Remote cameras have become a popular tool for monitoring wildlife. We used remote cameras to estimate bobcat (Lynx rufus) population abundance on the Utah Test and Training Range during two sample periods between 2015 and 2017. We used two statistical methods, closed capture mark-recapture (CMR) and mark-resight Poisson log-normal (PNE), to estimate bobcat abundance within the study area. We used the maximum mean distance moved method (MMDM) to calculate the effective sample area for estimating density. Additionally, we captured bobcats and estimated home range using minimum convex polygon (MCP) and kernel density estimation (KDE) methods. Bobcat abundance on the UTTR was 35-48 in 2017 and density was 11.95 bobcats/100 km2 using CMR and 16.69 bobcats/100 km2 using PNE. The North Range of the study area experienced a decline of 36-44 percent in density between sample periods. Density declines could be explained by natural predator prey cycles, by habituation to attractants or by an increase in home range area. We recommend that bobcat abundance and density be estimated regularly to establish population trends.To improve the management of bobcats on the Utah Test and Training Range (UTTR), we investigated bobcat (Lynx rufus) habitat use. We determined habitat use points by capturing bobcats in remote camera images. Use and random points were intersected with remotely sensed data in a geographic information system. Habitat variables were evaluated at the capture point scale and home range scale. Home range size was calculated using the mean maximum distance moved method. Scales and habitat variables were compared within generalized linear mixed-effects models. Our top model (AICc weight = 1) included a measure of terrain ruggedness, mean aspect, and land cover variables related to prey availability and human avoidance.

A geographic information system (GIS) was used to create landform measurements and maps for elevation, slope, aspect, landform index, relative phenologic change, and slope position for 3 topographic quadrangles in Virginia. A set of known observation points of the Northern dusky flying squirrel (Glaucomys sabrinus) was used to build 3 models to delineate sites with landform characteristics equivalent to those known points. All models were built using squirrel observation points from 2 topographic quadrangles. The first model, called "exclusionary", excluded those pixels with landform characteristics different from the known squirrel pixels based on histogram analyses. Logistic regression was used to create the other 2 models. Each model resulted in an image of pixels considered equivalent to the known squirrel pixels. Each model excluded approximately 65% of the Highland study area, but the exclusionary model excluded the fewest known squirrel pixels (12.62%). Both logistic regression models excluded approximately 10% more known squirrel pixels than the exclusionary approach. The models were tested in the area of a third quadrangle with points known to be occupied by squirrels. After the model was applied to the third topographic quadrangle, the exclusionary model excluded the least amount of full-area pixels (79.30%) and only 14.81% of the known squirrel pixels. The second logistic regression excluded 81.16 % of the full area and no known squirrel pixels. All models proved useful in quickly delineating pixels equivalent to areas where wildlife were known to occur. / Master of Science

The Puerto Rican Nightjar Caprimulgus noctitherus is an endangered species found in forest of southern Puerto Rico. I documented density of nightjars in Guánica Forest, the region of Guayanilla-Peñuelas, and Susúa Forest. The geographic range of the species was expanded because of this study and presence documented in a number of new localities. Stand level habitat model indicated forest type and midstory visual obscurity best predicted nightjar habitat. Landscape model predicted considerably more suitable nightjar habitat exists than had been previously estimated (> 30%) and highlighted several areas of importance for the species. I evaluated nightjar population estimation techniques and found use of point transects with lures (playback) and moon phase covariates generated best estimates. My results highlighted several sites currently under private ownership that should be protected or acquired. Establishment of new protected areas for the nightjar represents highest priority for conservation and eventual delisting of the species.

No description available.

