Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Mangrove dispersal disrupted by projected changes in global seawater density

Mangrove dispersal disrupted by projected changes in global seawater density Articles https://doi.org/10.1038/s41558-022-01391-9 Mangrove dispersal disrupted by projected changes in global seawater density 1,2 1 2,3 4  ✉ Tom Van der Stocken    , Bram Vanschoenwinkel , Dustin Carroll    , Kyle C. Cavanaugh and Nico Koedam The degree to which the distribution of mangrove forests will be impacted by climate change depends on the dispersal and establishment of sea-faring propagules, which drive forest rejuvenation, gene flow and range expansion. Climate change affects sea surface density via changes in temperature and salinity. However, these changes have not been mapped and it remains unclear how these factors may impact mangrove propagule dispersal. Here, we provide evidence for strong warming of coastal mangrove waters and elevated geographic variability in surface ocean density under representative concentration pathway RCP 8.5 by 2100. The largest changes will occur in the Indo West Pacific region, the primary hotspot of mangrove diversity. By comparing propagule densities to predicted sea surface density, we assessed potential effects on mangrove propagule disper- sal. In the future, a warmer and fresher ocean is likely to alter dispersal trajectories of mangrove propagules and increase rates of sinking in unsuitable offshore locations, potentially reducing the resilience of mangrove forests. angrove forests thrive along tropical and subtropi- and open-ocean water properties. The biogeographic implications cal shorelines and their distribution extends to warm of such density differences were recognized more than a century 1 18 Mtemperate regions . They are globally recognized for ago by Henry Brougham Guppy, who discussed ‘the far-reaching the valuable ecosystem services they provide but are expected influence on plant-distribution and on plant-development that the to be substantially influenced by climate change-related physical relation between the specific weight of seeds and fruits and the den- 3,4 processes in the future . Under warming winter temperatures, sity of sea-water must possess’. 5,6 poleward expansion is predicted for mangroves , with poten- Since the time of Guppy’s early observations, climate change tial implications for ecosystem structure and functioning, as from human activities has driven pronounced changes in ocean 7,8 well as human livelihoods and well-being . The global distribu- temperature and salinity, with further changes predicted through- tion, abundance and species richness of mangroves is governed out the twenty-first century . Ocean density is a nonlinear func- by a broad range of biotic and environmental factors, includ- tion of temperature, salinity and pressure ; therefore, these changes ing temperature and precipitation and diverse geomorphologi- may influence dispersal patterns of mangrove propagules by alter- 10 18 cal and hydrological gradients . Climate and aspects related to ing their buoyancy and floating orientation. As Guppy noted , coastal geography (for example, floodplain area) determine the ‘[for] plants whose seeds or fruits are not much lighter than sea- 11,12 availability of suitable habitat for establishment . However, the water […] the effect of increased density of the water is to extend potential for mangroves to track changing environmental condi- the flotation period’ or ‘to increase the number that floated for a tions and expand their distributions ultimately depends on dis- given period’. Guppy also reported that the seedlings of the wide- 11,13 persal . The importance of dispersal in controlling mangrove spread mangrove genera Rhizophora and Bruguiera present excep- distributions has been demonstrated by mangrove distributional tional examples of propagules with densities somewhere between 14 18 responses to historical climate variability , past mangrove (re) seawater and freshwater . Previous studies of the impacts of climate colonization of oceanic islands and from the long-term survival change on mangroves have focused on factors such as sea level rise, of mangrove seedlings planted beyond natural range limits . As altered precipitation regimes and increasing temperature and storm 4,21–23 such, quantifying changes in the factors that influence disper- frequency but the potential impact of climate-driven changes sal is important for understanding climate-driven distributional in seawater properties on mangroves has not yet been examined. responses of mangroves under future climate conditions. This is somewhat surprising, as the ocean is the primary disper- In mangroves, dispersal is accomplished by buoyant seeds and sal medium of this ‘sea-faring’ coastal vegetation and dispersal is fruits (hereafter referred to as ‘propagules’). In combination with a key process that governs a species’ response to climate change by prevailing currents, the spatial scale of this process, ranging from changing its geographical range. This knowledge gap contrasts with local retention to transoceanic dispersal over thousands of kilome- recent efforts to expose links between climate change and dispersal 13 17 tres , is determined by propagule buoyancy , that is, the density in other ecologically important marine taxa such as zooplankton 24–27 difference between that of propagules and the surrounding water. and fish species . Hence, the course of dispersal trajectories for propagules from these In this study, we investigate predicted changes in sea surface species depends on the interaction between spatiotemporal changes temperature (SST), sea surface salinity (SSS) and sea surface den- in both propagule density and that of the surrounding water, ren- sity (SSD) for coastal waters bordering mangrove forests (hereaf- dering this process sensitive to climate-driven changes in coastal ter referred to as ‘coastal mangrove waters’), over the next century. 1 2 Department of Biology, Vrije Universiteit Brussel, Brussels, Belgium. Earth Science Section, Jet Propulsion Laboratory, California Institute of Technology, 3 4 Pasadena, CA, USA. Moss Landing Marine Laboratories, San José State University, Moss Landing, CA, USA. Department of Geography, University of California, Los Angeles, CA, USA. e-mail: Tom.Van.Der.Stocken@vub.be Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 685 –3 °C kg m PSU Nature Climate ChaNge Articles 45° N 4 30° N 3.5 15° N 0° 3 15° S 2.5 30° S SST AEP IWP 45° S 2 45° N 1 30° N 15° N 0 0° 15° S –1 30° S SSS AEP IWP 45° S –2 45° N 0 30° N 15° N –1 0° 15° S –2 30° S SSD AEP IWP 45° S –3 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E Longitude Fig. 1 | global map showing the change in sea surface variables across mangrove bioregions under r CP 8.5. a–c, Change in SST (a), SSS (b) and SSD (c). 29,30 Changes in SST and SSS are based on present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database , from which SSD data were derived. The vertical line (19° E) separates the two major mangrove bioregions: the AEP and IWP. Using a biogeographic classification system for coastal and shelf a c 5.0 1.0 areas , we examine spatiotemporal changes in these surface 4.5 SST SSD 4.0 ocean properties, with a particular focus on the world’s two major 0.5 3.5 3.0 mangrove diversity hotspots: (1) the Atlantic East Pacific (AEP) 2.5 2.0 region, including all of the Americas, West and Central Africa 1.5 –0.5 and (2) the Indo West Pacific (IWP) region, extending from East 1.0 0.5 Africa eastwards to the islands of the central Pacific . Finally, we –1.0 synthesize available data on the density of mangrove propagules –1.5 for different mangrove species and explore the potential impact of 2.0 SSS 1.5 climate-driven changes in SSD on propagule dispersal. 1.0 –2.0 0.5 Global To assess changes in SST and SSS throughout the global range AEP –0.5 –2.5 of mangrove forests, we used present (2000–2014) and future –1.0 IWP (2090–2100) surface ocean properties from the Bio-ORACLE –1.5 –3.0 –2.0 29,30 RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 database . SSD estimates were derived from these variables –2.5 –3.0 using the UNESCO EOS-80 equation of state polynomial for sea- water . Changes in SST, SSS and SSD (Fig. 1) were calculated for four representative concentration pathways (RCPs) and derived for coastal waters closest to the 583,578 polygon centroids from 32 Fig. 2 | Change in surface ocean properties for coastal waters bordering the 2015 Global Mangrove Watch (GMW) database . After mangrove forests and in the two major mangrove bioregions, the aeP removing duplicates, our dataset contained 10,108 unique man- and iWP, for different r CPs. a–c, Variation in SST (a), SSS (b) and SSD grove occurrence locations, with corresponding present condi- (c) under various RCP scenarios. Grey indicates global distribution tions and predicted future changes in mean SST, SSS and SSD. (n = 10,108), orange denotes AEP (n = 3,190) and green represents IWP Under the low-warming scenario RCP 2.6, mean SST of coastal (n = 6,918). Data for SST and SSS consist of present-day (2000–2014) and mangrove waters is predicted to change by +0.64 (±0.11) °C 29,30 future (2090–2100) marine fields from the Bio-ORACLE database , from and mean SSS by −0.06 (±0.25) practical salinity units (PSU). which SSD data were derived. The cat-eye plots show the distribution Combined, this results in an average change in mean SSD of −0.25 −3 of the data. Median and mean values are indicated with black and white (±0.20) kg m in coastal mangrove waters by the late twenty-first circles, respectively, and the vertical lines represent the interquartile range. century (Supplementary Table 1). These values roughly double under RCP 4.5 (Supplementary Table 2), while under RCP 6.0, a change of +1.69 (±0.14) °C in mean SST, −0.21 (±0.42) PSU in −3 mean SSS and −0.71 (±0.32) kg m in mean SSD is predicted in SSS of −0.30 (±0.74) PSU (−2.01–1.26 PSU) and a correspond- −3 −3 (Supplementary Table 3). Under RCP 8.5, our study predicts a ing change in SSD of −1.17 (±0.56) kg m (−2.53–0.03 kg m ) change in SST of +2.84 (±0.21) °C (range 2.11–4.01 °C), a change (Supplementary Table 4). Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Latitude Latitude Latitude Change in SSS (PSU) Change in SST (°C) –3 Change in SSD (kg m ) Nature Climate ChaNge Articles 45° N 58 West Central Australian Shelf 57 Southwest Australian Shelf 56 Southeast Australian Shelf 55 East Central Australian Shelf 54 Southern New Zealand 30° N 9 53 Northern New Zealand 51 Agulhas 47 Warm Temperate Southwestern Atlantic 44 Galapagos 19 43 Tropical East Pacific 12 39 Central Polynesia 15° N 37 Hawaii 35 Tropical Southwestern Pacific 29 34 Northwest Australian Shelf 13 33 Northeast Australian Shelf 32 Sahul Shelf 0° 44 31 Eastern Coral Triangle 30 Western Coral Triangle 29 Tropical Northwestern Pacific 14 27 28 South Kuroshio 27 Java Transitional 15° S 26 Sunda Shelf 25 South China Sea 24 Andaman 34 33 23 Bay of Bengal 21 West and South Indian Shelf 30° S 47 20 Western Indian Ocean 19 Somali/Arabian 51 57 18 Red Sea and Gulf of Aden 17 Gulf of Guinea 16 West African Transition 14 Tropical Southwestern Atlantic 45° S 13 North Brazil Shelf 12 Tropical Northwestern Atlantic AEP IWP 11 Warm Temperate Northeast Pacific 9 Warm Temperate Northwest Pacific 6 Warm Temperate Northwest Atlantic 60° S 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E Longitude b c 4.5 1.5 1.0 4.0 0.5 3.5 –0.5 3.0 –1.0 –1.5 2.5 –2.0 SST SSS 2.0 –2.5 Longitude Longitude –0.5 –1.0 –1.5 –2.0 –2.5 SSD AEP IWP –3.0 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E Longitude Fig. 3 | global spatial variability in SST, SSS and SSD for coastal waters bordering mangrove forests under r CP 8.5. a, Global map showing the provinces (colour code and numbers) from the MEOW database used to investigate spatial patterns in mangrove coastal ocean water changes by 2100. b–d, Longitudinal gradient of the change in SST (b), SSS (c) and SSD (d) under RCP 8.5 in the AEP and the IWP mangrove bioregions; circles are coloured according to the MEOW province in which respective mangrove sites are located. Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E –3 Latitude Change in SSD (kg m ) Change in mean SST (°C) Change in mean SSS (PSU) Nature Climate ChaNge Articles a b 1,100 1,060 Present SSD 1,020 Ocean warming and freshening Future SSD Time Propagule Sink Ocean surface Present SSD Future SSD (RCP 8.5) Propagule density Horizontal floater Vertical floater Present ocean Time Sink Ocean Ocean surface surface Future ocean Time Fig. 4 | Potential effect of future declines in SSD on mangrove propagule dispersal. a, Range of reported propagule density values for wide-ranging mangrove species and present and future range of SSD for coastal waters along the range of those mangrove species. Mangrove propagule data are extracted from the literature (Supplementary Table 5). H. lit, Heritiera littoralis; X. gra, Xylocarpus granatum; A. ger, Avicennia germinans; A. mar, Avicennia marina; B. gym, Bruguiera gymnorrhiza; C. tag, Ceriops tagal; R. man, Rhizophora mangle; R. muc, Rhizophora mucronata. Bottom part adapted from ref. . b, Conceptual figure of the potential effects of ocean warming and freshening on mangrove propagule dispersal. Ocean warming and freshening drive changes in SSD and may reduce the timeframe for opportunistic colonization. For a propagule with a specific density and floating profile under present surface ocean conditions, reduced SSD of coastal and open-ocean waters may reduce floatation time (shaded area) and hence, reduce the proportion of long-distance dispersers. For simplicity, the density of propagules is assumed to increase linearly over time, although the actual increase may be nonlinear. Spatial variability in predicted surface ocean property changes (±0.57) PSU in the AEP and −0.68 (±0.44) PSU in the IWP region. was examined by considering the two major mangrove bioregions The maximum decrease in mean SSS (−2.01 PSU) is predicted for (AEP and IWP) (Fig. 2) and using the Marine Ecoregions of the the Gulf of Guinea in the AEP bioregion (Fig. 3c and Supplementary World (MEOW) biogeographic classification (Fig. 3). Both the Table 4). Within the IWP, the Western Indian Ocean region shows range and changes in mean SST were comparable for the AEP and little or no changes in SSS, which contrasts with the pronounced IWP mangrove bioregions, for all respective RCP scenarios (Fig. freshening trends predicted in the eastern part of this ocean basin 2a and Supplementary Tables 1–4). Under RCP 8.5, mean SST in and the Tropical West Pacific (Figs. 1b and 3c). Increased freshening both mangrove bioregions is predicted to warm ~2.8 °C by 2100, is predicted in the Bay of Bengal (SSS change: −1.17 ± 0.43 PSU), which is roughly 4.5 times the predicted increase in mean SST the Sunda Shelf (SSS change: −1.21 ± 0.29 PSU) and the Western under RCP 2.6 (Supplementary Tables 1 and 4). Predictions for Coral Triangle province (mean SSS change: −0.80 ± 0.17 PSU) (Fig. the RCP 8.5 scenario are generally consistent with reported global 3c and Supplementary Table 4). Within the AEP, salinity increases ocean temperature trends and show that the greatest warm- exceed +0.96 PSU in the Tropical Northwestern Atlantic, +0.80 in ing occurs in coastal waters near the Galapagos Islands (change the Warm Temperate Northwest Atlantic and +0.68 in the West in mean SST of 3.92 ± 0.06 °C). Pronounced SST increases are African Transition (Fig. 3c and Supplementary Table 4). The spa- also predicted for Hawaii (change in mean SST of 3.36 ± 0.05 °C), tial heterogeneity in SSS across the global range of mangrove forests the Southeast Australian Shelf (3.30 ± 0.25 °C), Northern and corresponds with observed changes in SSS . Trends in SSD (Fig. Southern New Zealand (3.25 ± 0.07 °C and 3.34 ± 0.02 °C, respec- 3d) strongly track changes in SSS (Fig. 3c) rather than SST. All RCP tively), Warm Temperate Northwest Pacific (3.27 ± 0.16 °C), the scenarios predict an overall decrease in SSD for both mangrove bio- Red Sea and Gulf of Aden (3.24 ± 0.08 °C), Somali/Arabian Coast regions; however, the predicted decrease in SSD in the IWP region (3.23 ± 0.15 °C), South China Sea (3.07 ± 0.10 °C), the Tropical was a factor of 2 (RCP 6.0) and 2.5 (RCP 2.6, RCP 4.5 and RCP 8.5) East Pacific (3.09 ± 0.15 °C) and the Warm Temperate Northwest stronger than in the AEP (Figs. 2 and 3d and Supplementary Atlantic (3.14 ± 0.13 °C) (Fig. 3b and Supplementary Tables 4). Tables 1–4). Predicted SSS changes exhibit an opposite trend in the AEP and Propagule density values from our literature survey range from −3 −3 IWP bioregions, with increased salinity in the AEP and reduced <600 kg m to >1,080 kg m for different mangrove species (Fig. 4 salinity in the IWP under global warming (RCP 2.6–RCP 8.5; Fig. and Supplementary Table 5). The low densities reported for Heritiera 2b); this is reflected in contrasting SSD changes in both mangrove littoralis propagules provide a strong contrast with the near-seawater bioregions (Fig. 2c) and associated with predicted global changes propagule densities reported for Avicennia and members of the in precipitation, with extensions of the rainy season over most Rhizophoraceae (Bruguiera, Rhizophora and Ceriops). Floating char- of the monsoon domains, except for the American monsoon . acteristics of the latter may be particularly sensitive to changes in SSD. Under RCP 8.5, the spatially averaged change in mean SSS is +0.51 To illustrate the potential influence of changing ocean conditions on Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange Propagule density H. lit X. gra A. ger A. mar B. gym C. tag R. man R. muc –3 Density (kg m ) Depth Depth Density Nature Climate ChaNge Articles a b –3 Present RCP 8.5 kg m 30° N 30° N 1,022 15° N 15° N 1,021 0° 0° 1,020 15 °S 15 °S –3 –3 ≤1,020 kg m ≤1,020 kg m 1,019 30 °S AEP IWP 30 °S AEP IWP 1,018 1,017 Longitude Longitude 1,016 c d 1,015 30° N 30° N 15° N 15° N 1,014 0° 0° 15 °S 15 °S –3 –3 ≤1,022 kg m ≤1,022 kg m 30 °S 30 °S AEP IWP AEP IWP Longitude Longitude Present RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 1,018 1,019 1,020 1,021 1,022 1,023 1,024 1,025 1,026 1,027 1,028 –3 SSD (kg m ) RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 1,018 1,019 1,020 1,021 1,022 1,023 1,024 1,025 1,026 1,027 1,028 –3 SSD (kg m ) −3 −3 Fig. 5 | Future changes in SSD. a–d, Spatial extent of coastal and open-ocean surface waters with a density ≤1,020 kg m (a,b) and 1,022 kg m (c,d), for present (2000–2014) (a,c) and future (2090–2100; RCP 8.5) (b,d) scenarios. Data are shown for surface ocean waters within the global latitudinal range of mangrove forests (between 32° N and 38° S). The two density thresholds considered are within the range of densities at which mangrove propagule buoyancy and floating orientation of several mangrove genera change, as reported in available literature. Black dots along the coast represent the global 32 −3 mangrove extent from the 2015 GMW dataset . Magenta-coloured circles represent SSD values <1,014 kg m . e,f, Ocean area with a density less than or equal to SSD (e) and future changes in the spatial extent of these regions (f) for different RCP scenarios. mangrove propagule dispersal, we considered threshold water density Our study shows changes in the physical and chemical properties −3 values (1,020 and 1,022 kg m ) that are within the range where elon- of coastal mangrove waters by the end of the twenty-first century gated propagules of important mangrove genera tend to change float- that could affect the distribution of propagules from widespread ing orientation (Fig. 4a). More specifically, we determined the ocean mangrove genera (Avicennia, Bruguiera, Ceriops and Rhizophora) surface area with an SSD below or equal to these thresholds under and probably more so within the IWP region, the primary hotspot different climate change scenarios (Fig. 5). Under RCP 8.5, the ocean of mangrove diversity, compared to the AEP. Propagules from surface covered by mangrove coastal waters (coastal waters border- these genera have densities that are close to that of seawater and −3 ing present mangrove forests) with a density ≤1,020 kg m increases experimental evidence shows that propagules in species of these 36–39 ~27% by 2100, notably more so in the IWP (~37%) than in the AEP genera typically become denser as they age . For mangroves in −3 (~6%) (Supplementary Table 6). A threshold of 1,022 kg m results large parts of the IWP, as well as the Gulf of Guinea in the AEP, in increases of roughly +11% (global), +12% (IWP) and +8% (AEP) declines in SSD could therefore promote local sinking rates and (Supplementary Table 7). Similar spatial patterns are observed for reduce the probability of successful long-distance dispersal due to open-ocean waters within the global latitudinal range of mangroves earlier propagule sinking before reaching suitable establishment (Fig. 5 and Supplementary Figs. 1 and 2). zones (Fig. 4). Indeed, future changes in surface ocean proper- Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 180° W 150° W 120° W 90° W 60° W 180° W 30° W 150° W 0° 120° W 30° E 90° W 60° E 60° W 90° E 30° W 120° E 0° 150° E 30° E 180° E 60° E 90° E 180° W 120° E 150° W 150° E 120° W 180° E 90° W 60° W 180° W 30° W 150° W 0° 120° W 30° E 90° W 60° E 60° W 90° E 30° W 120° E 0° 150° E 30° E 180° E 60° E 90° E 120° E 150° E 180° E Latitude Latitude ∆ area (%) Cumulative area (%) Latitude Latitude Nature Climate ChaNge Articles ties may impact dispersal differently at different scales. More spe- occur more frequently in the future. While our results suggest cifically, short-distance dispersal may be promoted and successful changes in dispersal patterns following climate-driven changes long-distance dispersal reduced since (1) freshening of local waters in coastal and open-ocean surface-water properties, the effects near the propagule release site and associated sinking can reduce also depend on adaptive capacity. Under current ocean condi- the fraction of propagules reaching open-ocean waters; and (2) tions, propagule densities that are just slightly lower than that of older propagules that have travelled longer distances are at risk of seawater are probably adaptive, since this ensures that they sink offshore sinking due to the combined effect of increased propagule in favourable coastal environments, that is, along coastal zones 36–39 density during transport and fresher coastal waters; as such, where SSS and SSD are lower due to freshwater influx from riv- increased ocean freshening and warming and resulting changes in ers. Yet, in future oceans the spatial extent of these lower-density SSD are likely to reduce the tail of the dispersal kernel—which cap- waters may expand further offshore, altering buoyancy character- tures long-distance dispersal events. Upon sinking, a propagule is at istics and the spatial distribution of propagules. The demographic least temporarily eliminated from the dispersing cohort and prone costs associated with ending up in unsuitable habitat (for exam- to mortality associated with stranding in unsuitable conditions. ple, due to offshore sinking) can get balanced when populations Empirical evidence of mangrove distributions limited by sinking of evolve to have lower propagule densities. As