Les invasions biologiques, deuxième menace majeure de la biodiversité, pose d'important défis pour la conservation de la biodiversité, et la recherche en éco-évolution. Les espèces invasives ont en effet été étudiées depuis plus de 150 ans, mais nos capacités à prédire leurs présences aujourd'hui et dans le futur reste rudimentaire. Ce problème est principalement dû à la difficulté d'estimer à la fois les composantes biotiques et abiotiques de la niche des espèces invasives, ainsi que leur évolution dans le temps et l'espace. L'objectif de ma thèse a été de travailler sur ces défis en améliorant les méthodes d'estimation de niche, en enrichissant notre compréhension du rôle des interactions biotiques dans le processus d'invasion, et en étudiant en détail comment les processus évolutifs peuvent affecter la dynamique spatio-temporelle des niches. Plus précisément, (1) à l'aide d'une revue de la littérature, j'ai commencé par décrire les limites des différentes approches de modélisation utilisées pour prédire la distribution des espèces invasives. (2) Ensuite, j'ai proposé un cadre de modélisation permettant d'améliorer l'estimation des niches abiotiques régionales. (3) Puis, je me suis intéressée à la caractérisation des interactions biotiques, et aux méthodes communément utilisées pour identifier les patrons de compétition symétrique en écologie des communautés. J'ai également implémenté un modèle de simulation d'assemblage de communautés pour tester la performance de ces méthodes. (4) Ces premières études m'ont permis d'étudier à la fois les composantes biotiques et abiotiques des communautés de plantes envahies dans les Alpes. (5) Finalement, j'ai étudié l'évolution de la niche environnementale chez une espèce invasive des Alpes françaises Ambrosia artemisiifolia L, à travers une approche reliant niche-trait-génétique. Dans l'ensemble, les résultats de ces études montrent à quel point les différentes facettes de l'écologie et l'évolution en invasion sont fortement intriquées. De plus, ils soulignent la nécessité d'une modélisation intégrant les processus écologiques et évolutifs pour pouvoir comprendre la dynamique des invasions et proposer des outils de protection de la biodiversité efficaces. / Biological invasions, the second major threat to biodiversity, pose significant challenges to conservation management and eco-evolutionary research. Even though invasion processes have been studied for more than 150 years, our capacity to predict their presence today and in the future is still rudimentary. This deficiency stems mainly from the difficulty involved in reliably assessing the ecological niche of an invader, i.e. those environmental and biotic conditions that allow the species to maintain viable populations. In particular, disentangling the abiotic and biotic components of the ecological niche and accounting for their changing over space and time due to evolutionary dynamics is difficult, albeit crucial for the quality of predictions. The main objective of my PhD has been to address these challenges by improving methodological approaches of niche estimation, advancing our understanding of the role of biotic interactions for invasion processes and studying in greater detail how evolution may affect spatio-temporal niche dynamics. More precisely, (1) with a comprehensive literature review, I started by describing the limits of the different modelling approaches usually applied to predict invasive species distributions. (2) Then, I provided a modelling framework for improving regional environmental niche estimations. (3) Thirdly, I focused on the identification of biotic interactions, and the methods commonly used to identify patterns of symmetric competition in ecological communities. I also implemented a simulation model of community assembly to test the efficiency of these methods. (4) In a fourth part, I studied invaded alpine plant communities and showed that characteristics of the biotic environment in these communities (e.g. symmetric vs. asymmetric competition) were good predictors of invaders' presence. (5) Finally, I provided a first example of a genetic-based, climatic niche expansion of the invasive weed Ambrosia artemisiifolia L. in the French Alps by combining information on its environmental niche, genetic structure and functional traits. Taken together, the results of these studies highlight how tightly the different facets of invasion ecology and evolution are interrelated and open the way to an integrated modelling approach that would advance both eco-evolutionary research on invasion dynamics and applied tools for biodiversity protection.

