Le hibou des marais ( Asio flammeus ) connait un déclin important en Amérique du Nord et son statut au Québec ne fait pas exception. Un enjeu majeur pour cette espèce est la destruction non intentionnelle des nids en milieux agricoles. Cette revue de la littérature explore les stratégies de conservation déployées dans l’ensemble de l’aire de répartition du hibou des marais afin de maximiser son succès de nidification. Nous avons trouvé 54 documents d’intérêt, mais aucune étude empirique permettant de mesurer l’efficacité des méthodes sur le sujet. En comparant les études incluant d’autres espèces d’oiseaux champêtres, nous recommandons une stratégie regroupant plusieurs mesures à court (p. ex. interventions d’urgence), à moyen (p. ex. modifications de pratiques agricoles) et à long terme (p. ex. sensibilisation, acquisition et intendance des terres). Cette stratégie devrait permettre d’augmenter le recrutement de l’espèce. Le présent ouvrage recueille et émet des recommandations sur les mesures de conservation qui pourraient être envisagées pour le hibou des marais dans le sud du Québec.

Many boreal species have declined during recent decades in North America. Various indexes suggest that populations of the Boreal Owl Aegolius funereus are declining across North America, but very few long-term, standardized monitoring schemes allow for reliable assessment. We combined various datasets monitoring Boreal Owls in eastern North America to assess its population trend. Using autumn migration monitoring from 1996 to 2023 at Tadoussac (Qu & eacute;bec, Canada) and Whitefish Point (Michigan, USA), we assessed population trends with Bayesian hierarchical generalized linear models. We also analyzed the trends in the proportion of juveniles and body condition over time. We correlated migration monitoring with participatory science observations recorded throughout the year to assess Boreal Owl population trends in eastern North America. We observed a dynamic of four-year cycles and a longer-term decline in relative abundance for both the total number of captured individuals and the number of juveniles alone. The proportion of juveniles and mean body condition both varied annually but showed stable trends over time. However, we detected a reduction in the recorded fat score over time, suggesting that conditions encountered in the boreal forest could be deteriorating. This study provides population trends for the Boreal Owl, an important bioindicator of the boreal ecosystem, and could ultimately support and orient the development of future monitoring projects during the breeding period. De nombreuses esp & egrave;ces bor & eacute;ales ont connu un d & eacute;clin au cours des derni & egrave;res d & eacute;cennies en Am & eacute;rique du Nord. Diff & eacute;rentes & eacute;tudes sugg & egrave;rent que les populations de nyctales de Tengmalm (Aegolius funereus) sont en d & eacute;clin en Am & eacute;rique du Nord, mais tr & egrave;s peu de programmes de suivi normalis & eacute;s et & agrave; long terme permettent une & eacute;valuation fiable. Nous avons combin & eacute; divers ensembles de donn & eacute;es de suivi des nyctales de Tengmalm dans l'est de l'Am & eacute;rique du Nord afin d'& eacute;valuer la tendance de sa population. Gr & acirc;ce au suivi des migrations automnales de 1996 & agrave; 2023 & agrave; Tadoussac (Qu & eacute;bec, Canada) et & agrave; Whitefish Point (Michigan, & Eacute;tats-Unis), nous avons & eacute;valu & eacute; les tendances d & eacute;mographiques avec des mod & egrave;les lin & eacute;aires g & eacute;n & eacute;ralis & eacute;s hi & eacute;rarchiques bay & eacute;siens. Nous avons aussi & eacute;tudi & eacute; la proportion de juv & eacute;niles et de la condition corporelle au cours des derni & egrave;res d & eacute;cennies. Nous avons corr & eacute;l & eacute; le suivi des migrations avec les observations de science participative enregistr & eacute;es tout au long de l'ann & eacute;e afin d'& eacute;valuer la tendance de la population de nyctales de Tengmalm dans l'est de l'Am & eacute;rique du Nord. Nous avons observ & eacute; une dynamique cyclique de quatre ans et un d & eacute;clin & agrave; plus long terme de l'abondance relative, tant pour le nombre total d'individus captur & eacute;s que pour le nombre de juv & eacute;niles. La proportion de juv & eacute;niles et l'indice moyen de condition corporelle variaient tous deux annuellement, mais pr & eacute;sentaient des tendances stables au fil du temps. Cependant, nous avons d & eacute;tect & eacute; une diminution du taux de gras enregistr & eacute; au fil du temps, ce qui sugg & egrave;re que les conditions rencontr & eacute;es dans la for & ecirc;t bor & eacute;ale pourraient se d & eacute;t & eacute;riorer. Cette & eacute;tude fournit des tendances d & eacute;mographiques pour la nyctale de Tengmalm, un bioindicateur important de l'& eacute;cosyst & egrave;me bor & eacute;al, et pourrait & agrave; terme appuyer le d & eacute;veloppement de futurs projets de surveillance pendant la p & eacute;riode de reproduction.

