Ecology

Study sitesThe paired 15N-tracer experiments were conducted in 13 forest sites, of which nine were in China, two in Europe and two in the USA. These sites vary in mean annual precipitation (MAP) from 700 to 2500 mm, in mean annual temperature (MAT) from 3 to > 20 °C, and in soil types (Fig. 1, Supplementary Table 1, Supplementary Table 2). Ambient N deposition (bulk/throughfall NH4+ plus NO3−) at the sites ranged from 6 to 54 kg N ha−1 yr−1. Forest types at the experimental sites include tropical forests in southern China, subtropical forests in central China, and temperate forests in northeastern China, Europe, and the USA. Data from the sites in Europe, the USA, and six of the nine sites in China have been reported previously. Detailed descriptions of these sites and the related data source references are summarized in Supplementary Table 1. Data for forests at the other three sites in China (Xishuangbanna, Wuyishan, and Maoershan) are originally presented here. The Xishuangbanna sites, which is located Xishuangbanna National Forest Reserve in Menglun, Mengla County, Yunnan Province, is a primary mixed forest dominated by the typical tropical forest tree species Terminalia myriocarpa and Pometia tomentosa. The Wuyishan forest, which is located in the Wuyi mountains in Jiangxi Province, is also a mature subtropical forest with Tsuga chinensis var. tchekiangensis as the dominant tree species in the canopy layer. Other common tree species in the forest include Betula luminifera and Cyclobalanopsis multinervis. Maoershan is a relatively young (45 years) larch (Larix gmelinii) plantation located at Laoshan Forest Research Station of Northeast Forestry University, Heilongjiang Province. A few tree species- Juglans mandshurica, Quercus mongolica, and Betula platyphylla- coexist with Larix gmelinii in the canopy. More information about these sites is also presented in Supplementary Table 1.
15N-tracer experimentAt all sites, small amounts of 15NH4+ or 15NO3− tracers (generally  20% in a 1-km pixel was defined as forest. Based on this, we estimated the total global forest area to be ≈42 million km2.Calculation of N-induced C sinkThe N-induced C sink was estimated via the stoichiometric upscaling method19, i.e., by multiplying the N retention in woody tissues of stems, branches, and coarse roots and in the soil with the C/N ratios in these compartments. The C sink due to NHx and or NOy deposition was calculated separately using Eq. (4) as follows:$${{{{{{mathrm{C}}}}}}}_{{{{{{mathrm{sink}}}}}}}={{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{dep}}}}}}}times left(,{!}^{15}{{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{org}}}}}}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{org}}}}}}}+{{,}^{15}}{{{{{{{mathrm{N}}}}}}}_{{{min }}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{min }}}+{{,}^{15}}{{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{wood}}}}}}}^{{{{{{mathrm{R}}}}}}}}times frac{{{{{{mathrm{C}}}}}}}{{{{{{mathrm{N}}}}}}}_{{{{{{mathrm{wood}}}}}}}times {{{{{mathrm{f}}}}}}right)$$
(4)
where Ndep is NHx or NOy deposition (kg N ha−1 yr−1); ({}^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{org}}}}}}}^{{{{{{rm{R}}}}}}}), ({}^{15}{{{{{{rm{N}}}}}}}_{{{min }}}^{{{{{{rm{R}}}}}}}) and ({}^{15}{{{{{{rm{N}}}}}}}_{{{{{{rm{wood}}}}}}}^{{{{{{rm{R}}}}}}}) indicate the fraction of deposited NHx or NOy allocated to organic layer, mineral soil, and woody biomass, respectively; and ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{{{{rm{org}}}}}}}), ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{min }}}), and ({frac{{{{{{rm{C}}}}}}}{{{{{{rm{N}}}}}}}}_{{{{{{rm{wood}}}}}}}) indicate C/N ratios in the soil organic layer, soil mineral layer and woody plant biomass, respectively. f is the fraction we applied to account for flexible C/N in response to elevated N deposition. At elevated N deposition, wood C/N ratio may decrease, and N accumulates without stimulating additional ecosystem C storage. To account for this scenario, we adopted a flexible stoichiometry51, in which the effects of N deposition on wood C/N ratios are accounted for by multiplying the C/N ratios of wood with a fraction f (from 1 to 0) depending on plant growth response to different rates of N deposition level (kg N ha−1 yr−1). Results of growth responses to experimental N addition and field N gradient studies show plant growth increased with increasing N deposition, flattening near 15–30 kg N ha−1 yr−1 and a reversal toward no enhanced growth response at about 100 kg N ha−1 yr−1 (ref. 36,52). Therefore, for N deposition More

