Supporting information
Study Sites & Sample Collection
Estuarine samples were taken from the Sydney harbour estuary (Port Jackson), which
connects the inland Paramatta River with the Tasman Sea. The system receives diverse water
inputs from inland tributaries, terrestrial run-off and storm water and contains strong salinity
and nutrient gradients (Supplementary Table S1. Samples were collected along a transect
spanning sites at the Parramatta river (33.8°S, 151.0 °W), Sydney Heads (33.8°S 151.3°W),
and Middle Harbour (33.7°S, 151.2° W) which incorporated salinities ranging from 12.28 ppt
to 33.3 ppt (Supplementary Table S1). Coastal and open ocean samples were collected during
an oceanographic voyage aboard the RV Southern Surveyor in October 2010 (Supplementary
Figure S2). Coastal samples covered an area of the Tasman sea between 28°S-33°S within the
continental shelf, and included samples collected from three river plume regions including
the Richmond, Clarence and Macleay river. Open ocean samples were collected across a
region spanning 29°S -32°S and incorporated several oceanographic features including the
East Australia Current and localized cold core eddies.
At all locations, 2 L water samples were collected from surface waters. For the coastal and
open water samples, physicochemical properties of the water column were measured with a
Seabird SBE911 CTD and water samples were collected using 10 L Niskin bottles. Estuarine
samples were collected in acid washed and triple rinsed poly propylene bottles. Estuarine
physicochemical properties were measured with a multiparameter water quality CTD
(YSITM). Samples for estuarine nutrient determination were filtered at the time of collection
(0.45μm) analysed using flow injection analysis (LaChatTM). Between collection and analysis
nutrient samples were kept frozen (-30°C) for not more than 3 months. Samples for collection
of microbial DNA were filtered onto 0.2µm polycarbonate filters (Millipore) and
immediately frozen. Total DNA was subsequently extracted using bead beating and chemical
lysis (Powerwater DNA isolation kit, MoBio, USA) with an additional heating step at 70°C
(10 minutes) to maximize fungal lysis.
ITS Sequencing
The fungal ITS region was amplified from community DNA using the ITS1F primer (3’CTTGGTCATTTAGAGGAAGTAA-5’) (Gardes and Bruns, 1993) and ITS4 primer (3’TCCTCCGCTTATTGATATGC-5’) using the following reaction conditions: a single-step
30 cycle PCR using HotStarTaq Plus Master Mix Kit (Qiagen) comprising 94 °C initial
denaturation (3 min), 28 cycles of 94 °C (30 sec) 53 °C (40 sec) and 72 °C (1 min), with a
final elongation step at 72 °C (5 min). The resultant amplicons were purified using Agencourt
Ampure beads (Agencourt Bioscience Corporation, USA) and sequenced using Roche 454
FLX Titanium (Molecular Research LP, Texas) instruments and reagents following the
manufacturers guidelines (Dowd et al., 2008).
Bioinformatics
Samples were de-multiplexed and processed using the QIIME package (Caporaso et al.,
2010). After sequencing, barcodes and primers were removed from the sequences, sequences
shorter than 200bp were removed, sequences with ambiguous base calls were removed, and
sequences with homopolymer runs exceeding 6 bp were removed. A 200bp cut-off was
chosen as this is a sequence quality related metric incorporated in the de-multiplexing and
quality control step of the QIIME pipeline (http://qiime.org/scripts/split_libraries.html).
Despite the potential that read-trimming may introduce a taxonomic bias for those organisms
with very short ITS regions, several other studies have used cut-offs of <200bp (e.g. De Beek
et al., 2014; Bokulich and Mills, 2013) or < 300bp (Amend et al., 2010) with no apparent
bias. Using a 200bp cut-off should have little taxonomic bias as ITS regions amplified with
the ITS1/ITS4 primer set span a region that ranges in size between 400bp and 800bp
(Bellermain et al., 2010). Additionally, despite the heterogeneity in length, the average size
of the full ITS region is 600bp across all fungal divisions (ODe Beek et al., 2014).
