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Puisque l’altération des habitats d’eau douce augmente, il devient critique d’identifier les composantes de l’habitat qui influencent les métriques de la productivité des pêcheries. Nous avons comparé la contribution relative de trois types de variables d’habitat à l’explication de la variance de métriques d’abondance, de biomasse et de richesse à l’aide de modèles d’habitat de poissons, et avons identifié les variables d’habitat les plus efficaces à expliquer ces variations. Au cours des étés 2012 et 2013, les communautés de poissons de 43 sites littoraux ont été échantillonnées dans le Lac du Bonnet, un réservoir dans le Sud-est du Manitoba (Canada). Sept scénarios d’échantillonnage, différant par l’engin de pêche, l’année et le moment de la journée, ont été utilisés pour estimer l’abondance, la biomasse et la richesse à chaque site, toutes espèces confondues. Trois types de variables d’habitat ont été évalués: des variables locales (à l’intérieur du site), des variables latérales (caractérisation de la berge) et des variables contextuelles (position relative à des attributs du paysage). Les variables d’habitat locales et contextuelles expliquaient en moyenne un total de 44 % (R2 ajusté) de la variation des métriques de la productivité des pêcheries, alors que les variables d’habitat latérales expliquaient seulement 2 % de la variation. Les variables les plus souvent significatives sont la couverture de macrophytes, la distance aux tributaires d’une largeur ≥ 50 m et la distance aux marais d’une superficie ≥ 100 000 m2, ce qui suggère que ces variables sont les plus efficaces à expliquer la variation des métriques de la productivité des pêcheries dans la zone littorale des réservoirs. / As freshwater fisheries become increasingly prone to habitat alteration, it is critical we identify the components of habitat that greatly influence fisheries productivity metrics. Using fish habitat modeling, we compared relative contributions of three types of habitat variables to explain variation in abundance, biomass and richness metrics, and identified habitat variables most effective at explaining these variations. During the summers of 2012 and 2013, fish communities in 43 littoral sites were sampled from Lac du Bonnet, a reservoir in southeastern Manitoba (Canada). Seven different sampling scenarios, consisting of different sampling methods, years and time periods, were used to measure relative abundance, biomass and richness metrics for all species combined per site. Three types of habitat variables were measured: local (i.e. within site), lateral (i.e. shore characterization) and contextual (i.e. position relative to landscape attributes) variables. Together local and contextual habitat variables explained on average 44% R2adj of the variation across fisheries productivity metrics, while only 2% R2adj of the variation was explained by lateral habitat variables. Specifically, macrophyte coverage, distance to tributaries ≥ 50 m wide, and distance to marshes ≥ 100,000 m2 ranked most significant across metrics, suggesting these habitat variables may be most effective at explaining variation in fisheries productivity metrics in the littoral zone of reservoirs.

Software development is an area in which companies not only need to keep up with the latest technology, but they additionally need to continuously increase their productivity to stay competitive in the industry. One company currently facing these challenges is Storytel - one of the strongest players on the Swedish audiobook market - with about a fourth of all employees involved with software development, and a rapidly growing workforce. With the purpose of understanding how the Storytel Tech Department is performing, this thesis maps Storytel’s productivity defined through the Four Key Metrics - Deployment Frequency, Delivery Lead Time, Mean Time To Restore and Change Fail Rate. A classification is made into which performance category (Low, Medium, High, Elite) the Storytel Tech Department belongs to through a deep-dive into the raw system data existing at Storytel, mainly focusing on the case management system Jira. A survey of the Tech Department was conducted, to give insights into the connection between human and technical factors influencing productivity (categorized into Culture, Environment, and Process) and estimated productivity. Along with these data collections, interviews with Storytel employees were performed to gather further knowledge about the Tech Department, and to understand potential bottlenecks and obstacles. All Four Key Metrics could be determined based on raw system data, except the metric Mean Time To Restore which was complemented by survey estimates. The generalized findings of the Four Key Metrics conclude that Storytel can be minimally classified as a ‘medium’ performer. The factors, validated through factor analysis, found to have an impact on the Four Key Metrics were Generative Culture, Efficiency (Automation and Shared Responsibility) and Number of Projects. Lastly, the major bottlenecks found were related to Architecture, Automation, Time Fragmentation and Communication. The thesis contributes with interesting findings from an expanding, middle-sized, healthy company in the audiobook streaming industry - but the results can be beneficial for other software development companies to learn from as well. Performing a similar study with a greater sample size, and additionally enabling comparisons between teams, is suggested for future research.