Modeling ocean distributions and abundances of natural- and hatchery-origin Chinook salmon stocks with integrated genetic and tagging data.

Background: Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations. Methods: We introduce and fit a comprehensive joint model that integrates data from CWT recoveries and GSI sampling to inform the marine life history of Chinook salmon stocks at spatial and temporal scales relevant to ongoing fisheries management efforts. In a departure from similar models based primarily on CWT recoveries, modeled stocks in the new framework encompass both hatchery- and natural-origin fish. We specifically model the spatial distribution and marine abundance of four distinct stocks with spawning locations in California and southern Oregon, one of which is listed under the U.S. Endangered Species Act. Results: Using the joint model, we generated the most comprehensive estimates of marine distribution to date for all modeled Chinook salmon stocks, including historically data poor and low abundance stocks. Estimated marine distributions from the joint model were broadly similar to estimates from a simpler, CWT-only model but did suggest some differences in distribution in select seasons. Model output also included novel stock-, year-, and season-specific estimates of marine abundance. We observed and partially addressed several challenges in model convergence with the use of supplemental data sources and model constraints; similar difficulties are not unexpected with integrated modeling. We identify several options for improved data collection that could address issues in convergence and increase confidence in model estimates of abundance. We expect these model advances and results provide management-relevant biological insights, with the potential to inform future mixed-stock fisheries management efforts, as well as a foundation for more expansive and comprehensive analyses to follow.

Signal and noise in metabarcoding data.

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing-both of which we can model-but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Toward quantitative metabarcoding.

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provide important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies-known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture-struggle to link genetic observations to the underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology, it becomes more important to develop ways to make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying polymerase chain reaction mechanism explains the observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arise, rather than transforming the data post hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

Introducing zoid: A mixture model and R package for modeling proportional data with zeros and ones in ecology.

Many ecological data sets are proportional, representing mixtures of constituent elements such as species, populations, or strains. Analyses of proportional data are challenged by categories with zero observations (zeros), all observations (ones), and overdispersion. In lieu of ad hoc data adjustments, we describe and evaluate a zero-and-one inflated Dirichlet regression model, with its corresponding R package (zoid), capable of handling observed data x $$ x $$ consisting of three possible categories: zeros, proportions, or ones. Instead of fitting the model to observations of single biological units (e.g., individual organisms) within a sample, we sum proportional contributions across units and estimate mixture proportions using one aggregated observation per sample. Optional estimation of overdispersion and covariate influences expand model applications. We evaluate model performance, as implemented in Stan, using simulations and two ecological case studies. We show that zoid successfully estimates mixture proportions using simulated data with varying sample sizes and is robust to overdispersion and covariate structure. In empirical case studies, we estimate the composition of a mixed-stock Chinook salmon (Oncorhynchus tshawytscha) fishery and analyze the stomach contents of Atlantic cod (Gadus morhua). Our implementation of the model as an R package facilitates its application to varied ecological data sets composed of proportional observations.

Environmental DNA provides quantitative estimates of Pacific hake abundance and distribution in the open ocean.

All species inevitably leave genetic traces in their environments, and the resulting environmental DNA (eDNA) reflects the species present in a given habitat. It remains unclear whether eDNA signals can provide quantitative metrics of abundance on which human livelihoods or conservation successes depend. Here, we report the results of a large eDNA ocean survey (spanning 86 000 km2 to depths of 500 m) to understand the abundance and distribution of Pacific hake (Merluccius productus), the target of the largest finfish fishery along the west coast of the USA. We sampled eDNA in parallel with a traditional acoustic-trawl survey to assess the value of eDNA surveys at a scale relevant to fisheries management. Despite local differences, the two methods yield comparable information about the broad-scale spatial distribution and abundance. Furthermore, we find depth and spatial patterns of eDNA closely correspond to acoustic-trawl estimates for hake. We demonstrate the power and efficacy of eDNA sampling for estimating abundance and distribution and move the analysis eDNA data beyond sample-to-sample comparisons to management relevant scales. We posit that eDNA methods are capable of providing general quantitative applications that will prove especially valuable in data- or resource-limited contexts.

Understanding PCR Processes to Draw Meaningful Conclusions from Environmental DNA Studies.

As environmental DNA (eDNA) studies have grown in popularity for use in ecological applications, it has become clear that their results differ in significant ways from those of traditional, non-PCR-based surveys. In general, eDNA studies that rely on amplicon sequencing may detect hundreds of species present in a sampled environment, but the resulting species composition can be idiosyncratic, reflecting species' true biomass abundances poorly or not at all. Here, we use a set of simulations to develop a mechanistic understanding of the processes leading to the kinds of results common in mixed-template PCR-based (metabarcoding) studies. In particular, we focus on the effects of PCR cycle number and primer amplification efficiency on the results of diversity metrics in sequencing studies. We then show that proportional indices of amplicon reads capture trends in taxon biomass with high accuracy, particularly where amplification efficiency is high (median correlation up to 0.97). Our results explain much of the observed behavior of PCR-based studies, and lead to recommendations for best practices in the field.

