Trophic interaction affects the metacommunity structures of free-living bacterioplankton and heterotrophic nanoflagellates in the Kuroshio region

Trophic interaction affects the metacommunity structures of free-living bacterioplankton and heterotrophic nanoflagellates in the Kuroshio region | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Molecular Ecology This is a preprint and has not been peer reviewed. Data may be preliminary. 10 March 2025 V1 Latest version Share on Trophic interaction affects the metacommunity structures of free-living bacterioplankton and heterotrophic nanoflagellates in the Kuroshio region Authors : Feng-Hsun Chang 0000-0002-8202-6364 [email protected] , Ariana Chih-Hsien Liu , Jinny Yang , Hiroaki Saito 0000-0002-5502-9076 , Yu Umezawa , Chung-Chi Chen , Sen Jan 0000-0002-4128-9715 , and Chih-hao Hsieh 0000-0001-5935-7272 Authors Info & Affiliations https://doi.org/10.22541/au.174160118.81536541/v1 359 views 247 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract not-yet-known not-yet-known not-yet-known unknown A substantial community variation of free-living bacterioplankton and their main predator heterotrophic nanoflagellates (HNFs) is often unexplained because their mutual dependency through presumed trophic interactions is often overlooked. Here, we collected bacterioplankton and HNFs from the surface layer and depth of chlorophyll-a maximum (DCM) along 13 degrees of latitude in the Kuroshio from Taiwan to Japan. We performed three-way variation partitioning analyses to unravel their reciprocal influences, in addition to dispersal limitation and environmental dissimilarity. Our analyses reveal that bacterioplankton and HNFs mutually and uniquely explain over 10% of compositional variation when both layers are analyzed collectively, underscoring the significance of trophic interactions. The vertical compositional variations of bacterioplankton and HNFs also reciprocally explain each other. Whereas, considering the surface layer alone, bacterioplankton uniquely explains 12.9% of HNF composition, but HNF makes a non-significant unique contribution to bacterial composition. In the DCM layer, the two trophic levels do not mutually explain each other. Dispersal limitation uniquely accounts for more than 20% of the compositional variation in each layer but does not significantly explain vertical variations. Environmental dissimilarity makes a minor contribution to both trophic levels. Our results suggest that influences of trophic interactions are more pronounced when considering vertical rather than horizontal variations. The horizontal dispersal limitation dominates the compositional variation within a water layer, while trophic interactions between bacterioplankton and HNFs mediate their compositional variation across depths. By considering two trophic levels, this study advances our understanding of metacommunity dynamics of free-living bacterioplankton and HNFs. Introduction In marine ecosystems, interactions between nanoflagellates and free-living bacterioplankton communities play a crucial role in mediating carbon flux, nutrient cycles, and various biogeochemical processes (Azam & Malfatti, 2007). The functional dynamics of this predator-prey pair are intricately linked to their community composition (Gravel et al. , 2011; Landa et al. , 2016). Therefore, it is critical to investigate the mechanisms underlying the compositional variation of bacterioplankton and nanoflagellates communities in space and time. The metacommunity concept is typically applied as a valuable framework to understand the spatial variation in bacterioplankton and nanoflagellates community composition (Leibold et al. , 2004). The metacommunity concept posits that dispersal and species sorting—including environmental filtering and biotic interactions—interact to determine community composition. (Leibold et al. , 2004). A more contemporary extension suggests examining compositional variation along a continuum of dispersal and species sorting (Winegardner et al. , 2012). This continuum framework has been applied to investigate bacterioplankton and nanoflagellate community composition across spatial gradients (Yeh et al. , 2015; Wu et al. , 2018). These studies reveal that the relative influence of dispersal and species sorting depends on multiple factors, including sampling depth (Junger et al. , 2023), organism’s abundance (Wu et al. , 2017), and body size (Wu et al. , 2018; Sun et al. , 2023b). Species sorting is generally thought to be stronger in surface waters, where environmental heterogeneity is higher, whereas dispersal effects become more dominant in deeper waters (Villarino et al. , 2022; Junger et al. , 2023; Deutschmann et al. , 2024; but see Milke et al. , 2022). Furthermore, the influence of species sorting on eukaryotic community composition is generally greater than its effect on prokaryotes, likely due to the size-plasticity hypothesis that smaller organisms are less environment filtered than larger organisms (Wu et al. , 2018). However, most studies assessing species sorting focus solely on abiotic environmental variables, overlooking the role of biotic interactions–particularly trophic interactions–which should be more explicitly considered (Leibold et al. , 2004; Kraft et al. , 2015) Beyond abiotic environmental variables, bacterioplankton composition has long been argued to be affected by their biological interactions with the nanoflagellates community, especially the selective grazing behavior of heterotrophic and mixotrophic nanoflagellates (Gonzalez et al. , 1990a; Hahn & Höfle, 1999; Pernthaler, 2005; Yang et al. , 2023). Selective grazing by the hetero- and mixotrophic nanoflagellates affects the availability of bacterial prey, which in turn influences the growth and composition of nanoflagellates (Verity, 1991). Recent studies reveal a positive association between the diversities of nanoflagellates and bacterioplankton (Yang et al. , 2018) and identify non-random processes, such as predation pressure, as the underlying mechanism (Chang et al. , 2021). In order to take trophic interaction into account, it is imperative to examine the reciprocal influence of the hetero- and mixotrophic nanoflagellates as well as bacterioplankton composition on each other’s compositional variation. In this study, we based on the most updated Mixoplankton Database (MDB) (Mitra et al. , 2023) and the compilation by (Yang et al. , 2018) to specifically select for hetero- and mixotrophic nanoflagellates in order to better capture the influences of trophic interaction as the main type of biological interactions. The compositional variation has been explored for the bacterioplankton and nanoflagellates metacommunity in the continental shelf region of the northwestern Pacific Ocean (Yeh et al. , 2015; Wu et al. , 2018; Sun et al. , 2023b). However, the existing research has offered limited insights along the Kuroshio region (Wu et al. , 2020a). The Kuroshio, a western