<p>Effective conservation and management of top predators requires a comprehensive understanding of their distributions and of the underlying biological and physical processes that affect these distributions. The Mid-Atlantic Bight shelf break system is a dynamic and productive region where at least 32 species of cetaceans have been recorded through various systematic and opportunistic marine mammal surveys from the 1970s through 2012. My dissertation characterizes the spatial distribution and habitat of cetaceans in the Mid-Atlantic Bight shelf break system by utilizing marine mammal line-transect survey data, synoptic multi-frequency active acoustic data, and fine-scale hydrographic data collected during the 2011 summer Atlantic Marine Assessment Program for Protected Species (AMAPPS) survey. Although studies describing cetacean habitat and distributions have been previously conducted in the Mid-Atlantic Bight, my research specifically focuses on the shelf break region to elucidate both the physical and biological processes that influence cetacean distribution patterns within this cetacean hotspot. </p><p>In Chapter One I review biologically important areas for cetaceans in the Atlantic waters of the United States. I describe the study area, the shelf break region of the Mid-Atlantic Bight, in terms of the general oceanography, productivity and biodiversity. According to recent habitat-based cetacean density models, the shelf break region is an area of high cetacean abundance and density, yet little research is directed at understanding the mechanisms that establish this region as a cetacean hotspot. </p><p>In Chapter Two I present the basic physical principles of sound in water and describe the methodology used to categorize opportunistically collected multi-frequency active acoustic data using frequency responses techniques. Frequency response classification methods are usually employed in conjunction with net-tow data, but the logistics of the 2011 AMAPPS survey did not allow for appropriate net-tow data to be collected. Biologically meaningful information can be extracted from acoustic scattering regions by comparing the frequency response curves of acoustic regions to theoretical curves of known scattering models. Using the five frequencies on the EK60 system (18, 38, 70, 120, and 200 kHz), three categories of scatterers were defined: fish-like (with swim bladder), nekton-like (e.g., euphausiids), and plankton-like (e.g., copepods). I also employed a multi-frequency acoustic categorization method using three frequencies (18, 38, and 120 kHz) that has been used in the Gulf of Maine and Georges Bank which is based the presence or absence of volume backscatter above a threshold. This method is more objective than the comparison of frequency response curves because it uses an established backscatter value for the threshold. By removing all data below the threshold, only strong scattering information is retained.</p><p>In Chapter Three I analyze the distribution of the categorized acoustic regions of interest during the daytime cross shelf transects. Over all transects, plankton-like acoustic regions of interest were detected most frequently, followed by fish-like acoustic regions and then nekton-like acoustic regions. Plankton-like detections were the only significantly different acoustic detections per kilometer, although nekton-like detections were only slightly not significant. Using the threshold categorization method by Jech and Michaels (2006) provides a more conservative and discrete detection of acoustic scatterers and allows me to retrieve backscatter values along transects in areas that have been categorized. This provides continuous data values that can be integrated at discrete spatial increments for wavelet analysis. Wavelet analysis indicates significant spatial scales of interest for fish-like and nekton-like acoustic backscatter range from one to four kilometers and vary among transects. </p><p>In Chapter Four I analyze the fine scale distribution of cetaceans in the shelf break system of the Mid-Atlantic Bight using corrected sightings per trackline region, classification trees, multidimensional scaling, and random forest analysis. I describe habitat for common dolphins, Risso’s dolphins and sperm whales. From the distribution of cetacean sightings, patterns of habitat start to emerge: within the shelf break region of the Mid-Atlantic Bight, common dolphins were sighted more prevalently over the shelf while sperm whales were more frequently found in the deep waters offshore and Risso’s dolphins were most prevalent at the shelf break. Multidimensional scaling presents clear environmental separation among common dolphins and Risso’s dolphins and sperm whales. The sperm whale random forest habitat model had the lowest misclassification error (0.30) and the Risso’s dolphin random forest habitat model had the greatest misclassification error (0.37). Shallow water depth (less than 148 meters) was the primary variable selected in the classification model for common dolphin habitat. Distance to surface density fronts and surface temperature fronts were the primary variables selected in the classification models to describe Risso’s dolphin habitat and sperm whale habitat respectively. When mapped back into geographic space, these three cetacean species occupy different fine-scale habitats within the dynamic Mid-Atlantic Bight shelf break system. </p><p>In Chapter Five I present a summary of the previous chapters and present potential analytical steps to address ecological questions pertaining the dynamic shelf break region. Taken together, the results of my dissertation demonstrate the use of opportunistically collected data in ecosystem studies; emphasize the need to incorporate middle trophic level data and oceanographic features into cetacean habitat models; and emphasize the importance of developing more mechanistic understanding of dynamic ecosystems.</p> / Dissertation

