Modelling the effects of stranding on the Atlantic salmon population in the Dale River, Norway

Sauterleute, Julian; Hedger, Richard D.; Hauer, Christoph; Pulg, Ulrich; Skoglund, Helge; Sundt-Hansen, Line Elisabeth Breivik; Bakken, Tor Haakon; Ugedal, Ola

doi:10.1016/j.scitotenv.2016.08.080

Cited by 32 publications

(41 citation statements)

References 37 publications

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“…In reality, the exact area dewatered will be dependent on more detailed characteristics of the channel within the section. Following from the work of Sauterleute et al (), our hypothesis was that the simulation of population dynamics over longitudinal reaches of 50 m in length would not be improved by using a more advanced 2‐D or 3‐D hydraulic model. Although a 1‐D approach may provide acceptable estimates of dewatering for a river with simple channel characteristics that is adequately surveyed (Casas‐Mulet, Alfredsen, Boissy, Sundt, & Ruther, ), a 2‐D approach has the potential for better modelling of the hydraulics if high resolution data are available (Vozinaki, Morianou, Alexakis, & Tsanis, ).…”

Section: Discussionmentioning

confidence: 99%

“…This model was developed to simulate Atlantic salmon population abundances across all life stages over long‐term (multidecadal) periods. It was calibrated using time‐series data on population characteristics (including juvenile abundance, smolt production, and size at age) for a salmon‐bearing river, the River Nausta in western Norway (Hedger, Sundt‐Hansen, Forseth, Diserud, et al, ), and has been used to simulate the effects of climate change (Hedger, Sundt‐Hansen, Forseth, Ugedal, et al, ), hydropeaking (Sauterleute et al, ), and habitat remediation (Bustos et al, ) on Atlantic salmon populations for several rivers in Norway, including the upper and lower watercourses of the River Mandalselva. It simulates individual Atlantic salmon life‐history processes (ontogeny, fecundity, mortality, and migration) using heuristic functions derived from field and laboratory experiments.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the abundance within a dewatered area at time T + 1 is directly proportional to the number at time T (Figure , right panel). Stranding mortality occurs within dewatered areas with every hydropeaking event, so the total probability of an individual experiencing stranding mortality within any given week ( M ) in the IBM simulation is calculated from the assigned stranding mortality probability ( S ), the proportion of the section that is dewatered (A), and the number of stranding events within that week (n; see Sauterleute et al, ):

M = 1 - {(1 - (S \times A))}^{n} .

…”

Section: Methodsmentioning

confidence: 99%

“…In both panels, the thick line shows the relationship between the population at time T and the population at time T + 1, the dashed line shows the relationship for an equivalent population for time T and time T + 1, and the solid arrows show how a population level at time T is transferred into a population level at time T + 1. In the left panel, the dotted arrow shows the total of biomass of parr that is not recruited within the section, some of which will move to another section (through parr migration) and some of which will be removed via density dependent mortality, and K sec shows the carrying capacity of the section (dependent on the weekly wetted area) probability of an individual experiencing stranding mortality within any given week (M) in the IBM simulation is calculated from the assigned stranding mortality probability (S), the proportion of the section that is dewatered (A), and the number of stranding events within that week (n; see Sauterleute et al, 2016):…”

Section: Individual-based Modelmentioning

confidence: 99%

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Modelling the effect of hydropeaking‐induced stranding mortality on Atlantic salmon population abundance

et al. 2018

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Studies of hydropeaking‐induced stranding mortality on fish populations have been confined to analysis of empirical data and/or short‐term hydraulic‐habitat modelling of individual events and are thus limited as to how they may be used to infer long‐term effects in fish populations. In this study, the effects of stranding mortality on an Atlantic salmon population were simulated using an individual‐based Atlantic salmon population model with the objective of determining the sensitivity of population dynamics to stranding. It was found that density‐dependent mortality (an alternative source of mortality in juvenile Atlantic salmon) partially compensated for stranding mortality, acting as a negative feedback mechanism that dampened change in population abundance. Stranding caused a perturbation in population dynamics, and effects of individual stranding events persisted in time across the life stages of the population. Effects on population abundance depended on the time of year when stranding was applied, both because of intra‐annual changes in stranding mortality probability and because of intra‐annual changes in the ability of density‐dependent mortality to compensate for stranding mortality. We concluded that empirical measurements of stranding mortality have limited potential for inference of overall effects on the population, and a more dynamic modelling approach, incorporating system feedback, allows for a better modelling of the impact of stranding. Sensitivity analysis showed that population abundance was highly sensitive to density‐dependent mortality, and we suggest that this area should be prioritized for further research when investigating the effects of hydropeaking on rivers.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

M = 1 - {(1 - (S \times A))}^{n} .

