Projected loss of brown macroalgae and seagrasses with global environmental change

Domain: earth_science
Published: 2024-06-24
Editorial outcome: major_revision

Abstract
Although many studies predict extensive future biodiversity loss and redistribution in the terrestrial realm, future changes in marine biodiversity remain relatively unexplored. In this work, we model global shifts in one of the most important marine functional groups—ecosystem-structuring macrophytes—and predict substantial end-of-century change. By modelling the future distribution of 207 brown macroalgae and seagrass species at high temporal and spatial resolution under different climate-change projections, we estimate that by 2100, local macrophyte diversity will decline by 3–4% on average, with 17 to 22% of localities losing at least 10% of their macrophyte species. The current range of macrophytes will be eroded by 5–6%, and highly suitable macrophyte habitat will be substantially reduced globally (78–96%). Global macrophyte habitat will shift among marine regions, with a high potential for expansion in polar regions.

Introduction
Anthropogenic climate change is driving an unprecedented redistribution of the Earth’s marine and terrestrial biota through extensive erosion, rearrangement, and shift of species ranges1,2. Such processes can alter existing biodiversity patterns and lead to the emergence of novel species assemblages, potentially disrupting important ecosystem services2. In the marine realm, brown macroalgae and seagrasses (hereafter ‘macrophytes’) play fundamental functional roles as both primary producers and habitat-forming organisms. Macrophyte-dominated habitats form extensive and highly productive biomes in shallow coastal areas worldwide, with brown macroalgal forests occupying an estimated 2.63 million km2 (ref. 3), and seagrass meadows potentially covering up to 1.65 million km2 (ref. 4). Canopy-forming brown macroalgae (including kelps and fucoids) and seagrasses enrich submerged vegetated habitats with complex, three-dimensional structures, offering shelter and food to many organisms5, including threatened6 and economically important species7.

Given their essential role in marine environments, macrophytes sustain both small-scale and industrial fisheries, thereby underpinning a major supply of protein for millions of people and playing an important role in global food security, especially in low-income areas7,8. Seagrasses are of particular socio-economic and cultural importance for Indigenous people who depend on the resources and services provided by seagrass ecosystems9,10. Brown macroalgae are also harvested directly or farmed for food consumption, medical use, or to produce animal feed, fertilisers, biofuels, and other commercial products11. Besides ensuring coastal protection12, brown algal canopies and seagrass stands also participate in marine biogeochemical cycles, with an estimated global primary production of 0.92 and 0.14–0.49 Pg C year−1, respectively3,12. Healthy brown macroalgal forests and seagrass meadows are important blue-carbon ecosystems that contribute to climate-change mitigation12–14. Together, the socio-economic services provided by brown macroalgae and seagrasses are estimated to be worth billions of dollars annually15–18.

Multiple stressors from global change now threaten all these ecological and socio-economical services19–21. Dunic and colleagues22 estimated that 19% of the surveyed seagrass area has already disappeared since 1880, and that the global loss of seagrass area for individual meadows is progressing at 1–2% year−1. Likewise, the geographical distribution of brown macroalgae has shifted23–25. The abundance of kelp (large brown macroalgal species of the order Laminariales) has slightly shrunk globally, but the magnitude and direction of change varies regionally, with declines in 38%, increases in 27%, and no evidence for change in 35% of the ecoregions where kelps are present25. Accelerating ocean warming has driven a poleward retreat of many species of brown macroalgae23,26.

Although changes in macrophyte distribution might become even more prominent in the coming decades due to accelerating global environmental change, predictions of the future distributions of brown macroalgae and seagrasses are scant, in stark contrast to the many studies modelling the future distribution of terrestrial plants27,28. Currently available models projecting the future distribution of marine macrophytes apply to regional or local scales only, and/or to a limited set of species29–33 (but see a recent global study34). Available studies focusing on the regional scale forecast substantial distributional shifts for both seagrasses30,32 and brown macroalgae31 by the end of this century. These shifts involve a high potential for local or regional extinctions30 and poleward expansions29. However, there are no comprehensive, global-scale models of the future distribution of ecosystem-structuring marine macrophytes.

Brown macroalgae and seagrasses display distinct global distributions, both in terms of latitudinal patterns of biodiversity and spatial location of diversity hotspots35–37. Such differences arise both from long and complex biogeographic processes—such as distinct patterns of speciation and dispersal38,39, and from taxon-specific ecological and environmental requirements. In general, temperature and light availability are commonly identified as important determinants of species distributions in both groups, followed by salinity, nutrient availability, wave energy, ice cover in polar regions, and the presence of suitable substrata3,36,40,41. Still, the relative importance and effects of these factors vary not only between the two groups but also within them. Thus, how the rapid shifts in these factors will reconfigure the distribution of habitat-forming marine macrophytes in future decades remains an open question, which calls for projections of both group-wide and species-specific responses to global change.

Here, we hypothesise that future increases in sea surface temperature will force brown macroalgae and seagrasses to retreat from lower to higher (and cooler) latitudes, albeit with substantial regional variation modulated by differences in salinity and surface primary productivity42,43, light availability, water quality, and various other anthropogenic stressors acting at local and regional scales25,44. Climate-driven shifts in species ranges will likely change macrophyte community composition and local species diversity, with losses expected in tropical regions, where conditions are already close to the upper limits of thermal tolerance of many resident species45. By contrast, the projected reduction in sea-ice cover and increasing sea temperatures might promote an expansion of macrophyte distribution into polar regions46, although potentially constrained by the availability of suitable substrata, declines in salinity, and increases in turbidity expected from sea-ice melting47.

We test such hypotheses quantitatively by modelling yearly changes in the distribution of 207 seagrass and brown macroalgal species at the global scale from 2015 to 2100, thereby substantially expanding the taxonomic coverage and spatial extent of previous work. We show that by the end of the century, (i) the global species diversity of brown macroalgae and seagrasses will decline, with marked regional and latitudinal variation, (ii) coastal areas will become less suitable to ecosystem-structuring macrophytes, with global macrophyte habitat distribution shifting among marine regions, and (iii) both brown macroalgae and seagrasses will lose a large fraction of their present range, which will be barely compensated by expansions into new, suitable areas.

Results
Modelling individual macrophyte distribution
We modelled the global distribution of 207 marine macrophyte species (185 brown macroalgae and 22 seagrasses; Supplementary Fig. 1b, c; Supplementary Data 1–2) from 2015 to 2100 at a 0.5° × 0.5° latitude/longitude resolution and under three different greenhouse gas-emissions scenarios (Shared Socio-economic Pathways SSP2-4.5, SSP3-7.0, SSP5-8.548). We first used a geometric criterion49 to define the largest possible polygon where the species could occur (i.e., the extent of occurrence50), based on a large dataset of occurrence records51. We then applied a random forest algorithm52 to obtain the area of occupancy50 of each species by determining habitat suitability (ranging from 0 to 1) within the species’ extent of occurrence, based on biologically relevant environmental and climatic variables (sea surface and air temperature, salinity, primary productivity, light, and sea-ice cover3,36,40,41; see Supplementary Fig. 1b and Methods for details). Our random forest models showed high accuracy, with an average out-of-bag validation score52 of 0.987 ± 0.001 (± standard error; minimum = 0.773). On average, sea surface temperature, air temperature, and light had the highest influence53 on predicting the distribution of both macrophyte groups (Supplementary Fig. 2).

Present and future macrophyte species diversity
We stacked and summed individual area-of-occupancy maps54 to obtain global maps of present and end-of-century macrophyte species diversity under different greenhouse gas-emissions scenarios (Fig. 1a, c; Supplementary Fig. 3a, c; Supplementary Fig. 4a, c; see Supplementary Fig. 5 for a map of linear species diversity). We obtained area-of-occupancy maps by determining habitat suitability within the boundaries of each species’ present-day extent of occurrence (assuming no dispersal in areas where the species has never been recorded). Present hotspots of brown macroalgal diversity occur along the Pacific and Atlantic coasts of North America, the Atlantic coast of Europe, and the south-eastern coast of Australia (Fig. 1a). We identified hotspots of seagrass diversity in the north-eastern coast of Australia and the Caribbean Sea (Fig. 1c).

We explored end-of-century losses and gains of macrophyte species diversity by comparing maps of end-of-century and present diversity (Fig. 1b, d; Supplementary Fig. 3b, d; Supplementary Fig. 4b, d). Changes in marine macrophyte species diversity will follow different regional patterns. Under an intermediate carbon-emissions scenario (SSP3-7.0), our model predicts that the Pacific coast of South America will face the most severe losses in brown macroalgal diversity by 2100, with more moderate losses occurring in the eastern Indo-Pacific, in eastern Africa, and along the North Atlantic coast of North America, Europe, and Africa (Fig. 1b). Diversity loss in these regions appears particularly severe under the most pessimistic emissions scenario (SSP5-8.5; Supplementary Fig. 4b). Gains in brown macroalgal diversity are mainly clustered along the eastern coast of Africa and the Arabian Peninsula, the western coast of Africa, and northern Australia (Fig. 1b). Hotspots of seagrass diversity loss will occur mainly along the Pacific coast of North America, the Atlantic and Mediterranean coasts of Europe, Baltic Sea, Black Sea, Korean Peninsula, and the north-western and south-eastern coasts of Australia (Fig. 1d). Gains are mainly expected in the Caribbean Sea, and at intermediate to high latitudes in the Northern Hemisphere—particularly along the Pacific and Atlantic coasts of North America, and Iceland.

Of the localities where ecosystem-structuring macrophytes are present, globally 21.7% will lose ≥10% of their present brown algal species diversity, and 17.4% will lose ≥10% of their current seagrass species diversity (SSP3-7.0). Overall, losses of local species diversity are likely to outweigh gains, resulting in a net reduction of the average number of macrophyte species occurring at a given location by 2100 (Fig. 2a, b; Supplementary Fig. 6a, b; Supplementary Fig. 7a, b). Diversity loss is predicted to be more severe for brown macroalgae than seagrasses. Specifically, we expect to lose 4.4% ± 0.1% (± standard error) of local species of brown macroalgae by 2100 under an intermediate emissions scenario (SSP3-7.0), compared to a loss of 2.6% ± 0.3% of seagrass species (Fig. 2a, b). These figures reach 6.5% ± 0.2% and 6.7% ± 0.5%, respectively, under SSP5-8.5 (Supplementary Fig. 7a, b).

When considering latitudinal patterns of diversity change throughout this century (Fig. 2c, d; Supplementary Fig. 6c, d; Supplementary Fig. 7c, d), the loss of brown macroalgae diversity appears particularly stark beyond 40° N and in the entire Southern Hemisphere, while gains mainly occur between 0-20° N (scenario SSP3-7.0; Fig. 2c, d). The loss of seagrass diversity appears more severe between 25–40° in both hemispheres, while gains occur in the tropics (around 20° N and 20° S) and beyond 50° N in the Northern Hemisphere (scenario SSP3-7.0; Fig. 2c, d). Conversely, both groups are projected to face widespread diversity loss in the tropics under a more pessimistic emissions scenario (SSP5-8.5; Supplementary Fig. 7c, d).

Future variation of global macrophyte habitat
We used a random forest algorithm52 to project present and future global habitat suitability for brown macroalgae and seagrasses at a resolution of 0.5° × 0.5° latitude/longitude for the period 2015–2100 and under different greenhouse gas-emissions scenarios48. Specifically, we modelled presence-absence of brown macroalgae (or seagrasses) per locality as a function of the same environmental and climatic variables we used to explore individual species’ distributions (Supplementary Fig. 1a, see Methods). We considered macroalgae (or seagrasses) to be present in a given locality if represented by at least one occurrence (of any species in the dataset). Our purpose here was therefore to identify global-scale changes in the areas capable of hosting macrophytes, regardless of their identity or local diversity. This approach conveys substantially different information from that attained by summing species-specific suitability maps (Supplementary Fig. 8), not only from a conceptual perspective but also because global habitat suitability models identify an independent set of non-linear relationships linking environmental variables to macrophyte occurrence. Our global habitat suitability models had high accuracy, with an average out-of-bag validation score52 of 0.817 ± 0.004 (± standard error; minimum = 0.806). The world’s coastlines will become progressively less suitable for macrophytes, with highly suitable habitat (suitability p > 0.9; see Methods) expected to decline by 96.5% for seagrasses and 77.8% for brown macroalgae by the end of the century under an intermediate emissions scenario (SSP3-7.0; Fig. 3). Under the most pessimistic emissions scenario (SSP5-8.5, Fig. 3), the decline in highly suitable macrophyte habitat is projected to be more severe for brown macroalgae (81%), but not for seagrasses (92.1%).

