Merging resource availability with isotope mixing models: the role of neutral interaction assumptions

EcoDiet: A hierarchical Bayesian model to combine stomach, biotracer, and literature data into diet matrix estimation

Although quantifying trophic interactions is a critical path to understanding and forecasting ecosystem functioning, fitting trophic models to field data remains challenging. It requires flexible statistical tools to combine different sources of information from the literature and fieldwork samples. We present EcoDiet, a hierarchical Bayesian modeling framework to simultaneously estimate food-web topology and diet composition of all consumers in the food web, by combining (1) a priori knowledge from the literature on both food-web topology and diet proportions; (2) stomach content analyses, with frequencies of prey occurrence used as the primary source of data to update the prior knowledge on the topological food-web structure; (3) and biotracers data through a mixing model (MM). Inferences are derived in a Bayesian probabilistic rationale that provides a formal way to incorporate prior information and quantifies uncertainty around both the topological structure of the food web and the dietary proportions. EcoDiet was implemented as an open-source R package, providing a user-friendly interface to execute the model, as well as examples and guidelines to familiarize with its use. We used simulated data to demonstrate the benefits of EcoDiet and how the framework can improve inferences on diet matrix by comparison with classical network MM. We applied EcoDiet to the Celtic Sea ecosystem, and showed how combining multiple data types within an integrated approach provides a more robust and holistic picture of the food-web topology and diet matrices than the literature or classical MM approach alone. EcoDiet has the potential to become a reference method for building diet matrices as a preliminary step of ecosystem modeling and to improve our understanding of prey-predator interactions.

Green macroalgae blooms (Ulva spp.) influence trophic ecology of juvenile flatfish differently in sandy beach nurseries

Opportunistic green macroalgae blooms increasingly affect coastal areas worldwide. Understanding their impacts on organisms that use this zone, such as juvenile flatfish, is critical. By combining stable isotope data, digestive tract contents and community analyses of flatfish and their potential prey (benthic macroinvertebrates) from two North-East Atlantic sandy beaches (one impacted by blooms and one not), we detected similar and species-specific trophic changes among three co-occurring species (sand sole, plaice and turbot). Across flatfish species, juveniles displayed more opportunistic foraging behavior at the impacted site. Differently, plaice and sand sole relied more on the additional basal resource (Ulva spp.) than turbot. Finally, sand sole and turbot presented a stronger diet shift at the impacted site than plaice. We hypothesize that the species-specific response to the blooms are mostly driven by how the flatfish detect their prey (using visual and/or chemical cues) and when they forage (diurnal or nocturnal foraging).

Quantifying learning in biotracer studies

Mixing models have become requisite tools for analyzing biotracer data, most commonly stable isotope ratios, to infer dietary contributions of multiple sources to a consumer. However, Bayesian mixing models will always return a result that defaults to their priors if the data poorly resolve the source contributions, and thus, their interpretation requires caution. We describe an application of information theory to quantify how much has been learned about a consumer's diet from new biotracer data. We apply the approach to two example data sets. We find that variation in the isotope ratios of sources limits the precision of estimates for the consumer's diet, even with a large number of consumer samples. Thus, the approach which we describe is a type of power analysis that uses a priori simulations to find an optimal sample size. Biotracer data are fundamentally limited in their ability to discriminate consumer diets. We suggest that other types of data, such as gut content analysis, must be used as prior information in model fitting, to improve model learning about the consumer's diet. Information theory may also be used to identify optimal sampling protocols in situations where sampling of consumers is limited due to expense or ethical concerns.

Specialized morphology corresponds to a generalist diet: linking form and function in smashing mantis shrimp crustaceans

Many animals are considered to be specialists because they have feeding structures that are fine-tuned for consuming specific prey. For example, "smasher" mantis shrimp have highly specialized predatory appendages that generate forceful strikes to break apart hard-shelled prey. Anecdotal observations suggest, however, that the diet of smashers may include soft-bodied prey as well. Our goal was to examine the diet breadth of the smasher mantis shrimp, Neogonodactylus bredini, to determine whether it has a narrow diet of hard-shelled prey. We combined studies of prey abundance, feeding behavior, and stable isotope analyses of diet in both seagrass and coral rubble to determine if N. bredini's diet was consistent across different habitat types. The abundances of hard-shelled and soft-bodied prey varied between habitats. In feeding experiments, N. bredini consumed both prey types. N. bredini consumed a range of different prey in the field as well and, unexpectedly, the stable isotope analysis demonstrated that soft-bodied prey comprised a large proportion (29-53 %) of the diet in both habitats. Using a Bayesian mixing model framework (MixSIAR), we found that this result held even when we used uninformative, or generalist, priors and informative priors reflecting a specialist diet on hard-shelled prey and prey abundances in the field. Thus, contrary to expectation, the specialized feeding morphology of N. bredini corresponds to a broad diet of both hard-shelled and soft-bodied prey. Using multiple lines of study to describe the natural diets of other presumed specialists may demonstrate that specialized morphology often broadens rather than narrows diet breadth.

Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models

Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.

