Abstract

Variations in stable hydrogen and oxygen isotope ratios in terrestrial animal tissues are used to reconstruct origin and movement. An underlying assumption of these applications is that tissues grown at the same site share a similar isotopic signal, representative of the location of their origin. However, large variations in tissue isotopic compositions often exist even among conspecific individuals within local populations, which complicates origin and migration inferences. Field-data and correlation analyses have provided hints about the underlying mechanisms of within-site among-individual isotopic variance, but a theory explaining the causes and magnitude of such variance has not been established. Here we develop a mechanistic modeling framework that provides explicit predictions of the magnitude, patterns, and drivers of isotopic variation among individuals living in a common but environmentally heterogeneous habitat. The model toolbox includes isoscape models of environmental isotopic variability, an agent-based model of behavior and movement, and a physiology-biochemistry model of isotopic incorporation into tissues. We compare model predictions against observed variation in hatch-year individuals of the songbird Spotted Towhee (Pipilo maculatus) in Red Butte Canyon, Utah, and evaluate the ability of the model to reproduce this variation under different sets of assumptions. Only models that account for environmental isotopic variability predict a similar magnitude of isotopic variation as observed. Within the modeling framework, behavioral rules and properties govern how animals nesting in different locations acquire resources from different habitats, and birds nesting in or near riparian habitat preferentially access isotopically lighter resources than those associated with the meadow and slope habitats, which results in more negative body water and tissue isotope values. Riparian nesters also have faster body water turnover and acquire more water from drinking (vs. from food), which exerts a secondary influence on their isotope ratios. Thus, the model predicts that local among-individual isotopic variance is linked first to isotopic heterogeneity in the local habitat, and second to how animals sample this habitat during foraging. Model predictions provide insight into the fundamental mechanisms of small-scale isotopic variance and can be used to predict the utility of isotope-based methods for specific groups or environments in ecological and forensic research.

Highlights

  • Variations in stable hydrogen (δ2H) and oxygen (δ18O) isotopic ratios of animal body tissues are commonly used in terrestrial migration ecology to reconstruct origin and migration

  • Variation is more similar in the E and E1B1 experiments, implying that the among-individual physiological differences considered here accentuate the tissue isotopic variability imparted by environmental heterogeneity, but by a relatively small amount. These experiments approximate the variation in the observed samples well, meaning that the model toolbox reproduces observed within-site amongindividual isotopic variance only when environmental isotopic variability is accounted for

  • Among the E experiments, betweenindividual variation is smallest in the E2A and E2C experiments (Figure 8). In the former case, that is likely because 16 slopeassociated model individuals died, compressing variation toward ranges typical of the riparian and meadow habitats; in the latter, reduced variance is attributable to the larger foraging area of individuals

Read more

Summary

Introduction

Variations in stable hydrogen (δ2H) and oxygen (δ18O) isotopic ratios of animal body tissues are commonly used in terrestrial migration ecology to reconstruct origin and migration (reviewed by Hobson, 1999; Hobson and Wassenaar, 2019). Isotopic data from known-origin samples commonly show high levels of variation even among conspecific individuals living at a common site (e.g., Kelly et al, 2002; Meehan et al, 2003; Smith and Dufty, 2005; Wunder et al, 2005; Rocque et al, 2006; Langin et al, 2007; Gow et al, 2012; Haché et al, 2012; van Dijk et al, 2014; Reese et al, 2018) Such variability can often be quantified and incorporated in geographic assignment tests (Hobson et al, 2012, 2014) but may limit the utility of such tests.

Methods
Results
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.