There have been growing concerns over the decline of healthy river ecosystems and the severe consequences this decline could have on biodiversity, ecosystem services, and human well-being. These concerns have led to increased efforts in river restoration around the globe, which aim to improve the ecological health and functioning of rivers. The restoration is usually done by implementing strategies such as hydromorphological adaptation and flow management. These measures, nevertheless, do not guarantee the recovery of river ecosystems. This is because there are multiple factors contributing to the success of restoration projects, which can vary depending on the specific characteristics of each river system. Habitat modeling, one of the most widely used ecological quality assessment tools for rivers, has been applied in the evaluation of restoration projects. An aquatic ecosystem is complex, and its dynamic nature requires a comprehensive understanding of the interconnections between biotic and abiotic components. These components also have a high degree of spatial and temporal variability. Therefore, it is crucial that approaches and modeling techniques be tailored to capture this dynamic. In the assessment of river restoration, for instance, habitat modeling needs to account for the changes in flow patterns, sediment transport, water quality, and habitat availability/quality for the key indicator species that result from the restoration efforts. This study addresses the need for developing an integrated approach to habitat modeling, particularly for macrozoobenthos, an important indicator of river health that plays a crucial role in the functioning of aquatic ecosystems. The primary research objective is to improve the existing modeling framework (TRiMM) by focusing on three key aspects: 1) expanding the prediction factors of physical habitat that influence habitat suitability for macrozoobenthos; 2) integrating fuzzy algorithms in the suitability assignment process; 3) incorporating species' (re-)colonization capacity and habitat temporal variability into habitat connectivity assessment. The model adopts the fuzzy logic method in the habitat module to account for the interactions between various factors described in the habitat template (Poff & Ward, 1990). Moreover, the model considers both spatial and temporal changes in habitat parameters by running a transient simulation over a specific time period relevant to the life cycle requirements of the target species. This allows for a more accurate representation of the dynamic nature of river habitats and provides valuable insights into how they may change over time. Additionally, the model incorporates species' (re-)colonization potentials into habitat connectivity analysis by considering their dispersal capabilities. This helps in understanding how changes in habitat parameters can affect the overall connectivity of river habitats, which is crucial for assessing the resilience and sustainability of the systems. The proposed transient habitat modeling (TRiMM 2.0) is applied to two case studies of low-order rivers in Germany. The first case study focuses on a river that has been restored after a period of degradation. The habitat model was tested with sampling data, and the results reveal that the model improved when additional variables related to habitat were included. The second case study was a simulation of habitat suitability and connectivity in a hypothetical river reach. Hydraulic and morphological factors (water depth, velocity, temperature, and sediment) are simulated over a period of four years using SRH-2D. The simulation results showed that hydraulic and morphological factors had a significant impact on sediment characteristics, which in turn influenced habitat suitability and connectivity. This study also highlights the importance of considering multiple variables and their interactions when assessing river habitats. Additionally, the use of transient modeling provides information about long-term changes in habitat quality and connectivity.:Abstract Kurzfassung Contents List of figures List of tables Nomencature Acknowledgement List of publications 1. General introduction 1.1. Research motivation 1.2. Statement of research objectives 1.3. Structure of the dissertation 2. Macrozoobenthos and stream’s ecology 2.1. Macrozoobenthos and their habitat 2.2. Factors influencing the distribution of macrozoobenthos 2.2.1. Food sources 2.2.2. Water quality 2.2.3. Physical habitat 2.2.4. Colonization process 2.2.5. Presence of other species 2.3. Spatial scale and temporal variability 2.4. Conclusion 3. State of the art in river habitat modeling 3.1. Habitat modeling and river ecology assessment 3.2. Habitat modeling principles 3.2.1. Habitat suitability curves method 3.2.2. Fuzzy logic method 3.2.3. Generalized additive models 3.3. Existing benthos habitat modeling 3.3.1. PHABSIM 3.3.2. RHYHABSIM 3.3.3. BITHABSIM 3.3.4. CASiMiR 3.3.5. HABFUZZ 3.4. TRiMM and further development 3.5. Conclusion 4. Basis for the modeling concept and methodological framework 4.1. Physical habitat template 4.1.1. Streamflow regime 4.1.2. Substrate regime 4.1.3. Thermal regime 4.2. Habitat connectivity 4.3. Species colonization and habitat connectivity 4.4. Analysis scales 4.5. Conclusion 5. Transient river habitat modeling for macrozoobenthos – TRiMM 2.0 5.1. Habitat model description 5.2. Input data preparation 5.2.1. Field survey 5.2.2. Hydro-morphodynamic models 5.3. Habitat suitability calculation 5.4. Patch-building and patch dynamics analysis 5.5. Habitat connectivity calculation 5.6. Conclusion 6. Model applications 6.1. Case study 1: Simulation of habitat suitability for macrozoobenthos in a small restored stream (Saxony, Germany) Abstract 6.1.1. Introduction 6.1.2. Material and Method 6.1.3. Results 6.1.4. Discussion 6.1.5. Conclusion 6.2. Case study 2: Application of TRiMM 2.0 to simulate benthic habitat quality in a hypothetical reach of Zschopau river 6.2.1. Introduction 6.2.2. Methodology 6.2.3. Results 6.2.4. Discussion 6.2.5. Conclusion 7. Summary and future outlook 8. References

