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Multiple studies have documented declines of forest breeding birds in the eastern United States, but the temporal and spatial scales of most studies limit inference regarding large scale bird-habitat trends. A potential solution to this challenge is integrating existing long-term datasets such as the U.S. Forest Service Forest Inventory and Analysis (FIA) program and U.S. Geological Survey Breeding Bird Survey (BBS) that span large geographic regions. The purposes of this study were to determine if FIA metrics can be related to BBS population indices at multiple spatial and temporal scales and to develop predictive models from these relationships that identify forest conditions favorable to forest songbirds. I accumulated annual route-level BBS data for 4 species guilds (canopy nesting, ground and shrub nesting, cavity nesting, early successional), each containing a minimum of five bird species, from 1966-2004. I developed 41 forest variables describing forest structure at the county level using FIA data from for the 2000 inventory cycle within 5 physiographic regions in 14 states (AL, GA, IL, IN, KY, MD, NC, NY, OH, PA, SC, TN, VA, and WV). I examine spatial relationships between the BBS and FIA data at 3 hierarchical scales: 1) individual BBS routes, 2) FIA units, and 3) and physiographic sections. At the BBS route scale, I buffered each BBS route with a 100m, 1km, and 10km buffer, intersected these buffers with the county boundaries, and developed a weighted average for each forest variable within each buffer, with the weight being a function of the percent of area each county had within a given buffer. I calculated 28 variables describing landscape structure from 1992 NLCD imagery using Fragstats within each buffer size. I developed predictive models relating spatial variations in bird occupancy and abundance to changes in forest and landscape structure using logistic regression and classification and regression trees (CART). Models were developed for each of the 3 buffer sizes, and I pooled the variables selected for the individual models and used them to develop multiscale models with the BBS route still serving as the sample unit. At the FIA unit and physiographic section scales I calculated average abundance/route for each bird species within each FIA unit and physiographic section and extrapolated the plot-level FIA variables to the FIA unit and physiographic section levels. Landscape variables were recalculated within each unit and section using NCLD imagery resampled to a 400 m pixel size. I used regression trees (FIA unit scale) and general linear models (GLM, physiographic section scale) to relate spatial variations in bird abundance to the forest and landscape variables. I examined temporal relationships between the BBS and FIA data between 1966 and 2000. I developed 13 forest variables from statistical summary reports for 4 FIA inventory cycles (1965, 1975, 1989, and 2000) within NY, PA, MD, and WV. I used linear interpolation to estimate annual values of each FIA variable between successive inventory cycles and GLMs to relate annual variations in bird abundance to the forest variables. At the BBS route scale, the CART models accounted for > 50% of the variation in bird presence-absence and abundance. The logistic regression models had sensitivity and specificity rates > 0.50. By incorporating the variables selected for the models developed within each buffer (100m, 1km, and 10km) around the BBS routes into a multiscale model, I was able to further improve the performance of many of the models and gain additional insight regarding the contribution of multiscale influences on bird-habitat relationships. The majority of the best CART models tended to be the multiscale models, and many of the multiscale logistic models had greater sensitivity and specificity than their single-scale counter parts. The relatively fine resolution and extensive coverage of the BBS, FIA, and NLCD datasets coupled with the overlapping multiscale approach of these analyses allowed me to incorporate levels of variation in both habitat and bird occurrence and abundance into my models that likely represented a more comprehensive range of ecological variability in the bird-habitat relationships relative to studies conducted at smaller scales and/or using data at coarser resolutions. At the FIA unit and physiographic section scales, the regression trees accounted for an average of 54.1% of the variability in bird abundance among FIA units, and the GLMs accounted for an average of 66.3% of the variability among physiographic sections. However, increasing the observational and analytical scale to the FIA unit and physiographic section decreased the measurement resolution of the bird abundance and landscape variables. This limits the applicability and interpretive strength of the models developed at these scales, but they may serve as indices to those habitat components exerting the greatest influences on bird abundance at these broader scales. The GLMs relating average annual bird abundance to annual estimates of forest variables developed using statistical report data from the 1965, 1975, 1989, and 2000 FIA inventories explained an average of 62.0% of the variability in annual bird abundance estimates. However, these relationships were a function of both the general habitat characteristics and the trends in bird abundance specific to the 4-state region (MD, NY, PA, and WV) used for these analyses and may not be applicable to other states or regions. The small suite of variables available from the FIA statistical reports and multicollinearity among all forest variables further limited the applicability of these models. As with those developed at the FIA unit and physiographic sections scales, these models may serve as general indices to the habitat components exerting the greatest influences on bird abundance trends through time at regional scales. These results demonstrate that forest variables developed from the FIA, in conjunction with landscape variables, can explain variations in occupancy and abundance estimated from BBS data for forest bird species with a variety of habitat requirements across spatial and temporal scales. / Ph. D.