The next steps in the study of missing individuals in networks: A comment on Smith et al. (2017)

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Social network analysis is now used widely to study social behaviour in humans and non-human animals, and missing individuals can represent a problem for network studies. This problem is becoming especially frequent in studies using bio-logging to collect interaction data, which is an approach used particularly frequently in the construction of animal networks. This therefore represents an important audience for Smith et al. (2017) who investigate how sub-sampling from networks impacts the outcome of subsequent analysis. Here I take advantage of the progress made by this paper to outline key issues that still require addressing to understand the effect of missing individuals on social network analysis

Graphite grain surface reactions in interstellar and protostellar environments

Surface reactions on warm (at least 60 K) graphite grains between lattice C atoms and impinging H and O atoms within or in the vicinity of H II regions can provide a prolific source of H2CO and other interstellar molecules. It is proposed that similar reactions in the primitive solar nebula led to the formation of the organic molecules found in carbonaceous chondrites. From this and related evidence, it is argued that most of the solid material in the solar system may have originated as interstellar grains

H2 recombination on interstellar grains

From a consideration of relevant theoretical and experimental data it is concluded that H atoms (but not H2 molecules) will be chemisorbed on interstellar graphite grains, with H2 formation proceeding efficiently for graphite grain temperatures less than 70 K. It is argued that graphite grains will act as the principal sites for H2 formation, with a formation rate of about 4 to the minus 17th cu cm per sec. Heating by H2 molecules formed by surface recombination is analyzed in the context of the available experimental data, and a heating rate is derived and compared with other suggested cloud heating mechanisms. It is concluded that H2 recombination will provide the largest heat source in diffuse clouds if the albedo of interstellar dust in the 912-1200 A region is high (about 0.9), whereas if the albedo in this wavelength region is lower (about 0.5), photoelectron ejection from grains will tend to predominate, and can explain observed cloud temperatures with a carbon depletion factor of approximately 2, a factor attributable to a normal interstellar abundance of graphite grains

Sputtering in interstellar shocks: a model for heavy element depletion

High-velocity (at least 100 km/s) shock fronts are found to provide an environment where grains can be destroyed by thermal sputtering. Application is made to supernova remnants. Sputtering of refractory grains in shocks associated with high-velocity clouds leads to cosmic abundances of heavy elements and if followed by deceleration of the high-velocity gas and dilution with ambient depleted matter. A correlation of depletion with systematic velocity is predicted. It is proposed that there is a residual underlying depletion associated with refractory grain formation and a subsequent trapping of gas-phase metallic species by adsorption. Sputtering of adsorbed monolayers in intermediate-velocity shocks (not exceeding 50 km/s) can account for a range of depletions in diffuse interstellar matter

Wildlife disease ecology from the individual to the population: Insights from a long-term study of a naturally infected European badger population

This is the final version of the article. Available from Wiley via the DOI in this record.Long-term individual-based datasets on host-pathogen systems are a rare and valuable resource for understanding the infectious disease dynamics in wildlife. A study of European badgers (Meles meles) naturally infected with bovine tuberculosis (bTB) at Woodchester Park in Gloucestershire (UK) has produced a unique dataset, facilitating investigation of a diverse range of epidemiological and ecological questions with implications for disease management. Since the 1970s, this badger population has been monitored with a systematic mark-recapture regime yielding a dataset of >15,000 captures of >3,000 individuals, providing detailed individual life-history, morphometric, genetic, reproductive and disease data. The annual prevalence of bTB in the Woodchester Park badger population exhibits no straightforward relationship with population density, and both the incidence and prevalence of Mycobacterium bovis show marked variation in space. The study has revealed phenotypic traits that are critical for understanding the social structure of badger populations along with mechanisms vital for understanding disease spread at different spatial resolutions. Woodchester-based studies have provided key insights into how host ecology can influence infection at different spatial and temporal scales. Specifically, it has revealed heterogeneity in epidemiological parameters; intrinsic and extrinsic factors affecting population dynamics; provided insights into senescence and individual life histories; and revealed consistent individual variation in foraging patterns, refuge use and social interactions. An improved understanding of ecological and epidemiological processes is imperative for effective disease management. Woodchester Park research has provided information of direct relevance to bTB management, and a better appreciation of the role of individual heterogeneity in disease transmission can contribute further in this regard. The Woodchester Park study system now offers a rare opportunity to seek a dynamic understanding of how individual-, group- and population-level processes interact. The wealth of existing data makes it possible to take a more integrative approach to examining how the consequences of individual heterogeneity scale to determine population-level pathogen dynamics and help advance our understanding of the ecological drivers of host-pathogen systems.The study is supported by the UK Department of Environment, Food and Rural Affairs. M.J.S. was supported by NE/M004546/1. J.L.M. research was motivated by NE/M010260/1 and currently supported by NE/L007770/1

