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Water resource development can lead to the significant alteration of natural flow regimes, which can have impacts on the many aquatic species that rely on both freshwater and estuarine environments to successfully complete their lifecycles. In tropical northern Australia, annual catches of commercially harvested white banana prawns (WBP) are highly variable in response to environmental conditions, namely rainfall and subsequent riverine flow. However, little is known about the spatial extent to which flow from individual rivers influences offshore WBP catch. In this study, we quantify how the relationship between WBP catch in the Gulf of Carpentaria is influenced by flow from the Mitchell River, Queensland Australia. We used a Bayesian framework to model both prawn presence and catch per unit effort, and found evidence that multiple components of the flow regime contribute to fishery catch. We also found evidence to suggest that the relationships between prawn presence and flow were spatially structured across the fishing ground. Our results suggest that attributing fishery catch to a single river remains challenging, though highlights the importance of maintaining natural flow regimes to support a highly valuable commercial fishery species in the face of potential water resource development. • Water resource development threatens natural flow regimes. • We modelled how riverine flow influences a highly valuable prawn fishery. • Multiple aspects of the flow regime were important for fishery catch. • Relationships between catch and flow were spatially structured in the fishery. • Our results inform management of ecosystems threatened by hydrologic development. [ABSTRACT FROM AUTHOR]

Hydrologic metrics have been used widely to quantify flow-ecology relationships; however, there are several challenges associated with their use, including the selection from a large number of available metrics and the limitation that metrics are a synthetic measure of a multi-dimensional flow regime. Using two case studies of fish species density and community composition, we illustrate the use of functional linear models to provide new insights into flow–ecology relationships and predict the expected impact of environmental flow scenarios, without relying on hydrologic metrics. The models identified statistically significant relationships to river flow over the 12 months prior to sampling (r2range 36–67%) and an environmental flow scenario that may enhance native species’ densities while controlling a non-native species. Hydrologic metrics continue to play an important role in ecohydrology and environmental flow management; however, functional linear models provide an approach that overcomes some of the limitations associated with their use.Editor Z.W. Kundzewicz; Guest editor M. AcremanCitation Stewart-Koster, B., Olden, J.D., and Gido, K.B., 2014. Quantifying flow–ecology relationships with functional linear models. Hydrological Sciences Journal, 59 (3–4), 629–644. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Studies of tropical floodplains have shown that algae are the primary source material for higher consumers in freshwater aquatic habitats. Thus, methods that can predict the spatial variation of algal productivity provide an important input to better inform management and conservation of floodplains. In this study, a prediction of the spatial variability in algal productivity was made for the Mitchell River floodplain in northern Australia. The spatial variation of aquatic habitat types and turbidity were estimated using satellite remote sensing and then combined with statistical modelling to map the spatial variation in algal primary productivity. Open water and submerged plants habitats, covering 79% of the freshwater flooded floodplain extent, had higher rates of algal production compared to the 21% cover of emergent and floating aquatic plant habitats. Across the floodplain, the predicted average algal productivity was 150.9 ± 95.47 SD mg C m−2 d−1 and the total daily algal production was estimated to be 85.02 ± 0.07 SD ton C. This study provides a spatially explicit representation of habitat types, turbidity, and algal productivity on a tropical floodplain and presents an approach to map 'hotspots' of algal production and provide key insights into the functioning of complex floodplain–river ecosystems. As this approach uses satellite remotely sensed data, it can be applied in different floodplains worldwide to identify areas of high ecological value that may be sensitive to development and be used by decision makers and river managers to protect these important ecological assets. [ABSTRACT FROM AUTHOR]

