Background The evolutionary relationships within Stomiiformes, a diverse order of deep-sea fishes dominating the mesopelagic and bathypelagic zones, remain contentious due to conflicting morphological...

Isotopic analysis as a method of assessing diet or geographical origin is now ubiquitous in archaeology, to the point where seemingly no project is co…

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Our study reveals how environmental factors and fishing pressure influence swordfish diet, highlighting trophic shifts under future climate scenarios. We provide a novel framework to strengthen our a...

Isotopic mixing models are widely used in ecology to quantify the diets of organisms. Most of these models assume that stable isotopic systems remain stable over time (a premise known as the steady-state hypothesis) and are therefore referred to as static mixing models. However, evidence shows that temporal dynamics—such as variations in isotopic turnover rates, diet shifts, and fluctuations in the isotopic signatures of both sources and consumers—can introduce significant bias into model outputs. Despite the recognition of such dynamics, the factors influencing bias and its implications remain underexplored. This study uses modelling and in silico experiments to characterize bias in mixing models resulting from temporal dynamics and to develop a dynamic mixing model that accounts for these effects. The results revealed that bias is strongly influenced by the interaction between the isotopic turnover rate and sampling frequency, emphasizing the importance of time variability in isotopic turnover. Additional sources of bias include the consumer’s isotopic signature prior to a dietary shift, which reflects the distance from equilibrium with the new diet. This bias can be further amplified by temporal fluctuations in source signatures. Our study also evaluates previously recommended strategies to mitigate bias, confirming their effectiveness in reducing errors in static models and providing additional guidelines for their application. Furthermore, it improves access to a dynamic mixing model, enabling direct comparison with static approaches and demonstrating its robustness and accuracy in estimating diets under dynamic conditions.

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Burning calories, burning ocean: metabolic rate in bivalves as a predictor of extinction selectivity through time and during rapid global warming

Metabolic rate dictates life’s tempo, yet how ecological and environmental factors integrate to shape metabolic traits remains contentious. Conside...

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

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