DOI: 10.5061/dryad.3ffbg79zm
title: Data and code from: Ocean deoxygenation and warming disrupt cooperation in coral reef fish mutualisms

description: Climate change is intensifying ocean deoxygenation and warming,
 compounding stressors that threaten tropical marine ecosystems. On coral
 reefs, cleaner wrasses sustain fish health and community stability by
 removing ectoparasites from client species during cooperative
 interactions. However, the mutualism is inherently unstable, as cleaners
 prefer to feed on clients’ protective mucus—a behaviour considered
 cheating. To maintain cooperation, clients use partner control strategies,
 while cleaners employ sophisticated social techniques to manage conflict
 and maximize food intake. In this study, cleaner wrasses (Labroides
 dimidiatus) and their clients (Naso elegans) were exposed for at least 33
 days to projected climate scenarios of ocean deoxygenation (90 % O2 sat),
 warming (+3 °C), and the combination of both stressors. We assessed the
 resilience of the mutualistic interaction through a cooperativeness task
 (feeding against preference test), measuring learning and cooperation, and
 through cleaning interactions. Deoxygenation and warming significantly
 reduced the proportion of time spent in cleaning interactions and led to
 the breakdown of behavioural strategies, including tactile stimulation and
 clients’ retaliation. Notably, dishonesty persisted at similar levels
 during natural interactions, likely due to altered client responses rather
 than maintained self-control. Clients under stress were more tolerant to
 dishonesty, allowing cheating to go unpunished. This is supported by the
 cooperativeness test, in which cleaners exposed to deoxygenation exhibited
 reduced cooperation and diminished ability to suppress preferred but
 non-cooperative behaviour, suggesting impaired cognitive control. Our
 results reveal that climate stressors impair cognitive and behavioural
 mechanisms underpinning cleaning mutualisms, potentially destabilising key
 trophic interactions on coral reefs under future ocean conditions.
description: # GENERAL INFORMATION This dataset comprises a code script (script.Rdm)
 with the associated raw data (data*.csv) from: "Ocean deoxygenation
 and warming disrupt cooperation in coral reef fish mutualisms." ## 1.
 Paper Citation B. P. Pereira; R. Oliveira; M. D. Martins; R. Rosa; J. R.
 Paula (2025). Ocean deoxygenation and warming disrupt cooperation in coral
 reef fish mutualisms. Behavioral
 Ecology.[https://doi.org/10.1093/beheco/araf152](https://doi.org/10.1093/beheco/araf152) ## 2. Originators Beatriz Palinhos Pereira ## 3. Contact information Name: Beatriz Palinhos Pereira Institution: MARE ULisboa email: [bppereira@ciencias.ulisboa.pt](mailto:bppereira@ciencias.ulisboa.pt) ## 4. Date of data collection September 2022 ## 5. Geographic location(s) of data collection Cascais, Portugal ## 6. Information about funding sources that supported the collection and curation of the data This work was supported by British Ecological Society through a Large Research Grant – LRB21/1004 to JRP. The study also received support from FCT—Fundação para a Ciência e Tecnologia, I.P., within the project grant PTDC/BIA-BMA/0080/2021—ChangingMoods ([https://doi.org/10.54499/PTDC/BIA-BMA/0080/2021](https://doi.org/10.54499/PTDC/BIA-BMA/0080/2021)) to JRP, the strategic project UIDB/04292/2020 granted to Centro de Ciências do Mar e do Ambiente, project LA/P/0069/2020 granted to the Associate Laboratory ARNET, and a PhD fellowship 2021.06590.BD to BPP. JRP was further supported supported by SPECO – Sociedade Portuguesa de Ecologia through the program “Projetos para Investigadores em Início de Carreira 2020” and by a fellowship from the ”la Caixa” Foundation (ID 100010434) with a fellowship code LCF/BQ/PR24/12050006. # ACCESS INFORMATION ## 1. Licenses/restrictions placed on the data CC0 ## 2. Data derived from other sources No data derived from other sources # DATA FILES AND VARIABLES ## 1. Data_FAP.csv Data from the feeding against preference (FAP) trials with cleaner wrasse (Labroides dimidiatus) across four treatment groups combining temperature (29 °C vs. 32 °C) and oxygen (100% vs. 90% air saturation). * treatment: Experimental treatment code (C = control, W = warming, D = deoxygenation, WD = warming + deoxygenation). * ID: Individual cleaner wrasse identification code. * temperature: Water temperature during the trial (°C). * O2: Oxygen saturation level (%). * SCORE: Standardised learning performance score in the FAP test. * W: Fish body weight (g). * TL: Fish total length (cm). * preference: Proportion of choices made towards the preferred food option. \##2. Data_Interactions.csv Data from natural cleaning interactions between cleaner wrasse (L. dimidiatus) and client fish (Naso elegans) under the same four treatment groups (temperature × oxygen). * treatment: Experimental treatment code (C = control, W = warming, D = deoxygenation, WD = warming + deoxygenation). * ID: Cleaner wrasse identification code. * O2: Oxygen saturation level (%). * temperature: Water temperature during the trial (°C). * n_interactions: Total number of cleaning interactions observed. * cleaner_start: Number of interactions initiated by the cleaner. * Prop_cleanerstart: Proportion of interactions initiated by the cleaner. * client_starts: Number of interactions initiated by the client. * n_posing: Number of posing events by the client. * n_chase: Number of chases by the client (retaliation). * n_jolts: Number of client jolts (indicator of cheating detection). * prop_jolts: Proportion of interactions with jolts. * n_ts: Number of tactile stimulations performed by the cleaner. * ts_duration: Total duration of tactile stimulations (s). * avg_int_duration: Mean duration of cleaning interactions (s). * total_int_duration: Total time spent in cleaning interactions (s). * prop_time_int: Proportion of the total observation period spent in cleaning interactions. * total_vid_time: Total video observation time (s). * time_not_int: Total time with no interaction (s). \#CODE SCRIPTS AND WORKFLOW \##1. script.Rdm R Markdown script containing all steps of data analysis, including: * Importing and inspecting raw data (`data_FAP.csv` and `data_Interactions.csv`). * Statistical analyses of learning performance (FAP test) and cleaning interactions (Video analysis). * Generation of figures for the manuscript. \#SOFTWARE VERSIONS All analyses were performed using R, version 4.3.0 (R Core Team, 2013). \##software name and version **loaded packages:** * library(MASS) * library(performance) * library(lattice) * library(ggplot2) * library(effects) * library(car) * library(predictmeans) * library(multcomp) * library(glmmTMB) * library(ggthemes) * library(ggplot2) * library(dplyr) * library(forcats) * library(hrbrthemes) * library(viridis) * library(r2glmm) * library(ggeffects) * library(sjmisc) * library(sjPlot) * library(emmeans) * source("HighstatLibV10.R") The HighstatLibV10.R library file from Highland Statistics is not provided due to copyright protection # REFERENCES R Core Team (2013). R: A Language And Environment For Statistical Computing. Vienna: R Core Team. Zuur, A. F. & Ieno, E. N. (2016). A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7(6), 636–645. [https://doi.org/10.1111/2041-210X.12577](https://doi.org/10.1111/2041-210X.12577) Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in ecology and evolution, 1(1), 3-14. [https://doi.org/10.1111/j.2041-210X.2009.00001.x](https://doi.org/10.1111/j.2041-210X.2009.00001.x) Zuur, A., Ieno, E., Walker, N., Saveliev, A. & Smith, G. (2009). Mixed Effects Models and Extensions in Ecology with R. Journal of Statistical Software, 32, 1-3. [https://doi.org/10.18637/jss.v032.b01](https://doi.org/10.18637/jss.v032.b01)