Northern bobwhite (<I>Colinus virginianus</I>) are important game birds associated with early successional habitats across the southeastern United States. In the past 30+ years there has been an almost universal decline in bobwhite population numbers despite a long history of management. The Virginia Bobwhite Quail Management Plan was implemented in 1996 to slow and stop the current population declines in Virginia. Virginia Department of Game and Inland Fisheries (VDGIF) personnel identified a lack of knowledge about the broad-scale, landscape level habitats in eastern Virginia. A large scale land cover map along with a detailed understanding of the spatial arrangements of bobwhite habitats will not only aid Virginia's management plan, but also allow focused efforts by our wildlife managers. I explored the possibilities of using remote sensing to map various habitats important to bobwhite. I compared several classification algorithms applied to Landsat TM imagery prior to selecting the classification method that best delineated early successional habitats. After method selection, a classified land cover map for the Coastal Plain and Piedmont of Virginia was generated. Using the classified images available from the first part of the study and 4 years of bobwhite call count data, I studied the landscape level habitat associations of bobwhite. A number of landscape metrics were calculated for the landscapes surrounding bobwhite call count routes and were used in two modeling exercises to differentiate between high and low bobwhite populations. Both pattern recognition (PATREC) and logistic regression models predicted levels of bobwhite abundance satisfactorily for the modeled (84.0% and 96.0% respectively) and independent (64.3% and 57.1%, respectively) data sets. The models were applied to remotely-sensed habitat maps to develop prediction maps expressing the quality of a landscape for supporting a high population of bobwhite based on existing land cover. Finally, I explored the possibility of eliminating the time consuming and very costly step of classifying a remotely-sensed image prior to examining its quality for a particular species. Using raw Landsat TM imagery and bobwhite call count data, I developed predictive logistic regression models expressing the quality of a landscape surrounding a pixel. The first model predicted the probability of the landscape supporting a high bobwhite population. Due to a number of stops with an average of zero, I was also able to generate a model that expressed the probability of the landscape supporting any number of bobwhite. This method also satisfactorily predicted high/low population and presence/absence for the modeled data (65.7% and 83.1%, respectively) and independent data (65.3% and 83.7%, respectively). The method described will allow for rapid assessment of our wildlife resources without having to classify remotely-sensed images into habitat classes prior to analyses. / Master of Science

The long-term success of wildlife conservation depends on maximizing the benefits of limited funds and data in pursuit of population and habitat objectives. The ultimate currency for wildlife management is progress toward long-term preservation of ample, wild, free wildlife populations and to this end, funds must be wisely spent and maximal use made from limited data. Through simulation-based analyses, I evaluated the efficacy of various models for estimating population abundance from harvest data. Because managers have different estimators to choose from and can also elect to collect additional data, I compared the statistical performance of different estimation strategies (estimator + dataset) relative to the financial cost of data collection. I also performed a value of information analysis to measure the impact that different strategies have on a representative harvest management decision. The latter analysis is not based on the cost of data, but rather on the management benefit derived from basing decisions on different datasets. Finally, I developed a hybrid modeling framework for mapping habitat quality or suitability. This framework makes efficient use of expert opinion and empirical validation data in a single, updatable statistical structure. I illustrate this method by applying it across an entire state.

