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Magnesium (Mg) is a mining-related contaminant in the Alligators Rivers Region of tropical northern Australia. A mesocosm experiment was used to assess Mg toxicity to aquatic freshwater assemblages. Twenty-five 2700-L tubs were arranged, stratified randomly, on the bed of Magela Creek, a seasonally flowing, sandy stream channel in the Alligator Rivers Region of northern Australia. The experiment comprised 5 replicates of 4 nominal Mg treatments, 2.5, 7.5, 23, and 68 mg L −1 , and a control. Phytoplankton biomass, and diatom, zooplankton, and macroinvertebrate assemblages present in the treatment tubs were sampled before and after Mg addition. A significant negative relationship between phytoplankton biomass and Mg was observed 4 wk after Mg addition as measured by chlorophyll a concentrations (r 2 = 0.97, p = 0.01). This result was supported by reductions in some major phytoplankton groups in response to increasing Mg concentrations, in the same experiment and from independent field studies. There was a significant negative relationship between zooplankton assemblage similarity (to control) and Mg concentrations (r 2 = 0.96, p = 0.002). Seven weeks after Mg addition, macroinvertebrate assemblages were dominated by 3 microcrustacean groups (Ostracoda, Cladocera, and Copepoda), each reaching maximum abundance at intermediate Mg concentrations (i.e., unimodal responses). The responses of phytoplankton and zooplankton were used to derive assemblage effect concentrations (Mg concentrations resulting in x% of the assemblage change [ECx]). Magnesium concentrations resulting in assemblage EC01 values were <3 mg L −1. Together with candidate guideline values from other laboratory-and field-based lines of evidence, the mesocosm EC01 values were incorporated into a weight-of-evidence framework for a robust regulatory approach to environmental protection.

The classification of savanna woodland tree species from high-resolution Remotely Piloted Aircraft Systems (RPAS) imagery is a complex and challenging task. Difficulties for both traditional remote sensing algorithms and human observers arise due to low interspecies variability (species difficult to discriminate because they are morphologically similar) and high intraspecies variability (individuals of the same species varying to the extent that they can be misclassified), and the loss of some taxonomic features commonly used for identification when observing trees from above. Deep neural networks are increasingly being used to overcome challenges in image recognition tasks. However, supervised deep learning algorithms require high-quality annotated and labelled training data that must be verified by subject matter experts. While training datasets for trees have been generated and made publicly available, they are mostly acquired in the Northern Hemisphere and lack species-level information. We present a training dataset of tropical Northern Australia savanna woodland tree species that was generated using RPAS and on-ground surveys to confirm species labels. RPAS-derived imagery was annotated, resulting in 2547 polygons representing 36 tree species. A baseline dataset was produced consisting of: (i) seven orthomosaics that were used for in-field labelling; (ii) a tiled dataset at 1024 × 1024 pixel size in Common Objects in Context (COCO) format that can be used for deep learning model training; (iii) and the annotations.

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