Factors influencing thermal variability and fish distribution in small boreal steams / by Lisa McKee.
Notice bibliographique
Résumé
The spatial variability of stream temperature is an important component of habitat within streams providing optimal temperatures for foraging and thermal refugia for sensitive species such as brook trout. Riparian shading and lateral contributions of groundwater through the hyporheic zone are the main contributors to spatial variability in stream temperature. The first two objectives of this study were to quantify thermal variability in stream systems through extensive mapping of streambed temperatures and to evaluate the influence of thermal variability on the stream fish distribution and community structure. The third objective was to examine associations between thermal variability and environmental variables at reach, riparian and catchment scales to identify features that may be used to characterize thermally important stream reaches. A total of 55 sample sites were surveyed during the warmest and driest season for Northwestern Ontario (mid-July - September) in streams from 4 catchments size classes; 1, 3, 5 and 10 km [squared]. Streambed thermal variability occurred on a sub-metre scale with temperature fluctuating up to 5.8 ?C across a transect perpendicular to stream flow. The maximum variability found was 10.1 ?C within a 50 m reach and 12.0 ?C within a 300 m survey. Thermal \nvariability was driven by cold streambed temperatures; 44 of 55 reaches had larger deviations below the mean streambed temperature than above the mean, which is an indication of cool groundwater entering the streambed. Fish species diversity and brook trout abundance was significantly higher in reaches with high thermal variability, while \nrainbow trout abundance was significantly lower. Fish species richness within a reach could be predicted as low (<5) or high (>5), with thermal variability as an independent variable using logistic regression. High (>0.10) or low (<0.10) rainbow trout abundance (fish/m[squared]) could also be predicted using thermal variability. Fish size was not found to be \nassociated to thermal variability. Furthermore, thermal variability was correlated with terrestrial variables associated with groundwater movement, including the amount of adjacent land contributing surface and subsurface runoff to the stream, also known as reach contributing area (RCA). Reaches with large RCAs had significantly higher levels of thermal variability compared to reaches with small RCAs. However, the relationship \nbetween thermal variability and RCA was only found for reaches in the two largest stream catchment size classes (5 and 10 km[squared]) due to the dominance of groundwater during base flow of the two smallest catchments (1 and 3 km[squared]). Areas of low and high thermal variability differed in landscape topography, terrestrial surface roughness, landform geology and streambed permeability, which are all related to groundwater flow. \nIn regions such as Northwestern Ontario, where hydrologic pathways are related to topographic features it is possible to use environmental features, such as RCA, to locate lateral groundwater inputs into streams. This predictive ability allows for identification, management and protection of valued ecosystem components important for the maintenance of ecological integrity of streams. \nStudy area : Nipigon Bay Basin, Northwestern Ontario.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 tête enseignante, 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 ».