Summary: Stable isotope analyses coupled with mixing models are being used increasingly to evaluate ecological management issues and questions. Such applications of stable isotope analyses often require simultaneous carbon and nitrogen analyses from the same sample. Correction of the carbon isotope values to take account of the varying content of ¹³C‐depleted lipids is then frequently achieved by a lipid‐normalization procedure using a model describing the relationship between change in δ¹³C following lipid removal and the original C:N ratio of a sample. We evaluated the applicability of two widely used normalization models using empirical data for muscle tissue from a wide range of fish and for aquatic invertebrates. Neither normalization model proved satisfactory, and we present some modifications that greatly improve the fit of one of the models to the fish muscle data. For invertebrates we found no clear relationship between change in δ¹³C following lipid removal and the original C:N ratio. We also examined the effect of lipid‐normalization on the output of a mixing model designed to calculate the proportional contribution of prey items to the diet of a consumer. Mixing model output was greatly influenced by whether prey or consumer values alone or together were lipid‐normalized and we urge caution in the interpretation of results from these models pending further experimental evidence. Synthesis and applications. We describe a revised lipid‐normalization model that should be applicable to a wide range of marine and freshwater fish species in studies applying stable isotope analyses to ecological management issues. However, we strongly advise against applying these kinds of lipid‐normalization models to aquatic invertebrate data. The interpretation of outputs from mixing models is greatly influenced by whether the carbon isotope data have been lipid‐normalized or not.

Stable isotope analysis provides a powerful tool to identify the energy sources which fuel consumers, to understand trophic interactions and to infer consumer trophic position (TP), an important concept that describes the ecological role of consumers in food webs. However, current methods for estimating TP using stable isotopes are limited and do not fulfil the complete potential of the isotopic approach. For instance, researchers typically use point estimates for key parameters including trophic discrimination factors and isotopic baselines, and do not explicitly include variance associated with these parameters when calculating TP. We present “tRophicPosition,” anrpackage incorporating a Bayesian model for the calculation of consumer TP at the population level using stable isotopes, with one or two baselines. It combines Markov Chain Monte Carlo simulations through JAGS and statistical and graphical analyses using R. We model consumer and baseline observations using relevant statistical distributions, allowing them to be treated as random variables. The calculation of TP—a random parameter—for one baseline follows standard equations linking¹⁵N enrichment per trophic level and the trophic position of the baseline (e.g. a primary producer or primary consumer). In the case of two baselines, a simple mixing model incorporating δ¹³C allows for the differentiation between two distinct sources of nitrogen, thus including heterogeneity derived from alternatives sources of δ¹⁵N. Methods currently implemented in “tRophicPosition” include loading, plotting and summarizing stable isotope data either from multiple sites and/or communities or a local assemblage; loading trophic discrimination factors from an internal database or generating them; defining and initializing a Bayesian model of TP; sampling posterior parameters; analysing, comparing and plotting posterior estimates of TP and other parameters; and calculating a parametric (non‐Bayesian) TP estimate. Additionally, full documentation including examples, multiple vignettes and code are available for download.

Understanding the drivers and implications of anthropogenic disturbance of ecological connectivity is a key concern for the conservation of biodiversity and ecosystem processes. Here, we review human activities that affect the movements and dispersal of aquatic organisms, including damming of rivers, river regulation, habitat loss and alteration, human-assisted dispersal of organisms and climate change. Using a series of case studies, we show that the insight needed to understand the nature and implications of connectivity, and to underpin conservation and management, is best achieved via data synthesis from multiple analytical approaches. We identify four key knowledge requirements for progressing our understanding of the effects of anthropogenic impacts on ecological connectivity: autecology; population structure; movement characteristics; and environmental tolerance/phenotypic plasticity. Structuring empirical research around these four broad data requirements, and using this information to parameterise appropriate models and develop management approaches, will allow for mitigation of the effects of anthropogenic disturbance on ecological connectivity in aquatic ecosystems.

Summary: 1. The adaptive radiation of fishes into benthic (littoral) and pelagic (lentic) morphs in post‐glacial lakes has become an important model system for speciation. Although these systems are well studied, there is little evidence of the existence of morphs that have diverged to utilize resources in the remaining principal lake habitat, the profundal zone. 2. Here, we tested phenotype‐environment correlations of three whitefish (Coregonus lavaretus) morphs that have radiated into littoral, pelagic and profundal niches in northern Scandinavian lakes. We hypothesized that morphs in such trimorphic systems would have a morphology adapted to one of the principal lake habitats (littoral, pelagic or profundal niches). Most whitefish populations in the study area are formed by a single (monomorphic) whitefish morph, and we further hypothesized that these populations should display intermediate morphotypes and niche utilization. We used a combination of traditional (stomach content, habitat use, gill raker counts) and more recently developed (stable isotopes, geometric morphometrics) techniques to evaluate phenotype‐environment correlations in two lakes with trimorphic and two lakes with monomorphic whitefish. 3. Distinct phenotype‐environment correlations were evident for each principal niche in whitefish morphs inhabiting trimorphic lakes. Monomorphic whitefish exploited multiple habitats, had intermediate morphology, displayed increased variance in gillraker‐counts, and relied significantly on zooplankton, most likely due to relaxed resource competition. 4. We suggest that the ecological processes acting in the trimorphic lakes are similar to each other, and are driving the adaptive evolution of whitefish morphs, possibly leading to the formation of new species.