Detecting changes in shorebird abundance in relation to habitat and climate at saline lakes of Saskatchewan, Canada
Bibliographic record
Abstract
Shorebird populations worldwide are experiencing alarming declines due to climate change, habitat alteration, and human disturbance. Many shorebird species migrate vast distances between wintering and breeding grounds, depending heavily on high-quality staging sites for rest and refuelling. While coastal stopover sites have been extensively studied, relatively few assessments have focused on inland saline lakes, which serve as vital but opportunistic stopovers for shorebirds migrating through North America's interior and may be candidates for prioritization as protected areas. This study investigates long-term changes in shorebird populations and their habitats by revisiting ten Saskatchewan saline lakes that previously hosted large shorebird numbers but have not been surveyed in over two decades. The scarcity of recent surveys at these lakes suggests that traditional monitoring efforts may be unsustainable in this region; therefore, this project also evaluates ways to improve shorebird survey methods using unmanned aerial vehicles (UAVs). The specific objectives of this research were to (1) analyze population trends by comparing current and historical shorebird counts, (2) determine if habitat features and land-cover at stopover sites has changed, (3) test the viability of UAV technology to enhance survey efficiency, and (4) evaluate current shorebird abundances in Saskatchewan for potential nomination under the Western Hemispheric Shorebird Reserve Network (WHSRN). To address these objectives, I conducted weekly surveys at 4 of the ten inland lakes and spring and fall migration counts for 6 of the ten lakes to count and identify shorebirds using established protocols from historical counts. Abundance across all species was notably lower in spring but higher in fall relative to historical surveys. Species-specific trends included a substantial rise in Wilson's Phalarope numbers in both spring and fall migrations, while tundra-breeding species like Semipalmated Sandpipers and Sanderling declined in spring but rose in fall when compared to historical numbers. Community measures, including species richness and the Shannon’s Index, generally trended downward, with species richness exhibiting a significant decline. Changes in land-cover at the study lakes was evaluated using historical series of satellite imagery. I found the area of water, grassland, and salt flats changed significantly, with area of water increasing while salt flats and grasslands showed a decrease. I further evaluated the accuracy and degree of disturbance from Unmanned Aerial Vehicles (UAV) as a potentially improved survey method. Species-level identification was most reliable at flight altitudes of 15 and 20 meters, especially for species like Sanderling, Red-necked Phalarope, American Avocet, and Stilt Sandpiper. UAV-based counts often exceeded ground counts by an average of 17%. While shorebirds exhibited heightened disturbance during UAV flights, these effects subsided immediately post-flight, with no avoidance of the survey area. Flight altitude did not influence disturbance levels, indicating that UAV surveys at 15 meters may be most reliable with minimal behavioral impact on shorebirds. Based on current surveys, Luck Lake is now the only new lake to meet the WHSRN criteria of regional importance based on maximum shorebird counts, whereas historically, all study lakes met WHSRN thresholds. These findings highlight the complexities of shorebird population and habitat dynamics over time and emphasize the utility of UAV technology for efficient, minimally invasive surveys. This research contributes critical insights for conservation efforts aimed at preserving shorebird staging habitats in the Prairie Pothole Region, an area increasingly vulnerable to environmental change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".