eBird, a citizen-science program developed by the Cornell Lab of Ornithology and National Audubon, allows users to enter bird sightings from around the world in order to develop a large scale data set for research. This study seeks to analyze eBird data and methods in order to determine if the data collected is robust enough to be usable as a basis for habitat management and, if so, to what extent. This is accomplished through a comparison of Piping Plover (Charadrius melodus) (a threatened shorebird of management concern) counts, trends, and methodologies made through a survey following a strict protocol versus data collected by eBird in three different areas (Bolivar Flats, Apfel Park, and San Luis Pass). Using descriptive statistics such as mean counts, counts adjusted for effort, and frequency, and confirming with Kruskal-Wallis tests, variation was found between eBird and survey data. eBird contained lower counts of Piping Plovers and a lower sighting frequency than survey data. When adjusting counts as a function of effort, similar results were found. Piping Plovers were found not to occur frequently at Bolivar Flats (9 birds over 2 surveys), while Apfel Park and San Luis Pass showed similar but inconclusive results. This study ultimately determined that, while of great use on large scales, use of eBird data on the local level, should be used with caution. Further study should be done to investigate sources of variation and methods to increase the effectiveness of eBird on small scales.

No description available.

<div> <div> <div> <p>Billions of birds migrate annually between breeding and wintering habitats, following transient resources. Though a majority of time is spent in breeding and wintering habitats, habitats used during the migratory periods are especially important for migrating birds. Migration and stopovers, where birds rest and refuel before continuing a migratory journey, are critical points in a bird’s annual lifecycle, and are important ecologically, socially, and economically. Populations of migratory birds are declining on a global scale, however, and proper management is vital to their persistence in an urbanizing environment. Indiana in the Midwestern United States is an important area in which to study stopover ecology of migratory birds, as it is a fragmented forest- urban-agricultural matrix almost entirely managed through private ownership. In this dissertation, I studied three questions of stopover ecology within the landscape context of the Midwestern United States, primarily using weather surveillance radar and eBird citizen science data. </p> <p><br></p><p>First, I studied spatiotemporal changes over an 11-year period (autumn 2005-2016 and spring 2006-2017) in densities of nocturnally migrating birds at two radar stations in Indiana. I found that mean density of migratory birds stopping over in Indiana declined by approximately 6.8% annually, but variability in stopover site use increased over the same period. This is consistent with other work completed on continental scales, and highlights the need for further conservation of migratory birds. Second, I studied patterns of stopover site use in Indiana during spring 2016- 2017 and autumn 2015-2016, identifying landscape and local factors associated with those patterns. I used both traditional land cover characteristics and a novel approach using human socioeconomic measures to describe these patterns, and found that socioeconomics, particularly the size of a housing unit, were among the most important predictors of migratory bird density in Indiana. The results from this study suggest that migratory birds are utilizing urban habitats, which are known to contain several novel hazards for birds, but that migratory birds will benefit greatly from interdisciplinary work focusing on urban habitats. Third, I explored a novel method of using weather surveillance radar and eBird citizen science data in combination with each other, to see if both measures provided similar estimates of bird abundances during stopover. Though I found no correlation between the two, I argue that eBird and radar still provide important and complementary insights for the field of migration ecology. Finally, I provide guidelines for private landowners in Indiana on management for declining populations of migratory birds. </p> </div> </div> </div>

