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Latitudinal Dynamics of Vibrio along the Eastern Coastline of Australia

Climate Change Cluster, University of Technology Sydney, Ultimo, NSW 2007, Australia
School of Integrative Plant Science, Cornell University, Ithaca, NY 14850, USA
Author to whom correspondence should be addressed.
Water 2022, 14(16), 2510;
Submission received: 8 July 2022 / Revised: 4 August 2022 / Accepted: 11 August 2022 / Published: 15 August 2022
(This article belongs to the Special Issue Waterborne Pathogens—Threats to Water Quality)


The marine genus of bacteria, Vibrio, includes several significant human and animal pathogens, highlighting the importance of defining the factors that govern their occurrence in the environment. To determine what controls large-scale spatial patterns among this genus, we examined the abundance and diversity of Vibrio communities along a 4000 km latitudinal gradient spanning the Australian coast. We used a Vibrio-specific amplicon sequencing assay to define Vibrio community diversity, as well as quantitative PCR and digital droplet PCR to identify patterns in the abundances of the human pathogens V. cholera, V. parahaemolyticus and V. vulnificus. The hsp60 amplicon sequencing analysis revealed significant differences in the composition of tropical and temperate Vibrio communities. Over 50% of Vibrio species detected, including the human pathogens V. parahaemolyticus and V. vulnificus, displayed significant correlations with either temperature, salinity, or both, as well as different species of phytoplankton. High levels of V. parahaemolyticus and V. vulnificus were detected in the tropical site at Darwin and the subtropical Gold Coast site, along with high levels of V. parahaemolyticus at the subtropical Sydney site. This study has revealed the key ecological determinants and latitudinal patterns in the abundance and diversity of coastal Vibrio communities, including insights into the distribution of human pathogens, within a region experiencing significant ecological shifts due to climate change.

1. Introduction

Understanding the distributional dynamics of a species or a community of organisms is pivotal to understanding their ecology, and how they may respond to environmental perturbations [1]. Across a wide range of organisms, species diversity and abundance patterns have been shown to be variable across latitudinal gradients, with many terrestrial and aquatic organisms displaying highest diversity levels at low latitudes [2,3,4]. Importantly, there is also growing evidence showing that latitudinal range shifts in multiple species are occurring as a consequence of climate change [5].
Latitudinal gradients have been shown to exist among microorganisms in both terrestrial [6] and aquatic environments [7,8], with two ecological processes often used to explain these patterns. The first of these is that diversity and productivity increase together because larger numbers of organisms are supported by larger rates of resource supply [9]. The second is that temperature and diversity increase together, as temperature impacts biological kinetics and therefore the rate of ecological processes such as speciation, ultimately resulting in an increase in diversity [10,11].
Previous studies (e.g., [12,13]) have demonstrated that marine bacterial species richness is correlated with temperature and inversely correlated with latitude. In addition to shaping the diversity of entire bacterial communities, latitude and temperature have been shown to be critical determinants of the distributions of keystone marine bacteria, including numerically abundant groups such as the SAR11 clade [11] and dominant primary producers, including the Cyanobacteria genera Synechococcus and Prochlorococcus [14].
Within coastal and estuarine ecosystems, members of the Vibrio genus, which can be particle associated [15], or free-living [16], are notable members of natural bacterial assemblages because of their often-important ecological relationships with marine animals, which can span mutualistic [17] to parasitic interactions with a wide diversity of species, including fish [18], corals [19], and shellfish [20]. In addition to impacting natural marine communities, diseases caused by Vibrio species are also particularly detrimental to the aquaculture sector, where they globally cause hundreds of millions of dollars in losses across a broad range of industries such as a USD 44 billion production loss to the shrimp industry between 2010 and 2016 [21], a USD 7.4 million production loss to the Malaysian seabass industry in the 1990s [22] and a substantial impact on the salmon industry in the 1990s [23].
In addition to impacting marine species and ecosystems, several Vibrio species are notable human pathogens. For example, V. cholerae causes the severe gastrointestinal disease cholera, which globally results in over 2.8 million cases and up to 95,000 deaths per year [24]. V. parahaemolyticus is a major cause of seafood poisoning, causing severe and sometimes fatal gastrointestinal disease in up to 30,000 people per year [25]. Infections by V. vulnificus can follow consumption of seafood as well, but can also result from direct exposure to seawater, and can be particularly dangerous, with up to a 50% mortality rate among infected individuals [26]. Notably, there are indications that the global distributions and seasonal dynamics of some of these pathogenic Vibrio species are shifting as a consequence of climate change [27,28,29], with evidence for poleward latitudinal range shifts [28].
A suite of environmental factors have been demonstrated to influence Vibrio abundance and diversity. Vibrio abundance has been shown to increase with nutrients such as phosphate [30], but in contrast decrease with increasing levels of nitrogen, dissolved oxygen, and pH [31]. The most widely observed determinants of Vibrio abundance, however, are salinity, temperature, and chlorophyll [29,30,32,33,34,35,36,37], with moderate salinity levels and elevated temperatures often leading to increased Vibrio abundances [32,37,38,39,40]. However, the environmental determinants and optimum conditions vary across Vibrio species [40]. For instance, V. cholerae, V. vulnificus and V. parahaemolyticus are generally more abundant within warm waters where the temperature exceeds 20 °C and salinity is below 10 ppt [40]. This is particularly relevant as climate change is increasing sea surface temperatures (SST), as well as increasing the frequency of severe rainfall events [41], which can result in decreased salinity in coastal environments. Together, these shifting environmental conditions have the potential to create ideal conditions for Vibrio blooms in the environment.
In light of the substantial environmental and human health importance of Vibrio species, we aimed to determine the patterns of abundance and diversity of the Vibrio community during the Australian summer using the high throughput hsp60 gene amplicon sequencing assay to characterise the Vibrio community across a large latitudinal transect spanning the entire eastern coastline of Australia, one of the largest latitudinal Vibrio ecology gradients studied to date. Previous studies have detected high levels of Vibrio, including the human pathogens V. cholerae, V. vulnificus and V. parahaemolyticus in tropical [42] and subtropical [43] Australian environments during summer months when water temperature is highest, but neither of these studies have investigated Vibrio ecology on such a large scale. This is particularly relevant within Australia, where there is evidence that climate change is increasing seawater temperatures more rapidly than in any other part of the southern hemisphere [44], making this region a climate change hotspot, and a potential location for substantial increases in the abundance of pathogenic Vibrio species.

2. Methods

2.1. Sampling Locations and Protocol

Water samples were collected once each from 28 coastal sites along a latitudinal gradient spanning Australia’s eastern coast, from Darwin (12°22′42.8″ S, 130°50′30.8″ E) to Hobart (42°51′5.7″ S, 147°31′16.2″ E) (Figure 1, Supplementary Table S1) in a sampling campaign conducted during the Australian summer (December 2017–January 2018) over a period of 48 days. To consider the impacts of salinity and the common occurrence of Vibrio in estuarine environments, at each location, both a river (R-Site) and a beach (B-Site) sample were collected, except for at Rockhampton (23°15′17.8″ S, 150°49′44.5″ E), where both samples were taken from the beach due to a lack of river access at the time of sampling, and Bundaberg (24°45′41.8″ S, 152°23′14.6″ E), where both samples were taken from the river due to no beach access at the time of sampling.
At each sampling site, 10 L of water was collected from the surface in tinted, plastic drums, which had been pre-rinsed with bleach and then miliQ water and then rinsed three times on site prior to sampling. Physiochemical parameters including temperature, dissolved oxygen, salinity and pH were measured in situ using a WTW multiprobe meter (Multi3430, Wertheim, Germany). Triplicate water samples were filtered through 0.22 μm pore size, 47 mm diameter filters (Millipore, DURAPORE PVDF. 22UM WH PL) using a peristaltic pump within 3 h of collection, with the volume of sample filtered recorded to allow for normalisation of gene copy numbers. Filters were immediately placed in dry ice, where they remained for the duration of the sampling trip. Upon returning to the lab, samples were stored at −80 °C until processing.

