Anthropogenic noise may interfere with avian acoustic communication, however some species alter the structure of vocalizations, to improve transmission in noise. Here, I conducted playback experiments to determine whether compressor stations, generator or grid-powered screw pump oil wells, and overall ambient noise levels affected responses of Savannah sparrows (Passerculus sandwichensis) to conspecific alarm calls at their nests. I also measured the structure of alarm calls, to determine whether Savannah sparrows altered vocalizations in noise. On control sites, Savannah sparrows responded to alarm calls by delaying provisioning visits. At compressor station sites, the loudest infrastructure treatment, they showed less of a delay. Close to compressor stations, Savannah sparrows lowered the frequency and increased the bandwidth of alarm calls. These findings suggest the compressor stations may interfere with anti-predator communication, but that Savannah sparrows can alter the structure of alarm calls at these sites, perhaps mitigating some effects of noise. / October 2016

Thesis (M. Sc.)--University of Alberta, 2010. / Title from pdf file main screen (viewed on Jan. 22, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Ecology, Department of Biological Sciences, University of Alberta. Includes bibliographical references.

I investigated the effect of energy sector development and introduced crested wheatgrass (Agropyron cristatum) on grassland birds on Canadian Forces Base Suffield. I conducted point counts and mapped breeding territories in 2007 and 2008 for Savannah sparrows (Passerculus sandwichensis), chestnut-collared longspurs (Calcarius ornatus), and Spragues pipits (Anthus spragueii). I found that Savannah sparrows favored areas with taller vegetation, human disturbances and crested wheatgrass in both years. Longspurs used shorter vegetation and in were tolerant of disturbance. Crested wheatgrass was avoided by longspurs in both years. Pipit territories contained similar vegetation to longspurs, were sensitive to disturbance, and avoided placing territories in areas containing crested wheatgrass or trails in both years. Well sites, pipelines and junctions were not avoided by the three species. My research suggests that reducing the number of trails and the spread of crested wheatgrass will increase habitat availability for sensitive species of grassland birds. / Ecology

No description available.

Increased growth of the mid-continent population of Lesser Snow Geese (LSGO) has led to the degradation of coastal salt marsh and sedge meadow habitats across Canadian Arctic and sub-Arctic ecosystems. It is believed that a human-induced trophic cascade caused by agricultural habitat modification along migratory routes and wintering grounds has contributed to the increase in LSGO numbers, which has resulted in the alteration of habitat quality and connectivity along northern breeding and stopover sites used by various avian species. This habitat degradation has been shown to decrease the presence and temporal persistence of ground-nesting passerine and shorebird species at a local level and may lead to decreases of Arctic / sub-Arctic breeding avian species across landscapes that LSGO utilize and degrade. In 1999, four paired study plots were established, and used in conjunction with a single study plot from 1976, in order to measure the composition of habitat parameters (barren ground extent; graminoid and shrub cover) and to estimate the number of avian nests found in these plots. Using this historical data along with our findings from 2010 and 2011, our main objectives were to: 1) document the change in the aforementioned habitat parameters over time; 2) estimate the local nesting occupancy rates of the common Savannah Sparrow (SAVS), a robust and adaptable ground nester; and 3) determine which habitat variables are indicative of the rates of change and occurrence of nesting by SAVS within the study plots. By using ANOVA, linear mixed effects, and multi-state occupancy models, results suggest that an increase in barren ground, decreases in shrub and graminoid cover, and a loss of connectivity between suitable nesting patches has led to a 10% (λ = 0.90) annual decline in the probability that SAVS nesting occurred across the study plots from 1999 to 2010. These model results may be used to estimate long-term trends in persistence of breeding SAVS and other similar ground-nesting avian species that share habitats with LSGO along Arctic and sub-Arctic ecosystems. (93 pages)

Many grassland songbird species exhibit sensitivity to patch size in North America’s fragmented prairie ecosystems, but the mechanisms explaining this area sensitivity are not well understood. I tested the effects of patch size and artificial conspecific location cues (song playback and decoys) on grassland songbird abundance in 23 northern tall-grass prairies in Manitoba, Canada, in 2010 and 2011. Richness and relative abundances increased with patch area; this effect was not explained by differences in local habitat structure, patch configuration, and adjacent matrix. Artificial cues elicited putative territory prospecting in small, previously unoccupied treatment patches from two focal species, Savannah Sparrow (Passerculus sandwichensis; n=3 treatment sites) and Le Conte’s Sparrow (Ammodramus leconteii; n=4 treatment sites), but not in control patches (n=3 for both focal species). Social information may influence the focal species’ settlement decisions, but the lack of permanent settlement response suggests social cues are unable to reverse their area sensitivity.

Many grassland songbird species exhibit sensitivity to patch size in North America’s fragmented prairie ecosystems, but the mechanisms explaining this area sensitivity are not well understood. I tested the effects of patch size and artificial conspecific location cues (song playback and decoys) on grassland songbird abundance in 23 northern tall-grass prairies in Manitoba, Canada, in 2010 and 2011. Richness and relative abundances increased with patch area; this effect was not explained by differences in local habitat structure, patch configuration, and adjacent matrix. Artificial cues elicited putative territory prospecting in small, previously unoccupied treatment patches from two focal species, Savannah Sparrow (Passerculus sandwichensis; n=3 treatment sites) and Le Conte’s Sparrow (Ammodramus leconteii; n=4 treatment sites), but not in control patches (n=3 for both focal species). Social information may influence the focal species’ settlement decisions, but the lack of permanent settlement response suggests social cues are unable to reverse their area sensitivity.