Les données sur l’occupation du territoire par la faune étant difficiles et coûteuses à obtenir, les études de priorisation des corridors de connectivité modélisent généralement les obstacles au déplacement et la qualité des habitats pour quelques espèces sentinelles, sur la base de cartes et d’opinions d’experts. L’objectif de la présente étude était de caractériser les obstacles au libre déplacement des organismes et l’occupation faunique dans le corridor modélisé du sud-ouest de la Mauricie (Québec, Canada) à l’aide d’inventaires terrain, d’analyses géomatiques et de données publiques sur les oiseaux et les mammifères. Les résultats montrent que les obstacles au déplacement sont associés à des pressions anthropiques dans la portion sud (p. ex., densité des routes, surfaces minéralisées), alors qu’ils sont de nature géomorphologique dans la portion nord du corridor (p. ex., densité des rivières, pentes fortes). L’utilisation du corridor par la faune terrestre (salamandres, oiseaux, grands mammifères) révèle des points chauds de déplacement faunique et de biodiversité au sud, parfois même à l’extérieur ou aux marges du tracé modélisé. À la lumière de nos analyses, le tracé du corridor sud-ouest de la Mauricie est modifié pour y inclure des noyaux de conservation.

We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach is able to find near-optimal solutions to problems that cannot be solved by an off-the-shelf solver in a reasonable time frame, or with reasonable computational resources. Our method substantially reduces the solve times required for large models, allowing mine planners to explore multiple scenarios in a timely fashion. Our approach is being used by Rio Tinto to solve large long-term mine planning problems, and has been responsible for generating millions of dollars in value insights.

Migration is the least-studied phase of the life cycle for many bird species, despite its importance to the full understanding of their life history traits and conservation. Between 2014 and 2023, we deployed tracking devices at Observatoire d'oiseaux de Tadoussac, Quebec and used the Motus Wildlife Tracking System to investigate migration patterns of 10 species that breed in boreal and Arctic habitats of eastern Canada, and migrate to wintering areas in the United States and South America. Several species were of special conservation concern in the United States and Canada. Motus receiving stations from Qu & eacute;bec to Colombia provided migratory movements for over 350 individual birds. We present and discuss tracking duration and distances, migration routes, stopover, flight statistics, and phenology of fall migration for these species. The array of Motus receivers in the region surrounding the tagging site detected many individuals clearly upon departure, allowing for comparisons of post-capture stopover duration and departure strategy. All tagged species stopped over at the tagging location following capture (mean 8.2 days +/- 6.7 SD), which could have been an effect of the capture and tagging process. Short distance migrants (mean 10.6 days +/- 7.6 SD) stopped over longer than long-distance migrants (mean 5.3 days +/- 3.8 SD). Prolonged (> 7 days) stopovers were detected elsewhere along the migratory routes for six of the species tagged. Eight species were detected during long-distance (> 100 km) migratory flights and estimated flight speeds were similar across species (mean 53.5 km/h +/- 22.3 SD). All but one species made primarily nocturnal departures for migratory flights, and three of the species with nocturnal departures were previously thought to be diurnal migrants. The Motus network allowed a reliable method to assess and compare migratory routes and timing for a variety of small birds nesting in Arctic and boreal ecosystems.