Efforts to reintroduce and maintain populations of pronghorn (Antilocapra americana) to the California Central Valley, specifically the Carrizo Plain National Monument (CPNM) and the Mojave Desert (Antelope Valley) portion of Tejon Ranch, have largely been unsuccessful due to dwindling numbers of translocated animals. The objective of this study was to improve upon previous models for the CPNM using aerial survey data and then apply the model to the Tejon Ranch. Aerial survey data collected from 2000-2010 on the CPNM was used to establish “use” and “non-use” areas in the model. Model variables included vegetation type (forest, shrub, grassland, semi-desert scrub, crops, and bare areas), slope, and road density. Vegetation and road density variables were treated categorically and slope as a continuous variable. Kernel density estimation (KDE) was used to estimate utilization distributions and home ranges (Fieberg 2007). An 80% isopleth was used to define “used” and “unused” habitat areas within the study site. Binary logistic regression was used to detect correlations between habitat variables and habitat use by pronghorn. Results of the regression analysis indicated overall significance with a p-value of < 0.0001 (testing that all slopes = 0). Each habitat variable comparison was made after adjusting for the other variables (e.g., slope effects were evaluated after adjusting for road density and vegetation type) and was found to be significant. Each variable coefficient was then included in a predictive equation and entered into GIS to generate a map to predict where pronghorn would likely be observed. Similar layers were created for the Tejon Ranch and the predictive equation was run with the CPNM statistical analysis. Limited conclusions about habitat suitability on the CPNM or the Tejon Ranch can be made based on the habitat data available for this model. While slope, road density, and vegetation type are all significant habitat variables influencing pronghorn habitat use, further study is needed to understand the mechanisms driving these relationships. With additional data expansion of the current habitat suitability model would help to further define pronghorn habitat use, specifically the creation of a focused model of a particular season, life history period, or individual animal use to identify more detailed habitat use patterns.

It is widely known that the world is losing biodiversity and primarily it is thought to be caused by anthropogenic activities. Many of these activities have been identified. However, we still lack a clear understanding of the causal relationships between human activities and the pressures they place on the environment and biodiversity. We need to know how ecosystems and individual species respond to changes in human activities and therefore how best to moderate our actions and reduce the rate of loss of biodiversity. One of the ways to detect these changes is to use indicators of ecosystem conditions. Indicators are statistics following changes in a particular factor usually over time. These indicators are used to summarise a complex set of data, and are seen as being representative of the wider situation in that field. So it can be assumed that if that particular factor is declining or improving, then the situation in general is also declining or improving. They are used to check the status and trends of biodiversity by both the public and policy makers. Indicators are also used to assess national performance and can be used to identify the actions required at the policy level. In this manner, they provide an important link between policy-makers and scientists collecting the data. The current thesis investigates the possibility of using bird species as indicators of biodiversity for better management of natural terrestrial ecosystems, by identifying their habitats according to various environmental factors. The study is established by drawing upon three main scientific areas: ecology, geographical information system (GIS), and statistical modelling. The Mornington Peninsula and Western Port Biosphere Reserve (MPWPBR) (Victoria, Australia) was chosen for the study area because of the combination of suburban and natural environments that made it optimum for this type of study. Once the study area was defined, the necessary data for the research were obtained from various sources. Birds Australia provided data on recorded observation of 271 bird species within the study area. Based on the nature of this study, seven species were selected for the study. The criteria for this selection are discussed in Chapter 3. Most literature state that the primary determinant for bird abundance is vegetation and land cover. Because of this, Ecological Vegetation Class (EVC) layer was used to determine which type(s) of vegetation have the greatest impact on habitat selection. Each species showed a relationship to a number of v vegetation types. These EVCs were combined to produce vegetation patches, and were considered as potentially suitable habitats of corresponding bird species. For each of the species, these habitat patches were analysed for the different aspects of patch characteristics (such as the level of patchiness, connectivity, size, shape, weighted distance between patches, etc.) by using the Landscape Context Tool (a GIS add-on). This process assisted the understanding of the importance of patch quality in habitat selection among different bird species by analysing the location of bird observation sites relative to habitat patches. In this way, the association between bird presence and the conditions of a habitat patch was identified by performing a discriminant function analysis. To investigate the probability of a species presence according to different environmental factors, a model of species distribution was created. Binary logistic regression was used to indicate the level of effect of each variable. The model was then successfully validated in the field. To define the indicators of environmental factors, it was essential to separate bird species based on their dependency on one or more of the studied variables. For this purpose, One-Way ANOVA was used. This analysis showed that some bird species can be considered as indicators of urban areas, while others could be good indicators of wellpreserved large forests. Finally, it must be mentioned that the type and quality of the datasets are crucial to this type of study, because some species have a higher degree of sensitivity to certain types of vegetation or land cover. Therefore, the vegetation data must be produced as detailed as possible. At the same time, the species data needs to be collected based on the presence and absence (versus presence-only) of the birds.