2.2. DNA Extraction

Microbial DNA was extracted from filters using the PowerWater DNA isolation Kit (QIAGEN) in accordance with the manufacturer’s instructions. DNA quantity and purity were then evaluated using a Nanodrop-1000 Spectrophotometer.

2.3. Quantitative PCR (qPCR)

Total bacterial community abundance, as well as the abundance of the total Vibrio community were measured using quantitative PCR (qPCR). Total bacterial abundance was quantified using the BACT1369F and PROK1492R 16S primer pair [45], while total Vibrio community abundance was quantified using the primer pair Vib1-f and Vib2-r [46], which targets the Vibrio-specific 16S rRNA gene. To quantify levels of V. parahaemolyticus we targeted the thermostable direct hemolysin (tdhS) gene [47] and to quantify levels of V. vulnificus, we targeted the V. vulnificus hemolysin (vvhA) gene [43]. To quantify V. cholerae, we targeted the outer-membrane protein ompW gene [48]. Additionally, we tested for toxigenic V. cholerae, targeting the enterotoxin gene ctxA [49]. For further qPCR assay details see Supplementary Table S2.
All qPCR analyses were performed on the BIO-RAD CFX384 Touch™ Real-Time PCR Detection System™, with gene copies calculated within each sample by plotting against their respective standard curve using BIO-RAD’s CFX MAESTROTM software version 1.1. Each qPCR analysis was run in triplicate, with all assays set-up in 5 μL reaction volumes that consisted of 2.5 μL BIO-RAD iTaq Universal SYBR® Green Supermix, 1.1 μL nuclease free water, 0.2 μL of each forward and reverse primer, and 1 μL of diluted (1:20) DNA template, with the exception of the ctxA assay, which consisted of 2.5 μL BIO-RAD iTaq Universal PROBES Supermix, 1 μL nuclease free water, 0.2 μL of each forward and reverse primer, 0.1 μL of probe and 1 μL of diluted (1:20) DNA template. Calibration curves were run with every plate and for each SYBR-based assay specificity was confirmed with a melt curve. Plate preparation was conducted using an epMotion® 5075 I Automated Liquid Handling System.

2.4. Digital Droplet Analysis

The abundance of V. cholerae, V. parahaemolyticus and V. vulnificus was initially determined via qPCR. While we detected no V. cholerae, we did detect V. parahaemolyticus and V. vulnificus, however these levels were very low and significant variation between technical replicates was observed. Digital droplet PCR (ddPCR) has increased precision when quantifying low copy numbers of a gene [50], and so, to quantify V. parahaemolyticus and V. vulnificus more precisely we used ddPCR. All ddPCR analyses were performed using the Bio-Rad QX200 digital droplet PCR instrument. For assay details see Supplementary Table S2. The ddPCR mix used comprised of 12.5 µL of EvaGreen Supermix (Bio-Rad), 0.2 μL of forward and reverse primers, 1 µL of diluted DNA template and nuclease free water up to 25 µL, with 20 µL then being used for droplet generation. The PCR reaction was performed on a C1000 Touch Thermal Cycler with 96-Deep Well reaction module (Bio-Rad) using the evergreen protocol with a single modification of the annealing/extension step, which was adjusted to 56 °C for V. vulnificus vvhA to suit the Tm of this primer set. The droplets were read with a QX200 Droplet Reader and QuantaSoft software version 1.7.4 (Bio-Rad). A single threshold was applied on all samples and negative controls were used, with the absolute number of gene copies converted to gene copies per litre.

2.5. 16S rRNA Gene Amplicon Sequencing

To characterise the total bacterial community composition in samples, the V3–V4 region of the bacterial 16S rRNA gene was amplified using the 341f/805r primer pair [51], with the following cycling conditions: 95 °C for 3 min followed by 25 cycles of: 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, and then 72 °C for 5 min with a final hold at 4 °C [52]. PCR amplicons were then sequenced using the Illumina MiSeq platform (300 bp paired-end analysis at the Ramaciotti Institute of Genomics, University of New South Wales, Sydney, NSW, Australia).
The DADA2 pipeline [53] was used to process the paired R1 and R2 fastq reads. Reads with ‘N’ bases were removed and primers were truncated using cutadapt [54]. Low quality terminal ends were removed by trimming reads (trunc (R1 = 280; R2 = 250)). To produce the highest number of merged reads after learning error rate and removing chimeric sequences, we used the dada2 removeBimeraDenovo program at the default threshold stringent minFoldParentOverAbundance = 1. Amplicon sequencing variants (ASVs) were annotated against the SILVA v138 database with a 50% probability cut-off. The ASV table was subsequently filtered to remove ASVs not assigned as kingdom bacteria, as well as any ASVs classified as chloroplast or mitochondria. The chloroplast data was removed and then ASVs were annotated using the PR2 version 4.14.0 [55] database with a 50% probability cut-off and then joined back to the bacteria dataset. Finally, the dataset was rarefied to 30,000 reads using vegan [56]. Sequences are available in the NCBI database (ID-PRJNA776096). For 16S pipeline script please see: (, accessed on 21 May 2022).

2.6. hsp60 Sequencing

To quantify the Vibrio community with greater species-level precision than is delivered by 16S rRNA gene amplicon sequencing, we employed a Vibrio-specific hsp60 gene amplicon sequencing assay, using the Vib-shpF3-23 and Vib-hspR401-422 primer pairing [57]. DNA was diluted 1:10 and 3 µL was used in a 30 µL PCR reaction volume comprising of 6 µL of 5 x Hi-Fi Buffer (Meridian Bioscience, Cincinnati, OH, United States), 3 µL of 10 mM dNTPs, 0.5 µL of high-fidelity velocity polymerase, 3 µL of 10 µM forward primer (Vib-shpF3-23), and 3 µL of 10 µM reverse primer (Vib-hspR401-422). Amplification was performed using the following PCR cycling conditions: one cycle of 98 °C for 2 min, 5 cycles of 98 °C for 30 s, 60 °C for 30 s, and 72 °C for 45 s, 21 cycles of 98 °C for 30 s, 60 °C for 30 s with a reduction of 0.5 °C per cycle (60 °C to 50 °C), and 72 °C for 45 s, 16 cycles of 98 °C for 30 s, 50 °C for 30 s, and 72 °C for 45 s, and a final extension time of 72 °C for 10 min. Amplicons were subsequently sequenced using the Illumina MiSeq platform (300 bp paired-end analysis at the Ramaciotti Institute of Genomics, University of New South Wales, Sydney, NSW, Australia).
As described in King et al. (2019), paired-end sequences were joined using FLASH [58] and trimmed using Mothur [59]. These fragments were then clustered, and chimeric sequences were removed using vsearch [60]. Cleaned sequences were BLASTed against a Vibrio-hsp60 reference dataset, discarding non-Vibrio sequences. The fasta file obtained was used to assign operational taxonomic units (OTUs) against the custom Vibrio-hsp60 reference dataset with the RDP classifier. As there was a large spread of sequences per sample, the data was not rarefied, but the sequences were normalised to the number of sequences per sample to provide the relative abundance for each taxon for each sample. For hsp60 pipeline script please see (, accessed on 21 May 2022).

2.7. Statistical Analysis

To test for differences in abiotic variables, qPCR and ddPCR data, between sampling sites and climatic zones, Kruskal–Wallis tests were used, followed by Mann–Whitney pairwise comparisons with Bonferroni corrected p-values. Correlations between qPCR data, ddPCR data and abiotic variables were determined using Spearman’s RS, with Bonferroni corrected p values. These statistics were performed in Past Version 4 [61].
To test for differences in microbial community composition (16S rRNA data) and Vibrio community composition (hsp60 data) between samples, as well as differences in alpha diversity measures in both data sets, we used the adonis function from Vegan [56] and the pairwise.adonis function from the PairwiseAdonis [62] R package. To determine how each sample within the 16S rRNA dataset were related to each other, we used the similarity profile routine test (SIMPROF) in the r package ‘clustsig’ [63]. For the 16S rRNA gene sequencing dataset, we found that the samples clustered into two significantly different groups and so ran the multipatt function in the indicspecies r package [64] to determine indicator ASVs within each group. Additionally, we used the correlation analysis package MicTools [65] to test for potential correlations between chloroplasts, the Vibrio community and abiotic variables, only accepting p-values < 0.05 and using Spearmans rs values for analysis.

