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Importance of Photosynthetic Symbionts in Sponges
No full textMaintenance and Update Frequency: asNeededStatement: 5 m transects using SCUBA, Diving PAM to determine photosynthetic sponges. Sample sponges that are positive. Determine distribuiton of photosynthetic symbionts in sponge using fluorescent microscopy, identify symbionts using 16S rDNA sequencing. Identify sponges using classical taxonomy.<b>Credit</b><br/>Jane Fromont, WA Museum; Simon Toze, CSIRO<b>Purpose</b><br/>Understanding of important interactions in our coastal ecosystems and the impacts of human activities including global warming.Data was collected on the biogeography and biodiversity of photosynthetic symbionts of marine sponges in Australia. From 2000, sponges from around Australia were sampled using a Diving PAM (pulse-amplitude modulation), molecular analyses and classical taxonomy to quantify the abundance of photosynthetic symbionts in sponges, particularly in temperate Western Australian waters
A systematic review of literature that assess the effects of sewage disposal on soft sediment assemblages.
No full textMaintenance and Update Frequency: notPlannedStatement: Searches were conducted on 3 databases (Biological Abstracts, Current Contents and Web of Science) for peer reviewed journal articles that were written in English and considered the effects of sewage effluent or sewage sludge from a point source on macroinfaunal invertebrate assemblages. Monitoring studies that were taken in a single gradient direction were excluded as they might represent a spatial gradient rather than a pollution gradient. Similarly, studies that were confounded with the effects of depth or time were also excluded. In studies were a control versus impact approach was used, more than 1 control site was required to be included in the review. Studies were also required to have replicate samples at each station to ensure that the estimates of the means used to infer an effect had reasonable precision.<b>Credit</b><br/>Keough, M.J., Prof<b>Purpose</b><br/>To review the literature and address the following questions about sewage disposal in marine environments:
1. What indicators are most commonly used in the monitoring of putative impacts in soft sediments?
2. How sensitive are these indicators in detecting impacts from sewage disposal.
3. Is there consistency between studies relating to the direction and magnitude of change in the indicators and is the magnitude of change considered a biologically important effect?
4. Do any characteristics of the studies influence the direction and magnitude of the observed effects?This review was a synthesis of studies that monitored the effects of sewage disposal in the marine environment. Twenty studies that were conducted between 1973 and 1997 and met specific design requirements (i.e. no confounding factors, appropriate replication) were included in the review. <br/><br/>Background data relating to discharge type, quantity, level of detail and sampling procedures of the investigation in each study were collated. Any impact that was detected was recorded for each endpoint. The magnitude of change or effect size was calculated for each study and was defined as the percent change at the impact site relative to the control sites. <br/><br/>There were some consistencies in the monitoring strategies and benthic responses between studies in different areas. Multivariate indicators and population level analyses were the most sensitive measures for detecting sewage related impacts. Abundance usually increased at outfall sites relative to controls (30-250% magnitude of change) while species richness, diversity and evenness tended to decrease (16-90% magnitude of change). The geographic extent of the studies were limited to temperate regions except for 1 study that was conducted in Antarctica
RAN CTD Profile Data - SMB DUYFKEN ProjectID: HI472DUY_H From: 2010-02-23 To: 2010-04-11
No full textStatement: Initial Processing
Incoming data undergoes gross range checks and removal of any corrupted or unrealistic data. Some CTD data is subject to a pressure offset error generally less than 1 decibar. A pressure offset error occurs when the pressure sensor does not read 0 dbar in air. In cases where the pressure offset error was observed before the CTD cast the pressure readings have been corrected for the offset which is stored in the global netCDF attribute named 'CTD_pressure_offset'. In some cases there is a separate recording for the downcast and upcast. The downcast data were selected as priority for better quality data. In rarer cases only an upcast was available so it is used by default.
Salinity on the PSS-78 scale is computed from conductivity, temperature and pressure using the UNESCO (1983) algorithm with temperature corrected from the ITS-90 scale to the ITS-68 scale used by the algorithm.
The raw CTD data contains a non-monotonic sequence of pressures due to up/down motion of ship/winch. A sequence of unique monotonic pressures up to the maximum value is extracted and then linearly interpolated to 1 decibar pressure levels with the corresponding interpolated temperature and salinity. Finally the interpolated files are converted to a set of netCDF files ready for QC.
Quality Control
Quality control (QC) involves viewing location of the data on a map, visual inspection of each profile of temperature and salinity and comparison with nearest neighbours and climatology. CTD profiles are checked for any density inversions i.e. density decreasing with depth by computing sigma-t density. A density inversion may indicate unrealistic salinity and/or temperature values. QC flags are applied to indicate whether data is good, suspect, bad or not tested. QC flags may apply at the 'whole profile' level or individual pressure levels.
CTD cast positions and times undergo a land and ship speed check. Casts with position on land or unrealistic speed between casts are flagged as failed position or time. Temperature and salinity profiles are then visually compared against a 3 standard deviation envelope from the CSIRO-CARS (2009) atlas. Profiles or segments of profiles with data outside the envelope are flagged as doubtful or failed. Profiles are also checked for consistency or doubtful features by comparing with previous/next casts (buddies) and also by comparison with historical CTD casts taken in the same area and season.<b>Credit</b><br/>Royal Australian Navy Hydrography and Metoc Branch<b>Purpose</b><br/>Part of the long term ocean monitoring program.This dataset contains quality controlled vertical profiles of pressure, temperature and salinity measured by a Conductivity, Temperature and Depth (CTD) probe. The dataset contains 7 CTD profiles (casts) obtained during RAN Hydrographic Survey cruise HI472DUY_H from SMB DUYFKEN. <br/><br/>The CTD was manufactured by Falmouth Scientific and is the "2-inch Micro CTD" model. This CTD type is referred to operationally as the HS_CTD. This CTD is fitted within an inductive type conductivity cell, a platinum thermometer and silicon pressure sensor.<br/><br/>The CTD temperature sensor is calibrated on the ITS-90 temperature scale against a master CTD using a controlled temperature bath. The pressure sensor is calibrated using a Druck Pressure Calibrator. The conductivity sensor is calibrated in a temperature controlled bath against seawater samples of known conductivity.<br/><br/>The HS_CTD is lowered and raised by a hand winch sampling at a rate of 1.83 Hz. Data files were recorded in the downcast and upcast direction but most data is from downcasts due to higher quality. Raw CTD pressure data is not always monotonic due to transient up/down motion of winch/ship. Raw pressure data is subsetted to give a monotonic (increasing) sequence and then linearly interpolated to 1 decibar pressure intervals and converted to netCDF format files. Data is then flagged with quality control flags after visual inspection and comparison to average climatology and historical CTD casts
Kimberley Benthic Habitat Survey - Video Data
No full textStatement: Underwater video cameras were used to collect video footage of the substrate and benthic biohabitat along 500 m transects. Video footage from 757 transects were recorded and stored in electronic format.