Changes in macrophyte habitat suitability show different regional trends for the two groups (Fig. 4; Supplementary Figs. 9–11). Under the intermediate emissions scenario (SSP3-7.0), our model predicts a decline in the extent of suitable habitat (p > 0.6) for brown macroalgae in most marine regions (temperate North Atlantic, most of the Pacific and Indo-Pacific, temperate Australasia, temperate South Africa, tropical Atlantic, and temperate South America), with expansions limited to the Arctic, western Indo-Pacific and Southern Ocean (Fig. 4a). Conversely, the extent of seagrass-suitable habitat (p > 0.6) is expected to decline in the temperate North Atlantic, Indo-Pacific, temperate Australasia, temperate South-Africa and tropical Atlantic, with increases in the temperate North Pacific and Arctic (Fig. 4a). When considering highly suitable habitat (p > 0.9, Supplementary Fig. 9), declines are expected in all the marine regions where present, except for an expansion of brown macroalgae in the Arctic.

Regional variation in the extent of macrophyte habitat will translate into a substantial change in the share of suitable global macrophyte habitat (p > 0.6) in each marine region (Fig. 4b). Globally, the proportion of suitable habitat for brown macroalgae will mainly increase in the Arctic (from 2.7 to 8.4%), in the temperate North Pacific (from 19.7 to 26.5%), in the western Indo-Pacific (from 1 to 2.1%), while declining particularly in the temperate North Atlantic (from 41.2 to 37.9%), tropical Atlantic (from 6.3 to 0%), central Indo-Pacific (from 2.6% to 0.1%), and eastern Indo-Pacific (from 1.3% to 0.1%). Major expansions of the proportion of global suitable seagrass habitat will occur in the temperate North Pacific (from 10.5 to 24.7%) and Arctic (from 0 to 4.6%), while a marked decline is expected in the tropical Atlantic (from 13.2 to 1%) and central Indo-Pacific (from 11.9 to 4%). A decrease in the extent of macrophyte habitat in a region (Fig. 4a) does not necessarily correspond to a decrease in the relative proportion of globally suitable macrophyte habitat in the region (Fig. 4b) and vice versa, because the absolute global extent of macrophyte-suitable habitat is also expected to change by 2100 (Fig. 4b, square brackets).

Erosion of average macrophyte area of occupancy
We explored future trajectories of average macrophyte area-of-occupancy extent by averaging the extension (km2) of the individual area of occupancy of all brown macroalgae (185 species) and seagrasses (22 species) at yearly intervals from 2015 to 2100 and under different greenhouse gas-emissions scenarios (see Supplementary Fig. 1d and Methods for details). Note that changes in the extent of the area of occupancy of each species can only be due to variation in habitat suitability within the boundaries of its present-day extent of occurrence (hence, we assume no dispersal in areas where the species has not yet been recorded). The analysis revealed similar outcomes for the two groups, with brown macroalgae expected to lose 5.8% ± 0.8% of their present area of occupancy by 2100, and 5.3% ± 2.9% for seagrasses under SSP3-7.0 (Fig. 5). We predict the loss to reach 11% ± 1.2% for brown macroalgae and 10.4% ± 4.1% for seagrasses under the most pessimistic scenario (SSP5-8.5; Fig. 5). Area-of-occupancy loss is unlikely to be distributed evenly among macrophyte species. Our models predict that most species will lose little of their present area of occupancy, while fewer species will experience severe losses by 2100 (Supplementary Fig. 12; Supplementary Table 1).

Future opportunities
We explored future opportunities for expansion, assuming that all species could colonise novel suitable habitats beyond the boundaries of their current extent of occurrence to an extent proportional to the current ratio between occupied and suitable areas (see Methods). Under this assumption, only seagrasses might moderately expand their present average area of occupancy by 2100 (Fig. 5, dashed lines), with gains of 2.4% ± 2.6% (standard error) under an intermediate emissions scenario (SSP3-7.0) and 2.3% ± 3.2% under the more pessimistic SSP5-8.5. In contrast, no gains are expected for brown macroalgae under any emissions scenario (Fig. 5, dashed lines).

Methods
Overview
We used extensive species occurrence data51 and a set of environmental and climatic layers at the global scale to map both ‘generic’ brown macroalgae and seagrass habitats (i.e., habitat suitable to host any brown macroalgal or seagrass species, regardless of species identity), and the distribution of 207 individual macrophyte species (Supplementary Fig. 1, Supplementary Data 1–2). We derived maps for both the present and the future (from 2015 to 2100, at yearly temporal resolution) under three greenhouse gas-emissions scenarios (Shared Socio-economic Pathways48 SSP2-4.5, SSP3-7.0, and SSP5-8.5), and explored future trajectories of change in macrophyte habitat extent and species diversity, as well as in individual species’ ranges (defined as “area of occupancy”). We did all analyses in Python 3 and R version 4.2.1. We did spatial data manipulation and analysis using the Geospatial Data Abstraction Library (GDAL), Rasterio, Scipy, and Shapely in Python 3.

Species occurrence data
We compiled occurrence records of canopy-forming brown macroalgae and seagrasses from a large published dataset51. The dataset includes 2,751,805 georeferenced observations obtained from online repositories, herbaria, peer-reviewed publications, and citizen-science programmes that have been taxonomically standardised, dereplicated, and checked for accuracy. We used a pruned version of the dataset, filtered further to exclude records erroneously georeferenced to occur on land, in areas with unsuitable light conditions for photosynthesis of marine forests, or outside the known distribution of each species. Our final dataset includes 800,119 records spanning 1665 to 2018 for 30 species of seagrasses (families Cymodoceaceae, Hydrocharitaceae, Posidoniaceae, and Zosteraceae) and 349 species of brown macroalgae (orders Fucales, Laminariales, and Tilopteridales). From the latter, we also excluded two species of floating Sargassum (S. fluitans and S. pusillum).

Environmental and climatic predictors
We selected a set of environmental and climatic predictors considered relevant for the distribution of both brown macroalgae and seagrasses: (i) sea surface temperature (°C), (ii) surface air temperature (°C), (iii) surface salinity (practical salinity units), (iv) surface primary productivity (organic carbon concentration in seawater, in g m-3 day-1), (v) light, in terms of surface incoming shortwave radiation (flux in the 0.2–4 µm wavelength band reaching a horizontal unit Earth surface, in W m-2), irradiance at bottom (photosynthetically active radiation reaching the sea bottom, in E m-2 year-1), (vi) sea-ice cover (% of grid cell area covered by ice), and (vii) depth (m).

We hypothesised that changes in the selected variables are determinants of the future distribution of brown macroalgae and seagrasses, and in particular that (i) the projected global increase in sea surface temperature42—and surface air temperature for intertidal macrophytes36—will exceed the physiological thresholds of many brown algal and seagrass species, thereby reducing their productivity, survival, growth, and reproduction—particularly in populations at the trailing edge of their range21,75,76; (ii) the projected regional anomalies in surface salinity43 will have varying effects on macrophyte survival, growth, and reproduction due to high interspecific variation in salinity tolerance76,77; (iii) regional increases in surface primary productivity associated with increased nutrient loading will reduce water clarity, thereby compromising macrophyte primary productivity78,79; and (iv) the projected reduction in sea-ice cover will increase the availability of substrata and sunlight for photosynthesis, potentially driving expansions of both groups in polar regions46.

We obtained mean monthly sea surface temperature, air temperature, surface salinity, surface primary productivity, and sea-ice cover at a resolution of 0.5° × 0.5° latitude/longitude for 2015–2100 and under three emissions scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5) from the Coupled Model Intercomparison Project Phase 680 (CMIP6; sea surface temperature, surface salinity, surface primary productivity, and sea-ice cover from www.dkrz.de/WDCC/ui/cerasearch/cmip6?input=CMIP6.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp845; air temperature from wdc-climate.de/ui/cmip6?input=CMIP6.ScenarioMIP.CNRM-CERFACS.CNRM-CM6-1-HR.ssp370). For incoming shortwave radiation at the surface, we averaged the monthly layers from 2019 to 2022 obtained from the Eumestat Climate Modelling Satellite Application Facility (wui.cmsaf.eu) at a resolution of 0.25° × 0.25° latitude/longitude. No future predictions of surface incoming shortwave radiation are available, so we maintained the same values for future predictions. We obtained depth from the 15 arc-second GEBCO interval grid (gebco.net). We downloaded data on the irradiance at the sea bottom (E m-2 year-1) from Bio Oracle (bio-oracle.org) at a 5 × 5 arc-minute resolution, considering the maximum values of radiance at the shallowest depth per cell. Where needed, we re-interpolated the original data to a resolution of 0.5° × 0.5° latitude/longitude.

Macrophyte extent of occurrence
For each macrophyte species, we extrapolated the largest possible polygon where the species could occur at present (2015–2020) from point occurrences using a α-hull method at a resolution of 0.05° × 0.05° latitude/longitude, following the procedure described in Strona and colleagues49. We only selected species with ≥10 occurrence records, reducing the initial number of species (379; 349 brown macroalgae and 30 seagrasses) to 207 (185 and 22, respectively). We then started from a small α (0.001) to obtain a hull including most of the occurrences and progressively increased α by increments of 0.005. At each step, we computed the ratio between the reduction in hull area relative to the previous hull and the relative decrease in the number of occurrences contained in the hull (with respect to the total number of occurrences available for the target species). We selected the final value of α when the ratio became <10. We then re-interpolated each polygon to a 0.5° × 0.5° resolution and clipped them with the light-at-bottom layer to exclude cells with limiting light conditions for macrophyte photosynthesis (<50 E m-2 year-1, as in ref. 51), and thus generated the present-day extent of occurrence for each species50.

Macrophyte habitat suitability (ecological niche model)
We used a random forest classifier (with the package Scikit-learn in Python 3) to model both (i) global macrophyte habitat suitability and (ii) species-specific habitat suitability at a resolution of 0.5° × 0.5° latitude/longitude (Supplementary Fig. 1). Random forest is a machine‐learning method based on an ensemble of bootstrapped decision trees52. Due to the high accuracy of predictions and the ability to handle complex interactions and collinearity among predictors81, random forests have been increasingly used to model species distributions, including marine taxa82,83. For both i and ii, we used occurrence data from Assis and colleagues51 and modelled the same set of n = 207 species selected with the α-hull procedure. We modelled global macrophyte habitat suitability separately for brown macroalgae and seagrasses, considering presence cells as those hosting ≥1 species of brown macroalgae and seagrasses, respectively. To model species-specific habitat suitability, we considered presence cells as those hosting at least one occurrence record. In both cases, we randomly sampled pseudo-absences from cells with no occurrence records, maintaining the sample size equal to the number of presence cells.

We calibrated the models using the monthly layers of the environmental predictors: sea surface temperature, air temperature, surface salinity, surface primary productivity, and sea-ice cover for 2015–2020 under the three emissions scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5), and the present values of surface incoming shortwave radiation and depth. In the individual models, we applied a variable-selection procedure where we iteratively removed the 10 least-important variables (starting from the full model), recomputing for each model the accuracy as an out-of-bag validation score52 and variable importance as the mean accumulation of impurity decrease. We then selected the most accurate model. The procedure substantially reduced the number of predictors in each model (55.8% ± 28.5, on average). This also resulted in the exclusion of the most correlated independent variables in 82% of the models. We generated (i) global maps of brown algae and seagrass habitat suitability, and (ii) individual suitability maps for each of the 207 macrophyte species for 2015–2100 (under the three emissions scenarios), with probability of occurrence p ranging from 0 to 1.

Analysis
We first modelled the future distribution of each of the 207 macrophyte species assuming no expansion beyond the boundaries of their present extent of occurrence. For this, we clipped individual habitat-suitability maps with the current extent-of-occurrence maps to obtain the area of occupancy50 of each species for 2015–2100 and under the three emissions scenarios (Supplementary Fig. 1b). To generate global maps of species diversity, we stacked individual area-of-occupancy maps (Supplementary Fig. 1c, e). We estimated species richness as the sum of the probabilities of occurrence p of all the macrophyte species present in each 0.5° × 0.5° grid cell54. We mapped end-of-century variation in global brown macroalgae and seagrass diversity as the log percentage change between 2100 and 2015 (100 × loge[diversity2100 / diversity2015]) to show diversity losses and gains on a symmetrical scale. We also explored global trajectories and latitudinal trends of future changes in macrophyte diversity. For the latter, we calculated the log percentage change in diversity relative to 2015 for the years 2016 to 2100 averaged across latitudes (at a resolution of 0.5° × 0.5° latitude/longitude).

