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The growing demand for freshwater resources has led to dam construction and water diversions in a majority of the world's large rivers. With an increasing demand for freshwater, trade‐offs between water allocations and the preservation of ecological connections between terrestrial and marine ecosystems are inevitable. The ecological links formed by rivers flowing into the ocean benefit many commercially fished species. The degree to which different species and the livelihoods of fishers are negatively impacted by changes in river flows due to water extraction or diversion is important for management across terrestrial and marine boundaries. Our objective was to predict how changes in freshwater flows from three wet–dry tropical rivers in northern Australia, that is, the Mitchell, Gilbert, and Flinders rivers, affect the commercial banana prawn (Penaeus merguiensis) catch. We used a novel spatiotemporal Bayesian approach to model the effects of river flows and key climate drivers on banana prawn catch. We then predicted how the loss of flow due to water extraction or diversion affected prawn catch. Our analyses of three water development scenarios found that catch was most impacted by water extraction during low flows. The impact of water extraction was greatest for a scenario with dams on the Mitchell River, where we predicted catch would decline by 53% during a year with low flow. Overall, our results imply that maintenance of low‐level flows is a crucial requirement for sustained fishery yields. We suggest that water managers must balance agricultural demand for water during drier years against the impact of water extraction on prawn fisheries during low‐flow years. Protecting low‐level flows during drier years is a priority for maintaining terrestrial–marine linkages for adjacent marine fisheries.

Environmental tracers, including both elemental concentrations and isotope ratios, are widely used to reconstruct the movement patterns of animals throughout landscapes. The methodology involves creating a map that describes the distribution of the environmental tracer across the landscape, an isoscape and then matching the values of the same tracer in the tissue of the animal (teeth, fish otolith, feathers) to determine provenance at one or more life stages. Classification models are commonly used to assign an individual to different areas of the isoscape. However, many of the current classification models are data intensive and may not account for (i) spatial autocorrelation (i.e. where an animal has moved is a function of where it was previously) inherent to data sets that use environmental tracers, (ii) species' movement ability which can influence region assignment or (iii) the propagation of errors from misallocation of locations early in the otolith time series. Here, we introduce a Bayesian classification model to estimate large‐scale movement patterns over the lifetime of freshwater fish that has relatively low data requirements, integrates spatial autocorrelation, offers an avenue to include movement capabilities and quantifies the uncertainty associated with the classification of fish movement throughout its life. We use a simulation study to test the accuracy of this model and then demonstrate functionality using a small otolith microchemistry data set (four species of fish collected at two sites) and a 87Sr/86Sr isoscape from the Mitchell River (Queensland, Australia) that accounts for spatial and temporal variation in water 87Sr/86Sr using water and mussel shell samples. The probabilistic framework of the Monte Carlo simulation allows uncertainty to be incorporated at each life stage, reducing the cumulative impact of misclassification and providing a more reliable reconstruction of lifetime movement patterns.

Freshwater ecosystems are highly vulnerable to global warming because 1) their chief drivers, water quality and flow regimes, are highly sensitive to atmospheric warming, and 2) they are already extremely threatened by a wide range of interacting anthropogenic pressures. Even relatively modest global warming of 1.5°C poses a considerable threat to freshwater ecosystems and the many critical services these provide to people. Shifts in the composition and function of freshwater ecosystems are widely anticipated with adverse consequences for ecosystem services, including those underpinning water and food security. While the extent and severity of effects is likely to be significantly reduced if global warming is limited to 1.5°C, concerted efforts to implement widely recognised priorities for policy and management are required to mitigate unavoidable impacts and reduce the likelihood of perverse outcomes of climate mitigation and adaptation efforts in other sectors—all of which rely on fresh water supply. Freshwater ecosystems and their services, including provision of fresh water, must therefore be considered first and foremost when developing and implementing any climate action.

Understanding the determinants of species' distributions and abundances is a central theme in ecology. The development of statistical models to achieve this has a long history and the notion that the model should closely reflect underlying scientific understanding has encouraged ecologists to adopt complex statistical methods as they arise. In this paper we describe a Bayesian hierarchical model that reflects a conceptual ecological model of multi-scaled environmental determinants of riverine fish species' distributions and abundances. We illustrate this with distribution and abundance data of a small-bodied fish species, the Empire gudgeon Hypseleotris galii, in the Mary and Albert Rivers, Queensland, Australia. Specifically, the model sought to address; 1) the extent that landscape-scale abiotic variables can explain the species' distribution compared to local-scale variables, 2) how local-scale abiotic variables can explain species' abundances, and 3) how are these local-scale relationships mediated by landscape-scale variables. Overall, the model accounted for around 60% of variation in the distribution and abundance of H. galii. The findings show that the landscape-scale variables explain much of the distribution of the species; however, there was considerable improvement in estimating the species' distribution with the addition of local-scale variables. There were many strong relationships between abundance and local-scale abiotic variables; however, several of these relationships were mediated by some of the landscape-scale variables. The extent of spatial autocorrelation in the data was relatively low compared to the distances among sampling reaches. Our findings exemplify that Bayesian statistical modelling provides a robust framework for statistical modelling that reflects our ecological understanding. This allows ecologists to address a range of ecological questions with a single unified probability model rather than a series of disconnected analyses.

