MIGRATION ECOLOGY AND ANNUAL CYCLE INFLUENCE ON CONTAMINANT CONCENTRATIONS IN HORNED GREBE (PODICEPS AURITUS CORNUTUS) BREEDING IN THE NORTHWEST TERRITORIES AND SASKATCHEWAN, CANADA
Bibliographic record
Abstract
Understanding the movements that migratory birds make and landscapes they use throughout their annual cycle is essential for developing sound management and conservation strategies, as it helps to identify when and where these strategies should be implemented. As individuals migrate between breeding, stopover, and non-breeding areas, they are exposed to a multitude of environmental conditions that can influence the health of populations in subsequent seasons. Therefore, it is crucial to assess the extent to which disparate populations co-occur between phases of the annual cycle (i.e., migratory connectivity) and are thus exposed to the same environmental conditions and pressures (Cohen et al. 2018). Within Canada, evidence suggests that breeding populations of Horned Grebe (Podiceps auritus) have experienced significant declines since 1970, most notably in the Prairie Pothole Region (PPR) (COSEWIC 2009). However, Horned Grebes are largely an understudied species, and existing knowledge gaps – particularly related to migratory connectivity and exposure to contaminants throughout the annual cycle – limit our understanding of the potential drivers behind these declines. My objective was to address key knowledge gaps by assessing migratory movements (Chapter 2) and analyzing contaminant burdens presumedly acquired on non-breeding and breeding sites (Chapter 3) of disparate breeding populations of Horned Grebes in Canada. In the former, I used geolocators and stable isotopes of head feathers to assign individuals to distinct non-breeding regions and estimate migratory connectivity. In the latter, I analyzed eggs and blood plasma for persistent organic pollutants (POPs) and neonicotinoids (NNIs), respectively and evaluated relationships between measured contaminant profiles, sources of nutrients in eggs, and breeding location. In my migration study, I found tracked birds exhibited significant population spread during the non-breeding period. Stable isotope analysis suggested that disparate breeding populations may also mix on non-breeding areas. Estimates of migratory connectivity were weak (MC = 0.08 +/- 0.07), suggesting conditions experienced on non-breeding areas will have a diffuse effect on breeding populations. This idea was supported by analysis of egg stable isotope values, which indicated that contaminant profiles observed in eggs are likely not representative of non-breeding area exposure; that is, eggs are not formed entirely of endogenous inputs but may primarily represent inputs from local food sources. Contaminant analyses revealed that types of POPs in eggs were similar between the two breeding populations, with all contaminants detected at relatively low levels; polychlorinated biphenyls (PCBs) were the dominant group – both proportionally and in total concentrations – followed by organochlorines (OCs), and brominated flame retardants (BFRs). More than half of all blood samples had detections of at least one NNI at or above the method reporting limit, with several individuals exposed to more than one NNI compound. Total concentrations of NNIs were greater in samples from the Boreal study site compared to samples from the Prairie site. I anticipated breeding area to have an effect on NNI profiles, but no relationship was detected. These findings demonstrate that exposure to agrochemicals may occur both within and beyond agricultural areas. Overall, my study highlights the importance of an annual cycle approach to understanding contaminant exposure in a migratory species, as my results indicate that Horned Grebe are likely more limited by conditions on breeding areas than by those on their non-breeding range. I recommend that conservation efforts, such as protection of critical habitats, should therefore focus on breeding areas to maximize benefits for Horned Grebes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".