Study areaThe QTP is located in southwestern China (25° ~ 40°N, 75° ~ 103°E), with a total area of 2.5 million km2 and an average elevation above 4000 m (Fig. 7). The QTP is mainly covered with permafrost and grassland, with areas of glacier and desert48. The QTP, also known as the “Asian Water Tower”49, is the source of 13 major Asian rivers (e.g., the Indus, Ganges, Brahmaputra, Yangtze, and Yellow Rivers). The QTP has a clod, arid climate, with an annual average temperature below 0 °C and an annual mean precipitation of 400 mm. The seasonal distribution of precipitation is uneven, with most precipitation concentrated in the period June to September. There is a decreasing trend in precipitation from the southeast to the northwest of the plateau50. Known as the “Roof of the World” and “Third Pole”, the QTP is also an area that is sensitive to global climate change, showing increasing warming and humidification in recent decades51. In addition, the QTP contains a diversity of ecosystems and fosters a historic ecological security barrier, which nurtures the development of animal husbandry and diverse cultures.Figure 7Geographical location of the QTP. The map was created using ArcMap 10.2, URL: http://www.esri.com.Full size imageData sourcesRCP scenarios and climate change datasetThe RCP scenarios released by the IPCC 5th Assessment Report52 supply a forecasting standard for climate change research. RCP values ranging from 2.6 to 8.5 reflect radiation forcing values in 2100 relative to the beginning of the Industrial Revolution in 175053. Different radiative forcing scenarios represent different future climate scenarios. RCPs consist of one high-emission scenario (8.5 ({text{W}} cdot {text{m}}^{ – 2}), RCP8.5), two medium-emission scenarios (6.0 ({text{W}} cdot {text{m}}^{ – 2}), RCP6.0; 4.5 ({text{W}} cdot {text{m}}^{ – 2}), RCP4.5), and one low-emission scenario (2.6 ({text{W}} cdot {text{m}}^{ – 2}), RCP2.6)54. In this study, we adopted the RCP2.6, RCP4.5 and RCP8.5 climate change scenarios choosing RCP4.5 to represent the medium emission scenario in consideration of increasing activity through global initiatives in response to climate change. Specific descriptions of each scenario are shown in Table 1.Table 1 The characteristics of each RCP scenario.Full size tableWe adopted the climate change dataset outputs from five global circulation models(GCMs) (namely GFDL-ESM2M, HadGEM2-ES, IPSLCM5ALR, MIROC-ESM-CHEM, and NorESM1-M) within the fifth phase of the Coupled Model Intercomparison Project (CMIP5)55. The dataset outputs from GCMs were downscaled to a resolution of 0.5° and bias-corrected with Water and Global Change (WATCH) data (Integrated Project Water and Global Change, http:/www.eu-watch.org/data_availability)56. The baseline period of the dataset is 1950–2005 and the forecast period is 2006–2099.The climate change dataset included daily precipitation, air pressure, solar radiation, air temperature, maximum air temperature, minimum air temperature, wind speed, and relative humidity.Auxiliary dataThe auxiliary data for our research include the following. (1) The land use and land cover (LULC) map was obtained from the Resource and Environment Science and Data Center (RESDC), Chinese Academy of Sciences (https://www.resdc.cn) for 1980, 1990, 1995, 2000, 2005, 2010, 2015 and 2020 at a 1 km resolution. The LULC data have six major classes: cropland, grassland, forestland, water, built-up land and barren land. (2) The spatial distribution of soil type data, digital elevation model (DEM), watershed boundaries and normalized difference vegetation index (NDVI) data with a resolution of 1 km were obtained from the RESDC. (3) Soil physical and chemical property data (available soil water capacity, absolute depth to bedrock, silt content, clay content, sand content and soil organic carbon content) were obtained from the International Soil Reference and Information Centre (ISRIC Data Hub) (https://data.isric.org) with a 1 km spatial resolution. (4) During 1986–2005 and 1986–2098 (RCP2.6; RCP4.5; RCP8.5), the permafrost datasets in the Northern Hemisphere (https://doi.org/10.12072/ncdc.CCI.db0032.2020) and the response of the alpine grassland ecosystem to climate change (RCP2.6, RCP4.5, and RCP8.5) in the permafrost region of the Qinghai-Tibet Plateau from 1981 to 2099 (https://doi.org/10.12072/ncdc.CCI.db0006.2020) were