Quantifying fisher responses to environmental and regulatory dynamics in marine systems

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Commercial fisheries are part of an inherently complicated cycle. As fishers have adopted new technologies and larger vessels to compete for resources, fisheries managers have adapted regulatory structures to sustain stocks and to mitigate unintended impacts of fishing (e.g., bycatch). Meanwhile, the ecosystems that are targeted by fishers are affected by a changing climate, which in turn forces fishers to further adapt, and subsequently, will require regulations to be updated. From the management side, one of the great limitations for understanding how changes in fishery environments or regulations impact fishers has been a lack of sufficient data for resolving their behaviors. In some fisheries, observer programs have provided sufficient data for monitoring the dynamics of fishing fleets, but these programs are expensive and often do not cover every trip or vessel. In the last two decades however, vessel monitoring systems (VMS) have begun to provide vessel location data at regular intervals such that fishing effort and behavioral decisions can be resolved across time and space for many fisheries. I demonstrate the utility of such data by examining the responses of two disparate fishing fleets to environmental and regulatory changes. This study was one of "big data" and required the development of nuanced approaches to process and model millions of records from multiple datasets. I thus present the work in three components: (1) How can we extract the information that we need? I present a detailed characterization of the types of data and an algorithm used to derive relevant behavioral aspects of fishing, like the duration and distances traveled during fishing trips; (2) How do fishers' spatial behaviors in the Bering Sea pollock fishery change in response to environmental variability; and (3) How were fisher behaviors and economic performances affected by a series of regulatory changes in the Gulf of Mexico grouper-tilefish longline fishery? I found a high degree of heterogeneity among vessel behaviors within the pollock fishery, underscoring the role that markets and processor-level decisions play in facilitating fisher responses to environmental change. In the Gulf of Mexico, my VMS-based approach estimated unobserved fishing effort with a high degree of accuracy and confirmed that the regulatory shift (e.g., the longline endorsement program and catch share program) yielded the intended impacts of reducing effort and improving both the economic performance and the overall harvest efficiency for the fleet. Overall, this work provides broadly applicable approaches for testing hypotheses regarding the dynamics of spatial behaviors in response to regulatory and environmental changes in a diversity of fisheries around the world.General introduction -- Chapter 1 Using vessel monitoring system data to identify and characterize trips made by fishing vessels in the United States North Pacific -- Chapter 2 Paths to resilience: Alaska pollock fleet uses multiple fishing strategies to buffer against environmental change in the Bering Sea -- Chapter 3 Vessel monitoring systems (VMS) reveal increased fishing efficiency following regulatory change in a bottom longline fishery -- General Conclusions

Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data

The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values

Fast And Automatic Floating Point Error Analysis With CHEF-FP

As we reach the limit of Moore's Law, researchers are exploring different paradigms to achieve unprecedented performance. Approximate Computing (AC), which relies on the ability of applications to tolerate some error in the results to trade-off accuracy for performance, has shown significant promise. Despite the success of AC in domains such as Machine Learning, its acceptance in High-Performance Computing (HPC) is limited due to stringent requirements for accuracy. We need tools and techniques to identify regions of code that are amenable to approximations and their impact on the application output quality to guide developers to employ selective approximation. To this end, we propose CHEF-FP, a flexible, scalable, and easy-to-use source-code transformation tool based on Automatic Differentiation (AD) for analyzing approximation errors in HPC applications. CHEF-FP uses Clad, an efficient AD tool built as a plugin to the Clang compiler and based on the LLVM compiler infrastructure, as a backend and utilizes its AD abilities to evaluate approximation errors in C++ code. CHEF-FP works at the source by injecting error estimation code into the generated adjoints. This enables the error-estimation code to undergo compiler optimizations resulting in improved analysis time and reduced memory usage. We also provide theoretical and architectural augmentations to source code transformation-based AD tools to perform FP error analysis. This paper primarily focuses on analyzing errors introduced by mixed-precision AC techniques. We also show the applicability of our tool in estimating other kinds of errors by evaluating our tool on codes that use approximate functions. Moreover, we demonstrate the speedups CHEF-FP achieved during analysis time compared to the existing state-of-the-art tool due to its ability to generate and insert approximation error estimate code directly into the derivative source.Comment: 11 pages, to appear in the 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS'23

Seismic velocity deviation log: An effective method for evaluating spatial distribution of reservoir pore types

Velocity deviation log (VDL) is a synthetic log used to determine pore types in reservoir rocks based on a combination of the sonic log with neutron-density logs. The current study proposes a two step approach to create a map of porosity and pore types by integrating the results of petrographic studies, well logs and seismic data. In the first step, velocity deviation log was created from the combination of the sonic log with the neutron-density log. The results allowed identifying negative, zero and positive deviations based on the created synthetic velocity log. Negative velocity deviations (below − 500 m/s) indicate connected or interconnected pores and fractures, while positive deviations (above + 500 m/s) are related to isolated pores. Zero deviations in the range of [− 500 m/s, + 500 m/s] are in good agreement with intercrystalline and microporosities. The results of petrographic studies were used to validate the main pore type derived from velocity deviation log. In the next step, velocity deviation log was estimated from seismic data by using a probabilistic neural network model. For this purpose, the inverted acoustic impedance along with the amplitude based seismic attributes were formulated to VDL. The methodology is illustrated by performing a case study from the Hendijan oilfield, northwestern Persian Gulf. The results of this study show that integration of petrographic, well logs and seismic attributes is an instrumental way for understanding the spatial distribution of main reservoir pore types