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Sustained productivity and the persistence of coral reef fisheries

Nature Sustainability volume 6, pages 1199–1209 (2023)Cite this article

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Abstract

Fishing-induced biomass depletion is common on coral reefs. Yet, fisheries persist, maintaining the livelihoods of millions of fishers. Understanding this persistence is key to sustained reef fisheries in a time of global changes. Here we combine snapshot fish surveys and individual models of growth and mortality in a novel framework to evaluate potential reef fisheries productivity across a whole Pacific country (Tonga) spanning a major fishing pressure gradient. We provide empirical evidence of compensatory ecological responses triggered by fishing on coral reefs. High fishing exploitation drove biomass declines, yet, for a given exploitation level, productivity was consistently larger than expected from the remaining biomass. This buffering response provided, on average, an extra ~20% or 0.24 kg ha−1 d−1 of target fish production—a sizeable proportion of reported coral reef fisheries yields. Such ‘buffering productivity’ was strongest in wave-exposed, shallower, benthic-diverse and structurally complex areas. Consequently, a reef’s capacity to deliver these responses is conditional on where it is located (that is, some habitats have higher propensity to support strong responses) and on its disturbance history (for example, episodic coral mortality that reduces structural complexity and benthic diversity). Thus, while compensatory buffering production may help explain persistent yields in biomass-depleted coral reef fisheries, the sustainability of these yields may be jeopardized by the impacts of climate change.

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Fig. 1: Using an extensive dataset of underwater fish counts from coral reefs in Tonga, South Pacific, we test the ‘buffering productivity’ hypothesis.
Fig. 2: Decoupled declines of reef fish biomass and productivity across a fishing pressure gradient.
Fig. 3: Buffering responses varied largely among the three regions investigated in Tonga.
Fig. 4: Buffering responses to fishing across Tonga’s coral reefs.
Fig. 5: Buffering productivity is mediated by habitat.

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Data availability

All data required to perform the analyses and generate the results in this manuscript are available from Zenodo at https://zenodo.org/record/7774820.

Code availability

All code required to perform the analyses and generate the results in this manuscript are available from Zenodo at https://zenodo.org/record/7774820.

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Acknowledgements

This is a contribution from the Research Hub for Coral Reef Ecosystem Functions, funded by the Australian Research Council through a Laureate Fellowship (FL190100062 to D.R.B.). Data collection was funded by the National Geographic Society (CP-137ER-17 to PSW). R.A.M. is currently supported by a Branco Weiss Fellowship Society in Science and a PSL Junior Fellowship. We thank S. Gordon and C. MacDonald for help throughout data collection and J. Robinson for constructive criticism.

Author information

Authors and Affiliations

  1. Research Hub for Coral Reef Ecosystem Functions, James Cook University, Townsville, Queensland, Australia

    Renato A. Morais & David R. Bellwood

  2. ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia

    Renato A. Morais, Patrick Smallhorn-West & David R. Bellwood

  3. Université Paris Sciences et Lettres, CRIOBE, UAR3278, Perpignan, France

    Renato A. Morais

  4. WorldFish, Bayan Lepas, Malaysia

    Patrick Smallhorn-West

  5. Wildlife Conservation Society, New York, NY, USA

    Patrick Smallhorn-West

  6. College of Science and Engineering, James Cook University, Townsville, Queensland, Australia

    Patrick Smallhorn-West, Sean R. Connolly & David R. Bellwood

  7. Smithsonian Tropical Research Institute, Balboa, Republic of Panama

    Sean R. Connolly

  8. Tonga Ministry of Fisheries, Nuku’alofa, Tonga

    Poasi Fale Ngaluafe, Siola’a Malimali & Tu’ikolongahau Halafihi

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  1. Renato A. Morais
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Contributions

R.A.M. conceived the study, with conceptual inputs from D.R.B., P.S.-W. and S.R.C., and practical inputs from P.F.N., T.H. and S.M. concerning study design. P.S.W. collected the data, with active institutional support from P.F.N., T.H. and S.M. R.A.M. performed the analysis and wrote the first draft. D.R.B., P.S.-W. and S.R.C. contributed substantially to revisions.

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Correspondence to Renato A. Morais.

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Extended data

Extended Data Fig. 1 Raw values of coral reef fish standing biomass and productivity in each of the 276 sites surveyed across three regions in Tonga.

See Methods section ‘Quantifying community standing biomass and productivity’ for definitions of biomass and productivity.

Extended Data Fig. 2 Coral reef fish community productivity-biomass relationships in three regions of Tonga.

Dots (n = 276) are the same site-level raw values from the Extended Data Fig. 1.