Conservation planning processes and wetland management require spatial estimations of aquatic habitats to support the maintenance of aquatic biodiversity. However, physical access to several wetlands and freshwater habitats can be restricted due to difficult topography and technological limitations associated with ground-based observations. In addition to these constraints, the distribution of some aquatic primary producers in freshwater habitats and floodplains that are difficult to reach further complicates large-scale assessment of aquatic habitats using traditional field-based techniques. The main objective of this study is to predict the spatial distribution of hot spots of primary producers (aquatic plant biomass) and assess large-scale inundation patterns in a large floodplain wetland (Flinders catchment) in the wet-dry tropics of Australia. To this end, remote sensing biophysical indicators (vegetation and inundation) were integrated with flood water depth in a classification tree model. Results indicate that in terms of floodplain total inundation, the Flinders wetland hydrology is rather restricted immediately after the summer wet season, the period when most primary production happens. While this can be attributed to the fact that much of the observed annual variability (93%) in rainfall and surface runoff (95%) occur during the wet season, post flood recession patterns are indicators that underpin the somewhat limited alimentation of the Flinders floodplain during this period. As observed in this study, post flood inundation extents in the summers of 2009 and 2019 declined by approximately 89% and 87% within fourteen and ten days, respectively. Despite having a significantly higher magnitude than the 2009 summer flood event, the 2019 extreme 'big wet' period did not translate to higher floodplain productivity (aquatic plant biomass and surface water distribution) immediately after summer as was the case in 2009. Furthermore, the predicted extents of aquatic plant biomass and total floodplain inundation in the downstream Flinders show substantial temporal variation and suggest the floodplain wetland hydrology is largely driven by inter-annual changes in annual rainfall. The extents of these hot spots of biomass accumulation were found to be considerably associated with total floodplain inundation extent (r = 0.94), rainfall (r = 0.81), and discharge (r = 0.68). Furthermore, advance statistical analyses show that downstream discharge and rainfall over the Flinders are significantly correlated (r = 0.72). In addition to this, observed amplitudes of discharge and rainfall in extreme wet and dry years coincided with floodplain inundation patterns and distribution of hot spots of primary producers. While these relationships emphasize the importance of flow in the nourishment of the downstream catchment, they further corroborate the composite influence of local rainfall and discharge on total floodplain inundation and hot spots of primary producers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Stressor exposure from global maps and in situ reporting aligns weakly. • Hydrological stressors have the highest agreement among data sources. • Stressor weights can be estimated from in situ stressor data. • Integration of in situ stressor monitoring can improve cumulative stress indices. • Ecological monitoring networks can help monitor ecological stressors globally. Aquatic ecosystems are exposed to a host of anthropogenic stressors whose combined effect can be synthesized with cumulative stress indices. The reliability of cumulative stress indices depends primarily on: 1) stressor incidence maps derived from remote sensing or modeling but rarely validated against on-the-ground observations, and 2) the weighting scheme used to combine multiple stressors into a cumulative index typically based on expert opinion. In this paper, we evaluate the exposure and weights for 13 aquatic stressor maps of the world's rivers with a comparison against local stress reporting across 1018 inland and coastal sites from the Ramsar Wetlands of International Importance. We found that globally-mapped and locally-reported stressors are poorly aligned overall (AUC-ROC = 0.50–0.63), and that concordance did not improve when stratifying by ecosystem types or continents. Agreement varied across individual stressors, was highest for hydrological stressors and lowest for habitat disconnectivity stressors. We estimated stressor weights from the comparison and found them to be remarkably aligned well with expert-generated weights, suggesting there is convergence on a stressor hierarchy across local and global scales. Our comparison illustrates the value of integrating data across scales to inform the calculation of global stressor indices. Continued systematic stressor monitoring across environmental observation networks is central to benchmarking maps of ecosystem stress globally. [ABSTRACT FROM AUTHOR]

Predator-prey models are often used in ecology to represent realistic encounters between species. Lotka-Volterra differential equations are famously used by ecologists to model the relationship between a predator and prey. Given ecological data collection is known to be time consuming and difficult, the purpose of this paper is to optimize the process and improve sampling efficiency. The method of sequential optimality is applied to a more complex population model with multiple basins of attraction, the Lotka-Volterra differential equations. In this application, various theoretical scenarios are simulated using varying sample sizes and timeframes to determine optimal designs. The first scenario uses a standard sample size and design window to represent a realistic budget for ecological data collection. The second scenario decreases the budget and extends the design window as a representation of a limited budget. The third scenario represents ample resources available for sampling. The three scenarios are used to then compare the optimal designs and help ecologists evaluate the method of sequential optimality. I, A and D optimal designs are compared in the simulation results. The I-optimality criterion tends to outperform the A and D criteria and is recommended as a primary selection when using the sequential optimality algorithm. This simulation study has further developed sequential optimality by predicting optimal sampling regimes according to the dynamics associated with the Lotka-Volterra differential equations. [ABSTRACT FROM AUTHOR]

