An inference procedure for behavioural studies combining numerical simulations, statistics and experimental results
Guarini, J.-M.; Coston-Guarini, J.; Deprez, T.; Chauvaud, L. (2019). An inference procedure for behavioural studies combining numerical simulations, statistics and experimental results. J. Mar. Biol. Ass. U.K. 99(1): 1-7. https://dx.doi.org/10.1017/s0025315417001783
In: Journal of the Marine Biological Association of the United Kingdom. Cambridge University Press/Marine Biological Association of the United Kingdom: Cambridge. ISSN 0025-3154; e-ISSN 1469-7769
The technical difficulties of performing underwater observation mean that marine ecologists have long relied on behavioural experiments to study reactions of marine organisms. In this article, we examine the underlying complexity of assumptions made in raceway experiments and we propose a statistical inference procedure tailored to this type of experimental protocol. As an example, experiments were performed to test if light of two different intensities affects the proximal behaviour (i.e. direct, local and immediate) of two species of crustaceans, the hermit crab (Pagurus bernhardus), and the green crab (Carcinus maenas). Individuals were collected in the vicinity of the Sven Loven Marine Center in Tjarnö (Sweden). Their movements in raceways were recorded and the statistical distance between the resulting experimental distribution and a simulated null distribution was used to compare their behaviour in two situations: dim (when they were expected to feed) and bright light (when they were expected to shelter). Initial tests indicated no differences of behaviour between dim and bright light for the two species. However, when compared with the reference state (here, a null distribution) the behaviour in dim light deviates significantly from the null distribution suggesting non-random behaviour. Our results suggest that efforts should be made to understand the behaviours of the individuals of these two species to establish a comprehensive reference state as a basis for comparison. This fundamental information should be a prerequisite before implementing experiments testing how potential disturbances affect individual organisms in behavioural ecology.
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