This record provides an overview of the NESP Marine and Coastal Hub small-scale study - "Coastal wetland restoration for Blue Carbon in northern Australia". For specific data outputs from this project, please see child records associated with this metadata. Investment in restoration of coastal wetland ecosystems is increasing due to concerns around habitat loss, water quality, decline in fish catches, coastal inundation and erosion, and climate change. Coastal wetlands, including mangroves, saltmarshes, seagrasses and tidal freshwater forests like Melaleuca have significant capacity to sequester carbon dioxide contributing to blue carbon stocks. They provide habitat for coastal fisheries and a range of biodiversity and are culturally important. This project aims to develop a method, that can be widely used across Australia, to prioritise coastal wetland restoration sites for Blue Carbon projects based on a value-based framework that considers biophysical suitability, balancing of wetland values, condition, regulation and policy adequacy, and economic feasibility. Planned Outputs • Spatial data outputs from the Fitzroy Basin QLD, south-west WA and northern Australia analysis [spatial dataset] • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]

This record provides an overview of the NESP Marine and Coastal Hub project 'Supporting recovery and management of migratory shorebirds in Australia'. For specific data outputs from this project, please see child records associated with this metadata. Coastal Australia is home to 37 regularly occurring migratory shorebird species, with many protected areas including Ramsar sites designated on the basis of shorebird populations. Many migratory shorebirds are declining rapidly, and hence the focus of conservation efforts at multiple levels of government in Australia and overseas. Excitingly, after decades of decline, many of Australia’s migratory shorebird populations are now showing improving trends (NESP MaC Project 1.21 - Australia’s Coastal Shorebirds: Trends and Prospects). However, we do not understand why the birds’ populations are stabilising, or how these gains can be converted into genuine population recovery to previous population levels. This project will combine analyses on more than a million observations of shorebirds banded in Australia with a comprehensive database of management actions to (i) create an annually updatable dashboard providing the key shorebird population parameters of reproductive output and survival, (ii) build a comprehensive database of conservation management actions for migratory shorebirds, indicating which actions are known to benefit reproductive output and survival, and (iii) create a shorebird management handbook that can be used by practitioners to guide action across Australia and around the East Asian – Australasian Flyway. Outputs will support DCCEEW’s international obligations in relation to Ramsar wetlands, the Convention on the Conservation of Migratory Species (CMS), bilateral migratory bird agreements with Japan (JAMBA), China (CAMBA) and the Republic of Korea (ROKAMBA) as well as feed directly into developing the new incarnation of the Australian Government’s Wildlife Conservation Plan for Migratory Shorebirds. Results have a pathway for regional and local implementation through BirdLife Australia’s Migratory Shorebirds Conservation Action Plan. Planned Outputs • Annually updateable dashboard providing estimates of reproductive output and survival • Searchable database of conservation management actions for migratory shorebirds • Shorebird habitat management handbook that can be used by practitioners • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]

The dataset consists of tables of means, with statistical differences indicated where they are significant, for measured crop performance, fertilizer N recovery and use efficiency at 6 field sites from Mackay to Freshwater. Runoff losses of N are also shown from sites at Freshwater and Silkwood. **This dataset is currently under embargo until December 2021. Optimizing fertilizer nitrogen (N) application rates to both sustain high levels of productivity and minimize any impacts on the surrounding ecosystem is challenging, especially under monsoonal wet season conditions in northern Australia. The inability of existing application strategies and fertilizer N products to achieve synchrony of mineral N supply with crop demand, or prevent rapid formation of nitrate-N (that is vulnerable to loss via gaseous or aqueous loss pathways) increases risks of inefficient N use. A blend of enhanced efficiency fertilizers (EEFs) with different modes of action has the best chance of lowering the risk of N losses and increasing crop N recovery, providing an opportunity to reduce fertilizer N rates without increasing the risks of productivity loss. Six field trials were established from Mackay to the wet tropics, with data collected from consecutive ratoon crops at each site. Yields and indices of N use efficiency were developed for crops receiving urea-N