For more detailed methods refer to final report, access via link to parent metadata record above.<b>Credit</b><br/>*Fry G; Heyward A; Wassenberg T; Colquhoun J; Pitcher R; Smith G; Ellis N; Stieglitz T; Taranto T; Keesing J; Irvine T; Pendrey R; Cheers S; Cook K; Thomson D; Vanderklift M; Brewer D; Brookes K; Cannard T; Chetwynd A; Hunt O; McLeod I; Speare P; Van der Velde T; Woodley S. *Researchers from Commonwealth Scientific and Industrial Research Organisation (CSIRO) - Marine and Atmospheric Research and AIMS (Australian Institute of Marine Science)Four locations along the Kimberley coast were video and depth surveyed to describe the substratum type, biohabitat type and the bathymetry of the seabed. Video and depth data was collected between 9 - 29 June 2008 from the vicinity of Gourdon Bay, Quondong – Coulomb Point, Perpendicular Head and Packer Island. Five supplementary transect lines, perpendicular to the shoreline, were also identified between the four locations. These were able to be surveyed when the vessels were in transit.<br/><br/>This video data was analysed to generate substrate and biohabitat distribution maps to be used by the Northern Development Taskforce as part of a selection process to rank the suitability of a range of locations for a proposed common-user liquefied natural gas hub precinct
Datasets relating to samples collected from the leeward off-reef area off Wistari Reef
No full textStatement: Surface samples were collected from boat transects across Wistari Reef. Deeper samples were obtained by diving. Samples were collected using a hand-held scoop and 600ml plastic bottles. Sediment samples were sieved in order to determine modal particle diameters, and sorting data. Samples were first dry sieved. Sieves of sizes -1.0phi to 2.5phi were stacked and installed on a sieving shaker. Samples were weighed out to approximately 250g, and each sample was sieved for 15 minutes to ensure correct separation of size fractions. Each fraction was then removed from the sieve and weighed. The size fractions 2.5phi, 3.0phi, 3.5phi and 4.0phi were pre-weighed, and wet sieved separately using a low pressure water hose, through smaller micropalaeontological sieves. Sediment textures are expressed in terms of mean grainsize, and sorting coefficient, represented by standard deviation.Statement: Scanned histograms are used to highlight the percentage of sediments within each grainsize bracket. The exact percentage figures are not available.
Mean grainsize of surface samples parameters: Average mean grainsize (phi), average sorting coefficient (phi), dominant coarse biota, dominant fine biota.
Grainsize of surface samples parameters: Sample, latitude (d,m,s), longitude (d,m,s), grainsize brackets (phi), percentage of sediments in grainsize brackets (%).<b>Credit</b><br/>Funded by The Australian Research Council (ARC)<b>Credit</b><br/>The Australian National University (ANU)<b>Purpose</b><br/>To provide an overview of the sedimentary zones of Wistari Reef. Conducted as part of a broader study looking at Holocene production and accumulation of sediments on Wistari Reef.Wistari Reef is a lagoonal platform reef, situated on the Tropic of Capricorn, within the Capricorn and Bunker region of the southern Great Barrier Reef. Surface samples were taken from the leeward off-reef floor to determine the mean grainsize and the sorting coefficient. Within this area the average mean grainsize is ~1.16phi, with an average sorting coefficient of 0.89phi. The sediments of the leeward off-reef floor are relatively mature. Most samples are moderately well-sorted medium sand, and are moderately well-rounded. Patch reefs, and small individual colonial and solitary corals contribute a small amount of coarser material to the sediment
Population dynamics of the infaunal bivalve, Soletellina alba