Le zooplancton et le micronecton sont les deux premiers échelons animaux de la chaine trophique marine. Bien que de tailles très différentes (200μm à 2mm pour le zooplancton, 2 à 20cm pour le micronecton), ces deux groupes d'espèces variées partagent un comportement singulier : les migrations nycthémérales. Ces migrations journalières entre la profondeur de jour et la surface de nuit induisent des flux de matière organique très importants entre les différentes profondeurs de l'océan. L'étude des cycles biogéochimiques océaniques a une grande importance pour l'étude du changement climatique. Cette étude est notamment conduite à travers le développement de modèles globaux de circulation océanique et de biogéochimie. La suite logique de ces développements est donc la modélisation du zooplancton et du micronecton. La gamme de modèles SEAPODYM modélise avec parcimonie la chaine trophique depuis le zooplancton jusqu'aux prédateurs supérieurs à l'aide de trois modèles. Cette thèse présente le modèle de biomasse de zooplancton SEAPODYM-LTL (pour lower trophic level, niveau trophique bas), ainsi qu'une analyse de sa sensibilité aux forçages. En effet, la particularité de ces modèles est leur forçage offline par des champs de courants, température et production primaire produits par d'autres modèles. Le modèle SEAPODYM-LTL est également comparé au modèle PISCES (NPZD), et présente des performances similaires à ce dernier dans le cas testé. Afin d'améliorer les prédictions du modèle SEAPODYM-MTL (mid-trophic level, i.e. le modèle de biomasse de micronecton), une méthodologie d'assimilation de données a été mise en place pour affiner la paramétrisation utilisée. Des données d'acoustique active (38kHz) sont donc utilisées pour enrichir le modèle. Cette méthodologie a été conçue autour d'un cas test présenté dans cette thèse. L'extension du jeu de données acoustiques assimilées au modèle a permis de mettre en évidence le besoin de mieux modéliser les profondeurs des couches verticales de SEAPODYM. Cela a été réalisé à l'aide du jeu de données acoustiques évoqué précédemment. Cette étude est également présentée dans cette thèse. / Zooplankton and micronecton are the first marine trophic levels. Different by their size (200μm to 2mm for zooplankton, 2 to 20cm for micronekton), this two groups undergo diel vertical migration from depth by day to the surface during the night. These migrations create major organic matter fluxes between the deep ocean and the surface. Biogeochemical cycles are of great importance for climate change studies. These studies are conducted with ocean global circulation model and biogeochemical model. The way to go is develop low and mid-trophic level modelling approaches. SEAPODYM ensemble of models are three parsimonious model of biomass at diverse level of the trophic chain, from zooplankton to top predators. This thesis introduce the zooplankton biomass model SEAPODYM-LTL (lower trophic level) and a forcing fields sensitivity analysis. Indeed, these model are forced off line by currents, temperature and primary production fields produced by other models. SEAPODYM-LTL has also been compared to PISCES (NPZD) and both have similar performance score in this study. In order to improve SEAPODYM-MTL (mid trophic level) predictions, a data assimilation framework has been developed to find a better parameterisation. 38kHz active acoustic data have been used to improve the model. This methodology has been develop thanks to a test case that we present in this thesis. The gathered acoustic dataset permitted to show the need of a better definition of vertical layer depths. It has been developed using the acoustic dataset. The related study is presented in this thesis.

Cetacean species face serious challenges worldwide due to the increasing noise pollution brought to their environment by human activities such as seismic exploration. Regulation of these activities is vaguely defined and uncoordinated. Visual observations and passive listening devices, aimed at preventing conflicts between human wealth and cetaceans’ health have some fundamental limitations and may consequently fail their mitigation purposes. Active sonar technology could be the optimal solution to implement mitigation of such human activities. In my thesis, the proper sonar unit was used to test the feasibility to detect cetaceans in situ. Omnidirectional sonars could be the optimal solution to monitor the presence of cetaceans in the proximity of potential danger areas. To use this class of sonar in a quantitative manner, the first step was to develop a calibration method. This thesis links in situ measurements of target strength (TS) with variation trends linked to the behavior, morphology and physiology of cetacean. The butterfly effect of a cetacean’s body was described for a fin whale insonified from different angles. A relationship between whale respiration and TS energy peaks was tested through a simple prediction model which seems very promising for further implementation. The effect of lung compression on cetacean TS due to increasing depth was tested through a basic mathematical model. The model fit the in situ TS measurements. TS measurements at depth of a humpback whale, when post-processed, correspond to TS measurements recorded at the surface. Sonar technology is clearly capable of detecting whale foot prints around an operating vessel. Sonar frequency response shows that frequencies between 18 and 38 kHz should be employed. This work has established a baseline and raised new questions so that active sonar can be developed and employed in the best interest for the whales involved in potentially harmful conflicts with man.