at rates equivalent to that derived from the local SIX EASY STEPS guidelines, or as urea or a blend of EEF’s applied at N rates calculated using a block-specific yield target (PZYP) based on mill records. Laboratory and field trials conducted at the University of Qld to investigate the implications of fertiliser application in concentrated, subsurface bands on the efficacy of different EEF technologies relative to a granular urea standard supported the research in sugarcane fields. These application methods represent the current industry standard practice, but the band environment may adversely influence the rate of N release and/or subsequent N transformations that determine crop N uptake or environmental loss. Methods: The project established a total of six field sites after the 2016 crop harvest, with all experiments commencing after harvest of the 1st or 2nd ratoon. The experimental design and plot size varied with site. In Silkwood, Freshwater and the Burdekin, plots consisted of large scale strips 6-8 cane rows wide and the length of the cane block, with yield (and in the case of Silkwood and Freshwater, runoff water quality) collected from the whole treated strip. The Burdekin trial contained three replicate strips of each treatment, but due to the extensive water quality monitoring equipment requirements at Silkwood and Freshwater, treatments were not replicated. At all other sites, trials consisted of smaller plot, replicated experiments in a randomized block design. Plot size was at least 6 cane rows wide times 30m long, and all treatments were replicated four times. The basis of fertilizer rates was either the District Yield Potential (DYP, currently used to determine the fertilizer N rates in 6ES) or the Productivity Zone Yield Potential (PZYP, used to determine N rates aligned to a spatially relevant yield target). The PZYP was calculated from the mean yield from block or satellite records over two or more crop cycles, plus 2 times standard error of that mean. As all sites were established in ratoon crops, plant crop yields were generally excluded from this calculation, especially where those yields were markedly higher than yields of the ratoons. In situations where large variation in yields occurred between La Nina and normal or drier seasons (e.g. in the wet tropics), separate PZYP targets were calculated to reflect the expected seasonal forecast (i.e. lower PZYP targets in forecast La Nina conditions). Each site hosted a Nil N treatment each year (fertilizer N was withheld for that growing season), but these plots/strips were relocated to new plot/strip locations annually. Having the Nil N treatment always located on a plot with a history of fertilizer N application provided a realistic assessment of the soil N supply which the fertilizer N application was designed to augment. Crop harvest and fertilizer application were conducted as per grower normal practice at each location, although at all sites there were no crops harvested in the 1st round. This was considered desirable, as it was expected that the best chance to assess the risks of reduced N rates and the efficacy of EEF’s would be under conditions where fertilizer N losses were more likely to occur (i.e., where the onset of the monsoonal wet season occurred before the crop had finished the majority of biomass N accumulation). Fertilizer N sources The same fertilizer N sources were used at each site. The fertilizer N standard was taken as granular urea, which was applied during the month following harvest of the preceding ratoon. This was compared to an EEF blend consisting of 1/3 times urea coated with the nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP, marketed commercially as Entec®) and 2/3 times polymer-coated urea with a reported 90-day release period (product of Everris Pty Ltd and marketed as Agromaster Tropical®). This blend was chosen as the best possible combination of products that would protect fertilizer N from risk of loss – initially by retaining the N in the NH4-N form, and subsequently by slowing the release of urea-N into the soil solution. Both products were applied using either stool-split (Burdekin, Freshwater, Mackay and Silkwood) or subsurface side-dress (Tully) fertilizer applicators, although it should be noted that on occasions the stool-split applicators did not always effectively close the fertilizer trench and cover the fertilizer band with soil. This suboptimal application strategy contributed to some confounding of the benefits of EEF use in some seasons due to greater loss risks to both the atmosphere and in runoff. Fertilizer N recovery, crop yield and indices of fertilizer N use efficiency Fresh and dry biomass and crop N content were determined from hand-cut biomass samples collected from 7-10 months after fertilizer application, on the assumption that at this stage, the crop N content would be at a maximum, and most relevant to the yield-determining processes. Crop N was partitioned between leaf/cabbage/dead leaf and stalks at that time. In situations where