NOAA National Marine Fisheries Service Northwest Fisheries Science Center

Plastic pollution is a planetary threat, affecting nearly every marine and freshwater ecosystem globally. In response, multilevel mitigation strategies are being adopted but with a lack of quantitative assessment of how such strategies reduce plastic emissions. We assessed the impact of three broad management strategies, plastic waste reduction, waste management, and environmental recovery, at different levels of effort to estimate plastic emissions to 2030 for 173 countries. We estimate that 19 to 23 million metric tons, or 11%, of plastic waste generated globally in 2016 entered aquatic ecosystems. Considering the ambitious commitments currently set by governments, annual emissions may reach up to 53 million metric tons per year by 2030. To reduce emissions to a level well below this prediction, extraordinary efforts to transform the global plastics economy are needed.

Continued growth and intensification of aquaculture production depends upon the development of sustainable protein sources to replace fish meal in aquafeeds. This document reviews various plant feedstuffs, which currently are or potentially may be incorporated into aquafeeds to support the sustainable production of various fish species in aquaculture. The plant feedstuffs considered include oilseeds, legumes and cereal grains, which traditionally have been used as protein or energy concentrates as well as novel products developed through various processing technologies. The nutritional composition of these various feedstuffs are considered along with the presence of any bioactive compounds that may positively or negatively affect the target organism. Lipid composition of these feedstuffs is not specifically considered although it is recognized that incorporating lipid supplements in aquafeeds to achieve proper fatty acid profiles to meet the metabolic requirements of fish and maximize human health benefits are important aspects. Specific strategies and techniques to optimize the nutritional composition of plant feedstuffs and limit potentially adverse effects of bioactive compounds are also described. Such information will provide a foundation for developing strategic research plans for increasing the use of plant feedstuffs in aquaculture to reduce dependence of animal feedstuffs and thereby enhance the sustainability of aquaculture.

NeEstimator v2 is a completely revised and updated implementation of software that produces estimates of contemporary effective population size, using several different methods and a single input file. NeEstimator v2 includes three single-sample estimators (updated versions of the linkage disequilibrium and heterozygote-excess methods, and a new method based on molecular coancestry), as well as the two-sample (moment-based temporal) method. New features include the following: (i) an improved method for accounting for missing data; (ii) options for screening out rare alleles; (iii) confidence intervals for all methods; (iv) the ability to analyse data sets with large numbers of genetic markers (10 000 or more); (v) options for batch processing large numbers of different data sets, which will facilitate cross-method comparisons using simulated data; and (vi) correction for temporal estimates when individuals sampled are not removed from the population (Plan I sampling). The user is given considerable control over input data and composition, and format of output files. The freely available software has a new JAVA interface and runs under MacOS, Linux and Windows.

Most surveys of mitochondrial DNA (mtDNA) in marine fishes reveal low levels of sequence divergence between haplotypes relative to the differentiation observed between sister taxa. It is unclear whether this pattern is due to rapid lineage sorting accelerated by sweepstakes recruitment, historical bottlenecks in population size, founder events, or natural selection, any of which could retard the accumulation of deep mtDNA lineages. Recent advances in paleoclimate research prompt a reexamination of oceanographic processes as a fundamental influence on genetic diversity; evidence from ice cores and anaerobic marine sediments document strong regime shifts in the world's oceans in concert with periodic climatic changes. These changes in sea surface temperatures, current pathways, upwelling intensities, and retention eddies are likely harbingers of severe fluctuations in population size or regional extinctions. Sardines (Sardina, Sardinops) and anchovies (Engraulis) are used to assess the consequences of such oceanographic processes on marine fish intrageneric gene genealogies. Representatives of these two groups occur in temperate boundary currents on a global scale, and these regional populations are known to fluctuate markedly. Biogeographic and genetic data indicate that Sardinops has persisted for at least 20 million years, yet the mtDNA genealogy for this group coalesces in less than half a million years and points to a recent founding of populations around the rim of the Indian-Pacific Ocean. Phylogeographic analysis of Old World anchovies reveals a Pleistocene dispersal from the Pacific to the Atlantic, almost certainly via southern Africa, followed by a very recent recolonization from Europe to southern Africa. These results demonstrate that regional populations of sardines and anchovies are subject to periodic extinctions and recolonizations. Such climate-associated dynamics may explain the low levels of nucleotide diversity and the shallow coalescence of mtDNA genealogies. If these findings apply generally to marine fishes, management strategies should incorporate the idea that even extremely abundant populations may be relatively fragile on ecological and evolutionary time scales.