No full textMaintenance and Update Frequency: notPlannedStatement: Please consult either the thesis, or the specific papers for details on the methods used.<b>Credit</b><br/>Dr Andrew Constable and Dr Peter Fairweather were supervisors of this PhD research.<b>Purpose</b><br/>To describe the population dynamics of Soletellina alba. This particular species was chosen because it exhibits mass mortality events that coincide with winter flooding, so part of the aim of this study was to determine the likely cause of these mortalitites, together with some of the biological traits that may allow this species to persist within highly dynamic intermittent estuaries.The population dynamics of the infaunal bivalve Soletellina alba were investigated at three sites situated in close proximity to the mouth of the Hopkins River estuary from 1997 to 1999. The distribution and abundance of juvenile and adult S.alba was very variable across all dates, sites and channel elevations (i.e. water depths). An experimental test comparing the recruitment of juveniles at different channel elevations and in sediments of varying particle size was conducted during 1999. The results of these tests showed that recruitment was greatest at the shallowest channel elevation and there was little evidence that sediment particle size influenced recruitment. In contrast to 1999, recruitment during 1997 or 1998 was very low.<br/><br/>Growth rates were monitored using tagged individuals held in caged and uncaged plots, which revealed that growth was highly variable among individuals, but not between sites. These tests also revealed that growth was negligible during the colder, winter months, and that the fastest growing individuals were capable of growing 0.2 mm/day.<br/><br/>Salinity tolerance experiments showed that bivalves exposed to low salinities (< 6 ppt), exhibited poorer condition and took longer to re-burrow into sediments than those exposed to greater salinities (> 14 ppt), while death of bivalves exposed to salinities < 1 ppt occurred after 8 days of exposure. These tests provide evidence that low salinities are probably the principal cause of mass mortalities that are observed during winter flooding, although the interaction between salinity, temperature and turbidity also deserve consideration in the future. <br/><br/>It is hypothesised that the survival of very young juveniles (between 0.5mm and 1mm shell length) and rapid growth rates are important features of the life history of S.alba that explain its successful persistence within the Hopkins River estuary. It is highly likely that this species is capable of completing its entire life cycle within the estuary. The absence of other nearby populations, and periods of mouth closure, are likely to greatly limit the potential contribution made by larvae entering from the surrounding marine environment. This study has added to our knowledge of how an infaunal bivalve copes with life in the intermittently closing estuaries that typify semi-arid coastlines in the Southern Hemisphere
RAN CTD Profile Data - SMB CASUARINA ProjectID: HI446(C)CAS_H From: 2009-01-18 To: 2009-02-22
No full textStatement: Initial Processing
Incoming data undergoes gross range checks and removal of any corrupted or unrealistic data. Some CTD data is subject to a pressure offset error generally less than 1 decibar. A pressure offset error occurs when the pressure sensor does not read 0 dbar in air. In cases where the pressure offset error was observed before the CTD cast the pressure readings have been corrected for the offset which is stored in the global netCDF attribute named 'CTD_pressure_offset'. In some cases there is a separate recording for the downcast and upcast. The downcast data were selected as priority for better quality data. In rarer cases only an upcast was available so it is used by default.
Salinity on the PSS-78 scale is computed from conductivity, temperature and pressure using the UNESCO (1983) algorithm with temperature corrected from the ITS-90 scale to the ITS-68 scale used by the algorithm.
The raw CTD data contains a non-monotonic sequence of pressures due to up/down motion of ship/winch. A sequence of unique monotonic pressures up to the maximum value is extracted and then linearly interpolated to 1 decibar pressure levels with the corresponding interpolated temperature and salinity. Finally the interpolated files are converted to a set of netCDF files ready for QC.
Quality Control
Quality control (QC) involves viewing location of the data on a map, visual inspection of each profile of temperature and salinity and comparison with nearest neighbours and climatology. CTD profiles are checked for any density inversions i.e. density decreasing with depth by computing sigma-t density. A density inversion may indicate unrealistic salinity and/or temperature values. QC flags are applied to indicate whether data is good, suspect, bad or not tested. QC flags may apply at the 'whole profile' level or individual pressure levels.