Preventing the arrival of invasive species is the most effective way of controlling their impact. Preventative strategies may be ‘offensive’ aimed at preventing the invader leaving colonised locations or ‘defensive’ aimed at preventing its arrival at uninvaded locations. The limited resources for invasive species control must be prioritized, particularly for numerous vulnerable locations or uncertainty about which sites are already invaded. We developed an integrative modelling framework to prioritise locations for either strategy by incorporating connectivity and habitat suitability. We applied this framework to a data set comprising 5189 water bodies in Wisconsin and Michigan, U.S.A, for zebra mussels Dreissena polymorpha and Eurasian watermilfoil Myriophyllum spicatum. We developed the framework with a spatial graph based on recreational boater movement and habitat suitability models. An historical graph comprised 3105 natural lakes connected in one of 18 components, whereas a total of 3944 water bodies (lakes and reservoirs) were connected in one of 13 separate components in a graph of the contemporary system. Habitat suitability models accounted for around half of the deviance in the distribution data for each species. There was a distinct spatial pattern in the levels of risk and subsequent recommended allocation of management interventions across several levels of investment. Higher risk water bodies were generally found in the largest component of the spatial graph. At comparatively low levels of investment, where managers target 5% of all locales to control D. polymorpha, the results suggested that 71% and 27% of this effort should be committed to defensive and offensive strategies, respectively, in the largest component. For M. spicatum, 92% and 8% of this effort should be allocated in this component to defensive and offensive strategies, respectively. It is only with much greater investment that water bodies in other components should be targeted. Synthesis and applications. Allocating limited resources to prevent the spread of invasive species is a challenge that transcends ecosystems and geography. We successfully identified a reduced number of locations to target for offensive and defensive intervention strategies for two species. This framework is readily applicable to other aquatic and terrestrial ecosystems vulnerable to invasive species.

1. Connectivity is regarded globally as a guiding principle for conservation planning, but due to difficulties in quantifying connectivity, empirical data remain scarce. Lack of meaningful connectivity metrics is likely leading to inadequate representation of important biological connections in reserve networks. Identifying patterns in landscape connectivity can, theoretically, improve the design of conservation areas. 2. We used a network model to estimate seascape connectivity for coral reef-associated fishes in a subtropical bay in Australia. The model accounted for two scales of connectivity: (i) within mosaics at a local scale and (ii) among these mosaics at a regional scale. Connections among mosaics were modelled using estimations of post-larval small and intermediate movement distances represented by home ranges of two fish species. 3. Modelled connectivity patterns were assessed with existing data on fish diversity. For fishes with intermediate home ranges (0-6 km), connectivity [quantified by the index Probability of Connectivity (dPC)] explained 51-60% of species diversity. At smaller home ranges (0-1 km), species diversity was associated closely with intramosaic connectivity quantified by the index dPCintra. 4. Mosaics and their region-wide connections were ranked for their contribution to overall seascape connectivity and compared against current positions and boundaries of reserves. Our matching shows that only three of the 10 most important mosaics are at least partly encompassed within a reserve, and only a single important regional connection lies within a reserve. 5. Synthesis and applications. Notwithstanding its formal recognition in reserve planning, connectivity is rarely accounted for in practice, mainly because suitable metrics of connectivity are not available in planning phases. Here, we show how a network analysis can be effectively used in conservation planning by identifying biological connectivity inside and outside present reserve networks. Our results demonstrate clearly that connectivity is insufficiently represented within a reserve network. We also provide evidence of key pathways in need of protection to avoid nullifying the benefits of protecting key reefs. The guiding principle of protecting connections among habitats can be achieved more effectively in future, by formally incorporating our findings into the decision framework.

[This corrects the article DOI: 10.1371/journal.pone.0262402.].

Coastal ecosystems can be degraded by poor water quality. Tracing the causes of poor water quality back to land-use change is necessary to target catchment management for coastal zone management. However, existing models for tracing the sources of pollution require extensive data-sets which are not available for many of the world’s coral reef regions that may have severe water quality issues. Here we develop a hierarchical Bayesian model that uses freely available satellite data to infer the connection between land-uses in catchments and water clarity in coastal oceans. We apply the model to estimate the influence of land-use change on water clarity in Fiji. We tested the model’s predictions against underwater surveys, finding that predictions of poor water quality are consistent with observations of high siltation and low coverage of sediment-sensitive coral genera. The model thus provides a means to link land-use change to declines in coastal water quality.

Development of skills in science communication is a well‐acknowledged gap in graduate training, but the constraints that accompany research (limited time, resources, and knowledge of opportunities) make it challenging to acquire these proficiencies. Furthermore, advisors and institutions may find it difficult to support graduate students adequately in these efforts. The result is fewer career and societal benefits because students have not learned to communicate research effectively beyond their scientific peers. To help overcome these hurdles, we developed a practical approach to incorporating broad science communication into any graduate‐school time line. The approach consists of a portfolio approach that organizes outreach activities along a time line of planned graduate studies. To help design the portfolio, we mapped available science communication tools according to 5 core skills essential to most scientific careers: writing, public speaking, leadership, project management, and teaching. This helps graduate students consider the diversity of communication tools based on their desired skills, time constraints, barriers to entry, target audiences, and personal and societal communication goals. By designing a portfolio with an advisor's input, guidance, and approval, graduate students can gauge how much outreach is appropriate given their other commitments to teaching, research, and classes. The student benefits from the advisors’ experience and mentorship, promotes the group's research, and establishes a track record of engagement. When graduate student participation in science communication is discussed, it is often recommended that institutions offer or require more training in communication, project management, and leadership. We suggest that graduate students can also adopt a do‐it‐yourself approach that includes determining students’ own outreach objectives and time constraints and communicating these with their advisor. By doing so we hope students will help create a new culture of science communication in graduate student education. Estrategias Prácticas para la Comunicación Científica para Estudiantes de Posgrado

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.