such, there is a need propagules under increasingly fresher conditions in estuaries was for quantitative data for inter- and intra-population variation in shown for mangroves along the Nakara River in Japan . Such effects the critical SSD at which mangrove propagules sink, which could are of considerable importance, since some of the most pronounced reflect underlying genetic variation in propagule density that changes in SSS and SSD are near major river outlets where many can fuel evolutionary change. Overall, our results suggest that of the most extensive mangrove areas in the world are found , such we may be entering a density-induced transition phase as part as the Ganges–Brahmaputra delta. In contrast to the wide-ranging of the Anthropocene, highlighting the importance of consider- mangrove genera Avicennia, Bruguiera, Ceriops and Rhizophora, ing future ocean property changes in evaluating the impacts of mangrove species such as H. littoralis and Xylocarpus granatum are climate change on mangrove ecosystems. Such information will unlikely to be affected by these ocean changes as their propagules complement knowledge on the effects of other impacting factors possess very low densities . and is important for predicting how altered environmental con- An additional level of complexity is that the floating orienta- ditions will affect these sensitive forests. Finally, while we con- tion of propagules from several widespread mangrove genera sidered mangroves as a model system in this study, our findings (Rhizophora and Ceriops) could move between horizontal and may be relevant also for other coastal taxa producing sea-drifted 38,40,41 49 vertical due to small changes in density . Changes in floating propagules, such as seagrasses and coastal strand communities orientation have been associated with the replacement of air in (Terminalia catappa, Barringtonia asiatica, Thespesia populnea, intercellular tissue by water via lenticels ; however, while the exact Hibiscus tiliaceus, Pisonia grandis, Pandanus spp. and so on). anatomical and physiological mechanisms underlying changes in mangrove propagule buoyancy are not yet fully understood, experi- Online content mental and modelling studies showed that these changes in floating Any methods, additional references, Nature Research report- orientation can strongly alter dispersal trajectories at the landscape ing summaries, source data, extended data, supplementary infor- 2 3 3 5 (10 –10 m), regional (10 –10 m), as well as the biogeographic scale mation, acknowledgements, peer review information; details of 5 7 (10 –10 m), via the relative effects of ocean and wind forces on author contributions and competing interests; and statements of 42–44 propagule transport . data and code availability are available at https://doi.org/10.1038/ Besides effects of changes in SSD, propagule dispersal may also s41558-022-01391-9. be directly impacted by SSS or SST. In Avicennia marina, propa- Received: 8 October 2021; Accepted: 13 May 2022; gule sinking has been associated with the shedding of the pericarp Published online: 30 June 2022 (Steinke, 1975, as cited in ref. ) and the time required for peri- carp shedding and the separation of the cotyledons increases with r eferences increasing salinity (Downton, 1982, as cited in ref. ). As such, 1. Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves (Earthscan future ocean freshening might decrease floating periods and poten- and James & James, 2010). tially dispersal distances in this species. Increases in SSS might also 2. Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. result in higher propagule mortality rates and lower germination Ecol. Monogr. 81, 169–193 (2011). success , whereas lower SSS could reduce propagule viability by 3. Ellison, J. Vulnerability assessment of mangroves to climate change and sea-level rise impacts. Wetl. Ecol. Manag. 23, 115–137 (2015). increasing the incidence of fungal infestation and rotting . Finally, 4. Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate projected increases in SST may facilitate mangrove expansion to change on mangrove ecosystems: a region by region overview. Ecosyst. Health higher latitudes in some regions by reducing the negative effect of Sust. 2, e01211 (2016). colder oceanic waters on propagule viability . For example, an ear- 5. Saintilan, N. et al. Mangrove expansion and salt marsh decline at mangrove lier study on the western South Atlantic latitudinal mangrove range poleward limits. Glob. Change Biol. 20, 147–157 (2014). 6. Cavanaugh, K. C. et al. Poleward expansion of mangroves is a threshold limit reported that temperatures <20 °C may limit the viability of response to decreased frequency of extreme cold events. Proc. Natl Acad. Sci. mangrove propagules during their dispersal along this coast and USA 111, 723–727 (2014). during subsequent establishment . However, since empirical data 7. Kelleway, J. J. et al. Review of the ecosystem service implications of mangrove on potential direct effects of SSS and SST on mangrove propagule encroachment into salt marshes. Glob. Change Biol. 23, 3967–3983 (2017). 8. Pecl, T. P. et al. Biodiversity redistribution under climate change: impacts on dispersal are deficient, effects of these variables require further ecosystems and human well-being. Science 355, 1389 (2017). investigation. 9. Osland, M. J. et al. Climatic controls on the global distribution, abundance, It is important to note that changes indicated by our study are and species richness of mangrove forests. Ecol. Monogr. 87, 341–359 (2017). based on changes in time-mean surface ocean properties and that 10. o Th m, B. G. Mangrove ecology and deltaic geomorphology: Tabasco, Mexico. the actual variability in SSD around these mean values could be J. Ecol. 55, 301–343 (1967). 11. Duke, N. C., Ball, M. C. & Ellison, J. C. Factors influencing biodiversity higher. Since mangroves thrive in a broad range of coastal set- and distributional gradients in mangroves. Glob. Ecol. Biogeogr. Lett. 7, tings, including estuaries, deltas, lagoons and open coast, their 27–47 (1998). propagules already encounter a wide range of water densities 12. Raw, J. L., Godbold, J. A., van Niekerk, L. & Adams, J. B. Drivers of mangrove today but our findings clearly illustrate that for important man- distribution at the high-energy, wave-dominated, southern African range grove regions worldwide exposure to lower-density waters will limit. Estuar. Coast. Shelf Sci. 226, 106296 (2019). Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 690 Nature Climate ChaNge Articles 13. Van der Stocken, T. et al. A general framework for propagule dispersal in 37. Robert, E. M. R. et al. Viviparous mangrove propagules of Ceriops tagal mangroves. Biol. Rev. 94, 1547–1575 (2019). and Rhizophora mucronata, where both Rhizophoraceae show different 14. Cavanaugh, K. C. et al. Climate-driven regime shifts in a mangrove–salt dispersal and establishment strategies. J. Exp. Mar. Bio. Ecol. 468, 45–54 marsh ecotone over the past 250 years. Proc. Natl Acad. Sci. USA 116, (2015). 21602–21608 (2019). 38. Tonné, N., Beeckman, H., Robert, E. M. R. & Koedam, N. Towards an 15. Woodroe ff , C. D. & Grindrod, J. Mangrove biogeography: the role unknown fate: the floating behaviour of recently abscised propagules of Quaternary environmental and sea-level change. J. Biogeogr. 18, from wide ranging Rhizophoraceae mangrove species. Aquat. Bot. 140, 23–33 (2017). 479–492 (1991). 16. Hoppe-Speer, S. C. L., Adams, J. B. & Rajkaran, A. Mangrove expansion and 39. Wang, W., Li, X. & Wang, M. Propagule dispersal determines mangrove population structure at a planted site, East London, South Africa. South. For. zonation at intertidal and estuarine scales. Forests 10, 245 (2019). 40. Rabinowitz, D. Dispersal properties of mangrove propagules. Biotropica 10, 77, 131–139 (2015). 47–57 (1978). 17. Van der Stocken, T. et al. Global-scale dispersal and connectivity in mangroves. Proc. Natl Acad. Sci. USA 116, 915–922 (2019). 41. Clarke, P. J., Kerrigan, R. A. & Westphal, C. J. Dispersal potential and early growth in 14 tropical mangroves: do early life history traits correlate with 18. Guppy, H. B. Observations of a Naturalist in the Pacic B fi etween 1896 and patterns of adult distribution? J. Ecol. 89, 648–659 (2001). 1899: Plant Dispersal Vol. II (Macmillan and Co., 1906). 42. Van der Stocken, T. et al. The role of wind in hydrochorous mangrove 19. Bindo, N. L. et a ff l. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 447–587 (WMO, 2019). propagule dispersal. Biogeosciences 10, 3635–3647 (2013). 43. Van der Stocken, T. et al. Interaction between water and wind as a driver of 20. Johnson, G. C. & Wijffels, S. E. Ocean density change contributions to sea passive dispersal in mangroves. PLoS ONE 10, e0121593 (2015). level rise. Oceanography 24, 112–121 (2011). 44. Van der Stocken, T. & Menemenlis, D. Modelling mangrove propagule 21. Alongi, D. M. The impact of climate change on mangrove forests. dispersal trajectories using high-resolution estimates of ocean surface winds Curr. Clim. Change Rep. 1, 30–39 (2015). and currents. Biotropica 49, 472–481 (2017). 22. Lovelock, E. C. et al. The vulnerability of Indo-Pacific mangrove forests to 45. Hutchings, P. & Saenger, P. e Ec Th ology of Mangroves (Univ. of Queensland sea-level rise. Nature 526, 559–563 (2015). Press, 1987). 23. Osland, M. J. et al. Mangrove forests in a rapidly changing world: global 46. Alleman, L. K. & Hester, M. W. Reproductive ecology of black mangrove change impacts and conservation opportunities along the Gulf of Mexico (Avicennia germinans) along the Louisiana coast: propagule production coast. Estuar. Coast. Shelf Sci. 214, 120–140 (2018). cycles, dispersal limitations, and establishment elevations. Estuar. Coast. 34, 24. O’Connor, M. I. et al. Temperature control of larval dispersal and the 1068–1077 (2011). implications for marine ecology, evolution, and conservation. Proc. Natl Acad. 47. Hickey, M. H. et al. Is climate change shifting the poleward limit of Sci. USA 104, 1266–1271 (2007). mangroves?. Estuar. Coast 40, 1215–1226 (2017). 25. Wilson, L. J. et al. Climate-driven changes to ocean circulation and their 48. Soares, M. L. G., Estrada, G. C. D. E., Fernandez, V. & Tognella, M. M. P. inferred impacts on marine dispersal patterns. Glob. Ecol. Evol. 25, 923–939 Southern limit of the western South Atlantic mangroves: assessment of the (2016). potential effects of global warming from a biogeographical perspective. 26. Poloczanska, E. S. et al. Responses of marine organisms to climate change Estuar. Coast. Shelf Sci. 101, 44–53 (2012). across oceans. Front. Mar. Sci. 3, 62 (2016). 49. Repolho, T. et al. Seagrass ecophysiological performance under ocean 27. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. warming and acidification. Sci. Rep. 7, 41443 (2017). Greater vulnerability to warming of marine versus terrestrial ectotherms. 50. Allen, M. et al. Raincloud plots: a multi-platform tool for robust data Nature 569, 108–111 (2019). visualization (version 2). Wellcome Open Res. 4, 63 (2021). 28. Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of 51. Van der Stocken, T., Vanschoenwinkel, B., De Ryck, D. & Koedam, N. Caught coastal and shelf areas. BioScience 57, 573–583 (2007). in transit: offshore interception of seafaring propagules from seven mangrove 29. Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species. Ecosphere 9, e02208 (2018). species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012). 30. Assis, J. et al. Bio-ORACLE v2.0: extending marine data layers for bioclimatic Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2017). published maps and institutional affiliations. 