provided by the National Cryosphere Desert Data Center (https://www.ncdc.ac.cn).Future land use simulation and validationIn this study, we used the Future Land Use Simulation model (FLUS) to simulate the LULC in 2030, 2050 and 2100 under the three RCP scenarios. This model was developed by57 and is available for download at (www.geosimulation.cn/flus.html). The FLUS model is an efficient land use simulation tool and has been widely used58,59. We selected the DEM, slope, precipitation, temperature, soil type, and permafrost distribution to calculate the suitability probability. Based on the land use transfer from 2010 to 2015, we calculated the total land use in 2030, 2050 and 2100 under three RCP scenarios by the Markov model. To validate the FLUS model, we set 2010 as the starting year and simulated the land use in 2015. The output results were compared with the real 2015 land use data, and we calculated the Kappa coefficient as follows:$$begin{array}{*{20}c} {Kappa = frac{{P_{0} – P_{C} }}{{P_{P} – P_{C} }}} \ end{array}$$
(1)
where (P_{0}) is the number of pixels converted correctly,(P_{C}) is the correct number of pixels to be converted in the random case, and (P_{P}) is the correct number of pixels to convert under ideal conditions.Assessment of ecosystem services under different RCP scenariosThis study assessed four ESs namely WY, SR, CS, and RMP, under climate change scenarios in 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2030 (short-term); 2050 (medium-term); and 2100 (long-term). We adopted the Integrated Valuation of Environmental Service and Tradeoffs (InVEST)60 model to assess the WY, SR, and CS ecosystem services. The InVEST model developed by the Natural Capital Project(www.naturalcapitalproject.org) is an effective model to evaluate ESs61 and is widely used in ES research on the QTP22,23,24,25. All spatial data were processed into a 1 km resolution and Albers projection by ArcGIS 10.2 before input into the InVEST model. The data requirements of the InVEST model and its processing are shown in Table S1. We use net primary productivity (NPP) to evaluate the RMP, and NPP can be used to represent the richness of biomass and the supply of organic materials. We adopted the Carnegie-Ames-Stanford Approach (CASA)62 model to estimate NPP.Water yieldWater yield is a key ecosystem service. It refers to the annual quantity of water available for human use, as measured by the supply of surface water per unit area63. We adopted the InVEST 3.9.0 water yield model to estimate WY services in the QTP region. The water yield model is based on the water balance principle64. The biophysical parameter table required by the model is shown in Table S2. The parameters in the biophysical table come from the published literature26,63,65. The annual WY is calculated as follows:$$begin{array}{*{20}{c}} {{Y_{xj}} = left( {1 – frac{{AE{T_{xj}}}}{{{P_x}}}} right){P_x}} end{array}$$
(2)
where (Y_{xj}) is the annual WY of land cover type j in pixel x; (P_{x}) is the annual average precipitation of pixel x; and (AET_{xj}) is the actual evapotranspiration of land cover type j in pixel x.$$begin{array}{*{20}c} {frac{{AET_{xj} }}{{P_{x} }} = frac{{1 + omega_{x} R_{xj} }}{{1 + omega_{x} R_{xj} + frac{1}{{R_{xj} }}}}} \ end{array}$$
(3)
where (omega_{x}) is a dimensionless nonphysical parameter representing soil properties under natural climate conditions. The calculation method is as follows:$$begin{array}{*{20}c} {omega_{x} = Zfrac{{AWC_{x} }}{{P_{x} }}} \ end{array}$$
(4)
where Z is a seasonal rainfall factor representing the regional precipitation distribution and other hydrogeological characteristics. The higher the Z value is, the less the seasonal constant Z affects the model results66. Since the QTP region belongs to the arid and cold climate zone in China, the Z value is set as 9. (AWC_{x}) is the soil effective water content of pixel X, which is determined by the soil depth and physical and chemical properties. (R_{xj}) is the Budyko dryness index, which is calculated as follows:$$begin{array}{*{20}c} {R_{xj} = frac{{K_{xj} cdot ET_{0} }}{{P_{x} }}} \ end{array}$$
(5)
where, (K_{xj}) is the reference crop evapotranspiration and (ET_{0}) is the reference evapotranspiration in pixel x. We adopted the modified Hargreaves method to calculate (ET_{0}).$$ET_{0} = 0.0013 times 0.408 times RA times (T_{av} + 17) times (TD – 0.0123P)^{0.76}$$
(6)