We used global macrophyte habitat-suitability maps, clipped with the light-at-bottom layer to exclude cells with limiting light conditions for macrophyte photosynthesis51 (<50 E m-2 year-1), to compute future trajectories of suitable habitat extension for brown algae and seagrasses both at a global scale and within each of the 12 world marine ecoregions85 occupied by the modelled species (worldwildlife.org/publications/marine-ecoregions-of-the-world-a-bioregionalization-of-coastal-and-shelf-areas). To calculate global habitat extension, we converted global macrophyte habitat suitability maps into binary matrices by applying different probability thresholds (p = 0.6, 0.7, 0.8, 0.9) and summed the area (km2) of the resulting cells. We calculated variation in habitat extension relative to 2015 using the ratio 100 × (area2100 - area2015) / area2015.

To calculate future trajectories of the average macrophyte area of occupancy from 2015 to 2100, we first multiplied, for each cell in the area-of-occupancy map, the probability of occurrence p by the corresponding cell extension (km2). We then summed the results to obtain the total extension of each species’ area of occupancy. We averaged the extension of the area of occupancy of all 185 brown macroalgae and 22 seagrass species, respectively. We also explored potential alternative future trajectories of macrophyte area of occupancy under a hypothesis of proportional expansion. Because we did not explicitly model macrophyte dispersal, we assumed that all macrophyte species would be able to colonise novel suitable areas outside their present extent of occurrence by the same proportion represented by the current ratio between occupied and suitable areas. For each species, we calculated the ratio R2015 between occupied and suitable (s) area for 2015 (R2015 = area of occupancy2015 / s2015) and computed the potential area of occupancy under the proportional-expansion hypothesis for each following year Y by multiplying R2015 by the future suitable area sY (sY R2015).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "OVERALL\nThis study deals with the projected loss of biodiversity and suitable habitat by global\nwarming of the most important foundation macrophytes of coastal marine ecosystems. For\ndoing so, the extent of occurrence, habitat suitability and area of occupancy are calculated\nfor 108 brown macroalgae and 15 seagrasses in the current climate (2015-2020) and under\nfuture IPCC scenarios, and individual patterns stacked to estimate species richness at each\ncell in the grid. The α-Hull method and Random Forest were performed and combined.\nMajor conclusions are the decline in species richness, drive by significant contractions in\nspecies distributions that are not compensate by potential species expansions to new areas.\nI believe the study of both observed and predicted patterns and trends of biodiversity\nglobally is an important topic in Biology, Global Change Science, and Biogeography, as we\nexpect major changes. However, the current ecological knowledge on such global\nbiogeographic patterns is not related to the results of this study, and thus we cannot\nconclude how it is improved or completed previous knowledge. On the contrary, the\nauthors pose that data may be not sufficient large for comparing with the diversity patterns\ndescribed before (sixth paragraph in the discussion), and thus they do not include a\ndiscussion from the biogeographic perspective. Alternatively, they develop nicely the\nimplications in global change scenarios for these two marine ecosystems, but I regret that in\ndoing so they narrow the discussion of the results for a marine audience of experimental\necologists. There are not general statements or ecological theory other than potential\nimpacts of climate change, and particularly the introduction and discussion sections do not\ndeal with general ideas, as I argue below. For this reason, and because the biological\ndatabase and species distribution modeling can be improved, I do not recommend the\npublication of this manuscript in Nature Communications.\nLIST OF COMMENTS\nThe manuscript is focus from the beginning on the macrophyte-dominated habitats without\nframing the research in a more general biogeographic context of interest for a wide\naudience. In particular, the authors center the introduction on the mentioned ecosystems\nfrom the first sentence, without making any reference or comparison with other systems,\nmarine or terrestrial, in regards of the biodiversity loss also experienced in those. I suggest\nstart de introduction on the topic of global patterns of and losses of biodiversity worldwide\nand introduce the reader on the current knowledge on this topic. For this the authors may\nimprove biological data by include the extensive records observed for macrophytes before\n2015 (see comments below).\nIn relation to the above, I suggest fuse and shorten the first and second paragraphs of the\nintroduction. I find well justified in the first paragraph the ecological relevance at a global\nscale of the studied systems, but do not see justification of a full paragraph on the\ncontribution of macrophytes to carbon sequestration, a topic that is no mentioned\nafterwards. I suggest shortening drastically this paragraph.\nBesides, the manuscript does not achieve general conclusions transferable to other\necological systems nor frame their findings on a biogeographic context in the discussion\nsection. Please compare your results with those from Keith et al.\n(https://doi.org/10.1111/geb.12132), Tittensor et al. (https://doi: 10.1038/nature09329), or\nShort et al. (https://doi.org/10.1016/j.jembe.2007.06.012), among others. Given the\nextensive literature and discussion on biodiversity global patterns and trends (mostly\nterrestrial), I encourage the authors to include this and similar literature using a\nbiogeographic approach. This may help in understanding how the predicted loss of these\ncanopy-forming species would impact the global marine biodiversity.\nThe authors develop Species Distribution Modeling (SDMs) using Random Forest, a method\nlinking presence records and pseudo-absences with the environmental layers of relevant\nenvironmental predictors. As mentioned in the introduction, this modeling is important for\npredicting habitat suitability. This modeling tool, largely use by biogeographers, has not\nbeen framed in this context and sound references of the theoretical base are not included.\nThe term SDM, or equivalent, is not mentioned throughout the manuscript. Please give a\nbrief explanation of what are these models and the state of the art, as there are not\nroutinely used in marine systems.\nRegarding the timeframe used in this study, I do not see justification what records previous\nto 2015 have not been used, as most observation of macrophytes distribution comprised in\nGBIF, OBIS, and Assis` databases were taken before. The extensive literature on\nbiogeography was reviewed by Lüning in its popular book of 1990, there is large information\non the distribution of the studied species that has been omitted without justification why.\nBesides, the data used in this study are affected by the heat weave of 2003 and later, as well\nas by the baseline warming observed before this date. Therefore, some historical\npopulations may be missing because of the warming, and thus the realized niche captured in\nthe SDMs may not represent the full conditions the species is able to cope with. Please\njustify the timeframe of this study and account that data are already affected by climate\nchange.\nAs the authors are probably aware, there is uncertainty in the ways biodiversity may be\npredicted in the future. In particular stacking predicted maps of areas-of occupancy do not\ninform on potential biological interactions and dispersal restrictions impeding the species to\nsurvive. The debate on how to improve predictions of biodiversity is open, and thus I\nsuggest the authors to mention this potential limitation and properly account for this topic\nin introduction, methods, and discussion. See for example Zhang et al.\n(https://doi.org/10.1111/ddi.12970).\nIn relation to the above, distributional contractions by climate change linked to stressful\nclimatic and physical conditions, and thus predicted with relatively confidence. On the\ncontrary, expansions to areas that are becoming climatically suitable are dependent on the\nability to disperse and overcome the inhibiting biotic interactions. This mater is explained by\nbiogeographers using the BAM diagram\n(https://www.youtube.com/playlist?list=PLJSfnPII0T5i_6Svb4L4VO-yAHwaQVFeU). Given\nthat macroalgae are not good dispersers and seagrasses rely on clonal growth, I believe that\nmacrophytes will find difficulties in expanding their limits. Post filtering using information on\ndispersal may improve the projections by SDMs (Anderson, https://doi:\n10.1111/nyas.12264). Instead, the authors argue that the current prevalence (ratio of\npresences/absences) of the species may capture dispersal limitations. I do not see how\nprevalence can inform on the elapsed time need for the species to reach the new area, that\nmay be longer than the date of the projection. Post filtering the SDMs output using the\ndispersal rates of some model-species my help in identifying if the potential dispersal of\nrelevant taxa may fell within the timeframe of the projections. I see clear theoretical\ndifferences between these two topics (dispersal Vs. prevalence-dominance) that are treated\nas a single response in paragraph starting in line 193, and in discussion in line 217. I believe\nprevalence is not a good proxy of dispersal, but please refer to previous references if so.\nSPECIFIC ISSUES\nLine 99- the authors are seeking for the “largest possible range where the species could\noccur” for calculating the extent of occurrence. For this I believe that data before 2015,\nparticularly the extensive ones from the 70s, 80s and 90s, shall be considered. I see no\njustification for not using all the biological information available and thus suggest extending\nthe database.\nThe lack of references in the “Results” and “Methods” sections is notorious. Line 98- lack of\nreferences on future climate scenarios, 100- lack of references on “extent of occurrence”\nand “area of occupancy” calculations, 104- lack of references of the validation method, 106-\nlack of references of the estimation of variance importance, 116- please add references to\ngive support of the approach for predicting biodiversity. This is not solved in the “Methods”\nsection where there are just two references on the niche modeling (numbers 28 and 76):\n370- lack of references to random forest and SDMs, 388- lack of references of the protocol\nused, among others.\nLine 110- How these results fit with the previous knowledge on the biodiversity global\npatterns of macrophytes and other relevant marine taxa?\nLine 120- The “gains” are probably better linked to the eltonian niche than to the one\ncaptured by SDMs. As argued above, gains cannot be given the same weight than losses, as\nare predicted with larger uncertainty. Please introduced this complexity in the ms. Are we\nreally expecting biodiversity gains in the Artic and sub-Artic (coast of Iceland)? (Line 124).\nBesides, the species itself is not predicted to expand but its suitable habitat. Authors are\nprojecting the species realized niche to the future rather than its presence. Please account\nfor these limitations.\nFig 1. and similar- With such a low number of species of seagrasses (I understand is a small\ntaxon in numbers), I do not see the need of use the logarithmic transformation of species\nrichness, but the richness itself, as done in Short et al.\n(https://doi.org/10.1016/j.jembe.2007.06.012). Besides, results of this study contradict\nthose from Short and other authors. For example, the diversity in the Mediterranean seems\ngreater than that of the Tropical Indo-Pacific contradicting previous knowledge. Moreover,\nFig. 1 show maxima logarithmic values in the Mediterranean, contradicting the text in line\n114.\nLine 140- please clarify and provide references supporting this approach of used pooled\ndata en SDMs, were the raw data of all species pooled as the input of biological records in a\nRandom Forest algorithm? or alternatively, did the authors combined the habitat suitability\nvalues of each species in a single final value?. I regret the exact methodology is not clear in\nthis paragraph. In line 296 it said “combine”, please be precise.\nLine 259- authors point here the potential importance of considered other factors not\nincluded in this study. I do not understand why this is given more weight that to the\ndiscussion of the responses to the environmental predictors that were considered, an\naspect that is not discussed in deep.\nLine 338- How you avoid autocorrelation between environmental predictors? this is well\nknown to be an potential issue in SDMs and other multivariate approaches (Elith,\nhttps://doi.org/10.1111/j.2041-210X.2010.00036.x ). The monthly means of the\nenvironmental parameters used in this study show necessary high autocorrelation. Please\naccount for this important issue when modeling.\nLine 381- the models are calibrated for the recent-present day layers but cannot be trained\nusing future conditions (SSP2-4,5, SSP3-7.0, AND SSP5-8.5) contrary to what is suggested in\nMethods. Training and predicting are mixed up and confused in the text: the models are first\ntrained to the current-recent conditions and afterwards the final algorithm applied to future\nscenarios, please explain separately these two phases of the modeling process.\nPlease end discussion making generalizations that arise necessary from comparisons with\nprevious work. With the exception of the topic of global decline of macrophytes due to\nclimate change, the authors do not include other general conclusion on how these results\ncontribute to our understanding of the patterns and trends of global diversity. I strongly\nbelieve the manuscript has to give same weight to climate change science and\nbiogeography.", "reviewer_2": "The authors use georeferenced species occurrence data for brown algae and seagrasses\nderived from online repositories as well as environmental data that are known to limit the\ndistribution of these species. They then modelled how these environmental factors will\nchange under future climate scenarios and what this would mean for the global distribution\nof these two groups of macrophytes. I am not a modeller so I can’t comment on the validity\nof methodological approaches used, but the environmental data used to drive their models\nare appropriate and the outcomes of the analyses seem logical and align with similar\nregional studies. Overall this paper provides the first global analysis of how these two\nimportant groups of macrophytes are likely to shift their ranges into the future. As with any\nmodelling approach there are a number of caveats that mean the predictions made in this\nmanuscript may not come to pass. On the whole these are the same for any similar study\nand there is little that the authors could do, but in places on think the caveats need to be\nmore strongly acknowledged. The authors also highlight that other studies separate model\nintertidal and subtidal species differently because drivers of their distribution are different.