3. Results

3.1. Environmental Conditions

Across the latitudinal gradient examined in this study, water temperatures (Figure 1B) varied significantly [p < 0.01] between tropical (12°22′42.8″ S–19°16′29.3″ S), subtropical (21°06′37.6″ S–33°91′00″ S), and temperate (35°00′46.0″ S–42°53′20.9″ S) climatic zones (mean: 31.25 ± 1.85 °C, 26 ± 2.38 °C, and 21 ± 2.61 °C, respectively). In contrast, salinity (Figure 1C) remained statistically indistinguishable between these zones (average 30.53 ± 9.36 ppt, 32.79 ± 6.40 ppt, and 35.70 ± 1.07 ppt, respectively). Of note however, four sites had significantly lower [p < 0.01] salinity than the mean salinity of the dataset (Darwin river: 22.54 ppt, Darwin beach: 10.30 ppt, Bundaberg river site 2: 19.26 ppt and Gold Coast river: 17.90 ppt), which coincided with rainfall events in the 3 days prior to sampling (Darwin: 24 mm, Bundaberg: 3.2 mm and Gold Coast: 17.4 mm). The Hobart sites had significantly lower [p < 0.01] dissolved oxygen levels than the other sites (Hobart beach: 9.48%, Hobart river: 10.01%), and were in fact 10-times lower than the mean dissolved oxygen observed along the latitudinal gradient. The pH within samples did not vary significantly between climatic zones.

3.2. Quantification of the Total Bacterial Community

Along the Eastern Coast of Australia, there were no correlations observed between levels of the 16S rRNA gene, used here as a proxy measure for bacterial biomass, and any of the environmental variables measured. The highest levels of 16S rRNA gene copies were observed within the subtropical region [mean: 1.69 × 1011 ± 1.24 × 1011 copies/L, n = 24], where levels were 61- and 68-times higher than levels detected within the tropical [mean: 1.05 × 1011 ± 7.55 × 1010 copies/L, n = 43] and temperate [mean: 1.00 × 1011 ± 5.58 × 1010 copies/L, n = 18] regions. Average levels of the 16S rRNA gene did not statistically differ [p > 0.05] between beach [mean: 1.39 × 1011 ± 1.12 × 1011 copies/L, n = 42] and river [mean: 1.56 × 1011 ± 1.40 × 1011 copies/L, n = 42] sites along the latitudinal gradient. However, the beach sites within the subtropical region displayed significantly higher [p < 0.01] levels of the 16S rRNA gene than the beach sites within the tropical and temperate regions. This pattern was not true of river sites, whereby across regions there was no statistically distinguishable difference.

3.3. Bacterial Community Analysis

Across the dataset, the 16S rRNA gene amplicon sequencing analysis revealed a total of 67, 218 unique bacterial ASVs. Both alpha diversity [F = 93.63, p < 0.01] and community composition [F = 104.7, p < 0.01] were significantly different between sites across the entire dataset. When beach and river sites were pooled, the most diverse climatic zone was the temperate zone, with both Shannons [F = 6.6, p < 0.05] and Simpson diversity [F = 16.9, p < 0.01] indexes within this zone being significantly higher than within the tropical zone. Simpsons diversity index was also significantly higher [F = 6.2, p < 0.05] within the temperate climatic zone than within the subtropical zone. When dividing the samples into river and beach sites, diversity did not differ significantly between the river sites within any of the climatic zones along the latitudinal gradient, with no significant difference in Chao1, Simpson or Shannons diversity index. In contrast, within the beach sites, Chao1 was significantly lower within the temperate climatic zone compared to the subtropical climatic zone [F = 8.39, p < 0.01].
To identify patterns in similarity among bacterial assemblages, we performed a SIMPROF analysis, which revealed a bifurcation of samples into two major groups that were significantly different [F = 21.472, p < 0.01] (Figure 2). The first group contained samples from only above a latitude of 30.29° S (the Sydney Beach site was an exception to this and clustered with these northern samples) and the second group contained samples from only below 30.29° S.
To determine what bacterial ASVs may be indicators of both the northern and southern communities, we used the multipatt function within the indicspecies R package. This analysis revealed 663 ASVs as indicators of the southern community, with the most abundant of these including a Rubritaleaceae ASV, a Flavobacteriaceae ASV and a Cyanobiaceae ASV, which contributed to 2.8%, 2.4% and 2.3% to the relative abundance of the southern community, respectively. There were also 464 ASVs identified as indicators of the northern community, with the most abundant of these consisting of a Rhodobacteraceae ASV and two Actinomarinaceae ASVs, contributing 1.4%, 1.6% and 0.8% to the relative abundance of the northern community, respectively. Of note, within the northern community indicator ASVs, 11 Vibrio spp. were identified. These Vibrio spp. included seven unassigned Vibrio spp., V. brasiliensis, V. maritimus, V. harveyi and V. sinaloensis.

3.4. Quantification of the Total Vibrio Community

Vibrio were detected via qPCR at all 28 sites sampled along the Australian eastern coastline (Figure 3A,B). Along the latitudinal gradient we observed no correlations between total Vibrio levels and any of the environmental variables.
The highest levels of total Vibrio occurred within the subtropical climatic zone [mean: 1.00 × 107 ± 9.29 × 106 copies/L, n = 41], where levels were 14-times higher than those detected within the tropical climatic zone [mean: 8.81 × 106 ± 1.13 × 107 copies/L, n = 22] and 184-times higher than the temperate climatic zone [mean: 3.54 × 106 ± 2.33 × 106 copies/L, n = 17]. However, in contrast to this overall pattern, the highest levels of Vibrio were detected within the Darwin river [mean: 3.05 × 107 ± 8.93 × 106 copies/L, n = 3] and Darwin beach [mean: 2.47 × 107 ± 1.30 × 107 copies/L] sites, where levels were significantly higher [p < 0.01] than any other site. Moreover, there were only three other regions (Rockhampton, Gold Coast and Sydney, Australia) (Figure 3A,B), which had total Vibrio levels exceeding 1 × 107 copies/L. The sites within these regions included the Rockhampton beach site 1 [mean: 2.13 × 107 ± 2.40 × 106 copies/L, n = 3], Rockhampton beach site 2 [mean: 1.25 × 107 ± 4.81 × 105 copies/L, n = 3], the Gold Coast beach, Gold Coast river, Sydney beach and Sydney river sites, all of which were significantly higher [p < 0.05] than any other site (excluding the Darwin sites) along the latitudinal gradient.
Across the entire dataset, average Vibrio levels were 37 times higher at the beach [mean: 9.57 × 106 ± 9.31 × 106 copies/L, n = 41] sites compared to river sites [mean: 7.01 × 106 ± 9.11 × 106 copies/L, n = 31], which was also true within the subtropical and temperate climatic zones, [subtropical beach mean: 1.25 × 107 ± 8.53 × 106 copies/L, n = 21; temperate beach mean: 3.41 × 106 ± 2.42 × 106 copies/L, n = 9], which were characterised by significantly higher [p < 0.01] Vibrio abundances than the river sites [subtropical mean: 7.46 × 106 ± 9.55 × 106 copies/L, n = 20, temperate mean: 3.67 × 106 ± 2.39 × 106 copies/L, n = 8].

