Spatial ecology and conservation of the critically endangered swift parrot

Conservation of highly mobile resource specialists depends on understanding where and when resources are available and how populations respond to resource configuration. These species are often resource specialists, which can make them vulnerable to resource bottlenecks in time and space. When they also have dynamic distributions, data collection and conservation planning is extremely challenging. Therefore, for species like the swift parrot, which is a highly mobile resource specialist with a dynamic distribution, ecologically relevant and spatiotemporally explicit estimates of distributions are urgently needed to guide conservation planning. Prior to this research little was known of spatiotemporal variation in the distribution of the critically endangered migratory swift parrot in its breeding range. The swift parrot requires co-occurrence of two key functional habitats to breed (nesting and foraging) and relies on the flowering of Eucalyptus globulus and E. ovata for food. The overall aim of this research was to better understand and quantify the spatial ecology of the species to improve conservation planning and outcomes. The main impetus for this research was continuing extensive habitat loss (as a result of industrial-scale logging and land clearance) without an understanding of i) the importance of the loss of key sites or locations and ii) the implications of the discovery of novel predator during the course of the study. Firstly, this thesis quantifies and describes a key functional habitat feature (i.e. nesting trees) to assist accurate identification of nesting habitat (Chapter 2). The research then uses data from a unique multi-year monitoring program to i) extend modelling approaches to account for imperfect detection and spatial autocorrelation, ii) quantify the strong link between changing food availability and the species distribution, and iii)quantify how this varies over time (Chapter 3). Then, using data sampled from each functional habitat the research quantifies annual change in the use, location and availability of functional habitats over the entire breeding range (Chapter 4). Finally, the abundance-occupancy relationship (AOR) is quantified temporally and spatially to better understand the implications of spatiotemporal changes in abundance and resource availability for the interpretation species distribution models (SDMs) (Chapter 5). This research reveals highly aggregated nesting behaviour of the swift parrot at multiple spatial scales, and provides one of the first macroecological examples to quantify a direct link between the spatiotemporal distribution of a highly mobile species and food availability. This spatiotemporal variation in food not only means the availability of functional habitats can vary dramatically between years, but also that an increase or decrease in one functional habitat does necessarily correspond to a relative increase or decrease in the other. This has important ramifications for interpreting SDMs, identifying when and where resource bottlenecks may occur, and the assessment of exposure to other spatially variable threats (e.g. predation). Further, the research shows the AOR for mobile species in dynamic distributions can be highly variable over time and space. Importantly, the results also highlight that locations with high predicted occupancy and/or abundance do not necessarily equate to areas of high quality habitat. This thesis delivers some of the first fundamental and quantitative insights into the spatial ecology of highly mobile species that rely on variable environments, and provides guidance towards informing and developing conservation plans for this difficult to study group of species

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Improving the usability of spatial point processes methodology : an interdisciplinary dialogue between statistics and ecology

The last few decades have seen an increasing interest and strong development in spatial point process methodology, and associated software that facilitates model fitting has become available. A lot of this progress has made these approaches more accessible to users, through freely available software. However, in the ecological user community the methodology has only been slowly picked up despite its obvious relevance to the field. This paper reflects on this development, highlighting mutual benefits of interdisciplinary dialogue for both statistics and ecology. We detail the contribution point process methodology has made to research on biodiversity theory as a result of this dialogue and reflect on reasons for the slow take-up of the methodology. This primarily concerns the current lack of consideration of the usability of the approaches, which we discuss in detail, presenting current discussions as well as indicating future directions.Publisher PDFPeer reviewe

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Information Driven Self-Organization of Agents and Agent Collectives

From a visual standpoint it is often easy to point out whether a system is considered to be self-organizing or not, though a quantitative approach would be more helpful. Information theory, as introduced by Shannon, provides the right tools not only quantify self-organization, but also to investigate it in relation to the information processing performed by individual agents within a collective. This thesis sets out to introduce methods to quantify spatial self-organization in collective systems in the continuous domain as a means to investigate morphogenetic processes. In biology, morphogenesis denotes the development of shapes and form, for example embryos, organs or limbs. Here, I will introduce methods to quantitatively investigate shape formation in stochastic particle systems. In living organisms, self-organization, like the development of an embryo, is a guided process, predetermined by the genetic code, but executed in an autonomous decentralized fashion. Information is processed by the individual agents (e.g. cells) engaged in this process. Hence, information theory can be deployed to study such processes and connect self-organization and information processing. The existing concepts of observer based self-organization and relevant information will be used to devise a framework for the investigation of guided spatial self-organization. Furthermore, local information transfer plays an important role for processes of self-organization. In this context, the concept of synergy has been getting a lot attention lately. Synergy is a formalization of the idea that for some systems the whole is more than the sum of its parts and it is assumed that it plays an important role in self-organization, learning and decision making processes. In this thesis, a novel measure of synergy will be introduced, that addresses some of the theoretical problems that earlier approaches posed

An information-theoretic approach to understanding the neural coding of relevant tactile features

