Alterations to the natural flow regime affect the structure and function of rivers and wetlands and contribute to loss of biodiversity worldwide. Although the effects of flow regulation have been relatively well studied, a lack of synthesis of the ecological consequences of low flows and droughts impedes research progress and our grasp of the mechanistic effects of human-induced water reductions on riverine ecosystems. We identified 6 ecologically relevant hydrological attributes of low flow (antecedent conditions, duration, magnitude, timing and seasonality, rate of change, and frequency) that act within the temporal hierarchy of the flow regime and a spatial context. We synthesized the literature to propose 4 principles that outline the mechanistic links between these low-flow attributes and the processes and patterns within riverine ecosystems. First, low flows control the extent of physical aquatic habitat, thereby affecting the composition of biota, trophic structure, and carrying capacity. Second, low flows mediate changes in habitat conditions and water quality, which in turn, drive patterns of distribution and recruitment of biota. Third, low flows affect sources and exchange of material and energy in riverine ecosystems, thereby affecting ecosystem production and biotic composition. Last, low flows restrict connectivity and diversity of habitat, thereby increasing the importance of refugia and driving multiscale patterns in biotic diversity. These principles do not operate in isolation, and many of the ecological pathways that are affected by low flows are likely to overlap or occur simultaneously, potentially resulting in synergistic and complex effects. Last, we outlined major human-induced threats to low-flow hydrology and how they act upon the ecologically relevant hydrological attributes of low flow to affect potential changes in riverine ecosystem integrity. The mechanistic links described in this synthesis can be used to develop and test hypotheses of low-flow hydrological-ecological response relationships in a cause-effect framework that will have value for both research and river flow management. Continued experimental research and ongoing consolidation of ecological information will improve our understanding and ability to predict consequences of low-flow alteration on river, floodplain, and estuarine ecosystems.

/pdf/mechanistic-effects-of-low-flow-hydrology-on-riverine-2vtphzn7ic.pdf

Mechanistic effects of low-flow hydrology on riverine ecosystems: ecological principles and consequences of alteration

Dynamic energy budget (DEB) theory offers a systematic, though abstract, way to describe how an organism acquires and uses energy and essential elements for physiological processes, in addition to how physiological performance is influenced by environmental variables such as food density and temperature. A 'standard' DEB model describes the performance (growth, development, reproduction, respiration, etc.) of all life stages of an animal (embryo to adult), and predicts both intraspecific and interspecific variation in physiological rates. This approach contrasts with a long tradition of more phenomenological and parameter-rich bioenergetic models that are used to make predictions from species-specific rate measurements. These less abstract models are widely used in fisheries studies; they are more readily interpretable than DEB models, but lack the generality of DEB models. We review the interconnections between the two approaches and present formulae relating the state variables and fluxes in the standard DEB model to measured bioenergetic rate processes. We illustrate this synthesis for two large fishes: Pacific bluefin tuna (Thunnus orientalis) and Pacific salmon (Oncorhynchus spp.). For each, we have a parameter-sparse, full-life-cycle DEB model that requires adding only a few species-specific features to the standard model. Both models allow powerful integration of knowledge derived from data restricted to certain life stages, processes and environments.

/pdf/integrating-dynamic-energy-budget-deb-theory-with-rox0kzcyi9.pdf

Integrating dynamic energy budget (DEB) theory with traditional bioenergetic models

Invertebrate drift, the downstream transport of aquatic invertebrates, is a fundamental ecological process in streams with important management implications for drift-feeding fishes. Despite long-s...

