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A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data

June 19, 2023
  1. Rapidly advancing animal telemetry technologies paired with new statistical models can provide insight into the behaviour of otherwise unobservable free-living animals. Changes in behaviour apparent from pairing telemetry with statistical models often occur as animals undertake key life-history activities, such as reproduction. For many species that are secretive or occupy remote areas, these life-history events are difficult to detect with conventional survey techniques, and consequently, vital rates are difficult to estimate.
  2. We present a hierarchical modelling framework, which integrates movement data observed via animal-borne telemetry and optional, infrequent survey data, to estimate individual- and population-level reproductive success. The approach combines a mechanistic movement model and survival model, and allows for assessing the effects of hypothesized individual and environmental covariates on reproductive success. We first tested our approach with simulated data, and then applied it to movement data from migratory golden eagles (Aquila chrysaetos) breeding in southcentral Alaska across four breeding seasons.
  3. We show that results supported our biological hypotheses that changes in movement coincided with the timing of reproductive failures, and that changes in movement could be used to assess breeding success (and failure) at the individual and population levels. The analysis also provided evidence of inter-annual variation in population-level nest success and the timing of nesting failures.
  4. This new approach is adaptable to many species that care for young and can be tracked with telemetry devices, and can provide not only individual-level information useful for testing ecological hypotheses, but estimates of demographic parameters that can directly inform conservation and management if tagged animals are representative of the population.
Publication Year 2023
Title A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data
DOI 10.1111/2041-210X.14159
Authors Joseph Michael Eisaguirre, Perry J. Williams, Julia C. Brockman, Stephen B. Lewis, Christopher P. Barger, Greg A. Breed, Travis L. Booms
Publication Type Article
Publication Subtype Journal Article
Series Title Methods in Ecology and Evolution
Index ID 70245183
Record Source USGS Publications Warehouse
USGS Organization Alaska Science Center Ecosystems