Multi-Tissue Stable Isotope Analysis and Acoustic Telemetry Reveal Seasonal Variability in the Trophic Interactions of Juvenile Bull Sharks in a Coastal Estuary

Understanding how natural and anthropogenic drivers affect extant food webs is critical to predicting the impacts of climate change and habitat alterations on ecosystem dynamics. In the Florida Everglades, seasonal reductions in freshwater flow and precipitation lead to annual migrations of aquatic taxa from marsh habitats to deep-water refugia in estuaries. The timing and intensity of freshwater reductions, however, will be modified by ongoing ecosystem restoration and predicted climate change. Understanding the importance of seasonally pulsed resources to predators is critical to predicting the impacts of management and climate change on their populations. As with many large predators, however, it is difficult to determine to what extent predators like bull sharks (Carcharhinus leucas) in the coastal Everglades make use of prey pulses currently. We used passive acoustic telemetry to determine whether shark movements responded to the pulse of marsh prey. To investigate the possibility that sharks fed on marsh prey, we modelled the predicted dynamics of stable isotope values in bull shark blood and plasma under different assumptions of temporal variability in shark diets and physiological dynamics of tissue turnover and isotopic discrimination. Bull sharks increased their use of upstream channels during the late dry season, and although our previous work shows long-term specialization in the diets of sharks, stable isotope values suggested that some individuals adjusted their diets to take advantage of prey entering the system from the marsh, and as such this may be an important resource for the nursery. Restoration efforts are predicted to increase hydroperiods and marsh water levels, likely shifting the timing, duration and intensity of prey pulses, which could have negative consequences for the bull shark population and/or induce shifts in behaviour. Understanding the factors influencing the propensity to specialize or adopt more flexible trophic interactions will be an important step in fully understanding the ecological role of predators and how ecological roles may vary with environmental and anthropogenic changes.

Climate forecast data (Nino3.4 SST, Arctic sea ice extent) and observational references, link to files in Rdata format

This data set contains seasonal forecasts of sea surface temperature and Arctic sea ice extent from state-of-the-art climate models, along with observational references used to evaluate those forecasts. Common skill scores like the correlation between modelled and observed time series are also reported.

Exploring Mechanisms of Bacterial Adaptation to Seasonal Temperature Change

This research examines three potential mechanisms by which bacteria can adapt to different temperatures: changes in strain-level population structure, gene regulation and particle colonization. For the first two mechanisms, I utilize bacterial strains from the Vibrionaceae family due to their ease of culturability, ubiquity in coastal environments and status as a model system for marine bacteria. I first examine vibrio seasonal dynamics in temperate, coastal water and compare the thermal performance of strains that occupy different thermal environments. Our results suggest that there are tradeoffs in adaptation to specific temperatures and that thermal specialization can occur at a very fine phylogenetic scale. The observed thermal specialization over relatively short evolutionary time-scales indicates that few genes or cellular processes may limit expansion to a different thermal niche. I then compare the genomic and transcriptional changes associated with thermal adaptation in closely-related vibrio strains under heat and cold stress. The two vibrio strains have very similar genomes and overall exhibit similar transcriptional profiles in response to temperature stress but their temperature preferences are determined by differential transcriptional responses in shared genes as well as temperature-dependent regulation of unique genes. Finally, I investigate the temporal dynamics of particle-attached and free-living bacterial community in coastal seawater and find that microhabitats exert a stronger forcing on microbial communities than environmental variability, suggesting that particle-attachment could buffer the impacts of environmental changes and particle-associated communities likely respond to the presence of distinct eukaryotes rather than commonly-measured environmental parameters. Integrating these results will offer new perspectives on the mechanisms by which bacteria respond to seasonal temperature changes as well as potential adaptations to climate change-driven warming of the surface oceans.

