Assessment of Duckling Abundance as a Biological Indicator of Wetland Health in the Prairie Pothole Region
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».