Causes and consequences of invertebrate drift in running waters: from individuals to populations and trophic fluxes

Empirical relationships between stream flow and ecological responses (flow–ecology relationships) are essential for establishing environmental flows and evaluating tradeoffs between instream values and out-of-stream uses. Establishing the shape of flow–ecology relationships (i.e. slope, linearity versus nonlinearity) is particularly important to avoid crossing ecological thresholds in water management.
This review focuses on ecological responses to discharge at low summer flows when out-of-stream water demand is often highest, and identifying ecological contexts where nonlinearities are most likely. Most physical attributes (temperature, dissolved oxygen, available habitat) and ecological responses (energy flow, fish survival, recruitment, community structure) show at least some evidence of nonlinear relationships with flow, although assumptions of linearity may be reasonable across limited discharge ranges which may include low flows.
Nonlinearities are most likely in systems that are near existing thresholds (e.g. cold-water transitional fish communities that are close to upper thermal tolerances). The probability of nonlinearities is likely to increase under future landuse and climate change scenarios, particularly in combination with other stressors, such as eutrophication, which may greatly accelerate temperature-related decline in dissolved oxygen under climate warming.
Managers need to anticipate changes in flow–ecology relationships and develop management systems that are robust to change. Field programmes to establish the slope and linearity of local flow–ecology relationships are essential for regional management, but developing generalisable flow–ecology relationships that are transferrable to regions with limited resources also needs to be a priority.
Generalised relationships can be generated through meta-analysis of empirical flow–ecology relationships, and may prove especially useful if they can capture how environmental and ecological context (channel size and morphology, landuse, flow regime, antecedent conditions, habitat or taxonomic guild) affect flow–ecology relationships. For instance, linking empirical data from flow–ecology relationships to available habitat predicted by physical habitat simulation models (e.g. PHABSIM) may provide a better mechanistic basis for modelling ecological responses, while providing much needed validation for habitat simulation approaches. This would also help bridge the gap between emerging holistic environmental flow modelling approaches and more traditional habitat simulation methods.

Developing flow-ecology relationships: implications of nonlinear biological responses for water management.

There is an increasing demand for tools to assist in the management of environmental flows in rivers. Changes in river discharge act on biota through a hydraulic template which is mediated by channel morphology. Hence environmental flow assessments also need to consider channel morphology, especially if morphology has been altered by human activities. Computer models describing the preferences of fish for hydraulic microhabitats have been applied to environmental flow problems since the mid 1970s. Salmonids have been a particular focus for these methods. Other reviews have provided comprehensive coverage of the basic features and principles of such models. These are briefly discussed before focusing on developments that have occurred in the last 15 years and whose application has so far been infrequent. These include improvements to the representation of hydraulics at reach-scale and of longer river sections, and improved representation of interacting physical variables that describe habitat. The central theme is the spatial coverage and fundamental granularity of such models. Despite a broad literature, there is a lack of documented examples of the application of hydraulic-habitat models through all stages in the environmental flow decision-making process. The review concludes with four short examples which illustrate the use of model output.

Hydraulic‐habitat modelling for setting environmental river flow needs for salmonids

Process-based modelling of invertebrate drift transport, net energy intake and reach carrying capacity for drift-feeding salmonids

We compared a process-based invertebrate drift and drift-feeding net rate of energy intake (NREI) model and a traditional hydraulic-habitat model (using the RHYHABSIM [River Hydraulics and Habitat Simulation] software program) for predicting the flow requirements of 52-cm Brown Trout Salmo trutta in a New Zealand river. Brown Trout abundance predicted by the NREI model for the constant drift concentration–flow scenarios were asymptotic or linear, depending on drift concentration, increasing through the mean annual low flow (MALF; 17 m3/s). However, drift concentration increased with flow, consistent with passive entrainment. The predicted fish abundance–flow relationship based on flow-varying drift concentration increased logistically, and more steeply, with flow through the MALF and beyond. Predictions for the relationship between weighted useable area (WUA) and flow were made for three sets of drift-feeding habitat suitability criteria (HSC) developed on three midsized and one large New Zealand ...

Can Weighted Useable Area Predict Flow Requirements of Drift-Feeding Salmonids? Comparison with a Net Rate of Energy Intake Model Incorporating Drift–Flow Processes

Lon H. Kelly

Papers

Process-based modelling of invertebrate drift transport, net energy intake and reach carrying capacity for drift-feeding salmonids

Can Weighted Useable Area Predict Flow Requirements of Drift-Feeding Salmonids? Comparison with a Net Rate of Energy Intake Model Incorporating Drift–Flow Processes