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With a little help from my friends: individual and collaborative performance during trail clearing in leaf-cutting ants

Citation

Alma, Andrea; Farji-Brener, Alejandro G; Elizalde, Luciana (2020), With a little help from my friends: individual and collaborative performance during trail clearing in leaf-cutting ants, Dryad, Dataset, https://doi.org/10.5061/dryad.bzkh18959

Abstract

Social organisms express collaborative behaviours, allowing them to solve problems that exceed their individual capabilities. Group coordination and environmental context are some of the factors that may determine the performance of individual and collaborative strategies. Using the trail-clearing behaviour of leaf-cutting ants, we evaluated experimentally for both strategies whether the success probability and clearing time depend on problem characteristics and context. We placed obstacles of different sizes and shapes (problem characteristics), in trails with different foragers’ fluxes and soil roughness (context) in 10 field nests of Atta cephalotes, and compared removal success (i.e. if ants could remove obstacles) and time of individual and collaborative strategies. Very large obstacles could only be removed collaboratively, confirming individual limitations for transporting large objects. For all obstacle shapes, collaborative removals were more successful but took longer, suggesting that coordination among individuals delays these actions. Individual strategies were faster, regardless of ant flux. However, as ant flux increased, removal success was higher for collaborative than for individual removals. Lastly, trail roughness had no effect. This work highlights one advantage of sociality, the option of collaboratively solving problems that exceed the individual abilities. In addition, it reveals the associated costs of joint actions, since they can be time-consuming presumably due to coordination problems.Social organisms express collaborative behaviours, allowing them to solve problems that exceed their individual capabilities. Group coordination and environmental context are some of the factors that may determine the performance of individual and collaborative strategies. Using the trail-clearing behaviour of leaf-cutting ants, we evaluated experimentally for both strategies whether the success probability and clearing time depend on problem characteristics and context. We placed obstacles of different sizes and shapes (problem characteristics), in trails with different foragers’ fluxes and soil roughness (context) in 10 field nests of Atta cephalotes, and compared removal success (i.e. if ants could remove obstacles) and time of individual and collaborative strategies. Very large obstacles could only be removed collaboratively, confirming individual limitations for transporting large objects. For all obstacle shapes, collaborative removals were more successful but took longer, suggesting that coordination among individuals delays these actions. Individual strategies were faster, regardless of ant flux. However, as ant flux increased, removal success was higher for collaborative than for individual removals. Lastly, trail roughness had no effect. This work highlights one advantage of sociality, the option of collaboratively solving problems that exceed the individual abilities. In addition, it reveals the associated costs of joint actions, since they can be time-consuming presumably due to coordination problems.

Methods

Sheet ‘Obstacle size effect’- Data on the effect of obstacle size on removal decision and time for individual and collaborative strategies. We used generalized linear mixed models (GLMM) with nest as a random factor. The response variables were (1) the proportion of successful removals with Binomial distribution (removal decision column), and (2) the removal time with Normal distribution (we log-transformed this variable). The explanatory variable was the interaction between the removal strategies and the obstacle size, and we included the size of clearing ant/s (we used a mean size for collaborative removals), and ant flux as co-variables.

Sheet ‘Obstacle shape effect’- Data on the effect of obstacle shape on removal decision and time for individual and collaborative strategies. We used generalized linear mixed models (GLMM) with nest as a random factor. The response variables were (1) the proportion of successful removals with Binomial distribution (removal decision column), and (2) the removal time with Normal distribution (we log-transformed this variable). The explanatory variable was the interaction between the removal strategies and the obstacle shape, and we included the size of clearing ant/s (we used a mean size for collaborative removals), obstacle mass and ant flux as co-variables.

Sheet ‘Ant flux effect’- Data on the effect of ant flux on removal decision and time for individual and collaborative strategies. We used generalized linear mixed models (GLMM) with nest as a random factor. The response variables were (1) the proportion of successful removals with Binomial distribution (removal decision column), and (2) the removal time with Normal distribution (we log-transformed this variable). The explanatory variable was the interaction between the removal strategies and the ant flux, and we included the size of clearing ant/s (we used a mean size for collaborative removals), and obstacle mass as co-variables.

Sheet ‘Trail roughness effect’- Data on effect of trail roughness on removal decision and time for individual and collaborative strategies. We used generalized linear mixed models (GLMM) with nest as a random factor. The response variables were (1) the proportion of successful removals with Binomial distribution (removal decision column), and (2) the removal time with Normal distribution (we log-transformed this variable). The explanatory variable was the interaction between the removal strategies and the trail roughness, and we included the size of clearing ant/s (we used a mean size for collaborative removals), obstacle mass and ant flux as co-variables.

Statistical analyses were performed in the R environment (R Development Core Team, 2013) using the package MASS (Venables and Ripley, 2002) and ‘nlme’ (Pinheiro et al., 2017).