DOI: 10.1016/j.watres.2018.10.093
Scopus记录号: 2-s2.0-85056191279
论文题名: Modelling the effects of multiple stressors on respiration and microbial biomass in the hyporheic zone using decision trees
作者: Mori N. ; Debeljak B. ; Škerjanec M. ; Simčič T. ; Kanduč T. ; Brancelj A.
刊名: Water Research
ISSN: 431354
出版年: 2019
起始页码: 9
结束页码: 20
语种: 英语
英文关键词: Ecosystem processes
; Freshwater biofilm
; Hyporheic zone
; Machine learning
; Stressors
; Water quality
Scopus关键词: Artificial intelligence
; Biofilms
; Biomass
; Decision trees
; Digital storage
; Ecosystems
; Electron transport properties
; Forestry
; Groundwater
; Land use
; Learning systems
; Plants (botany)
; Runoff
; Stream flow
; Temperature
; Trees (mathematics)
; Water quality
; Ecosystem process
; Environmental features
; Environmental variables
; Freshwater biofilms
; Hyporheic zone
; Physical and chemical characteristics
; Predictive performance
; Stressors
; Catchments
; ammonia
; calcium ion
; nitrite
; potassium
; protein
; sulfate
; water
; aquatic ecosystem
; biofilm
; biomass
; catchment
; data set
; decision analysis
; ecosystem approach
; environmental gradient
; environmental stress
; freshwater
; hyporheic zone
; machine learning
; microorganism
; modeling
; respiration
; water quality
; Article
; catchment
; decision tree
; electron transport
; environmental factor
; hydraulic conductivity
; land use
; machine learning
; microbial biomass
; microbial respiration
; nonhuman
; pH
; priority journal
; protein content
; sediment
; summer
; temperature sensitivity
; urban area
; water quality
; Slovenia
英文摘要: Integrity of freshwater surface- and groundwater ecosystems and their ecological and qualitative status greatly depends on ecological processes taking place in streambed sediments overgrown by biofilm, in the hyporheic zone (HZ). Little is known about the interactions and effects of multiple stressors on biologically driven processes in the HZ. In this study, machine learning (ML) tools were used to provide evidence-based information on how stressors and ecologically important environmental factors interact and drive ecological processes and microbial biomass. The ML technique of decision trees using the J48 algorithm was applied to build models from a data set of 342 samples collected over three seasons at 24 sites within the catchments of five gravel-bed rivers in north-central Slovenia. Catchment-scale land use data and reach-scale environmental features indicating the HZ morphology and physical and chemical characteristics of water were used as predictive variables, while respiration (R) and microbial respiratory electron transport system activity (ETSA) were used as response variables indicating ecological processes and total protein content (TPC) indicating microbial biomass. Separate models were built for two HZ depths: 5–15 cm and 20–40 cm. The models with R as a response variable have the highest predictive performance (67–89%) showing that R is a good indicator of complex environmental gradients. The ETSA and TPC models were less accurate (42–67%) but still provide valuable ecological information. The best model show that temperature when combined with selected water quality elements is an important predictor of R at depth of 5–15 cm. The ETSA and TPC models show the combined effects of temperature, catchment land use and selected water quality elements on both response variables. Overall, this study provides new knowledge on how ecological processes occurring in the HZ respond to catchment and reach-scale variables, and provides evidence-based information about complex interactions between temperature, catchment land use and water quality. These interactions are highly dependent on the selection of the response variable, i.e., each response variable is influenced by a specific combination of predictive environmental variables. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/122178
Appears in Collections: 气候变化事实与影响
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作者单位: National Institute of Biology, Department of Organisms and Ecosystems Research, Večna pot 111, Ljubljana, 1000, Slovenia; University of Ljubljana, Faculty of Civil and Geodetic Engineering, Jamova 2, Ljubljana, Slovenia; Jožef Stefan Institute, Department of Environmental Sciences, Jamova 39, Ljubljana, 1000, Slovenia; University of Nova Gorica, School for Environmental Sciences, Vipavska 13, Nova Gorica, 5000, Slovenia
Recommended Citation:
Mori N.,Debeljak B.,Škerjanec M.,et al. Modelling the effects of multiple stressors on respiration and microbial biomass in the hyporheic zone using decision trees[J]. Water Research,2019-01-01