…”

Section: Methodsmentioning

confidence: 99%

Section: Individual-based Modelmentioning

confidence: 99%

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Modelling the effect of hydropeaking‐induced stranding mortality on Atlantic salmon population abundance

et al. 2018

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“…It is the most affordable renewable energy source, and run-of-river hydroelectric power plants have the highest energy payback ratio (267 versus 39 for wind and nine for solar photovoltaic) [4]. However, they contribute greatly to the degradation of river ecosystems and biodiversity [5,6]: Retention structures are obstacles to the longitudinal connectivity of river habitats [7], reduce hydrodynamics, and foster exotic species invasion [8]; hydropeaking causes dewatering, fish stranding and modifies fish assemblage [9,10]; flow modifications have severe consequences for river ecosystems [8], and particularly for the fish population [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Model-Based Evaluation of the Effects of River Discharge Modulations on Physical Fish Habitat Quality

Zingraff‐Hamed

Noack

Greulich

et al. 2018

Water

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Abstract:The increase in minimum flows has rarely been considered to mitigate the ecological impact of hydroelectric power plants because it requires a site-specific design and expensive long-term monitoring procedure to identify the most beneficial scenario. This study presents a model-based method to estimate, within the model constraints, the most sustainable scenario of water resource sharing between nature and human needs. We studied physical habitat suitability of the Isar River in Munich (Germany) for three protected fish species: Thymallus thymallus L., Hucho hucho L., and Chondostroma nasus L. The analysis combined a high-resolution two-dimensional (2D) hydromorphological model with expert-based procedures using Computer Aided Simulation Model for Instream Flow Requirements (CASiMIR). We simulated a range of minimum discharges from 5 to 68.5 m 3 /s and four scenarios: (A) maximum use of the resource for humans; (B) slight increase in the minimum water flow; (C) medium increase in the minimum water flow; and, (D) without diversion for hydroelectric production. Under the current hydromorphological conditions, model outputs showed that different life stages of the fish species showed preferences for different scenarios, and that none of the four scenarios provided permanently suitable habitat conditions for the three species. We suggest that discharge management should be combined with hydromorphological restoration actions to re-establish parts of the modified channel slope and/or parts of the previously lost floodplain habitat in order to implement a solution that favors all species at the same time. The modeling procedure that is presented may be helpful to identify the discharge scenario that is most efficient for maintaining target fish species under realistic usage conditions.

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Automated analysis of lateral river connectivity and fish stranding risks—Part 1: Review, theory and algorithm

2020

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Riverine fish stranding is of significant concern due to its potentially devastating impacts on fish populations already at risk. Because stranding is dependent on a wide range of biotic and abiotic factors, it is difficult to accurately identify and parameterize fish stranding risks for various river topographies, fish species/lifestages and flow ramping scenarios. This article presents a literature review, new concepts and a novel Python3 algorithm for post‐processing two‐dimensional hydrodynamic numerical model results to identify spatially explicit locations where fish stranding is likely, such as but not limited to downstream of hydropeaking facilities. Compared to previous stranding algorithms, this one is novel in its use of graph theory to find optimal fish escape routes and for its embedding in the free, open‐source river analysis software River Architect. Guided by biological parameter selection and supplied with two‐dimensional hydrodynamic model rasters, River Architect's Stranding Risk module is suitable for characterization of existing pool stranding risks, alternative flow regime and topographic design evaluation and post‐project assessment of rivers during flow recessions.

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Modelling the effects of stranding on the Atlantic salmon population in the Dale River, Norway

Cited by 32 publications

References 37 publications

Modelling the effect of hydropeaking‐induced stranding mortality on Atlantic salmon population abundance

Modelling the effect of hydropeaking‐induced stranding mortality on Atlantic salmon population abundance

Model-Based Evaluation of the Effects of River Discharge Modulations on Physical Fish Habitat Quality

Automated analysis of lateral river connectivity and fish stranding risks—Part 1: Review, theory and algorithm

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