Data

No-take marine reserves promote oligotrophic reef bacterioplankton communities across the Great Barrier Reef

James Cook University
Terzin, Marko ; K. Gruber (data creator), Renee ; J. Robbins (external advisor), Steven ; C. Bell (data creator), Sara ; S. Webster (external advisor), Nicole ; E. Dougan, Katherine ; Zaugg, Julian ; J. Emslie, Michael ; M. Ceccarelli, Daniela ; Hugenholtz, Philip ; Yeoh, Yun Kit ; Laffy, Patrick ; Bourne, David
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://researchdata.jcu.edu.au//published/7136fca0098711f1ac7ecf6b4103ada8&rft.title=No-take marine reserves promote oligotrophic reef bacterioplankton communities across the Great Barrier Reef&rft.identifier=https://researchdata.jcu.edu.au//published/7136fca0098711f1ac7ecf6b4103ada8&rft.publisher=Australia's Integrated Marine Observing System (IMOS)&rft.description= This dataset consists of prokaryotic metagenome-assembled genomes (pMAGs) and paired environmental data from 48 offshore GBR reefs, used to compare microbial communities between No-Take Marine Reserves (NTMRs) and fished zones across the Great Barrier Reef Marine Park (GBRMP). The data was collected to assess how marine zoning influences seawater microbiome structure, function, and ecological networks in relation to reef health indicators. What the data consists of: Metagenomic assemblies: 5,283 prokaryotic metagenome-assembled genomes (pMAGs) from hybrid (Illumina-Nanopore) and short-read assemblies, dereplicated at 95% Average Nucleotide Identity (ANI) sequence similarity to 876 “species-resolved” pMAGs. Environmental variables: 54 continuous variables including: Physicochemical variables: Ammonium (NH₄⁺), Nitrite (NO₂⁻), Nitrate (NO₃⁻), Phosphate (PO₄³⁻), Silicate (Si), Total Dissolved Nitrogen (TDN), Total Dissolved Phosphorus (TDP), Dissolved Organic Carbon (DOC), Particulate Organic Carbon (POC), Particulate Nitrogen (PN), Particulate Phosphorus (PP), Temperature, Salinity, Chlorophyll a (Chl-a), Phaeophytin (Phaeo), Total Suspended Solids (TSS), and in-situ Chl-a fluorescence. Benthic cover: Substrate: Abiotic, Sand, Unknown Algae: crustose coralline algae, macroalgae, turf algae Hard corals: Acropora groups (bottlebrush, branching, digitate, encrusting, submassive, tabulate) and non-Acropora morphology: (branching, encrusting, foliose, massive, submassive), and other corals including mushroom coral, and a fire coral Millepora (hydrozoan). Soft corals: arborescent, encrusting, arborescent and encrusting, capitate, lobate, massive other biota: sponges, zoanthids, and other organisms Fish data: Overall fish biomass abundances collapsed at trophic groups: carnivore, corallivore, detritivore, herbivore, invertivore, piscivore Sample metadata: Reef zoning status (NTMR vs. fished), geographic sector, sampling date, and replicate information. Process of analysis applied: Genome assembly & annotation: Hybrid assembly with Aviary pipeline, taxonomic classification with GTDB-Tk, functional annotation with anvi’o and KEGG modules. Indicator analysis: Multivariate INTegrative Sparse Partial Least Squares Discriminant Analysis (MINT sPLS-DA) to identify microbial MAGs indicative of reef zoning. Multivariate INTegrative Sparse Partial Least Squares (MINT sPLS) to correlate microbial abundance data with continuous environmental variables. Network analysis: Co-occurrence network connectedness and cohesion metrics to compare