boundary current spanning originates from the split of the Pacific North Equatorial Current around 12°N (Qiu & Lukas, 1996) and flows along the east coast of Taiwan and Japan before turning eastward at around 35°N (Qiu, 2001). Characterized by warm, saline, oligotrophic, and low-productivity water (Saito, 2019), the Kuroshio constantly interacts with the continental shelf of the East China Sea (Jan et al. , 2017) and results in active turbulence and eddies along its path (Nagai et al. , 2019). The unique hydrography and large geographic expansion of Kuroshio make it an ideal region to investigate the compositional variation of bacterioplankton and nanoflagellates. This study aims first to depict the compositional variation of both hetero- and mixotrophic nanoflagellates as well as bacterioplankton. Second, we assess the relative importance of dispersal, abiotic environments, and biological interactions in structuring these two trophic levels along the Kuroshio region in both the surface and DCM layers. According to the aforementioned literature, we hypothesize that (1) both hetero- and mixotrophic nanoflagellates as well as bacterioplankton are more affected by the environment at the surface while more affected by dispersal at DCM. In addition, (2) the hetero- and mixotrophic nanoflagellates are more affected by environments, while bacterioplankton are more affected by dispersal. Finally, we hypothesize that (3) the influence of hetero- and mixotrophic nanoflagellates as well as bacterioplankton composition on each other’s compositional variation is comparable to the influence of abiotic environment and dispersal. This study seeks to enhance comprehension of metacommunity dynamics and elucidate the functional roles of free-living bacterioplankton as well as hetero- and mixotrophic nanoflagellates in the Kuroshio region of the northwestern Pacific Ocean. Materials and Methods Sampling sites Samples of bacterioplankton and HNFs were collected from 30 sampling sites, spanning a substantial range of the Kuroshio Current, extending from approximately 22°N to 35°N in November 2015 (Figure 1). The sampling sites encompassed 21 locations along the BS, TS, TK, OK, and TE transects, which were surveyed using the R/V Hakuho Maru. The other 9 sites were along the OKTV transect and covered by the R/V Ocean Researcher I. Sample collection At each sampling site, 20 L of seawater was collected from the surface, and another 20 L was collected from the DCM layer. The surface layer was collected at 5-m depth. The DCM layer ranged from 5 to 105 m depth and was determined as the peak of the real-time fluorescence profile measured when lowering a fluorometer (AQUAtracka III, Chelsea) mounted on a metal rosette wheel. On the metal rosette wheel, Teflon-coated 12-L Niskin-X bottles (on the R/V Hakuho Maru) or 20-L GoFlo bottles (on the R/V Ocean Researcher I) were mounted to collect seawater at the determined depth. All Niskin-X and GoFlow bottles were acid rinsed before use. The 20-L seawater was first pre-filtered through a screen mesh with 20-μm openings to remove large debits and organisms such as zooplankton. The pre-filtered seawater was then sequentially filtered onto Millipore Isopore TM hydrophilic polycarbonate membranes with 1.2-μm and 0.2-μm pore sized to collect nanoflagellates and free-living bacterioplankton respectively (Yang et al. , 2018). The hetero- and mixotrophic nanoflagellates were further selected for in the following bioinformatics procedures, following the Mixoplankton Database (Mitra et al. , 2023) and Yang et al. (2018) (see below). The filter membranes were immediately frozen with liquid nitrogen and then stored at -20℃ until molecular analysis. At the surface and the DCM layer, we also measured the seawater temperature (℃) and salinity (‰) with a conductivity, temperature, and density profiler (CTD profiler, Sea-Bird Electronics, Bellevue, WA, United States) mounted on the metal rosette wheel. In addition, we also collected water samples at these two layers for measuring chlorophyll-a concentration (as a proxy of phytoplankton) (Behrenfeld & Falkowski, 1997) as well as nutrient concentrations. Specifically, we measured the chlorophyll-a (µg/L), dissolved inorganic nitrogen (NO 2 +NO 3 ; μM) and phosphate (μM) following the methods developed by Saito et al. , 2002 and Gong et al. , 2003, which is slightly revised from the standardized protocol (Parsons et al. , 1984). In detail, chlorophyll-a concentration was measured by filtering seawater on GF/F filters under a low vacuum (< 0.02 MPa). The filters collected on the R/V Hakuho Maru were immediately immersed in 5 mL of N,N-dimethylformamide, and the filters collected on the R/V Ocean Researcher I were immediately immersed in 10 ml of 90% acetone. All filters were kept at -20℃ for 24 hours to extract chlorophyll-a, and the chlorophyll-a concentration was measured by a fluorometer (10-AU-005; Turner Designs) following the protocol of (Welschmeyer, 1994). Dissolved inorganic phosphate was measured by allowing seawater samples to react with a composite reagent containing molybdic acid, ascorbic acid, and trivalent antimony. The resulting complex is reduced to give a blue solution, which is measured at 885 nm. Dissolved inorganic nitrogen was the sum of nitrate and nitrite. Nitrate was measured by running seawater samples through a column containing cadmium filings coated with metallic copper to reduce it to nitrite. The reduced nitrite and the nitrite in the seawater were determined by diazotizing with sulfanilamide and coupling with N-(l-naphthyl)-ethylenediamine to form a highly colored azo dye and finally be measured spectrophotometrically. The measurements of all environmental variables were deposited with links to BioProject accession number PRJNA798158 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/). DNA extraction, sequencing and sequence processing To obtain the community composition of nanoflagellates and free-living bacterioplankton, we extract total DNA from the 1.2-μm and 0.2-μm pore size filters, respectively, using the PowerWater DNA Extraction Kit (PowerWater, Qiagen) according to the manufacturer’s instructions. DNA extracts from the 1.2-μm and 0.2-μm pore size filters were used as templates of a two-step polymerase chain reaction (PCR) to amplify the 18S rRNA gene for microeukaryotes and 16S rRNA gene for bacterioplankton. For the 18S rRNA, we amplified the V4 region (∼400 bp) with the forward primer FIA-TAReuk454FWD1 (5’-[forward index adaptor]-CCAGCA(G / C)C(C / T)GCGG-TAATTCC-3’) and reverse primer RIA-TAReukREV3 (5’-[reverse index adaptor]-ACTTTCGTTCTTGAT(C / T)(A / G)A-3’) (Stoeck et al. , 2010). For the 16S rRNA, we amplified the V5–V6 region (∼300 bp) with the forward primer FIA-787F (5’-[forward index adaptor]-ATTAGATACCCNGGTAG-3’) and reverse primer RIA-1046R (5’-[reverse index adaptor]-CGACAGCCATGCANCACCT-3’) (Cai et al. , 2013). Amplicons from PCR were sequenced through the Illumina Miseq300 