Spartina anglica is an exotic perennial grass that can rapidly colonise the intertidal zone of temperate estuaries and lagoons. Consequently, there is considerable concern about its impact on estuarine flora and fauna. This study provides the first investigation of ecological impacts by S. anglica in Australia. The objective was to investigate the impacts of S. anglica on benthic macroinvertebrate communities inhabiting mudflat and native saltmarsh habitats at Little Swanport estuary, Tasmania. The null hypothesis that species richness and species abundance of benthic macroinvertebrates in exotic S. anglica marsh does not differ from adjacent native saltmarsh and mudflat habitats was tested. Eighteen species and 3716 macroinvertebrates were collected from 60 intertidal core samples in three habitats. Species richness, total abundance of invertebrates, crustacean abundance and mollusc abundance of mudflat communities were significantly (P < 0.05) lower when compared to those inhabiting adjacent S. anglica marsh and native saltmarsh. However, species richness and total abundance of invertebrates of native saltmarsh and S. anglica marsh did not differ significantly. Ordination of macroinvertebrate data clearly separated mudflat sites from vegetated sites but showed remarkable similarity between exotic and native vegetated sites.

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Sustainable management and conservation of the world’s oceans requires effective monitoring, evaluation, and reporting (MER). Despite the growing political and social imperative for these activities, there are some persistent and emerging challenges that marine practitioners face in undertaking these activities. In 2015, a diverse group of marine practitioners came together to discuss the emerging challenges associated with marine MER, and potential solutions to address these challenges. Three emerging challenges were identified: (i) the need to incorporate environmental, social and economic dimensions in evaluation and reporting; (ii) the implications of big data, creating challenges in data management and interpretation; and (iii) dealing with uncertainty throughout MER activities. We point to key solutions to address these challenges across MER activities: (i) integrating models into marine management systems to help understand, interpret, and manage the environmental and socio-economic dimensions of uncertain and complex marine systems; (ii) utilizing big data sources and new technologies to collect, process, store, and analyze data; and (iii) applying approaches to evaluate, account for, and report on the multiple sources and types of uncertainty. These solutions point towards a potential for a new wave of evidence-based marine management, through more innovative monitoring, rigorous evaluation and transparent reporting. Effective collaboration and institutional support across the science–management–policy interface will be crucial to deal with emerging challenges, and implement the tools and approaches embedded within these solutions.

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Summary A robust scientific conclusion is the result of a rigorous scientific process. In observational ecology, this process involves making inferences about a population from a sample. The sample is crucial, and is the result of implementing a survey design. A good survey design ensures that the data from the survey are capable of answering the research question. Better designs, such as spatially balanced designs, will also be as precise as possible given the constraints of the budget. Many study areas will have previously sampled ‘legacy sites’ that already have accumulated a time series of observations. For estimating trent, it is often beneficial to include these sites within a new survey. In this paper, we propose a method to incorporate the locations of legacy sites into new spatially balanced survey designs to ensure spatial balance among all sample locations. Simulation experiments indicate that incorporating the spatial location of legacy sites increases spatial balance and decreases uncertainty in inferences (smaller standard errors in mean estimates) when compared to designs that ignore legacy site locations. We illustrate the process of incorporating legacy sites using a proposed survey of a large marine reserve in South‐Eastern Australia, although the method is applicable to all environments. Our approach allows for integration of legacy sites into a new spatially balanced design, increasing efficiency. Scientists, managers and funders alike will benefit from this methodology – it provides a tool to provide efficient survey designs around established ones, including in‐the‐field adjustments. In this way, it can aid integrated monitoring programmes. An R‐package that implements these methods, called MBHdesign, is available from CRAN.