31. Fofono, P ff . & Millard, R. C. Algorithms for computation of fundamental properties of seawater. UNESCO Techn. Papers in Mar. Sci. 44 (UNESCO, 1983). Open Access This article is licensed under a Creative Commons 32. Bunting, P. et al. The Global Mangrove Watch—a new 2010 global baseline of Attribution 4.0 International License, which permits use, sharing, adap- mangrove extent. Remote Sens. 10, 1669 (2018). tation, distribution and reproduction in any medium or format, as long 33. Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. as you give appropriate credit to the original author(s) and the source, provide a link to Nat. Clim. Change 8, 499–503 (2018). the Creative Commons license, and indicate if changes were made. The images or other 34. Moon, S. & Ha, K. J. Future changes in monsoon duration and precipitation third party material in this article are included in the article’s Creative Commons license, using CMIP6. Clim. Atmos. Sci. 3, 45 (2020). unless indicated otherwise in a credit line to the material. If material is not included in 35. Durack, P. J. & Wijffels, S. E. Fifty-year trends in global ocean salinities and the article’s Creative Commons license and your intended use is not permitted by statu- their relationship to broad-scale warming. J. Clim. 23, 4342–4362 (2010). tory regulation or exceeds the permitted use, you will need to obtain permission directly 36. Kadoya, T. & Inoue, T. Spatio-temporal pattern of specific gravity of from the copyright holder. To view a copy of this license, visit http://creativecommons. mangrove diaspore: implications for upstream dispersal. Ecography 38, org/licenses/by/4.0/. 472–479 (2015). © The Author(s) 2022 Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 691 Nature Climate ChaNge Articles correspond with a 2015 GMW occurrence point (the centroids of each 2015 GMW Methods polygon feature). The resulting coordinates for A. germinans (n = 2,964), A. marina Ocean data. In this study we examine changes in SST, SSS and SSD over the next (n = 6,419), B. gymnorrhiza (n = 6,040), C. tagal (n = 5,653), H. littoralis (n = 5,100), century. SSD has been largely overlooked in studies that examine the response of R. mangle (n = 2,345), R. mucronata (n = 5,348) and X. granatum (n = 5,891) can be marine organisms to climate-driven ocean changes, despite its potential influence viewed as species-specific GMW data. Each of these computations was performed on the dispersal patterns and colonization potential of passive propagules with using the MATLAB 2020b programming software. near-seawater densities. We calculated changes in mean SST and SSS over the next century using present (2000–2014) and future (2090–2100) data from 29,30 Spatial analyses. Spatial patterns in projected ocean property changes over the Bio-ORACLE , a database of GIS rasters providing geophysical, biotic and next century were explored by considering bioregion- and province-levels to subset environmental data for surface and benthic marine realms (https://www.bio-oracle. our GMW 2015 data. Bioregion subsetting consists of coastal Bio-ORACLE cells org/). These data consist of uniformly constructed rasters provided at a spatial in the AEP (between −180° and 19° longitude) and IWP (between 19° and 180° resolution of 5 arcmin (~0.08° or ~9.2 km at the Equator). Present layers were longitude) bioregion, whereas the province-level waters consist of a subsetting generated with climate data describing monthly means for the period 2000–2014, using the provinces provided by the MEOW dataset . acquired from preprocessed global ocean ARMOR reanalyses that combine remotely sensed and in situ observations, while future data were produced by Reporting summary. Further information on research design is available in the averaging output from atmosphere–ocean general circulation models provided Nature Research Reporting Summary linked to this article. by the Coupled Model Intercomparison Project Phase 5 (CMIP5) . We are aware of the limitations of remotely sensed coastal ocean data and climate change projections of fine-scale near-shore circulation patterns, which may deviate Data availability from in situ observations . However, using time-averaged SST and SSS data and All the datasets used for analyses during this study are publicly available and can considering spatially averaged patterns ensures that we are capturing general trends 29,30 be accessed at: https://www.bio-oracle.org/ (marine data layers ); https://data. in coastal ocean changes. SSD was derived from these variables using the UNESCO unep-wcmc.org/datasets/45 (global mangrove extent ); https://www.iucnredlist. EOS-80 equation of state polynomial for seawater . Changes were calculated for org/ (geographic range of the species considered); https://data.unep-wcmc.org/ four RCP scenarios: RCP 2.6 (490 CO e before 2100 and then decline), RCP 4.5 28 2 datasets/38 (provinces from the MEOW database ). All remaining data that (650 CO e at stabilization aer 2100), R ft CP 6.0 (850 CO e at stabilization aer 2100) ft 2 2 support the findings of this study are freely available at https://doi.org/10.5061/ and RCP 8.5 (>1,370 CO e in 2100) (see ref. for more details about RCPs). dryad.66t1g1k4c (ref. ). Global mangrove range data. The GMW provides high-resolution (0.8 arcsec or Code availability ~25 m) global mangrove extent baseline maps based on Landsat sensor spectral Computations of SSD were conducted using the UNESCO EOS-80 equation of composite data and Advanced Land Observing Satellite (ALOS) Phased Arrayed state polynomial for seawater (sw_dens.m from the MATLAB seawater package) . L-band Synthetic Aperture Radar (PALSAR) data for the years 1996, 2007, 2008, Other MATLAB codes used during the current study are available from the 2009, 2010, 2015 and 2016 (ref. ). For this study, we used the 2015 mangrove corresponding author upon reasonable request. extent baseline map since it most closely matches the end year of the period (2000–2014) considered for generating the Bio-ORACLE raster data of present-day marine environmental conditions . Using the QGIS 3.10.10 software, centroids r eferences were computed for each GMW 2015 polygon (n = 583,578) and considered as our 52. Smit, J. S. et al. A coastal seawater temperature dataset for biogeographic global mangrove occurrence dataset. Global mangrove occurrence points were studies: large biases between in situ and remotely-sensed data sets around the assigned to the nearest grid cell (Euclidean distance) in the Bio-ORACLE fields. coast of South Africa. PLOS ONE 8, e81944 (2013). Since multiple occurrence points might have been assigned to the same grid 53. Moss, R. et al. The next generation of scenarios for climate change research cell, duplicates were removed, resulting in unique grid cell records for the GMW and assessment. Nature 463, 747–756 (2010). 2015 mangrove range (n = 10,108). Each of these steps was performed using the 54. Quisthoudt, K. et al. Disentangling the effects of global climate and regional MATLAB 2020b programming software. land-use change on the current and future distribution of mangroves in South Africa. Biodivers. Conserv. 22, 1369–1390 (2013). Mangrove species-specific data. A literature survey was conducted to collect data 55. Cavanaugh, K. C. et al. Sensitivity of mangrove range limits to climate on mangrove propagule density values that were interpreted against the range of variability. Glob. Ecol. Biogeogr. 27, 925–935 (2018). present and future SSD values within the range of Avicennia germinans (AEP), 56. McKee, L. K. Seedling recruitment patterns in a Belizean mangrove forest: Avicennia marina (IWP), Bruguiera gymnorrhiza (IWP), Ceriops tagal (IWP), effects of establishment ability and physico-chemical factors. Oecologia 101, Heritiera littoralis (IWP), Rhizophora mangle (AEP), Rhizophora mucronata 448–460 (1995). (IWP) and Xylocarpus granatum (IWP). This selection of mangrove species in our 57. Van der Stocken, T. et al. Future changes in mangrove coastal water literature survey is based on the availability of propagule density data. Such data are properties. Dryad https://doi.org/10.5061/dryad.66t1g1k4c (2022). surprisingly limited but nevertheless allow for a first assessment of species-specific differences in the sensitivity to SSD changes. Additionally, propagules of the a cknowledgements selected species are representative for the variety of propagule morphotypes T.V.d.S. is supported by the EU Horizon 2020 Framework Programme for Research and found among mangrove species globally and include the most widely distributed Innovation under the Marie Skłodowska-Curie actions Individual Fellowship (MSCA-IF) mangrove species (Avicennia spp. and Rhizophora spp.) for which distributional with grant agreement no. 896888 (GLOMAC). K.C.C. is supported by the NASA Land 5,54,55 changes have been reported and predicted . In the case that specific gravity Cover and Land Use Change (LCLUC) Program with grant no. 80NSSC21K0296. 36,56 −3 values were reported , we multiplied these values by 999.97 kg m (the density of water at 4 °C, which is the standard density used to compute specific gravity) to a uthor contributions obtain density values. T.V.d.S. designed the study, compiled and analysed the data, produced the figures, The 2015 GMW database used for our global analysis does not contain interpreted the results and wrote the manuscript with contributions and suggestions coordinates for species-specific mangrove distributions. Hence, data for different from B.V., D.C., K.C. and N.K. T.V.d.S and D.C. computed sea surface density fields. B.V. species were generated using vector layers obtained from the IUCN Red List provided extensive feedback on various drafts and revisions of the manuscript. website (https://www.iucnredlist.org/). The IUCN data for each species consist of a single polygon feature that was intersected with a 1:10 m global coastline downloaded from the Natural Earth database (https://www.naturalearthdata.com/) Competing interests in QGIS 3.10.10. Before this intersection, we buffered the species distribution The authors declare no competing interests. polygons using a buffer distance equal to the spatial resolution of the Bio-ORACLE marine data layers (0.5 arcmin) to avoid losing data where the original IUCN species distribution polygons did not overlap with the global coastline data. a dditional information Point features were generated at 1 km distance for each line feature representing Supplementary information The online version contains supplementary material a species range, using the QChainage plugin in QGIS 3.10.10 and longitude and available at https://doi.org/10.1038/s41558-022-01391-9. latitude information was added to each point for A. germinans (n = 4,610), A. Correspondence and requests for materials should be addressed to marina (n = 11,231), B. gymnorrhiza (n = 9,665), C. tagal (n = 9,134), H. littoralis Tom Van der Stocken. (n = 7,984), R. mangle (n = 3,431), R. mucronata (n = 9,519) and X. granatum Peer review information Nature Climate Change thanks Ken Krauss, Erik Yando and the (n = 8,890). Since the IUCN data show mangrove presence along vast stretches of other, anonymous, reviewer(s) for their contribution to the peer review of this work. coast (for example, Somalia and East Madagascar) where mangroves are absent in the 2015 GMW data, we extracted ocean data for the IUCN coordinates that Reprints and permissions information is available at www.nature.com/reprints. Na Ture CliMa Te ChaNge | www.nature.com/natureclimatechange http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Climate Change Springer Journals

Mangrove dispersal disrupted by projected changes in global seawater density

Loading next page...
