Biogeochemical tracers found in the hard parts of organisms are frequently used to answer key ecological questions by linking the organism with the environment. However, the biogeochemical relationship between the environment and the biogenic structure becomes less predictable in higher organisms as physiological processes become more complex. Here, we use the simultaneous combination of biogeochemical tracers and fish growth analyzed with a novel modeling framework to describe physiological and environmental controls on otolith chemistry in an upwelling zone. First, we develop increasingly complex univariate mixed models to describe and partition intrinsic (age effects) and extrinsic (environmental parameters) factors influencing fish growth and otolith element concentrations through time. Second, we use a multivariate mixed model to investigate the directionality and strength between element‐to‐element and growth relationships and test hypotheses regarding physiological and environmental controls on element assimilation in otoliths. We apply these models to continuous element (Na, Sr, Mg, Ba, Li) and growth increment profiles (monthly resolution over 17 yr) derived from otoliths of reef ocean perch (Helicolenus percoides), a wild‐caught, site‐attached, fully marine fish. With a conceptual model, we hypothesize that otolith traits (elements and growth) driven by environmental conditions will correlate both within an otolith, reflecting the time dependency of growth and element assimilation, and among individuals that experience a similar set of external conditions. We found some elements (Sr:Ca and Na:Ca) are mainly controlled by physiological processes, while other elements (Ba:Ca and Li:Ca) are more environmentally influenced. Within an individual fish, the strength and direction of correlation varies among otolith traits, particularly those under environmental control. Correlations among physiologically regulated elements tend to be stronger than those primarily controlled by environmental drivers. Surprisingly, only Ba:Ca and growth are significantly correlated among individuals. Failure to appropriately account for intrinsic effects (e.g., age) led to inflated estimates of among individual correlations and a depression of within individual correlations. Together, the lack of among‐individual correlations of otolith traits in properly formulated models and the biases that can be introduced by not including appropriate intrinsic covariates suggest that caution is needed when assuming multi‐elemental signatures are reflective solely of shared environments.
Most species of bryozoans have short-lived larvae with limited dispersal potential, yet many of these species possess global distributions. In this study, we report the first occurrence from the western Atlantic Ocean of the widely distributed arborescent bryozoan Tricellaria inopinata d 'Hondt and Occhipinti-Ambrogi, 1985. This species was collected in Eel Pond, Woods Hole, Massachusetts, in September 2010. At that time, T. inopinata colonies had already formed dense conspecific aggregations at some collection sites, despite the presence of several other arborescent bryozoans. Sites were monitored throughout 2011 to track the success of this introduction, and to assess the reproductive timing of T. inopinata in Eel Pond. To determine the likelihood of T. inopinata persisting in Eel Pond and competing with previously established bryozoans, rates of metamorphic initiation, metamorphic completion, and overall offspring survivability were compared to one of the other dominant arborescent species. Finally, we provide taxonomic details to aid in identifying these animals, consider the potential mode of transport, and discuss the potential ecological implications resulting from this introduction.
Identifying trends and drivers of fish growth in commercial species is important for ongoing sustainable management, but there is a critical shortage of long-term datasets in marine systems. Using otolith (ear bone) sclerochronology and mixed-effects modeling, we reconstructed nearly four decades (37 yr) of growth across four oceanographically diverse regions in an iconic fishery species, snapper (Chrysophrys auratus). Growth was then related to environmental factors (sea surface temperature, chlorophyll-a, and Southern Oscillation Index) and population performance indicators (recruitment and commercial catch). Across the decades, growth rates declined in the two most productive fishery regions. Chlorophylla (a measure of primary productivity) was the best predictor of growth for all regions, but direction and magnitude of the relationships varied, indicating regional-specific differences in intra-specific competition. Sea surface temperature was positively correlated with fish growth, but negatively correlated after temperature reached optimum thermal maxima, which suggests individuals in warmer regions may be under thermal stress. Growth also decreased at the extremes of the Southern Oscillation Index, indicating fish growth is impeded in significant climatic events. Contrasting relationships between growth, catch, and recruitment indicated regional-specific density-dependent effects, with growth positively correlated with population size in one region but negatively correlated in another. Our results indicate that under future ocean warming and increased frequency of extreme climate events, fish growth and fisheries productivity are likely to be affected. Furthermore, the interactive effects of extrinsic factors also indicated that stressors on fisheries should be managed collectively. We show that otolith chronologies are an effective method to assess long-term trends and drivers of growth in fishery species. Such informed ecological predictions will help shape the sustainable management of fisheries under future changing climates.
Growth modelling is a fundamental component of fisheries assessments but is often hindered by poor quality data from biased sampling. Several methods have attempted to account for sample bias in growth analyses. However, in many cases this bias is not overcome, especially when large individuals are under-sampled. In growth models, two key parameters have a direct biological interpretation: L0, which should correspond to length-at-birth and L∞, which should approximate the average length of full-grown individuals. Here, we present an approach of fitting Bayesian growth models using Markov Chain Monte Carlo (MCMC), with informative priors on these parameters to improve the biological plausibility of growth estimates. A generalised framework is provided in an R package ‘BayesGrowth’, which removes the hurdle of programming an MCMC model for new users. Four case studies representing different sampling scenarios as well as three simulations with different selectivity functions were used to compare this Bayesian framework to standard frequentist growth models. The Bayesian models either outperformed or matched the results of frequentist growth models in all examples, demonstrating the broad benefits offered by this approach. This study highlights the impact that Bayesian models could provide in age and growth studies if applied more routinely rather than being limited to only complex or sophisticated applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.