European coastal monitoring programmes may fail to identify impacts on benthic macrofauna caused by bottom trawling.

Bottom trawling (hereafter trawling) is the dominant human pressure impacting continental shelves globally. However, due to ongoing data deficiencies for smaller coastal vessels, the effects of trawling on nearshore seabed ecosystems are poorly understood. In Europe, the Water Framework Directive (WFD) provides a framework for the protection and improvement of coastal water bodies. It requires member states to track the status of 'biological quality elements' (including benthic macrofauna) using WFD-specific ecological indicators. While many of these metrics are sensitive to coastal pressures such as nutrient enrichment, little is known about their ability to detect trawling impacts. Here, we analysed a comprehensive data set of 5885 nearshore benthic samples - spatiotemporally matched to high-resolution trawling and environmental data - to examine how these pressures affect coastal benthos. In addition, we investigated the ability of 8 widely-used benthic monitoring metrics to detect impacts on benthic biological quality. We found that abundance (N) and species richness (S) were strongly impacted by bottom trawling. A clear response to trawling was also observed for the WFD-specific Benthic Quality Index (BQI). Relationships between N and S, and trawling were particularly consistent across the study area, indicating sensitivity across varying environmental conditions. In contrast, WFD indices such as AZTIs Marine Biotic Index (AMBI), multivariate AMBI (M-AMBI), and the Danish Quality Index (DKI), were unresponsive to trawling. In fact, some of the most heavily trawled areas examined were classified as being of 'high/good ecological status' by these indices. A likely explanation for this is that the indices are calculated using species sensitivity scores, based on expected species response to eutrophication and chemical pollution. While the BQI also uses species sensitivity scores, these are based on observed responses to disturbance gradients comprising a range of coastal pressures. Given the prominent use of AMBI and DKI throughout Europe, our results highlight the considerable risk that the metrics used to assess Good Ecological Status (GES) under the WFD may fail to identify trawling impacts. As trawling represents a widespread source of coastal disturbance, fishing impacts on benthic macrofauna may be underestimated, or go undetected, in many coastal monitoring programmes around Europe.

Estimation of Individual Growth Trajectories When Repeated Measures Are Missing.

Individuals in a population vary in their growth due to hidden and observed factors such as age, genetics, environment, disease, and carryover effects from past environments. Because size affects fitness, growth trajectories scale up to affect population dynamics. However, it can be difficult to estimate growth in data from wild populations with missing observations and observation error. Previous work has shown that linear mixed models (LMMs) underestimate hidden individual heterogeneity when more than 25% of repeated measures are missing. Here we demonstrate a flexible and robust way to model growth trajectories. We show that state-space models (SSMs), fit using R package growmod, are far less biased than LMMs when fit to simulated data sets with missing repeated measures and observation error. This method is much faster than Markov chain Monte Carlo methods, allowing more models to be tested in a shorter time. For the scenarios we simulated, SSMs gave estimates with little bias when up to 87.5% of repeated measures were missing. We use this method to quantify growth of Soay sheep, using data from a long-term mark-recapture study, and demonstrate that growth decreased with age, population density, weather conditions, and when individuals are reproductive. The method improves our ability to quantify how growth varies among individuals in response to their attributes and the environments they experience, with particular relevance for wild populations.

How well can body size represent effects of the environment on demographic rates? Disentangling correlated explanatory variables.

Demographic rates are shaped by the interaction of past and current environments that individuals in a population experience. Past environments shape individual states via selection and plasticity, and fitness-related traits (e.g. individual size) are commonly used in demographic analyses to represent the effect of past environments on demographic rates. We quantified how well the size of individuals captures the effects of a population's past and current environments on demographic rates in a well-studied experimental system of soil mites. We decomposed these interrelated sources of variation with a novel method of multiple regression that is useful for understanding nonlinear relationships between responses and multicollinear explanatory variables. We graphically present the results using area-proportional Venn diagrams. Our novel method was developed by combining existing methods and expanding upon them. We showed that the strength of size as a proxy for the past environment varied widely among vital rates. For instance, in this organism with an income breeding life history, the environment had more effect on reproduction than individual size, but with substantial overlap indicating that size encompassed some of the effects of the past environment on fecundity. This demonstrates that the strength of size as a proxy for the past environment can vary widely among life-history processes within a species, and this variation should be taken into consideration in trait-based demographic or individual-based approaches that focus on phenotypic traits as state variables. Furthermore, the strength of a proxy will depend on what state variable(s) and what demographic rate is being examined; that is, different measures of body size (e.g. length, volume, mass, fat stores) will be better or worse proxies for various life-history processes.

Distribution and biomass of gelatinous zooplankton in relation to an oxygen minimum zone and a shallow seamount in the Eastern Tropical North Atlantic Ocean.