Math 412 - Topology with Applications

Highlights of Data Expedition: • Students explored daily observations of local climate data spanning the past 35 years. • Topological Data Analysis, or TDA for short, provides cutting-edge tools for studying the geometry of data in arbitrarily high dimensions. • Using TDA tools, students discovered intrinsic dynamical features of the data and learned how to quantify periodic phenomenon in a time-series. • Since nature invariably produces noisy data which rarely has exact periodicity, students also considered the theoretical basis of almost-periodicity and even invented and tested new mathematical definitions of almost-periodic functions. Summary The dataset we used for this data expedition comes from the Global Historical Climatology Network. “GHCN (Global Historical Climatology Network)-Daily is an integrated database of daily climate summaries from land surface stations across the globe.” Source: https://www.ncdc.noaa.gov/oa/climate/ghcn-daily/ We focused on the daily maximum and minimum temperatures from January 1, 1980 to April 1, 2015 collected from RDU International Airport. Through a guided series of exercises designed to be performed in Matlab, students explore these time-series, initially by direct visualization and basic statistical techniques. Then students are guided through a special sliding-window construction which transforms a time-series into a high-dimensional geometric curve. These high-dimensional curves can be visualized by projecting down to lower dimensions as in the figure below (Figure 1), however, our focus here was to use persistent homology to directly study the high-dimensional embedding. The shape of these curves has meaningful information but how one describes the “shape” of data depends on which scale the data is being considered. However, choosing the appropriate scale is rarely an obvious choice. Persistent homology overcomes this obstacle by allowing us to quantitatively study geometric features of the data across multiple-scales. Through this data expedition, students are introduced to numerically computing persistent homology using the rips collapse algorithm and interpreting the results. In the specific context of sliding-window constructions, 1-dimensional persistent homology can reveal the nature of periodic structure in the original data. I created a special technique to study how these high-dimensional sliding-window curves form loops in order to quantify the periodicity. Students are guided through this construction and learn how to visualize and interpret this information. Climate data is extremely complex (as anyone who has suffered from a bad weather prediction can attest) and numerous variables play a role in determining our daily weather and temperatures. This complexity coupled with imperfections of measuring devices results in very noisy data. This causes the annual seasonal periodicity to be far from exact. To this end, I have students explore existing theoretical notions of almost-periodicity and test it on the data. They find that some existing definitions are also inadequate in this context. Hence I challenged them to invent new mathematics by proposing and testing their own definition. These students rose to the challenge and suggested a number of creative definitions. While autocorrelation and spectral methods based on Fourier analysis are often used to explore periodicity, the construction here provides an alternative paradigm to quantify periodic structure in almost-periodic signals using tools from topological data analysis.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.

Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: an extreme scaling experiment, links to sourcecode of model and grides

The Model for Prediction Across Scales (MPAS) is a novel set of Earth system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and the use of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This concept allows one to include the feedback of regional land use information on weather and climate at local and global scales in a consistent way, which is impossible to achieve with traditional limited area modelling approaches. Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different high-performance computing (HPC) sites with different architectures and compilers. We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes. We further demonstrate the model performance of MPAS in terms of its capability to reproduce the dynamics of the West African monsoon (WAM) and its associated precipitation in a pilot study. Constrained by available computational resources, we compare 11-month runs for two meshes with observations and a reference simulation from the Weather Research and Forecasting (WRF) model. We show that MPAS can reproduce the atmospheric dynamics on global and local scales in this experiment, but identify a precipitation excess for the West African region. Finally, we conduct extreme scaling tests on a global 3?km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70?% parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3?km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities.

Holocene precipitation change in different monsoon sub-regions (time-slices and transient data) simulated by different global climate models, with links to model results

The recently proposed global monsoon hypothesis interprets monsoon systems as part of one global-scale atmospheric overturning circulation, implying a connection between the regional monsoon systems and an in-phase behaviour of all northern hemispheric monsoons on annual timescales (Trenberth et al., 2000). Whether this concept can be applied to past climates and variability on longer timescales is still under debate, because the monsoon systems exhibit different regional characteristics such as different seasonality (i.e. onset, peak, and withdrawal). To investigate the interconnection of different monsoon systems during the pre-industrial Holocene, five transient global climate model simulations have been analysed with respect to the rainfall trend and variability in different sub-domains of the Afro-Asian monsoon region. Our analysis suggests that on millennial timescales with varying orbital forcing, the monsoons do not behave as a tightly connected global system. According to the models, the Indian and North African monsoons are coupled, showing similar rainfall trend and moderate correlation in rainfall variability in all models. The East Asian monsoon changes independently during the Holocene. The dissimilarities in the seasonality of the monsoon sub-systems lead to a stronger response of the North African and Indian monsoon systems to the Holocene insolation forcing than of the East Asian monsoon and affect the seasonal distribution of Holocene rainfall variations. Within the Indian and North African monsoon domain, precipitation solely changes during the summer months, showing a decreasing Holocene precipitation trend. In the East Asian monsoon region, the precipitation signal is determined by an increasing precipitation trend during spring and a decreasing precipitation change during summer, partly balancing each other. A synthesis of reconstructions and the model results do not reveal an impact of the different seasonality on the timing of the Holocene rainfall optimum in the different sub-monsoon systems. They rather indicate locally inhomogeneous rainfall changes and show, that single palaeo-records should not be used to characterise the rainfall change and monsoon evolution for entire monsoon sub-systems.

(Table 2) Seasonal flux data and percentages of major bulk components of total flux at the mesotrophic sediment trap site CB from 1988 - 2012

