Beyond simple linear mixing models: Process-based isotope partitioning of ecological processes

Kiona Ogle, Colin Tucker, Jessica M. Cable

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

Stable isotopes are valuable tools for partitioning the components contributing to ecological processes of interest, such as animal diets and trophic interactions, plant resource use, ecosystem gas fluxes, streamflow, and many more. Stable isotope data are often analyzed with simple linear mixing (SLM) models to partition the contributions of different sources, but SLM models cannot incorporate a mechanistic understanding of the underlying processes and do not accommodate additional data associated with these processes (e.g., environmental covariates, flux data, gut contents). Thus, SLM models lack predictive ability. We describe a process-based mixing (PBM) model approach for integrating stable isotopes, other data sources, and process models to partition different sources or process components. This is accomplished via a hierarchical Bayesian framework that quantifies multiple sources of uncertainty and enables the incorporation of process models and prior information to help constrain the source-specific proportional contributions, thereby potentially avoiding identifiability issues that plague SLM models applied to "too many" sources. We discuss the application of the PBM model framework to three diverse examples: temporal and spatial partitioning of streamflow, estimation of plant rooting profiles and water uptake profiles (or water sources) with extension to partitioning soil and ecosystem CO2 fluxes, and reconstructing animal diets. These examples illustrate the advantages of the PBM modeling approach, which facilitates incorporation of ecological theory and diverse sources of information into the mixing model framework, thus enabling one to partition key process components across time and space.

Original languageEnglish (US)
Pages (from-to)181-195
Number of pages15
JournalEcological Applications
Volume24
Issue number1
DOIs
StatePublished - Jan 2014

Fingerprint

partitioning
isotope
stable isotope
streamflow
diet
ecological theory
ecosystem
trophic interaction
animal
water uptake
rooting
resource use
gas
modeling
soil

Keywords

  • Diet sourcing
  • Ecosystem fluxes
  • Hierarchical Bayesian framework
  • Hydrograph partitioning
  • IsoError
  • IsoSource
  • Mixing models
  • MixSIR
  • Plant water sources
  • Process-Based models
  • SIAR
  • Soil respiration
  • Stable isotopes
  • Trophic interactions

ASJC Scopus subject areas

  • Ecology

Cite this

Beyond simple linear mixing models : Process-based isotope partitioning of ecological processes. / Ogle, Kiona; Tucker, Colin; Cable, Jessica M.

In: Ecological Applications, Vol. 24, No. 1, 01.2014, p. 181-195.

Research output: Contribution to journalArticle

Ogle, Kiona ; Tucker, Colin ; Cable, Jessica M. / Beyond simple linear mixing models : Process-based isotope partitioning of ecological processes. In: Ecological Applications. 2014 ; Vol. 24, No. 1. pp. 181-195.
@article{190fc86adc6646dcbad4776218b768f4,
title = "Beyond simple linear mixing models: Process-based isotope partitioning of ecological processes",
abstract = "Stable isotopes are valuable tools for partitioning the components contributing to ecological processes of interest, such as animal diets and trophic interactions, plant resource use, ecosystem gas fluxes, streamflow, and many more. Stable isotope data are often analyzed with simple linear mixing (SLM) models to partition the contributions of different sources, but SLM models cannot incorporate a mechanistic understanding of the underlying processes and do not accommodate additional data associated with these processes (e.g., environmental covariates, flux data, gut contents). Thus, SLM models lack predictive ability. We describe a process-based mixing (PBM) model approach for integrating stable isotopes, other data sources, and process models to partition different sources or process components. This is accomplished via a hierarchical Bayesian framework that quantifies multiple sources of uncertainty and enables the incorporation of process models and prior information to help constrain the source-specific proportional contributions, thereby potentially avoiding identifiability issues that plague SLM models applied to {"}too many{"} sources. We discuss the application of the PBM model framework to three diverse examples: temporal and spatial partitioning of streamflow, estimation of plant rooting profiles and water uptake profiles (or water sources) with extension to partitioning soil and ecosystem CO2 fluxes, and reconstructing animal diets. These examples illustrate the advantages of the PBM modeling approach, which facilitates incorporation of ecological theory and diverse sources of information into the mixing model framework, thus enabling one to partition key process components across time and space.",
keywords = "Diet sourcing, Ecosystem fluxes, Hierarchical Bayesian framework, Hydrograph partitioning, IsoError, IsoSource, Mixing models, MixSIR, Plant water sources, Process-Based models, SIAR, Soil respiration, Stable isotopes, Trophic interactions",
author = "Kiona Ogle and Colin Tucker and Cable, {Jessica M.}",
year = "2014",
month = "1",
doi = "10.1890/1051-0761-24.1.181",
language = "English (US)",
volume = "24",
pages = "181--195",
journal = "Ecological Appplications",
issn = "1051-0761",
publisher = "Ecological Society of America",
number = "1",

