Comparing migratory connectivity across species : the importance of considering the pattern of sampling and the processes that lead to connectivity

Measuring the degree of migratory connectivity – how much and where different populations of species mix as they migrate over their annual cycle – is important because it informs the understanding of the evolution of migration, how populations will be affected by both habitat and climate change, and which areas to prioritize for conservation. But existing measures of connectivity may be difficult to compare because they measure different things and are confounded by sampling bias. Here we use tagging data from all available published landbird tracks up to July 2019 (224 populations, 86 species and 1524 individuals tracked in the three main global flyways) to identify robust measures to compare migratory connectivity across species. We consider two widely used descriptive measures: (1) degree of breeding population overlap on the non-breeding grounds and (2) Mantel correlation, which tests the degree of spatial autocorrelation between the breeding and non-breeding individuals; as well as one causative measure of the main process that leads to connectivity patterns: migratory spread of individuals from the same breeding population across the non-breeding area. We investigated the sensitivity of these three measures to the distance between breeding locations of sampled populations (breeding distance) and their sample size. We also considered the confounding effects of migration distance because longer migrations decreased overlap and increased Mantel correlations and migratory spread. We found that the degree of overlap between breeding populations on the non-breeding grounds decreased with increasing breeding distance and increased with increasing sample size. Mantel correlation coefficients also increased significantly with increasing breeding distance; sample size did not affect accuracy, but precision was greatly improved above a sample size of about 15 individuals. Migratory spread, however, was independent of breeding distance; sample size had only small effects on accuracy and precision, with no significant effects when more than four individuals per population were included. Furthermore, migratory spread was highly positively correlated with the maximum non-breeding range. Overlap and Mantel correlations were highly confounded by the spatial pattern and amount of sampling, whereas migratory spread was relatively unconfounded, even by migratory distance. Although any descriptive migratory connectivity measure can help set priorities by determining current areas for conservation on the non-breeding grounds, migratory spread, which leads to these patterns, needs fewer data, is comparable, and gives information on evolutionary flexibility and so ability to deal with changing habitat and climate.Publisher PDFPeer reviewe

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

Unknowns and unknown unknowns: from dark sky to dark matter and dark energy

Answering well-known fundamental questions is usually regarded as the major goal of science. Discovery of other unknown and fundamental questions is, however, even more important. Recognition that "we didn't know anything" is the basic scientific driver for the next generation. Cosmology indeed enjoys such an exciting epoch. What is the composition of our universe? This is one of the well-known fundamental questions that philosophers, astronomers and physicists have tried to answer for centuries. Around the end of the last century, cosmologists finally recognized that "We didn't know anything". Except for atoms that comprise slightly less than 5% of the universe, our universe is apparently dominated by unknown components; 23% is the known unknown (dark matter), and 72% is the unknown unknown (dark energy). In the course of answering a known fundamental question, we have discovered an unknown, even more fundamental, question: "What is dark matter? What is dark energy?" There are a variety of realistic particle physics models for dark matter, and its experimental detection may be within reach. On the other hand, it is fair to say that there is no widely accepted theoretical framework to describe the nature of dark energy. This is exactly why astronomical observations will play a key role in unveiling its nature. I will review our current understanding of the "dark sky", and then present on-going Japanese project, SuMIRe, to discover even more unexpected questions.Comment: 11 pages, 7 figures, to appear in the proceedings of SPIE Astronomical Instrumentation "Observational frontiesr of astronomy for the new decade", based on a plenary talk on June 28, 201

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN