Les aménagements gagnants d'une CLAAC : ce qu'en disent les étudiants

Comprend des références bibliographiques

Les conditions d'efficacité des classes d'apprentissage actif

PA-2013-012La présente recherche a été subventionnée par le ministère de l’Éducation et de l’Enseignement supérieur dans le cadre du Programme d’aide à la recherche sur l’enseignement et l’apprentissage (PAREA).Comprend des références bibliographiques

Les habitudes technologiques au cégep : résultats d'une enquête effectuée auprès de 30 724 étudiants

Texte en anglais.Bibliographie : pages 55-56

Statistical analysis of fish ladder attractivity on the Nord-Est Sainte-Marguerite River.

passe migratoire; échelle à poissons; rivière Sainte-Marguerite; Québec

Comparison of a deterministic and statistical approach for the prediction of thermal indices in regulated and unregulated river reaches: case study of the Fourchue River (Québec, Canada).

Water temperature is an important factor modifying fish distribution patterns and community abundance in streams, and this is especially true for salmonids. Knowing that dams often modify the thermal regime of rivers, understanding these changes is of crucial importance for fish habitat management. This study aims to improve knowledge about the impact of dams on the thermal regime of rivers during the summer season and to assess the relative efficiency of two modelling tools used to predict water temperature downstream of dams. A deterministic model (Stream Network Temperature (SNTEMP)) and a statistical model based on a canonical correlation analysis were calibrated on the Fourchue River (St-Alexandre-de-Kamouraska, Québec, Canada) upstream and downstream of a reservoir. SNTEMP was used to simulate mean water temperature time series using meteorological inputs and discharge. The statistical model was used to directly estimate thermal indices (descriptive statistics of the thermal regime). The two models were compared based on their efficiency to estimate thermal indices such as mean and maximum monthly water temperatures and other parameters of importance in the understanding of the distribution and growth of ichthyofauna. Water temperature was monitored at 18 locations in the Fourchue River during the summers of 2011 and 12 locations in 2012 to describe the thermal regime and calibrate the models. The statistical model achieved better results than SNTEMP in estimating most of the thermal indices, especially the mean and maximum daily ranges with root mean square errors of 4.1 and 4.9° C, respectively, for SNTEMP as compared to 0.5 and 1.1° C for the leave-one-out validation and 0.6 and 1.4° C for the split-sample mode for the statistical model. The better performance of the statistical model for metrics related to thermally stressful events for fish makes it more appealing as a management tool for water resources and fisheries managers. However, SNTEMP should be considered when the objective is to investigate the impact of climate change, reservoir operations or other anthropogenic impacts

Stream Temperature Modeling Using Functional Regression Models.

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches

Inclusion of water temperature in a fuzzy logic Atlantic salmon (Salmo salar) parr habitat model.

As water temperature is projected to increase in the next decades and its rise is clearly identified as a threat for cold water fish species, it is necessary to adapt and optimize the tools allowing to assess the quantity and quality of habitats with the inclusion of temperature. In this paper, a fuzzy logic habitat model was improved by adding water temperature as a key determinant of juvenile Atlantic salmon parr habitat quality. First, salmon experts were consulted to gather their knowledge of salmon parr habitat, then the model was validated with juvenile salmon electrofishing data collected on the Sainte-Marguerite, Matapedia and Petite-Cascapedia rivers (Québec, Canada). The model indicates that when thermal contrasts exist at a site, cooler temperature offered better quality of habitat. Our field data show that when offered the choice, salmon parr significantly preferred to avoid both cold areas (<15 °C) and warm areas (>20.5 °C). Because such thermal contrasts were not consistently present among the sites sampled, the model was only validated for less than 60% of the sites. The results nevertheless indicate a significant correlation between median Habitat Quality Index and parr density for the Sainte-Marguerite River (R² = 0.38). A less important, albeit significant (F-test; p = 0.036) relationship was observed for the Petite-Cascapedia river (R² = 0.14). In all instances, the four-variable (depth, velocity, substrate size and temperature) model provided a better explanation of parr density than a similar model excluding water temperature

A new look at habitat suitability curves through functional data analysis.

Habitat suitability curves (HSC) synthesize the preference of a species for important habitat variables and are, therefore, key components of various fish habitat models. However, HSC are developed at large scales (e.g. river or regional scales) that do not consider the differences that exist in available habitat conditions at smaller scales. To address this problem, a new look at HSC is taken through functional data analysis (FDA). It is an appropriate framework adapted for HSC construction because in FDA, each observation is a curve or a function. To illustrate the potential of FDA for HSC, a dataset of Atlantic salmon (Salmo salar) parr density and habitat variables constructed on two rivers was used. Functional regression models (FRM) were built to predict site-specific HSC based on the available habitat conditions for three salmon parr habitat variables: water depth, mean flow velocity and median substrate size. FRM explained a greater proportion of the variation in site-specific HSC (respectively 38.0%, 53.3% and 45.5% for depth, substrate size and velocity) compared to traditional HSC developed at the scale of each river or regionally that poorly fitted site-specific HSC. When HSC were aggregated into habitat suitability indices (HSI), weak relationships were found between HSI and parr density (R² < 5%) for all models (traditional HSC and FRM). This study demonstrates that FDA is an innovative framework that can be used to predict more representative site-specific HSC adapted to differences in local available habitat. The results suggested that its potential should be further exploited in habitat modelling