A meta-analysis of amino acid δ\u3csup\u3e15\u3c/sup\u3eN trophic enrichment factors in fishes relative to nutritional and ecological drivers

The use of amino acid (AA) nitrogen stable isotopes (δ15N) from consumer tissues aims to provide precise estimates of trophic position (TP), but the drivers of AA isotope fractionation remain unclear. In particular, the main factors driving the variability in TEFAA among taxonomic groups and trophic levels remain largely unexplained, which challenges the application of universal values for TEFs. While the relationship between protein content and quality and TEFs has been examined, studies have yielded inconsistent results, and the role of protein and lipid nutritional requirements as well as feeding regime have not been considered. Likewise, drivers that influence physiological and nutritional processes have not been examined relative to TEFAA variation. We conducted a meta-analysis of controlled feeding experiments within a single group, teleosts fishes, to evaluate the relationship between five nutritional factors (protein and lipid content, protein and lipid content relative to nutritional requirements, and feeding regime) and three ecological drivers (diet type, life stage, and habitat type) on TEFAA. We considered a broad range of protein levels (8–71%) in diets and found no relationship between source TEFAAs and percent protein relative to nutritional requirements, whereas lipid content relative to nutrient requirements, feeding regime and habitat type partially explain the variability in TEFs of Lys, but not for Phe and Met TEFs. The variability for the latter was representative of robust source AAs. Among trophic AAs, Asp, Ile, Pro, and Leu TEFs were significantly higher in species from brackish than marine habitats possibly due to osmoregulation involvement. TEFGlu was sensitive to protein content and feeding regime within teleosts, but relatively constant when comparing TEFs among teleosts, non-teleosts, and all taxa. Our results indicate that TEFAA is less variable within a single taxon than among multiple taxa and that such variation is not negligible. Our results indicate that δ15NAA values could provide better TP estimates if using taxon-specific values, and highlights the need to explain the mechanisms of AA fractionation and quantify the variability in TEFs used during error propagation for TP estimates

Inferences to estimate consumer’s diet using stable isotopes: Insights from a dynamic mixing model

Stable isotope ratios are used to reconstruct animal diet in trophic ecology via mixing models. Several assumptions of stable isotope mixing models are critical, i.e., constant trophic discrimination factor and isotopic equilibrium between the consumer and its diet. The isotopic turnover rate (λ and its counterpart the half-life) affects the dynamics of isotopic incorporation for an organism and the isotopic equilibrium assumption: λ involves a time lag between the real assimilated diet and the diet estimated by mixing models at the individual scale. Current stable isotope mixing model studies consider neither this time lag nor even the dynamics of isotopic ratios in general. We developed a mechanistic framework using a dynamic mixing model (DMM) to assess the contribution of λ to the dynamics of isotopic incorporation and to estimate the bias induced by neglecting the time lag in diet reconstruction in conventional static mixing models (SMMs). The DMM includes isotope dynamics of sources (denoted δs), λ and frequency of diet-switch (ω). The results showed a significant bias generated by the SMM compared to the DMM (up to 50% of differences). This bias can be strongly reduced in SMMs by averaging the isotopic variations of the food sources over a time window equal to twice the isotopic half-life. However, the bias will persist (∼15%) for intermediate values of the ω/λ ratio. The inferences generated using a case study highlighted that DMM enhanced estimates of consumer’s diet, and this could avoid misinterpretation in ecosystem functioning, food-web structure analysis and underlying biological processes