Modelled annual abundance of humpback whales Megaptera novaeangliae around Bermuda, 2011-2020

• Main Authors: Grove, Thomas, King, Ruth, Henry, Lea-Anne, Stevenson, Andrew
• Format: Dataset
• Language: English
• Published: PANGAEA 2022
• Subjects: Abundance; Bermuda_humpback_whale_demographic_survey; Bermuda; Atlantic Ocean; cetacean; humpback whale; iAtlantic; Integrated Assessment of Atlantic Marine Ecosystems in Space and Time; Megaptera novaeangliae; Model; Time series; Humpback Whale; North Atlantic
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.945442; https://doi.org/10.1594/PANGAEA.945442

Modelled demographic parameters of North Atlantic humpback whales (Megaptera novaeangliae) visiting Bermuda are provided for every year from 2011 to 2010. The data set was constructed to determine abundance and abundance trends of humpback whales visiting Bermuda, a migratory stopover. Photographs of the ventral side of humpback whale tail flukes were taken during dedicated vessel-based surveys between December and May each year between 2010 and 2020, and were daily whenever possible (weather-permitting). Surveys followed a haphazard regime to maximize encounters with whales and focused spatially on the southwestern Bermuda Platform and the Challenger Bank seamount*. Surveys were conducted in closing mode; whales were approached and photographed on detection, and a focal follow was conducted at each encounter. Images of suitable quality were used for individual photo-identification via scarring patterns and coloration (Calambokidis et al., 2001). The resulting catalogue was used to construct annual sighting histories for each identifiable animal. Annual demographic parameters, including abundance, were reconstructed from sighting histories using capture-recapture (CR) methods. A Cormack-Jolly-Seber (CJS) model (Lebreton et al., 1992) was fit through maximum likelihood estimation to estimate annual apparent survival (Φ) and detection probability (p). The final model specification was informed by goodness-of-fit tests and AICc. To account for variable annual effort, p was linked to modified annual survey effort (based on number of survey days) via logistic regression (resulting in a different p value for each year). To account for transient animals that only visit the study area once, Φ was stratified into survival following the first sighting (Φ0) and survival following subsequent sightings (Φ1+). From the output of this CJS model, the following parameters were derived: annual rate of transience (T), number of transient animals (NT), number of non-transient animals (NR) and total abundance (Ntot). Ntot was ...