This study analyzes the remaining suitable habitat of the one-horned rhinoceros, Rhinoceros unicornis, in Royal Chitwan National Park of Nepal. An April 2003 Landsat image was classified into eight land cover types: wetland, sand, water, mixed forest, sal forest, agriculture, settlement, and grassland. This image was converted into habitat suitability maps using cover, food, and water. The rhinoceros prefers grassland habitat with oxbow lakes and closed canopy during the monsoon season. Nominal values of five parameters were used to create a map of habitat suitability index. The map was categorized into four habitat classes: highly unsuitable, unsuitable, moderately suitable habitat, and suitable. Landscape metrics, patch metrics and class metrics associated with habitat were determined through the use of FRAGSTATS.

This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting cetacean distribution patterns based on environmental variables such as bathymetric and physical properties, and for exploring the potential ecological implications that contribute to cetacean spatial distributions. We can advance conservation efforts of cetacean populations by expanding our knowledge of their habitats and distribution. Generalized additive models (GAMs) were developed to predict the spatial distribution patterns of sperm whales (Physeter macrocephalus), pilot whales (Globicephala spp.), bottlenose dolphins (Tursiops truncatus), and Atlantic spotted dolphins (Stenella frontalis) based on significant physical parameters along the continental shelf-break region in the Mid-Atlantic Bight. Data implemented in the GAMs were collected in the summer of 2006 aboard the NOAA R/V Gordon Gunter. These included visual cetacean survey data collected along with physical data at depth via expendable bathythermograph (XBT), and conductivity-temperature-depth (CTD) instrumentation. Additionally, continual surface data were collected via the ship’s flow through sensor system. Interpolations of physical data were created from collected point data using the inverse distant weighted method (IDW) to estimate the spatial distribution of physical data within the area of interest. Interpolated physical data, as well as bathymetric (bottom depth and slope) data were extracted to overlaid cetacean sightings, so that each sighting had an associated value for nine potentially significant physical habitat parameters. A grid containing 5x5 km grid cells was created over the study area and cetacean sightings along with the values for each associated habitat parameter were summarized in each grid cell. Redundant parameters were reduced, resulting in a full model containing temperature at 50 m depth, mixed layer depth, bottom depth, slope, surface temperature, and surface salinity. GAMs were fit for each species based on these six potentially significant parameters. The resultant fit models for each species predicted the number of individuals per km2 based on a unique combination of environmental parameters. Spatial prediction grids were created based on the significant habitat parameters for each species to illustrate the GAM outputs and to indicate predicted regions of high density. Predictions were consistent with observed sightings. Sperm whale distribution was predicted by a combination of depth, sea surface temperature, and sea surface salinity. The model for pilot whales included bottom slope, and temperature at 50 m depth. It also indicated that mixed layer depth, bottom depth and surface salinity contributed to group size. Similarly, temperature at 50 m depth was significant for Atlantic spotted dolphins. Predicted bottlenose dolphin distribution was determined by a combination of bottom slope, surface salinity, and temperature at 50 m depth, with mixed layer depth contributing to group size. Distribution is most likely a sign of prey availability and ecological implications can be drawn from the habitat parameters associated with each species. For example, regions of high slope can indicate zones of upwelling, enhanced vertical mixing and prey availability throughout the water column. Furthermore, surface temperature and salinity can be indicative of patchy zones of productivity where potential prey aggregations occur. The benefits of these models is that collected point data can be used to expand our knowledge of potential cetacean “hotspots” based on associations with physical parameters. Data collection for abundance estimates, higher resolution studies, and future habitat surveys can be adjusted based on these model predictions. Furthermore, predictive habitat models can be used to establish Marine Protected Areas with boundaries that adapt to dynamic oceanographic features reflecting potential cetacean mobility. This can be valuable for the advancement of cetacean conservation efforts and to limit potential vessel and fisheries interactions with cetaceans, which may pose a threat to the sustainability of cetacean populations.

