A Paleolimnological Perspective on Avian Population Dynamics and Their Effect on Lake Productivity in a Rapidly Warming Arctic
Bibliographic record
Abstract
Rapid warming is radically transforming the Arctic, including its freshwater ecosystems. Regionally, warmer temperatures and greater nutrient inputs from a greening landscape are increasing waterbodies' productivity. However, nutrients from the feces produced by growing avian nesting colonies in some locations can have a much greater effect on aquatic primary production, as undisturbed Arctic catchments otherwise provide little nutrients to waterbodies. Therefore, a more complete assessment of climate warming's influence on Arctic waterbodies should consider how warming may influence avian population dynamics. In this thesis, markers for primary production (chlorophyll a, C/N ratio), avian fecal inputs (δ¹⁵N, sterols/stanols), and paleotemperature (branched glycerol diakyl glycerol tetraethers (brGDGTs)) are analyzed in lake sediment cores from the Arctic Archipelago to explore whether these variables are related. Chapter 2 looks at a sediment core from a known seabird-impacted pond on Cape Vera (Devon Island, Nunavut), where previous studies had suggested that warming may be responsible for the significant Northern Fulmar (Fulmarus glacialis) population growth inferred from lacustrine sedimentary depth profiles of avian markers. Our brGDGT-based paleotemperature reconstructions show rapid months above freezing (MAF) warming, which is corroborated by a short temperature record and a longer-term warming trend seen in ice core records from the nearby Devon Ice Cap and tracks the seabird population increase also inferred from sedimentary markers. Chapter 3 examines whether primary production in Cambridge Bay region (Nunavut) lakes is determined by climate warming and/or nutrient inputs from goose droppings using sediment cores from three lakes, including the only known eutrophic lake from the region. Paleotemperature reconstructions largely indicate non-significant MAF warming in all lakes, and significant increases in primary production markers tracking goose fecal markers are observed in 2 lakes. The lack of a primary production increase in the third, non-goose-impacted lake suggests that warming has had a minimal impact on primary production compared to avian-derived nutrients. Altogether, this thesis highlights the impact warming may have on lake primary production by positively influencing migratory bird populations. This work also provides some of the first paleotemperature reconstructions relying on the sedimentary relative abundances of brGDGTs from the Arctic Archipelago and showcases their validity, as we were able to correctly discern two distinct local temperature trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".