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Response of harmful dinoflagellate distribution in the China seas to global climate change
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Changyou Wang1, *, Yuxing Tang1, Bernd Krock2, Yiwen Xu1, Zhuhua Luo1, 3, 4, Zhaohe Luo3, *
Acta Oceanologica Sinica | 2024, 43(12) : 102 - 112
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Acta Oceanologica Sinica | 2024, 43(12): 102-112
• Marine Biology •
Response of harmful dinoflagellate distribution in the China seas to global climate change
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Changyou Wang1, *, Yuxing Tang1, Bernd Krock2, Yiwen Xu1, Zhuhua Luo1, 3, 4, Zhaohe Luo3, *
Affiliations
  • 1 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2 Alfred Wegener Institut, Helmholtz Zentrum für Polar– und Meeresforschung, Bremerhaven D–27570, Germany
  • 3 Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
  • 4 Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
Published: 2024-12-25 doi: 10.1007/s13131-024-2451-3
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By establishing a distribution and environmental factor database of 21 typical harmful dinoflagellates in global waters, the MaxEnt model was used to predict shifts in the habitat of harmful dinoflagellates in Chinese waters under global climate change. The results revealed that offshore distance was the most important predictive factor and that surface seawater temperature (SST), primary productivity, and nitrate concentration were the key ecological factors influencing the distribution of harmful dinoflagellates. Under the low greenhouse gas emission scenario defined by the Intergovernmental Panel on Climate Change (IPCC), by approximately 2050, 17 of the 21 harmful dinoflagellate species in high-suitability areas (HSA) will migrate northward, six species will migrate eastward, and six species will expand their HSA. By 2100, approximately 18 of the 21 harmful dinoflagellate species in HSA will have migrated northward, seven species will have migrated eastward, and four species will have expanded their HSA. Notably, the HSA content of highly toxic Alexandrium minutum is expected to increase by 13.4% and 9.4% by 2050 and 2100, respectively. Under the high greenhouse gas emissions, there will be 17 species migrating northward, 6 species migrating eastward, and 4 species increasing in their size in HSA by 2050; moreover, there will be 16 species migrating northward, 2 migrating eastward, and 4 species according to their size of HSA by 2100. Specifically, the HSA of A. minutum is predicted to increase by 7.0% and 25.9% by 2050 and 2100, respectively. Notably, A. ostenfeldii, which is currently seldom present in the China seas, is predicted to exhibit an HSA in most coastal areas of the Yellow Sea, the Bohai Sea, the Hangzhou Bay, the Zhejiang Coast, and the Beibu Gulf of the South China Sea. Conversely, the HSA of Noctiluca scintillans, a typical red-tide species, will be reduced by 7%–90%. The northward migration of Karenia mikimotoi exceeded 100 km and 300 km under low and high greenhouse gas emission scenarios, respectively. These changes underscore the significant impact of climate change on the distribution and habitat suitability of harmful dinoflagellates, thus indicating a potential shift in their ecological dynamics and consequent effects on marine ecosystems.

harmful dinoflagellate  /  climate change  /  suitable habitat  /  MaxEnt  /  species distribution shifts
Changyou Wang, Yuxing Tang, Bernd Krock, Yiwen Xu, Zhuhua Luo, Zhaohe Luo. Response of harmful dinoflagellate distribution in the China seas to global climate change[J]. Acta Oceanologica Sinica, 2024 , 43 (12) : 102 -112 . DOI: 10.1007/s13131-024-2451-3
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1 Introduction
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Since the early 20th century, the average surface seawater temperature (SST) worldwide has increased by 0.6℃, causing the distribution range of warm water species to expand towards the poles and the distribution range of cold-water species to shrink (Cai et al., 2020; IPCC, 2019). Since the 1960s, the SST in the China seas has exhibited a significant upward trend. An increasing number of heat waves are occurring as sea levels continue to increase (Cai et al., 2019). In the context of global climate change, the eastern area of China, particularly the East China Sea, is likely to be one of the areas with the greatest future global warming and sea level rise (Cai et al., 2019, 2020; IPCC, 2019, 2021). Harmful algal blooms (HABs) in China predominantly consist of dinoflagellates, with an increasing occurrence of toxic and harmful species (Guo et al., 2014). The unpredictability of these blooms that are significant marine ecological disasters has intensified for coastal stakeholders due to global climate change (Lin et al., 2019; Yu and Chen, 2019; Gu et al., 2022). Therefore, understanding the distribution patterns of harmful dinoflagellates and predicting their future trends can not only provide new theories, technologies, and methods for the prevention and control of harmful dinoflagellate bloom disasters but can also provide scientific support to related industries such as marine fisheries and aquaculture, ultimately reducing economic losses caused by future disasters.
Species distribution models such as Biomod2, MaxEnt, openModeller, and ModEco have been used to analyze the correlation between species distribution data and environmental factors, primarily in the context of terrestrial habitat biogeography (Bertram et al., 2019; Li et al., 2019; Yan et al., 2020). Among these, the MaxEnt model has been extensively employed to predict potential distribution areas of invasive species, identify suitable habitats for endangered and economically important species, and assess species distribution under climate change scenarios (Bertram et al., 2019; Li et al., 2019; Meshgi et al., 2019; Zhang et al., 2019; Yan et al., 2020). Compared to terrestrial species, there are limited reports regarding the use of the MaxEnt model to study the distribution of marine species. Limited data detailing the distribution of marine species are partly responsible for this. Another reason is that planar changes in upper marine habitats are relatively small, and the planar resolution of general marine surveys is low compared with that of terrestrial habitats (Wang et al., 2010; Xie et al., 2019; Melo-Merino et al., 2020). In particular, for global ocean surveys, the grid is above 0.5° in longitude and latitude (http://mds.nmdis.org.cn/).