In the Pacific Ocean, air and ocean temperatures, atmospheric carbon dioxide, landings of anchovies and sardines, and the productivity of coastal and open ocean ecosystems have varied over periods of about 50 years. In the mid-1970s, the Pacific changed from a cool "anchovy regime" to a warm "sardine regime." A shift back to an anchovy regime occurred in the middle to late 1990s. These large-scale, naturally occurring variations must be taken into account when considering human-induced climate change and the management of ocean living resources.

We review commonly used population definitions under both the ecological paradigm (which emphasizes demographic cohesion) and the evolutionary paradigm (which emphasizes reproductive cohesion) and find that none are truly operational. We suggest several quantitative criteria that might be used to determine when groups of individuals are different enough to be considered 'populations'. Units for these criteria are migration rate (m) for the ecological paradigm and migrants per generation (Nm) for the evolutionary paradigm. These criteria are then evaluated by applying analytical methods to simulated genetic data for a finite island model. Under the standard parameter set that includes L = 20 High mutation (microsatellite-like) loci and samples of S = 50 individuals from each of n = 4 subpopulations, power to detect departures from panmixia was very high ( approximately 100%; P < 0.001) even with high gene flow (Nm = 25). A new method, comparing the number of correct population assignments with the random expectation, performed as well as a multilocus contingency test and warrants further consideration. Use of Low mutation (allozyme-like) markers reduced power more than did halving S or L. Under the standard parameter set, power to detect restricted gene flow below a certain level X (H(0): Nm < X) can also be high, provided that true Nm < or = 0.5X. Developing the appropriate test criterion, however, requires assumptions about several key parameters that are difficult to estimate in most natural populations. Methods that cluster individuals without using a priori sampling information detected the true number of populations only under conditions of moderate or low gene flow (Nm < or = 5), and power dropped sharply with smaller samples of loci and individuals. A simple algorithm based on a multilocus contingency test of allele frequencies in pairs of samples has high power to detect the true number of populations even with Nm = 25 but requires more rigorous statistical evaluation. The ecological paradigm remains challenging for evaluations using genetic markers, because the transition from demographic dependence to independence occurs in a region of high migration where genetic methods have relatively little power. Some recent theoretical developments and continued advances in computational power provide hope that this situation may change in the future.

Tire tread particles turn streams toxic For coho salmon in the U.S. Pacific Northwest, returning to spawn in urban and suburban streams can be deadly. Regular acute mortality events are tied, in particular, to stormwater runoff, but the identity of the causative toxicant(s) has not been known. Starting from leachate from new and aged tire tread wear particles, Tian et al. followed toxic fractions through chromatography steps, eventually isolating a single molecule that could induce acute toxicity at threshold concentrations of ∼1 microgram per liter. The compound, called 6PPD-quinone, is an oxidation product of an additive intended to prevent damage to tire rubber from ozone. Measurements from road runoff and immediate receiving waters show concentrations of 6PPD-quinone high enough to account for the acute toxicity events. Science , this issue p. 185

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.

ldne is a program with a Visual Basic interface that implements a recently developed bias correction for estimates of effective population size (N(e) ) based on linkage disequilibrium data. The program reads genotypic data in standard formats and can accommodate an arbitrary number of samples, individuals, loci, and alleles, as well as two mating systems: random and lifetime monogamy. ldne calculates separate estimates using different criteria for excluding rare alleles, which facilitates evaluation of data for highly polymorphic markers such as microsatellites. The program also introduces a jackknife method for obtaining confidence intervals that appears to perform better than parametric methods currently in use.