3.5. Vibrio Diversity

Vibrio-specific hsp60 gene amplicon sequencing identified the occurrence of 59 unique Vibrio species across the entire dataset, with the most abundant species including V. campbellii and V. harveyi, which were detected in 93% and 89% of samples, respectively (Figure 4). All sites had a significantly different [F = 7.5, p < 0.001] Shannon’s diversity and a significantly different [F = 7.15, p < 0.001] community composition. No significant differences were observed in Vibrio diversity between tropical, subtropical and temperate zones, nor did any of the diversity indices correlate with environmental variables. There was, however, a significant difference [F = 4.5, p < 0.01] in Vibrio community composition between the tropical and temperate climatic zones, which was driven by V. campbellii and V. harveyi, accounting for 24% and 6% of the dissimilarity between these two regions, respectively. Vibrio community composition did not differ between river and beach sites, nor was there a significant difference between tropical and subtropical, or subtropical and temperate climatic zones.
Among the most abundant Vibrio species, the relative abundance of V. harveyi (mean relative abundance = 7.69%, n = 78) was positively correlated with temperature, DO% and latitude [rs = 0.38, 0.08 and 0.38, p < 0.01], and negatively correlated with salinity and pH [rs = −0.43 and −0.54, p < 0.01]. The relative abundance of V. harveyi was also positively correlated [rs = 0.03, p < 0.05] with the relative abundance of one phytoplankton ASV belonging to the genus Tetraselmis. The relative abundance of V. campbellii (mean relative abundance = 34.94%, n = 78) was positively correlated with temperature and latitude [rs = 0.18 and 0.10, p < 0.01] and negatively correlated with DO%, salinity and pH [rs = −0.10. −0.32 and −0.46, p < 0.01]. V. campbellii relative abundance was also positively correlated with 17 phytoplankton ASVs, which were mostly members of the Bacillariophyta family. The strongest correlation [rs = 0.1] with an ASV from the Phaeocystis genera.
Among known human pathogens, V. parahaemolyticus OTUs were detected in 78% (n = 22/24) of samples, with their relative abundance positively correlated with latitude [rs = 0.26, p < 0.01] and negatively correlated with salinity and pH [rs = −0.71 and −0.52, p < 0.01] (Figure 5). V. parahaemolyticus relative abundance was also significantly correlated with two Raphid-Pennate ASVs [rs = 0.28 and 0.12, p < 0.05], an Arcocellulus ASV [rs = 0.09, p < 0.05], two Olisthodiscus ASVs [rs = 0.09 and 0.02, p < 0.05], a Teleaulax ASV [rs = 0.09, p < 0.05] and a Marsupiomonas ASV [rs = 0.09, p < 0.05]. V. vulnificus OTUs were recorded at three sites, namely the tropical environments at Bundaberg R2, Darwin B and Darwin R, where the relative abundance of this pathogen displayed a positive correlation to temperature, latitude and DO% [rs = 0.06, 0.06 and 0.07, p < 0.01] while displaying a negative correlation to salinity [rs = −0.47, p < 0.01].

3.6. Potentially Pathogenic Vibrio Species: Vibrio Cholerae, Vibrio parahaemolyticus and Vibrio vulnificus

V. cholerae was not detected in any sample using qPCR, which is consistent with the results from the hsp60 sequencing analyses where this species was also not detected. V. parahaemolyticus was detected in 79% (22/28) of samples, with the highest levels detected at the Cooktown river site (15°27′37.8″ S, 145°14′57.6″ E) [mean: 2.56 × 105 ± 4.43 × 105 copies/L, n = 3] and the Jervis Bay beach site (35°00′46.0″ S, 150°41′38.5″ E) [mean: 9.5 × 104 ± 1.65 × 105 copies/L, n = 3]. On average, the tropical climatic zone had the highest levels of V. parahaemolyticus [mean: 4.59 × 104 ± 1.66 × 105 copies/L, n = 21], where they were 310-times higher than levels within the subtropical climatic zone and 1814-times and significantly [p < 0.05] higher than levels within the temperate climate zone (Figure 3E,F). V. vulnificus was detected in 57% (16/28) of samples, with the highest levels detected at the Coffs Harbour beach site [mean: 1.34 × 104 ± 2.61 × 103 copies/L, n = 2] and the Gold Coast river site [mean: 1.26 × 104 ± 1.60 × 104 copies/L, n = 3]. On average, the subtropical climatic zone had the highest levels of V. vulnificus [mean: 3.65 × 103 ± 6.22 × 103 copies/L, n = 34], where they were 26 times higher than levels within the tropical climatic zone [mean: 2.90 × 103 ± 4.13 × 103 copies/L, n = 22] and 44 times higher than levels within the temperate climatic zone [mean: 2.54 × 103 ± 3.03 × 103 copies/L, n = 23] (Figure 3C,D). In contrast to the patterns seen in the hsp60 gene amplicon sequencing data, no significant correlations were observed between V. vulnificus or V. parahaemolyticus and any measured environmental variables. However, this may be due to the limited proportion of samples that these organisms were detected in, and it is notable that both organisms occurred in greatest abundance in warm water tropical and subtropical environments, such as Cooktown and the Gold Coast.

4. Discussion

The principal goal of this study was to characterise patterns in the diversity and abundance of Vibrio species, along a large latitudinal gradient spanning the eastern coastline of Australia, which is a region that has been identified as a climate change hotspot [44]. Along this transect, we observed the highest levels of Vibrio bacteria in Darwin, the northernmost and warmest site sampled, as well as positive correlations between both pathogenic and non-pathogenic Vibrio species with temperature, salinity and phytoplankton ASVs.
Bacterial communities are often governed by differing environmental variables such as temperature [66] along latitudinal gradients. Previous studies have demonstrated that some bacterial populations change with latitude along the eastern coastline of Australia, including poleward increases in the abundance of members of the Marine Roseobacter Group [67], and latitudinal shifts in the abundance of aerobic anoxygenic phototrophic bacteria [49]. In this study, we observed a notable bifurcation of the total bacterial community into two significantly different groups that separated at approximately 30° S, into a northern and southern group of samples. Upon examination of the taxa responsible for this split, 11 Vibrio ASVs were defined as indicators of the northern community, which is consistent with prior observations that many Vibrio species show a preference for warmer waters [30,32,33,34,36,37,39,68,69].

4.1. Latitudinal Trends in Vibrio Abundance and Diversity

Vibrio often exist in a higher abundance at low latitudes where water temperature is high [42,70]. We observed the greatest levels of Vibrio spp. in Darwin, the lowest latitude at which we sampled, as well as high levels in Rockhampton, the Gold Coast and in Sydney. Not only were these sites amongst the sites with the warmest temperatures (Figure 1B), but two of these sites, Darwin and the Gold Coast River site, also had the lowest salinities. This finding aligns with other studies [37,39,68,69], which have also found a higher abundance of Vibrio bacteria at low salinities and high temperatures. While not examined in this study, in a recent study, Vibrio spp. were discovered to form biofilms on microplastic particles and, their occurrence on these microplastics was correlated to proximity to major cities [71], which may explain high levels of Vibrio bacteria at these locations, which are all highly urbanised. Another potential reason for these differences may be due to temporal changes, which were not accounted for in this study, with each site being sampled once within a period of 48 days during the Australian summer.
We observed a significant difference in community composition between all sites and between the tropical and temperate regions of the coastline. These differences were largely driven by heterogeneity in the relative abundance of V. campbellii, V. azureus and V. harveyi OTUs, whereby V. campbellii and V. harveyi OTUs were more abundant in the tropical sites and V. azureus OTUs were more abundant at the temperate sites. Notably, strains of all of these species are known marine pathogens, infecting shrimp [72], oysters [73], and fish species [18]. These patterns show some similarities to those observed in other large-scale examinations of Vibrio diversity, where V. campbellii and V. harveyi were also amongst the most abundant Vibrio species isolated from corals, fireworms, soil and water, along the tropical coast of Brazil [74]. Additionally, V. campbellii, V. azureus and V. harveyi OTUs displayed positive correlations with specific phytoplankton groups identified in the 16S rRNA gene amplicon sequencing analysis. These observations point towards a potentially important role of phytoplankton in defining the abundance of some Vibrio species, which is consistent with previous observations (e.g., [35]) that have observed strong correlations between Vibrio and chlorophyll a. In this case, it is possible that species—specific relationships underpin this pattern, which has been observed before during a yearlong time-series, where an unknown Vibrio species increased from baseline relative abundance of 0–2% up to 54%, closely following an increase in the relative abundance of the diatom Chaetoceros compressus [66].
Of the human pathogens detected within the community data, a V. vulnificus OTU was detected at the northernmost sampling location Darwin, where water temperatures were <32 °C, as well as the Bundaberg river 2 site. V. parahaemolyticus OTUs occurred within 78% (22/28) of sites but were also most abundant in Darwin. V. vulnificus OTUs levels displayed a significant, albeit weak, correlation with temperature across the transect, but a much stronger negative correlation to salinity, which is consistent with previous observations [31,37]. V. parahaemolyticus was correlated positively with phytoplankton ASVs including a Raphid-Pennate ASV, an Arcocellulus ASV, two Olisthodiscus ASVs, a Teleaulax ASV and a Marsupiomonas ASV. Whilst V. parahaemolyticus has previously been shown to display ecological interactions with phytoplankton [75], to our knowledge this is the first evidence for links between V. parahaemolyticus and these specific phytoplankton taxa.