Timescales alter the inferred strength and temporal consistency of intraspecific diet specialization

Many populations consist of individuals that differ substantially in their diets. Quantification of the magnitude and temporal consistency of such intraspecific diet variation is needed to understand its importance, but the extent to which different approaches for doing so reflect instantaneous vs. time-aggregated measures of individual diets may bias inferences. We used direct observations of sea otter individuals (Enhydra lutris nereis) to assess how: (1) the timescale of sampling, (2) under-sampling, and (3) the incidence- vs. frequency-based consideration of prey species affect the inferred strength and consistency of intraspecific diet variation. Analyses of feeding observations aggregated over hourly to annual intervals revealed a substantial bias associated with time aggregation that decreases the inferred magnitude of specialization and increases the inferred consistency of individuals' diets. Time aggregation also made estimates of specialization more sensitive to the consideration of prey frequency, which decreased estimates relative to the use of prey incidence; time aggregation did not affect the extent to which under-sampling contributed to its overestimation. Our analyses demonstrate the importance of studying intraspecific diet variation with an explicit consideration of time and thereby suggest guidelines for future empirical efforts. Failure to consider time will likely produce inconsistent predictions regarding the effects of intraspecific variation on predator-prey interactions.

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.

Bias in diet determination: incorporating traditional methods in Bayesian mixing models

There are not “universal methods” to determine diet composition of predators. Most traditional methods are biased because of their reliance on differential digestibility and the recovery of hard items. By relying on assimilated food, stable isotope and Bayesian mixing models (SIMMs) resolve many biases of traditional methods. SIMMs can incorporate prior information (i.e. proportional diet composition) that may improve the precision in the estimated dietary composition. However few studies have assessed the performance of traditional methods and SIMMs with and without informative priors to study the predators’ diets. Here we compare the diet compositions of the South American fur seal and sea lions obtained by scats analysis and by SIMMs-UP (uninformative priors) and assess whether informative priors (SIMMs-IP) from the scat analysis improved the estimated diet composition compared to SIMMs-UP. According to the SIMM-UP, while pelagic species dominated the fur seal’s diet the sea lion’s did not have a clear dominance of any prey. In contrast, SIMM-IP’s diets compositions were dominated by the same preys as in scat analyses. When prior information influenced SIMMs’ estimates, incorporating informative priors improved the precision in the estimated diet composition at the risk of inducing biases in the estimates. If preys isotopic data allow discriminating preys’ contributions to diets, informative priors should lead to more precise but unbiased estimated diet composition. Just as estimates of diet composition obtained from traditional methods are critically interpreted because of their biases, care must be exercised when interpreting diet composition obtained by SIMMs-IP. The best approach to obtain a near-complete view of predators’ diet composition should involve the simultaneous consideration of different sources of partial evidence (traditional methods, SIMM-UP and SIMM-IP) in the light of natural history of the predator species so as to reliably ascertain and weight the information yielded by each method.

Trophic omnivory across a productivity gradient: intraguild predation theory and the structure and strength of species interactions

Intraguild predation theory centres on two predictions: (i) for an omnivore and an intermediate predator (IG-prey) to coexist on shared resources, the IG-prey must be the superior resource competitor, and (ii) increasing resource productivity causes the IG-prey's equilibrium abundance to decline. I tested these predictions with a series of species-rich food webs along New Zealand's rocky shores, focusing on two predatory whelks, Haustrum haustorium, a trophic omnivore, and Haustrum scobina, the IG-prey. In contrast to theory, the IG-prey's abundance increased with productivity. Furthermore, feeding rates and allometric considerations indicate a competitive advantage for the omnivore when non-shared prey are considered, despite the IG-prey's superiority for shared prey. Nevertheless, clear and regular cross-gradient changes in network structure and interaction strengths were observed that challenge the assumptions of current theory. These insights suggest that the consideration of consumer-dependent functional responses, non-equilibrium dynamics, the dynamic nature of prey choice and non-trophic interactions among basal prey will be fruitful avenues for theoretical development.

Probabilistic patterns of interaction: the effects of link-strength variability on food web structure

Patterns of species interactions affect the dynamics of food webs. An important component of species interactions that is rarely considered with respect to food webs is the strengths of interactions, which may affect both structure and dynamics. In natural systems, these strengths are variable, and can be quantified as probability distributions. We examined how variation in strengths of interactions can be described hierarchically, and how this variation impacts the structure of species interactions in predator-prey networks, both of which are important components of ecological food webs. The stable isotope ratios of predator and prey species may be particularly useful for quantifying this variability, and we show how these data can be used to build probabilistic predator-prey networks. Moreover, the distribution of variation in strengths among interactions can be estimated from a limited number of observations. This distribution informs network structure, especially the key role of dietary specialization, which may be useful for predicting structural properties in systems that are difficult to observe. Finally, using three mammalian predator-prey networks (two African and one Canadian) quantified from stable isotope data, we show that exclusion of link-strength variability results in biased estimates of nestedness and modularity within food webs, whereas the inclusion of body size constraints only marginally increases the predictive accuracy of the isotope-based network. We find that modularity is the consequence of strong link-strengths in both African systems, while nestedness is not significantly present in any of the three predator-prey networks.