The MaxEnt model is a species distribution model based on maximum entropy theory (Elith et al., 2011). The maximum entropy principle is a criterion for selecting the probability distribution of random variables that best fits the objective situation (Soofi, 2000). Based on this understanding, it is possible to infer the probability distribution of unknown events from the information of known finite events, as the actual observed real event exhibits the maximum number of approaches to achieve this (Bertram et al., 2019; Li et al., 2019; Meshgi et al., 2019; Yan et al., 2020). The MaxEnt model has been continuously improved and can achieve good prediction results with limited environmental variables and species distribution data (Phillips et al., 2004, 2006, 2017; Phillips and Dudík, 2008). The application of the MaxEnt model to predict species distribution exhibits a small dependence on the sample size of the species distribution data, thus making it suitable for objective situations where there are limited survey data regarding the distribution of harmful dinoflagellates in the ocean (Merow et al., 2013).
Using the MaxEnt model, this study aims to (1) predict the changes in suitable areas for harmful dinoflagellates under global climate change conditions, (2) elucidate the impact of environmental factors such as seawater warming on the potential distribution of harmful dinoflagellates, and (3) discuss new situations faced by marine fisheries, aquaculture, disaster prevention, and reduction.
2 Materials and methods
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2.1 Database development and distribution area division for harmful dinoflagellates
Using database retrieval (ocean biographic information system, OBIS), literature review (https://www.sciencedirect.com; http://www.springer.com; https://www.cnki.net), and field investigation, we collected information detailing 21 typical, globally distributed harmful dinoflagellate species, including their locations, occurrence times, SST, and salinity, to establish a database of harmful dinoflagellate distribution areas (Table 1). Global ocean assimilation data from the same period were used to supplement missing SST and salinity data for harmful dinoflagellates (China National Marine Science Data Center, 2021).
Referring to the gridding method of the global ocean reanalysis system (China National Marine Science Data Center, 2021), the grids were divided into sizes of 0.5° × 0.5° in terms of longitude and latitude. Longitudes 0°−180°−0° were divided into 720 grids, and latitudes from 75°S to 85°N were divided into 348 grids. Grid sizes gradually change according to 0.4°, 0.3°, 0.25°, 0.25°, 0.3°, and 0.4° between –7.5° and 7.5°. The global oceans were divided into 176 340 distribution blocks, and the distribution areas of the 21 dinoflagellates in the global oceans were counted (Table 2). In areas with a high survey frequency, there were numerous records of harmful dinoflagellates, resulting in multiple records in the same distribution area. This makes it possible for survey frequency to affect the prediction results of the model. Therefore, records from the same distribution area were merged to retain only one record from each distribution area.
2.2 Environmental layer variable selection scheme
Marine data layers for ecological modeling have attracted the attention of numerous scholars, with over 600 references or applications in related research (http://www.bio-oracle.org). Bio-ORACLE (version 2.2) data provides 18 global geophysical, chemical, biological, and climate data layers with a common spatial resolution (5 arcmin, 9.2 km at the equator) (Tyberghein et al., 2012; Assis et al., 2018). Additionally, we created a data layer for seawater depth and offshore distance based on Natural Earth data at a 1 m:10 m scale (https://www.naturalearthdata.com/) and saved the data in the same format as in Bio-ORACLE version 2.2. The available Bio-ORACLE (version 2.2) for future environmental variables was produced using climate data derived from IPCC predictions (Assis et al., 2018; IPCC, 2019).
Greenhouse gas emission scenarios are the basis for estimating future climate change. The fifth evaluation report of IPCC adopts a new generation scenario termed “Representative Concentration Pathways” (RCPs) (IPCC, 2013). The RCP8.5 scenario (8.5 W/m2) is the highest greenhouse gas emission scenario. This scenario assumes the largest population, a low technological innovation rate, and slow energy improvement, resulting in slow income growth. This will lead to a long-term high energy demand and high greenhouse gas emissions while lacking policies to address climate change. The RCP2.6 scenario (2.6 W/m2) is a scenario that limits the global average temperature rise to within 2℃. This scenario was the lowest in terms of greenhouse gas emissions and radiative forcing. From 2010 to 2100, cumulative greenhouse gas emissions will be reduced by 70% compared to those of the baseline year. To achieve this, it is necessary to completely change the energy structure and greenhouse gas emissions other than CO2 and to promote the use of biomass energy and forest restoration (Shen and Wang, 2013; IPCC, 2019). Based on the current marine data layers for ecological modeling (Bio-ORACLE version 2.2) and combined with future change trends under RCP8.5 and RCP2.6, 50 data layers of environmental variables for 2050 and 2100 were created by overlaying current environmental variables with future increments. These environmental variables consisted of six functions of ten dominant environmental factors, including seawater temperature, salinity, ocean current rate, sea ice thickness, depth, offshore distance, dissolved oxygen, nitrate concentration, phosphate concentration, and primary productivity. The functions include the long-term average of the maximum records per year (suffix Lt.max), the long-term average of the minimum records per year (suffix Lt.min), the long-term average (Suffix Mean), maximum records (Suffix Max), minimum records (Suffix Min), and the range given by the average of the absolute difference between the minimum and maximum records per year (suffix range).
