No evidence for general intelligence in a fish

script (txt) and metadata (csv)<br>script-g.txt: script to run with program R studio<br>PCA-n69.csv: metadata for the PCA analysis, pairwise correlations analysis, and body length vs capture site analysis (Figs. 1, 2, S5, and S6).Site: site of capture; crest = high fish density; lagoon = low fish densityYear: when experiments doneBW: Body Weight in gramBL: Body Length in centimeterReversal learning: ranking in the reversal learning taskDetour task: ranking in the detour taskNumerical competence: ranking in the numerical competence taskFeeding against preference: ranking in the feeding against preference task<br>perf-n69.csv: metadata for the performance analysis across capture sites (Fig. 3).Reversal Learning: RL_TTS = Total Trials to SucceedDetour Task: DT_bump = number of bumps before reaching the reward plate (failed trials included)Numerical Competence: NC_succ80 = percentage success in the last 80 trials, all combinationsFeeding Against Preference: Featen = mean number of flake items eaten prior to eat a prawn item Site: crest = high fish density / lagoon = low fish density<br>transect-data2018.csv: metadata for the density analysis (Fig. S2).day: when data collectedtime: at what time the data were collectedlocation: lagoon = low fish density; crest = high fish densitytransect: 1 transect = 150m2n cleaners: number of cleaners seen the 150m2 transectm2: how many m2per 100m2: number of cleaners per 100m2<br>OP-n52.csv: metadata for the above chance significance.Site: crest = high fish density; lagoon = low fish densityPercentage success: percentage success over 80 trials in object permanence. <br>NC.chance: metadata to verify that cleaners as a group performed above chance in the NC task <br><br>