Assessment of Duckling Abundance as a Biological Indicator of Wetland Health in the Prairie Pothole Region
Bibliographic record
Abstract
Located in the central portion of North America, the Prairie Pothole Region (PPR) is one of the most biologically productive ecosystems in the world. Since European settlement, the region has undergone extensive human development, largely from agricultural practices, urban settlements, and in-part from climate change. Wetlands are often the last remaining natural ecosystems in many parts of the PPR, and they harbor critical habitat for numerous organisms, including nesting and stop-over habitat for the majority of North America’s waterfowl. Due to widespread impacts from agricultural practices, the health and condition of wetlands is frequently degraded across the PPR which may affect their ability to support key species. I hypothesized that the presence and productivity of locally breeding waterfowl may be indicators of wetland condition. I investigated relationships between duckling abundance and measurements of wetland quality to determine the potential use of ducklings as biological indicators of wetland health. In 58 wetlands located at 6 transect sites in central Saskatchewan over 2 years (2018-2019), I examined multiple factors which may determine wetland health including water quality (e.g. pH, conductivity, and Pesticide Toxicity Index), habitat characteristics (e.g. floating vegetation density, maximum water depth, percentage of surrounding grassland, and the extent of degradation of adjacent terrestrial wetland vegetation from agricultural practices), aquatic macroinvertebrate abundance and biomass, and duckling counts of dabbler species derived from bi-weekly surveys throughout the brood-rearing season. I tested relationships between dabbling duckling abundance and several wetland health measurements using generalized linear zero-inflated Poisson models. Model-averaged parameters and 95% confidence intervals (CI) indicated a significant negative effect of conductivity [-0.35 (CI: -0.83, -0.05)], a moderate adverse impact from pH [-0.26 (-0.75, 0.01)], and a slight negative, but nonsignificant effect from pesticides measured using an acute Pesticide Toxicity Index [-3.85 (-8.71, 0.54)]. Based on model-averaged confidence intervals, I found that floating vegetation density negatively impacted dabbling duckling abundance [-0.016 (-0.026, -0.006)], while maximum water depth of wetlands had a positive effect [0.703 (0.391, 1.015)]. Lastly, I found a positive association between aquatic macroinvertebrate abundance and dabbling duckling abundance (SE= 0.1144; P-Value= 0.0374). Based on my findings and the current understanding of the relationship between wetland characteristics and biological productivity, I conclude that duckling abundance could potentially be used as a biological indicator of wetland health in the PPR. This insight may be useful for wetland conservation efforts in the region due to the high likelihood that human impacts from agrochemicals, drainage, and vegetation removal will continue to increase and the costs to monitor wetlands is high using traditional methods. Therefore, it is necessary to have accessible integrative tools for effectively monitoring wetland health. Based on results of my study, I suggest that surveys of duckling abundance could aid in this effort.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".