Screening variables that can provide more information to the model and establish a model with good performance using as few variables as possible can improve the adaptability and accuracy of the model predictions (Wang et al., 2023a). The collinearity of environmental variables can exert adverse effects on the modeling process and the interpretation of results (Beaumont, 1981; Wang et al., 2023b; Kariya et al., 2024). To avoid multicollinearity between variables, highly correlated variables were removed, and collinearity analysis was performed for each type of environmental variable. For variables with collinearity, only representative variables were retained, whereas those with collinearity were excluded. A correlation analysis of the 50 related environmental variables was performed using MATLAB 2021. For a group of environmental variables that were significantly correlated at a 0.01 level with a correlation coefficient greater than 0.8 and considering the representativeness and reliability of the data, non-biological environmental variables were preferred, followed by biological environmental variables. Among the non-biological environmental function variables, the physical environmental variables were preferred, followed by the chemical environmental variables. The dominant environmental variables are presented in Table 3.
Noncollinear variables of the nine selected environmental factors, including ocean current rate, sea ice thickness, temperature, salinity, dissolved oxygen, nitrate, primary productivity, depth, and offshore distance, were used as environmental variables to run the MaxEnt model. The relative contribution and permutation importance of each variable to the predictive ability of the model were calculated (Phillips et al., 2006, 2017; Phillips and Dudík, 2008; Merow et al., 2013), and the importance of environmental factors in regard to the distribution of dinoflagellates was determined based on an analysis of the minimum rank of the relative contribution and permutation importance:
$ {\mathrm{EFR}}_i = \frac{{\displaystyle\sum\limits_1^{{n}} \displaystyle\sum\limits_{j = 1}^6 {\min ({P_{i1}},...,{P_{{{ij}}}})} }}{n} ,$
EFRi, importance rank of i environmental factors on the distribution of dinoflagellates; j, dinoflagellate species; Pij, rank of relative contribution and importance of environmental factors for i environmental factors of j species; n, number of dinoflagellate species.
2.3 Classification of suitability levels for harmful dinoflagellates and calculation of displacement of suitable areas
The potential distribution probabilities of the 21 harmful dinoflagellates in the global ocean were calculated using the MaxEnt model, and the distribution probabilities in the China seas and adjacent waters were extracted to construct a distribution map of these harmful dinoflagellates (Supplementary Figures). Based on the IPCC report regarding the classification for likelihood assessment and the actual location of harmful dinoflagellates in the Chinese seas (IPCC, 2019), the level and corresponding range of the suitable area are defined, where a presence probability <0.05 is an unsuitable area, 0.05≤ presence probability <0.33 is a low suitable area, 0.33≤ presence probability <0.66 is a moderate suitable area (MSA), and presence probability ≥0.66 is a high suitable area (HSA).
The average longitude and latitude coordinates of the distribution areas with the same suitability level for harmful dinoflagellates were calculated as the center coordinates of their suitable areas. The differences in the center coordinates between the present suitable area and the future suitable area for harmful dinoflagellates under RCP2.6 and RCP8.5 were defined as the displacement of the suitable area, and the differences in the size between the present HSA and the future HSA under RCP2.6 and RCP8.5 were calculated as the increase (or decrease) in the size of harmful dinoflagellate distribution areas. The average presence probability of harmful dinoflagellate species in the China seas was defined as the arithmetic mean of the presence probabilities of these 21 analyzed harmful dinoflagellate species and was used to quantitatively express the changes in dinoflagellates in a specific distribution area under the background of global change.
3 Results and discussion
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3.1 Changes in suitable area under RCP2.6 Scenario
Under RCP2.6, by 2050, 17 of the 21 analyzed harmful dinoflagellate species in HSAs will exhibit northward migration compared to their current distributions. Notable exceptions were Alexandrium ostenfeldii, Azadinium spinosum, Gonyaulax verior, and Margalefidinium polykrikoides (Table 4). Specifically, Akashiwo sanguinea, Coolia monotis, Dinophysis acuminata, Gonyaulax spinifera, Gymnodinium catenatum, Karenia mikimotoi, Karlodinium veneficum, Lingulodinium polyedra, Noctiluca scintillans, Ostreopsis ovata, and Prorocentrum lima that have moved northward for over 100 km, reaching 245.9 km, 174.1 km, 147.7 km, 433.0 km, 173.1 km, 102.3 km, 130.4 km, 486.3 km, 182.3 km, 360.3 km, and 178.5 km, respectively (Table 5). In addition to the northward migration, several species displayed eastward movement within their HASs, albeit to a lesser extent. Among these, A. sanguinea exhibited the greatest eastward migration, exceeding 50 km. Other species with notable eastward shifts included A. ostenfeldii, C. monotis, M. polykrikoides, N. scintillans, and Polykrikos hartmannii. Furthermore, six species exhibited an increase in HSA size (Table 4). Alexandrium minutum, A. ostenfeldii, A. spinosum, C. monotis, P. lima, and Protoceratium reticulatum increased by 13.4%, 13.6%, 8.5%, 0.05%, 0.17%, and 0.64%, respectively.