4.2. Latitudinal Patterns in Vibrio Pathogens

Some strains of V. cholerae cause cholera, a disease responsible for ~100,000 deaths annually [24]. However, non-cholera causing V. cholerae have also been implicated in milder forms of gastroenteritis, otitis and life-threatening necrotizing fasciitis [76]. However, neither environmental nor toxigenic strains of V. cholerae were detected in any samples in this study. This is in contrast to recent studies that have detected low levels (101–103 copies/mL) of non-toxigenic V. cholerae in some of the Australian coastal environments examined here, including Darwin [42] and Sydney Harbour [43]. Seasonal differences in the timing of sampling may explain these divergent observations.
V. parahaemolyticus can cause foodborne disease and wound infections [25]. The highest levels of V. parahaemolyticus were detected in the tropical regions of the east Australian coast, where levels of this species were an order of magnitude higher than those observed in the temperate regions. The highest levels of V. parahaemolyticus were observed in Cooktown, where abundances reached levels similar to those recorded by Padovan et al. (2021), in the tropical waters around the Darwin area. However, it is notable that very high levels of V. parahaemolyticus were also observed in temperate samples, as far south as Jervis Bay (35°00′46.0″ S, 150°41′38.5″ E).
V. vulnificus causes foodborne disease and severe wound infections, which can often result high rates of mortality [77]. The abundance of V. vulnificus was not restricted to tropical waters, with levels 20 and 30 times higher in the subtropical sites compared to the tropical and temperate sites, respectively. The highest levels of V. vulnificus were recorded at the Coffs Harbour beach (30°18′17.4″ S, 153°08′34.9″ E) and Gold Coast river (28°10′44.1″ S, 153°32′30.1″ E) sites. Notably, these levels were similar to levels previously recorded in tropical northern Australian waters [42].

4.3. Implications of High Levels of Vibrio Bacteria along Australia’s East Coast

This is the first study to examine the spatial dynamics of Vibrio abundance and diversity along Australia’s eastern coast and has revealed levels of Vibrio to be higher in the tropical and subtropical sites compared to the temperate sites, with the highest levels in Darwin, the northernmost site, which was characterised by the highest water temperature and lowest salinity. Additionally, we observed multiple Vibrio species that displayed significant correlations with temperature, salinity and phytoplankton ASVs.
Notably, Vibrio abundances were not always most strongly driven by temperature, which is contrast to numerous previous studies [30,32,33,34,36,37,39,68,69]. Models of future SST scenarios predict 38,000 km of new coastal areas by 2100 being suitable for Vibrio growth as a result of climate change [28], as well as an increase in Vibrio cases of 1.93 times for every 1 °C increase in SST [78]. This is particularly concerning for Australia, whereby the SST is predicted to rise 1.5–3 °C by 2070, translating to a 2.9–5.8 times increase in the abundance of Vibrio spp. in Australian waters. While the highest levels of total Vibrio were indeed detected in the very warm water tropical sites in Darwin, it is notable that we also observed high levels in higher latitude locations, including sub-tropical sites at Gold Coast and in Sydney. Notably these are two very densely populated coastal areas, where high levels of recreational use of waterways occurs, highlighting potential human health hazard through the exposure to these pathogens [79].

5. Conclusions

The study of ecological patterns in the abundance and diversity of organisms is essential for developing an understanding of where and when they are abundant and for predicting distributions in future scenarios. We observed significant differences in both the entire bacterial and Vibrio communities between the northern and southern sites. Across this transect, both pathogenic and non-pathogenic Vibrio species correlated positively with temperature and negatively with salinity. Interestingly, we also observed correlations between several pathogenic Vibrio species and specific phytoplankton species, highlighting ecological relationships with phytoplankton as a potentially important, but often over-looked, determinant of Vibrio spatial and temporal dynamics. During the summer period that this study was performed, we detected pathogenic Vibrio species not only in warm tropical waters, but also southerly latitudes, including the highly urbanised environments, where opportunities for human exposure are significant. Given that ocean temperatures are rising [80] and severe rainfall events are increasing [41] as a consequence of climate change, many coastal environments may become more ideal habitats for pathogenic Vibrio species, indicating a potentially heightened human health risk.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1. List of locations sampled, detailing date, latitude, longitude, sample type and Koppen-Classification. Table S2. Primers used in study.

Author Contributions

J.R.S., N.S. and V.B. designed the study. N.L.R.W. and V.B. took all samples. qPCR was performed by N.L.R.W. ddPCR and hsp60 PCR were performed by N.L.R.W., N.S., A.B. and N.L.R.W. performed the 16S rRNA gene sequencing analysis. N.L.R.W. and W.L.K. performed the hsp60 gene sequencing analysis. N.L.R.W. and J.R.S. prepared and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.


This research was supported by an Australian Research Council grant (DP210101610) to J.R.S.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All abiotic, qPCR and ddPCR data are available in the Supplementary Materials. Sequences are available in the NCBI database (ID—PRJNA776096). Scripts are available at (, accessed on 24 May 2022).


