A primer on coupled state-switching models for multiple interacting time series

State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation sequences whose underlying state variables interact. In this paper, we provide an overview of the modelling techniques related to coupling in state-switching models, thereby forming a rich and flexible statistical framework particularly useful for modelling correlated time series. Simulation experiments demonstrate the relevance of being able to account for an asynchronous evolution as well as interactions between the underlying latent processes. The models are further illustrated using two case studies related to a) interactions between a dolphin mother and her calf as inferred from movement data; and b) electronic health record data collected on 696 patients within an intensive care unit.Comment: 30 pages, 9 figure

Dolphin echolocation behaviour during active long-range target approaches

Financial support was provided by the US Office of Naval Research Code 32 (Mine Countermeasures, Acoustics Phenomenology & Modeling Group). M.L. and P.T.M. were funded by frame grants from the National Danish Research Council (Det Frie Forskningsråd) and by a Semper Ardens grant from the Carlsberg Foundation. M.L.’s travel expenses were covered by grants from Augustinus Fonden and DAS-Fonden (Danish Acoustical Society, Dansk Akustisk Selskab). F.H.J. was funded by an AIAS-COFUND fellowship from Aarhus Institute of Advanced Studies under the EU's Seventh Framework Programme (Agreement No. 609033).Echolocating toothed whales generally adjust click intensity and rate according to target range to ensure that echoes from targets of interest arrive before a subsequent click is produced, presumably facilitating range estimation from the delay between clicks and returning echoes. However, this click-echo-click paradigm for the dolphin biosonar is mostly based on experiments with stationary animals echolocating fixed targets at ranges below ∼120 m. Therefore, we trained two bottlenose dolphins instrumented with a sound recording tag to approach a target from ranges up to 400 m and either touch the target (subject TRO) or detect a target orientation change (subject SAY). We show that free-swimming dolphins dynamically increase interclick interval (ICI) out to target ranges of ∼100 m. TRO consistently kept ICIs above the two-way travel time (TWTT) for target ranges shorter than ∼100 m, whereas SAY switched between clicking at ICIs above and below the TWTT for target ranges down to ∼25 m. Source levels changed on average by 17log10(target range), but with considerable variation for individual slopes (4.1 standard deviations for by-trial random effects), demonstrating that dolphins do not adopt a fixed automatic gain control matched to target range. At target ranges exceeding ∼100 m, both dolphins frequently switched to click packet production in which interpacket intervals exceeded the TWTT, but ICIs were shorter than the TWTT. We conclude that the click-echo-click paradigm is not a fixed echolocation strategy in dolphins, and we demonstrate the first use of click packets for free-swimming dolphins when solving an echolocation task.Publisher PDFPeer reviewe

High rates of click communication in wild harbour porpoises (Phocoena phocoena)

This upload contains the raw data (.dtg files) from two of the six harbour porpoise deployments of this study (hp12_293a and hp15_218a). It also contains the Matlab scripts used to acoustically and visually process the data (runAUDIT.m, d3audithp.m) and how to automatically detect and save cues for individual clicks within the marked click trains of interest (runPCclickdetector.m, detectTagclicks3.m), in this case, calls and buzzes. Finally, it contains a script that saves the time and sample cues of all detected clicks within each click train in individual files (PCfakeFMPCperm.m) and a script that enables manual correction of those click trains in which clicks have been falsely detected or missed (PCfixfakeFMPCT.m). Additional scripts used to run these scripts can be found in the Tag Toolbox that can be accessed and downloaded on www.animaltags.org.