positive/negative microbial interactions between zones. Regression of microbial genomic traits (genome size, GC content, and metabolic pathway completeness scores) against microbial network properties. Predictive modeling: Random Forest models to predict environmental variables from microbial abundances; microbial niche modeling (RO method) to infer tolerance ranges. GLMMs to test environmental differences (physicochemical, benthic cover, and fish abundance/biomass variables) between zones. Software/equipment used to create/collect the data: Seawater collection and filtration (metagenomics & physicochemical): Niskin bottles Divers (for some samples) Sterivex™-GP Pressure Filters (0.22 µm pore size, Millipore) Minisart® syringe prefilters (5 µm pore size, Sartorius) Peristaltic pump Research vessels: RV Solander RV Cape Ferguson (AIMS Long-Term Monitoring Program vessels) In-situ sensor data as part of IMOS Ships of Opportunity Sensors underway systems: SBE 38 digital oceanographic thermometer (Sea-Bird Scientific) SBE 21 SeaCAT Thermosalinograph (Sea-Bird Scientific) ECO-FLNTU-RT fluorometer (WET Labs) Standardised AIMS Long-Term Monitoring Program (LTMP) protocols: Benthic cover assessment (in-situ reef surveys): SCUBA surveys along permanently marked transects (5 × 50 m per site) Digital underwater cameras for benthic imagery Fixed-point (quincunx pattern) digital overlay for substrate classification Specialised point image classifier software for benthic cover analysis Fish abundance and biomass estimation (in-situ reef surveys): Visual belt transect surveys: 5 m width for large-bodied mobile fishes (250 m² area) 1 m width for small-bodied fishes (50 m² area) Species identification and size estimation using predefined size bins FishBase-derived length-weight relationships for fish biomass calculation Laboratory processing: Water Chemistry (physicochemical): Filtration: Whatman GF/F filters (0.7 µm), Polycarbonate filters (0.4 µm), Sartorius Minisart syringe filters (0.45 µm) Analyzers: Seal AA3 segmented flow analyzer (for NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, Si) Carbon/Nitrogen analyzer: Shimadzu TOC-L carbon analyzer with SSM-5000A solid sample module (POC) and TNM-L nitrogen module (PN) Fluorometer: Turner Designs 10AU fluorometer (for Chl-a and Phaeo) Digestion: Hot acid persulfate digestion (for PP analysis) Gravimetric analysis: For Total Suspended Solids (TSS) Hybrid metagenomics: DNA extraction: phenol:chloroform:isoamyl alcohol extraction with ethanol precipitation with Lysozyme (100 mg/mL) in lysis buffer NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) Qubit 3 fluorometer (Thermo Fisher Scientific) Sequencing facility: Microba Life Sciences Ltd. (Brisbane, QLD) Sequencing platform: Illumina NovaSeq with Nextera Flex library preparation Long-read sequencing (subset of samples) with Oxford Nanopore PromethION R9.4 chemistry Software/equipment used to manipulate/analyse the data: Metagenome assembly, binning and taxonomic classification: Long-read processing: Guppy v5.0.16 (superaccuracy basecalling), Porechop (adapter/barcode trimming) Assembly pipeline: Aviary v0.3.3 for hybrid (Illumina-Nanopore) and short-read assemblies Binning & dereplication: CoverM v0.6 for 95% ANI dereplication Taxonomic classification: Genome Taxonomy Database Toolkit (GTDB-Tk, release R214) Read mapping: minimap2 v2.18 (via CoverM) for abundance estimation Functional