Patterns and Drivers of Recent Peatland Carbon Accumulation in Northeastern Canada

Northern peatlands are an important component of the global carbon (C) cycle and have been a net sink of atmospheric C during the Holocene. Under current climate warming conditions, the future sink-source balance of these peatlands is uncertain. In particular, peatlands near the southern limit of permafrost are likely to be sensitive to changes in topography as well as climate. In order to predict how the sink-source balance may change, this thesis focuses on determining the generality of observed patterns of C accumulation in Northeastern Canada. The methodological approach in this thesis is unique. A total of 30 cores were taken from 9 peatlands located in 3 ecoclimatic regions along the North Shore of the Gulf of St Lawrence. This replication of records allows for climate-scale (allogenic) signals to be separated from the internal or local factors (autogenic), and for statistical testing of differences between regions and within sites over time. Trends in carbon accumulation rates (CAR) were analysed on three levels: (1) within individual sites along a hydrological or microtopography gradient, (2) between overall regions located along a climatic or permafrost gradient, and (3) over time on a multi-centennial scale. Lead-210 (210Pb) dating was used throughout the analysis to increase temporal resolution for the last 150-200 years of C accumulation. The method was thoroughly tested from preparation to analysis and found to produce reliable results, comparable with other dating methods. These dates were then used to develop combined age-depth models for longer-term context. Replicated records of 210Pb inventories and fallout rates were also used to address questions of deposition patterns and post-depositional mobility in peat profiles. Total inventories decreased with water table depth, with lichen hummocks having significantly higher inventories. One site also received significantly higher 210Pb deposition than the other two, as it is more sheltered from the Gulf influence. Recent carbon accumulation rates for the 150-year period for all microforms across all regions was 62.1 ± 4.4 g C m-2 a-1, and were highest for Sphagnum hummocks (79.9 ± 8.9 g C m-2 a-1) and lowest for dry lichen hummocks (42.7 ± 6.2 g C m-2 a-1). Patterns and trends at this scale were mainly driven by autogenic processes, including incomplete decomposition in the acrotelm peat. Models of peat accumulation related to acrotelm thickness were found to be overly simplistic, as carbon accumulation for intermediate microforms showed large natural variability driven by changing ecohydrological feedbacks, in part due to permafrost degradation at one of the sites. Over a multi-centennial scale, carbon accumulation rates were driven by a combination of climatic changes and ecohydrological feedbacks due to shifts in the microform configuration in response to permafrost degradation. Changes in carbon accumulation rates were detected and coincided with Little Ice Age temperature/solar minima (including the Spörer, Maunder and Dalton Minima), permafrost degradation since the 1950s, and recent climatic changes in the mid-1990s. Snow cover and exposure of sites and microforms were found to play an important role, rather than solely climatic variables. Rapid Sphagnum re-establishment in post-permafrost degraded features and increasing temperatures meant that carbon accumulation was highest for the northernmost site in the transect. Age-depth models using a combination of lead-210 and radiocarbon dates allowed for the calculation of carbon accumulation rates at a decadal resolution. While peat carbon sequestration is projected to increase in northern regions, the fate of peatland C near the southern limit of permafrost is complex. Future studies seeking to interpret recent changes should include multiple cores and consider both regional climatic and local ecohydrological drivers.College of Life and Environmental Sciences, University of ExeterUniversité du Québec à Montreal (UQAM

Simplicial Multivalued Maps and the Witness Complex for Dynamical Analysis of Time Series

Topology based analysis of time-series data from dynamical systems is powerful: it potentially allows for computer-based proofs of the existence of various classes of regular and chaotic invariant sets for high-dimensional dynamics. Standard methods are based on a cubical discretization of the dynamics and use the time series to construct an outer approximation of the underlying dynamical system. The resulting multivalued map can be used to compute the Conley index of isolated invariant sets of cubes. In this paper we introduce a discretization that uses instead a simplicial complex constructed from a witness-landmark relationship. The goal is to obtain a natural discretization that is more tightly connected with the invariant density of the time series itself. The time-ordering of the data also directly leads to a map on this simplicial complex that we call the witness map. We obtain conditions under which this witness map gives an outer approximation of the dynamics, and thus can be used to compute the Conley index of isolated invariant sets. The method is illustrated by a simple example using data from the classical H\'enon map.Comment: laTeX, 9 figures, 32 page

Bayesian analysis of 210Pb dating

In many studies of environmental change of the past few centuries, 210Pb dating is used to obtain chronologies for sedimentary sequences. One of the most commonly used approaches to estimate the ages of depths in a sequence is to assume a constant rate of supply (CRS) or influx of `unsupported' 210Pb from the atmosphere, together with a constant or varying amount of `supported' 210Pb. Current 210Pb dating models do not use a proper statistical framework and thus provide poor estimates of errors. Here we develop a new model for 210Pb dating, where both ages and values of supported and unsupported 210Pb form part of the parameters. We apply our model to a case study from Canada as well as to some simulated examples. Our model can extend beyond the current CRS approach, deal with asymmetric errors and mix 210Pb with other types of dating, thus obtaining more robust, realistic and statistically better defined estimates.Comment: 22 Pages, 4 Figure

Mobile Learning Innovation in Information Literacy Skills Training

The Mobile Information Literacy (MIL) tool is a user-friendly literacy app to help university students hone their information literacy skills through mobile technology

Gender issues in initial teacher education

The purpose of the study was to examine preservice teachers' perceptions towards gender issues in education. It also examined whether gender issues are an integral component of prospective teacher training. This was done using focus group methodology supplemented by "self appraisal" questionnaires. Five themes were persistently a part of the students' perceptions of gender issues. Theses themes were: a) the prescribed roles of males and females ; b) the invisibility of gender inequity; c) the importance of practice teaching; d) the belief that gender issues are female issues and; e) the resistance to gender equity initiatives. One theme that emerged from the focus group sessions that was not found in the literature was that of harassment of female student teachers by male teachers and students. The results also suggest that gender continues to be a major organizational construct within educational institutions and that the problematization of gender continues to be ignored despite increased awareness and discussion of gender inequity in education

Topological Data Analyses of Time Series Using Witness Complexes

Real-time regime shift detection between chaotic dynamical systems via time series analysis demands quick and correct, theoretically guaranteed methods. Often the best implemented techniques in the field are well-motivated heuristics and even interpretable statistics are scarce. Topological data analysis can contribute to the canon of traditional methods for analyzing nonlinear time series but is not computationally cheap. We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - to model the data and assess its performance across a range of model parameters affecting computational efficiency. We then explore how the topology of witness complexes changes across this range of model parameters. We next define a simplicial complex whose construction incorporates the temporal information available with time series data. We experimentally show that this construction results in filtrations with fewer simplices and improved topological signature. We apply our techniques to synthetic time series data including numerical solutions of classical low dimensional chaotic systems Lorenz and Rössler systems of ODEs as well as regimes of the higher dimensional Brunel neuronal network model and experimental live voltage recordings of musical instruments

Topological Regularization via Persistence-Sensitive Optimization

Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to ensure its smoothness. Recently, topological methods have emerged as a way to provide a more precise and expressive control over the solution, relying on persistent homology to quantify and reduce its roughness. All such existing techniques back-propagate gradients through the persistence diagram, which is a summary of the topological features of a function. Their downside is that they provide information only at the critical points of the function. We propose a method that instead builds on persistence-sensitive simplification and translates the required changes to the persistence diagram into changes on large subsets of the domain, including both critical and regular points. This approach enables a faster and more precise topological regularization, the benefits of which we illustrate with experimental evidence.Comment: The first two authors contributed equally to this wor