Currently, A. ostenfeldii, A. spinosum, and Gambierdiscus toxicus are limited to the China seas and adjacent waters (Gu et al., 2013; Xu et al., 2021). However, under RCP2.6 in 2050, A. ostenfeldii is expected to receive HSAs in most coastal areas of the Yellow Sea and on the northern and southern coasts of the Bohai Sea, Hangzhou Bay, Zhejiang Province, and Beibu Gulf of the South China Sea (Fig. S1c). Azadinium spinosum is projected to receive HSAs along the northern coast of the Bohai Sea, the northern and eastern coasts of the Yellow Sea, the eastern East China Sea, and the Beibu Gulf of the South China Sea (Fig. S1e). Gambierdiscus toxicus is expected to receive HSAs in most of the South China Sea and the eastern East China Sea (Fig. S1h). Under RCP2.6 2050, the size of areas of high suitability for A. sanguinea in the China seas will decrease by approximately 28.6% and will only be distributed along the coast of Beibu Gulf and the Taiwan Strait, with smaller patches along the eastern and southern coasts of the Korean Peninsula (Fig. S1a). Gonyaulax verior is currently present in the Bohai Sea, Yellow Sea, and South China Sea, but the China seas and adjacent waters will become unsuitable habitats by 2050 under RCP2.6 (Fig. S1j). Polykrikos hartmanii is also not expected to receive HSA in the China seas under RCP2.6 in 2050 (Fig. S1r). Significant reductions in HSA size have been reported in several species, including Azadinium poporum, D. acuminata, G. toxicus, G. spinifera, G. catenatum, K. mikimotoi, K. veneficum, L. polyedra, M. polykrikoides, N. scintillans, O. ovata, P. hartmanii, and Pyrodinium bahamense var. compressibum, with decreases ranging from 6.6% to 80% (Figs S1d, f−i, k−r, and u).
A total of 18 out of 21 harmful dinoflagellate species in HSAs are predicted to migrate northward under RCP2.6 in 2010 compared to the present ones, with the exceptions of A. spinosum, G. verior, and M. polykrikoides (Table 4). In particular, A. sanguinea, C. monotis, D. acuminata, G. spinifera, G. catenatum, L. polyedra, N. scintillans, O. ovata, and P. lima are anticipated to move northward by over 100 km, ultimately reaching 231.2 km, 159.8 km, 131.0 km, 375.3 km, 138.5 km, 457.6 km, 132.7 km, 254.2 km, and 166.0 km, respectively (Table 5). An eastward migration was also expected for A. sanguinea, A. ostenfeldii, C. monotis, M. polykrikoides, N. scintillans, P. hartmannii, and Pyrodinium bahamense var. compressum, although predominantly in A. sanguinea, which is predicted to move more than 50 km eastward. Additionally, four species, including A. minutum, A. ostenfeldii, A. spinosum, and P. reticulatum, increased the size of HSAs by 9.4%, 7.3%, 3.8%, and 0.003%, respectively (Table 5). Compared to that predicted in 2050, the size of A. sanguinea in the HSAs of the China seas will have expanded from 140 000 km2 to 150 000 km2 in 2100 and is mainly distributed along the coast of the Beibu Gulf, the Taiwan Strait, and the coast of the Korean Peninsula (Fig. S2a). The HSAs of A. ostenfeldii are mainly distributed along the northern and southern coasts of the Bohai Sea, the eastern and western coasts of the Yellow Sea, Hangzhou Bay, and the coast of Zhejiang Province, but they have been reduced in size in the Yellow Sea compared to the present size. There were also sporadic HSAs of A. ostenfeldii along the coast of Beibu Gulf in the South China Sea (Fig. S2c). Azadinium spinosum exhibits large HSAs along the eastern coast of the Yellow Sea, the eastern East China Sea, and the Beibu Gulf of the South China Sea; however, several small HSAs occur along the coast of the Shandong Peninsula. Compared with that predicted for 2050, the size of A. spinosum is expected to decrease by 2100 (Fig. S2e). Similar to the prediction for 2050, G. verior will not exhibit HSAs in the China seas or adjacent waters (Fig. S2j) in 2100, nor will P. hartmanii in the China seas (Fig. S2r). However, G. toxicus possesses areas of high suitability in the South China sea and East China sea (Fig. S2h).
The HSA sizes of A. sanguinea, A. poporum, C. monotis, D. acuminata, G. toxicus, G. spinifera, G. catenatum, K. mikimotoi, K. veneficum, L. polyedra, M. polykrikoides, N. scintillans, O. ovata, P. hartmanii, P. lima, and Pyrodinium bahamense var. compressibum will exhibit a decrease of 4.7% to 78.6% by 2100 (Figs 2a, d, f−i, k−s, u) compared to the present ranges.
As a whole, under the RCP2.6, the overall suitable area, an average presence probability of all 21 analyzed harmful dinoflagellate species is ≥0.33, and they will move northward for over 240 km and 226 km by 2050 and 2100, respectively, compared to their current area. The overall suitable area is expected to decrease by 19.1% and 16.4% by 2050 and 2100, respectively. However, the meridional migration distance of the total HSA was <15 km (Fig. 1).
3.2 Changes in suitable area under RCP8.5 Scenario
In 2050, 17 of the 21 harmful dinoflagellate species in HSAs will migrate northward under RCP8.5 compared to the present values, with the exception of A. spinosum, G. verior, M. polykrikoides, and P. hartmannii. In particular, A. sanguinea, C. monotis, D. acuminata, G. catenatum, N. scintillans, and P. lima moved northward by over 100 km. Lingulodinium polyedra moved over 500 km, O. ovata moved over 600 km, and G. spinifera moved over 800 km, respectively (Table 5). Moreover, A. sanguinea, A. ostenfeldii, C. monotis, M. polykrikoides, N. scintillans, and P. hartmannii migrated eastward in highly suitable areas, although the eastward migration distance was relatively small. Only A. sanguinea moved eastward over 50 km. Only 4 of the 21 harmful dinoflagellate species (A. minutum, A. ostenfeldii, A. spinosum, and C. monotis) increased in size by 7.0%, 3.3%, 11.3%, and 0.3%, respectively (Table 5). Similar to RCP2.6 2050, G. verior and P. hartmanii did not receive HSAs in the China seas (Figs S3j, r). Dinophysis acuminata causes HSAs along the entire coast of the China seas, but its distribution area has decreased by 4.1% compared to the present distribution. The HSA sizes of A. sanguinea, A. poporum, D. acuminata, G. toxicus, G. spinifera, G. catenatum, K. mikimotoi, K. veneficum, L. polyedra, M. polykrikoides, N. scintillans, O. ovata, P. hartmanii, P. lima, P. reticulatum, and Pyrodinium bahamense var. compressibum decreased by 29.8% on average, ranging from 1.6% to 89.5% (Table 5) (Figs S3a, d, g−i, k−u).