The American alligator (Alligator mississippiensis) has been frequently studied in large reservoirs and coastal marshes. Large ontogenetic shifts in their diet and morphology have been linked with changes in habitat use, with adult males using deep, open water and juveniles and nesting females relying on vegetated marsh. In certain regions of the inland portion of the alligator’s range, these different aquatic habitats are represented by seasonal wetlands and riverine systems that are separated by a terrestrial matrix. Ontogenetic habitat shifts, therefore, would require overland movements between systems, which has important implications for conservation of the species. I tested several commonly used methods of surveying alligator populations to determine the most effective method of studying alligators in seasonal wetlands. I then used systematic trapping, nest surveys and radio telemetry to determine habitat use and overland movement rates by different sex and size classes. I found that seasonal wetlands provided nesting and nursery sites for these inland alligator populations, but that both juveniles undergoing an ontogenetic shift and nesting females move between the wetlands and riverine systems. Overland movements by alligators between the wetland and riverine habitats establish a level of functional connectivity between these aquatic ecosystems. I constructed a habitat suitability index of both the wetlands and the surrounding landscape to determine which patch and landscape characteristics were important to wetland use by alligators. I found that both descriptive wetland characteristics and the spatial relationships between wetlands were important predictors of alligator use. Overland movement was related to upland landuse as well as distance between aquatic habitats. Conserving a variety of wetland sizes and types within an intact upland matrix is critical to maintaining connectivity across the landscape. Furthermore, understanding how species may act as mobile links between ecosystems, particularly those with ontogenetic niche shifts, illustrates the importance of approaching conservation from a landscape perspective.

The American alligator (Alligator mississippiensis) has been frequently studied in large reservoirs and coastal marshes. Large ontogenetic shifts in their diet and morphology have been linked with changes in habitat use, with adult males using deep, open water and juveniles and nesting females relying on vegetated marsh. In certain regions of the inland portion of the alligator’s range, these different aquatic habitats are represented by seasonal wetlands and riverine systems that are separated by a terrestrial matrix. Ontogenetic habitat shifts, therefore, would require overland movements between systems, which has important implications for conservation of the species. I tested several commonly used methods of surveying alligator populations to determine the most effective method of studying alligators in seasonal wetlands. I then used systematic trapping, nest surveys and radio telemetry to determine habitat use and overland movement rates by different sex and size classes. I found that seasonal wetlands provided nesting and nursery sites for these inland alligator populations, but that both juveniles undergoing an ontogenetic shift and nesting females move between the wetlands and riverine systems. Overland movements by alligators between the wetland and riverine habitats establish a level of functional connectivity between these aquatic ecosystems. I constructed a habitat suitability index of both the wetlands and the surrounding landscape to determine which patch and landscape characteristics were important to wetland use by alligators. I found that both descriptive wetland characteristics and the spatial relationships between wetlands were important predictors of alligator use. Overland movement was related to upland landuse as well as distance between aquatic habitats. Conserving a variety of wetland sizes and types within an intact upland matrix is critical to maintaining connectivity across the landscape. Furthermore, understanding how species may act as mobile links between ecosystems, particularly those with ontogenetic niche shifts, illustrates the importance of approaching conservation from a landscape perspective.

