Protected fish spawning aggregations as self-replenishing reservoirs for regional recovery.

Dispersal of eggs and larvae from spawning sites is critical to the population dynamics and conservation of marine fishes. For overfished species like critically endangered Nassau grouper (Epinephelus striatus), recovery depends on the fate of eggs spawned at the few remaining aggregation sites. Biophysical models can predict larval dispersal, yet these rely on assumed values of key parameters, such as diffusion and mortality rates, which have historically been difficult or impossible to estimate. We used in situ imaging to record three-dimensional positions of individual eggs and larvae in proximity to oceanographic drifters released into egg plumes from the largest known Nassau grouper spawning aggregation. We then estimated a diffusion-mortality model and applied it to previous years' drifter tracks to evaluate the possibility of retention versus export to nearby sites within 5 days of spawning. Results indicate that larvae were retained locally in 2011 and 2017, with 2011 recruitment being a substantial driver of population recovery on Little Cayman. Export to a nearby island with a depleted population occurred in 2016. After two decades of protection, the population appears to be self-replenishing but also capable of seeding recruitment in the region, supporting calls to incorporate spawning aggregation protections into fisheries management.

Unifying error structures in commonly used biotracer mixing models.

Mixing models are statistical tools that use biotracers to probabilistically estimate the contribution of multiple sources to a mixture. These biotracers may include contaminants, fatty acids, or stable isotopes, the latter of which are widely used in trophic ecology to estimate the mixed diet of consumers. Bayesian implementations of mixing models using stable isotopes (e.g., MixSIR, SIAR) are regularly used by ecologists for this purpose, but basic questions remain about when each is most appropriate. In this study, we describe the structural differences between common mixing model error formulations in terms of their assumptions about the predation process. We then introduce a new parameterization that unifies these mixing model error structures, as well as implicitly estimates the rate at which consumers sample from source populations (i.e., consumption rate). Using simulations and previously published mixing model datasets, we demonstrate that the new error parameterization outperforms existing models and provides an estimate of consumption. Our results suggest that the error structure introduced here will improve future mixing model estimates of animal diet.

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.

Analyzing mixing systems using a new generation of Bayesian tracer mixing models.

The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software-the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.

A deconvolutional Bayesian mixing model approach for river basin sediment source apportionment.

Increasing complexity in human-environment interactions at multiple watershed scales presents major challenges to sediment source apportionment data acquisition and analysis. Herein, we present a step-change in the application of Bayesian mixing models: Deconvolutional-MixSIAR (D-MIXSIAR) to underpin sustainable management of soil and sediment. This new mixing model approach allows users to directly account for the 'structural hierarchy' of a river basin in terms of sub-watershed distribution. It works by deconvoluting apportionment data derived for multiple nodes along the stream-river network where sources are stratified by sub-watershed. Source and mixture samples were collected from two watersheds that represented (i) a longitudinal mixed agricultural watershed in the south west of England which had a distinct upper and lower zone related to topography and (ii) a distributed mixed agricultural and forested watershed in the mid-hills of Nepal with two distinct sub-watersheds. In the former, geochemical fingerprints were based upon weathering profiles and anthropogenic soil amendments. In the latter compound-specific stable isotope markers based on soil vegetation cover were applied. Mixing model posterior distributions of proportional sediment source contributions differed when sources were pooled across the watersheds (pooled-MixSIAR) compared to those where source terms were stratified by sub-watershed and the outputs deconvoluted (D-MixSIAR). In the first example, the stratified source data and the deconvolutional approach provided greater distinction between pasture and cultivated topsoil source signatures resulting in a different posterior distribution to non-deconvolutional model (conventional approaches over-estimated the contribution of cultivated land to downstream sediment by 2 to 5 times). In the second example, the deconvolutional model elucidated a large input of sediment delivered from a small tributary resulting in differences in the reported contribution of a discrete mixed forest source. Overall D-MixSIAR model posterior distributions had lower (by ca 25-50%) uncertainty and quicker model run times. In both cases, the structured, deconvoluted output cohered more closely with field observations and local knowledge underpinning the need for closer attention to hierarchy in source and mixture terms in river basin source apportionment. Soil erosion and siltation challenge the energy-food-water-environment nexus. This new tool for source apportionment offers wider application across complex environmental systems affected by natural and human-induced change and the lessons learned are relevant to source apportionment applications in other disciplines.

Estimating incidence of vision-reducing cataract in Africa: a new model with implications for program targets.

OBJECTIVE: To estimate the incidence of vision-reducing cataract in sub-Saharan Africa and use these data to calculate cataract surgical rates (CSR) needed to eliminate blindness and visual impairment due to cataract. METHODS: Using data from recent population-based, standardized, rapid-assessment surveys, we calculated the age-specific prevalence of cataract (including operated and unoperated eyes) from surveys in 7 "districts" across Africa. This was done at 3 levels of visual acuity. Then we used the age-specific prevalence data to develop a model to estimate age-specific incidence at different visual acuities, taking into account differences in mortality rates between those with cataract compared with those without. The model included development of opacity in the first eye and second eye of people older than 50 years. The incidence data were used to calculate target cataract surgical rates. RESULTS: Incidence and CSR needs varied significantly in different sites and were lower in some than expected. Cataract surgical rates may depend on genetic, environmental, or cultural variations and will vary with population structure, which is not uniform across Africa. CONCLUSION: Africa should not be viewed as homogeneous in terms of cataract incidence or CSR needed. These CSR calculations should be useful for more appropriate planning of human resources and service delivery on the continent. The methodology can be applied to other population-based data as they become available to determine appropriate CSR targets.