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

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).