Bird populations within the same species may follow different migratory strategies and phenology depending on their breeding location and latitude, and migratory strategies may be influenced by important stopover sites. Understanding these strategies and identifying important stopover sites is crucial for the conservation of species with regionally varying population trends. In this study, Euphagus carolinus (Rusty Blackbird) from 2 populations in eastern North America were affixed with NanoTag (Lotek) transmitters and tracked using the Motus Wildlife Tracking System to determine migratory routes and connectivity, stopover locations, and wintering areas. During fall migration, birds tagged at Observatoire d'oiseaux de Tadoussac, Quebec and breeding sites in New England maintained separate migratory routes north of 43 degrees latitude, as indicated by positive Mantel statistics of migratory connectivity, before converging on stopover areas in the Chesapeake and Delaware Bays region of the mid-Atlantic U.S. Migratory strategy differed between the 2 populations: birds from New England spent similar to 2 months longer at breeding latitudes than birds from Quebec, and Quebec birds spent more time at fall stopover sites and wintering latitudes. Birds from both populations made >1-week stopovers during spring and fall migrations and made long-distance (up to 645 km) nocturnal flights. The few winter detections suggested that E. carolinus from New England wintered at more southern latitudes than birds from Quebec. Land cover data around stopover sites indicated that E. carolinus were positively associated with percent cover of wooded wetlands, croplands, and hay/pasture. Results from this study could help identify and protect regionally important stopover and wintering areas for E. carolinus, a species that has experienced dramatic long-term population declines linked to habitat loss in the nonbreeding range.

Ongoing climate change can affect migration phenology in a variety of species. We assessed autumn migration phenology of Northern Saw-whet Owls (Aegolius acadicus) using 25 years of banding data from 7 sites throughout eastern North America. Using a linear mixed model, we found a significant trend toward a later passage for the median passage date. Phenological changes in migration could be a way to cope with changing environmental conditions.

Hydropower is a renewable source of energy that relies on efficient water planning and management. As the behavior of this natural resource is difficult to predict, water managers therefore use methods to help the decision-making process. Reinforcement Learning (RL) has been shown to be a potentially effective approach to overcome the limitations of the Stochastic Dynamic Programming (SDP) method that is commonly used for water management. However, convergence to a robust and efficient operating policy from RL methods requires large amounts of data, while long-term historical data is not always available. The objective of this study consists in using tools to generate long-term hydrological series to obtain an efficient parameterization of the management policy. This presentation introduces a comparison of calibration datasets used in a RL method for the optimal control of a hydropower system. This method aims to find a feedback policy that maximizes the production of a hydropower system over a mid-term horizon. Three streamflow datasets are compared on a real hydropower system for RL calibration: 1) the historical streamflow (35 years), 2) streamflow simulated by a hydrological model driven by a high-resolution large-ensemble climate model data (3500 years) from the ClimEx project, and 3) streamflow simulated by a hydrological model driven by climate data generated with a stochastic weather generator (5000 years). The GR4J hydrological model is employed for the hydrologic modelling aspect of the work. The reinforcement learning method is applied on the Lac-Saint-Jean water resources system in Quebec (Canada), where the hydrological regime is snowmelt-dominated. A bootstrapping method where multiple calibration and validation sets were resampled is used to conduct a robust statistical analysis for comparing the methods’ performance. The performance of the calibrated management policy is evaluated with respect to the operational constraints of the system as well as the overall energy production. Preliminary results show that is possible to achieve effective management policies by using tools to generate long-term hydrological series to feed a RL method.

The phenology of migrating birds is shifting with climate change. For instance, short-distance migrants wintering in temperate regions tend to delay their migration in fall during spells of warmer temperature. However, some species do not show strong shifts, and the factors determining which species will react to temperature changes by delaying their migration are poorly known. In addition, it is not known whether a slower migration or a postponed departure creates the observed delays in fall migration because most studies occur far south of the boreal breeding areas making it difficult to separate those 2 mechanisms. We used 22 yr of data at a northern observatory in eastern North America, at the southern edge of the boreal forest, to examine how 21 short-distance migrants responded to changing temperatures. We investigated if those species responding to temperature share life-history features (i.e. diet, size, total migration distance, breeding habitat, timing of migration). The period of migration in each species was, by far, the most important factor predicting the response of a species to temperature. Eight of the 13 species migrating in October changed their migration onset with temperature (usually by delaying migration by 1-2 days/degrees C), while the migration timing of none of the 8 species migrating in September was dependent on temperature. Furthermore, the absence of a greater migration delay by birds breeding farther from the study site (i.e. Arctic-breeding birds) suggests the mechanism is a postponed departure rather than a slower migration. We conclude that temperature variations in late fall influence the conditions on the breeding grounds, so that birds still present at that time benefit more from postponing their departure in warm weather.

