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Bioenergetic habitat suitability model for drift-feeding salmonids and guidance on its use in hydraulic habitat modelling
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... Bioenergetics-based drift-foraging models were developed for better understanding fitness-based habitat selection, incorporating physical habitat suitability and aspects of food acquisition (Fausch 2014). They were then adapted for more applied research, including fish growth and ecological flow assessment (Hayes et al. 2000;Piccolo et al. 2014;Rosenfeld et al. 2014;Dodrill et al. 2016;Naman et al. 2020). Hayes et al. (2007) harnessed salmonid drift-foraging models to hydraulic models in a process-based modelling method to predict NREI and trout abundance as a function of flow. ...
... For instance, Webb (1991) suggested that drag at a given water velocity in the field (spontaneous swimming) increases by a factor of 3 compared with that in flumes with laminar current used to estimate steady / forced swimming costs. Other published factors range from 2 to 20 (see Webb 1991 and see Hayes et al. 2020 for information on uncertainties in bioenergetic drift foraging models). The predictions of the trout NREI model are sensitive to flow varying drift concentration. ...
We present an overview of a process-based modelling approach for predicting how change in flow affects drift density, net rate of energy intake (NREI) and numbers of drift-feeding salmonids. It involves linking an existing two-dimensional flow model (River2D) with models of invertebrate drift transport and drift-foraging which we have developed. We describe, demonstrate and partially test our models in an application on a 80 m × 20 m pool on a New Zealand river. We show how these models realistically capture hydraulic, drift dispersion and bioenergetics drift-foraging processes to predict the relationship between stream flow, habitat quality and quantity (in terms of NREI), and carrying capacity for drift-feeding salmonids. Overall, the 2D hydraulic model made good predictions of water levels, depths and water velocity at the calibration flow and a lower (validation) flow. The drift transport model made good predictions of the spatial distribution of invertebrate drift density throughout the pool at low flow after it was calibrated against observed drift density at the higher flow. The model correctly predicted that drift density would decline downstream and into the margins due to the process of settling dominating over entry from the stream bed, and that drift would be carried further downstream and laterally as flow increased. The foraging model made a reasonable prediction (6–7) of the numbers of 0.5 m adult brown trout observed (5) in the pool. It accurately predicted that trout should be distributed down the thalweg where net rate of energy intake (NREI) was highest, but when NREI was adjusted for depletion by feeding fish the predicted drift-feeding locations were more closely spaced (bunched) than observed fish locations. Our process-based modelling approach has important implications for improving biological realism in predictions of the response of drift-feeding fishes to flow change within the context of the IFIM.
... Suitable data are lacking for most lotic species. Bioenergetics habitat suitability models (Hayes et al. 2019) relate flow conditions and food concentrations to the energy demands of fish. ...
Predicting land-use and land-management effects on stream and river biota is an important aspect of land-water management, yet there are no collations of what methods are available to carry out those assessments nor guidance on which methods to use. This paper summarises a range of methods with examples of their applications, comments on their strengths and weaknesses, evaluates them against a set of criteria, and provides guidance on method selection. Assessment methods include empirical statistical and mechanistic models, Bayesian networks, likelihood–consequence risk assessments, scoring methods, and hybrid methods, some of which can be informed by expert elicitation. An evaluation matrix for methods indicated that no single method is ideal, and selection of methods needs to carefully consider factors such as the physico-chemical stressor or biotic impact of interest, the intended stakeholders, and the scales of assessment. One emergent principle is the separation of relationships between land use and stressors from assessments of stressors and biota, for which alternative methods could be used. A tiered approach is recommended, whereby simple methods with low resource and time requirements are applied first, followed by more sophisticated methods for selected aspects if needed. There is a need for more ready-made methods at the screening level, as well as development of new methods to address remaining gaps such as multiple stressors.
... Hughes and Kelly (1996) found that the "Webb factor" applied to their sub-model of swimming costs underestimated the energy costs of foraging manoeuvres of Arctic grayling calculated by hydrodynamics. As a result, Hayes et al. (2016Hayes et al. ( , 2020 developed an additional multiplying factor to allow for acceleration and turning (TC) that increased exponentially with the velocity at which the prey was moving (V m/s): ...