The effects of landscape structure on wildlife populations have drawn more attention from ecologists and wildlife managers as landscapes have rapidly changed worldwide. The objectives of this study were to (1) conduct a statewide habitat suitability assessment for wild turkeys (Meleagris gallopavo) in Mississippi using machine learning methods; (2) determine landscape-abundance relationships of wild turkeys at 2 spatial scales; and (3) measure genetic distinction of wild turkey populations in Mississippi. I found that habitat suitability for wild turkeys was positively related to amount of forest cover. Wild turkey relative abundance peaked at an optimal hardwood forest proportion of 0.29 and increased with enhanced landscape configuration at the annual dispersal scale, supporting the landscape composition hypothesis. Using microsatellite analysis of 224 birds, I found 3 distinct genetic clusters in Mississippi; however, population genetic differentiation neither fit to the isolation by distance or isolation by resistance models but may have behavioral cues.

I compared the performance of habitat models created from data of differing reliability. Because the reliability is dependent on the probability of detecting the species, I experimented to estimate detectability for a salamander species. Based on these estimates, I investigated the sensitivity of habitat models to varying detectability. Models were created using a database of amphibian and reptile observations at Fort A.P. Hill, Virginia, USA. Performance was compared among modeling methods, taxa, life histories, and sample sizes. Model performance was poor for all methods and species, except for the carpenter frog (Rana virgatipes). Discriminant function analysis and ecological niche factor analysis (ENFA) predicted presence better than logistic regression and Bayesian logistic regression models. Database collections of observations have limited value as input for modeling because of the lack of absence data. Without knowledge of detectability, it is unknown whether non-detection represents absence. To estimate detectability, I experimented with red-backed salamanders (Plethodon cinereus) using daytime, cover-object searches and nighttime, visual surveys. Salamanders were maintained in enclosures (n = 124) assigned to four treatments, daytime__low density, daytime__high density, nighttime__low density, and nighttime__high density. Multiple observations of each enclosure were made. Detectability was higher using daytime, cover-object searches (64%) than nighttime, visual surveys (20%). Detection was also higher in high-density (49%) versus low-density enclosures (35%). Because of variation in detectability, I tested model sensitivity to the probability of detection. A simulated distribution was created using functions relating habitat suitability to environmental variables from a landscape. Surveys were replicated by randomly selecting locations (n = 50, 100, 200, or 500) and determining whether the species was observed, based on the probability of detection (p = 40%, 60%, 80%, or 100%). Bayesian logistic regression and ENFA models were created for each sample. When detection was 80 __ 100%, Bayesian predictions were more correlated with the known suitability and identified presence more accurately than ENFA. Probability of detection was variable among sampling methods and effort. Models created from presence/absence data were sensitive to the probability of detection in the input data. This stresses the importance of quantifying detectability and using presence-only modeling methods when detectability is low. If planning for sampling as an input for suitability modeling, it is important to choose sampling methods to ensure that detection is 80% or higher. / Ph. D.

