Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Marine Science

Major Professor

David F. Naar, Ph.D.

Co-Major Professor

Ernst B. Peebles, Ph.D.

Committee Member

David L. Jones, Ph.D.

Committee Member

Steven A. Murawski, Ph.D.

Committee Member

Theodore S. Switzer, Ph.D.


fisheries, ecosystem management, multivariate statistics, time-series, simulated data


The work in this dissertation represents an attempt to investigate multiple temporal and spatial scales of inquiry relating to the variability of marine resources throughout the Gulf of Mexico large marine ecosystem (Gulf LME). This effort was undertaken over two spatial extents within the greater Gulf LME using two different time-series of fisheries monitoring data. Case studies demonstrating simple frameworks and best practices are presented with the aim of aiding researchers seeking to reduce errors and biases in scientific decision making. Two of the studies focused on three years of groundfish survey data collected across the West Florida Shelf (WFS), an ecosystem that occupies the eastern portion of the Gulf LME and which spans the entire latitudinal extent of the state of Florida. A third study was related to the entire area covered by the Gulf LME, and explored a 30-year dataset containing over 100 long-term monitoring time-series of indicators representing (1) fisheries resource status and structure, (2) human use patterns and resource extractions, and (3) large- and small-scale environmental and climatological characteristics. Finally, a fourth project involved testing the reliability of a popular new clustering algorithm in ecology using data simulation techniques.

The work in Chapter Two, focused on the WFS, describes a quantitatively defensible technique to define daytime and nighttime groundfish assemblages, based on the nautical twilight starting and ending times at a sampling station. It also describes the differences between these two unique diel communities, the indicator species that comprise them, and environmental drivers that organize them at daily and inter-annual time scales. Finally, the differential responses in the diel, and inter-annual communities were used to provide evidence for a large-scale event that began to show an environmental signal in 2010 and subsided in 2011 and beyond. The event was manifested in the organization of the benthic fishes beginning weakly in 2010, peaking in 2011, and fully dissipating by 2012. The biotic effects of the event appeared to disproportionately affect the nighttime assemblage of fishes sampled on the WFS.

Chapter Three explores the same WFS ecosystem, using the same fisheries-independent dataset, but also includes explicit modeling of the spatial variability captured by the sampling program undertaking the annual monitoring effort. The results also provided evidence of a disturbance that largely affected the nighttime fish community, and which was operating at spatial scales of variability that were larger than the extent of the shelf system itself. Like the previous study, the timing of this event is coincident with the 2010 Deepwater Horizon oil spill, the subsequent sub-marine dispersal of pollutants, and the cessation of spillage. Furthermore, the spatial models uncovered the influence of known spatial-abiotic gradients within the Gulf LME related to (1) depth, (2) temperature, and (3) salinity on the organization of daytime groundfish communities. Finally, the models developed also described which non-spatially structured abiotic variables were important to the observed beta-diversity. The ultimate results were the decomposition of the biotic response, within years and divided by diel classification, into the (1) pure-spatial, (2) pure-abiotic, (3) spatial-abiotic, and (4) unexplained fractions of variation. This study, along with that in Chapter Two, also highlighted the relative importance of the nighttime fish community to the assessment of the structure and function of the WFS, and the challenges associated with adequately sampling it, both in space and time.

Because one focus of this dissertation was to develop low-decision frameworks and mathematically defensible alternatives to some common methods in fisheries ecology, Chapter Five employs a clustering technique to identify regime states that relies on hypothesis testing and the use of resemblance profiles as decision criteria. This clustering method avoids some of the arbitrary nature of common clustering solutions seen in ecology, however, it had never been rigorously subjected to numerical data simulation studies. Therefore, a formal investigation of the functional limits of the clustering method was undertaken prior to its use on real fisheries monitoring data, and is presented in Chapter Four. The results of this study are a set of recommendations for researchers seeking to utilize the new method, and the advice is applied in a case study in Chapter Five.

Chapter Five presents the ecosystem-level fisheries indicator selection heuristic (EL-FISH) framework for examining long-term time-series data based on ecological monitoring for resources management. The focus of this study is the Gulf LME, encompassing the period of 1980-2011, and it specifically sought to determine to what extent the natural and anthropogenic induced environmental variability, including fishing extractions, affected the structure, function, and status of marine fisheries resources. The methods encompassed by EL-FISH, and the resulting ecosystem model that accounted for ~73% of the variability in biotic resources, allowed for (1) the identification and description of three fisheries resource regime state phase shifts in time, (2) the determination of the effects of fishing and environmental pressures on resources, and (3) providing context and evidence for trade-offs to be considered by managers and stakeholders when addressing fisheries management concerns. The EL-FISH method is fully transferrable and readily adapts to any set of continuous monitoring data.