\nThis study does not separate intertidal and subtidal species, but no justification is given for\nwhy this is appropriate in this study. This is a concern and I would argue that the authors\nshould model these species separately as other studies have for the reasons stated or\nprovide an evidence based justification for why it is okay in this instance. The number of\nspecies modelled (123) and the geographical coverage (12 eco-regions) make this study\nnovel. I enjoyed reading the manuscript, but feel that authors need to explain how their\napproach of not separating intertidal and subtidal species is an appropriate approach given\nthe different drivers of distribution for these species and therefore at this stage I believe\nthat this manuscript can not be accepted for publication in its current state.\nMinor comments\nLines 35 & 36 The references should come after the areal extent of the two habitats\nLine 54 It should be made clear that both DOC and POC can be transported to deep marine\nsediments\nLine 64 Krumhansl et al 2016 PNAS 113: 13785-137990 should be cited at the end of this line\nand it might also be worth stating how many ecoregions have reported losses\nLines 67-70 I suggest these sentences are restructured as the first suggests that there are no\nstudies, when there are regional studies and then the second sentence acknowledges that\nthis is the case.\nLine 78 It seems strange that only salinity and primary productivity are noted here. Light will\nbe a key driver of these species distributions as will other interacting anthropogenic\nstressors. This is acknowledged in the discussion, but should be incorporated here as well.\nLines 81-82 Some macrophytes are likely to expand into the Arctic with reductions in sea-ice\ncover, but the picture is more complicated as outline in Filbee-Dexter et al Global and\nplanetary Change 172: 1-14 review paper. I suggest caveating this sentence to acknowledge\nthe more complex picture.\nLine 123 There is also a hotspot of loss on the west coast of Australia that deserves a\nmention, particularly as this area supports a high diversity of seagrass species.\nLines 129-131 I suggest stating what the losses under the other climate change scenarios are\nand refer the reader to the SOM\nLine 133 Add negatively before impacted\nLine 134 I suggest adding that these changes are particularly stark from 20 degrees north\nLine 135-137 I suggest it might be worth highlighting the gains and then losses around 20\ndegrees south\nLines 164-166 It isn’t clear to whether the values in parentheses are the increases/\ndecreases between 2015-2100 or the range of values the models provided. Please clarify\nLine 241-243 From my understanding there is increasing concern of brown macroalgae\nreaching the Antarctic via the rafting of, particularly species with air bladders, that have\nbeen dislodged baring reproductive material. It may be worth acknowledging this as a\nmethod of dispersal, but not one that would be picked up with this modelling approach.\nLine 245 It is only under the worst case scenario that any brown algal species losses most of\ntheir range. This should be acknowledged.\nLine 249 I believe that Macrocystis pyrifera has a listing designation in Australia that might\nbe worth acknowledging if this species is included in the analysis\nFigure 2 It should be stated in the figure legend what the blue and red represent in terms of\ngains and losses\nFigure 4 If this was upped to a higher probability how many regions would be excluded. I\nwould be tempted to use as a high a probability as possible e.g. p>0.9 as described in the\ntext around Fig 3 and only use this lower value for the regions with limited suitable cells\nunless this is the case across all/majority of regions and if so this needs to be state.\nSOM Fig 3 and 4 are only discussed in the discussion and all SOM figures should be\nnumbered in the order in which they appear.", "reviewer_3": "This study uses habitat suitability modelling and climate projections to show how global\ndistributions of large brown macroalgae (LBMs) and seagrasses are likely to change over the\nnext 70 years or more. The approach is solid, if a little opaque in parts, with the findings only\ncompletely clear after a close study of the methods. Species with sufficient records to\nestablish credible global distributions are modelled individually and the results combined to\nshow how species diversity and total suitable habitats might change. The second part of the\nwork models all seagrasses and LBMs together to produce similar projections to those from\nthe combined individual species models, without adding much to the work. Dropping the\ntotal macrophyte modelling would allow for greater focus on how the findings are\nsupported by the methodology. The changes in macrophyte results shown in Figs 3 to 5\ncould be produced from the summation of the species-specific models.\nSome confusion for this reader was generated by the very similar-sounding terms for quite\ndifferent measures of species ranges and suitable habitats. ‘Extent of occurrence’ covers the\nrange of observations of each species, while ‘area of occupancy’ refers the overlap of\n‘extent of occurrence’ and modelled maps of present-day and predicted future suitable\nhabitat (lines 389-393). Future ‘area of occupancy’ can never therefore never exceed\npresent-day ‘extent of occurrence’. ‘Area’ is also used interchangeably to mean the summed\nextent of a species distribution and the distribution itself.\nModels of future distributions assumed no expansion of ranges (line 388). Adopting this\ndecision meant that the focus was only the loss of present-day ranges, with largely negative\nimpacts on future diversity. For these largely temperate species with no tropical\ncounterparts to replace them with warming, declining diversity would be expected in their\npresent-day temperate diversity hotspots. But the diversity change calculated here (lines\n395-396) does not take account of potential gains from range expansions of warm-affinity\nspecies within temperate zones, inflating the likely losses in diversity.\n‘Proportional expansion’ (lines 411), the assumption that species will continue to occupy a\nconstant proportion of the predicted globally suitable habitat, is a fair enough proposition\nand better than that newly suitable habitat will be instantly occupied once it becomes\navailable through climate change.\nMore detailed comments follow:\nlines 26-27. Losses. It is not yet clear at this point that gains in range and suitable habitats\nare excluded, and expansions focussed on macrophyte habitat in general.\nlines 34-75. This is a nice summary of the literature on the main drivers of change in these\ngroups, but the number of references could be reduced.\nline 120. The odd gains of species diversity for LBMs in the northwest Indian Ocean are hard\nto understand (Fig. 2b). Are existing tropical species predicted to expand to areas previously\nunoccupied? Similarly, gains for seagrasses east Africa (Fig. 2d) need some explanation.\nFigure 3. The greater losses of seagrass habitat extent under the less-extreme climate\nscenarios of SSP2 and SSP3 than SSP5 are difficult to reconcile. Why is that happening?\nFigure 4. Information presented here is essentially similar to Figs 1 and 2, with declines in\ntropical regions, stasis in cold temperate regions and increases in polar regions where\nsuitable habitats may exist – here the Arctic only.\nFigure 5. As before, distinction between contraction of existing distributions (solid lines give\nchanges in area of occupancy) and potential expansion into newly suitable areas (dashed\nlines) makes it difficult to see a net gain or loss in LDMs and seagrasses."}, "author_response": "Reviewer 1\nI believe the study of both observed and predicted patterns and trends of biodiversity globally is an\nimportant topic in Biology, Global Change Science, and Biogeography, as we expect major changes.\nHowever, the current ecological knowledge on such global biogeographic patterns is not related to\nthe results of this study, and thus we cannot conclude how it is improved or completed previous\nknowledge. On the contrary, the authors pose that data may be not sufficient large for comparing\nwith the diversity patterns described before (sixth paragraph in the discussion), and thus they do not\ninclude a discussion from the biogeographic perspective. Alternatively, they develop nicely the\nimplications in global change scenarios for these two marine ecosystems, but I regret that in doing so\nthey narrow the discussion of the results for a marine audience of experimental ecologists. There are\nnot general statements or ecological theory other than potential impacts of climate change, and\nparticularly the introduction and discussion sections do not deal with general ideas, as I argue\nbelow. For this reason, and because the biological database and species distribution modeling can be\nimproved, I do not recommend the publication of this manuscript in Nature Communications.\nRESPONSE #1: We have now framed our study in a broader biogeographic context both in\nthe Introduction (L34–37):\n“Anthropogenic climate change is driving an unprecedented redistribution of the\nEarth’s marine and terrestrial biota through extensive erosion, rearrangement, and\nshift of species ranges1,2. Such processes can alter existing biodiversity patterns and\nlead to the emergence of novel species assemblages, potentially disrupting important\necosystem services2.”\nL79-89:\n\"Brown macroalgae and seagrasses display distinct global distributions, both in terms\nof diversity gradients and spatial location of diversity hotspots35–37. Such differences\narise both from long and complex biogeographic processes38,39, and from taxon-\nspecific ecological and environmental requirements. In general, temperature and light\navailability are commonly identified as important determinants of species\ndistributions in both groups, followed by salinity, nutrient availability, wave energy,\nice cover in polar regions, and the presence of suitable substrata3,36,40,41. Still, the\nrelative importance and effects of these factors varies not only between the two\ngroups, but also within them. Thus, how the rapid shifts in these factors will\nreconfigure the distribution of habitat-forming marine macrophytes in future decades\nremains an open question, which calls for projections of both group-wide and\nspecies-specific responses to global change.\"\nand the Discussion (L269-279):\n“Our projections of macrophyte diversity and habitat suitability vary substantially\nover the global range. In general, present patterns of brown macroalgal species\ndiversity are consistent with those previously described by others35,36. Contrary to\nmost marine coastal taxa58, brown macroalgae show highest diversity in temperate\nregions such as the north-western Pacific, the north Atlantic and south-eastern\nAustralia. Conversely, the highest species diversity of seagrasses is mainly found in\nthe tropics37,59 (particularly in the Tropical Indo-Pacific bioregion). However, our\nmap of current seagrass species diversity only partially overlaps with those of Short\nand colleagues37,59 given that our models did not identify prominent diversity\nhotspots such as the insular Indo-Pacific and the coast of south-western Australia.\nThe reason for this mismatch is that our analyses were focused on a specific set of\nseagrass species, for which sufficient occurrence data exist to make reliable\npredictions.\"\nWe have also improved and clarified the methodology throughout the manuscript.\nThe manuscript is focus from the beginning on the macrophyte-dominated habitats without framing\nthe research in a more general biogeographic context of interest for a wide audience. In particular,\nthe authors center the introduction on the mentioned ecosystems from the first sentence, without\nmaking any reference or comparison with other systems, marine or terrestrial, in regards of the\nbiodiversity loss also experienced in those. I suggest start de introduction on the topic of global\npatterns of and losses of biodiversity worldwide and introduce the reader on the current knowledge\non this topic. For this the authors may improve biological data by include the extensive records\nobserved for macrophytes before 2015 (see comments below).\nIn relation to the above, I suggest fuse and shorten the first and second paragraphs of the\nintroduction. I find well justified in the first paragraph the ecological relevance at a global scale of\nthe studied systems, but do not see justification of a full paragraph on the contribution of\nmacrophytes to carbon sequestration, a topic that is no mentioned afterwards. I suggest shortening\ndrastically this paragraph.\nRESPONSE #2: We have now framed the study into the broader biogeographic context by\nopening the Introduction with more general statements on projected patterns of biodiversity\nchange in terrestrial and marine ecosystems (L34–37) (see Response #1). We have also\nfused and shortened paragraph 1 and 2 of the Introduction (L46–58).\nBesides, the manuscript does not achieve general conclusions transferable to other ecological\nsystems nor frame their findings on a biogeographic context in the discussion section. Please\ncompare your results with those from Keith et al. (https://doi.org/10.1111/geb.12132), Tittensor et\nal. (https://doi: 10.1038/nature09329), or Short et al. (https://doi.org/10.1016/j.jembe.2007.06.012),\namong others. Given the extensive literature and discussion on biodiversity global patterns and\ntrends (mostly terrestrial), I encourage the authors to include this and similar literature using a\nbiogeographic approach. This may help in understanding how the predicted loss of these\ncanopy-forming species would impact the global marine biodiversity.