Programmation génétique appliquée à l'imagerie hyperspectrale pour l'évaluation d'une variable biophysique au sein d'une grande culture : cas de l'azote dans un champ de maïs

L'imagerie hyperspectrale de télédétection offre d'innombrables opportunités pour la gestion durable des ressources naturelles. L'agriculture de précision est une approche récente qui prend en considération l'hétérogénéité biophysique des cultures, lors de l'application d'intrants (engrais, herbicides...). Nous proposons une nouvelle méthode fondée sur les principes de la programmation génétique et des indices de végétation; l'objectif est d'élaborer un modèle décrivant une variable biophysique d'un champ, pour évaluer précisément sa variabilité et agir localement. La validation de notre approche est réalisée sur des mesures d'azote (variable biophysique étudiée) relevées dans un champtest de maïs de l'Université McGill (Montréal). Le meilleur modèle obtenu explique 84.83% de la variance d'un jeu de données non apprises avec une erreur de généralisation de 14.34%, améliorant ainsi les résultats de la littérature

An agent-based model for the sustainable management of navigation activities in the Saint Lawrence Estuary

Natural resource managers of protected areas are concerned with the management of human activities potentially harmful to ecosystems’ health and/or integrity. These systems where human interact with natural resources are called social-ecological systems (SES) and possess the characteristics of complex adaptive systems (e.g. co-evolution). The SES of navigation activities and whales interacting within the Saguenay–St. Lawrence Marine Park (SSLMP) and the projected St. Lawrence Estuary Marine Protected Area in Quebec, Canada, has been investigated and modelled using the agent-based modelling (ABM) technology: The resulting Marine Mammal and Maritime Traffic Simulator (3MTSim) is designed to support marine protected area managers in their effort to reduce the frequency and intensity of boat-whale co-occurrences within the St Lawrence Estuary and mitigate the risks of vessel strikes. This dissertation presents the building process of the 3MTSim’s boat ABM. The knowledge extracted from analyses of gathered and collected data relative to all forms of sailing and motorized navigation supported the decision to first focus on the modelling of commercial excursions (including whale-watching trips), cargo ships, and cruise liners. Data analyses allowed, for the first time, to draw a comprehensive portrait of navigation activities throughout the region where whales congregate in great numbers during the summer season. Among others, a quantitative analysis led to an accurate estimate of the total navigation time within each separate ecosystem of the region. This study identified areas intensively used by maritime traffic such as the mouth of the Saguenay River and offshore Les Escoumins. Several field campaigns carried out in the context of this project allowed to link some undesirable collective patterns of whale-watching excursions (regarding both whale conservation and SSLMP visitors’ experience) with contextual factors including whale species’ abundance and distribution, management gaps, and companies and captains’ decisions. The bounded rationality framework was chosen to investigate captains’ decision making and more generally the dynamics of the whole whale-watching SES. A portrait of the decision strategies followed by whale-watching captains has been drawn. The results will lead to a set of recommendations regarding the sustainable management of whale-watching excursions in and around the SSLMP. Results from field investigations and data analyses have fed the model building process, including an explicit representation of the whale-watching captains’ decision making. Data analyses revealed that cargo ships and ocean liners tend to follow predictable routes with low variability. Consequently, a complex behavioural modelling approach was deemed unnecessary in favour of a statistical approach, justified by the large volume of high-quality historical data available for both components. The pattern-oriented modelling approach proved appropriate for selecting a valid model of whale-watching excursions. Model simulations confirmed that whale-watching captains do favour the observation of a few rare rorqual species (e.g. humpback whales), leaving aside the most abundant one, namely the minke whales. Therefore, 3MTSim was run to quantify the impact that whale-watching captains changing their decision strategy could have on both whale exposure to boats (conservation concern) and excursion content (commercial concern). It was found that captains willing to avoid crowded observation sites and/or seeking to increase the diversity of species observed could have statistically significant benefits regarding conservation issues without affecting important features of their excursions. Finally, the convincing performance of the 3MTSim’s boat ABM ensures its safe use as a decision-support tool for management insofar as model limitations are understood and accounted for in the results and discussion

Comparing polycentric configuration for adaptive governance within community forests: Case studies in Eastern North America

Looking at two cases of community forests (CF) in Eastern North America, this article examines their institutional features in order to assess whether they are conducive to adaptive governance. To do so, this article presents CFs as manifestations of polycentric governance, which allow identifying the complex networks of relations existing between different actors involved in governance at many scales. Polycentric governance is assumed to have a higher adaptability to changing factors. To better capture the variables conducive to adaptive governance in CFs, we draw on the socio-ecological system (SES) framework. The study shows that variables from the SES framework are useful in identifying features of polycentricity in CFs. Moreover, these variables highlight mechanisms of adaptability in CF governance, namely: interaction between organizations and actors, multiplicity of complementary rules from different organizations and structures of governance. Moreover, ongoing communication with the forest users and learning among actors appear key for CF governance’s adaptability

Agents, Individuals, and Networks: Modeling Methods to Inform Natural Resource Management in Regional Landscapes