annotation and metabolic profiling using gene prediction & annotation: anvi’o v8 with Prodigal v2.6.3 (ORF prediction), HMMER v3.3.2, and based on the KEGG Orthology (KO) functional database via anvi-run-kegg-kofams Metabolic pathway analysis: anvi-estimate-metabolism for KEGG module completeness Environmental Data processing: Benthic cover analysis: Specialised point image classifier software for digital image analysis Fish biomass calculation: FishBase-derived length-weight relationships, custom R scripts for biomass aggregation Physicochemical data was obtained from the AIMS Water Quality team Microbiome and environmental data integration (Analysis environment: R v4.3.2 / RStudio): Microbiome data handling: phyloseq v1.46.0, microbiome v1.24.0 (CLR transformation) Multivariate analysis: mixOmics v6.26.0 (PCA, sPLS-DA, MINT sPLS-DA, MINT sPLS) Generalized linear mixed models: glmmTMB v1.1.10, DHARMa v0.4.7 for diagnostics Machine learning: randomForest v4.7-1.2 for predictive modeling of environmental metrics from microbiomes Co-occurrence networks: Custom R implementation of connectedness/cohesion metrics (following methods of Hernandez et al. 2021 and Herren and McMahon 2017) Microbial niche modeling: Robust optimum (RO) method for computing niche tolerance ranges (Q1, Q2, Q3) Network modularity: Calculated from correlation-based co-occurrence networks Data wrangling & visualization: tidyverse v2.0.0, ggplot2 v3.5.1 Spatial mapping: ggspatial, sf, raster, dataaimsr, gisaimsr General software for figure finalization: Inkscape v0.92.5 Analysis code availability: https://github.com/mterzin/fishy_microbes &rft.creator=Terzin, Marko &rft.creator=K. Gruber (data creator), Renee &rft.creator=J. Robbins (external advisor), Steven &rft.creator=C. Bell (data creator), Sara &rft.creator=S. Webster (external advisor), Nicole &rft.creator=E. Dougan, Katherine &rft.creator=Zaugg, Julian &rft.creator=J. Emslie, Michael &rft.creator=M. Ceccarelli, Daniela &rft.creator=Hugenholtz, Philip &rft.creator=Yeoh, Yun Kit &rft.creator=Laffy, Patrick &rft.creator=Bourne, David &rft.date=2025&rft.relation=to be added upon publication&rft.coverage=east=147.29475; north=-18.62552; projection=WGS84&rft.coverage=east=147.43341; north=-18.62166; projection=WGS84&rft.coverage=east=147.55211; north=-18.73423; projection=WGS84&rft.coverage=east=147.72155; north=-18.73288; projection=WGS84&rft.coverage=east=147.5709; north=-18.6065; projection=WGS84&rft.coverage=east=147.57555; north=-18.57226; projection=WGS84&rft.coverage=east=147.71591; north=-18.65388; projection=WGS84&rft.coverage=east=147.38445; north=-18.2575; projection=WGS84&rft.coverage=east=147.07521; north=-18.61726; projection=WGS84&rft.coverage=east=146.87271; north=-18.4731; projection=WGS84&rft.coverage=east=147.05325; north=-18.47656; projection=WGS84&rft.coverage=east=147.0545; north=-18.43503; projection=WGS84&rft.coverage=east=146.98581; north=-18.42411; projection=WGS84&rft.coverage=east=146.99845; north=-18.46205; projection=WGS84&rft.coverage=east=146.56712; north=-17.81295; projection=WGS84&rft.coverage=east=146.53186; north=-17.7896; projection=WGS84&rft.coverage=east=146.38676; north=-17.57812; projection=WGS84&rft.coverage=east=146.40219; north=-17.46753; projection=WGS84&rft.coverage=east=146.47807; north=-17.20274; projection=WGS84&rft.coverage=east=146.47433; north=-17.22383; projection=WGS84&rft.coverage=east=146.23148; north=-16.8474