\nRESPONSE #3: We have now expanded the comparison with previous studies on global\npatterns of diversity and distribution of marine macrophytes and other marine taxa by\nincluding the suggested references (L270–279):\n“In general, present patterns of brown macroalgal species diversity are consistent\nwith those previously described by others35,36. Contrary to most marine coastal\ntaxa58, brown macroalgae show highest diversity in temperate regions such as the\nnorth-western Pacific, the north Atlantic and south-eastern Australia. Conversely, the\nhighest species diversity of seagrasses is mainly found in the tropics37,59 (particularly\nin the Tropical Indo-Pacific bioregion). However, our map of current seagrass\nspecies diversity only partially overlaps with those of Short and colleagues37,59 given\nthat our models did not identify prominent diversity hotspots such as the insular\nIndo-Pacific and the coast of south-western Australia. The reason for this mismatch\nis that our analyses were focused on a specific set of seagrass species, for which\nsufficient occurrence data exist to make reliable predictions.”\nHowever, we have refrained from expanding the comparison to terrestrial systems because\nthat milieu falls out of the scope of our manuscript.\nThe authors develop Species Distribution Modeling (SDMs) using Random Forest, a method\nlinking presence records and pseudo-absences with the environmental layers of relevant\nenvironmental predictors. As mentioned in the introduction, this modeling is important for\npredicting habitat suitability. This modeling tool, largely use by biogeographers, has not\nbeen framed in this context and sound references of the theoretical base are not included.\nThe term SDM, or equivalent, is not mentioned throughout the manuscript. Please give a\nbrief explanation of what are these models and the state of the art, as there are not\nroutinely used in marine systems.\nRESPONSE #4: We now provide more information and clarify the modelling approach we\nused. Specifically, we now provide theoretical background on the random forest algorithm in\nthe Results and Methods, as well as examples of its application in modelling species\ndistributions in marine systems (e.g., L447–450):\n“Random forest is a machine‐learning method based on an ensemble of bootstrapped\ndecision trees52. Due to the high accuracy of predictions and the ability to handle\ncomplex interactions and collinearity among predictors81, random forests have been\nincreasingly used to model species distributions, including marine taxa82,83.”\nThat said, we disagree that random forests would be something new or strange in the marine\n(or any other) context, or that they would be “largely use[d] by biogeographers”. Given the\nreviewer’s emphasis on general patterns and theory for different realms, we stress that\nrandom-forest methods are routinely used across all fields and realms of ecology — a\nstatement we now support with additional references.\nRegarding the timeframe used in this study, I do not see justification what records previous to 2015\nhave not been used, as most observation of macrophytes distribution comprised in GBIF, OBIS, and\nAssis` databases were taken before. The extensive literature on biogeography was reviewed by\nLüning in its popular book of 1990, there is large information on the distribution of the studied\nspecies that has been omitted without justification why.\nBesides, the data used in this study are affected by the heat weave of 2003 and later, as well as by\nthe baseline warming observed before this date. Therefore, some historical populations may be\nmissing because of the warming, and thus the realized niche captured in the SDMs may not\nrepresent the full conditions the species is able to cope with. Please justify the timeframe of this\nstudy and account that data are already affected by climate change.\nRESPONSE #5: This is a misunderstanding of our approach. but we concede that this\naspect was not clear in the previous version of the manuscript. We have now clarified that\nthe dataset we used (Assis et al. 2020) includes records from 1665 to 2018. We only filtered\nthe original version of the dataset to exclude species with few occurrence records (< 10), but\nwe did use occurrences recorded before 2015 for our models.\nAs the authors are probably aware, there is uncertainty in the ways biodiversity may be predicted in\nthe future. In particular stacking predicted maps of areas-of occupancy do not inform on potential\nbiological interactions and dispersal restrictions impeding the species to survive. The debate on\nhow to improve predictions of biodiversity is open, and thus I suggest the authors to mention this\npotential limitation and properly account for this topic in introduction, methods, and discussion.\nSee for example Zhang et al. (https://doi.org/10.1111/ddi.12970).\nRESPONSE #6: We agree that predicting biodiversity is challenging, and that stacking\nindividual distribution maps has potential limitations, including not accounting for\ninterspecific interactions. As we discuss in the text, the main goal of our approach is to\nexplore changes in the potential future distribution and diversity of macrophytes; we are not\naiming to predict future community composition. As we discuss in L350–351, not\naccounting for species interactions and potential dispersal restrictions makes our projections\nof future macrophyte ranges and diversity conservative. We have now emphasized these\nissues in the Discussion (L337–342):\n“In addition, biotic interactions can modify brown macroalgal and seagrass\nassemblages through top-down mechanisms68,69, and will therefore likely play a role\nin modulating the future distribution of both groups. For example, increased grazing\npressure caused by the expansion of tropical herbivorous fishes has depleted brown\nalgal cover in many temperate regions68. Interspecific interactions between\nindividual macrophyte taxa — such as competition or facilitation — might contribute\nadditional complexity70.”\nIn relation to the above, distributional contractions by climate change linked to stressful climatic\nand physical conditions, and thus predicted with relatively confidence. On the contrary, expansions\nto areas that are becoming climatically suitable are dependent on the ability to disperse and\novercome the inhibiting biotic interactions. This mater is explained by biogeographers using the\nBAM diagram (https://www.youtube.com/playlist?list=PLJSfnPII0T5i_6Svb4L4VO-\nyAHwaQVFeU). Given that macroalgae are not good dispersers and seagrasses rely on clonal\ngrowth, I believe that macrophytes will find difficulties in expanding their limits. Post filtering using\ninformation on dispersal may improve the projections by SDMs (Anderson, https://doi:\n10.1111/nyas.12264). Instead, the authors argue that the current prevalence (ratio of\npresences/absences) of the species may capture dispersal limitations. I do not see how prevalence\ncan inform on the elapsed time need for the species to reach the new area, that may be longer than\nthe date of the projection. Post filtering the SDMs output using the dispersal rates of some model-\nspecies my help in identifying if the potential dispersal of relevant taxa may fell within the\ntimeframe of the projections. I see clear theoretical differences between these two topics (dispersal\nVs. prevalence-dominance) that are treated as a single response in paragraph starting in line 193,\nand in discussion in line 217. I believe prevalence is not a good proxy of dispersal, but please refer\nto previous references if so.\nRESPONSE #7: We agree that post-filtering the species distribution model output with\ninformation on the species dispersal would be a powerful approach to model potential\nexpansions into newly suitable areas. While we initially considered this approach, our\ndecision to use prevalence as a proxy for future dispersal was dictated by the paucity of data\non the dispersal ability and recruitment of most of the species we modelled — especially for\nbrown macroalgae. Moreover, many other biotic and abiotic factors — including oceanic\ncurrents, wind speed, storms, temperature, nutrients, light and substrate availability, presence\nof competitors and grazers — can affect the dispersal and recruitment of both macrophyte\ngroups. Given this complexity and the broad geographic and taxonomic coverage of our\nstudy, explicitly modelling macrophyte dispersal would be an endeavour too great and too\nspeculative to be incorporated in our analyses.\nWe are aware of the limitations of using current prevalence rates as a proxy for the\nvarious potential factors affecting the ability to colonize suitable areas, as we outlined in the\nDiscussion (L260–266). However, our ‘proportional expansion’ hypothesis provides an\nintermediate scenario between assuming no dispersal into novel suitable areas (hence,\nmodelling macrophyte range erosion alone), and assuming full dispersal into novel suitable\nareas — two equally unrealistic scenarios.\nLine 99- the authors are seeking for the “largest possible range where the species could occur” for\ncalculating the extent of occurrence. For this I believe that data before 2015, particularly the\nextensive ones from the 70s, 80s and 90s, shall be considered. I see no justification for not using all\nthe biological information available and thus suggest extending the database.\nRESPONSE #8: As we explained in Response #5, the dataset we used (Assis et al. 2020)\nincludes records from 1665 to 2018, and we have therefore included occurrences recorded\nbefore 2015 in ours analyses.\nThe lack of references in the “Results” and “Methods” sections is notorious. Line 98- lack of\nreferences on future climate scenarios, 100- lack of references on “extent of occurrence” and “area\nof occupancy” calculations, 104- lack of references of the validation method, 106- lack of\nreferences of the estimation of variance importance, 116- please add references to give support of\nthe approach for predicting biodiversity. This is not solved in the “Methods” section where there\nare just two references on the niche modeling (numbers 28 and 76): 370- lack of references to\nrandom forest and SDMs, 388- lack of references of the protocol used, among others.\nRESPONSE #9: We have now expanded the cited reference (especially in the Methods).\nLine 110- How these results fit with the previous knowledge on the biodiversity global patterns of\nmacrophytes and other relevant marine taxa?\nRESPONSE #10: We now treat these results in more depth in the Discussion (L270–279):\n“In general, present patterns of brown macroalgal species diversity are consistent\nwith those previously described by others35,36. Contrary to most marine coastal\ntaxa58, brown macroalgae show highest diversity in temperate regions such as the\nnorth-western Pacific, the north Atlantic and south-eastern Australia. Conversely, the\nhighest species diversity of seagrasses is mainly found in the tropics37,59 (particularly\nin the Tropical Indo-Pacific bioregion). However, our map of current seagrass\nspecies diversity only partially overlaps with those of Short and colleagues37,59 given\nthat our models did not identify prominent diversity hotspots such as the insular\nIndo-Pacific and the coast of south-western Australia. The reason for this mismatch\nis that our analyses were focused on a specific set of seagrass species, for which\nsufficient occurrence data exist to make reliable predictions.”\nLine 120- The “gains” are probably better linked to the eltonian niche than to the one captured by\nSDMs. As argued above, gains cannot be given the same weight than losses, as are predicted with\nlarger uncertainty. Please introduced this complexity in the ms. Are we really expecting biodiversity\ngains in the Artic and sub-Artic (coast of Iceland)? (Line 124). Besides, the species itself is not\npredicted to expand but its suitable habitat. Authors are projecting the species realized niche to the\nfuture rather than its presence. Please account for these limitations.\nRESPONSE #11: Correct. We projected the geographic space where abiotic conditions will\nallow species to persist. The species true presence in this space will be further constrained\nby a suite of factors, including biotic conditions (e.g., species interactions) and by mobility.\nWe concur that this makes range losses more certain than gains, because while the former\ncan be determined by the loss of suitable abiotic conditions alone, the latter are also\nconstrained by biotic and dispersal factors. We now emphasise these points in the Discussion\n(L291–294):\n“However, diversity losses are predicted with greater certainty than gains, because\nwhile the former can be determined by the loss of suitable abiotic conditions alone,\nthe latter are also constrained by biotic and dispersal components that we did not\nincorporate in our models.”\nHowever, we obtained the species diversity maps by summing the area-of-occupancy maps\nproduced by cropping the species-specific suitability maps with the extent of occurrence\nobtained from the occurrence records of each species based on an α-hull method (see\nMethods and Supplementary Fig. 1). In the area-of-occupancy maps, this restricts future\nsuitable localities to the geographic area where the species can likely disperse (because it\nhas already been recorded there), thereby partially buffering the issue related to dispersal. As\nfor the biotic factors, we could not incorporate these in our models. Inclusion would\nintroduce more uncertainty on the real future distribution of each macrophyte species, also\naffecting our projections of macrophyte diversity (explained in L345–349). However, the\ninfluence of biotic interactions on species distributions might diminish at large geographical\nscales and coarse resolutions (Eltonian noise hypothesis; Soberón & Nakamura, 2009).\nFig 1. and similar- With such a low number of species of seagrasses (I understand is a small taxon\nin numbers), I do not see the need of use the logarithmic transformation of species richness, but the\nrichness itself, as done in Short et al. (https://doi.org/10.1016/j.jembe.2007.06.012). Besides, results\nof this study contradict those from Short and other authors. For example, the diversity in the\nMediterranean seems greater than that of the Tropical Indo-Pacific contradicting previous\nknowledge. Moreover, Fig. 1 show maxima logarithmic values in the Mediterranean, contradicting\nthe text in line 114.