biomass sampling was conducted a little earlier than desirable (e.g. due to an impending cyclone), smaller numbers of whole stalk samples were again collected for dry matter and N concentration immediately prior to harvest (to determine the partitioning of N between harvested and non-harvested portions of the crop), and stalk N concentration from the final harvest was used in combination with cane yields to estimate crop N removal. Yields were determined by commercial harvest in the case of the large strip plots, with the bins collected from each strip weighed and ccs determined at the mill. In the case of the small plot trials, yields were determined from small plot hand harvesting and ccs was determined by near infrared spectroscopy. A number of indices of N use efficiency were calculated from these data including – -Agronomic Efficiency of fertilizer N use (AgronEffN) = Fertilizer N rate/(YieldN1 – YieldN0) = kg fertilizer N required to produce an additional tonne of cane yield. In this calculation, YieldN1 is the cane yield at fertilizer rate N1, while YieldN0 is the yield with no N applied. -Nitrogen uptake efficiency (NUpE) = (Crop N1 – Crop N0)/Fertilizer N rate = the additional crop N uptake/kg fertilizer N applied. In this calculation, N1 is the biomass N content for N rate 1, while N0 is the biomass N content with no applied N fertilizer. -Nitrogen Utilization Efficiency (NUtE) = Yield/Crop N content = t cane produced/kg of crop N uptake. This figure is a very useful indicator of trial sites where yield is constrained by factors other than N (e.g. waterlogging). Runoff and drainage losses of N Surface water runoff and drainage below the root zone (1 m depth) were monitored in four of the fertilizer rate treatments at Silkwood and Freshwater sites. In addition, strategic sampling in the farm drain around the Silkwood block was undertaken. Surface water samples were collected by automated samplers, with sampling undertaken across the hydrograph (Freshwater) or as an integrated composite of runoff from each individual runoff event (Silkwood). Runoff samples were analysed for sediment, total nitrogen, urea, ammonium-N, and nitrate-N (in addition to other constituents). Drainage samples were collected from 5 lysimeter barrels in each of the treatments with runoff monitoring (totalling 20 barrels) on a weekly to monthly basis at Silkwood. Drainage samples were analysed for nitrate-N and ammonium-N concentrations. Process studies of banded urea and EEFs A series of laboratory studies have been undertaken as part of a PhD program and subsequent short postdoc appointment to quantify the N dynamics relating to banded EEF’s (controlled release/coated products or stabilized products), due to concerns that the band environment may interfere with the operation of the EEF technology. This work has resulted in a series of publications that detail the key findings, but that also include supplementary data to provide background information. These papers are available from the NESP website. Limitations of the data: Data collectively represent variable soil types and seasonal conditions, so extrapolation from particular sites to other seasons, regions or soil types should be undertaken with extreme caution. Similarly, the performance of the EEF fertilizer blend used in this study is specific to the products used, and extrapolation to a broader range of EEF technologies would not be appropriate. This project played a key role in developing the research approach that was used in the Reef Trust 4 project EEF60 run by the Canegrowers organization, which represented a much broader testing of the combination of EEF fertilisers and reduced N rates on productivity and runoff water quality. Project scientists also set on the technical advisory group for that project. Format: The data from the field trial program consists of a series of spreadsheets containing crop summary data for each successive crop season at all sites. As of Jan 2021, the dataset is complete for 3 crop seasons at all sites and a 4th crop season at sites in Tully, Freshwater and Mackay. Data are presented as treatment means with statistical significance (P<0.05) indicated where appropriate. Due to the specific and controlled nature of the chemical assays of fertiliser N performance in bands, data is presented in detail in the technical publications and the related Supplementary material published with those manuscripts. These are available from the journal articles themselves, which can be assessed from the website. Similarly, there are a number of linked publications from research into the interaction between fertiliser application strategies and EEF technologies that were conducted at UQ Gatton, using maize as a test crop. NESP project 5.11 contributed to some of the analytical costs of this research (the trials were funded primarily by contributions from UQ and Kingenta Australia) where it complimented the NESP objectives, with that funding acknowledged in the manuscripts under development. However, the data does not relate to