Modelling the impacts of climate change on thermal habitat suitability for shallow-water marine fish at a global scale

Abstract

Understanding and predicting the response of marine communities to climate change at large spatial scales, and distilling this information for policymakers, are prerequisites for ecosystem-based management. Changes in thermal habitat suitability across species' distributions are especially concerning because of their implications for abundance, affecting species' conservation, trophic interactions and fisheries. However, most predictive studies of the effects of climate change have tended to be sub-global in scale and focused on shifts in species' range edges or commercially exploited species. Here, we develop a widely applicable methodology based on climate response curves to predict global-scale changes in thermal habitat suitability. We apply the approach across the distributions of 2,293 shallow-water fish species under Representative Concentration Pathways 4.5 and 8.5 by 2050-2100. We find a clear pattern of predicted declines in thermal habitat suitability in the tropics versus general increases at higher latitudes. The Indo-Pacific, the Caribbean and western Africa emerge as the areas of most concern, where high species richness and the strongest declines in thermal habitat suitability coincide. This reflects a pattern of consistently narrow thermal ranges, with most species in these regions already exposed to temperatures above inferred thermal optima. In contrast, in temperate regions, such as northern Europe, where most species live below thermal optima and thermal ranges are wider, positive changes in thermal habitat suitability suggest that these areas are likely to emerge as the greatest beneficiaries of climate change, despite strong predicted temperature increases.

Fig 1. A Gaussian climate response curve.

This curve is based on the 10^th, 50^th and 90^th quantiles in the baseline sea surface temperatures (SSTs) [50] occupied by Atlantic sturgeon (Acipenser oxyrinchus) [51]. The estimated 50^th quantile (the species’ thermal index, STI) is shown by the vertical dashed line and labelled. Below the STI, the ‘cold’ half of the distribution is shown in blue; an increase in temperature for populations occupying these temperatures (for example, from 10 to 12°C, shown) is assumed to lead to an increase in the CRCS. In contrast, above the STI, the ‘warm’ half of the distribution is shown in red; an increase in temperature for populations occupying these temperatures (for example, from 20 to 22°C, shown) is assumed to lead to a decline in the CRCS. The diamond-shaped points mark the predicted CRCS for two hypothetical populations, each occupying an example starting temperature T₀; the circular points mark the CRCS of those populations after an assumed 2°C rise in temperature (ΔT) following climate change, at temperature T₀ + ΔT. Arrows show the assumed direction of change in the CRCS for these two hypothetical populations in the cold and warm halves of the distribution.

Fig 2. Spatial variation in species’ sensitivity to temperature change within Exclusive Economic Zones.

A, B, The mean species’ thermal range (STR) across all species in each cell derived from (A) sea surface temperature (SST) and (B) sea bottom temperature (SBT). C, D, The mean species’ thermal bias (STB) derived from (C) SST and (D) SBT. Adjacent to each map, the coloured line shows the mean STR or STB across all cells in each latitudinal band, following same colour scheme as for the map. Background coastline data are from Natural Earth (public domain).

Fig 3. The mean predicted change in the index of thermal habitat suitability, E(ΔCRCS_i,j), under future sea surface temperature (SST) change for two climate experiments (RCP 4.5 and 8.5) over mid-century (M) and late-century (L) timescales.

In each 0.5° grid cell, the mean predicted change in the CRCS, calculated over all species whose predicted distributions overlap with that cell, is shown. Predictions are only shown within Exclusive Economic Zones. Adjacent to each map, the coloured line shows the mean E(ΔCRCS_i,j) across all cells in each latitudinal band, following same colour scheme as for the map, with lower values (in red) to the left and higher values (in green) to the right. Background coastline data are from Natural Earth (public domain).

Fig 4

The mean predicted change in the index of thermal habitat suitability (CRCS) under future sea surface temperature change, synthesised across the coastal areas of all Exclusive Economic Zones (EEZs) containing more than five modelled species, in (A) Asia, (B) Africa, (C) the Americas, (D) Oceania and (E) Europe. For each EEZ, four bars are shown, denoting the average change in CRCS across all grid cells in that EEZ (E(ΔCRCS_EEZ)), calculated from the mean predicted changes in each grid cell (E(ΔCRCS_i,j)), under mid-century RCP 4.5 (bottom bar, lightest hatching) and RCP 8.5 (second bar) and late-century RCP 4.5 (third bar) and RCP 8.5 (top bar, densest hatching) experiments. For each EEZ, the colour of the top bar (the late-century RCP 8.5 experiment) indicates the EEZ’s (absolute) mid-point latitudinal location, which may be influenced by overseas territories. Error bars mark the interquartile range. Numbers in brackets after each EEZ indicate the number of modelled species.

Fig 5

The proportion of species predicted to experience declines in the index of thermal habitat suitability (CRCS) under future sea surface temperature change in the coastal areas of all Exclusive Economic Zones (EEZs) containing more than five modelled species, in (A) Asia, (B) Africa, (C) the Americas, (D) Oceania and (E) Europe. For each EEZ, four bars are shown, denoting the proportion of species predicted to experience declines in CRCS in the EEZ (Pr(ΔCRCS_EEZ < 0)), under mid-century RCP 4.5 (bottom bar, lightest hatching) and RCP 8.5 (second bar) and late-century RCP 4.5 (third bar) and RCP 8.5 (top bar, densest hatching) experiments. For each EEZ, the colour of the top bar (the late-century RCP 8.5 experiment) indicates the EEZ’s (absolute) mid-point latitudinal location, which may be influenced by overseas territories. Error bars mark the interquartile range. Numbers in brackets after each EEZ indicate the number of modelled species.

Fig 6. A comparison of predicted changes in the index of thermal habitat suitability (CRCS) between late-century climate experiments across Exclusive Economic Zones (EEZs).

A, The mean predicted change in CRCS (E(ΔCRCS_EEZ)) under RCP 8.5 as a function of the mean predicted change under RCP 4.5. B, Density distributions of the proportion of species predicted to experience declines in CRCS across EEZs (Pr(ΔCRCS_EEZ < 0)). In A, each point represents an EEZ; point size is proportional to the number of modelled species. The diagonal dashed line represents the line y = x. The solid black line surrounded by the grey confidence envelope marks the robust regression line ± 95% confidence intervals. The vertical and horizontal dotted lines mark a mean predicted change in CRCS of 0 under RCP 4.5 and 8.5 respectively. For EEZs below the line y = x, the predicted change in CRCS under RCP 8.5 is more negative than under RCP 4.5. For EEZs above the line, the predicted change in CRCS under RCP 8.5 is more positive. In B, the light and dark grey lines represent RCP 4.5 and 8.5 respectively. The upper and lower rugs mark the proportion of species predicted to experience declines in each EEZ under RCP 4.5 and 8.5 respectively. In both panels, EEZs are coloured according to their (absolute) mid-point latitudinal location.