Dynamic soaring in the winds of change: The effects of wind and oceanography on the population and spatial ecology of seabirds

Seabirds are marine top predators regarded as indicators of the environmental changes occurring in their supporting ecosystems. The analytical lens of this thesis focusses on seabird belonging to the order Procellariiformes, which have similar life-histories characterised by high life expectancy and delayed sexual maturity. Furthermore, despite acting as central place foragers during breeding, most procellariiform seabirds can perform foraging trips covering thousands of kilometres by extracting energy from the wind through a flight behaviour known as "dynamic soaring". The overarching aim of my thesis is to understand the pathways through which wind and oceanographic processes affect the demography, population dynamics, foraging ecology and spatial distribution of seabirds. Focussing on the black-browed albatross (Thalassarche melanophris) as a model organism, we developed integrated population models to investigate the effects of wind and oceanographic fluctuations on the population breeding and survival processes. By analysing a demographic database spanning nearly two decades, we found that the population breeding parameters were negatively impacted by higher sea surface temperatures and positively affected by stronger winds, presumably through bottom-up environmental processes modulating food availability and accessibility. Survival was relatively constant and was only influenced by deeper ecosystem changes acting at larger spatio-temporal scales. Furthermore, our results revealed the high sensitivity of the population to the survival rate of the poorly understood sub-adult life history stages, which comprised approximately half of the total population size. We then studied the occurrence of albatross chick mortality events not caused by predation. Our results showed that, while albatross chicks weighed less in years with warmer sea temperatures, chick malnutrition and environmentally-driven food regulation did not explain the observed patterns of mortality. Rather, nestlings mortality events unrelated to predation were clustered at small scales in time and space, suggesting that part of the pronounced inter-annual variability in albatross breeding success was modulated by the prevalence of an unidentified infectious disease. By developing state-space models, we quantified a previously hypothesised, but never empirically documented "habitat-mediated" pathway linking environmental conditions to the breeding processes of a social monogamous population. Specifically, we found a higher prevalence of divorce in challenging years characterised by warmer sea surface temperatures, documenting the direct disruptive effects of ocean warming on the social monogamous bonds of albatrosses. Our work then focussed on the hypermobile Desertas petrel (Pterodroma deserta) and Bulwer's petrel (Bulweria bulwerii) as model organisms to investigate role of winds in shaping the flight behaviour and the foraging ecology of dynamic soaring seabirds during the breeding season. Desertas petrels used favourable winds to maximise their ground speed and distance covered throughout their round-trip foraging movements, among the longest recorded in any animal. Bulwer's petrels, on the other hand, exploited the stable North Atlantic trade winds, exhibiting a striking selectivity for crosswinds and engaging in crosswind zig-zag flight throughout large sections of their tracks. Under stable winds, this strategy enabled them to maximise the distance travelled and the probability of detecting odour plumes along the round trip. Crucially, the movement patterns of these two species suggest that seabirds have a priori knowledge of the regional winds and can plan their round-trip with an expectation of predicted wind conditions and costs of flight to return back to their colony. Collectively, the findings of my thesis highlight the sensitivity of seabirds to changes in oceanographic conditions and their reliance on winds to sustain their extreme life-history. Given the accelerating pace of global change and its dramatic effects on marine ecosystems, monitoring the diagnostic responses of these "sentinels" of the global ocean and, crucially, predicting their future performance is a conservation goal of upmost importance.Falkland Islands Government – Environmental Studies Budge

Long-Range Autocorrelations of CpG Islands in the Human Genome

In this paper, we use a statistical estimator developed in astrophysics to study the distribution and organization of features of the human genome. Using the human reference sequence we quantify the global distribution of CpG islands (CGI) in each chromosome and demonstrate that the organization of the CGI across a chromosome is non-random, exhibits surprisingly long range correlations (10 Mb) and varies significantly among chromosomes. These correlations of CGI summarize functional properties of the genome that are not captured when considering variation in any particular separate (and local) feature. The demonstration of the proposed methods to quantify the organization of CGI in the human genome forms the basis of future studies. The most illuminating of these will assess the potential impact on phenotypic variation of inter-individual variation in the organization of the functional features of the genome within and among chromosomes, and among individuals for particular chromosomes

Mining Hidden Markov Models in Sequences of Characters Using Recurrent Neural Networks

Restoring damaged historical manuscripts and making them available to the large public has been of great interest for humanities researchers long before computers provided assistance for this task. Current technologies and models make this process easier, more accurate, and capable of discovering parts that were previously unknown. I use Recurrent Neural Networks for uncovering hidden Markov models in sequences of characters from historic manuscripts. Such manuscripts are typically written in some archaic language, which makes the underlying machine learning problem inherently difficult, as not much training data is available, in general. I use bidirectional, hierarchical models for sequences of one or more characters, trained on the existent manuscript data. I tested my model and present experimental results using an Old English manuscript