This research was supported by an Australian Research Council grant (DP210101610) to J.R.S. We acknowledge the support of multiple team members from the OMG group including James O’Brien and Martin Ostrowski for their help and advice regarding the analysis of the 16S rRNA gene sequencing data. We would also like to acknowledge Anna Padovan and Karren Gibb for their assistance and advice when taking samples in Darwin.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Chapin, S.F.; Zavaleta, E.S.; Eviner, V.T.; Naylor, R.L.; Vitousek, P.M.; Reynolds, H.L.; Hooper, D.U.; Lavorel, S.; Sala, O.E.; Hobbie, S.E.; et al. Consequences of changing biodiversity. Nature 2000, 405, 234–242. [Google Scholar] [CrossRef] [PubMed]
  2. Fischer, A.G. Latitudinal Variations in Organic Diversity. Evolution 1960, 14, 64–81. [Google Scholar] [CrossRef]
  3. Hillebrand, H. Strength, slope and variability of marine latitudinal gradients. Mar. Ecol. Prog. Ser. 2004, 273, 251–267. [Google Scholar] [CrossRef]
  4. Pianka, E.R. Latitudinal Gradients in Species Diversity: A review of Concepts. Am. Nat. 1966, 100, 33–46. [Google Scholar] [CrossRef]
  5. Sunday, J.M.; Bates, A.E.; Dulvy, N.K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Chang. 2012, 2, 686–690. [Google Scholar] [CrossRef]
  6. Bahram, M.; Hildebrand, F.; Forslund, S.K.; Anderson, J.L.; Soudzilovskaia, N.A.; Bodegom, P.M.; Bengtsson-Palme, J.; Anslan, S.; Coelho, L.P.; Harend, H.; et al. Structure and function of the global topsoil microbiome. Nature 2018, 560, 233–237. [Google Scholar] [CrossRef] [PubMed]
  7. Raes, E.J.; Bodrossy, L.; van de Kamp, J.; Bissett, A.; Waite, A.M. Marine bacterial richness increases towards higher latitudes in the eastern Indian Ocean. Limnol. Oceanogr. Lett. 2018, 3, 10–19. [Google Scholar] [CrossRef]
  8. Roy, K.; Jablonski, D.; Valentine, J.W.; Rosenberg, G. Marine latitudinal diversity gradients: Tests of causal hypotheses. Proc. Natl. Acad. Sci. USA 1998, 95, 3699–3702. [Google Scholar] [CrossRef] [PubMed]
  9. Brown, J.H. Two decades of homage to santa rosalia: Toward a general theory of diversity. Integr. Comp. Biol. 1981, 21, 877–888. [Google Scholar] [CrossRef]
  10. Allen, A.P.; Brown, J.H.; Gillooly, J.F. Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 2002, 297, 1545–1548. [Google Scholar] [CrossRef] [PubMed]
  11. Brown, J.H.; Gillooly, J.F.; Allen, A.P.; Savage, V.M.; West, G.B. Toward a Metabolic Theory of Ecology. Ecol. Soc. Am. 2004, 85, 1771–1789. [Google Scholar] [CrossRef]
  12. Fuhrman, J.A.; Steele, J.A.; Hewson, I.; Schwalbach, M.S.; Brown, M.V.; Green, J.L.; Brown, J.H. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl. Acad. Sci. USA 2008, 105, 7774–7778. [Google Scholar] [CrossRef] [PubMed]
  13. Pommier, T.; Canbäck, B.; Riemann, L.; Boström, K.H.; Simu, K.; Lundberg, P.; Tunlid, A.; Hagström, Å. Global patterns of diversity and community structure in marine bacterioplankton. Mol. Ecol. 2007, 16, 867–880. [Google Scholar] [CrossRef]
  14. Flombaum, P.; Gallegos, J.L.; Gordillo, R.A.; Rincón, J.; Zabala, L.L.; Jiao, N.; Karl, D.M.; Li, W.K.W.; Lomas, M.W.; Veneziano, D.; et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc. Natl. Acad. Sci. USA 2013, 110, 9824–9829. [Google Scholar] [CrossRef] [PubMed]
  15. Liang, J.; Liu, J.; Wang, X.; Lin, H.; Liu, J.; Zhou, S.; Sun, H.; Zhang, X.-H. Spatiotemporal Dynamics of Free-Living and Particle-Associated Vibrio Communities in the Northern Chinese Marginal Seas. Appl. Environ. Microbiol. 2019, 85, e00217-19. [Google Scholar] [CrossRef]
  16. Li, N.; Dong, K.; Jiang, G.; Tang, J.; Xu, Q.; Li, X.; Kang, Z.; Zou, S.; Chen, X.; Adams, J.M.; et al. Stochastic processes dominate marine free-living Vibrio community assembly in a subtropical gulf. FEMS Microbiol. Ecol. 2020, 96, fiaa198. [Google Scholar] [CrossRef]
  17. Nyholm, S.V.; McFall-Ngai, M.J. A lasting symbiosis: How the Hawaiian bobtail squid finds and keeps its bioluminescent bacterial partner. Nat. Rev. Microbiol. 2021, 19, 666–679. [Google Scholar] [CrossRef]
  18. Istiqomah, I.; Sukardi, M.; Isnansetyo, A. Review Vibriosis Management in Indonesian Marine Fish Farming. E3S Web Conf. 2020, 147, 01001. [Google Scholar] [CrossRef]
  19. Kushmaro, A.; Banin, E.; Loya, Y.; Stackebrandt, E.; Rosenberg, E. Vibrio shiloi sp. nov., the causative agent of bleaching of the coral Oculina patagonica. Int. J. Syst. Evol. Microbiol. 2001, 51, 1383–1388. [Google Scholar] [CrossRef]
  20. Bruto, M.; Labreuche, Y.; James, A.; Piel, D.; Chenivesse, S.; Petton, B.; Polz, M.F.; Le Roux, F. Ancestral gene acquisition as the key to virulence potential in environmental Vibrio populations. ISME J. 2018, 12, 2954–2966. [Google Scholar] [CrossRef] [PubMed]
  21. Tang, K.F.J.; Bondad-Reantaso, M.G. Impacts of acute hepatopancreatic necrosis disease on commercial shrimp aquaculture. Rev. Sci. Tech. 2019, 38, 477–490. [Google Scholar] [CrossRef] [PubMed]
  22. Seong Wei, L.; Wee, W. Diseases in Aquaculture. Res. J. Anim. Vet. Sci. 2014, 7, 1–6. [Google Scholar]
  23. Lafferty, K.D.; Harvell, C.D.; Conrad, J.M.; Friedman, C.S.; Kent, M.L.; Kuris, A.M.; Powell, E.N.; Rondeau, D.; Saksida, S.M. Infectious diseases affect marine fisheries and aquaculture economics. Annu. Rev. Mar. Sci. 2015, 7, 471–496. [Google Scholar] [CrossRef] [PubMed]
  24. Ali, M.; Nelson, A.R.; Lopez, A.L.; Sack, D.A. Updated global burden of cholera in endemic countries. PLoS Negl. Trop. Dis. 2015, 9, e0003832. [Google Scholar] [CrossRef] [PubMed]
  25. Daniels, N.A.; Mackinnon, L.; Bishop, R.; Altekruse, S.; Ray, B.; Hammond, R.M.; Thompson, S.; Wilson, S.; Bean, N.H.; Griffin, P.M.; et al. Vibrio parahaemolyticus infections in the United States, 1973–1998. J. Infect. Dis. 2000, 181, 1661–1666. [Google Scholar] [CrossRef]
  26. Rippey, S.R. Infectious diseases associated with molluscan shellfish consumption. Clin. Microbiol. Rev. 1994, 7, 419–425. [Google Scholar] [CrossRef]
  27. Baker-Austin, C.; Trinanes, J.; Gonzalez-Escalona, N.; Martinez-Urtaza, J. Non-Cholera Vibrios: The Microbial Barometer of Climate Change. Trends Microbiol. 2017, 25, 76–84. [Google Scholar] [CrossRef]
  28. Trinanes, J.; Martinez-Urtaza, J. Future scenarios of risk of Vibrio infections in a warming planet: A global mapping study. Lancet Planet. Health 2021, 5, e426–e435. [Google Scholar] [CrossRef]
  29. Vezzulli, L.; Colwell, R.R.; Pruzzo, C. Ocean Warming and Spread of Pathogenic Vibrios in the Aquatic Environment. Microb. Ecol. 2013, 65, 817–825. [Google Scholar] [CrossRef]
  30. Eiler, A.; Johansson, M.; Bertilsson, S. Environmental influences on Vibrio populations in northern temperate and boreal coastal waters (Baltic and Skagerrak Seas). Appl. Environ. Microbiol. 2006, 72, 6004–6011. [Google Scholar] [CrossRef]
  31. Blackwell, K.D.; Oliver, J.D. The ecology of Vibrio vulnificus, Vibrio cholerae, and Vibrio parahaemolyticus in North Carolina Estuaries. J. Microbiol. 2008, 46, 146–153. [Google Scholar] [CrossRef] [PubMed]
  32. Froelich, B.; Bowen, J.; Gonzalez, R.; Snedeker, A.; Noble, R. Mechanistic and statistical models of total vibrio abundance in the neuse river estuary. Water Res. 2013, 47, 5783–5793. [Google Scholar] [CrossRef] [PubMed]