Cette recherche explore comment l’infrastructure et les utilisations d’eBird, l’un des plus grands projets de science citoyenne dans le monde, se développent et évoluent dans le temps et l’espace. Nous nous concentrerons sur le travail d’eBird avec deux de ses partenaires latino-américains, le Mexique et le Pérou, chacun avec un portail Web géré par des organisations locales. eBird, qui est maintenant un grand réseau mondial de partenariats, donne occasion aux citoyens du monde entier la possibilité de contribuer à la science et à la conservation d’oiseaux à partir de ses observations téléchargées en ligne. Ces observations sont gérées et gardées dans une base de données qui est unifiée, globale et accessible pour tous ceux qui s’intéressent au sujet des oiseaux et sa conservation. De même, les utilisateurs profitent des fonctionnalités de la plateforme pour organiser et visualiser leurs données et celles d’autres. L’étude est basée sur une méthodologie qualitative à partir de l’observation des plateformes Web et des entrevues semi-structurées avec les membres du Laboratoire d’ornithologie de Cornell, l’équipe eBird et les membres des organisations partenaires locales responsables d’eBird Pérou et eBird Mexique. Nous analysons eBird comme une infrastructure qui prend en considération les aspects sociaux et techniques dans son ensemble, comme un tout. Nous explorons aussi à la variété de différents types d’utilisation de la plateforme et de ses données par ses divers utilisateurs. Trois grandes thématiques ressortent : l’importance de la collaboration comme une philosophie qui sous-tend le développement d’eBird, l’élargissement des relations et connexions d’eBird à travers ses partenariats, ainsi que l’augmentation de la participation et le volume des données. Finalement, au fil du temps on a vu une évolution des données et de ses différentes utilisations, et ce qu’eBird représente comme infrastructure. / This research explores the evolution of the infrastructure and uses of eBird, one of the world’s largest citizen science projects. It concentrates on the work of eBird with two of its local partners in Latin America who manage regional portals in Mexico and Peru. eBird allows users throughout the world to contribute their observations of birds online and so to advance the case of science and conservation. These observations are stored and managed in a unified, global database that is freely accessible to all who are interested in birds and their conservation. Participants can use the platform’s various functionalities to organize and visualize their data as well as that of others. The research follows a qualitative methodology based on observation of the eBird platform and on interviews with members of the Cornell Lab of Ornithology, the eBird team and members of local organizations responsible for eBird in Peru and Mexico. We analyze eBird as an infrastructure whose technical and social sides are interrelated and need to be examined simultaneously. We also explore how the eBird team conceives the uses of the eBird platform and the data it contains. Three major themes emerge: the philosophy of collaboration underlying the development of eBird, the extension and diversification of eBird through its network of partnerships and a corresponding increase in both participation and volume of data. Finally, we also observe an evolution in the type and variety of uses for eBird observations and the eBird infrastructure itself.

No description available.

La diversité bêta est une mesure essentielle pour décrire l'organisation de la biodiversité dans l'espace. Le calcul des contributions locales à la diversité bêta (LCBD), en particulier, permet d'identifier des sites à forte unicité écologique montrant une diversité exceptionnelle au sein d'une région d'intérêt. Jusqu’à présent, l'utilisation des LCBD s'est restreinte à des échelles locales ou régionales avec un petit nombre de sites. Dans ce mémoire, j'ai examiné si les modèles de répartition d'espèces (SDM) permettent d'évaluer l'unicité écologique sur de plus grandes étendues spatiales. J'ai également étudié l’effet des changements d’échelle sur la quantification de la diversité bêta. Pour ce faire, j'ai utilisé la base de données eBird et des arbres de régression additifs bayésiens pour prédire la répartition des parulines en Amérique du Nord. J'ai ensuite calculé les LCBD sur ces prédictions, ce qui permet de couvrir de plus grandes étendues spatiales et un nombre de sites plus élevé. Mes résultats ont montré que les SDM fournissent des estimations d'unicité fortement corrélées avec les données observées et montrant une association spatiale statistiquement significative. Ils ont également montré que la relation entre la richesse et les LCBD varie selon la région et l'étendue spatiale et qu'elle est influencée par la proportion d'espèces rares dans les communautés. Ainsi, les sites identifiés comme uniques peuvent varier selon les caractéristiques de la région étudiée. Ces résultats montrent que les SDM peuvent être utilisés pour prédire l'unicité écologique, ce qui pourrait permettre d'identifier d'importantes cibles de conservation au sein de régions non échantillonnées. / Beta diversity is an essential measure to describe the organization of biodiversity through space. The calculation of local contributions to beta diversity (LCBD), specifically, allows the identification of sites with high ecological uniqueness and exceptional diversity within a region of interest. To this day, LCBD indices have primarily been used on regional and smaller scales, with relatively few sites. Furthermore, their use is typically restricted to strictly sampled sites with known species composition, leading to gaps in spatial coverage on broad extents. Here, I examined whether species distribution models (SDMs) can be used to assess ecological uniqueness over broader spatial extents and investigated the effect of scale changes on beta diversity quantification. To this aim, I used observations recorded in the eBird database and Bayesian additive regression trees to model warbler species composition in North America, then computed LCBD indices on the predictions, thus covering a broader spatial extent and a higher number of sites. My results showed that SDMs provide uniqueness estimates highly correlated with observed data with a statistically significant spatial association. They also showed that the relationship between richness and LCBD values varies according to the region and the spatial extent and that it is affected by the proportion of rare species in communities. Sites identified as unique may therefore vary according to regional characteristics. These results show that SDMs can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.