Size, growth, and density data for shallow-water sea urchins from Mexico to the Aleutian Islands, Alaska, 1956-2016.

Size, growth, and density have been studied for North American Pacific coast sea urchins Strongylocentrotus purpuratus, S. droebachiensis, S. polyacanthus, Mesocentrotus (Strongylocentrotus) franciscanus, Lytechinus pictus, Centrostephanus coronatus, and Arbacia stellata by various workers at diverse sites and for varying lengths of time from 1956 to present. Numerous peer-reviewed publications have used some of these data but some data have appeared only in graduate theses or the gray literature. There also are data that have never appeared outside original data sheets. Motivation for studies has included fisheries management and environmental monitoring of sewer and power plant outfalls as well as changes associated with disease epidemics. Studies also have focused on kelp restoration, community effects of sea otters, basic sea urchin biology, and monitoring. The data sets presented here are a historical record of size, density, and growth for a common group of marine invertebrates in intertidal and nearshore environments that can be used to test hypotheses concerning future changes associated with fisheries practices, shifts of predator distributions, climate and ecosystem changes, and ocean acidification along the Pacific Coast of North America and islands of the north Pacific. No copyright restrictions apply. Please credit this paper when using the data.

Spatial distribution of environmental DNA in a nearshore marine habitat.

In the face of increasing threats to biodiversity, the advancement of methods for surveying biological communities is a major priority for ecologists. Recent advances in molecular biological technologies have made it possible to detect and sequence DNA from environmental samples (environmental DNA or eDNA); however, eDNA techniques have not yet seen widespread adoption as a routine method for biological surveillance primarily due to gaps in our understanding of the dynamics of eDNA in space and time. In order to identify the effective spatial scale of this approach in a dynamic marine environment, we collected marine surface water samples from transects ranging from the intertidal zone to four kilometers from shore. Using PCR primers that target a diverse assemblage of metazoans, we amplified a region of mitochondrial 16S rDNA from the samples and sequenced the products on an Illumina platform in order to detect communities and quantify their spatial patterns using a variety of statistical tools. We find evidence for multiple, discrete eDNA communities in this habitat, and show that these communities decrease in similarity as they become further apart. Offshore communities tend to be richer but less even than those inshore, though diversity was not spatially autocorrelated. Taxon-specific relative abundance coincided with our expectations of spatial distribution in taxa lacking a microscopic, pelagic life-history stage, though most of the taxa detected do not meet these criteria. Finally, we use carefully replicated laboratory procedures to show that laboratory treatments were remarkably similar in most cases, while allowing us to detect a faulty replicate, emphasizing the importance of replication to metabarcoding studies. While there is much work to be done before eDNA techniques can be confidently deployed as a standard method for ecological monitoring, this study serves as a first analysis of diversity at the fine spatial scales relevant to marine ecologists and confirms the promise of eDNA in dynamic environments.

A framework for inferring biological communities from environmental DNA.

Environmental DNA (eDNA), genetic material recovered from an environmental medium such as soil, water, or feces, reflects the membership of the ecological community present in the sampled environment. As such, eDNA is a potentially rich source of data for basic ecology, conservation, and management, because it offers the prospect of quantitatively reconstructing whole ecological communities from easily obtained samples. However, like all sampling methods, eDNA sequencing is subject to methodological limitations that can generate biased descriptions of ecological communities. Here, we demonstrate parallels between eDNA sampling and traditional sampling techniques, and use these parallels to offer a statistical structure for framing the challenges faced by eDNA and for illuminating the gaps in our current knowledge. Although the current state of knowledge on some of these steps precludes a full estimate of biomass for each taxon in a sampled eDNA community, we provide a map that illustrates potential methods for bridging these gaps. Additionally, we use an original data set to estimate the relative abundances of taxon-specific template DNA prior to PCR, given the abundance of DNA sequences recovered post-PCR-and-sequencing, a critical step in the chain of eDNA inference. While we focus on the use of eDNA samples to determine the relative abundance of taxa within a community, our approach also applies to single-taxon applications (including applications using qPCR), studies of diversity, and studies focused on occurrence. By grounding inferences about eDNA community composition in a rigorous statistical framework, and by making these inferences explicit, we hope to improve the inferential potential for the emerging field of community-level eDNA analysis.

Assessing trade-offs to inform ecosystem-based fisheries management of forage fish.