Software is now available to apply a salmonid bioenergetic drift-foraging model to generate values of net energy intake (NEI) over a range of water depths and velocities. The predictions can be used to build univariate “habitat” suitability curves or multivariate “habitat” selection models for use in instream habitat modelling programs. Capture success and swimming cost sub-models are basic components of the bioenergetic model and there is a need to understand their influence of NEI predictions. Examination of the swimming cost sub-models showed a surprising amount of variation between species and models and this was attributed to the amount and range of data used for their derivation and the different methods of formulating the swimming cost equations. Predictions of optimal velocity for large fish (>96 g) was influenced by the choice of swimming cost sub-model but optimal velocities for smaller fish were dependent on the capture success sub-model. More research is needed to validate the capture success sub-model, especially for larger fish sizes. Swimming costs while intercepting prey, and the cost of swimming in natural streams with turbulence, are other factors that remain uncertain.
Net energy intake (NEI) models are useful for quantifying mechanisms driving habitat selection in drift-feeding stream fishes; nonetheless, their complexity has limited their application in conservation. We evaluated the validity of assumptions and the performance of multiple variants of an exemplar NEI model for juvenile Chinook Salmon (Oncorhynchus tshawytscha), Dolly Varden Char (Salvelinus malma), and Arctic Grayling (Thymallus arcticus) in interior Alaska. We tested model assumptions that: (1) drift concentration, (2) fish visual reaction area, and (3) swimming cost do not vary meaningfully within the range of focal velocities occupied by drift-feeding stream fishes and can therefore be treated as constants or ignored. We then compared the predictive success of complex and simplified model variants. Comparisons of literature and field data indicated model assumptions were: (1) plausible, (2) plausible, and (3) implausible, respectively. Simplified model variants generally performed as well or better than the complex model. Drift concentration, visual reaction field, and swimming cost are important components of drift-feeder habitat selection; however, the difficulty of accurately estimating these variables may currently limit the utility of complex NEI models. Simplified NEI models are pragmatic tools for addressing urgent conservation needs and can guide development of complex NEI models as estimation techniques improve.
Habitat Suitability Curves (HSCs) are the biological component of habitat simulation tools used to evaluate instream flow management trade‐offs (e.g., the Physical Habitat Simulation Model). However, traditional HSCs based on empirical observations of habitat use relative to availability have been criticized for generating biased estimates of flow requirements and for being poorly transferable across locations. For fish like salmonids that feed on drifting invertebrates, bioenergetics‐based foraging models that relate habitat conditions to net energy gain offer an alternative approach that addresses some of these shortcomings. To make this technique more accessible for practitioners, we present free and user‐friendly software for generating bioenergetics‐based HSCs. The software also allows sensitivity analyses of HSCs to factors like fish size or prey abundance as well as direct integration of hydraulic data. While some caveats remain, bioenergetic HSCs should offer a more rigorous and credible means for quantifying habitat suitability for instream flow modelling.
Hydraulic heterogeneity can strongly influence habitat selection by stream fishes. Velocity gradients created by channel roughness and flow obstructions may be particularly important for species that feed on drifting invertebrates, where maintaining focal points in low velocity microhabitats adjacent to faster water allows fish to scan a larger water volume for prey while minimizing swimming costs. However, these velocity gradients are rarely integrated into habitat suitability criteria used for defining instream flow requirements, which are generally based on mean column velocity measurements at the focal point location. It is also unclear how velocity gradient exploitation differs among sympatric drift‐feeding species. We measured the use of velocity gradients by two sympatric juvenile salmonids, Coho Salmon Oncorhynchus kisutch and Steelhead Trout O. mykiss, in a mid‐order cobble‐boulder dominated river. We compared focal point velocities of fish to adjacent velocities within their foraging area, and compared the magnitude of velocity and kinetic energy gradients between species. We then explored how lateral velocity gradients may bias instream flow assessments by deriving two sets of velocity habitat suitability curves (HSCs): conventional HSCs using average water column velocities measured at focal point locations; and spatially averaged HSCs incorporating adjacent velocities (4 body lengths from focal points). These contrasting HSCs were then used as input into the physical habitat simulation model to predict the influence of flow on habitat availability. Both species often used focal velocities that were lower than adjacent points, but the magnitude of these velocity gradients was higher for Steelhead Trout, consistent with known differences in foraging behaviour, habitat selection, and physiology. Incorporating adjacent velocities into HSCs resulted in a ~ 40% (Steelhead Trout) and ~ 10% (Coho Salmon) increase in flows predicted to optimize habitat availability. Thus, small scale heterogeneity in velocity used by drift‐feeding fish can lead to large biases in flow assessments.