Under RCP8.5 as projected for 2100, the HSAs of 16 of the 21 harmful dinoflagellate species will shift northward compared to their current positions, with the exception of A. spinosum, G. verior, M. polykrikoides, O. ovata, and P. hartmannii. Particularly, A. minutum, A. ostenfeldii, C. monotis, D. acuminata, G. catenatum, K. mikimotoi, K. veneficum, P. lima, P. reticulatum, and Pyrodinium bahamense var. compressibum are predicted to move northward by over 100 km. Noctiluca scintillans moved over 500 km, A. sanguinea and L. polyedra moved over 600 km, and G. spinifera moved over 1 200 km (Table 5). Moreover, C. monotis and P. hartmannii migrated eastward in highly suitable areas, although their eastward migration distances were relatively small. Only C. monotis moved more than 50 km eastward. Additionally, four species (A. minutum, A. ostenfeldii, A. spinosum, and C. monotis) exhibited an increase in the size of HSA by 25.9%, 12.1%, 16.9%, and 24.5%, respectively. Gonyaulax verior, O. ovata, and P. hartmanii did not produce HSAs in the China seas (Figs S4j, q and r).
The sizes of A. sanguinea, A. poporum, D. acuminata, G. toxicus, G. spinifera, G. catenatum, K. mikimotoi, K. veneficum, L. polyedra, M. polykrikoides, N. scintillans, P. hartmanii, P. lima, P. reticulatum, and Pyrodinium bahamense var. compressibum were predicted to decrease significantly, ranging from 8.5% to 94.5%, by 2100 (Table 5) (Figs S4a, d, g−i, k−p, r−u).
Under RCP8.5, the overall suitable area will move northward by over 265 km and 535 km by 2050 and 2100, respectively, compared to the current area. The overall suitable area is expected to decrease by 22.7% and 42.9% by 2050 and 2100, respectively. However, the overall suitable area will move westward by 26 km by 2050 and 150 km by 2100 (Fig. 1).
3.3 Impact of global changes on the habitat of harmful dinoflagellates
Although 15 variables consisting of nine environmental factors were used in the MaxEnt model for each species (Table 3), five of them contributed to an average of 85% (ranging from 71% to 97%) of the predictive power of the model (Table 6). Therefore, this discussion focused on the five most influential environmental factors.
According to the EFR calculated using the relative contribution and permutation importance of each environmental variable (Phillips and Dudík, 2008; Phillips et al., 2017; Merow et al., 2013), offshore distance was the most important predictive factor and ranked among the top five most important environmental factors for all 21 dinoflagellate species, with 12 species ranking first (Table 7). Seawater depth was ranked among the five most important environmental factors for 19 species, with seven species ranking first. This indicates that the 21 dinoflagellate species primarily belong to nearshore environments (Qi et al., 2003; Lin et al., 2019), which is consistent with the importance of altitude in terrestrial habitats (Wang et al., 2017). Spatial variables in the ocean, such as seawater depth and offshore distance, also strongly influenced the distribution of harmful dinoflagellates in seawater (Table 6). Only A. sanguinea and C. monotis will move more than 50 km eastward and will not move more than 100 km. This was irrespective of RCP2.6 or 8.5 (Table 5), indicating that the behavior of nearshore species did not change.
Surface seawater temperature (SST) has emerged as a critical ecological factor, ranking among the top five most important environmental factors for 18 dinoflagellates, with K. mikimotoi ranking first. Karenia mikimotoi exhibits the second largest cumulative area of harmful algal blooms (HABs), possibly being the most important HAB species in China in terms of the economic losses (Liu et al., 2015; Lü et al., 2019; Gu et al., 2022; Ministry of Natural Resources of the People’s Republic of China, 1998−2022). The rise in SST would promote the northward migration of K. mikimotoi more than 100 km and 300 km under RCP 2.6 and 8.5, respectively (Table 5). This will likely lead to K. mikimotoi becoming a common species in the Yellow Sea and increase the risk of developing HAB (Wang et al., 2024). Furthermore, SST ranked among the top ecological factors for 11 dinoflagellate species: C. monotis, G. toxicus, G. verior, G. catenatum, L. polyedra, N. scintillans, O. ovata, P. hartmannii, P. reticulatum, and Pyrodinium bahamense var. compressum, indicating that changes in temperature have a significant effect on their distribution. Interestingly, the contribution of offshore distance to the prediction of dinoflagellate distribution was greater than that of temperature (Table 7), as the studied dinoflagellates were primarily nearshore species, and temperature changes were not as significant as offshore distance changes.
Primary productivity was the second most important ecological factor and ranked among the five most important environmental factors for the 13 dinoflagellate species, with A. spinosum ranking first. Although A. spinosum has not been documented in the China seas as a de novo producer of azaspiracids (Tillmann et al., 2009), it is currently larger than 619 100 km2 with an average increase of 10% under global climate change (Table 5). Their potential expansion poses a significant risk to marine fisheries and aquaculture systems. Cysts of A. spinosum have been identified along the coast of the China seas (Wang et al., 2022). Moreover, primary productivity ranks among the top 1 in the ecological factors for seven dinoflagellates, A. minutum, A. poporum, Karenia mikimotoi, L. polyedra, N. scintillans, and P. lima, indicating great potential for the expansion of these heterotrophic or mixotrophic species, with further increases in primary productivity and eutrophication levels in the China seas and adjacent waters (Burkholder et al., 2008).