\nRESPONSE #12: We show seagrass species richness on a logarithmic scale for consistency\nwith the species richness map of brown macroalgae. We now provide a map of seagrass\nspecies richness on a linear scale in the Supplementary Information (Supplementary Fig. 5,\nalso shown below). We agree that our results on seagrass diversity are not fully in line with\nthose of Short et al. (2007) and others. As we outline in the Discussion (L311–318), this is\nbecause we chose to focus our analyses on a specific set of seagrass species (n = 22 versus ~\n72 seagrass species globally described to date), for which sufficient occurrence data exist (≥\n10 occurrence records) to make reliable predictions. This is an important choice, because it\nallows us to focus on the more common and/or better-studied species for which current\ndistributional data will likely be informative about underlying climatic drivers.\nSupplementary Fig. 5 Present global macrophyte species diversity. We obtained present-\nday diversity by stacking individual area-of-occupancy maps (see Methods). a Shows brown\nmacroalgae, b shows seagrasses. Unlike Fig. 1, the expected number of species in the figure\nis on a linear scale.\nLine 140- please clarify and provide references supporting this approach of used pooled data en\nSDMs, were the raw data of all species pooled as the input of biological records in a Random\nForest algorithm? or alternatively, did the authors combined the habitat suitability values of each\nspecies in a single final value?. I regret the exact methodology is not clear in this paragraph. In line\n296 it said “combine”, please be precise.\nRESPONSE #13: We now clarify our approach in the Methods (L450–457; see also\nSupplementary Fig. 1). We used the random forest algorithm for two reasons: (i) modelling\nspecies-specific habitat suitability and (ii) modelling global macrophyte habitat suitability.\nFor i, we generated species-specific models based on all the occurrences of the target\nspecies. For ii, we generated a model to predict the overall suitability of a given locality to\nhost brown algae (or seagrasses), regardless of species identity. In this second approach, we\ntherefore used presence/absence of any brown algal (or seagrass) species as the dependent\nvariable (i.e., we deemed a grid cell as a presence when it hosted at least one brown alga or\nseagrass record, regardless of species identity).\nLine 259- authors point here the potential importance of considered other factors not included in\nthis study. I do not understand why this is given more weight that to the discussion of the responses\nto the environmental predictors that were considered, an aspect that is not discussed in deep.\nRESPONSE #14: We have now expanded the text describing the responses to the\nenvironmental predictors in the models (L321–325):\n“Under all emissions scenarios, sea surface temperature and air temperature —\ntogether with surface incoming shortwave radiation — appeared to be the major\ndeterminant of macrophyte distribution. These results correspond with the general\nexpectation that distributional shifts of marine taxa mainly track changes in ocean\ntemperature1.”.\nLine 338- How you avoid autocorrelation between environmental predictors? this is well known to\nbe an potential issue in SDMs and other multivariate approaches (Elith,\nhttps://doi.org/10.1111/j.2041-210X.2010.00036.x ). The monthly means of the environmental\nparameters used in this study show necessary high autocorrelation. Please account for this\nimportant issue when modeling.\nRESPONSE #15: The random forest algorithm is generally insensitive to collinearity and\ninteractions among predictors (Breiman 2001; Cutler et al. 2007; Di Franco et al. 2016;\nDuffy et al. 2016). Unlike linear models that take into account all variables in relation to one\nanother, decision trees in the random forests are constructed independently using a random\nsubset of predictors, and thus tend to be de-correlated. Additionally, because the final\noutcome aggregates predictions from multiple trees, if some trees make predictions based on\ncorrelated variables, the final voting process tends to reduce the impact of individual trees\npotentially biased by variable correlation. However, to cope with any potential collinearity\nissues that might remain, we have now done a backward automatic variable selection for\neach model, reducing the number of predictors to 10.\nLine 381- the models are calibrated for the recent-present day layers but cannot be trained\nusing future conditions (SSP2-4,5, SSP3-7.0, AND SSP5-8.5) contrary to what is suggested in\nMethods. Training and predicting are mixed up and confused in the text: the models are first\ntrained to the current-recent conditions and afterwards the final algorithm applied to future\nscenarios, please explain separately these two phases of the modeling process.\nRESPONSE #16: We trained the models on the three different emissions scenarios for\npresent-day layers for consistency (see also the method used by Strona and Bradshaw 2022).\nPlease end discussion making generalizations that arise necessary from comparisons with\nprevious work. With the exception of the topic of global decline of macrophytes due to climate\nchange, the authors do not include other general conclusion on how these results contribute to our\nunderstanding of the patterns and trends of global diversity. I strongly believe the manuscript has to\ngive same weight to climate change science and biogeography.\nRESPONSE #17: We have now added more general statements on how our results relate to\npatterns and trends in global biodiversity (L359-362):\n“Overall, the substantial and geographically diverse redistribution of habitat-forming\nmarine macrophytes projected in this study provides compelling evidence for the\npervasive and intricate impacts of climate change on marine life1,74.”\nReviewer 2\nThe authors use georeferenced species occurrence data for brown algae and seagrasses derived\nfrom online repositories as well as environmental data that are known to limit the distribution of\nthese species. They then modelled how these environmental factors will change under future climate\nscenarios and what this would mean for the global distribution of these two groups of macrophytes.\nI am not a modeller so I can’t comment on the validity of methodological approaches used, but the\nenvironmental data used to drive their models are appropriate and the outcomes of the analyses\nseem logical and align with similar regional studies. Overall this paper provides the first global\nanalysis of how these two important groups of macrophytes are likely to shift their ranges into the\nfuture. As with any modelling approach there are a number of caveats that mean the predictions\nmade in this manuscript may not come to pass. On the whole these are the same for any similar\nstudy and there is little that the authors could do, but in places on think the caveats need to be more\nstrongly acknowledged. The authors also highlight that other studies separate model intertidal and\nsubtidal species differently because drivers of their distribution are different. This study does not\nseparate intertidal and subtidal species, but no justification is given for why this is appropriate in\nthis study. This is a concern and I would argue that the authors should model these species\nseparately as other studies have for the reasons stated or provide an evidence based justification for\nwhy it is okay in this instance. The number of species modelled (123) and the geographical\ncoverage (12 eco-regions) make this study novel. I enjoyed reading the manuscript, but feel that\nauthors need to explain how their approach of not separating intertidal and subtidal species is an\nappropriate approach given the different drivers of distribution for these species and therefore at\nthis stage I believe that this manuscript can not be accepted for publication in its current state.\nRESPONSE #18: We have now accounted for the difference between intertidal and subtidal\nspecies. We identified potential issues in making a pre-selection of subtidal versus intertidal\nspecies and in using different modelling approaches to project their future potential\ndistribution at the global scale. In fact, some macrophytes (e.g., Fucus vesiculosus) occupy\nsubtidal habitats in certain geographic areas and intertidal habitats in others. Instead, we\nexpanded the model and let it identify potential differences in the drivers of distribution\nbetween intertidal versus subtidal species. In fact, our previous model already included\ndepth at high spatial resolution, so it was already accounting for potential differences in\nhabitat preference among species to some extent. However, our new models now also\ninclude monthly surface air temperature — a variable that influences the distribution of\nintertidal marine macrophytes (Fragkopoulou et al. 2022). As we explained in Response #15,\nwe also implemented an automated variable-selection procedure to minimize the ratio\nbetween number of independent variables and number of observations.\nLines 35 & 36 The references should come after the areal extent of the two habitats\nRESPONSE #19: Done.\nLine 54 It should be made clear that both DOC and POC can be transported to deep marine\nsediments\nRESPONSE #20: Following the suggestion of Reviewer 1 (Response #2), we decided to\nshorten the paragraph, and we have removed reference to dissolved organic carbon and\nparticulate organic carbon.\nLine 64 Krumhansl et al 2016 PNAS 113: 13785-137990 should be cited at the end of this line and\nit might also be worth stating how many ecoregions have reported losses\nRESPONSE #21: Done.\nLines 67-70 I suggest these sentences are restructured as the first suggests that there are no studies,\nwhen there are regional studies and then the second sentence acknowledges that this is the case.\nRESPONSE #22: We have now rephrased the second sentence to improve clarity (L71-75):\n“Currently available models projecting the future distribution of marine macrophytes\napply to regional or local scales only, and/or to a limited set of species29–33 (but see a\nrecent global study34). Available studies focusing on the regional scale forecast\nsubstantial distributional shifts for both seagrasses30,32 and brown macroalgae31 by\nthe end of this century.”\nLine 78 It seems strange that only salinity and primary productivity are noted here. Light will be a\nkey driver of these species distributions as will other interacting anthropogenic stressors. This is\nacknowledged in the discussion, but should be incorporated here as well.\nRESPONSE #23: We now mention other drivers of marine macrophyte distribution (L90–\n94):\n“Here, we hypothesize that future increases in sea surface temperature will\nforce brown macroalgae and seagrasses to retreat from lower to higher (and\ncooler) latitudes, albeit with substantial regional variation modulated by\ndifferences in salinity and surface primary productivity42,43, light availability,\nwater quality, and various other anthropogenic stressors acting at local and\nregional scales25,44.”\nLines 81-82 Some macrophytes are likely to expand into the Arctic with reductions in sea-ice\ncover, but the picture is more complicated as outline in Filbee-Dexter et al Global and planetary\nChange 172: 1-14 review paper. I suggest caveating this sentence to acknowledge the more complex\npicture.\nRESPONSE #24: We have now elaborated on the potential limiting factors to macrophyte\nexpansion in the Arctic (L97–100):\n“By contrast, the projected reduction in sea-ice cover and increasing sea\ntemperatures might promote an expansion of macrophyte distribution into polar\nregions46, although potentially constrained by the availability of suitable substrata,\ndeclines in salinity, and increases in turbidity expected from sea-ice melting47.”\nLine 123 There is also a hotspot of loss on the west coast of Australia that deserves a mention,\nparticularly as this area supports a high diversity of seagrass species.\nRESPONSE #25: We now mention the hotspot of loss in Western Australia in the Results\n(L151).\nLines 129-131 I suggest stating what the losses under the other climate change scenarios are and\nrefer the reader to the SOM\nRESPONSE #26: Done (L162–163):\n“These figures reach 6.5% ± 0.2% and 6.7% ± 0.5%, respectively, under SSP5-8.5\n(Supplementary Fig. 7a, b).”\nLine 133 Add negatively before impacted\nRESPONSE #27: In light of the results obtained with the new, improved models, we have\nremoved the sentence because it was no longer relevant.\nLine 134 I suggest adding that these changes are particularly stark from 20 degrees north\nLine 135-137 I suggest it might be worth highlighting the gains and then losses around 20 degrees\nsouth\nRESPONSE #28: We have now described them more extensively (L164–172):\n“When considering latitudinal patterns of diversity change throughout this century\n(Fig. 2c,d; Supplementary Fig. 6c,d; Supplementary Fig. 7c,d), the loss of brown\nmacroalgae diversity appears particularly stark beyond 40º N and in the entire\nSouthern Hemisphere, while gains mainly occur between 0-20º N (scenario SSP3-\n7.0; Fig. 2c,d). The loss of seagrass diversity appears more severe between 25–40º in\nboth hemispheres, while gains occur in the tropics (around 20º N and 20º S) and\nbeyond 50º N in the Northern Hemisphere (scenario SSP3-7.0; Fig. 2c, d).\nConversely, both groups are projected to face widespread diversity loss in the tropics\nunder a more pessimistic emissions scenario (SSP5-8.5; Supplementary Fig. 7c,d).”\nLines 164-166 It isn’t clear to whether the values in parentheses are the increases/ decreases\nbetween 2015-2100 or the range of values the models provided. Please clarify\nRESPONSE #29: For each marine region, the two values in parentheses represent the share\n(%) of global macrophyte habitat in the region (i) in 2015 and (ii) in 2100. We have now\nclarified this.\nLines 241-243 From my understanding there is increasing concern of brown macroalgae\nreaching the Antarctic via the rafting of, particularly species with air bladders, that have been\ndislodged baring reproductive material. It may be worth acknowledging this as a method of dispersal,\nbut not one that would be picked up with this modelling approach.\nRESPONSE #30: We have modified the sentence accordingly (L304–307).\n“Conversely, our models did not predict a strong expansion of macrophyte habitat in\nthe Southern Ocean, which is consistent with the independent expectation arising\nfrom the biogeographic isolation of the Antarctic continent62, although dispersal to\nthis region might occur via rafting for some species63. ”\nLine 245 It is only under the worst case scenario that any brown algal species losses most of their\nrange. This should be acknowledged.