We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when increasing a variable, the user is able to predict if a function increases or decreases, but is unable to quantify the amount by which it varies. We refer to this type of problems as "monotonic grey box" optimization problems. Our objective is to develop an algorithmic mechanism that exploits this monotonic information to find a feasible solution as quickly as possible. With this goal in mind, we have built a theoretical foundation through a thorough study of monotonicity on cones of multivariate functions. We introduce a trend matrix and a trend direction to guide the Mesh Adaptive Direct Search (Mads) algorithm when optimizing a monotonic grey box optimization problem. Different strategies are tested on a some analytical test problems, and on a real hydroelectric dam optimization problem.

Migration routes vary greatly among small passerine species and populations. It is now possible to determine the routes over great distances and long periods of time with emerging monitoring networks. We tracked individual Swainson’s Thrush (Catharus ustulatus), Bicknell’s Thrush (Catharus bicknelli) and Gray-cheeked Thrush (Catharus minimus) in northeastern Quebec and compared their migration routes and paces across an array of radio-telelemetry stations in North America. Swainson’s Thrush migrated further inland than the other two species. Individuals from all three species slowed their migration pace in the southeastern United States, and Swainson’s Thrush was more likely to stopover than Bicknell’s Thrush. Although individuals were tagged in a small area within or close to their breeding range, the results document the variability of migration routes between species with similar ecological characteristics and provide detailed material to be used for migration studies with broader taxonomic or ecological scope.

Maintenance of power generators is essential for reliable and efficient electricity production. Because generators under maintenance are typically inactive, optimal planning of maintenance activities must consider the impact of maintenance outages on the system operation. However, in hydropower systems finding a minimum cost maintenance schedule is a challenging optimization problem due to the uncertainty of the water inflows and the nonlinearity of the hydroelectricity production. Motivated by an industrial application problem, we formulate the hydropower maintenance scheduling problem as a two-stage stochastic program, and we implement a parallelized Benders decomposition algorithm for its solution. We obtain convex subproblems by approximating the hydroelectricity production using linear inequalities and indicator variables, which account for the nonlinear effect of the number of active generators in the solution. For speeding up the execution of our decomposition algorithm, we tailor and test seven techniques, including three new applications of special ordered sets, presolve and warm start for Benders acceleration. Given the large number of possible configurations of these acceleration techniques, we illustrate the application of statistical methods and computational experiments to identify the best performing configuration, which achieved a fourfold speedup of the decomposition algorithm. Results in an industrial setting confirm the high scalability on the number of scenarios of our parallelized Benders implementation. (C) 2020 Elsevier B.V. All rights reserved.

Due to the low detectability of Northern Saw-whet Owls (Aegolius acadicus; hereafter, NSWO) throughout their annual cycle, standardized monitoring during migration allows for population assessments over time. We assessed age-class population trends in NSWO throughout eastern North America using banding data from 7 sites over a 25 year period. Using a mixed linear model, we did not detect any significant trends over time for the total owl count, adult owl count, and juvenile owl count from 1992 to 2017. During the period when all 7 sites were active from 2001 to 2017, trend estimates remained nonsignificant despite showing negative slopes. We confirmed this nonsignificant, negative trend through a similar mixed linear model of NSWO data from Christmas Bird Counts. Our results suggest that NSWO populations across eastern North America have been relatively stable since 1992 throughout their migration and winter ranges and demonstrate the value of standardized banding data for monitoring the regional population status of NSWO.