  33. Heidelberg, J.F.; Heidelberg, K.B.; Colwell, R.R. Seasonality of chesapeake bay bacterioplankton species. Appl. Environ. Microbiol. 2002, 68, 5488–5497. [Google Scholar] [CrossRef] [PubMed]
  34. Nigro, O.D.; Hou, A.; Vithanage, G.; Fujioka, R.S.; Steward, G.F. Temporal and spatial variability in culturable pathogenic Vibrio spp. in Lake Pontchartrain, Louisiana, following hurricanes katrina and rita. Appl. Environ. Microbiol. 2011, 77, 5384–5393. [Google Scholar] [CrossRef] [PubMed]
  35. Turner, J.W.; Good, B.; Cole, D.; Lipp, E.K. Plankton composition and environmental factors contribute to Vibrio seasonality. ISME J. 2009, 3, 1082–1092. [Google Scholar] [CrossRef] [PubMed]
  36. Wetz, J.J.; Blackwood, A.D.; Fries, J.S.; Williams, Z.F.; Noble, R.T. Trends in total Vibrio spp. and Vibrio vulnificus concentrations in the eutrophic Neuse River Estuary, North Carolina, during storm events. Aquat. Microb. Ecol. 2008, 53, 141–149. [Google Scholar] [CrossRef]
  37. Oberbeckmann, S.; Fuchs, B.M.; Meiners, M.; Wichels, A.; Wiltshire, K.H.; Gerdts, G. Seasonal Dynamics and Modeling of a Vibrio Community in Coastal Waters of the North Sea. Microb. Ecol. 2012, 63, 543–551. [Google Scholar] [CrossRef] [PubMed]
  38. Hsieh, J.L.; Fries, J.S.; Noble, R.T. Dynamics and predictive modelling of Vibrio spp. in the Neuse River Estuary, North Carolina, USA. Environ. Microbiol. 2008, 10, 57–64. [Google Scholar] [CrossRef] [PubMed]
  39. Randa, M.A.; Polz, M.F.; Lim, E. Effects of temperature and salinity on Vibrio vulnificus population dynamics as assessed by quantitative PCR. Appl. Environ. Microbiol. 2004, 70, 5469–5476. [Google Scholar] [CrossRef] [PubMed]
  40. Takemura, A.F.; Chien, D.M.; Polz, M.F. Associations and dynamics of vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 2014, 5, 38. [Google Scholar] [CrossRef] [PubMed]
  41. Madakumbura, G.D.; Goldenson, N.; Hall, A. Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets. Nat. Commun. 2021, 12, 3944. [Google Scholar] [CrossRef] [PubMed]
  42. Padovan, A.; Siboni, N.; Kaestli, M.; King, W.L.; Seymour, J.R.; Gibb, K. Occurrence and dynamics of potentially pathogenic vibrios in the wet-dry tropics of northern Australia. Mar. Environ. Res. 2021, 169, 105405. [Google Scholar] [CrossRef] [PubMed]
  43. Siboni, N.; Balaraju, V.; Carney, R.; Labbate, M.; Seymour, J.R. Spatiotemporal dynamics of Vibrio spp. within the Sydney Harbour estuary. Front. Microbiol. 2016, 7, 460. [Google Scholar] [CrossRef] [PubMed]
  44. Hobday, A.J.; Loug, J.M. Projected climate change in Australian marine and freshwater environments. Mar. Freshw. Res. 2011, 62, 1000–1014. [Google Scholar] [CrossRef]
  45. Suzuki, M.T.; Taylor, L.T.; DeLong, E.F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5′-nuclease assays. Appl. Environ. Microbiol. 2000, 66, 4605–4614. [Google Scholar] [CrossRef] [PubMed]
  46. Thompson, F.L.; Iida, T.; Swings, J. Biodiversity of Vibrios. Microbiol. Mol. Biol. Rev. 2004, 68, 403–431. [Google Scholar] [CrossRef] [PubMed]
  47. Rizvi, A.V.; Bej, A.K. Multiplexed real-time PCR amplification of tlh, tdh and trh genes in Vibrio parahaemolyticus and its rapid detection in shellfish and Gulf of Mexico water. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 2010, 98, 279–290. [Google Scholar] [CrossRef]
  48. Gubala, A.J.; Proll, D.F. Molecular-beacon multiplex real-time PCR assay for detection of Vibrio cholerae. Appl. Environ. Microbiol. 2006, 72, 6424–6428. [Google Scholar] [CrossRef] [PubMed]
  49. Bibiloni-Isaksson, J.; Seymour, J.R.; Ingleton, T.; van de Kamp, J.; Bodrossy, L.; Brown, M.V. Spatial and temporal variability of aerobic anoxygenic photoheterotrophic bacteria along the east coast of Australia. Environ. Microbiol. 2016, 18, 4485–4500. [Google Scholar] [CrossRef]
  50. Hindson, C.M.; Chevillet, J.R.; Briggs, H.A.; Gallichotte, E.N.; Ruf, I.K.; Hindson, B.J.; Vessella, R.L.; Tewari, M. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat. Methods 2013, 10, 1003–1005. [Google Scholar] [CrossRef] [PubMed]
  51. Takahashi, S.; Tomita, J.; Nishioka, K.; Hisada, T.; Nishijima, M. Development of a Prokaryotic Universal Primer for Simultaneous Analysis of Bacteria and Archaea Using Next-Generation Sequencing. PLoS ONE 2014, 9, e105592. [Google Scholar] [CrossRef]
  52. Illumina. 16S Metagenomic Sequencing Library. Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System; Illumina: San Diego, CA, USA, 2013; pp. 1–28. [Google Scholar]
  53. Callahan, B.J.; Mcmurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef]
  54. Martin, M. Cutadapt Removes Adapter Sequences from High-Throughput Sequencing Reads. EMBnet J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  55. Guillou, L.; Bachar, D.; Audic, S.; Bass, D.; Berney, C.; Bittner, L.; Boutte, C.; Burgaud, G.; De Vargas, C.; Decelle, J.; et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013, 41, 597–604. [Google Scholar] [CrossRef]
  56. Dixon, P. Computer program review VEGAN, a package of R functions for community ecology. J. Veg. Sci. 2003, 14, 927–930. [Google Scholar] [CrossRef]
  57. King, W.L.; Siboni, N.; Kahlke, T.; Green, T.J.; Labbate, M.; Seymour, J.R. A New High Throughput Sequencing Assay for Characterizing the Diversity of Natural Vibrio Communities and Its Application to a Pacific Oyster Mortality Event. Front. Microbiol. 2019, 10, 2907. [Google Scholar] [CrossRef]
  58. Magoč, T.; Salzberg, S.L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011, 27, 2957–2963. [Google Scholar] [CrossRef] [PubMed]
  59. Schloss, P.D.; Westcott, S.L.; Ryabin, T.; Hall, J.R.; Hartmann, M.; Hollister, E.B.; Lesniewski, R.A.; Oakley, B.B.; Parks, D.H.; Robinson, C.J.; et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 2009, 75, 7537–7541. [Google Scholar] [CrossRef] [PubMed]
  60. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahé, F. VSEARCH: A versatile open-source tool for metagenomics. PeerJ 2016, 4, e2584. [Google Scholar] [CrossRef]
  61. Hammer, Ø.; Harper, D.A.T.; Ryan, P.D. Past: Paleontological statistics software package for education and data analysis. Curr. Sci. 2001, 4, 4–9. [Google Scholar]
  62. Martinez Arbizu, P. PairwiseAdonis: Pairwise Multilevel Comparison Using, R Package version 0.4; GitHub, Inc.: San Francisco, CA, USA, 2020. [Google Scholar]
  63. Whitaker, D.; Christman, M. Package ‘clustsig’, version 1.0; GitHub, Inc.: San Francisco, CA, USA, 2010. [Google Scholar]
  64. De Caceres, M.; Jansen, F. Indicspecies: Relationship between Species and Groups of Sites; R Package Version 1.7.5; GitHub, Inc.: San Francisco, CA, USA, 2018. [Google Scholar]
  65. Albanese, D.; Riccadonna, S.; Donati, C.; Franceschi, P. A practical tool for maximal information coefficient analysis. GigaScience 2018, 7, 1–8. [Google Scholar] [CrossRef] [PubMed]
  66. Gilbert, J.A.; Steele, J.A.; Caporaso, J.G.; Steinbrück, L.; Reeder, J.; Temperton, B.; Huse, S.; McHardy, A.C.; Knight, R.; Joint, I.; et al. Defining seasonal marine microbial community dynamics. ISME J. 2012, 6, 298–308. [Google Scholar] [CrossRef] [PubMed]
