Supporting information for "Pollen DNA metabarcoding identifies regional provenance and high plant diversity in Australian honey". Abstract: Accurate identification of the botanical components of honey can be used to establish its geographical provenance, while also providing insights into honey bee (Apis mellifera L.) diet and foraging preferences. DNA metabarcoding has been demonstrated as a robust method to identify plant species from pollen and pollen-based products, including honey. We investigated the use of pollen metabarcoding to identify the floral sources and local foraging preferences of honey bees using 15 honey samples from six bioregions from eastern and western Australia. We used two plant metabarcoding markers, ITS2 and the trnL P6 loop. Both markers combined identified a total of 55 plant families, 67 genera and 43 species. The trnL P6 loop marker provided significantly higher detection of taxa, detecting an average of 15.6 taxa per sample, compared to 4.6 with ITS2. Most honeys were dominated by Eucalyptus and other Myrtaceae species, with a few honeys dominated by Macadamia (Proteaceae) and Fabaceae. Metabarcoding detected the nominal primary source provided by beekeepers amongst the top five most abundant taxa for 85% of samples. We found that eastern and western honeys could be clearly differentiated by their floral composition, and clustered into bioregions with the trnL marker. Comparison with previous results obtained from melissopalynology shows that metabarcoding can detect similar numbers of plant families and genera, but provides significantly higher resolution at species level. Our results show that pollen DNA metabarcoding is a powerful and robust method for detecting honey provenance and examining the diet of honey bees. This is particularly relevant for hives foraging on the unique and diverse flora of the Australian continent, with the potential to be used as a novel monitoring tool for honey bee floral resources.

These data were collected to trial passive eDNA collection methods in tropical and temperate waters for the detection of fishes. The data were generated as part of a feasibility study within the Environomics Future Science Platform. The goal of the project was to develop low-cost, low-tech, easy to deploy methods for biomonitoring studies.

Data from multibeam echosounder surveys taken as part of the Ningaloo Outlook project are classified into various seafloor cover types according to their hardness, rugosity and depth. The classifications are validated with towed video ground truth where it is available. Three AOIs are classified, two that were explicitly part of the Ningaloo Outlook Deep Reefs project and a third transect that was acquired incidentally while RV Investigator was transiting between locations. Due to the nature of the acquired data, two different approaches were taken for the classification, the first approach used multibeam backscatter angular response curves along with rugosity as input to a maximum likelihood classifier. The second approach used flattened multibeam backscatter (i.e. with the angular effects removed), along with rugosity as inputs to a Random Forest Classifier. Estimates of the accuracy of the classifiers are produced, where possible, along with area statistics for the different substratum observed in the classified maps. The maps are GeoTIFFs with text based classification keys. [Note:] Area 3a only

Data from multibeam echosounder surveys taken as part of the Ningaloo Outlook project are classified into various seafloor cover types according to their hardness, rugosity and depth. The classifications are validated with towed video ground truth where it is available. Three AOIs are classified, two that were explicitly part of the Ningaloo Outlook Deep Reefs project and a third transect that was acquired incidentally while RV Investigator was transiting between locations. Due to the nature of the acquired data, two different approaches were taken for the classification, the first approach used multibeam backscatter angular response curves along with rugosity as input to a maximum likelihood classifier. The second approach used flattened multibeam backscatter (i.e. with the angular effects removed), along with rugosity as inputs to a Random Forest Classifier. Estimates of the accuracy of the classifiers are produced, where possible, along with area statistics for the different substratum observed in the classified maps. The maps are GeoTIFFs with text based classification keys. [Note:] 120m Transect only

Data from multibeam echosounder surveys taken as part of the Ningaloo Outlook project are classified into various seafloor cover types according to their hardness, rugosity and depth. The classifications are validated with towed video ground truth where it is available. Three AOIs are classified, two that were explicitly part of the Ningaloo Outlook Deep Reefs project and a third transect that was acquired incidentally while RV Investigator was transiting between locations. Due to the nature of the acquired data, two different approaches were taken for the classification, the first approach used multibeam backscatter angular response curves along with rugosity as input to a maximum likelihood classifier. The second approach used flattened multibeam backscatter (i.e. with the angular effects removed), along with rugosity as inputs to a Random Forest Classifier. Estimates of the accuracy of the classifiers are produced, where possible, along with area statistics for the different substratum observed in the classified maps. The maps are GeoTIFFs with text based classification keys. [Note:] Area 5 only

