Some (fish might) like it hot: Habitat quality and fish growth from past to future climates

Reeder, W. J.; Gariglio, Frank; Carnie, Ryan; Tang, Chunling; Isaak, Daniel J.; Chen, Qiuwen; Yu, Zhongbo; McKean, J. A.; Tonina, Daniele

doi:10.1016/j.scitotenv.2021.147532

Cited by 7 publications

(4 citation statements)

References 81 publications

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“…Reductions in juvenile size‐at‐age may contribute to higher mortality rates (Quinn, 2005) and forced emigration is also usually disadvantageous. Whereas warmer water temperatures may sometimes increase growth because of high metabolic rates if sufficient food is also available; higher fish densities caused by a decrease in rearing habitat could negate the effect (Reeder et al., 2021).…”

Section: Resultsmentioning

confidence: 99%

“…The quality of local spawning habitat, expressed as cell suitability index, CSI, was calculated at the cell scale, by using the geometric mean of the suitability index of each variable (Ecohydraulic modeling, Equation 3, Figure S8 in Supporting Information S1). We quantified the weighted useable area, WUA (SI: Ecohydraulic modeling, Equation 4), to provide a reach scale index of habitat availability (Reeder et al, 2021). The substrate grain size and sand concentrations were mapped in field surveys during 2011.…”

Section: Methodsmentioning

confidence: 99%

“…Comparisons between predicted monthly VIC discharges and those measured at Bear Valley Creek showed discrepancies around 24% of the historical values (1922–1957) and less than 10% in daily discharge collected between 2006 and 2008 (VIC model validation, Figure S3 in Supporting Information ). The three emission scenarios provided similar future results, so we selected the EM predictions for further analysis (Reeder et al., 2021) because EM predictions weight the effect of all GSMs scenarios (Climate‐change scenarios comparison: Figure S5 in Supporting Information ).…”

Section: Methodsmentioning

confidence: 99%

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Climate Change Shrinks and Fragments Salmon Habitats in a Snow‐Dependent Region

Tonina

McKean

Isaak

et al. 2022

Geophysical Research Letters

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The large majority of climate change research for aquatic organisms has focused on stream water temperature and discounted the importance of flow changes associated with evolving precipitation regimes. Using a high-resolution stream topographic map that supported a series of linked flow and fish habitat quality models, our research describes how observed historical flow reductions and projected future reductions affect local habitat quality, distributions, and connectivity for a salmon population. We demonstrated that as climate change continues to change flow regimes in snow-dependent regions, it will cause fragmentation and reduce the overall volume of available habitats in headwater streams where salmon and other cold-water fish species spawn and grow during early life stages.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Climate Change Shrinks and Fragments Salmon Habitats in a Snow‐Dependent Region

Tonina

McKean

Isaak

et al. 2022

Geophysical Research Letters

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“…

Numerical models have become essential tools for investigating biophysical processes in river ecosystems (Reeder et al, 2021) and informing decisions related to water resources management (Benjankar et al, 2018), and ecological conservation (Wheaton et al, 2018). Multi-dimensional numerical models provide a means of exploring interactions between flow fields, sediment transport, and river morphology in two or three dimensions (2D or 3D) (Shimizu et al, 2020).

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confidence: 99%

Evaluating the Sensitivity of Multi‐Dimensional Model Predictions of Salmon Habitat to the Source of Remotely Sensed River Bathymetry

Harrison

Legleiter

Sridharan

et al. 2022

Water Resources Research

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Multi‐dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two‐dimensional (2D) and three‐dimensional (3D) hydrodynamic models for a 1.6‐km reach of a large gravel‐bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth‐averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near‐bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high‐resolution bathymetry used as model input. Model predictions of winter‐run Chinook salmon (Oncorhynchus tshawytscha) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear‐flowing, relatively shallow large rivers with sufficient accuracy to support multi‐dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.

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