Automated ice-core layer-counting with strong univariate signals

We present an automated process for determining the annual layer chronology of an ice-core with a strong annual signal, utilising the hydrogen peroxide record from an Antarctic Peninsula ice-core as a test signal on which to count annual cycles and explain the methods. The signal is de-trended and normalised before being split into sections with a deterministic cycle count and those that need more attention. Possible reconstructions for the uncertain sections are determined which could be used as a visual aid for manual counting, and a simple method for assigning probability measures to each reconstruction is discussed. The robustness of this process is explored by applying it to versions of two different chemistry signals from the same stretch of the NGRIP (North Greenland Ice Core Project) ice-core, which shows more variation in annual layer thickness, with and without thinning to mimic poorer quality data. An adapted version of these methods is applied to the more challenging non-sea-salt sulphur signal from the same Antarctic Peninsula core from which the hydrogen peroxide signal was taken. These methods could readily be adapted for use on much longer datasets, thereby reducing manual effort and providing a robust automated layer-counting methodology

Modelling and inference for the movement of interacting animals

1. Statistical modelling of animal movement data is a rapidly growing area of research. Typically though, these models have been developed for analysing the tracks of individual animals and we lose sight of the impact animals have on each other with regards to their movement behaviours. We aim to develop a model with a flexible social framework that allows us to capture that information. 2. Our approach is based on the concept of social hierarchies, and this is embedded in a multivariate diffusion process which models the movement of a group of animals. The possibility of switching between behavioural states facilitates dynamic social behaviours and we augment the observed data with sampled state switching times in order to model the animals' behaviour naturally in continuous time. In addition, this enables us to carry out exact inference in a Bayesian setting with the benefits of being able to handle regular, irregular and missing data. All movement and behaviour parameters are estimated with Markov chain Monte Carlo methods. 3. We examine the capability of our model with simulated data before fitting it to GPS locations of five wild olive baboons Papio anubis. The results enable us to identify which animals are influencing the movement of others and when, which provides both a dynamic and long-term static insight into the group's social behaviours. 4. Our model offers a flexible method in continuous time with which to model the network of social interactions within animal movement. Doing so avoids the limitations caused by a discrete-time approach and it allows us to capture rich information with regards to a group's social structure, leading to constructive applications in conservation and management decisions. However, currently it is a computationally expensive task to fit the model to data, which in turns limits extending the model to more fruitful but complex cases such as heterogeneity in space or individual characteristics. Furthermore, our social hierarchy approach assumes all relevant animals are tracked and that any interactions have some ordering, both of which narrow the scope within which this approach is appropriate

Improving the visual communication of environmental model projections

Environmental and ecosystem models can help to guide management of changing natural systems by projecting alternative future states under a common set of scenarios. Combining contrasting models into multi-model ensembles (MMEs) can improve the skill and reliability of projections, but associated uncertainty complicates communication of outputs, affecting both the effectiveness of management decisions and, sometimes, public trust in scientific evidence itself. Effective data visualisation can play a key role in accurately communicating such complex outcomes, but we lack an evidence base to enable us to design them to be visually appealing whilst also effectively communicating accurate information. To address this, we conducted a survey to identify the most effective methods for visually communicating the outputs of an ensemble of global climate models. We measured the accuracy, confidence, and ease with which the survey participants were able to interpret 10 visualisations depicting the same set of model outputs in different ways, as well as their preferences. Dot and box plots outperformed all other visualisations, heat maps and radar plots were comparatively ineffective, while our infographic scored highly for visual appeal but lacked information necessary for accurate interpretation. We provide a set of guidelines for visually communicating the outputs of MMEs across a wide range of research areas, aimed at maximising the impact of the visualisations, whilst minimizing the potential for misinterpretations, increasing the societal impact of the models and ensuring they are well-placed to support management in the future

Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data

Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events

Inference in MCMC step selection models

Habitat selection models are used in ecology to link the spatial distribution of animals to environmental covariates, and identify preferred habitats. The most widely used models of this type, resource selection functions, aim to capture the steady‐state distribution of space use of the animal, but they assume independence between the observed locations of an animal. This is unrealistic when location data display temporal autocorrelation. The alternative approach of step selection functions embed habitat selection in a model of animal movement, to account for the autocorrelation. However, inferences from step selection functions depend on the underlying movement model, and they do not readily predict steady‐state space use. We suggest an analogy between parameter updates and target distributions in Markov chain Monte Carlo (MCMC) algorithms, and step selection and steady‐state distributions in movement ecology, leading to a step selection model with an explicit steady‐state distribution. In this framework, we explain how maximum likelihood estimation can be used for simultaneous inference about movement and habitat selection. We describe the local Gibbs sampler, a novel rejection‐free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive its likelihood function for several important special cases. In a simulation study, we verify that maximum likelihood estimation can recover all model parameters. We illustrate the application of the method with data from a zebra

Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models

1.Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. 2.Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time scales, e.g. step lengths obtained from GPS tags every hour, dive depths obtained from time‐depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi‐stream and multi‐scale data is lacking. 3.We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple, connected behavioural processes, each of which operates at the time scale at which the corresponding variables were observed. 4.By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time scales, and in particular help to draw a much more comprehensive picture of an animal's movement patterns, e.g. with regard to long‐term vs. short‐term movement strategies. 5.The suggested approach is illustrated in two real‐data applications, where we jointly model i) coarse‐scale horizontal and fine‐scale vertical Atlantic cod (Gadus morhua) movements throughout the English Channel, and ii) coarse‐scale horizontal movements and corresponding fine‐scale accelerations of a horn shark (Heterodontus francisci) tagged off the Californian coast

Modelling group movement with behaviour switching in continuous time

This article presents a new method for modelling collective movement in continuous time with behavioural switching, motivated by simultaneous tracking of wild or semi‐domesticated animals. Each individual in the group is at times attracted to a unobserved leading point. However, the behavioural state of each individual can switch between ‘following’ and ‘independent’. The ‘following’ movement is modelled through a linear stochastic differential equation, while the ‘independent’ movement is modelled as Brownian motion. The movement of the leading point is modelled either as an Ornstein‐Uhlenbeck (OU) process or as Brownian motion (BM), which makes the whole system a higher‐dimensional Ornstein‐Uhlenbeck process, possibly an intrinsic non‐stationary version. An inhomogeneous Kalman filter Markov chain Monte Carlo algorithm is developed to estimate the diffusion and switching parameters and the behaviour states of each individual at a given time point. The method successfully recovers the true behavioural states in simulated data sets , and is also applied to model a group of simultaneously tracked reindeer (Rangifer tarandus)

3D Coronal Density Reconstruction and Retrieving the Magnetic Field Structure during Solar Minimum

Measurement of the coronal magnetic field is a crucial ingredient in understanding the nature of solar coronal phenomena at all scales. We employed STEREO/COR1 data obtained during a deep minimum of solar activity in February 2008 (Carrington rotation CR 2066) to retrieve and analyze the three-dimensional (3D) coronal electron density in the range of heights from 1.5 to 4 Rsun using a tomography method. With this, we qualitatively deduced structures of the coronal magnetic field. The 3D electron density analysis is complemented by the 3D STEREO/EUVI emissivity in the 195 A band obtained by tomography for the same CR. A global 3D MHD model of the solar corona was used to relate the reconstructed 3D density and emissivity to open/closed magnetic field structures. We show that the density maximum locations can serve as an indicator of current sheet position, while the locations of the density gradient maximum can be a reliable indicator of coronal hole boundaries. We find that the magnetic field configuration during CR 2066 has a tendency to become radially open at heliocentric distances greater than 2.5 Rsun. We also find that the potential field model with a fixed source surface (PFSS) is inconsistent with the boundaries between the regions with open and closed magnetic field structures. This indicates that the assumption of the potential nature of the coronal global magnetic field is not satisfied even during the deep solar minimum. Results of our 3D density reconstruction will help to constrain solar coronal field models and test the accuracy of the magnetic field approximations for coronal modeling.Comment: Published in "Solar Physics

A general framework for combining ecosystem models

When making predictions about ecosystems, we often have available a number of different ecosystem models that attempt to represent their dynamics in a detailed mechanistic way. Each of these can be used as a simulator of large-scale experiments and make projections about the fate of ecosystems under different scenarios to support the development of appropriate management strategies. However, structural differences, systematic discrepancies and uncertainties lead to different models giving different predictions. This is further complicated by the fact that the models may not be run with the same functional groups, spatial structure or time scale. Rather than simply trying to select a “best” model, or taking some weighted average, it is important to exploit the strengths of each of the models, while learning from the differences between them. To achieve this, we construct a flexible statistical model of the relationships between a collection of mechanistic models and their biases, allowing for structural and parameter uncertainty and for different ways of representing reality. Using this statistical meta-model, we can combine prior beliefs, model estimates and direct observations using Bayesian methods and make coherent predictions of future outcomes under different scenarios with robust measures of uncertainty. In this study, we take a diverse ensemble of existing North Sea ecosystem models and demonstrate the utility of our framework by applying it to answer the question what would have happened to demersal fish if fishing was to stop