Rapid winter warming could disrupt coastal marine fish community structure

Abstract

Marine ecosystems are under increasing threat from warming waters. Winter warming is occurring at a faster rate than summer warming for ecosystems around the world, but most studies focus on the summer. Here, we show that winter warming could affect coastal fish community compositions in the Mediterranean Sea using a model that captures how biotic associations change with sea surface temperature to influence species’ distributions for 215 fish species. Species’ associations control how communities are formed, but the effect of winter warming on associations will be on average four times greater than that of summer warming. Projections using future climate scenarios show that 60% of coastal Mediterranean grid cells are expected to lose fish species by 2040. Heavily fished areas in the west will experience diversity losses that exacerbate regime shifts linked to overexploitation. Incorporating seasonal differences will therefore be critical for developing effective coastal fishery and marine ecosystem management.

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Fig. 1: Winter SST gradients have larger effects on Mediterranean fish communities.
Fig. 2: Warming SSTs will lead to pronounced changes for fish communities across the Mediterranean Sea.
Fig. 3: Range responses will differ across fish species, with bottom dwellers expected to show substantially reduced ranges in response to warming temperatures.
Fig. 4: Winter SSTs impact how species associate and assemble into communities.

Data availability

The Mediterranean fish binary occurrence data and IPCC SRES A2 SST projections that support the findings of this study are described in ref. 31 and are available in the Ecological Archives (accession E096-203-D1).

Code availability

All R code needed to extract data from public repositories, replicate all of the analyses and generate the figures is presented in the Supplementary Information file and stored in a licensed GitHub repository (https://github.com/nicholasjclark/Mediterranean-Fishes-MRF).

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Acknowledgements

We thank the creators and maintainers of the FishMed database for facilitating access to occurrence and IPCC projection data. The co-occurrence modelling was undertaken on facilities at the Research Computing Centre at the University of Queensland, which is supported by the Australian Commonwealth Government. N.J.C. is supported by a University of Queensland Early Career Research Grant (UQECR1946913). C.I.F. is supported by a Rutherford Discovery Fellowship from the Royal Society of New Zealand (UOO1803).

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N.J.C. conceived of the idea, conducted the analyses and wrote most of the first draft of the paper. J.T.K. and C.I.F. contributed conceptual advice and helped with writing the paper and with refining later drafts.

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Correspondence to Nicholas J. Clark.

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Peer review information Nature Climate Change thanks Sandro Azaele, Marta Coll and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Schematic overview of the ensemble modelling approach and examples of key outputs produced at each step of analysis.

Schematic overview of the ensemble modelling approach and examples of key outputs produced at each step of analysis. A spatial Conditional Random Fields (CRF) model was trained on 1980 binary occurrence vectors for 215 fish species across 8,154 coastal sample sites in the Mediterranean Sea, using mean summer and mean winter Sea Surface Temperatures as external predictors. To generate predictions for a range of climate scenarios, simulation from the CRFs posterior predictive distribution was accomplished using a multivariate boosted regression tree that learned complex, nonlinear relationships and prioritised those predictors that had large influences on covariance in species’ occurrence probabilities. These simulations allowed for more direct comparisons among historical and future predictions, avoiding the biases that can occur when comparing observed and predicted community measurements.

Extended Data Fig. 2 Predicted historical and future metrics of fish community species richness, functional diversity and network modularity across coastal grid cells in the Mediterranean Sea.

Predicted historical (1980) and future (2040) metrics of fish community species richness, functional diversity and network modularity across coastal grid cells in the Mediterranean Sea. Predictions were based on sea surface temperate (SST) estimates using IPCC SRES A2 climate scenarios. Note that functional diversity and network modularity metrics are unitless and are therefore presented as standardised estimates where very low: ≤ 7.5 percentile; low: 7.5 – 37.5 percentile; moderate: 37.5 – 62.5 percentile; high: 62.5 – 92.5 percentile; very high: ≥ 92.5 percentile.

Extended Data Fig. 3 Geographical distributions of coastal fishing zones and populous coastal cities in the Mediterranean Sea.

Geographical distributions of coastal fishing zones (Geographical Subareas; GSAs) and populous coastal cities in the Mediterranean Sea. Regions highlighted in colour correspond to key geographical areas that are expected to experience marked changes in their coastal fish communities in response to warming sea surface temperatures.

Extended Data Fig. 4 Relationships between winter warming classification and predicted changes in coastal fish community richness, functional diversity and network modularity between 1980 and 2040 IPCC SRES A2 climate scenarios.

(a) Coastal sample sites in the Mediterranean Sea classified according to whether a grid cell is predicted to surpass a 13 °C winter sea surface temperature (SST) threshold by the year 2040. (b) Relationships between winter warming classification and predicted changes in coastal fish community richness, functional diversity and network modularity between 1980 and 2040 IPCC SRES A2 climate scenarios. Boxplots show: medians (lines within boxes), 25% and 75% quantiles (hinges) and 5% and 95% quantiles (whiskers).

Extended Data Fig. 5 Average predicted changes in coastal fish species community richness, functional diversity and network modularity across GSAs between 1980 and 2040 IPCC SRES A2 climate scenarios.

(a, b, c) Geographical variation in fishing pressures across Mediterranean Sea GSAs, calculated as total landings per km2 of coastal shelf area, for total fishes, demersal species and pelagic species. (d, e, f) Average predicted changes in coastal fish species community richness, functional diversity and network modularity across GSAs between 1980 and 2040 IPCC SRES A2 climate scenarios.

Extended Data Fig. 6 Predicted latitudinal distributions in the 1980 and 2040 time periods for species of economic and conservation importance.

Predicted latitudinal distributions in the 1980 and 2040 time periods for species of economic and conservation importance, including non-indigenous species with the greatest potential impacts (according to the General Fisheries Commission for the Mediterranean). Range sizes were calculated by summing the predicted presence / absence vectors in each time period across all 8,154 grid cells.

Extended Data Fig. 7 Predicted longitudinal distributions in the 1980 and 2040 time periods for species of economic and conservation importance.

Predicted longitudinal distributions in the 1980 and 2040 time periods for species of economic and conservation importance, including non-indigenous species with the greatest potential impacts (according to the General Fisheries Commission for the Mediterranean). Range sizes were calculated by summing the predicted presence / absence vectors in each time period across all 8,154 grid cells.

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Clark, N.J., Kerry, J.T. & Fraser, C.I. Rapid winter warming could disrupt coastal marine fish community structure. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-0838-5

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