; projection=WGS84&rft.coverage=east=146.19447; north=-16.77893; projection=WGS84&rft.coverage=east=146.1028; north=-16.63941; projection=WGS84&rft.coverage=east=146.0159; north=-16.50097; projection=WGS84&rft.coverage=east=145.86415; north=-16.04517; projection=WGS84&rft.coverage=east=151.90408; north=-23.1775; projection=WGS84&rft.coverage=east=152.50674; north=-21.86969; projection=WGS84&rft.coverage=east=152.58116; north=-21.94489; projection=WGS84&rft.coverage=east=152.65945; north=-21.99904; projection=WGS84&rft.coverage=east=152.46706; north=-21.99809; projection=WGS84&rft.coverage=east=152.31361; north=-21.96005; projection=WGS84&rft.coverage=east=151.9245; north=-23.2657; projection=WGS84&rft.coverage=east=151.77; north=-23.504; projection=WGS84&rft.coverage=east=151.72284; north=-23.53219; projection=WGS84&rft.coverage=east=152.25743; north=-23.76276; projection=WGS84&rft.coverage=east=152.2836; north=-23.80801; projection=WGS84&rft.coverage=east=152.35922; north=-23.86598; projection=WGS84&rft.coverage=east=145.84686; north=-16.07261; projection=WGS84&rft.coverage=east=144.87929; north=-14.20223; projection=WGS84&rft.coverage=east=144.09326; north=-13.8559; projection=WGS84&rft.coverage=east=144.24026; north=-13.92542; projection=WGS84&rft.coverage=east=144.44693; north=-13.96599; projection=WGS84&rft.coverage=east=143.75682; north=-12.38134; projection=WGS84&rft.coverage=east=143.8674; north=-12.3457; projection=WGS84&rft.coverage=east=143.29448; north=-11.59805; projection=WGS84&rft.coverage=east=143.32346; north=-11.35312; projection=WGS84&rft.coverage=east=143.26387; north=-11.12721; projection=WGS84&rft.coverage=east=143.24783; north=-11.0198; projection=WGS84&rft.coverage=&rft_rights=The dataset is copyright and is licensed under the Creative Commons Attribution 3.0 Australia Licence. You are free to copy, distribute, and adapt the work, provided you attribute the source as specified by the data owners.&rft_rights=CC BY: Attribution 3.0 AU http://creativecommons.org/licenses/by/3.0/au&rft_subject=Marine Protected Areas&rft_subject=No-Take Marine Reserves&rft_subject=Ecosystem Resilience&rft_subject=Coral Reef Management&rft_subject=Microbial Communities&rft_subject=Microbial Species Co-occurrence Network Analysis&rft_subject=Fishing Impact&rft_subject=Bacterial Metagenomes&rft_subject=Reef Health Indicators&rft_subject=Sequence analysis&rft_subject=Bioinformatics and computational biology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Biological network analysis&rft_subject=Bioinformatics and computational biology not elsewhere classified&rft_subject=Community ecology (excl. invasive species ecology)&rft_subject=Ecology&rft_subject=Marine and estuarine ecology (incl. marine ichthyology)&rft_subject=Microbial ecology&rft_subject=Microbiology&rft_subject=Microbiology not elsewhere classified&rft_subject=Ecosystem function&rft_subject=Ecological applications&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Environmental assessment and monitoring&rft_subject=Environmental management&rft_subject=Conservation and biodiversity&rft_subject=Environmental management&rft_subject=Assessment and management of benthic marine ecosystems&rft_subject=Marine systems and management&rft_subject=ENVIRONMENTAL MANAGEMENT&rft_subject=Assessment and management of pelagic marine ecosystems&rft_subject=Marine biodiversity&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