Weather generators are usually used to produce an ensemble of climate time series for vulnerability assessments and impact studies in hydrological and agricultural communities. Multisite and multivariate weather generators (MMWGs) have many advantages over single-site counterparts in terms of coupling with distributed models for the assessment of spatial variability in various impact sectors. However, the existing MMWGs usually suffer from limitations in preserving multisite and multivariate dependencies at multiple time scales, as well as preserving the low-frequency variability of climate variables. This study proposes a new MMWG which preserves low-frequency climate variability by coupling annual, monthly and daily weather generators into a single model. Specifically, the daily precipitation and temperature time series generated by a widely used multisite daily weather generator is adjusted using monthly and annual climate time series generated by a first-order linear autoregressive model. This combination preserves the multisite and multivariate attributes, as well as low-frequency variability at monthly and annual scales. The performance of the proposed MMWG was evaluated by comparing the baseline model against that of its variants with monthly or annual adjustments for climate generation. The performance was also assessed using a hydrological model over two watersheds with different hydroclimatic characteristics. The results show that the proposed weather generator performs well with respect to reproducing the marginal distributional attributes, multisite and multivariate dependencies, and climate variability at the daily, monthly and annual scales. Weather generators with either monthly or annual adjustments (and not both) only improve the simulations in multisite and multivariate dependencies and low-frequency variability at the corresponding time scales. In terms of hydrological modeling, the proposed model consistently performs better than the baseline model and its variants with only monthly or annual adjustments in representing the mean and variance of monthly and annual streamflows. It also performs better in representing the frequency distribution of mean and extreme streamflow events. Overall, the proposed MMWG can effectively produce multisite and multivariate climate time series with low-frequency variability and has a strong potential for use in climate change impact studies.

This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

Despite decades of operational use, stochastic dynamic programming (SDP) is still a popular method for solving hydropower management optimization problems. From an operational perspective, there are many advantages to using this type of method: it provides a feedback operating policy that can be used for simulation purposes, marginal values of water stored in reservoirs are easy to compute, and it is relatively simple and easy to understand. However, for systems with more than two or three reservoirs, some issues arise that must be resolved in order to create efficient and fast operational software. This paper presents a case study which solved a problem of four reservoirs by sampling SDP (SSDP). Several improvements were proposed, such as using parallelization techniques, efficient discretization of the state space, and piecewise linear approximation of the water value function utilizing a strategy similar to Benders cuts as in stochastic dual dynamic programming, to build fast, efficient, and robust SSDP operational software. Program implementation details and numerical results were presented for a real hydropower system owned by Rio Tinto in Canada.

Stochastic dynamic programming is one of the most widely used optimization techniques for water system optimization. In this study, four methods for estimating transition probabilities have been evaluated to determine how they influence water system performance for short-term operating policies. The methods are counting, ordinary least-squares regression, robust linear model regression and multivariate conditional distribution. Two discretization schemesequal-width interval and equal-frequency and data transformationhave also been included in the study as sources of uncertainty. The study was carried out for three water systems: the Outardes River, Manicouagan River, and Lac Saint-Jean, located in Quebec, Canada. The results show that the water system configuration played a significant role in the performance of the transition probabilities. The discretization scheme and data transformation had a considerable influence on the counting and regression methods, whereas they had less of an impact on the multivariate conditional distribution. The robust linear models with equal-frequency discretization without data transformation gave satisfactory results for all the water systems. (c) 2017 American Society of Civil Engineers.

This paper presents a novel method to treat a chance constrained formulation of the hydropower reservoir management problem. An advantage of this methodology is that it is easily understandable by the decision makers. However, when using explicit optimization methods, the optimal operating policy requires to be simulated over multiple scenarios to validate the feasibility of the constraints. A blackbox optimization framework is used to determine the parameters of the chance constraints, embedding the chance constrained optimization problem and the simulation as the blackbox. Numerical results are conducted on the Kemano hydropower system in Canada.

Abstract. This paper presents an analysis of the effects of biased Extended Streamflow Prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon testbed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data was fed to the hydrological model and the synthetic streamflow thus generated was considered as a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method’s response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test-bed in which the entire system is precisely and accurately modeled. The ensemble climate data forecasts, the hydrological modeling and ESP forecast generation, optimization model and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focused on one hydropower system both with and without minimum base load constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore increases the odds of forced spillage by attempting to maximize short-term profit by keeping a higher net head. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider optimal trajectories.