\nRESPONSE #31: We have removed the sentence from the Discussion.\nLine 249 I believe that Macrocystis pyrifera has a listing designation in Australia that might be\nworth acknowledging if this species is included in the analysis.\nRESPONSE #32: In light of the results obtained with the new, improved models, we have\nremoved the paragraph because it was no longer relevant.\nFigure 2 It should be stated in the figure legend what the blue and red represent in terms of\ngains and losses\nRESPONSE #33: Done.\nFigure 4 If this was upped to a higher ability how many regions would be excluded. I would be\ntempted to use as a high a probability as possible e.g. p>0.9 as described in the text around Fig 3\nand only use this lower value for the regions with limited suitable cells unless this is the case across\nall/majority of regions and if so this needs to be state.\nRESPONSE #34: We now provide a separate figure in the Supplementary Information\n(Supplementary Fig. 8, also shown below) where we display trajectories of highly suitable\nhabitat only (p > 0.9), excluding a total of three marine regions. However, we have refrained\nfrom using two different thresholds within the same figure in the main text, because it would\nbe difficult to interpret the bar plots in panel b.\nSupplementary Fig. 8 Variation in the extent of suitable macrophyte habitat (p > 0.9)\nacross marine regions (emissions scenario SSP3-7.0). We calculated global macrophyte\nhabitat suitability using a machine-learning approach (see Methods) for the period 2015–\n2100, and applied a threshold of p = 0.9 to select only highly suitable macrophyte habitat\n(only marine regions where highly suitable macrophyte habitat is present are shown). a\nVariation in macrophyte habitat extent (km2) for brown macroalgae (purple) and seagrasses\n(pink) within each marine region, aggregated every 5 years. b Comparison of the percentage\nof global suitable macrophyte habitat in each marine region between 2015 and 2100 for\nbrown macroalgae (upper bar plots) and seagrasses (lower bar plots). Colours refer to\nmarine regions as shown in a. Square brackets show total global suitable habitat extension.\nSOM Fig 3 and 4 are only discussed in the discussion and all SOM figures should be numbered in\nthe order in which they appear.\nRESPONSE #35: We have added references to all the Supplementary figures in the Results\nand numbered them in the order in which they appear.\nReviewer 3\nThis study uses habitat suitability modelling and climate projections to show how global\ndistributions of large brown macroalgae (LBMs) and seagrasses are likely to change over the next\n70 years or more. The approach is solid, if a little opaque in parts, with the findings only\ncompletely clear after a close study of the methods. Species with sufficient records to establish\ncredible global distributions are modelled individually and the results combined to show how\nspecies diversity and total suitable habitats might change. The second part of the work models all\nseagrasses and LBMs together to produce similar projections to those from the combined individual\nspecies models, without adding much to the work. Dropping the total macrophyte modelling would\nallow for greater focus on how the findings are supported by the methodology. The changes in\nmacrophyte results shown in Figs 3 to 5 could be produced from the summation of the species-specific\nmodels.\nSome confusion for this reader was generated by the very similar-sounding terms for quite different\nmeasures of species ranges and suitable habitats. ‘Extent of occurrence’ covers the range of\nobservations of each species, while ‘area of occupancy’ refers the overlap of ‘extent of occurrence’\nand modelled maps of present-day and predicted future suitable habitat (lines 389-393). Future\n‘area of occupancy’ can never therefore never exceed present-day ‘extent of occurrence’. ‘Area’ is\nalso used interchangeably to mean the summed extent of a species distribution and the distribution\nitself.\nRESPONSE #36: We have now clarified the different purposes and interpretations of the\ntwo approaches, paying particular attention to the potential confounding issues associated\nwith our chosen terminology. Regardless, we have thoroughly explored the reviewer’s\nsuggestions, and summed species-specific area-of-occupancy maps to obtain global habitat\nsuitability maps, both considering the mean and maximum habitat suitability (p) value per\ngrid cell (among the suitability values of all species present in each cell). Confirming our\nstatement above, the resulting patterns of habitat suitability are substantially different from\nthose that we obtained from the generic habitat suitability model. We show the maps for the\nyear 2015 (scenario SSP3-7.0) for comparison below.\nWe are aware that modelling generic macrophyte habitat suitability separately might\nseem redundant. However, the two analyses convey substantially different messages. This is\nbecause the generic habitat model, by being trained on the full set of conditions, does not\nnecessarily represent the ‘sum’ of the individual models; instead, it identifies an independent\nset of non-linear relationships linking environmental variables to the occurrence of generic\nmacrophyte habitat. This model is also expected to be statistically more robust than the\nindividual models, because of the much larger pool of training observations.\nGlobal macrophyte habitat suitability maps obtained with different approaches (year\n2015, scenario SSP3-7.0). a–b maps obtained from the global habitat suitability model we\nused; c–d maps obtained from the summation of species-specific area-of-occupancy maps,\nconsidering the mean suitability value (p) per cell; e–f maps obtained from the sum of\nspecies-specific area-of-occupancy maps, considering the maximum suitability value (p) per\ncell. a,c,e brown macroalgae (n = 185 species); b,d,f seagrasses (n = 22 species). Coloured\nscalebars show habitat suitability (p).\nModels of future distributions assumed no expansion of ranges (line 388). Adopting this decision\nmeant that the focus was only the loss of present-day ranges, with largely negative impacts on\nfuture diversity. For these largely temperate species with no tropical counterparts to replace them\nwith warming, declining diversity would be expected in their present-day temperate diversity\nhotspots. But the diversity change calculated here (lines 395-396) does not take account of potential\ngains from range expansions of warm-affinity species within temperate zones, inflating the likely\nlosses in diversity.\n‘Proportional expansion’ (lines 411), the assumption that species will continue to occupy a constant\nproportion of the predicted globally suitable habitat, is a fair enough proposition and better than\nthat newly suitable habitat will be instantly occupied once it becomes available through climate\nchange.\nRESPONSE #37: We agree that limiting range expansions to the extent of occurrence of\neach species might inflate diversity losses, giving a more pessimistic scenario of diversity\nchange. However, we also explored potential expansions beyond the limits of each species’\nextent of occurrence under the ‘proportional expansion’ hypothesis (Fig. 5).\nlines 26-27. Losses. It is not yet clear at this point that gains in range and suitable habitats are\nexcluded, and expansions focussed on macrophyte habitat in general.\nRESPONSE #38: We have now reworded the sentence to clarify that we are referring to\ndifferent aspects of the future of macrophytes — specifically, their local diversity/species\nrichness, loss of current range (which, by definition excludes future gains in areas outside\nit), and global loss of habitat (L27–32):\n“… we estimate that by 2100, local macrophyte diversity will decline by 3–4% on\naverage, with 17 to 22% of localities losing at least 10% of their macrophyte species\nglobally. The current range of macrophytes will be eroded by 5–6%, and highly\nsuitable macrophyte habitat will be substantially reduced globally (78–96%). Global\nmacrophyte habitat will shift among marine regions, with a high potential for\nexpansion in polar regions. ”\nlines 34-75. This is a nice summary of the literature on the main drivers of change in these groups,\nbut the number of references could be reduced.\nRESPONSE #39: We have shortened the Introduction (see Response #2) and the number of\nreferences.\nline 120. The odd gains of species diversity for LBMs in the northwest Indian Ocean are hard to\nunderstand (Fig. 2b). Are existing tropical species predicted to expand to areas previously\nunoccupied? Similarly, gains for seagrasses east Africa (Fig. 2d) need some explanation.\nRESPONSE #40: The diversity in neighbouring areas appears to be stable. An increase in\ndiversity in the north-western Indian Ocean (blue cells) might be due to an increase in\nsuitable habitat for local species (i.e., within their extent of occurrence). As for seagrasses,\nour new and improved models did not show any gains in diversity in East Africa.\nFigure 3. The greater losses of seagrass habitat extent under the less-extreme climate scenarios of\nSSP2 and SSP3 than SSP5 are difficult to reconcile. Why is that happening?\nRESPONSE #41: The results we obtained with the new models do not show such patterns\n(Fig. 3).\nFigure 4. Information presented here is essentially similar to Figs 1 and 2, with declines in tropical\nregions, stasis in cold temperate regions and increases in polar regions where suitable habitats may\nexist – here the Arctic only.\nRESPONSE #42: As we stated in Response #36, modelling global macrophyte habitat\nsuitability separately conveys a different kind of information than the sum of single area-of-\noccupancy maps. The magnitude of regional shifts in the extent of suitable habitat for\nmacrophytes (Fig. 4) does not necessarily mirror changes in macrophyte species diversity in\nthat marine region (Fig. 1). For example, in the tropical Atlantic (Fig. 4a), suitable habitat\nfor brown macroalgae (p > 0.6) is projected to diminish by the end of the current century,\nwhile this negative trend is not evident in the map of brown macroalgal species diversity\n(Fig. 1b), where both diversity gains and losses occur in the region. Likewise, the western\nIndo-Pacific is projected to face a high loss of suitable habitat for seagrasses (Fig. 4a), which\ndoes not show in the map of seagrass species diversity (Fig. 1d).\nFigure 5. As before, distinction between contraction of existing distributions (solid lines give\nchanges in area of occupancy) and potential expansion into newly suitable areas (dashed lines)\nmakes it difficult to see a net gain or loss in LDMs and seagrasses.\nRESPONSE #43: We have now clarified the figure legend:\n“Solid lines represent mean variation in macrophyte current area of occupancy\n(assuming no expansion beyond the species’ extent of occurrence); dashed lines\nrepresent mean expected change in macrophyte area of occupancy assumingthe\ncurrent ratio between occupied and climatically suitable range will remain constant\n(proportional expansion; see Methods); this allows for expansion beyond the limits\nof a species’ extent of occurrence.”\nReferences cited in this response\nAssis, J., Fragkopoulou, E., Frade, D., Neiva, J., Oliveira, A., Abecasis, D. et al. (2020). A fine-tuned global distribution\ndataset of marine forests. Scientific Data, 7, 119. doi:10.1038/s41597-020-0459-x\nBreiman, L. (2001). Random forests. Machine learning 45, 5–32\nCutler, D.R., Edwards Jr, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. et al. (2007). Random forests for\nclassification in ecology. Ecology, 88, 2783-2792. doi:10.1890/07-0539.1\nDi Franco, A., Thiriet, P., Di Carlo, G., Dimitriadis, C., Francour, P., Gutiérrez, N.L. et al. (2016). Five key attributes\ncan increase marine protected areas performance for small-scale fisheries management. Scientific Reports, 6, 38135.\ndoi:10.1038/srep38135\nDuffy, J.E., Lefcheck, J.S., Stuart-Smith, R.D., Navarrete, S.A. & Edgar, G.J. (2016). Biodiversity enhances reef fish\nbiomass and resistance to climate change. Proceedings of the National Academy of Sciences of the USA, 113, 6230-\n6235. doi:10.1073/pnas.1524465113\nFragkopoulou, E., Serrão, E.A., De Clerck, O., Costello, M.J., Araújo, M.B., Duarte, C.M. et al. (2022). Global\nbiodiversity patterns of marine forests of brown macroalgae. Global Ecology and Biogeography, 31, 636-648.\ndoi:10.1111/geb.13450\nShort, F., Carruthers, T., Dennison, W. & Waycott, M. (2007). Global seagrass distribution and diversity: A bioregional\nmodel. Journal of Experimental Marine Biology and Ecology 350, 3-20\nSoberón, J., & Nakamura, M. (2009). Niches and distributional areas: concepts, methods, and assumptions. Proceedings\nof the National Academy of Sciences, 106(supplement_2), 19644-19650.\nStrona, G. & Bradshaw, C.J.A. (2022). Coextinctions dominate future vertebrate losses from climate and land use\nchange. Science Advances, 8, eabn4345. doi:10.1126/sciadv.abn4345\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nI have reviewed my previous comments to the original submission, and the new reviewed\nmanuscript in comparison to the previous version, and I believe the authors have done\nmajor changes following the suggestions raised in the review. The manuscript has gain\ninterest for a wide audience and improved in clarity. Besides, my concerns have been\nanswered whit sound arguments, and the information requested is now included in the\nrevised version, as for example the clarification on the time frame of the presence records,\nor the previous knowledge on the latitudinal patterns of macrophytes, among other major\nchanges. I am thus changing my suggestion to “publishable” after few additional issues that I\nlisted below.\nThe new paragraph in the introduction dealing with the biogeographic patterns of the\nmacrophytes need to be \"polished\" to explain the ideas with precision:\n-line 80. “Distinct” in what manner? these two groups exhibit contrasting latitudinal\npatterns in biodiversity, please explain in brief, the idea is left \"open\".\n-line 82, what complex processes do the authors refer here?, please explain.\n-line 83, what environmental requirements? please clarify. This contrast with the idea that\nboth groups dependent on light and temperature in line 83, and therefore, depending on\nsimilar environmental requirements?.