Markov-switching generalized additive models

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation and further illustrated by modelling how energy price in Spain depends on the Euro/Dollar exchange rate

Continuous-time modelling of behavioural responses in animal movement

Funding: TM, RG, CH, and LT were funded by the US office of Naval Research, Grant N000141812807. This work was supported by the US Fleet Forces Command through the Naval Facilities Engineering Command Atlantic under Contract No. N62470-15-D-8006, Task Order 50, Issued to HDR, Inc.There is great interest in ecology to understand how wild animals are affected by anthropogenic disturbances, such as sounds. For example, behavioural response studies are an important approach to quantify the impact of naval activity on marine mammals. Controlled exposure experiments are undertaken where the behaviour of animals is quantified before, during, and after exposure to a controlled sound source, often using telemetry tags (e.g., accelerometers, or satellite trackers). Statistical modelling is required to formally compare patterns before and after exposure, to quantify deviations from baseline behaviour. We propose varying-coefficient stochastic differential equations (SDEs) as a flexible framework to model such data, with two components: (1) time-varying baseline dynamics, modelled with non-parametric or random effects of time-varying covariates, and (2) a nonparametric response model, which captures deviations from baseline. SDEs are specified in continuous time, which makes it straightforward to analyse data collected at irregular time intervals, a common situation for animal tracking studies. We describe how the model can be embedded into a state-space modelling framework to account for measurement error. We present inferential methods for model fitting, model checking, and uncertainty quantification (including on the response model). We apply this approach to two behavioural response study data sets on beaked whales: a satellite track, and high-resolution depth data. Our results suggest that the whales’ horizontal movement and vertical diving behaviour changed after exposure to the sound source, and future work should evaluate the severity and possible consequences of these responses. These two very different examples showcase the versatility of varying-coefficient SDEs to measure changes in behaviour, and we discuss implications of disturbances for the whales’ energetic balance.PostprintPeer reviewe

Open population maximum likelihood spatial capture-recapture

Funding: Part-funded by UK EPSRC grant EP/K041061/1 (DB); Richard Glennie was funded by the Carnegie Trust.Open population capture‐recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture‐recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack‐Jolly‐Seber and Jolly‐Seber models, with and without activity center movement. The method is applied to a 12‐year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root‐mean‐square error in predicting population density compared to closed population models.PostprintPeer reviewe

Incorporating animal movement into distance sampling

R. Glennie gratefully acknowledges the Carnegie Trust for funding his work on this research project.Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an explicit animal movement model is incorporated into distance sampling, combining distance sampling survey data with animal telemetry data. Detection probability depends on the entire unobserved path the animal travels. The intractable integration over all possible animal paths is approximated by a hidden Markov model. A simulation study shows the method to be negligibly biased (<5%) in scenarios where conventional distance sampling overestimates abundance by up to 100%. The method is applied to line transect surveys (1999–2006) of spotted dolphins (Stenella attenuata) in the eastern tropical Pacific where abundance is shown to be positively biased by 21% on average, which can have substantial impact on the population dynamics estimated from these abundance estimates and on the choice of statistical methodology applied to future surveysPreprintPostprintPeer reviewe

Understanding the stochastic partial differential equation approach to smoothing

DLM was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, being managed by the U.S. Navy's Living Marine Resources program under Contract No. N39430-17-C-1982.Correlation and smoothness are terms used to describe a wide variety of random quantities. In time, space, and many other domains, they both imply the same idea: quantities that occur closer together are more similar than those further apart. Two popular statistical models that represent this idea are basis-penalty smoothers (Wood in Texts in statistical science, CRC Press, Boca Raton, 2017) and stochastic partial differential equations (SPDEs) (Lindgren et al. in J R Stat Soc Series B (Stat Methodol) 73(4):423–498, 2011). In this paper, we discuss how the SPDE can be interpreted as a smoothing penalty and can be fitted using the R package mgcv, allowing practitioners with existing knowledge of smoothing penalties to better understand the implementation and theory behind the SPDE approach.Publisher PDFPeer reviewe

The INNs and outs of antibody nonproprietary names

An important step in drug development is the assignment of an International Nonproprietary Name (INN) by the World Health Organization (WHO) that provides healthcare professionals with a unique and universally available designated name to identify each pharmaceutical substance. Monoclonal antibody INNs comprise a –mab suffix preceded by a substem indicating the antibody type, e.g., chimeric (-xi-), humanized (-zu-), or human (-u-). The WHO publishes INN definitions that specify how new monoclonal antibody therapeutics are categorized and adapts the definitions to new technologies. However, rapid progress in antibody technologies has blurred the boundaries between existing antibody categories and created a burgeoning array of new antibody formats. Thus, revising the INN system for antibodies is akin to aiming for a rapidly moving target. The WHO recently revised INN definitions for antibodies now to be based on amino acid sequence identity. These new definitions, however, are critically flawed as they are ambiguous and go against decades of scientific literature. A key concern is the imposition of an arbitrary threshold for identity against human germline antibody variable region sequences. This leads to inconsistent classification of somatically mutated human antibodies, humanized antibodies as well as antibodies derived from semi-synthetic/synthetic libraries and transgenic animals. Such sequence-based classification implies clear functional distinction between categories (e.g., immunogenicity). However, there is no scientific evidence to support this. Dialog between the WHO INN Expert Group and key stakeholders is needed to develop a new INN system for antibodies and to avoid confusion and miscommunication between researchers and clinicians prescribing antibodies

Optically stimulated luminescence dating as a geochronological tool for late quaternary sediments in the Red Sea region

This chapter concerns the use of luminescence methods as geochronological tools for dating Late Quaternary sediments in the Red Sea region. The dating methods all use stimulated luminescence to register signals developed in mineral systems in response to long term exposure to ionising radiation in the environment. The principles of luminescence dating are outlined followed by discussion of its application to the Arabian Peninsula, where, particularly in SE Arabia and parts of the interior, a growing corpus of work is emerging, which is helping to define past arid or humid periods of importance to palaeoclimatology and to archaeology. Turning to the Red Sea, studies conducted within the DISPERSE project are presented both in marine and terrestrial settings. The motivation for much of this work concerns definition of the environmental conditions and chronologies for hominin and human dispersion through Arabia. Data are presented which identify, for the first time, late Pleistocene evidence on the inner continental shelf near the Farasan Islands, using material from the 2013 cruise of RV AEGAEO . Results are also presented from the littoral fringe of southwest Saudi Arabia, identifying units associated with MIS5 which have palaeo-environmental and archaeological significance. It is to be hoped that further research in coming decades will continue to extend the regional chronology for the littoral fringe of the Red Sea. In this respect luminescence dating has potential to help define the environmental history of this important area, to assist with assigning marine and terrestrial features into unique stages of Quaternary climate cycles, and to promote better understanding of human-environment interactions in this dynamic area