Nitrate concentration was the third most important ecological factor and ranked among the top five most important environmental factors for seven dinoflagellate species, with no species ranking first. However, nitrate concentration was ranked among the top ecological factors for the four dinoflagellate species, A. sanguinea, A. ostenfeldii, D. acuminata, and G. toxicus (Table 7). This indicates that aggravated eutrophication is beneficial for the diffusion of these autotrophic and mixotrophic species and could even trigger blooms in the China seas and adjacent waters (Burkholder et al., 2008; Wang et al., 2024). Akashirvo sanguinea has frequently bloomed in Chinese waters since 1998 and has been categorized as a species that causes irregular blooms (Chen et al., 2019). Alexandrium ostenfeldii, occurring usually in coastal areas at high latitudes, was reported to be present in the China seas in 2011 (Gu, 2011). Akashirvo sanguinea, D. acuminata, and G. toxicus are examples of free-living, mixotrophic, and harmful algal species that thrive in eutrophic estuarine marine coastal waters (Burkholder et al., 2008; Chen et al., 2019). These records partially support the importance of nutrients for dinoflagellate species during autotrophy and mixotrophy.
Ice thickness was ranked among the top ecological factors for M. polykrikoides and O. ovata, dissolved oxygen for G. spinifera and P. reticulatum, salinity for K. veneficum, and ocean current velocity for P. bahamense var. compressum ranked among the top ecological factors for these organisms.
According to the IPCC Assessment Report (IPCC, 2021), the increases in temperature are an important projection of global climate change, and global mean SST would increase by 0.64–0.73℃ with a range from 0.20℃ to 1.27℃ under RCP2.6 from 2050 to 2100. The projected change would be up to 0.95–2.58℃ with a range from 0.60℃ to 3.51℃ under RCP8.5 from 2050 to 2100 (IPCC, 2019). An increase in SST will significantly affect biodiversity and adaptability (IPCC, 2019; Cai et al., 2020). As presented in Table 5, more than 70%–80% of the species would suffer a decrease in HSA in the near term (2050) and at the end of the century (2100) relative to current levels. Some species, such as Osteopsis ovata, have completely lost their HSA in the China seas, posing a significant threat to biodiversity. Conversely, although A. ostenfeldii, A. spinosum, and G. toxicus rarely occur in the China seas and adjacent waters, they possess a suitable area with a current size of more than 2.2×105 km2 regardless of the RCP (Figs S1−4), indicating an increase in their adaptability. Nevertheless, HSA for G. verior is lacking in the China seas, both currently and in the future, regardless of the RCP.
The northward migration of 70%–80% of dinoflagellate species poses a serious challenge to China, possibly resulting in the emergence of new types of HABs in the northern China seas (Gu et al., 2022). HABs in China have primarily occurred in the Bohai Sea and southeastern coastal waters of the China seas over the past 30 years (Zhang, 2013; Guo et al., 2015); however, the frequency and area of HABs caused by dinoflagellates in the Yellow Sea have increased in recent years (Ministry of Natural Resources of the People’s Republic of China, 1998–2022; Wang et al., 2023a). In particular, as one of the top disasters caused by dinoflagellate blooms in China, N. scintillans exhibit the highest frequency of HABs as dominant organisms in recent years, while the K. mikimotoi and G. catenatum exert the most serious effects of HABs caused by toxic species on social life and production (Gu et al., 2013; Lü et al., 2019; Li, 2021; Ministry of Natural Resources of the People’s Republic of China, 1998–2022; Wang et al., 2023a). A previous study reported that 173 species of HAB organisms could produce toxins or exert toxic effects (http://www.marinspecies.org/hab), 94 of which are dinoflagellates (Lin et al., 2019). The northward migration of harmful dinoflagellates affects the primary productivity and ecosystem structure in the China seas and adjacent waters. Therefore, more attention should be paid to the future biogeographical impacts of climate change on toxin-producing species and to proactive response measures. Although suggestions for improving the information level in HAB monitoring, risk assessment, and graded HABs forecasting have been proposed based on the characteristics of HAB disasters along the coast of the China seas (Guo et al., 2014; Zhou et al., 2020), the environmental management of coastal habitats and ecosystems in relation to marine fisheries and aquaculture should be studied further.
4 Conclusions
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Based on the calculation of the EFR for the distribution of dinoflagellates, offshore distance was observed to be the most important predictive factor for detecting HSA (similar to the altitude of terrestrial habitats), and this is an important environmental variable affecting species distribution. SST was the most important ecological factor and ranked among the top ecological factors for 11 out of 21 species and led to the northward migration of 14 species, particularly toxic species such as K. mikimotoi and G. catenatum. This poses a serious challenge for the environmental management of coastal habitats and ecosystems in China. Moreover, primary productivity was ranked among the top ecological factors for seven heterotrophic and mixotrophic species, and nitrate concentration was ranked among the top ecological factors for four autotrophic species, thus indicating an increase in the risk of changes in HSA with boosted primary productivity and aggravated eutrophication in the China seas and adjacent waters.
Greater than 70%–80% of species would suffer a decrease in HSA and northward migration in the near term and at the end of the century. This may result in the emergence of new types of HABs in the northern area of the China seas and exert great impacts on biodiversity and adaptability in the future. Global climate change poses a serious challenge to China, and efforts should be made to encourage further research to examine the future biogeographical impacts of toxin-producing species and develop countermeasures for the environmental management of coastal habitats and ecosystems.