platform. All sequences were then processed with the DADA2 pipeline version 1.16 (Callahan et al. , 2016) to filter, trim, dereplicate, merge paired reads, and remove primers, phix and chimeras. The processed 18S rRNA and 16S rRNA gene sequences were assembled into amplicon sequence variants (ASVs). Taxonomy assignment was performed on the ASVs of 18S and 16S rRNA gene based on the PR2 v4.12 and Silva v138 database, respectively (Quast et al. , 2012; Vaulot et al. , 2022). From the 18S AVSs, hetero- and mixotrophic nanoflagellates were specifically selected according to the Mixoplankton Database (MDB(Mitra et al. , 2023)) and compilation of Yang et al. (Supplementary Table 1 from Yang et al. , 2018). From the 16S ASVs, the kingdom of ”bacteria” was selected to focus on bacterioplankton. Detailed methods and codes for ASVs are explained in Supplement I. The sequence data without artificial sequences and conditionally mandatory parameters for amplicons were also deposited in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/) with accession number PRJNA798158. Compositional variation To depict the compositional variation of heterotrophic and mixotrophic nanoflagellates and bacterioplankton community in the Kuroshio region, we first subsampled the two communities once to the minimum number of reads among all samples (10700 for bacterioplankton and 420 for hetero- and mixotrophic nanoflagellates) to address the disparity issue (i.e., unequal reads among stations). We then applied Chao et al’s method to rarefy both hetero- and mixotrophic nanoflagellates and bacterioplankton communities in order to have a fair among-station comparison (Chao et al. , 2014). The rarefication procedures yield 90.66±1.56% and 92.38±2.23% coverage for hetero- and mixotrophic nanoflagellates and bacterioplankton. The rarefied composition of hetero- and mixotrophic nanoflagellates and bacterioplankton were Hellinger transformed (Legendre & Gallagher, 2001) before calculating the community dissimilarity among all pairs of 58 samples (the DCM happened to be at the surface at the TK3 and TS1 sites). The community dissimilarity was represented by the abundance-weighted UniFrac distance to incorporate phylogenetic information (Lozupone Catherine & Knight Rob, 2005). Additionally, both abundance-unweighted UniFrac distance and Bray-Curtis dissimilarity metrics were computed and implemented in the variation partitioning analysis (see below) to assess the influence of incorporating phylogeny and relative abundance in our analysis. Results from abundance weighted UniFrac distance were visualized in figures, while results from the other two metrics were summarized in a table. Explaining compositional variation We first conducted permutational multivariate analysis of variance (PERMANOVA) to examine compositional variation across layers and latitudes for heterotrophic and mixotrophic nanoflagellates, as well as bacterioplankton. Additionally, we performed PERMANOVA to assess how compositional variation could be explained independently by each of the five available environmental variables. Beyond analyzing each variable independently, we reduced the highly multidimensional factors – bacterioplankton or nanoflagellate composition, environmental dissimilarity, and spatial autocorrelation– into three composite explanatory components: biological, environmental, and dispersal, respectively. For the biological component, we adopted a two-step approach (Yeh & Fuhrman, 2022) and did not use the full community matrices directly because the number of amplicon sequence variants (ASVs) for heterotrophic and mixotrophic nanoflagellates (377) and bacterioplankton (8116) exceeded the sample size (58). First, we applied a Hellinger transformation followed by correspondence analysis (CA) and retained the first few CA axes (17 for nanoflagellates and 10 for bacterioplankton) that captured at least 70% of the compositional variation. Next, we used stepwise forward selection to identify CA axes significantly associated with the other trophic level’s community composition (10 CA axes for nanoflagellates and 6 for bacterioplankton), which together formed the biological explanatory component. For environmental dissimilarity, we standardized the five environmental variables to a mean of zero and unit variance before computing Euclidean distances. We then performed principle component analysis (PCA) and selected the first few principal axes that retained at least 70% of total variation as the environmental explanatory component. To approximate the dispersal component, we first calculated Moran’s Eigenvector Maps (MEM) to maximize spatial autocorrelation separately for hetero- and mixotrophic nanoflagellates as well as bacterioplankton independently (Dray et al. , 2006, 2012). We then selected the eigenvectors that are significantly associated with the spatial autocorrelation (three for both nanoflagellates and bacterioplankton) and computed Euclidean distances among these eigenvectors to represent the dispersal explanatory component. Using these three explanatory components, we first performed PERMANOVA to assess their individual contributions to compositional variation. We then conducted variation partitioning analysis to quantify their relative importance in explaining the community composition of bacterioplankton as well as hetero- and mixotrophic nanoflagellates. This analysis was performed across all possible sample pairs from the surface and deep chlorophyll maximum (DCM) layers to evaluate compositional variation across the entire Kuroshio region. We also repeated the analysis separately for the surface and DCM layers. Prior to variation partitioning, we applied Detrended Correspondence Analysis (DCA) to determine the appropriate response model (Hill & Gauch, 1980). The longest gradient length of DCA were 2.58 and 2.01 for bacterioplankton and nanoflagellates community, respectively, so that we performed partial distance-based redundancy analysis (pdbRDA) as a way of variation partition analysis (Peres-Neto et al. , 2006). To complement the variation partitioning analysis, we also adopted the null model approach developed by Stegen et al (Stegen et al. , 2012, 2013). This approach has been successfully applied in marine microbial ecology studies (Wu et al. , 2018; Logares et al. , 2020; Junger et al. , 2023). The analysis involves two main steps. First, we inferred selection processes from ASV phylogenetic turnover. Because the method assumes a phylogenetic signal (Cavender-Bares et al. , 2009; Stegen et al. , 2013), we tested whether closely related taxa (based on 16S and 18S rRNA gene phylogenies) share similar habitat preferences. Using Mantel correlograms to compare ASV environmental preference and phylogenetic distances, we confirmed a phylogenetic signal at short phylogenetic distances (Figure S1), consistent with previous studies (Stegen et al., 2013; Junger et al., 2023). Next, we calculated phylogenetic turnover with the