In the above formula, (T_{av}) represents the average daily maximum temperature and minimum temperature, (TD) represents the difference between the daily maximum temperature and minimum temperature, (RA) represents astronomical radiation (MJm-2d-1) and P represents precipitation (mm/month).Soil retentionSoil retention refers to the ability of various land cover types to prevent soil erosion. The InVEST 3.9.0 sediment delivery ratio (SDR) was employed to estimate SR services in the QTP region. The SDR model is based on the Revised Universal Soil Loss Equation (RUSLE)67, and the model is calculated as follows:$$begin{array}{*{20}c} {SR = R*K*LS – R*K*LS*C*P} \ end{array}$$
(7)
$$begin{array}{*{20}c} {L = left( {frac{gamma }{22.3}} right)^{{frac{beta }{1 + beta }}} } \ end{array}$$
(8)
$$begin{array}{*{20}c} {beta = frac{{sin frac{theta }{0.0896}}}{{left[ {3.0, *,left( {sin theta } right)^{0.8} +, 0.56} right]}}} \ end{array}$$
(9)
$$begin{array}{*{20}c} {S = 65.41*sin^{2} theta + 4.56*sin theta + 0.065} \ end{array}$$
(10)
where SR is the total amount of soil retention (tons ha-1 a-1), LS is the topographic factor, and LS is calculated from the slope length factor (L) and slope steepness factor (S). C is the vegetation and management factor. P is the support practice factor. C and P are shown in Table S2. R is the rainfall erosivity index(MJ mm ha-1 h-1 a-1), which was calculated via monthly precipitation28. K is the soil erodibility, which was calculated from the sand, silt, clay and organic soil moisture contents68. R and K are calculated as follows:$$begin{array}{*{20}c} {R = mathop sum limits_{i = 1}^{12} left( { – 1.5527 + 0.179P_{i} } right)} \ end{array}$$
(11)
$$begin{array}{*{20}c} {K = 0.1317*left{ {0.2 + 0.3*exp left[ { – 0.0256*SANleft( {1 – frac{SIL}{{100}}} right)} right]} right}} \ {*left( {frac{SIL}{{CLA – SIL}}} right)^{0.3} *left( {1 – frac{0.25*SOM}{{SOM + exp 3.72 – 2.95*SOM}}} right)} \ {quad quad*left( {1 – frac{{0.7*1 – frac{SAN}{{100}}}}{{begin{array}{*{20}c} {1 – frac{SAN}{{100}} + exp left( { – 5.51 + 22.9*left( {1 – frac{SAN}{{100}}} right)} right)} \ end{array} }}} right)} \ end{array}$$
(12)
where Pi is the precipitation in month i. SAN, SIL, CLA, and SOM are the contents of sand, silt, clay and organic moisture, respectively. Other parameters are shown in Table S1.Carbon storageCarbon storage services refer to the carbon that ecosystems store in vegetation, soil and debris. The InVEST 3.9.0 carbon model uses a simple method to estimate CS based on land use data. The carbon pools in this model include four categories: aboveground carbon, belowground carbon, soil organic carbon and dead organic matter. This model simplifies the carbon cycle, and the change in carbon storage is mainly caused by change in land use69. The carbon pools for land use types were set according to published literature70,71,72. The carbon storage is calculated as follows:$$begin{array}{*{20}c} {{text{C}}_{{{text{total}}}} = C_{above} + C_{below} + C_{soil} + C_{dead} } \ end{array}$$
(13)
where ({text{C}}_{{{text{total}}}}), (C_{above}), (C_{below}), (C_{soil}) and (C_{dead}) are the total carbon storage, aboveground carbon, belowground carbon, soil organic carbon and dead organic matter, respectively.Raw material provisionRaw material supply refers to the organic matter provided by the ecosystem for human production and life, such as pasture and wood. In this study, RMP was quantified by the annual NPP. The NPP in the QTP region is calculated by the CASA model, which is a light use efficiency model driven by climate and remote sensing data73,74. The CASA model has been widely used to estimate NPP in terrestrial ecosystems75,76. In the CASA model, NPP is calculated as follows:$$begin{array}{*{20}c} {NPPleft( {x,t} right) = APARleft( {x,t} right) times varepsilon left( {x,t} right)} \ end{array}$$
(14)
where, (APARleft( {x,t} right)) is the photosynthetically active radiation(MJ m-2) absorbed by pixel x in month t, (varepsilon left( {x,t} right)) is the actual light energy utilization rate(gC MJ-1), and the (APARleft( {x,t} right)) calculation method is as follows:$$begin{array}{*{20}c} {APARleft( {x,t} right) = SOLleft( {x,t} right) times FPARleft( {x,t} right) times 0.5} \ end{array}$$
(15)
In the formula, (SOLleft( {x,t} right)) is the total solar radiation in pixel x in month t(MJ M-2); (FPARleft( {x,t} right)) is the absorption ratio of photosynthetically active radiation by vegetation, which is determined by the normalized difference vegetation index (NDVI); and the constant 0.5 is the proportion of photosynthetically active radiation to the total radiation. (SOLleft( {x,t} right)) can be calculated by the solar shortwave radiation as follows:$$begin{array}{*{20}c} {SOLleft( {x,t} right) = a_{s} + b_{s} frac{n}{N}R_{s} } \ end{array}$$
