To aggregate or not? Capturing the spatio-temporal complexity of the thermal regime. (August 2016)

• Record Type: Journal Article
• Authors: Turschwell, Mischa P.; Peterson, Erin E.; Balcombe, Stephen R.; Sheldon, Fran
• Is Part Of: Ecological indicators. Volume 67(2016)
• Page Start: 39
• Page End: 48
• Publication Date: 2016-08
• Subjects: Spatial stream-network -- Temperature metrics -- Prediction -- Thermal regime -- Climate warming -- Rivers; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2016.02.014
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Spatial models have improved prediction over random forest and non-spatial models. Aggregated temperature metrics are more accurate for thermal magnitude and duration. Frequency metrics are best represented by daily temperature metrics. Spatial dependence and dataset need to be considered when deciding metric choice. Accurate representation of thermal regime is vital for conservation and management. Abstract: Freshwater stream systems are under immense pressure from various anthropogenic impacts, including climate change. Stream systems are increasingly being altered by changes to the magnitude, timing, frequency, and duration of their thermal regimes, which will have profound impacts on the life-history dynamics of resident biota within their home range. Although temperature regimes have a significant influence on the biology of instream fauna, large spatio-temporal temperature datasets are often reduced to a single metric at discrete locations and used to describe the thermal regime of a system; potentially leading to a significant loss of information crucial to stream management. Models are often used to extrapolate these metrics to unsampled locations, but it is unclear whether predicting actual daily temperatures or an aggregated metric of the temperature regime best describes the complexity of the thermal regime. We fit spatial statistical stream-network models (SSNMs), random forest and non-spatial linear models to stream temperature data from the Upper Condamine River in QLD, Australia and used them to semi-continuously predict metrics describing the magnitude, duration, and frequency of the thermal regime through space and time. We compared both daily and aggregated temperature metrics and found that SSNMs always had more predictive ability than the random forest models, but both models outperformed the non-spatial linear model. For metrics describing thermal magnitude and duration, aggregated predictions were most accurate, while metrics describing the frequency of heating events were better represented by metrics based on daily predictions generated using a SSNM. A more comprehensive representation of the spatio-temporal thermal regime allows researchers to explore new spatio-temporally explicit questions about the thermal regime. It also provides the information needed to generate a suite of ecologically meaningful metrics capturing multiple aspects of the thermal regime, which will increase our scientific understanding of how organisms respond to thermal cues and provide much-needed information for more effective management actions.