abundance-weighted β-mean nearest taxon distance ( βMNTD ) metric (Stegen et al. , 2013), which measures the average phylogenetic distance between each ASV and its closest relative across community pairs. We then generated a null distribution of βMNTD using 999 randomizations to simulate turnover without selection. The deviation of the observed βMNTD from the null βMNTD values was computed as the β-nearest taxon index ( βNTI ), where | βNTI | > 2 indicates that that selection processes are significant (Stegen et al. , 2013). For communities with | βNTI | ≤ 2, we then evaluated taxonomic turnover using the Raup-Crick metric ( RC bray ) based on Bray-Curtis dissimilarities (Chase & Myers, 2011; Stegen et al. , 2013). RC bray values, normalized to range from –1 to 1, with | RC bray | > 0.95 signifying significant differences from the null expectation at 5% type-I error rate (i.e., α = 0.05). Values within ±0.95 suggest assembly by ecological drift, whereas values > +0.95 or < –0.95 indicate dispersal limitation or homogenizing dispersal, respectively (Chase & Myers, 2011). The null model approach and variation partitioning analysis provide complementary information to better decipher the processes underlying the compositional variation (Meynard et al. , 2013; Vellend et al. , 2014). Computation We used the “phyloseq” package to perform sequence subsampling to achieve parity in a total number of reads (McMurdie & Holmes, 2013), the “iNEXT” package to perform rarefaction (Hsieh et al. , 2022), and the “adespatial” package to construct Moran’s Eigenvector Maps (Dray et al. , 2023), and the “vegan” package to perform PERMANOVA (Oksanen et al. , 2022). All packages were built, and computation was carried out in R ver. 4.2.2. Compositional variation in combined surface and DCM layers: Associations with individual explanatory components We observed significant compositional differences in the abundance-weighted UniFrac distance for both free-living bacterioplankton as well as hetero- and mixotrophic nanoflagellates across sampling layers (circles vs. triangles in Figure 2A), which accounted for 18% and 13% of the variation, respectively (1 st bar in Figure 2B). Regarding latitudinal variation, the abundance-weighted UniFrac distance of both microbial communities exhibited significant shifts from lower to higher latitudes when both layers were analyzed together (warmer vs. cooler colored dots in Figure 2A). The latitudinal shift was more pronounced in bacterioplankton, with latitude explaining 13% of the variation compared to 8% for hetero- and mixotrophic nanoflagellates (2 nd bar in Figure 2B). These differences remained statistically significant when implementing abundance unweighted distance and the Bray-Curtis dissimilarity (Figure S2-S7). The compositional variation of both microbial communities was best explained by the composite biological component. The hetero- and mixotrophic nanoflagellates composition independently explained 49% of bacterial variation; bacterioplankton composition independently explained 34% of nanoflagellate variation (Figure 2B). The second most influential composite explanatory component was dispersal, which accounted for 44% and 28% of the compositional variation in bacterioplankton as well as hetero- and mixotrophic nanoflagellates, respectively (Figure 2B). The composite environmental component, which consisted of the first two principal axes that accounted for 77.7% of the total environmental variation (Figure S8), came last and independently explained approximately 20% of the compositional variation in both microbial communities (Figure 2B). Among all the environmental variables, nutrient availability emerged as the primary driver of compositional variations for both communities. Phosphate and dissolved inorganic nitrogen each independently explained bacterioplankton as well as hetero- and mixotrophic nanoflagellates (3 rd and 4 th bars in Figure 2B). Temperature and chlorophyll- a concentration also significantly explained ~10% of the compositional variation for both microbial communities (5 th and 6 th bars in Figure 2B). Salinity had the least impact and was not significant for hetero- and mixotrophic nanoflagellates (7 th bars in Figure 2B). Partitioning the unique contributions of explanatory components in combined surface and DCM layers Variation partitioning analysis revealed that biological and dispersal components are the most prominent components that contributed nearly equally to the compositional variation of both free-living bacterioplankton as well as hetero- and mixotrophic nanoflagellates (Figure 3A). Specifically, the abundance-weighted UniFrac distance of heterotrophic/mixotrophic nanoflagellates uniquely explained 15.5% of bacterioplankton compositional variation, while the corresponding metric for bacterioplankton uniquely explained 10.5% of nanoflagellate compositional variation (yellow circles in Figure 3A). Dispersal limitation also played a substantial role, uniquely accounting for 16.7% of bacterioplankton variation and 8.4% of hetero- and mixotrophic nanoflagellates variation (blue circles in Figure 3A). In contrast, environmental dissimilarity had a relatively minor effect, explaining only 5.5% of hetero- and mixotrophic nanoflagellates variation and showing no significant unique contribution to bacterioplankton composition (p = 0.25; green circles in Figure 3A). When implementing abundance unweighted UniFrac and Bray-Curtis dissimilarity for bacterioplankton, the contributions of the three explanatory components remained largely the same, but the unique contribution of hetero- and mixotrophic nanoflagellates reduced to 6-8% (Table 1). When implementing these two metrics for hetero- and mixotrophic nanoflagellates, the unique influence of bacterial dissimilarity remained consistent and environmental dissimilarity still played a minor (<5%), but significant role (Table 1). However, when implementing abundance-unweighted UniFrac distance for hetero- and mixotrophic nanoflagellates, the unique influence of dispersal limitation became non-significant (Table 1). The null model analysis further supported these findings, showing that dispersal-related components (dispersal limitation and homogeneous dispersal) and selection were the dominant forces shaping bacterial community composition, contributing 58.7% and 36.7% of the variation, respectively (light green and blue bars in the left panel of Figure 3B). For hetero- and mixotrophic nanoflagellates, dispersal also played a more pronounced role, accounting for 47.4% of variation, whereas selection contributed only 11.7% (light green and blue bars in the right panel of Figure 3B). These findings underscore the dominant roles of biological interactions and dispersal in shaping the compositional variation of the two microbial communities. Compositional variation in the surface layer: Associations with individual explanatory components When focusing on the surface layer, both bacterioplankton and