Predicting Breeding Status of a Forest Songbird from Singing Rate
Notice bibliographique
Résumé
For male breeding songbirds, song rate varies throughout the breeding season and tends to be correlated with breeding-cycle stages. Although these patterns have been well documented, to our knowledge, this relationship has not been used to predict a bird’s breeding status through acoustic monitoring. The first objective of this study was to determine if variation in song rate can be used to predict the breeding status of the Olive-sided Flycatcher (Contopus cooperi; OSFL), a Species at Risk in Canada. In 2016, song rates from 27 male OSFLs in Alberta and the Northwest Territories were collected from human observers (n = 454 5-min counts), and breeding status (i.e. single, paired, and feeding young) was monitored throughout the breeding season. I evaluated the predictive ability of three modeling approaches (i.e. regression, hierarchical, and machine learning) using model sensitivity and specificity. The hierarchical model was the best at predicting all three breeding statuses, with 69%, 50% and 87% sensitivities and 80%, 82% and 78% specificities for predicting single, paired, and feeding young, respectively. This resulted in a mean sensitivity of 69%, compared with 54% and 50% from the regression and machine learning models, respectively. A second objective was to use the hierarchical modelling framework to predict breeding status from song rates collected by Autonomous Recording Units (ARUs) processed using automatic recognition software. For 24 of these OSFLs, I collected 4,302 5-min song counts and used daily song rate to compare the relationship of rates and breeding status as determined by ARUs versus human-observers. We then tested four hierarchical models accounting for imperfect detection. Song rates derived from ARU data followed a similar pattern to that of human-observer song rates, where single males had the higher rates, paired males had lower rates, and those feeding young had lowest rates, but the absolute values for rates were much lower with ARUs. All ARU data predictive models performed poorly at predicting single (sensitivity range 0 – 7%) and well at predicting paired (sensitivity range 77 – 84%). The ARU models had mixed success at predicting feeding young (sensitivity range of 25 – 68%) but adjusting for imperfect detection did not improve model sensitivity to predict any breeding statuses. Low predictive ability was likely due to the low detectability of ARUs (e.g. bird movement out of detection range of ARU) and the automatic recognition software we used. Considering the high predictive ability of models using human-observer data and that the challenges currently associated with our acoustic processing methods can be addressed, I recommend that the breeding status of forest birds should be monitored using acoustic data. I provided a hierarchical modelling framework than can be applied to other species and improved to account for bird movement or number of conspecifics. This novel approach could provide a cost-effective tool to infer much needed demographic information over large spatial extents, and inform species status assessments, recovery strategies, and management plans for many species of conservation interest.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».