Open Licence view details
CC-BY

CC BY: Attribution 3.0 AU
http://creativecommons.org/licenses/by/3.0/au

The dataset is copyright and is licensed under the Creative Commons Attribution 3.0 Australia Licence. You are free to copy, distribute, and adapt the work, provided you attribute the source as specified by the data owners.

Access:

Open view details

Open: free access under license

Contact Information



Full description

This dataset consists of prokaryotic metagenome-assembled genomes (pMAGs) and paired environmental data from 48 offshore GBR reefs, used to compare microbial communities between No-Take Marine Reserves (NTMRs) and fished zones across the Great Barrier Reef Marine Park (GBRMP). The data was collected to assess how marine zoning influences seawater microbiome structure, function, and ecological networks in relation to reef health indicators.

What the data consists of:

Metagenomic assemblies: 5,283 prokaryotic metagenome-assembled genomes (pMAGs) from hybrid (Illumina-Nanopore) and short-read assemblies, dereplicated at 95% Average Nucleotide Identity (ANI) sequence similarity to 876 “species-resolved” pMAGs.

Environmental variables: 54 continuous variables including:

  • Physicochemical variables: Ammonium (NH₄⁺), Nitrite (NO₂⁻), Nitrate (NO₃⁻), Phosphate (PO₄³⁻), Silicate (Si), Total Dissolved Nitrogen (TDN), Total Dissolved Phosphorus (TDP), Dissolved Organic Carbon (DOC), Particulate Organic Carbon (POC), Particulate Nitrogen (PN), Particulate Phosphorus (PP), Temperature, Salinity, Chlorophyll a (Chl-a), Phaeophytin (Phaeo), Total Suspended Solids (TSS), and in-situ Chl-a fluorescence.
  • Benthic cover:
    • Substrate: Abiotic, Sand, Unknown
    • Algae: crustose coralline algae, macroalgae, turf algae
    • Hard corals: Acropora groups (bottlebrush, branching, digitate, encrusting, submassive, tabulate) and non-Acropora morphology: (branching, encrusting, foliose, massive, submassive), and other corals including mushroom coral, and a fire coral Millepora (hydrozoan).
    • Soft corals: arborescent, encrusting, arborescent and encrusting, capitate, lobate, massive
    • other biota: sponges, zoanthids, and other organisms
  • Fish data:
    • Overall fish biomass
    • abundances collapsed at trophic groups: carnivore, corallivore, detritivore, herbivore, invertivore, piscivore

Sample metadata: Reef zoning status (NTMR vs. fished), geographic sector, sampling date, and replicate information.

Process of analysis applied:

  • Genome assembly & annotation: Hybrid assembly with Aviary pipeline, taxonomic classification with GTDB-Tk, functional annotation with anvi’o and KEGG modules.
  • Indicator analysis: Multivariate INTegrative Sparse Partial Least Squares Discriminant Analysis (MINT sPLS-DA) to identify microbial MAGs indicative of reef zoning. Multivariate INTegrative Sparse Partial Least Squares (MINT sPLS) to correlate microbial abundance data with continuous environmental variables.
  • Network analysis: Co-occurrence network connectedness and cohesion metrics to compare positive/negative microbial interactions between zones. Regression of microbial genomic traits (genome size, GC content, and metabolic pathway completeness scores) against microbial network properties.
  • Predictive modeling: Random Forest models to predict environmental variables from microbial abundances; microbial niche modeling (RO method) to infer tolerance ranges.
  • GLMMs to test environmental differences (physicochemical, benthic cover, and fish abundance/biomass variables) between zones.

Software/equipment used to create/collect the data:

Seawater collection and filtration (metagenomics & physicochemical):

  • Niskin bottles
  • Divers (for some samples)
  • Sterivex™-GP Pressure Filters (0.22 µm pore size, Millipore)
  • Minisart® syringe prefilters (5 µm pore size, Sartorius)
  • Peristaltic pump

Research vessels:

  • RV Solander
  • RV Cape Ferguson (AIMS Long-Term Monitoring Program vessels)

In-situ sensor data as part of IMOS Ships of Opportunity Sensors underway systems:

  • SBE 38 digital oceanographic thermometer (Sea-Bird Scientific)
  • SBE 21 SeaCAT Thermosalinograph (Sea-Bird Scientific)
  • ECO-FLNTU-RT fluorometer (WET Labs)

Standardised AIMS Long-Term Monitoring Program (LTMP) protocols:

  • Benthic cover assessment (in-situ reef surveys):
    • SCUBA surveys along permanently marked transects (5 × 50 m per site)
    • Digital underwater cameras for benthic imagery
    • Fixed-point (quincunx pattern) digital overlay for substrate classification
    • Specialised point image classifier software for benthic cover analysis
  • Fish abundance and biomass estimation (in-situ reef surveys):
    • Visual belt transect surveys:
      • 5 m width for large-bodied mobile fishes (250 m² area)
      • 1 m width for small-bodied fishes (50 m² area)
    • Species identification and size estimation using predefined size bins
    • FishBase-derived length-weight relationships for fish biomass calculation

