Statistical models for animal telemetry data with applications to harbor seals in the Gulf of Alaska

2017 Spring.Includes bibliographical references.Much is known about the general biology and natural history of harbor seals (Phoca vitulina), but questions remain about the aquatic and terrestrial space use of these marine mammals. This is in large part because methods for examining the spatial ecology of harbor seals are poorly developed. The objective of this dissertation is to pair existing telemetry data with contemporary spatio-temporal modeling to quantify the space use and resource selection of harbor seals in the coastal waters of southern Alaska. Recent extensions to models for analyzing animal telemetry data address complications such as autocorrelation and telemetry measurement error; however, additional challenges remain, especially in the context of analyzing Argos satellite telemetry data collected on marine mammals like harbor seals. For example, existing methods assume elliptical (or circular) patterns of measurement error, even though Argos satellite telemetry devices impose more complicated error structures on the data. Constraints, or barriers, to animal movement present another complication. Harbor seals and other marine mammals are constrained to move within the marine environment, and mechanistic models that do not adhere to movement barriers yield unreliable inference. Therefore, a primary goal of this research is to develop statistical tools that account for these nuances and provide rigorous, ecologically relevant inference. Even though the models presented in this dissertation were specifically developed with Argos satellite telemetry data and harbor seals in mind, the methods are general and can be applied to other species and types of telemetry data. This dissertation consists of five chapters. In Chapter 1, I briefly discuss the general biology of harbor seals, focusing on what is known about their spatial habits in Alaska. I then summarize trends in Alaskan harbor seal abundance, a topic that motivated my research as well as the work of many others. I describe the existing Alaska Department of Fish and Game telemetry data sets that are available for examining harbor seal spatial ecology, commonly-used statistical methods for analyzing animal telemetry data, and conclude with the objectives of my research and an outline for the remainder of the dissertation. In Chapter 2, I propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. The model consists of two general components: a model for the true, but unobserved, animal locations that reflects prior knowledge about constraints to animal movement, and a model for the observed telemetry locations that is conditional on the true locations. I apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with telemetry measurement error and movement constraints. I then apply the framework to obtain inference concerning aquatic resource selection and space use for harbor seals near Kodiak Island, Alaska. Chapters 3 and 4 shift the focus from inference concerning aquatic space use and resource selection, to inference concerning the use of coastal resources (i.e., haul-out sites) by harbor seals. In Chapter 3, I present a fully model-based approach for estimating the location of central places (e.g., haul-out sites, dens, nests, etc.) from telemetry data that accounts for multiple sources of uncertainty and uses all of the available locational data. The model consists of an observation model to account for large telemetry measurement error and animal movement, and a highly flexible mixture model (a Dirichlet process) to identify the location of central places. Ancillary behavioral data (e.g., harbor seal dive data obtained from the satellite-linked depth recorders) are also incorporated into the modeling framework to obtain inference concerning temporal patterns in central place use. Based on the methods developed in Chapter 3, I present a comprehensive analysis of the spatio-temporal patterns of haul-out use for harbor seals near Kodiak Island in Chapter 4. Chapter 4 also extends previously developed methods to examine the affect of covariates on haul-out site selection and to obtain population-level inference concerning haul-out use. I conclude, in Chapter 5, with some general thoughts about analyzing animal telemetry data, as well as potential future research directions

Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

Can Rats Reason?

Since at least the mid-1980s claims have been made for rationality in rats. For example, that rats are capable of inferential reasoning (Blaisdell, Sawa, Leising, & Waldmann, 2006; Bunsey & Eichenbaum, 1996), or that they can make adaptive decisions about future behavior (Foote & Crystal, 2007), or that they are capable of knowledge in propositional-like form (Dickinson, 1985). The stakes are rather high, because these capacities imply concept possession and on some views (e.g., Rödl, 2007; Savanah, 2012) rationality indicates self-consciousness. I evaluate the case for rat rationality by analyzing 5 key research paradigms: spatial navigation, metacognition, transitive inference, causal reasoning, and goal orientation. I conclude that the observed behaviors need not imply rationality by the subjects. Rather, the behavior can be accounted for by noncognitive processes such as hard-wired species typical predispositions or associative learning or (nonconceptual) affordance detection. These mechanisms do not necessarily require or implicate the capacity for rationality. As such there is as yet insufficient evidence that rats can reason. I end by proposing the ‘Staircase Test,’ an experiment designed to provide convincing evidence of rationality in rats

A pilot inference study for a beta-Bernoulli spatial scan statistic

The Bernoulli spatial scan statistic is used to detect localised clusters in binary labelled point data, such as that used in spatial or spatio-temporal case/control studies. We test the inferential capability of a recently developed beta-Bernoulli spatial scan statistic, which adds a beta prior to the original statistic. This pilot study, which includes two test scenarios with 6,000 data sets each, suggests a marked increase in power for a given false alert rate. We suggest a more extensive study would be worthwhile to corroborate the findings. We also speculate on an explanation for the observed improvement

The roots of self-awareness

In this paper we provide an account of the structural underpinnings of self-awareness. We offer both an abstract, logical account-by way of suggestions for how to build a genuinely self-referring artificial agent-and a biological account, via a discussion of the role of somatoception in supporting and structuring self-awareness more generally. Central to the account is a discussion of the necessary motivational properties of self-representing mental tokens, in light of which we offer a novel definition of self-representation. We also discuss the role of such tokens in organizing self-specifying information, which leads to a naturalized restatement of the guarantee that introspective awareness is immune to error due to mis-identification of the subject

Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models

We propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of variables in the census tract level of the 19 Departmental capitals of Uruguay. In particular, we propose an index that combines different sources of information via a set of micro-environmental indicators and geographical location in the country. Our index is based on a new class of spatially hierarchical factor models that explicitly account for the different levels of hierarchy in the country, such as census tracts within the city level, and cities in the country level. We compare our approach with that obtained when data are aggregated in the city level. We show that our proposal outperforms current and standard approaches, which fail to properly account for discrepancies in the region sizes, for example, number of census tracts. We also show that data aggregation can seriously affect the estimation of the cities vulnerability rankings under benchmark models.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS497 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)