Extended Data Fig. 3 Decoupled declines of reef fish biomass and productivity across a fishing pressure gradient.

Here, declining trajectories of biomass and productivity with fishing pressure are overlaid for the three regions investigated in Tonga. Lines are posterior medians from the posterior distribution of predictions from two spatially explicit INLA models (that is, biomass and productivity, see Methods). Arrows connect the productivity and biomass curves of each region at the highest fishing pressure.

Extended Data Fig. 4 As fishing pressure increased across Tonga’s coral reefs, so did their level of exploitation and buffering productivity.

This process, however, slightly decelerates after 10-15% of the maximum observed fishing pressure for the three regions (A). Increased fishing pressure induced buffering responses that varied from very shallow in Tongatapu to very steep in Ha’apai (based on n = 230 sites with non-zero fishing) (B). In both (A) and (B), black lines represent posterior medians, and coloured lines represent 100 simulated individual draws from the posterior distribution of predictions from two spatially explicit INLA models (see Methods).

Extended Data Fig. 5 Relative buffering responses to fishing across Tonga’s coral reefs.

(A) Posterior distributions of relative buffering productivity values for each site (each line, with 10,000 values/draws per line), coloured based on their median value, following the same colour scheme from Fig. 4a. Note that the y-axis is square-root transformed. (B) The distribution of 10,000 means of relative buffering productivity across all surveyed sites, one for each draw from the posterior distribution. Yellow vertical line and label represent the median of the 10,000 means. While (A) portrays the uncertainty in the buffering responses for each site, (B) portrays the uncertainty in the overall buffering productivity across all surveyed sites.

Extended Data Fig. 6 Effect size of variables quantifying size- and trophic-structure of fish communities on buffering productivity responses at the site level in Tonga.

Effect sizes are standardised variability in the response (buffering productivity), across the range of each predictor, and are based on n = 230 sites with non-zero fishing. Circles represent the posterior medians; narrow error bars delimit the 90% highest density intervals [HDI90], and broad error bars the 50% HDI from a spatially explicit INLA model (see Methods). MnWeight = mean individual body weight of fish communities; SizSpe = community biomass size-spectrum exponent; relHer to relPre = relative productivity of herbivorous, invertivorous, planktivorous and piscivorous fishes.

Extended Data Fig. 7 Effect size of the main habitat drivers of buffering productivity in the three regions studied in Tonga.

Effect sizes are measured as the proportional variability in the response (buffering productivity), across the range of each predictor (driver), for each region, and are based in n = 230 sites with non-zero fishing. Circles represent the posterior medians; narrow error bars delimit the 90% highest density intervals [HDI90], and broad error bars the 50% HDI from 10,000 draws from the posterior distribution of predictions from two spatially explicit INLA models (see Methods). BathySlope = bathymetric slope as estimated from a global model of digital elevation and seafloor topography; DistLand = distance to the nearest land; Dis20mDep = distance to the 20 m depth isobath; BentPCo1 is the first axis of the benthic community Principal Coordinates Analyses (see Methods); StruCompl = structural complexity; WaveExpos = wave exposure.

Extended Data Fig. 8 Among region variation in the main habitat drivers of buffering productivity in Tonga’s coral reefs.

Data points are observed values for each site in each region, while violin plots depict the density of values across the range observed. WaveExpos = wave exposure; StruCompl = structural complexity; BentPCo1 is the first axis of the benthic community Principal Coordinates Analyses (see Methods); Dis20mDep = distance to the 20 m depth isobath; DistLand = distance to the nearest land, BathySlope = bathymetric slope as estimated from a global model of digital elevation and seafloor topography.

Extended Data Fig. 9 Principal Coordinate Analysis of the benthic community composition across 276 sites in Tonga, South Pacific.

The three main axes (PCoA1 to PCoA3) accounted for 89% of the total variability and are here shown in biplots of PCoA1 vs PCoA2, PCoA2 vs PCoA3, and PCoA3 vs PCoA2. The six most abundant benthic components across the study are highlighted in black arrows and text (left-hand side). Barplots on the right-hand side include the proportional cover of each of these six components in each site (bar) ordered from negative to positive values of each of the three axes. Turf = algal turf, CCA = calcareous coralline algae, SoftCo = soft coral, AcroBr = branching Acropora, FavMus = favids and mussids.

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Supplementary Data

Supplementary Table 1, and Analyses 1 and 2.

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Morais, R.A., Smallhorn-West, P., Connolly, S.R. et al. Sustained productivity and the persistence of coral reef fisheries. Nat Sustain 6, 1199–1209 (2023). https://doi.org/10.1038/s41893-023-01137-1

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