Laboratory processing:

  • Water Chemistry (physicochemical):
    • Filtration: Whatman GF/F filters (0.7 µm), Polycarbonate filters (0.4 µm), Sartorius Minisart syringe filters (0.45 µm)
    • Analyzers: Seal AA3 segmented flow analyzer (for NH₄⁺, NO₂⁻, NO₃⁻, PO₄³⁻, Si)
    • Carbon/Nitrogen analyzer: Shimadzu TOC-L carbon analyzer with SSM-5000A solid sample module (POC) and TNM-L nitrogen module (PN)
    • Fluorometer: Turner Designs 10AU fluorometer (for Chl-a and Phaeo)
    • Digestion: Hot acid persulfate digestion (for PP analysis)
    • Gravimetric analysis: For Total Suspended Solids (TSS)
  • Hybrid metagenomics:
    • DNA extraction: phenol:chloroform:isoamyl alcohol extraction with ethanol precipitation with Lysozyme (100 mg/mL) in lysis buffer
    • NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific)
    • Qubit 3 fluorometer (Thermo Fisher Scientific)
    • Sequencing facility: Microba Life Sciences Ltd. (Brisbane, QLD)
    • Sequencing platform: Illumina NovaSeq with Nextera Flex library preparation
    • Long-read sequencing (subset of samples) with Oxford Nanopore PromethION R9.4 chemistry

Software/equipment used to manipulate/analyse the data:

Metagenome assembly, binning and taxonomic classification:

  • Long-read processing: Guppy v5.0.16 (superaccuracy basecalling), Porechop (adapter/barcode trimming)
  • Assembly pipeline: Aviary v0.3.3 for hybrid (Illumina-Nanopore) and short-read assemblies
  • Binning & dereplication: CoverM v0.6 for 95% ANI dereplication
  • Taxonomic classification: Genome Taxonomy Database Toolkit (GTDB-Tk, release R214)
  • Read mapping: minimap2 v2.18 (via CoverM) for abundance estimation
  • Functional annotation and metabolic profiling using gene prediction & annotation: anvi’o v8 with Prodigal v2.6.3 (ORF prediction), HMMER v3.3.2, and based on the KEGG Orthology (KO) functional database via anvi-run-kegg-kofams
  • Metabolic pathway analysis: anvi-estimate-metabolism for KEGG module completeness

Environmental Data processing:

  • Benthic cover analysis: Specialised point image classifier software for digital image analysis
  • Fish biomass calculation: FishBase-derived length-weight relationships, custom R scripts for biomass aggregation
  • Physicochemical data was obtained from the AIMS Water Quality team

Microbiome and environmental data integration (Analysis environment: R v4.3.2 / RStudio):

  • Microbiome data handling: phyloseq v1.46.0, microbiome v1.24.0 (CLR transformation)
  • Multivariate analysis: mixOmics v6.26.0 (PCA, sPLS-DA, MINT sPLS-DA, MINT sPLS)
  • Generalized linear mixed models: glmmTMB v1.1.10, DHARMa v0.4.7 for diagnostics
  • Machine learning: randomForest v4.7-1.2 for predictive modeling of environmental metrics from microbiomes
  • Co-occurrence networks: Custom R implementation of connectedness/cohesion metrics (following methods of Hernandez et al. 2021 and Herren and McMahon 2017)
  • Microbial niche modeling: Robust optimum (RO) method for computing niche tolerance ranges (Q1, Q2, Q3)
  • Network modularity: Calculated from correlation-based co-occurrence networks

Data wrangling & visualization: tidyverse v2.0.0, ggplot2 v3.5.1 Spatial mapping: ggspatial, sf, raster, dataaimsr, gisaimsr General software for figure finalization: Inkscape v0.92.5

