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For the 0% treatment, all biofilm was removed using a razor blade, and for the 100% coverage treatment, no biofilm was removed. Biofilm was removed from the outer 5 cm of the glass pane for all treatments so that each pane had a 38 × 38 cm square of biofilm in the centre of the pane. Resource distributions became less patchy and more evenly distributed as biofilm coverage increased from 25 to 100%. Our treatments comprised five levels of resource distribution: 0, 25, 50, 75 and 100% algal biofilm coverage. To ensure rapid and consistent growth of biofilm, these plastic containers were placed in environmental chambers at 24☌ and constant, bright light for two weeks before use in foraging trials. To grow the biofilm, glass panes were submerged for 14 days in large, clear plastic containers (10 panes per container) filled with water from the mesocosm where snails were collected (see above). We created experimental landscapes by growing algal biofilm on 43 × 43 cm glass panes ( figure 1). Third, we performed a temporal analysis to test the prediction that µ varied over time as the internal states of snails changed, particularly in treatments containing resources, as snails switched from foraging to searching for mates, a refuge or escape after they became satiated. Trajectories that are dominated by short displacements, are highly tortuous and have a µ > 3 approximate Brownian walks (Brownian-like walks), whereas trajectories that contain relatively more long displacements, are less tortuous and have 1 3) in high-resource environments, where snails could continuously graze and movement is dominated by short displacements. the distance an organism moves before making an appreciable turn in a different direction) and µ is the exponent of this power law, which is determined by the shape of the distribution. The general power-law probability density function is expressed as P( l i) ≈ l i −µ, where l is the displacement length (i.e. Aspects of the environment strongly influence organismal movement, but less is known about how patterns of movement change with time.īiologists often use power-law probability density functions to distinguish among patterns of organismal movement. Despite this complexity, growing evidence suggests that animal movement is governed by general rules, and elucidating these rules should lead to a more predictive science of ecology with basic and applied benefits. Movement is complex, changing through space and time, and is undertaken by an enormous diversity of species in ever-changing environments. Movement can be energetically costly and can increase the risk of predation, and so mobile organisms need to balance these costs with the perceived benefits. The relative movements between organisms drive the strength and outcome of many ecological interactions, thereby linking individual movement to the dynamics of populations and communities. Mobile organisms move to locate resources and avoid adverse conditions and enemies. Thus, external and internal factors interacted to shape the inherently flexible movement of these snails. These changes in movement patterns through time were similar across all treatments that contained resources. Our temporal analysis revealed that movement patterns changed predictably for snails that satiated their hunger and then performed other behaviours. Different patterns of movement between resource and bare patches explained movement at larger spatial scales movement was ballistic-like Lévy in resource-free landscapes, Lévy in landscapes with intermediate resource coverage and approximated Brownian in landscapes covered in resources. The average snail speed was slower in resource patches, where snails spent most of their time. Brownian-like) within resource patches but straighter (i.e.
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Resource distribution strongly affected snail movement. Specifically, we tracked snail movements on experimental landscapes where resource (algal biofilm) distribution varied from 0 to 100% coverage and quantified how that movement changed over a 24 h period. Here, we explored how resource distribution interacted with the internal state of organisms to drive patterns of movement. Movement enables mobile organisms to respond to local environmental conditions and is driven by a combination of external and internal factors operating at multiple scales.