  67. O’Brien, J.; McParland, E.L.; Bramucci, A.R.; Siboni, N.; Ostrowski, M.; Kahlke, T.; Levine, N.M.; Brown, M.V.; van de Kamp, J.; Bodrossy, L.; et al. Biogeographical and seasonal dynamics of the marine Roseobacter community and ecological links to DMSP-producing phytoplankton. ISME Commun. 2022, 2, 16. [Google Scholar] [CrossRef]
  68. Johnson, C.N.; Flowers, A.R.; Noriea, N.F.; Zimmerman, A.M.; Bowers, J.C.; DePaola, A.; Grimes, D.J. Relationships between environmental factors and pathogenic vibrios in the northern gulf of Mexico. Appl. Environ. Microbiol. 2010, 76, 7076–7084. [Google Scholar] [CrossRef] [PubMed]
  69. Lipp, E.K.; Rodriguez-palacios, C.; Rose, J.B. The Ecology and Etiology of Newly Emerging Marine Diseases; Springer: Dordrecht, The Netherlands, 2001; Volume 159, pp. 165–173. [Google Scholar] [CrossRef]
  70. Wong, Y.Y.; Lee, C.W.; Bong, C.W.; Lim, J.H.; Narayanan, K.; Sim, E.U.H. Environmental control of Vibrio spp. abundance and community structure in tropical waters. FEMS Microbiol. Ecol. 2019, 95, fiz176. [Google Scholar] [CrossRef]
  71. Kesy, K.; Labrenz, M.; Scales, B.S.; Kreikemeyer, B.; Oberbeckmann, S. Vibrio colonization is highly dynamic in early microplastic-associated biofilms as well as on field-collected microplastics. Microorganisms 2021, 9, 76. [Google Scholar] [CrossRef]
  72. Defoirdt, T.; Sorgeloos, P. Monitoring of Vibrio harveyi quorum sensing activity in real time during infection of brine shrimp larvae. ISME J. 2012, 6, 2314–2319. [Google Scholar] [CrossRef]
  73. Green, T.J.; Siboni, N.; King, W.L.; Labbate, M.; Seymour, J.R.; Raftos, D. Simulated Marine Heat Wave Alters Abundance and Structure of Vibrio Populations Associated with the Pacific Oyster Resulting in a Mass Mortality Event. Microb. Ecol. 2019, 77, 736–747. [Google Scholar] [CrossRef]
  74. Chimetto Tonon, L.A.; Silva, B.S.D.O.; Moreira, A.P.B.; Valle, C.; Alves, N.; Cavalcanti, G.; Garcia, G.; Lopes, R.M.; Francini-Filho, R.B.; De Moura, R.L.; et al. Diversity and ecological structure of vibrios in benthic and pelagic habitats along a latitudinal gradient in the Southwest Atlantic Ocean. PeerJ 2015, 3, e731. [Google Scholar] [CrossRef]
  75. Hartwick, M.A.; Berenson, A.; Whistler, C.A.; Naumova, E.N.; Jones, S.H. The seasonal microbial ecology of plankton and plankton-associated Vibrio parahaemolyticus in the northeast united states. Appl. Environ. Microbiol. 2021, 87, e02973-20. [Google Scholar] [CrossRef]
  76. Vezzulli, L.; Baker-Austin, C.; Kirschner, A.; Pruzzo, C.; Martinez-Urtaza, J. Global emergence of environmental non-O1/O139 Vibrio cholerae infections linked with climate change: A neglected research field? Environ. Microbiol. 2020, 22, 4342–4355. [Google Scholar] [CrossRef]
  77. Baker-Austin, C.; Stockley, L.; Rangdale, R.; Martinez-Urtaza, J. Environmental occurrence and clinical impact of Vibrio vulnificus and Vibrio parahaemolyticus: A European perspective. Environ. Microbiol. Rep. 2010, 2, 7–18. [Google Scholar] [CrossRef]
  78. Baker-Austin, C.; Trinanes, J.A.; Taylor, N.G.H.; Hartnell, R.; Siitonen, A.; Martinez-Urtaza, J. Emerging Vibrio risk at high latitudes in response to ocean warming. Nat. Clim. Chang. 2013, 3, 73–77. [Google Scholar] [CrossRef]
  79. Baker-Austin, C.; Oliver, J.D.; Alam, M.; Ali, A.; Waldor, M.K.; Qadri, F.; Martinez-Urtaza, J. Vibrio spp. infections. Nat. Rev. Dis. Prim. 2018, 4, 8. [Google Scholar] [CrossRef]
  80. Doney, S.C.; Ruckelshaus, M.; Emmett Duffy, J.; Barry, J.P.; Chan, F.; English, C.A.; Galindo, H.M.; Grebmeier, J.M.; Hollowed, A.B.; Knowlton, N.; et al. Climate change impacts on marine ecosystems. Ann. Rev. Mar. Sci. 2012, 4, 11–37. [Google Scholar] [CrossRef]
Figure 1. (A) Map of sampling locations. At each location, a sample was taken at the beach and in the river with the exception of Rockhampton where both samples were beach sites and Bundaberg where both sites were river sites. (B) Water temperature at each site in °C. (C) Salinity at each site in ppt.
Figure 1. (A) Map of sampling locations. At each location, a sample was taken at the beach and in the river with the exception of Rockhampton where both samples were beach sites and Bundaberg where both sites were river sites. (B) Water temperature at each site in °C. (C) Salinity at each site in ppt.
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Figure 2. NMDS (stress = 0.1269098) showing the results of a SIMPROF (coloured categories) and the bacterial community composition similarities between locations. This cut-off occurred at latitude 30.29° S.
Figure 2. NMDS (stress = 0.1269098) showing the results of a SIMPROF (coloured categories) and the bacterial community composition similarities between locations. This cut-off occurred at latitude 30.29° S.
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Figure 3. Results from (A,B) Total Vibrio qPCR, (C,D) Vibrio vulnificus ddPCR, (E,F) Vibrio parahaemolyticus ddPCR. (A,C,E) are river sites, while (B,D,F) are beach sites. Size scale represents copies/L of each assay.
Figure 3. Results from (A,B) Total Vibrio qPCR, (C,D) Vibrio vulnificus ddPCR, (E,F) Vibrio parahaemolyticus ddPCR. (A,C,E) are river sites, while (B,D,F) are beach sites. Size scale represents copies/L of each assay.
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Figure 4. Stacked bar-plot of the top 10 most abundant Vibrio species from the hsp60 gene amplicon sequencing data along the East Coast of Australia. Data is normalised to the total Vibrio qPCR data (grey).
Figure 4. Stacked bar-plot of the top 10 most abundant Vibrio species from the hsp60 gene amplicon sequencing data along the East Coast of Australia. Data is normalised to the total Vibrio qPCR data (grey).
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Figure 5. Heatmap displaying the correlations from MicTools analysis between environmental variables and Vibrio species from hsp60 dataset. Each cell is either coloured or white, with coloured cells representing pearsonR R value and white cells representing no significant correlation.
Figure 5. Heatmap displaying the correlations from MicTools analysis between environmental variables and Vibrio species from hsp60 dataset. Each cell is either coloured or white, with coloured cells representing pearsonR R value and white cells representing no significant correlation.
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Williams, N.L.R.; Siboni, N.; King, W.L.; Balaraju, V.; Bramucci, A.; Seymour, J.R. Latitudinal Dynamics of Vibrio along the Eastern Coastline of Australia. Water 2022, 14, 2510.

AMA Style

Williams NLR, Siboni N, King WL, Balaraju V, Bramucci A, Seymour JR. Latitudinal Dynamics of Vibrio along the Eastern Coastline of Australia. Water. 2022; 14(16):2510.

Chicago/Turabian Style

Williams, Nathan L. R., Nachshon Siboni, William L. King, Varunan Balaraju, Anna Bramucci, and Justin R. Seymour. 2022. "Latitudinal Dynamics of Vibrio along the Eastern Coastline of Australia" Water 14, no. 16: 2510.

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