Supplemental Information. The Evolution of Lateralization. in Group Hunting Sailfish

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1 Current Biology, Volume 27 Supplemental Information The Evolution of Lateralization in Group Hunting Sailfish Ralf H.J.M. Kurvers, Stefan Krause, Paul E. Viblanc, James E. Herbert-Read, Paul Zaslansky, Paolo Domenici, Stefano Marras, John F. Steffensen, Morten B.S. Svendsen, Alexander D.M. Wilson, Pierre Couillaud, Kevin M. Boswell, and Jens Krause

2 Figure S1. Related to Figure 2. (A) Histogram shows the number of times an individual sailfish was observed. Note that sailfish which were observed only once or twice (dotted bars) were excluded from all analyses. (B) The relationship between the number of times an individual was observed and its laterality index. The transparency of the dots represents the number of individuals, with darker dots representing more than one sailfish. To test whether the number of times an individual was observed affected its absolute laterality index, we used a Generalized Linear Mixed Model (GLMM) with an individual s absolute laterality index as response variable and the number of times it was observed as fixed effect. Group was fitted as a random term, and we used binomial errors and a logit-link function. The number of times an individual sailfish was observed did not affect its absolute laterality index (GLMM: est ± se = ± 0.08, z = , P = 0.23).

3 Figure S2. Related to Figure 3. (A, D, G) Individuals with a higher absolute laterality index had a higher capture success. Each dot represents at least one sailfish with darker dots representing more than one sailfish. Line is linear regression line with 95% confidence intervals. (B, E, H) Sailfish had a higher capture success when using their preferred side than when using their non-preferred side. n indicates the number of observed events. (C, F, I) With increasing absolute laterality index of sailfish, the larger the difference in capture success between preferred and non-preferred side. Results are shown for three different thresholds: including sailfish which were observed at least (A-C) 3, (D-F) 4, or (G-I) 5 times.

4 Figure S3. Related to Figure 3. There was no significant difference in capture success between individuals which predominantly attacked to the left side and individuals which predominantly attacked to the right side. Both types appeared equally often in our observations. Only sailfish with a laterality index of 0.3 or higher where included in this analysis, but the exact threshold value used did not alter these results. Capture success is defined as the fraction of attacks which resulted in successful prey capture. n = number of different sailfish. Results are shown for sailfish for which we observed at least (A) 3, (B) 4, or (C) 5 attacks (all P > 0.1).

5 Figure S4. Related to Figure 2G. The number of micro-teeth exceeding 0.2 mm in width on the left and right side for 12 sailfish bill tips of 5 cm length; divided in 20 zones of 2.5 mm. Zone 1 represents the bill tip. Bills of individuals 1-3 had significantly more micro-teeth on the left than on the right side (paired t-test, P < 0.05), individuals 4-10 had significantly more microteeth on the right side compared to the left side (P < 0.05), and individuals 11 and 12 had no significant difference in the number of micro-teeth comparing left and right (P > 0.05).

6 SUPPLEMENTAL EXPERIMENTAL PROCEDURES Statistical analyses To test whether an individual s LI affected its capture success; we used a generalized linear mixed model (GLMM) with capture successful (yes/no) as response variable. As fixed effect we fitted absolute LI. As random effect we fitted individual nested within group and we used binomial errors and a logit-link function since the response variable was binary. To test whether sailfish were more successful when using their preferred side than when using their nonpreferred side, we used a GLMM with capture successful (yes/no) as response variable and as fixed effect side used (preferred/non-preferred). As random effect we fitted individual nested in group and we used binomial errors and a logit-link function. To test whether LI affected the difference in capture success between preferred and non-preferred side use, we calculated for each individual the difference in capture success between using its preferred side and nonpreferred side. High values thus imply that individuals are more successful with their preferred side relative to their non-preferred side. This variable was used as a response variable in a LMM (not a GLMM since values can be negative), using absolute LI as a fixed effect. Note that this analysis is done on the level of the individual, therefore only group was fitted as random effect. In all models described above, we also fitted the number of sailfish in the group and the estimated number of sardines (log-transformed) as fixed effects. For the GLMMs, we used the glmer function from the lme4 package [S1] and for the LMM we used the lme function from the nlme package [S2] in R (v ). Interrater agreement To test for inter-rater agreement, two different observers independently rated the attack movement direction (left/right) and capture success (yes/no) of all the 365 observed attacks. We used the kappa2 function from the irr package in R to calculate Cohen s kappa [S3]. Inter-rater agreement was high for both measures (left/right attack: Cohen s kappa = 0.91, percentage of cases with disagreement: 4.38%; prey captures: Cohen s kappa = 1, no disagreements). In case of disagreement, the assessment of the more experienced observer was adopted. Micro-CT imaging Scans were performed on a Skyscan 1172 (Brucker-CT, Kontich, Belgium) with an effective pixel size of 7 µm, at 65 KeV, using an Aluminium filter, reconstructed (NRecon v , Brucker-CT, Kontich, Belgium) and visualized (CTvox 2.4, Brucker-CT, Kontich, Belgium). SUPPLEMENTAL REFERENCES S1. Bates, D., Maechler, M., Bolker, B., and Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1. S2. Pinheiro, J., Bates, D., DebRoy, S., and Sarkar, D. (2014). R Core Team (2014) nlme: linear and nonlinear mixed effects models. R package version See R-project. org/package= nlme. S3. Gamer, M., Lemon, J., Fellows, I., and Singh, P. (2012). irr: Various coefficients of interrater reliability and agreement. R package version 0.84.