The remote Kimberley coast of north-western Australia is one of the few marine environments domains on earth largely unaffected by human use. However, the region is undergoing increasing economic importance as a destination for tourism and significant coastal developments associated with oil and gas exploration. The objective of the project was to reconstruct a timeline of inferred water quality changes from the sediment record for a selected set of sites in the Kimberley, Western Australia. The project made use of palaeoecological approaches to reconstruct a chronology of change over the last approximately 100 years using a series of biogeochemical proxies for phytoplankton composition and biomass, temperature and terrestrial influences. Where possible these were matched to historical land/water use, meteorological or hydrological observational records. The project examined sediment cores from three coastal locations, Koolama Bay (King George River), Cygnet Bay and Roebuck Bay. Each sampling location provided a contrast with which to evaluate changes over either a spatial or temporal gradient of human or natural influence. Sediment cores (up to 1.5 m) were obtained from each of these locations in the expectation that they would provide a time series for about the last 100 years. A set of parameters was measured along the core length (every 1-2 cm) for some or all cores depending on the particular focus for the location: 210Pb and 137Cs; 15N isotope; 13C isotope; Carbon/Nitrogen ratio; Sedimentation rate and grain size; Total Organic Carbon (TOC) and Total Nitrogen (TN); Biosilicate; Biomarkers; TEX86; long chain n-alkanes (C27+C29+C31); Elemental carbon (or black carbon). Rainfall data was obtained from the Australian Bureau of Meteorology website (www.bom.gov.au). Stream flow data was obtained from the Western Australian Department of Water website (www.water.wa.gov.au). Historical bushfire data was obtained from the Western Australian Department of Parks and Wildlife. The metadata record only relates to data generated as part of the sediment analysis.

Name, Brief Description and owner: Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA Relevant component details: make, model, serial number, firmware version, settings: Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area. Cameras used: 2 x Canon HG 25 (forward facing) with the follow settings: • Focus: Manual (3.0m) • Rec Program: P) • Image stabilizer: OFF • Facial recognition: OFF • Recording mode: MXP • Frame rate: PF25 1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds. Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal. Contains folders: Backwards, Forwards, New Convert, Videos Done

Name, Brief Description and owner: Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA Relevant component details: make, model, serial number, firmware version, settings: Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area. Cameras used: 2 x Canon HG 25 (forward facing) with the follow settings: • Focus: Manual (3.0m) • Rec Program: P) • Image stabilizer: OFF • Facial recognition: OFF • Recording mode: MXP • Frame rate: PF25 1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds. Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal. Contains files: 1.01.*.avi to 7.08.*.avi

Name, Brief Description and owner: Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA Relevant component details: make, model, serial number, firmware version, settings: Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area. Cameras used: 2 x Canon HG 25 (forward facing) with the follow settings: • Focus: Manual (3.0m) • Rec Program: P) • Image stabilizer: OFF • Facial recognition: OFF • Recording mode: MXP • Frame rate: PF25 1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds. Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal. Contains files: 8.01.*.avi to 12.11.*.avi

Name, Brief Description and owner: Baited Remote Underwater stereo-Video (BRUV) systems (6 in total) were used. All equipment (BRUVs, weights, cameras, lights, ropes, etc.) belonged to UWA Relevant component details: make, model, serial number, firmware version, settings: Stereo‐BRUV systems consisted of a frame, protecting 2 convergent video cameras inside waterproof housings (plus one rear-facing video camera) and 2 lights (one forward-facing and one rear-facing), attached to a base bar, with a baited container fixed in front of the cameras. Systems were tethered by rope to surface buoys to facilitate relocation and retrieval. Weights were added to frames due to the current and depth in the area. Cameras used: 2 x Canon HG 25 (forward facing) with the follow settings: • Focus: Manual (3.0m) • Rec Program: P) • Image stabilizer: OFF • Facial recognition: OFF • Recording mode: MXP • Frame rate: PF25 1 x GoPro Hero 3+ (backwards facing), taking photos every 60 seconds. Cameras were calibrated at UWA prior to and at the conclusion of the field trip, using SeaGIS software Cal. Contains files: 30.01.*.avi to 31.06.*.avi