(16)
where, (R_{s}) is the solar shortwave radiation(MJ M-2 d-1), n is the actual sunshine time(hours), N is the time of day(hours), and (frac{n}{N}) is the relative sunshine time; The constants (a_{s} = 0.25) and (b_{s} = 0.5).And the (varepsilon left( {x,t} right)) is calculated as follows:$$begin{array}{*{20}c} {varepsilon left( {x,t} right) = T_{varepsilon 1} left( {x,t} right) times T_{varepsilon 2} left( {x,t} right) times W_{varepsilon } left( {x,t} right) times varepsilon_{max} } \ end{array}$$
(17)
where, (T_{varepsilon 1}) and (T_{varepsilon 2}) are the stress factors of cold and heat, respectively; (W_{varepsilon }) is the water stress factor, reflecting the influence of water conditions; (varepsilon_{max}) is the maximum light use efficiency(gC MJ-1) under the optimal conditions, in this study, (varepsilon_{max}) is 0.389.Trend analysisThe Mann–Kendall nonparametric test and Sen’s slope estimator were used to analyze the trend of ESs in the QTP region. The Mann–Kendall method is widely used to analyze climatic and hydrological time series variation trends77. The advantage of the Mann–Kendall test is that it does not require the sample to follow a certain distribution, allows the existence of missing values, is not affected by a small number of outliers, and has strong quantitative ability78. The Mann–Kendall test is as follows:$$begin{array}{*{20}c} {S = mathop sum limits_{i}^{n – 1} mathop sum limits_{j = i + 1}^{n} sgnleft( {x_{j} – x_{i} } right)} \ end{array}$$
(18)
For time series data, i.e., {x1, x2, …, xn}, n is the length of the data, and (sgnleft( {x_{j} – x_{i} } right)) is derived as:$$begin{array}{*{20}c} {sgnleft( {x_{j} – x_{i} } right) = left{ {begin{array}{*{20}c} { + 1,x_{j} – x_{i} > 0} \ {0,x_{j} – x_{i} = 0} \ { – 1,x_{j} – x_{i} < 0} \ end{array} } right.} \ end{array}$$ (19) In this study, we set the significance level of (alpha = 0.05), when (left| Z right| le Z_{1 - alpha /2}) accepts the null hypothesis. Otherwise, the null hypothesis is rejected, and the trend is statistically significant.$$begin{array}{*{20}c} {Z = left{ {begin{array}{*{20}l} frac{S - 1}{{sqrt {VARleft( S right)} }},&quad S > 0 \ 0,&quad S = 0 \ frac{S + 1}{{sqrt {VARleft( S right)} }},&quad S < 0 \ end{array} } right.} \ end{array}$$ (20) $$begin{array}{*{20}c} {VARleft( S right) = left{ {nleft( {n - 1} right)left( {2n + 5} right) - mathop sum limits_{j = 1}^{p} t_{j} left( {t_{j} - 1} right)left( {2t_{j} + 5} right)} right} div 18} \ end{array}$$ (21) where p is the number of nodes in the dataset and (t_{j}) is the length of the nodes.Sen’s slope estimator is an estimation method based on the median and its insensitivity to outliers78.$$begin{array}{*{20}c} {beta = Medianleft( {frac{{x_{j} - x_{i} }}{j - i}} right)} \ end{array}$$ (22) Trade-offs and synergy analysisSynergies and trade-offs were used to describe the relationships among the ESs. A trade-off analysis was conducted to reflect the difference in ESs and their responses to climate change. Trade-offs are when ESs change in the opposite direction. Synergies are when ESs change in the same direction79. Correlation analysis is often used to evaluate trade-offs and synergies between ESs2. To analyze the trade-offs and synergies of ESs at different administrative and natural scales, we allocated the ES values at the 10 km (pixel), county and watershed scales by the “zonal statistic” module of ArcGIS 10.2, and conducted minimum–maximum normalization in R4.0.3 (www.R-project.com). To analyze the relationship between any two of the four ES types, the R package PerformanceAnalytics was adopted to measure the Spearman correlation matrix at different scales. More

