A series of related research studies over 15 years assessed the effects of prawn trawling on sessile megabenthos in the Great Barrier Reef, to support management for sustainable use in the World Heritage Area. These large-scale studies estimated impacts on benthos (particularly removal rates per trawl pass), monitored subsequent recovery rates, measured natural dynamics of tagged megabenthos, mapped the regional distribution of seabed habitats and benthic species, and integrated these results in a dynamic modelling framework together with spatio-temporal fishery effort data and simulated management. Typical impact rates were between 5 and 25% per trawl, recovery times ranged from several years to several decades, and most sessile megabenthos were naturally distributed in areas where little or no trawling occurred and so had low exposure to trawling. The model simulated trawl impact and recovery on the mapped species distributions, and estimated the regional scale cumulative changes due to trawling as a time series of status for megabenthos species. The regional status of these taxa at time of greatest depletion ranged from ∼77% relative to pre-trawl abundance for the worst case species, having slow recovery with moderate exposure to trawling, to ∼97% for the least affected taxon. The model also evaluated the expected outcomes for sessile megabenthos in response to major management interventions implemented between 1999 and 2006, including closures, effort reductions, and protected areas. As a result of these interventions, all taxa were predicted to recover (by 2–14% at 2025); the most affected species having relatively greater recovery. Effort reductions made the biggest positive contributions to benthos status for all taxa, with closures making smaller contributions for some taxa. The results demonstrated that management actions have arrested and reversed previous unsustainable trends for all taxa assessed, and have led to a prawn trawl fishery with improved environmental sustainability.

Microplastics are persistent environmental contaminants found in marine environments worldwide. Microplastic particles isolated from coastlines in the Canterbury region of New Zealand were quantified and characterised. Sediment samples were collected from 10 locations representing exposed-beach, estuarine and harbour environments in both urban and non-urban settings. Particles were isolated from sediments using an NaCl densityseparation procedure and quantified and characterised with a combination of optical/fluorescence imaging and micro-Raman spectroscopy. Microplastics were detected at eight out of 10 locations, at concentrations ranging from 0-45.4 particles kg −1 of dry sediment. The majority of microplastics were identified as polystyrene (55%), polyethylene (21%) and polypropylene (11%). Microplastic concentrations in exposed-beach environments were significantly greater than in harbour and estuarine environments.

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New sensor streams are being generated at a rapidly increasing rate. The sources of these streams are a diverse set of networked sensors, diverse both in sensing hardware and sensing modality. Machine learning algorithms are ideally placed to develop generalized methods for stream analysis. One exemplar problem is the detection and analysis of periodic structure within these streams. Our contribution is the proposal of a new machine learning framework that (i) classifies a signal as periodic or aperiodic, (ii) further analyses the signal to find periodic structure using a neural network, and (iii) groups the motifs in the periodic signals using a modified Self Organising Map algorithm. We also demonstrate the framework using data generated by an Oyster heart rate sensor. We find that the generalized approach our classifier improves the detection of signal periods by reducing the number of functions classified as periodic from 11% to 9%; however, most benefit occurs for period calculation with the number of erroneously calculated periods reducing from 14% to 4%.