Operational taxonomic units (OTUs) were defined as sequences sharing 97% similarity (Buée
et al., 2009) using UCLUST (Edgar, 2010). Additionally, fungal ITS1 regions were extracted
from the full length amplicons using ITSx (Bengtsson‐ Palme, et al 2013) and analysed as
above to account for the potential influence of length heterogeneity. This analysis yielded
fewer O.T.Us (7956 O.T.Us) compared to full length amplicons (19260 O.T.Us) however
beta-diversity patterns were maintained in this dataset (Supporting Information Figure S4)
indicating the ITS region used does not greatly influence the partitioning of habitats in this
dataset. Overall, 97.2% of the sequences analysed contained fungal ITS1 regions identified
using fungi specific Hidden Markov Models (Bengtsson‐ Palme, et al 2013) indicating that
the diversity we profiled was indeed fungal. Samples were rarefied to 1082 sequences to
ensure even sampling depth. Representative sequences of each OU were compared against
the UNITE database (http://unite.ut.ee/index.php) (Kõljalg et al., 2005) using BLAST (E<105
) (Altschul et al., 1991) to assign taxonomy.
Statistical analysis using multivariate and network analysis
OTU abundance data was square root transformed and Bray-Curtis similarity between
profiles was ordinated using multidimensional scaling (MDS), while Analysis of Similarities
(ANOSIM) was applied to determine if differences in factors (habitats) were significantly
different from a randomly permutated distribution. Distance based multivariate analysis for
linear models using Primer 6 (version 6.1.11, Primer-E, Clarke and Gorley, 2006), which
describes the relationship between multivariate analysis and environmental variables based
on a resemblance matrix using permutations was employed to link biotic patterns to
environmental variables. The environmental matrix (Euclidean distance) was generated
using available metadata (supporting table S1) with the exception of latitude and longitude
and those variables for which insufficient data was collected across the whole dataset
(TDN,TDP,TN,TP,POP,DON,DOP,POC) and excluding those samples for which insufficient
environmental data was collected (7 samples). Values which were below the detection limit
were treated as zeroes.
The OTU network was generated in QIIME using the make_otu_network.py python script
(Caporaso et al., 2010, http://qiime.org/scripts/make_otu_network.html) and visualized using
Cytoscape (version 3.0.1, National resource for network biology, Shannon et al., 2003).
Nodes in the bi-partite network represented both samples and individual OTUs. Edges
connect OTUs to the samples in which they occur with the samples clustering based on the
degree to which they share OTUs. Edge weights were determined using the abundance of
individual OTUs in each sample. A stochastic spring-embedded layout was utilized in
Cytoscape (“Edge-Weighted Spring Embedded”) in which nodes act like physical objects that
repel each other, and edges act a springs with a spring constant and a resting length. Nodes
are organized in a way that minimizes forces in the network bringing together samples that
share the most OTUs, scaled by abundance.
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Supporting information Figure S1. Distance based linear model showing correlation of
environmental variables to 454-sequencing data defined at 97% sequence identity (Estuary ,
Coastal  and Ocean )
Supporting information Figure S2. Map of sampling sites from Sydney Harbour Estuary
(Panel A) and Coastal and Ocean sites (Panel B, CH=coast-harbour (range), CR=coast-river
(Yellow), C=coast (Green), STS=South Tasman Sea (maroon), TS=Tasman Sea (pink),
EAC=East Australian current (Brown), CCE=Cold core eddy (Blue))
Supporting information figure S3. Analysis using extracted ITS1 regions. A)
Multidimensional scaling of relative OTU abundance (Estuary , Coastal  and Ocean
),and (B) Network analysis of OTU partitioning between samples. Sample clustering
represents the degree to which OTUs are shared between samples, with samples grouping
closer together if they share more OTUs. Black nodes represent OTUs, coloured nodes
represent sample type (red = estuary; green = coastal; blue = open ocean) and edges (lines)
connect OTUs to samples in which they occur (coloured by the habitat of the connected
sample). Edge weights represent the number of sequences from each OTU that occurred in
each sample. ITS1 OTUs were clustered at 97% identity using UCLUST (Edgar, 2010).
Networks were generated using QIIME (Caporaso et al 2010) and visualized using Cytoscape
(Shannon et al, 2003 ). All samples were rarefied to 1045 sequences per sample to equalize
sampling depth.
Supplementary Fig. S1
A
B
Supplementary Fig. S2
Supplementary Fig. S3