Diversities of the phytoplanktonic and zooplanktonic communities
A total of 251 waters samples were collected from 12 sites along the coast of China, covering more than 13,000 km (Fig. S1). 12 water chemical variables were measured for each sample (i.e. pH, salinity, COD, DO, Pb2+, Cu2+, Zn2+, As3+, NO2-N, NO3-N, NH4-N and Chlorophyll-a concentration) (Table S1). The samples could be grouped into three geographic regions, Bohai Sea (BS), East China Sea (ECS), and South China Sea (SCS), representing the northern, middle, and southern regions of the Chinese coastal marine ecosystems respectively.
A total 307 phytoplanktonic species and 311 zooplanktonic species were identified, and the total planktonic density counted in each sample. The relative abundance of plankton species in all sampling sites are summarized at the genus level (Fig. 1). The Paracalanus was the dominant zooplanktonic genus in most sampling sites, except Site2 and Site8. In those two stations, Acartia and Amphorellopsis have the highest relative abundance, respectively (Fig. 1A). In the phytoplankton, the Skelectonema was the major genus in most sites. But the Chactoceros was the dominant genus in Site5 and Site6, and the Coscinodiscus has a higher relative abundance than others in the Site9 (Fig. 1B). The northern region has the highest total zooplanktonic density, and then rapid decline along with the latitude decrease (Fig. 1A). The phytoplankton has opposite tendencies, the southern region has a higher total density than the middle and northern regions (Fig. 1B). The observed richness of phytoplankton and zooplankton both showed significant geographic differences among all sampling sites (Fig. S2A and S2D, Table S2, Kruskal-Wallis test, P<0.05). More precisely, we found they had the lowest richness in the northernmost site 1 and then richness, in general, gradually increased as latitude decreased, though the maximum richness did not appear in the southernmost site, Site 12 (Fig. S2A and S2D). By grouping the closest sites into three regions (BS, ECS, and SCS), this trend became much clearer (Fig. S2B and S2E; Kruskal-Wallis test, P<0.001) with both phytoplanktonic and zooplanktonic richness showing significant linear correlation to latitude (Fig. S2C and S2F; P<0.001). Together, these results indicated there were significant latitudinal gradients in the richness of both zooplankton and phytoplankton.
Phytoplankton and zooplankton exhibiting distinct features
Coastal ecosystem zooplankton and phytoplankton community relationships were visualized via non-metric multidimensional scaling (NMDS) of the compositional dissimilarities, showed that both the zooplanktonic and phytoplanktonic communities of the 12 sites formed distinct clusters as confirmed by similarity analysis (ANOSIM, permutations=999, p=0.001), permutational multivariate analysis of variance (ADONIS, permutations=999, p=0.001), and multi response permutation procedure (MRPP, permutations=999, p=0.001) (Fig. 2). Together these results indicated that the species composition of both the phytoplanktonic and zooplanktonic communities was significantly different among the 12 sampling sites.
To reveal the biogeographic patterns of both phytoplanktonic and zooplanktonic communities and the drivers of plankton beta diversity, we measured the distance-decay relationships (DDRs) between beta diversity (Sorensen) and geographic distances (Fig. S3). The slopes of both DDRs reflected the spatial turnover rates at which planktonic dissimilarity significantly increased with geographic distance (P=0.01). Permutation test showed that the spatial turnover rate (the slope) of the zooplanktonic community (0.074) was significantly steeper (P<0.001) than the phytoplanktonic community (0.047), suggesting the zooplanktonic community had greater differences than phytoplankton across larger spatial scales.
Environmental factors on shaping these two planktonic communities
To identify which environmental factor(s) and/or if geographic distance played more important role(s) in driving phytoplanktonic and zooplanktonic communities, partial Mantel tests (Spearman’s correlation, permutations=999) were implemented (Table 1). Geographic distance showed a significant contribution to both zooplanktonic (r=0.23, P=0.001) and phytoplanktonic (r=0.24, P=0.001) communities. Compared to the phytoplanktonic community (r=0.30, P=0.001), the joint environmental factors showed greater contribution to the zooplanktonic community (r=0.49, P=0.001). Canonical Correlation Analysis (CCA) was used to identify the environmental and spatial variations in shaping the phytoplanktonic and zooplanktonic community structures (Fig. S4). Total 16 spatial factors (PCNM1-16) were generated to explore the contribution of spatial in constructing the planktonic communities. 10 spatial and 9 environmental factors were contributed significantly to explaining the zooplanktonic community composition. For the phytoplanktonic community, the significant factors consisted of 5 spatial and 7 environmental factors (Table S3). The results of variation partitioning analysis (VPA) revealed that the spatial variables contributed a substantially larger proportion of variation relative to environmental factors to the zooplankton (16.463%) and phytoplankton (9.279%) community, suggesting spatial limitation could be an important factor determining the compositions of both phytoplankton and zooplankton in this latitudinal gradient.