Objective: Traditional theories in neuroscience state that tactile afferents present in the glabrous skin of the human hand encode tactile information following a submodality segregation strategy, meaning that each modality (eg. motion, vibration, shape, ... ) is encoded by a different afferent class. Modern theories suggest a submodality convergence instead, in which different afferent classes work together to capture information about the environment through tactile sense. Typically, studies involve electrophysiological recordings of tens of afferents. At the same time, the human hand is filled with around 17.000 afferents. In this thesis, we want to tackle the theoretical gap this poses. Specifically, we aim to address whether the peripheral nervous system relies on population coding to represent tactile information and whether such population coding enables us to disambiguate submodality convergence against the classical segregation. Approach: Understanding the encoding and flow of information in the nervous system is one of the main challenges of modern neuroscience. Neural signals are highly variable and may be non-linear. Moreover, there exist several candidate codes compatible with sensory and behavioral events. For example, they can rely on single cells or populations and also on rate or timing precision. Information-theoretic methods can capture non-linearities while being model independent, statistically robust, and mathematically well-grounded, becoming an ideal candidate to design pipelines for analyzing neural data. Despite information-theoretic methods being powerful for our objective, the vast majority of neural signals we can acquire from living systems makes analyses highly problem-specific. This is so because of the rich variety of biological processes that are involved (continuous, discrete, electrical, chemical, optical, ...). Main results: The first step towards solving the aforementioned challenges was to have a solid methodology we could trust and rely on. Consequently, the first deliverable from this thesis is a toolbox that gathers classical and state-of-the-art information-theoretic approaches and blends them with advanced machine learning tools to process and analyze neural data. Moreover, this toolbox also provides specific guidance on calcium imaging and electrophysiology analyses, encompassing both simulated and experimental data. We then designed an information-theoretic pipeline to analyze large-scale simulations of the tactile afferents that overcomes the current limitations of experimental studies in the field of touch and the peripheral nervous system. We dissected the importance of population coding for the different afferent classes, given their spatiotemporal dynamics. We also demonstrated that different afferent classes encode information simultaneously about very simple features, and that combining classes increases information levels, adding support to the submodality convergence theory. Significance: Fundamental knowledge about touch is essential both to design human-like robots exhibiting naturalistic exploration behavior and prostheses that can properly integrate and provide their user with relevant and useful information to interact with their environment. Demonstrating that the peripheral nervous system relies on heterogeneous population coding can change the designing paradigm of artificial systems, both in terms of which sensors to choose and which algorithms to use, especially in neuromorphic implementations

Spatio-temporal modelling of stable isotopes in tree Mediterranean species (Quercus ilex L. and Pinus Halepensis Mill.): a climatic and ecophysiological view

Trees hold important secrets that may be essential in order to face the unprecedented current environmental challenges. The basis of this thesis is to use a combination of modern tools such as stable isotopes, Geographical Information Systems (GIS), Point Process statistics to retrieve climatic and ecophysiological information from forests at different spatial and temporal scales. We focus on two typical coexisting Mediterranean species: holm oak (Quercus ilex L.) and Aleppo pine (Pinus halepensis Mill.). At the regional scale: we generated carbon isotope (D13C) landscapes (isoscapes) of each species, later converted to annual precipitation maps: and we also showed that in Aleppo pine, annual precipitation drives D13C, RG and NDVI, but the three variables hold complementary information. At the local scale, we focus on a mixed forest stand in which both species coexist. By combining water isotopes and point process statistics, we could interpret tree-to-tree interactions in terms of water use (including seasonal variation).We found evidences of a dynamic niche segregation.Los árboles poseen secretos importantes que pueden ser esenciales para afrontar los actuales retos medioambientales. La idea central de esta tesis es usar una combinación de herramientas modernas, tales como Sistemas de Información Geográfica (SIG), estadística de Procesos Puntuales, para extraer información de los bosques a diferentes escalas espaciotemporales. Nos centramos en dos especies mediterráneas: la encina (Quercus ilex L.) y el pino carrasco (Pinus halepensis Mill.). A escala regional, generamos paisajes de distribución isotópica (isoscapes) de carbono (D13C) para cada especie, derivando así mapas de precipitación anual; mostramos que la precipitación controla la variabilidad espaciotemporal de la D13C, crecimiento radial (CR) e índice de vegetación (NDVI) para el pino, aunque ofrecen información complementaria. A escala local, nos centramos en una masa mixta donde ambas especies coexisten. Combinando isótopos de agua y estadística de Procesos Puntuales, interpretamos las interacciones árbol-árbol en el uso de agua (incluyendo variación estacional). Encontramos evidencias de una segregación de nicho hidrológico dinàmica.Els arbres amaguen secrets importants que poden ser essencials per enfrontar-se als actuals canvis ambientals sense precedents. El fonament d’aquesta tesi és fer servir una combinació d’eines innovadores com ara isòtops estables, Sistemes d’Informació Geogràfica (SIG), o estadística de processos puntuals a fi d’obtenir informació climàtica i ecofisiològica dels boscos a diferents escales temporals i espacials. L’objecte d’estudi són dues espècies típicament mediterrànies: alzina (Quercus ilex L.) i pi blanc (Pinus halepensis Mill) A escala regional, generem paisatges de distribució isotòpica (isoscapes) de (D13C) per cada espècie; trobem que la variació espacial i temporal en D13C, creixement radial (CR) i els índexs de vegetació (NDVI) pel pi blanc, ve donat per la precipitació anual, encara que ofereixen informació complementària. A escala local, ens centrem en una massa mixta on les dues espècies coexisteixen. Combinant isòtops d’aigua i estadística de processos puntuals hem pogut interpretar interaccions arbre-arbre en l’ús de l’aigua (incloent variació estacional). Trobem evidències d’una segregació de nínxol hidrològic dinàmica

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page