}

TY - JOUR

T1 - Beyond simple linear mixing models

T2 - Process-based isotope partitioning of ecological processes

AU - Ogle, Kiona

AU - Tucker, Colin

AU - Cable, Jessica M.

PY - 2014/1

Y1 - 2014/1

N2 - Stable isotopes are valuable tools for partitioning the components contributing to ecological processes of interest, such as animal diets and trophic interactions, plant resource use, ecosystem gas fluxes, streamflow, and many more. Stable isotope data are often analyzed with simple linear mixing (SLM) models to partition the contributions of different sources, but SLM models cannot incorporate a mechanistic understanding of the underlying processes and do not accommodate additional data associated with these processes (e.g., environmental covariates, flux data, gut contents). Thus, SLM models lack predictive ability. We describe a process-based mixing (PBM) model approach for integrating stable isotopes, other data sources, and process models to partition different sources or process components. This is accomplished via a hierarchical Bayesian framework that quantifies multiple sources of uncertainty and enables the incorporation of process models and prior information to help constrain the source-specific proportional contributions, thereby potentially avoiding identifiability issues that plague SLM models applied to "too many" sources. We discuss the application of the PBM model framework to three diverse examples: temporal and spatial partitioning of streamflow, estimation of plant rooting profiles and water uptake profiles (or water sources) with extension to partitioning soil and ecosystem CO2 fluxes, and reconstructing animal diets. These examples illustrate the advantages of the PBM modeling approach, which facilitates incorporation of ecological theory and diverse sources of information into the mixing model framework, thus enabling one to partition key process components across time and space.

AB - Stable isotopes are valuable tools for partitioning the components contributing to ecological processes of interest, such as animal diets and trophic interactions, plant resource use, ecosystem gas fluxes, streamflow, and many more. Stable isotope data are often analyzed with simple linear mixing (SLM) models to partition the contributions of different sources, but SLM models cannot incorporate a mechanistic understanding of the underlying processes and do not accommodate additional data associated with these processes (e.g., environmental covariates, flux data, gut contents). Thus, SLM models lack predictive ability. We describe a process-based mixing (PBM) model approach for integrating stable isotopes, other data sources, and process models to partition different sources or process components. This is accomplished via a hierarchical Bayesian framework that quantifies multiple sources of uncertainty and enables the incorporation of process models and prior information to help constrain the source-specific proportional contributions, thereby potentially avoiding identifiability issues that plague SLM models applied to "too many" sources. We discuss the application of the PBM model framework to three diverse examples: temporal and spatial partitioning of streamflow, estimation of plant rooting profiles and water uptake profiles (or water sources) with extension to partitioning soil and ecosystem CO2 fluxes, and reconstructing animal diets. These examples illustrate the advantages of the PBM modeling approach, which facilitates incorporation of ecological theory and diverse sources of information into the mixing model framework, thus enabling one to partition key process components across time and space.

KW - Diet sourcing

KW - Ecosystem fluxes

KW - Hierarchical Bayesian framework

KW - Hydrograph partitioning

KW - IsoError

KW - IsoSource

KW - Mixing models

KW - MixSIR

KW - Plant water sources

KW - Process-Based models

KW - SIAR

KW - Soil respiration

KW - Stable isotopes

KW - Trophic interactions

UR - http://www.scopus.com/inward/record.url?scp=84893349324&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893349324&partnerID=8YFLogxK

U2 - 10.1890/1051-0761-24.1.181

DO - 10.1890/1051-0761-24.1.181

M3 - Article

C2 - 24640543

AN - SCOPUS:84893349324

VL - 24

SP - 181

EP - 195

JO - Ecological Appplications

JF - Ecological Appplications

SN - 1051-0761

IS - 1

ER -