School of Agriculture, Food and Ecosystem Sciences - Research Publications
Permanent URI for this collection
Search Results
1 - 4 of 4
-
ItemNo Preview AvailableSmall artificial impoundments have big implications for hydrology and freshwater biodiversity(WILEY, 2022-04)Headwater streams are critical for freshwater ecosystems. Global and continental studies consistently show major dams as dominant sources of hydrological stress threatening biodiversity in the world’s major rivers, but cumulative impacts from small artificial impoundments (SAIs) concentrated in headwater streams have rarely been acknowledged. Using the Murray Darling River basin (Australia) and the Arkansas River basin (US) as case studies, we examined the hydrological impacts of SAIs. The extent of their influence is considerable, altering hydrology in 280–380% more waterways as compared to major dams. Hydrological impacts are concentrated in smaller streams (catchment area <100 km2), raising concerns that the often diverse and highly endemic biota found in these systems may be under threat. Adjusting existing biodiversity planning and management approaches to address the cumulative effects of many small and widely distributed artificial impoundments presents a rapidly emerging challenge for ecologically sustainable water management.
-
ItemUsing State-and-Transition Simulation Models (STSMs) to Explore Dynamic Population Responses to Drought Cycles in Freshwater Ecosystems(FRONTIERS MEDIA SA, 2022-08-22)Climate variability and change pose significant threats to aquatic biodiversity, particularly in areas with low and variable streamflow. Quantifying the magnitude of risk from these threats is made more difficult by the variable responses of individual species to hydrologic stress. Patterns of population decline and recovery in response to drought cycles will depend on both the resistance traits (e.g., tolerance to harsh environmental conditions) and resilience traits (e.g., fecundity, age at maturity), both of which vary considerably among species. Collectively these traits can give rise to varied, and lagged patterns of decline and recovery in response to hydrologic variability, which ultimately can affect population viability in drought prone environments and in response to a changing climate. Such population cycles are typically modelled based on demographic rates (mortality and recruitment) under different climate conditions. However, such models are relatively data intensive, limiting their widespread development. A less precise but more tractable approach is to adopt state-and-transition approaches based on semi-quantitative population states (or population size estimates), and modelled transitions between states under different hydrologic conditions. Here we demonstrate the application of such models to a suite of diverse taxa, based on an expert elicitation of expected state-changes across those different taxa under a range of different flow conditions. The model results broadly conform with population changes observed in response to a major drought in the case-study system, mimicking the observed lags in recovery of species with different life-histories. Stochastic simulations of population cycles under scenarios of more protracted drought provide a semi-quantitative measure of the potential risk to different species under each scenario, as well as highlighting the large uncertainties that can arise when taking into account stochastic (rather than deterministic) state-transitions.
-
ItemThe politicisation of science in the Murray-Darling Basin, Australia: discussion of 'Scientific integrity, public policy and water governance'(Taylor & Francis, 2021-10-30)Many water scientists aim for their work to inform water policy and management, and in pursuit of this objective, they often work alongside government water agencies to ensure their research is relevant, timely and communicated effectively. A paper in this issue, examining 'Science integrity, public policy and water governance in the Murray-Darling Basin, Australia’, suggests that a large group of scientists, who work on water management in the Murray-Darling Basin (MDB) including the Basin Plan, have been subject to possible ‘administrative capture'. Specifically, it is suggested that they have advocated for policies favoured by government agencies with the objective of gaining personal benefit, such as increased research funding. We examine evidence for this claim and conclude that it is not justified. The efforts of scientists working alongside government water agencies appear to have been misinterpreted as possible administrative capture. Although unsubstantiated, this claim does indicate that the science used in basin water planning is increasingly caught up in the politics of water management. We suggest actions to improve science-policy engagement in basin planning, to promote constructive debate over contested views and avoid the over-politicisation of basin science.
-
ItemInforming Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation Cycles(SPRINGER, 2018-03)One important aspect of adaptive management is the clear and transparent documentation of hypotheses, together with the use of predictive models (complete with any assumptions) to test those hypotheses. Documentation of such models can improve the ability to learn from management decisions and supports dialog between stakeholders. A key challenge is how best to represent the existing scientific knowledge to support decision-making. Such challenges are currently emerging in the field of environmental water management in Australia, where managers are required to prioritize the delivery of environmental water on an annual basis, using a transparent and evidence-based decision framework. We argue that the development of models of ecological responses to environmental water use needs to support both the planning and implementation cycles of adaptive management. Here we demonstrate an approach based on the use of Conditional Probability Networks to translate existing ecological knowledge into quantitative models that include temporal dynamics to support adaptive environmental flow management. It equally extends to other applications where knowledge is incomplete, but decisions must still be made.