nanoflagellates showed significant compositional changes with latitude (warmer vs. cooler colored dots in Figure 4A). Again, bacterioplankton exhibited a stronger latitudinal pattern than hetero- and mixotrophic nanoflagellates, with latitude explaining 23% vs. 13% of the variation (1 st bar in Figure 4B). These compositional changes were primarily driven by the dispersal component that independently explained 59% of the variation in bacterioplankton and 43% in nanoflagellates. The composite biological component was the second most important factor, explaining 40% of bacterioplankton variation and 35% of nanoflagellate variation (Figure 4B). The composite environmental component alone explained 32% of the variation in bacterioplankton and 25% in heterotrophic and mixotrophic nanoflagellates (Figure 4B). Among the environmental variables, temperature and chlorophyll‑ a concentration were the most influential ones that individually accounted for 25% and 20% of the variation, respectively (2 nd and 3 rd bars in the left panel of Figure 4B). In contrast, nutrient availability played a relatively minor role, with phosphate and dissolved inorganic nitrogen explaining 8% and 16% of the variation, respectively, and salinity showing no significant association (5 th to 7 th bars in the left panel of Figure 4B). For heterotrophic and mixotrophic nanoflagellates, all five abiotic variables were significantly related to compositional variation, but each contributed only around 10% (2 nd to 7 th bars in the right panel of Figure 4B). not-yet-known not-yet-known not-yet-known unknown Partitioning the unique contributions of explanatory components in the surface layer Variation partitioning analysis indicated that dispersal was the dominant factor for bacterioplankton, uniquely contributing 28.5% of the variation (p<0.01; blue circle in the left panel of Figure 5A). In contrast, environmental dissimilarity uniquely explained only 5.8% of the bacterial variation (p<0.01; green circle in the left panel of Figure 5A), and the unique contribution of hetero- and mixotrophic nanoflagellates was not significant (yellow circle in the left panel of Figure 5A). On the other hand, none of the three explanatory components made a significant unique contribution to the compositional variation of hetero- and mixotrophic nanoflagellates (Right panel of Figure 5A). When two other dissimilarity metrics were applied, the overall contributions of the three explanatory components remained consistent (Table 1). The null model approach further revealed that, for bacterioplankton, the dispersal component accounted for 54.3% of the variation, followed closely by selection at 40% (Left panel of Figure 5B). In contrast, ecological drift was the leading process for heterotrophic and mixotrophic nanoflagellates, accounting for 46.2% of their variation (Right panel of Figure 5B). These findings suggest that dispersal is the dominant unique explanatory factor for bacterioplankton, followed by environmental dissimilarity, while none of the three components is uniquely significant for hetero- and mixotrophic nanoflagellates. Compositional variation in DCM layer: Associations with individual explanatory components Both bacterioplankton and nanoflagellates showed significant compositional changes with latitude in the DCM layer (warmer vs. cooler colored dots in Figure 6A). Latitude explained 39% of bacterioplankton compositional variation and 13% of hetero- and mixotrophic nanoflagellate variation (1 st bar in Figure 4B). For bacterioplankton, the dispersal component independently explained the largest proportion of variation (60%), followed by the biological (28%) and environmental (23%) component (Figure 4B). In contrast, for hetero- and mixotrophic nanoflagellates, the environmental component played the dominant role (30%), while the biological and dispersal component each contributed approximately 20% (Figure 4B). Among all environmental variables, nutrient availability was a primary driver of this variation: phosphate and dissolved inorganic nitrogen independently accounted for 26% and 21% of bacterioplankton variation, respectively, and 19% and 16% of nanoflagellate variation, respectively. (2 nd and 3 rd bars in the left panel of Figure 6B). not-yet-known not-yet-known not-yet-known unknown Partitioning the unique contributions of explanatory components in the DCM layer When focusing on the DCM layer, dispersal limitation emerged as the only factor uniquely contributing to 27.6% of bacterioplankton compositional variation (blue circle in the left panel of Figure 7A). This dominance of dispersal limitation was also observed using two other dissimilarity metrics (Table 1). However, when these alternative metrics were used, nanoflagellate influence became significant, albeit accounting for less than 10% of the variation (Table 1). For hetero- and mixotrophic nanoflagellates, no single component uniquely and significantly explained nanoflagellate compositional variation when abundance-weighted UniFrac distance is applied. However, with abundance-unweighted UniFrac, biological and dispersal components became significant, and with Bray-Curtis dissimilarity, biological and environmental components were significant (Table 1). Supporting these findings, the null model analysis indicated that dispersal-related factors accounted for 61.1% of bacterioplankton variation, and ecological drift explained 48.1% of nanoflagellate variation (Table 1). Collectively, these results highlight the primary role of dispersal limitation in structuring bacterioplankton communities within the DCM layer. However, the drivers of heterotrophic and mixotrophic nanoflagellate composition remain complex and less clearly defined, varying depending on the dissimilarity metric employed. Discussion Compositional variation of bacterioplankton as well as heterotrophic and mixotrophic nanoflagellates Our findings reveal significant compositional shifts in both bacterioplankton and heterotrophic/mixotrophic nanoflagellates across depths and latitudes (Figure 2A, 4A, and 6A), consistent with previous studies (Sunagawa et al. , 2015; Ibarbalz et al. , 2019). Vertical compositional differences in this region have been attributed to strong thermal stratification and nutrient limitation in the surface layer relative to the DCM (Liu et al. , 2022). For latitudinal compositional variation, our results indicate that temperature is the primary driver in the surface layer (Figure 4), whereas nutrient availability exerts the strongest influence in the DCM layer (Figure 6). This aligns with previous research showing that bacterioplankton and nanoflagellate communities respond to different environmental factors depending on depth (Wu et al. , 2020b; Wang et al. , 2021). In the surface layer, temperature plays a dominant role in structuring bacterioplankton communities, likely due to the pronounced temperature gradient from lower to higher latitudes (Figure S8). Seawater temperature