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We used AquaMaps18,32 to obtain species occurrence data, and extracted eleven ecologically and biologically relevant traits (seven continuous and four categorical) from FishBase19. All calculations were done separately for the two group of fishes (bony fishes and cartilaginous fishes). Our workflow is in Supplementary Fig. 1.Step 1- InputOccupancy Data (assemblage matrix)We extracted occupancy data from AquaMaps18,32. This online database provides half-degree grid cell species occurrences based on data from GBIF44 and OBIS45 complemented with information from FishBase19 and SeaLifeBase46. AquaMaps gives probabilities of the occurrence of a given species between 0 (based on the environmental envelope indicating there is no chance of finding the species in that grid cell) to 1 (highest probability to find the species in that grid cell)32.These occurrence data are then combined with an algorithm implemented by AquaMaps using “estimates of environmental preferences with respect to depth, water temperature, salinity, primary productivity, and association with sea ice or coastal areas”. For more information see Kesner-Reyes, et al.32.In our analysis, we selected occurrence data (the presence of a given species in a certain half degree grid cell) with a probability >0.9. To ensure our results were not simply an artefact of using a high probability of occurrence we also examined probabilities higher than 0.7 and 0.5. Further analyses were repeated independently for each of these probabilities. After this initial step, we allocated each half-degree grid cell to a 2° grid cell. We created the 2° grid cells using features available in ArcGIS47. These occupancy data also allowed us to compute the species richness of each 2° grid cell.We applied all analyses described here (see Supplementary Fig. 1) separately for the Coastal Systems and High Seas of each of the Seven Oceanic Regions (see Supplementary Fig. 3). This gives us seven Coastal Systems and seven High Seas, a total of 14 assemblage matrices for each of the probabilities (probability  > 0.9, 0.7 and 0.5).Trait Data Compilation (trait matrix)Our final occurrence data (each of the 14 systems) were split between marine bony fishes (11,961 Actinopterygii species) and cartilaginous fishes (866 Elasmobranchii species). For each species in both groups of fish we assembled biologically and ecologically relevant traits from the most recent version of the FishBase database19. This gave us seven continuous and four categorical traits that are largely uncorrelated with one another, (see Supplementary Fig. 9a and b):Environmental Traits: (1) Position in Water Column—The vertical position of a species indicates its feeding habitat19 and its influence on the process of transferring nutrients through the water column48,49. This trait has 8 categories. (2) Maximum Depth (m)—This trait reflects the environmental conditions that each species occur27. (3) Mean Temperature Preference (°C)—Thermal variations in preferences indicates the species tolerances to changes in temperature50,51,52.Life History Traits: (4) Growth (k)—This coefficient parameter (k = 1 year−1) is derived from the von Bertalanffy growth function (Lt = L ∞ (1-exp(−K(t−t0)))). Faster growth rates are associated with higher k values19. (5) (Q/B)—this trait represents the proportional ratio between food consumption (Q) and biomass (B) and can be used as a proxy for trophic interactions, evidencing the flow of energy in the system53,54. (6) Trophic Level—the position of a given species in the food chain is expressed as its trophic level and as discussed by Froese and Pauly19 can be assessed as the amount of “energy-transfer steps to that level”. The trophic level also gives information on interactions between species, for example predator-prey and trophic cascades55.Morphological Traits: (7) Body Shape—The body shape of a species relates to ecological behaviours, such as migration patterns56. We divided this trait into 38 categories (see Supplementary Table 1). (8) Swimming Mode—the mobile strategy adopted by fish species has a direct relationship with ecology and behaviour57. Following FishBase we used 12 different swimming modes (see Supplementary Table 1) based on anatomical and morphological features; these traits provide information on the functional role that each species plays.Reproductive Traits: (9) Generation Time—defined by FishBase as “the time period from birth to average age of reproduction”. (10) Length of First Maturity (mm)—body length when around 50% of a given species becomes mature58,59. (11) Reproductive Guild—15 categories of reproductive guild as defined by FishBase (see Supplementary Table 1). Reproductive traits have a direct influence on population dynamics and species resilience60,61, and are therefore commonly used in fisheries management62.These traits were selected for their ecological and biological relevance as described above. We tested the correlations of the traits to ensure complementarity, and as