Stable isotopes are a powerful tool for ecologists, often used to assess contributions of different sources to a mixture (e.g. prey to a consumer). Mixing models use stable isotope data to estimate the contribution of sources to a mixture. Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. We developed a Bayesian-mixing model that estimates probability distributions of source contributions to a mixture while explicitly accounting for uncertainty associated with multiple sources, fractionation and isotope signatures. This model also allows for optional incorporation of informative prior information in analyses. We demonstrate our model using a predator-prey case study. Accounting for uncertainty in mixing model inputs can change the variability, magnitude and rank order of estimates of prey (source) contributions to the predator (mixture). Isotope mixing models need to fully account for uncertainty in order to accurately estimate source contributions.

In many marine species, high levels of gene flow ensure that the genetic signal from population differentiation is weak. As a consequence, various errors associated with estimating population genetic parameters that might normally be safely ignored assume a relatively greater importance. This fact has important implications for the use of genetic data to address two common questions in fishery conservation and management: (1) How many stocks of a given species are there? and (2) How much gene flow occurs among stocks? This article discusses strategies to maximize the signal:noise ratio in genetic studies of marine species and suggests a quantitative method to correct for bias due to a common sampling problem. For many marine species, however, genetic methods alone cannot fully resolve these key management questions because the amount of migration necessary to eliminate most genetic evidence of stock structure (only a handful of individuals per generation) will generally be inconsequential as a force for rebuilding depleted populations on a time scale of interest to humans. These limitations emphasize the importance of understanding the biology and life history of the target species-first, to guide design of the sampling program, and second, so that additional information can be used to supplement indirect estimates of migration rates based on genetic data.

BACKGROUND As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth. ADVANCES On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy. OUTLOOK Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans. Emerging models are beginning to incorporate six key biological mechanisms that can improve predictions of biological responses to climate change. Models that include biological mechanisms have been used to project (clockwise from top) the evolution of disease-harboring mosquitoes, future environments and land use, physiological responses of invasive species such as cane toads, demographic responses of penguins to future climates, climate-dependent dispersal behavior in butterflies, and mismatched interactions between butterflies and their host plants. Despite these modeling advances, we seldom have the detailed data needed to build these models, necessitating new efforts to collect the relevant data to parameterize more biologically realistic predictive models.

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.

Climate change is a pervasive and growing global threat to biodiversity and ecosystems. Here, we present the most up-to-date assessment of climate change impacts on biodiversity, ecosystems, and ecosystem services in the U.S. and implications for natural resource management. We draw from the 4th National Climate Assessment to summarize observed and projected changes to ecosystems and biodiversity, explore linkages to important ecosystem services, and discuss associated challenges and opportunities for natural resource management. We find that species are responding to climate change through changes in morphology and behavior, phenology, and geographic range shifts, and these changes are mediated by plastic and evolutionary responses. Responses by species and populations, combined with direct effects of climate change on ecosystems (including more extreme events), are resulting in widespread changes in productivity, species interactions, vulnerability to biological invasions, and other emergent properties. Collectively, these impacts alter the benefits and services that natural ecosystems can provide to society. Although not all impacts are negative, even positive changes can require costly societal adjustments. Natural resource managers need proactive, flexible adaptation strategies that consider historical and future outlooks to minimize costs over the long term. Many organizations are beginning to explore these approaches, but implementation is not yet prevalent or systematic across the nation.