 
/lp/springer-journals/mangrove-dispersal-disrupted-by-projected-changes-in-global-seawater-jbBSq2UO0m

References (60)

Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2022
ISSN
1758-678X
eISSN
1758-6798
DOI
10.1038/s41558-022-01391-9
Publisher site
See Article on Publisher Site

Abstract

Articles https://doi.org/10.1038/s41558-022-01391-9 Mangrove dispersal disrupted by projected changes in global seawater density 1,2 1 2,3 4  ✉ Tom Van der Stocken    , Bram Vanschoenwinkel , Dustin Carroll    , Kyle C. Cavanaugh and Nico Koedam The degree to which the distribution of mangrove forests will be impacted by climate change depends on the dispersal and establishment of sea-faring propagules, which drive forest rejuvenation, gene flow and range expansion. Climate change affects sea surface density via changes in temperature and salinity. However, these changes have not been mapped and it remains unclear how these factors may impact mangrove propagule dispersal. Here, we provide evidence for strong warming of coastal mangrove waters and elevated geographic variability in surface ocean density under representative concentration pathway RCP 8.5 by 2100. The largest changes will occur in the Indo West Pacific region, the primary hotspot of mangrove diversity. By comparing propagule densities to predicted sea surface density, we assessed potential effects on mangrove propagule disper- sal. In the future, a warmer and fresher ocean is likely to alter dispersal trajectories of mangrove propagules and increase rates of sinking in unsuitable offshore locations, potentially reducing the resilience of mangrove forests. angrove forests thrive along tropical and subtropi- and open-ocean water properties. The biogeographic implications cal shorelines and their distribution extends to warm of such density differences were recognized more than a century 1 18 Mtemperate regions . They are globally recognized for ago by Henry Brougham Guppy, who discussed ‘the far-reaching the valuable ecosystem services they provide but are expected influence on plant-distribution and on plant-development that the to be substantially influenced by climate change-related physical relation between the specific weight of seeds and fruits and the den- 3,4 processes in the future . Under warming winter temperatures, sity of sea-water must possess’. 5,6 poleward expansion is predicted for mangroves , with poten- Since the time of Guppy’s early observations, climate change tial implications for ecosystem structure and functioning, as from human activities has driven pronounced changes in ocean 7,8 well as human livelihoods and well-being . The global distribu- temperature and salinity, with further changes predicted through- tion, abundance and species richness of mangroves is governed out the twenty-first century . Ocean density is a nonlinear func- by a broad range of biotic and environmental factors, includ- tion of temperature, salinity and pressure ; therefore, these changes ing temperature and precipitation and diverse geomorphologi- may influence dispersal patterns of mangrove propagules by alter- 10 18 cal and hydrological gradients . Climate and aspects related to ing their buoyancy and floating orientation. As Guppy noted , coastal geography (for example, floodplain area) determine the ‘[for] plants whose seeds or fruits are not much lighter than sea- 11,12 availability of suitable habitat for establishment . However, the water […] the effect of increased density of the water is to extend potential for mangroves to track changing environmental condi- the flotation period’ or ‘to increase the number that floated for a tions and expand their distributions ultimately depends on dis- given period’. Guppy also reported that the seedlings of the wide- 11,13 persal . The importance of dispersal in controlling mangrove spread mangrove genera Rhizophora and Bruguiera present excep- distributions has been demonstrated by mangrove distributional tional examples of propagules with densities somewhere between 14 18 responses to historical climate variability , past mangrove (re) seawater and freshwater . Previous studies of the impacts of climate colonization of oceanic islands and from the long-term survival change on mangroves have focused on factors such as sea level rise, of mangrove seedlings planted beyond natural range limits . As altered precipitation regimes and increasing temperature and storm 4,21–23 such, quantifying changes in the factors that influence disper- frequency but the potential impact of climate-driven changes sal is important for understanding climate-driven distributional in seawater properties on mangroves has not yet been examined. responses of mangroves under future climate conditions. This is somewhat surprising, as the ocean is the primary disper- In mangroves, dispersal is accomplished by buoyant seeds and sal medium of this ‘sea-faring’ coastal vegetation and dispersal is fruits (hereafter referred to as ‘propagules’). In combination with a key process that governs a species’ response to climate change by prevailing currents, the spatial scale of this process, ranging from changing its geographical range. This knowledge gap contrasts with local retention to transoceanic dispersal over thousands of kilome- recent efforts to expose links between climate change and dispersal 13 17 tres , is determined by propagule buoyancy , that is, the density in other ecologically important marine taxa such as zooplankton 24–27 difference between that of propagules and the surrounding water. and fish species . Hence, the course of dispersal trajectories for propagules from these In this study, we investigate predicted changes in sea surface species depends on the interaction between spatiotemporal changes temperature (SST), sea surface salinity (SSS) and sea surface den- in both propagule density and that of the surrounding water, ren- sity (SSD) for coastal waters bordering mangrove forests (hereaf- dering this process sensitive to climate-driven changes in coastal ter referred to as ‘coastal mangrove waters’), over the next century. 1 2 Department of Biology, Vrije Universiteit Brussel, Brussels, Belgium. Earth Science Section, Jet Propulsion Laboratory, California Institute of Technology, 3 4 Pasadena, CA, USA. Moss Landing Marine Laboratories, San José State University, Moss Landing, CA, USA. Department of Geography, University of California, Los Angeles, CA, USA. e-mail: Tom.Van.Der.Stocken@vub.be Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 685 –3 °C kg m PSU Nature Climate ChaNge Articles 45° N 4 30° N 3.5 15° N 0° 3 15° S 2.5 30° S SST AEP IWP 45° S 2 45° N 1 30° N 15° N 0 0° 15° S –1 30° S SSS AEP IWP 45° S –2 45° N 0 30° N 15° N –1 0° 15° S –2 30° S SSD AEP IWP 45° S –3 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E Longitude Fig. 1 | global map showing the change in sea surface variables across mangrove bioregions under r CP 8.5. a–c, Change in SST (a), SSS (b) and SSD (c). 29,30 Changes in SST and SSS are based on present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database , from which SSD data were derived. The vertical line (19° E) separates the two major mangrove bioregions: the AEP and IWP. Using a biogeographic classification system for coastal and shelf a c 5.0 1.0 areas , we examine spatiotemporal changes in these surface 4.5 SST SSD 4.0 ocean properties, with a particular focus on the world’s two major 0.5 3.5 3.0 mangrove diversity hotspots: (1) the Atlantic East Pacific (AEP) 2.5 2.0 region, including all of the Americas, West and Central Africa 1.5 –0.5 and (2) the Indo West Pacific (IWP) region, extending from East 1.0 0.5 Africa eastwards to the islands of the central Pacific . Finally, we –1.0 synthesize available data on the density of mangrove propagules –1.5 for different mangrove species and explore the potential impact of 2.0 SSS 1.5 climate-driven changes in SSD on propagule dispersal. 1.0 –2.0 0.5 Global To assess changes in SST and SSS throughout the global range AEP –0.5 –2.5 of mangrove forests, we used present (2000–2014) and future –1.0 IWP (2090–2100) surface ocean properties from the Bio-ORACLE –1.5 –3.0 –2.0 29,30 RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 database . SSD estimates were derived from these variables –2.5 –3.0 using the UNESCO EOS-80 equation of state polynomial for sea- water . Changes in SST, SSS and SSD (Fig. 1) were calculated for four representative concentration pathways (RCPs) and derived for coastal waters closest to the 583,578 polygon centroids from 32 Fig. 2 | Change in surface ocean properties for coastal waters bordering the 2015 Global Mangrove Watch (GMW) database . After mangrove forests and in the two major mangrove bioregions, the aeP removing duplicates, our dataset contained 10,108 unique man- and iWP, for different r CPs. a–c, Variation in SST (a), SSS (b) and SSD grove occurrence locations, with corresponding present condi- (c) under various RCP scenarios. Grey indicates global distribution tions and predicted future changes in mean SST, SSS and SSD. (n = 10,108), orange denotes AEP (n = 3,190) and green represents IWP Under the low-warming scenario RCP 2.6, mean SST of coastal (n = 6,918). Data for SST and SSS consist of present-day (2000–2014) and mangrove waters is predicted to change by +0.64 (±0.11) °C 29,30 future (2090–2100) marine fields from the Bio-ORACLE database , from and mean SSS by −0.06 (±0.25) practical salinity units (PSU). which SSD data were derived. The cat-eye plots show the distribution Combined, this results in an average change in mean SSD of −0.25 −3 of the data. Median and mean values are indicated with black and white (±0.20) kg m in coastal mangrove waters by the late twenty-first circles, respectively, and the vertical lines represent the interquartile range. century (Supplementary Table 1). These values roughly double under RCP 4.5 (Supplementary Table 2), while under RCP 6.0, a change of +1.69 (±0.14) °C in mean SST, −0.21 (±0.42) PSU in −3 mean SSS and −0.71 (±0.32) kg m in mean SSD is predicted in SSS of −0.30 (±0.74) PSU (−2.01–1.26 PSU) and a correspond- −3 −3 (Supplementary Table 3). Under RCP 8.5, our study predicts a ing change in SSD of −1.17 (±0.56) kg m (−2.53–0.03 kg m ) change in SST of +2.84 (±0.21) °C (range 2.11–4.01 °C), a change (Supplementary Table 4). Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Latitude Latitude Latitude Change in SSS (PSU) Change in SST (°C) –3 Change in SSD (kg m ) Nature Climate ChaNge Articles 45° N 58 West Central Australian Shelf 57 Southwest Australian Shelf 56 Southeast Australian Shelf 55 East Central Australian Shelf 54 Southern New Zealand 30° N 9 53 Northern New Zealand 51 Agulhas 47 Warm Temperate Southwestern Atlantic 44 Galapagos 19 43 Tropical East Pacific 12 39 Central Polynesia 15° N 37 Hawaii 35 Tropical Southwestern Pacific 29 34 Northwest Australian Shelf 13 33 Northeast Australian Shelf 32 Sahul Shelf 0° 44 31 Eastern Coral Triangle 30 Western Coral Triangle 29 Tropical Northwestern Pacific 14 27 28 South Kuroshio 27 Java Transitional 15° S 26 Sunda Shelf 25 South China Sea 24 Andaman 34 33 23 Bay of Bengal 21 West and South Indian Shelf 30° S 47 20 Western Indian Ocean 19 Somali/Arabian 51 57 18 Red Sea and Gulf of Aden 17 Gulf of Guinea 16 West African Transition 14 Tropical Southwestern Atlantic 45° S 13 North Brazil Shelf 12 Tropical Northwestern Atlantic AEP IWP 11 Warm Temperate Northeast Pacific 9 Warm Temperate Northwest Pacific 6 Warm Temperate Northwest Atlantic 60° S 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E Longitude b c 4.5 1.5 1.0 4.0 0.5 3.5 –0.5 3.0 –1.0 –1.5 2.5 –2.0 SST SSS 2.0 –2.5 Longitude Longitude –0.5 –1.0 –1.5 –2.0 –2.5 SSD AEP IWP –3.0 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E Longitude Fig. 3 | global spatial variability in SST, SSS and SSD for coastal waters bordering mangrove forests under r CP 8.5. a, Global map showing the provinces (colour code and numbers) from the MEOW database used to investigate spatial patterns in mangrove coastal ocean water changes by 2100. b–d, Longitudinal gradient of the change in SST (b), SSS (c) and SSD (d) under RCP 8.5 in the AEP and the IWP mangrove bioregions; circles are coloured according to the MEOW province in which respective mangrove sites are located. Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E 180° W 150° W 120° W 90° W 60° W 30° W 0° 30° E 60° E 90° E 120° E 150° E 180° E –3 Latitude Change in SSD (kg m ) Change in mean SST (°C) Change in mean SSS (PSU) Nature Climate ChaNge Articles a b 1,100 1,060 Present SSD 1,020 Ocean warming and freshening Future SSD Time Propagule Sink Ocean surface Present SSD Future SSD (RCP 8.5) Propagule density Horizontal floater Vertical floater Present ocean Time Sink Ocean Ocean surface surface Future ocean Time Fig. 4 | Potential effect of future declines in SSD on mangrove propagule dispersal. a, Range of reported propagule density values for wide-ranging mangrove species and present and future range of SSD for coastal waters along the range of those mangrove species. Mangrove propagule data are extracted from the literature (Supplementary Table 5). H. lit, Heritiera littoralis; X. gra, Xylocarpus granatum; A. ger, Avicennia germinans; A. mar, Avicennia marina; B. gym, Bruguiera gymnorrhiza; C. tag, Ceriops tagal; R. man, Rhizophora mangle; R. muc, Rhizophora mucronata. Bottom part adapted from ref. . b, Conceptual figure of the potential effects of ocean warming and freshening on mangrove propagule dispersal. Ocean warming and freshening drive changes in SSD and may reduce the timeframe for opportunistic colonization. For a propagule with a specific density and floating profile under present surface ocean conditions, reduced SSD of coastal and open-ocean waters may reduce floatation time (shaded area) and hence, reduce the proportion of long-distance dispersers. For simplicity, the density of propagules is assumed to increase linearly over time, although the actual increase may be nonlinear. Spatial variability in predicted surface ocean property changes (±0.57) PSU in the AEP and −0.68 (±0.44) PSU in the IWP region. was examined by considering the two major mangrove bioregions The maximum decrease in mean SSS (−2.01 PSU) is predicted for (AEP and IWP) (Fig. 2) and using the Marine Ecoregions of the the Gulf of Guinea in the AEP bioregion (Fig. 3c and Supplementary World (MEOW) biogeographic classification (Fig. 3). Both the Table 4). Within the IWP, the Western Indian Ocean region shows range and changes in mean SST were comparable for the AEP and little or no changes in SSS, which contrasts with the pronounced IWP mangrove bioregions, for all respective RCP scenarios (Fig. freshening trends predicted in the eastern part of this ocean basin 2a and Supplementary Tables 1–4). Under RCP 8.5, mean SST in and the Tropical West Pacific (Figs. 1b and 3c). Increased freshening both mangrove bioregions is predicted to warm ~2.8 °C by 2100, is predicted in the Bay of Bengal (SSS change: −1.17 ± 0.43 PSU), which is roughly 4.5 times the predicted increase in mean SST the Sunda Shelf (SSS change: −1.21 ± 0.29 PSU) and the Western under RCP 2.6 (Supplementary Tables 1 and 4). Predictions for Coral Triangle province (mean SSS change: −0.80 ± 0.17 PSU) (Fig. the RCP 8.5 scenario are generally consistent with reported global 3c and Supplementary Table 4). Within the AEP, salinity increases ocean temperature trends and show that the greatest warm- exceed +0.96 PSU in the Tropical Northwestern Atlantic, +0.80 in ing occurs in coastal waters near the Galapagos Islands (change the Warm Temperate Northwest Atlantic and +0.68 in the West in mean SST of 3.92 ± 0.06 °C). Pronounced SST increases are African Transition (Fig. 3c and Supplementary Table 4). The spa- also predicted for Hawaii (change in mean SST of 3.36 ± 0.05 °C), tial heterogeneity in SSS across the global range of mangrove forests the Southeast Australian Shelf (3.30 ± 0.25 °C), Northern and corresponds with observed changes in SSS . Trends in SSD (Fig. Southern New Zealand (3.25 ± 0.07 °C and 3.34 ± 0.02 °C, respec- 3d) strongly track changes in SSS (Fig. 3c) rather than SST. All RCP tively), Warm Temperate Northwest Pacific (3.27 ± 0.16 °C), the scenarios predict an overall decrease in SSD for both mangrove bio- Red Sea and Gulf of Aden (3.24 ± 0.08 °C), Somali/Arabian Coast regions; however, the predicted decrease in SSD in the IWP region (3.23 ± 0.15 °C), South China Sea (3.07 ± 0.10 °C), the Tropical was a factor of 2 (RCP 6.0) and 2.5 (RCP 2.6, RCP 4.5 and RCP 8.5) East Pacific (3.09 ± 0.15 °C) and the Warm Temperate Northwest stronger than in the AEP (Figs. 2 and 3d and Supplementary Atlantic (3.14 ± 0.13 °C) (Fig. 3b and Supplementary Tables 4). Tables 1–4). Predicted SSS changes exhibit an opposite trend in the AEP and Propagule density values from our literature survey range from −3 −3 IWP bioregions, with increased salinity in the AEP and reduced <600 kg m to >1,080 kg m for different mangrove species (Fig. 4 salinity in the IWP under global warming (RCP 2.6–RCP 8.5; Fig. and Supplementary Table 5). The low densities reported for Heritiera 2b); this is reflected in contrasting SSD changes in both mangrove littoralis propagules provide a strong contrast with the near-seawater bioregions (Fig. 2c) and associated with predicted global changes propagule densities reported for Avicennia and members of the in precipitation, with extensions of the rainy season over most Rhizophoraceae (Bruguiera, Rhizophora and Ceriops). Floating char- of the monsoon domains, except for the American monsoon . acteristics of the latter may be particularly sensitive to changes in SSD. Under RCP 8.5, the spatially averaged change in mean SSS is +0.51 To illustrate the potential influence of changing ocean conditions on Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange Propagule density H. lit X. gra A. ger A. mar B. gym C. tag R. man R. muc –3 Density (kg m ) Depth Depth Density Nature Climate ChaNge Articles a b –3 Present RCP 8.5 kg m 30° N 30° N 1,022 15° N 15° N 1,021 0° 0° 1,020 15 °S 15 °S –3 –3 ≤1,020 kg m ≤1,020 kg m 1,019 30 °S AEP IWP 30 °S AEP IWP 1,018 1,017 Longitude Longitude 1,016 c d 1,015 30° N 30° N 15° N 15° N 1,014 0° 0° 15 °S 15 °S –3 –3 ≤1,022 kg m ≤1,022 kg m 30 °S 30 °S AEP IWP AEP IWP Longitude Longitude Present RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 1,018 1,019 1,020 1,021 1,022 1,023 1,024 1,025 1,026 1,027 1,028 –3 SSD (kg m ) RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 1,018 1,019 1,020 1,021 1,022 1,023 1,024 1,025 1,026 1,027 1,028 –3 SSD (kg m ) −3 −3 Fig. 5 | Future changes in SSD. a–d, Spatial extent of coastal and open-ocean surface waters with a density ≤1,020 kg m (a,b) and 1,022 kg m (c,d), for present (2000–2014) (a,c) and future (2090–2100; RCP 8.5) (b,d) scenarios. Data are shown for surface ocean waters within the global latitudinal range of mangrove forests (between 32° N and 38° S). The two density thresholds considered are within the range of densities at which mangrove propagule buoyancy and floating orientation of several mangrove genera change, as reported in available literature. Black dots along the coast represent the global 32 −3 mangrove extent from the 2015 GMW dataset . Magenta-coloured circles represent SSD values <1,014 kg m . e,f, Ocean area with a density less than or equal to SSD (e) and future changes in the spatial extent of these regions (f) for different RCP scenarios. mangrove propagule dispersal, we considered threshold water density Our study shows changes in the physical and chemical properties −3 values (1,020 and 1,022 kg m ) that are within the range where elon- of coastal mangrove waters by the end of the twenty-first century gated propagules of important mangrove genera tend to change float- that could affect the distribution of propagules from widespread ing orientation (Fig. 4a). More specifically, we determined the ocean mangrove genera (Avicennia, Bruguiera, Ceriops and Rhizophora) surface area with an SSD below or equal to these thresholds under and probably more so within the IWP region, the primary hotspot different climate change scenarios (Fig. 5). Under RCP 8.5, the ocean of mangrove diversity, compared to the AEP. Propagules from surface covered by mangrove coastal waters (coastal waters border- these genera have densities that are close to that of seawater and −3 ing present mangrove forests) with a density ≤1,020 kg m increases experimental evidence shows that propagules in species of these 36–39 ~27% by 2100, notably more so in the IWP (~37%) than in the AEP genera typically become denser as they age . For mangroves in −3 (~6%) (Supplementary Table 6). A threshold of 1,022 kg m results large parts of the IWP, as well as the Gulf of Guinea in the AEP, in increases of roughly +11% (global), +12% (IWP) and +8% (AEP) declines in SSD could therefore promote local sinking rates and (Supplementary Table 7). Similar spatial patterns are observed for reduce the probability of successful long-distance dispersal due to open-ocean waters within the global latitudinal range of mangroves earlier propagule sinking before reaching suitable establishment (Fig. 5 and Supplementary Figs. 1 and 2). zones (Fig. 4). Indeed, future changes in surface ocean proper- Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 180° W 150° W 120° W 90° W 60° W 180° W 30° W 150° W 0° 120° W 30° E 90° W 60° E 60° W 90° E 30° W 120° E 0° 150° E 30° E 180° E 60° E 90° E 180° W 120° E 150° W 150° E 120° W 180° E 90° W 60° W 180° W 30° W 150° W 0° 120° W 30° E 90° W 60° E 60° W 90° E 30° W 120° E 0° 150° E 30° E 180° E 60° E 90° E 120° E 150° E 180° E Latitude Latitude ∆ area (%) Cumulative area (%) Latitude Latitude Nature Climate ChaNge Articles ties may impact dispersal differently at different scales. More spe- occur more frequently in the future. While our results suggest cifically, short-distance dispersal may be promoted and successful changes in dispersal patterns following climate-driven changes long-distance dispersal reduced since (1) freshening of local waters in coastal and open-ocean surface-water properties, the effects near the propagule release site and associated sinking can reduce also depend on adaptive capacity. Under current ocean condi- the fraction of propagules reaching open-ocean waters; and (2) tions, propagule densities that are just slightly lower than that of older propagules that have travelled longer distances are at risk of seawater are probably adaptive, since this ensures that they sink offshore sinking due to the combined effect of increased propagule in favourable coastal environments, that is, along coastal zones 36–39 density during transport and fresher coastal waters; as such, where SSS and SSD are lower due to freshwater influx from riv- increased ocean freshening and warming and resulting changes in ers. Yet, in future oceans the spatial extent of these lower-density SSD are likely to reduce the tail of the dispersal kernel—which cap- waters may expand further offshore, altering buoyancy character- tures long-distance dispersal events. Upon sinking, a propagule is at istics and the spatial distribution of propagules. The demographic least temporarily eliminated from the dispersing cohort and prone costs associated with ending up in unsuitable habitat (for exam- to mortality associated with stranding in unsuitable conditions. ple, due to offshore sinking) can get balanced when populations Empirical evidence