has long been recognized as a key determinant of both microbial survival and metabolic function in marine ecosystems (Abreu et al. , 2023) so it will influence the community composition (Chow et al. , 2013; Ward et al. , 2017). In contrast, nutrient availability emerges as the primary factor in the DCM layer, likely because it is the only environmental variable exhibiting significant latitudinal variation at this depth (Figure S8). In addition, nutrient availability is a well-established constraint on both bacterioplankton and nanoflagellate communities in marine ecosystems (Cermeño et al. , 2008; James et al. , 2022). Together, these findings highlight depth-dependent shifts in the dominant environmental factors of microbial community composition, with temperature structuring surface communities and nutrient availability playing a greater role in the DCM. Influences of composite abiotic environment Although each abiotic environmental variable individually explains the composition of bacterioplankton as well as hetero- and mixotrophic nanoflagellates, the variation partitioning analysis reveals that the composite environmental component is the least important for both communities, compared to dispersal and biological component regardless of the water layer (Table 1). The null model approach also supports this finding that selection, which theoretically encompasses both environmental and biological components, is always outperformed by dispersal-related components, i.e., dispersal limitation and homogeneous dispersal (Table 1). This finding not only rejects our first hypothesis but also contradicts many previous studies, both at global and regional scales, showing that these microbes are mainly explained by environments rather than by dispersal (Soininen, 2014; Sunagawa et al. , 2015; Milke et al. , 2022; Junger et al. , 2023; Sun et al. , 2023a). However, some studies at smaller spatial scales demonstrate that dispersal can be more important than environments (Wu et al. , 2017, 2018). We posit that this contradiction likely arises from the relatively low environmental heterogeneity during our sampling in the Kuroshio (Figure S8). Not all the environmental variables show significant change across latitudes. For example, temperature and chlorophyll-a concentration only significantly decreased in the surface but not in the DCM layer. Whereas, when sampling across a larger spatial scale and including more heterogeneous environments, for example, crossing the continental shelf of the East China Sea, Kuroshio, and the northwestern Pacific Ocean, the unique contribution of environmental component is observed to be more substantial than that of dispersal (Wu et al. , 2020b). Alternatively, the low explanatory power of environments may be explained by its significant correlation with dispersal; indeed, a significant correlation between environmental and dispersal components is observed for bacterioplankton (R 2 of PERMANOVA = 0.2, p-value = 0.03) but not for nanoflagellates (R 2 of PERMANOVA = 0.07, p-value = 0.23) when two water layers are analyzed together. As a consequence, the environmental component is non-significant for bacterioplankton but significant for nanoflagellates. Previous studies have shown that the importance of environments can be masked by dispersal (Gilbert & Bennett, 2010; Bauman et al. , 2018) because the dispersal component consists of not only dispersal-related processes but also other unmeasured environmental variables that are spatially structured (Cottenie, 2005; Legendre et al. , 2009; Smith & Lundholm, 2010). Accordingly, we suggest that either the weak environmental gradient or the correlation between the environmental and dispersal components in our study region renders both microbial communities primarily governed by dispersal limitation and biological interactions, as will be discussed in the later section. Influences of dispersal In addition to abiotic factors, dispersal emerges as a significant explanatory component, uniquely explaining 16.7% of bacterioplankton composition and 8.4% of hetero- and mixotrophic nanoflagellate composition along the Kuroshio (Figure 3). Moreover, when each layer is analyzed independently, the influence of dispersal limitation is more pronounced for bacterioplankton but becomes non-significant for nanoflagellates (Figure 5 and 6). The null model approach further supports this pattern, consistently showing that dispersal explains a larger proportion of bacterioplankton variation than the nanoflagellate variation (Figure 3, 5 and 6, and Table 1). This partially supports the hypothesis that smaller bacterioplankton are more influenced by dispersal than by environmental selection, as suggested by previous studies (Logares et al. , 2018; Wu et al. , 2018). However, our findings seem to contradict several studies showing that eukaryotes, including nanoflagellates, are more affected by dispersal than prokaryotes (Milke et al. , 2022; Junger et al. , 2023). We propose that the observed discrepancy arises from how the dispersal component is interpreted in variation partitioning analysis, particularly in relation to dispersal limitation and homogeneous dispersal. Due to their higher abundance, faster growth rates, and smaller body size, bacterioplankton are more readily transported by ocean currents than nanoflagellates (Villarino et al. , 2018). While their high dispersal potential reduces the effects of dispersal limitation, it simultaneously increases their susceptibility to homogeneous dispersal (Logares et al. , 2018). In fact, this is evident in our null model results, where dispersal limitation and homogeneous dispersal contribute 38.1% and 20.6% of community turnover in bacterioplankton, compared to 39.2% and 8.2% in nanoflagellates when both water layers are analyzed together (Figure 3). In the variation partitioning analysis, the dispersal component encompasses dispersal-related processes—including both dispersal limitation and homogeneous dispersal—as well as other unmeasured but spatially structured environmental variables (Cottenie, 2005; Legendre et al. , 2009; Smith & Lundholm, 2010). Accordingly, the unique explanatory power of dispersal component thus appears higher for bacterioplankton than for nanoflagellates. However, these contrasting findings highlight the need for further studies, including experimental manipulations and finer-scale biophysical modeling, to disentangle the relative roles of dispersal and environment in structuring microbial communities across trophic levels. Influences of biological interaction Beyond the effects of the abiotic environment and dispersal, our analysis demonstrates that when both water layers are considered together, each microbial community is better explained by the composition of the other (Figure 3). More importantly, the explanatory power of these biological associations surpasses that of abiotic factors and is comparable to dispersal across the expansive Kuroshio region. This