A more than two-decadal sediment trap record from the Eastern Boundary Upwelling Ecosystem (EBUE) off Cape Blanc, Mauritania, is analysed with respect to deep ocean mass fluxes, flux components and their variability on seasonal to decadal timescales. The total mass flux revealed interannual fluctuations which were superimposed by fluctuations on decadal timescales. High winter fluxes of biogenic silica (BSi), used as a measure of marine production (mostly by diatoms) largely correspond to a positive North Atlantic Oscillation (NAO) index (December-March). However, this relationship is weak. The highest positive BSi anomaly was in winter 2004-2005 when the NAO was in a neutral state. More episodic BSi sedimentation events occurred in several summer seasons between 2001 and 2005, when the previous winter NAO was neutral or even negative. We suggest that distinct dust outbreaks and deposition in the surface ocean in winter and occasionally in summer/autumn enhanced particle sedimentation and carbon export on short timescales via the ballasting effect. Episodic perturbations of the marine carbon cycle by dust outbreaks (e.g. in 2005) might have weakened the relationships between fluxes and large-scale climatic oscillations. As phytoplankton biomass is high throughout the year, any dry (in winter) or wet (in summer) deposition of fine-grained dust particles is assumed to enhance the efficiency of the biological pump by incorporating dust into dense and fast settling organic-rich aggregates. A good correspondence between BSi and dust fluxes was observed for the dusty year 2005, following a period of rather dry conditions in the Sahara/Sahel region. Large changes of all bulk fluxes occurred during the strongest El Niño-Southern Oscillation (ENSO) in 1997-1999 where low fluxes were obtained for almost 1 year during the warm El Niño and high fluxes in the following cold La Niña phase. For decadal timescales, Bakun (1990) suggested an intensification of coastal upwelling due to increased winds (''Bakun upwelling intensification hypothesis''; Cropper et al., 2014) and global climate change. We did not observe an increase of any flux component off Cape Blanc during the past 2 and a half decades which might support this. Furthermore, fluxes of mineral dust did not show any positive or negative trends over time which might suggest enhanced desertification or ''Saharan greening'' during the last few decades.

Warmer, deeper and greener mixed layers in the north Atlantic subpolar gyre over the last 50 years.

Shifts in global climate resonate in plankton dynamics, biogeochemical cycles, and marine food webs. We studied these linkages in the North Atlantic subpolar gyre (NASG), which hosts extensive phytoplankton blooms. We show that phytoplankton abundance increased since the 1960s in parallel to a deepening of the mixed layer and a strengthening of winds and heat losses from the ocean, as driven by the low frequency of the North Atlantic Oscillation (NAO). In parallel to these bottom-up processes, the top-down control of phytoplankton by copepods decreased over the same time period in the western NASG, following sea surface temperature changes typical of the Atlantic Multi-decadal Oscillation (AMO). While previous studies have hypothesized that climate-driven warming would facilitate seasonal stratification of surface waters and long-term phytoplankton increase in subpolar regions, here we show that deeper mixed layers in the NASG can be warmer and host a higher phytoplankton biomass. These results emphasize that different modes of climate variability regulate bottom-up (NAO control) and top-down (AMO control) forcing on phytoplankton at decadal timescales. As a consequence, different relationships between phytoplankton, zooplankton, and their physical environment appear subject to the disparate temporal scale of the observations (seasonal, interannual, or decadal). The prediction of phytoplankton response to climate change should be built upon what is learnt from observations at the longest timescales.

Warmer, deeper and greener mixed layers in the north Atlantic subpolar gyre over the last 50 years.

Shifts in global climate resonate in plankton dynamics, biogeochemical cycles, and marine food webs. We studied these linkages in the North Atlantic subpolar gyre (NASG), which hosts extensive phytoplankton blooms. We show that phytoplankton abundance increased since the 1960s in parallel to a deepening of the mixed layer and a strengthening of winds and heat losses from the ocean, as driven by the low frequency of the North Atlantic Oscillation (NAO). In parallel to these bottom-up processes, the top-down control of phytoplankton by copepods decreased over the same time period in the western NASG, following sea surface temperature changes typical of the Atlantic Multi-decadal Oscillation (AMO). While previous studies have hypothesized that climate-driven warming would facilitate seasonal stratification of surface waters and long-term phytoplankton increase in subpolar regions, here we show that deeper mixed layers in the NASG can be warmer and host a higher phytoplankton biomass. These results emphasize that different modes of climate variability regulate bottom-up (NAO control) and top-down (AMO control) forcing on phytoplankton at decadal timescales. As a consequence, different relationships between phytoplankton, zooplankton, and their physical environment appear subject to the disparate temporal scale of the observations (seasonal, interannual, or decadal). The prediction of phytoplankton response to climate change should be built upon what is learnt from observations at the longest timescales.

Environmental influence on the seasonal movements of satellite-tracked ocean sunfish Mola mola in the north-east Atlantic

Determining the habitat use of mobile marine species is important for understanding responses to climate change and aids the implementation of management and conservation measures. Inference of preferred habitat use has been greatly improved by combining satellite-based oceanographic data with animal tracking techniques. Although there have been several satellite-tracking studies on ocean sunfish Mola mola, limited information is available about either horizontal or vertical environmental preferences. In this study, both geographical movements and diving behaviour of ocean sunfish were explored together with the environmental factors influencing this species’ space use in the north-east Atlantic.

Environmental influence on the seasonal movements of satellite-tracked ocean sunfish Mola mola in the north-east Atlantic

Determining the habitat use of mobile marine species is important for understanding responses to climate change and aids the implementation of management and conservation measures. Inference of preferred habitat use has been greatly improved by combining satellite-based oceanographic data with animal tracking techniques. Although there have been several satellite-tracking studies on ocean sunfish Mola mola, limited information is available about either horizontal or vertical environmental preferences. In this study, both geographical movements and diving behaviour of ocean sunfish were explored together with the environmental factors influencing this species’ space use in the north-east Atlantic.