of mangrove distributions limited by sinking of evolve to have lower propagule densities. As such, there is a need propagules under increasingly fresher conditions in estuaries was for quantitative data for inter- and intra-population variation in shown for mangroves along the Nakara River in Japan . Such effects the critical SSD at which mangrove propagules sink, which could are of considerable importance, since some of the most pronounced reflect underlying genetic variation in propagule density that changes in SSS and SSD are near major river outlets where many can fuel evolutionary change. Overall, our results suggest that of the most extensive mangrove areas in the world are found , such we may be entering a density-induced transition phase as part as the Ganges–Brahmaputra delta. In contrast to the wide-ranging of the Anthropocene, highlighting the importance of consider- mangrove genera Avicennia, Bruguiera, Ceriops and Rhizophora, ing future ocean property changes in evaluating the impacts of mangrove species such as H. littoralis and Xylocarpus granatum are climate change on mangrove ecosystems. Such information will unlikely to be affected by these ocean changes as their propagules complement knowledge on the effects of other impacting factors possess very low densities . and is important for predicting how altered environmental con- An additional level of complexity is that the floating orienta- ditions will affect these sensitive forests. Finally, while we con- tion of propagules from several widespread mangrove genera sidered mangroves as a model system in this study, our findings (Rhizophora and Ceriops) could move between horizontal and may be relevant also for other coastal taxa producing sea-drifted 38,40,41 49 vertical due to small changes in density . Changes in floating propagules, such as seagrasses and coastal strand communities orientation have been associated with the replacement of air in (Terminalia catappa, Barringtonia asiatica, Thespesia populnea, intercellular tissue by water via lenticels ; however, while the exact Hibiscus tiliaceus, Pisonia grandis, Pandanus spp. and so on). anatomical and physiological mechanisms underlying changes in mangrove propagule buoyancy are not yet fully understood, experi- Online content mental and modelling studies showed that these changes in floating Any methods, additional references, Nature Research report- orientation can strongly alter dispersal trajectories at the landscape ing summaries, source data, extended data, supplementary infor- 2 3 3 5 (10 –10 m), regional (10 –10 m), as well as the biogeographic scale mation, acknowledgements, peer review information; details of 5 7 (10 –10 m), via the relative effects of ocean and wind forces on author contributions and competing interests; and statements of 42–44 propagule transport . data and code availability are available at https://doi.org/10.1038/ Besides effects of changes in SSD, propagule dispersal may also s41558-022-01391-9. be directly impacted by SSS or SST. In Avicennia marina, propa- Received: 8 October 2021; Accepted: 13 May 2022; gule sinking has been associated with the shedding of the pericarp Published online: 30 June 2022 (Steinke, 1975, as cited in ref. ) and the time required for peri- carp shedding and the separation of the cotyledons increases with r eferences increasing salinity (Downton, 1982, as cited in ref. ). As such, 1. Spalding, M., Kainuma, M. & Collins, L. World Atlas of Mangroves (Earthscan future ocean freshening might decrease floating periods and poten- and James & James, 2010). tially dispersal distances in this species. Increases in SSS might also 2. Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. result in higher propagule mortality rates and lower germination Ecol. Monogr. 81, 169–193 (2011). success , whereas lower SSS could reduce propagule viability by 3. Ellison, J. Vulnerability assessment of mangroves to climate change and sea-level rise impacts. Wetl. Ecol. Manag. 23, 115–137 (2015). increasing the incidence of fungal infestation and rotting . Finally, 4. Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate projected increases in SST may facilitate mangrove expansion to change on mangrove ecosystems: a region by region overview. Ecosyst. Health higher latitudes in some regions by reducing the negative effect of Sust. 2, e01211 (2016). colder oceanic waters on propagule viability . For example, an ear- 5. Saintilan, N. et al. Mangrove expansion and salt marsh decline at mangrove lier study on the western South Atlantic latitudinal mangrove range poleward limits. Glob. Change Biol. 20, 147–157 (2014). 6. Cavanaugh, K. C. et al. Poleward expansion of mangroves is a threshold limit reported that temperatures <20 °C may limit the viability of response to decreased frequency of extreme cold events. Proc. Natl Acad. Sci. mangrove propagules during their dispersal along this coast and USA 111, 723–727 (2014). during subsequent establishment . However, since empirical data 7. Kelleway, J. J. et al. Review of the ecosystem service implications of mangrove on potential direct effects of SSS and SST on mangrove propagule encroachment into salt marshes. Glob. Change Biol. 23, 3967–3983 (2017). 8. Pecl, T. P. et al. Biodiversity redistribution under climate change: impacts on dispersal are deficient, effects of these variables require further ecosystems and human well-being. Science 355, 1389 (2017). investigation. 9. Osland, M. J. et al. Climatic controls on the global distribution, abundance, It is important to note that changes indicated by our study are and species richness of mangrove forests. Ecol. Monogr. 87, 341–359 (2017). based on changes in time-mean surface ocean properties and that 10. o Th m, B. G. Mangrove ecology and deltaic geomorphology: Tabasco, Mexico. the actual variability in SSD around these mean values could be J. Ecol. 55, 301–343 (1967). 11. Duke, N. C., Ball, M. C. & Ellison, J. C. Factors influencing biodiversity higher. Since mangroves thrive in a broad range of coastal set- and distributional gradients in mangroves. Glob. Ecol. Biogeogr. Lett. 7, tings, including estuaries, deltas, lagoons and open coast, their 27–47 (1998). propagules already encounter a wide range of water densities 12. Raw, J. L., Godbold, J. A., van Niekerk, L. & Adams, J. B. Drivers of mangrove today but our findings clearly illustrate that for important man- distribution at the high-energy, wave-dominated, southern African range grove regions worldwide exposure to lower-density waters will limit. Estuar. Coast. Shelf Sci. 226, 106296 (2019). Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 690 Nature Climate ChaNge Articles 13. Van der Stocken, T. et al. A general framework for propagule dispersal in 37. Robert, E. M. R. et al. Viviparous mangrove propagules of Ceriops tagal mangroves. Biol. Rev. 94, 1547–1575 (2019). and Rhizophora mucronata, where both Rhizophoraceae show different 14. Cavanaugh, K. C. et al. Climate-driven regime shifts in a mangrove–salt dispersal and establishment strategies. J. Exp. Mar. Bio. Ecol. 468, 45–54 marsh ecotone over the past 250 years. Proc. Natl Acad. Sci. USA 116, (2015). 21602–21608 (2019). 38. Tonné, N., Beeckman, H., Robert, E. M. R. & Koedam, N. Towards an 15. Woodroe ff , C. D. & Grindrod, J. Mangrove biogeography: the role unknown fate: the floating behaviour of recently abscised propagules of Quaternary environmental and sea-level change. J. Biogeogr. 18, from wide ranging Rhizophoraceae mangrove species. Aquat. Bot. 140, 23–33 (2017). 479–492 (1991). 16. Hoppe-Speer, S. C. L., Adams, J. B. & Rajkaran, A. Mangrove expansion and 39. Wang, W., Li, X. & Wang, M. Propagule dispersal determines mangrove population structure at a planted site, East London, South Africa. South. For. zonation at intertidal and estuarine scales. Forests 10, 245 (2019). 40. Rabinowitz, D. Dispersal properties of mangrove propagules. Biotropica 10, 77, 131–139 (2015). 47–57 (1978). 17. Van der Stocken, T. et al. Global-scale dispersal and connectivity in mangroves. Proc. Natl Acad. Sci. USA 116, 915–922 (2019). 41. Clarke, P. J., Kerrigan, R. A. & Westphal, C. J. Dispersal potential and early growth in 14 tropical mangroves: do early life history traits correlate with 18. Guppy, H. B. Observations of a Naturalist in the Pacic B fi etween 1896 and patterns of adult distribution? J. Ecol. 89, 648–659 (2001). 1899: Plant Dispersal Vol. II (Macmillan and Co., 1906). 42. Van der Stocken, T. et al. The role of wind in hydrochorous mangrove 19. Bindo, N. L. et a ff l. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 447–587 (WMO, 2019). propagule dispersal. Biogeosciences 10, 3635–3647 (2013). 43. Van der Stocken, T. et al. Interaction between water and wind as a driver of 20. Johnson, G. C. & Wijffels, S. E. Ocean density change contributions to sea passive dispersal in mangroves. PLoS ONE 10, e0121593 (2015). level rise. Oceanography 24, 112–121 (2011). 44. Van der Stocken, T. & Menemenlis, D. Modelling mangrove propagule 21. Alongi, D. M. The impact of climate change on mangrove forests. dispersal trajectories using high-resolution estimates of ocean surface winds Curr. Clim. Change Rep. 1, 30–39 (2015). and currents. Biotropica 49, 472–481 (2017). 22. Lovelock, E. C. et al. The vulnerability of Indo-Pacific mangrove forests to 45. Hutchings, P. & Saenger, P. e Ec Th ology of Mangroves (Univ. of Queensland sea-level rise. Nature 526, 559–563 (2015). Press, 1987). 23. Osland, M. J. et al. Mangrove forests in a rapidly changing world: global 46. Alleman, L. K. & Hester, M. W. Reproductive ecology of black mangrove change impacts and conservation opportunities along the Gulf of Mexico (Avicennia germinans) along the Louisiana coast: propagule production coast. Estuar. Coast. Shelf Sci. 214, 120–140 (2018). cycles, dispersal limitations, and establishment elevations. Estuar. Coast. 34, 24. O’Connor, M. I. et al. Temperature control of larval dispersal and the 1068–1077 (2011). implications for marine ecology, evolution, and conservation. Proc. Natl Acad. 47. Hickey, M. H. et al. Is climate change shifting the poleward limit of Sci. USA 104, 1266–1271 (2007). mangroves?. Estuar. Coast 40, 1215–1226 (2017). 25. Wilson, L. J. et al. Climate-driven changes to ocean circulation and their 48. Soares, M. L. G., Estrada, G. C. D. E., Fernandez, V. & Tognella, M. M. P. inferred impacts on marine dispersal patterns. Glob. Ecol. Evol. 25, 923–939 Southern limit of the western South Atlantic mangroves: assessment of the (2016). potential effects of global warming from a biogeographical perspective. 26. Poloczanska, E. S. et al. Responses of marine organisms to climate change Estuar. Coast. Shelf Sci. 101, 44–53 (2012). across oceans. Front. Mar. Sci. 3, 62 (2016). 49. Repolho, T. et al. Seagrass ecophysiological performance under ocean 27. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. warming and acidification. Sci. Rep. 7, 41443 (2017). Greater vulnerability to warming of marine versus terrestrial ectotherms. 50. Allen, M. et al. Raincloud plots: a multi-platform tool for robust data Nature 569, 108–111 (2019). visualization (version 2). Wellcome Open Res. 4, 63 (2021). 28. Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of 51. Van der Stocken, T., Vanschoenwinkel, B., De Ryck, D. & Koedam, N. Caught coastal and shelf areas. BioScience 57, 573–583 (2007). in transit: offshore interception of seafaring propagules from seven mangrove 29. Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species. Ecosphere 9, e02208 (2018). species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012). 30. Assis, J. et al. Bio-ORACLE v2.0: extending marine data layers for bioclimatic Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2017). published maps and institutional affiliations. 