Physical and topographic characteristics can structure pelagic habitats and affect the plankton community composition. For example, oxygen minimum zones (OMZs) are expected to lead to a habitat compression for species with a high oxygen demand, while upwelling of nutrient-rich deep water at seamounts can locally increase productivity, especially in oligotrophic oceanic waters. Here we investigate the response of the gelatinous zooplankton (GZ) assemblage and biomass to differing oxygen conditions and to a seamount in the Eastern Tropical North Atlantic (ETNA) around the Cape Verde archipelago. A total of 16 GZ taxa (>1100 specimens) were found in the upper 1000 m with distinct species-specific differences, such as the absence of deep-living species Atolla wyvillei and Periphylla periphylla above the shallow seamount summit. Statistical analyses considering the most prominent groups, present at all stations, namely Beroe spp., hydromedusae (including Zygocanna vagans, Halicreas minimum, Colobonema sericeum, Solmissus spp.) and total GZ, showed a strong positive correlation of abundance with temperature for all groups, whereas oxygen had a weak negative correlation only with abundances of Beroe spp. and hydromedusae. To account for size differences between species, we established length-weight regressions and investigated total GZ biomass changes in relation to physical (OMZ) and topographic characteristics. The highest GZ biomass was observed at depths of lowest oxygen concentrations and deepest depth strata at the southeastern flank of the seamount and at two stations south of the Cape Verde archipelago. Our data suggest that, irrespective of their patchy distribution, GZ organisms are ubiquitous food web members of the ETNA, and their habitat includes waters of low oxygen content.

The prevalence and persistence of sigma virus, a biparentally transmitted parasite of Drosophila melanogaster.

QUESTION: How do vertically transmitted parasites persist? ORGANISMS: Drosophila melanogaster (host) and sigma virus (parasite). FIELD SITE: Peach stands in northern Georgia, USA, on a transect between Macon and Athens. EMPIRICAL METHODS: We estimated prevalence in the field. We also estimated male and female transmission in the laboratory, using field-collected animals as parents. We further quantified patrilineal (father to son) transmission in the laboratory, and estimated cost of infection (virulence) by quantifying decreased egg production of infected flies. MATHEMATICAL METHODS: Discrete-time, deterministic models for prevalence; analysis of stability of disease-free and endemic equilibria; numerical computation of equilibria based on empirical estimates. KEY ASSUMPTIONS: Random mating, discrete generations, cost of infection to females only. PREDICTIONS AND CONCLUSIONS: The model allows persistence under parameter estimates obtained for this population. Uncertainty in parameters leads to wide confidence intervals on the predicted prevalence, which may be systematically underestimated due to Jensen's inequality. Male transmission is required for persistence, and multiple generations of strictly patrilineal transmission are possible in the laboratory, albeit with decreasing transmission efficiency.

Statistical modeling of patterns in annual reproductive rates.

Reproduction by individuals is typically recorded as count data (e.g., number of fledglings from a nest or inflorescences on a plant) and commonly modeled using Poisson or negative binomial distributions, which assume that variance is greater than or equal to the mean. However, distributions of reproductive effort are often underdispersed (i.e., variance < mean). When used in hypothesis tests, models that ignore underdispersion will be overly conservative and may fail to detect significant patterns. Here we show that generalized Poisson (GP) and Conway-Maxwell-Poisson (CMP) distributions are better choices for modeling reproductive effort because they can handle both overdispersion and underdispersion; we provide examples of how ecologists can use GP and CMP distributions in generalized linear models (GLMs) and generalized linear mixed models (GLMMs) to quantify patterns in reproduction. Using a new R package, glmmTMB, we construct GLMMs to investigate how rainfall and population density influence the number of fledglings in the warbler Oreothlypis celata and how flowering rate of Heliconia acuminata differs between fragmented and continuous forest. We also demonstrate how to deal with zero-inflation, which occurs when there are more zeros than expected in the distribution, e.g., due to complete reproductive failure by some individuals.

A method for detecting positive growth autocorrelation without marking individuals.

In most ecological studies, within-group variation is a nuisance that obscures patterns of interest and reduces statistical power. However, patterns of within-group variability often contain information about ecological processes. In particular, such patterns can be used to detect positive growth autocorrelation (consistent variation in growth rates among individuals in a cohort across time), even in samples of unmarked individuals. Previous methods for detecting autocorrelated growth required data from marked individuals. We propose a method that requires only estimates of within-cohort variance through time, using maximum likelihood methods to obtain point estimates and confidence intervals of the correlation parameter. We test our method on simulated data sets and determine the loss in statistical power due to the inability to identify individuals. We show how to accommodate nonlinear growth trajectories and test the effects of size-dependent mortality on our method's accuracy. The method can detect significant growth autocorrelation at moderate levels of autocorrelation with moderate-sized cohorts (for example, statistical power of 80% to detect growth autocorrelation ρ (2) = 0.5 in a cohort of 100 individuals measured on 16 occasions). We present a case study of growth in the red-eyed tree frog. Better quantification of the processes driving size variation will help ecologists improve predictions of population dynamics. This work will help researchers to detect growth autocorrelation in cases where marking is logistically infeasible or causes unacceptable decreases in the fitness of marked individuals.

Generalized linear mixed models: a practical guide for ecology and evolution.

How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.