Perils and pitfalls of mixed-effects regression models in biology

This is the final version. Available on open access from PeerJ via the DOI in this recordData Availability: The following information was supplied regarding data availability: The R code used to conduct all simulations in the paper is available in the Supplemental Files.Biological systems, at all scales of organisation from nucleic acids to ecosystems, are inherently complex and variable. Biologists therefore use statistical analyses to detect signal among this systemic noise. Statistical models infer trends, find functional relationships and detect differences that exist among groups or are caused by experimental manipulations. They also use statistical relationships to help predict uncertain futures. All branches of the biological sciences now embrace the possibilities of mixed-effects modelling and its flexible toolkit for partitioning noise and signal. The mixed-effects model is not, however, a panacea for poor experimental design, and should be used with caution when inferring or deducing the importance of both fixed and random effects. Here we describe a selection of the perils and pitfalls that are widespread in the biological literature, but can be avoided by careful reflection, modelling and model-checking. We focus on situations where incautious modelling risks exposure to these pitfalls and the drawing of incorrect conclusions. Our stance is that statements of significance, information content or credibility all have their place in biological research, as long as these statements are cautious and well-informed by checks on the validity of assumptions. Our intention is to reveal potential perils and pitfalls in mixed model estimation so that researchers can use these powerful approaches with greater awareness and confidence. Our examples are ecological, but translate easily to all branches of biology.University of Exete

Considering connections between Hollywood and biodiversity conservation

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Cinema offers a substantial opportunity to share messages with a wide audience. Given its global range and potentially high impact, there is an urgent need for research that evaluates the effects of this form of visual media on conservation outcomes. Cinema can influence the awareness and behaviours of non-specialist audiences, and could therefore play an important positive and/or negative role in biodiversity conservation through behavioural change and social pressure on key stakeholders and policy makers. Limited awareness about the potential benefits and limitations of cinema for conservation, as well as a lack of evidence about impacts, currently hinder our ability to learn from previous and ongoing initiatives, and to engage productively with the movie industry. We discuss the key opportunities and risks that arise from cinematic representations of conservation issues and species of concern, making use of examples and case studies where they are available. We additionally provide a framework that enables conservationists to better understand and engage with the film industry, highlighting how this can facilitate engagement with the movie industry, harness its potential, and improve work to mitigate any negative consequences. A robust evidence base is key for evaluating and planning these engagements, and for informing related policy and management decisions. This article is protected by copyright. All rights reserved.NERC (grant number: NE/M004546/1), Darwin Initiative and the University of Exeter unrelated to this work

Analysing animal social network dynamics: the potential of stochastic actor-oriented models

This is the final version of the article. Available from the publisher via the DOI in this record.Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network-based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor-oriented models (SAOMs) are a principal example. SAOMs are a class of individual-based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high-resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning

The use of multilayer network analysis in animal behaviour

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.We gratefully acknowledge the 806 supporters of MX16: the UC Davis Institute for Social Sciences, the U.S. Army Research Office 807 under Multidisciplinary University Research Initiative Award No. W911NF-13-1-0340, the UC 808 Davis Complexity Sciences Center, the UC Davis Anthropology Department, the UC Davis 809 Graduate Student Association, the UC Davis Department of Engineering, and the UC Davis 810 Office of Research.Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected ‘layers’ of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Natural Environment Research Council (NERC)National Science Foundation (NSF) Graduate Research FellowshipNFS IOS grantNIH R01NERC standard gran

Can multilayer networks advance animal behavior research?

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Interactions among individual animals — and between these individuals and their environment — yield complex, multifaceted systems. The development of multilayer network analysis offers a promising new approach for studying animal social behavior and relating it to eco-evolutionary dynamics