• Introduced a cloud-based framework to assess changes in floodplain productivity. • Upstream flows drive floodplain inundation and aquatic plant biomass accumulation. • Local rainfall has limited impact on the productivity of downstream catchment. • Climate variability is a key constraint to changes in floodplain productivity. • Flows are primary drivers of floodplain inundation in the Mitchell catchment. Rivers in tropical systems across the world are well known for their strong connections with floodplain wetlands. However, increased water needs and changing climate could drive water-management policy that leads to major changes in flow or surface water availability. These changes could have deleterious effects on ecological functions and ecosystem services provided by these freshwater ecosystems (e.g., groundwater recharge, food sources for fish, etc.). In this study, we introduce a cloud-based monitoring framework using multi-resolution satellite observations (2007 - 2020), historical surface inundation (1984 - 2015), and gauge data to better understand these connections in a large river catchment in tropical Queensland (Mitchell River catchment). A key driver and indicator of floodplain productivity, surface water inundation and aquatic biomass distribution respectively, are investigated to provide insight that underpins the prioritization of future surface water developments in this region. Results show that the characteristics of floodplain inundation; loss, persistence, gain and frequency, vary in time and space, and are driven by climate variability. The largest gain (7200 Ha) and persistence (8437 Ha) in total surface inundation were observed in the wet seasons during the periods of 2007 - 2019 , respectively. In the downstream catchment, permanent water surface area was approximately 4.5% (of the total downstream region) compared to the upstream region (2.7%). There is a higher proportion of new permanent water features (16.8%) and new seasonal (30%) surface water classes in the upstream are in contrast to the downstream section (0.7% and 9.6%, respectively). Using multivariate statistics, floodplain productivity was linked to inter-annual changes in flow and rainfall. A partial least squares regression (PLSR) model shows upstream flows drive changes in total floodplain inundation (observed vs predicted, r = 0.95 , p = 0.000). Flows and local rainfall accounted for approximately 60% (r = 0.77 , p = 0.005) and 25% (r = 0.50 , p = 0.120) of the variability in total floodplain inundation, respectively. Different PLSR modelling scenarios confirmed that upstream flows are primary drivers of floodplain inundation and substantial lost in total surface inundation could happen if flows are altered. Just as local rainfall has limited impact on total floodplain inundation (r = 0.92 , p = 0.000 ; for model excluding local rainfall), aquatic plant biomass accumulations are also strongly associated with upstream flows (e.g., r = 0.82). Given the dependence of downstream water level (r = 0.89) and floodplain inundation on upstream flows, sustainable management of river flows in the Mitchell River is fundamentally important to floodplain productivity. [ABSTRACT FROM AUTHOR]

• Floodplain productivity and inundation assessed using remote sensing indicators. • River discharge is a stronger indicator of hot spots of biomass accumulation. • Floodplain inundation and primary production are driven by flow and local rainfall. • Upstream catchment contributes to the connectivity and productivity of floodplain. • Fluctuations in climate will be key constraints on freshwater habitats. The ecological communities supported by freshwater habitats and wetlands that persist from floodplain inundation generate numerous cultural, recreational and economic values via commercial fisheries and other human uses of these habitats. However, the alteration of flow connectivity, degradation, and disruption of physical processes that sustain different levels of organisms (e.g., primary producers) are threats that affect aquatic biodiversity and the productivity of these habitats. Therefore, the large scale assessment of freshwater habitats for proactive water resources planning and the development of climate change mitigation and resource management strategies is essential. However, such monitoring and assessment is complicated by the inaccessibility of many large wetland systems during times of inundation, making in situ sampling unfeasible at a time when high levels of aquatic primary producers are generating food and energy sources for higher order consumers. To understand the physical dynamics of aquatic primary producers and freshwater habitats (1991–2019) during such times in a large floodplain river in northern Australia (Gilbert catchment), this study integrated Landsat-derived modified normalised difference water index and normalised difference vegetation index in a classification tree model. Thereafter, key hydrological drivers (rainfall and river discharge) of floodplain productivity and connectivity were assessed using a range of multivariate techniques. Results show that the floodplain had a high aquatic plant biomass during the summer wet season based on the correlation between aquatic biomass accumulation (hot spots) and inundation (r = 0.83 @ phase lag < 1 month) during such period. River discharge (r = 0.68 @ lag = 1 month) at downstream Gilbert catchment appear to be a relatively stronger indicator of hot spots of floodplain biomass accumulation as opposed to local rainfall (r = 0.57 @ lag = 3 months). While the downstream discharge explains a significant proportion of variability in the leading orthogonal mode of rainfall (r = 0.83), statistical relationships developed between discharge/rainfall and the distribution of aquatic primary producers confirm that both rivers (flow) and local rainfall are key optimal predictors of inundation and sites of primary producers with river flows being a better indicator of the latter. It is therefore argued that hydro-meteorological fluctuations will be key constraints on freshwater habitats and the growth of primary producers, albeit, human disturbance of flow can impact on floodplain productivity. As illustrated in this study, such constraints are reflected in the spatial patterns and changing characteristics (connectivity and spatial heterogeneity) of hot spots of primary producers and freshwater habitats (Palustrine, Lacustrine, and Riverine ecosystems). [ABSTRACT FROM AUTHOR]

A large proportion of the uncertainty surrounding catchment sediment budget modelling has been attributed to sediment supplied from riverbank erosion. Some of the variables influencing riverbank erosion are bend curvature, specific streampower, riparian vegetation, and in some instances sand and gravel extraction. The empirical relationship between these variables and observed riverbank erosion across 78 km of the Upper Brisbane River, Australia was investigated. No significant relationship was found between curvature, specific streampower and riverbank erosion. The role of riparian vegetation relative to sediment supply from riverbank erosion varied with spatial location, susceptibility of a reach to erosion, and human disturbance such as sand and gravel extraction. Despite not having data on substrate type the model described approximately 37% of the variation in observed riverbank erosion. It appears that inclusion of a management practice factor in riverbank erosion models is justified, where appropriate, and may improve model performance. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]