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Meta-analysis reveals weak associations between reef fishes and corals

Nature Ecology & Evolution volume 8, pages 676–685 (2024)Cite this article

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Abstract

Habitat associations underpin species ecologies in high-diversity systems. Within tropical, shallow water coral reefs, the relationship between fishes and corals is arguably the most iconic and highly scrutinized. A strong relationship between fishes and reef-building hard corals is often assumed, a belief supported by studies that document the decline of reef fishes following coral loss. However, the extent of this relationship is often unclear, as evidenced by conflicting reports. Here we assess the strength of this ecological association by relying on literature that has surveyed both fishes and corals synchronously. We quantitatively synthesize 723 bivariate correlation coefficients (from 66 papers), published over 38 years, that relate fish metrics (abundance, biomass and species richness) with the percentage of hard coral cover. Remarkably, despite extensive variation, the pattern of association on a global scale reveals a predominantly positive, albeit weak (|r| < 0.4), correlation. Even for commonly hypothesized drivers of fish–coral associations, fish family and trophic group, associations were consistently weak. These findings question our assumptions regarding the strength and ubiquity of fish–coral associations, and caution against assuming a direct and omnipresent relationship between these two iconic animal groups.

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Fig. 1: Coral use among fishes and the correlation coefficients (r) between fish metrics (abundance, biomass and species richness) and the percentage of coral cover.
Fig. 2: Posterior distribution of correlation coefficients (r) between fish metrics (abundance, biomass and species richness) and the percentage of coral cover.
Fig. 3: Posterior distribution of correlation coefficients (r) between fish metrics (abundance, biomass and species richness) and the percentage of coral cover across two distinct geographic realms.
Fig. 4: Posterior distribution of correlation coefficients (r) between fish abundance and the percentage of coral cover.

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Data availability

The datasets generated and/or analysed during our study is available at https://doi.org/10.25903/chbr-5x77.

Code availability

The codes generated during our study is available at https://doi.org/10.25903/chbr-5x77.

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Acknowledgements

We would like to thank S. Nakagawa for providing foundational work on performing biological meta-analyses and subsequent statistical clarifications. We also thank members of the Reef Function Hub for insightful discussions, and H. Yan and M. Logan for assistance and clarifications in statistical modelling. Funding was provided by the Australian Research Council (Laureate grant LF190100062 to D.R.B.), with a postdoctoral fellowship to A.C.S. and a PhD scholarship to P.M.

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Authors and Affiliations

  1. Research Hub for Coral Reef Ecosystem Functions, College of Science and Engineering, James Cook University, Townsville, Queensland, Australia

    Pooventhran Muruga, Alexandre C. Siqueira & David R. Bellwood

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  1. Pooventhran Muruga
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  2. Alexandre C. Siqueira
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  3. David R. Bellwood
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Contributions

P.M., A.C.S. and D.R.B. conceived the study. P.M. collected the data, performed the analyses and wrote the first draft of the paper. P.M., A.C.S. and D.R.B. contributed substantially to revisions.

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Correspondence to Pooventhran Muruga.

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Extended data

Extended Data Fig. 1 Workflow of systematic review process.

Flow chart shows the search string used to gather research articles from two databases and the decision tree (criteria) used to assess the suitability of papers to be included in the meta-analysis.

Extended Data Fig. 2 Distribution (raw data) of re-calculated correlation coefficients (r) across fish metrics.

Data was extracted from 66 papers (n = 723). (a) fish abundance, (b) biomass, and (c) species richness. Conventional limits are used to denote the strength of association (for example weak, moderate, strong). Based on raw data, median correlations are weak across all metrics. Note that raw data does not account for hierarchical structure of multiple effect sizes from the same study (see methods).

Extended Data Fig. 3 Funnel plots displaying the relationship between Fisher’s-z transformed correlation coefficients and their respective standard errors for each of the fish metrics.

(a) fish abundance, (b) biomass, and (c) species richness. Estimates were derived from fitting a three-level hierarchical random effects model (effect size nested within study). Roughly symmetrical scatter of data points suggests the absence of publication bias. This was further assessed using Egger’s regression tests conducted on each fish metric using the standard error of Fisher’s-z transformed correlation coefficients. Tests indicate the absence of publication bias across all metrics (p-value: abundance = 0.908, biomass = 0.647, richness = 0.441).

