Data from: Statistically testing the role of individual learning and decision-making in trapline foraging
Ayers, Carolyn A.; Armsworth, Paul R.; Brosi, Berry J. (2018), Data from: Statistically testing the role of individual learning and decision-making in trapline foraging, Dryad, Dataset, https://doi.org/10.5061/dryad.h6r7p2s
Trapline foraging, a behavior consisting of repeated visitation to spatially fixed resources in a predictable sequence, has been observed over diverse taxa and is important ecologically for efficient resource gathering. Despite this, few null models exist to test the significance of suspected traplines, particularly for studies interested in the role of individual decision-making in the formation of traplines versus the role of resource layouts and random movement patterns. Here we present a spatially explicit, individual-based null model, which may be used to test whether resource layout and realistic forager movement may account for sequence repeats in suspected traplines. In our model, we generate resource visitation sequences by modeling a forager without spatial memory using a random walk to discover and visit spatially-fixed resources. We quantify traplining using Determinism, a metric derived from recurrence quantification analysis. Using both simulated and empirical bee foraging data, we compared our model with two existing null models—a completely random model and a sample randomization model. The former creates null sequences by randomly selecting available resources, while the latter randomizes the order of visits in observed sequences. We found that our model has a higher propensity of being (correctly) rejected than a sample randomization model for trapliners, and a lower propensity of being (incorrectly) rejected for non-trapliners compared to a completely random model. The use of a spatially explicit individual-based null model to test the statistical significance of patterns in empirical data is a novel approach that may be useful for other spatial and individual-based processes.
National Science Foundation, Award: DEB-1120572