CTD cast positions and times undergo a land and ship speed check. Casts with position on land or unrealistic speed between casts are flagged as failed position or time. Temperature and salinity profiles are then visually compared against a 3 standard deviation envelope from the CSIRO-CARS (2009) atlas. Profiles or segments of profiles with data outside the envelope are flagged as doubtful or failed. Profiles are also checked for consistency or doubtful features by comparing with previous/next casts (buddies) and also by comparison with historical CTD casts taken in the same area and season.<b>Credit</b><br/>Royal Australian Navy Hydrography and Metoc Branch<b>Purpose</b><br/>Part of the long term ocean monitoring program.This dataset contains quality controlled vertical profiles of pressure, temperature and salinity measured by a Conductivity, Temperature and Depth (CTD) probe. The dataset contains 36 CTD profiles (casts) obtained during RAN Hydrographic Survey cruise HI446(C)CAS_H from SMB CASUARINA. <br/><br/>The CTD was manufactured by Falmouth Scientific and is the "2-inch Micro CTD" model. This CTD type is referred to operationally as the HS_CTD. This CTD is fitted within an inductive type conductivity cell, a platinum thermometer and silicon pressure sensor.<br/><br/>The CTD temperature sensor is calibrated on the ITS-90 temperature scale against a master CTD using a controlled temperature bath. The pressure sensor is calibrated using a Druck Pressure Calibrator. The conductivity sensor is calibrated in a temperature controlled bath against seawater samples of known conductivity.<br/><br/>The HS_CTD is lowered and raised by a hand winch sampling at a rate of 1.83 Hz. Data files were recorded in the downcast and upcast direction but most data is from downcasts due to higher quality. Raw CTD pressure data is not always monotonic due to transient up/down motion of winch/ship. Raw pressure data is subsetted to give a monotonic (increasing) sequence and then linearly interpolated to 1 decibar pressure intervals and converted to netCDF format files. Data is then flagged with quality control flags after visual inspection and comparison to average climatology and historical CTD casts
RAN CTD Profile Data - HMAS PALUMA ProjectID: HI544PAL_M From: 2014-04-09 To: 2014-06-04
No full textStatement: Initial Processing
Incoming data undergoes gross range checks and removal of any corrupted or unrealistic data. Some CTD data is subject to a pressure offset error generally less than 1 decibar. A pressure offset error occurs when the pressure sensor does not read 0 dbar in air. In cases where the pressure offset error was observed before the CTD cast the pressure readings have been corrected for the offset which is stored in the global netCDF attribute named 'CTD_pressure_offset'.
Salinity on the PSS-78 scale is computed from conductivity, temperature and pressure using the UNESCO (1983) algorithm with temperature corrected from the ITS-90 scale to the ITS-68 scale used by the algorithm.
For this CTD model the response time (time constant) of the temperature sensor (100 ms) is slower than the conductivity cell (25ms). This leads to a phenomenon called 'salinity spiking' in the salinity data. Spiking is caused by the fact that calculated salinity is a function of conductivity, temperature and pressure [S=f(C,T,z)]. The temperature response lags the conductivity response resulting an error in the calculation of salinity. This is evident as noticeable spikes in the salinity profiles especially at depths where temperature is rapidly changing e.g in the ocean thermocline. The spiking was reduced reduced by applying a 2.5 sample time shift (0.1 s) to the temperature values and using the shifted temperature value in the re-computation of the salinity. An array of raw pressure, temperature and de-spiked salinity is then passed to the next processing stage.
The raw CTD data contains a non-monotonic sequence of pressures due to up/down motion of ship/winch. A sequence of unique monotonic pressures up to the maximum value is extracted and then linearly interpolated to 1 decibar pressure levels with the corresponding interpolated temperature and salinity. Finally the interpolated files are converted to a set of netCDF files ready for QC.
Quality Control
Quality control (QC) involves viewing location of the data on a map, visual inspection of each profile of temperature and salinity and comparison with nearest neighbours and climatology. CTD profiles are checked for any density inversions i.e. density decreasing with depth by computing sigma-t density. A density inversion may indicate unrealistic salinity and/or temperature values. QC flags are applied to indicate whether data is good, suspect, bad or not tested. QC flags may apply at the 'whole profile' level or individual pressure levels.