finding supports our third hypothesis: the compositional variation of bacterioplankton and nanoflagellates is reciprocally linked. We carefully attribute this association to trophic interactions with an awareness that recognizing nanoflatellates’ trophic strategy is a rapidly evolving field, especially for mixotrophy encompassing numerous degrees and types (Mitra et al. , 2016; Flynn et al. , 2019; Millette et al. , 2024) that cannot be exhaustively addressed here. The role of trophic interactions in shaping microbial community composition has long been discussed (Zhang et al. , 2007; Bonilla-Findji et al. , 2009; Cram et al. , 2016). Our study pioneers in demonstrating the reciprocal dependency of community composition, indicating reflection of trophic interactions between bacterioplankton and hetero- and mixotrophic nanoflagellates. Incorporating each other’s compositional variation in metacommunity analyses reduces unexplained variation by more than 10%, addressing concerns about the oversight of biological interactions. Albeit acknowledging the presence of unmeasured variables and the challenge of inferring causation from association, our analysis significantly enhances the understanding of bacterioplankton and HNF metacommunity structure in the Kuroshio region. Comparing the analyses based on phylogenetic distance with Bray-Curtis dissimilarity reveals that trophic interactions influence bacterioplankton composition more strongly when phylogenetic distance is incorporated in the joint analysis of the surface and deep chlorophyll maximum (DCM) layers (Table 1). In contrast, the effect of trophic interaction on nanoflagellate community composition remains relatively constant across different dissimilarity metrics (Table 1). This partially supports the idea that trophic interactions in food webs are constrained by species’ phylogeny, particularly when phylogenetic signals are significant ((Eklöf et al. , 2011; Stouffer et al. , 2012; Dalla Riva & Stouffer, 2016). In our study, significant phylogenetic signals are detected in both microbial communities (Figure S1), suggesting that species’ phylogeny can serve as a proxy for functional traits (Mouquet et al. , 2012). Traits of bacterioplankton that may influence nanoflagellate grazing preferences include larger cell size (Gonzalez et al. , 1990b; Gonzàlez, 1996), physiologically active (Longnecker et al. , 2010; Sintes & del Giorgio, 2014), lower motility (Matz & Jürgens, 2005), and/or lack grazing resistant mechanisms (Pernthaler, 2005), such as certain cell wall properties (Jezbera et al. , 2005) or aggregation behavior (Tophøj et al. , 2018). These traits tend to be phylogenetically conserved, meaning that phylogenetically similar bacterial species are expected to interact with similar sets of grazers (Cattin et al. , 2004; Bersier & Kehrli, 2008; Naisbit et al. , 2012; Peralta, 2016). However, there are some studies suggesting that nanoflagellates’ feeding preferences are not strictly phylogenetically constrained (Matz & Jürgens, 2001; Baltar et al. , 2016). Therefore, we interpret our findings with caution and recognize that further investigation—such as direct imaging or experimental validation—is needed to confirm these interactions. not-yet-known not-yet-known not-yet-known unknown Concluding remarks We investigate the metacommunity structure of bacterioplankton and heterotrophic and mixotrophic nanoflagellates in the Kuroshio region. Our goal is to reveal the influence of abiotic environment, dispersal, and, crucially, biological interaction that implies trophic interaction. Our results highlight that the primary explanatory components are biological interaction and dispersal when two water layers are analyzed together. Contrastingly, abiotic environmental dissimilarity plays a comparatively minor role in shaping the metacommunity structure of these microorganisms. Noteworthy is the increased prominence of dispersal in the context of horizontal variations within a water layer, whereas biological interactions become more influential in vertical variation across water layers. Consequently, we posit that horizontal dispersal predominantly governs the metacommunity structure of bacterioplankton and nanoflagellates within a water layer, while biological interactions mediate the compositional variation of these organisms across depth in the Kuroshio region. These findings not only advance our comprehension of metacommunity dynamics but also provide valuable insights into the functional roles of free-living bacterioplankton and nanoflagellates in the northwestern Pacific Ocean. Acknowledgements We thank Hon-Tsen Yu for providing facilities and advice on laboratory work. Funding Statement This work was supported by the National Center for Theoretical Sciences, Foundation for the Advancement of Outstanding Scholarship, and the National Science and Technology Council, Taiwan. Competing Interests The authors declare no competing financial interests. Data Availability Statement The sequence data have been deposited in the NCBI Sequence Read Archive (SRA) under the accession numbers: PRJNA662424 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA662424). The scripts for generating figures and tables of this manuscript are stored on Dryad (https://doi.org/10.5061/dryad.cfxpnvxd1). References Abreu, C.I., Dal Bello, M., Bunse, C., Pinhassi, J. & Gore, J. (2023) Warmer temperatures favor slower-growing bacteria in natural marine communities. Science Advances , 9 , eade8352.Azam, F. & Malfatti, F. (2007) Microbial structuring of marine ecosystems. 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Figure legends Figure 2 Principal coordinates analysis (PCoA) and PERMANOVA results for bacterioplankton as well as hetero- and mixotrophic nanoflagellates based on weighted phylogenetic distance when two layers are analyzed together. (A) PCoA ordination plots of bacterioplankton (left) and hetero- and mixotrophic nanoflagellates (right), illustrating compositional variation across sampling sites. Points represent samples, color-coded by transect line. The percentages on the axes indicate the proportion of variance explained by the first two PCoA axes. (B) PERMANOVA results showing the explanatory power (R 2 ) of various environmental, biological, and spatial factors in shaping community composition. Bars represent the R 2 of each factor, with statistical significance indicated by asterisks (*p<0.05, **p<0.01). Environmental factors include latitude, temperature, chlorophyll-a, phosphate, dissolved inorganic nitrogen (DIN), and salinity, whereas composite variables represent principal components of environmental variation (Envi_PC1&2), composition of the other trophic level (Bio), and dispersal limitation (MEM). Figure 3 Variation partitioning and ecological processes contributing to bacterioplankton as well as hetero-and mixotrophic nanoflagellate (NF) community composition based on weighted phylogenetic distance. (A) Venn diagrams illustrating