Acknowledgements
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We would like to thank Editage (www.editage.cn) for English language editing.
Funding
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  • The National Key Research and Development Program of China under contract Nos 2019YFE0124700 and 2022YFC3106002.
References
Less
Assis J, Tyberghein L, Bosch S, et al. 2018. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3): 277–284, doi: 10.1111/geb.12693
Beaumont C D. 1981. Regression diagnostics—Identifying influential data and sources of collinearity. Journal of the Operational Research Society, 32(2): 157–158
Bertram J, Newman E A, Dewar R C. 2019. Comparison of two maximum entropy models highlights the metabolic structure of metacommunities as a key determinant of local community assembly. Ecological Modelling, 407: 108720, doi: 10.1016/j.ecolmodel.2019.108720
Burkholder J M, Glibert P M, Skelton H M. 2008. Mixotrophy, a major mode of nutrition for harmful algal species in eutrophic waters. Harmful Algae, 8(1): 77–93, doi: 10.1016/j.hal.2008.08.010
Cai Rongshuo, Han Zhiqiang, Yang Zhengxian. 2020. Impacts and risks of changing ocean on marine ecosystems and dependent communities and related responses. Climate Change Research (in Chinese), 16(2): 182–193
Cai Rongshuo, Tan Hongjian, Guo Haixia. 2019. Responses and compound risks of the coastal China areas to global change. Journal of Applied Oceanography (in Chinese), 38(4): 514–527
Chen Baohong, Wang Kang, Lin Hui. 2019. Akashiwo sanguinea blooms in Chinese waters in 1998–2017. Marine Pollution Bulletin, 149: 110652, doi: 10.1016/j.marpolbul.2019.110652
China National Marine Science Data Center. 2021. Further analysis of global monthly average data (in Chinese). http://mds.nmdis.org.cn/[2019-08-06/2021-05-16]
Elith J, Phillips S J, Hastie T, et al. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1): 43–57, doi: 10.1111/j.1472-4642.2010.00725.x
Gu Haifeng. 2011. Morphology, phylogenetic position, and ecophysiology of Alexandrium ostenfeldii (Dinophyceae) from the Bohai Sea, China. Journal of Systematics and Evolution, 49(6): 606–616, doi: 10.1111/j.1759-6831.2011.00160.x
Gu Haifeng, Liu Tingting, Vale P, et al. 2013. Morphology, phylogeny and toxin profiles of Gymnodinium inusitatum sp. nov., Gymnodinium catenatum and Gymnodinium microreticulatum (Dinophyceae) from the Yellow Sea, China. Harmful Algae, 28: 97−107
Gu Haifeng, Wu Yiran, Lü Songhui, et al. 2022. Emerging harmful algal bloom species over the last four decades in China. Harmful Algae, 111: 102059, doi: 10.1016/j.hal.2021.102059
Guo Hao, Ding Dewei, Lin Feng’ao, et al. 2015. Characteristics and patterns of red tide in China coastal waters during the last 20 a. Advances in Marine Science (in Chinese), 33(4): 547–558
Guo Hao, Lin Feng’ao, Liu Yongjian, et al. 2014. High-incidence HABs causative species in China coastal waters and the forewarning method based on the HABs risk index. Marine Environmental Science (in Chinese), 33(1): 94–98
IPCC. 2013. Summary for policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group Ⅰ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 3–29
IPCC. 2019. Summary for policymakers. In: Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 3–34
IPCC. 2021. Summary for policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group Ito the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, 3–32
Kariya T, Kurata H, Hayashi T. 2024. A modelling framework for regression with collinearity. Journal of Statistical Planning and Inference, 228: 95–115, doi: 10.1016/j.jspi.2023.07.001
Li Xueding. 2021. Occurrence characteristics of the red tide in Fujian coastal waters during the last two decades. Marine Environmental Science (in Chinese), 40(4): 601–610
Li Jiangyue, Chang Hong, Liu Tong, et al. 2019. The potential geographical distribution of Haloxylon across Central Asia under climate change in the 21st century. Agricultural and Forest Meteorology, 275: 243–254, doi: 10.1016/j.agrformet.2019.05.027
Lin Senjie, Ji Nanjing, Luo Hao. 2019. Recent progress in marine harmful algal bloom research. Oceanologia et Limnologia Sinica (in Chinese), 50(3): 495–510
Liu Guiying, Ge Kun, Song Lun, et al. 2015. The research trend of Karenia mikimotoi. Marine Sciences (in Chinese), 39(9): 117–122
Lü Songhui, Cen Jingyi, Wang Jianyan, et al. 2019. The research status quo, hazard, and ecological mechanisms of Karenia mikimotoi red tide in coastal waters of China. Oceanologia et Limnologia Sinica (in Chinese), 50(3): 487–494
Melo-Merino S M, Reyes-Bonilla H, Lira-Noriega A. 2020. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecological Modelling, 415: 108837, doi: 10.1016/j.ecolmodel.2019.108837
Merow C, Smith M J, Silander J A. 2013. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography, 36(10): 1058–1069, doi: 10.1111/j.1600-0587.2013.07872.x
Meshgi B, Majidi-Rad M, Hanafi-Bojd A A, et al. 2019. Ecological niche modeling for predicting the habitat suitability of fascioliasis based on maximum entropy model in southern Caspian Sea littoral, Iran. Acta Tropica, 198: 105079, doi: 10.1016/j.actatropica.2019.105079
