SEDIMENT PHOSPHORUS CYCLING AND SPECIATION ACROSS CANADIAN LAKES: THE ROLE OF REGIONAL GEOLOGY, GEOGRAPHY, LAND USE, AND LAKE CHARACTERISTICS
Bibliographic record
Abstract
Lakes are tremendously variable, and Canada alone has millions of lakes. The high degree of variability we find among lakes is a function of climate, watershed attributes, human activities in the watershed, and lake characteristics including their physical, chemical, and biotic attributes. One of the most important attributes of lakes is their trophic status. High phosphorus (P) loading leads to higher (eutrophic or hypereutrophic) lake trophic states, defined by algae and cyanobacteria rich ecosystems, which are often associated with low benthic oxygen, and degraded ecosystem services. As a result of this key role of P in impacting lake ecosystems, tremendous research effort has been focused on understanding P sources to lakes, and P pools and cycling. The sediment P pool plays a critical role in lakes, partly because its characteristics influence how vulnerable P is to remobilization, via the process of internal P loading. While we may have a strong understanding of P cycling processes in lakes, and significant geochemical differences in P pools, our ability to comprehend key regional differences, and really recognise the potential importance of sediment pools and fluxes to lake P budgets at the scale of thousands to millions of lakes we see in the landscape, remains limited. Informed by the growing field of landscape limnology, my PhD dissertation works to help recognise the attributes of catchments and lakes that help define the dominant sediment P pools and understand the risk of internal loading at large spatial scales. In this research, I used data from 236 lakes spanning 12 Canadian ecozones, sampled during the NSERC Lake Pulse Network, to assess the geographic variation of sediment P species in surface sediments to further our understanding of the permanence of P storage, and the controls on P release. Using P sequential fractionation to quantify the lake sediment P composition, I found a wide variability at the national level, with the highest contrast in sediment P composition between lakes from the Prairies and Boreal Plains ecozones (i.e., mid west Canada) vs. Atlantic Maritime and Atlantic Highlands (i.e., east Canada), and Pacific Maritime ecozone (i.e., western Canada). The watershed soil composition, lake pH, depth, and lake sediment and water chemistry and composition were all associated with the dissimilarity in sediment P composition. This dissimilarity was indicative of high variation in internal P loading (IPL), the release of P from sediments, which is an important source of nutrients within many lakes and a process that can delay ecosystem recovery from eutrophication. To further understand IPL risk, I measured porewater P (soluble reactive P – SRP) concentrations and gradients between sediment porewater and overlying water. I found a 2400 fold variation in porewater SRP concentration across sampled lakes, with the highest values generally found in prairie lakes. High SRP was also associated with lakes with high pH and salinity, shallow depths, high trophic status (e.g., eutrophic and hyper eutrophic), and low bulk sediment Al:P and Fe:P molar ratios (attributes also common in the prairies). As the Prairies and Boreal Plains showed the highest dissimilarity in sediment P composition, and the highest porewater SRP concentration of all the other sampled ecozones, I focussed to improve our understanding of the drivers of sediment P in this region. Despite significant variation between the two ecozones of various watershed and lake attributes known to affect sediment P cycling (e.g., lake trophic status, pH, salinity, bulk sediment elemental composition, and land use variables), porewater SRP concentration and IPL were indistinguishable between the Prairies and Boreal Plains ecozones. Nevertheless, my research showed that porewater SRP in the prairie region is most strongly associated with soil types (e.g., chernozemic and luvisolic), land use in the watershed (e.g., agriculture, natural landscapes, and forestry), and lake pH. This PhD research helps improve our understanding of P budgets and storage at landscape scale and helps advance approaches to estimate IPL risk across unsampled lakes, contributing to our knowledge of lake nutrient budgets and lake management using principles of landscape limnology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".