Extended Data Fig. 4 Posterior distribution of correlation coefficients (r) between fish metrics and different categories of coral morphology.

Models were conducted separately for (a) fish abundance and (b) fish species richness. Estimates were derived from posterior samples extracted from a hierarchical Bayesian model. The plot displays the sample size n (number of r, number of papers from which r were extracted), and the credible interval (probability) of the estimates. Conventional limits are used to denote the strength of association (for example weak, moderate), with r = 0 (dashed line) indicating no association. Note: complex coral refers to coral with branching structures (for example Acropora sp.). Non-complex coral refers to coral without any branching structures (for example massive Porites sp.). Total Live CC (coral cover) refers to surveys that sample all live coral cover. Median r values indicate weak associations across different coral morphologies, with the exception of Total live CC for richness (moderate).

Extended Data Fig. 5 Posterior distribution of correlation coefficients (r) between fish metrics and percent coral cover for surveys that sampled both fish and coral within same transect (Yes) or separately (No).

Estimates were derived from posterior samples extracted from a hierarchical Bayesian models conducted separately for (a) fish abundance, (b) biomass, and (c) species richness. Each plot displays the sample size n (number of r, number of papers from which r were extracted) for each model, and the credible interval (probability) of the estimates. Conventional limits are used to denote the strength of association (for example weak, moderate, strong), with r = 0 (dashed line) indicating no association. Median r values indicate weak fish-coral associations irrespective of fish and coral being sampled within the same transect.

Extended Data Fig. 6 Relationships between correlation coefficients (r) and publication year (of individual studies).

Estimates for black lines (mean) and coloured 95% credible interval (probability) were derived from posterior samples extracted from hierarchical Bayesian models conducted separately for r between percent coral cover and (a) fish abundance, (b) biomass, and (c) species richness. Each plot displays the sample size n (number of r, number of papers from which r were extracted) for each model. Points reflect re-calculated r values extracted from individual papers. Across all fish metrics, credible interval values for slopes include 0 (abundance: −0.019–0.002, biomass: −0.014–0.029, richness: −0.027–0.004), which suggest no specific trend (that is the year of publication of each study does not affect the reported fish-coral association), and predominantly weak associations regardless of publication year.

Extended Data Fig. 7 Relationships between correlation coefficients (r) estimates and the duration (no. of years) of dataset (used to calculate r).

Estimates for black lines (mean) and coloured 95% credible interval (probability) were derived from posterior samples extracted from hierarchical Bayesian models conducted separately for r between percent coral cover and (a) fish abundance, (b) biomass, and (c) species richness. Each plot displays the sample size n (number of r, number of papers from which r were extracted) for each model. Points reflect re-calculated r values extracted from individual papers. Across all fish metrics credible interval values for slopes include 0 (abundance: −0.019–0.002, biomass: −0.112–0.062, richness: −0.187–0.082), which suggest no specific trend (that is the duration of study does not affect the reported fish-coral association) and predominantly weak associations regardless of duration of sampling.

Extended Data Fig. 8 Relationships between correlation coefficients (r) estimates and mean percent coral cover (used to calculate r).

Estimates for black lines (mean) and coloured 95% credible interval (probability) were derived from posterior samples extracted from hierarchical Bayesian models conducted separately for r between mean percent coral cover and (a) fish abundance, (b) biomass, and (c) species richness. Each plot displays the sample size n (number of r, number of papers from which r were extracted) for each model. Points reflect re-calculated r values extracted from individual papers. Across all fish metrics credible interval values for slopes include 0 (abundance: −0.005–0.006, biomass: −0.021–0.004, richness: −0.033–0), which suggest no specific trend (that is, the mean coral cover sampled within study does not affect the reported fish-coral association).

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Muruga, P., Siqueira, A.C. & Bellwood, D.R. Meta-analysis reveals weak associations between reef fishes and corals. Nat Ecol Evol 8, 676–685 (2024). https://doi.org/10.1038/s41559-024-02334-7

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