Ministry of Natural Resources of the People’s Republic of China. 1998–2022. Bulletin of China Marine Disaster (1998–2022) (in Chinese). https://www.mnr.gov.cn/sj/sjfw/hy/gbgg/zghyzhgb/index_1.html[2023-12-13/2023-04-08]
Phillips S J, Anderson R P, Dudík M, et al. 2017. Opening the black box: An open-source release of MaxEnt. Ecography, 40(7): 887–893, doi: 10.1111/ecog.03049
Phillips S J, Anderson R P, Schapire R E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4): 231–259, doi: 10.1016/j.ecolmodel.2005.03.026
Phillips S J, Dudík M. 2008. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography, 31(2): 161–175, doi: 10.1111/j.0906-7590.2008.5203.x
Phillips S J, Dudík M, Schapire R E. 2004. A maximum entropy approach to species distribution modeling. In: Proceedings of the Twenty-First International Conference on Machine Learning. Banff, Alberta, Canada: ACM, 83
Qi Yuzao, Zou Jingzhong, Liang Song. 2003. Red Tide along the Coast of China (in Chinese). Beijing: Science Press, 1–2
Shen Yongping, Wang Guoya. 2013. Key findings and assessment results of IPCC WGI Fifth Assessment Report. Journal of Glaciology and Geocryology (in Chinese), 35(5): 1068–1076
Soofi E S. 2000. Principal information theoretic approaches. Journal of the American Statistical Association, 95(452): 1349–1353, doi: 10.1080/01621459.2000.10474346
Tillmann U, Elbrächter M, Krock B, et al. 2009. Azadinium spinosum gen. et sp. nov. (Dinophyceae) identified as a primary producer of azaspiracid toxins. European Journal of Phycology, 44(1): 63–79, doi: 10.1080/09670260802578534
Tyberghein L, Verbruggen H, Pauly K, et al. 2012. Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21(2): 272–281, doi: 10.1111/j.1466-8238.2011.00656.x
Wang Rulin, Li Qing, Feng Chuanhong, et al. 2017. Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling. Acta Ecologica Sinica (in Chinese), 37(24): 8556–8566
Wang Changyou, Wang Xiulin, Liang Shengkang, et al. 2010. The methods for calibrating average concentration in the domain and estimating average annual concentration of pollutants in the sea area being partially or totally different from target domains. Haiyang Xuebao (in Chinese), 32(2): 155–160
Wang Changyou, Xu Yiwen, Gu Haifeng, et al. 2023a. Expansion risk of the toxic dinoflagellate Gymnodinium catenatum blooms in Chinese waters under climate change. Ecological Informatics, 75: 102042, doi: 10.1016/j.ecoinf.2023.102042
Wang Changyou, Xu Yiwen, Gu Haifeng, et al. 2024. Potential geographical distribution of harmful algal blooms caused by the toxic dinoflagellate Karenia mikimotoi in the China Sea. Science of the Total Environment, 906: 167741, doi: 10.1016/j.scitotenv.2023.167741
Wang Endong, Zhang Lianjun, Chen Hong, et al. 2023b. Collinearity-oriented sensitivity analysis for patterning energy factor significance in buildings. Journal of Building Engineering, 73: 106685, doi: 10.1016/j.jobe.2023.106685
Wang Zhaohui, Zhang Yuning, Wang Wenting, et al. 2022. Distribution of dinoflagellate cysts in surface sediments on the Dongshan Bay, Fujian Province, China. Journal of Tropical Oceanography (in Chinese), 41(4): 154–162
Xie Pengfei, Gu Yanbin, Sui Weina, et al. 2019. Big data challenges for species distribution models in predicting potential distribution of marine species. Marine Information (in Chinese), 34(1): 51–61
Xu Yixiao, He Xilin, Lee Wai Hin, et al. 2021. Ciguatoxin-producing dinoflagellate Gambierdiscus in the Beibu Gulf: First report of toxic Gambierdiscus in Chinese Waters. Toxins (Basel), 13(9): 643, doi: 10.3390/toxins13090643
Yan Huyong, Feng Lei, Zhao Yufei, et al. 2020. Predicting the potential distribution of an invasive species, Erigeron canadensis L., in China with a maximum entropy model. Global Ecology and Conservation, 21: e00822
Yu Zhiming, Chen Nansheng. 2019. Emerging trends in red tide and major research progresses. Oceanologia et Limnologia Sinica (in Chinese), 50(3): 474–486
Zhang Qingtian. 2013. Review on the annual variation of red tides in China Sea. Environmental Monitoring in China (in Chinese), 29(5): 98–102
Zhang Danhua, Hu Yuanman, Liu Miao. 2019. Potential distribution of Spartinal alterniflora in China coastal areas based on MaxEnt niche model. Chinese Journal of Applied Ecology (in Chinese), 30(7): 2329–2337
Zhou Jian, Wang Wei, Wu Zhihong, et al. 2020. The basic characteristics and prevention countermeasures of red tide in Shandong coast waters. Marine Environmental Science (in Chinese), 39(4): 537–543
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Year 2024 volume 43 Issue 12
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doi: 10.1007/s13131-024-2451-3
  • Receive Date：2023-03-21
  • Online Date：2025-11-19
  • Published：2024-12-25
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  • Received：2023-03-21
  • Accepted：2024-09-25
Funding
The National Key Research and Development Program of China under contract Nos 2019YFE0124700 and 2022YFC3106002.
Affiliations
    1 School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2 Alfred Wegener Institut, Helmholtz Zentrum für Polar– und Meeresforschung, Bremerhaven D–27570, Germany
    3 Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
    4 Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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