CTD cast positions and times undergo a land and ship speed check. Casts with position on land or unrealistic speed between casts are flagged as failed position or time. Temperature and salinity profiles are then visually compared against a 3 standard deviation envelope from the CSIRO-CARS (2009) atlas. Profiles or segments of profiles with data outside the envelope are flagged as doubtful or failed. Profiles are also checked for consistency or doubtful features by comparing with previous/next casts (buddies) and also by comparison with historical CTD casts taken in the same area and season.<b>Credit</b><br/>Royal Australian Navy Hydrography and Metoc Branch<b>Purpose</b><br/>Part of the long term ocean monitoring program.This dataset contains quality controlled vertical profiles of pressure, temperature and salinity measured by a Conductivity, Temperature and Depth (CTD) probe. The dataset contains 76 CTD profiles (casts) obtained during RAN Hydrographic Survey cruise HI544PAL_M from HMAS PALUMA.<br/><br/>The CTD was manufactured by Applied Microsystems Limited and is the "Micro CTD" model. This CTD type is referred to operationally as the MVP200_CTD. This CTD is fitted with a 4 electrode platinized conductivity cell, thermistor temperature sensor and a semiconductor strain gauge pressure sensor.<br/><br/>The CTD sensors are calibrated at the manufacturer Applied Microsystems Limited on a 12-18 month schedule. The CTD temperature sensor is calibrated against 'Hart' temperature standards. The pressure sensor is calibrated using 'Budenburg Deadweight' standards. The conductivity sensor is calibrated using 'Hart' temperature standards and seawater samples of known conductivity.<br/><br/>The CTD sensors are mounted to a fish-shape probe. The probe is controlled by an electric winch (MVP200 type). For downcasts the fish is allowed to free-fall (winch is in 'free-wheel' mode) under its own weight at about 2-3 ms-1 and then is winched back. Data is recorded in downcast and usually in upcast direction at a sampling rate of 25 Hz. The downcast data is of higher quality because sensors encounter undisturbed seawater that flows through the nose of the fish. On the upcast the fish is flipped around (tail first) and the sensors in the nose encounter disturbed flow from the fish tail.<br/><br/>This type of CTD is prone to a phenomenon called 'salinity spiking' caused by a mismatch between the response times of the temperature and conductivity sensor. Data undergoes a salinity de-spiking routine to correct for this. See the history metadata for further details on the salinity de-spiking process.<br/><br/>Raw CTD pressure data is not always monotonic due to transient up/down motion of winch/ship. Raw pressure data is subsetted to give a monotonic (increasing) sequence and then linearly interpolated to 1 decibar pressure intervals. Data is then flagged with quality control flags after visual inspection and comparison to average climatology and historical CTD casts
Effects of provisioning bottlenose dolphins in Cockburn Sound: injuries, entanglements, and changes to ranging and social behaviours
No full textMaintenance and Update Frequency: notPlannedStatement: - Behavioural surveys -
Behavioural surveys and focal individual follows were used to collect ecological and behavioural data on the sub-population in Cockburn Sound.
- Defining conditioned dolphins -
Between 1993-2003 a sub-set of dolphins exhibited a series of behavioural events indicating that they were conditioned to human interaction by food reinforcement. Dolphins were classified as conditioned to human interaction by food reinforcement based on observations of the following set of behavioural events: 1) an active and directed approach towards a stationary boat; 2) maintenance of close proximity (within 2m) to the beam (side) or stern of stationary vessels for an extended period (>30 seconds) and 3) acceptance of food from humans (if offered).
- High-risk behaviours during provisioning interactions -
Samuals and Dejder (2004) described several components of human-dolphin interactions that they predicted would increase the risk of injury, illness, or death to humans or dolphins during provisioning interactions with humans in Cockburn Sound:
1) physical contact with (or close proximity to) humans;
2) maintenance of close proximity to vessels;
3) acceptance of food from humans;
4) acceptance of a foreign (non-fish) object from humans;
5) maintenance of close proximity to deployed fishing gear; and
6) participation in interactions in areas of poor water quality and high boat traffic. Instances of these behaviours were documented during observations of provisioned animals interacting with humans.
- Case-study approach for behavioural changes -
By inspection, the number of observations for provisioned individuals varied. To investigate behavioural changes associated with provisioning a case study approach was adopted and analysis of behavioural changes limited to five individuals for whom there were moderate to high number of post-provisioning observations.
