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.

Using StorAge Selection functions to quantify ecohydrological controls on the time-variant age of evapotranspiration, soil water, and recharge

“…Similar to other studies, the was variable with respect to soil wetness conditions and water availability (Ogle et al, 2014;Volkmann et al, 2016;Barbeta and Peñuelas, 2017). Drier average conditions at Site A resulted in root-uptake from deeper 20 depths and older water ages, however year-round wet upper soil conditions at Site B resulted in higher from near surface waters.…”

“…The sources of and from depth likely change in time due to water availability (Ogle et al, 2014;Volkmann et al, 2016;Barbeta and Peñuelas, 2017). To estimate the temporal variability of and sources, the beta distribution was 20 assumed to be representative of the probability of water source from each depth within the modelling domain (0 -Z):…”

“…The identification of root-water uptake ages and fluxes is often difficult; this is due to the often unknown root densities and soil moisture distributions that influence the spatial location of preferential root-uptake volumes (Brantley et al, 2017). Isotopes have been shown to be a useful tool to identify root-uptake source through mass-balance of 10 soil isotopic compositions and xylem water samples (Ogle et al, 2014;Geris et al, 2017;Sprenger et al, 2017b). Similar to root-uptake, evaporation fluxes and age from depth are difficult to quantify due to limitations in measured energy fluxes (Xiao et al, 2011(Xiao et al, , 2012 which may be used to infer evaporation sources during dry and wet conditions (Sakai et al, 2011).…”

Process-based isotope partitioning of winter soil respiration in a subalpine ecosystem reveals importance of rhizospheric respiration

“…The stable isotope approach suffers from difficulties in measuring the isotopic composition of the mixture and the potential sources (Bowling et al 2009). Further, small differences (less than *4 %) between the d 13 C of rhizospheric and heterotrophic respiration, as in most studies that rely on natural abundances of 13 C, result in natural variability overwhelming the isotopic signal (Bowling et al 2008), unless further process knowledge is incorporated to account for natural variability (Ogle et al 2014). Partitioning of soil respiration is further complicated in the snow-covered season because deep snow prevents easy access to the soil.…”

“…Partitioning of soil respiration is further complicated in the snow-covered season because deep snow prevents easy access to the soil. However, stable isotope partitioning is the least invasive approach and has been successfully applied in numerous systems (Ogle et al 2014). Any partitioning method, including stable isotopes, can be improved by integrating process information (e.g., Zobitz et al 2008) and accounting for important sources of uncertainty (Ogle and Pendall, in review), which we do in this study.…”