Summary: 1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance.2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use.3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built‐in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated.4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple‐covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance.5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap.6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state‐of‐the‐art software that implements these methods is described that makes the methods accessible to practising ecologists.

Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture‐recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers.Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast‐developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics‐based density estimation, illustrated with examples from real‐world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic‐based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture‐recapture. The methods are also applicable to other aquatic and terrestrial sound‐producing taxa.We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds, amphibians, and insects, especially in situations where inferences are required over long periods of time. There is considerable work ahead, with several potentially fruitful research areas, including the development of (i) hardware and software for data acquisition, (ii) efficient, calibrated, automated detection and classification systems, and (iii) statistical approaches optimized for this application. Further, survey design will need to be developed, and research is needed on the acoustic behaviour of target species. Fundamental research on vocalization rates and group sizes, and the relation between these and other factors such as season or behaviour state, is critical. Evaluation of the methods under known density scenarios will be important for empirically validating the approaches presented here.

We discuss hidden Markov‐type models for fitting a variety of multistate random walks to wildlife movement data. Discrete‐time hidden Markov models (HMMs) achieve considerable computational gains by focusing on observations that are regularly spaced in time, and for which the measurement error is negligible. These conditions are often met, in particular for data related to terrestrial animals, so that a likelihood‐based HMM approach is feasible. We describe a number of extensions of HMMs for animal movement modeling, including more flexible state transition models and individual random effects (fitted in a non‐Bayesian framework). In particular we consider so‐called hidden semi‐Markov models, which may substantially improve the goodness of fit and provide important insights into the behavioral state switching dynamics. To showcase the expediency of these methods, we consider an application of a hierarchical hidden semi‐Markov model to multiple bison movement paths.

Summary: Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/plant distribution. Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods. We review recent developments in the field and consider the likely directions of future research before focussing on a popular approach based on generalized additive models. In particular, we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two‐stage model and smoothing in areas with complex boundaries. The methods discussed are available in anRpackage developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.

Funding for R. Bruintjes, J. Purser, A. N. Radford, S. D, Simpson and M. A. Wale was provided by the UK Department for Environment Food and Rural Affairs (Defra). N.D. Merchant received travel funding from Ocean Networks Canada. RW was supported by a Marie Curie International Incoming Fellowship within the 7th European Community Framework Programme (Project CONCEAL, FP7, PIIF-GA-2009-253407), and received travel funding to attend IMCC3 from the Society for Conservation Biology (SCB) Marine Section and the International Whaling Commission’s Climate Change steering group (with thanks to Mark Simmonds). A.J. Wright also received travel funding to attend IMCC3 from the SCB Marine Section. Date of Acceptance: 28/05/2015Anthropogenic underwater noise is now recognized as a world-wide problem, and recent studies have shown a broad range of negative effects in a variety of taxa. Underwater noise from shipping is increasingly recognized as a significant and pervasive pollutant with the potential to impact marine ecosystems on a global scale. We reviewed six regional case studies as examples of recent research and management activities relating to ocean noise in a variety of taxonomic groups, locations, and approaches. However, as no six projects could ever cover all taxa, sites and noise sources, a brief bibliometric analysis places these case studies into the broader historical and topical context of the peer-reviewed ocean noise literature as a whole. The case studies highlighted emerging knowledge of impacts, including the ways that non-injurious effects can still accumulate at the population level, and detailed approaches to guide ocean noise management. They build a compelling case that a number of anthropogenic noise types can affect a variety of marine taxa. Meanwhile, the bibliometric analyses revealed an increasing diversity of ocean noise topics covered and journal outlets since the 1940s. This could be seen in terms of both the expansion of the literature from more physical interests to ecological impacts of noise, management and policy, and consideration of a widening range of taxa. However, if our scientific knowledge base is ever to get ahead of the curve of rapid industrialization of the ocean, we are going to have to identify naïve populations and relatively pristine seas, and construct mechanistic models, so that we can predict impacts before they occur, and guide effective mitigation for the most vulnerable populations.Publisher PDFPeer reviewe

Recent developments in animal tracking technology have permitted the collection of detailed data on the movement paths of individuals from many species. However, analysis methods for these data have not developed at a similar pace, largely due to a lack of suitable candidate models, coupled with the technical difficulties of fitting such models to data. To facilitate a general modeling framework, we propose that complex movement paths can be conceived as a series of movement strategies among which animals transition as they are affected by changes in their internal and external environment. We synthesize previously existing and novel methodologies to develop a general suite of mechanistic models based on biased and correlated random walks that allow different behavioral states for directed (e.g., migration), exploratory (e.g., dispersal), area‐restricted (e.g., foraging), and other types of movement. Using this “toolbox” of nested model components, multistate movement models may be custom‐built for a wide variety of species and applications. As a unified state‐space modeling framework, it allows the simultaneous investigation of numerous hypotheses about animal movement from imperfectly observed data, including time allocations to different movement behavior states, transitions between states, the use of memory or navigation, and strengths of attraction (or repulsion) to specific locations. The inclusion of covariate information permits further investigation of specific hypotheses related to factors driving different types of movement behavior. Using reversible‐jump Markov chain Monte Carlo methods to facilitate Bayesian model selection and multi‐model inference, we apply the proposed methodology to real data by adapting it to the natural history of the grey seal (Halichoerus grypus) in the North Sea. Although previous grey seal studies tended to focus on correlated movements, we found overwhelming evidence that bias toward haul‐out or foraging locations better explained seal movement than did simple or correlated random walks. Posterior model probabilities also provided evidence that seals transition among directed, area‐restricted, and exploratory movements associated with haul‐out, foraging, and other behaviors. With this intuitive framework for modeling and interpreting animal movement, we believe that the development and application of custom‐made movement models will become more accessible to ecologists and non‐statisticians.