\nI believe the set of environmental predictors probably show autocorrelation, as in particular\nsea surface and air temperatures, thought the authors do not include estimations on this\nautocorrelation (as for example Pearson Coefficients between pairs or Variation Inflation\nFactors VIFs). Nevertheless, the authors provide references supporting Random Forest can\nperform in such conditions in their rebuttal letter. There are indeed some\napplications/packages dealing with this potential issue, as for example:\nhttps://blasbenito.github.io/spatialRF/#reducing-multicollinearity-in-the-predictors. I thus\nbelieve the algorithm and statistical approach is correct but would suggest the authors to\ninclude such information on the magnitude of autocorrelation, and information on the\nprecise software or package used to perform the RF analysis, the options of parametrization\nused to lover this potential issue, and the technical details related to this concern.\nReviewer #2 (Remarks to the Author):\nThe authors have done an excellent job of incorporating the three reviewers comments and\nthis has significantly improved the manuscript. Where the comments of reviewers have not\nbeen incorporated this is either because that material is no longer in the manuscript or the\nauthors, in my opinion, provide a strong justification for not doing so. This manuscript is a\nvery interesting read and is novel in providing the first global assessment of likely changes in\ndistribution and richness of key marine ecosystem engineers. The authors have taken the\ntime in the current version to more fully acknowledge the limitations of the techniques\nused, but this isn't a failing of the paper just the natural limitations of SDM modelling - an\napproach widely used for predicting for species distributions. I believe this manuscript will\nbe of interest to a broad audience and I recommend publication with some very minor\ncorrections (see below). I would also like to thanks the authors for their very clear response\nto reviewers. It made it very easy to link the reviewer comment with the response,\nespecially with the inclusion of the change in text.\nMinor comments\nLine 29 I suggest removing globally as somewhat redundant\nLine 123 I would indicate sea and air temperature\nLine 127 I would use light instead of shortwave radiation as it is more accessible\nLine 271 Suggest deleting 'over the global range' and replace with 'across ecoregions' as it is\nmore reflective of the comparisons undertaken\nReviewer #3 (Remarks to the Author):\nMy points concerned the need for clearer terminology and a more upfront message that the\nstudy emphasised loss of existing distributions in the way that the models were set up\n(Response #37). The reasons for the latter focus on losses are clear enough, but this aspect\nis lost in the detail – not obvious from the Abstract for example. It was good to see the\nexploration of the differences between the maps of summed suitability models and that of\noverall macrophyte suitability. The figure on p15 of the rebuttal would be a useful addition\nto the supplement (after Supplementary Fig. 3). The difference between the two\napproaches (stacked SDMs and modelled macrophyte suitability) highlights an important\nissue: data-poor species models may be more likely to predict presence in unsuitable areas\nand less likely in more suitable areas than the data-rich all macrophyte one. Stacked species\nmodels may be more likely to give higher species diversity (Fig. 1a) in unsuitable areas as a\nconsequence. A final point is that the new Fig. 3 still shows greater losses of seagrasses for\nless extreme climate scenarios SSP2 and SSP3 than SSP5, especially for higher suitability\nthresholds (0.7-0.9). There might be a reason for this but a comment should be made.\nReviewer 1\nI have reviewed my previous comments to the original submission, and the new reviewed\nmanuscript in comparison to the previous version, and I believe the authors have done major\nchanges following the suggestions raised in the review. The manuscript has gain interest for a wide\naudience and improved in clarity. Besides, my concerns have been answered whit sound arguments,\nand the information requested is now included in the revised version, as for example the\nclarification on the time frame of the presence records, or the previous knowledge on the latitudinal\npatterns of macrophytes, among other major changes. I am thus changing my suggestion to\n“publishable” after few additional issues that I listed below.\nThe new paragraph in the introduction dealing with the biogeographic patterns of the macrophytes\nneed to be \"polished\" to explain the ideas with precision:\n-line 80. “Distinct” in what manner? these two groups exhibit contrasting latitudinal patterns in\nbiodiversity, please explain in brief, the idea is left \"open\".\nRESPONSE #1: We have now clarified the sentence (L81–83):\n“Brown macroalgae and seagrasses display distinct global distributions, both in terms\nof latitudinal patterns of biodiversity and spatial location of diversity hotspots35–37.”\n-line 82, what complex processes do the authors refer here?, please explain.\nRESPONSE #2: The biogeographic processes that drove the current distribution of brown\nmacroalgae and seagrasses are explained in detail in the cited literature (Bolton 2010 and\nLarkum et al. 2018). We chose not to delve into the topic extensively in the text, but we\nhave now added a short sentence to clarify to which general processes we refer (L83–85):\n“Such differences arise both from long and complex biogeographic processes —\nsuch as distinct patterns of speciation and dispersal38,39, and from taxon-specific\necological and environmental requirements.”\n-line 83, what environmental requirements? please clarify. This contrast with the idea that\nboth groups dependent on light and temperature in line 83, and therefore, depending on similar\nenvironmental requirements?.\nRESPONSE #3: Temperature and light are primary drivers of the distribution of both\ngroups. However, in general, brown macroalgae and seagrasses have different optima and\ntolerance limits both for temperature and light. Most species of brown macroalgae are in\ntemperate waters (Fragkopoulou et al. 2022), while the highest diversity of seagrasses\noccurs in warm, tropical waters (Short et al. 2007). Moreover, seagrasses generally have\nhigher minimum light requirements than brown macroalgae (Duarte 1991).\nI believe the set of environmental predictors probably show autocorrelation, as in particular\nsea surface and air temperatures, thought the authors do not include estimations on this\nautocorrelation (as for example Pearson Coefficients between pairs or Variation Inflation Factors VIFs).\nNevertheless, the authors provide references supporting Random Forest can perform in such\nconditions in their rebuttal letter. There are indeed some applications/packages dealing with this\npotential issue, as for example: https://blasbenito.github.io/spatialRF/#reducing-multicollinearity-\nin-the-predictors. I thus believe the algorithm and statistical approach is correct but would suggest\nthe authors to include such information on the magnitude of autocorrelation, and information on\nthe precise software or package used to perform the RF analysis, the options of parametrization\nused to lover this potential issue, and the technical details related to this concern.\nRESPONSE #4: We have further addressed the issue of autocorrelation among variables as\nrequested. We have calculated the average R2 between each pair of variables used in the\nsingle-species models before and after variable selection for each emissions scenario (SSP2-\n4.5, SSP3-7.0, SSP5-8.5). We report in the boxplot below the distribution of the mean R2 for\nthe pairwise variable comparison for each model in the three emissions scenarios. On\naverage, pairwise variable correlation was < 0.3. The variable selection procedure reduced\nthe number of variables per model by 56% on average and removed the most correlated\nvariables in 82% of models. However, as we discussed in our previous response to\nreviewers, in principle, autocorrelation among predictors does not affect the performance of\nthe random forest algorithm (Breiman 2001; Dormann et al. 2013). We now provide further\ndetails on the variable selection procedure in the Methods (L473–481):\n“In the individual models, we applied a variable-selection procedure where we\niteratively removed the 10 least-important variables (starting from the full model),\nrecomputing for each model the accuracy as an out-of-bag validation score52 and\nvariable importance as the mean accumulation of impurity decrease. We then\nselected the most accurate model. The procedure substantially reduced the number of\npredictors in each model (55.8% ± 28.5 on average). This also resulted in the\nexclusion of the most correlated independent variables in 82% of the models.”\nAutocorrelation (R2) among variables selected in the species-specific models.\nDistribution of the mean R2 calculatedamong each pair of variables used in every species-\nspecific model after variable selection (for the year 2015) for all the three emissions\nscenarios (SSP2-4.5, SSP3-7.0, SSP5-8.5).\nReviewer 2\nThe authors have done an excellent job of incorporating the three reviewers comments and this has\nsignificantly improved the manuscript. Where the comments of reviewers have not been\nincorporated this is either because that material is no longer in the manuscript or the authors, in\nmy opinion, provide a strong justification for not doing so. This manuscript is a very interesting\nread and is novel in providing the first global assessment of likely changes in distribution and\nrichness of key marine ecosystem engineers. The authors have taken the time in the current version\nto more fully acknowledge the limitations of the techniques used, but this isn't a failing of the paper\njust the natural limitations of SDM modelling - an approach widely used for predicting for species\ndistributions. I believe this manuscript will be of interest to a broad audience and I recommend\npublication with some very minor corrections (see below). I would also like to thanks the authors\nfor their very clear response to reviewers. It made it very easy to link the reviewer comment with the\nresponse, especially with the inclusion of the change in text.\nMinor comments\nLine 29 I suggest removing globally as somewhat redundant\nRESPONSE #5: Removed.\nLine 123 I would indicate sea and air temperature\nRESPONSE #6: Done.\nLine 127 I would use light instead of shortwave radiation as it is more accessible\nRESPONSE #7: Done.\nLine 271 Suggest deleting 'over the global range' and replace with 'across ecoregions' as it is more\nreflective of the comparisons undertaken\nRESPONSE #8: Replaced.\nReviewer 3\nMy points concerned the need for clearer terminology and a more upfront message that the study\nemphasised loss of existing distributions in the way that the models were set up (Response #37). The\nreasons for the latter focus on losses are clear enough, but this aspect is lost in the detail – not\nobvious from the Abstract for example.\nRESPONSE #9: We had already discussed this aspect in the Results (L233–236):\n“Changes in the extension of the area of occupancy of each species can only be due to\nvariation in habitat suitability within the boundaries of its present-day extent of\noccurrence (hence, we assume no dispersal in areas where the species has not yet been\nrecorded).”\nHowever, we have now highlighted the issue also in the paragraph on present and future\nmacrophyte species diversity (L136–139):\n“We obtained area-of-occupancy maps by determining habitat suitability within the\nboundaries of each species’ present-day extent of occurrence (assuming no dispersal in\nareas where the species has never been recorded).”\nIt was good to see the exploration of the differences between the maps of summed suitability models\nand that of overall macrophyte suitability. The figure on p15 of the rebuttal would be a useful\naddition to the supplement (after Supplementary Fig. 3).\nRESPONSE #10: We have now added the figure to the Supplementary Information\n(Supplementary Fig. 8).\nThe difference between the two approaches (stacked SDMs and modelled macrophyte suitability)\nhighlights an important issue: data-poor species models may be more likely to predict presence in\nunsuitable areas and less likely in more suitable areas than the data-rich all macrophyte one.\nStacked species models may be more likely to give higher species diversity (Fig. 1a) in unsuitable\nareas as a consequence.\nRESPONSE #11: We agree that models fitted with few occurrences might not yield accurate\npredictions compared to more data-rich models. However, in general, our species-specific\nmodels had high accuracy, with an average out-of-bag validation score of 0.987 ± 0.001 (±\nstandard error; minimum = 0.773).\nA final point is that the new Fig. 3 still shows greater losses of seagrasses for less extreme climate\nscenarios SSP2 and SSP3 than SSP5, especially for higher suitability thresholds (0.7-0.9). There\nmight be a reason for this but a comment should be made.\nRESPONSE #12: We have now added a comment in the Results (L197–200):\n“Under the most pessimistic emissions scenario (SSP5-8.5, Fig.3), the decline in\nhighly suitable macrophyte habitat is projected to be more severe for brown\nmacroalgae (81%), but not for seagrasses (92.1%).”\nReferences cited in this response\nBolton, J. J. The biogeography of kelps (Laminariales, Phaeophyceae): a global analysis with new insights from recent\nadvances in molecular phylogenetics. Helgol. Mar. Res. 64, 263–279 (2010)\nBreiman L (2001) Random forests. Mach Learn 45(1):5-32. doi:10.1023/A:1010933404324\nDormann CF, et al. (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their\nperformance. Ecography 36(1):27-46. doi:10.1111/j.1600-0587.2012.07348.x\nDuarte, C. M. (1991). Seagrass depth limits. Aquatic botany, 40(4), 363-377.\nFragkopoulou, E., Serrão, E. A., De Clerck, O., Costello, M. J., Araújo, M. B., Duarte, C. M., ... & Assis, J. (2022).\nGlobal biodiversity patterns of marine forests of brown macroalgae. Global Ecology and Biogeography, 31(4), 636-\n648.\nLarkum, A. W. D., Waycott, M. & Conran, J. G. Evolution and Biogeography of Seagrasses. in Seagrasses of Australia:\nStructure, Ecology and Conservation (eds. Larkum, A. W. D., Kendrick, G. A. & Ralph, P. J.) 3–29 (Springer\nInternational Publishing, 2018). doi:10.1007/978-3-319-71354-0_1.\nShort, F., Carruthers, T., Dennison, W., & Waycott, M. (2007). Global seagrass distribution and diversity: a bioregional\nmodel. Journal of experimental marine biology and ecology, 350(1-2), 3-20."}]

Decision letter