Modélisation de l'utilisation de l'habitat du béluga du Saint-Laurent en fonction de ses proies à l'embouchure de la rivière Saguenay et à la baie Sainte-Marguerite

Le béluga du Saint-Laurent est une espèce menacée au Canada et protégée par la Loi sur les espèces en péril du Canada. La détermination des fonctions biologiques de ses habitats essentiels est nécessaire afin d’assurer le rétablissement de la population. Parcs Canada a entamé en 2009 un suivi des proies du béluga dans deux de ses aires de fréquentation intensive situées dans le Parc marin du Saguenay–Saint-Laurent : l’embouchure de la rivière Saguenay et la baie Sainte-Marguerite. L’étude de l’abondance et de la distribution des proies est réalisée par sondage hydroacoustique le long de transects à l’aide d’un échosondeur multifréquences. Un protocole d’observations systématiques du béluga est mené simultanément aux sondages hydroacoustiques à partir de sites terrestres. Le premier objectif de cette étude est de développer la méthodologie concernant le traitement, la classification et la cartographie des données hydroacoustiques échantillonnées. L’objectif principal consiste à déterminer si l’abondance et la distribution des proies pélagiques ont une influence sur l’utilisation de ces deux habitats par le béluga. La cartographie de la biomasse relative de poissons a été réalisée pour la couche de surface, la couche en profondeur et pour l’ensemble de la colonne d’eau par krigeage ordinaire pour les deux habitats pour les 29 transects. À la baie Sainte-Marguerite, le nombre de bélugas observés augmente avec la biomasse relative des proies en surface et en profondeur. À l’embouchure de la rivière Saguenay, les résultats n’ont pas été concluants. Les résultats suggèrent que l’alimentation pourrait être l’une des fonctions biologiques de la baie Sainte-Marguerite.

Contributions of local, lateral and contextual habitat variables to explaining variation in fisheries productivity metrics in the littoral zone of a reservoir

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.

Evaluating the Potential Impact of a Gas Pipeline on Whimbrel Breeding Habitat in the Outer Mackenzie Delta, Northwest Territories

We used ground surveys to identify breeding habitat for Whimbrel (Numenius phaeopus) in the outer Mackenzie Delta, Northwest Territories, and to test the value of high-resolution IKONOS imagery for mapping additional breeding habitat in the Delta. During ground surveys, we found Whimbrel nests (n = 28) in extensive areas of wet-sedge low-centered polygon (LCP) habitat on two islands in the Delta (Taglu and Fish islands) in 2006 and 2007. Supervised classification using spectral analysis of IKONOS imagery successfully identified additional areas of wet-sedge habitat in the region. However, ground surveys to test this classification found that many areas of wet-sedge habitat had dense shrubs, no standing water, and/or lacked polygon structure and did not support breeding Whimbrel. Visual examination of the IKONOS imagery was necessary to determine which areas exhibited LCP structure. Much lower densities of nesting Whimbrel were also found in upland habitats near wetlands. We used habitat maps developed from a combination of methods, to perform scenario analyses to estimate the potential effects of the Mackenzie Gas Project on Whimbrel habitat. Assuming effective complete habitat loss within 20 m, 50 m, or 250 m of any infrastructure or pipeline, the currently proposed pipeline development would result in loss of 8%, 12%, or 30% of existing Whimbrel habitat. If subsidence were to occur, most Whimbrel habitat could become unsuitable. If the facility is developed, follow-up surveys will be required to test these models.

Wave exposure as a predictor of benthic habitat distribution on high energy temperate reefs

The new found ability to measure physical attributes of the marine environment at high resolution across broad spatial scales has driven the rapid evolution of benthic habitat mapping as a field in its own right. Improvement of the resolution and ecological validity of seafloor habitat distribution models has, for the most part, paralleled developments in new generations of acoustic survey tools such as multibeam echosounders. While sonar methods have been well demonstrated to provide useful proxies of the relatively static geophysical patterns that reflect distribution of benthic species and assemblages, the spatially and temporally variable influence of hydrodynamic energy on habitat distribution have been less well studied. Here we investigate the role of wave exposure on patterns of distribution of near-shore benthic habitats. A high resolution spectral wave model was developed for a 624 km2 site along Cape Otway, a major coastal feature of western Victoria, Australia. Comparison of habitat classifications implemented using the Random Forests algorithm established that significantly more accurate estimations of habitat distribution were obtained by including a fine-scale numerical wave model, extended to the seabed using linear wave theory, than by using depth and seafloor morphology information alone. Variable importance measures and map interpretation indicated that the spatial variation in wave-induced bottom orbital velocity was most influential in discriminating habitat classes containing the canopy forming kelp Ecklonia radiata, a foundation kelp species that affects biodiversity and ecological functioning on shallow reefs across temperate Australasia. We demonstrate that hydrodynamic models reflecting key environmental drivers on wave-exposed coastlines are important in accurately defining distributions of benthic habitats. This study highlights the suitability of exposure measures for predictive habitat modeling on wave-exposed coastlines and provides a basis for continuing work relating patterns of biological distribution to remotely-sensed patterns of the physical environment.