shown in the (Supplementary Fig. 9a and b), these traits are largely uncorrelated. We also took into consideration the data gaps inevitable in a large data set such as FishBase (traits were selected if a maximum of 30% of data were missing). To overcome this limitation we applied random forest algorithms to fill the missing traits63, by using the package “missForest”64.Step 2—Rarity indicesSpecies restrictednessWe calculated species restrictedness (Resi) by dividing the species geographical extent (GE = number of 2° grid cells that a species occurs in based on the assemblage matrix compiled from AquaMaps) by the total number of grid cells (TOT), minus one (see Supplementary Fig. 2). We scaled the values between 0 (species occurring in all 2° grid cells) and 1 (the most restricted species). We used the function “restrictedness” in the “funrar” package to do this calculation16.Functional distinctivenessFunctional distinctiveness (Disi) quantifies the level of dissimilarity in trait combination between species16,22 (see Supplementary Fig. 2). This index is the average of functional distance of a given species compared with all other species in the assemblage16.We calculated how distinct or common each species is by using the function “distinctiveness_global” available in the “funrar” package16. We then scaled the values found between 0 (species with common combinations of traits) and 1 (species with the most dissimilar combination of traits). This analysis was conducted using presence/absence data.Functional uniquenessFunctional uniqueness quantifies the level of species isolation in the multidimensional functional space16,17. This index is calculated by quantifying the distance of each species in relation to its nearest neighbour16. This mathematical approach applied to multidimensional functional space was adapted from the mean nearest neighbour distance developed initially to calculate the phylogenetic distance between species65 (see equation descriptions at the Supplementary Fig. 2).Step 3—Selecting rare species & Step 4—Rarity biogeographyQuartile analysisWe examined the distribution of values for species restrictedness and functional distinctiveness (or species restrictedness and functional uniqueness) and used the quartile criterion (performed using the base R function “quantile” from the package “stats” in R Core Team66) as proposed by Gaston20 to identify the rare species. By this definition the species considered rare lie in the top quartile of both metrics (i.e. values between 0 (less restricted) and 1 (more restricted)). We next assigned the observed number of rare species (Step 4), as defined above, to each 2° grid cell. The analysis was undertaken separately for Actinopterygii and Elasmobranchii.Step 5—Null modelDoes the number of rare species in a given grid cell differ from the null expectation? To answer this question we applied a null model approach based on the curveball algorithm67. This algorithm keeps constant the total number of species (rare + non-rare) and the number of grid cells that each species occurs. It then randomizes the presence and absence of all species following these thresholds. We ran the model for 2000 iterations; in each loop it randomizes the occurrences of all species, identifies where the rare species are falling and then counts the total number of rare species in each grid cell.To quantify how the observed number of rare species differ from the null expectation we then use Standardized Effect Sizes (SES) as follows:$${{{{{rm{SES}}}}}}=({{{{{rm{X}}}}}}-{{{{{rm{Y}}}}}})/{{{{{rm{Z}}}}}}$$X as the number of rare species observed in each grid cell,Y as the average of rare species found from the null model after 2000 interactions andZ as the standard deviation from Y.A positive SES indicates more rare species than would be expected by chance and a negative SES fewer than expected.We are using 14 different systems (7 Coastal Systems and 7 High Seas (see Supplementary Fig. 3)) for 2 groups of organisms (bony and cartilaginous fish), 2 functional rarity indices (distinctiveness and uniqueness), and using 3 different probabilities of occurrences (prob. >0.9, >0.7 and >0.5).We then have the following “roadmap”:

i.

Scales—7 Coastal Systems and 7 High Seas.

ii.

Groups—bony and cartilaginous fish.

iii.

Indices—distinctiveness and uniqueness.

iv.

Probability of occurrences— >0.9, >0.7 and >0.5.

v.

Total—168 independent cases analysis (each having its own assemblage and trait matrices.

