Incorporating multi-scale structures and physiological processes into the modeling of animal movement.

HMMs are commonly used to model animal movement data and infer aspects of animal behavior. Their ability to connect an observation process to an underlying state process, generally serving as a proxy for a finite set of animal behaviors of interest, matches the intuition that the observed movements stem from an underlying (unobserved) behavioral process. We can further extend the HMM framework to consist of multiple state processes to reflect that different behaviors are identified by different compositions of the observed movement processes. We refer to this extension as a multi-scale HMM whereby one state process is connected to the underlying behaviors that generate the movements at the temporal scale at which the data are processed and another is connected to a larger-scale behavioral process, defined as a composition of fine-scale behavioral states. We present two formulations of the multi-scale HMM. We illustrate the application of multi-scale HMMs in four real-data examples, vertical movements of harbor porpoises observed in the field, and garter snake movement data collected as part of an experimental design, in chapter 2 and under two different formulations applied to tiger shark data in chapter 3. HMMs again play a feature role in chapter 4, where we aim to connect movement and physiology dynamics and their evolution and interaction over time. A long-sought goal in ecology is to connect movement with population dynamics. For many species and especially for ungulates, there is a known link between condition (e.g. fat reserves) and the probability of survival and reproduction. Assuming a particular genetic makeup and physiology, condition reflects the history of behavioral decisions, including movement and habitat use. However, the condition of an animal can also have a direct implication on the types of movements that it performs and the habitats that it visits. Movement data for ungulates are typically collected at a fine temporal scale, e.g. a position recorded by a GPS device every five or ten minutes. However, fat reserves cannot be measured remotely and must be done manually. This in turn creates a mismatch in the temporal scale at which the two data streams are observed, i.e. every five minutes for movement vs approximately once a month for condition. Further, the temporal mismatch leads to various challenges when jointly modeling the two processes. For the movement model, we use discrete-time, finite-state HMMs with the positional data of the sheep serving as the observation process and the underlying state process serving as a proxy for behaviors of interest. To incorporate condition as a potential covariate affecting the movement, and thus behavioral, process, we make use of the physiological equations that describe the evolution of body fat in Merino sheep in order to predict daily values of the condition process. The physiological equations are expressed as a function of the states inferred by HMM, as well as the distance that the sheep travels

The hot hand in professional darts

Ötting M, Langrock R, Deutscher C, Leos-Barajas V. The hot hand in professional darts. Journal of the Royal Statistical Society. Series A. 2020;183(2):565-580.We investigate the hot hand hypothesis in professional darts in a nearly ideal setting with minimal to no interaction between players. Considering almost 1 year of tournament data, corresponding to 167492 dart throws in total, we use state space models to investigate serial dependence in throwing performance. In our models, a latent state process serves as a proxy for a player's underlying form, and we use auto-regressive processes to model how this process evolves over time. Our results regarding the persistence of the latent process indicate a weak hot hand effect, but the evidence is inconclusive

Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals

The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.Fil: Ruiz Suarez, Sofia Helena. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Leos Barajas, Vianey. North Carolina State University; Estados UnidosFil: Alvarez Castro, Ignacio. Universidad de la República; UruguayFil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentin

Métodos de clasificación supervisada en series temporales

Una serie de tiempo se puede definir como una secuencia de medidas a lo largo del tiempo que describen el comportamiento de cierto sistema. El mismo puede cambiar a lo largo del tiempo debido a cambios tanto internos como externos, permitiendo así distinguir diferentes estados o condiciones del sistema. El problema de clasificación en este caso tiene como objetivo tanto reconocer los instantes de cambio de estado en el sistema como las categorías de los mismos. Existe una variedad de campos en donde este problema es aplicable, por ejemplo en medicina para reconocer distintos ritmos o frecuencias cardíacas de un paciente según un electrocardiograma; en computación para el problema del reconocimiento de voz e incluso en ecología para el reconocimiento de comportamientos en animales a partir de acelerómetros y GPS. Diferentes enfoques y metodologías pueden ser utilizados con este propósito, y dependerá de los datos disponibles y la naturaleza de los mismos su verdadera utilidad. Metodologías del tipo Machine Learning, tales como Random Forest o Support Vector Machines han sido utilizadas con éxito para resolver problemas de clasificación. Sin embargo, estas metodologías tal cual se presentan no consideran la temporalidad de los datos, por lo que es necesario agregar esta condición dentro del análisis. Los modelos hidden Markov model son una alternativa para este problema. Estos modelos de serie de tiempo estocástico incluyen la configuración temporal dentro de su estructura, lo que los hace una alternativa potente para resolver este problema. Mediante simulaciones en R comparamos estos dos enfoques y exponemos las fortalezas y limitaciones de ambos. Estudiamos las consecuencias de aplicar técnicas suponiendo independencia temporal y analizamos los escenarios en donde es posible aplicar estas técnicas para la clasificación de series temporales.Sociedad Argentina de Informática e Investigación Operativ

Spline-based nonparametric inference in general state-switching models

State‐switching models combine immense flexibility with relative mathematical simplicity and computational tractability and, as a consequence, have established themselves as general‐purpose models for time series data. In this paper, we provide an overview of ways to use penalized splines to allow for flexible nonparametric inference within state‐switching models, and provide a critical discussion of the use of corresponding classes of models. The methods are illustrated using animal acceleration data and energy price data.PostprintPeer reviewe

Métodos de clasificación supervisada en series temporales

Una serie de tiempo se puede definir como una secuencia de medidas a lo largo del tiempo que describen el comportamiento de cierto sistema. El mismo puede cambiar a lo largo del tiempo debido a cambios tanto internos como externos, permitiendo así distinguir diferentes estados o condiciones del sistema. El problema de clasificación en este caso tiene como objetivo tanto reconocer los instantes de cambio de estado en el sistema como las categorías de los mismos. Existe una variedad de campos en donde este problema es aplicable, por ejemplo en medicina para reconocer distintos ritmos o frecuencias cardíacas de un paciente según un electrocardiograma; en computación para el problema del reconocimiento de voz e incluso en ecología para el reconocimiento de comportamientos en animales a partir de acelerómetros y GPS. Diferentes enfoques y metodologías pueden ser utilizados con este propósito, y dependerá de los datos disponibles y la naturaleza de los mismos su verdadera utilidad. Metodologías del tipo Machine Learning, tales como Random Forest o Support Vector Machines han sido utilizadas con éxito para resolver problemas de clasificación. Sin embargo, estas metodologías tal cual se presentan no consideran la temporalidad de los datos, por lo que es necesario agregar esta condición dentro del análisis. Los modelos hidden Markov model son una alternativa para este problema. Estos modelos de serie de tiempo estocástico incluyen la configuración temporal dentro de su estructura, lo que los hace una alternativa potente para resolver este problema. Mediante simulaciones en R comparamos estos dos enfoques y exponemos las fortalezas y limitaciones de ambos. Estudiamos las consecuencias de aplicar técnicas suponiendo independencia temporal y analizamos los escenarios en donde es posible aplicar estas técnicas para la clasificación de series temporales.Sociedad Argentina de Informática e Investigación Operativ

El diseño cualitativo y el contexto social : Una experiencia de investigación cualitativa con población infantil en contextos sociales carenciados

En este trabajo presentamos una experiencia de Investigación Acción Participativa en la que se consideró el criterio de idoneidad contextual tanto en la fase de diseño como en la de trabajo empírico. Tal experiencia tuvo lugar en dos barrios de la periferia de la ciudad de Hermosillo, Sonora (México), cuyos participantes son niños de educación primaria con edades que oscilan entre los 9 y los 13 años. Dichos barrios enfrentan condiciones de vulnerabilidad social derivadas de carencias y problemas económicos, urbanos y sociales. Se trata de una propuesta orientada al cambio (de actitudes y prácticas medioambientales), para lo cual se ha hecho necesario acoplar la estrategia de investigación Acción Participativa tanto al contexto específico de cada entorno barrial como a los actores implicados en el proyecto.Facultad de Humanidades y Ciencias de la Educació

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