Quantitative habitat suitability models (HSMs) are frequently used to inform the conservation and management of lotic organisms, often in the context of instream flow management. Correlative statistical models relating hydraulic variables to habitat preferences (habitat suitability curves based on use:availability ratios) are the most common form of HSM, but face significant criticism on the grounds that habitat preference may not reflect the fitness consequences of habitat use. Consequently, there has been a drive to develop mechanistic approaches that link habitat to direct correlates of fitness.
Bioenergetic foraging models relating hydraulic conditions to energy balance are particularly well‐developed for drift‐feeding fishes (e.g. salmonids) and show promise as a more mechanistic approach to modelling suitability. However, these models are rarely validated empirically or quantitatively compared with correlative HSMs. We addressed these gaps by comparing the ability of a bioenergetics‐based HSM and two correlative HSMs (a traditional suitability index and a resource selection function) to predict density and growth of stream salmonids (juvenile steelhead, Oncorhynchus mykiss , and coastal cutthroat trout, Oncorhynchus clarki ).
Suitability estimates differed between the approaches, with both correlative models predicting higher suitability relative to the bioenergetic model at shallow depths and low to intermediate velocities, but lower suitability as depth increased. The bioenergetic model explained over 90% of variation in trout growth, compared to c . 50% for the correlative model. The bioenergetic model was also better at predicting fish density; however, the improvement was less striking and a high proportion of variation remained unexplained by either method.
Differences in suitability estimates between approaches probably reflect biotic interactions (e.g. territorial displacement or predation risk) that decouple realised habitat use from energetics‐based estimates of habitat quality. Results highlight fundamental differences between correlative HSMs, based on observed habitat use, and mechanistic HSMs, based on the physiology and behaviour of the focal taxa. They also suggest that mechanistic bioenergetics‐based models provide more rigorous estimates of habitat suitability for drift‐feeding stream fishes. The bioenergetics approach is readily accessible to instream flow practitioners because model predictions are expressed in terms of traditional habitat suitability curves.
This study advances understanding of the flow dependency of invertebrate drift in rivers and its relevance to drift-feeding fish. Background drift concentration varied spatially and with flow over natural flow recession (lower mid-range to low flow) in a reach of a New Zealand river, largely consistent with passive entrainment. Seven taxonomic groups (dominated by Leptophlebiidae and Chironomidae) exhibited positive drift concentration–flow relationships, and one (sandy/stony-cased caddisflies (Conoesucidae)) exhibited negative relationships. A mechanistic drift transport model accurately predicted the slope, but not y intercept, of the drift concentration–flow relationship for the total drift community that positively responded to flow but performed more poorly at the taxon or size-class level. Partitioning the relative influence of drift entry and dilution revealed that positive drift concentration–flow relationships arose from entry overwhelming dilution with increasing flow. Drift transport models have potential for predicting relative (%) effects of flow change on concentration and rate of drift-prone invertebrates. This paves the way for drift transport models to inform inputs to net rate of energy intake models for drift-feeding fish.
Bioenergetics modeling is a widely used tool in fisheries management and research. Although popular, currently available software (i.e., Fish Bioenergetics 3.0) has not been updated in over 20 years and is incompatible with newer operating systems (i.e., 64-bit). Moreover, since the release of Fish Bioenergetics 3.0 in 1997, the number of published bioenergetics models has increased appreciably from 56 to 105 models representing 73 species. In this article, we provide an overview of Fish Bioenergetics 4.0 (FB4), a newly developed modeling application that consists of a graphical user interface (Shiny by RStudio) combined with a modeling package used in the R computing environment. While including the same capabilities as previous versions, Fish Bioenergetics 4.0 allows for timely updates and bug fixes and can be continuously improved based on feedback from users. In addition, users can add new or modified parameter sets for additional species and formulate and incorporate modifications such as habitat-dependent functions (e.g., dissolved oxygen, salinity) that are not part of the default package. We hope that advances in the new modeling platform will attract a broad range of users while facilitating continued application of bioenergetics modeling to a wide spectrum of questions in fish biology, ecology, and management.
• 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.
