| Summary: | The hierarchical model of provisioning posits that parents employ a strategic, sequential use of three provisioning tactics as offspring demand increases (e.g., due to increasing brood size and age). Namely, increasing delivery rate (reducing intervals between provisioning visits), expanding provisioned diet breadth, and adopting variance-sensitive provisioning. We evaluated this model in an Arctic breeding population of Peregrine falcons ( Falco peregrinus tundrius ) by analyzing changes in inter-visit-intervals (IVIs) and residual variance in IVIs across 7 study years. Data was collected using motion-sensitive nest camera images and analyzed using Bayesian mixed effect models. We found strong support for a decrease in IVIs (i.e., increase in delivery rates) between provisioning visits and an increase in residual variance in IVIs with increasing nestling age, consistent with the notion that peregrines shift to variance-prone provisioning strategies with increasing nestling demand. However, support for predictions made based on the hierarchical model of tactics for coping with increased brood demand was equivocal as we did not find evidence in support of expected covariances between random effects (i.e., between IVI to an average-sized brood (intercept), change in IVI with brood demand (slope) or variance in IVI). Overall, our study provides important biological insights into how parents cope with increased brood demand. Funding provided by: Natural Sciences and Engineering Research Council Crossref Funder Registry ID: https://ror.org/01h531d29 Award Number: Funding provided by: University of Alberta Crossref Funder Registry ID: https://ror.org/0160cpw27 Award Number: Funding provided by: Mitacs Crossref Funder Registry ID: https://ror.org/00cjrc276 Award Number: Funding provided by: Government of Nunavut Crossref Funder Registry ID: https://ror.org/03wf6h922 Award Number: Funding provided by: Nunavut General Monitoring Plan Crossref Funder Registry ID: http://dx.doi.org/10.13039/100015878 Award Number: Data ...
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