Reconciling the size‐dependence of marine particle sinking speed

Sinking particles are critical to the ocean's “biological pump,” sequestering carbon from the atmosphere. Particles' sinking speeds are a primary factor determining fluxes and subsequent ecological and climatic impacts. While size is a key determinant of particles' sinking speeds, observations suggest a variable size-sinking relationship, affected by other particle properties, resulting in substantial spread in parameterizations of particle sinking and fluxes. We compile particle size-sinking observations and apply hierarchical Bayesian statistical models to resolve the size-sinking relationship while accounting for other factors. We find an overall scaling close to the general Navier-Stokes drag equation, and differences between particle types, open ocean versus coastal/laboratory particles, and in situ versus ex situ methods. These results can help harmonize how Earth system models parameterize particle fluxes and support a weaker size-dependence than often assumed, with implications for the flux contribution of small particles and the predicted future shrinking of marine particle populations

Predator decline leads to decreased stability in a coastal fish community

Fisheries exploitation has caused widespread declines in marine predators. Theory predicts that predator depletion will destabilise lower trophic levels, making natural communities more vulnerable to environmental perturbations. However, empirical evidence has been limited. Using a community matrix model, we empirically assessed trends in the stability of a multispecies coastal fish community over the course of predator depletion. Three indices of community stability (resistance, resilience and reactivity) revealed significantly decreasing stability concurrent with declining predator abundance. The trophically downgraded community exhibited weaker top-down control, leading to predator-release processes in lower trophic levels and increased susceptibility to perturbation. At the community level, our results suggest that high predator abundance acts as a stabilising force to the naturally stochastic and highly autocorrelated dynamics in low trophic species. These findings have important implications for the conservation and management of predators in marine ecosystems and provide empirical support for the theory of predatory control

Predator decline leads to decreased stability in a coastal fish community

Abstract Fisheries exploitation has caused widespread declines in marine predators. Theory predicts that predator depletion will destabilise lower trophic levels, making natural communities more vulnerable to environmental perturbations. However, empirical evidence has been limited. Using a community matrix model, we empirically assessed trends in the stability of a multispecies coastal fish community over the course of predator depletion. Three indices of community stability (resistance, resilience and reactivity) revealed significantly decreasing stability concurrent with declining predator abundance. The trophically downgraded community exhibited weaker top-down control, leading to predator-release processes in lower trophic levels and increased susceptibility to perturbation. At the community level, our results suggest that high predator abundance acts as a stabilising force to the naturally stochastic and highly autocorrelated dynamics in low trophic species. These findings have important implications for the conservation and management of predators in marine ecosystems and provide empirical support for the theory of predatory control

Next-generation ensemble projections reveal higher climate risks for marine ecosystems

Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning

Evaluating the benefits of bayesian hierarchical methods for analyzing heterogeneous environmental datasets: a case study of marine organic carbon fluxes

Large compilations of heterogeneous environmental observations are increasingly available as public databases, allowing researchers to test hypotheses across datasets. Statistical complexities arise when analyzing compiled data due to unbalanced spatial sampling, variable environmental context, mixed measurement techniques, and other reasons. Hierarchical Bayesian modeling is increasingly used in environmental science to describe these complexities, however few studies explicitly compare the utility of hierarchical Bayesian models to simpler and more commonly applied methods. Here we demonstrate the utility of the hierarchical Bayesian approach with application to a large compiled environmental dataset consisting of 5,741 marine vertical organic carbon flux observations from 407 sampling locations spanning eight biomes across the global ocean. We fit a global scale Bayesian hierarchical model that describes the vertical profile of organic carbon flux with depth. Profile parameters within a particular biome are assumed to share a common deviation from the global mean profile. Individual station-level parameters are then modeled as deviations from the common biome-level profile. The hierarchical approach is shown to have several benefits over simpler and more common data aggregation methods. First, the hierarchical approach avoids statistical complexities introduced due to unbalanced sampling and allows for flexible incorporation of spatial heterogeneitites in model parameters. Second, the hierarchical approach uses the whole dataset simultaneously to fit the model parameters which shares information across datasets and reduces the uncertainty up to 95% in individual profiles. Third, the Bayesian approach incorporates prior scientific information about model parameters; for example, the non-negativity of chemical concentrations or mass-balance, which we apply here. We explicitly quantify each of these properties in turn. We emphasize the generality of the hierarchical Bayesian approach for diverse environmental applications and its increasing feasibility for large datasets due to recent developments in Markov Chain Monte Carlo algorithms and easy-to-use high-level software implementations

