An animal's movement speed affects all behaviors and underlies the intensity of an activity, the time it takes to complete it, and the probability of successfully completing it, but which factors determine how fast or slow an animal chooses to move? Despite the critical importance of an animal's choice of speed (hereafter designated as "speed-choice"), we still lack a framework for understanding and predicting how fast animals should move in nature. In this article, we develop a framework for predicting speed that is applicable to any animal-including humans-performing any behavior where choice of speed occurs. To inspire new research in this area, we (1) detail the main factors likely to affect speed-choice, including organismal constraints (i.e., energetic, physiological, and biomechanical) and environmental constraints (i.e., predation intensity and abiotic factors); (2) discuss the value of optimal foraging theory in developing models of speed-choice; and (3) describe how optimality models might be integrated with the range of potential organismal and environmental constraints to predict speed. We show that by utilizing optimality theory it is possible to provide quantitative predictions of optimal speeds across different ecological contexts. However, the usefulness of any predictive models is still entirely dependent on being able to provide relevant mathematical functions to insert into such models. We still lack basic knowledge about how an animal's speed affects its motor control, maneuverability, observational skills, and vulnerability to predators. Studies exploring these gaps in knowledge will help facilitate the field of optimal performance and allow us to adequately parameterize models predicting the speed-choice of animals, which represents one of the most basic of all behavioral decisions.
This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2015.
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#4,177,427
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#375
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Outputs of similar age from Integrative & Comparative Biology
#2
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Altmetric has tracked 34,400,738 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,916 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done well, scoring higher than 80% of its peers.
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We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.