Analysis code availability: https://github.com/mterzin/fishy_microbes

Created: 2025-09-25

Data time period: 23 11 2019 to 17 07 2020

Data time period: November 2019 - July 2020

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related Publications

Related People

147.29475,-18.62552

147.29475,-18.62552

147.43341,-18.62166

147.43341,-18.62166

147.55211,-18.73423

147.55211,-18.73423

147.72155,-18.73288

147.72155,-18.73288

147.5709,-18.6065

147.5709,-18.6065

147.57555,-18.57226

147.57555,-18.57226

147.71591,-18.65388

147.71591,-18.65388

147.38445,-18.2575

147.38445,-18.2575

147.07521,-18.61726

147.07521,-18.61726

146.87271,-18.4731

146.87271,-18.4731

147.05325,-18.47656

147.05325,-18.47656

147.0545,-18.43503

147.0545,-18.43503

146.98581,-18.42411

146.98581,-18.42411

146.99845,-18.46205

146.99845,-18.46205

146.56712,-17.81295

146.56712,-17.81295

146.53186,-17.7896

146.53186,-17.7896

146.38676,-17.57812

146.38676,-17.57812

146.40219,-17.46753

146.40219,-17.46753

146.47807,-17.20274

146.47807,-17.20274

146.47433,-17.22383

146.47433,-17.22383

146.23148,-16.8474

146.23148,-16.8474

146.19447,-16.77893

146.19447,-16.77893

146.1028,-16.63941

146.1028,-16.63941

146.0159,-16.50097

146.0159,-16.50097

145.86415,-16.04517

145.86415,-16.04517

151.90408,-23.1775

151.90408,-23.1775

152.50674,-21.86969

152.50674,-21.86969

152.58116,-21.94489

152.58116,-21.94489

152.65945,-21.99904

152.65945,-21.99904

152.46706,-21.99809

152.46706,-21.99809

152.31361,-21.96005

152.31361,-21.96005

151.9245,-23.2657

151.9245,-23.2657

151.77,-23.504

151.77,-23.504

151.72284,-23.53219

151.72284,-23.53219

152.25743,-23.76276

152.25743,-23.76276

152.2836,-23.80801

152.2836,-23.80801

152.35922,-23.86598

152.35922,-23.86598

145.84686,-16.07261

145.84686,-16.07261

144.87929,-14.20223

144.87929,-14.20223

144.09326,-13.8559

144.09326,-13.8559

144.24026,-13.92542

144.24026,-13.92542

144.44693,-13.96599

144.44693,-13.96599

143.75682,-12.38134

143.75682,-12.38134

143.8674,-12.3457

143.8674,-12.3457

143.29448,-11.59805

143.29448,-11.59805

143.32346,-11.35312

143.32346,-11.35312

143.26387,-11.12721

143.26387,-11.12721

143.24783,-11.0198

143.24783,-11.0198

dcmiPoint: east=147.29475; north=-18.62552; projection=WGS84

dcmiPoint: east=147.43341; north=-18.62166; projection=WGS84

dcmiPoint: east=147.55211; north=-18.73423; projection=WGS84

dcmiPoint: east=147.72155; north=-18.73288; projection=WGS84

dcmiPoint: east=147.5709; north=-18.6065; projection=WGS84

dcmiPoint: east=147.57555; north=-18.57226; projection=WGS84

dcmiPoint: east=147.71591; north=-18.65388; projection=WGS84

dcmiPoint: east=147.38445; north=-18.2575; projection=WGS84

dcmiPoint: east=147.07521; north=-18.61726; projection=WGS84

dcmiPoint: east=146.87271; north=-18.4731; projection=WGS84

dcmiPoint: east=147.05325; north=-18.47656; projection=WGS84

dcmiPoint: east=147.0545; north=-18.43503; projection=WGS84

dcmiPoint: east=146.98581; north=-18.42411; projection=WGS84

dcmiPoint: east=146.99845; north=-18.46205; projection=WGS84

dcmiPoint: east=146.56712; north=-17.81295; projection=WGS84