Using Botnet Technologies to Counteract Network Traffic Analysis

Botnets have been problematic for over a decade. They are used to launch malicious activities including DDoS (Distributed-Denial-of-Service), spamming, identity theft, unauthorized bitcoin mining and malware distribution. A recent nation-wide DDoS attacks caused by the Mirai botnet on 10/21/2016 involving 10s of millions of IP addresses took down Twitter, Spotify, Reddit, The New York Times, Pinterest, PayPal and other major websites. In response to take-down campaigns by security personnel, botmasters have developed technologies to evade detection. The most widely used evasion technique is DNS fast-flux, where the botmaster frequently changes the mapping between domain names and IP addresses of the C&C server so that it will be too late or too costly to trace the C&C server locations. Domain names generated with Domain Generation Algorithms (DGAs) are used as the \u27rendezvous\u27 points between botmasters and bots. This work focuses on how to apply botnet technologies (fast-flux and DGA) to counteract network traffic analysis, therefore protecting user privacy. A better understanding of botnet technologies also helps us be pro-active in defending against botnets. First, we proposed two new DGAs using hidden Markov models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) which can evade current detection methods and systems. Also, we developed two HMM-based DGA detection methods that can detect the botnet DGA-generated domain names with/without training sets. This helps security personnel understand the botnet phenomenon and develop pro-active tools to detect botnets. Second, we developed a distributed proxy system using fast-flux to evade national censorship and surveillance. The goal is to help journalists, human right advocates and NGOs in West Africa to have a secure and free Internet. Then we developed a covert data transport protocol to transform arbitrary message into real DNS traffic. We encode the message into benign-looking domain names generated by an HMM, which represents the statistical features of legitimate domain names. This can be used to evade Deep Packet Inspection (DPI) and protect user privacy in a two-way communication. Both applications serve as examples of applying botnet technologies to legitimate use. Finally, we proposed a new protocol obfuscation technique by transforming arbitrary network protocol into another (Network Time Protocol and a video game protocol of Minecraft as examples) in terms of packet syntax and side-channel features (inter-packet delay and packet size). This research uses botnet technologies to help normal users have secure and private communications over the Internet. From our botnet research, we conclude that network traffic is a malleable and artificial construct. Although existing patterns are easy to detect and characterize, they are also subject to modification and mimicry. This means that we can construct transducers to make any communication pattern look like any other communication pattern. This is neither bad nor good for security. It is a fact that we need to accept and use as best we can

Multi-scale movement of demersal fishes in Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2019Information on the movement of migratory demersal fishes such as Pacific halibut, Pacific cod, and sablefish is needed for management of these valuable fisheries in Alaska, yet available methods such as conventional tagging are too coarse to provide detailed information on migration characteristics. In this dissertation, I present methods for characterizing seasonal and annual demersal fish movement at multiple scales in space and time using electronic archival and acoustic tags. In Chapter 1, acoustic telemetry and the Net Squared Displacement statistic were used to identify and characterize small-scale movement of adult female Pacific halibut during summer foraging in a Marine Protected Area (MPA). The dominant movement pattern was home range behavior at spatial scales of less than 1 km, but a more dispersive behavioral state was also observed. In Chapter 2, Pop-up Satellite Archival Tags (PSATs) and acoustic tags were deployed on adult female Pacific halibut to determine annual movement patterns relative to MPA boundaries. Based on observations of summer home range behavior, high rates of year-round MPA residency, migration timing that largely coincided with winter commercial fisheries closures, and the demonstrated ability of migratory fish to return to previously occupied summer foraging areas, the MPA is likely to be effective for protecting both resident and migrant Pacific halibut brood stock year-round. In Chapter 3, I adapted a Hidden Markov Model (HMM) originally developed for geolocation of Atlantic cod in the North Sea for use on demersal fishes in Alaska, where maximum daily depth is the most informative and reliable geolocation variable. Because depth is considerably more heterogeneous in many regions of Alaska compared to the North Sea, I used simulated trajectories to determine that the degree of bathymetry heterogeneity affected model performance for different combinations of likelihood specification methods and model grid sizes. In Chapter 4, I added a new geolocation variable, geomagnetic data, to the HMM in a small-scale case study. The results suggest that the addition of geomagnetic data could increase model performance over depth alone, but more research is needed to continue validation of the method over larger areas in Alaska. In general, the HMM is a flexible tool for characterizing movement at multiple spatial scales and its use is likely to enrich our knowledge about migratory demersal fish movement in Alaska. The methods developed in this dissertation can provide valuable insights into demersal fish spatial dynamics that will benefit fisheries management activities such as stock delineation, stock assessment, and design of space-time closures.Rasmuson Fisheries Research Center and the Pollock Conservation Cooperative Research CenterChapter 1: Characterizing Pacific halibut movement and habitat in a Marine Protected Area using net squared displacement analysis methods -- Chapter 2: Interannual site fidelity of Pacific halibut: potential utility of protected areas for management of a migratory demersal fish -- Chapter 3 Effect of study area bathymetric heterogeneity on parameterization and performance of a depth depth-based geolocation model for demersal fishes -- Chapter 4 Potential utility of geomagnetic data for geolocation of demersal fish in the North Pacific Ocean -- General conclusion -- References -- Appendix A: Geolocation of demersal fishes in the North Pacific Ocean: Hidden Markov model framework and data likelihood models