Landscapes are complex systems. Landscape dynamics are the result of multiple interacting biophysical and socioeconomic processes that are linked across a broad range of spatial, temporal, and organizational scales. Understanding and describing landscape dynamics poses enormous challenges and demands the use of new multiscale approaches to modeling. In this synthesis article, we present three regional systems - i.e., a forest system, a marine system, and an agricultural system - and describe how hybrid, bottom-up modeling of these systems can be used to represent linkages across scales and between subsystems. Through the use of these three examples, we describe how modeling can be used to simulate emergent system responses to different conservation policy and management scenarios from the bottom up, thereby increasing our understanding of important drivers and feedback loops within a landscape. The first case study involves the use of an individual-based modeling approach to simulate the effects of forest harvesting on the movement patterns of large mammals in Canada's boreal forest and the resulting emergent population dynamics. This model is being used to inform forest harvesting and management guidelines. The second case study combines individual and agent-based approaches to simulate the dynamics of individual boats and whales in a marine park. This model is being used to inform decision-makers on how to mitigate the impacts of maritime traffic on whales in the Saint Lawrence Estuary in eastern Canada. The third example is a case study of biodiversity conservation efforts on the Eyre Peninsula, South Australia. In this example, the social-ecological system is represented as a complex network of interacting components. Methods of network analysis can be used to explore the emergent responses of the system to changes in the network structure or configuration, thus informing managers about the resilience of the system. These three examples illustrate how bottom-up modeling approaches may contribute to a new landscape science based on scenario building, to find solutions that meet the multiple objectives of integrated resource management in social-ecological systems

The Gradient-Boosting Method for Tackling High Computing Demand in Underwater Acoustic Propagation Modeling

Agent-based models return spatiotemporal information used to process time series of specific parameters for specific individuals called “agents”. For complex, advanced and detailed models, this typically comes at the expense of high computing times and requires access to important computing resources. This paper provides an example on how machine learning and artificial intelligence can help predict an agent-based model’s output values at regular intervals without having to rely on time-consuming numerical calculations. Gradient-boosting XGBoost under GNU package’s R was used in the social-ecological agent-based model 3MTSim to interpolate, in the time domain, sound pressure levels received at the agents’ positions that were occupied by the endangered St. Lawrence Estuary and Saguenay Fjord belugas and caused by anthropomorphic noise of nearby transiting merchant vessels. A mean error of 3.23 ± 3.76(1σ) dB on received sound pressure levels was predicted when compared to ground truth values that were processed using rigorous, although time-consuming, numerical algorithms. The computing time gain was significant, i.e., it was estimated to be 10-fold higher than the ground truth simulation, whilst maintaining the original temporal resolution

The Gradient-Boosting Method for Tackling High Computing Demand in Underwater Acoustic Propagation Modeling

Agent-based models return spatiotemporal information used to process time series of specific parameters for specific individuals called “agents”. For complex, advanced and detailed models, this typically comes at the expense of high computing times and requires access to important computing resources. This paper provides an example on how machine learning and artificial intelligence can help predict an agent-based model’s output values at regular intervals without having to rely on time-consuming numerical calculations. Gradient-boosting XGBoost under GNU package’s R was used in the social-ecological agent-based model 3MTSim to interpolate, in the time domain, sound pressure levels received at the agents’ positions that were occupied by the endangered St. Lawrence Estuary and Saguenay Fjord belugas and caused by anthropomorphic noise of nearby transiting merchant vessels. A mean error of 3.23 ± 3.76(1σ) dB on received sound pressure levels was predicted when compared to ground truth values that were processed using rigorous, although time-consuming, numerical algorithms. The computing time gain was significant, i.e., it was estimated to be 10-fold higher than the ground truth simulation, whilst maintaining the original temporal resolution

Le système d’identification automatique (AIS), un outil pour la gestion d’aires marines protégées : revue des applications au parc marin du Saguenay–Saint-Laurent

Les diverses conséquences des activités de navigation sur le milieu marin sont maintenant reconnues et de plus en plus documentées, en particulier sur les mammifères marins. Dans les dernières années, les gouvernements se sont engagés à rehausser le niveau de protection du milieu marin, notamment par la création d’aires marines protégées. Le système d’identification automatique (AIS), implémenté à l’origine pour la sécurité maritime et la gestion du trafic, est devenu un outil indispensable pour la gestion des activités de navigation dans un contexte de conservation de l’environnement marin. Afin de présenter différentes applications des données AIS dans la gestion d’une aire marine protégée, nous utilisons comme étude de cas le parc marin du Saguenay–Saint-Laurent, reconnu pour la diversité des espèces de mammifères marins qui le fréquentent et pour l’intensité du trafic maritime. Les exemples portent sur la description de l’utilisation de l’espace maritime par les activités de navigation, sur l’évaluation et la modélisation de leurs effets environnementaux et sur le suivi de la conformité à des mesures de gestion. En plus d’illustrer les différents avantages d’utilisation des données AIS, une revue critique sur les limites de ces données en conservation est également présentée.The diverse impacts of shipping activities on the marine environment are now recognized and increasingly documented, particularly with regards to marine mammals. In recent years, governments have committed to enhancing the protection of the marine environment, notably through the creation of marine protected areas. The Automatic Identification System (AIS), originally introduced for maritime safety and traffic control, has become an essential tool for the management of shipping activities for the conservation of marine environments. The present paper uses the Saguenay–St. Lawrence Marine Park (Québec, Canada), recognized for its high diversity of marine mammals and intensive traffic, to highlight the different potential uses of AIS data in the management of a marine protected area. Examples presented include describing baseline vessel use of a maritime area, assessing and modelling the environmental impacts of the shipping activities, and monitoring compliance to management measures. The different benefits of using AIS data in a conservation context are illustrated, and their limitations are outlined

Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert’s workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset’s annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 [email protected]). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall

Correction: Lagrois et al. Low-to-Mid-Frequency Monopole Source Levels of Underwater Noise from Small Recreational Vessels in the St. Lawrence Estuary Beluga Critical Habitat. <i>Sensors</i> 2023, <i>23</i>, 1674

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