Isotopic evidence for niche partitioning and the influence of anthropogenic disturbance on endemic and introduced rodents in central Madagascar

We applied a multi-isotope approach to examine aspects of niche partitioning, competition, and mobility for rodents in the Central Highlands of Madagascar. Specifically, we used carbon (δ¹³C), nitrogen (δ¹⁵N), and strontium (⁸⁷Sr/⁸⁶Sr) isotope ratios in bone to investigate diet and mobility for endemic tufted tail rats (Eliurus spp.), and introduced black rats (Rattus rattus) and house mice (Mus musculus) within and outside a fragment of montane humid forest in the Ambohitantely Special Reserve. There was a clear spatial segregation in trapping success for different species: Eliurus was only in the forest interior and edge, Mus only outside of the fragment in a marsh and park housing complex, and Rattus in all habitats except the housing complex. We find only moderate support for mobility of rodents among habitats. Mus may routinely move between the marsh and housing complex. However, regular movement between the forest edge and interior, or between the forest fragment and surrounding grassland is not supported. Taxa appear to target different foods: Rattus tends to feed at a higher trophic level than Eliurus, and Mus consumes some C₄ resources. To date, strontium isotopes have been underutilized in ecological research. Here, we show that they are highly complementary to carbon and nitrogen isotope data. Even in localities with relatively uniform underlying geology, it may be possible to distinguish individuals that regularly forage in different habitats.

Stable isotope discrimination factors and between-tissue isotope comparisons for bone and skin from captive and wild green sea turtles (Chelonia mydas)

RATIONALE

The ecological application of stable isotope analysis (SIA) relies on taxa- and tissue-specific stable carbon (Δ¹³ C) and nitrogen (Δ¹⁵ N) isotope discrimination factors, determined with captive animals reared on known diets for sufficient time to reflect dietary isotope ratios. However, captive studies often prohibit lethal sampling, are difficult with endangered species, and reflect conditions not experienced in the wild.

METHODS

We overcame these constraints and determined the Δ¹³ C and Δ¹⁵ N values for skin and cortical bone from green sea turtles (Chelonia mydas) that died in captivity and evaluated the utility of a mathematical approach to predict discrimination factors. Using stable carbon (δ¹³ C values) and nitrogen (δ¹⁵ N values) isotope ratios from captive and wild turtles, we established relationships between bone stable isotope (SI) ratios and those from skin, a non-lethally sampled tissue, to facilitate comparisons of SI ratios among studies using multiple tissues.

RESULTS

The mean (±SD) Δ¹³ C and Δ¹⁵ N values (‰) between skin and bone from captive turtles and their diet (non-lipid-extracted) were 2.3 ± 0.3 and 4.1 ± 0.4 and 2.1 ± 0.6 and 5.1 ± 1.1, respectively. The mathematically predicted Δ¹³ C and Δ¹⁵ N values were similar (to within 1‰) to the experimentally derived values. The mean δ¹⁵ N values from bone were higher than those from skin for captive (+1.0 ± 0.9‰) and wild (+0.8 ± 1.0‰) turtles; the mean δ¹³ C values from bone were lower than those from skin for wild turtles (-0.6 ± 0.9‰), but the same as for captive turtles. We used linear regression equations to describe bone vs skin relationships and create bone-to-skin isotope conversion equations.

CONCLUSIONS

For sea turtles, we provide the first (a) bone-diet SI discrimination factors, (b) comparison of SI ratios from individual-specific bone and skin, and (c) evaluation of the application of a mathematical approach to predict stable isotope discrimination factors. Our approach opens the door for future studies comparing different tissues, and relating SI ratios of captive to wild animals.

Best practices for use of stable isotope mixing models in food-web studies

Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinterpreted. We address major challenges to their effective application. Mixing models have increased rapidly in sophistication. Current models estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate complexities such as variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and concentration dependence. For proper implementation of mixing models, we offer the following suggestions. First, mixing models can only be as good as the study and data. Studies should have clear questions, be informed by knowledge of the system, and have strong sampling designs to effectively characterize isotope variability of consumers and resources on proper spatio-temporal scales. Second, studies should use models appropriate for the question and recognize their assumptions and limitations. Decisions about source grouping or incorporation of concentration dependence can influence results. Third, studies should be careful about interpretation of model outputs. Mixing models generally estimate proportions of assimilated resources with substantial uncertainty distributions. Last, common sense, such as graphing data before analyzing, is essential to maximize usefulness of these tools. We hope these suggestions for effective implementation of stable isotope mixing models will aid continued development and application of this field.