Table 1. Partial Mantel tests showing the correlations between planktonic community compositions and environmental distance or geographic distance.
|
Partial
|
Zooplankton
|
Phytoplankton
|
|
rM
|
P
|
rM
|
P
|
Geographic
distance
|
Environmental
factors
|
0.23
|
0.001
|
0.24
|
0.001
|
Environmental
factors
|
Geographic
distance
|
0.49
|
0.001
|
0.30
|
0.001
|
Interaction network between two planktonic communities
The Inter-Domain Ecological Network (IDEN) approach [23] was implemented to analyze the interaction between phytoplanktonic and zooplanktonic species in the three regions. These three bipartite networks were obvious topological differences (Fig. 3). The structure of the southern region (SCS) phytoplankton-zooplankton network (Fig. 3C) showed higher complexity and connectivity than those of other two regions (BS and ECS) (Fig. 3A and 3B). According the topological indexes of these bipartite networks (Table 2), the network size (number of nodes), number of links, and connectance all demonstrated that the interactions between phytoplanktonic and zooplanktonic species became closer from north to south. This suggested that the interactions between phytoplankton and zooplankton also had a latitudinal gradient along the Chinese coastline.
Table 2. Network topological structure properties for the planktonic bipartite networks of the three regions. (BS) Bohai Sea, (ECS) East China Sea, (SCS) South China Sea.
|
BS
|
ECS
|
SCS
|
No.phy
|
66
|
87
|
63
|
No.zoo
|
39
|
68
|
42
|
Total link
|
254
|
587
|
1294
|
Positive Link
|
186
|
444
|
711
|
Negative Link
|
68
|
143
|
583
|
Connectance
|
0.0987
|
0.0992
|
0.1015
|
Web asymmetry
|
0.2571
|
0.1226
|
0.1013
|
Links per species
|
2.4190
|
3.7871
|
5.7004
|
No.of compartments
|
2
|
4
|
4
|
Specialization
asymmetry
|
-0.1204
|
-0.701
|
-0.0626
|
Modularity
|
0.5356
|
0.4709
|
0.3313
|
No. of modules
|
6
|
10
|
11
|
In order to distinguish the most important species, the networks were further classified into sub-structures, called modules, which contained groups of species that have intensive interactions with each other, but few interactions with members from other modules. The topological role of each node (species) could be measured by using its within-module connectivity (Zi) and among-module connectivity (Pi). All planktonic species in the ZiPi plot could be divided into four categories: peripherals, connectors, module hubs, and network hubs. The latter three were considered as keystone taxa in the bipartite networks (Fig S5). The phytoplankton-zooplankton networks were primarily composed of keystone taxa and their neighbor (Fig. 3D‒F). Subnetworks for the interactions among keystone taxa and their neighbors were analyzed to identify the role of these members in structuring the planktonic communities. These sub-networks contained the majority of planktonic organisms and had the same structure variation patterns as the full networks, in being more complex and tighter in the middle and southern regions (ECS and SCS) than the northern region (BS).
The specific interactions between common important phytoplanktonic and zooplanktonic species in the bipartite networks were studied further detail. First, the subnetworks for the interactions among three predominant planktonic group, (diatoms, copepoda, and dinoflagellates), were analyzed to reveal how the relationships between them changed along the latitudinal gradient (Fig. 3G‒I). The maximum proportion of diatoms was found in the northern region (BS, 62.5%), and then gradually decreased with decreasing latitude (55.17% for ECS and 54.48% for SCS). The proportion of dinoflagellates (10.42% for ES, 12.65% for ECS, and 13.80% for SCS) in the subnetworks, showed an increasing trend from north to south. In addition, a lower percentage of copepoda was observed in the BS (27.08%) than in ECS (32.18%) or SCS (31.72%) (Fig. 3G‒I). We found the percentage of interaction types (positive and negative relationships) also changed with latitude. Positive relationships made up the majority of planktonic interactions (68.04%) in the middle region (ECS), and then decreased towards the south (SCS, 52.43%) and north (BS, 60.94%) regions. In addition, 17 planktonic species (10 phytoplanktonic species and 7 zooplanktonic species) were found in all three planktonic networks, and those organisms were linked with different neighbors in three regions. However, none of the planktonic links appeared two or more times in the three regions, indicating that the relationship between zooplankton and phytoplankton was altered between geographic regions, and that planktonic species link with different neighbors when they appeared in different the locations.
Based on the above bipartite network, we extracted a subnetwork which only contained the common planktonic species observed in all three sampling regions with their neighbors (Fig. 4). Most of these common planktonic species (8 phytoplankton and 5 zooplankton) had almost equivalent neighbors in all three regions (the spots in the middle of Fig. 4). However, the phytoplanktonic Coscinodiscus oculus-iridis and zooplanktonic Centropages orsinii were both associated with more neighbors in northern (BS) region than the other two regions, while phytoplanktonic Chaetocero lorenzianus and zooplanktonic Oncaea clevei had more and more neighbors from north to south. Although the majority of planktonic species were associated with consistent planktonic neighbors in three regions, some interactions have been altered among regions. For example, C. lorenzianus showed interactions with Planktonic larvae in both middle (ECS) and southern (SCS), but only associated with Copepoda in northern (BS). The phytoplanktonic C. oculus-iridis has a specific relationship in different regions, it was only associated with Copepoda and Chaetognatha in the middle (ECS) and southern (SCS) respectively. These results demonstrated the interactions between phytoplankton and zooplankton had a clear spatial pattern while different planktonic species could contact with specific taxa in different regions.