Dans le contexte général de l’érosion de la biodiversité, deux espèces d’oiseaux forestiers, le Grand Tétras (Tetrao urogallus) et la Gélinotte des bois (Bonasa bonasia), présentes dans le massif Jurassien sont menacées par la perte et la fragmentation de leur habitat à l’échelle régionale. En particulier, dans le massif Jurassien l’extension progressive des tâches de régénération du hêtre induit la transformation du couvert végétal constitué de myrtilles et d’herbacées favorable en un habitat fermé défavorable. Le destin de ces deux espèces emblématiques dépend pour la première d’actions de gestions et pour la seconde d’une meilleure connaissance de la distribution et de la dynamique des populations. La coupe des zones de régénération fait partie des principales actions envisagées pour restaurer l’habitat forestier. Cependant ces actions de gestion ou de suivi des populations sont couteuses en temps et en argent. Ainsi, l’opportunité d’utiliser deux jeux de données LiDAR (Light Detection and Ranging) couvrant la majorité de l’aire de distribution des deux espèces dans le massif Jurassien a initié le projet de cartographie des habitats de chaque espèce et de la présence des tâches de régénération du hêtre en utilisant des modèles de distribution d’espèces (SDMs). L’objectif est de soutenir les gestionnaires dans leurs décisions et actions grâce à la production de prédictions spatiales adaptées. La réalisation de cet objectif dépend de la fiabilité des modèles produits, mais aussi de la bonne transmission des résultats par le chercheur aux gestionnaires qui ne sont pas familiers avec les méthodes utilisées. Dans un premier temps, le choix d’une méthode de modélisation appropriée (correction du biais d’échantillonnage, échelles, algorithmes) par rapport aux caractéristiques des jeux de données et aux objectifs a été évalué. Dans un second temps, l’utilisation de variables environnementales LiDAR orienté-objet (arbres et trouées) pour faciliter l’appropriation des résultats par les gestionnaires a été testée. Enfin, les résultats obtenus ont permis la création de modèles multi-échelles et de carte de prédictions pour chacune des espèces démontrant la capacité du LIDAR de représenter la structure de la végétation qui influence la présence des espèces d’oiseaux forestières étudiées. Des modèles de distribution de la régénération du hêtre ont pu aussi être créés à une échelle fine. / In the general context of biodiversity erosion, two forest bird species occurring in the French Jura massif, the Capercaillie (Tetrao urogallus) and the Hazel Grouse (Bonasa bonasia), are threatened by habitat loss and fragmentation at the regional scale. In particular, intensive beech regeneration patches extension in the Jura massif is leading to the transformation of the understory cover, once suitable with bilberry and herbaceous vegetation, to closed unfavorable habitat. The fate of those two emblematic species is depending for the first on future management actions and for the second on a better knowledge of the species population’s dynamics and occurrences. In particular, the cutting of the beech regeneration patches is one of the efficient management actions undertaken to restore the habitat. However, management actions and surveys are money and time consuming due to the large area that need to be covered. The opportunity to use two Light Detection and Ranging (LiDAR) datasets covering a major part of the distribution of the two species in the Jura massif initiated the phD project, with the objective to support managers in their decisions and actions by the creation of adapted distribution predicted maps using Species Distribution Models (SDMs) (Hazel Grouse, Capercaillie and beech regeneration). The realization of this objective is depending on the reliability of the models produced and on the capacity of the researcher to transfer the results to managers who are not familiar with modeling methods. In a first step, the choice of the appropriate modeling method regarding the datasets characteristics and the objectives was investigated (sampling bias correction, scales, and algorithms). In addition, the use of object-oriented LiDAR predictors (trees and gaps) pertinent from both species and managers point of view to facilitate the results transfer was tested. The results obtained were used to create appropriate multi-scale SDMs and to predict distribution maps for both target species, demonstrating the capacity of LiDAR to represent vegetation structures that influence the targeted forest bird species occurrences. Models at a fine scale were also created to map the beech regeneration distribution in the Jura massif.