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Data collection for fresh-water regions of The Ecosystem Health Monitoring Program (EHMP), in southeast Queensland, Australia, involves the sampling of over 130 sites among 19 catchments twice per year and has been ongoing for over ten years. The sampling design was derived following an exhaustive process of indicator and site selection to develop a composite indicator that represented aquatic ecosystem health. After 13 years of implementation, there was an interest in identifying redundancies in sampling to reduce sampling costs without making a substantial impact on the integrity of the program and its capacity to report on ecosystem health. This paper focuses on identifying a subset of sites and times that could be removed from sampling with a minimal impact on the subsequent ecosystem health scores. Herein, Mixed models are employed to assess a variance structure from which optimality criteria are utilized to identify the scheme. Integer programs are then used to ensure specific practical constraints are observed. [ABSTRACT FROM AUTHOR]

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]

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]

• 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]