Elicitator : an expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited, or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modeling, assessing detectability or eradication, ecological condition assessments, risk analysis, and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible, and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.

Reliable event transfer in wireless sensor networks deployed for emergency response

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find application in battlefield awareness to sense chemicals and other gases used in chemical and biological warfare. An application that has become increasingly attractive in the post 9/11 era is the use of wireless sensor networks for emergency (first) response in mass casualty incidents. Reliability of data and event transfer is of critical importance to emergency response applications. In this work we propose a reliable event transfer mechanism making use of an overlay network of relay nodes. The overlay network removes the burden of data relaying from the sensor nodes and results in increasing the lifetime of the network. Simulation results prove the benefits of such an architecture. Reliability is increased 10-30% with reduction in event traffic of 60-80%.

Lease based addressing for event-driven wireless sensor networks

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find applications in battlefield awareness and emergency (first) response situations. While unique addressing is not a requirement of many data collecting applications of wireless sensor networks it is vital for the success of applications such as emergency response. Data that cannot be associated with a specific node becomes useless in such situations. In this work we propose an addressing mechanism for event-driven wireless sensor networks. The proposed scheme eliminates the need for network wide Duplicate Address Detection (DAD) and enables reuse of addresses.

Dynamic addressing in wireless sensor networks without location awareness

Sensor Networks have applications in diverse fields. They can be deployed for habitat modeling, temperature monitoring and industrial sensing. They also find applications in battlefield awareness and emergency (first) response situations. While unique addressing is not a requirement of many data collecting applications of wireless sensor networks, it is vital for the success of applications such as emergency response. Data that cannot be associated with a specific node becomes useless in such situations. In this work we propose a novel dynamic addressing mechanism for wireless sensor networks that are not location-aware. The scheme enables successful reuse of addresses in event-driven wireless sensor networks introducing minimal latencies and efficiently addressing packet loss. It also eliminates the need for network-wide Duplicate Address Detection (DAD) to ensure uniqueness of network level addresses.

Distance software: design and analysis of distance sampling surveys for estimating population size

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance. 2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use. 3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated. 4. A first step in analysis of distance sampling data is modeling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance. 5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap. 6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modeling analysis engine for spatial and habitat-modeling, and information about accessing the analysis engines directly from other software. 7. Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of- the-art software that implements these methods is described that makes the methods accessible to practicing ecologists.

Nesting In The Clouds: Evaluating And Predicting Sea Turtle Nesting Beach Parameters From Lidar Data

Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization. Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.

Spatial Relationships among Hydroacoustic, Hydrographic and Top Predator Patterns: Cetacean Distributions in the Mid-Atlantic Bight

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.

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.

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.

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.

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.

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.

A modeling approach to identify optimal habitat and suitable fishing grounds for neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean

We developed a habitat suitability index (HSI) model to understand and identify the optimal habitat and potential fishing grounds for neon f lying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean. Remote sensing data, including sea surface temperature, sea surface salinity, sea surface height, and chlorophyll-a concentrations, as well as fishery data from Chinese mainland squid f leets in the main fishing ground (150–165°E longitude) from August to October, from 1999 to 2004, were used. The HSI model was validated by using fishery data from 2005. The arithmetic mean modeling with three of the environmental variables—sea surface temperature, sea surface height anomaly, and chlorophyll- a concentrations—was defined as the most parsimonious HSI model. In 2005, monthly HSI values >0.6 coincided with productive fishing grounds and high fishing effort from August to October. This result implies that the model can reliably predict potential f ishing grounds for O. bartramii. Because spatially explicit fisheries and environmental data are becoming readily available, it is feasible to develop a dynamic, near real-time habitat model for improving the process of identifying potential fishing areas for and optimal habitats of neon flying squid.

Modeling the Effects of Habitat Fragmentation on the Movements of the Mongolian Gazelle in the Eastern Mongolian Steppe

The Mongolian gazelle, Procapra gutturosa, resides in the immense and dynamic ecosystem of the Eastern Mongolian Steppe. The Mongolian Steppe ecosystem dynamics, including vegetation availability, change rapidly and dramatically due to unpredictable precipitation patterns. The Mongolian gazelle has adapted to this unpredictable vegetation availability by making long range nomadic movements. However, predicting these movements is challenging and requires a complex model. An accurate model of gazelle movements is needed, as rampant habitat fragmentation due to human development projects - which inhibit gazelles from obtaining essential resources - increasingly threaten this nomadic species. We created a novel model using an Individual-based Neural Network Genetic Algorithm (ING) to predict how habitat fragmentation affects animal movement, using the Mongolian Steppe as a model ecosystem. We used Global Positioning System (GPS) collar data from real gazelles to “train” our model to emulate characteristic patterns of Mongolian gazelle movement behavior. These patterns are: preferred vegetation resources (NDVI), displacement over certain time lags, and proximity to human areas. With this trained model, we then explored how potential scenarios of habitat fragmentation may affect gazelle movement. This model can be used to predict how fragmentation of the Mongolian Steppe may affect the Mongolian gazelle. In addition, this model is novel in that it can be applied to other ecological scenarios, since we designed it in modules that are easily interchanged.