Therefore, as mentioned above, the null model ran for 2000 iterations in each of those independent cases. The final matrices from these initial steps contain grid cells as rows and as columns we have the raw number of rare species along with the SES values for each. These matrices were important to map our results.Step 6—Mapping the resultsAfter the above steps (and using the matrices with the results), to visualise the results for Coastal Systems we plotted the geographic distribution of rarity, measured using the observed number of rare species, based on species Restrictedness and functional Distinctiveness using Fig. 1a, c, and the results from the SES using Fig. 1b, d. Meanwhile in Fig. 2 we constructed the same plots for the High Seas. The complementary results of the alternative approach using species Restrictedness and functional Uniqueness are shown in Supplementary Fig. 4 (for Coastal Systems) and Supplementary Fig. 5 (for High Seas).The flow chart in Supplementary Fig. 1 provides step by step details of what was done for each of the 168 independent cases explained at the “roadmap” above. The comprehensive list of all rare fish species found for each system is available in Supplementary Table 2.Further analysesLatitudinal rarity biogeographyWe then produced the density plots of the positive SES values (using the function geom_density from the package ggplot268) to further understand these patterns in relation to latitude (Fig. 3, from c to j). These were compared with the latitudinal gradient of species richness (Fig. 3a, b). The main text discusses results focused on rarity measured using the probability of occurrence higher than 0.9 (Fig. 3). We also examined density of positive SES values across the latitudinal distribution using the probability of occurrences higher than 0.7 and 0.5 (see Supplementary Fig. 6a–d for probability >0.7 and Supplementary Fig. 6e–f for probability >0.5).Spatial autocorrelationWe constructed distance decay plots to examine spatial autocorrelation, and fitted a quantile regression to these relationships. The results are illustrated in Supplementary Fig. 7, which shows the distance decay calculated by pairwise differences (Supplementary Fig. 7a—Coastal Systems and b—High Seas for bony fish, and Supplementary Fig. 7c—Coastal Systems and d—High Seas for cartilaginous fish) between a given grid cell and all other grid cells present in the Northwest Pacific Ocean. These plots provide reassurance that spatial autocorrelation is not obscuring the results we report.Sensitivity analysisWe performed a sensitivity analysis to ensure that the environmental traits “Depth” and “Mean Temperature Preference” had no major influence on determining the level of distinctiveness and uniqueness of the species. We did this by excluding each trait in turn from the analysis (each of those were removed individually and a third time without both together) and compared the results with the full analysis. We found strong correlations in rarity estimates in all cases (see Supplementary Fig. 8a, b (for bony fish (distinctiveness and uniqueness) respectively, c and d (for cartilaginous fish (distinctiveness and uniqueness)).Trait correlation analysisWe tested the correlation between traits to ensure that those were largely uncorrelated, as shown in Supplementary Fig. 9a, b.Supplementary analysisWe tested the possible influence of sampling effect on the rarity hotspots observed by creating random fill matrices and comparing those with the observed matrices from four scenarios: Northwest Pacific Coast (bony and cartilaginous fish species) and Southwest Pacific Coast (bony and cartilaginous species). The subsequent results showed no evidence of sampling effect (see Supplementary Fig. 11).Mapping marine protected areas (MPAs)We used the MPAs shapefiles provided by the UNEP-WCMC and IUCN69 to measure the level of congruence between marine protected areas and hotspots of rarity. The distances between each MPA and the centroid of each grid cell were calculated using the spatial analysis tool in ArcGIS (the unit of the distance calculated is in decimal degrees). We then assigned each MPA to its nearest 2° degree grid cell centroid (the distance cut point used was < 0.75 decimal degrees (the distance from a given MPA to a grid cell centroid)). We plotted these global spatial patterns from the 2° grid cells indicating either congruence or mismatches between Marine Protected Areas (MPAs) and Rarity Hotspots (species rare in both dimensions of biodiversity; taxonomically—highest restrictedness and functionally—highest distinctiveness) (Fig. 4a and b, bony and cartilaginous fish respectively).Habitat specializationAll species were classified according to their position in the water column (bathydemersal, bathypelagic, benthopelagic, demersal, pelagic neritic, pelagic oceanic and reef associated (as categorized in FishBase19)); here we are using this trait as a proxy for “habitat specialization”. We then used a G test, and Cramer’s V (using the functions GTest and CramerV from the package DescTools70) to compare the frequency distribution in habitat specialization between rare and non-rare species (see Supplementary Fig. 10 for all frequency distributions and statistical results).Forms of functional rarity classification schemeIn their 2017 paper Violle et al.17, suggested 12 forms of functional rarity. We believe that the approach applied here is similar to the classification scheme they described as: “Rare traits irrespective of the scale and the species pool”. The authors pointed to two possible extremes: rare traits (exhibited by range-restricted species) and common traits (supported by many widespread species). In this case, our approach identifies species that are both geographically restricted within each of the 14 systems (coastal and high seas systems) and present a distinct (or unique) combination of traits. Our approach to the classification of rarity differs slightly, however, in that we follow Gaston’s approach20 of quantile distribution as illustrated in Supplementary Fig. 1, step 3 QUARTILES.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Effects of NaCl concentrations on growth indicators of R. soongorica seedlingsAs shown in Table 1, when compared with control A (i.e., 0 mM NaCl), both the fresh weight and root/shoot ratio of R. soongorica in group B (i.e., 200 mM NaCl) were significantly higher. However, both fresh weight and root/shoot ratio gradually decreased in group C (i.e., 500 mM NaCl). When the NaCl concentration reached that of group C (i.e., 500 mM NaCl), the growth of R. soongorica was significantly inhibited. The fresh weight of above-ground and root tissues was respectively 43.82% and 50.99% that of the control, and these differences were significant (P  More