleven marine biogeographical provinces.
Major taxa studied
Reef fish and shrimps.
We combined field and literature data to test whether recurrent patterns in mutualistic networks (nestedness, modularity) describe the distributions of marine cleaning interactions. Nested network structures suggest that some cleaner species interact with many clients while the others clean fewer, predictable subsets of these clients; modular network structures suggest that cleaners and clients interact within defined, densely connected subsets of species. We used linear mixed models to evaluate whether the life‐history traits of cleaners contribute to the emergence of these patterns locally and whether environmental and geographical factors influence the network structures.
Marine cleaning networks were more nested than modular. Nestedness was prevalent in communities with dedicated cleaners (ones that feed exclusively by cleaning), whereas communities with only facultative cleaners (ones that clean opportunistically) were generally unstructured. Cleaner type and taxa were the only traits shaping networks, with dedicated fish cleaners contributing disproportionally more than facultative cleaners and shrimps to the emergence of nestedness. Although cleaner species seem concentrated around the tropics and biodiversity centres, we did not detect an influence of environmental and geographical factors on network structures.
Dedicated species are key in shaping the structure of marine cleaning mutualistic networks. By relying exclusively on cleaning to feed, dedicated cleaners interact with most of the available clients and form the network core, whereas the opportunistic facultative species tend to clean the most common clients. We hypothesize that trophic niche variation and phenotypic specialization are major drivers of this asymmetry in marine mutualisms. Our study strengthens the links between biotic interactions at the community level and the distribution of species and specializations at larger spatial scales.