Fragmentation and thermal risks from climate change interact to affect persistence of native trout in the Colorado River basin

Roberts, James J.; Fausch, Kurt D.; Peterson, Douglas P.; Hooten, Mevin B.

doi:10.1111/gcb.12136

Cited by 74 publications

(103 citation statements)

References 87 publications

(121 reference statements)

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“…interpolation). For the models that included a RNS, RMSE (0.7-0.9 • C) was approximately half that reported by previous studies, although it should be noted that these studies were conducted at considerably larger spatial scales (Moore et al, 2013;Roberts et al, 2013;Wehrly et al, 2009). The RMSE of models without a RNS (0.9-1.8 • C) was generally similar or slightly better than reported by other studies.…”

Section: The Importance Of Rnsmentioning

confidence: 65%

“…The performance of the single-catchment LS and LS_T a models in this study compared favourably to regional models of T w max (Moore et al, 2013;Roberts et al, 2013;Wehrly et al, 2009) when predictions were made for the catchment in which the models were developed (i.e. interpolation).…”

Section: The Importance Of Rnsmentioning

confidence: 99%

“…In common with similar modelling studies (Hrachowitz et al, 2010;Imholt et al, 2011;Jackson et al, 2017b;Ruesch et al, 2012), no interactions were considered between covariates due to data constraints.…”

Section: Single-catchment Modelsmentioning

confidence: 99%

“…Large-scale models are required to provide information at the spatial scales appropriate to management decisions, i.e. catchment (Chang and Psaris, 2013;Hrachowitz et al, 2010;Imholt et al, 2011Imholt et al, , 2013Jackson et al, 2017b;Steel et al, 2016), regional (Hill et al, 2013;Isaak et al, 2012;Ruesch et al, 2012) and national scales.…”

mentioning

confidence: 99%

“…The development of affordable, reliable, accurate T w data loggers has led to a rapid increase in T w monitoring (Sowder and Steel, 2012), to the point that staff time, data storage and quality control are often now the greatest limitations on data collection . At the same time, there have been substantial developments in spatial statistical modelling approaches (Ver Hoef et al, 2006Ver Hoef and Peterson, 2010;Isaak et al, 2014;Jackson et al, 2017b;O'Donnell et al, 2014;Peterson et al, 2013;Rushworth et al, 2015), monitoring network design (Dobbie et al, 2008;Jackson et al, 2016;Som et al, 2014), spatial datasets (e.g. shapefiles incorporating covariates such as in "The National Stream Internet Project" (Isaak et al, 2011) or gridded air temperature datasets (Perry and Hollis, 2005a, b)) and spatial analysis tools (Isaak et al, 2011(Isaak et al, , 2014Peterson et al, 2013;Peterson and Ver Hoef, 2014).…”

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confidence: 99%

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Can spatial statistical river temperature models be transferred between catchments?

Jackson

Fryer

Hannah

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. There has been increasing use of spatial statistical models to understand and predict river temperature (T w ) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses T w data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum T w (T w max ) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (T a ) metric (LS_T a models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of T w max . However, the predictions were biased when mean T w max differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_T a models transferred less well, with particularly poor performance when the relationship with the T a metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between T w max and the T a metric, indicating that the T a metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting T w at unmonitored locations across multiple catchments and larger spatial scales.Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

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Section: The Importance Of Rnsmentioning

confidence: 65%

Section: The Importance Of Rnsmentioning

confidence: 99%

Section: Single-catchment Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Can spatial statistical river temperature models be transferred between catchments?

Jackson

Fryer

Hannah

et al. 2017

Hydrol. Earth Syst. Sci.

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Climate change and freshwater fisheries

Harrod

2015

Freshwater Fisheries Ecology

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Climate change is among the most serious environmental challenge facing humanity and the ecosystems that provide the goods and services on which it relies. Climate change has had a major historical influence on global biodiversity and will continue to impact the structure and function of natural ecosystems, including the provision of natural services such as fisheries. Freshwater fishery professionals (e.g. fishery managers, fish biologists, fishery scientists and fishers) need to be informed regarding the likely impacts of climate change. Written for such an audience, this chapter reviews the drivers of climatic change and the means by which its impacts are predicted. It details observed and future predicted effects of climate change on freshwater ecosystems across Arctic, temperate and tropical climatic zones and highlights those areas where data are currently limiting our understanding of how and if climate change is affecting freshwater fisheries. Observed and predicted consequences of climatic change on the ecology of freshwater fishes are examined at different levels of biological organization, including individual, population, community and ecosystem levels, and future scenarios are discussed. The chapter examines the likely impacts of such changes on the exploitation and management of freshwater fishes across recreational, artisanal and commercial fishery sectors, including observations on the vulnerability of fishers themselves to climate change.

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Fragmentation and drying ratchet down Great Plains stream fish diversity

Perkin

Gido

Costigan

et al. 2014

Aquatic Conservation

108

150

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Stream fragmentation alters the structure of aquatic communities on a global scale, generally through loss of native species. Among riverscapes in the Great Plains of North America, stream fragmentation and hydrologic alteration (flow regulation and dewatering) are implicated in the decline of native fish diversity. This study documents the spatio–temporal distribution of fish reproductive guilds in the fragmented Arkansas and Ninnescah rivers of south‐central Kansas using retrospective analyses involving 63 years of fish community data. Pelagic‐spawning fishes declined throughout the study area during 1950–2013, including Arkansas River shiner (Notropis girardi) last reported in 1983, plains minnow (Hybognathus placitus) in 2006, and peppered chub (Macrhybopsis tetranema) in 2012. Longitudinal patterns in fish community structure in both rivers consisted of strong breaks associated with dams, and pelagic‐spawning fishes were missing from shorter fragments upstream of those barriers. Among downstream and longer fragments, probability of occurrence for pelagic‐spawning fishes declined or fell to zero during periods of drought. Based on these data, interactions between fragmentation and drying are hypothesized as operating as an ecological ratchet mechanism in which forward movement toward pelagic‐spawning fish extirpation occurs during desiccation, and reciprocated reverse movement toward recolonization following return of flows is blocked by fragmentation. The ratchet mechanism is capable of explaining the long‐term 'ratcheting down' of fish diversity in Great Plains rivers and has implications for managing biodiversity in fragmented riverscapes where water is scarce or might become so in the future. Copyright © 2014 John Wiley & Sons, Ltd.

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Fragmentation and thermal risks from climate change interact to affect persistence of native trout in the Colorado River basin

Cited by 74 publications

References 87 publications

Can spatial statistical river temperature models be transferred between catchments?

Can spatial statistical river temperature models be transferred between catchments?

Climate change and freshwater fisheries

Fragmentation and drying ratchet down Great Plains stream fish diversity

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