Counter culture: causes, extent and solutions of systematic bias in the analysis of behavioural counts.

We often quantify the rate at which a behaviour occurs by counting the number of times it occurs within a specific, short observation period. Measuring behaviour in such a way is typically unavoidable but induces error. This error acts to systematically reduce effect sizes, including metrics of particular interest to behavioural and evolutionary ecologists such as R2 , repeatability (intra-class correlation, ICC) and heritability. Through introducing a null model, the Poisson process, for modelling the frequency of behaviour, we give a mechanistic explanation of how this problem arises and demonstrate how it makes comparisons between studies and species problematic, because the magnitude of the error depends on how frequently the behaviour has been observed as well as how biologically variable the behaviour is. Importantly, the degree of error is predictable and so can be corrected for. Using the example of parental provisioning rate in birds, we assess the applicability of our null model for modelling the frequency of behaviour. We then survey recent literature and demonstrate that the error is rarely accounted for in current analyses. We highlight the problems that arise from this and provide solutions. We further discuss the biological implications of deviations from our null model, and highlight the new avenues of research that they may provide. Adopting our recommendations into analyses of behavioural counts will improve the accuracy of estimated effect sizes and allow meaningful comparisons to be made between studies.

Making spatial-temporal marine ecosystem modelling better - A perspective.

Marine Ecosystem Models (MEMs) provide a deeper understanding of marine ecosystem dynamics. The United Nations Decade of Ocean Science for Sustainable Development has highlighted the need to deploy these complex mechanistic spatial-temporal models to engage policy makers and society into dialogues towards sustainably managed oceans. From our shared perspective, MEMs remain underutilized because they still lack formal validation, calibration, and uncertainty quantifications that undermines their credibility and uptake in policy arenas. We explore why these shortcomings exist and how to enable the global modelling community to increase MEMs' usefulness. We identify a clear gap between proposed solutions to assess model skills, uncertainty, and confidence and their actual systematic deployment. We attribute this gap to an underlying factor that the ecosystem modelling literature largely ignores: technical issues. We conclude by proposing a conceptual solution that is cost-effective, scalable and simple, because complex spatial-temporal marine ecosystem modelling is already complicated enough.

The behavioural response of European seabass (Dicentrarchus labrax) to chlorinated seawater effluents.

Seawater chlorination is widely used for coastal, marine industries for the prevention of fouling. Using a choice chamber system, we investigated the influence of chlorinated seawater at typical concentrations occurring near chlorinated cooling water discharges, on the behaviour of juvenile seabass (Dicentrachus labrax). These studies showed that there was evidence of an effect of chlorination, with models predicting 22% fewer fish present in the chlorine dosed chamber compared to the undosed chamber in all control runs (mean number of fish in the dosed chamber in all control runs was 2.23 in comparison to 1.74 when chlorine was present). These data suggest that when food is available D. labrax will enter areas that are at mean chlorination levels of 0.04 mg l-1, which are typical close to the discharge of power station cooling water but are more likely to move away once the food supply is reduced.

LeMaRns: A Length-based Multi-species analysis by numerical simulation in R.

Fish stocks interact through predation and competition for resources, yet stocks are typically managed independently on a stock-by-stock basis. The need to take account of multi-species interactions is widely acknowledged. However, examples of the application of multi-species models to support management decisions are limited as they are often seen as too complex and lacking transparency. Thus there is a need for simple and transparent methods to address stock interactions in a way that supports managers. Here we introduce LeMaRns, a new R-package of a general length-structured fish community model, LeMans, that characterises fishing using fleets that can have different gears and species catch preferences. We describe the model, package implementation, and give three examples of use: determination of multi-species reference points; modelling of mixed-fishery interactions; and examination of the response of community indicators to dynamical changes in fleet effort within a mixed-fishery. LeMaRns offers a diverse array of options for parameterisation. This, along with the speed, comprehensive documentation, and open source nature of the package makes LeMans newly accessible, transparent, and easy to use, which we hope will lead to increased uptake by the fisheries management community.

Making the most of survey data: Incorporating age uncertainty when fitting growth parameters.

Individual growth is an important parameter and is linked to a number of other biological processes. It is commonly modeled using the von Bertalanffy growth function (VBGF), which is regularly fitted to age data where the ages of the animals are not known exactly but are binned into yearly age groups, such as fish survey data. Current methods of fitting the VBGF to these data treat all the binned ages as the actual ages. We present a new VBGF model that combines data from multiple surveys and allows the actual age of an animal to be inferred. By fitting to survey data for Atlantic herring (Clupea harengus) and Atlantic cod (Gadus morhua), we compare our model with two other ways of combining data from multiple surveys but where the ages are as reported in the survey data. We use the fitted parameters as inputs into a yield-per-recruit model to see what would happen to advice given to management. We found that each of the ways of combining the data leads to different parameter estimates for the VBGF and advice for policymakers. Our model fitted to the data better than either of the other models and also reduced the uncertainty in the parameter estimates and models used to inform management. Our model is a robust way of fitting the VBGF and can be used to combine data from multiple sources. The model is general enough to fit other growth curves for any taxon when the age of individuals is binned into groups.

Transcriptomes of Plant Gametophytes Have a Higher Proportion of Rapidly Evolving and Young Genes than Sporophytes.

Reproductive traits in plants tend to evolve rapidly due to various causes that include plant-pollinator coevolution and pollen competition, but the genomic basis of reproductive trait evolution is still largely unknown. To characterize evolutionary patterns of genome wide gene expression in reproductive tissues in the gametophyte and to compare them to developmental stages of the sporophyte, we analyzed evolutionary conservation and genetic diversity of protein-coding genes using microarray-based transcriptome data from three plant species, Arabidopsis thaliana, rice (Oryza sativa), and soybean (Glycine max). In all three species a significant shift in gene expression occurs during gametogenesis in which genes of younger evolutionary age and higher genetic diversity contribute significantly more to the transcriptome than in other stages. We refer to this phenomenon as "evolutionary bulge" during plant reproductive development because it differentiates the gametophyte from the sporophyte. We show that multiple, not mutually exclusive, causes may explain the bulge pattern, most prominently reduced tissue complexity of the gametophyte, a varying extent of selection on reproductive traits during gametogenesis as well as differences between male and female tissues. This highlights the importance of plant reproduction for understanding evolutionary forces determining the relationship of genomic and phenotypic variation in plants.