Data from: An open spatial capture–recapture model for estimating density, movement, and population dynamics from line-transect surveys

• Main Authors: Gowan, Timothy (10743338), Crum, Nathan (10743341), Roberts, Jason (8864576)
• Format: Software
• Language: unknown
• Published: 2021
• Subjects: Biotechnology; Ecology; Inorganic Chemistry; Biological Sciences not elsewhere classified; Mathematical Sciences not elsewhere classified; density estimation; distance sampling; hierarchical model; population dynamics; right whale; spatial capture-recapture; North Atlantic
• Online Access: https://doi.org/10.5281/zenodo.4633989

The purpose of many wildlife population studies is to estimate density, movement, or demographic parameters. Linking these parameters to covariates, such as habitat features, provides additional ecological insight and can be used to make predictions for management purposes. Line-transect surveys, combined with distance sampling methods, are often used to estimate density at discrete points in time, whereas capture–recapture methods are used to estimate movement and other demographic parameters. Recently, open population spatial capture–recapture models have been developed, which simultaneously estimate density and demographic parameters, but have been made available only for data collected from a fixed array of detectors and have not incorporated the effects of habitat covariates. We developed a spatial capture–recapture model that can be applied to line-transect survey data by modeling detection probability in a manner analogous to distance sampling. We extend this model to a) estimate demographic parameters using an open population framework and b) model variation in density and space use as a function of habitat covariates. The model is illustrated using simulated data and aerial line-transect survey data for North Atlantic right whales in the southeastern United States, which also demonstrates the ability to integrate data from multiple survey platforms and accommodate differences between strata or demographic groups. When individuals detected from line-transect surveys can be uniquely identified, our model can be used to simultaneously make inference on factors that influence spatial and temporal variation in density, movement, and population dynamics.