Les réseaux neuronaux profonds sont des outils prometteurs pour l'évaluation de la biodiversité aviaire, en particulier pour la détection des chants et la classification acoustique des espèces. Toutefois, on connaît mal l’étendue de leur capacité de généralisation face à la variation intraspécifique présente dans les chants d’oiseaux, ce qui pourrait mener à des biais. Notre étude porte sur l'évaluation des performances de BirdNET, un réseau neuronal profond, pour le traitement d’un corpus d'enregistrements audio caractérisés par une variation intraspécifique significative, en utilisant l’exemple du chant du bruant des prés (Passerculus sandwichensis). Dans la population de l'île de Kent, au Nouveau-Brunswick, les individus sont suivis et enregistrés grâce à leurs bagues de couleur et la présence de microdialectes est solidement documentée. Nous avons recueilli et annoté 69 606 chants provenant de 52 individus et analysé ces données à l'aide d’une version récente de BirdNET. Nos résultats révèlent que BirdNET démontre une précision globale suffisante, prédisant correctement 81,9 % des chants, ce qui dépasse les résultats rapportés par ses développeurs. Toutefois, nous avons observé une variation considérable dans les scores de confiance et les taux de prédiction exactes entre les individus, ce qui suggère des biais potentiels. Cependant, nos recherches n'ont pas mis en évidence de variation entre les résultats des différents microdialectes, ce qui souligne la relative robustesse de l'algorithme. Nous avançons que la variation observée entre les individus est due au fait que certains d’entre eux chantent systématiquement plus près des microphones, résultant en des chants plus clairs donc plus faciles à identifier. Pour mieux comprendre le processus de prise de décision de BirdNET, nous avons tenté de produire des cartes d'activation de classe, qui constituent un outil précieux pour identifier les éléments d’un chant qui déterminent une prédiction. Cependant, il ne nous a pas été possible d’obtenir des cartes d’activation de classe d’après la version actuellement disponible du code de BirdNET sans avoir recours à des connaissances avancées en informatique. L'accès à des outils explicatifs adaptés aux innovations récentes dans les architectures de réseaux neuronaux 4 profonds serait crucial pour mieux interpréter les résultats et renforcer la confiance des utilisateurs. Nos résultats soulignent la nécessité de poursuivre les recherches sur la capacité de généralisation des réseaux neuronaux profonds pour la bioacoustique en utilisant des ensembles de données monospécifiques portant sur de plus longues périodes ou des aires de répartition géographique plus vastes. En outre, l'extension de cette étude à des espèces ayant des répertoires plus importants ou des différences plus subtiles entre le chant des individus pourrait nous informer davantage sur les limites et le potentiel des algorithmes d'apprentissage profond pour la détection et la classification acoustiques des espèces. En conclusion, notre étude démontre les performances prometteuses de BirdNET pour le traitement d'un large corpus de chants de bruants des prés, et confirme son potentiel en tant qu'outil précieux pour l'évaluation de la biodiversité aviaire. Les biais dus aux techniques d’enregistrement et la variation dans les taux de succès observés entre les individus méritent d'être étudiés plus en détail. / Machine learning, particularly deep neural networks, has gained prominence as a valuable tool in ecological studies and wildlife conservation planning. In the field of avian biodiversity assessment, deep neural networks have shown remarkable promise, particularly in acoustic species detection and classification. Despite their success, a critical knowledge gap exists concerning the generalization ability of these algorithms across intraspecific variation in bird song. This raises concerns about potential biases and misinterpretation of results. This study focuses on evaluating the performance of BirdNET, a deep neural network, in processing audio recordings characterized by significant intraspecific variation in the Savannah Sparrow (Passerculus sandwichensis) song. Savannah Sparrows are an ideal candidate for this investigation, given their well-studied population on Kent Island, New Brunswick, Canada. Each male sings a unique, unchanging song throughout its life, and the population exhibits well-documented geographical microdialects. We collected a large corpus of Savannah Sparrow songs using autonomous and focal recorders on Kent Island, yielding a total of 69,606 manually annotated songs from 52 different sparrows. We analyzed the audio data using BirdNET-Analyzer. The resulting confidence scores were used to assess the algorithm's performance across microdialects and individual birds. Our results revealed that BirdNET exhibited considerable overall accuracy, correctly predicting 81.9% of the songs, which surpassed the results reported by the developers of BirdNET. We observed variations in BirdNET's confidence scores among individual birds, suggesting potential biases in its classifications. However, our investigation indicated no evidence of distinct biases towards specific microdialects, highlighting the algorithm's relative robustness across these groups. We suspect that the variation observed amongst individuals is caused by the fact that some were singing consistently closer to microphones, yielding clearer songs. To gain insights into BirdNET's decision-making process, we sought to employ class activation maps, a valuable tool for identifying essential song elements contributing to species predictions. However, we were unable to produce class activation maps from the current version of BirdNET 6 without advanced computer science skills. Access to informative tools adapted to recent innovations in deep neural network architectures for bioacoustic applications is crucial for understanding and interpreting results better. Such tools would enhance user confidence and favour accountability for conservation decisions based on these predictions. Our findings underscore the need for further research investigating the generalization capacity of deep neural networks in bioacoustics on single-species datasets with more extensive intraspecific variation and broader geographical ranges. Additionally, expanding this investigation to species with larger song repertoires or more subtle inter-individual song differences could provide valuable insights into the limits and potential of deep learning algorithms for acoustic species detection and classification. In conclusion, our study demonstrates BirdNET's promising performance in processing a large corpus of Savannah Sparrow songs, highlighting its potential as a valuable tool for avian biodiversity assessment. Biases and variations in confidence scores observed across individual birds warrant further investigation.