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Montréal, Canada

Dumas A.,Coastal Zones Research Institute | Lopez S.,University of Leon | Kebreab E.,University of California at Davis | Gendron M.,Environnement Illimite Inc. | And 2 more authors.
CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources | Year: 2012

Simple growth models have proven helpful to study and predict growth trajectories of fish with different life histories and from various environments, and have found numerous applications in ecology, fisheries and aquaculture worldwide. The most applied simple models (e.g. von Bertalanffy) convey little information about life history traits, are facing criticism, and their goodness of fit is assessed using a limited number of statistical outputs. This review challenges the von Bertalanffy and certain statistical outputs using real-life data, and proposes more explanatory alternatives. The length-at-age relationship from three different wild populations of four fish species (lake herring, lake whitefish, northern pike and walleye) is described using the monomolecular, Schumacher, Gompertz, logistic, von Bertalanffy and Richards equations. Comparison between models was based on least squares and likelihood theories involving several statistical outputs currently encountered in model comparison and selection studies. The analysis demonstrates that the monomolecular, Schumacher and Richards equations often stood as alternatives to the von Bertalanffy and highlighted residual sum of squares, standard error (SE) of parameter estimates and, to a lesser extent, Akaike weight as statistical outputs facilitating discrimination between candidate models. Based on visual appraisal, different equations fitted similar trajectories across the 12 growth profiles. However, the assumptions and statistical performance served to select the most appropriate models and discern life history traits of the species under study. Results indicate that comparison and selection of models should consider not only the statistical performance of equations but also the purpose of the study along with the biological/physical interpretation of parameters. © CAB International 2012.

Kovacs J.M.,Nipissing University | De Santiago F.F.,Environnement Illimite Inc. | Bastien J.,Environnement Illimite Inc. | Lafrance P.,Environnement Illimite Inc.
Wetlands | Year: 2010

We provide a baseline account as to the type of mangrove that is typical for Guinea, Africa using field based and remotely sensed data. Specifically, the mangroves of the estuarine islands of Mabala and Yélitono were classified using satellite and airborne optical remote sensing data. Mangroves were mapped according to four classes: tall red (Rhizophora racemosa), medium red (R. racemosa), dwarf red (R. mangle and R. harisonii), and black mangrove (Avicennia germinans). Producer's and user's accuracies for the mapping of mangrove from non-mangrove areas were both 98%. When separating amongst the mangrove classes most of the confusion resulted from the medium red mangrove class. Of the 10,442 ha of mangrove mapped, approximately 30% were classified as riverine, dominated by tall R. racemosa. The remaining mangrove areas were dominated by dwarf mangrove of either Rhizophora or A. germinans. Biophysical parameter data collected from 56 transects varied considerably amongst the classes. For the tallest mangrove class, the mean values of height, DBH, estimated LAI, stem density and basal area recorded were 13 m, 15.1 cm, 4.3, 838 stems/ha, and 25.9 m2/ha, respectively. In contrast, for A. germinans, values of 3 m, 4.6 cm, 1.5, 2,877 stems/ha, and 6.0 m2/ha were calculated, respectively. © 2010 Society of Wetland Scientists.

Rivers and streams are unstable environments in which estimation of energetic costs and benefits of habitat utilization are the daunting exercise. Empirical models of food consumption may be used to estimate energetic benefits based on abiotic and biotic conditions in patches of habitat. We performed thirty daily surveys of fish stomach contents to estimate the consumption rates for juvenile Atlantic salmon (Salmo salar) in a river. The data were used to assess whether variations of daily consumption rates existed within the river, and to develop empirical models that could predict fish consumption rates using abiotic and biotic conditions as independent variables. Daily consumption rates based on stomach content surveys in the field (range: 0.15-1.49g dry/(100g wet day)) varied significantly depending on habitat patch (500-1000m2), summer period, and sampling year. Variables such as water temperature, numerical density of salmon, water depth and moon phase explained 83-93% of the variations in daily food consumption rates. Daily consumption rates tended to increase with water temperature and depth, and were also higher near a full moon. However, they tended to decrease with the numerical density of salmon. Our work suggests that empirical models based on independent variables that are relatively simple to estimate in the field may be developed to predict fish consumption rates in different habitat patches in a river. © 2013.

de Santiagoa F.,University of Western Ontario | de Santiagoa F.,Environnement Illimite Inc. | Kovacs J.M.,Nipissing University | Lafrance P.,Environnement Illimite Inc.
International Journal of Remote Sensing | Year: 2013

The principal objective of this study was to determine the accuracy of an object-based image analysis (OBIA) approach in classifying mangroves from spaceborne synthetic aperture radar (SAR) data, specifically Advanced Land Observation Satellite (ALOS), phased array L-band synthetic aperture radar (PALSAR), and single-polarized (HH) and dual-polarized (HH + HV) L-bands. The accuracy of the object parameters was examined to determine the optimal colour and shape ratios for the hierarchical classification. At the first level of classification (mangroves from non-mangroves), the results indicate that it is possible to accurately separate mangrove areas from saltpan and water/shallow zones using both sets of SAR images for the Mabala and Yélitono islands of southern Guinea. The final accuracies, based on the most optimal object parameters, were 91.1% and 92.3% for the single- and dual-polarized data, respectively. At the second level of classification, separation among the three mangrove classes identified was most accurate when using the dual-polarized data, at an overall accuracy of only 63.4%. The three mangrove classes identified included tall red mangrove (Rhizophora racemosa), dwarf red mangrove (R. mangle and R. harisonii), and black mangrove (Avicennia germinans). Using the optimal combination of parameters, the extent to which a filter could be used to improve the accuracy was examined. At this level, it was determined that the dual-polarized data, filtered with a 3 × 3 Lee speckle filter and a segmentation scale of 5, resulted in an overall accuracy of 64.9%. Consequently, it is recommended that for persistently cloud-covered regions, such as Guinea, ALOS PALSAR data using an OBIA could be useful as a quick method for mapping and monitoring mangroves. © 2013 Copyright Taylor and Francis Group, LLC.

Demarty M.,Environnement Illimite Inc. | Bastien J.,Environnement Illimite Inc. | Tremblay A.,Hydro - Quebec
Biogeosciences | Year: 2011

Surface water pCO2 and pCH4 measurements were taken in the boreal zone of Québec, Canada, from summer 2006 to summer 2008 in Eastmain 1 reservoir and two nearby lakes. The goal of this follow-up was to evaluate annual greenhouse gas (GHG) emissions, including spring emissions (N.B. gross emissions for reservoir), through flux calculations using the thin boundary layer model. Our measurements underscored the winter CO2 accumulation due to ice cover and the importance of a reliable estimate of spring diffusive emissions as the ice breaks up. We clearly demonstrated that in our systems, diffusive CH4 flux (in terms of CO2 equivalent) were of minor importance in the GHG emissions (without CH 4 accumulation under ice), with diffusive CO2 flux generally accounting for more than 95% of the annual diffusive flux. We also noted the extent of spring diffusive CO2 emissions (23% to 52%) in the annual carbon budget. © 2011 Author(s).

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