Multi-frequency backscatter acoustic profilers (echosounders) are used to measure biological and physical phenomena in the ocean in ways that are not possible with optical methods. Echosounders are commonly used on ocean observatories and by commercial fisheries but require significant manual effort to classify species of interest within the collected echograms. The work presented in this thesis tackles the challenging task of automating the identification of fish and other phenomena in echosounder data, with specific application to aggregations of juvenile salmon, schools of herring, and bubbles of air that have been mixed into the water. U-Net convolutional neural networks (CNNs) are used to accomplish this task by identifying classes at the pixel level. The data considered here were collected in Okisollo Channel on the coast of British Columbia, Canada, using an Acoustic Zooplankton and Fish Profiler at four frequencies (67.5, 125, 200, and 455 kHz). The entrainment of air bubbles and the behaviour of fish are both governed by the surrounding physical environment. To improve the classification, simulated channels for water depth and solar elevation angle (a proxy for sunlight) are used to encode the CNNs with information related to the environment providing spatial and temporal context. The manual annotation of echograms at the pixel level is a challenging process, and a custom application was developed to aid in this process. A relatively small set of annotations were created and are used to train the CNNs. During training, the echogram data are divided into randomly-spaced square tiles to encode the models with robust features, and into overlapping tiles for added redundancy during classification. This is done without removing noise in the data, thus ensuring broad applicability. This approach is proven highly successful, as evidenced by the best-performing U-Net model producing F1 scores of 93.0%, 87.3% and 86.5% for herring, salmon, and bubble classes, respectively. These models also achieve promising results when applied to echogram data with coarser resolution. One goal in fisheries acoustics is to detect distinct schools of fish. Following the initial pixel level classification, the results from the best performing U-Net model are fed through a heuristic module, inspired by traditional fisheries methods, that links connected components of identified fish (school candidates) into distinct school objects. The results are compared to the outputs from a recent study that relied on a Mask R-CNN architecture to apply instance segmentation for classifying fish schools. It is demonstrated that the U-Net/heuristic hybrid technique improves on the Mask R-CNN approach by a small amount for the classification of herring schools, and by a large amount for aggregations of juvenile salmon (improvement in mean average precision from 24.7% to 56.1%). / Graduate

There are a large number of research vessels and fishing vessels equipped with echosounders plying the world ocean, making continual observations of the ocean interior. Developing data collation programmes (e.g. Integrated Marine Observing System) and automated, repeatable analyses techniques enable the upper c. 1,200 meters of the world ocean to be sampled routinely, and for their characteristic deep scattering layers (DSLs) to be compared. Deep scattering layers are comprised of zooplankton (e.g. euphausiids) and fish, particularly myctophids or lantern fish, and comprise the majority of sub-surface biomass. Here we present, by the analysis of a global acoustic dataset, a mesopelagic biogeography of the sea. This was accomplished by (i) the collation and processing of a global active acoustic dataset, (ii) the development of a standardised and automated method of sound scattering layer (SSL) extraction and description, (iii) the derivation of the environmental drivers of DSL depth and biomass, (iv) the definition of a mesopelagic biogeography based on the drivers of DSL metrics and (v) the prediction, using output from the NEMO-MEDUSA-2.0 coupled model, of how the metrics and biogeography may change by 2100. Key findings include, the development of the Sound Scattering Layer Extraction Method (SSLEM) the inference that primary production, water temperature and wind stress are key drivers in DSL depth and biomass and that mesopelagic fish biomass may increase by 2100. Such an increase is a result of increased trophic efficiency from the shallowing of DSLs and rising water temperatures, suggesting, that as the climate warms the ocean is becoming more efficient. The biophysical relationships and biogeography derived here, serve to improve our understanding of mesopelagic mid-trophic level dynamics in open-ocean ecosystems. This will aid both fisheries and conservation management, which now adopt more holistic approaches when monitoring and evaluating ecosystem health and stability.