Monitoring riverine thermal regimes on stream networks: Insights into spatial sampling designs from the Snoqualmie River, WA

Marsha, Amy; Steel, E. Ashley; Fullerton, Aimee H.; Sowder, Colin

doi:10.1016/j.ecolind.2017.08.028

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…Stream temperature predictions were made twice daily to represent daily minimum (6 am) and maximum (6 pm) temperatures. Details about the empirical data and model construction are provided in Steel et al (2016) and Marsha et al (2018), and summarized here. Predictions were based on observations of water temperature monitored every 30 min at >40 locations throughout the mainstem, the three forks, and other tributaries ( Fig.…”

Section: Thermal Regimesmentioning

confidence: 99%

Individual‐based simulations suggest mixed impacts of warmer temperatures and a nonnative predator on Chinook salmon

et al. 2020

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As the climate changes, warmer water temperatures may stress populations of native coldwater fishes. Simultaneously, nonnative warmwater predators may expand their ranges and interact with already at‐risk native fish populations. To explore the independent and combined effects of these two stressors on threatened salmon, we present a case study of simulated Chinook salmon Oncorhynchus tshawytscha and largemouth bass Micropterus salmoides interacting under two thermal regimes in the Snoqualmie River, Washington, USA. We applied an individual‐based and spatially explicit model that tracks fish movement and growth. We evaluated changes in Chinook salmon emergence date, outmigration date, mass, and survival. We ran simulations for four scenarios: (1) Baseline, run without either stressor, (2) Warm, run with warmer temperatures, (3) Predator, run with largemouth bass, and (4) Warm‐Predator, run with both stressors. We assessed outcome metrics relative to the Baseline scenario. In the Warm scenario, salmon emerged 37 d and outmigrated 55 d earlier. There were 61% more subyearling migrants that were 31% smaller, and 72% fewer yearlings. In the Predator scenario, salmon survival decreased 64% for subyearlings and 69% for yearlings, and subyearlings were 7% smaller. In the Warm‐Predator scenario, salmon emerged 39 d and outmigrated 59 d earlier, subyearling survival increased 22%, subyearling mass decreased 37%, and 93% fewer yearlings survived. Our results suggest that warmer temperatures shift emergence and outmigration; predation by nonnative species is a threat to salmon survival; and life history strategies experience these stressors in different ways. Whereas subyearling production benefited from warmer temperatures more than it was hurt by predation, yearling production was depressed by both stressors independently and combined. Managers can use our individual‐based and spatially explicit approach to identify key times and areas to address exposure to extreme temperatures, overlap with nonnative species, and their interactive effects on threatened salmon. Our case study addressed three pressing needs identified in the literature: investigate impacts of nonnative species on threatened native salmon, build tools to evaluate management options where bass and salmon overlap, and explore how freshwater fishes will contend with multiple interactive stressors.

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Section: Thermal Regimesmentioning

confidence: 99%

Individual‐based simulations suggest mixed impacts of warmer temperatures and a nonnative predator on Chinook salmon

et al. 2020

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“…This result is not surprising as larger watersheds generally encompass higher variation in river/landscape characteristics which create challenges to modeling. Fewer sites per watershed than suggested by the sensitivity analysis would likely be required if training datasets contained fewer gaps and if logger locations were chosen to cover spatial gradients (Marsha et al 2018). Sensitivity analyses suggested that at least four years of data was sufficient; however, including longer time-series and more diverse climate years would PeerJ reviewing PDF | (2019:03:36199:1:1:NEW 2 Aug 2019)…”

Section: Discussion Interactions and Modeling Performancementioning

confidence: 99%

“…Spatial modeling techniques, which utilize statistical auto-correlation to describe how upstream sites influence downstream sites, have recently become popular to predict stream temperature across entire watersheds (e.g., Peterson et al 2013, Isaak et al 2014, Jackson et al 2017. Most spatial modeling techniques require dense monitoring networks (Marsha et al 2018) and, with a few exceptions (e.g., Jackson et al 2018, Hocking et al 2018, primarily been used to predict temporally summarized metrics, such as the mean August max weekly temperature (Isaak et al 2017) or the maximum weekly mean stream temperature (Ruesch et al 2012), as opposed to continuous estimates of temperature. This is likely a consequence of spatial correlations and covariate relationships changing with climatic variability (Steel et al 2016, Jackson et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape (v0.1)"

2019

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Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85-1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

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“…This led to the development of covariance functions that are specifically designed to describe the unique spatial relationships found in streams data [4,10]. Geostatistical models fit to streams data describe a number of in-stream relationships in a way that is scientifically consistent with the hydrological features of natural streams and, as such, are increasingly being used for broad-scale monitoring and modelling of stream networks; see, for example, Isaak et al [11] and Marsha et al [12], both model temperature in streams, with Marsha et al [12] further considering questions of site placement and sample size based on their data.…”

Section: Introductionmentioning

confidence: 99%

SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

et al. 2020

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Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

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Monitoring riverine thermal regimes on stream networks: Insights into spatial sampling designs from the Snoqualmie River, WA

Cited by 19 publications

References 26 publications

Individual‐based simulations suggest mixed impacts of warmer temperatures and a nonnative predator on Chinook salmon

Individual‐based simulations suggest mixed impacts of warmer temperatures and a nonnative predator on Chinook salmon

Peer Review #1 of "Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape (v0.1)"

SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

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