the proportion of compositional variation explained by environmental dissimilarity (green), dispersal limitation (blue), and biological interactions (yellow) for bacterioplankton (left) and heterotrophic/mixotrophic nanoflagellates (right). Percentages represent the variation uniquely explained by each component, with corresponding 𝑝-values indicating statistical significance. Residual variation represents the unexplained portion of compositional variation. (B) Contributions of different ecological processes to bacterioplankton (left) and hetero- and mixotrophic NF (right) community. Bars represent the proportion of variance attributed to selection (light green), dispersal limitation (blue), homogeneous dispersal (purple), and ecological drift (dark purple). not-yet-known not-yet-known not-yet-known unknown Figure 5 Variation partitioning and ecological processes contributing to bacterioplankton as well as hetero-and mixotrophic nanoflagellate (NF) community composition based on weighted phylogenetic distance in the surface layer of Kuroshio. (A) Venn diagrams illustrating the proportion of compositional variation explained by environmental dissimilarity (green), dispersal limitation (blue), and biological interactions (yellow) for bacterioplankton (left) and heterotrophic/mixotrophic nanoflagellates (right). Percentages represent the variation uniquely explained by each component, with corresponding 𝑝-values indicating statistical significance. Residual variation represents the unexplained portion of compositional variation. (B) Contributions of different ecological processes to bacterioplankton (left) and hetero-and mixotrophic NF (right) community. Bars represent the proportion of variance attributed to selection (light green), dispersal limitation (blue), homogeneous dispersal (purple), and ecological drift (dark purple). Figure 6 Principal coordinates analysis (PCoA) and PERMANOVA results for bacterioplankton as well as hetero- and mixotrophic nanoflagellates based on weighted phylogenetic distance in deep chlorophyll- a maximum (DCM) layer. (A) PCoA ordination plots of bacterioplankton (left) and hetero- and mixotrophic nanoflagellates (right), illustrating compositional variation across sampling sites. Points represent samples, color-coded by transect line. The percentages on the axes indicate the proportion of variance explained by the first two PCoA axes. (B) PERMANOVA results showing the explanatory power (R 2 ) of various environmental, biological, and spatial factors in shaping community composition. Bars represent the R 2 of each factor, with statistical significance indicated by asterisks (*p<0.05, **p<0.01). Environmental factors include latitude, temperature, chlorophyll-a, phosphate, dissolved inorganic nitrogen (DIN), and salinity, while composite variables represent principal components of environmental variation (Envi_PC1&2), composition of the other trophic level (Bio), and dispersal limitation (MEM). Figure 7 Variation partitioning and ecological processes contributings to bacterioplankton as well as hetero-and mixotrophic nanoflagellate (NF) community composition based on weighted phylogenetic distance in the DCM layer of Kuroshio. (A) Venn diagrams illustrating the proportion of compositional variation explained by environmental dissimilarity (green), dispersal limitation (blue), and biological interactions (yellow) for bacterioplankton (left) and heterotrophic/mixotrophic nanoflagellates (right). Percentages represent the variation uniquely explained by each component, with corresponding 𝑝-values indicating statistical significance. Residual variation represents the unexplained portion of compositional variation. (B) Contributions of different ecological processes to bacterioplankton (left) and hetero-and mixotrophic NF (right) community. Bars represent the proportion of variance attributed to selection (light green), dispersal limitation (blue), homogeneous dispersal (purple), and ecological drift (dark purple). Table 1 Results of variation partitioning and null model approach in different water layers with different community dissimilarity matrices. Notes: The components for variation partitioning analysis are the other community [B], environment [E], dispersal [D], the other community independent of environment and dispersal [B|E+D], environment independent of the other community and dispersal [E|B+D], dispersal independent of the other community and environment [D|B+E]. The components of the null model approach are selection [S], dispersal limitation [DL], homogeneous dispersal [HD], and drift [Drift]. The number below each explanatory component represents the % of explained variance, with statistical significance indicated by asterisks (*p<0.05, **p<0.01) and bold font. UniFrac distance (abundance weighted) [B|E+D] [E|B+D] [D|B+E] Residual [B|E+D] [E|B+D] [D|B+E] Residual Two layers 15.5* 2.2 16.7** 32.6 10.5* 5.5* 8.4* 52.1 Surface 6.5 5.8** 28.6* 24.4 9.4 4.2 13.8 42.5 DCM 4.1 5.7 27.6** 31.4 6.6 8.7 6.9 64.4 Null model approach [S] [DL] [HD] [Drift] [S] [DL] [HD] [Drift] Two layers 36.7 38.1 20.6 4.6 11.7 39.2 8.2 40.5 Surface 40.0 11.3 43.0 5.8 7.4 30.6 15.2 46.2 DCM 36.7 38.1 20.6 4.6 11.7 39.2 8.2 40.5 UniFrac distance (abundance unweighted) [B|E+D] [E|B+D] [D|B+E] Residual [B|E+D] [E|B+D] [D|B+E] Residual Two layers 6.1** 4.4** 14.5** 61.7 9.3** 3.8* 5.8 66.1 Surface 3.5 7.3* 25.1* 50.8 2.7 6.2 22.0 57.8 DCM 8.4* 6.4 22.8** 53.5 14.4* 6.6 8.8* 63.6 Bray-Curtis dissimilarity [B|E+D] [E|B+D] [D|B+E] Residual [B|E+D] [E|B+D] [D|B+E] Residual Two layers 8.5** 2.3 12.4** 44.6 4.2** 5.5** 7.6** 53.2 Surface 2.8 6.8** 23.4** 41.1 3.3 6.2 21.7 44.1 DCM 4.1* 5.4 18.0** 45.6 5.8* 8.4* 6.9 66.7 Information & Authors Information Version history V1 Version 1 10 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Molecular Ecology Keywords free-living bacterioplankton heterotrophic nanoflagellates kuroshio metacommunity mixotrophic nanoflagellates northwestern pacific ocean Authors Affiliations Feng-Hsun Chang 0000-0002-8202-6364 [email protected] National Taiwan University View all articles by this author Ariana Chih-Hsien Liu National Taiwan University View all articles by this author Jinny Yang University of Michigan Department of Ecology and Evolutionary Biology View all articles by this author Hiroaki Saito 0000-0002-5502-9076 The University of Tokyo View all articles by this author Yu Umezawa Tokyo University of Agriculture and Technology View all articles by this author Chung-Chi Chen National Taiwan Normal University View all articles by this author Sen Jan 0000-0002-4128-9715 National Taiwan University View all articles by this author Chih-hao Hsieh 0000-0001-5935-7272 Institute of Oceanography View all articles by this author Metrics & Citations Metrics Article Usage 359 views 247 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Feng-Hsun Chang, Ariana Chih-Hsien Liu, Jinny Yang, et al. 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