Twenty-first century conservation is centered on negotiating trade-offs between the diverse needs of people and the needs of the other species constituting coupled human-natural ecosystems. Marine forage fishes, such as sardines, anchovies, and herring, are a nexus for such trade-offs because they are both central nodes in marine food webs and targeted by fisheries. An important example is Pacific herring, Clupea pallisii in the Northeast Pacific. Herring populations are subject to two distinct fisheries: one that harvests adults and one that harvests spawned eggs. We develop stochastic, age-structured models to assess the interaction between fisheries, herring populations, and the persistence of predators reliant on herring populations. We show that egg- and adult-fishing have asymmetric effects on herring population dynamics--herring stocks can withstand higher levels of egg harvest before becoming depleted. Second, ecosystem thresholds proposed to ensure the persistence of herring predators do not necessarily pose more stringent constraints on fisheries than conventional, fishery driven harvest guidelines. Our approach provides a general template to evaluate ecosystem trade-offs between stage-specific harvest practices in relation to environmental variability, the risk of fishery closures, and the risk of exceeding ecosystem thresholds intended to ensure conservation goals are met.

The origin of female-biased sex ratios in intertidal seagrasses (Phyllospadix spp.).

Flowering sex ratios of dioecious plants are commonly male-biased but rarely female-biased. While greater costs of reproduction from females have been repeatedly demonstrated and explain male biases, male reproductive costs almost never exceed female costs, making the origins of female biases enigmatic. I investigated the seagrasses Phyllospadix scouleri and P. serrulatus (surfgrasses), which have some of the most extreme female-biased sex ratios documented (>90% female), to identify the mechanisms driving sex ratio bias. I developed sex-linked amplified fragment length polymorphism (AFLP) markers and applied them to three P. scouleri life stages at four sites to determine when during the life cycle sex ratio bias arises. Sex ratios were even among seedlings but became more female-biased at later life stages, indicating that sex ratios were driven by male-biased mortality. To identify when during the life cycle sex ratio bias developed, I examined sex differences in survival among seedlings and three aspects of reproductive costs that could potentially generate biased sex ratios under field conditions. No differences in seedling survival between the sexes were detected, and there was no evidence of substantial sex differences in costs of reproduction. I found no support for a trade-off between current and future reproduction or between reproductive investment and growth. Thus, costs of reproduction appear unlikely to drive sex ratio bias in surfgrass. Instead, small sex differences in growth and survival spread across the life cycle appear to be responsible for female-biased sex ratios and suggest that life history trade-offs other than reproductive costs drive sex ratio bias.

The ecological and evolutionary drivers of female-biased sex ratios: two-sex models of perennial seagrasses.

Among sexually reproducing species, differences between the sexes within species are ubiquitous. Despite the clear effect of sex differences on sex ratios and population growth rates, demographic models rarely consider both sexes explicitly. Here I explore the causes of extreme female-biased sex ratios in two marine angiosperms (Phyllospadix spp.). Using demographic data, I develop two-sex matrix projection models to assess the magnitude of demographic differences necessary to generate observed sex ratios and the consequences of sex differences for population growth rates. I demonstrate that small sex differences in survival can generate biased sex ratios, but the importance of sexual reproduction differs markedly between species. Even in the absence of a direct trade-off between sexual and asexual reproduction, the presence of two reproductive modes affects both the importance of sex and the sex-ratio bias. Using sensitivity analyses, I quantify the contribution of shared and sex-specific vital rates and show that until males become rare, the sensitivity of sex-specific vital rates is small relative to that of shared vital rates. I demonstrate that placing sex differences in the context of a demographic model that includes biologically motivated life-history trade-offs can explain the maintenance of sex-specific life histories and the persistence of skewed sex ratios.

SKEWED SEX RATIOS, POLLEN LIMITATION, AND REPRODUCTIVE FAILURE IN THE DIOECIOUS SEAGRASS PHYLLOSPADIX.

Dioecious mating systems in angiosperms, in which individuals are unisexual, require that pollen disperses between male and female individuals and may cause dioecious species to be vulnerable to reduced reproductive success via pollen limitation. This potential cost has rarely been documented, in part because of a dearth of studies investigating abiotic pollen dispersal, which tends to be associated with dioecy. I document widespread variation in reproductive success in an ecologically important, habitat-forming group, the seagrasses (marine angiosperms), using two species of the dioecious genus Phyllospadix. At multiple sites in the state of Washington, USA, I demonstrate pervasive male rarity (all sites <24% male by area). Male abundance is shown to predict female reproductive success at two scales. Within sites, the ratio of maturing seeds to total ovules declines rapidly with increasing distance to the nearest male, while among sites, mean seed : ovule ratios scale positively with male abundance. At some sites, less than 1 in 100 ovules mature into seeds. A field experiment conducted at a high pollen availability site shows that manipulating pollen availability could produce the range of seed sets observed across sites, but pollen limitation was not definitively demonstrated. Overall my results are consistent with pollen limitation in Phyllospadix and comprise the first strong evidence of this phenomenon in seagrass populations. In addition, seed production rates predicted local seedling recruitment, demonstrating a measurable demographic consequence of low pollen availability.