the core objectives of the project ‘Effects of fertilizer nitrogen (N) application rate and Enhanced Efficiency Fertilizers on sugarcane productivity, efficiency of N use and loss of N in runoff’, and so experimental data will not be provided in eAtlas. It will be reported in detail in the publications, as per the laboratory component. Data Dictionary: -EEF – Enhanced Efficiency Fertilizer. In the context of these trials, refers to a blend of 1/3 urea coated with the nitrification inhibitor DMPP (Entec ®) and 2/3 polymer coated urea (Agromaster Tropical ®) with a reported 90d release period. -Agronomic Efficiency of fertilizer N use (AgronEffN) = Fertilizer N rate/(YieldN1 – YieldN0) = kg fertilizer N required to produce an additional tonne of cane yield. In this calculation, YieldN1 is the cane yield at fertilizer rate N1, while YieldN0 is the yield with no N applied. -Nitrogen uptake efficiency (NUpE) = (Crop N1 – Crop N0)/Fertilizer N rate = the additional crop N uptake/kg fertilizer N applied. In this calculation, N1 is the biomass N content for N rate 1, while N0 is the biomass N content with no applied N fertilizer. -Nitrogen Utilization Efficiency (NUtE) = Yield/Crop N content = t cane produced/kg of crop N uptake. This figure is a very useful indicator of trial sites where yield is constrained by factors other than N (e.g. waterlogging). Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.11_On-farm-nitrogen-management **This dataset is currently under embargo until December 2021

This record provides an overview of the NESP Marine and Coastal Hub study - Project 3.18 – Robust citizen science for reef habitat assessment in support of management. For specific data outputs from this project, please see child records associated with this metadata. By mobilising tourism vessels and thousands of citizens, the Great Reef Census (GRC) has demonstrated that citizen-based infrastructure can undertake reconnaissance of hundreds of reefs and garner private donor support. While these data are already used by managers, there remain core scientific questions regarding the acquisition, quality, and optimisation of such data for reef management. Here, we create a robust approach to citizen science that can be scaled up to reefs generally. Specifically, our project will (1) maximise the quality of data on key habitats by combining machine and human learning (in partnership with Dell Technologies) while conducting a rigorous testing of data quality, (2), operationalise a field deployment strategy that maximises the value of citizen data for management and mapping and (3) provides annual maps of reef state and ecological importance that feeds into decision-making by marine managers. It specifically responds to MAC Hub priority research areas 2023 (citizen science and/or new technologies in assessing condition and status of marine habitats and species). Planned Outputs • Maps of reef habitat type, reef state, ecological importance of reefs [spatial dataset] • GRC data on reef images and reef state [tabular dataset] • Final technical report with analysed data and a short summary of recommendations for policy makers of key findings [written]

The aim of this component of the study was to determine how photosynthetic rates related to growth rates both into terms of skeletal deposition and organic carbon accrual associated with linear extension, and with organic carbon accrual associated with fat deposition or lipid enrichment of existent tissue. The study was based on the coral Acropora millepora located at Lizard Island (-14°40’, 145°27’) and at Heron Island (-23°26', 151°54') in the Great Barrier Reef (GBR) of Australia, with data collected for winter and summer months. Coral colonies were monitored for two years, with both summers breaching maximum mean monthly maximum sea surface temperatures, but by less than the NOAA Bleach Watch trigger of 1°C, Lizard Island colonies proved to be highly susceptible to whole colony mortality, with a loss of 2 of 5 colonies monitored. Heron Island colonies proved to be more robust with no whole colony mortality. Interestingly, comparisons of the growth rate of Lizard colonies in summer to that of Heron colonies in winter showed that despite winter heterotrophy in Heron colonies, compared to summer autotrophy in Lizard colonies: Corals had greater linear extension in the winter at Heron, than they did in the summer at Lizard. Heron and Lizard colonies at these times were equally fat with non-significant differences in lipid per surface area. Heron colonies appear to maintain their weight (areal lipid concentrations) and growth (positive linear extension) despite a potential reliance on heterotrophy, whilst Lizard colonies appear to struggle despite high rates of photosynthesis. The study therefore reinforces the notion that projections regarding coral health need to greater appreciate the mixotrophic lifestyle of corals. The following parameters were measured: Temperature in degrees C; Pnet max as micromol O2 h-1 cm-2; Lipid per surface area as mass per cm-2;