Rebuilding marine life

The UN Sustainable Development Goal 14 aims to “conserve and sustainably use the oceans, seas and marine resources for sustainable development”. Achieving this goal will require rebuilding the marine life-support systems that deliver the many benefits society receives from a healthy ocean. In this Review we document the recovery of marine populations, habitats and ecosystems following past conservation interventions. Recovery rates across studies suggest that substantial recovery of the abundance, structure, and function of marine life could be achieved by 2050, should major pressures, including climate change, be mitigated. Rebuilding marine life represents a doable Grand Challenge for humanity, and ethical obligation, and a smart economic objective to achieve a sustainable future

How repetitive are genomes?

BACKGROUND: Genome sequences vary strongly in their repetitiveness and the causes for this are still debated. Here we propose a novel measure of genome repetitiveness, the index of repetitiveness, I(r), which can be computed in time proportional to the length of the sequences analyzed. We apply it to 336 genomes from all three domains of life. RESULTS: The expected value of I(r )is zero for random sequences of any G/C content and greater than zero for sequences with excess repeats. We find that the I(r )of archaea is significantly smaller than that of eubacteria, which in turn is smaller than that of eukaryotes. Mouse chromosomes have a significantly higher I(r )than human chromosomes and within each genome the Y chromosome is most repetitive. A sliding window analysis reveals that the human HOXA cluster and two surrounding genes are characterized by local minima in I(r). A program for calculating the I(r )is freely available at . CONCLUSION: The general measure of DNA repetitiveness proposed in this paper can be efficiently computed on a genomic scale. This reveals a broad spectrum of repetitiveness among diverse genomes which agrees qualitatively with previous studies of repeat content. A sliding window analysis helps to analyze the intragenomic distribution of repeats

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

Genome-Wide Influence of Indel Substitutions on Evolution of Bacteria of the PVC Superphylum, Revealed Using a Novel Computational Method

Whole-genome scans for positive Darwinian selection are widely used to detect evolution of genome novelty. Most approaches are based on evaluation of nonsynonymous to synonymous substitution rate ratio across evolutionary lineages. These methods are sensitive to saturation of synonymous sites and thus cannot be used to study evolution of distantly related organisms. In contrast, indels occur less frequently than amino acid replacements, accumulate more slowly, and can be employed to characterize evolution of diverged organisms. As indels are also subject to the forces of natural selection, they can generate functional changes through positive selection. Here, we present a new computational approach to detect selective constraints on indel substitutions at the whole-genome level for distantly related organisms. Our method is based on ancestral sequence reconstruction, takes into account the varying susceptibility of different types of secondary structure to indels, and according to simulation studies is conservative. We applied this newly developed framework to characterize the evolution of organisms of the Planctomycetes, Verrucomicrobia, Chlamydiae (PVC) bacterial superphylum. The superphylum contains organisms with unique cell biology, physiology, and diverse lifestyles. It includes bacteria with simple cell organization and more complex eukaryote-like compartmentalization. Lifestyles range from free-living organisms to obligate pathogens. In this study, we conduct a whole-genome level analysis of indel substitutions specific to evolutionary lineages of the PVC superphylum and found that indels evolved under positive selection on up to 12% of gene tree branches. We also analyzed possible functional consequences for several case studies of predicted indel events