With exploding human populations and landscapes that are changing, an increasing number of wildlife species are brought to the brink of extinction. In Canada, the eastern hog-nosed snake, 'Heterodon platirhinos', is found in a limited portion of southern Ontario. Designated as threatened by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC), this reptile has been losing its habitat at an alarming rate. Due to the increase in development of southern Ontario, it is crucial to document what limits the snake’s habitat to direct conservation efforts better, for the long-term survival of this species. The goals of this study are: 1) to examine what environmental parameters are linked to the presence of the species at a landscape scale; 2) to predict where the snakes can be found in Ontario through GIS-based habitat suitability models (HSMs); and 3) to assess the role of biotic interactions in HSMs. Three models with high predictive power were employed: Maxent, Boosted Regression Trees (BRTs), and the Genetic Algorithm for Rule-set Production (GARP). Habitat suitability maps were constructed for the eastern hog-nosed snake for its entire Canadian distribution and models were validated with both threshold dependent and independent metrics. Maxent and BRT performed better than GARP and all models predict fewer areas of high suitability when landscape variables are used with current occurrences. Forest density and maximum temperature during the active season were the two variables that contributed the most to models predicting the current distribution of the species. Biotic variables increased the performance of models not by representing a limiting resource, but by representing the inequality of sampling and areas where forest remains. Although habitat suitability models rely on many assumptions, they remain useful in the fields of conservation and landscape management. In addition to help identify critical habitat, HSMs may be used as a tool to better manage land to allow for the survival of species at risk.

With exploding human populations and landscapes that are changing, an increasing number of wildlife species are brought to the brink of extinction. In Canada, the eastern hog-nosed snake, 'Heterodon platirhinos', is found in a limited portion of southern Ontario. Designated as threatened by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC), this reptile has been losing its habitat at an alarming rate. Due to the increase in development of southern Ontario, it is crucial to document what limits the snake’s habitat to direct conservation efforts better, for the long-term survival of this species. The goals of this study are: 1) to examine what environmental parameters are linked to the presence of the species at a landscape scale; 2) to predict where the snakes can be found in Ontario through GIS-based habitat suitability models (HSMs); and 3) to assess the role of biotic interactions in HSMs. Three models with high predictive power were employed: Maxent, Boosted Regression Trees (BRTs), and the Genetic Algorithm for Rule-set Production (GARP). Habitat suitability maps were constructed for the eastern hog-nosed snake for its entire Canadian distribution and models were validated with both threshold dependent and independent metrics. Maxent and BRT performed better than GARP and all models predict fewer areas of high suitability when landscape variables are used with current occurrences. Forest density and maximum temperature during the active season were the two variables that contributed the most to models predicting the current distribution of the species. Biotic variables increased the performance of models not by representing a limiting resource, but by representing the inequality of sampling and areas where forest remains. Although habitat suitability models rely on many assumptions, they remain useful in the fields of conservation and landscape management. In addition to help identify critical habitat, HSMs may be used as a tool to better manage land to allow for the survival of species at risk.

With exploding human populations and landscapes that are changing, an increasing number of wildlife species are brought to the brink of extinction. In Canada, the eastern hog-nosed snake, 'Heterodon platirhinos', is found in a limited portion of southern Ontario. Designated as threatened by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC), this reptile has been losing its habitat at an alarming rate. Due to the increase in development of southern Ontario, it is crucial to document what limits the snake’s habitat to direct conservation efforts better, for the long-term survival of this species. The goals of this study are: 1) to examine what environmental parameters are linked to the presence of the species at a landscape scale; 2) to predict where the snakes can be found in Ontario through GIS-based habitat suitability models (HSMs); and 3) to assess the role of biotic interactions in HSMs. Three models with high predictive power were employed: Maxent, Boosted Regression Trees (BRTs), and the Genetic Algorithm for Rule-set Production (GARP). Habitat suitability maps were constructed for the eastern hog-nosed snake for its entire Canadian distribution and models were validated with both threshold dependent and independent metrics. Maxent and BRT performed better than GARP and all models predict fewer areas of high suitability when landscape variables are used with current occurrences. Forest density and maximum temperature during the active season were the two variables that contributed the most to models predicting the current distribution of the species. Biotic variables increased the performance of models not by representing a limiting resource, but by representing the inequality of sampling and areas where forest remains. Although habitat suitability models rely on many assumptions, they remain useful in the fields of conservation and landscape management. In addition to help identify critical habitat, HSMs may be used as a tool to better manage land to allow for the survival of species at risk.