In this paper, we review recent advances in stable isotope mixing models (SIMMs) and place them into an overarching Bayesian statistical framework, which allows for several useful extensions. SIMMs are used to quantify the proportional contributions of various sources to a mixture. The most widely used application is quantifying the diet of organisms based on the food sources they have been observed to consume. At the centre of the multivariate statistical model we propose is a compositional mixture of the food sources corrected for various metabolic factors. The compositional component of our model is based on the isometric log‐ratio transform. Through this transform, we can apply a range of time series and non‐parametric smoothing relationships. We illustrate our models with three case studies based on real animal dietary behaviour. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Genetic methods are routinely used to estimate contemporary effective population size (N e) in natural populations, but the vast majority of applications have used only the temporal (two-sample) method. We use simulated data to evaluate how highly polymorphic molecular markers affect precision and bias in the single-sample method based on linkage disequilibrium (LD). Results of this study are as follows: (1) Low-frequency alleles upwardly bias [Formula: see text], but a simple rule can reduce bias to <about 10% without sacrificing much precision. (2) With datasets routinely available today (10-20 loci with 10 alleles; 50 individuals), precise estimates can be obtained for relatively small populations (N e < 200), and small populations are not likely to be mistaken for large ones. However, it is very difficult to obtain reliable estimates for large populations. (3) With 'microsatellite' data, the LD method has greater precision than the temporal method, unless the latter is based on samples taken many generations apart. Our results indicate the LD method has widespread applicability to conservation (which typically focuses on small populations) and the study of evolutionary processes in local populations. Considerable opportunity exists to extract more information about N e in nature by wider use of single-sample estimators and by combining estimates from different methods.

Assignment methods, which use genetic information to ascertain population membership of individuals or groups of individuals, have been used in recent years to study a wide range of evolutionary and ecological processes. In applied studies, the first step of articulating the biological question(s) to be addressed should be followed by selection of the method(s) best suited for the analysis. However, this first step often receives less attention than it should, and the recent proliferation of assignment methods has made the selection step challenging. Here, we review assignment methods and discuss how to match the appropriate methods with the underlying biological questions for several common problems in ecology and conservation (assessing population structure; measuring dispersal and hybridization; and forensics and mixture analysis). We also identify several topics for future research that should ensure that this field remains dynamic and productive.

Integrated ecosystem assessments challenge the broader scientific community to move beyond the important task of tallying insults to marine ecosystems to developing quantitative tools that can support the decisions national and regional resource managers must make.

Climate change became real for many Americans in 2012 when a record heat wave affected much of the United States, and Superstorm Sandy pounded the Northeast. At the same time, a less visible heat wave was occurring over a large portion of the Northwest Atlantic Ocean. Like the heat wave on land, the ocean heat wave affected coastal ecosystems and economies. Marine species responded to warmer temperatures by shifting their geographic distribution and seasonal cycles. Warm-water species moved northward, and some species undertook local migrations earlier in the season, both of which affected fisheries targeting those species. Extreme events are expected to become more common as climate change progresses (Tebaldi et al., 2006; Hansen et al., 2012). The 2012 Northwest Atlantic heat wave provides valuable insights into ways scientific information streams and fishery management frameworks may need to adapt to be effective as ocean temperatures warm and become more variable.

Rising atmospheric carbon dioxide (CO 2 ) levels, from fossil fuel combustion and deforestation, along with agriculture and land-use practices are causing wholesale increases in seawater CO 2 and inorganic carbon levels; reductions in pH; and alterations in acid-base chemistry of estuarine, coastal, and surface open-ocean waters. On the basis of laboratory experiments and field studies of naturally elevated CO 2 marine environments, widespread biological impacts of human-driven ocean acidification have been posited, ranging from changes in organism physiology and population dynamics to altered communities and ecosystems. Acidification, in conjunction with other climate change–related environmental stresses, particularly under future climate change and further elevated atmospheric CO 2 levels, potentially puts at risk many of the valuable ecosystem services that the ocean provides to society, such as fisheries, aquaculture, and shoreline protection. Thisreview emphasizes both current scientific understanding and knowledge gaps, highlighting directions for future research and recognizing the information needs of policymakers and stakeholders.