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Kollisionen von Vögeln mit Windturbinen haben sich zu einer bedenklichen Quelle für die Gefährdung besonders von Populationen seltenerer Vogelarten entwickelt. Allerdings wird im Allgemeinen auch bestätigt, dass die Nutzung der Windenergie unverzichtbar ist. Das Hauptziel dieser Arbeit war es, die Relevanz der Wechselwirkungen zu verstehen, die zwischen technischen Infrastrukturen und den von Kollisionen betroffenen Vogelarten auf der Landschaftsebene stattfinden. Da sowohl von der Landschaft beeinflusst werden. Unter Nutzung der durch gezielte Nachsuche gefundenen Opfer der am häufigsten von Kollisionen betroffenen Artengruppen paradoxerweise as als Proxy für das Vorkommen von Arten, und Durch die Anwendung verschiedener Techniken zur Modellierung der Artenverbreitung (SDMs) die “kollisionsempfindliche Nische “für jede der Vogelgruppen beschrieben. Obwohl die vorhergesagten Gebiete mit potenziellen Kollisionsrisiko insgesamt nur kleine, aber stark verteilt im ungefährdes Bundeslandes hatten. Greifvögel mit die breiteste Nische, die zudem signifikante Überlappungen mit den kollisionsempfindlichen Nischen der anderen Gruppen aufwies. Die niedrig bewerteten Gebiete weiter differenziert, die als tatsächliche „Bereiche ohne Risiko“ interpretiert wurden, für weitere geplante Winkraftanlagen. Zusätzlich die jeweiligen Potentiale und Gefärdungen für Kollisionen auf der Basis der regionalen Dichteverteilungen der Arten in Brandenburg mit Ensemble-Methoden von Boosted Regression Trees wird ebenfalls bewertet. Zusammenfassend, diese Analysen paradigmatisch, sowohl die Gebiete als auch die Entfernungen zu den Grenzlinien der verschiedenen Landnutzungsformen ein höheres Risiko für die Kollision von Individuen der untersuchten Arten mit Windkraftanlagen ergibt ermitteln . Dieser Ansatz kann es möglich machen, zukünftige Windparkerweiterungen in der Landschaft im die möglichst kollisionsfreie und naturverträglicheStandorte in der Landschaft. / Although, it is well recognized that harnessing wind energy is highly indispensable, but collisions of birds at wind turbines has also developed simultaneously, concerning multiple bird species. With wind being strongly affected by the landscape and the behaviour of birds also being strongly influenced by the landscape, the main objective of the thesis was to understand the relevance of interactions between wind energy infrastructures and bird species from an ecological perspective of the landscape. Utilizing the carcass collision datasets of the frequently-hit bird-groups paradoxically as proxies for species presence, collision sensitive ecological distances to different land-use types were ascertained, by employing multiple techniques of species distribution modelling (SDMs), to delineate their respective collision sensitive niche employing the capabilities of machine learning algorithms. The predicted areas were specialized and highly dispersed across the federal state, with raptors showing the broadest niche and significant overlaps with the other groups. Based on estimated collision probabilities of the assessed areas (between 0 and 1), further segregations differentiated only those areas with negligible collision probabilities, <0.05, which were interpreted as the actual "no risk areas, suggesting any further planned additions of wind turbines to be suitably positioned only in these “safer” areas. Additionally, these collision probabilities were translated to strike susceptibilities, by relating them to the regional density distributions of the species as well. Summarizing, these analyses paradigmatically ascertained collision risk areas, and especially the collision sensitive distances from different land-use types to these areas, enabling the accurate guidance of future wind farm expansions in the landscape. Ultimately, formulating novel wind turbine allocation strategies to minimize avian collisions, making them as compatible as possible.

This study examines land cover changes and their projected future impacts on bird species conservation in and around Nairobi National Park, Kenya. 2016 and 2023 Satellite imagery, analyzed through Google Earth Engine and the MOLUSCE plugin were used to assess changes in five land cover types: grassland, shrubland, forest, urban, and water. Bird population data from the Kenya Bird Map were used to evaluate birds' responses to land cover changes. Birds were categorized into five habitat guilds—grassland-dependent, shrubland-dependent, forest-dependent, water-dependent, and urban-dependent bird species. Between 2016 and 2023, grassland and shrubland decreased by 14.21% and 5.54%, respectively, and urban increased by 19.85%. 2040 projection indicates declines in grassland (19.6%), shrubland (16%), and forest (5.64%), and increase in urbanization by 58.8% which reflect fluctuations in bird populations: grassland-dependent species declined by 27.5%, shrubland-dependent species declined by 6.3%, while forest-dependent, water-dependent, and urban-dependent species increased by 168%, 35.7%, and 101.5%.