dcmiPoint: east=146.53186; north=-17.7896; projection=WGS84

dcmiPoint: east=146.38676; north=-17.57812; projection=WGS84

dcmiPoint: east=146.40219; north=-17.46753; projection=WGS84

dcmiPoint: east=146.47807; north=-17.20274; projection=WGS84

dcmiPoint: east=146.47433; north=-17.22383; projection=WGS84

dcmiPoint: east=146.23148; north=-16.8474; projection=WGS84

dcmiPoint: east=146.19447; north=-16.77893; projection=WGS84

dcmiPoint: east=146.1028; north=-16.63941; projection=WGS84

dcmiPoint: east=146.0159; north=-16.50097; projection=WGS84

dcmiPoint: east=145.86415; north=-16.04517; projection=WGS84

dcmiPoint: east=151.90408; north=-23.1775; projection=WGS84

dcmiPoint: east=152.50674; north=-21.86969; projection=WGS84

dcmiPoint: east=152.58116; north=-21.94489; projection=WGS84

dcmiPoint: east=152.65945; north=-21.99904; projection=WGS84

dcmiPoint: east=152.46706; north=-21.99809; projection=WGS84

dcmiPoint: east=152.31361; north=-21.96005; projection=WGS84

dcmiPoint: east=151.9245; north=-23.2657; projection=WGS84

dcmiPoint: east=151.77; north=-23.504; projection=WGS84

dcmiPoint: east=151.72284; north=-23.53219; projection=WGS84

dcmiPoint: east=152.25743; north=-23.76276; projection=WGS84

dcmiPoint: east=152.2836; north=-23.80801; projection=WGS84

dcmiPoint: east=152.35922; north=-23.86598; projection=WGS84

dcmiPoint: east=145.84686; north=-16.07261; projection=WGS84

dcmiPoint: east=144.87929; north=-14.20223; projection=WGS84

dcmiPoint: east=144.09326; north=-13.8559; projection=WGS84

dcmiPoint: east=144.24026; north=-13.92542; projection=WGS84

dcmiPoint: east=144.44693; north=-13.96599; projection=WGS84

dcmiPoint: east=143.75682; north=-12.38134; projection=WGS84

dcmiPoint: east=143.8674; north=-12.3457; projection=WGS84

dcmiPoint: east=143.29448; north=-11.59805; projection=WGS84

dcmiPoint: east=143.32346; north=-11.35312; projection=WGS84

dcmiPoint: east=143.26387; north=-11.12721; projection=WGS84

dcmiPoint: east=143.24783; north=-11.0198; projection=WGS84

Subjects
Assessment and Management of Benthic Marine Ecosystems | Assessment and Management of Pelagic Marine Ecosystems | Biological Sciences | Bacterial Metagenomes | Bioinformatics and Computational Biology | Bioinformatics and Computational Biology Not Elsewhere Classified | Biological Network Analysis | Community Ecology (Excl. Invasive Species Ecology) | Conservation and Biodiversity | Coral Reef Management | Environmental Management | Environmental Sciences | Ecological Applications | Ecology | Ecosystem Resilience | Ecosystem Function | Environmental Assessment and Monitoring | Environmental Management | Environmental Management | Fishing Impact | Marine Protected Areas | Marine and Estuarine Ecology (Incl. Marine Ichthyology) | Marine Biodiversity | Marine Systems and Management | Microbial Communities | Microbial Species Co-occurrence Network Analysis | Microbial Ecology | Microbiology | Microbiology Not Elsewhere Classified | No-Take Marine Reserves | Reef Health Indicators | Sequence Analysis |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers
  • Local : researchdata.jcu.edu.au//published/7136fca0098711f1ac7ecf6b4103ada8