- Changes in ranging and association patterns -
To investigate whether dolphins in Cockburn Sound changed their ranging patterns after becoming conditioned to human provisioning, the size and location of post-provisioning range of case study individuals were compared to their pre-provisioning range size and location. The methods used to determine ranging patterns of individuals were reviewed in Chapter 2 (Section 2.6 of thesis).
- Male proximity to reproductive-age females -
Changes in the frequency with which case study males were in close proximity to reproductive-age females post-provisioning were examined.
- Observations of injury and mortality -
Observations of injuries related to human interactions were compiled by the authors from direct observation or from personal communication.
See section 5.2 for further detailed methodology<b>Credit</b><br/>Donaldson, Rebecca<b>Purpose</b><br/>To assist in the ecosystem-based conservation of dolphins within Cockburn Sound.Unpublished data and analyses from Rebecca Donaldson (1993-1997) and original data collected for the thesis (2000-2003) from Cockburn Sound were used to:<br/>1) evaluate the link between high-risk behaviour by dolphins during provisioning interactions and rates of human-induced injury;<br/>2) contrast the behaviour of known individuals before and after they engaged in provisioning interactions; and<br/>3) examine the incidence of unregulated provisioning interactions within a population-level context in which the baseline demographic parameters were known
2021 State of the Environment Report Marine Chapter – Expert Assessment – State and Trend – Inner shelf (0-30 m) – reef fish species
No full textStatement: QUALITY OF DATA USED IN THE ASSESSMENT
High. Quantitative and standardised data collected using consistent methods by experienced observers.<b>Credit</b><br/>Peer reviews of this assessment were provided by:
Mike Emslie (Australian Institute of Marine Science)The Marine chapter of the 2021 State of the Environment (SoE) report incorporates multiple expert templates developed from streams of marine data. This metadata record describes the Expert Assessment "State and Trend of inner shelf (0-30 m) – reef fish species". <br/>***A PDF of the full Expert Assessment, including figures and tables (where provided) is downloadable in the "On-line Resources" section of this record as "EXPERT ASSESSMENT 2021 - Inner shelf (0-30 m) – reef fish species"***<br/><br/>----------------------------------------<br/><br/>DESCRIPTION OF SPECIES/HABITAT/COMMUNITY/PROCESS FOR EXPERT ASSESSMENT<br/>Bony and cartilaginous fish assemblages found on coastal rocky and coral reefs <30 m depth around the Australian continent, including both exploited and unexploited species.<br/><br/>DATA STREAM(S) USED IN EXPERT ASSESSMENT<br/>Underwater visual census data from Reef Life Survey, the Australian Institute of Marine Science, and the Australian Temperate Reef Collaboration. National synoptic change (mean 2011-2015 period vs mean 2016-2020 period) and 18 long-term monitoring locations spread nationally. See national reefs case study for details.<br/><br/>----------------------------------------<br/><br/>2021 SOE ASSESSMENT SUMMARY [see attached Expert Assessment for full details]<br/><br/>• 2021 •<br/>Assessment grade: Poor<br/>Assessment trend: Highly variable among species<br/>Confidence grade: Adequate<br/>Confidence trend: Adequate<br/>Comparability: High. This is the second sequential assessment based on standardised quantitative data from the same sources.<br/>• 2016 •<br/>Assessment grade: Poor<br/>Assessment trend: Deteriorating<br/>Confidence grade: Adequate high quality evidence and high level of consensus<br/>Confidence trend: Adequate high quality evidence and high level of consensus<br/>Comparability: Grade and trend are somewhat comparable to the 2011 assessment<br/>• 2011 •<br/>Assessment grade: Poor<br/>Assessment trend: Stable<br/>Confidence grade: Limited evidence or limited consensus<br/>Confidence trend: Limited evidence or limited consensus<br/><br/>----------------------------------------<br/><br/>CHANGES SINCE 2016 SOE ASSESSMENT<br/>not supplie