31. Fofono, P ff . & Millard, R. C. Algorithms for computation of fundamental properties of seawater. UNESCO Techn. Papers in Mar. Sci. 44 (UNESCO, 1983). Open Access This article is licensed under a Creative Commons 32. Bunting, P. et al. The Global Mangrove Watch—a new 2010 global baseline of Attribution 4.0 International License, which permits use, sharing, adap- mangrove extent. Remote Sens. 10, 1669 (2018). tation, distribution and reproduction in any medium or format, as long 33. Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. as you give appropriate credit to the original author(s) and the source, provide a link to Nat. Clim. Change 8, 499–503 (2018). the Creative Commons license, and indicate if changes were made. The images or other 34. Moon, S. & Ha, K. J. Future changes in monsoon duration and precipitation third party material in this article are included in the article’s Creative Commons license, using CMIP6. Clim. Atmos. Sci. 3, 45 (2020). unless indicated otherwise in a credit line to the material. If material is not included in 35. Durack, P. J. & Wijffels, S. E. Fifty-year trends in global ocean salinities and the article’s Creative Commons license and your intended use is not permitted by statu- their relationship to broad-scale warming. J. Clim. 23, 4342–4362 (2010). tory regulation or exceeds the permitted use, you will need to obtain permission directly 36. Kadoya, T. & Inoue, T. Spatio-temporal pattern of specific gravity of from the copyright holder. To view a copy of this license, visit http://creativecommons. mangrove diaspore: implications for upstream dispersal. Ecography 38, org/licenses/by/4.0/. 472–479 (2015). © The Author(s) 2022 Na Ture CliMa Te ChaNge | VOL 12 | JULy 2022 | 685–691 | www.nature.com/natureclimatechange 691 Nature Climate ChaNge Articles correspond with a 2015 GMW occurrence point (the centroids of each 2015 GMW Methods polygon feature). The resulting coordinates for A. germinans (n = 2,964), A. marina Ocean data. In this study we examine changes in SST, SSS and SSD over the next (n = 6,419), B. gymnorrhiza (n = 6,040), C. tagal (n = 5,653), H. littoralis (n = 5,100), century. SSD has been largely overlooked in studies that examine the response of R. mangle (n = 2,345), R. mucronata (n = 5,348) and X. granatum (n = 5,891) can be marine organisms to climate-driven ocean changes, despite its potential influence viewed as species-specific GMW data. Each of these computations was performed on the dispersal patterns and colonization potential of passive propagules with using the MATLAB 2020b programming software. near-seawater densities. We calculated changes in mean SST and SSS over the next century using present (2000–2014) and future (2090–2100) data from 29,30 Spatial analyses. Spatial patterns in projected ocean property changes over the Bio-ORACLE , a database of GIS rasters providing geophysical, biotic and next century were explored by considering bioregion- and province-levels to subset environmental data for surface and benthic marine realms (https://www.bio-oracle. our GMW 2015 data. Bioregion subsetting consists of coastal Bio-ORACLE cells org/). These data consist of uniformly constructed rasters provided at a spatial in the AEP (between −180° and 19° longitude) and IWP (between 19° and 180° resolution of 5 arcmin (~0.08° or ~9.2 km at the Equator). Present layers were longitude) bioregion, whereas the province-level waters consist of a subsetting generated with climate data describing monthly means for the period 2000–2014, using the provinces provided by the MEOW dataset . acquired from preprocessed global ocean ARMOR reanalyses that combine remotely sensed and in situ observations, while future data were produced by Reporting summary. Further information on research design is available in the averaging output from atmosphere–ocean general circulation models provided Nature Research Reporting Summary linked to this article. by the Coupled Model Intercomparison Project Phase 5 (CMIP5) . We are aware of the limitations of remotely sensed coastal ocean data and climate change projections of fine-scale near-shore circulation patterns, which may deviate Data availability from in situ observations . However, using time-averaged SST and SSS data and All the datasets used for analyses during this study are publicly available and can considering spatially averaged patterns ensures that we are capturing general trends 29,30 be accessed at: https://www.bio-oracle.org/ (marine data layers ); https://data. in coastal ocean changes. SSD was derived from these variables using the UNESCO unep-wcmc.org/datasets/45 (global mangrove extent ); https://www.iucnredlist. EOS-80 equation of state polynomial for seawater . Changes were calculated for org/ (geographic range of the species considered); https://data.unep-wcmc.org/ four RCP scenarios: RCP 2.6 (490 CO e before 2100 and then decline), RCP 4.5 28 2 datasets/38 (provinces from the MEOW database ). All remaining data that (650 CO e at stabilization aer 2100), R ft CP 6.0 (850 CO e at stabilization aer 2100) ft 2 2 support the findings of this study are freely available at https://doi.org/10.5061/ and RCP 8.5 (>1,370 CO e in 2100) (see ref. for more details about RCPs). dryad.66t1g1k4c (ref. ). Global mangrove range data. The GMW provides high-resolution (0.8 arcsec or Code availability ~25 m) global mangrove extent baseline maps based on Landsat sensor spectral Computations of SSD were conducted using the UNESCO EOS-80 equation of composite data and Advanced Land Observing Satellite (ALOS) Phased Arrayed state polynomial for seawater (sw_dens.m from the MATLAB seawater package) . L-band Synthetic Aperture Radar (PALSAR) data for the years 1996, 2007, 2008, Other MATLAB codes used during the current study are available from the 2009, 2010, 2015 and 2016 (ref. ). For this study, we used the 2015 mangrove corresponding author upon reasonable request. extent baseline map since it most closely matches the end year of the period (2000–2014) considered for generating the Bio-ORACLE raster data of present-day marine environmental conditions . Using the QGIS 3.10.10 software, centroids r eferences were computed for each GMW 2015 polygon (n = 583,578) and considered as our 52. Smit, J. S. et al. A coastal seawater temperature dataset for biogeographic global mangrove occurrence dataset. Global mangrove occurrence points were studies: large biases between in situ and remotely-sensed data sets around the assigned to the nearest grid cell (Euclidean distance) in the Bio-ORACLE fields. coast of South Africa. PLOS ONE 8, e81944 (2013). Since multiple occurrence points might have been assigned to the same grid 53. Moss, R. et al. The next generation of scenarios for climate change research cell, duplicates were removed, resulting in unique grid cell records for the GMW and assessment. Nature 463, 747–756 (2010). 2015 mangrove range (n = 10,108). Each of these steps was performed using the 54. Quisthoudt, K. et al. Disentangling the effects of global climate and regional MATLAB 2020b programming software. land-use change on the current and future distribution of mangroves in South Africa. Biodivers. Conserv. 22, 1369–1390 (2013). Mangrove species-specific data. A literature survey was conducted to collect data 55. Cavanaugh, K. C. et al. Sensitivity of mangrove range limits to climate on mangrove propagule density values that were interpreted against the range of variability. Glob. Ecol. Biogeogr. 27, 925–935 (2018). present and future SSD values within the range of Avicennia germinans (AEP), 56. McKee, L. K. Seedling recruitment patterns in a Belizean mangrove forest: Avicennia marina (IWP), Bruguiera gymnorrhiza (IWP), Ceriops tagal (IWP), effects of establishment ability and physico-chemical factors. Oecologia 101, Heritiera littoralis (IWP), Rhizophora mangle (AEP), Rhizophora mucronata 448–460 (1995). (IWP) and Xylocarpus granatum (IWP). This selection of mangrove species in our 57. Van der Stocken, T. et al. Future changes in mangrove coastal water literature survey is based on the availability of propagule density data. Such data are properties. Dryad https://doi.org/10.5061/dryad.66t1g1k4c (2022). surprisingly limited but nevertheless allow for a first assessment of species-specific differences in the sensitivity to SSD changes. Additionally, propagules of the a cknowledgements selected species are representative for the variety of propagule morphotypes T.V.d.S. is supported by the EU Horizon 2020 Framework Programme for Research and found among mangrove species globally and include the most widely distributed Innovation under the Marie Skłodowska-Curie actions Individual Fellowship (MSCA-IF) mangrove species (Avicennia spp. and Rhizophora spp.) for which distributional with grant agreement no. 896888 (GLOMAC). K.C.C. is supported by the NASA Land 5,54,55 changes have been reported and predicted . In the case that specific gravity Cover and Land Use Change (LCLUC) Program with grant no. 80NSSC21K0296. 36,56 −3 values were reported , we multiplied these values by 999.97 kg m (the density of water at 4 °C, which is the standard density used to compute specific gravity) to a uthor contributions obtain density values. T.V.d.S. designed the study, compiled and analysed the data, produced the figures, The 2015 GMW database used for our global analysis does not contain interpreted the results and wrote the manuscript with contributions and suggestions coordinates for species-specific mangrove distributions. Hence, data for different from B.V., D.C., K.C. and N.K. T.V.d.S and D.C. computed sea surface density fields. B.V. species were generated using vector layers obtained from the IUCN Red List provided extensive feedback on various drafts and revisions of the manuscript. website (https://www.iucnredlist.org/). The IUCN data for each species consist of a single polygon feature that was intersected with a 1:10 m global coastline downloaded from the Natural Earth database (https://www.naturalearthdata.com/) Competing interests in QGIS 3.10.10. Before this intersection, we buffered the species distribution The authors declare no competing interests. polygons using a buffer distance equal to the spatial resolution of the Bio-ORACLE marine data layers (0.5 arcmin) to avoid losing data where the original IUCN species distribution polygons did not overlap with the global coastline data. a dditional information Point features were generated at 1 km distance for each line feature representing Supplementary information The online version contains supplementary material a species range, using the QChainage plugin in QGIS 3.10.10 and longitude and available at https://doi.org/10.1038/s41558-022-01391-9. latitude information was added to each point for A. germinans (n = 4,610), A. Correspondence and requests for materials should be addressed to marina (n = 11,231), B. gymnorrhiza (n = 9,665), C. tagal (n = 9,134), H. littoralis Tom Van der Stocken. (n = 7,984), R. mangle (n = 3,431), R. mucronata (n = 9,519) and X. granatum Peer review information Nature Climate Change thanks Ken Krauss, Erik Yando and the (n = 8,890). Since the IUCN data show mangrove presence along vast stretches of other, anonymous, reviewer(s) for their contribution to the peer review of this work. coast (for example, Somalia and East Madagascar) where mangroves are absent in the 2015 GMW data, we extracted ocean data for the IUCN coordinates that Reprints and permissions information is available at www.nature.com/reprints. Na Ture CliMa Te ChaNge | www.nature.com/natureclimatechange

Journal

Nature Climate ChangeSpringer Journals

Published: Jul 1, 2022

There are no references for this article.